Jekyll2021-11-29T22:52:54+00:00https://paul-mora.com/feed.xmlEconometrics & Data ScienceData Science Blog PostsPaul MoraVarious techniques on dealing with multi-output classification2021-11-29T00:00:00+00:002021-11-29T00:00:00+00:00https://paul-mora.com/multi-output/classification/multi-label/python/Various-techniques-on-dealing-with-multi-output-classification<p>This post evaluates different approaches when facing a multi-output within a classification problem. A multi-output describes a combination of several outputs put together. For example, a multi-output would describe a situation in which the classification model would not only have to predict whether the image shows a dog or a cat, but also which color the animal is.</p>
<p>There are two main approaches for this problem. We could either split this task into multiple single-classification models, in which each model outputs exactly one attribute. The alternative would be to use exactly one model which was designed to handle a multi-output in the first place. This post applies both methods on a scraped dataset in order to shed some light on the question when to go for which approach.</p>
<p>The entire code-base for this project can be found <a href="https://github.com/data4help/shiny-bassoon">here</a>.</p>
<h2 id="data">Data</h2>
<p>Instead of taking a pre-made image dataset, we scraped the meta information and images of the athletes participating in the water disciplines of the Tokyo 2020 Olympics. As the image below shows us nicely, the database contains information about the country the athlete is participating for, as well as the gender of the person (information in the red squares). The decision for this <a href="https://www.fina.org/competitions/5/olympic-games-tokyo-2020/athletes?gender=&discipline=&countryId=">website</a> was made given the good data quality (e.g. all athletes were shot from the same angle) as well as the (okayish) amount of images of people. The image below also shows a case in which we no image of the athlete was provided (first athlete). In these situation we disregarded the information altogether.</p>
<div>
<center>
<img src="/assets/post_images/multi_class/blogpost/website.png" width="1000" />
</center>
</div>
<p>After scraping the meta information from the website, we made one small addition to our dataset, namely the inclusion of the continent information. That is because we preferred to predict the continent attribute of an athlete rather than the country. The main reason for this decision was the limited amount of images we have for each country. In total we were able to scrape around 1.000 images of athletes with their meta information attached. Given that we have 196 official countries on earth, predicting each country separately would have resulted in approximately (1000 // 196) five images for each country (assuming that it is evenly distributed). That amount is way too little for training an image classification model, even when using the power of transfer learning. We therefore decided to map every country to the six continents: Europe, Africa, Oceania, South America, North America and Asia (sadly no athlete is from Antarctica).</p>
<p>Adding the information of the athlete’s continent sounds easier than it turned out to be. That is because of the country information provided on the website we scraped our data from. As it can be seen from the image above, the website provides us with a three letter country-code. More specifically the country-code provided on the website is a so-called <em>National Olympic Committee (NOC)</em> country-code. The issue with this country code is, that it is rarely used anywhere other than the Olympics. Therefore, there are pretty much no mapping tables to translate NOC country-codes to continents. In order to still do so, we first mapped the NOC country-codes to more well known country-codes, such as <a href="https://en.wikipedia.org/wiki/List_of_ISO_3166_country_codes">ISO 3166</a>. This mapping was done by scraping the mapping table from a third-party <a href="https://www.worlddata.info/countrycodes.php">website</a>. Afterwards we made use of the handy Python package <code> pycountry_convert</code> in order to map the ISO code to the continent specification. The table below shows the final result of the meta information we collected from the website. One notes that we also translated the date of birth to the actual age, using November 2021 as the reference date.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">import</span> <span class="nn">pickle</span>
<span class="nb">file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s">"../data/processed/general/meta_df.pickle"</span><span class="p">,</span> <span class="s">"rb"</span><span class="p">)</span>
<span class="n">pickle</span><span class="p">.</span><span class="n">load</span><span class="p">(</span><span class="nb">file</span><span class="p">).</span><span class="n">head</span><span class="p">()</span>
</code></pre></div></div>
<div>
<style scoped="">
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>number</th>
<th>country_code</th>
<th>sports</th>
<th>gender</th>
<th>age</th>
<th>continent</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>1</td>
<td>ALB</td>
<td>Swimming</td>
<td>Male</td>
<td>17.996263</td>
<td>Europe</td>
</tr>
<tr>
<th>1</th>
<td>2</td>
<td>ALB</td>
<td>Swimming</td>
<td>Female</td>
<td>23.220189</td>
<td>Europe</td>
</tr>
<tr>
<th>2</th>
<td>3</td>
<td>ALG</td>
<td>Open Water</td>
<td>Female</td>
<td>32.707037</td>
<td>Africa</td>
</tr>
<tr>
<th>3</th>
<td>4</td>
<td>ALG</td>
<td>Swimming</td>
<td>Female</td>
<td>28.055333</td>
<td>Africa</td>
</tr>
<tr>
<th>4</th>
<td>5</td>
<td>ALG</td>
<td>Swimming</td>
<td>Male</td>
<td>29.232633</td>
<td>Africa</td>
</tr>
</tbody>
</table>
</div>
<h2 id="data-imbalance">Data Imbalance</h2>
<p>Before moving to the actual classification modeling, we have to check the class imbalance in our data, in order to adjust for potential imbalances. That is necessary, since when a classification model overly sees one class (a.k.a majority class) compared to another (a.k.a minority class), it fails to learn and understand the distinguishing factors between these two classes. From the chart below, we can see that there is quite a bit of imbalance in the data. Furthermore, the two continents <em>Oceania</em> and <em>South America</em> have so few observations, that we decided to drop these two continents, as the amount of images is too low relative to the other classes.</p>
<div>
<center>
<img src="/assets/post_images/multi_class/figures/task_preprocess_classification/label_imbalance.png" width="500" />
</center>
</div>
<p>One valid form of countering a class-imbalance within a classification problem is to adjust the <strong>sample weight</strong> for each of the priorly selected categories. This weight makes the model understand the severity of getting a certain observation wrong. Predicting an observation incorrectly which has a higher weight impacts the model’s training much more, than misclassifying an observation from the majority class. In the following chart, we can see that the weight is quite low for European observations, but quite high for African observations. That is because the class weight is inversely related to the amount of observations in the data. Since we have so many observation from <em>Europe</em>, the model should not stress so much about getting all of them right, but since we have so few <em>African</em> observations, the model should really pay attention to those ones.</p>
<div>
<center>
<img src="/assets/post_images/multi_class/figures/task_preprocess_classification/sample_weights.png" width="500" />
</center>
</div>
<p>In order to get a better feeling of how difficult this classification task is, we should take a look at our observations. In the following we show example images from all eight categories. There are eight categories, since we are dealing with all possible combinations of two genders and four continents. This information about the length of the output space, is also important to know, as it allows to approximately (since it does not account for imbalance) calculate what performance a random classification model would achieve, namely 1/8 = 12.5%.</p>
<p>Furthermore, the images below give us a good indication of how difficult the task at hand is. This is especially visible when comparing the visual appearance people from <em>North America</em> and <em>Europe</em>. Since both continents have a high amount of white people, it is quite difficult to tell them apart. On the other hand, it is not difficult to tell whose <em>male</em> or <em>female</em>. In the following we will see that the model confirms these hypothesis of how difficult which classification is.</p>
<div>
<center>
<img src="/assets/post_images/multi_class/figures/task_preprocess_classification/label_examples.gif" width="1000" />
</center>
</div>
<h2 id="classification-methods">Classification Methods</h2>
<p>After exploring the data and adjusting the class-imbalance, it is now time to talk about the different possibilities to deal with the <strong>multi-output</strong> of this classification problem. All methods mainly represent a different way to encode the target label each observation has. This is because machine learning model is unable to make use of the string target: <em>male_europe</em>. We have to encode that target into a numeric format. Of course there are many different methods of how one could do that, three of which are detailed below and applied on our dataset.
Regardless of the encoding technique, all encoding mechanisms are then stacked on the pre-trained network <a href="https://towardsdatascience.com/step-by-step-vgg16-implementation-in-keras-for-beginners-a833c686ae6c">VGG16</a>, by using transfer learning. The idea and workings behind transfer learning are out-of-scope for this post but can be learned about in <a href="https://paul-mora.com/transfer-learning/clustering/python/Using-the-power-of-transfer-learning-for-building-a-flower-classifier/">this</a> other post of mine.</p>
<h3 id="multi-class">Multi-Class</h3>
<p>Arguably, the most popular choice is the so-called <strong>multi-class</strong> handling. A multi-class describes the situation in which we assign each combination of the two outputs an unique id. When for example facing a <em>female</em> person from <em>Asia</em>, we would simply say that this combination is encoded to class 1. A <em>male</em> person from <em>Asia</em> would then be (for example) class 2, and so on. From these two examples, one notices that this approach scales badly. Having <em>n</em> categories in the first output space, and <em>m</em> in the second, would result in $n \cdot m$ combinations.</p>
<p>Another weakness of this approach is that we treat all labels completely different, even though they may have a partial overlap. If for example our classifier predicts <em>male_asia</em> as <em>female_asia</em>, the loss function of the model is unaware that it got 50% of the target labels correct. It treats these two classes as something entirely different. That behavior can of course also have its benefits, namely for tasks in which we classify objects which are fundamentally something else. Herein the behavior of not getting partial credit is also desired. In the following we see an example of this encoding technique.</p>
<div>
<center>
<img src="/assets/post_images/multi_class/blogpost/multiclass.png" width="300" />
</center>
</div>
<h3 id="multi-label">Multi-Label</h3>
<p>A second viable option is the so-called <strong>multi-label</strong> option. That method represents a generalization of the multi-class option, in which no constraint is given of how many output classes can be set to true or false. To better illustrate that, let us imagine an output space in which the only two continents from which a participant could come from is <em>Europe</em> or <em>Asia</em>, and that the person’s gender is constraint to <em>Female</em> and <em>Male</em>. In this scenario the output space of a multi-label approach would look the following way:</p>
\[[Bool_{Asia}, Bool_{Europe}, Bool_{Female}, Bool_{Male}]\]
<p>In the equation above <em>Bool</em> represents a boolean, which can be set either true (1) or False (0). Following that logic, a female Asian would therefore be encoded as [1, 0, 1, 0]. This encoding method is strikingly different to the multi-class approach, as multiple outputs can be true. For that reason multi-label encoding is used when it is unclear how many outputs could be shown in the image. If we would face a group photo of a male European <strong>and</strong> a female Asian, the multi-class encoding technique would have a hard time, whereas the multi-label approach would simply return [1, 1, 1, 1], because all categories are visible on that image.</p>
<p>Given that in our classification exercise we only face images from exactly one person, this type of encoding cannot play its main strength. Though it is still a strong contender, since it is able to learn from interaction effects. That is because if the model predicts <em>male_asia</em>, even though the correct label is <em>female_asia</em>, the classification model understands that it was partially correct. That is the main advantage of the multi-label encoding compared to the multi-class. Furthermore, it scales massively better than the multi-class problem. In a situation in which the two output spaces have the lengths of <em>n</em> and <em>m</em> respectively, then increasing the output space of <em>n</em> by one unit, would increase the total output layer of the multi-label approach by one. In the following we see an example of the multi-label technique:</p>
<div>
<center>
<img src="/assets/post_images/multi_class/blogpost/multilabel.png" width="400" />
</center>
</div>
<h3 id="chained-single-classification">Chained Single Classification</h3>
<p>Lastly, another option of how to deal with our multiple-output classification problem is that we break down the problem into several disjoint classification themselves. In our scenario, that would mean that we train a classification model for the person’s gender and continent information separately. In the end we would then hand a new image to the two fitted classification models and retrieve the respective information from each. The first model would for example tell us that the person is <em>Male</em> and the second model would give us the additional information that the person is predicted to be <em>Asian</em>.</p>
<p>The strengths of this approach is that each model can solely focus on one task at once and therefore can potentially achieve a better prediction result on a certain task than any of the previously mentioned models can. Of course, this approach also has its flaws. First of all, we need one model per output category, meaning that this approach is computationally expensive. Second of all, the model cannot use any interaction effects. This can be illustrated through the following example. Let us imagine that females from <em>Asia</em> look fundamentally different than females from other continents. A classification model which solely distinguishes between male and female, without any continent information will have a hard time to understand why it fails so often with female Asians. This is because, this classifier lacks the additional important information that the female is Asian and therefore has to be treated differently than other females. The previously mentioned classification methods though makes use of this additional continent information, and are therefore able to deal with that problem.</p>
<p>In the following we see an example of how an image is encoded when using the chained single classification.</p>
<div>
<center>
<img src="/assets/post_images/multi_class/blogpost/chain.png" width="500" />
</center>
</div>
<h2 id="data-processing-and-augmentation">Data Processing and Augmentation</h2>
<p>As mentioned earlier, all of the three presented methods are basically nothing other than different ways of how to encode the multi-output target. All three approaches though are of course not enough to classify the image itself. One needs an underlying Convolutional network to do the main work. Within all kinds of image classification tasks, it is relatively unwise to train a new neural network from scratch, given the extensive amount of pre-trained models available. One condition when using a pre-trained model, is that we are using the same kind of pre-processing of our images, as the pre-trained model used when it was initially trained. From the images below we can nicely see what kind of transformation is applied when comparing the images under the heading “Validation/ Testing” and “Original”.</p>
<p>Another important aspect to consider when having a small image dataset, is the power of data augmentation. Data augmentation describes the process of artificially increasing the amount of data through transforming (augmenting) the already existing database, by e.g. stretching, zooming, flipping the original image. The image under the header “Training” from the figure below shows nicely how the image under the header “Validation/ Testing” was stretched and zoomed. Through these altering of the original images, the model sees quite a bit more <em>different</em> images, than it would if we feed it only the unaltered images. The exact augmentation settings used in this project, can be found in the parameters configuration file on github.</p>
<div>
<center>
<img src="/assets/post_images/multi_class/figures/task_train_classification_multilabel/augmentation.png" width="500" />
</center>
</div>
<h2 id="results">Results</h2>
<p>In the following we present and discuss the results of the project. We do that in the following fashion: First we evaluate the results of each approach separately, pointing out the main strengths and weaknesses of each approach and hypothesis on the reasons for that performance. Afterwards, we show a head-to-head comparison of the different approaches and try to shed some light on why a certain approach worked better than others. Lastly, we point out the key-learnings of this project and what could be improved.</p>
<h3 id="multi-label-1">Multi-Label</h3>
<p>A common method when evaluating a classification model is the so-called <a href="https://en.wikipedia.org/wiki/Confusion_matrix">confusion-matrix</a>. Each row of that matrix shows one actual target class, whereas the columns show what was predicted by the classification model. A perfect classifier would have all observations along the diagonal of the matrix.</p>
<p>In the following figure we plotted such a confusion matrix for our classification model. In order to showcase the performance a bit better, we slightly tweaked the confusion matrix though. Instead of displaying the absolute amount of observations in each cell, we show the percentage value of the sum of each row. The value in the top left for example tells us that 12% of <em>africa_female</em> were actually classified as <em>africa_female</em>. Two boxes to the right we also find that 25% of <em>africa_female</em> were classified as <em>asia_female</em>. Of course, a perfect result would show 100% along the diagonal of the confusion matrix. It is furthermore to be said that all predictions were made on a hold-out test dataset, which represents 15% of the sub-selected image dataset and therefore represent around 150 images.</p>
<p>As already implicitly noted in the prior example, the performance for <em>africa_female</em> is quite poor. Though, it has to be said that the performance of all categories is at least four times better than for the <em>africa_female</em> category. For all other categories, the algorithm classified on average around 50% or more of the test-images correctly. It is furthermore nicely visible that the misclassification happen mostly along the continent dimension and not on the gender attribute. Taking the <em>europe_female</em> category as an example. Herein we find that 49% of all images of that category are correctly classified as <em>europe_female</em>. The other two larger buckets for that category are <em>asia_female</em> (16%) and <em>female_north america</em>, meaning that the algorithm understood the difference between male and female, though had more problems to understand the continent attribute of that person. The very same phenomena is also visible for the <em>male_north america</em> category. Interestingly, the algorithm had no problem of telling <em>European</em> and <em>North American</em> males apart, but rather to distinguish <em>North American</em>, <em>African</em> and <em>Asian</em>.</p>
<p>The fact that the classification model performs much better along the gender attribute compared to the continent attribute is also understandable. Given the migration flows around the world, it is pretty much impossible to tell with certainty where somebody was born, simply by their appearance. Though, which gender a person is, is easier to tell in comparison.</p>
<p>Overall, the performance of the multi-label approach is rather decent, of course it has its weaknesses along the continent dimension, though as mentioned above, this is also difficult task in general.</p>
<div>
<center>
<img src="/assets/post_images/multi_class/figures/task_train_classification_multilabel/confusion_matrix.png" width="500" />
</center>
</div>
<p>In order to shed more light on which observation the algorithm was able to classify, and which one the classifier failed on, we provide some examples below to get a better feeling. On the <strong>left side</strong> we see all the examples which the classifier was able to predict, whereas the <strong>right side</strong> shows the failures of our model. By looking at the examples the classifier failed at, it becomes obvious why this is such a difficult classification task.</p>
<div>
<center>
<img src="/assets/post_images/multi_class/figures/task_train_classification_multilabel/pos_neg.gif" width="500" />
</center>
</div>
<h3 id="multi-class-1">Multi-Class</h3>
<p>Many of the findings of the multi-label classifications also show up during the evaluation of the multi-class metrics. For example, the misclassification also happens mostly on the continent dimension rather than the gender dimension. In a direct comparison with the multi-label classification, we find that the multi-class algorithm performs better than the multi-label approach for 75% (6/8) of the eight categories. That finding suggests that all categories have something quite unique about them and that the inability to recognize partial correctness (e.g. classifying gender correctly, but incorrect continent) does not lead to a superior prediction performance.</p>
<p>It would be quite interesting to see whether this superior performance would also remain if we increase the number of continents and/or introduce another output dimension to the model.</p>
<div>
<center>
<img src="/assets/post_images/multi_class/figures/task_train_classification_multiclass/confusion_matrix.png" width="500" />
</center>
</div>
<p>When looking at which samples the multi-class classification model got wrong, we actually find several familiar faces which the multi-label method was also unable to correctly predict.</p>
<div>
<center>
<img src="/assets/post_images/multi_class/figures/task_train_classification_multiclass/pos_neg.gif" width="500" />
</center>
</div>
<h3 id="independent-single-classification">Independent Single Classification</h3>
<p>The assessment of the results for the chained classification can be split up into its individual components. Namely we can directly assess how well the algorithms performed on both dimensions respectively. Since this analysis holds some interesting pieces, we therefore go over the evaluation of both categories one by one.</p>
<h4 id="gender">Gender</h4>
<p>The confusion matrix below confirms what we hypothesized already when evaluating the performance of the two previous models. The classification algorithm performs relatively strong along the gender dimension. Most of the predictions are correct, with the algorithm being slightly stronger detecting males (97%) compared to females (89%).</p>
<div>
<center>
<img src="/assets/post_images/multi_class/figures/task_train_classification_chain/gender/confusion_matrix.png" width="500" />
</center>
</div>
<p>When looking at the examples which the algorithm failed to detect, we see that most observations where that happened are females with a short hair-cut. This does make sense as short hair-cuts are primarily worn by man, and since the algorithm is learning nothing other than statistics, we also expected such behavior of the algorithm. When we are saying that the algorithm learns to understand what gender a person is, it is of course not measuring the Testosterone level of the person, but rather it looks at similarities of the two clusters. Therefore the model is rather detecting whether the person fits more criteria of a male/female person than anything else.</p>
<div>
<center>
<img src="/assets/post_images/multi_class/figures/task_train_classification_chain/gender/pos_neg.gif" width="500" />
</center>
</div>
<h4 id="continent">Continent</h4>
<p>When evaluating the model’s performance along the continent dimension, we have to remind ourselves that the classification happened without any indication of the gender of the person. Meaning that the algorithm would have to ignore the visual differences of the gender and focus only on other features which would indicate the origin of a person.</p>
<p>The result below shows clearly that this task is considerably more difficult than the previous one. For the <em>Europe</em> class, the algorithm even fails on more than half (47%) of its predictions.</p>
<div>
<center>
<img src="/assets/post_images/multi_class/figures/task_train_classification_chain/continent/confusion_matrix.png" width="500" />
</center>
</div>
<div>
<center>
<img src="/assets/post_images/multi_class/figures/task_train_classification_chain/continent/pos_neg.gif" width="500" />
</center>
</div>
<h4 id="total">Total</h4>
<p>Last but not least we take a look at the combination of the two single classification tasks. The confusion matrix is relatively empty in the top right and bottom left corner, showing that there was basically no misclassification on the gender dimension. Though, the disperse top left and bottom right corner show that there was quite a bit misclassification along the continent dimension.</p>
<p>Given that this algorithm performs worse on average than the previous two classification techniques, it seems to be the case that the classification of the continent is significantly enhanced when knowing the gender of the person. That would suggest that there is a significant interaction effect between gender and continent.</p>
<div>
<center>
<img src="/assets/post_images/multi_class/figures/task_train_classification_chain/confusion_matrix.png" width="500" />
</center>
</div>
<h3 id="overall-assessment">Overall Assessment</h3>
<p>In order to better compare the models head-to-head, we plotted several classification evaluation metrics in the figure below. Herein we see that the multi-class model is slightly ahead of the other two methods for all but one classification criteria. On second place we the see the multi-label model, which manages to outperform the multi-class model in terms of its <em>Precision</em>. That suggests that for some reason the multi-label makes fewer False Positive mistakes. On the disappointing third and last place we find the combined single-classification. This results suggests that failing to incorporate any interaction effects between the gender and continent attribute worsens the overall prediction power of the combination.</p>
<div>
<center>
<img src="/assets/post_images/multi_class/blogpost/results.png" width="700" />
</center>
</div>
<h3 id="hamming-loss">Hamming Loss</h3>
<p>The figure above has one potentially flaw though which should be mentioned, namely that it fails to award points for a partially correct prediction. That is because of the nature of the target. All three classifiers return in the end a prediction such as <em>male_europe</em>. This very prediction is then compared to the true label. If the true label is <em>male_europe</em>, then we speak of a correct prediction. Though, a <em>male_asia</em> and a <em>africa_female</em> would be both equally incorrect, even though that <em>male_asia</em> is much closer to the truth to the true label than <em>africa_female</em>, where both of the attributes are incorrectly classified.</p>
<p>In order to also attribute partial credit, we have to pick another evaluation metric than the ones we showed in the figure above. All metrics we looked at in the previous figure are commonly used within classification tasks, though when having a multi-output problem, we should also look at something called <strong>hamming loss</strong>. This loss metric originates from the concept of <a href="https://en.wikipedia.org/wiki/Hamming_distance">hamming-distance</a>. Herein we evaluate the number of symbol changes between two strings. For example, the number of symbol changes to go from <em>010</em> to <em>000</em> is exactly one, namely we change the 1 in the first string to a zero. Changing from <em>111</em> to <em>000</em> would represent the largest number of symbol changes necessary to change from one string to another.</p>
<p>This very method can neatly applied to our prediction problem, since it recognizes that the number of symbol changes between <em>male_europe</em> and <em>male_africa</em> is smaller than between <em>male_europe</em> and <em>female_africa</em>. The figure below shows the results when calculating the hamming loss for all three approaches. One has to remember that we are looking at a <strong>loss</strong>, therefore a smaller score is better. In contrast to the previous figure, the multi-label approach outperforms the other two. That makes also somewhat sense, as we used a loss somewhat related to hamming loss (binary accuracy) for the training of the multi-label algorithm.</p>
<div>
<center>
<img src="/assets/post_images/multi_class/blogpost/hamming_loss.png" width="700" />
</center>
</div>
<h2 id="conclusion">Conclusion</h2>
<p>These results lead to quite interesting conclusions. First of all, interaction effects are important. That was particularly visible when looking at the performance of the combined single-classification model. Secondly, whether to use multi-class or multi-label depends heavily what the objective of the classification task is. If we face a black and white scenario, in which a partially correct prediction is worth nothing, then we should use multi-class. If on the other hand, also partial correctness is useful, then we should opt for the multi-label approach. Last but not least. The multi-class algorithm scales the worst. When having a large multi-output space this approach seems likely to fail quicker than the multi-label, which scales considerably better.</p>
<h2 id="outlook-and-improvements">Outlook and Improvements</h2>
<p>Last but not least we briefly go over several potential improvements to the model performance, if there would have been more time on that project.</p>
<h3 id="using-chained-classification-instead-independent-combined-classification">Using Chained-Classification instead Independent Combined Classification</h3>
<p>One weakness of the combination of two single-classification seems to be the failure of incorporating the gender information when predicting the continent attribute of a person. That seems to be the case given the struggle of the combination classification for the continent attribute. One potential solution for that weakness is the so-called <em>Classifier Chain</em>, as it is described by scikit-learn <a href="https://scikit-learn.org/stable/auto_examples/multioutput/plot_classifier_chain_yeast.html">here</a>. That approach does exactly what it is assumed is missing for our combination prediction. It incorporates the prediction of the previous classifier into the input space of the following classification method. For our example that would mean that we could include the gender information when classifying the continent attribute. Unfortunately, the implementation from scikit-learn does not easily let us use tensorflow with it, which was the reason it was not implemented right away. Though, given the results, this approach could seriously increase the performance of the combination of individual classifiers.</p>
<h3 id="better--more-images">Better & More Images</h3>
<p>When looking through the web at other people’s projects of classifying a person’s origin, the datasets people work with are most of the times much cleaner than ours. The projects on the web usually gather and validate their images manually and therefore have much less noise through immigration compared to our dataset, which simply takes the data from the website as a given. It is more than likely that when removing the observations which introduce the noise to our dataset, that we would be more than able to reproduce the classification performance of the other projects on the web.</p>Paul MoraThis post evaluates different approaches when facing a multi-output within a classification problem. A multi-output describes a combination of several outputs put together. For example, a multi-output would describe a situation in which the classification model would not only have to predict whether the image shows a dog or a cat, but also which color the animal is.Designing products with WGANs2021-06-17T00:00:00+00:002021-06-17T00:00:00+00:00https://paul-mora.com/adversial%20networks/python/Designing-with-WGANs<p>This post elaborates on the idea of using Generative Adversarial Networks to design new products, such as car rims. Every time a company is looking for a new design of a product, hundreds of designers start their work and produce multiple suggestions each. Given the nature of the business, only one out of these suggestions is going to be implemented in the end, with the all other suggestions mostly thrown away. This large inefficiency could be resolved by the possibility of creating new design suggestions as easy as a click on a button. This is where GANs come in. By feeding them with images of already existing and economically successful images, the model is ultimately able to generate new design suggestions which are not possible to tell apart from real ones. For that purpose we used a few images car rims and implemented a Wasserstein GAN with Gradient Penalty in order to see how well the model can generate new design suggestions.</p>
<p>The post starts with a brief discussion about the history of GANs, before elaborating on the use case of design and fashion. Afterwards the data for this project is introduced and some data processing steps are introduced. Finally we show the Python code with which this project is implemented and conclude with the results in the end. For a more mathematical introduction to GANs and WGANs, please refer to my previous post about this topic.</p>
<h1 id="gan-and-wgan">GAN and WGAN</h1>
<p>Since 2014 and the first paper of Generative Adversial Networks [1], it became difficult to keep track of all papers suggesting new implementation of GAN models. Arguably the most significant during the time right after the original papers was the addition of Deep Convolutional Neural Networks to GANs [2]. This method was particularly favorable for image data. As already outlined in my previous post, GANs are relatively instable and oftentimes get stuck during their training process. There are <a href="https://developers.google.com/machine-learning/gan/problems">multiple potential reasons</a> for a GAN model to get stuck, ranging from mode collapse over to vanishing gradients. One rather successful solution to this failures is the in 2017 proposed Wasserstein GAN (WGAN)[3].</p>
<p>The main difference between the Wasserstein GAN and “ordinary” is the way the model measures the distance between real data and generated fake data. Whereas the traditional GAN model is using the so-called <a href="https://en.wikipedia.org/wiki/Jensen–Shannon_divergence">Jenson-Shannon Divergence</a>, the Wasserstein GAN is using, as the name already suggests the Wasserstein divergence. The main benefit of Wasserstein is that it does not diverge when the distributions of real and fake data are fairly far away from one another. Knowing how far away two distributions are from one another is crucial, since this information is used to update the weights of the Neural Network the generative model comprises of. These updates are conducted through Backpropagation and is the central mechanism of how the generative model improves over time. By telling the model how good or bad it created new fake data, the model has then the chance to use that feedback to improve in its next iteration. Therefore, it is detrimental if the measure of how bad the model is, is diverging. In this case, very bad and “normal” bad are quantitatively the same, and the model has a harder time to improve from these situations. Using the Wasserstein Distance is therefore to be preferred. For a more elaborated explanation of GANs and WGANs as well as a high level mathematical explanation of their foundations, please refer to my previous blog-post about the fundamental concept of GANs.</p>
<h2 id="-and-gradient-penalty">… and gradient penalty</h2>
<p>Even though the Wasserstein paper rightfully claimed a lot of fame, it had one flaw. The way the Wasserstein paper is calculating the gradient update for the generative model is by using gradient clipping. That literally means that the gradients have to be in the range of -0.01 and 0.01. Everything bigger or smaller than this range is simply cut. As the paper states:</p>
<p><em>Weight clipping is a clearly terrible way to enforce a Lipschitz constraint… However, we do leave the topic of enforcing Lipschitz constraints in a neural network setting for further investigation, and we actively encourage interested researchers to improve on this method.</em></p>
<p>This call for improvement was also quickly heard. In the same year, Gulrajani et al published an improved version [4]. This new version of the Wasserstein GAN is using some heavy mathematics (check the appendix of the <a href="https://arxiv.org/pdf/1704.00028.pdf">original paper</a> if you feel like it) to implement a gradient penalty which does not require any unfavorable weight clipping anymore.</p>
<h1 id="gans-and-its-applications-in-design-and-fashion">GANs and its applications in Design and Fashion</h1>
<p>The range of use cases for GANs are <a href="https://machinelearningmastery.com/impressive-applications-of-generative-adversarial-networks">immense</a>. Furthermore, next to many flashy applications, GANs can also help to mitigate several real-life problems. One of which is the workings of the creative industry. Design and fashion are fields in which an insane amount of manual labor is constantly thrown away. In this day and age, the design of products became at least as important as its functionality. Whenever a company is planning to launch a new product, hundreds of designers submit multiple design suggestions, hoping to please the lead-designer. Since there is mostly one product and one design, all design suggestion except for one are being left unused and forgotten. The amount of inefficiency is arguably as large as the frustration of many designers, working long hours for nothing.</p>
<p>The described scenario would be different if new design suggestions would come as easy as a click on a button. This is where the GAN comes in. Being able to take inspiration by many other products, a GAN model could come up with a design which looks different but indistinguishable to the design of an already existing (and potentially approved) designs.</p>
<h1 id="use-case-car-rim-design">Use Case: Car Rim Design</h1>
<p>To showcase the power of Generative Adversial Networks within the field of design, we chose to create new designs for car rims. Car rims have the benefit that they mostly are not overly complicated in regards to their design. This comes beneficial since generating a new image out of nothing but thin air is quite a difficult task even for neural networks.</p>
<p>Since the GAN is expected to learn which design patterns are acceptable for car rims, it is important to provide the model with a good quality of images. That means that there should be minimal noise, meaning no distraction factors. The best case scenario would therefore be if the images are all shoot from the same angle and also cropped similarly. Furthermore, having gray-scale images also helps since the model then has to learn only a third of the pixel values.</p>
<p>Trying to satisfy the outlined criteria above is difficult, since when blindly looking for new images, one rarely finds a whole lot of images shoot in a very similar way. Oftentimes we find a car attached to the rims (surprise), and other distortions like angle and cropping.</p>
<p>For that reason we looked at the different rim dealerships, as they sometimes provide all their products lined up in a very similar fashion. Our search led us to <a href="https://www.imagewheels.co.uk"><em>imagewheels.co.uk</em></a>, which provided images of car rims in a favorable way. Not only did they shoot every photo nearly exactly the same, they also provided a dark background. This is beneficial to the model given that the GAN can therefore solely focus on the rim instead of the noise going on in the background.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="n">np</span>
<span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="n">plt</span>
<span class="n">number_of_examples</span> <span class="o">=</span> <span class="mi">5</span>
<span class="n">original_filenames</span> <span class="o">=</span> <span class="n">os</span><span class="p">.</span><span class="n">listdir</span><span class="p">(</span><span class="s">"../data/original"</span><span class="p">)[:</span><span class="n">number_of_examples</span><span class="p">]</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="n">ncols</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">original_filenames</span><span class="p">))</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">file_name</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">original_filenames</span><span class="p">):</span>
<span class="n">image</span> <span class="o">=</span> <span class="n">Image</span><span class="p">.</span><span class="nb">open</span><span class="p">(</span><span class="sa">f</span><span class="s">"../data/original/</span><span class="si">{</span><span class="n">file_name</span><span class="si">}</span><span class="s">"</span><span class="p">)</span>
<span class="n">np_array</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">array</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">].</span><span class="n">imshow</span><span class="p">(</span><span class="n">np_array</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">].</span><span class="n">axis</span><span class="p">(</span><span class="s">"off"</span><span class="p">)</span>
</code></pre></div></div>
<center>
<img src="/assets/post_images/felgen_gan/output_5_0.png" width="750" align="center" />
</center>
<p>The images above show some examples of the images taken from the website. Even though the images are mostly gray, they still have three color channels, and therefore significantly more pixel values than gray-scale images. Since we are only interested in the overall design of the rim and not at all in its coloring, we therefore convert the image to gray-scale. Additional to that, we are reducing the complexity of the images even further, by turning every pixel value of the image either into black or white.</p>
<p>Even though the background of the sample images above looks entirely black, their pixel value is not exactly completely black. Therefore we manually help a bit. We do that by first checking which pixel value the very top left corner is. This pixel value then serves us as an indicator of what the background pixel value is, through which we can subset the image and turn everything (truly) black.</p>
<p>Additionally we only want to put emphasis on brighter details. That means that we <a href="https://www.youtube.com/watch?v=O4irXQhgMqg">paint everything black</a> that is below a certain threshold level. That threshold is subjective and was manually adjusted until a sweet spot was found which left enough details of the rims. The resulting image can be seen below.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">background_color</span> <span class="o">=</span> <span class="mi">255</span>
<span class="n">detail_color</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">threshold_level</span> <span class="o">=</span> <span class="mi">50</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="n">ncols</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">original_filenames</span><span class="p">))</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">file_name</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">original_filenames</span><span class="p">):</span>
<span class="n">image</span> <span class="o">=</span> <span class="n">Image</span><span class="p">.</span><span class="nb">open</span><span class="p">(</span><span class="sa">f</span><span class="s">"../data/original/</span><span class="si">{</span><span class="n">file_name</span><span class="si">}</span><span class="s">"</span><span class="p">)</span>
<span class="n">np_array</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">array</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
<span class="c1"># Adjustments
</span> <span class="n">current_background_color</span> <span class="o">=</span> <span class="n">np_array</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
<span class="n">np_array</span><span class="p">[</span><span class="n">np_array</span><span class="o">==</span><span class="n">current_background_color</span><span class="p">]</span> <span class="o">=</span> <span class="n">background_color</span>
<span class="n">np_array</span><span class="p">[</span><span class="n">np_array</span><span class="o"><</span><span class="n">threshold_level</span><span class="p">]</span> <span class="o">=</span> <span class="n">background_color</span>
<span class="n">np_array</span><span class="p">[</span><span class="n">np_array</span><span class="o">!=</span><span class="n">background_color</span><span class="p">]</span> <span class="o">=</span> <span class="n">detail_color</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">].</span><span class="n">imshow</span><span class="p">(</span><span class="n">np_array</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s">"gray"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">].</span><span class="n">axis</span><span class="p">(</span><span class="s">"off"</span><span class="p">)</span>
</code></pre></div></div>
<center>
<img src="/assets/post_images/felgen_gan/output_7_0.png" width="750" align="center" />
</center>
<p>From the examples above one can clearly see the difference before and after our adjustment. Turning the car rims into black and white created a better contrast, and it became easier to identify the overall design.</p>
<p>The major drawback of our data is the limited amount we are working with. Even though the images are all nicely standardized, since they come from the same website, the price for that is that we can only take as many images as there are shown on the website. That left us with around 100 images, which is not a lot when working with GANs. Nevertheless, the GAN was able to produce some decent rim designs as we will see in the end of this post.</p>
<h1 id="code-implementation">Code Implementation</h1>
<p>As already mentioned in the beginning of this post, we are using a Wasserstein GAN with gradient penalty. The difference to a basic DCGAN is though only visible in the implementation of the Discriminator, which because of the adjustments is now called the critic. The main difference is that the output of the critic is not a probability of the image being real, which is the case for the generator. Furthermore, the usage of the Wasserstein divergence instead of the Jenson-Shannon divergence prevents most mode collapses and the vanishing of gradients. A more technical explanation of these phenomena can be found in my previous post.</p>
<p>In contrast to many GAN implementations on the web, I packaged my code which allows for easier changing of parameters and usage through the terminal. To give a good overview how the different files interact, we take a look into the folder structure.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">import</span> <span class="nn">seedir</span> <span class="k">as</span> <span class="n">sd</span>
<span class="n">sd</span><span class="p">.</span><span class="n">seedir</span><span class="p">(</span><span class="s">"../source"</span><span class="p">,</span> <span class="n">style</span><span class="o">=</span><span class="s">"lines"</span><span class="p">,</span> <span class="n">exclude_folders</span><span class="o">=</span><span class="s">"__pycache__"</span><span class="p">)</span>
</code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>source/
├─.DS_Store
├─config.json
├─utils/
│ ├─config.py
│ └─args.py
├─generator.py
├─loader.py
├─trainer.py
├─critic.py
└─main.py
</code></pre></div></div>
<h2 id="loader">Loader</h2>
<p>The first thing we have to do on our GAN model is to prepare the images. For that we apply the adjustment we outlined above and turn the colored images into black and whites. Furthermore, we resize them. This is necessary since the original images are quite large. When working with GANs, one has to be aware that the bigger the desired output image is, the more training is required. For that reason, most GANs are using no images bigger than 128x128 (with some <a href="https://machinelearningmastery.com/a-gentle-introduction-to-the-biggan/">exceptions</a> of course). In our case we are working with even smaller images, namely 64x64. The benefit of using the smaller images size is that the model is able to mimic the distribution of real images faster. Though, smaller images also contain a lower level of detail, therefore it is a bit of a trade-off.</p>
<p>Furthermore, we normalize the image in order for them to have a mean and standard deviation equal to 0.5 each. This normalization of input values is necessary in order to stabilize the model and is done not only for GANs, but for every neural network application.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1">### loader.py
</span>
<span class="c1"># %% Packages
</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="n">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="n">plt</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torchvision.transforms</span> <span class="k">as</span> <span class="n">transforms</span>
<span class="kn">import</span> <span class="nn">torchvision.datasets</span> <span class="k">as</span> <span class="n">dset</span>
<span class="kn">import</span> <span class="nn">torchvision.utils</span> <span class="k">as</span> <span class="n">vutils</span>
<span class="c1"># %% Class
</span>
<span class="k">class</span> <span class="nc">DataLoader</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">):</span>
<span class="bp">self</span><span class="p">.</span><span class="n">config</span> <span class="o">=</span> <span class="n">config</span>
<span class="bp">self</span><span class="p">.</span><span class="n">base_dir</span> <span class="o">=</span> <span class="n">os</span><span class="p">.</span><span class="n">getcwd</span><span class="p">()</span>
<span class="bp">self</span><span class="p">.</span><span class="n">create_black_white_images</span><span class="p">()</span>
<span class="bp">self</span><span class="p">.</span><span class="n">loader</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">load_images</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">load_images</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="s">"""
Loading the black and white images and transforming them accordingly
:return: Returning a dataloader from PyTorch
"""</span>
<span class="n">transform</span> <span class="o">=</span> <span class="n">transforms</span><span class="p">.</span><span class="n">Compose</span><span class="p">([</span>
<span class="n">transforms</span><span class="p">.</span><span class="n">Grayscale</span><span class="p">(),</span>
<span class="n">transforms</span><span class="p">.</span><span class="n">ToTensor</span><span class="p">(),</span>
<span class="n">transforms</span><span class="p">.</span><span class="n">Normalize</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span>
<span class="p">])</span>
<span class="n">dataset_path</span> <span class="o">=</span> <span class="sa">f</span><span class="s">"</span><span class="si">{</span><span class="bp">self</span><span class="p">.</span><span class="n">base_dir</span><span class="si">}</span><span class="s">/data/black_white"</span>
<span class="n">dataset</span> <span class="o">=</span> <span class="n">dset</span><span class="p">.</span><span class="n">ImageFolder</span><span class="p">(</span><span class="n">root</span><span class="o">=</span><span class="n">dataset_path</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">)</span>
<span class="n">loader</span> <span class="o">=</span> <span class="n">torch</span><span class="p">.</span><span class="n">utils</span><span class="p">.</span><span class="n">data</span><span class="p">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">loader</span><span class="p">.</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="bp">self</span><span class="p">.</span><span class="n">plot_example_images</span><span class="p">(</span><span class="n">loader</span><span class="p">)</span>
<span class="k">return</span> <span class="n">loader</span>
<span class="k">def</span> <span class="nf">plot_example_images</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">loader</span><span class="p">):</span>
<span class="s">"""
Using the data loader in order to plot some example images
:param loader: DataLoader from PyTorch
"""</span>
<span class="n">example_images</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">loader</span><span class="p">))[:</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">loader</span><span class="p">.</span><span class="n">batch_size</span><span class="p">]</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">axs</span><span class="p">.</span><span class="n">axis</span><span class="p">(</span><span class="s">"off"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">.</span><span class="n">set_title</span><span class="p">(</span><span class="s">"Example Images"</span><span class="p">)</span>
<span class="n">batch_images</span> <span class="o">=</span> <span class="n">vutils</span><span class="p">.</span><span class="n">make_grid</span><span class="p">(</span><span class="n">example_images</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">normalize</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">image_grid</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">batch_images</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
<span class="n">axs</span><span class="p">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">image_grid</span><span class="p">)</span>
<span class="n">fname</span> <span class="o">=</span> <span class="sa">f</span><span class="s">"</span><span class="si">{</span><span class="bp">self</span><span class="p">.</span><span class="n">base_dir</span><span class="si">}</span><span class="s">/reports/figures/example_images.png"</span>
<span class="n">fig</span><span class="p">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">fname</span><span class="o">=</span><span class="n">fname</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s">"tight"</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">close</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">create_black_white_images</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="s">"""
Creating the black and white versions of the original images
"""</span>
<span class="k">if</span> <span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">loader</span><span class="p">.</span><span class="n">create_new_images</span><span class="p">:</span>
<span class="n">all_file_names</span> <span class="o">=</span> <span class="n">os</span><span class="p">.</span><span class="n">listdir</span><span class="p">(</span><span class="s">"./data/original"</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">file_name</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">all_file_names</span><span class="p">):</span>
<span class="n">image</span> <span class="o">=</span> <span class="n">Image</span><span class="p">.</span><span class="nb">open</span><span class="p">(</span><span class="sa">f</span><span class="s">"</span><span class="si">{</span><span class="bp">self</span><span class="p">.</span><span class="n">base_dir</span><span class="si">}</span><span class="s">/data/original/</span><span class="si">{</span><span class="n">file_name</span><span class="si">}</span><span class="s">"</span><span class="p">).</span><span class="n">convert</span><span class="p">(</span><span class="s">"L"</span><span class="p">)</span>
<span class="n">resized_image</span> <span class="o">=</span> <span class="n">image</span><span class="p">.</span><span class="n">resize</span><span class="p">(</span>
<span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">loader</span><span class="p">.</span><span class="n">target_size</span><span class="p">,</span> <span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">loader</span><span class="p">.</span><span class="n">target_size</span><span class="p">)</span>
<span class="p">)</span>
<span class="n">np_array</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">array</span><span class="p">(</span><span class="n">resized_image</span><span class="p">)</span>
<span class="n">current_background_color</span> <span class="o">=</span> <span class="n">np_array</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
<span class="n">np_array</span><span class="p">[</span><span class="n">np_array</span><span class="o">==</span><span class="n">current_background_color</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">loader</span><span class="p">.</span><span class="n">background_color</span>
<span class="n">np_array</span><span class="p">[</span><span class="n">np_array</span><span class="o"><</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">loader</span><span class="p">.</span><span class="n">detail_color_level</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">loader</span><span class="p">.</span><span class="n">background_color</span>
<span class="n">np_array</span><span class="p">[</span><span class="n">np_array</span><span class="o">!=</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">loader</span><span class="p">.</span><span class="n">background_color</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">loader</span><span class="p">.</span><span class="n">detail_color_level</span>
<span class="n">im</span> <span class="o">=</span> <span class="n">Image</span><span class="p">.</span><span class="n">fromarray</span><span class="p">(</span><span class="n">np_array</span><span class="p">)</span>
<span class="n">saving_path</span> <span class="o">=</span> <span class="sa">f</span><span class="s">"</span><span class="si">{</span><span class="bp">self</span><span class="p">.</span><span class="n">base_dir</span><span class="si">}</span><span class="s">/data/black_white/images/</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s">.jpg"</span>
<span class="n">im</span><span class="p">.</span><span class="n">save</span><span class="p">(</span><span class="n">saving_path</span><span class="p">)</span>
</code></pre></div></div>
<h2 id="generator">Generator</h2>
<p>The generator of this GAN model is exactly the same as for a DCGAN. That is because all adjustments of the Wasserstein divergence and gradient penalty are made on the discriminator (which is therefore called the critic instead).</p>
<p>In contrast to other implementations online, we are using <em>LeakyRelU</em> instead “normal” <em>RelU</em>. That is because of the suggestions of <a href="https://github.com/soumith/ganhacks">ganhacks</a>. According to this guideline, which is written by <a href="https://github.com/facebook">Facebook AI research</a>, it is also advised to use a <em>dropout</em> layer at multiple layers of the generative model, which we therefore also do. Though instead of using a dropout level of 0.5, as the guide suggests, we are using only 0.2, since the lower rate proved to result in better images in our scenario.</p>
<p>From the in-line comments of the code below we can nicely see how the size of the image increases over time. Starting with an image size of 4x4, we slowly work our way up, until we get to the desired image size of 64x64. When implementing any convolution model, it is always worthwhile to do the arithmetic, in order to see whether the specifications for padding, stride and etc. work out. We have to be a bit careful in this scenario, as we are using convolution transpose to increase the image size. The formula of how to calculate the resulting image size given the input parameters is stated below. Note that since we are working with square images, we are only showing the calculation for the height of the image, which is denoted by $H$. Additionally, the stride is denoted as $S$, the padding as $P$, the dilation as $D$ and the kernel size as $K$.</p>
\[\begin{align}
H_{out} = \left( H_{in} - 1 \right) \cdot S - 2 \cdot P + D \cdot \left( K - 1 \right) + 1
\end{align}\]
<p>Applying the formula above on our data, we then can calculate the size of the image at every layer. The following table summarizes the results of these calculations. Note that for the first layer we did not state an input size as here the noise vector is simply projected to the size of the kernel.</p>
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<table class="tg">
<thead>
<tr>
<th class="tg-c3ow">Layer Number</th>
<th class="tg-c3ow">Input Size</th>
<th class="tg-c3ow">Kernel Size</th>
<th class="tg-c3ow">Stride</th>
<th class="tg-c3ow">Padding</th>
<th class="tg-c3ow">Dilation</th>
<th class="tg-c3ow">Output Size</th>
</tr>
</thead>
<tbody>
<tr>
<td class="tg-c3ow">1</td>
<td class="tg-c3ow">/</td>
<td class="tg-c3ow">4</td>
<td class="tg-c3ow">1</td>
<td class="tg-c3ow">0</td>
<td class="tg-c3ow">1</td>
<td class="tg-c3ow">4</td>
</tr>
<tr>
<td class="tg-c3ow">2</td>
<td class="tg-c3ow">4</td>
<td class="tg-c3ow">4</td>
<td class="tg-c3ow">2</td>
<td class="tg-c3ow">1</td>
<td class="tg-c3ow">1</td>
<td class="tg-c3ow">8</td>
</tr>
<tr>
<td class="tg-c3ow">3</td>
<td class="tg-c3ow">8</td>
<td class="tg-c3ow">4</td>
<td class="tg-c3ow">2</td>
<td class="tg-c3ow">1</td>
<td class="tg-c3ow">1</td>
<td class="tg-c3ow">16</td>
</tr>
<tr>
<td class="tg-c3ow">4</td>
<td class="tg-c3ow">16</td>
<td class="tg-c3ow">4</td>
<td class="tg-c3ow">2</td>
<td class="tg-c3ow">1</td>
<td class="tg-c3ow">1</td>
<td class="tg-c3ow">32</td>
</tr>
<tr>
<td class="tg-c3ow">5</td>
<td class="tg-c3ow">32</td>
<td class="tg-c3ow">4</td>
<td class="tg-c3ow">2</td>
<td class="tg-c3ow">1</td>
<td class="tg-c3ow">1</td>
<td class="tg-7btt">64</td>
</tr>
</tbody>
</table>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1">### generator.py
</span>
<span class="c1"># %% Packages
</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="n">nn</span>
<span class="c1"># %% Class
</span>
<span class="k">class</span> <span class="nc">Generator</span><span class="p">(</span><span class="n">nn</span><span class="p">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">):</span>
<span class="bp">self</span><span class="p">.</span><span class="n">config</span> <span class="o">=</span> <span class="n">config</span>
<span class="nb">super</span><span class="p">(</span><span class="n">Generator</span><span class="p">,</span> <span class="bp">self</span><span class="p">).</span><span class="n">__init__</span><span class="p">()</span>
<span class="n">fm</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">model</span><span class="p">.</span><span class="n">feature_maps</span>
<span class="bp">self</span><span class="p">.</span><span class="n">main</span> <span class="o">=</span> <span class="n">nn</span><span class="p">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="bp">self</span><span class="p">.</span><span class="n">_block</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">model</span><span class="p">.</span><span class="n">noise_dim</span><span class="p">,</span> <span class="n">fm</span> <span class="o">*</span> <span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="c1"># 4
</span> <span class="bp">self</span><span class="p">.</span><span class="n">_block</span><span class="p">(</span><span class="n">fm</span> <span class="o">*</span> <span class="mi">16</span><span class="p">,</span> <span class="n">fm</span> <span class="o">*</span> <span class="mi">8</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="c1"># 8
</span> <span class="bp">self</span><span class="p">.</span><span class="n">_block</span><span class="p">(</span><span class="n">fm</span> <span class="o">*</span> <span class="mi">8</span><span class="p">,</span> <span class="n">fm</span> <span class="o">*</span> <span class="mi">4</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="c1"># 16
</span> <span class="bp">self</span><span class="p">.</span><span class="n">_block</span><span class="p">(</span><span class="n">fm</span> <span class="o">*</span> <span class="mi">4</span><span class="p">,</span> <span class="n">fm</span> <span class="o">*</span> <span class="mi">2</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="c1"># 32
</span> <span class="n">nn</span><span class="p">.</span><span class="n">ConvTranspose2d</span><span class="p">(</span><span class="n">fm</span> <span class="o">*</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="c1"># 64
</span> <span class="n">nn</span><span class="p">.</span><span class="n">Tanh</span><span class="p">()</span> <span class="c1"># [-1, 1]
</span> <span class="p">)</span>
<span class="k">def</span> <span class="nf">_block</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">):</span>
<span class="k">return</span> <span class="n">nn</span><span class="p">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="n">nn</span><span class="p">.</span><span class="n">ConvTranspose2d</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">),</span>
<span class="n">nn</span><span class="p">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">out_channels</span><span class="p">),</span>
<span class="n">nn</span><span class="p">.</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">model</span><span class="p">.</span><span class="n">leaky_relu_rate</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="bp">True</span><span class="p">),</span>
<span class="n">nn</span><span class="p">.</span><span class="n">Dropout</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">model</span><span class="p">.</span><span class="n">dropout_rate</span><span class="p">)</span>
<span class="p">)</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
<span class="n">output</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">main</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="k">return</span> <span class="n">output</span>
</code></pre></div></div>
<h2 id="critic">Critic</h2>
<p>The main difference between the critic and the discriminator is that the output of this model is not normalized between 0 and 1 anymore. Instead of providing probabilities how certain the model is that the image is real, the critic is simply outputting the value of its loss function. This method has the benefit of making the generator understand better how far away the fake data distribution is from the distribution of the real images.</p>
<p>In contrast to the generator, the critic is using traditional convolutions. Therefore, the formula to calculate the outputted image size given certain input parameters is a bit different. Using the same notation for padding, stride and etc., the formula is:</p>
\[\begin{align}
H_{out} = \frac{H_{in} + 2 \cdot P - D \cdot \left( K - 1 \right) - 1}{S} + 1
\end{align}\]
<p>Following that formula the we quickly also find how the image size is reduced from 64x64 down to one number, meaning a size of 1x1.</p>
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.tg {border-collapse:collapse;border-color:#aaa;border-spacing:0;}
.tg td{background-color:#fff;border-color:#aaa;border-style:solid;border-width:1px;color:#333;
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<table class="tg">
<thead>
<tr>
<th class="tg-c3ow">Layer Number</th>
<th class="tg-c3ow">Input Size</th>
<th class="tg-c3ow">Kernel Size</th>
<th class="tg-c3ow">Stride</th>
<th class="tg-c3ow">Padding</th>
<th class="tg-c3ow">Dilation</th>
<th class="tg-c3ow">Output Size</th>
</tr>
</thead>
<tbody>
<tr>
<td class="tg-c3ow">1</td>
<td class="tg-c3ow">64</td>
<td class="tg-c3ow">4</td>
<td class="tg-c3ow">2</td>
<td class="tg-c3ow">1</td>
<td class="tg-c3ow">1</td>
<td class="tg-c3ow">32</td>
</tr>
<tr>
<td class="tg-c3ow">2</td>
<td class="tg-c3ow">32</td>
<td class="tg-c3ow">4</td>
<td class="tg-c3ow">2</td>
<td class="tg-c3ow">1</td>
<td class="tg-c3ow">1</td>
<td class="tg-c3ow">16</td>
</tr>
<tr>
<td class="tg-c3ow">3</td>
<td class="tg-c3ow">16</td>
<td class="tg-c3ow">4</td>
<td class="tg-c3ow">2</td>
<td class="tg-c3ow">1</td>
<td class="tg-c3ow">1</td>
<td class="tg-c3ow">8</td>
</tr>
<tr>
<td class="tg-c3ow">4</td>
<td class="tg-c3ow">8</td>
<td class="tg-c3ow">4</td>
<td class="tg-c3ow">2</td>
<td class="tg-c3ow">1</td>
<td class="tg-c3ow">1</td>
<td class="tg-c3ow">4</td>
</tr>
<tr>
<td class="tg-c3ow">5</td>
<td class="tg-c3ow">4</td>
<td class="tg-c3ow">4</td>
<td class="tg-c3ow">2</td>
<td class="tg-c3ow">0</td>
<td class="tg-c3ow">1</td>
<td class="tg-7btt">0</td>
</tr>
</tbody>
</table>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1">### critic.py
</span>
<span class="c1"># %% Packages
</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="n">nn</span>
<span class="c1"># %% Class
</span>
<span class="k">class</span> <span class="nc">Critic</span><span class="p">(</span><span class="n">nn</span><span class="p">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">):</span>
<span class="bp">self</span><span class="p">.</span><span class="n">config</span> <span class="o">=</span> <span class="n">config</span>
<span class="nb">super</span><span class="p">(</span><span class="n">Critic</span><span class="p">,</span> <span class="bp">self</span><span class="p">).</span><span class="n">__init__</span><span class="p">()</span>
<span class="n">fm</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">model</span><span class="p">.</span><span class="n">feature_maps</span>
<span class="bp">self</span><span class="p">.</span><span class="n">main</span> <span class="o">=</span> <span class="n">nn</span><span class="p">.</span><span class="n">Sequential</span><span class="p">(</span> <span class="c1"># 64
</span> <span class="n">nn</span><span class="p">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">fm</span> <span class="o">*</span> <span class="mi">2</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="c1"># 32
</span> <span class="n">nn</span><span class="p">.</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="bp">True</span><span class="p">),</span>
<span class="bp">self</span><span class="p">.</span><span class="n">_block</span><span class="p">(</span><span class="n">fm</span> <span class="o">*</span> <span class="mi">2</span><span class="p">,</span> <span class="n">fm</span> <span class="o">*</span> <span class="mi">4</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="c1"># 16
</span> <span class="bp">self</span><span class="p">.</span><span class="n">_block</span><span class="p">(</span><span class="n">fm</span> <span class="o">*</span> <span class="mi">4</span><span class="p">,</span> <span class="n">fm</span> <span class="o">*</span> <span class="mi">8</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="c1"># 8
</span> <span class="bp">self</span><span class="p">.</span><span class="n">_block</span><span class="p">(</span><span class="n">fm</span> <span class="o">*</span> <span class="mi">8</span><span class="p">,</span> <span class="n">fm</span> <span class="o">*</span> <span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="c1"># 4
</span> <span class="n">nn</span><span class="p">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">fm</span> <span class="o">*</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">bias</span><span class="o">=</span><span class="bp">False</span><span class="p">),</span> <span class="c1"># 1
</span> <span class="p">)</span>
<span class="k">def</span> <span class="nf">_block</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">):</span>
<span class="k">return</span> <span class="n">nn</span><span class="p">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="n">nn</span><span class="p">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="bp">False</span><span class="p">),</span>
<span class="n">nn</span><span class="p">.</span><span class="n">InstanceNorm2d</span><span class="p">(</span><span class="n">out_channels</span><span class="p">),</span>
<span class="n">nn</span><span class="p">.</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">model</span><span class="p">.</span><span class="n">leaky_relu_rate</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="p">)</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
<span class="n">output</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">main</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="k">return</span> <span class="n">output</span>
</code></pre></div></div>
<h2 id="trainer">Trainer</h2>
<p>The models are then loaded into the training class. The training class starts by initializing the weights of the generator and the critic, before setting up the optimizers. Following the suggestions from ganhacks, we are using the <em>Adam</em> optimizer. The learning rate, as well as the values for the betas are taken from the original paper, namely 1e-4.</p>
<p>Note that we are creating some “constant” noise which is used to see how our generative model improves over time. Through the constant noise we can plot the output of the very same input after every adjustment of the model in order to see the change in output. The emphasis on visualization at various points is also one of the main reasons why this class contains so many more lines of code compared to the other files. Given that GANs do not have a objectively agreed on loss function to assess model performance, the visual inspection of results is particularly important.</p>
<p>Given that we are working with a relatively small amount of images (around 100), we have to ramp up the number of epochs the model is training with. In contrast to 50 epochs, which is used for example in the <a href="https://www.tensorflow.org/tutorials/generative/dcgan">tensorflow guide</a> when generating new MNIST pictures, we are using 1000 epochs. The small amount of images also reduces the batch size we can use. In contrast to the usual 32, 64, or even 128, we are using a batch size of a mere 8.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1">### trainer.py
</span>
<span class="c1"># %% Packages
</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">datetime</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="n">plt</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="n">np</span>
<span class="kn">import</span> <span class="nn">imageio</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torchvision</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="n">nn</span>
<span class="kn">import</span> <span class="nn">torch.optim</span> <span class="k">as</span> <span class="n">optim</span>
<span class="kn">from</span> <span class="nn">torch.utils.tensorboard</span> <span class="kn">import</span> <span class="n">SummaryWriter</span>
<span class="c1"># %% Class
</span>
<span class="k">class</span> <span class="nc">GanTrainer</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">generator</span><span class="p">,</span> <span class="n">critic</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">config</span><span class="p">):</span>
<span class="bp">self</span><span class="p">.</span><span class="n">generator</span> <span class="o">=</span> <span class="n">generator</span>
<span class="bp">self</span><span class="p">.</span><span class="n">critic</span> <span class="o">=</span> <span class="n">critic</span>
<span class="bp">self</span><span class="p">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span>
<span class="bp">self</span><span class="p">.</span><span class="n">config</span> <span class="o">=</span> <span class="n">config</span>
<span class="bp">self</span><span class="p">.</span><span class="n">device</span> <span class="o">=</span> <span class="s">"cpu"</span>
<span class="c1"># Initialization empty containers
</span> <span class="bp">self</span><span class="p">.</span><span class="n">generator_loss</span> <span class="o">=</span> <span class="p">[]</span>
<span class="bp">self</span><span class="p">.</span><span class="n">critic_loss</span> <span class="o">=</span> <span class="p">[]</span>
<span class="bp">self</span><span class="p">.</span><span class="n">image_list</span> <span class="o">=</span> <span class="p">[]</span>
<span class="c1"># Creation of tensorboard and training model
</span> <span class="bp">self</span><span class="p">.</span><span class="n">sw_real</span><span class="p">,</span> <span class="bp">self</span><span class="p">.</span><span class="n">sw_fake</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">create_tensorboard</span><span class="p">()</span>
<span class="bp">self</span><span class="p">.</span><span class="n">train_gan</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">gradient_penalty</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">critic</span><span class="p">,</span> <span class="n">real</span><span class="p">,</span> <span class="n">fake</span><span class="p">):</span>
<span class="s">"""
This method calculates the gradient penalty, which is the essential component of the WGAN with
gradient penalty
:param critic: Critic Model
:param real: Batch of real images
:param fake: Batch of fake images created by the generator
:return:
"""</span>
<span class="n">batch_size</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="n">real</span><span class="p">.</span><span class="n">shape</span>
<span class="n">epsilon</span> <span class="o">=</span> <span class="n">torch</span><span class="p">.</span><span class="n">rand</span><span class="p">((</span><span class="n">batch_size</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)).</span><span class="n">repeat</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">).</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">device</span><span class="p">)</span>
<span class="n">interpolated_images</span> <span class="o">=</span> <span class="n">real</span> <span class="o">*</span> <span class="n">epsilon</span> <span class="o">+</span> <span class="n">fake</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">epsilon</span><span class="p">)</span>
<span class="n">mixed_scores</span> <span class="o">=</span> <span class="n">critic</span><span class="p">(</span><span class="n">interpolated_images</span><span class="p">)</span>
<span class="n">gradient</span> <span class="o">=</span> <span class="n">torch</span><span class="p">.</span><span class="n">autograd</span><span class="p">.</span><span class="n">grad</span><span class="p">(</span>
<span class="n">inputs</span><span class="o">=</span><span class="n">interpolated_images</span><span class="p">,</span>
<span class="n">outputs</span><span class="o">=</span><span class="n">mixed_scores</span><span class="p">,</span>
<span class="n">grad_outputs</span><span class="o">=</span><span class="n">torch</span><span class="p">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">mixed_scores</span><span class="p">),</span>
<span class="n">create_graph</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
<span class="n">retain_graph</span><span class="o">=</span><span class="bp">True</span>
<span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">gradient</span> <span class="o">=</span> <span class="n">gradient</span><span class="p">.</span><span class="n">view</span><span class="p">(</span><span class="n">gradient</span><span class="p">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="n">gradient_norm</span> <span class="o">=</span> <span class="n">gradient</span><span class="p">.</span><span class="n">norm</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">gradient_penalty</span> <span class="o">=</span> <span class="n">torch</span><span class="p">.</span><span class="n">mean</span><span class="p">((</span><span class="n">gradient_norm</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
<span class="k">return</span> <span class="n">gradient_penalty</span>
<span class="k">def</span> <span class="nf">create_tensorboard</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="s">"""
Setting up the tensorboard
:return:
"""</span>
<span class="n">sw_real</span> <span class="o">=</span> <span class="n">SummaryWriter</span><span class="p">(</span><span class="sa">f</span><span class="s">"./logs/</span><span class="si">{</span><span class="n">datetime</span><span class="p">.</span><span class="n">datetime</span><span class="p">.</span><span class="n">now</span><span class="p">():</span><span class="o">%</span><span class="n">Y</span><span class="o">-%</span><span class="n">m</span><span class="o">-%</span><span class="n">d</span><span class="si">}</span><span class="s">/real"</span><span class="p">)</span>
<span class="n">sw_fake</span> <span class="o">=</span> <span class="n">SummaryWriter</span><span class="p">(</span><span class="sa">f</span><span class="s">"./logs/</span><span class="si">{</span><span class="n">datetime</span><span class="p">.</span><span class="n">datetime</span><span class="p">.</span><span class="n">now</span><span class="p">():</span><span class="o">%</span><span class="n">Y</span><span class="o">-%</span><span class="n">m</span><span class="o">-%</span><span class="n">d</span><span class="si">}</span><span class="s">/fake"</span><span class="p">)</span>
<span class="k">return</span> <span class="n">sw_real</span><span class="p">,</span> <span class="n">sw_fake</span>
<span class="k">def</span> <span class="nf">weights_init</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">m</span><span class="p">):</span>
<span class="s">"""
Weight initialization method for the generator and critic
:param m: The inputted model (either generator or critic)
:return:
"""</span>
<span class="n">classname</span> <span class="o">=</span> <span class="n">m</span><span class="p">.</span><span class="n">__class__</span><span class="p">.</span><span class="n">__name__</span>
<span class="k">if</span> <span class="n">classname</span><span class="p">.</span><span class="n">find</span><span class="p">(</span><span class="s">"Conv"</span><span class="p">)</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
<span class="n">nn</span><span class="p">.</span><span class="n">init</span><span class="p">.</span><span class="n">normal_</span><span class="p">(</span><span class="n">m</span><span class="p">.</span><span class="n">weight</span><span class="p">.</span><span class="n">data</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">classname</span><span class="p">.</span><span class="n">find</span><span class="p">(</span><span class="s">"BatchNorm"</span><span class="p">)</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
<span class="n">nn</span><span class="p">.</span><span class="n">init</span><span class="p">.</span><span class="n">normal_</span><span class="p">(</span><span class="n">m</span><span class="p">.</span><span class="n">weight</span><span class="p">.</span><span class="n">data</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">)</span>
<span class="n">nn</span><span class="p">.</span><span class="n">init</span><span class="p">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">m</span><span class="p">.</span><span class="n">bias</span><span class="p">.</span><span class="n">data</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">plot_losses</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="s">"""
Method to plot the loss function of critic and generator
:return:
"""</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
<span class="n">axs</span><span class="p">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">generator_loss</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">"Generator Loss"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">critic_loss</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">"Critic Loss"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s">"Iterations"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s">"Loss"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">fname</span> <span class="o">=</span> <span class="s">"./reports/figures/loss.png"</span>
<span class="n">fig</span><span class="p">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">fname</span><span class="o">=</span><span class="n">fname</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s">"tight"</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">close</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">save_epoch_image</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">grid</span><span class="p">,</span> <span class="n">epoch</span><span class="p">):</span>
<span class="s">"""
Saving a snapshot of fixed noise in order to see the progress made by the mode
:param grid: Grid from PyTorch
:param epoch: Integer indicating which epoch we are at
:return:
"""</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">image_grid</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">grid</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
<span class="n">axs</span><span class="p">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">image_grid</span><span class="p">)</span>
<span class="n">axs</span><span class="p">.</span><span class="n">axis</span><span class="p">(</span><span class="s">"off"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s">"Fake Images at Epoch Number </span><span class="si">{</span><span class="n">epoch</span><span class="si">}</span><span class="s">"</span><span class="p">)</span>
<span class="n">fname</span> <span class="o">=</span> <span class="sa">f</span><span class="s">"./reports/epoch_examples/epoch_</span><span class="si">{</span><span class="n">epoch</span><span class="si">}</span><span class="s">.png"</span>
<span class="n">fig</span><span class="p">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">fname</span><span class="o">=</span><span class="n">fname</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s">"tight"</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">close</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">save_model</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="s">"""
Saving the generator model in order to be used outside of the trainer.py
:return:
"""</span>
<span class="n">model_path</span> <span class="o">=</span> <span class="s">"./model/generator.h5"</span>
<span class="n">torch</span><span class="p">.</span><span class="n">save</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">generator</span><span class="p">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="n">model_path</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">build_gif</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="s">"""
Method which builds a gif from all the snapshots created. This helps assess progress made by the model
:return:
"""</span>
<span class="n">images</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">file_names</span> <span class="o">=</span> <span class="n">os</span><span class="p">.</span><span class="n">listdir</span><span class="p">(</span><span class="s">"./reports/epoch_examples"</span><span class="p">)</span>
<span class="n">file_names</span><span class="p">.</span><span class="n">sort</span><span class="p">(</span><span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">float</span><span class="p">(</span><span class="n">x</span><span class="p">.</span><span class="n">strip</span><span class="p">(</span><span class="s">"epoch_"</span><span class="p">).</span><span class="n">strip</span><span class="p">(</span><span class="s">".png"</span><span class="p">)))</span>
<span class="k">for</span> <span class="nb">file</span> <span class="ow">in</span> <span class="n">file_names</span><span class="p">:</span>
<span class="n">images</span><span class="p">.</span><span class="n">append</span><span class="p">(</span><span class="n">imageio</span><span class="p">.</span><span class="n">imread</span><span class="p">(</span><span class="sa">f</span><span class="s">"./reports/epoch_examples/</span><span class="si">{</span><span class="nb">file</span><span class="si">}</span><span class="s">"</span><span class="p">))</span>
<span class="n">imageio</span><span class="p">.</span><span class="n">mimsave</span><span class="p">(</span><span class="s">"./reports/figures/epoch.gif"</span><span class="p">,</span> <span class="n">images</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">train_gan</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="s">"""
Training the model in its entirety
:return:
"""</span>
<span class="bp">self</span><span class="p">.</span><span class="n">generator</span><span class="p">.</span><span class="nb">apply</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">weights_init</span><span class="p">)</span>
<span class="bp">self</span><span class="p">.</span><span class="n">critic</span><span class="p">.</span><span class="nb">apply</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">weights_init</span><span class="p">)</span>
<span class="n">optimizer_generator</span> <span class="o">=</span> <span class="n">optim</span><span class="p">.</span><span class="n">Adam</span><span class="p">(</span>
<span class="bp">self</span><span class="p">.</span><span class="n">generator</span><span class="p">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">trainer</span><span class="p">.</span><span class="n">learning_rate</span><span class="p">,</span> <span class="n">betas</span><span class="o">=</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">)</span>
<span class="p">)</span>
<span class="n">optimizer_critc</span> <span class="o">=</span> <span class="n">optim</span><span class="p">.</span><span class="n">Adam</span><span class="p">(</span>
<span class="bp">self</span><span class="p">.</span><span class="n">critic</span><span class="p">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">trainer</span><span class="p">.</span><span class="n">learning_rate</span><span class="p">,</span> <span class="n">betas</span><span class="o">=</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">)</span>
<span class="p">)</span>
<span class="n">constant_noise</span> <span class="o">=</span> <span class="n">torch</span><span class="p">.</span><span class="n">randn</span><span class="p">(</span>
<span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">trainer</span><span class="p">.</span><span class="n">number_of_examples</span><span class="p">,</span> <span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">trainer</span><span class="p">.</span><span class="n">noise_dimension</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">device</span>
<span class="p">)</span>
<span class="bp">self</span><span class="p">.</span><span class="n">generator</span><span class="p">.</span><span class="n">train</span><span class="p">()</span>
<span class="bp">self</span><span class="p">.</span><span class="n">critic</span><span class="p">.</span><span class="n">train</span><span class="p">()</span>
<span class="n">global_step</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">trainer</span><span class="p">.</span><span class="n">number_of_epochs</span><span class="p">):</span>
<span class="k">for</span> <span class="n">real_image</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="bp">self</span><span class="p">.</span><span class="n">data</span><span class="p">.</span><span class="n">loader</span><span class="p">:</span>
<span class="n">current_batch_size</span> <span class="o">=</span> <span class="n">real_image</span><span class="p">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="c1"># Critic Training
</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">trainer</span><span class="p">.</span><span class="n">critic_iterations</span><span class="p">):</span>
<span class="n">noise</span> <span class="o">=</span> <span class="n">torch</span><span class="p">.</span><span class="n">randn</span><span class="p">(</span>
<span class="n">current_batch_size</span><span class="p">,</span> <span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">trainer</span><span class="p">.</span><span class="n">noise_dimension</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">device</span>
<span class="p">)</span>
<span class="n">fake_image</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">generator</span><span class="p">(</span><span class="n">noise</span><span class="p">)</span>
<span class="n">critic_real</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">critic</span><span class="p">(</span><span class="n">real_image</span><span class="p">).</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="n">critic_fake</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">critic</span><span class="p">(</span><span class="n">fake_image</span><span class="p">).</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="n">gradient_penalty</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">gradient_penalty</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">critic</span><span class="p">,</span> <span class="n">real_image</span><span class="p">,</span> <span class="n">fake_image</span><span class="p">)</span>
<span class="n">loss_critic</span> <span class="o">=</span> <span class="p">(</span>
<span class="o">-</span> <span class="p">(</span><span class="n">torch</span><span class="p">.</span><span class="n">mean</span><span class="p">(</span><span class="n">critic_real</span><span class="p">)</span> <span class="o">-</span> <span class="n">torch</span><span class="p">.</span><span class="n">mean</span><span class="p">(</span><span class="n">critic_fake</span><span class="p">))</span>
<span class="o">+</span> <span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">trainer</span><span class="p">.</span><span class="n">lambda_gp</span> <span class="o">*</span> <span class="n">gradient_penalty</span><span class="p">)</span>
<span class="p">)</span>
<span class="bp">self</span><span class="p">.</span><span class="n">critic</span><span class="p">.</span><span class="n">zero_grad</span><span class="p">()</span>
<span class="n">loss_critic</span><span class="p">.</span><span class="n">backward</span><span class="p">(</span><span class="n">retain_graph</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">optimizer_critc</span><span class="p">.</span><span class="n">step</span><span class="p">()</span>
<span class="c1"># Generator Training
</span> <span class="n">gen_fake</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">critic</span><span class="p">(</span><span class="n">fake_image</span><span class="p">).</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="n">loss_gen</span> <span class="o">=</span> <span class="o">-</span><span class="n">torch</span><span class="p">.</span><span class="n">mean</span><span class="p">(</span><span class="n">gen_fake</span><span class="p">)</span>
<span class="bp">self</span><span class="p">.</span><span class="n">generator</span><span class="p">.</span><span class="n">zero_grad</span><span class="p">()</span>
<span class="n">loss_gen</span><span class="p">.</span><span class="n">backward</span><span class="p">()</span>
<span class="n">optimizer_generator</span><span class="p">.</span><span class="n">step</span><span class="p">()</span>
<span class="c1"># Print losses occasionally and print to tensorboard
</span> <span class="k">if</span> <span class="n">epoch</span> <span class="o">%</span> <span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">trainer</span><span class="p">.</span><span class="n">print_epochs_after</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">epoch</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="k">print</span><span class="p">(</span>
<span class="sa">f</span><span class="s">"Epoch [</span><span class="si">{</span><span class="n">epoch</span><span class="si">}</span><span class="s">/</span><span class="si">{</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">trainer</span><span class="p">.</span><span class="n">number_of_epochs</span><span class="si">}</span><span class="s">] "</span>
<span class="sa">f</span><span class="s">"Loss D: </span><span class="si">{</span><span class="n">loss_critic</span><span class="p">:.</span><span class="mi">4</span><span class="n">f</span><span class="si">}</span><span class="s">, loss G: </span><span class="si">{</span><span class="n">loss_gen</span><span class="p">:.</span><span class="mi">4</span><span class="n">f</span><span class="si">}</span><span class="s">"</span>
<span class="p">)</span>
<span class="bp">self</span><span class="p">.</span><span class="n">plot_losses</span><span class="p">()</span>
<span class="bp">self</span><span class="p">.</span><span class="n">save_model</span><span class="p">()</span>
<span class="bp">self</span><span class="p">.</span><span class="n">build_gif</span><span class="p">()</span>
<span class="c1"># Tensorboard
</span> <span class="k">with</span> <span class="n">torch</span><span class="p">.</span><span class="n">no_grad</span><span class="p">():</span>
<span class="n">fake_examples</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">generator</span><span class="p">(</span><span class="n">constant_noise</span><span class="p">)</span>
<span class="n">real_examples</span> <span class="o">=</span> <span class="n">real_image</span><span class="p">[:</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">trainer</span><span class="p">.</span><span class="n">number_of_examples</span><span class="p">]</span>
<span class="n">img_grid_real</span> <span class="o">=</span> <span class="n">torchvision</span><span class="p">.</span><span class="n">utils</span><span class="p">.</span><span class="n">make_grid</span><span class="p">(</span><span class="n">real_examples</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">normalize</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">img_grid_fake</span> <span class="o">=</span> <span class="n">torchvision</span><span class="p">.</span><span class="n">utils</span><span class="p">.</span><span class="n">make_grid</span><span class="p">(</span><span class="n">fake_examples</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">normalize</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="bp">self</span><span class="p">.</span><span class="n">sw_real</span><span class="p">.</span><span class="n">add_image</span><span class="p">(</span><span class="s">"Real"</span><span class="p">,</span> <span class="n">img_grid_real</span><span class="p">,</span> <span class="n">global_step</span><span class="o">=</span><span class="n">global_step</span><span class="p">)</span>
<span class="bp">self</span><span class="p">.</span><span class="n">sw_fake</span><span class="p">.</span><span class="n">add_image</span><span class="p">(</span><span class="s">"Fake"</span><span class="p">,</span> <span class="n">img_grid_fake</span><span class="p">,</span> <span class="n">global_step</span><span class="o">=</span><span class="n">global_step</span><span class="p">)</span>
<span class="c1"># Filling containers
</span> <span class="bp">self</span><span class="p">.</span><span class="n">generator_loss</span><span class="p">.</span><span class="n">append</span><span class="p">(</span><span class="n">loss_gen</span><span class="p">.</span><span class="n">item</span><span class="p">())</span>
<span class="bp">self</span><span class="p">.</span><span class="n">critic_loss</span><span class="p">.</span><span class="n">append</span><span class="p">(</span><span class="n">loss_critic</span><span class="p">.</span><span class="n">item</span><span class="p">())</span>
<span class="c1"># Save epoch example image
</span> <span class="bp">self</span><span class="p">.</span><span class="n">save_epoch_image</span><span class="p">(</span><span class="n">img_grid_fake</span><span class="p">,</span> <span class="n">epoch</span><span class="p">)</span>
<span class="n">global_step</span> <span class="o">+=</span> <span class="mi">1</span>
</code></pre></div></div>
<h2 id="main">Main</h2>
<p>Lastly we import all priorly shown files into the main.py and execute the code from the terminal. It is to be said that GANs take a very long time to train, especially when one’s computer does not have a GPU. In order to speed up training, one could either use a cloud solution, or make use of <a href="https://colab.research.google.com/?utm_source=scs-index">Google Colab</a>’s free (but limited) usage.</p>
<p>One of the main benefits of the usage of a <code> config </code> file, like we do, is that the user can easily change several parameters of the model, without having to step into any of the Python scripts. This is especially convenient for users external to the development of this project.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1">### main.py
</span>
<span class="c1"># %% Packages
</span>
<span class="kn">from</span> <span class="nn">utils.args</span> <span class="kn">import</span> <span class="n">get_args</span>
<span class="kn">from</span> <span class="nn">utils.config</span> <span class="kn">import</span> <span class="n">process_config</span>
<span class="kn">from</span> <span class="nn">loader</span> <span class="kn">import</span> <span class="n">DataLoader</span>
<span class="kn">from</span> <span class="nn">critic</span> <span class="kn">import</span> <span class="n">Critic</span>
<span class="kn">from</span> <span class="nn">generator</span> <span class="kn">import</span> <span class="n">Generator</span>
<span class="kn">from</span> <span class="nn">trainer</span> <span class="kn">import</span> <span class="n">GanTrainer</span>
<span class="c1"># %% Script
</span>
<span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
<span class="n">args</span> <span class="o">=</span> <span class="n">get_args</span><span class="p">()</span>
<span class="n">config</span> <span class="o">=</span> <span class="n">process_config</span><span class="p">(</span><span class="n">args</span><span class="p">.</span><span class="n">config</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s">"Load Discriminator"</span><span class="p">)</span>
<span class="n">critic</span> <span class="o">=</span> <span class="n">Critic</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s">"Load Generator"</span><span class="p">)</span>
<span class="n">generator</span> <span class="o">=</span> <span class="n">Generator</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s">"Load Data"</span><span class="p">)</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s">"Training the model"</span><span class="p">)</span>
<span class="n">trainer</span> <span class="o">=</span> <span class="n">GanTrainer</span><span class="p">(</span><span class="n">generator</span><span class="p">,</span> <span class="n">critic</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">config</span><span class="p">)</span>
<span class="k">if</span> <span class="n">__name__</span> <span class="o">==</span> <span class="s">"__main__"</span><span class="p">:</span>
<span class="n">main</span><span class="p">()</span>
</code></pre></div></div>
<h1 id="results">Results</h1>
<p>After training the model for around a day, we find some decent results. Below one can see the progress the model made from the very beginning. It is nicely visible that the model started with a black blob, but then quickly started to recognize the need for a round shape and some white spots in the middle. It then took the model quite a bit more time before recognizing what kind of design the rims are supposed to have in order to be mistaken for a real one. After around 300-400 epochs there is not too much change anymore and the model performance stagnates. At this point one could have already stopped training as not much is happening more. One way to get a higher level of detail would be to increase the image size, since details would then be easier to spot for the model.</p>
<center>
<img src="/assets/post_images/felgen_gan/epoch_64.gif" width="750" align="center" />
</center>
<p>One significant drawback of GANs is the lack of a proper loss function. Since the generator and the critic are playing against each other in some sort of a zero-sum game, it is unclear what kind of loss metric we should look at in order to assess the model performance. The usage of the Wasserstein divergence helps a bit here. That is because the original paper is documenting a correlation between the loss of the generative model and the overall image quality. However, the same paper also states that:</p>
<p>…<em>we do not claim that this is a new method to quantitatively evaluate generative models yet. The constant scaling factor that depends on the critic’s architecture means it’s hard to compare models with different critics.</em></p>
<center>
<img src="/assets/post_images/felgen_gan/wgan_loss.png" width="750" align="center" />
</center>
<p><a href="https://arxiv.org/abs/1701.07875">Source</a></p>
<p>Contrasting that image from the original paper to our loss function, we can see from the blue line that an overall decline is visible. Therefore using the loss of the generative model could indeed serve us as an indication of how well the model is doing.</p>
<h2 id="outlook">Outlook</h2>
<p>Lastly, it to be said that we are overall quite happy with the results of our rim designs. In the future different implementations of GANs (e.g. StyleGans) could be tried in order to see whether a more satisfying results are obtainable. Additionally it would be beneficial to find a use-case for which we have more images. Especially the big design studios around the world must have tons of such data and it will be only a matter of time until your next T-Shirt is going to be designed by an AI.</p>
<h1 id="appendix">Appendix</h1>
<h2 id="references">References</h2>
<p>[1] - Goodfellow, Ian J., et al. “Generative adversarial networks.” arXiv preprint arXiv:1406.2661 (2014).
APA</p>
<p>[2] - Radford, Alec, Luke Metz, and Soumith Chintala. “Unsupervised representation learning with deep convolutional generative adversarial networks.” arXiv preprint arXiv:1511.06434 (2015).
APA</p>
<p>[3] - Arjovsky, Martin, Soumith Chintala, and Léon Bottou. “Wasserstein generative adversarial networks.” International conference on machine learning. PMLR, 2017.</p>
<p>[4] - Gulrajani, Ishaan, et al. “Improved training of wasserstein gans.” arXiv preprint arXiv:1704.00028 (2017).</p>
<h2 id="config-file">Config File</h2>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">import</span> <span class="nn">json</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s">"../source/config.json"</span><span class="p">)</span> <span class="k">as</span> <span class="n">json_file</span><span class="p">:</span>
<span class="n">config_file</span> <span class="o">=</span> <span class="n">json</span><span class="p">.</span><span class="n">load</span><span class="p">(</span><span class="n">json_file</span><span class="p">)</span>
<span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">config_file</span><span class="p">.</span><span class="n">items</span><span class="p">():</span>
<span class="k">print</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
</code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>loader
{'create_new_images': False, 'target_size': 64, 'detail_color_level': 50, 'background_color': 255, 'detail_color': 0, 'batch_size': 8}
model
{'leaky_relu_rate': 0.2, 'noise_dim': 100, 'dropout_rate': 0.2, 'feature_maps': 64}
trainer
{'learning_rate': 0.0001, 'batch_size': 8, 'noise_dimension': 100, 'number_of_epochs': 1000, 'lambda_gp': 10, 'number_of_examples': 16, 'critic_iterations': 5, 'print_epochs_after': 5}
</code></pre></div></div>Paul MoraThis post elaborates on the idea of using Generative Adversarial Networks to design new products, such as car rims. Every time a company is looking for a new design of a product, hundreds of designers start their work and produce multiple suggestions each. Given the nature of the business, only one out of these suggestions is going to be implemented in the end, with the all other suggestions mostly thrown away. This large inefficiency could be resolved by the possibility of creating new design suggestions as easy as a click on a button. This is where GANs come in. By feeding them with images of already existing and economically successful images, the model is ultimately able to generate new design suggestions which are not possible to tell apart from real ones. For that purpose we used a few images car rims and implemented a Wasserstein GAN with Gradient Penalty in order to see how well the model can generate new design suggestions.Generative Adversial Networks and Wasserstein Addition2021-06-07T00:00:00+00:002021-06-07T00:00:00+00:00https://paul-mora.com/generative%20models/python/Generative-Adversial-Networks-and-Wasserstein-Addition<p>This blog-post elaborates on the workings of Generative Adversial Networks (GANs). Particularly we are looking at the high-level mathematics and intuition of GANs. Furthermore, we are looking into the weaknesses of GANs and proposed enhancements. One development of GANs we are looking deeper into is called the Wasserstein GAN (WGAN), which introduced a new distribution distance function. In the very end of the blog-post we are showing the results of applying such a WGAN model on flag images from all around the world with promising results.</p>
<h1 id="concept-of-gans">Concept of GANs</h1>
<p>Generative Adversial Networks [1], or also known as GANs are all about creating artificial data, which with enough training resembles real data to the extent that neither a machine nor a human can tell the difference between real and fake anymore. This technique allows for sheer endless opportunities in nearly all aspects of our lives. Though, it also creates a debate about ethical usage of such technology, since it will soon be possible to create images which look completely realistic, but are actually the mere product of a machine, so-called <a href="https://en.wikipedia.org/wiki/Deepfake">deepfakes</a>. This post deals rather with the mathematical idea of GANs than with the ethical questions. That is not because the ethical aspects are in any form less important, but talking about the ethical aspects of such a technology would deserve its own post and not a mere side-note of another one.</p>
<p>When talking about a GAN, the first thing to understand is that a GAN does not consist out of one model only. In fact there are two models competing with each other. These two models are referred to as <em>Discriminator</em> and <em>Generator</em>. As the name arguably already suggests, the <em>Generator</em>’s job is to create the artificial image, whereas the <em>Discriminator</em> is trying to tell which image belongs to the real images and which one is fake.</p>
<center>
<img src="/assets/post_images/gan/ppt/working_gan.png" width="750" align="center" />
</center>
<p>The workings of the GAN are oftentimes compared to the never ending battle between art-forgers and the police. In the beginning the art-forgers are very bad at painting. Therefore all of their forgeries are pretty easy to tell apart from Van Gogh’s or Monet’s paintings. Luckily for the art-forgers though, they are always perfectly aware by exactly how much their forgery was away from the quality of a real painting. Based on that information, they update their painting technique and try again. At the same time, the police is also getting better at judging which painting is a fraud and which one is not. In the limits both the police as well as the art-forgers are constantly getting better, and with them, the created forgeries.</p>
<p>This analogy pretty accurately depicts what is happening when training a GAN. It is important to understand that every image, for example the images of national flags, can be described by a probability distribution. This distribution is rather complex and high dimensional. Furthermore, the distribution is also unknown to us. In order to create new flag images, we would have to learn this distribution. There are multiple ways to do so. The arguably most famous ways are <strong>Variational Autoencoders</strong> (VAE) and <strong>Generative Adversial Networks</strong>.</p>
<p>Variational Autoencoders, as the name already suggests, encode the real images and break them down into the most important features of the image. Afterwards, these feature are decoded again and it is tried to output the image we initially inputted. Afterwards we are comparing how different the inputted and outputted images are and update our network accordingly. The exact way how the encoding and decoding of a Variational Autoencoder work is left for a future post, since we are focusing more on the second method, namely GANs.</p>
<p>In contrast to VAEs, the general idea of GANs is that if we would like to sample from a complex, high dimensional distribution such as images, we just sample from a simple distribution and apply a transformation to it and through that approximate the complex distribution. This transformation is where neural networks come into play, since they are the perfect tool to approximate complex functions and distributions.</p>
<p>In practice that means that we are using random noise, usually taken from a Gaussian Distribution as the input of the <em>Generator</em>, which then outputs an image. This input vector is referred to as a latent vector. Which number within the input vector is controlling which shape or color from the resulting image is learned by the network alone. Through adjusting the weight parameters, the <em>Generator</em> is then learning which transformation is necessary in order to turn the distribution of the latent vector into the distribution of real images (i.e. how to transform $p_{z}$ into $p_{data}$).</p>
<p>For the <em>Discriminator</em> we are also using a neural network, but with another intent. Instead of starting with random latent vector and outputting an image, we are feeding the flattened image into the <em>Discriminator</em> and output a probability for whether the image is real.</p>
<h1 id="objective-function-of-gans">Objective Function of GANs</h1>
<p>To get a better feeling how GANs are trained, we take a look at the objective function. The objective function of GANs is slightly different from a standard objective function because we have two models to optimize rather than one.
More specifically, whereas the <em>Discriminator</em> would like to minimize the loss of separating real and fake images, the <em>Generator</em> is trying to maximize exactly this loss. The <em>Generator</em> would like the <em>Discriminator</em> to fail.</p>
<p>These objective functions where we have a minimization and maximization problem at the same time, are called <em>minimax</em> functions. In the following function the <em>Generator</em> is denoted as <em>G</em>, and the <em>Discriminator</em> is denoted as <em>D</em>.</p>
\[\begin{align}
\min_{G} \max_{D} & \; V(D, G) \\
V(D, G) = & \; \mathbb{E}_{x \sim P_{data}(x)} \left[log(D(x)) \right] + \mathbb{E}_{z \sim P_{z}(z)} \left[1-log(D(G(z)) \right]
\end{align}\]
<p>$P_{data}(x)$ describes the distribution of the real dataset. $P_{z}(z)$ describes the distribution of $z$, which is usually Gaussian. $G(z)$ describes the output of the <em>Generator</em> when we feed in the vector $z$. Furthermore, $D(x)$ describes the probability that the <em>Discriminator</em> thinks that the input $x$ is real. Equivalently, $D(G(z))$ describes the result of the <em>Discriminator</em> when feeding in the output of the <em>Generator</em>.</p>
<p>In the following we are now rewriting this objective function to gain further insights. We start by replacing the expectation symbols by what they actually stand for, a weighted average. Since we are dealing with a continuous problem, we are using an integral instead a summation sign.</p>
\[\begin{align*}
&= \int_{x} p_{data}(x) \; \log(D(x)) \; dx + \int_{z} p_z (z) \; \log(1-D(G(Z))) \; dz
\label{eq:rewritten_objective} \tag{1}
\end{align*}\]
<p>Now we would like to replace every instance of $z$ with an expression of $x$. This is done in order to have fewer variables to work with. This task is easily done after realizing that the data $x$ is nothing other than the output of the <em>Generator</em> which itself took $z$ as an input. Therefore we know:</p>
\[\begin{align}
x = G(z) \quad \rightarrow \quad z = G^{-1}(x) \quad \rightarrow \quad dz = (G^{-1})'(x)dx
\end{align}\]
<p>With these helper equations we can now replace any instance of $z$ in $\eqref{eq:rewritten_objective}$. This step results in the following equation:</p>
\[\begin{align*}
&= \int_{x} p_{data}(x) \; \log(D(x)) \; dx + \int_{x} p_z (G^{-1}(x)) \; \log(1-D(x))(G^{-1})'(x)) \; dx
\end{align*}\]
<p>We note that there is still one $z$ in the equation above, namely $p_z$. In order to also replace this part of the expression we are using a mathematical property of <strong>probability density functions</strong>. Namely, that if one variable is a monotonic transformation of another variable, then the differential area must be invariant under change of variables.</p>
<hr />
<h5 id="digression-probability-density-function">Digression: Probability density function</h5>
<p>If we have a monotonic transformation, for example \(Y = g(X)\), then it follows that \(|f_Y(y) dy = f_X(x) dx|\).
<a href="https://en.wikipedia.org/wiki/Probability_density_function#Dependent_variables_and_change_of_variables">That is because that the probability contained in a differential area bust me invariant under change of variables.</a> That is because $f_X(x)dx$ represents nothing other than mass under the density curve. $f_X(x)$ represents the height of the density function and $dx$ the width. A monotonic transformation of the product of height and width of the density function might change the level of height and width, but not the product. Because of the following we can write:</p>
\[\begin{align}
f_Y(y) = \left| \frac{dx}{dy} \right| f_X(x) = \left| \frac{d}{dy} (x) \right| f_X(x) = \left| \frac{d}{dy} (g^{-1} (y)) \right| f_X(g^{-1} (y)) = \left| (g^{-1} (y)) \right| f_X(g^{-1} (y))
\end{align}\]
<hr />
<p>Given that we are assuming that the transformation the <em>Generator</em> is applying on the latent vector $z$ is monotonic, we can rewrite $p_z (G^{-1}(x)) (G^{-1})’(x))$ as simply $p_g$. After replacing also the last occurrence of the latent vector $z$, our final equation looks like the following:</p>
\[\begin{align*}
&= \int_{x} p_{data}(x) \; \log(D(x)) \; dx + \int_{x} p_g \; \log(1-D(x)) dx \\
&= \int_{x} p_{data}(x) \; \log(D(x)) \; + p_g \; \log(1-D(x)) dx \\
\end{align*}\]
<p>After eliminating every occurrence of $z$ within our equation, we now shift our attention to the inner maximization problem. The idea here is that we would like to maximize the objective functions with respect to D. We are maximizing this, since we would like that the <em>Discriminator</em> is able to separate fake and real images.</p>
\[\begin{align}
\max_{D} V(D, G) = \int_{x} p_{data}(x) \; \log(D(x)) \; + p_g \; \log(1-D(x)) dx
\label{eq:only_x_function} \tag{2}
\end{align}\]
<p>Maximizing this function means that we have to take the first derivative of that function and set it equal to zero. Note that we are taking the derivative of that function at the position of $x$. Because of that, we are not seeing any integral sign in the equation below.</p>
\[\begin{align}
\frac{\partial}{\partial D(x)} p_{data}(x) \; \log(D(x)) \; + p_g \; \log(1-D(x)) &= 0 \\
\rightarrow \frac{p_{data}(x)}{D(x)} - \frac{p_g(x)}{1-D(x)} &= 0 \\
\rightarrow D(x) &= \frac{p_{data}(x)}{p_{data}(x) + p_g(x)}
\end{align}\]
<p>This result is quite important. It tells us what the optimal level of the <em>Discriminator</em> is for a given <em>Generator</em>. We are now able to use that result and insert it into our objective function in $\eqref{eq:only_x_function}$. We do that in order to find the resulting level of the <em>Generator</em> when using the optimal value of the <em>Discriminator</em>.</p>
\[\begin{align}
&= \int_{x} p_{data}(x) \; \log(D(x)) \; + p_g \; \log(1-D(x)) dx\\
&= \int_{x} p_{data}(x) \; \log(D^*(x)) \; + p_g \; \log(1-D^*(x)) dx\\
&= \int_{x} p_{data}(x) \; \log\left( \frac{p_{data}(x)}{p_{data}(x) + p_g(x)} \right) \; + p_g \; \log\left( \frac{p_{g}(x)}{p_{data}(x) + p_g(x)} \right) dx\\
\end{align}\]
<p>After getting the equation to that not very intuitive shape, we have to make a mathematically allowed, but strange looking adjustment. Namely, we have to divide and multiply the denominator by positive two. Note that the following equations make use of that fact that $log(\frac{1}{2} \cdot A) = log(A) - log(2)$.</p>
\[\begin{align}
&= \int_{x} p_{data}(x) \; \log\left( \frac{p_{data}(x)}{2\frac{p_{data}(x) + p_g(x)}{2}} \right) \; + p_g \; \log\left( \frac{p_{g}(x)}{2\frac{p_{data}(x) + p_g(x)}{2}} \right) dx \\
&= \int_{x} p_{data}(x) \; \log\left( \frac{p_{data}(x)}{\frac{p_{data}(x) + p_g(x)}{2}} \right) \; + p_g \; \log\left( \frac{p_{g}(x)}{\frac{p_{data}(x) + p_g(x)}{2}} \right) dx - 2\log(2) \\
\end{align}\]
<p>The equation we arrived at yet is also known as the <strong>Kullback-Leibler Divergence</strong> (aka <a href="https://en.wikipedia.org/wiki/Kullback–Leibler_divergence">KL-Divergence</a>). KL-Divergence is used to quantify the difference of two distributions.</p>
\[\begin{align}
&= KL \left[ p_{data}(x) || \frac{p_{data} + p_g}{2} \right] + KL \left[ p_{g}(x) || \frac{p_{data} + p_g}{2} \right] - 2 log(2)
\label{eq: kl_divergence} \tag{3}
\end{align}\]
<p>We are interested in minimizing that quantity with regards to the <em>Generator</em> since if the difference in distribution of $p_g$ and $p_{data}$ is small, we are creating fake data which is very similar to real data.</p>
<p>When trying to minimize the expression above, we have to note that KL-Divergence cannot be negative, as it is a measure of similarity. Therefore we know that expression $\eqref{eq: kl_divergence}$ is minimal when the KL-Divergence parts are equal to zero.</p>
<p>The difference in two distribution is only zero if and only if the distributions of $p_{data}(x)$ (or $p_{g}(x)$) and $\frac{p_{data} + p_g}{2}$ are equal. For that to be the case, the following would have to be fulfilled:</p>
\[\begin{align}
KL \left[ p_{data}(x) || \frac{p_{data}(x) + p_{g}(x)}{2} \right] &= 0 \\
\rightarrow p_{data}(x) &= \frac{p_{data}(x) + p_{g}(x)}{2} \\
\rightarrow p_{data}(x) &= p_g(x)
\end{align}\]
<p>That is a very intuitive result. The <em>Generator</em> reaches its best performance when the distribution of the real data $p_{data}$ is equal to the distribution of the data it creates, namely $p_{g}$.</p>
<p>In fact, equation $\eqref{eq: kl_divergence}$ is describing a special case of KL-Divergence, since both $p_{data}$ and $p_{g}$ are compared to an average of themselves. This special case is called symmetric KL-Divergence. In fact, this phenomena even has its own name, namely the <a href="https://en.wikipedia.org/wiki/Jensen–Shannon_divergence">Jenson-Shannon Divergence</a> (aka JS-Divergence). Knowing this, we can rewrite $\eqref{eq: kl_divergence}$ to:</p>
\[\begin{align}
&= KL \left[ p_{data}(x) || M \right] + KL \left[ p_{g}(x) || M \right] - 2 \log(2) \\
& \text{where M equals $\frac{1}{2}(p_g(x) + p_{data}(x))$} \\
&= 2 \cdot JSD \left[ p_{data}(x) || p_g(x) \right] - 2 \log(2)
\end{align}\]
<p>This result tells us, that training the <em>Generator</em> actually means that we are trying to minimize the distance between two distributions. Being able to quantify that distance is important, since we can use it to tell the <em>Generator</em> what went wrong every time it created new images.</p>
<h1 id="the-many-problems-of-gans">The many problems of GANs</h1>
<p>Generative Adversarial Networks are an incredible strong tool for many tasks. Though, they are also famous to be very instable. That problem arises because of multiple reasons, some of which are discussed in this section.</p>
<h2 id="optimizing-two-functions-at-a-time">Optimizing two functions at a time</h2>
<p>One of the biggest problem is the aim to optimize two models in an iterative process. As we already know, GANs are build iteratively. That means that <em>Discriminator</em> and <em>Generator</em> update their weights after one another, not taking the other model into consideration. <a href="https://proceedings.neurips.cc/paper/2016/file/8a3363abe792db2d8761d6403605aeb7-Paper.pdf">Salimans et al 2016</a> describe training a GAN rather fittingly with finding a <a href="https://en.wikipedia.org/wiki/Nash_equilibrium">Nash Equilibrium</a> to a two-player non-cooperative game. A Nash Equilibrium describes a situation in which in neither player has an incentive to unilaterally deviate from their strategy. That means that the <em>Discriminator</em> and <em>Generator</em> have both minimum loss with respect to their weight parameters. The problem is that on the way to a minimum, every change of the <em>Discriminator</em>’s weight parameters can increase the loss of the <em>Generator</em> and vice versa.</p>
<p>Salimans et al. 2016 then also present a very intuitive example to illustrate that argument. Let us assume that the <em>Generator</em> has influence on $g$ in order to minimize the expression $f_g(g) = g<em>d$, whereas the *Discriminator</em> can control $d$ in order to minimize the expression $f_d(d) = -g*d$.</p>
<p>When minimizing a function within gradient descent we are taking the derivative of that function and multiply the result by the learning rate in order to go a step towards the minimum of that function. The derivative of these functions respectively is:</p>
\[\begin{align}
\frac{\partial f_g}{\partial g} &= -d \\
\frac{\partial f_d}{\partial d} &= g \\
\end{align}\]
<p>After knowing what the partial derivative of the two functions are, we can now go ahead, multiply the result with the learning rate $\eta$ (assumed to be 0.1) and add it to the prior level of $g$ and $d$ respectively. The following snippet of code implements our problem and also shows the result of that function.</p>
<p>As we can see the resulting function $g*d$ is oscillating with an ever growing amplitude, which gets worse over time and contributes substantially to the instability of the model.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="n">plt</span>
<span class="kn">import</span> <span class="nn">_config</span>
<span class="n">d</span> <span class="o">=</span> <span class="n">g</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">f_d</span> <span class="o">=</span> <span class="n">d</span> <span class="o">*</span> <span class="n">g</span>
<span class="n">learning_rate</span> <span class="o">=</span> <span class="mf">0.1</span>
<span class="n">f_d_list</span><span class="p">,</span> <span class="n">d_list</span><span class="p">,</span> <span class="n">g_list</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">):</span>
<span class="n">d_new</span> <span class="o">=</span> <span class="n">d</span> <span class="o">+</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">g</span>
<span class="n">g_new</span> <span class="o">=</span> <span class="n">g</span> <span class="o">-</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">d</span>
<span class="n">f_d</span> <span class="o">=</span> <span class="n">d</span> <span class="o">*</span> <span class="n">g</span>
<span class="n">d</span> <span class="o">=</span> <span class="n">d_new</span>
<span class="n">g</span> <span class="o">=</span> <span class="n">g_new</span>
<span class="n">d_list</span><span class="p">.</span><span class="n">append</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
<span class="n">g_list</span><span class="p">.</span><span class="n">append</span><span class="p">(</span><span class="n">g</span><span class="p">)</span>
<span class="n">f_d_list</span><span class="p">.</span><span class="n">append</span><span class="p">(</span><span class="n">f_d</span><span class="p">)</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">axs</span><span class="p">.</span><span class="n">plot</span><span class="p">(</span><span class="n">f_d_list</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">"g*d"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">.</span><span class="n">plot</span><span class="p">(</span><span class="n">g_list</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">"g"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">.</span><span class="n">plot</span><span class="p">(</span><span class="n">d_list</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">"d"</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></div></div>
<center>
<img src="/assets/post_images/gan/output_13_0.png" width="750" align="center" />
</center>
<h2 id="vanishing-gradients">Vanishing Gradients</h2>
<p>Another common problem of GANs are vanishing gradients. This problem describes the situation in which the <em>Discriminator</em> is doing too good of a job. In the most extreme case we would have a perfect <em>Discriminator</em> which would detect every real sample $D(x) = 1, \forall \; x \in p_{data}$ as real, and every fake image as fake $D(x) = 0 \forall x \; \in p_g$.</p>
<center>
<img src="/assets/post_images/gan/ppt/vanishing_gradient.png" width="750" align="center" />
</center>
<p><em>First, we trained a DCGAN for 1, 10 and 25 epochs. Then, with the generator fixed we train a discriminator from scratch. We see the error quickly going to 0, even with very few iterations on the discriminator. This even happens after 25 epochs of the DCGAN, when the samples are remarkably good and the supports are likely to intersect, pointing to the non-continuity of the distributions. Note the logarithmic scale. For illustration purposes we also show the accuracy of the discriminator, which goes to 1 in sometimes less than 50 iterations. This is 1 even for numerical precision, and the numbers are running averages, pointing towards even faster convergence.</em>
Source: <a href="https://arxiv.org/pdf/1701.04862.pdf">Arjovsky and Bottou, 2017</a></p>
<p>This problem leads us to a situation in which the speed of learning is seriously dampened, or even completely stopped. That is because the <em>Discriminator</em> is sending not enough information for the <em>Generator</em> to make any progress, meaning that the <em>Generator</em> does not know how to improve.</p>
<h2 id="mode-collapse">Mode collapse</h2>
<p>Another problem of GANs are so-called <em>Mode collapses</em>. A <em>mode</em> describes one possible output of the real samples. For example, the MNIST dataset, which contains images of 10 hand-written digits, contains 10 modes.</p>
<p>A mode collapse then describes a situation in which the <em>Generator</em> found a way to produce one or a very limited amount of samples to produce, which the <em>Discriminator</em> is unable to reject. The result of that situation is that the <em>Generator</em> is constantly producing the same subset of data and does not even try to significantly change it anymore, since the <em>Discriminator</em> got stuck in a local minima and does not send meaningful feedback anymore.</p>
<p>The example below shows how the <em>Generator</em> at some point started to solely produce images from the same mode.</p>
<center>
<img src="/assets/post_images/gan/ppt/mode_collapse.png" width="750" align="center" />
</center>
<p>Source: <a href="https://arxiv.org/pdf/1611.02163.pdf">Metz, Luke, et al.</a></p>
<h1 id="wassterstein-gan">Wassterstein GAN</h1>
<p>One solution to the last two problems would be to use a different solution of to measure the distance between $p_g$ and $p_{data}$, called Wasserstein Distance and the resulting Wasserstein GAN [3].</p>
<h2 id="motivation-of-wasserstein-distance">Motivation of Wasserstein Distance</h2>
<p>Before starting to talk about how this new distance functions works, we start by motivating why we would even need a different distance measure next to JS-Distance and KL-Divergence. The reason for that is that both the JS and KL distance are quite bad in scenarios where the two distributions are relatively far apart.</p>
<p>In the following we illustrate what is meant by that. We do that by comparing a distribution called p (shown in blue) with three other distributions whose means are 5, 25, and 100 units away. (Note that we shift all distributions ten units to the right, in order to mitigate negative and zero values. That is necessary since the KL-Divergence cannot handle negative values.)</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="n">np</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="n">sns</span>
<span class="n">BASE_LEVEL</span> <span class="o">=</span> <span class="mi">10</span>
<span class="n">SIZE</span> <span class="o">=</span> <span class="mi">10_000</span>
<span class="n">RANDOM_STATE</span> <span class="o">=</span> <span class="mi">42</span>
<span class="n">data_dict</span> <span class="o">=</span> <span class="p">{</span>
<span class="s">"p"</span><span class="p">:</span> <span class="n">np</span><span class="p">.</span><span class="n">random</span><span class="p">.</span><span class="n">normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="n">BASE_LEVEL</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">SIZE</span><span class="p">),</span>
<span class="s">"q1"</span><span class="p">:</span> <span class="n">np</span><span class="p">.</span><span class="n">random</span><span class="p">.</span><span class="n">normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="p">(</span><span class="n">BASE_LEVEL</span><span class="o">+</span><span class="mi">5</span><span class="p">),</span> <span class="n">size</span><span class="o">=</span><span class="n">SIZE</span><span class="p">),</span>
<span class="s">"q2"</span><span class="p">:</span> <span class="n">np</span><span class="p">.</span><span class="n">random</span><span class="p">.</span><span class="n">normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="p">(</span><span class="n">BASE_LEVEL</span><span class="o">+</span><span class="mi">25</span><span class="p">),</span> <span class="n">size</span><span class="o">=</span><span class="n">SIZE</span><span class="p">),</span>
<span class="s">"q3"</span><span class="p">:</span> <span class="n">np</span><span class="p">.</span><span class="n">random</span><span class="p">.</span><span class="n">normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="p">(</span><span class="n">BASE_LEVEL</span><span class="o">+</span><span class="mi">100</span><span class="p">),</span> <span class="n">size</span><span class="o">=</span><span class="n">SIZE</span><span class="p">)</span>
<span class="p">}</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">data_dict</span><span class="p">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">sns</span><span class="p">.</span><span class="n">kdeplot</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">key</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">axs</span><span class="p">,</span> <span class="n">fill</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">axs</span><span class="p">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="p">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></div></div>
<center>
<img src="/assets/post_images/gan/output_15_0.png" width="750" align="center" />
</center>
<p>Now we are calculating the KL- and JS-Divergence between the base-distribution (blue) and the other three. Note that we are using <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.rel_entr.html#scipy.special.rel_entr">rel_entr</a> instead of <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.kl_div.html">kl_div</a>, given that the latter is slightly different implemented to the definition of KL-Divergence we used earlier.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">from</span> <span class="nn">scipy.spatial.distance</span> <span class="kn">import</span> <span class="n">jensenshannon</span>
<span class="kn">from</span> <span class="nn">scipy.special</span> <span class="kn">import</span> <span class="n">rel_entr</span>
<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">norm</span>
<span class="n">ITERATIONS</span> <span class="o">=</span> <span class="mi">5_000</span>
<span class="n">kld_list</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">jsd_list</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">x1</span> <span class="o">=</span> <span class="n">norm</span><span class="p">.</span><span class="n">rvs</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="n">BASE_LEVEL</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">SIZE</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">RANDOM_STATE</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">ITERATIONS</span><span class="p">,</span> <span class="mi">5</span><span class="p">):</span>
<span class="n">x2</span> <span class="o">=</span> <span class="n">norm</span><span class="p">.</span><span class="n">rvs</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="p">(</span><span class="n">BASE_LEVEL</span><span class="o">+</span><span class="n">i</span><span class="p">),</span> <span class="n">size</span><span class="o">=</span><span class="n">SIZE</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">RANDOM_STATE</span><span class="p">)</span>
<span class="n">jsd</span> <span class="o">=</span> <span class="n">jensenshannon</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">)</span>
<span class="n">jsd_list</span><span class="p">.</span><span class="n">append</span><span class="p">(</span><span class="n">jsd</span><span class="p">)</span>
<span class="n">kld</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="nb">abs</span><span class="p">(</span><span class="n">np</span><span class="p">.</span><span class="nb">sum</span><span class="p">(</span><span class="n">rel_entr</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">)))</span>
<span class="n">kld_list</span><span class="p">.</span><span class="n">append</span><span class="p">(</span><span class="n">kld</span><span class="p">)</span>
<span class="n">stand_jsd_list</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">array</span><span class="p">(</span><span class="n">jsd_list</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="p">.</span><span class="nb">max</span><span class="p">(</span><span class="n">jsd_list</span><span class="p">)</span>
<span class="n">stand_kld_list</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">array</span><span class="p">(</span><span class="n">kld_list</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="p">.</span><span class="nb">max</span><span class="p">(</span><span class="n">kld_list</span><span class="p">)</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
<span class="n">axs</span><span class="p">.</span><span class="n">plot</span><span class="p">(</span><span class="n">stand_kld_list</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">"KL-Distance"</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">"black"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">.</span><span class="n">plot</span><span class="p">(</span><span class="n">stand_jsd_list</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">"JS-Distance"</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">"purple"</span><span class="p">)</span>
<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">dist</span><span class="p">,</span> <span class="n">color</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">([</span><span class="s">"q1"</span><span class="p">,</span> <span class="s">"q2"</span><span class="p">,</span> <span class="s">"q3"</span><span class="p">],</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> <span class="p">[</span><span class="s">"orange"</span><span class="p">,</span> <span class="s">"green"</span><span class="p">,</span> <span class="s">"red"</span><span class="p">]):</span>
<span class="n">axs</span><span class="p">.</span><span class="n">vlines</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">dist</span><span class="p">,</span> <span class="n">ymin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">ymax</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">linestyles</span><span class="o">=</span><span class="s">"dashed"</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">name</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">)</span>
<span class="n">axs</span><span class="p">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="p">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></div></div>
<center>
<img src="/assets/post_images/gan/output_17_0.png" width="750" align="center" />
</center>
<p>As we can see from the chart above, both, the KL- and the JS-Divergence level out fairly soon. This represents a problem since the <em>Generator</em> is not getting enough information in order to meaningful improve. It is not sufficiently communicated how bad/good of a job the <em>Generator</em> is doing. That leads then to a situation where the gradient is close to zero and the <em>Generator</em> is not able to learn anything.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">wasserstein_distance</span>
<span class="n">wasserstein_list</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">ITERATIONS</span><span class="p">,</span> <span class="mi">5</span><span class="p">):</span>
<span class="n">x2</span> <span class="o">=</span> <span class="n">norm</span><span class="p">.</span><span class="n">rvs</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="p">(</span><span class="n">BASE_LEVEL</span><span class="o">+</span><span class="n">i</span><span class="p">),</span> <span class="n">size</span><span class="o">=</span><span class="n">SIZE</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">RANDOM_STATE</span><span class="p">)</span>
<span class="n">wd</span> <span class="o">=</span> <span class="n">wasserstein_distance</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">)</span>
<span class="n">wasserstein_list</span><span class="p">.</span><span class="n">append</span><span class="p">(</span><span class="n">wd</span><span class="p">)</span>
<span class="n">stand_wasserstein_list</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">array</span><span class="p">(</span><span class="n">wasserstein_list</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="p">.</span><span class="nb">max</span><span class="p">(</span><span class="n">wasserstein_list</span><span class="p">)</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
<span class="n">axs</span><span class="p">.</span><span class="n">plot</span><span class="p">(</span><span class="n">stand_kld_list</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">"KL-Distance"</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">"black"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">.</span><span class="n">plot</span><span class="p">(</span><span class="n">stand_jsd_list</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">"JS-Distance"</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">"purple"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">.</span><span class="n">plot</span><span class="p">(</span><span class="n">stand_wasserstein_list</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">"Wasserstein-Distance"</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">"red"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="p">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></div></div>
<center>
<img src="/assets/post_images/gan/output_19_0.png" width="750" align="center" />
</center>
<h2 id="what-exactly-is-the-wasserstein-distance">What exactly is the Wasserstein Distance</h2>
<p>From the chart above we can see why we would rather use the Wasserstein Distance to assess the similarity between two distributions. In contrast to KL- and JS-Divergence, the Wasserstein Distance provides us with a nicely scalable way to assess the similarity between two distributions.</p>
<p>The Wasserstein Distance is also called the <strong>Earth Mover’s Distance</strong>, given that it can be interpreted as the minimum amount of effort of transforming one distribution into the shape of another distribution. <em>Effort</em> is defined here as the amount of probability mass times the distance we are moving the mass.</p>
<p>The general workings of this distance measure become clearer when considering a discrete example. We consider two discrete distributions $A$ and $B$, which both have 14 probability mass units (14 is just a random number here). These probability mass units are divided on the four different piles in the following way:</p>
\[\begin{align}
A_1 &= 5, A_2 = 2, A_3 = 2, A_4 = 5 \\
B_1 &= 3, B_2 = 3, B_3 = 5, B_4 = 3
\end{align}\]
<p>Of course, there are multiple ways how we could transform these two distributions in order for them to match up. There are even more possible transformations with the smallest effort. In the following we are showing one potential way of how to do it, but be aware that there could be more than just that one.</p>
<p>When trying to transform distribution $A$ into distribution $B$ with the smallest amount of effort, the following steps are possible:</p>
<ul>
<li>Move 2 units from $A_1$ to $A_2 \rightarrow A_1$ and $B_1$ match up</li>
<li>Move 1 units from $A_2$ to $A_3 \rightarrow A_2$ and $B_2$ match up</li>
<li>Move 2 units from $A_4$ to $A_3 \rightarrow A_3$ and $B_3$ & $A_4$ and $B_4$ match up</li>
</ul>
<p>The overall costs of transforming distribution A into distribution B is then equal to five. This was calculated by simply
adding up the number of probability mass units moved (since they were all moved a distance of one respectively). More
generalizable, one could use the formula \(W = \sum |\sigma_i|\), in which \(\sigma_{i+1} = \sigma_{i} + A_i - B_i\). Using this formula, the calculation would look the following way:</p>
\[\begin{align}
\sigma_0 &= 0 \\
\sigma_1 &= 0 + 5 - 3 = 2 \\
\sigma_2 &= 2 + 2 - 3 = 1 \\
\sigma_3 &= 1 + 2 - 5 = -2 \\
\sigma_4 &= -2 + 5 - 3 = 0 \\
\rightarrow W &= \sum | \sigma_i | = 5
\end{align}\]
<center>
<img src="/assets/post_images/gan/ppt/wasserstein.png" width="500" align="center" />
</center>
<p>In the continuous case, this formula changes to:</p>
\[W(p_r, p_g) = \underset{\gamma \in \prod(p_r, p_g)}{inf} E_{(x, y) \sim \gamma} \left[ || x- y ||\right]\]
<p>This formula describes the very same concept we had for the discrete case, but now for a continuous version. The <a href="https://en.wikipedia.org/wiki/Infimum_and_supremum">infimum</a> operator describes that we are interested in the <em>greatest lower bound</em>, hence the smallest effort of transforming distribution x to distribution y.</p>
<p>Given that there is not <em>one way</em> of how to transform distribution x and y in order to match these two distributions, we are considering all possible combinations. That fact is denoted by $\prod (p_r, p_g)$.
On the other hand, ||x-y|| describes the distance that we have to push probability mass of x, in order to align with the distribution of y. Lastly we have $\gamma (x, y)$ which describes the percentage of probability mass of x that would be needed to move around in order to align with distribution of y. When multiplying the percentage of distribution mass that is needed to be moved with the distance the probability mass would have to move we would arrive at the formula above:</p>
\[\sum_{x, y} \gamma (x, y) ||x-y|| = E_{(x, y) \sim \gamma} ||x-y||\]
<p>It is to be said though calculating all possible combinations is not tractable to calculate all possible combinations of how to match up the two distributions. Meaning that $\prod (p_r, p_g)$ is too large. The authors of the WGAN paper [4] therefore come up with a transformation based on the Kantorovich-Rubinstein duality. This transformation changes our formula to:</p>
\[W(p_r, p_g) = \frac{1}{K} \underset{||f||_L \leq K}{sup} E~p_r \left[f(x) \right] - E~p_g \left[f(x) \right]\]
<p>Herein we are using the so-called supremum, which is the opposite of the infimum, meaning that we are interested in the maximum value. Furthermore, the newly introduced function \(f\)
is demanded to satisfy $||f||_L \leq K$, which means it should be K-Lipschitz continuous.</p>
<hr />
<h5 id="digression-lipschitz-continuity">Digression: Lipschitz continuity</h5>
<p>A Lipschitz continuous functions is nothing other than a continuous function with a limited slope parameter. That becomes
clearer when taking a look at the mathematical definition. A function is said to be Lipschitz continuous if
$|f(x) - f(y)| \leq L \cdot |x-y| \quad \forall x, y \in \mathbf{R}$</p>
<p>If we now apply a simple transformation for that formula and ensure that $x \neq y$, then we find that</p>
\[\begin{align}
|f(x) - f(y)| \leq L \cdot | x-y | \\
\left| \frac{f(x) - f(y)}{x-y} \right| \leq L
\end{align}\]
<p>From the left side of the formula above we see that this is nothing other than the slope parameter of the function, which in the limits of x converging towards y represents the derivative of the function.</p>
<hr />
<p>How exactly that Lipschitz continuity is applied to the Wasserstein distance formula is heavy mathematics and not scope of this blog-post. The appendix of the original paper sheds some light on the derivation for those who are interested. If we now consider that the function $f$ is obeying the K-Lipschitz restriction, then we find that the Wasserstein distance is defined as:</p>
\[W(p_r, p_g) = \underset{w \in W}{max} E_{x ~ p_r} [f_w(x)] - E_{z~p_r(z)}[f_w(g_{\theta}(z))]\]
<p>In which $f_w$ is K-Lipschitz continuous. Ensuring this Lipschitz continuity during training is difficult, and even the original paper is struggling to do so, suggesting to clip the gradient updated weights between [-0.01, 0.01]. The authors even state in the original paper -<em>Weight clipping is a clearly terrible way to enforce a Lipschitz constraint</em> -. This clipping of parameters also does not completely resolve the problem of vanishing gradients and slow convergence, since the hard-coded clipping of the weights cannot solve all problems.</p>
<p>Since then there were multiple suggestions of how to work-around this problem, <a href="https://arxiv.org/pdf/1704.00028.pdf">the most famous one</a> working with a gradient penalty [4].</p>
<h1 id="results-from-using-a-wgan">Results from using a WGAN</h1>
<p>After the theoretical explanation of GAN and WGAN we implemented a working example into Python, using PyTorch. Herein we used flag images from countries all around the world with the aim to create new ones.</p>
<p>We obtained the flag images from <a href="https://www.countries-ofthe-world.com/flags-of-the-world.html">here</a>. Given the small amount of images (~250) we applied heavy data augmentation on the images. For this example we implemented the described WGAN, given its better performance over the standard GAN.</p>
<p>From the image below we can see the resulting flags. Even though many of them are not on the level to be used as the new national flag of any emerging country, but the result is still satisfying, considering that these flags were literally created out of random-noise.</p>
<center>
<img src="/assets/post_images/gan/flag_results.png" width="1000" align="center" />
</center>
<h1 id="reference-list">Reference List</h1>
<p>[1] Goodfellow, Ian J., et al. “Generative adversarial networks.” arXiv preprint arXiv:1406.2661 (2014).</p>
<p>[2] Salimans, Tim, et al. “Improving GANs using optimal transport.” arXiv preprint arXiv:1803.05573 (2018).</p>
<p>[3] Arjovsky, Martin, Soumith Chintala, and Léon Bottou. “Wasserstein generative adversarial networks.” International conference on machine learning. PMLR, 2017.</p>
<p>[4] Gulrajani, Ishaan, et al. “Improved training of wasserstein gans.” arXiv preprint arXiv:1704.00028 (2017).</p>Paul MoraThis blog-post elaborates on the workings of Generative Adversial Networks (GANs). Particularly we are looking at the high-level mathematics and intuition of GANs. Furthermore, we are looking into the weaknesses of GANs and proposed enhancements. One development of GANs we are looking deeper into is called the Wasserstein GAN (WGAN), which introduced a new distribution distance function. In the very end of the blog-post we are showing the results of applying such a WGAN model on flag images from all around the world with promising results.Using the power of transfer learning for building a flower classifier2021-05-21T00:00:00+00:002021-05-21T00:00:00+00:00https://paul-mora.com/transfer-learning/clustering/python/Using-the-power-of-transfer-learning-for-building-a-flower-classifier<p>In this blog-post we discuss the concept of transfer-learning and show an implementation in Python, using tensorflow. Namely, we are using the pre-trained model <em>MobileNetV2</em> and apply it on the <em>Oxford Flower 102</em> dataset, in order to build a flower classification model. Lastly, we deploy the trained model on an ios device to make live predictions, using the phone’s camera. The Github Repository for this project can be found <a href="https://github.com/paulmora-statworx/flower_detection">here</a>.</p>
<div>
<center>
<img src="/assets/post_images/transfer_learning/gif/testing_gif.gif" width="300" align="center" />
</center>
</div>
<h1 id="the-concept-of-transfer-learning">The concept of Transfer-Learning</h1>
<p>The concept of transfer-learning is best understood when applied to real-life examples. If, for example, a person has played the piano for many years, this person will have an easier time picking up guitar, compared to a person who never played any musical instrument before.</p>
<p>That does not imply, of course, that the person who has prior piano experience is going to be perfect at playing the guitar right away, but the yearlong finger practice of playing piano and the development of having a good ear in music, makes it easier for that person to learn.</p>
<p>It is important to note that this synergy effect were only possible because playing piano and playing the guitar are somewhat related. If the person who played piano for many years is now trying to learn to play American Football, it is less likely that this person will have an advantage. Hence, whether the previous training of concept A is beneficial for concept B depends on whether concept A and B bear any similarities to one another.</p>
<p>The very same idea applies to transfer-learning. Let us assume that we have a model which is very strong in detecting whether an apple is portrayed in a given image. Chances are good that we could use this model to detect pears as well, since pears and apples are similar (both round fruits). It will definitely not be as strong as a model that was trained entirely on pears, but in situation where we do not have that many trainings images of pears, it is better than nothing.</p>
<p>That the apple detection would work also on detecting pears is only because apples and pears bear some common characteristics, like for example color and shape. If we would, on the other hand, take the apple detection model and try to detect cars with it, we will likely have terrible performance.</p>
<p>The question might arise why we are not simply building one model for each job: one model for detecting apples, one model for detecting pears, and so on. The reasons why this is sometimes not feasible are multi-fold. It could be, for example, that we lack the computational power, the time, or even the amount of images needed to train a classification model that performs well. Especially the latter reason is a common problem within image detection.</p>
<center>
<img src="/assets/post_images/transfer_learning/ppt/starting_point.png" width="500" align="center" />
</center>
<p>The reason why a small amount of images leads to a poor performing model is easily understood when considering the workings of a neural network. When initializing a neural network, all weight parameters are initialized randomly. Through the training process these weight parameters are constantly adjusted, using back-propagation based on gradient descent. If we do not have enough images of the object we would like to classify, the network is not going to have a sufficient amount of data in order to adjust the weights appropriately (i.e., learn).</p>
<p>Transfer-learning can help in those situations. That is because of the workings of convolutional neural networks (CNNs), which are the gold standard when working with image data. While many details of CNNs are not fully explored, it is established that especially the lower levels of those models learn general shapes and patterns of the images. In the final layers of the networks all these shapes and patterns are put together to make up for the final object.</p>
<p>When using a pre-trained model on a different domain than it was originally trained for, we cannot make use of the top-layers of the pre-trained model since they are too specific to the initial use-case. The lower-levels of the network, on the other hand, come in very handy since detecting shapes and patterns is an essential part of image recognition, as is going to be needed for the new domain as well.</p>
<p>Therefore, we simply separate the pre-trained model into two pieces. The first piece, the layers below the very top are referred to as the <strong>base-model</strong>. The job of these layers is to detect shapes and patterns and also to combine those. The second piece are the top-layers. These layers are specific to the objects the model is classifying. This second piece is unwanted when changing the domain the model is supposed to classify, and therefore simply removed.</p>
<p>After removing the top-layer from the base-model, we simply stack one or several untrained layers on top of the headless base-model. It is important to note that the new top-layers can also have a different amount of categories compared to the previous top-layers. That means, if the pre-trained model was originally trained to classify 50 different dog breeds, we can remove the last dense layer which outputs a vector with length 50, and add a layer with 30 categories, in order to classify 30 different cat breeds.</p>
<div>
<center>
<img src="/assets/post_images/transfer_learning/ppt/base_layer.png" width="500" align="center" />
</center>
</div>
<p>When stacking one or more top-layers on top of the lower levels, we have to re-train the entire model. It is important to note that we are solely intending to train the top-layers, not the base-model. We achieve that by freezing the weights of the base-model and therefore only training the newly added top layer with image data from the new domain. That is because jointly training the pre-trained model with the randomly initialized top-layers would result in gradient updates that are too large, and the pre-trained model would forget what it originally learned.</p>
<p>Through that approach the model is much faster in correctly classifying the objects from the new domain, since it does not spend time with learning how to detect shapes and patterns, but solely on how to identify what the combination of such shapes and patterns represent.</p>
<p>After training the top-layers we then can go one step further and train the entire model a bit more, by unfreezing some top-layers of the base-model and re-training those as well. It is crucial at this point to use a smaller learning rate, in order to not cause large gradient updates which would shake the foundation of the model.</p>
<h1 id="mobilenet-v2">MobileNet V2</h1>
<p>After covering the general idea of transfer-learning, we are now moving in the direction of implementing our own transfer-learning model. For that we first have to find a solid pre-trained model. As explained above, it is important for the cross-learning effects to happen that the pre-trained model is generalizable. Therefore, most of these pre-trained models are initially trained on very large image datasets with hundreds of categories. For that reason, many of the pre-trained models are trained using the <a href="https://www.image-net.org">ImageNet database</a>. This database offers 1000 categories and over one million training images.</p>
<p>When choosing which pre-trained model to go for, we were influenced in our decision by the fact that we are interested in deploying our final model on a mobile device. This causes obvious limitations in storage space, processing speed and energy usage, due to the decreased capactities of mobile phones. Lucky for us, there is a pre-trained model for exactly that purpose, namely the <a href="(https://arxiv.org/pdf/1704.04861.pdf)"><strong>MobileNet V2</strong></a>, which was developed by Google.</p>
<h2 id="model-architecture">Model architecture</h2>
<p>Before implementing that model right away, we take a look at its workings and what makes the model the better choice for being deployed on a mobile phone in contrast to any other model. For that we take a look at the <a href="https://arxiv.org/pdf/1801.04381v4.pdf">original paper</a>. More specifically, the model architecture of the network. The graphic below shows the different layers which were used when initially training this model. Each line represents one layer, which is repeated <em>n</em> times.</p>
<div>
<center>
<img src="/assets/post_images/transfer_learning/external_images/original_model.png" width="500" align="center" />
</center>
</div>
<p><a href="https://arxiv.org/pdf/1801.04381v4.pdf">Source</a></p>
<p>The MobileNetV2 starts with a traditional convolution before starting to apply the so-called bottleneck operator. This operator is elaborated on in more detail in the following sections. The main idea of these bottleneck layers is that they try to maintain the same level of accuracy as original convolutional networks, while being computationally cheaper. The size of the feature maps being passed from one layer to the next, are denoted by the variable <em>c</em>, while the stride parameter is denoted as <em>s</em>. The parameter <em>t</em> describes the <em>expansion factor</em>, which is the amount by which the number of channels are expanded within the bottleneck methodology.</p>
<p>As with most image classification models we notice how the number of channels gradually increases, while the image size decreases before collapsing the image into a 1x1 with k channels using average pooling and again a traditional convolution.</p>
<h3 id="bottleneck-sequence">Bottleneck Sequence</h3>
<p>The main difference between the workings of the MobileNetV2 and other image classification model architectures are the usage of these so-called bottlenecks. The original paper describes bottlenecks as a sequence of three things - expansion layers, a depth wise convolution, and a projection layer. The following table, which was extracted from the original paper, nicely shows how the input and output sizes change when applying the bottleneck sequence.</p>
<p>One noteworthy aspect we gain from the table is that this approach is not using any pooling mechanism, and is altering the image’s height and width solely using the stride parameter.</p>
<div>
<center>
<img src="/assets/post_images/transfer_learning/external_images/bottleneck_sizes.png" width="500" align="center" />
</center>
</div>
<p><a href="https://arxiv.org/pdf/1801.04381v4.pdf">Source</a></p>
<h4 id="expansion-layer">Expansion Layer</h4>
<p>As the name suggests, what the expansion layer does is that it increases the number of feature maps of the output. This is done by applying multiple 1x1 kernels on the image and through that, increasing the size of the channels without altering the height or width of the image. This can also be seen from the table above. The original paper describes this process as a form of <em>unzipping</em> the image into a larger workbench. By how much we are increasing the number feature maps is a user defined input defined as <em>t</em>. The original paper set the default value equal to six.</p>
<h4 id="depth-wise-convolution">Depth wise convolution</h4>
<p>After increasing the number of channels through the expansion layer, we then apply a so-called depth-wise convolution. Depth wise convolution is very similar to traditional convolution, with the only difference being that the result is not one pixel within a feature map, but multiple.</p>
<p>To better understand that, we quickly explain the workings of traditional convolution. Traditional convolution applies one (usually) squared kernel to an image, which then calculates the dot product with every feature map respectively and then calculates a weighted sum of all dot product results in return one single number. The resulting number of feature maps when applying a kernel to an image is equal to one.</p>
<p>When applying depth wise convolution, we still apply the kernel to the image and calculate the dot product for every feature map. The difference is that we are then not summing the results of <em>all</em> feature maps together, but rather only sum the dot products for each feature map individually. This approach results in us having the same amount of feature maps before and after applying the convolution. This is also visible by looking at the second row of the table above, in which it says that both the input and output are equal to $tk$.</p>
<p><img src="/assets/post_images/transfer_learning/external_images/depthwise_conv.png" alt="" /></p>
<p><a href="https://machinethink.net/blog/googles-mobile-net-architecture-on-iphone/">Source</a></p>
<h4 id="projection-layer">Projection layer</h4>
<p>Lastly, we apply a so-called projection layer. What this layer is doing is that it shrinks the number of feature maps. This is done by simply using again a 1x1 kernel, but this time not in order to increase the number of feature maps, but rather in order to decrease them. The amount by which the projection layer shrinks the number of feature maps is a user-defined input, denoted as <em>c</em>.</p>
<p><img src="/assets/post_images/transfer_learning/external_images/pointwise_conv.png" alt="" /></p>
<p><a href="https://machinethink.net/blog/googles-mobile-net-architecture-on-iphone/">Source</a></p>
<h4 id="graphical-example">Graphical Example</h4>
<p>To better understanding the combined workings of the bottleneck sequence, we will take a look at an example. Let us assume that we have an image with the sizes 128x128x16. When applying the expansion layer we increase the number of feature maps of the image. Using the default expansion factor of six, the number of channels increases to $16*6=96$, while not changing the width or height of the image.</p>
<p>The second step would then be to apply the depth wise convolution. Given that the depth wise convolution does not change the number of channels, we do not have any change in the number of feature maps. Assuming a stride equal to 1, we also do not change the number of height or width.</p>
<p>Lastly, we decrease the number of feature channels again, using the projection layer. Herein we set the number of desired output channels equal to 24, which is therefore going to be the resulting number of output channels.</p>
<p><img src="/assets/post_images/transfer_learning/ppt/filtering_steps.png" alt="" /></p>
<h4 id="motivation">Motivation</h4>
<p>The question might be asked why we are doing all of this instead of simply using traditional convolution. The reason for this is mainly already answered by the invention of <a href="https://arxiv.org/pdf/1704.04861.pdf">MobileNetV1</a>. In contrast to MobileNetV2, V1 consists only of the depth wise convolution plus the projection layer. This combination is by the original paper referred to as <strong>Depth wise separable convolution</strong>.</p>
<p>In order to gain a more mathematical understanding of why the depth wise separable convolution is computationally beneficial is found when considering the number of computations both methods have to go through. Traditional convolutions have a computational cost of:</p>
\[D_K \cdot D_K \cdot M \cdot N \cdot D_F \cdot D_F\]
\[\begin{align}
D_K &= \textrm{Kernel size} \\
M &= \textrm{Number of input channels} \\
N &= \textrm{Number of output channels} \\
D_F &= \textrm{Feature map size} \\
\end{align}\]
<p>In contrast to traditional convolution, depth wise convolution does not take the number of output channels into consideration, since it creates as many output channels as it has input channels by definition. Adjusting the number of output channels is then conducted by the projection layer. Both costs are then in the end summed together, resulting in the computational cost of the depth wise separable convolution:</p>
\[D_K \cdot D_K \cdot M \cdot D_F \cdot D_F + M \cdot N \cdot D_F \cdot D_F\]
\[\begin{align}
D_K &= \textrm{Kernel size} \\
M &= \textrm{Number of input channels} \\
N &= \textrm{Number of output channels} \\
D_F &= \textrm{Feature map size} \\
\end{align}\]
<p>When then calculating how many times the depth wise separable convolution is superior to the traditional convolution, we find the following:</p>
\[\frac{D_K \cdot D_K \cdot M \cdot D_F \cdot D_F + M \cdot N \cdot D_F \cdot D_F}{D_K \cdot D_K \cdot M \cdot N \cdot D_F \cdot D_F} = \frac{1}{N} + \frac{1}{D^2_K}\]
\[\begin{align}
D_K &= \textrm{Kernel size} \\
M &= \textrm{Number of input channels} \\
N &= \textrm{Number of output channels} \\
D_F &= \textrm{Feature map size} \\
\end{align}\]
<p>Assuming a Kernel size of 3, we then find that the convolution method of MobileNet is 8 to 9 times more efficient compared to traditional convolution.</p>
<p>The computation gains are the main motivation of using MobileNet overall. The difference between V2 and V1 are mostly the idea of zipping and unzipping the image within each bottleneck sequence, which further reduces the computational costs.</p>
<h4 id="side-note-relu6">Side note: ReLU6</h4>
<p>It is also interesting to note that the MobileNetV2 does not use a traditional ReLU function, but rather a so-called ReLU6 activation function. As the name probably already suggests, this caps all positive values at positive six, preventing the activations from becoming too large.</p>
<div>
<center>
<img src="/assets/post_images/transfer_learning/external_images/relu6.png" width="500" align="center" />
</center>
</div>
<h1 id="oxford-flower-102">Oxford Flower 102</h1>
<p>In order to show the power of transfer-learning we chose the Oxford Flower 102 dataset, which can be found <a href="https://www.robots.ox.ac.uk/~vgg/data/flowers/102/">here</a>. This dataset contains, as the name suggests, 102 different categories of flowers in the United Kingdom. Each flower category contains between 40 and 258 images, respectively. Obviously, this amount of data is far too little to train a sophisticated neural network from, which makes it a good testing example of transfer-learning.</p>
<div>
<center>
<img src="/assets/post_images/transfer_learning/external_images/flowers.jpeg" width="500" align="center" />
</center>
</div>
<p><a href="https://www.researchgate.net/figure/Examples-of-images-in-the-Oxford-Flower-102-Dataset-Corresponding-categories-are-given_fig7_318204948">Source</a></p>
<h1 id="code-implementation">Code implementation</h1>
<p>The implementation of this transfer learning example was done in tensorflow. Choosing tensorflow over PyTorch did not have any particular reason, as both frameworks have a significant amount of content about transfer-learning.
The repository for this project is found <a href="https://github.com/paulmora-statworx/flower_detection">here</a>. The final implementation of the trained model into an ios application is conducted following the tutorial from <a href="https://github.com/tensorflow/examples/tree/master/lite/examples/image_classification/ios">tensorflow’s repository</a>.</p>
<h2 id="data-structure">Data Structure</h2>
<p>In contrast to many other blog-posts which try to show intuitive output after every code cell, this post works a bit differently given the packaged code. Instead, this post shows all classes used in building the model individually and elaborates on its workings.</p>
<p>In order to have a better understanding of how the different classes interact with each other, we start by showing the <code> src </code> folder for this project.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">import</span> <span class="nn">seedir</span> <span class="k">as</span> <span class="n">sd</span>
<span class="n">sd</span><span class="p">.</span><span class="n">seedir</span><span class="p">(</span><span class="s">"../src"</span><span class="p">,</span> <span class="n">style</span><span class="o">=</span><span class="s">"lines"</span><span class="p">,</span> <span class="n">exclude_folders</span><span class="o">=</span><span class="s">"__pycache__"</span><span class="p">)</span>
</code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>src/
├─config.json
├─data_loader.py
├─utils/
│ ├─config.py
│ └─args.py
├─coreml_converter.py
├─model.py
├─trainer.py
└─main.py
</code></pre></div></div>
<p>As usually done, all major classes are constructed in their own file and then called and executed within <code> main.py </code>. All kinds of hyper-parameters for every class are stated within the <code> config.json </code> file and called by using an argparser function defined in the folder <code>utils</code>.</p>
<h2 id="data-loader">Data Loader</h2>
<p>The first step we have to do after downloading the images is to load them into Python. This step was less straight-forward than originally thought. This is because the Oxford Flower dataset has the interesting property of having substantially more test data than training data. This might be an interesting challenge for many, but for our use-case, in which we would like to end up building a strong classification model, we would rather have more trainings data.</p>
<p>In order to self-set the train to test data ratio, we have to unpack all images and shuffle them ourselves. These steps are outlined in the methods <code> _loading_images_array </code> and <code> _load_labels </code>. Since labels and images are stored within two separate files, we have to make sure to correctly align and match image and label. This is done by sorting the image names in ascending order before attaching the labels. The labels have to be one-hot encoded, in order to be properly used within the prediction algorithm.</p>
<p>We decided to use the preprocessing class <code> ImageDataGenerator </code> from the preprocessing package from tensorflow. Using this preprocessing method allows us to easily apply the appropriate data augmentation settings for the images. One has to be aware that when using the pre-trained model MobileNetV2, one has to apply the related pre-processing function for that very model. This is necessary since the input images have to resemble the same kind of images which were used when training the model in the first place.</p>
<p>Furthermore, we applied several data augmentation techniques to the training data. This is commonly done in situation in which we have only a limited amount of training data. In contrast to the training data, the validation and test data is not augmented in any way other than the necessary MobileNetV2 pre-processing. Classifying the flower category becomes a much harder challenge for the model when the image is heavily augmented. Since the validation data is not altered in any way, those examples are much easier for the model, which results in a better performance of on the validation data in contrast to the training data, a phenomena which rarely occurs.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1"># %% Packages
</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="n">np</span>
<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="n">tf</span>
<span class="kn">from</span> <span class="nn">scipy.io</span> <span class="kn">import</span> <span class="n">loadmat</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">OneHotEncoder</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras.preprocessing.image</span> <span class="kn">import</span> <span class="n">ImageDataGenerator</span>
<span class="c1"># %% Classes
</span>
<span class="k">class</span> <span class="nc">OxfordFlower102DataLoader</span><span class="p">:</span>
<span class="s">"""
This class loads the images and labels and embeds them into ImageDataGenerators.
"""</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">):</span>
<span class="bp">self</span><span class="p">.</span><span class="n">config</span> <span class="o">=</span> <span class="n">config</span>
<span class="p">(</span>
<span class="bp">self</span><span class="p">.</span><span class="n">train_generator</span><span class="p">,</span>
<span class="bp">self</span><span class="p">.</span><span class="n">val_generator</span><span class="p">,</span>
<span class="bp">self</span><span class="p">.</span><span class="n">test_generator</span><span class="p">,</span>
<span class="p">)</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">create_generators</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">create_generators</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="s">"""
This method loads the labels and images, which are already split into train, test and validation.
Furthermore, we add an additional step to the preprocessing function, which is required for the pre-trained
model. Afterwards we create ImageGenerators from tensorflow for train, test and validation.
:return: ImageDataGenerator for training, validation and testing
"""</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_val</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_val</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">_image_and_labels</span><span class="p">()</span>
<span class="n">train_augment_settings</span><span class="p">,</span> <span class="n">test_augment_settings</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">_add_preprocess_function</span><span class="p">()</span>
<span class="c1"># Data Augmentation setup initialization
</span> <span class="n">train_data_gen</span> <span class="o">=</span> <span class="n">ImageDataGenerator</span><span class="p">(</span><span class="o">**</span><span class="n">train_augment_settings</span><span class="p">)</span>
<span class="n">valid_data_gen</span> <span class="o">=</span> <span class="n">ImageDataGenerator</span><span class="p">(</span><span class="o">**</span><span class="n">test_augment_settings</span><span class="p">)</span>
<span class="n">test_data_gen</span> <span class="o">=</span> <span class="n">ImageDataGenerator</span><span class="p">(</span><span class="o">**</span><span class="n">test_augment_settings</span><span class="p">)</span>
<span class="c1"># Setting up the generators
</span> <span class="n">training_generator</span> <span class="o">=</span> <span class="n">train_data_gen</span><span class="p">.</span><span class="n">flow</span><span class="p">(</span>
<span class="n">x</span><span class="o">=</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y_train</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">data_loader</span><span class="p">.</span><span class="n">batch_size</span>
<span class="p">)</span>
<span class="n">validation_generator</span> <span class="o">=</span> <span class="n">valid_data_gen</span><span class="p">.</span><span class="n">flow</span><span class="p">(</span>
<span class="n">x</span><span class="o">=</span><span class="n">X_val</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y_val</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">data_loader</span><span class="p">.</span><span class="n">batch_size</span>
<span class="p">)</span>
<span class="n">test_generator</span> <span class="o">=</span> <span class="n">test_data_gen</span><span class="p">.</span><span class="n">flow</span><span class="p">(</span>
<span class="n">x</span><span class="o">=</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y_test</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">data_loader</span><span class="p">.</span><span class="n">batch_size</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">training_generator</span><span class="p">,</span> <span class="n">validation_generator</span><span class="p">,</span> <span class="n">test_generator</span>
<span class="k">def</span> <span class="nf">_add_preprocess_function</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="s">"""
This function adds the pre-processing function for the MobileNet_v2 to the settings dictionary.
The pre-processing function is needed since the base-model was trained using it.
:return: Dictionaries with multiple items of image augmentation
"""</span>
<span class="n">train_augment_settings</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">data_loader</span><span class="p">.</span><span class="n">train_augmentation_settings</span>
<span class="n">test_augment_settings</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">data_loader</span><span class="p">.</span><span class="n">test_augmentation_settings</span>
<span class="n">train_augment_settings</span><span class="p">.</span><span class="n">update</span><span class="p">(</span>
<span class="p">{</span>
<span class="s">"preprocessing_function"</span><span class="p">:</span> <span class="n">tf</span><span class="p">.</span><span class="n">keras</span><span class="p">.</span><span class="n">applications</span><span class="p">.</span><span class="n">mobilenet_v2</span><span class="p">.</span><span class="n">preprocess_input</span>
<span class="p">}</span>
<span class="p">)</span>
<span class="n">test_augment_settings</span><span class="p">.</span><span class="n">update</span><span class="p">(</span>
<span class="p">{</span>
<span class="s">"preprocessing_function"</span><span class="p">:</span> <span class="n">tf</span><span class="p">.</span><span class="n">keras</span><span class="p">.</span><span class="n">applications</span><span class="p">.</span><span class="n">mobilenet_v2</span><span class="p">.</span><span class="n">preprocess_input</span>
<span class="p">}</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">train_augment_settings</span><span class="p">,</span> <span class="n">test_augment_settings</span>
<span class="k">def</span> <span class="nf">_image_and_labels</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="s">"""
This method loads labels and images and afterwards split them into training, validation and testing set
:return: Trainings, Validation and Testing Images and Labels
"""</span>
<span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">_load_labels</span><span class="p">()</span>
<span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">_loading_images_array</span><span class="p">()</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
<span class="n">X</span><span class="p">,</span>
<span class="n">y</span><span class="p">,</span>
<span class="n">train_size</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">data_loader</span><span class="p">.</span><span class="n">train_size</span><span class="p">,</span>
<span class="n">random_state</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">data_loader</span><span class="p">.</span><span class="n">random_state</span><span class="p">,</span>
<span class="n">shuffle</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
<span class="n">stratify</span><span class="o">=</span><span class="n">y</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_val</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_val</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
<span class="n">X_train</span><span class="p">,</span>
<span class="n">y_train</span><span class="p">,</span>
<span class="n">train_size</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">data_loader</span><span class="p">.</span><span class="n">train_size</span><span class="p">,</span>
<span class="n">random_state</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">data_loader</span><span class="p">.</span><span class="n">random_state</span><span class="p">,</span>
<span class="n">shuffle</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
<span class="n">stratify</span><span class="o">=</span><span class="n">y_train</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">X_train</span><span class="p">,</span> <span class="n">X_val</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_val</span><span class="p">,</span> <span class="n">y_test</span>
<span class="k">def</span> <span class="nf">_load_labels</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="s">"""
Loading the matlab file and one-hot encodes them.
:return: Numpy array of one-hot encoding labels
"""</span>
<span class="n">imagelabels_file_path</span> <span class="o">=</span> <span class="s">"./data/imagelabels.mat"</span>
<span class="n">image_labels</span> <span class="o">=</span> <span class="n">loadmat</span><span class="p">(</span><span class="n">imagelabels_file_path</span><span class="p">)[</span><span class="s">"labels"</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
<span class="n">image_labels_2d</span> <span class="o">=</span> <span class="n">image_labels</span><span class="p">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">encoder</span> <span class="o">=</span> <span class="n">OneHotEncoder</span><span class="p">(</span><span class="n">sparse</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
<span class="n">one_hot_labels</span> <span class="o">=</span> <span class="n">encoder</span><span class="p">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">image_labels_2d</span><span class="p">)</span>
<span class="k">return</span> <span class="n">one_hot_labels</span>
<span class="k">def</span> <span class="nf">_loading_images_array</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="s">"""
Loading the flower images and resizes them into the appropriate size. Lastly we turn the images into a numpy array
:return: Numpy array of the images
"""</span>
<span class="n">image_path</span> <span class="o">=</span> <span class="s">"./data/jpg"</span>
<span class="n">image_file_names</span> <span class="o">=</span> <span class="n">os</span><span class="p">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">image_path</span><span class="p">)</span>
<span class="n">image_file_names</span><span class="p">.</span><span class="n">sort</span><span class="p">()</span>
<span class="n">image_array_list</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">image_file_name</span> <span class="ow">in</span> <span class="n">image_file_names</span><span class="p">:</span>
<span class="n">tf_image</span> <span class="o">=</span> <span class="n">tf</span><span class="p">.</span><span class="n">keras</span><span class="p">.</span><span class="n">preprocessing</span><span class="p">.</span><span class="n">image</span><span class="p">.</span><span class="n">load_img</span><span class="p">(</span>
<span class="n">path</span><span class="o">=</span><span class="sa">f</span><span class="s">"</span><span class="si">{</span><span class="n">image_path</span><span class="si">}</span><span class="s">/</span><span class="si">{</span><span class="n">image_file_name</span><span class="si">}</span><span class="s">"</span><span class="p">,</span>
<span class="n">grayscale</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span>
<span class="n">target_size</span><span class="o">=</span><span class="p">(</span>
<span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">data_loader</span><span class="p">.</span><span class="n">target_size</span><span class="p">,</span>
<span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">data_loader</span><span class="p">.</span><span class="n">target_size</span><span class="p">,</span>
<span class="p">),</span>
<span class="p">)</span>
<span class="n">img_array</span> <span class="o">=</span> <span class="n">tf</span><span class="p">.</span><span class="n">keras</span><span class="p">.</span><span class="n">preprocessing</span><span class="p">.</span><span class="n">image</span><span class="p">.</span><span class="n">img_to_array</span><span class="p">(</span><span class="n">tf_image</span><span class="p">)</span>
<span class="n">image_array_list</span><span class="p">.</span><span class="n">append</span><span class="p">(</span><span class="n">img_array</span><span class="p">)</span>
<span class="k">return</span> <span class="n">np</span><span class="p">.</span><span class="n">array</span><span class="p">(</span><span class="n">image_array_list</span><span class="p">)</span>
</code></pre></div></div>
<p>Given that we have quite a large number of flower categories to predict (102), and the fact that these categories are not balanced, we have to make sure that we have the same proportion of each class within the training, validation and test data in order to have a stronger model and a more meaningful model evaluation. This balance is ensured by using the <code> stratify </code> argument within the train-test split from <code> sklearn </code>. The following image shows the result of using that parameter: We can see that we have same proportions within the train, test and validation data.</p>
<p><img src="/assets/post_images/transfer_learning/figures/relative_distribution.png" alt="" /></p>
<p>In order to also have a better understanding what the pre-processing of the images actually looks like, we show in the following nine example images from the trainings data. We see that all images are much darker than the original ones we saw before. That change of lighting comes from the MobileNetV2 pre-process function we applied. The image in the very middle of the lower matrix nicely shows the level of distortion we apply to the images. These augmentations of images are especially useful in cases like this one where we have such training little data, since it artificially increases the pool of images we can train our model with. It is to be said, though, that we are not applying these distortions on the test and validation data, since these heavy distortions don’t occur in the model’s final application and should therefore not be considered in the model’s performance on real flower-images.</p>
<div>
<center>
<img src="/assets/post_images/transfer_learning/figures/sample_images.png" width="500" align="center" />
</center>
</div>
<h2 id="model">Model</h2>
<p>Now it is time to build our model. This is done in two steps. The first step loads the pre-trained model and freezes all parameters within it. We then stack a dense layer on top and solely train these weights. Furthermore, we add a dropout layer in order to prevent the overfitting of the model.</p>
<p>The second step, as already outlined in the explanation of transfer-learning, then describes the fine-tuning process of transfer-learning. Herein we unfreeze several of the top-layers of the pre-trained model and train them using a small learning rate in order to marginally adjust the pre-trained model in a beneficial direction.</p>
<p>We are using RMSprop for compiling the model, as well as a learning rate of 1e-3 for the training within the first step, and a learning rate of 1e-4 for the fine-tuning.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1"># %% Packages
</span>
<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="n">tf</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras</span> <span class="kn">import</span> <span class="n">Sequential</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras.layers</span> <span class="kn">import</span> <span class="n">Dense</span><span class="p">,</span> <span class="n">Dropout</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras.applications</span> <span class="kn">import</span> <span class="n">MobileNetV2</span>
<span class="c1"># %% Classes
</span>
<span class="k">class</span> <span class="nc">OxfordFlower102Model</span><span class="p">:</span>
<span class="s">"""
This class is initializing the model
"""</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">):</span>
<span class="bp">self</span><span class="p">.</span><span class="n">config</span> <span class="o">=</span> <span class="n">config</span>
<span class="bp">self</span><span class="p">.</span><span class="n">base_model</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">build_model</span><span class="p">()</span>
<span class="n">tf</span><span class="p">.</span><span class="n">random</span><span class="p">.</span><span class="n">set_seed</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">model</span><span class="p">.</span><span class="n">random_seed</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">build_model</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="s">"""
This method build the basic model. The basic model describes the pre-trained model plus a dense layer
on top which is individualized to the number of categories needed. The model is also compiled
:return: A compiled tensorflow model
"""</span>
<span class="n">pre_trained_model</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">initialize_pre_trained_model</span><span class="p">()</span>
<span class="n">top_model</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">create_top_layers</span><span class="p">()</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
<span class="n">model</span><span class="p">.</span><span class="n">add</span><span class="p">(</span><span class="n">pre_trained_model</span><span class="p">)</span>
<span class="n">model</span><span class="p">.</span><span class="n">add</span><span class="p">(</span><span class="n">top_model</span><span class="p">)</span>
<span class="n">model</span><span class="p">.</span><span class="nb">compile</span><span class="p">(</span>
<span class="n">loss</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">model</span><span class="p">.</span><span class="n">loss</span><span class="p">,</span>
<span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">model</span><span class="p">.</span><span class="n">metrics</span><span class="p">],</span>
<span class="n">optimizer</span><span class="o">=</span><span class="n">tf</span><span class="p">.</span><span class="n">keras</span><span class="p">.</span><span class="n">optimizers</span><span class="p">.</span><span class="n">RMSprop</span><span class="p">(</span>
<span class="n">learning_rate</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">model</span><span class="p">.</span><span class="n">learning_rate</span>
<span class="p">),</span>
<span class="p">)</span>
<span class="n">model</span><span class="p">.</span><span class="n">summary</span><span class="p">()</span>
<span class="k">return</span> <span class="n">model</span>
<span class="k">def</span> <span class="nf">unfreeze_top_n_layers</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">ratio</span><span class="p">):</span>
<span class="s">"""
This method unfreezes a certain number of layers of the pre-trained model and combines it subsequently with the
pre-trained top layer which was added within the 'create_top_layers' method and trained within the 'build_model'
class
:param model: Tensorflow model which was already fitted
:param ratio: Float of how many layers should not be trained of the entire model
:return: Compiled tensorflow model
"""</span>
<span class="n">base_model</span> <span class="o">=</span> <span class="n">model</span><span class="p">.</span><span class="n">layers</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">trained_top_model</span> <span class="o">=</span> <span class="n">model</span><span class="p">.</span><span class="n">layers</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">base_model</span><span class="p">.</span><span class="n">trainable</span> <span class="o">=</span> <span class="bp">True</span>
<span class="n">number_of_all_layers</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">base_model</span><span class="p">.</span><span class="n">layers</span><span class="p">)</span>
<span class="n">non_trained_layers</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">number_of_all_layers</span> <span class="o">*</span> <span class="n">ratio</span><span class="p">)</span>
<span class="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="n">base_model</span><span class="p">.</span><span class="n">layers</span><span class="p">[:</span><span class="n">non_trained_layers</span><span class="p">]:</span>
<span class="n">layer</span><span class="p">.</span><span class="n">trainable</span> <span class="o">=</span> <span class="bp">False</span>
<span class="n">fine_tune_model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
<span class="n">fine_tune_model</span><span class="p">.</span><span class="n">add</span><span class="p">(</span><span class="n">base_model</span><span class="p">)</span>
<span class="n">fine_tune_model</span><span class="p">.</span><span class="n">add</span><span class="p">(</span><span class="n">trained_top_model</span><span class="p">)</span>
<span class="n">adjusted_learning_rate</span> <span class="o">=</span> <span class="p">(</span>
<span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">model</span><span class="p">.</span><span class="n">learning_rate</span> <span class="o">/</span> <span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">model</span><span class="p">.</span><span class="n">learning_rate_shrinker</span>
<span class="p">)</span>
<span class="n">fine_tune_model</span><span class="p">.</span><span class="nb">compile</span><span class="p">(</span>
<span class="n">loss</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">model</span><span class="p">.</span><span class="n">loss</span><span class="p">,</span>
<span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">model</span><span class="p">.</span><span class="n">metrics</span><span class="p">],</span>
<span class="n">optimizer</span><span class="o">=</span><span class="n">tf</span><span class="p">.</span><span class="n">keras</span><span class="p">.</span><span class="n">optimizers</span><span class="p">.</span><span class="n">RMSprop</span><span class="p">(</span><span class="n">learning_rate</span><span class="o">=</span><span class="n">adjusted_learning_rate</span><span class="p">),</span>
<span class="p">)</span>
<span class="n">fine_tune_model</span><span class="p">.</span><span class="n">summary</span><span class="p">()</span>
<span class="k">return</span> <span class="n">fine_tune_model</span>
<span class="k">def</span> <span class="nf">initialize_pre_trained_model</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="s">"""
This method calls the pre-trained model. In this case we are loading the MobileNetV2
:return: Tensorflow model
"""</span>
<span class="n">image_shape</span> <span class="o">=</span> <span class="p">(</span>
<span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">data_loader</span><span class="p">.</span><span class="n">target_size</span><span class="p">,</span>
<span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">data_loader</span><span class="p">.</span><span class="n">target_size</span><span class="p">,</span>
<span class="mi">3</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">base_model</span> <span class="o">=</span> <span class="n">MobileNetV2</span><span class="p">(</span>
<span class="n">input_shape</span><span class="o">=</span><span class="n">image_shape</span><span class="p">,</span> <span class="n">include_top</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">pooling</span><span class="o">=</span><span class="s">"avg"</span>
<span class="p">)</span>
<span class="n">base_model</span><span class="p">.</span><span class="n">trainable</span> <span class="o">=</span> <span class="bp">False</span>
<span class="k">return</span> <span class="n">base_model</span>
<span class="k">def</span> <span class="nf">create_top_layers</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="s">"""
Creating the tensorflow top-layer of a model
:return: Tensorflow Sequential model
"""</span>
<span class="n">top_model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
<span class="n">top_model</span><span class="p">.</span><span class="n">add</span><span class="p">(</span>
<span class="n">Dense</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">model</span><span class="p">.</span><span class="n">number_of_categories</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s">"softmax"</span><span class="p">)</span>
<span class="p">)</span>
<span class="n">top_model</span><span class="p">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="n">rate</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">model</span><span class="p">.</span><span class="n">dropout_rate</span><span class="p">))</span>
<span class="k">return</span> <span class="n">top_model</span>
</code></pre></div></div>
<h2 id="trainer">Trainer</h2>
<p>As the last class, we define the training process. This class first triggers the training of the base-model, which consists of the pre-trained model with the shallow top-layers on top for ten epochs. Afterwards, we call the unfreezing method of the model, which is defined within the model class, and continue training for ten more epochs. In order to stop any potential overfitting we use Early-stopping. This method stops the model training after the validation accuracy leveled for a user-defined number of epochs (we chose three).</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1"># %% Packages
</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="n">plt</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras.callbacks</span> <span class="kn">import</span> <span class="n">EarlyStopping</span>
<span class="c1"># %% Classes
</span>
<span class="k">class</span> <span class="nc">OxfordFlower102Trainer</span><span class="p">:</span>
<span class="s">"""
This class is training the base-model and fine-tunes the model
"""</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">data_generator</span><span class="p">,</span> <span class="n">config</span><span class="p">):</span>
<span class="bp">self</span><span class="p">.</span><span class="n">config</span> <span class="o">=</span> <span class="n">config</span>
<span class="bp">self</span><span class="p">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">model</span>
<span class="bp">self</span><span class="p">.</span><span class="n">train_data_generator</span> <span class="o">=</span> <span class="n">data_generator</span><span class="p">.</span><span class="n">train_generator</span>
<span class="bp">self</span><span class="p">.</span><span class="n">val_data_generator</span> <span class="o">=</span> <span class="n">data_generator</span><span class="p">.</span><span class="n">val_generator</span>
<span class="bp">self</span><span class="p">.</span><span class="n">loss</span> <span class="o">=</span> <span class="p">[]</span>
<span class="bp">self</span><span class="p">.</span><span class="n">acc</span> <span class="o">=</span> <span class="p">[]</span>
<span class="bp">self</span><span class="p">.</span><span class="n">val_loss</span> <span class="o">=</span> <span class="p">[]</span>
<span class="bp">self</span><span class="p">.</span><span class="n">val_acc</span> <span class="o">=</span> <span class="p">[]</span>
<span class="bp">self</span><span class="p">.</span><span class="n">_init_callbacks</span><span class="p">()</span>
<span class="k">print</span><span class="p">(</span><span class="s">"Train the base Model!"</span><span class="p">)</span>
<span class="bp">self</span><span class="p">.</span><span class="n">train_model</span><span class="p">()</span>
<span class="k">print</span><span class="p">(</span><span class="s">"Fine tune the Model!"</span><span class="p">)</span>
<span class="bp">self</span><span class="p">.</span><span class="n">train_fine_tune</span><span class="p">()</span>
<span class="bp">self</span><span class="p">.</span><span class="n">save_model</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">_init_callbacks</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="p">.</span><span class="n">custom_callbacks</span> <span class="o">=</span> <span class="p">[</span>
<span class="n">EarlyStopping</span><span class="p">(</span>
<span class="n">monitor</span><span class="o">=</span><span class="s">"val_accuracy"</span><span class="p">,</span>
<span class="n">mode</span><span class="o">=</span><span class="s">"max"</span><span class="p">,</span>
<span class="n">patience</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">trainer</span><span class="p">.</span><span class="n">early_stopping_patience</span><span class="p">,</span>
<span class="p">)</span>
<span class="p">]</span>
<span class="k">def</span> <span class="nf">train_model</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="s">"""
This method is training the base_model
:return: /
"""</span>
<span class="n">history</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">model</span><span class="p">.</span><span class="n">base_model</span><span class="p">.</span><span class="n">fit</span><span class="p">(</span>
<span class="bp">self</span><span class="p">.</span><span class="n">train_data_generator</span><span class="p">,</span>
<span class="n">verbose</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">trainer</span><span class="p">.</span><span class="n">verbose_training</span><span class="p">,</span>
<span class="n">epochs</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">trainer</span><span class="p">.</span><span class="n">number_of_base_epochs</span><span class="p">,</span>
<span class="n">validation_data</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">val_data_generator</span><span class="p">,</span>
<span class="n">callbacks</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">custom_callbacks</span><span class="p">,</span>
<span class="p">)</span>
<span class="bp">self</span><span class="p">.</span><span class="n">append_model_data</span><span class="p">(</span><span class="n">history</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">train_fine_tune</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="s">"""
This method is unfreezing some layers of the already trained model and re-trains the model
:return: /
"""</span>
<span class="n">total_epochs</span> <span class="o">=</span> <span class="p">(</span>
<span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">trainer</span><span class="p">.</span><span class="n">number_of_base_epochs</span>
<span class="o">+</span> <span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">trainer</span><span class="p">.</span><span class="n">number_of_fine_tune_epochs</span>
<span class="p">)</span>
<span class="bp">self</span><span class="p">.</span><span class="n">fine_tune_model</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">model</span><span class="p">.</span><span class="n">unfreeze_top_n_layers</span><span class="p">(</span>
<span class="bp">self</span><span class="p">.</span><span class="n">model</span><span class="p">.</span><span class="n">base_model</span><span class="p">,</span> <span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">trainer</span><span class="p">.</span><span class="n">percentage_of_frozen_layers</span>
<span class="p">)</span>
<span class="n">fine_tune_history</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">fine_tune_model</span><span class="p">.</span><span class="n">fit</span><span class="p">(</span>
<span class="bp">self</span><span class="p">.</span><span class="n">train_data_generator</span><span class="p">,</span>
<span class="n">verbose</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">trainer</span><span class="p">.</span><span class="n">verbose_training</span><span class="p">,</span>
<span class="n">initial_epoch</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">trainer</span><span class="p">.</span><span class="n">number_of_base_epochs</span><span class="p">,</span>
<span class="n">epochs</span><span class="o">=</span><span class="n">total_epochs</span><span class="p">,</span>
<span class="n">validation_data</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">val_data_generator</span><span class="p">,</span>
<span class="n">callbacks</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">custom_callbacks</span><span class="p">,</span>
<span class="p">)</span>
<span class="bp">self</span><span class="p">.</span><span class="n">append_model_data</span><span class="p">(</span><span class="n">fine_tune_history</span><span class="p">)</span>
<span class="bp">self</span><span class="p">.</span><span class="n">plot_history</span><span class="p">(</span><span class="s">"fine_tune_model"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">append_model_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">history</span><span class="p">):</span>
<span class="s">"""
This method is
:param history: Tensorflow model history
:return: /
"""</span>
<span class="bp">self</span><span class="p">.</span><span class="n">loss</span><span class="p">.</span><span class="n">extend</span><span class="p">(</span><span class="n">history</span><span class="p">.</span><span class="n">history</span><span class="p">[</span><span class="s">"loss"</span><span class="p">])</span>
<span class="bp">self</span><span class="p">.</span><span class="n">val_loss</span><span class="p">.</span><span class="n">extend</span><span class="p">(</span><span class="n">history</span><span class="p">.</span><span class="n">history</span><span class="p">[</span><span class="s">"val_loss"</span><span class="p">])</span>
<span class="bp">self</span><span class="p">.</span><span class="n">acc</span><span class="p">.</span><span class="n">extend</span><span class="p">(</span><span class="n">history</span><span class="p">.</span><span class="n">history</span><span class="p">[</span><span class="s">"accuracy"</span><span class="p">])</span>
<span class="bp">self</span><span class="p">.</span><span class="n">val_acc</span><span class="p">.</span><span class="n">extend</span><span class="p">(</span><span class="n">history</span><span class="p">.</span><span class="n">history</span><span class="p">[</span><span class="s">"val_accuracy"</span><span class="p">])</span>
<span class="k">def</span> <span class="nf">plot_history</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">title</span><span class="p">):</span>
<span class="s">"""
This method is plotting the accuracy and loss of the plots
:param title: str - Used to save the png
:return: /
"""</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">axs</span> <span class="o">=</span> <span class="n">axs</span><span class="p">.</span><span class="n">ravel</span><span class="p">()</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">].</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">loss</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">"Training"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">].</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">val_loss</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">"Validation"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">].</span><span class="n">set_title</span><span class="p">(</span><span class="s">"Loss"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">].</span><span class="n">axvline</span><span class="p">(</span>
<span class="n">x</span><span class="o">=</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">trainer</span><span class="p">.</span><span class="n">number_of_base_epochs</span> <span class="o">-</span> <span class="mi">1</span><span class="p">),</span>
<span class="n">ymin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">ymax</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="s">"BaseEpochs"</span><span class="p">,</span>
<span class="n">color</span><span class="o">=</span><span class="s">"green"</span><span class="p">,</span>
<span class="n">linestyle</span><span class="o">=</span><span class="s">"--"</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">].</span><span class="n">legend</span><span class="p">()</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">].</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">acc</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">"Training"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">].</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">val_acc</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">"Validation"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">].</span><span class="n">set_title</span><span class="p">(</span><span class="s">"Accuracy"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">].</span><span class="n">axvline</span><span class="p">(</span>
<span class="n">x</span><span class="o">=</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">config</span><span class="p">.</span><span class="n">trainer</span><span class="p">.</span><span class="n">number_of_base_epochs</span> <span class="o">-</span> <span class="mi">1</span><span class="p">),</span>
<span class="n">ymin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">ymax</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="s">"BaseEpochs"</span><span class="p">,</span>
<span class="n">color</span><span class="o">=</span><span class="s">"green"</span><span class="p">,</span>
<span class="n">linestyle</span><span class="o">=</span><span class="s">"--"</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">].</span><span class="n">legend</span><span class="p">()</span>
<span class="n">fig</span><span class="p">.</span><span class="n">savefig</span><span class="p">(</span><span class="sa">f</span><span class="s">"./reports/figures/history_</span><span class="si">{</span><span class="n">title</span><span class="si">}</span><span class="s">.png"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">save_model</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="s">"""
Saving the fine-tuned model
:return: /
"""</span>
<span class="n">path</span> <span class="o">=</span> <span class="s">"./models/oxford_flower102_fine_tuning.h5"</span>
<span class="bp">self</span><span class="p">.</span><span class="n">fine_tune_model</span><span class="p">.</span><span class="n">save</span><span class="p">(</span><span class="n">filepath</span><span class="o">=</span><span class="n">path</span><span class="p">)</span>
</code></pre></div></div>
<h2 id="run-the-model">Run the model</h2>
<p>Lastly we call the aforementioned classes within the <code> main.py </code> file and trigger them one after another.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1"># %% Packages
</span>
<span class="kn">from</span> <span class="nn">utils.args</span> <span class="kn">import</span> <span class="n">get_args</span>
<span class="kn">from</span> <span class="nn">utils.config</span> <span class="kn">import</span> <span class="n">process_config</span>
<span class="kn">from</span> <span class="nn">model</span> <span class="kn">import</span> <span class="n">OxfordFlower102Model</span>
<span class="kn">from</span> <span class="nn">data_loader</span> <span class="kn">import</span> <span class="n">OxfordFlower102DataLoader</span>
<span class="kn">from</span> <span class="nn">trainer</span> <span class="kn">import</span> <span class="n">OxfordFlower102Trainer</span>
<span class="c1"># %% Main Script
</span>
<span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
<span class="n">args</span> <span class="o">=</span> <span class="n">get_args</span><span class="p">()</span>
<span class="n">config</span> <span class="o">=</span> <span class="n">process_config</span><span class="p">(</span><span class="n">args</span><span class="p">.</span><span class="n">config</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s">"Creating the Data Generator!"</span><span class="p">)</span>
<span class="n">data_loader</span> <span class="o">=</span> <span class="n">OxfordFlower102DataLoader</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s">"Creating the Model!"</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">OxfordFlower102Model</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s">"Creating the Trainer!"</span><span class="p">)</span>
<span class="n">trainer</span> <span class="o">=</span> <span class="n">OxfordFlower102Trainer</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">data_loader</span><span class="p">,</span> <span class="n">config</span><span class="p">)</span>
<span class="k">if</span> <span class="n">__name__</span> <span class="o">==</span> <span class="s">"__main__"</span><span class="p">:</span>
<span class="n">main</span><span class="p">()</span>
</code></pre></div></div>
<h2 id="model-evaluation">Model evaluation</h2>
<p>The plot below gives us some interesting insights into how the model training went. We see that the model reaches a relatively strong performance after only a small amount of training epochs, but then seems to starting leveling off. After the fine-tuning kicks in, we then witness a significant drop in accuracy, which suggests that the learning rate was probably too high, triggering too large weight changes within the back-propagation of the network. However, after a couple of epochs, the model is back on track, reaching performance levels which were not attainable earlier.</p>
<p>Looking at the examples from the tensorflow <a href="https://www.tensorflow.org/tutorials/images/transfer_learning">website</a>, we did not spot any drop in accuracy to the extent that we encountered. Unsure whether that problem was only due to a potentially higher learning rate, we tried a range of learning rates, always encountering the same problem. We therefore suspect that the drop of the learning rate is likely going to be a result of having so little training data, compared to the example shown on the tensorflow website, which uses the well-known <a href="https://www.kaggle.com/c/dogs-vs-cats">cats vs. dogs dataset</a>.</p>
<p>Overall we are happy with the model performance, which reaches an accuracy of <strong>93.17%</strong> on the unseen test data.</p>
<div>
<center>
<img src="/assets/post_images/transfer_learning/figures/history_fine_tune_model.png" width="700" align="center" />
</center>
</div>
<h1 id="phone-application">Phone Application</h1>
<p>Finally, we thought it would be a nice to deploy the model on a mobile application, as that was also the motivation of choosing the MobileNetV2 network. In order to do so, one has to convert the <code>h5</code> format the model is currently saved as, into a <code>tflite</code> file. Doing that compresses the model and brings it into the right format for the job. Furthermore, we have to sort and store all the labels into a text file and put them into the <code> Model </code> folder of the application parent folder.</p>
<p>This app folder is pulled from the official tensorflow repository, found <a href="https://github.com/tensorflow/examples/tree/master/lite/examples/image_classification/ios">here</a>.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">sd</span><span class="p">.</span><span class="n">seedir</span><span class="p">(</span><span class="s">"../app/model"</span><span class="p">,</span> <span class="n">style</span><span class="o">=</span><span class="s">"lines"</span><span class="p">,</span> <span class="n">exclude_folders</span><span class="o">=</span><span class="s">"__pycache__"</span><span class="p">)</span>
</code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>model/
├─.DS_Store
├─oxford_flower_102.tflite
├─labels_flowers.txt
├─mobilenet_quant_v1_224.tflite
├─labels.txt
└─.gitignore
</code></pre></div></div>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1"># %% Packages
</span>
<span class="kn">import</span> <span class="nn">json</span>
<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="n">tf</span>
<span class="c1"># %% Loading models and data
</span>
<span class="c1"># Model
</span><span class="n">keras_path</span> <span class="o">=</span> <span class="s">"./models/oxford_flower102_fine_tuning.h5"</span>
<span class="n">keras_model</span> <span class="o">=</span> <span class="n">tf</span><span class="p">.</span><span class="n">keras</span><span class="p">.</span><span class="n">models</span><span class="p">.</span><span class="n">load_model</span><span class="p">(</span><span class="n">keras_path</span><span class="p">)</span>
<span class="n">converter</span> <span class="o">=</span> <span class="n">tf</span><span class="p">.</span><span class="n">lite</span><span class="p">.</span><span class="n">TFLiteConverter</span><span class="p">.</span><span class="n">from_keras_model</span><span class="p">(</span><span class="n">keras_model</span><span class="p">)</span>
<span class="n">tflite_model</span> <span class="o">=</span> <span class="n">converter</span><span class="p">.</span><span class="n">convert</span><span class="p">()</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s">"./models/oxford_flower_102.tflite"</span><span class="p">,</span> <span class="s">"wb"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">f</span><span class="p">.</span><span class="n">write</span><span class="p">(</span><span class="n">tflite_model</span><span class="p">)</span>
<span class="c1"># Labels
</span><span class="n">labels_path</span> <span class="o">=</span> <span class="s">"./data/cat_to_name.json"</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">labels_path</span><span class="p">)</span> <span class="k">as</span> <span class="n">json_file</span><span class="p">:</span>
<span class="n">labels_dict</span> <span class="o">=</span> <span class="n">json</span><span class="p">.</span><span class="n">load</span><span class="p">(</span><span class="n">json_file</span><span class="p">)</span>
<span class="n">sorted_labels_dict</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">labels_dict</span><span class="p">.</span><span class="n">items</span><span class="p">(),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
<span class="n">label_values</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">sorted_labels_dict</span><span class="p">]</span>
<span class="n">textfile</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s">"./models/labels_flowers.txt"</span><span class="p">,</span> <span class="s">"w"</span><span class="p">)</span>
<span class="k">for</span> <span class="n">element</span> <span class="ow">in</span> <span class="n">label_values</span><span class="p">:</span>
<span class="n">textfile</span><span class="p">.</span><span class="n">write</span><span class="p">(</span><span class="n">element</span> <span class="o">+</span> <span class="s">"</span><span class="se">\n</span><span class="s">"</span><span class="p">)</span>
<span class="n">textfile</span><span class="p">.</span><span class="n">close</span><span class="p">()</span>
</code></pre></div></div>
<p>After some adjustment in xcode, we can then deploy the app on any iOs device and use the camera for live prediction. The result of which can be seen on the gif below.</p>
<div>
<center>
<img src="/assets/post_images/transfer_learning/gif/testing_gif.gif" width="300" align="center" />
</center>
</div>Paul MoraIn this blog-post we discuss the concept of transfer-learning and show an implementation in Python, using tensorflow. Namely, we are using the pre-trained model MobileNetV2 and apply it on the Oxford Flower 102 dataset, in order to build a flower classification model. Lastly, we deploy the trained model on an ios device to make live predictions, using the phone’s camera. The Github Repository for this project can be found here.Dynamic Time Warping: Explanation and extensive testing on audio and tabular data2021-04-24T00:00:00+00:002021-04-24T00:00:00+00:00https://paul-mora.com/classification/time-series/clustering/python/Dynamic-Time-Warping-Explanation-and-Testing-on-Audio-and-Tabular-Data<p><em>Explaining the concept of Dynamic-Time-Warping and testing it extensively on sound and tabular data.</em></p>
<h1 id="introduction">Introduction</h1>
<p>When working with time-series data, the question will at some point occur how to do time-series clustering. This question could occur in multiple scenarios and areas of Data Science.</p>
<p>For once it could happen when working with <strong>sound data</strong>. One could potentially try to cluster certain sound-waves together since they represent the same spoken word. Sound is a particularly interesting case, since we would also like the clustering algorithm to be invariant to time, since the same word could be spoken in different speeds. The machine should understand that when somebody says <em>Hello</em> or <em>Heeeelllooo</em>, they mean the same thing.</p>
<p>Another use case of time-series classification/ clustering is when trying to find which time-series are sufficiently similar in order to use them to build a <strong>pooled forecasting model</strong>. A pooled model describes situation in which we are not building one model for each time-series we would like to predict, but fitting one model on multiple entities. When doing that, we should be convinced that there is cross-learning potential. Therefore, trying to first cluster similar time-series together could substantial help to know which time-series could be jointly used for a pooled model.</p>
<p>Lastly, when working with time-series data, it could happen that two or more series have <strong>different lengths</strong>. This problem is oftentimes a deal-breaker for many models, as most classification/ cluster algorithms are not equipped to deal with a varying amount of features.</p>
<p>All of these problems can be solved with <strong>Dynamic Time Warping</strong> (DTW), which is the topic of this post. We start by showing a basic situation in which using the euclidean distance would lead us to the incorrect conclusion and show how DTW can be beneficial. Afterwards we elaborate on the mathematical idea of DTW and illustrate its workings, using a simple example. After that we implement the algorithm into Python code. In order to show the potential of DTW, we show-case different usage areas, ranging from sound-data of different complexity to tabular data.</p>
<p>We start the post by importing all the relevant packages we need later on. Note that we also import a self-written <em>_config</em> file in which only basic <code>matplotlib</code> settings are specified. Through importing these configurations in the beginning, we do not have to specify things like <em>font-size</em> every time we create a plot.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="n">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="n">pd</span>
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
<span class="kn">from</span> <span class="nn">pydub</span> <span class="kn">import</span> <span class="n">AudioSegment</span>
<span class="kn">import</span> <span class="nn">IPython.display</span> <span class="k">as</span> <span class="n">ipd</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="n">plt</span>
<span class="kn">from</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">import</span> <span class="n">cm</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="n">sns</span>
<span class="kn">from</span> <span class="nn">fastdtw</span> <span class="kn">import</span> <span class="n">fastdtw</span>
<span class="kn">from</span> <span class="nn">tslearn.neighbors</span> <span class="kn">import</span> <span class="n">KNeighborsTimeSeriesClassifier</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">accuracy_score</span><span class="p">,</span> <span class="n">f1_score</span><span class="p">,</span> <span class="n">precision_score</span><span class="p">,</span> <span class="n">recall_score</span>
<span class="kn">from</span> <span class="nn">scipy.spatial.distance</span> <span class="kn">import</span> <span class="n">euclidean</span>
<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">GradientBoostingClassifier</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">GridSearchCV</span>
<span class="n">sys</span><span class="p">.</span><span class="n">path</span><span class="p">.</span><span class="n">append</span><span class="p">(</span><span class="s">"../src/"</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">_config</span>
</code></pre></div></div>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">DATA_PATH</span> <span class="o">=</span> <span class="s">"../data"</span>
<span class="n">FIGURES_PATH</span> <span class="o">=</span> <span class="s">"../reports/figures"</span>
</code></pre></div></div>
<h1 id="problem-at-hand">Problem at hand</h1>
<p>Given that the similarity between two time-series could be easily interpreted as the distance of two vectors, the idea could quickly arise to use the <strong>euclidean distance</strong>. The euclidean is defined as the sum of the squared elements of each corresponding element of two vectors. Or even more formal: The euclidean distance between vector p and q is defined as \(d(p, q) = \sqrt{\sum^n_{i=1} (q_i - p_i)^2}\).</p>
<p>As already visible by the subscript <em>i</em> for each element of p <strong>and</strong> q, it is necessary for both time series to have equal length. This of course is especially problematic with sound-data. Here it could happen that you say the same word at a different pace, which would lead the sound-files to have different lengths. One solution to that problem would be to simply clip the longer file, in order to match the size of the shorter file.</p>
<p>Below we find two functions for calculating the euclidean distance. The first implements the aforementioned clipping, whereas the second functions implements the calculation of the Euclidean distance.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">def</span> <span class="nf">cut_time_series</span><span class="p">(</span><span class="n">array1</span><span class="p">,</span> <span class="n">array2</span><span class="p">):</span>
<span class="n">length_array1</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">array1</span><span class="p">)</span>
<span class="n">length_array2</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">array2</span><span class="p">)</span>
<span class="k">if</span> <span class="n">length_array1</span> <span class="o"><</span> <span class="n">length_array2</span><span class="p">:</span> <span class="c1"># we have to clip the second series
</span> <span class="n">array2</span> <span class="o">=</span> <span class="n">array2</span><span class="p">[:</span><span class="n">length_array1</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span> <span class="c1"># we have to clip the first series
</span> <span class="n">array1</span> <span class="o">=</span> <span class="n">array1</span><span class="p">[:</span><span class="n">length_array2</span><span class="p">]</span>
<span class="k">return</span> <span class="n">array1</span><span class="p">,</span> <span class="n">array2</span>
<span class="k">def</span> <span class="nf">euclidean_distance_calculator</span><span class="p">(</span><span class="n">array1</span><span class="p">,</span> <span class="n">array2</span><span class="p">):</span>
<span class="n">clipped_array1</span><span class="p">,</span> <span class="n">clipped_array2</span> <span class="o">=</span> <span class="n">cut_time_series</span><span class="p">(</span><span class="n">array1</span><span class="p">,</span> <span class="n">array2</span><span class="p">)</span>
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">clipped_array1</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">clipped_array2</span><span class="p">),</span> <span class="s">"Not equal length!"</span>
<span class="n">dist</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">linalg</span><span class="p">.</span><span class="n">norm</span><span class="p">(</span><span class="n">clipped_array1</span><span class="o">-</span><span class="n">clipped_array2</span><span class="p">)</span>
<span class="k">return</span> <span class="nb">round</span><span class="p">(</span><span class="n">dist</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
</code></pre></div></div>
<p>Even though the euclidean distance is a quickly understood and easily implementable method, it is heavily flawed when trying to find true similarities between different time-series. This is visualized through the example below. Here we have three Signals. The first two signals represent sine waves with a different frequency as well as amplitude. The third signal represents a mere straight line with a small slope.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">AMPLITUDE1</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">AMPLITUDE2</span> <span class="o">=</span> <span class="mi">3</span><span class="o">/</span><span class="mi">4</span>
<span class="n">SAMPLE_RATE</span> <span class="o">=</span> <span class="mi">8000</span>
<span class="n">FREQUENCY1</span> <span class="o">=</span> <span class="mi">6</span>
<span class="n">FREQUENCY2</span> <span class="o">=</span> <span class="mi">3</span>
<span class="n">sample</span> <span class="o">=</span> <span class="mi">8000</span>
<span class="n">time</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">arange</span><span class="p">(</span><span class="n">SAMPLE_RATE</span><span class="p">)</span>
<span class="n">signal1</span> <span class="o">=</span> <span class="n">AMPLITUDE1</span> <span class="o">*</span> <span class="n">np</span><span class="p">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="p">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">FREQUENCY1</span> <span class="o">*</span> <span class="n">time</span> <span class="o">/</span> <span class="n">SAMPLE_RATE</span><span class="p">)</span>
<span class="n">signal2</span> <span class="o">=</span> <span class="n">AMPLITUDE2</span> <span class="o">*</span> <span class="n">np</span><span class="p">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="p">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">FREQUENCY2</span> <span class="o">*</span> <span class="n">time</span> <span class="o">/</span> <span class="n">SAMPLE_RATE</span><span class="p">)</span>
<span class="n">signal3</span> <span class="o">=</span> <span class="p">(</span><span class="n">time</span> <span class="o">*</span> <span class="mf">0.00005</span><span class="p">)</span> <span class="o">-</span> <span class="mf">0.2</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">axs</span><span class="p">.</span><span class="n">plot</span><span class="p">(</span><span class="n">time</span><span class="p">,</span> <span class="n">signal1</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">"Signal 1"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">.</span><span class="n">plot</span><span class="p">(</span><span class="n">time</span><span class="p">,</span> <span class="n">signal2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">"Signal 2"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">.</span><span class="n">plot</span><span class="p">(</span><span class="n">time</span><span class="p">,</span> <span class="n">signal3</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">"Signal 3"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="p">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></div></div>
<p><img src="/assets/post_images/dtw/output_9_0.png" alt="png" /></p>
<p>Given that the former two signals represent a sine curve, we would like our similarity algorithm to also find a bigger similarity (a.k.a smaller distance) between Signal 1 and 2 compared to Signal 3. When applying the euclidean distance to this problem we find the following:</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">print</span><span class="p">(</span><span class="sa">f</span><span class="s">"The euclidean distance between Signal 1 and Signal 2 is </span><span class="si">{</span><span class="n">euclidean_distance_calculator</span><span class="p">(</span><span class="n">signal1</span><span class="p">,</span> <span class="n">signal2</span><span class="p">)</span><span class="si">}</span><span class="s">"</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="sa">f</span><span class="s">"The euclidean distance between Signal 2 and Signal 3 is </span><span class="si">{</span><span class="n">euclidean_distance_calculator</span><span class="p">(</span><span class="n">signal2</span><span class="p">,</span> <span class="n">signal3</span><span class="p">)</span><span class="si">}</span><span class="s">"</span><span class="p">)</span>
</code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>The euclidean distance between Signal 1 and Signal 2 is 79.06
The euclidean distance between Signal 2 and Signal 3 is 51.1
</code></pre></div></div>
<p>As already feared, the euclidean distance suggests that Signal 2 and 3 are actually more similar than Signal 1. This behavior is, because of the aforementioned reasons, undesired. A powerful alternative of the euclidean distance is the so-called <strong>Dynamic-Time-Warping</strong> (DTW) distance. Using the <code>fastdtw</code> implementation, we find a result which aligns with our initial intuition.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">print</span><span class="p">(</span><span class="sa">f</span><span class="s">"The DTW distance between Signal 1 and Signal 2 is </span><span class="si">{</span><span class="nb">round</span><span class="p">(</span><span class="n">fastdtw</span><span class="p">(</span><span class="n">signal1</span><span class="p">,</span> <span class="n">signal2</span><span class="p">)[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">2</span><span class="p">)</span><span class="si">}</span><span class="s">"</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="sa">f</span><span class="s">"The DTW distance between Signal 2 and Signal 3 is </span><span class="si">{</span><span class="nb">round</span><span class="p">(</span><span class="n">fastdtw</span><span class="p">(</span><span class="n">signal2</span><span class="p">,</span> <span class="n">signal3</span><span class="p">)[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">2</span><span class="p">)</span><span class="si">}</span><span class="s">"</span><span class="p">)</span>
</code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>The DTW distance between Signal 1 and Signal 2 is 2858.61
The DTW distance between Signal 2 and Signal 3 is 3993.83
</code></pre></div></div>
<h1 id="the-workings-of-dynamic-time-warping">The workings of Dynamic-Time-Warping</h1>
<p>The fundamental differences between the euclidean distance and the DTW distance is that the former is simply comparing the sequences element by element, whereas the second algorithm is actively trying to look for the closest possible connections between two observations within each series.</p>
<p>To better show that, let us define the following two sequences:
\(A = a[0], a[1], ..., a[n] \quad B = b[0], b[1], ..., b[m]\)</p>
<p>What DTW is now doing is that it tries to match element $a[i]$ to the closest element of B, regardless of whether it is also at position <em>i</em>. Hence it would be possible to connect element $a[i]$ with element $b[i+4]$, if that would minimize the <em>distance</em> between these two. This <em>distance</em> is oftentimes calculated by using the euclidean distance, but other distance measures are possible.</p>
<p>The magic of Dynamic-Time-Warping is the so-called <strong>Warping Path</strong>. The warping-path matches the different elements within A and B in such a way that the aggregated distance of all elements is minimized. Before showing how the warping function is doing that, we first introduce some boundaries on how the matching is allowed to happen.</p>
<h2 id="restrictions-on-warping-function">Restrictions on Warping Function</h2>
<p>As already mentioned, Dynamic-Time-Warping allows each element from one sequence to match with one or even multiple other elements from another series, even if this element is not at the same index position. Though, there are some restrictions on how DTW is allowed to do that. The original paper from Hiroaki Sakoe and Seibi Chiba 1978 [1] state the following five conditions.</p>
<ol>
<li>
<p><strong>Monotonic conditions</strong>
This condition says that we are not allowed to match the following element, if we have not matched the current element already. Meaning that we are not allowed to go back in time
\(i(k-1) \leq i(k) \quad \text{and} \quad j(k-1) \leq j(k)\)</p>
</li>
<li>
<p><strong>Continuity conditions</strong>
When finding the optimal warping path, we are not allowed to jump in time
\(i(k) - i(k-1) \leq 1 \quad \text{and} \quad j(k) - j(k-1) \leq 1\)</p>
</li>
<li>
<p><strong>Boundary conditions</strong>
This condition ensures that both sequences start on their first element and end on their last element
\(i(1) = 1, j(1) = 1 \quad \text{and} \quad i(K) = I, j(K) = J\)</p>
</li>
<li>
<p><strong>Adjustment window condition</strong>
This condition ensures that matching partners are not further apart than a predefined distance, defined by the positive integer <em>r</em>
\(|i(k) - j(k)| \leq r\)</p>
</li>
<li>
<p><strong>Slope constraint condition</strong>
The slope constraint is put in place in order to prevent the warping path to go too extremely into one direction, which would lead to undesirable distortions</p>
</li>
</ol>
<h2 id="distance-matrix">Distance Matrix</h2>
<p>Now it is time to explore how the optimal warping path is found. In order to understand that, we have to introduce yet another concept, called the <strong>distance matrix</strong>. The concept of which is easily understood though. <em>The distance matrix quantitatively describes the cost of each possible matching path between two or more sequences.</em></p>
<p>Building such a distance-matrix happens in two steps. The first step is to calculate some sort of a base-cost matrix. The second step is then to iteratively filling the distance-matrix, using the base-cost matrix from the first step. This base-cost describes the distance between every combination of points of two sequences. For illustrative purposes, consider the following two sequences:</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">array1</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
<span class="n">array2</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
</code></pre></div></div>
<p>We then build a matrix which contains the distance between each combination of two elements from the sequences. As a measure for the distance we are using the squared distance between the two elements. The following function implements this for our two sequences. Note that we want the beginning of the two sequences to start in the lower left corner. In order for that to happen we have to reverse the second array.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">def</span> <span class="nf">euclidean_distance_matrix</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">dist</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">)):</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)):</span>
<span class="n">dist</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">-</span><span class="n">y</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="o">**</span><span class="mi">2</span>
<span class="k">return</span> <span class="n">dist</span>
<span class="n">euc_array</span> <span class="o">=</span> <span class="n">euclidean_distance_matrix</span><span class="p">(</span><span class="n">array1</span><span class="p">,</span> <span class="n">array2</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="n">euc_df</span> <span class="o">=</span> <span class="n">pd</span><span class="p">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">euc_array</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">array1</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">array2</span><span class="p">)</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">sns</span><span class="p">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">euc_df</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">axs</span><span class="p">,</span> <span class="n">annot</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></div></div>
<p><img src="/assets/post_images/dtw/output_18_0.png" alt="png" /></p>
<p>In order to make sure that the concept of the base-cost matrix is understood, let us consider an example from the chart above. For instance, the third cell from the top and second from the left. At this position the first array (shown on the columns) has a value of 2 and the second array (shown on the index) has a value of 4. The squared distance between these two elements is equal to 4, which is also shown in the matrix. This value of 4 can be understood as the cost of matching these two elements.</p>
<p>After we build the entire base-cost matrix, it is now time to fill the distance-matrix. Filling the distance-matrix does not happen all at once. Rather it happens one after another, since each element depends on the previously calculated value. This approach is also referred to as <strong>dynamic programming</strong>. A good analogy of understanding how we fill in the distance matrix is to imagine that we have a chess-king, which starts in the lower left of the matrix and then goes and explores every path of the matrix. For those who do not know, a chess king can only move one square at a time in every direction. The procedure begins by filling in the bottom left element of the base-cost matrix into our yet still empty distance matrix (in our case a zero).</p>
<p>The chess king then starts exploring the boundary cells of the distance-matrix. Every time the chess king moves to a new square <strong>he incurs the cost of the base-cost matrix, but he also drags with him the minimum adjacent cost of the squares behind him</strong>. The graphic below shows the result of that step. The calculation of the blue 22, for example was done by checking the base-cost element at that position (9) and summing the previous value to it (13). The same applies to the red 5 in the bottom row. Here again we first check the element of the base-cost matrix at that position (4) and sum the previous value (1) to it.</p>
<p><img src="/assets/post_images/dtw/cost_matrix1.png" alt="" /></p>
<p>Again, at every step the chess king occurs the cost of the base-cost matrix at that position, <strong>plus</strong> the minimum value of the cells adjacently behind him: (i-1, j), (i, j-1) or (i-1, j-1). Or mathematically formulated: \(r(i, j) = d(x_i, y_j) + min\{r(i-1, j), \quad r(i, j-1), \quad r(i-1, j-1)\}\)</p>
<p>For which $d(x_i, y_j)$ describes the squared distance. Through this dynamic programming approach we then fill the entire matrix.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">def</span> <span class="nf">compute_accumulated_cost_matrix</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">distances</span> <span class="o">=</span> <span class="n">euclidean_distance_matrix</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="c1"># Initialization
</span> <span class="n">cost</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span>
<span class="n">cost</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">distances</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">)):</span>
<span class="n">cost</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">distances</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">cost</span><span class="p">[</span><span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)):</span>
<span class="n">cost</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">distances</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">+</span> <span class="n">cost</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">j</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span>
<span class="c1"># Accumulated warp path cost
</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">)):</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)):</span>
<span class="n">cost</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span>
<span class="n">cost</span><span class="p">[</span><span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">j</span><span class="p">],</span> <span class="c1"># insertion
</span> <span class="n">cost</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="o">-</span> <span class="mi">1</span><span class="p">],</span> <span class="c1"># deletion
</span> <span class="n">cost</span><span class="p">[</span><span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">j</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="c1"># match
</span> <span class="p">)</span> <span class="o">+</span> <span class="n">distances</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span>
<span class="k">return</span> <span class="n">cost</span>
<span class="c1"># Plotting the results
</span><span class="n">acc_cost_array</span> <span class="o">=</span> <span class="n">compute_accumulated_cost_matrix</span><span class="p">(</span><span class="n">array1</span><span class="p">,</span> <span class="n">array2</span><span class="p">)</span>
<span class="n">acc_df</span> <span class="o">=</span> <span class="n">pd</span><span class="p">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">acc_cost_array</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">array1</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">array2</span><span class="p">)</span>
<span class="n">acc_df</span> <span class="o">=</span> <span class="n">acc_df</span><span class="p">.</span><span class="n">iloc</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">sns</span><span class="p">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">acc_df</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">axs</span><span class="p">,</span> <span class="n">annot</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></div></div>
<p><img src="/assets/post_images/dtw/output_22_0.png" alt="png" /></p>
<p>To make that concept a bit clearer, let us consider another example. The value 9 in the third row from the top and second column from the left was calculated in the following way. We start by taking a look what the base-cost matrix is at this position. From the cost matrix above, we find that the base-cost at this point is a four. Now we find the minimum value from all adjacently backward values , which is a five. Summing those two values up, leads us to nine.</p>
<p>The last step of calculating the DTW is now to find the <strong>optimal warping path</strong> which results in the lowest accumulated cost when walking from the lower left to the upper right of the distance matrix. As mentioned earlier, finding such a path must follow certain restrictions which we outlined earlier.</p>
<p>In the following we are using the Python implementation <code>fastdtw</code>, to calculate and visualize the optimal warping path.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">dtw_distance</span><span class="p">,</span> <span class="n">warp_path</span> <span class="o">=</span> <span class="n">fastdtw</span><span class="p">(</span><span class="n">array1</span><span class="p">,</span> <span class="n">array2</span><span class="p">,</span> <span class="n">dist</span><span class="o">=</span><span class="n">euclidean</span><span class="p">)</span>
<span class="n">cost_matrix</span> <span class="o">=</span> <span class="n">compute_accumulated_cost_matrix</span><span class="p">(</span><span class="n">array1</span><span class="p">,</span> <span class="n">array2</span><span class="p">)</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">sns</span><span class="p">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">cost_matrix</span><span class="p">,</span> <span class="n">annot</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">square</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s">"YlGnBu"</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
<span class="n">ax</span><span class="p">.</span><span class="n">invert_yaxis</span><span class="p">()</span>
<span class="n">path_x</span> <span class="o">=</span> <span class="p">[</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">warp_path</span><span class="p">]</span>
<span class="n">path_y</span> <span class="o">=</span> <span class="p">[</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">warp_path</span><span class="p">]</span>
<span class="n">path_xx</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="o">+</span><span class="mf">0.5</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">path_x</span><span class="p">]</span>
<span class="n">path_yy</span> <span class="o">=</span> <span class="p">[</span><span class="n">y</span><span class="o">+</span><span class="mf">0.5</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">path_y</span><span class="p">]</span>
<span class="n">ax</span><span class="p">.</span><span class="n">plot</span><span class="p">(</span><span class="n">path_xx</span><span class="p">,</span> <span class="n">path_yy</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">'blue'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></div></div>
<p><img src="/assets/post_images/dtw/output_24_0.png" alt="png" /></p>
<p>Furthermore, the package also has that capability of plotting how the warping path matching translates into the matching of the individual elements of the two sequences. Through the small snippet of code we get an impressive and intuitive idea how each element is compared to a counterpart of the other series.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">fig</span><span class="p">.</span><span class="n">patch</span><span class="p">.</span><span class="n">set_visible</span><span class="p">(</span><span class="bp">False</span><span class="p">)</span>
<span class="n">ax</span><span class="p">.</span><span class="n">axis</span><span class="p">(</span><span class="s">'off'</span><span class="p">)</span>
<span class="k">for</span> <span class="p">[</span><span class="n">map_x</span><span class="p">,</span> <span class="n">map_y</span><span class="p">]</span> <span class="ow">in</span> <span class="n">warp_path</span><span class="p">:</span>
<span class="n">ax</span><span class="p">.</span><span class="n">plot</span><span class="p">([</span><span class="n">map_x</span><span class="p">,</span> <span class="n">map_y</span><span class="p">],</span> <span class="p">[</span><span class="n">array1</span><span class="p">[</span><span class="n">map_x</span><span class="p">],</span> <span class="n">array2</span><span class="p">[</span><span class="n">map_y</span><span class="p">]],</span> <span class="s">'--k'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
<span class="n">ax</span><span class="p">.</span><span class="n">plot</span><span class="p">(</span><span class="n">array1</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">"red"</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s">"o"</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">"Array1"</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">ax</span><span class="p">.</span><span class="n">plot</span><span class="p">(</span><span class="n">array2</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">"blue"</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s">"o"</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">"Array2"</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">ax</span><span class="p">.</span><span class="n">set_title</span><span class="p">(</span><span class="s">"DTW Matching"</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s">"bold"</span><span class="p">)</span>
<span class="n">ax</span><span class="p">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="p">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></div></div>
<p><img src="/assets/post_images/dtw/output_26_0.png" alt="png" /></p>
<p>It is important that the concept of the distance-matrix is clear to the reader. In case a more visual explanation is desired, we recommend the following <a href="https://www.youtube.com/watch?v=_K1OsqCicBY">tutorial video</a>.</p>
<h1 id="warping-band">Warping Band</h1>
<p>Of course not all time-series are as short as the two example sequences we used above. Especially audio data can be reach extensive lengths. When considering the finding of the optimal warping path from above, it quickly becomes a daunting task to find the optimal warping path with that many possibilities. In order to limit the amount of possibilities, and for making the problem computationally feasible, the <strong>window-size parameter</strong> is introduced. This parameter was already mentioned as the fourth restrictions from the original paper. In practice this window parameter looks like the following:</p>
<p><img src="/assets/post_images/dtw/warping_band.png" alt="" />
<em>Source: https://pyts.readthedocs.io/en/stable/auto_examples/metrics/plot_sakoe_chiba.html#sphx-glr-auto-examples-metrics-plot-sakoe-chiba-py</em></p>
<p>As visible from the charts above, we can see that by cutting off the upper left and lower right corner, the amount of possibilities goes down drastically. The size of this window parameter is a hyper-parameter set by the user. Hence, every time we are writing about a relative band size, we are referring to this concept.</p>
<h1 id="implementation-of-dtw-in-python">Implementation of DTW in Python</h1>
<p>Given how powerful this algorithm is and the fact that this concept was not discovered yesterday (the original paper published in 1981), one would think that there is a commonly agreed code implementation in Python of that algorithm. For many years that was also the case and it was called <code>fastdtw</code>, implemented by Salvador, Stan and Chan Philip [2]. Their <a href="https://pypi.org/project/fastdtw/">implementation</a> was and still is wildly popular and used in much of academic’s work. In fact, this post initially also used fastdtw for everything upcoming.</p>
<p>The appeal of fastdtw is, as its name already suggests, it is fast. It does that by approximating the true value of the warping path instead of trying to calculate the true value itself. That is done through a combination of three steps, called: <em>Coarsening</em>, <em>Projection</em> and <em>Refinement</em>. What these three steps do is to first shrink the original time-series into a smaller one, find the optimal warping path using the smaller version and then scale it back to the original size. In order to make that rescaling more accurate, we then <em>refine</em> the warping path with superior matches within a neighborhood of the projected path. The size of this <em>neighborhood</em> is determined by the parameter <em>radius</em> which represents a hyper-parameter of the <code>fastdtw</code> implementation.</p>
<p>Nearly at the end of this writing this post, we then found a relatively new (2020) paper with the edgy title <em>FastDTW is approximate and Generally Slower than the Algorithm it Approximates</em>, written by Renjie Wu and Eamonn J. Keogh (they are both very interesting people who’s personal websites are worth to taking a look at). From this title it is already clear that this paper is not here to make any friends. They even conclude their impressive piece of work by stating a quote of an academic who used the <code>fastdtw</code> implementation and found more accurate and faster results when using the implementation of Wu and Keogh. Since they did us the honor and made their Python <a href="https://wu.renjie.im/research/fastdtw-is-slow/">implementation</a> public, we are using their implementation in the following.</p>
<p>For the rest of this post we are using the implementation by Wu and Eamonn with a slight addition in order to be able to compare time-series of different lengths. Namely, we are padding the shorter series to reach the length of the longer one.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">def</span> <span class="nf">adjust_length</span><span class="p">(</span><span class="n">longer_array</span><span class="p">,</span> <span class="n">shorter_array</span><span class="p">):</span>
<span class="s">""" Pad the smaller series in order to have the same length as the longer array
:param longer_array: the longer of the two sequences
:param shorter_array: the shorter of the two sequences
:return: the padded shorter sequences, which now has the same length as the longer array
"""</span>
<span class="n">difference_in_length</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">longer_array</span><span class="p">)</span> <span class="o">-</span> <span class="nb">len</span><span class="p">(</span><span class="n">shorter_array</span><span class="p">)</span>
<span class="n">padding_zeros</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">difference_in_length</span><span class="p">)</span>
<span class="n">adjusted_array</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">shorter_array</span><span class="p">,</span> <span class="n">padding_zeros</span><span class="p">])</span>
<span class="k">return</span> <span class="n">adjusted_array</span>
<span class="k">def</span> <span class="nf">dtwupd</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">r</span><span class="p">):</span>
<span class="s">""" Compute the DTW distance between 2 time series with a warping band constraint
:param a: the time series array 1
:param b: the time series array 2
:param r: the size of Sakoe-Chiba warping band
:return: the DTW distance
"""</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="o"><</span> <span class="nb">len</span><span class="p">(</span><span class="n">b</span><span class="p">):</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">adjust_length</span><span class="p">(</span><span class="n">longer_array</span><span class="o">=</span><span class="n">b</span><span class="p">,</span> <span class="n">shorter_array</span><span class="o">=</span><span class="n">a</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">adjust_length</span><span class="p">(</span><span class="n">longer_array</span><span class="o">=</span><span class="n">a</span><span class="p">,</span> <span class="n">shorter_array</span><span class="o">=</span><span class="n">b</span><span class="p">)</span>
<span class="n">m</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="n">k</span> <span class="o">=</span> <span class="mi">0</span>
<span class="c1"># Instead of using matrix of size O(m^2) or O(mr), we will reuse two arrays of size O(r)
</span> <span class="n">cost</span> <span class="o">=</span> <span class="p">[</span><span class="nb">float</span><span class="p">(</span><span class="s">'inf'</span><span class="p">)]</span> <span class="o">*</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">r</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">cost_prev</span> <span class="o">=</span> <span class="p">[</span><span class="nb">float</span><span class="p">(</span><span class="s">'inf'</span><span class="p">)]</span> <span class="o">*</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">r</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">m</span><span class="p">):</span>
<span class="n">k</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">r</span> <span class="o">-</span> <span class="n">i</span><span class="p">)</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">i</span> <span class="o">-</span> <span class="n">r</span><span class="p">),</span> <span class="nb">min</span><span class="p">(</span><span class="n">m</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">i</span> <span class="o">+</span> <span class="n">r</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
<span class="c1"># Initialize all row and column
</span> <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">j</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">b</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">cost</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">c</span> <span class="o">*</span> <span class="n">c</span>
<span class="n">k</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">continue</span>
<span class="n">y</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="s">'inf'</span><span class="p">)</span> <span class="k">if</span> <span class="n">j</span> <span class="o">-</span> <span class="mi">1</span> <span class="o"><</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">k</span> <span class="o">-</span> <span class="mi">1</span> <span class="o"><</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">cost</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span>
<span class="n">x</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="s">'inf'</span><span class="p">)</span> <span class="k">if</span> <span class="n">i</span> <span class="o"><</span> <span class="mi">1</span> <span class="ow">or</span> <span class="n">k</span> <span class="o">></span> <span class="mi">2</span> <span class="o">*</span> <span class="n">r</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">cost_prev</span><span class="p">[</span><span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
<span class="n">z</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="s">'inf'</span><span class="p">)</span> <span class="k">if</span> <span class="n">i</span> <span class="o"><</span> <span class="mi">1</span> <span class="ow">or</span> <span class="n">j</span> <span class="o"><</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">cost_prev</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
<span class="c1"># Classic DTW calculation
</span> <span class="n">d</span> <span class="o">=</span> <span class="n">a</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">-</span> <span class="n">b</span><span class="p">[</span><span class="n">j</span><span class="p">]</span>
<span class="n">cost</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">)</span> <span class="o">+</span> <span class="n">d</span> <span class="o">*</span> <span class="n">d</span>
<span class="n">k</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="c1"># Move current array to previous array
</span> <span class="n">cost</span><span class="p">,</span> <span class="n">cost_prev</span> <span class="o">=</span> <span class="n">cost_prev</span><span class="p">,</span> <span class="n">cost</span>
<span class="c1"># The DTW distance is in the last cell in the matrix of size O(m^2) or at the middle of our array
</span> <span class="n">k</span> <span class="o">-=</span> <span class="mi">1</span>
<span class="k">return</span> <span class="n">cost_prev</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
</code></pre></div></div>
<h1 id="audio-files---numbers">Audio Files - Numbers</h1>
<p>As already mentioned earlier, the concept of Dynamic-Time-Warping was originally invented for sound research. Sound can be seen as a time-series given that it represents nothing other than <a href="https://www.physicsclassroom.com/class/sound/u11l1c.cfm">pressure over time</a>.</p>
<p>To assess the performance of the Python implementation of DTW, I recorded myself saying the three words “one”, “two” and “three” in five different speed levels, resulting in 15 audio files. The following function and output below gives an idea how that would sound like. More explicitly, we are showing the medium-speed and quick version of saying the word “one”.</p>
<p>Note that because of the default .m4a format of <em>QuickTimePlayer</em>, we had to first change the format of the sound file before we can display the audio file. Furthermore, it was not possible to loop the displaying of the sound-files, which is the reason of using two cells in the following.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">def</span> <span class="nf">show_audio</span><span class="p">(</span><span class="n">m4a_path</span><span class="p">):</span>
<span class="n">m4a_file</span> <span class="o">=</span> <span class="n">AudioSegment</span><span class="p">.</span><span class="n">from_file</span><span class="p">(</span><span class="n">m4a_path</span><span class="p">)</span>
<span class="n">wav_file</span> <span class="o">=</span> <span class="s">"../wav.wav"</span>
<span class="n">m4a_file</span><span class="p">.</span><span class="n">export</span><span class="p">(</span><span class="n">wav_file</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s">"wav"</span><span class="p">)</span>
<span class="nb">file</span> <span class="o">=</span> <span class="n">ipd</span><span class="p">.</span><span class="n">Audio</span><span class="p">(</span><span class="n">wav_file</span><span class="p">,</span> <span class="n">rate</span><span class="o">=</span><span class="mi">22050</span><span class="p">)</span>
<span class="n">os</span><span class="p">.</span><span class="n">remove</span><span class="p">(</span><span class="n">wav_file</span><span class="p">)</span>
<span class="k">return</span> <span class="nb">file</span>
<span class="n">show_audio</span><span class="p">(</span><span class="sa">f</span><span class="s">"</span><span class="si">{</span><span class="n">DATA_PATH</span><span class="si">}</span><span class="s">/numbers/one_1.m4a"</span><span class="p">)</span>
</code></pre></div></div>
<audio controls="controls">
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bAF0AXQBcAF0AWQBVAE4ASwBIAD4AQQBIAEMAQQBEAEAANAAtACkAJgAlACEAGQATAA0ACAD///T/7v/q/9//0v/Q/8j/w//C/8D/xP/C/7f/s/+0/7P/tv+6/8H/xv/L/8r/xf/Q/9f/0v/J/8X/wv+//8L/wv/L/83/x//J/8v/z//M/8z/0//V/9P/0P/S/9T/1f/S/87/xP+//7n/q/+m/6X/o/+h/57/mv+Z/5v/n/+i/6b/rP+s/6j/pf+l/6X/qf+r/6z/q/+m/6f/pf+o/6//s/+2/7n/u//B/83/1P/c/+X/6v/t//H/9P/3////CQALAAoAEwAYABMAFQAeACMAJwAqACsAKwAwADAALAArACQAIQAkACUAIgAhACcAKQAoACgAJwAjACYAKQAtADIANwA+AD8AQQBFAEoAUQBXAFgAWQBaAFsAXQBdAFwAWABbAFkAUQBSAFUATwBTAFsAYgBqAHEAeQB9AH4AfgCCAIkAiwCFAIMAiwCJAIQAgwCEAH0AeAB1AG8AcQBvAGwAawBpAG0AbwBvAGsAawBtAGgAagBpAGMAXABXAFcAUgBRAEoAQgBEAEIAQQA+AD4ANwAxADUANAAxADMALwApAC0ALwArACMAIQAfABgAEgAQAAwAAwD9//n/9v/0//H/6//p/+z/5//j/+T/5//r/+z/6P/o/+j/5v/o/+T/2v/X/9P/0//V/9D/zv/O/8r/yf/I/8X/y//J/8L/xv/E/8f/x//E/8f/xv/I/8T/xf/G/8H/x//G/7r/uP+8/7//v/+9/77/vv+6/7j/tf+0/7n/tf+u/7H/sf+t/6//rv+u/7X/uf+0/6//r/+r/6r/q/+s/6r/rP+u/67/r/+x/7b/u//E/8//zf/F/8//1f/Q/9X/3f/e/97/3//e/+b/7//v/+//8v/w/+z/7v/u/+3/8f/6//T/6//w//D/6v/o/+3/7f/s/+X/3f/a/9P/0v/Q/8v/yv/F/8D/wv/D/8b/yf/I/8r/zf/Q/9L/1P/W/9n/2//f/+b/4v/l/+z/8f/z/+//8v/z//X/+P/6//r//f8CAP///f/9/wAAAQAJABAADQALAAsADAALAA4ADQAJAAsACgACAAQABQD+/////v/9//r/8//u/+n/7f/s/+j/6f/s/+r/6f/t/+r/5v/q/+n/6f/q/+n/7f/r/+L/3v/m/+L/1//b/9j/zv/Q/9T/1f/T/9D/0P/M/9H/1v/V/9r/2//e/9//3v/h/+X/6f/m/+n/7P/r/+//9P/1//b/+f///wMA/v/6//j/+f/6//T/+P/3//D/7f/s/+v/4//f/9//3f/Z/9f/2P/V/9X/1v/P/8r/zP/R/9D/z//J/8j/z//N/8r/y//Q/9D/0v/Z/9P/zv/P/9L/zv/L/8//zP/L/8n/w/+8/8P/yP/A/8L/wv/B/8j/yv/H/8f/yf/J/8b/yf/J/8b/zP/Q/8//0f/W/9f/0v/Q/9P/1v/X/9f/2P/V/9P/1P/R/9P/1//R/8//0v/P/8r/yP/G/8f/y//K/8r/y//P/8z/yP/K/8j/yf/E/8H/xP/E/8X/w//D/8T/wf+7/7j/uf+2/7P/uP+7/77/wv/F/83/0//Y/9b/2P/c/9f/0//V/9b/1v/X/9r/3P/d/9z/2P/b/97/2f/W/9T/1v/X/9r/2//U/9T/0//X/9f/1f/Y/9b/2f/j/9//3P/f/97/4f/e/9r/2f/f/97/3P/c/9n/3v/b/9v/3//c/9j/3P/e/9r/3//h/+f/5v/g/+b/5P/o/+7/7//s/+7/8f/u//D/8v/0//D/7v/u/+v/8f/z/+7/7v/t/+z/7v/v/+z/6v/r/+z/7P/u//D/7v/w//P/8P/z//b/8//v//H/9f/z//j///8AAP3/+//6//j/+v/7//j/+v/5//b/+f/4//f//f/8//n/+v///wEA//8CAAkACAAIAAsABgADAAUACAANABMAEgAOAAsACwAPAAsACQAJAAUABQAIAAoADAANAAwADQAKAAoACQAGAAsACwALAA8AFAATABMAFwATABIAEgARABIAEAASABUAFwAbACAAHQAcAB4AGQAXABoAGgAWABgAGQAWABsAHQAaABoAHgAdABcAGwAgAB8AJQAmACAAJAAlACEAHgAhACIAHwAfACIAIgAiACYAKgAkACEAJAAgACEAJAAhABwAFwAWABYAFQAQAA0AEQAQAA0AEQAUAA8AFAAWAA8ADgATABQAEgAUABgAHAAgAB8AHwAiACAAHgAaACAAIgAaABoAHgAfAB0AIwAiACAAHwAaACAAHQAdAB0AHQAiAB4AHAAXABYAEwAMAAkADAALAAUACQAKAAcADAARAAwACQARABAACgAMABMAEwATABUAEQAQABIAFgASAA8AFgAWABgAGAAXABoAGQAYABgAHwAjACIAIQAiACUAJAAqACkAKwAvACwAMgAzADIAMwAxAC8AMgA3ADYAMwAwAC0ALQAwACoAKAAtACsAKwAtADAAJwAkACYAJAAoACYAJgAnACsALQAnACYALgAyADEAMQAxADYAOAA4ADoAOgBBAEQAQAA/AEAAPgA7ADsAPABAAD0APABDAEgARwBFAEgARAA9ADwAQAA9ADwAPgA/AEEAQABAADwAOQA2AC8ANQA1AC4ALAAsADAAMAAqAC4AMgArADEANgAyADQAMwAwACwAKgAnACcAKwAuAC0AKwAuACsAKAAsACsAKQAsACwAKQAoACgAKAApACsAKAAkACIAJAAjACIAJgAnACQAIQAjACEAHgAfAB0AGgAaAB0AHQAaAB0AHAAZABsAFwAYABwAHAAZABMAEAANAA8AEwAUABAADAAMAAkADgAOAAoACAADAAIA/////wAA+//7//z//v///wAA/v/9//7/+v/6//7////9//v/+v/3//b//f8AAPz///8AAPn//f8CAAIA/f/+/wQA/v8BAAEA+v/+/wAA/P/3//v//f/8/wQAAwD//wEAAAD9//j/+f/0//X//v/4//j/+f/6//z/9//0/+z/6v/q/+b/6v/w/+z/5v/o/+z/6P/k/+f/5v/k/+H/4f/n/+n/6f/q/+3/6//q/+3/6v/m/+b/5v/o/+n/6v/p/+r/7v/v/+z/6P/s/+z/6//r/+n/6v/s/+r/6f/o/+f/6v/k/9//5v/l/9//4P/k/+L/3//a/9r/4f/n/+P/3f/c/9j/2v/g/+X/3v/a/93/2v/c/9j/1//S/9D/0P/K/9T/1f/R/9P/1f/T/83/1f/T/8//2f/e/9//2v/c/93/2//l/+T/5f/p/+j/5f/m/+7/7P/w//f/8v/t//L/9f/y//P/8v/z//b/8f/w//L/8P/u/+v/7f/w//D/8v/2//X/9v/5//n/9//s/+v/8P/x//b/8v/z//L/8P/w/+z/7P/q/+n/5v/k/+f/5f/m/+f/6P/n/+b/5//k/+n/7//t/+7/8P/y//X/+P/4//n//P/1//P/9f/3//j/9P/3//X/9P/5//r/+f/6//z//P8CAAMA+//9/wEA/P/3//v/+//2//j/+//6//r//f/8//j/9//0//P/+P/4//H/8v/5//f/8//1//f/+v/7//T/7//z//T/8v/0//H/6v/o/+b/6f/o/+b/5//q/+f/5v/o/+P/4//g/+D/5v/l/+n/6//i/93/3v/g/+L/5//r/+r/7//p/+j/6//q/+z/7f/w/+//8//3//z//P/3//T/7v/p/+n/7f/n/+f/7f/r/+L/4f/m/+X/4P/Z/+D/5P/f/9v/4P/i/9//4v/d/9z/4f/g/93/3f/k/+b/5f/o//H/8f/z//j/9P/y//P/9//5//r//v/+//z/AAAAAP3/AgAEAP//AwADAPv//v8EAAQABwAJAAgACgAHAAsAEgAMAAoADAAKAAYABAACAAUACQABAAAAAAD6//f/9f/9//n/8v/2//T/8f/x//T/8v/0//j/9P/x//P/9//z//T/9//z//T//P/3//H/+f/8/wAAAgAAAAMAAgD+//z//P/9//7//v8AAP7//f8BAAEA/v///wUAAAD4//z////8//v////+//3////8//7/AAD+//z/+////////v8CAAIAAQACAAIA/f/2//b/+f/6//v/+P/4//v/+P/2//f/+v/5//X/+f/z//P/+v/9//3/+f8AAAQAAQACAAIABwAMAAoACwAVABgAFAAVABcAGgAZABYAFQAXABwAGgAbAB8AGQAWABgAHwAfABUAFQAZABgAFQASABAAFQAaABUAEgAPABIAFAASABIAEQAUABIADwAMAAoADgAPAAsACwAPABMAFwAVABUAEQASAA8AAgD9/wAACgAMABEAFgAVABgAEgAPAA8AEAANAAYABAAJAAoACAAKABAAEgANAAoACwAGAAMACAACAP3///8DAAUABQAGAAIAAgAAAPr//f/9//v/+f/3//f/+v/+/wEAAAAAAP//+P/4//j/9f/3//z/+//7///////9//v/9v/y/+7/8v/z//D/8P/w//f/9//0//n/+v/+//////8AAAAA///5//r/+v/2//r//f/7//v//v/9//z//f/+//v//v8GAAUABAAHAAgACgAPAA0ACAAJAA0ACwAGAA4AEAAPABUAFAAQAAwAEQARAAsAEgAVABcAGQATABUAGgAYABYAGAAYABYAEgATABAADAAMAAwACgANABQAEgAWABcADwAUABQAEwAWABgAGgAXABIAEwAYABoAFAASABQAEwAYABkAGQAhACUAHgAaACAAIAAfACQAKQAuADAALgAqACsAKwAlACYAKQAoACcAKwAvACsALQAzAC4AJgApADMAKwAkACgAKQAvAC0ALQAxAC4AMAAtACoALwAtACkAJgAmACUAJwAoACgAKwArACoAJQAkACcAJAAlACYAKgAtACsAKQAlACgAKQAnACUAIAAeABwAHwAkACcAIgAjACIAFgAWABoAFgARABEADgAKAAsABwAIAA0ACAACAAkADAAAAPn//P/6//n/AAD7//n/+//z/+//+f8AAPf/8//3//f/8P/1//3/9P/x//L/8//4//v/9f/0//f/+f/4//r/AAD9//v/9//v//H/+f/4//P/9f/3//n/+//9//n/9v/7//v/+P/9/wEA/f/+/wEAAwADAAAA//8CAAUAAQABAAQA///7/wAAAAABAAUACAAHAAQACAAJAAQA/v/9/wIAAwACAAUABgAGAAcAAwAEAAkABwAFAAUACQAMAAgABgAHAAoADgALAAsACwAKAA0ACAAMABMAEAANABIAFQATABYAEgANAA8AEQARAAwAEAASAA4ADAARABUADQANABEAEQANAAkADwANAAoACwANABAAEAATABYAFgAXABsAGgAaABwAGwAeABkAFwAaAB0AHgAcABwAHgAhACEAGgASABMAFgAYABkAFgAYABgAGAAYABUAEwARABAAEQATABIAEwAVABUAFAAVABUAFgAYABEAEAAPABAAFwAVABUAFQATABUAFQASAAwADQARAA4ACwALAA4ADAAOAAgAAwAOAA0ADQARAA8AEAAOABAAEAANABIADwAKAAsADAALAAcABwAKAAYADQAPAAYABQAGAAUAAAABAAUAAAD//wEA/f/7//n/9f/1//X/9f/1//f/8v/t//L/9f/1//T/9//1/+3/7f/w/+v/5//m/+v/6//s/+//6f/n/+j/5//k/+D/5v/n/+f/5//k/+v/7v/o/+r/5//n/+j/5P/i/9//5v/u/+v/7P/s/+7/7v/y//b/8P/v/+v/6//t/+n/7P/t/+3/6//n/+j/6v/w//D/6v/v//X/8//x//T/9v/0//n///8AAAMACQAGAAIABgAEAAYABQADAAgABwAEAAgACAADAAEA/f/4//n//P/8//n/+f/3//b/9//2//j/8//v//P/9v/2//z//f/5//z//P/6//r//f/6//j/AgABAPf/+/8BAP//AgAHAP3/+f8CAAcAAAD3//r///8DAAYAAgAAAAIABQAIAAQAAQAFAAUABwAIAAQACAALAAkACgAIAAkACgAHAAQACQAGAP7//f/6//v///////n/+P/+//n/+v8AAPz//P8AAAAAAAD//wAABAAEAP7///8BAAAA/v/4//n//v/+/wIA/P/3//r/+//5//f/9//2//P/8P/w/+z/7f/s/+j/5v/k/+f/4f/h/+X/5P/g/9v/3P/c/9//4v/b/9f/3v/d/9n/3f/h/+L/4f/o/+b/3v/e/+H/4//g/93/4f/j/+H/4v/g/9r/3P/e/97/3v/a/9X/2//b/9f/2P/Y/9n/0v/W/9j/2P/d/93/3P/b/+T/5P/e/97/2v/a/97/3P/b/9//4v/m/+b/5//q/+f/5//n/+b/5P/o/+L/2//c/+D/4P/c/97/3v/b/9r/4//l/+D/4v/h/+b/6f/o/+f/6v/r/+X/6//q/+f/6v/t//H/8//1//H/7v/v//L/9f/3//b/7P/s//D/8//0/+z/7f/0//n/+//6//v/+//4//n//P/6//7//f/5//z/+//9////+//8/wAA/P/3//b/+v////v/+f/2//P/+f/5//T/9v/1//D/9P/1//X/9P/3//z/+v/6//r/+v/+//7/+P/3//v////+//n/9f/w//T/+f/2//X/+P/4//b/+f/1//P/9P/0//P/9P/5//T/7P/y//L/7//z//X/8f/0//j/9P/y/+//8P/0//T/9P/w/+n/6f/x//D/8P/y/+//7//w//D/8P/z/+7/7P/0/+7/5f/p/+b/5//s/+r/6v/o/+r/7f/t/+z/5//r/+//6P/i/+b/7f/s/+f/5//q/+P/3v/d/97/4f/d/97/3v/Z/9f/1//Y/9v/3v/Z/9X/2f/X/9T/1f/f/+T/4//j/9//4f/k/+P/5f/m/+P/5f/k/+L/5f/l/+X/4f/g/+L/6P/k/9v/3v/b/9r/2//W/9f/1//Z/9n/4f/k/9z/2//f/9//3//e/9v/2//c/9z/2f/c/+P/4//g/97/3f/a/9r/3P/f/+P/4P/e/97/3//i/93/2//d/9//3v/h/+X/4f/d/9r/3v/e/9r/2//b/93/3f/f/+H/3//f/97/3//d/9//5v/j/+H/5f/p/+X/5P/n/+T/5v/p/+n/6//t/+f/4f/k/+b/5P/k/+b/5v/l/+j/6//o/+r/6//m/+b/5v/n/+v/7f/v//H/9f/7//b/9v/6//P/9P/6//b/9f/3//b/9f/2//v/9v/y//j/+P/3//z/+v/2//v//P/3//j/+f/+//3/9f/1//f/9v/z//b//P/4//n//v/9/wAAAQD+//7/AwAEAP3/+//7//v/AAAEAAIAAQAGAAUAAAAGAAkABwAEAAUADAAQABQAEQATABoAFgAVABcAHAAiAB8AHgAeAB0AHgAYABgAGwAaABgAHAAgAB0AGgAXABgAFgATABMAFQAVABYAGgAYABQAEwAWABQAEAASABMAEQAOABQAFwASABQAFQAZAB0AFgARABkAGgAVABcAEwATABkAFAAQABAAEgASAAkABgAIAAcAAwACAAYABQAEAAcABQAFAAIAAgABAP3//v///wUACQAHAAMAAwAJAAYAAwAAAP7/+//9/wEA///9//j/+v/+//v/+v/2//r/AAD9//z/+v/6//r/9v/z//f/+//6////AQD+////BAAFAAcACAAJAAoADgAQAAsACgAMAAgAAAACAAwADQAPABAAEgAQAAwADQAHAAkAFgAVAA0ACQAKAA8AEgASABYAHAAbABYAFAARAA4AEwAUABUAFwASABIAFgAcABgAEgAaABsAFwAVABMAEgATAA4ADAANAAgACQAIAAsACwAHAAYABwAPAA0ADgAOAAsAEgAVABkAHAAaABgAGAAYABgAHAAjACYAIQAcAB0AHwAkACEAIAAgABcAFgAYABoAGQAUABkAHgAeABoAGAAZABwAHAAaABkAGgAeAB0AHQAbAB4AIQAdACEAIwAlACAAHQAcABcAGQAXABkAGQAcACEAIAAhACAAIgAkACQAHQAdACUAJgAlACMAJgAnACQAJQAoACMAGgAhACUAJAAlACIAJwAoACYAJQAjACYAJQAhACAAIAAjACMAIgAiACQAKAAoACYAKQArACUAIwAnACQAIQAkACUAKQAqACgAKAAqACoAJQAhACEAHwAjACUAIQAbAB8AIgAdACAAIgAlACIAFwAcACEAJAAjACAAKQApACUAJQAlACsALQApACUAKAAqACgALAAvAC8AKAAkACkAKAAtAC0AJQArACsAKgAuAC4ALAAlACcAJQAeAB0AIwAgAB0AJAAjACAAIgAkACUAJQAkACEAJAApACQAIAAkACgAKgAkACcAKwApADAALQAsAC0ALgAtAC0AKwAnAC0AMQAsACoALQAvACoAKgAtAC4ALQAoACYAJwAoACoAKAAlACQAIgAkACcAKQAlACUAJQAnACwAIgAcABgAFwAcABwAHAAeACAAHwAZABsAHgAiACQAIwAgACAAIwAhACEAIAAjAB4AGQAdABsAIAAhAB0AGwAaABwAHQAgACAAGwAZABkAGwAdABsAHAAbABwAHQAaABsAGwAbABsAGwAVABUAGQAVABcAGgAbABYAEwAZABwAGQAZABgAGQAbABoAFQAUABQAFQASABYAHgAbAB0AIgAhACAAIgAnACcAJgAkACEAIwAmACMAIAAiACEAGwAdAB0AHAAgABsAFgAWABUAFQASABUAGAAWABQAFwAaABgAFQASABQAGgAZAA8ADQASABMAEQARABMAEgATABUAEwAVABMAEAAPAAoACgAOAA8AEgAOAA0AEQANAA8AEQAPAAsACwALAAcACAAKAAsABgAKAAwACAALAAsADgAOAAkABgAJAA4ACQAJAAUABAAOAA0ACgALAAwACQACAAIAAAACAAUABwALAAsABwAHAAwADQAHAAQACgAJAAYACgAKAAYABwAIAAQACQAKAAYABwAIAAcABwAFAAQAAQD+//3/+f/4/wAAAAD2//j/+f/1//j/9P/w//b/+P/z//L/+f/9//j/9f/4//f/9//7///////+/wIABQACAAEAAwAFAAEA/f8CAAEAAAAAAAEACAAEAPz///8BAP7/+v/6//r/9v/1//X/9f/2//r/+f/3//j/+//4//j/+v/3//n/9//z/+//8//2//L/9v/6//f/9P/7//v/9P/4//z/+f/5//3/+v/2//n/9P/x//X/9v/y//T/9P/w//L/8P/v/+3/6//w/+//8v/y/+n/7f/y//b/9f/4//j/9v/4//n/9//w/+z/8f/y/+7/8P/v//H/9P/w//T/8v/t/+j/4f/k/+j/6f/r/+f/6P/q/+n/6v/r/+z/6v/o/+3/7P/q//D/7f/v//H/7//s/+n/7P/q/+f/5v/l/+n/5P/f/9//3v/f/+L/4v/e/93/5P/j/9z/4P/i/+D/3P/f/+D/3v/l/+X/4//i/+P/3v/b/97/2f/X/9j/2f/a/9X/1f/U/9L/1f/U/9L/0v/O/8v/zf/P/8r/x//O/83/zP/P/83/zv/O/87/zP/J/9H/0f/L/83/0P/N/8n/yP/I/8r/yP/H/8n/yv/M/8z/zf/M/8//0v/L/87/0v/S/9H/0f/U/9b/0f/T/9b/0f/b/9r/1f/a/9j/2f/a/9z/3f/Z/9v/3v/a/9n/3v/b/9b/2f/Y/9f/2f/Y/97/4P/g/+D/2//Z/9z/2v/Y/9j/1v/T/9D/1v/W/9X/3P/Z/9n/1P/T/9D/z//V/9P/0f/T/9j/1P/U/9f/3P/e/9r/3f/b/9r/2v/b/9v/2//f/+H/4v/e/97/3v/e/9v/2//h/97/2v/c/9//4v/h/+H/3//c/97/4P/e/9v/3//f/93/4P/h/+T/5f/i/+f/6//n/+b/5//k/9//4v/m/+D/3v/m/+P/3f/k/+f/5P/g/9//5P/j/+D/3//e/+P/4v/g/+P/4//j/9//3P/g/+D/4P/f/9z/2v/X/9f/1//a/9//3f/b/9n/2v/b/9v/1v/R/9T/2f/a/9j/3f/f/9z/2P/Z/9r/3P/c/93/5P/i/+X/5//l/+X/3//j/+f/4f/m/+r/4//j/+X/4f/d/9v/3P/i/9//2//f/9//4f/j/+H/4f/k/+L/2//d/97/3P/f/97/2f/Y/9z/3//g/9//3v/d/93/2v/V/9b/2f/W/9D/0P/U/9b/1//Z/9j/1v/Z/9b/1f/Y/9X/1f/Z/9b/1f/Y/9f/2f/a/9r/1//X/9j/2f/V/9P/2f/Z/9v/3P/c/9z/3f/i/+L/4f/k/+j/7P/v//L/7P/s//L/8v/y//D/8P/u//D/9//0//P/9f/7//7//P////3/AQAHAAgABgADAAEAAAACAAIAAgADAAcACgAJAAkACwAJAAYACgANAAsACwAOAA4ACgAJAA0ADwAMAAsABgACAAQACgALAAwADwAQABEADwAOAA8AFAAUAA4AEQARAAwACwARABUADwARABIAEwAUABQAGQAZABMAFAAZABYAFAAVABUAGAAUABQAFAAQABQAFgASABMAFAASABMAFQAXABUAEwAXABcAEAAWABsAFwAXABYAFwAZABoAGgAaABwAHAAYABYAFwAZABkAFwAYABQAFAAWABYAFAATABAACwAPAA8ADAANABAADgAPABMADgAKAAkACQAHAAwADwAMAA0ACwAKAAcABwAJAAYABAADAAMABAAEAAMACQALAAkAEAATABQAFgAVABMADwAPAA4ADQAUABgAEwAUABQAFAAVABIAEgAUABIAEAAWABYAFQAaABsAGwAXABkAHgAdABgAGgAfABoAGwAcACEAIwAeAB0AHQAhAB8AHwAdABoAHgAcAB8AIAAfACEAIgAiACEAHgAeACMAJAAgAB4AIAAcACAAIQAeACQAKAAlACIAJQAqACsAKgAuAC4ALAAyADIAMAAyADQAMgAxADQANgA0ADMAMAAvADQAOgA6ADkANwA1ADUAOQA3ADYANAAxADEALwAyADQAMwA1ADQAMAAtADQAOAA1ADUAMgAyADMAMwA0ADMAMgAwADEANgA4ADYALwAwADQANQA1ADQANQAzADEAMAAyADMAMQAvAC8ALQAvADIALgAuAC0AKwArACwALAApACwAKQAqAC4ALgApACIAJQAmACIAIQAmACYAIAAhACIAJQAjAB4AIgAmACYAJQAoACoAKwArACYAKQArACUAJAAjACEAIQAlACcAIAAdACEAIgAeABkAEgASABcAFgATABQAEQAOABIAEQAOAA0ACgAFAAQACgAMAAoACAAJAAgACQAFAAQACwAFAAIABgAIAAkABAAGAAkACQAJAAcACQAJAAUAAQACAAEAAwAJAAkACQAHAAYABQAEAAYAAwAAAAAAAQD+//v//v///wAABAAFAAYACQAJAAMABAAIAP//AAADAPz/+////wEAAAACAAEA/f/5//n/+v/0//b/+f/4//v/+//2//T/+P/3//f/9v/3//j/9f/2//f//f/+//z//f/8//3/+f/5//z/+f/4//n/+//4//v//f/2//3/BwAAAAAABwAEAAAAAQAEAAYACQAIAAYABgAIAAgABgAIAAkAAwAKAAwABgAEAAAAAgAGAAYAAwD//wMAAQACAAQAAAD+//3//f///wIAAwACAAYABAAAAAYAAwD//wQABwAFAP3//f///wQABAACAAkABwAGAAYACwALAAMABAAGAAoABwAGAAgABwAJAAsACQAGAAMAAAD//wIAAgAAAAEAAwABAPn/9//7//7/AwABAP//BAAGAAMAAQACAAMAAwAAAP3/AQADAAIAAwADAAYABwAJAAwACgAKAAkABAAAAP7///8AAAAA/v///wAA+f/2//r/9P/w//D/7v/t/+//7v/q/+7/8P/u//D/7f/q/+r/7f/v/+r/7//1//T/9v/z//D/7//1//f/7//y//H/8v/1//T/8P/t//D/8f/t/+3/7//r/+j/6f/p//D/7//q/+r/7P/q/+P/4f/i/97/3f/g/+L/4//n/+f/4//k/+f/4P/h/+P/4P/j/+b/5v/j/+r/6v/i/+T/5//r/+r/7P/w/+//7P/r/+z/6v/q/+r/6//p/+z/7v/r/+7/7//q/+j/7//w/+r/6v/r/+z/6v/r/+n/5v/o/+j/4v/j/+f/5f/l/+r/6v/o/+v/7P/r/+7/7P/s/+3/7v/s/+j/6v/u/+7/7P/t/+7/7P/s/+7/7v/r/+3/8f/t/+3/7//u/+//8f/v/+b/5//o/+T/5//p/+v/8P/o/+T/6v/s/+r/5//o/+n/6v/q/+f/6P/m/+T/6P/l/+T/4//h/+L/4P/j/+T/4//j/+X/6P/m/+r/6f/o/+v/7//w/+//8f/u/+7/8f/x//P/8v/u//D/8//y//H/8//0//L/8v/0//H/8v/z//X//P/7//j/9//5//n/9v/v//H/8f/x//X/9//3//X/9P/u/+//7P/n/+v/6//r/+n/7f/r/+j/7P/w//D/6f/s/+r/5//p/+f/5v/l/+b/5P/k/+T/4v/j/9//3f/f/+D/4P/i/+b/5P/i/+L/4P/e/9v/2v/h/97/2P/a/9j/1v/b/+D/2f/X/9n/1f/X/9b/1//V/9T/2P/W/97/3//c/93/3//k/+P/4f/e/+P/5f/o/+j/5//t/+r/6f/p/+3/7P/n/+r/5//l/+P/4//l/+j/6v/o/+b/5//s/+z/6f/o/+3/8f/y//D/8v/0//H/9P/3//T/8//u//L/9P/v/+//8P/t//L/8//z//X/9v/8//7/+v/7//z/9//2//r/+f/5//j/+v/5//n//f/5//z/AgABAAMAAwACAAIABAAMAA4ADwANAAwADwASABMADAAPAA4ACQAOAAsACQAKAAsACwAPABEADgAUABYAFQAYABYAFQARABMAFgAXABgAFAAXABcAEQASABMADwAQABAAEwAUAA4AEAAOAA0ADQAQABMACQAMABUAEwAVABUAGQAZABgAFwAYAB4AHAAZABwAIAAfABsAGwAYABgAFAAQABIAEQAUABIADAALAAoADwANAAYABQAKAAwABwAJAAcAAgAGAAcAAwAGAAoABwAJAAkABAAHAAcACQAQAAwADgAPAA8ADwAPABYAFwAYABoAGQAXABUAFAARABcAGQASABEAEAAQABAAEQAQAA0ADwAPAAkACgAJAAUACQAJAAUACQANAA0ABwAIAA0ABwAIAAwABwAHAAkABgALAA4ACQAJAAsACgAHAAoACQAEAA8ADQALABYACwANAA8ADgASAAkAEwANABAAFQAOABcADwATABYAGgAcABwAIQAbACAAHgAiACEAIAAhABgAFgAbACMAHwAcAB4AGwAUABgAGwAWABkAGQAbAB4AGwAZABkAFQARABEAFQAWABcAGgAZABoAIAAfABoAGQAZAB4AGAAVABMADgARAA8AEQAUABcAFwASABUAGAAQABIAGAATABUAFAAQABAAEQASABMAFQATABUAGwAeABcAFgAfACMAHgAbAB4AGwAXABYAGAAZABUAFgAbABsAHQAkACUAIQAgACQAHwAcACEAHwAYABYAGQAYABsAHAAXABYAEwATABAAEQAWABMAEQAPAAsACQAKAA0ADQAKAAkADAANAAoABgAGAAkADAAMAAsACQAIAAYABgAKAAkABQAIAAgABAAFAAgACQAGAAYACAAJAAMA/v8AAAQAAQAAAAcABAD//wAAAQACAP//AgAHAAgAAwAFAAYAAQAGAAcABgAGAAIAAgABAP////8FAAEA/f8AAP3//v////3//v/7/wAABAAEAAEA/v8BAAAAAAD//wAA///+//7/+//+//3/9//3//z//v/5//r//v8CAAAA/v8FAAMABAAKAA0ADwAOAA4ACQALAA4ADQAMAAcABwAJAAoABwAHAAoABQACAAUABwD/////BgAHAAIA////////BgAEAP7/AgAFAAAA/v8AAPz//v8CAAEABgAIAAcABwAJAAoACQAMAA4ADwAOAA0AEQARABIAEwAPAA8ADwARAA8AEQATABMAEwAUABcAFQAXABQAEwAQABIAEQAKAAwADAALAAgACAAIAAkABgACAAUABQAFAAQAAQAEAAMAAAABAAAAAwAHAAQAAgAGAAkABQADAAMAAwADAAUAAgAEAAUAAgAHAAYABQAJAAgACQAGAAgACgAJAAgACQAMAAoACAALAAoABgAJAAoACwANAAYACAALAAoADAANAA8ADAAJAAcABQAFAAYACAAJAAsACgAKAAwADwALAAUACgANAAwACQAKABEAEQANAAsACgAIAAYABwAKAAoACgAKAA8AEAANAA0ADQAQAA8AEgAWABEADwAYAB0AGwAcAB8AHQAZABsAIQAdABcAGwAaABUAFQAYABkAFwAWABYAFQAQAAwADgAQAA4ACwANAA0ACgAMAAwACQANAA8AEAATAA4ADQARAA4AEgAYABcAFAAWABcAFAATABMAEQAKAAoAEwAPAAsAEQAKAAkADgAGAAUACQAOAAYA//8FAPz/AQACAAAABQABAAQABQAEAP//BAACAPr/AAD+////+//6//n/+f/6//j/+//3//j//P/6//j/+//+//3//f/7//v/+P/8//3/+//8//z//P/4//f/9//3//j/9//1//L/8//1//T/8//y//T/8v/y//H/8f/4//X/9P/1//P/8f/u//T/7v/s//P/8P/w/+//8v/x/+7/7//u/+7/7P/u/+7/8f/2//X/8//y//H/8f/2//n/+P/1//X/9v/3//v/+//6//7/AwABAAAABQAGAAIAAwAHAAQABAADAAIA/f8BAAUA/v////3/+P/3//v/+//9/wAAAQAFAAIAAQABAP//AwADAAMABQAEAAgABgACAAMAAwAFAAcACAADAAQABQABAAIAAgABAAEAAgABAAIAAgD+/wAA///8//3//v/8//r//v/+//3//P/+/////P8BAP7/+f/9//3//f/8//z/+v/4//3//f/7//r/+f/6//j/9P/5//r/9v/z//L/8v/0//H/7f/y/+//7P/v/+r/6P/s/+z/6v/r/+r/5v/l/+P/5f/i/+D/4v/f/9//3//b/93/2v/a/9n/1f/Y/9r/2f/V/9j/2v/Y/9v/1f/Y/9z/1//V/9f/2P/T/9b/2//X/9T/1//e/9n/2//d/9j/2v/V/9D/z//S/9H/0f/S/8//0//W/9f/0f/W/9z/2v/a/9j/2v/V/9T/2f/Y/9j/2f/V/9X/1f/Q/9P/0//S/9H/1P/W/9D/zv/S/9X/2P/Z/9f/2P/e/9r/1f/V/9j/2v/a/9r/2v/c/93/3P/d/9//3f/c/9z/3f/e/+H/3v/h/+n/4v/c/9//3v/c/+H/5P/k/+j/5v/j/+T/5//q/+r/7P/s/+j/6f/n/+X/5v/n/+f/6P/q/+b/4v/j/+P/4P/f/+D/4//h/+D/5f/k/9//5P/n/+L/5f/r/+f/4//n/+n/5f/p/+v/5v/n/+3/6v/o/+z/6//q/+z/7f/v/+3/7//u/+3/9f/z//L/9//3//T/9f/2//D/8f/2//z/+P/2//n/9//4//f/8v/3//3/9f/z//L/7f/u/+//6//r/+z/6v/q/+z/7P/v/+//7P/s/+z/7//u/+r/6//q/+z/7f/t/+3/6//r/+j/5v/o/+j/5P/l/+n/6v/p/+P/4f/m/+f/5v/o/+f/5//o/+f/6v/u/+v/5//m/+X/5//o/+T/5f/l/+X/6f/n/+T/6f/r/+b/4//i/+L/5P/e/9v/3P/Z/9r/3f/f/9z/3v/d/9//4v/d/+D/4P/i/+H/4f/k/+H/4f/g/93/4P/i/+L/5v/k/+T/6//u//L/7v/w//L/7v/s/+3/8P/x//j/+P/3//b/9f/1//j/+//2//f/+P/4//r//P/7//j/+f/5//z/+v/6////+v/6//3////9//j//P/+//7//f/9//7/+v/2//T/+P/5//n/+v/8//7//f/8//3//f/9/wAAAQAAAP//AwADAP//AQADAAYABgACAAMABgAIAAUACAAMAAcACAALAA4ADwALAAwADQAMABIAFAASABEAEAARABUAFAATABQAFQAaABwAHAAbABYAFQAZABUAEgASABUAFgAQAAwADAAPABAACwAMAA8ADQAMAAoACwAQABIAFAAPAA4AEwASABEAFAAXABQAEgATABMAEgAUABMADwAOABAAEQARABIAEAARABYAFQAUABYAFQASABEADwANAA0ACwAMAA8ADgAMAA8AEQASABUAFwAWABYAGwAWABAAEwARABEAEgAQAA0ADQARAA8AEQAaABIADQAPABEAFAASABMAEQAVABcAFAASABIAEwANABMAGAAWABUAEwAUABIAFAAUABUAGwAcABoAGgAbABkAHQAbABkAHAAbABkAEgARABQAGwAZABEAEQAQAA4ACgANAA8ADQAOAA4AEAAPABAAEQAOAAwAEAATABIADQAQABUAEwARABAADwALAAYABwAOABYAFAARABAADgASABIAEwAZABsAGgAXABsAGQAWABsAGwAZAB4AHwAbABsAHwAgAB8AIwAkACEAIQAhACUAKQAqACcAJAAoACkAKAAlACYALAApACgAKQAsACkAJAAoACwAJQAcAB4AIQAgACIAJAAkACMAJQAoACUAJgApACgAKgArAC8ALAApAC0ALAArAC0AMAAxADEALgApACkAKQArACgAJAAoACgAJgAnACcAKwArACMAIQAlACMAIAAdAB4AIgAjACQAHwAfACAAHgAbABwAIgAiACEAHwAfAB0AHQAdABcAGwAYABYAGAAUABoAGAAXAB0AGwAZABkAGwAcAB8AGQAcAB4AGgAcABgAHAAZABgAHQAYABYAFwATAA4AEgAXABUAEQASABEADwATABAADAAOAA8ACwANABIAEgATABMAEwAXABUAEAARABIAFAAWABEAEQAQABIAFQAOABAAEgAMAAoAEAAVABAADQAOABAAEQATABEADgANAA8AFQAUAA8ADwARABAADgAQAA0ACwANAAwACwALAAoADQAOABAADQAOABIADwAOAAoADgAOAAcADAAIAAsAEwARAA4ACwAPAA8ADwAPAA0ADAALAA0ADwARAA4ABQAEAAsACQALAA0ACAANABAAFAARAAsAEAAVABIAFAAWABMAFwAZABQAFQAcABoAGAAdACEAIgAeABwAHAAfAB8AGwAaABsAIQAlAB8AHwAgABsAFwAYABwAGgAcACEAHwAeAB4AGwAZABsAGAASABIAEwATAA4ACwAKAAsADwANAAsADgAKAAkACwANAA4ADQAKAAkADAALAAkACQAJAAkADAAMAAkACwASABEADQAPAAoABAAJAAwABwAKAAsADQAQAAoABwAHAAgABAABAAAAAQABAP3/+//2//v/+v/4/wEAAgABAPz/AgACAP3//P/7//z/+P/5//f/9P/2//b/9v/1//b/9v/3//j/9P/z//f//P/7//v//P/7//3/+//3//3/AAD///n/9v/3//r////+////AAD+/wEABAACAAMAAwD+/////v8DAAIA/f/8////BAAAAP7//f/8//r/+//8//r//v////3/+//9//7//P/7//3//f/7//3/+f/3//j/+P/3//X/9v/6//f/9v/5//j/9v/3//z//P/5//n/+v/9/////v/8//v/AAD+//v//f/7//3//P/7//7/AAD+//f/+f/8//f/9v/x/+7/8//y//P/8P/u//L/8//1//X/9f/1//j/8//y//z//P/7//r//P/+//3/AAACAAQABAADAP//AgAFAAQACQAHAAMABAAIAAQAAgACAAMABAABAP7//f8AAP7/+v/4//r//v///////f8AAAAA+v/6//n/+f/7//r/+P/4//r/+f/2//X/8//y//r/+f/2//b/+P/5//n//v/7//v/+//7//r/+P/5//f/+f/6//j/+P/6//z/+v/4//b/9f/3//v//P/6//v/+v/3//r/+//6//z/+f/6//T/8//3//P/9f/4//r/9//1//n/9v/2//X/8f/y//L/8P/w/+//7v/r/+v/6f/q/+f/5P/p/+X/5P/o/+f/5f/m/+j/4//i/+r/6v/k/+H/5P/l/+L/5f/l/9//4f/h/+L/4//e/9//4v/g/9//3P/d/9//3v/i/+L/5P/l/+X/5f/k/+L/5f/n/+b/5v/m/+T/4//l/+b/6f/q/+n/5P/p//D/6//p/+r/5//s//H/6f/m/+n/5v/i/+X/6f/k/+n/7f/q/+3/7f/u/+7/8f/z/+//7f/y//X/8//4//j/8v/x//P/+P/2//L/8f/y//P/7//t//L/8//w/+v/6v/o/+P/5//o/+X/4v/j/+X/6f/u/+3/6f/o/+v/6//r/+v/7v/v/+//8f/z//f/8//u/+7/8f/1//L/9v/3//H/8v/w/+3/7//v/+7/8P/1//P/8v/1//L/9f/5//n/9f/2//v/+f/5//n//v/9//7/AwABAAAA+f/5//r/9//y//D/9v/7//n/9f/2//T/9v/3//H/8f/w//X/9//x//L/8//v/+z/7//w/+z/7P/r/+z/7f/v//D/8P/0//L/8v/2//X/8//z//X/8//2//n/8//w//X/+v/5//X/+P/1//P/9f/x//T/9P/y/+7/7v/y/+z/7f/x//L/7P/p/+r/5P/p/+j/5v/q/+r/7P/s/+//9P/u/+3/7//s//H/9v/0//T/9P/1//n//P/5//f/9f/w/+z/6//t/+j/5//q/+b/5//o/+j/5//p/+v/5//n/+r/7P/v/+7/6v/t/+z/5f/l/+n/6P/l/+f/6f/p/+r/7v/r/+X/5//p/+3/6f/k/+b/6P/p/+n/6//r/+3/6//n/+X/4//n/+T/3//d/9z/3//g/93/4P/e/9n/3v/f/+L/4//a/97/4v/c/9z/3v/e/+T/4//i/+L/4v/g/+X/5f/d/+T/5v/n/+j/6f/q/+n/6//o/+//8P/w//L/8//w/+7/8P/s//D/7P/t//H/7f/v//D/8P/u/+//7f/s/+7/8f/0//T/+f/6//j//f/+//n//P8AAP3//f8CAAMAAAAIAAcAAwADAAIA///+/wEAAwAIAAkABAAHAAgACQAFAAYACQAFAAQABQAIAAgACAAIAAYACgAFAAYACgAHAAcABgAFAAgABgAJAAsABgAGAAYADQAMAAYABQADAAMAAgD///r//f/9//3/+v/4//n/+v8BAP3//P/9/////f/7/wEAAQAFAAIAAAAEAAQABQAAAAEA/v/8//3/AAABAPz/AgABAPj//f8AAPz/+v8AAPz/+v////7//v/9/////v/7//z/AAAAAPr/+v/3//f/9//0//f/9v/1//P/+P/5//P/8//2//j/9//6//////8AAAIAAAD//wcABgAAAAAA/f/+//7//f/9/////v/4//b/+P/2//T/9v/3//L/7//v//D/8P/v//L/7//s//D/8P/v//H/7//w//H/7f/t//H/8P/v//L/9f/y//D/9v/2//b/9f/1//r/+v////3/+//8//n//P/5//n//f/8//z/+v/4//f/+f/5//j//f/5//n/+//y//D/8v/z//T/9//6//b/+P/8//j/9v/6//z//f/3//X/+v/7//v/+P/7//v/9v/4//b/+//3//b//f/5/////v/9/wIAAAACAAAAAAABAAAAAQD/////AwAGAAUABgAGAAQABwAKAAgACAAHAAcACgAIAAYABAAFAAcABQADAAEAAQD//wAAAAD7//v/+//6//v/+//8//j/+//8//L/+/8AAPX//P/7//H/9f/2//T/9f/4//X/7//3//n//f/5//T/AAD6//n/9//y//n/+f/3//P/+f/5//j/+v/0//X/+v////z///8FAAMABwAGAAkACwAGAA4AEAAJAAYADAAQAAwADAAFAAAAAQAFAAQA//8BAAIA///8//7/AAAAAAIA/v8BAAIAAwALAA0ADgAQAA8ADQAOABIAEQALAAwADgAOABEAEAAQAA8AEAAPAAsACwAKAAQABgAJAAcABwALABAADwAOAA4AEAAQABAAFQASABMAEwANABMAFwAXABQAFgAZABUAFQAVABcAFwAVABUAFgAYABUAFgAZABsAGgAdACMAHwAbABwAHwAfAB8AHwAeABwAGAAaABgAGgAfABYAFQAeAB0AFgAVABsAGwAYABUAEgAVABcAFgAXABkAGQAXABcAFwAaABoAFQATABgAHAAYABYAGgAaABgAGgAbAB0AGwAbABoAGQAgABwAGgAbABsAHAAZAB0AGwAXABwAHwAcABsAGgAaABkAFAAXABYAEwAUABEAEAAQAA8ADwASABUADwAOABYAGAAbACEAIAAgACEAHQAYABsAHwAaABgAHgAhACEAHgAeACIAKAAmAB8AIwAmACEAIAAjACAAHAAcABwAHgAfAB4AHAAcABsAFgAXABgAFQASABMAFAASABAAEgARAA8AEAASABYAFQAVABcAGAAdABsAFwAYABwAHgAcABwAHAAaABoAGQAXABcAGQAYABoAHwAfABoAGgAgABwAFgAZABsAGwAfACAAHwAhACAAIAAhACIAHgAZABoAGwAcACEAJAAgAB8AIgAhACEAIgAiACMAIgAiACMAIgAkACgAKQAhACMAJQAdAB8AHQAcABoAFwAZABgAGQAXABkAIQAjAB0AGwAdAB4AIAAaAB4AIgAgAB4AGwAdAB4AHwAbABcAGAAaABkAFQAaABwAGgAZABgAGAAaABkAGQAaABYAGwAaABgAGgAbABwAHAAeABsAHQAeABsAGwAdAB8AGwAcABsAGQAaABsAHgAeAB4AGgAbACAAHgAbAB0AHgAcABoAGQAaAB4AHgAdAB0AHQAeABcAFQAZABcAFgAWABMAEwAXABYAEgAQABIAEgAPABMADAAHAA0AEAAOAA4ADgAOABIAEgARABQAEQASABUAFgAXABUAFgAaABsAFgAUABoAGQASABMAEwATABMADwAOAA0AEAAQAAsACQAHAAgABQAFAAYACAAIAAQACAAEAP//BgAGAAYACQAGAAIAAgAGAAIA/P/8/////v8AAAIA/v/+/wEA/v/7/////v/9/wAAAQD+//7////6//n/+f/2//X/8f/y//D/8P/z//H/9f/y/+3/8P/v//H/8f/v/+//7v/s/+r/6v/n/+3/7P/p/+n/5f/o/+j/5v/p/+n/6f/o/+b/6v/p/+j/7P/s//D/8v/1//f/9v/z/+z/7f/u//D/7v/t/+7/7f/v/+7/7P/s//H/8P/v//L/9f/z//H/8v/z//T/8v/y/+//7v/v//P/9v/4//j/8v/3//r/+v/6//v//P/3//v/+v/5//v//f/8////BAD+//7/BQAGAAEABAAKAAgABAAEAAYACAANAAsACAALAA0ACQAIAAUA/f/4//j//P/3//T/+//8//v/+f/3//b/9v/3//b/9v/z//P/9v/v/+//9P/w//D/8f/w/+//8v/x//H/7v/s/+v/6v/p/+j/6//p/+n/6P/o/+3/6v/q/+b/6f/y//P/7f/r/+3/6//u//L/7f/r/+v/5v/n//D/8v/w//D/8f/v//L/9P/y/+//7f/t/+b/4f/l/+L/4f/e/9j/2//g/9//2//d/93/2v/X/9X/1//X/9r/2f/V/9b/1f/T/9n/3f/b/9r/3v/i/+L/4f/h/+H/4f/j/+T/4f/j/+P/4v/l/+j/7P/q/+X/6f/r/+3/7//t/+//8//0/+7/7f/u/+z/7f/r/+j/5//n/+n/7P/t/+r/4//j/+T/5P/g/93/4P/g/+D/4P/i/+T/3v/i/+b/5f/k/9//4f/k/+P/4v/e/93/3P/a/9r/2v/a/93/4v/p//X/+P/0/+z/3//e/+f/4//S/8X/xv/L/9H/2//e/+T/7f/q/+j/8f/9//3//P/0/9//zf/B/8f/0f/V/8z/wf/J/+b//f8DAAAA8//u/+f/5//t/+v/5f/j/+X/4f/p/+//+P8BAAMA+v/w//r/AAACAP3/9v/t/+n/6P/o//D/9v/8////AAD8//P/8f/y//b/9//y/+b/4//q//T/+f/4//f/9//6//3///8EAAIA/f/1//H/7P/s//H/7f/1//v/+P/4//j//f/8//z/+//4//b/8f/z//n////8//L/7//5/wIAAwD6//T/9f/3//v/+v8AAP3/9f/y//T/+/8BAAgABgD9//z/+v/0//P//f8AAPr/9v/4/wEABAAFAAUABgAFAPj/8P/0//n/+P/3//b/8//1//b/9P/2//z/+v/2//T/7//w//D/7//v//X/9//w//H/+v8EAAYAAQADAAMA9v/k/+f/+P8BAPv/6v/o//3/DgAMAAAAAQAPAA8ABAD+////CAAAAPD/8f/8/wUADQAVABQAEwAPAAoABQAJAA4AAQD3/+//7v/z//7/AwD5//P/8P/3/wIAAAD8//z////+//T/6//0//v/9//y/+3/9v///wcAEAANAAoABwD+//z/+//3//n/+/8AAAEAAgALAAwACwALAAsACAAKAAsADAARAA4ACwAFAAYADAALABEADwAMABUAGQAYABQAEQAVABkAGAAWABAAEAAaACUAJwAhABoAGAAaABgAGwAjACMAFwALABIAIQAmACMAHwAaABYAGAAZABcAFwATAAcAAwANABIAEQASABYAGgAdABsAGAAXABcAFgAVABYAGAAVAA0ADwATABoAGgATABMAGgAfAB0AHQAiAB0AFgAXABUAGAAdABcAGAAZABgAFwAcACAAFQARABAAEQAQAA4AEgAOAAoACgAIAAUACQAJAAYACQAHAA0AFAAVABQAEgARAA0ADAAVABYADgANAA8AEQAZABoAFQAPABMAFgAVABgAFwAWABMAEQAVABcAFAAMAAsADQAPABAAEQARAA4AEQAQABAAFQARABMAEAAOABAAEgAUAAsAEQAQAAwAEAASABgAFgAZABwAIAAgAB4AIgAgACMAHwAdAB0AHAAbABsAHwAZABkAGwAaAB0AHAAaABYAFwAbABoAFgAYABcAEAAPABEAEQAQABIADgALAAsACQAMABAAEgATABYAGQAVABYAGgAZABMAEAATABQAEgARABQAEwATABMAEQATABQAEQANAA8AEQARAA8ACwAMAAoACQAOAA4ADAAOAAoACwAOAA0ACwANABIAEAAPABEAFQAVAA0ADwAPABAAEwAUABQAEwASABEAEQAQAA0ACwALAAsADAAIAAcACQALAA4ACwAQAA4ADQASAAwACQAHAAgACgAKAAkABwALAAwACAAHAAkAAwD+/wAAAwABAP3///////z//f/+//v//P////z/AQACAPr/+v/3//T/+v/+//3/9//0//X/+f/5//j/9v/z//T/9f/1//X/9P/z//L/8P/v//D/7f/r/+n/6//r/+f/7P/r/+n/7P/y//P/8v/0//P/9f/3//f/+v/7//b/8P/1//n/+P/5//n/9//1//b/8f/w//P/8P/v//D/7v/m/+b/6f/s//L/8//v/+//9v/5//j/+f/6//X/9f/6//X/+P/4//b/9//3//f/+P////7/9v/x//X/9f/y//T/+P/1/+z/7//t/+//+P/5//n/9//2//X//P////7/AAAAAP///v8FAAwACAAEAAcABwAGAAoADgAPAAsACAAPABAACgANAAoACAAPAA4ACAAIAA8ADQAMAAkACAAKAAkACgADAAEAAgAEAAIA/v/9/wIABQD9/wAABAADAAUABwAIAAMAAQAEAAEAAgAHAAUAAQAAAAYACAAIAA0ACgAMAAwACgAHAAYACwAIAAYAAwAFAAcACAAGAAMACAAEAPz//P8CAPz/9v/4//T/8v/z//X/9P/3//b/7//z//X/9v/0//P/9v/z//H/8P/2//T/7//w//H/8f/w//b/+P/1//X/9v/4//X/9v/8//n/9f/4//3/BgAHAP7/+v/7//z/AQAEAP7/+v/5//n/+v/7/wAA/f/4//T/8f/x//T/+v/6//L/8f/2//X/9P/0//b/8//v//D/8v/0//L/7//u//L/8v/y//H/8f/z//L/9v/1//X/9f/v//D/8f/u/+n/7v/w/+r/7//v//H/7//t/+7/6//x//T/8v/y//f/+f/4//X/8v/0//T/+P/2//T/9P/z//j/9//4//L/7//v//H/9P/z//j/8v/s/+7/8P/z//P/8f/y/+//7//y//L/8//v//H/8//x//H/7v/u//H/8v/x//D/7v/u//H/7v/s/+//7//u/+r/6f/w//P/8//u//P/+v/2//T/9P/3//b/8//4//n/9P/2//b/9P/5//7/+v/1//v//P/5//r//f/7//b/+P/0//f/+v/0//H/8f/2//f/9f/3//f/9v/y//H/9P/2//X/8//y//D/9v/3//H/8P/x//H/8f/1//f/+f/6//f/9v/6//3///8BAP///v/9//3/AAABAP//+//8//////8BAAMAAQD9////AAD//wEA/f/6//j/9P/2//n/+//5//X/8P/0//n/+v/7//T/9P/1//X/9//z/+//7P/t/+r/6//v/+7/7f/o/+r/7P/q/+3/7//u/+z/7f/t/+//8v/1//X/8P/v/+3/8v/1//P/9P/x//L/8//0//n/+P/3//b/8f/w//L/8f/w/+//7f/t/+z/7v/z//T/7P/p/+7/8P/z//X/9//2//T/8f/x//P/8//x//L/9f/z/+7/6//u//T/8f/v/+//8P/0/+7/7f/1//b/9P/1//P/8f/y//T/9v/0//L/9P/z//P/9v/3//n/9//2//f/9v/4//f/9//3//n/9v/1//f/9//6//b/+P/5//H/8//z//P/8P/x//P/8f/1//T/9P/y//P/+P/3//r//P/9//7/AAD9//z//v///wAA//8CAAIAAQD///3/AQACAAIAAgAAAP//AwACAAEAAgABAAQABQAHAAcABQAFAAUADAAMAAkACAAJAA4ACwAIAAsACAAFAAUACAAFAAYACgADAAEABgAFAAUACQAIAAQAAwAFAAUACAAKAAkABwAKAA0ACwALAA0ACwAFAAcABgAAAAEA//8BAAAAAAAAAAAABgAGAAYAAQD9//3/AAAAAP7/AAABAAQA//8AAAYAAgAAAAEABgAJAAsACQAJAAsACgAKAAoADQANAAYABwAIAAYACwAMAAoACQAIAAkABwAFAAgACAADAAIAAQAEAAUA/v/7//7//f/8//v/+P/8//n/8//0//X/+P/1//L/9v/6//r/9//4//v//v/7//v//v////z/+v////7/9//1//X/9v/6//v/9v/1//P/8//2//b/9v/0//b/9P/x//T/9f/w//T/+//3//f/+v/6//v/+v/4//T/+P/9//r/+//5//L/8v/4//n/9f/0//L/8f/1//X/+P/2//H/+P/0//T/9//2//j/+P/8//z/+v/9/wIAAgD+/wMAAwADAAYA//8BAAQAAQACAAMAAwD9//7/AwABAAAAAgD///z/AQACAP7/AAACAPz/+/////z//P/9/wAA//8BAAQAAgABAAIABgAJAAoABQAFAAIA/f8CAAYABAD//wEAAAACAAgAAQD9//v/+v/7////AQD9////AgD+//v//v8BAP7//f/8//z//f8DAAIA+/8DAAMAAQAAAP//AgAAAAIAAQAGAA0ABAD9//3/AgAAAP3//v8DAAQA+//7/wAA/f/6//n/+//9//r/9//4//n//f/+//z/+//8/wAA/f/9////AwADAAEABgAHAAIAAQACAAMACAAJAAYABwAIAAUABgAJAAgABQACAAQABAD9////BwAFAAIAAgD+/wAACAAGAAMABAAFAAkACgAFAAUACAAJAAgACwAPAA0ABwAJAA8ADAANAA0ADAAKAAoADwALAAoABQADAAcABQACAPz/+v////z/+P/3//n/+P/2//b/9v/0//P/8P/w//L/8//y/+//7v/x//H/9P/2//b/9f/0//j/+//5//f/9P/u/+//8f/z//L/7P/t//L/8//x/+3/7P/s/+v/6//t//H/8P/p/+r/6P/n/+z/7f/v/+7/8v/y//H/9f/z//L/8v/3//j/9//7//r//f/7//v/AAD+/wAAAAD9///////8/wEAAAD9//7/AQACAAIAAgABAAUACwAJAAcACQAPABMADwAOABQAGQAYABsAIAAaABQAFQAVABcAGwAZABkAHgAeABoAHgAhABoAGAAbAB4AHAAZABoAHQAcABgAGAAZABoAGgAXABgAHAAZABgAHAAcABoAGgAbABMAFQAbAB0AIAAdAB4AHQAaABoAHAAbAB0AHwAcABoAGAAZABkAGAATABMAFAATABkAGwAZABcAHQAeABsAHAAXABkAGgAaABwAGwAcAB4AIAAeAB8AHQAbABoAFQAdAB0AGgAfAB4AHAAcABwAGQAdAB0AGgAfAB8AIgAeABcAFgAXABkAGAAYABkAGwAZAB0AIAAfACMAIgAkACYAJAAoACcAIQAiACMAHwAeABwAIQAjAB8AIAAiACYAIwAhACMAIAAaABoAGwAYABgAGQAYABMAEwAUABIAFgATAA0ABQACAAMAAAADAAUAAgADAAcABQAEAAgABwAFAAUABgAHAAcADwAPAAgACwAPAA0ADAASABAADQAQABEADgAMABIAFAAUABAADQANAAwADAAMABAADQAKAAwADQAPABAAEgASABIAEgATABMAFQAWABYAGQAbABsAHQAbABoAIAAgAB4AHgAgACoAKQAmACsALQAwADIANgAvACoALwAnACUAJAAjAB8AIAAiAB0AIQAiACEAIAAjACAAGAAfACEAHwAfACIAIQAgACIAIgAmACYAIgAjACwALQAnACcAJgAlACUAKAAlAB8AIwAgAB0AHwAdABwAGwAaABkAGQAaABwAGwAaABoAHQAgAB4AGgAXABwAHQAbABkAGQAYABUAGgAZABIADQAMAA0AEAAUABEADwANAAwADQAJAAcACAAIAAkABQADAAYABwAGAAYACAAKAAgABwAMAAsACAAKAAkADQANABAAEgARABYAFAAPABIAFQARAAwADwAQABEAFAAVABUAEwAYABwAGAAcABwAFwAZABwAHAAZABcAGAAWABcAGAAWABgAGwAYABEADwAPAAwADAANAAsACQAHAAkACgAIAAcACgAPAA8AEAAVABcAEwAOAA0AEAAUABIADQAUABIADwAUABQAFgAYABUAEgASABMAFAAWABYAFgASABEAEgASABoAHQAaABsAHQAaABgAGAAXABUAFQASABIAFwASABAAEAAPAAoACAAMAAoACQAMAA0ADAANAA0ADgAUABUADQAOABUADgAIAA4ADAAJAA8AEAAKAAYACAACAP7/BQAHAAMA/v/9//7/AgD///v/AQAGAAMAAAABAP///v/9//z//f////7//f8AAAEAAwACAAIABAACAP7////9////AwD+//7/AgACAAQAAgACAAAAAQABAP7///8BAAMA+v/5//v/9f/3//r/+P/3//L/7//v/+//9f/3//H/7v/w/+//7v/w/+3/6v/p/+n/6v/s/+z/7P/s/+//7v/o/+j/5v/o/+v/7P/p/+H/6P/t/+v/6//s/+7/7P/s/+3/7P/q/+j/6//r/+f/6f/t/+z/6v/u/+z/6//o/+P/6v/n/+T/4//o/+n/4//o/+X/4//m/+j/4v/c/97/3P/b/9z/3P/a/9n/2v/Z/9v/1//V/9f/1f/W/9j/2f/X/9P/0v/S/9H/1//Y/9D/z//S/9P/1//X/9b/2//X/9f/2f/V/9n/1f/U/9j/1f/V/9b/2P/V/9H/z//P/9L/1P/T/8//0P/O/83/z//R/9T/1P/U/9X/1v/Y/9f/1v/X/9j/1v/Z/9v/1//W/9j/2v/f/93/2f/c/9z/1//X/9j/2P/c/+H/5f/h/93/3//g/9//2//b/9z/1//a/93/2f/a/9j/2//d/9//4f/g/+H/3//g/+T/5P/n/+P/4P/m/+f/5P/g/+P/4P/d/9//2//c/93/3f/j/+T/4P/g/+H/3//g/+T/4v/j/+j/5//o/+v/7v/w/+//6v/m/+3/7//n/+j/7//2//T/8P/v//D/7//v//L/9P/x/+//8v/y/+r/6f/w//H/7P/x//D/6v/x//H/6v/r/+v/6v/r/+v/8P/v/+z/6//p/+r/7P/r/+z/6f/m/+j/5P/n/+b/5P/m/+X/5//s/+3/5//r/+z/5//q/+r/6v/s/+3/7f/r/+n/6//s/+n/5f/k/+j/6f/m/+X/5v/h/+D/4f/g/+X/4P/g/+P/4v/m/+L/3v/c/97/4P/e/9//3//g/97/2//Z/9n/2f/V/9n/2//Z/9n/1v/W/9b/0//S/9j/4P/j/+P/4v/i/+P/4//h/+T/5v/j/+P/5v/r/+j/5//r/+X/6v/t/+z/7v/w//D/6v/p/+z/6f/q/+7/7P/t/+3/6v/r/+z/6v/k/+T/5//m/+f/6P/k/+D/6P/s/+b/6//q/+T/5P/q/+z/6P/p/+j/5f/p/+j/5v/l/+b/5//l/+b/5//r/+//7v/p/+z/8P/u//L/9f/y/+3/8P/z//H/9v/2//T/9P/5//z/9//5//3//P/4//j/+//3//r/AAD+//v/+f/4//n/+v/3//f/9//6//j/+f8AAP//AAD9//v/+//+////9//2//b/+P/6//n/+f/6//z/+//5//f/9//6//b/9P/6//3/+//3//n/+//6//r/+v/6//b/+f/7//j/+P/4//r/9//2//z/+//5//z///////z/+/8AAAQAAgABAAIAAgACAAQABwAFAAYABgAEAAQAAwAEAAUABwAFAP3//P/8//3//f/6//v/+v/6//v//v////7//v/9///////8//z//f/5//f//P/4//j/+//5//r//v8BAPv//f8CAAAAAAD8//r//f///wIABAADAP////8BAAIAAwD//////v/8//z//P/7////AgD8//z/AAAAAPv//P/+//3/AAD9//z/+P/1//n/+v/8//r/+f/6//3/+v/4//j/9f/5//f/9f/1//j//f/7//b/+P/8//j/+v/7//n/+v/7//v/9P/2//X/9//5//X/9f/0//X/9f/6//z/+v/6//r/+f/2//b/9P/3//j/9//8//v/+P/4//r/+v/6//r/+//7//f/9//3//b/+f/3//n/+//6//r/+v/7//r/+P/1//T/9f/4//n/+P/9/wAAAAD8//v/AgADAP//AAADAAMAAwAHAAwADwAPAA8ADAAKAA0ACwAJAAsACQALAAwACgAHAAYADAAJAAMAAwAEAAEA/f/+/////////wEAAQD/////AAADAAQABQACAAAAAAABAAUABQAFAAQABwAGAAQACgAKAAsABwAGAAoACQAKAA4ADwANABAAEQAQABEADgALAA8AEAANAAwACwAOAA0AEQARAA8AEwAUABIADQARABQAEwATABQAFAAUABQAEgAUABIADgAQABIAEQANAA0AEAAUABQAEAANAA4AEwATAA4ADgAOABAADgAMAAwACQANABEADwANAAwADAALAA4ADgAOABQAFgAWABgAFgASABUAFwAZABgAEwAOABAAFAATABUAFwAXABIAEAASABQAEgAQAA0ACwASAA4ACgALAAoACQABAAIADAANAAcAAwAFAAgACAAEAAQABQAGAAgACQAMAA0ACAAHAAUABAAEAAUABgAEAAcADAALAAsADgAKAAYACwAJAAkABgAGAAUAAwAJAAwACgAMABUAEQAJAAoACwANAAwADQAMAAsACAAGAAwADwALAAgACwAIAAYACQAHAAUACAAHAP3/AgAKAAQAAgABAP7//P/8//3//f/+//////8DAAMAAAAAAAEAAgD8////AgD///3/+v/6//f/9v/4//3//v/7//7/+/////7/+/8AAP//BgAFAAQABAAAAAMABQAGAAIABAAHAAMABwANAAwACAAMAA0ACAAGAAkACQALAA0ACQAKABEADwANABIAEgARABEAEgATABMAGwAXABUAFwAXABwAHAAdAB4AHQAeAB0AHAAbABsAGwAbAB4AHgAeABwAFgAWABkAHAAcABoAHgAYABcAGQAZABoAFgAWABYAEwASABYAFAAUABkAFQATABcAGAAXABcAGgAYABQAGQAYABcAGAAUABkAGQAaABwAGgAfACAAGQAZABoAGQAaABcAFgAaABoAFgAVABYAFwAaABQAFQAXABUAFAARABAAEAAUABYAFAAUABgAGAAWABYAGQAbABgAGQAZABkAGQAZABgAGwAeAB0AHgAeACAAIwAfABsAHQAfABwAIQAmACIAIAAkACcAKwAtACkAKQApACkAKAAlACkAKwAoACoAKgAnACQAJwAoACgALAAsAC4AMQA1ADUANgA2ADYAOgA4ADgAOAA4ADoAPAA9AD0AOwA4ADkAOAA6ADsAOAA7ADgANQA2ADMAMwA0ADMAMQAyACwAKwAxACsAJwAqACoAKQAoACYAJwAnACUAJAAlACsAKwArAC8ALAAoACkAJwAqAC8AKQAmACkAJwAnACkAJQAjACUAJgAjACMAJAAfAB8AIwAhAB4AIgAhABgAHgAaABMAFQAZABkAFAAWABYAFAATABYAFwATABIADgALAA8AEQANAAwADQANAAoABQAHAAUAAwAEAAEAAQAAAAAAAAADAAMABAAIAAYABwAKAA0ADQANAA4ACgAOABAADQALAA8ADQANABMAEQARAA8ADwAQABMAEgARABAACQAIAAgACwAKAAoADgAKAA0AEAARAAwACQAGAAQACgALAAcABgAIAAUABQAKAAcAAwACAP///P8DAAgABQACAAIAAgAFAA4ACgACAAYACgAPABEAEAAOAAwADQAQAA8ADQAQAA8ADAAQAA4ADQAPAA0ADgARABAAEAANAA0AEAAOABEAEwAPABAAFAASAA8AFgAYABEAEAAUABEAEAAZABgAFQAaABkAGQAaABoAGgAXABYAGAAWABQAFAAUABMADwAPABIAEQAMAAoAEAANAAcACQALAAgADQANAAcACwAQABIADQANABAACAAFAAgACAAIAAYABgAGAAUABgAEAAYABwAIAAgABAAFAAYACAAIAAQAAQD9//v//P8AAP//+//3//r/+v/5////+//5//z/+v/4//X/+P/5//T/8f/z//L/7//y//H/7P/v//D/7f/u/+7/7v/w//X/8v/v//L/7v/v//H/8P/s/+v/6f/k/+b/6//l/+H/4f/h/+T/5v/n/+D/3//l/+X/4f/g/+b/4P/e/9z/3//g/9z/3v/Y/9j/1v/Q/9L/0v/U/9T/z//Q/9P/0P/S/9j/3P/e/9b/zv/P/9f/3P/h/9v/zv/U/+L/3//X/9r/1f/S/97/4v/c/97/4v/c/9v/2v/P/9L/4v/f/9L/0P/T/9P/2P/Y/9H/0//W/9P/1//g/9v/1P/U/9f/2f/c/+L/4v/f/9z/4f/k/+L/5f/m/+L/3v/h/97/4f/o/+P/3v/g/+b/5f/k/+L/3//d/9n/2P/a/93/1f/U/9r/3P/d/9n/1//Z/9n/1P/Y/9z/2//Y/9v/2v/X/93/2P/Y/+D/3//Z/9X/3P/f/9//4P/d/9n/2P/Y/97/4//g/93/3f/d/93/5P/p/+T/4v/l/+n/6//m/+T/6f/o/+j/6//n/+T/5f/m/+b/5v/j/+X/4f/h/+T/4P/i/+D/3//f/+D/4P/h/+X/5f/k/+P/5v/j/9z/3P/d/9z/3f/e/9r/2v/d/93/2P/Y/9v/2P/Z/9v/2P/W/9b/2f/b/9z/2f/Y/9X/1f/V/9L/1P/Q/9D/zv/L/83/zv/T/83/zP/N/8r/x//L/9D/z//R/9L/1P/V/9T/1P/V/9X/1v/Z/9b/2P/a/9j/1f/S/9n/3P/Z/93/3v/b/9v/2v/Y/9r/2f/Z/9X/0v/W/9T/1P/X/9X/1//W/9H/0v/R/87/0f/W/9b/1//X/9n/2P/Z/9v/1//e/9//3P/b/9v/2v/X/97/3//b/9v/2f/f/+D/4P/d/9z/4v/m/+j/5//o/+n/6v/q/+r/6//p/+v/6//t/+7/8//1//H/9//2//b/+//9//3//f8DAAMA/f/7//3//P/7//3/+////wAA+//6//v/+v/7//7/+v/4//r/+f/7//z//f/+//r/+v/4//b/9//4//3//P/4//n/+f/1//X/+f/6//r/+P/7//z/9f/z//j/+v/7//v/+f/4//X/8//y//H/9f/2/+//8f/3//n//f8BAAUAAQAAAP7//f8FAAQAAQD//wQAAgD8/wIAAAD//wMAAwAHAAgACAAJAAYACgAKAAoADQANAA0ABgAFAAkACgANAA4ADAAMAA8ADgAKABAAEQAMAA0ADgAOAAoADAAQAAgABwAHAAYACQAMAAoACwASABAADgAQAA4AEAAUABQAEwAVABgAFAANAA0ADQAQABMAFQAYABkAFwARABQAGwAaABoAFAATABoAHAAdABsAHQAeAB8AIAAdACEAIgAgACIAJwAoACcALAAqACYALQAuACsAKwAtACwAJgAnACkAIgAiACgAKgAnACcAJgAmACkAJAAiACUAJAAhACIAIwAfACAAJAAjACQAJQAkACcAJQApACUAJAApACYAKgAsACoAJwAlACYAKQAmACQAJgAmACQAIQAhACMAJAAkACEAIAAlACYAKAAsACgAJwApACkAJwArADIALwAxADQAMQAvAC0AMQA0ADEAMQAzADIANAA3ADYANQA0ADEAMAAxACsALAAtACoALAAsAC8AMgAtACcAKAAqAC0ALAAmACgAKAAnACcAKQArACwAKQAqAC0ALgAvACsALwAvACwAKwApACsALAAsACsALgArACcAKQAnACYAKQAmACQAJQAnACMAIgAmACIAIAAhACAAIwAiACEAHgAeACEAHwAeAB4AHgAgACAAHQAeACEAHgAdACIAIwAhAB8AIwAiABkAGwAcABwAHgAfACEAHQAaABkAGAAbAB0AGAAVABUAEwAVABgAGgAXABMAEwATABgAHwAdABoAGgAWABQAGQAZABYAFwAZABQAEwAYABcADwATABkAFwAUABAAEQASABAADwARABUAFQALAAoADgAKAAsACwAKAAkACgAMAAUABQAJAA0ADAAEAAUABwAHAAUABgAJAAkACQAKAAsACgAKAAkACQAKAAYACQANAA0ACgALAA0ACwALAAwACgAIAAoACQAIAAkABwAIAAoADQAMAA0ADwAMAA4ADAAJAAwADAAIAAgABgAJAAgAAgACAAMA/v/7//v//P/+//7//P/9//z/+/8AAAIA/f/9//3//f/+//7///////////8BAAIAAgAEAAIA/f/9/wAAAAACAAIA+//8//7/+//+/wEA/v/6//v/AAD6//r////8//z//f/6//n//v8BAPv/+P/+//r/9//6//n/+f/7//z/+//+//3/+P/6//3/+v/3//r/+P/y//L/9v/y//P/+P/4//X/9f/y/+3/7//t/+n/6//u/+3/5v/q/+3/5v/m/+f/5f/k/+X/5P/j/+P/3f/f/+b/5f/g/+L/4P/f/+D/3P/h/+X/5P/g/9//5v/h/+L/5P/i/+L/5P/l/+H/4v/n/+T/4v/l/+b/4P/h/+b/4//h/+H/4P/h/9//3f/c/9z/4f/e/9v/3v/c/9f/1v/Y/9//3v/c/+D/4v/j/+L/4f/i/+H/3v/k/+X/4v/j/+P/3f/d/+H/2v/a/9//3f/X/9z/3//Z/9v/2v/b/9r/2P/e/+H/3v/f/+T/4v/g/9//4f/h/+D/5//n/+T/4//k/+L/5//o/+j/7P/o/+r/6//t/+3/6v/t/+b/5P/q/+r/5//r/+z/6//t/+7/7P/o/+b/5//p/+X/6P/p/+P/5v/m/+f/5f/o/+j/5v/p/+X/4v/j/+T/4f/g/+T/5//m/+T/5//o/+j/7P/r/+n/7P/p/+n/7f/q/+j/6//t/+//6//m/+j/5v/q//H/7//u/+//7v/u/+z/6v/r//H/9P/t/+7/9f/z//D/8P/u/+7/7v/s/+3/7f/s/+z/8f/z/+7/7f/u/+7/6v/v//f/9f/x/+//8v/x/+3/7v/w/+//6v/q/+z/6v/p/+3/7v/s/+v/5f/i/+j/6//s/+z/7v/t/+r/7v/t//D/8//z//T/9P/3//T/8f/w//D/8//x//L/9f/2//b/9v/5//f/+P/6//T/+P/8//r/+f/+//3/9v/3//3//v/6//b/8f/t//L/9f/y//P/9P/x/+7/8f/w/+//7v/s/+//8P/v/+//7f/s//D/8v/y//D/7P/t/+7/6v/n/+v/7v/s/+r/5//j/+L/6f/q/+L/4f/g/+H/5P/l/+T/4v/l/+f/5P/n/+r/5//l/+j/6//p/+v/7v/q/+7/7v/t//P/+f/6//f/+v/4//P/8v/2//j/8v/y//L/8P/v/+z/7P/s/+//7f/s/+z/6f/u/+7/6v/r/+3/5//m/+v/5//l/+T/5v/n/+v/7P/r/+z/5f/n/+z/7f/t/+z/6//s/+3/7v/y/+z/6//v/+z/6//q/+f/6P/s/+n/7P/v//D/8f/v//H/9f/4//v/+//5//n/+f/8//3/+v/9/wAAAwADAAAAAgADAAAA/v8DAAAA+v8EAAgACQAIAAIAAwAIAAkACAADAAUABQACAAIAAAAAAP//AQD+//7//v/2//j////+//j//v8CAP////8CAAAA+////wIAAQAAAAYABAD//wIABAAFAAEAAgACAAAAAwAFAAAAAgAEAAMABgADAP//AgABAAAAAwACAAAA/v8AAAIA//8CAAIAAgAGAAgABQAAAAQABgAAAP//AQAAAP7//v8AAAIAAQACAAAAAgAGAP7/AAABAP7/BAAFAAQABAAHAAcABgAEAAMABQAEAAYACAAFAAcACwAJAAkADwAPAAwADQAJAAoADAAKAA0ADwAOAAoACQALAAsADgAOAAgACAALAAgABwAJAAsADQAPABEADAAIAAYABAAIAAwADAALAA0ADQALAA4ADgAOABEAEAAPABAAEgAPAAsADgAOAAsACwAMAAwACQAIAAYABwAIAAMAAQACAAYACQAJAAUABQAGAAUABQAFAAcACQAJAAkAEAAPAAkADAAOABEAEgATAA8ACwAPABEAFAAYABkAGQAaABsAHAAeAB8AGAAYACMAIQAaAB4AIQAgACIAIwAfACEAIgAcABwAGwAeABsAGQAeABsAGAAbABoAFQAXABkAEAATABYAFQATABMAGQAXABUAGAAYABkAHAAeAB4AHgAcAB4AIQAgABoAHgAiAB8AIAAjACEAGwAeACAAHQAdABwAHAAeACEAIAAhACMAHwAfAB8AHgAfACEAIwAhAB4AIQAgACAAIgAlACMAJAAoACgAJgAfACIAJAAkACcAJgAkACMAJwAjACEAIwAfABsAHAAbABYAFQAaABoAFQAUABIAEAARABEAEQATABIAEAATABUAEQAOABUAGQATABUAHgAeABkAGQAYABsAHAAdABwAGwAeABsAHAAdABsAFwAXABcAFwAZABgAFAASABUAFgAUABIAEgARAAwAEAAQAAwADAAOAAwACQAJAAkACgAKAA4ADgAPABAADgAVABcAFAAXABkAFQATABcAGgAUABEAFQASAA4AFQAWABMAGAAYABcAGAAZABoAGQAXABIAEwASABMAGQAWABQAFgAXABIAEgAXABEADwAVABcAFAAVABgAFwAYABYAFgAdAB4AHAAaABoAHQAZABsAHAAZABgAGQAcABsAHgAcABYAGAAbABsAHQAdABsAGgAeAB8AGgAbABwAFgAYABwAGgAYABwAHAAZABkAGQAVABUAGAAYABcAFwAVABUAGAAVABEAFQAWABIAEQAQAA0ACQAKAA0ADAALAA8ADwANABAAFAATABEAFQAXABEAEwAWABYAFwAXABUAGAAgAB4AFwAaAB4AFwAYABsAFwAZABoAGAASABcAGAAPABIAEwAQAAkACgAMAAUABQANAA4ACAAJAAsABwALAA4ACgAMAA0ACwARABcAEgARABUAEwAQABAAEQAPAAgACQALAAgACgAIAAoADAAOABQAFQAUABQAFwAVABQAEwAWABcAFgAYABQAFwAUABEAFgAZABQADAAOABIAFAAQAAsACgALAA0ADQANAAgACQAGAAMABQAGAAkABAAAAAAAAgABAP//AwAGAAYAAwAEAAUABAADAP//AAACAAUAAwADAAQAAgAAAAEABAADAAYABwAEAAYABgAGAAIABAAIAAMAAQAFAAcABwAIAAsADAAMAAoABwAIAAsACQAFAAcACwAJAAgACQAMAAkABQAGAAoACgAJAA0ACwAJAAcACAANAAkACAAHAAYABwAEAAIABAAAAP3/AQAAAP3//f8AAAMAAgACAAQABgADAAUABwAGAAUABgAJAAgACAALAAwACQAGAAgADQAHAAMABgADAAEAAwAAAP7/AQABAP7//v/7//r/9//6//z/+f/9//3///8AAP7////+//n/+f/8//v//P/9///////6//n/9v/4//T/8f/3//j/9P/w//H/8f/y//L/8P/u/+7/7f/q/+7/7v/r/+j/6f/o/+f/6f/q/+v/6P/m/+L/4//j/9//2//c/+L/3//f/+T/4P/f/+H/5P/l/+T/4//h/+D/3//e/9z/3v/d/9//4f/g/93/3//k/9//3P/e/9z/3P/h/+T/3f/c/+P/4//h/+H/4//g/9//4v/l/+b/4P/i/+T/4f/h/+L/4//j/+T/4P/e/9//3//f/93/3v/d/9z/3v/c/9n/3P/f/9z/3v/j/+P/4v/m/+b/6P/p/+L/4P/j/+T/4P/i/+P/3//j/+L/4P/g/+H/4//g/+H/4f/e/9r/2v/Z/9n/3f/f/+D/4P/h/+H/3f/Z/93/4v/h/+H/4//k/+P/4//m/+v/5//l/+b/4v/i/+L/5f/m/+H/4f/l/+P/3//h/+X/3v/d/9//3v/b/9n/2//a/9v/2//c/9//3//k/+P/4//k/+b/5//m/+j/6f/r/+z/6v/t/+//7f/r/+v/6f/o/+z/6P/n/+z/8P/z/+//6//u//D/7v/x//L/8//y//T/+P/2//H/8//1//H/8//x//T/8//x//b/9f/z//D/7//t/+v/5//m/+7/6//m/+n/7f/s/+3/7f/m/+T/5//m/+P/6P/q/+P/5P/m/+b/5//l/+j/5//p/+7/7v/w//D/6//t//H/7//x//L/8P/w//P/8//v/+v/7//y/+3/7//u/+r/7//w//P/9P/0//P/8f/4//n/9v/4//X/9P/2//f/9//2//j/9v/y//L/8//0//H/7//w/+7/7//v//D/8P/u/+v/7P/u/+//7//s/+7/8f/x/+3/6f/s/+3/8v/w/+v/7//x/+//7v/y//H/8P/t/+z/8v/y//T/9//y//b/9//x//D/8f/1//L/8//2//X/+f/5//j/9v/4//b/9v/2//H/9v/3//j/+P/7//v//P////r/+f/2//X/+P/9/wAA//8BAAIAAwACAAQABAAEAAQA/v8AAAIAAAACAAMAAwAEAAMAAgABAAYACwAHAAYACwANAA0ACwAMAAwACgAMAA4ACwAKAAoACQAJAAkACwANAAwADQAOAAsACQALAA8ADgAJAAwACwAMABAACQAIAAsACQAIAAwADQALAA0ACgAMAAoACAAJAAUABgAHAAUA/v/+/////P8BAAQAAgABAAYAAwD7//3////8//n//P/6//v//f/4//j/+f/7//n//f/+//z////9//z//f8AAAIA//8CAAIA/P8CAAkABQD+//7//f/7//7//v/7//j/+v/7//j/+//9/wEAAAAAAP///f////v///8BAPv//f/7//j/9v/0//b/+v/8//j/+P/9//3/+//7//7/+//8//z/+//+//v/+f/2//f/+P/1//b/9v/3//r//P/7//z/+//3//j/+v/9//j/8v/5//f/9v/5//X/+f/7//r/9//4//z/+P/4//b/9f/y//H/9v/z//b/+v/5//r/+v/1//n/+f/2//3/+v/5//3//v/3//T/+P/x//H/9P/0//X/9f/8//3//P/+//z//v/+//n//f8AAP3//f////7/AAAAAAAAAAAHAAsACgAKAAsADAARABMAEgAXABMAEgATABQAFQAVABcAFQAXABkAGQAcAB4AIQAfAB0AHwAgAB4AGwAcAB0AHQAeAB8AIAAfACIAJgAhABwAIAAfABoAGQAaABcAFwAZABgAHwAfABkAGwAcABkAFAAUABkAGAAYABgAFgAZABoAFwAXABcAFwASABMAGAAVABUAFwASAA8AFAAYABcAFwAWABQAFAAXABYAFQAXABUAFAAbAB0AGwAaABoAGgAZABYAEwAWABcAFwAWABMAFwAZABEADQAPAAwABgAJAAkABQAIAAYABQAFAAUAAwD9////AQD//wAAAAD9/////v/7//v//f/+//7/AQACAAAA/P8AAAAA/v///wEABAADAAkACgAJAAsACgAIAAoACAAFAAkABwAIAAgABwAIAAEAAgAGAAYABAAGAAUAAgABAAAAAAADAAgABAD+/wUABAD9/wQABQAEAAcAAwADAAAAAgD9//7/AwD9//7/+f/4//r/+v/9//7/AwAFAAIAAAACAAMAAgD+////AAD9//z/+v8BAAQAAwABAAEABgADAP3/AAABAP//AgAEAAYABgAHAAsACAAKABIAFgATABAAFQAVABIAEwARAA8ADAAJAAYABwAOAAsABwAJAAoABwAEAAgABAADAAkABgAGAAUAAgACAAUAAwADAAgABwAFAAgACQAFAAUACgAKAAMAAQAEAAUACgAIAAYACgAHAAUACAAGAAUABQAIAAoABgAIAAoACwAMAAwADQAPABQAEwAUABEAFAAbABQAEgATABYAFQARABMAEQARABMAEwASABIADwANAA8ADgAQABMAEwALAAkADwALAAsADAAKAAwADgANAAsACgAIAAkACgALAAsACQAIAAUAAwABAP//AAACAAAA/f/9////AAADAAEA/v8CAAEAAQAEAAMAAAACAAMA/v/+/wAA+//4//z//P/7/wAAAgABAAEAAAAAAAEAAwADAAEAAwABAAMABQADAAIABQAHAAgACQAIAAgABAAEAAQAAQACAP7//f/8//v/+v/9//r/9v////v/9P/1//X/9//4//n//P8AAAEAAQACAAAA/v/9//v///8DAAEAAAD9//r/AAACAP3/AQAFAP3//P8AAAEABAAFAAUABAAEAAIAAAAEAAIA/P/8/wAA/v/8//3//P/6//3/AAD9/wAA/v/4//n/9v/z//j//P/7//n/+//6//j/9//3//f/+P/5//n/+v/9//f/+P///wAA/v/7//b/+P/8//n/+P/5//v/+f/2//r/+P/9/wAA+v/+/wAA/v/8/////P/3//z//f8AAAAA/P/+//3//f/8//7//v/7//3//f/4//T/+P/4//b/9v/0//f/8//y//b/9v/0//H/8//w/+3/8v/0//L/7f/s/+3/7v/w//H/7//v//L/7//r/+v/8f/1//T/8P/x//P/8f/y//L/9P/0//P/9P/y//H/8v/0//L/8f/0//j/+f/5//f/9f/4//b/8f/z//T/8//2//b/9P/4//b/9P/4//v/+f/8//v/+//9//3/AQD+//7/+//7//3//P/8//v//f/8/////f/7//3/+//5//r//f/4//j/+f/5//z//f////3//P/8/////v/6//r/+//5//j/9//4/wAA+//3//j/9//0//P/+f/2//b/+P/2//T/8v/1//X/9f/0//f/9//3//f/8//2//X/7v/v/+3/6f/r/+3/6//s/+3/7P/p/+v/7f/w//P/8v/v/+j/6f/v//H/8f/v/+//8//2//H/8f/0//D/8P/0//b/8//z//n/9f/y//P/9f/3//j/+v/4//X/8v/w//H/8//y//H/8//w/+z/8P/0//P/8v/1//n/+P/3//f/9//0//L/7v/v//T/8v/x//H/8v/0//b/+v/9//v/+f/7//v/AAADAAAA+////wQA/v8BAAYABAD//wEABwD+//3/BgACAP//AAD//wAA/v/+/wMABAABAAQAAwABAAQABAAHAP///P////7//v/7//3/+v/7//7//f/7//T/+f/5//T/+P/5//j/+v/6//r/+//6//n//P/+//z///8CAAIAAwADAAMABQAGAAIABQAGAAEAAAAEAAgAAgD7//7/AwADAAYABgAFAAgABwAFAAYACQAKAAkACwALAAsACgAIAAgABgAJAAsACAAIAAYABQAFAAcACQAJAAkABgAHAAgAAwABAAQABgAHAAgABwAKAAoACAAIAAUABgACAAAABQAEAAoACgAGAAgABgAGAAYABwAFAAEA//8AAP7//v8DAAQABAACAAIABAACAAQABQAGAAgACAALAAoACAAFAAYADAAMAAgABQAGAAUABAAEAAUAAwAHAAoACAALAAkACgAGAAgAEQAQAA8ADgAQABMAFwAYABUAFAAWABgAFAAVABgAGQAZABkAHAAeABoAGgAcABsAHQAcAB8AHgAZABoAHQAiACEAIAAhACIAIwAhACEAIgAiAB8AHAAcABkAFAAVABgAGgAZABoAGgAWABcAGwAZABgAGQAZABoAGQAbAB4AGwAZABgAHAAdABsAGAAXABUAEgAVABcAFgAVABUAFQAXABgAFwAUABcAHAAbABwAFwAWABwAIAAhABwAHAAeAB4AGgAXABwAHwAfACAAIwAiACEAIQAhACAAIgAiABYAGQAeABgAGQAZABcAFAAPAAwADQAPABAAEQAMAAwAEQAQABAAEAASABQAEwASABQAGAAXABQAFwAWABgAFwAWABcAFgAaABwAHwAcABUAEwATABUAEQAUABQADQAPABIADQAGAAoACQADAAYABwALAA8AEAASABIAEgANAAsADAAJAAsAEQAOAAsADwAQAAwADgATABIADgAQABIAEwASABMAEgAVABYAFwAZABgAFQAQABUAFgAVABgAFQAVABcAFwAWABYAGQAZABsAHAAfACQAHwAaAB0AIQAiACMAIgAhAB8AGwAcABoAGAAXABQAEAAOAA0ADAAPAAwADgATABIADgANAA0ACAAEAAgADgAKAAoACQAHAAYAAwAFAAYACAAJAAcACAAIAAcABAABAAAAAQADAP///P8BAAIA/v/9/wAAAwABAAEAAQABAAUABAD9//7/BAABAP//AwACAAIAAgAEAAEA//8DAAMABwAGAAYA///7/wEA/P///wIAAwACAAMAAQD+/wIAAAD//wAAAwADAAEABAABAAMABAAEAAYABgABAAUABwD+/wAAAAABAAUABwAGAAAA///9//z//f/7//3/AAABAP3/+v/9//3/AQABAP3//v///wAA/v/4//j/+P/5//f/9v/5//b/9P/y//T/9v/z/+//7v/x//P/7v/s/+3/7//u/+v/7f/s/+z/7P/m/+j/6//o/+n/6P/p/+7/7v/u//L/8v/w/+//7//x//D/7//r/+n/6v/p/+j/5//o/+n/5//o/+j/6v/q/+f/5P/i/+f/6P/i/+P/4//h/+T/5P/l/+T/5v/s/+3/8f/w/+//8P/v//H/9v/2/+7/8f/2//b/9v/2//r/+v/2//T/9f/1//H/8P/s/+3/8f/y/+3/7P/u//H/9P/x//L/8//x//L/8P/0//f/8//x/+7/7//w/+z/8v/y/+7/8P/z//L/8f/0//X/8v/z//n/9v/0//b/9P/x//L/8//x//b/8v/t//D/7//v/+7/8v/y/+7/7f/t/+//8P/0//X/8v/z//L/8f/w//H/8v/z//X/8v/x//P/8v/w//D/9P/y//D/9P/1//L/9f/2//D/7v/w//L/7//u/+3/7P/s/+//7P/o/+r/6//s/+7/7//u/+z/7P/r/+j/6//v/+n/5v/o/+v/7//s/+r/7P/u/+7/7//x//P/8//z//H/7P/u/+7/7//u/+z/8f/s/+v/8f/u/+n/7//w/+v/7v/o/+b/6v/t/+//7f/w//L/9P/t/+z/6//s/+//6v/s/+r/6P/m/+P/5f/j/+L/4//i/9//4//g/9z/3v/g/+X/5f/h/+H/5v/j/+H/5f/k/+L/4//p/+j/5P/i/+P/5f/n/+n/5P/e/9z/4v/i/97/4f/i/+H/4P/h/+D/3f/b/97/3f/d/93/3P/d/9//3v/b/97/3//f/+D/4//f/97/5v/k/+D/3f/h/+P/4//k/+L/5f/g/+D/5v/k/+X/5//p/+f/4//j/+T/4//h/9z/3v/j/+T/5//t/+v/6//t/+n/6v/t//P/7v/p/+v/5//n/+P/4v/h/+H/5P/f/+H/4//k/9//3//h/97/3//e/+D/4f/h/+D/3P/d/97/3f/e/97/3v/m/+j/5f/m/+r/7P/r/+v/6P/o/+f/6f/q/+n/8P/s/+n/6//q/+j/6P/u//H/7v/r/+7/7//u/+3/6v/v/+//6v/v//D/7//s/+//8//x//b/9P/v//D/8P/w//H/9f/x//D/8f/s/+z/7P/t//D/8//2//f/+v/5//X/9P/1//f/9//3//j/+v/6//f/9//3//X/+P/6//v//f/6//X/9f/6//z//f8BAAAA+//5//3//v/8//n/+f/7//n//v/7//n/+v/8//7/9v/5//z/+P/3//b/+f/7//n/9//6//v/+f/9/wEA/v/7//7////9////AgD9//r//P/+/////f/7////AAD9//v////9//j//f/8//v/+//6//7//P/7//3//P/6//r//P/7//v///////z/AAAAAP///f/7/wAA/v/8//////8BAAMAAQACAAUABAAFAAYAAgAEAAsADAAMAAcABgAJAAYABQAIAAcAAwADAAMAAwAEAAYACAAKAAwACQAFAAUABgAEAAMABQADAAUABgAEAAMABQAEAAIAAgACAAEAAgADAAEAAwAEAP3///8EAAYABAAGAA0ABwADAAgADAALAAsADQAMAAsADQAOAA4AEQAVABEADQAQABMAEwAPAAsACwAMAAoACQAKAAwADAAHAAoACwAKAAwACQAIAAgACAALAAwACgAKAA0ABgAHAA4ACQAKAA0ADAAOABEADQANAA8ADQANAA8ADgAIAAsACgAGAAcACgALAAsADQAKAAoADwAOAAsADAAQAA8AEAAPAA4AEAARABQADgAOABAAEAAUABAAEAATABIAEQAPAA4ADAAMAA4ADwAMAAwAEQAOABAAEQATABUAEgAVABsAHAAXABYAGwAhAB8AGQAaABkAFQAZABwAFwAWABcAFwAZABoAGgAZABgAHAAiAB4AGAAYABcAFAATABQAEwASABIAFQAVABQAEQARABcAFQAMAA4AFAASABMAEwARABYAEgAKAAwADQAOAAoACgAPABIAEQANAAwACgANAA4ACAANAA8AEAAMAAoADQAOABAADgAQABMAEwAZABoAGQAaABsAHQAiAB8AHgAiACMAIQAcABwAGgAbAB4AHwAdABgAGgAaABkAGwAgABwAGAAeAB8AGwAYAB8AHwAdACMAIwAfABkAHgAiAB0AGwAfACAAHAAfAB0AHgAkACIAIAAfACEAHgAYABsAHAAYAB0AIAAdABsAFwAdAB8AHQAiACIAHgAfACMAIQAeABkAHwAiABwAIQAlACYAIQAhACAAHQAfABoAFgAZAB0AHgAZABkAHgAcABgAFwAXABoAGAASABcAGgAaABsAGQAcAB0AHgAiAB8AHgAeAB0AGwAZABkAFgAUABEAFgAaABQAFQAYABgAFgAWABcAGAAXABQAFwAbABoAEgATABcAEAAPABAADwAKAAoACwALABAADQAJAAsAEQANAAoADQAHAAoAEQATABQAEwARABIAFQAXABYAFQAUABIAFAAYABkAFgAWABYAFgAYABkAGwAZABcAFAARABQAEwAUABcAFwAVABUAGQAZABgAFQASABUAEAAKAAsACQAMAAoACAALAA4ACwAIAAkABgAKAAkABwALAAcADQAQAAsACQALABAADgASAA8ACwAPABAAEgASABQAEQAQABEADQANAA8AEwANAAsADwAQABIAEQAPAA4AEwARAA4ADwALAAkABQAHAAUABAAFAAAAAQABAAYACQAFAAYACwAIAAYABwAIAA8ADQAJAA0AEgAOAAkADgAQAA8ADQANAA0ACwAOAA8ADgATABUAEQARABMAFQATAA8ADwAOAA4ACgAMABMAEwARABEAFAAXABIADwAQAA4ADQARABUAFAASABQAGAAYABcAFQAUABIAEwAUABAAEQARABMAFwAUABUAFQAUABYAFwATABAAFQAUABEAEwAYABwAHgAdAB0AIAAZABYAGwAaABgAGQAZABkAGwAYABYAGQAbABcAEwATABAAEQASABEAEQATABQAEQARABYAFQAOAAoACgALAAoACwARABAADAANABEAEgARAA0ADQAQAA0ADgAMAAkACwAJAAoADQAOAAwADAAKAAUABAACAAMAAwADAAMAAwAFAAQABAAEAAQAAQD+/wAA//8CAP7//P8DAAEA+v/9/wMAAAD+//n//P8AAAAA///6///////+//r//P/7//T/+v/4//j/+P/6//n/8//4//r/+f/4//v/+f/1//X/8f/x//P/8v/w//P/8P/q/+7/8f/u/+v/6v/p/+f/5f/l/+n/6v/n/+r/6//p/+f/6v/p/+L/6P/s/+n/6v/r/+7/6//q/+r/6v/r/+n/6//q/+z/7f/q/+3/6//t/+v/6v/v/+z/6f/o/+3/7v/u//H/8P/u//D/9f/x/+//8f/x/+//6v/t//P/9f/0//P/9f/6//b/8f/z//X/9P/t/+7/8f/u//D/8f/w/+//7P/p/+b/5//l/+f/5//l/+j/5v/k/+X/5//n/+b/6P/p/+r/7f/q/+b/5v/m/+b/5//l/+f/7P/s/+3/7v/v/+z/6P/s/+z/6//u/+z/7f/x/+v/5v/v//D/6v/s//D/7v/u//L/8P/z//D/6//t/+//8//z//P/9f/x//T/+//5//H/9P/3//b/9v/4//f/8//z//P/8v/1//n/9P/w//L/9f/z/+7/7//y//H/6//r/+3/7P/s/+j/7P/v/+z/7v/p/+r/5//i/+b/5v/n/+L/4//l/+D/4P/e/9r/3//f/9j/3P/d/9//4f/f/+L/4v/j/+D/4f/j/+T/4//g/9//3v/h/+T/5f/k/+b/5//n/+P/4v/l/+T/6P/n/+X/5f/l/+X/4//o/+z/7v/p/+j/7//t/+3/7v/x//L/8f/z/+//7//0//T/7//s/+z/7v/s/+j/6//t/+v/6P/n/+X/3//h/+b/4P/a/97/3//k/+L/4P/h/+H/4//j/+f/5P/i/+T/4//k/9//3//g/97/3P/c/9z/4P/k/+P/4v/g/+D/3v/b/97/3f/c/9z/3f/i/+P/4P/i/+T/5P/k/+T/5//l/+X/6v/n/+D/4v/m/+X/4//k/+T/5P/l/+P/5P/m/+P/5P/l/+D/2//f/+P/3//h/+H/4f/n/+r/6P/m/+j/4//h/+j/5v/j/93/4v/l/+T/5v/m/+3/7v/q/+j/5v/r/+//7P/s/+3/7//z//P/8P/x//T/8//w/+7/8f/y/+3/6v/u//P/8f/u/+j/6//w/+3/7P/q/+3/6f/n/+//7f/q/+z/7f/s/+r/6P/o/+z/6v/l/+3/7v/o/+v/7P/u/+r/6v/v/+z/7f/y/+//6f/s/+3/7P/r/+j/6f/k/+H/5P/j/+H/5P/j/+D/4f/h/+P/4v/i/+L/5P/l/+b/5v/l/+r/5P/j/+r/6//r/+v/8f/s/+r/7//w//D/7f/t/+j/5P/k/+X/6f/q/+r/5P/j/+P/3v/k/+T/3f/d/+L/4P/h/+T/5P/i/+P/6//n/+X/6v/q/+b/5//p/+f/6P/n/+j/6v/n/+n/6P/s//D/6P/l/+j/5//s/+3/5//n/+z/7f/r/+7/7f/r//D/7v/s/+3/7//y//X/8//u//b/8v/u//X/8f/w/+//8P/0//P/8v/z//b/8v/v//L/9f/v/+v/7f/u/+7/7//u/+7/7f/u//H/8f/w/+//8f/y/+7/7P/u/+v/6//v/+7/6v/q/+z/7v/u/+z/8P/u/+3/8v/y//X/9f/4//f/9f/4//n/+P/3//j/9f/1//f/9//6//r/+v/7//v//P/7//X/9f/4//P/9//6//n/AAABAP7//v8AAAIAAQD+//7/AgD/////AwADAAYABAACAAQAAwAAAAAAAgADAAMAAgADAAAAAAADAAQABAABAAAA//8AAP7///8CAAEAAAD9/wEAAQD//wEAAQABAPv/+//9/////f/1//z/AQACAAMAAAD+/wAAAAD+/wAAAgADAAEAAQACAAAAAgD///7/AQACAAMAAAADAAIA/P/9//r//P///wAAAgAFAAUAAQACAAEA/v///wIAAQD+/wQAAgAEAAUA/v8DAAgABQACAAQABwAFAAkABgAAAAIAAwAGAAcA/f/5//v//v/7//z/AwAAAP7/AQAAAP3///8GAP//+//+/wIABQABAAIABAAIAAwACQANAA4ACwAJAAkADQANAAwACgALAAsABwAFAAkADAAKAAoADgANAAsADwAQAA0ADQAQABEADAAOABMAEQAPAA4AEwAXABMAEQAUABMAEQARAA8AFQAYABAADgATABUAFAAVABUAFQATABYAGAATABcAHAAZABgAGQAXABkAGQAXABoAHQAeAB0AHwAiAB4AHwAhACEAIQAiACYAJwAkACIAIQAiACUAJgAjACcAKgAnACUAJAApACoAJgAlACkAKgArACsAKQAmACYAKgApAC0ALwAwADEAMgAyADAANAA6ADUAMAA4ADoAMwA4ADsAOAA7ADsAOgA7ADoANQAzADgANQA2ADYANAA1ADAALwAtACsAKwAtAC0AJgAmACkAJgAlACkALgAoACgALwAoACQAKQArACYAJwArACgAJwAsAC0AKgAoACwALQArACwAKAApACwAKgAsADMAOAAxADIANAAyAC4AKwAoACYAKgAmACYAKgAnACQAJgAmACAAHQAfAB4AGwAbABwAGgAXABsAHAAXABwAHgAcABcAGQAeABwAGQAYABoAGgAcABsAHQAeABwAIQAgABwAHQAhACEAHwAgACIAJQAiACAAIQAlACQAIQAkACcAJwAlACcAJgAkACcAJQAkACUAIgAfACAAIgAjACQAJQAhACIAIwAmACkAIwAkACMAIAAiACQAJgAkACYAJgAmACYAJgAlACAAHwAiACIAIQAlACYAKgAuACkAKQAoACsALAApACoALQAsACsALAArACkAJwAoACgAKgArACYAIgAiACYAKAAmACQAKwAtACoAKQAnACoAKQAhACIAKAAiACIAJAAhAB8AIAAiACEAHwAgACAAGwAbAB8AHwAcAB0AHgAdABoAFQAZABoAGgAaABkAGwAaABsAHQAbABkAHgAeABoAHgAhAB8AFwAVABUAFQAZABsAHQAhAB8AGQAYABwAIAAcABsAHAAXABYAGQAZABcAFwAYABcAFwAZABsAHAAfAB8AGwAYABcAFgAWABYAEgASABIAEAAMAAsACwAGAAcACQAHAAQACAAMAAsACgAKAA8ADQANAA8AEQAVABAADgAQABQAEAALAA8ADAAMAAwACQAJAAkACAAJAA8ADgAJAAsADAAGAAEAAQAAAP7/AAAEAAAAAQADAAEAAAD8//3/AgD+//3////7//r//P/+//v//v/+//r///////j/9v/5//r/+f/4//r/+//+//3/9v/7////+f/5//7//P/9/wMABAADAAAA//8AAAAAAwAEAAIAAQAEAAEAAQABAAIABAADAAQAAwAEAAYABwAHAAIAAwAFAAAAAQABAP7//v////v/+f/8//z/+//6//v//f/9////+P/6/wIA/v/6//7////7//v//f/9//r/+f/8//n/9//4//X/9v/7//n/8v/x//f/9//1//T/8f/y//P/8v/x/+//9P/0//D/8//v/+z/7//x//X/9f/0//P/9f/1//P/8//z//H/7v/s//D/7//p/+r/6v/r/+7/6//o/+f/5v/k/+H/4P/c/9r/2v/b/9z/2v/b/97/3//e/9r/3f/k/+T/4//j/+X/4P/d/97/2//d/9z/3P/a/9j/4v/j/9//2//Z/9r/2//e/9f/0//U/9X/1f/S/9f/2v/Z/9z/2//c/9v/2f/d/93/4f/n/+b/5P/l/+X/4//l/+b/6P/t/+7/6v/q/+3/5v/n/+n/5v/l/+X/5v/h/+T/5f/f/93/4f/g/9r/2v/c/9n/2//a/9f/2v/Y/9f/1//Z/9r/1//X/9T/1P/U/9b/1v/V/9n/3P/d/9j/1v/Z/9r/2P/W/9r/4f/m/+L/4//k/+P/4v/h/+P/4f/j/+P/4v/m/+j/6//o/+n/7v/r/+3/8v/y//T/9//1//f/+v/4//j/+v/8//j/9//4//P/9P/3//f/8//0//f/8v/0//n/9//x//H/9v/z//L/8P/s/+//7P/q/+3/8P/t/+j/7v/w/+v/8P/x/+n/5//o/+j/5//k/+H/5P/i/+D/3v/g/+f/6f/q/+f/6P/m/+P/6P/n/+b/5//o/+r/6//r/+j/7P/u/+z/6v/q/+z/6v/n/+j/5//p/+v/6f/m/+z/7//q/+j/6f/o/+f/6f/r/+3/7//u/+v/7v/w/+//8v/0//P/8//y/+//7f/t/+//8P/q/+r/8v/x//D/8v/y//H/7//x//D/8P/w//H/9P/z/+7/7P/y//P/8f/0//L/8P/y//X/8//x//T/9v/1//b/+P/2//L/8//z//H/9P/0//b/8//y//n/9f/x//f/+f/4//n//f/+//7/AQADAAEA/v8AAAEA///+//3//f/6//7/AAD//wEA///+////AQADAAIAAAD6//f/+//9//7/+v/8//7///8AAP//AgD/////AwAEAAMA/v8BAAEAAAAHAAUABAAEAAgABwAFAAkACgANAAoACAALAA0ADAAHAAgADQAJAAIACAANAAoADQAPABUAGAAXABsAGwAZABsAHQAcABoAGgAcABwAGwAdABgAGAAaABcAGwAaABkAGgAYABcAFQAVABcAFQAVABoAGAAZABoAFgAZABkAGQAeAB4AHAAaABwAHgAbABkAFgAZABcAFwAbABgAGQAZABwAGwAbABsAEwAYABkAFAAQAAsADQAQAA4ADAAQAA4AEAAUABEAEgAPABAADgAKAA0ADQALAAoADwARABIAEQAQABMAEQASABEADQAPABQAEwAOABEAEwASABIAEwAWABEADwAPABAAFQAUABMAEwAQABIAEgASABUAGAAVABMAFQASABEAEwAVAA4AEAANAAoAEgAKAAQACQAKAAYABwAFAAMABQADAAIA/f8AAAUA/f/5//z/+//5//j/9//5//f/9v/5//r//P/5//j/+f/5//3/AAAGAAMABAAHAAUABgAJAAwABgAEAAcABwAGAAoADAAPAA4ADQANAA0AEQANAA0ADgAMAA4ADgAOAAwADgASABAADQARAA8ACAAJAAwADAALAAkACAAKAAgACwAMAAoADAAMAAgAAwALAA0ACwALAA0ADgALAAwADAAMAAwADAAKAA0ADAALAAwACQAEAAIACAAIAAYACQAJAAYAAwAGAAYACAAIAAQABwAMAAsABgAIAAkABwAHAAgACAAJAAYAAgAEAAYACAAEAAUACwAJAAYABAADAAYABgADAAIACgALAAQAAwAHAAgAAQADAAYAAwAEAAYABgACAAIAAAABAAYABAAAAAAA/v/+/wIABAADAAAAAQACAP//AAACAAQABAD///3//////////f/7//7/AAAAAAAA/v/7//z//v////3/+v/4//r//f/6//f/+P/5//r/+v/2//P/9//8//r/+v/8//v/+f/3//b/+P/5//f/9v/1//T/8//1//X/8f/v//D/8v/y//D/7f/s/+z/7P/p/+j/5v/j/+r/6//p/+n/6v/m/+T/5//i/+L/5v/l/+X/5f/o/+f/4v/l/+r/5v/n/+b/5//r/+X/5P/o/+n/5f/m/+n/5P/i/+D/4P/j/+n/6v/o/+v/7P/r/+n/5v/i/+T/5//o/+b/5f/l/+X/5//n/+r/6f/i/+D/4v/j/+D/5f/o/+f/5//n/+r/6P/o/+n/6P/r//D/6//n/+3/7//y//P/8//z//L/8f/z//n/9v/x//H/9P/x/+v/7//q/+L/5//q/+b/5f/h/+T/6P/l/+P/3//m/+f/5v/v/+3/6v/o/+f/4//l/+j/5f/k/+X/5f/j/+X/4//m/+f/3//i/+f/5f/h/+H/4P/h/+P/4f/g/93/4f/k/+L/5f/h/9v/3v/f/9z/3//l/+b/4v/n/+n/5P/p/+r/5P/m/+n/5f/o/+j/6//t/+z/7f/q//D/7v/o/+j/7//t/+n/7P/u/+3/6v/o/+X/6//u/+j/5f/n/+r/5v/q/+//6v/q/+z/7f/p/+f/6v/k/+L/5f/o/+r/6v/q/+z/7f/s/+v/6v/s/+v/6//x/+3/7v/z//P/9P/z//f/+P/x/+r/6v/s/+b/5//s/+z/6P/j/+r/7//r/+f/5//q/+j/5//l/+H/4P/k/+j/4//j/+b/5f/f/+L/5//i/+L/4v/h/+L/5P/n/+n/7f/n/+b/6//q/+r/7P/y/+//6f/s//D/7v/n/+z/8P/t/+3/8f/y/+//9f/z//H/8v/z//r/+v/6//n/+f/8//j/9P/6//z/9f/3//r/+f/4//7/AgABAAMABQAAAP7/AgABAAEAAQD+/wMACQAHAAYACgAKAAEAAgAKAAoACQAIAAsACgAMABEADgAQABIADAAKAAwACgANABMAEQARABQAFgASABEAFgAVABMAEwASABYAGAAVABYAGQAbABoAGgAcABkAGAAaABwAIAAhAB4AGAAZAB8AHgAaABsAGwAaAB0AHQAeACEAHwAdABwAHwAlACYAJgAmACYAIgAgACUAKAApACcAJgAoACkAKgAnACUAJQArACoAJQAmACMAIgAiACMAJgAlACUAJAAlACgAKAAnACQAIwAmACYAJAAiACYALQAsACoAKwAtACcAJAAnACQAJAAlACYAJQAkACYAIwAiACUAJgAjACQAJgAmACgAJwAoACkAJwAlAB8AIQAjAB4AHAAfAB8AHQAeAB8AHgAeACEAHQAaAB4AHQAcAB0AGAATABQAFwAVABIAFwAWABUAFwATABMAEgARABAAEAASAA4AEAAOAAwADQAKAAsACgAHAAUABwAEAAIABwAJAAYABQAJAAcACQALAAgACAAGAAkACwAKAAkACwAMAAkADAANAA0ADQARABEACQAMABAAEQATABAAEAARABIADwALAAwACwAMAA0AEAAQAA8AEwATABIAEwARABQAFQARABMAEQAQABMAFQAVABUAFQARABcAHAAWABIAFAAbABsAGwAgABwAGQAYABQAFAAXABIADwAUABUAFgAZABkAGAAbAB0AHgAbABsAHQAeAB0AHgAbABoAHAAXABsAHwAZABgAGAAUABEAEgAOAA4AEQARABMAFgAXABQAFAAVABAAEQAYABUADgARABEAEQARABIAEQAOABEADgAOABEADQAHAAYABgAHAAcABgAGAAQACQAIAAIAAQAAAAMA/f/5//v/+v/4//b/9v/1//P/8f/w//D/7v/s/+z/6//o/+n/6v/t//D/7v/u/+//7f/s/+3/7f/x//D/7f/w/+//7//y//H/7P/w//H/7f/u//H/8v/0//P/8f/y//L/8v/x//T/9P/v//P/8v/s/+r/7P/t/+7/8P/s/+v/6v/r//D/7//v/+7/7f/u/+7/7//w//H/8P/y//P/8v/z//D/8P/t/+j/6P/t/+r/5v/r/+n/5f/i/+X/5f/b/9r/3f/h/+H/3P/a/9j/2f/Z/9n/3P/f/93/2//Z/9f/1f/U/9b/1f/V/9P/1f/b/9f/1//X/9X/1v/U/9T/0v/L/8r/zf/N/8r/y//M/8v/x//H/8v/yv/K/8r/zP/P/9L/0P/T/9n/1v/T/9X/1P/S/9b/1//V/9b/1//W/9P/0v/R/8//0P/P/83/1P/V/8//z//T/9b/0v/P/9L/zf/L/8//0v/S/9P/1f/T/9b/1//W/9X/2P/Z/9P/0//V/9b/2f/Z/9X/2f/e/97/3v/f/+D/3P/a/9//4P/i/+H/3//h/+L/4f/e/9z/4f/j/9z/2v/d/93/4v/m/+T/4//k/+L/3v/f/97/3P/b/9z/3P/d/9//2v/V/9f/2f/Y/9P/1P/b/9j/1//Y/9X/1//T/9P/1v/Y/9f/1v/Y/9X/0//Q/9P/1f/S/9n/1v/R/9P/2f/a/9P/1f/Y/9n/2//Z/9r/2f/Z/93/3P/b/9v/2//Z/9T/1//c/9//4P/e/97/3f/f/+D/3//f/+D/5P/n/+X/4//g/+L/4//f/+H/4//k/+f/6P/l/+P/5P/i/+b/5v/h/+T/4//f/97/3f/e/9//5f/h/+D/5f/l/+P/4v/l/+L/4//n/+D/3P/j/+f/4P/m//D/7//y//X/9f/2//n//P/6//f/+v/6//n////9/wEABAD//wAAAgAGAAgAAwADAAQA/v8BAAgAAwACAAIABAAFAAYADAAJAAkADAANAA8ADQANABIAEwAQAA8AEgANAAUACQAMAA0ADwAPAA4ADAAPABMAEAARABUAFQATABEAEgASAA0AEAAUABIAEwAQABAAEwAUABoAGQAbABsADwARABQAEAASABAAEQARABYAGAAYABoAGwAdABsAHwAdAB4AJAAgACAAIwAlACUAIgAmACkAJwAoACYAIwAjACUAKgApACYAJwAiACMAKAAkACIAIQAlACoAJwAkACUALAAqACgAKwAsACwAJwArAC8ALwAuACkAKgAsAC0AKwAsAC8ALgAvAC0ALQAvAC8AMQAuAC0AKwAoACwALwAvAC0ALAAsACkALwA0ADAALAArAC8ALwAvADIALQArAC4ANQA1AC4AMAAuACwALQAuACsALgA3AC8ALAAvAC4ALAAsAC4ALwAyADMAMQAuAC8AMQAvADQAMQAvADUANQA3ADgAOwA7ADgAOwBCAEMAPwBDAEEAQQBFAEQAQQBEAEUAPwA/AEMAQgA+AEIAQwBBAEAAPgA7ADkAPQA7ADkAPQBCAEAAPAA/AD0AOwA9AEAARgBIAEoASQBHAEEAQwBGAEgASQBIAEwATABNAE8ATgBOAEsATQBPAEwATABNAEsATgBSAFAAUQBPAE4AUQBPAFUAVABPAE8ATgBRAFAAUQBSAE4AUABNAE8AUwBSAFEATgBQAFEATwBVAFMAUgBWAFYAVQBTAFMAUgBMAEkARwBJAEwATQBMAEkARwBFAEIARgBJAEgASABNAFAATwBQAE0ATQBNAE0ATABKAEoASgBJAEkASQBHAEcASQBMAEkASABNAEkARQBHAEcAQgBEAEcASQBJAEYASABJAEYAPwBCAEYAQgA7ADoAOwA6ADkAOAA5ADYANQAzAC4ALwArACcAKAAsAC4ALwAyAC8ALgAwADEAMwAwADMANAArAC4AMgAuACoALgAxACwALAAsACoAKgAqACUAHQAeACIAIAAZABkAHQAXABIAFQAWABUAFAASABEAFAAOAA4AEgANAA0ADwAUABQAFwAdABwAHAAbAB0AHgAbABwAGwAVABIAEQARABMAFQASABAADgAKABAAEQAMAAwACwAIAAQACQAMAAYABwAEAAQABQADAAQABAADAAQAAQD//wMABQAAAAEAAwAAAAIAAgADAAEA/v//////AAD9//v/+v//////+f/3//b/+//5//f/9v/0//T/9f/5//b/8//y//X/+P/0/+//7f/w/+7/8P/z//T/9P/0//b/8v/z//D/7f/u/+z/7f/p/+r/7P/n/+T/5P/k/+j/6P/k/+f/6P/l/+H/4//j/+D/4f/b/9z/4P/d/9z/3//g/93/2P/a/9z/1v/V/9b/0v/N/9P/1f/R/9X/1f/V/9T/1v/Z/9b/0//T/9T/1v/V/9H/0f/X/9j/0v/U/9f/0v/Q/9T/1f/U/9f/2P/W/9H/0P/U/9P/1P/Q/8//0f/R/9D/0P/S/9P/1P/U/9L/z//N/8//1P/W/9T/0v/T/9b/3P/Z/9L/1f/R/8z/0P/Q/83/y//M/8z/yf/L/8n/yP/G/8b/xv/C/8L/vP+8/8D/vf+7/77/u/+3/7j/uP+4/7f/u/+8/7j/vP+9/77/v/+9/7//wv/C/8P/x//K/8r/xv/F/8r/yv/J/8j/yP/I/8X/xf/G/8b/xf/D/73/vf/D/8D/wf/D/8H/wf/A/8H/v//G/8z/xf/E/8f/xv/H/8n/x//I/83/zv/H/8j/y//F/7//v//D/8b/xP/E/8L/wf+//7z/wP/B/73/vf/B/8L/w//G/8X/xf/D/8n/yf/B/8L/x//K/8f/yP/L/83/zv/L/83/zP/K/8b/xv/L/8n/x//I/8n/zP/M/8v/yv/H/8f/yv/P/87/y//O/83/zf/S/9T/1P/S/9L/zf/Q/9T/0P/Q/9P/1//T/9H/0v/W/9f/2P/f/9v/2f/Y/9r/3f/X/9X/1//a/9r/1//W/9j/2f/e/+D/2//b/9//4P/c/9n/2v/e/+D/3//d/93/4v/e/9r/2//Z/9z/3f/b/9z/3P/b/9z/3P/X/9n/3P/a/9v/3P/d/9r/1v/Y/9v/3f/c/93/3//f/+D/4P/g/9n/2f/e/9//4P/i/+X/4f/e/+H/4P/e/+D/3P/c/+L/4v/g/+P/5v/m/+j/5P/g/+f/5f/c/93/4v/j/93/3P/j/+D/4f/h/97/4P/i/+D/3//l/+T/4f/k/+f/5v/s/+z/5f/n/+z/6v/i/+f/6//t/+//7v/u/+3/8v/u/+z/8P/v/+z/5f/p/+7/7f/u/+v/6//v/+z/7P/w//D/8P/r/+z/7f/r/+v/7f/x/+//8P/t/+z/8//1//P/8f/u//H/9f/2//r/+//4//f/+v/5//X/9P/1//n/9//5//v/9v/5//j/+P/4//n/+f/6/wAA/////wAA/f///wIAAgAEAAYABgAGAAUACQAJAAkADgAMAAsADgAQAAwAEQAWABMAFQAYABgAEQASABUAEwAUABUAGQAVABAADwAQABUAFQAPAAwADQARABUAFQAQAA0ADAALAAoADgASABAADQAPABEAEgAVABQAFAAVABMAEwASABUAFgAWABMAEwAVABgAGwAYABUAFQAXABYAFgAYABgAFQASABMAFgAWABUAEQAQABIADQAOABMAFAATABQAFQASABEAEQASABQAFAAVABUAFQAVABUAFAAUABAAEQARABIAEwAQABAADAAMAA8AEAAMAAsAEAATABQAEgASABIAEgASABQAFgAUABQAEwAQAA8ADgAPABMAFAASABMAFgAUABEAEAANAAoABgAGAAkACgALAAwADgALAAkADgAQAAwABwAFAAoADAAJAAgABQAIAAoACwALAA8AEwAJAAcACgAKAAwADwAQAAwADQAPAA0ADwAMAAIABQAIAAEAAQAEAAMABAADAPv/+/8AAAMAAAACAAcAAgADAAMABAADAAIACgAMAAsADAAOAAsACwAKAAgADwAJAAoADAAQABEACwARAA8ACwALAA0ADQAPABAACgALAA0ADwAOAA0ADwAOAA8ADQAKAAwAEQAOAA0AEwASABAADwAOAA4ACQALAA4AEAARABAAEAASABMAEQATABMAEgARABQAFQASABAAEwAWABUAFQATABEAEQATABUAEAAPABUAEwAQABEAEAAPABIAFAAUABYAFgASABEAFwAVABUAGAAVABUAFQAVABMAFAAWABYAFAAYABoAGQAVABMAFQARABEAEAAQABUAEgAOABIAEgARABkAGQAYABYAFgAcABkAFQAZABoAGgAaABwAIgAiAB0AHwAmACMAGgAeACEAIQAfACAAIAAcAB0AHgAeABoAFwAVABMAFwAYABMAEgAVABQAEQATABMAFQAXABoAGgAXABYAFAAVABsAGgAYABoAHAAYABcAHQAXAA0AEAAQABEAEgAOAAwACQAIAA0ADwAOAA0ADAALAAoADAAPAA4ADAAMAA8ADwAPABMAFQAVABUAGAAaABQAFgAZABcAFQATABQAEwAUABEACAAPAA4ABwAHAAUABgAFAAMAAwAFAAYABgAIAAkACgAMAAoADAALAA0ADwALAAwADQARABAADgATABMAEgAQABAADwAQABAADwASABEAEgANAAsAEQAQAAkACQAMAAgACQALAAkACAAKAAUABQAJAAUABwAGAAUABQAHAAoAAwD//wMAAgACAAYACAAGAAcACgAEAAUACwAIAAMABgAHAAIAAgABAAAAAAD8//z/AwAGAP//AAADAP////8EAAMA//8GAAQABAAJAAUACwALAAgADwASABMACAAHAA4ACQAJAA0ADAAMAAkABgAHAAoADgAKAAcABwAEAAIABQAHAAMAAgAEAAcABgAJAAgABQAJAAUABQAHAAUABQAIAAcABAAHAAoACwAMAAsADQAPAA8ADQANABQAFAARABAAEwATABEAFgATABEAEgARABAADAARABIADwAKAAcADQAOAAkACQANAAwACQAQABMADQAKAA8AEQAPAA8ADAAMABAAEQAQAA0ACwAQABEACQAKABEAEAAMAAwADwAPAAcABgAKAAoADAAGAAUABwAHAAkACQAKAAoADQALAAsAEQAUABEADwAVABIADQANAA0ADgAQAA0ACQALAAgACAALAAwADAANAA8ADQAMAA8ACgAIAAsACAAIAAcABgALAAgADAAOAAYACQAHAAEAAwAJAAYAAgAGAAgACwANAAoACAALAAoABAAFAAkADAAJAAYAAgACAAEA+/8EAAUABAACAP3/AgABAAMAAQD+/wEAAgAFAAYABQAGAAUABAADAAUABwAJAAYABgANAA8AEAAMAAkACwAMABEADwAPAA4ADQANAAsADQAQABEAEAASABQAEwAQABQAGAAUABMAEwAUABMAEwAVABIAEgARAA4ADgASABIAEAAQAA0ADgASABUAFAATABIADgARAA8ACwAOAA4ADwARABIADwAMAAkADQAQAA0ADgARABAADgANAA0ADAALAA8AEwANAA4ADwAIAAYACAANABAADAALAA4ADgAGAAQACwALAAUABAAFAAMAAgD+//z///8EAAQA//8AAAAA//////3/+//9//v/+//6//j/+P/2//r/9//1//P/8//y/+v/8f/0//H/8P/0//L/7P/v//D/7v/q/+j/6v/o/+X/5v/n/+T/5P/l/+T/4//l/+f/6P/n/+j/6f/n/+f/6P/o/+b/5P/n/+b/5v/m/+f/6f/p/+n/6P/q/+v/6v/q/+//8f/u/+v/7P/t/+n/6f/r/+v/6v/v//D/7//w/+//8f/t/+//8v/y//D/7f/0//L/6//x//b/8f/w//T/7f/s//D/8v/u/+7/8P/t/+n/6//t/+7/8P/u//H/8v/z//T/8v/x/+7/8P/z//L/8v/1//P/9P/1//D/7//y//P/8f/y//H/7v/v//L/8f/u/+//8P/z//D/8P/z//X/9//z//j/+P/0//P/9v/5//f//P/5//f/+P/1//H/8v/3//P/8v/0//f/8v/v//H/7f/v//L/8P/p/+z/8//r/+z/9P/0/+3/7f/2//T/8//z/+//8f/0/+7/7P/0//L/8P/w//D/7//t//P/9P/0//H/7f/w/+7/7P/q/+r/7f/r/+3/7//s/+v/6f/o/+X/5v/p/+j/6P/h/+L/5f/h/+D/4f/g/9//3//g/+D/3P/e/9v/1//b/9v/4f/f/9//5P/f/+L/4//h/+L/4v/j/+b/5//j/+j/6v/o/+f/5//n/+X/6//s/+j/6f/n/+j/6P/l/+X/5v/l/97/2f/f/97/3f/f/9z/3//e/9j/1f/Y/9j/1P/V/9b/1f/U/9j/1P/T/9n/2f/V/9H/2f/a/9T/1v/W/9X/1//U/9P/1v/Y/9T/0f/V/9X/0v/P/8//0P/O/83/z//U/9b/2P/W/9L/1f/X/9b/1f/X/9f/2P/W/9X/2P/Z/9n/1//Z/9f/1v/Y/9n/2P/X/9f/1v/X/9j/1v/S/9b/2v/W/9T/2P/X/9T/1v/X/9n/2v/a/9j/1P/Y/9f/0v/R/9D/0f/R/87/0P/R/87/0f/T/9H/0v/P/9D/0P/O/9H/0f/Q/8//1P/T/8//0//U/9L/y//K/8z/yv/L/8//0P/P/9D/1f/W/9L/1f/Y/9f/3P/e/93/4P/d/+D/5f/l/+D/4f/s/+v/5//o/+b/5f/n/+P/4f/n/+j/5f/i/+X/5//i/+f/5v/h/+n/6f/m/+b/6P/o/+f/5//q/+7/6f/n/+L/4//m/+X/6v/l/+D/4f/i/+H/4f/f/9n/3//j/+P/4//l/+X/6P/t/+f/4//p/+z/6f/p/+f/5v/p/+r/5v/l/+v/6//n/+f/6P/o/+T/4//o/+7/7//y//X/9P/3//b/9//4//j/+P/4//j/+//+//3/AAACAAUACAAIAA8AEAAIAAkADQANAAwACgAMAAwADQANAAsADAAOAAsACAALAAwABgAEAAcACQAHAAYABwAFAAMABwAHAAQACQAKAAoABgAHAAoABwAJAAcACAAJAAkACQAJAA4ADQAPABMAEAAOABEAFQAVABUAFQAVABYAGAAXABgAFwAVABgAGwAgAB8AHgAiACYAIwAhACYAJgAmACMAJgApACYAJgAqACoAJgAlACYAJAAkACkAKAAnACoALQAtACoALgAxACoAJgAuADMAMwAzADQAMAAxADUAMgAyADcAOAAyADEANgAvACwAMAAxADEALQArACoAKQApACYAJwApACMAIwAoACUAIwAnACMAIQAnACcAJQAoACsAKQAmACkAKQAqAC4ALgAqACoAMAAvAC0ALgAsACwALQAtAC0ALgAwAC4AKQAoACwALgAsAC0AKwAsACwAKgAsACoAKQArAC8ALwAsACsAJwArADAAKwAqACoAKwAtACsAKwApACgAKQAnACoAKwAnACcAKgApACcAKQApACcAJwAjACQAKAAmACIAJwApACoAKgAqACcAIgAqACwAJwAoACsAKwAoACwALgAsACsALAApACcAKwAoACcAKwApACsAMAAuAC0AMQAzADQANQA0ADYANgA2ADUAMwA1ADUANgA1ADgAOgA2ADQANAA1ADAALAAwAC8AKAAmACsALAAsAC0ALQAuAC4ALQAuAC4AJwAmAC0ALQAqACoALwAvAC0AMAAuACwALAAuAC4ALQAxADIAMAAqACoALQApACoAKQAoACkAJwApACgAIwAeAB8AIQAhACQAJQAhACIAIgAjACgAKAAlACcAJgAiACQAKAAoACcAIgAgACMAJQAmAB8AIAAhAB4AHgAeACAAHgAeACIAIAAeAB4AHgAfACEAHgAeAB8AHwAfAB4AIQAgAB4AIgAoACcAIwAlACIAIwApACkAKgAoACQAIwAmACYAJgAmACQAIQAlACgAJAAjACcAKAAkACUAJwAkACMAJgAqACUAJQAlACEAIQAgAB8AGwAbABwAGwAcABsAGwAZABsAHAAaAB0AIQAeABwAHgAeACIAIQAdAB8AHwAhACIAIAAfACMAIwAgACAAIQAgAB0AHwAfAB4AIwAkACIAJAAiAB4AGgAXABsAHQAeAB0AHAAbABgAEwASABIADgAOABAAEAATABQAEwARABAADQAOABUAFgARAA8ADwALAA0AEQAMAAoADQAIAAEABgAKAAYAAwAEAAUABQAKAA4AEAAPAAsABwAHAAoACAAEAAQABwAGAAUABAAAAAMABgAFAAIAAwAEAAIAAwADAAUABQAIAAsACgAMAAsACQAMAA8AEQAMAAcACQAIAAUABQD+/wAAAQD7//3//v/+////+//6//r/+v/4//f/+//5//j/+f/5//n/+P/5//X/8//0//P/8//2//r/9//5//v/+P/5//3//f/8//n/+P/5//z//v/7//z//v/+//j/8P/x//T/8v/x//L/7//t/+r/6//s/+r/6v/u/+z/6f/v//D/6f/m/+j/5//o/+v/6f/l/+n/6v/n/+n/5//j/+T/5v/i/+L/6f/m/+X/5//j/9//4f/j/+P/5f/h/+H/5v/o/+b/4f/o/+v/5P/j/+f/5P/g/+P/4P/c/+D/4//e/9v/2//e/93/2//c/9//4f/g/+H/3//b/9//4P/e/9//3//d/9n/2//f/9r/2v/d/+D/4P/f/9//4P/i/97/2//d/9v/2f/b/9n/2f/e/9r/1v/X/9n/1v/R/9X/1//Z/9b/0f/T/9b/2P/X/9j/2//d/9z/2//g/+D/3v/g/+P/3//h/+L/4f/l/+T/4f/i/+X/5f/m/+r/6f/k/+P/5v/j/9//4P/k/+L/3//g/+H/5//p/+X/6P/r/+v/6f/s/+7/6//s/+n/5v/l/+b/5v/k/+n/5//l/+r/5//i/+H/5P/m/+X/6f/n/+L/5v/k/+L/4f/j/+X/3v/f/97/3v/g/9r/2f/i/+X/3//g/+P/4f/f/97/4f/j/+P/4f/b/9z/3v/d/9//3//e/9//2v/a/93/3P/b/9n/2//f/+L/4//e/93/3f/b/9v/3v/f/93/2v/a/9//3v/d/9z/2//e/97/2v/Y/9n/2f/d/97/3v/c/9n/2//a/9v/2v/U/9P/0f/P/9H/0//X/9X/1f/a/9j/0//W/9f/0//Q/9H/0f/U/9X/1f/T/9X/1//U/9n/2//a/9z/3v/g/9//4//n/+T/4//k/+X/5f/n/+j/5P/j/+H/3v/b/9z/2//b/97/2v/a/93/2//X/9n/2v/X/9v/2v/Y/9f/2v/c/9n/2f/Z/97/3v/d/9//3//c/97/3//h/+b/4v/l/+j/5//j/+H/5//l/+P/5v/n/+f/4v/k/+j/5//s/+n/5P/i/+P/6v/o/+r/6v/p/+r/6//y//L/7f/q/+n/7P/q/+n/5//l/+b/5v/m/+P/5f/p/+n/5v/i/+f/5v/i/+P/5f/n/+f/6f/t/+//7P/p/+z/7v/u/+//9P/2//b/+f/6//r/AAABAPv/+v/+//z/+P/7//z/+v/7//j/9P/0//b/+P/0//P/8v/1//b/8//x//D/8//v/+7/8P/t/+n/5P/p/+r/6P/q/+j/6v/s/+z/7f/s/+r/6P/m/+j/6//n/+n/6//q/+v/7P/u/+r/7P/x/+z/6P/r//D/7//u//D/7f/s/+z/6f/p/+z/5//m/+f/6P/p/+n/6f/l/+n/6P/m/+b/4P/i/+f/5P/m/+j/5f/m/+X/5P/i/+X/4//e/+X/5P/h/9//3v/l/+X/5P/l/+T/6v/p/+n/7v/t/+7/6v/r/+3/7f/x//H/6//s//T/8//s/+v/7f/t/+v/6P/q/+z/6//u/+v/7f/y//n/+f/0//X/8v/y//H/8f/w//D/9P/z//T/8//1//j/+v/5//X/+f/3//b/+v/3//X/9P/2//X/9//5//T/9f/4//j/+f/6//z//v/9////AAD+//7/AgD///7/BgAIAAoACwAHAAcACAAMAAkABwAMAAwABwAIAAsACwAMAAsACwAMAA0ACwAMAA8ACQAHAA4ADQAIAAYACAAHAAUACgAKAAUABwANAAsABQAGAAYABQAIAAgABwAHAAYABQAKAAsACAAEAAUABgAFAAsACQAFAAkADAAIAAkACQAIAAgACQAOAAsACgALAAgADAAOAAoABgANAA8ACwAKAAkACQAGAAcABQACAAYABwAFAAYABgAFAAMAAQAAAAAA/v/7/wAAAgAAAAIAAwAAAAIABgAFAAYABQAFAAcACgAKAAgACQAIAAcACAAGAAEAAgAJAAgAAQAFAAgABwAJAAwADgALAA0ADQAMAAsACgAQABAAEQAQABAAEQAPABQAEgATABQAEQAJAAkADgAPAA0ACgALAAMABwAOAA0ACgAFAAsABwAEAAgACQAJAAcADAAIAAQADAAOAAkABwAIAAYACgAOAAwACwANAA8AEQAQABEAEQANABAAFAATABQAFAAUABYAFQAWABkAFgAYAB0AFwATABYAGQAXABMAFAAVABYAGAAZABYAGgAaABgAHgAaABsAHAAUABYAGwAYABgAGwAbABkAFQAVABEADgAPABMAFgAUABgAFgAZAB8AGgAaABoAGgAbABoAGwAdAB4AHQAdAB0AHgAgABwAHAAbABwAIgAdABwAHgAdAB4AIAAfAB8AHwAdACIAIwAlACcAIgAjACUAIgAgACMAIgAjAB0AGQAeAB4AIQAcABsAHgAdACMAIgAnACoAJgAlACkAKQAqACsAJgAlAB8AIQAgABwAIAAdABgAFQAVABcAFgAZABgAFAAUABYAFQASABYAFAASABMAEwAWABUAGQAZABgAGAAXABsAGAAXABUAFwAbABsAGgAZABkAFwAcABoAGQAXABkAIQAaABMAFgAXABUAGQAbABcAFAAUABQAEgAWABcAGAAUAA8AEAANAA4AEAAQABIAEwATABEAEQAPAAkACwANAAwACgAIAAwAEQAOAA4AEwAYABMADwAUABQAFwAYABUAFAAUAB0AGwAXABwAHQAhACEAHAAdACIAIQAgACIAIAAfAB8AIQAhAB4AIAAhACAAIQAjACEAIAAhACMAHwAdACEAJAAjACEAIgAjACMAIwAiAB8AIgAlACMAIwAgAB0AHAAgACQAIAAhACcAJwAlACQAIAAdAB8AIAAfACAAIgAnACUAIgAjACAAIgAkAB8AHwAiAB8AGwAfAB8AGgAfACEAHwAdABwAHgAbABwAIQAiACAAHwAdACEAJAAhACMAJAAhACAAIQAgABwAHgAgACIAJAAhACIAIAAeACAAIgAhAB8AHwAbAB8AIwAgACAAIwAfABsAHwAhACIAIgAkACQAIwAjACIAJQAjACQAIwAfACEAIgAjACYAJgAlACQAIwAhACIAIwAjACcAJgApACwALAAuACYAIgAoACsAKAAnACoAJwAkACQAJgAoACUAJAAlACMAIgAhACEAIQAdAB0AIQAfAB0AHAAaABwAGwAbABoAFwAeABwAGgAgACUAJgAjACMAJQApAC0ALQArACgALAAsACsAKwApAC4ALgAoACkAKQAqACYAJgApACYAKAAnACoAKgAkACQAJAAgAB0AHAAYAB0AHQAdAB4AGgAYABcAHAAdABoAFwAZABkAGQAcABYAFQAYABoAHQAbABoAGQAVABgAFgATABgAGQAdAB0AGwAaABoAHAAZABUAGgAcABoAGwAYABYAFAAZABwAFwAaABkAFAAVABQAFAAVABAAEAARAA8ADwAGAAQACAAGAAcABwACAAEABQAIAAkACAAKAAkABAAHAAsACwAHAAoACQAJAA8ADgAQABQAFwAUAA4AEwAZABIADgASAA0ACgAJAAgACwANAAwACwAQAA0ABAAEAAgACAAFAAQABAAEAAMA/P/8/wMA/f/4//z/AAD9//r///8CAAMAAQADAAIAAAAGAAgACAAHAAYABwAGAAcAEAAPAA4ADwAQAA0ACQAOAAkABwAJAAcACgAPAAsABAAHAAgABwAHAAgACgAHAAUAAwACAAIAAQABAAEAAwACAP///v/8//f/9//2/+//7//y//D/7//y/+7/7v/v/+//7v/s/+7/6//t//D/6//u//H/7//s/+7/8f/w/+r/6//x/+//7f/s/+//7P/u//D/6P/o/+r/6f/n/+n/5f/j/+f/5//p/+b/7P/r/+b/6v/q/+j/5f/r/+j/5//r/+b/6v/r/+z/6//r/+r/6P/p/+j/6//o/+z/7v/r/+z/6f/p/+j/5//o/+j/5v/h/+D/4v/i/+L/4f/i/+L/4v/k/+X/5f/f/93/4//j/+L/5f/l/+X/6P/m/+f/7f/p/+T/4P/i/+f/5f/k/+H/4P/g/97/4f/j/+b/5f/k/+f/5P/h/+L/4//k/+P/3//g/97/3f/i/+D/3//h/+D/3f/c/93/2v/a/9v/3//i/97/3f/f/+T/5P/i/+L/4v/l/+L/4//l/+H/4P/b/9r/3v/c/9v/2//f/9//2//a/9r/1//W/9z/2//a/9j/2f/b/9b/2P/X/9j/1//U/9n/2f/T/9L/1f/S/83/0P/U/9H/z//U/9D/zf/Q/9H/0f/O/8n/y//N/8v/y//L/8z/yP/I/8z/yf/E/8L/x//I/8f/xv/D/8P/wf/E/8T/wv/D/8T/xP/D/8H/vv+//7//vP+5/7r/vP/C/8P/wP/D/8X/xP++/8P/x//E/8T/w//F/8n/yv/E/7//xf/F/77/v//C/77/v//B/8H/vv++/8D/uv+4/7z/uv+5/73/uP+z/7f/t/+4/7r/u/+6/7f/t/+6/7X/tf+5/7f/uP+2/7b/uf+8/8H/vf+5/7v/uf+9/73/uv+6/7X/tf+6/7j/uv+//7z/uf+7/7v/v//E/8L/wv/D/8X/x//G/8r/yv/F/8X/yP/L/87/z//L/83/2P/b/9T/2P/c/9f/1v/Z/9j/1v/V/9f/0v/T/9b/0v/O/9L/2f/P/8z/z//M/87/zP/R/9L/1P/S/8v/zf/P/9L/0v/T/9f/1P/Y/9n/1v/W/9b/2P/Y/9j/2P/Z/9n/0//W/9r/1f/V/9f/2f/W/9T/2f/d/9j/1f/Y/9j/2//a/9z/3//d/9z/2f/a/9v/3f/d/9z/3P/c/+D/3P/b/93/3f/a/9f/2v/W/9L/1P/W/9b/2f/b/9j/2v/d/9z/2P/Y/9z/1//V/9j/1f/V/9b/1P/W/9b/1//S/9T/2v/X/9T/0P/T/9X/1P/W/9b/z//K/9L/0P/O/9P/zv/K/8v/zv/O/8z/z//M/9H/1v/V/9T/0v/X/9f/1//b/9v/2v/b/9z/3P/h/+P/3//j/+T/2v/W/9v/4v/j/9//3v/d/97/3P/b/9v/3P/g/93/3//k/+P/5f/l/+L/4f/f/9//4P/g/9//5f/l/+b/6v/o/+v/6P/r/+3/6v/r/+b/4//h/+T/5f/h/9//3v/n/+r/5P/i/+H/4v/l/+b/4f/h/+r/7P/r/+r/6v/q/+3/7//s/+//8f/x//T/9v/2//b/+f/8//n/+/////z/+f/3//f/+f/3//X/+v/+//j/9v/5//f/9f/2//n/9//0//P/8//0//L/9P/3//f/+v/9//z/AQADAAAA/P///wUAAQADAAwADQAHAAsAEgARABEAFAAVABQAFQAWABQAEQAQABIAFwATAA0AEgAWABAABgALAA4ACwANAAwADAAMAA8AFAAXABQAEgAXABUAFAAWABcAGgAZAB0AHwAfAB4AGgAeACIAIAAiACcAJQAlACkAJwAlACUAJgApACYAJgAmAB8AHgAeAB0AHwAfACIAJgAoACIAIgAnACMAHgAfACEAIAAgACIAJwAoACcALAAuAC4AMQAsACwALwAxAC0AKwAtACsALAAtAC0ALQAwAC8AKwAsAC0ALQAtAC8AMQAuACwAKAAqADAAMgAzADEAMQAzADkAOQA3ADoAOAA4ADsAPAA6AD0APAA2ADkAOQA4ADMAMQAxADIAMwAuACsALQAvACwALgAsACsAMwAxADIANgA2ADMAMwA6ADoANwA7ADwAOwA8ADwAOgA4ADgAMwAyADYAMwAyADIAMQAvAC0ALQAuAC0ALAAuAC4ALAAsACsALwA0ADIAMQAxADEANQA5ADkANgA1ADgAOAA1ADYAMwAzADgAOgA6ADcAOAA+ADsAOAA5ADYANgA0ADQAMwAxADIALQAuAC8ALAAtADMAMQAtADMANAAzADIAMAAwADIANgAyAC4AMgA3ADUAMwA1ADQANAAyADAALwAyADIALwAxAC0AKwAxADAAKQAqACsAJAAiACYAJwAmACgAKQArACwAKwApACkALgAyADEAMQAuACsALQAtACwAKwAnACUAKAAlACYAKgAnACQAIAAgACAAHwAiACIAHgAbABwAHwAgAB0AIQAnACYAIwAiACUAJwAnACMAJAAnACMAJQApACgAJgAjACQAJQAlACUAJQApACMAIQAnACgAKwApACcAJwAoACsAKAAmACwALQApACoAKwAtACsAKAApACYAKQApACcAJwAmACUAJgAjACIAJgAiAB8AHgAhACIAGwAZACAAIgAeAB4AHAAeAB0AHQAhAB8AHQAbABwAGQAZABoAHQAhABsAGwAeABwAHwAgAB4AIAAhAB4AHQAdAB0AHQAeAB0AHQAeAB8AIwAmACYAIwAhAB0AHAAdABwAIQAhACEAHgAcACIAIgAcABoAGwAYABkAFwAYABkAGQAbABoAGwAbABoAGgAfAB8AGwAbABsAHAAZABoAGQAWABMAFwAYABIAFAAXABQAFAAUABMAFAAWABMAEAAPAA8ACgAOABAADQARABEADgALAAwACwAGAAgADAAMAAkABwAIAA0ACgAFAAkADAAKAAgACQAKAAgABAAEAAoACwAHAAsACwAJAA4ADAAPABEADwAQAAwADwAKAAkACAAFAAcABQADAAEABAAIAAcABQAFAAQABAAHAAoABwAGAAMA/v8DAAUABQACAAIACAAHAAoACQAMAAsACAAMAA0ADQAKAAkACAAEAAQABQACAP//AgAIAAkABgAIAAgABwAHAAkACAAEAAQABgAGAAUACwALAAQACAALAAkACQAJAAwADgAPAA0ACwALAAsACgAJAA0ADQAHAAYABAAFAAkACQALAAoABwALAAgACgAMAAcABgAGAAcACQANAAcACgAPAAsACQAFAAoACwAFAAQAAQACAAMABAAGAAYABwADAAAA///7//7/AAD9//n//P8CAP7/+v/3//j/9//3//r/+f/6//r/AAAHAAMABAAEAAMABQABAAEACgAIAAIAAwADAAEAAwAFAAMA//8CAAYABAAFAAcAAgD//wIAAwAHAAgABwAEAAYADgAKAAkACQAHAAgACAAIAAQACAALAAgACgAJAAcABQAJAAwABwAIAAwACwAOAA4ADAAQAA4ADgAKAAsADQALAAkACQAKAAYABwALAAsACQAHAAYABgAHAAgABwABAAEABQAHAAsACgAIAAsADgAHAAcADQAKAAYABwALAAcACQALAAgADAAMAAwACwAJAAgABgAGAAgABwAHAAcABAAEAAUABwAGAAQAAwABAAMABAAEAAEAAQABAAAAAwABAP3//f8AAAMABgAKAAkACQAGAAgACwAIAAYACAAGAAMACAAJAAUABAAFAAUABwAKAAMAAgAKAAkABwANAA8ACgAHAAkACQAKAAsABQADAAYABAAIAAcABgAKAAUABwAHAAcABwADAAcABwAKAAcABAAKAAkABAACAAUABQADAAQAAwAHAAUAAgAFAAEAAgAHAAUABAACAAEABAAHAAQAAAACAAIA//8CAAUABQAHAAkABgACAAMABwAHAAoACQAKAA4ADwAMAAYACAAJAAkACQAIAAYAAwAIAAQABQAJAAIA//8AAAIA/v/8//3/+v/6//z//P/8//3//v/8/wAAAgD//wQAAwD+////BAACAP3//v8CAAAA+v///wAA//////z//P/6//n/+P/6//X/9//8//j/9f/z//z//f/3//r/+P/6//7/AQACAAEAAgABAAEAAgACAAIAAAAFAAQAAAD8//v/BAACAPz/+//9/wAA///+//3///8AAAAA//8AAAIAAAD9////AAD8/wAAAAD9/wEAAAAAAPv/+f/7//n//P/9//v////+//n/9//1//T/8f/x//T/8f/x//X/8//z//X/8//v/+//8v/y/+7/8f/5//b/7f/u//H/9P/2//X/9P/0//P/7f/q/+//8v/u//T/+P/w//D/9P/1//X/9f/z/+7/8P/w/+z/7P/r/+v/8f/x//D/7v/r/+v/6v/q/+f/5P/j/+T/5//k/+T/4v/i/+H/4v/i/+H/5P/j/97/2//h/+L/4f/g/93/4f/g/9z/3P/h/+D/2f/a/9//3P/d/9z/2v/c/93/3P/b/93/4P/d/9j/4P/j/9z/3f/j/+j/5f/h/+T/6P/o/+f/6P/i/9z/5f/s/+P/4v/n/+X/6v/s/+v/6//n/+f/5f/j/+T/5P/m/+T/6f/s/+b/5f/k/+P/5P/h/93/3v/h/+D/3v/k/+X/4f/f/+D/4//j/+H/3v/e/9z/3P/f/+H/4v/k/+b/5f/h/+H/3//a/9n/2P/Y/9j/1f/X/9z/3v/a/9v/3f/a/9j/2//b/9n/4P/h/+H/4//i/+H/4P/j/+P/4v/i/+X/4//k/+f/5P/k/+P/5P/l/+f/5//k/+T/6f/u/+r/7v/v/+3/7//u/+7/7P/s/+r/6v/s/+b/6P/t/+r/6P/t//P/8v/x//P/9P/z//L/8//z//L/8//0//L/9f/1//T/9//z/+7/7f/s/+r/6v/o/+X/6v/q/+j/6P/n/+L/3P/b/93/4P/h/+T/5//m/+b/4v/j/+X/4f/j/+P/4f/g/+T/4f/d/+b/6v/i/+D/5v/h/+H/5P/f/+L/4v/g/+T/6f/n/+H/4//g/+D/5P/j/9//3v/j/+H/3f/c/93/4v/i/9//3P/c/97/3//g/9//3v/d/+D/3//e/+D/3P/a/+H/4//i/+P/4v/j/+P/5f/m/97/3P/d/9z/3P/c/93/4P/e/9z/3f/e/9v/3f/f/9r/3f/i/9z/2P/d/9//2//a/9v/2//Y/9j/3P/c/93/3f/e/9z/3//h/9z/2//c/9//4v/h/+H/4v/k/+L/4P/g/+L/3//d/9//3v/d/9//4//i/+P/4//k/+v/6v/q/+v/6//t/+//7//u/+//6//p/+X/5P/t/+v/5//p/+b/6P/r/+n/5f/k/+j/6P/k/+L/5P/g/+L/5P/i/+b/4v/i/+L/4//n/+X/4//l/+f/5//s/+n/6P/n/+j/6//p/+//7v/r//H/8v/u//L/9P/w//f/+v/1//f//v/5//L/+v/9//f/9P/3//X/9v/7//f/9v/4//r/9v/2//v/9//4//X/7//y//f/9P/0//f/+f/+//v//v8DAP3/9//+////9//6//j/9//7//r/+//7//r/+f/4//X/+P/5//b/+v/7//z/AAD9//v//v8BAAAA/P8AAP7/8//5/////v8CAAIA/P/4//n/AgD7//T/+//4//X/+f/6//f/+P/6//b/9P/4//j/9v/4//b/9P/6//n/+f/4//P/9f/3//T/8f/v/+3/7f/w/+//8f/0//H/7v/x//D/8v/2//L/7v/w//f/9v/3//n/+f/5//r//P/8/////f8AAP7/+v////n/+P/9//3//P/+/wAA/v///wAAAgABAAIAAwACAP//AAABAP7/AAACAP7/AQAEAAEAAQADAAQABgAEAAIACQAJAAkAEQAMAAsACwALABMADwAMABUAGAATABQAGAAWABQAGQAYABUAGQAXABYAGwAXABEAFwAVABMAGAAaABwAGwAbAB4AHwAfACEAIwAkACoAJAAgACYAIwAeACQAKAAfACEAKQAiACMAKQAnACQAJQAoACcAJQAoACUAJQAkABwAIQAkAB8AHwAdABsAHwAbABQAEgATABIAEAARABQAFQAVABcAFAAaABUADQAXAA8ACgARAA0ACwASABYAEwAPAA8ADwAMABEAEgAMABAAGAAUABQAGwAYABYAGQAaABUAFwAdABwAJwAnAB4AJQAoACIAJwAtACsALQAqACoALQAtACwAKwAuACkAJQAoACYAHwAhACAAFwAcAB8AGgAfACMAHwAcACEAHAAQABUAGAAUAB8AIgAaACIAKAAjACMAJQAjAB0AHgArACIAHAAqACMAGwAlACMAGwAhACUAIAAhACYAIgAcAB8AHgAZABoAGwAcAB0AHQAdACMAJQAfACIAJQAiACcAKAAoACwAKwArADEAKwAoAC8ALAAkACUAKQAsACsAJwAnACkAJwAjACQAIQAfACIAIAAjAC8ALQAvAD8APAA/AE4ASwBRAGEAaAByAIcAkwCYAK8AwwDTAOwA+AAIARsBLgE7ATwBSQFPAVQBYgFmAWoBcQF2AXMBcAFyAWsBXwFYAVcBUgFTAU4BRQFEATQBJAEUAfcA2AC6AKIAkAB6AGwAYABPAD8AMgAfAPj/2//F/57/i/94/13/Uf9E/zL/Iv8S//3+2v7B/rD+ff52/n/+Mv5I/o3+KP5O/qT+Ff5K/oj+7P1B/nL+8v1M/n7+OP6B/qL+gf6u/sP+t/7I/uL+7P78/iX/Qv9X/5T/of+j/8b/tf+p/7f/x/+m/9P/CgDj/ysARQAzAG8AYAA7AGIAQQAhAFEAMQAxAFoARgBkAIMAbwB6AHIAWwBWAEAANgA9ADkASgBZAFMAagBmAGQAYwBOAFQAPwAyAEAAOgBIAFoAUwBrAHEAZwB1AG4AawBwAGYAggCSAIkAsQCrAKEAvwCoAJYAsACmAJMArwC0ALoAxQDKANMAxQC3ALYArACIAHwAbQBYAFQAUwBYAEgAQgAzACEAFADw/9r/xP+j/5z/nP9//3f/dP9N/zb/K/8Q/wf/+f7f/tn+2P7a/tr+0/7J/s3+wP6o/qf+mP6L/pz+of6h/rb+xv7J/tL+1v7N/tH+1P7a/vH+/f4b/zr/U/91/5D/sv/W/+3/AAAlAE8AcgChANMA/gA8AZMB2AEBAjACawK8AiEDWgN+A8UDBQQpBDkEVwRVBEgEjQS9BMoE9ARCBZUFxwXPBdcFEwYYBgkGCwb+BQkGIAZlBo4Glwa2BuMGEQf9BtkGzwbrBgIHBwdAB2sHlAexB6cHiwdNBykH7wanBkwG9gX9BesFzwWjBWYFRAUYBdUEWATrA4MDMgMFA6cCVwImAvgBjgH+AJQAGwCk/zn/xP5q/i/+Cv7k/av9QP3Q/Jz8MfyM+/r6g/oJ+oz5TPkK+cT4k/hh+CT4uPc+9+H2pvZQ9tn1r/Wb9Wr1N/Xc9I30VPQX9ObzwfOY84vzrPPI89nz3vP78xH05PPB857ze/No81LzWfOD87fz8vM49Gj0bvRx9IT0nPSd9Kr0z/Tk9An1NPVb9YL1k/Wf9az1vfXR9fj1MPZt9rT2E/eN9+P3EvhK+HT4dPhx+IT4oPi++Pz4U/mk+f35UPqd+tf6Avss+1b7hvue+7D70PsB/DX8Wvx+/KD8vfzG/M/85/z5/B/9YP2i/eX9Kf5u/p/+tv61/rn+1P7q/vb+Cf9C/3X/wP8TAC8AbACyANMA+wAIAfEAGAFlAYEBrQHxARwCWwKrAq8CpALaAvgCDgNaA4cDrQMgBHQEngT1BDIFSwWGBZwFkwXOBRAGMgZzBsUGEQd5B88H7wcQCEQIaAiSCMwI5ggKCVYJownUCe0J/wkaCkEKSwpDCkwKbwqhCtwKFws9C2oLsgvWC8ILtwuyC7ELzAvaC9oLEQxrDI4MnwzGDNEM3AzxDNYMyAzhDOgMAA0wDTMNOg10DYYNbg1ZDUcNQw1TDVINSA1mDYYNkA2eDYoNTw1ADToNDg3/DO0M4QwCDQAN2gy+DK0MhwxMDAMMmwtECwILyQqQCkIKBwr0CdYJaQn6CIwI/AeBBwcHjQYYBrYFUgXtBIEE9QOLAxkDawLAAUMBvQArAKP/Gf+s/lD+7P2J/Rj9dvzh+2/76fpi+t/5avkO+bP4Tfje93D3BPek9kr26/WM9Tf18vSZ9Df07/PF84vzKfPd8qjycPIo8t7xqvF78WrxTvE48S/xAfH08O/wvPCH8Ibwl/CC8GvwXfBU8FPwcfCN8I7wn/C28N/wE/EU8QHxF/E78WjxmPGo8cbxDfJZ8n/ysvIF80rzqPPx8/vzD/Rd9J70yvT59CX1kPUP9nL2vPbm9i73hvfI9/T3GPg0+GX4y/gO+Tb5gPnp+Uf6p/r5+gP7Iftj+3b7hvvQ+xL8MvyC/O/8FP04/Y79r/3b/Sz+bf6s/tj+Bv87/4z/0P/v/xgATwCLAKIAywD1ACEBgQHrAVwCfgK2AjwDiAOnA9IDIwRqBOcEWgV+BdgFOQZ8BrwGPweKB5EHCghoCJwI+QhpCcQJDApZCqkKFwtLCzELUgunC8YL9wtQDHwMqgzkDPkMIQ0+DRsNUA2VDUwNIg1YDW0NVg1NDRcNzAzaDNQMgAw7DN8LjAvRC/sLiwtkC24LIQvcCoAK5gmYCXUJCwm3CKYIaQhCCGsIEQh5B1sHSgf5Bp8GQgbWBakFrgWABUMFKgXjBKYEqQRWBMwDpQOpA3MDYgNiAzYDKgM1AxwD3QKqAnMCdAKdAjwCCgJPAkMCUwKMAnECZALLAtkCuAIbA0YDWAPUA0cETASVBAQFxQSwBLcEXgR4BMAE0QQoBa8FCAZ6BhgHHwffBuQG6wbiBr8GoAbIBkgHugcgCIgIkgjRCCIJxggxCOwH4wfCB8UHvAe9BykIWQhVCD8I4gdgBx4HxAbzBYkFUAX3BPEEuAQuBO4D3wNkA5wC8gEXAVwACgB5/6v+AP6U/TL9iPyq+876Ivp6+c34K/iK9xX3svZA9nn1mPTx82DzuvLH8ejwYPAA8KzvWu8j79Dube4v7sntJO1w7ATsuOtb6znrJusf6xTr7urO6qjqiepX6kHqOuoI6hbqS+pv6oTqrOrj6uzqCusW6wjrEOs262nriOvV6yPsfOzY7ADtL+1O7YTtu+3f7SjuYO6Z7v7uce+p7+fvPfBT8H/w4PAj8UfxnfEC8mXy4PIz80vzefO+883zzfPU8/rzTPSw9DH1pfUg9pr2Avdg9333kvfe9zr4Z/il+BH5Xvnd+Wr61PpI+837T/z2/N/9T/7E/tD/rQCIAbMCgwMXBAwFvgUXBqMGBAdiBx4IxQhICSYKGQvMC7MMlg0GDqgOYw+WD9kPTBB3EMAQURG/EQwSYhKHEqASwhKKEkcSPBIpEiQSVxJmEk8SfhKOEmASKBK3ETwR0hA0EG0P3g5xDgUOwQ17DTAN8gyoDDQMpwsSC2EKxwlTCeAIagj5B6oHWAfRBnIGHgaZBToF6gSRBFcEPwQ6BEgEPwQMBOEDmwMpA7QCPALLAYwBgQGHAZwBqQGlAZkBZgEOAb4AdQANAK3/Zf8e/+X+t/53/j7+C/65/Wv9DP2X/En8HPzl+8n73PvE+6/7rvty+yf78fqn+kv6Qfpg+k36XPqc+tn6QfvD+wb8P/yj/AD9U/2p/fr9Yv4a/+v/mwA1Ac0BYgINA+EDtgSlBcEG8gcvCVcKQAvkC5kMUQ3YDYAOSQ8JEPcQNxJOEygUKRUSFq8WLBeGF6gX6hdQGKAYIxm2GRoaWxp6Gj8a0RlwGdoYNxivF14XTRceF7YWSRbuFVIViBSjE3oSMxEBEPkOBA4XDf0LCwtyCosJaAhIBwcGogRzA2MCLAEdAEH/YP5r/UX86vrK+cz4bvcB9vj0KfR18+XyQvKV8fnwavDT7+3uye3L7BXsbuvL6mXqLuoa6hHq3+mN6R7pq+hP6Pjnf+cN5wznNeco5/Hmxua35pvmgeY75s7lh+WH5a3lrOWx5eXlSeaV5ofmTuYn5hXm+OX25fLl7+VO5vLmbuep58fnDeh76MTo4Oj26EDpsukt6rPqOuu96z3svuwN7R3tRO2k7RjuZ+7N7pjvaPAN8bjxQPJd8m7yn/Kx8tPy6vIL83bzG/Sp9Bb1wPVL9pX24vY/92b3a/fG9yD4dvgG+bb5c/oy+xP85Py1/Zv+ff+WAMYBrAJfAzAE6QRhBbsFDAZhBt8GmAddCBkJ3wnLCsMLhwzmDBgNaQ2eDbANtg3VDSgOoA46D7MP4w/KD6gPgA8JD2AO2A21DaENlA3UDRAOMg42DioO5w1CDYgM0AsfC2AKowlDCTAJFgncCMQItAhjCBYI1wdrB/8G3gbiBtMGywa8BqEGigZQBgAG2gXiBesFEgaEBhsHtwdYCP4IbAl7CXsJawkjCdcItwjBCPUIYQnLCTAKmAq/CrkKoApaCv0JqAlfCSEJ6AiyCIUIRQjcB3gHDgdyBtAFQwXOBG4EGwTXA44DSQP/ApQCCwJkAb0ALgC8/1z/Ff/g/rz+sf6m/pD+af42/gD+3P3R/dv9DP5N/pz+A/9m/7r/CwBIAHAAqQAEAX8BGQLSAqkDjwSLBXwGVAc8CB4J2QmACjML9AvbDOQN3g7eD/MQ+hHlEtITmRQhFdkVvhZ/FzYYABmuGUEayBoCGwUbFRsAG9Ya5RroGssa+BpLG00bIBvvGnsa2RkOGRAYKRdoFqsVBhWYFCIUlBMFE0YSPhEREO8O2w2vDG4LWAqCCacIyQfvBtYFwwS+A50CeQExAOL+0f33/Aj8BvtB+nX5gfik99X23/Xs9CH0XPPE8jnyp/E98dDwNPCS7xfvm+4V7p7tJu267G3sS+wz7O3rnutW6wHrmOoH6oTpL+nh6I/oduh/6FXoIujy56HnKue15l7mCuax5XPlQuUM5fbk8+Ti5MbknuRs5ELkIuQB5Ovj9+Mt5GXkqOQJ5V3lkeXK5QvmKOZM5pLm5OY555Tn/+dx6PPob+nP6RrqVeqx6iTrjevt613s6ux77QLuXO6f7u3uOO97777vEfBu8PvwsvE48pHy9vKC8//zWvSm9OL0WvUj9gT31Pe4+MD54vos/FT9Jf7d/rn/cwAFAasBOgLOAqUDoQSABWYGYwdOCD8JJwrGCj0L4wujDEkN5A2GDi8P5w+UEBoRhxHKEesR/REAEgcSDBIlElUShBLBEvsSLBNSE00TMRMXE+MSkxJPEhISphErEdIQlBBVEP8Pmw9KDxUP1w6HDkEO9Q2iDXUNRw3vDH8M+QtpC+kKfQoHCrQJjAluCXQJgAl3CVcJQAkICYII8Ad/BykHxAZgBgkGvwWMBWIFJwXKBFsE2wNqAwEDgQL3AXgBFAGdAAYAaf/C/gz+Vf2m/Oz7RPvH+l76/fmf+UH57vit+FH4y/dN99r2ZPb89ab1b/Vb9Vv1avV79Yv1qfXX9Qj2OfZ79tn2U/ft95b4NfnU+Xz6J/vJ+2b8AP2z/ZP+j/+jAMIB/AJBBHAFhAaUB6IIngmqCrYLnQyRDcMOAhBNEawS2xMNFW0WrBfAGNsZ2Bq6G9wc/B3HHnkfMCDFID4hriHoIR0iiCLSIgQjTiNtI2IjcyNrIxQjqSIvIooh2SA0IGUfgx7KHfccCBwUGwAazhiWF1cWAxXDE5oSaBEWELcOYA0MDJsKAAlgB7IFIAS0AiQBhv8H/qv8Zvs8+gn5xveU9l31EvTj8sjxo/Ck77/u3e0p7Yns2+s265vqA+qE6Svp0Oht6CTo3ueq56Lng+c25/DmxeaV5nLmV+Yq5ibmSuZY5mDmbuZf5jbmEuba5ZnleuVw5WrlWeVL5VTlX+Vq5XLlhOWg5cDl4+UM5kPme+a/5hvng+fc5yLoc+jB6PzoPOmL6dzpM+qv6jfrtOs67LHsE+197e/tXe627gHvO++S7wPwVPCc8OPwKfF88cnx//Et8mjyq/II82bzpfPk8yf0aPSU9I/0gPSB9JP0svTH9Nb08vQu9Yv1+/V59hT3svcs+Lf4Wfn8+an6J/uF++/7VPyp/Ov8J/1J/YX9CP6h/kr/7P+bAEsBwwERAnYC4gIJA0YDtAMDBHAEFQWxBTMGoQbhBgwHPgc4BxEHJwdRB4EH5Qd7CA4JiAn2CTQKUgpiCnIKcQpYCkEKLQpvCrUKygoACzcLXQt+C6QLtgvBC8gL2QsaDF4MlgzVDB4NLw0DDfIMBA38DN8MBg1aDbUNLQ6xDv0OCA8SD/sOvw5oDvkNqA2DDV4NMQ05DUoNNg0ADaIMFAxsC+QKSQqOCegIYwjwB3EH0gYGBjYFdQTCA/4CPAKWAfIAdgAVAJj/Bf+X/jn+tP0R/XD86/t1+yT77vrD+sT63/rw+gr7GfsD+wr7NPtb+5D7+ft6/Of8d/0k/rv+Of+v/yYAogA9Ac8BbgJXA10EUQVIBmQHXAgeCeEJkwoqC9oLqAxbDRAO/A4SECARDhLcEq4TwxT0FdMWfRdrGHsZcxpLG+cbVxzaHJAdBh4jHjgeax7pHmgfjB+AH7kfLiBKIA4gtx9bH/Mekx4rHmcdqxxAHMkbGRtBGlUZdBisF5wWNxX2E+sSwhFyEC8P2A2QDH0LPQqVCNYGXAUMBKICAAFL/+397fzD+0762viY94z2hPVg9BDz3/Ee8WnwZ+9f7qvtIO2O7OzrIeuE6kTqCuqo6Sfpt+iN6Kvokej+54DnbOdj5yfn4Oar5pTmoear5o7mVuZG5mXmYeYO5qTlgeWY5YflNuXu5O/kHeU65Snl+eTd5PjkOOVX5TTlM+WJ5fvlSeZT5lrmquYW51jnguet5+7nd+gf6Xzpr+kY6rrqYuvJ6+PrIey37ETtke3D7fztUO7I7i/vOO8r73Xv0+8G8BzwH/BS8MXwGfEi8SPxN/FD8XPxl/Fl8TDxPPFg8X3xj/Fz8Xfx0fE88qTyEvOg8y30x/SM9VL2Fve/92j4Fvmk+RL6d/rk+kj7x/tq/C79Ev75/uf/8gD2AbwChANUBAsFngUlBtIGkgdpCCsJ2gmZCkILtgvzCwsMAwwcDFQMlQziDC0Nmg0UDoAOrg6lDpoOfw5qDjoO3Q1wDTwNPg0sDQwN0gytDKAMhgxFDOoLswuMC38LdgtoC2ELaAtqCzIL6wqyCpkKggpqCmcKgQrLCh8LdAuaC5wLlQuCC3ILKwu+Cl0KQwpOCkMKQAo0CiMK/gm3CU8J1ghkCOwHhAcUB6gGQQbOBT4FgwTgA0cDnQLaAR4BlQASAJ3/Nf/d/on+O/7n/Xr9+vxl/PH7n/tn+zH7HPtE+2T7d/uN+7T72fsN/FP8j/zo/Fj98P2g/jz/3P+kAIABLgK+AkwD6wOrBIcFgwaUB7gI2gkBCyAMAw29DYUOWQ8FEKUQVxEqEgYT6BPvFOMVxxbfF/wYxhlzGi0b6hvmHLIdHh6vHk4foR++H+MfwB+PH+AfFCDzH+sf9h/sH+4fsR/0Hlke6x01HXcc2Rv1GgQacxnKGKwXgRZsFRUUsxJVEcYPbA5MDQwMwwqMCT4I6QaiBSEEawK+AED/6P1x/Nn6eflF+Av3+vXs9Lzzo/Ko8Zbwle+h7pTtzuxF7Izr5eqZ6ibqiukw6dDoTOgF6Nvnn+eJ53/nY+dh52rnO+cc5yjnB+fY5tnm8OYF5zXnYed+57rn5efq593ntOeX57Tnz+fI5+TnIOhS6Hnohuh86JTovujT6AXpO+lk6cXpUurI6h3reevb60Lsley37NLsCe1Q7bbtN+6V7u/ue+8H8GXwqvDs8C/xfvG98evxKfJl8qHy8vIk8xrzF/Mz80DzOPMo8xrzI/NI82HzW/Mv8/DyvPKB8i3yvfFy8UzxHvH18NXww/C58LrwpvB18HHwpvAM8Z7xIfJ78vXyx/Oo9Fv14vVH9tn2jfcW+IT4BPmk+Ur6Pvta/ED9Of5T/1cAHAHRAX8CHgPMA1EE5wSmBWMGLwchCAIJhwkDCnYKwQoACycLMAtVC8YLLAyXDAANKA1vDcANyQ2+DbwNlw12DYwNbg0mDSwNPw02DTcNMQ0dDR8NHw0TDRsNEQ30DCINZQ1zDXgNYQ1DDT8NTg0/DSINKQ0/DZsNCQ4yDlYOdw5nDkoORQ4HDqoNdA0gDeMM3gzHDIMMOgzlC0MLoQoGCk4JnAjnBxYHcAYJBmIFjgS8A9AC9wFKAX8Ah/+3/gv+Z/3R/ET8vvs9+8D6U/oM+rr5TPn0+L/4q/jA+Or4//gh+WH5tfkT+l36qvof+777S/zw/NT9tv6O/3QAWQEfAv0C3AOfBHUFWQZXB38IvgnNCtQLBA0SDgYP5A+VED0RGRIIE+ETwxSQFX0WuhfaGJEZJxr6GhIcQR0NHn8eIx8nIDQh+iF2Iqgi9CKpIygkDyTRI+cjTSSPJGQkDyT6Iw4kzSMVIzgibiHMIDQgYB85HiAdbxy+G6EaIBmRFy4W3RRuE7YR8g97DjcNzws9CqAI9AZ1Be8DBQL4/0z+/PyI+/z5cfgX9wL25PSF8wTyw/Cy78ruxe2m7M7rQ+vP6ivqoulA6d/ocejs52rn/ObD5rTmmeZ45m7mlubV5tvmp+Z/5oPmleaZ5pPmiuae5uHmGOcp5yfnVOee57TnnOd/57HnHOhi6F/odejk6FzpsOnH6b/pzekz6rTq7+ou64br/euW7Cjtg+3D7SDud+7R7h/va++97yjwtPAt8Ynx1/FL8sLy8/Lx8jfzo/Pl8zP0VfRi9Kb09fT99P709fSm9IP0dPQl9Mjzi/NF8wPz/fLH8lLy6fFz8QLxpfAd8HPvC+/H7mjuHO7p7bPtle177U7tTO2N7fTthO4T73DvE/Ak8Q7ywPJW89Pzf/Rt9RL2i/ZQ9yv4W/nT+uj7zfw8/r3/twCiAWYCCgMSBBMFiwUbBg4H8gf6CAMKYQqmCmYLzgu7C8QL2gv8C4MMCA0gDV8NwQ3wDRwOCA6ODUwNYQ06DecMsAx0DGAMfwxwDEwMLQz6C/8LEgzPC7AL4Av6CyIMVgxWDFoMjAxpDBMMJAwmDA0MWgygDLcMMg3GDeEN9g0gDu4Nog1wDQkNrAyrDHoMCgz8CwMMsAtPC60KtgkTCZAIsQfFBgcGWgXXBGIElgOXArQB6wAkADv/Nv5Y/bT8KPy1+0P7w/qM+ln66Pl6+SH51vi0+LD4jfiw+ET5zfkt+pj66fop+8H7WPy6/FH9Iv4Q/zwAbwFjAnsDjQQnBdEF2AaxB1wISQlMCmcLwAzrDeEO0w+fEF8ROxLPEiwT7BO8FEUV3hWTFkYXFhjKGFkZGxrNGnQbUhwlHd4d0B7mH6ogSyEMIrUiCiMjIxYjJCNxI5cjvSPpI+sjCCQ+JBUkaCOTIsch2yDOH70eqh2kHKYbrBqfGVAYzxZaFeQTChL5DyoOcAy5CvMINAebBf8DaQLBABj/S/2U+wn6SPiK9if17fOX8kDx/+8Q7zXuOe0e7A7rQurJ6Y/pCOl06C3oIugU6MnnVOfl5sjm0+bN5tvm/OYz56bnDej659XnE+he6G7oIujb5xXonejr6PPo9OgY6WzpxOm+6VHpSOmf6eDp3Om06eLpXurH6tzq4uoD60zrv+sG7O7r2es97NvsXO2g7c7tOO7D7izvZu9/75Dv0+9X8Knw0fAX8YLx1fHv8evxBvJ18qfyjfKV8rTyx/II8zzz8fKl8ozydfIx8sPxQfHV8KDwLvDE757vT+/Y7lLut+0X7a7sN+x969rqVerq6cnpiun36NTo+Ojd6NjoHOmP6WPqmOtX7PfsO+6b7+jwA/KY8jXzhPTH9Wv2Uvds+H35NfsT/WP+1v+JAR4DsQTgBXAGEgdECCwJ0wmtCkcLQAzEDdQOPA97D7oP8Q8mEOQPIA/wDisPNQ9FDzsPGw8pD10PGg+QDjIOyg1xDRUNZAzGC4gLRAsWC+oKigouCjEKQQonCi0K9wnYCUYKjwqKCpkKgQpXCngKfQo5CjEKdwreCnoL+gsIDDMMagxEDBIMtwsAC2EKRwo6CvsJpgk7CQ0J6QhOCEwHOQY+BXkEvwOyAosB4wCfADAAWv80/kn95/xr/IH7gvq0+T35GvkO+cL4ivig+MP4wPip+Jf4j/jF+Bv5ivkg+vP67/vj/Kv9Kf6x/oj/lABeAQEC7gI4BMQFLQdTCDUJHAohC9oLWgzxDNINww6+D8AQtBHXEvsT1xRSFZ8V5hVLFuEWaBe+Fy4Y6RiiGWsaIxttG3MbqBsKHG4cUR2FHlcfFiAVIRciaCOnJJYkSCTMJAMl8CT7JFYklyNsJHQlKiX1JPAknCSVJOgjuSHjH/oepR39G5Qa7BiTF0YXShYyFAgS9w8EDgAMgQnCBrwEUQPFAQkADv5E/Az7qvmZ93P1xPPG8ify1vAF7/3ts+3/7Afs0epZ6QHpaOka6Tno1+fz54fo9uhP6LrnBehF6B3oAeiQ5z/n+OfM6PPoMum86SrqnOqH6p/pLel96aLpiul66aLpIur26mDrI+vZ6t/qHOsN64zqJepY6rXqF+tR61rryOtq7JHsQOwT7CXsbuzc7OjsuewN7aHt8u1b7q7ur+717k/vVu8u7yXvHe8a71fvc++O79bv4e+Y73/vT+/p7uLu0+6J7mDuSO7x7ZLtUu3B7PvrL+ti6uDpi+n16Eno5+eE5y/nzuYm5kblceQd5OjjauOz4nfiwOIC41DjvONc5E/l2Oai6Cjqf+sj7SrvV/FI8530GfYc+O/5QfuZ/Cz+0/8wAtsE0AarCP0KhA23DwwRnxFDEkMTGRSUFDsVGhbqFisYWhnVGa4ZVhkMGUoYRxdUFo4V3xRJFO4ToRMfE3AS2hFHEUQQ4w7NDegM3wvkCigKgQnQCGIIKAi3BwwHZQb8BdMFqgVkBUMFcgXSBWMGygaCBg4GDQZBBi4G0QW6BVwGiweLCAIJKAk/CUYJAQlPCF0HrQZZBl4GWwYNBucFDAbsBUcFTgQEA9cB3QDl/9n+/f1g/fH84PyN/MH78vpm+tD5G/mI+Az41vcB+E74t/hp+SX6uPo5+4v7z/tX/AH9u/2h/sr/RAHrAloEfgWhBogHSggeCe8J5QoPDEYNhw7/DzYRGBLkElgTZxOTE9oT0hMXFL8UYxUMFt0WkhfvFyIY9ReiF18X+RZ5FlcWtxYpF8AXfRgKGWUZ1Bn/GegZzxnJGUgaWBufHMId3R7rH9Ug1SGBIlgiECIwIloikiKsImUiVyL4Ilcj+iKMIukhEyFZICgfTR2WGzgaxxg/F50V2xNhEggRLw8CDdwKkwhYBjoE9wEIAIf++fxE+2v5jvf49Zj03/L88J7vze517inuTu1K7MrriesK6zvqROmm6NDoM+lU6ZLp0eku6sjq+uqT6nbq1Or16vvqBusK63XrRuyR7J7s9+w37W7tfe0C7TvsCuw47FrsZuxc7LDsX+3e7brtVu3r7L7s3+yx7C3s/OtT7NzsU+1Z7UPtfu3m7RbuGO4C7s7tG+6s7r3uhO6C7qTuuu6q7nLuUO5v7oDuYO5A7vrtyu3s7dztTu3I7J/sfewK7Fnrl+oJ6uPppekm6Xrov+dF5wbnkeZp5T3kneMw46Hi3OH14Dbgwt9b39zeTN793fjdH94x3vjd5d1d3jrfyt9v4OPh/eOI5lbpyuu+7STwEfMa9rP4cPou/Lj+YQF3A4AFkAeyCZUM0A+LEgsVexeaGaEbAR1RHZodOh6WHvgekx/QHy8gKSHFIWMhSCC/HpYd5RyAG10ZaBfiFfUUUxTAEpQQ9A62DZQMegufCYgHhQa8BYQEOQPVAZYAUQA3AED/Wf70/eT9OP5U/rz90f3Y/rj/SABZAOX/wf89AFIALABRAKkAowH/ArEDsAOxA6IDcgMFAw0CJwEAARgB/QDZAIgAMgASAKT/k/5C/Qz8O/u7+uj5//ii+Jv4nvjG+Lr4Xfgu+Dz4XvhZ+Dv4Xfgn+TD6Afsv/KH93P7n/+gAwgGlApgDsAQiBowHBwnACnYMvw2/DpsPFBBdEN8QrxGCEiwT0xO5FI8V8BX3FcYVchX/FHQU8hOlE2YTJBMdEygTJRNsE9MTkRO6EjwSJBIMEtwRfxFuESUSRBODFNcVjBa8FpkX2BiNGeQZWRp6Gzsdvh61H7ggnSEBIqciSyM0IzUjuSMxJFgkGyS2I4Aj5SKbIW4geh8lHtkc5xtvGo8Y5BY8FVwTLBHMDsoM/wqWCCUGRARFAhoAUf6w/PH6gvl4+Fz36vUR9Gjyc/F/8Fbvhe4V7sLt0u0j7u/ta+387M/s1OyX7FPsjOwR7XHt0O1E7nDuke7Q7uTu5u7v7invk+/J76/vpO/y70Xwg/C48K3wpfDi8BXx//C/8KLwufDz8BLxBfEg8Uzxc/F48VzxRvFi8czxCfL28dfx6PET8hDyz/FQ8eHw6PA58UfxA/G58KHwlPBG8Kfvze4V7q/tSe2u7PbrNeup6jbqc+l76MjnY+fp5jrmV+V/5A7kyOMg4yTiKOGL4GPgFuBF31re3N2t3ZTdON113Nfbt9us20TbxtpW2jjanNoA2yvbctss3BLdyd1e3uTeGeA/4pjkG+cC6ljt0fBd9O33ufoh/dr/ogIuBYAHvAkpDO0OwhGOFIUXdxoSHZYf6yGCI54kXiWpJXQl5SSRJJ0kqCRXJBck+SM+I/YhYiBdHvAbkRlhFwwVrBJjEDcOFwzFCa8H6AXfA7cBFwAU/wb+ufxb+0/6j/mi+K731/Yb9s31KvaR9p/2Hvcl+Cb5EvrF+pb7iPxF/Z79wP0h/ob+Yf9uACgB9AH2AhUEsgSpBCUEkQMxA7gCTQL5AbsB1gE3AnoCOgK4AT0BlgCp/3z+ov1m/VP9OP0o/UX9sP1g/uL+9/4e/7D/iABxAQQCXgI/A6sEAQYTBxMIBgkbClELXgxBDR8OHA82EGERMxK8EnoT5BPQE3IT8xKaEl4SHxKlEWwRahFMES4RnRCoD6cOtQ2yDKoL3AorCtEJrwmVCasJugnHCQAKXgqmCswKbAuDDNwNUw+jECwSAxTyFZQXtBirGeoanRw8Ho4f3SBQIiAkJSauJ3Mo+ShxKZsplClFKckoMiiwJ4MnDyc7Jhwl6COhIt0guR5eHCUaABiZFVcTOhH2Dt8MwQpNCN4FuQOUAU//LP1Y++D5dfjo9oD1cvRZ8zvyW/FS8G/vBu+v7lvuH+4a7l/uzu4S7+7u3O747hTvR+9q74Tv0+9u8B7xlfHB8ffxYfKX8oXyV/JB8ojyEPOI873zJvTK9Df1c/Vy9V71Y/WD9Z71o/XN9Qr2SPZ79n72dfZv9lb2I/bh9ZL1N/X59Mf0lPRJ9MrzVvP98oPyxvHf8O7vE+967vXtMO1J7GLrtuow6mvpX+g/51nmouX65EHkd+PS4jzivOEp4Vzgzt943wTfmd5Z3kreSd5I3hbepN1j3UPdC9283FbcDtwM3DfcW9xh3CHcFNyN3PvcPt1n3eLdqd5Y3/jfUeAF4TLireOy5eLn8OoU72zzI/dV+h/+MwK5BTkIFApxDGkPRRJmFCEWWBixG8YfGyORJeondirXLAkusi1HLNUqrimCKHEnHSbgJGkkJSQzIwQhIB5WG6kYnBXIEeoNoArWBzoFFwKo/rv7q/m/94n1Y/PB8QjxNfAK7+ztA+197CXsB+y166vrJ+y+7Mjt8u4a8ITxU/Na9Wv3Q/lF+v/62vuX/Dr91f2p/vb/ywF2A7cEjwWuBX8FIgWIBNIDMQPpAvUCWwPOAygEWQQoBL8DLwNOAkgBowBPACMAFwA/AOgA4gG9AmMDIgQGBQcG6gZSB3cH6wcHCVkKSwv7C/oMPw42D68PxQ/dDzgQoxDwEC0RbBGdEZAR6BDGD5sOjw1uDBkL3AkdCd0Icwi3B94G/AUQBQ8EPgNmApwBVQF3AboB3gFEAuICrAOoBIYFxgYvCKkJVgscDRQPMRHwE6EWFxm9GyEeciBjIugjViXqJp4oESrBK3ct2i4cMMIw2zCVMPUvAi/XLY0sGSvaKX4osibIJLwiiyAcHnUbwBhEFgIUfhGzDtcLMQnvBr4EXgLx/8D95Ps1+m34c/ac9HXzvfL68TnxsfB78FLw9u9b7+Lure7C7gnvOe+e73DwevFA8ozys/IC84Tz//NN9NP0hfUz9uT2YfeY96b3Avht+In4pPjP+Bj5Vvlo+Xv5xfk++qL6APtM+1b7avt++2D7BvuT+mD6Rvob+uT5s/mX+V355/gt+HL3zPYB9i31W/SM893y//HT8K/vlu527VDsNOtH6pHp5+gN6P/m4+X95B7kHuMn4kLhnOA+4N/fSt/D3mveP95L3jje69253c/d+93f3aHdmN1y3WfdnN273frdS96H3qPe0N7x3hHfTN8W3+He8N4B3xTfBN8E327fVOAz4eThyOKl44jkrOUZ5+LoF+sS7q7xyvVc+uP+MwM1B8UKFg4yEbkT4BUiGKEaWx3zH5AirCX/KBMs0C7lMCUysTIrMpowey7SK+AoeSZ8JJEiICGaH34d+hotGJoUFxCRC0MHPQNX/zj7dPeo9DLyRe9s7DPqnOi65+Hmo+UA5UTlxuX/5drlAOYS547opumu6gns3+018IPywvRi9y36w/wT/wwBWgI2AwEEfQQQBQ8GKQdXCK4J4wqcC9MLVAtaCmQJDAiiBuUFogWnBc8F4gUHBmMGTQZ6BY4EvQMTA6ECPwIXAn4CbwOEBLYFBQcZCBMJ2gl6CiQLnAvNC/kLlAxtDTYOnA6QDpgOyQ7ODmQOgQ2bDCwM5AtXC34KVQkICOUGaAVxA5kB9v+7/tv9I/2d/Fn8Rfwu/Bj8sPs9+2D7qvv1+5j8t/1S/2sBxAPWBdYHEgpoDMIOwhBsEo4UXxddGoYd9yB1JDAo2yuYLngwoTFPMuMyIzMZM3IzSDQDNZg10jVjNY40SDONMWQvvyzuKUkneiQ9IRUeBhvfF/UUZRIpEAQOqAseCZ4GEgQ7AWv+ufse+Rn3t/We9HjzQ/I78Wjwye8178juju6H7gjvn+/y7xLwG/B78CLxpfHt8X7yhfOL9GT1E/ax9nn3cfhr+Tr69/rT+6P8Gv08/VT9mP3A/dj9Cv5B/oz+xv7u/uL+rf5+/nP+ov6d/lD+6/2q/VT9mPyo+336Rvlc+LL36fbx9QP1K/Ru84PyWfFF8D3vD+7m7NbroupW6RLoz+au5dnkKOR149jiX+Ic4szhKeF/4DjgHeDt39Tf1t8B4DDgOuBQ4F7gXOCV4AvhaOHB4ULizuJH427jc+Or493j+OMO5Azk/OPs4/vj6+N+4wrjHuN44z7jzuKv4sDi8uI043Ljk+P348jkkOX45UvmSudS6Szsae9282f4xP2ZAwMJZA0eEVcUGReRGZYbSR2OHzUi7iQDKBYrAi7nMFgzIzUgNrE1IjSsMQ0uGipVJpEiHx9vHOgZkxdJFS4SkQ5uCt8F3gDw+2n3//Ik757rkejw5ZTjjuH03/HeR95h3rfeIN8t4E7hfuLp43/lLuco6TnrJO2S7yjymPQk98b5jvyV/3YC0gTTBksIVQkACioKKgpkCs4KJwuGCwAMYAwjDCAL3AmTCBoHvQWbBL8DgwPTAyIEDgTqA7EDYgMLA4oCQwJOApkCFQPcA+wE/QUrB2kIpQmjCloLHwyqDOIMEA0sDRINCg0dDdMMSgyAC3UKUQndB18GCwXlA+0CCgIJAbz/Vv6l/MH6Gvl79+713/SK9MT0VPUd9uX2nPeh+AX6Sftk/MH9o//vAXIEDgfHCb4M/g8wE0AWVRkmHN0ehSHnI08mzigrK0ItOS/2MGsywzOXNPc0HTXiNHI0CTSEM3QyKTHjL1MucywiKmQnbCSiITEfvRwbGokXYxVNE+wQYA6TC7YIHQbVA8YBDwCR/vD8gftS+kv5OPjl9qL1hfTQ82Hz3fJQ8vLxCfJA8l3yjvLU8ijzgPPS8yT0gPQH9bv1kfaJ95L4tPnK+qf7Vfzv/IX9Mv7+/sv/nwByAUgCJAOgA7gDoQOOA5MDNgOiAiACtgFgAdAAAADx/gf+W/2O/H37P/oV+eb3uPZs9fHzhPIs8fvv0+6k7VrsEevv6cHou+fu5jzmsOUv5b7kY+Tv43fjI+P74gTjK+Of41Dk5ORD5X/lnuXF5RrmQOY35jHmZebo5k/ngueC52/nk+fZ5+rngOf55nnm/OWh5RDlduQk5CfkWeRX5Bnk0+Ny47Di+OGb4W3hX+Fg4ZbhCOKL4vziD+Mb473j8uQ85g7n2Of26FHqpOt97JHt6u/u8z/5If8lBfgKaBBYFRIZCxsWHCUdjh5XIAQidiM0JWwn+Cl0LDsuWS9KMOkwHzBiLT0pPSQ0H1oarxXnEUsPqw1uDO8KcwjGBHwA6fsl92DyH+6/6m7ozuZm5RzkpOJh4czgyuAZ4Znhs+J+5H3mW+jw6YPrb+3C7zrydPRt9lf4OfoE/KP9N/8wAXoD8gVZCDIKfgsSDOoLSQtfCmoJvgisCPEI+wiQCN0H6gbDBYMEHQMnAh8CywLDA4AE3ATiBNcElwTcAxYDnwLEAmsDcgSsBdUGHQiOCegK6gt4DIUMQwzOCxALOQp2CdQIiQhtCEkIDwiBB2QGqgSaAmsAY/7M/In7iPrw+cH5nfke+TT4Avfz9V/1PvVz9f71KPfS+Kb6f/xh/lwAngIpBZoHFQrJDGcP3hE4FIEW2RhMG/QduyBtI9glACgSKs0rRi2aLqEvZjD/ME4xKjGUMHEvLS4GLc8r8SqiKpgqWCq1KbooQyfrJNMhuB62G/wY5RYZFR0TQBG2DyIObQx6CqIIIQevBf8DAgIWACH+Hfxw+hn5B/hZ9wP36Pak9gb2W/Wn9O7zdPNz89XzZ/Qo9fH1nPb79k/3zPcy+Lv4e/mR+gn8i/3I/t7/AwHxAewC5gOeBDgFkAXIBdcFfAXwBJYETQT2A9EDuwMyA0sCOgHc/0b+ePye+v34ovdZ9vb0t/Nx8gzxqO9F7vXs1esL617quOkZ6Y/oIuid5yvn6ubr5h/nZ+fV5zzojuj56Hjp6elw6ivr6+u47IztDe5V7oLude4/7gHuve1z7UXtBe2P7PzrZevm6lbqmunL6EXoC+iU58XmxuXt5Efkm+MC44XiL+JF4rji0uJy4hDi2eHW4djhxuHy4aLiceNy5IblGua75tTnIOla6mHrROxP7XXufO/t8ObyvvVT+hkAHwYYDMcRtxaNGuYc4B2XHlMfMSBmIb0iYSRcJpMowSq9LDUuNS+CL2MuIiyPKIQj1R1SGFMTTQ97DCwKjQiUBwcGWgOY/yf7nPYz8uXtBurK5lHk2eLJ4ejgS+AK4Ifgh+Gw4gLkeuXD5j3oBeqe60ztf+9C8iT16fdC+i385P1w/90AKALUAwEGPwgbCmgLNQyIDIcMEAxgC+sKrwpeCt0JKAkRCMsGZAUQBDwD8QILA4cDWgQwBcsF4QWiBSEFYwTPA3MDVgNsA94DnQSCBaoG6gc2CV8KGAtbCygLTwraCC8HkQU6BFwD5wKnAmcC6QEbAQEAhf67/Nr6D/m29+j2Xfbz9ZX1SfVO9Xj1ofXo9X32lPcT+br6RPz2/ej/7AFfBDEH+gnPDNUPuBJCFZQXnxmMG2gdPh9CIUMjDiWFJsYnDClLKkorAyyuLPYs3yyaLKMrAiozKGwm0ySAI3gi+yEaIg0ifSFtIOIeNh1OGzgZQheaFWMUnBPFEmgRFBDoDrQNigwdC6IJgAhjB+UFHgQnAiwAoP5f/Rz8B/s7+on5tfiJ9x72svSE8/7y7vIk88/zvfSZ9Tf2mfbC9gb3fvc7+FL5pPpq/Ez+8f8/AZICHwSOBdEGygefCE4Jngl8CRAJfQjSB3kHLAeqBgIGIAXsA0QCMwDh/Zz7cfmC98D18vM/8rbwOe+b7f7rp+q+6RXpkOgo6LnncedM5zbnJOdO59rnwOjp6dzqpOts7Crtu+0d7onuO+838CLx7fGQ8u7y+PLZ8qXyTPLv8ZvxVvHS8Ozvye6l7YTsPusS6iXpdujg51nnkeZz5V3kTOOC4urhTeH+4CPhluEP4jriJeJh4tniZ+MN5H3k6eSR5Ujmt+bV5uDmQOcG6MnohumN6rPryuz17bnuJu/a78Hw2fF68/71kvlq/vwDkgn+DpQTUBc6Gu4b2hyZHW0elx9XIe4iWyRwJgkpuiubLZcu0i7hLaMrzieOIpMcBhc3EkAOfgs3CU8HAAY6BBwBJf17+Efzee7w6bvlg+IR4Hfevt1P3TXdwd2z3sffF+GV4knkKub059bpHuzG7rTx8PRA+Ev7CP6IAMkCygSmBpIIkwqsDJ4O8A+vEMwQXhDCDwYPFg4lDaEMDAwwCxYKbAigBt0E/wJyAa0AXQBuAOMAPAGGAcoBAQL0AYYBJgHkAOEAGQFfAf4BMAPCBHYGOwifCVYKnApHCmQJDQg3BnIEPAOHAvYBnAFzAewAFwAA/3b9lPun+fj3hvam9SX1wPSr9Or0b/Xm9WX2D/cN+In5H/vM/ML++ACMA2kGPQkwDEUPVhJSFQsYVRpdHHAeYyAjItUjfiUdJ5coxyl1KrkqtCp3Ki4qyylMKa4o0Ce+JnElpyOWIYgffx2nGwAasxjOF+wW8xU5FYwUkhOpEmARsw+eDqoNbAwyC/8JCQmBCCUIzQedBzUHrQZLBjUFdAOwAQMAn/6S/a/80vtC+/n6rvo8+nj55Pi7+Mr4GvlE+V75CPrp+nH71/sd/GP8T/1w/kT/TQB2AcgCegTbBeYGDgj1CIYJ6gnPCTgJtgg0CGYHyQZMBrwFTgWsBI0D4QH1/wL+GPwk+in4fvbk9JHzcfIG8anvfu5j7WrsqesU69XqyerL6hnreevH60vsz+wq7bTtO+7M7pzvX/AC8b3xnvJp8yf0yPQL9Qf14vSQ9CL0zvOb83jzRfPr8l/ynPGe8H7vMO7F7Irrguqo6cnoxOfC5gHmVOWi5PbjVuPj4nbiL+IT4u3h0OHm4SPiZOK34hDjaeO04/PjSOSi5O7kM+V85dDlLOZk5nrmnebR5hPnxOfB6MfpV+vy7HvujfDy8sv14fkZ/6sEhgrhD1wUCBhZGvIbTR11Hh4gdCKrJKImpiiWKowsBy6xLrku1C3tK/wokySPHjsYdhIxDQAJxAUXAyABsf/O/c36V/YF8STshect45rfz9xE2xbbX9vL25ncut2l30bi9+St51zq+Ox+7wDyTvSO9mj5tPwPACQD9wVWCHIKYQyvDQ4PWhBeEWYSBhOyEpcROhBqDoMMrgrUCGAHXgaOBYEEPAPbAVsAEf8S/lz9Qf21/UX+6P6O//n/bAAGAWsBygFiAg4D4QP2BCMGKwdmCLoJxQpwC5YLQgtfCh8JbAeaBe8DOALlAN3/7P4I/iz9JvzA+jn5Wfd19RL0DvNe8g/yL/K98sDz9fQK9kr37fjD+tv8Nf+VARME3AbKCaQMww8cE0MWGBmxG9Ydhh8wIWYiMCNHJJslzCbDJy8oACjaJ3Enaib9JCgjPyGSH/AdJByVGrAZKRmxGAcY/hbNFcIUwxNqEgkR5Q84DzEPJg/FDncOhg6aDp0OaA59DbAMsgylDEAMxAvcCusJKQm/B8wFHQSZAlUBpgCo/yz+B/1C/IL7fvqN+eX4cfg9+Ab4xPey9xj4zfi6+dX6/fuH/Tj/qQD0AU0DmATcBQgHzAe7CPAJygoiC3oLpQt1C1ALxQrYCREJeAimB1UGmQSAAmgAZf4e/IP5Gvch9W/z2/E18LHuo+0K7ZDsEex56wfrCOsl6x3r/+o46/vrL+137l3vNvCC8fnyIfT99JH1JfYE98r3D/gJ+Av4Cfj094f31PYu9rf1aPX49Dz0KvMV8i3xDPBw7rzsRev66c/ocufa5Yrkm+Pn4l7i9+Gz4c/hJeI+4jniM+Ix4jriVeJn4n3i5eJm4wDkleQB5aPlOuZ05rzmNedi513nPeea5r/l7uRR5DjkY+T35FPm0Of76E/qo+v07EXvC/ML+MP94QMfCv8P8BQ6GBoaahv1HPEe4yCbInEkvCY1KZwrci1LLs4uzS5tLV4qhCVlH7EYEBL5C4QGJAIP/yz9//up+lD4tfRE8Hjr9uaF4lDeT9u32W7Z1tlx2ozbgt1U4N7jgOff6oDu//Ej9Sf4cPp//D7/SAJOBTgIngqPDG0OBRA5EVYSWBMsFKgUpRTYEz4SLhBLDSMKegccBQcDVAH1/87+Af4i/dD7iPq5+Yj51/lo+u/6hvt8/G79G/4H/ycAVwH0AoYEvgUJB2QIcAlyCsQL3wyUDQsO8A0HDXgLZAnyBpYEZQKGAB//8P0A/T78QPvm+XH4B/eB9dPzXfJp8f3wMPHA8YHy5vPh9Sr4tPpG/cv/UwLlBIgHHgqSDEsPYxJ1FWMYLRujHcgfnCEhI1Ak8CRoJdAlDSYiJsAlESU2JBQjrCEDIBgeMRyaGj4ZAhjEFkMVohMwEgwRChDxDgAOOw2nDEwMzgteC00Lfwu0CwYMOgzbC1YL/wqiChkK1gnqCc0Jsgl+CdgI6gcIB0cGtQVgBREF+wTpBKEENARQA00CjAHRADAA3v++/+z/hgAdAYkB9gFDAn4C2AIaAzUDYwOeA94DGARoBN0EqwXEBrUHeQjkCPMIowjjB+UGxgXLBPgDRQOAAqoBtwB3/1X+Jf2n+0n6O/kh+Ar3APbV9MDz0PL58VfxG/EB8R3xbPGC8cXxZfIS88XzgvTM9An10PWn9g/3+/b29kP3rPcC+Bz4//cR+Bz4vvcu91H2jPVf9TT1RvQQ8yjyMPFR8F3vAu4P7YLsvesw6/zqb+pp6Wjonufm5h7mL+V75FPkZuRG5ATk8+Pu4xfkTOT440bj0OLU4q3iD+Jl4QvhNeFt4SjhdODe367fb9/33pDekN7Z3hXfV9/g34rgiOFV41TlF+fg6OLqKO1C8BT1TPuFAjgKjREfGFYddCD8ITgjeiQ1Ji8o1ymOK3EtZS8bMTkyTzLWMbgwcy1gKGcitxukFBIOOgjZAqz+ovts+cv3zfXj8kbvOusC59zi4t4R3P3aGtsq3LDdJ98L4V7jx+XA6EDs/u/88973Rvs7/tYAUQPaBQ8IOQp0DOoN1A6cDz8QtRAWEU8RGBGtEOYPyA4VDY4K1QcXBYYChQDj/kr9SPwK/LD7R/vN+jb6UPoE+xj8f/0O/8kAogJVBFEFJQYgBxwIWQm2Cu4LyQzMDbsORQ/ZDxIQ2A8xD/wNFwyXCcgGvAPoAF7+JPxo+h75OPhQ9zj27vSE8z/yCfH172DvdO8n8FzxCPPF9JL25/ir+7X+xgHcBCAIhQvbDuMR+RT9F8oajR3bH6Mh6SK+I0gkoiTTJMck7iQXJdkkJSQHI6kh4B+9HVobzRhNFgQUGRJVEPIO4g3vDIwMbQwaDNEL+gtvDCkNOQ78DnYPuQ/CD/kPThCdEB4RBxLtEm4TzxPrE9YTyBNYE5YSpREpEAUOrwt9CVYHVwWlA04CSQFtALP/9v4b/iL93/uG+on50fhm+IX4Qvld+m77rPz2/S3/cwC5AU8DFAXUBm0InAmECiwLmwv7C0oMcwxrDFEM8gs4C1AKLwk/CGoHPwbuBHEDtgHp/wz+Jvwh+jL4ZPa79JPzhfKn8UbxN/Et8RbxIPEX8Vzx1vE28qzyE/Nd87jzPfS89Fj1RvZe93P4Vfnd+Rr6OPrq+Vb50/gs+Hr3zfYZ9k31cvS68wXzOfJF8WHwiu+P7rDtxOyx67Hq2ukV6R7oT+fr5qXmdOYy5vLl0OWk5WTlM+Uu5Q7lv+R55PTjHONS4snhdOE64UPhieHb4ebhj+FL4drgFuCa3zffr96L3o3eZN6B3v/ew98g4dTiUOQx5kTo/unS69Tt3vAD9pD8qwNqCyQTixmBHqgh9yLVI74kriXmJlgo5CmgK0Mtuy4oMJ4wJDDMLm0rACaeH2gYTRDkCMMC3f2R+mT4FPcV9vv04fJS7yrr5Oae4l/eh9th2jraY9t+3TTgTuO/5mPqMe7V8fn0A/iJ+r788/75AEIDCgaqCM4KEA3MDqwPMhBSECoQ+A+XD8MO5g3bDHYL8wniB5UFWgPmAJv+0Pwy+9H5Z/l8+Xr5wPkM+n/6c/uY/KP9wf4hAFwBuQJFBJIFyQb3By0JPgosC9YLSQwCDb4NXA51Du4N3gxQC0IJiwbdAzYBkf5R/G76wvht96z2F/aw9Tb1SvRL8y7yDfEs8PTvpfAd8kn03Pbg+Qb9OwB8A3kGWQn3C3oO5hBdE+gVVxjfGi4dGB+JIIshEyI9IlIiByJtIZsgsR+jHmAd+xtZGsgYCxcXFRMT6hDYDhkN5wvGCvkJpwlhCXQJiAloCWYJ8wktC/MMKw8/ERcTghQ/FYUVChVBFC0UYxSAFOUUeBXhFVUWrxY8Fg8VTBPAEKoNdApcB3cE6QESAAL/Y/4//o/+xf6b/h/+/fxG+6b5Pvh/98H30fit+vv8hP8JAvkDRAVzBnMHBAi0CHYJDgq6Cm0LEgzGDGENlg2SDS8NNQzwCnEJ7geXBl0FMgQ0A04CEAHa/5z+8Pw8+3H5qvcb9uf0IfSv877z6PMP9Dz0XfSD9IH0f/R89KD0z/Ta9Bv1jPUZ9ub2AfgQ+QL6uPoF+/n6iPri+Sr5kPju9yf3bPaf9ef0Q/SZ8+PyIPJT8V3wce927mPtc+ys6/nqOOqE6d3oTejn53jn8eZ05vHlOuWA5NPjROPg4oDiVOIs4ufhp+E84Z3g198y35ne891x3SndV92h3fnddt7P3gbfJN9J313fb9+t39Pf6N8v4Kngl+Ej4xzlpOeJ6kzt4+9D8tf0hfiB/VUD5gnREPcWHBzzH14i7iO7JHAlOibWJk0nrCdUKBkp/imbKlkqVym9JnYiHR27Fr4P8Aj8AtP9/vmC99z1NvWi9HXzgfF17sLq1eYG4y7gjt793dHerODl4njlheir6/rui/Kd9Vv4wPrG/LT+dQCSAuoEXAeiCagLaA37DR4OUw5sDoUOZw49Du0Nfg3BDHoLvgmaB2MFRwNoAZP/yf24/In8qvza/Aj9Nf3G/Zr+qP+iAEUBCAK+Am0DCQSQBF4FaAauB+sI8AllCo8KDAt+C/oLOAz+C14LDQpACNMFKwO4AKj+Nv0l/IX7Bvu9+rn6i/oB+i35T/hQ9372GPYM9o327/cN+oT8Ov8RAvsEtwdkCgYNIA/dEIsSQhT3FbsXoxlSG+AcFR60HtMejB5MHuwdhR0THVMcUBsPGrEY9hYMFR0TARHWDqgMogrvCMgHJwfjBtAGxQa2BoQGPQYFBgoGOgalBpoHAgmwCoYMiA5kEO4RQhMPFLEUxBUsF5sYGhqAGx0cAxxvGzIalxgGF70VcBS5Er8Qqw5dDAUKKwhtBq0EiQNdAokAl/62/KP60vi29x33F/fZ9yf5nvoT/Hj9t/6g/24AZAFbAo8DGAWxBh0ITAleCjsL4At9DBQNhQ3QDRAOzg0JDS0MEAvdCe0I9AenBl4FCQRrAtUAUf/8/QP9Pfxf+0z6J/kX+B/3AvbR9OrzR/PT8pfygPJ/8qjyEfN+877zCvR19Nb0N/Wk9eD18/Up9lj2c/a+9iD3fvfE97n3dvcR93H2zfU59Yn01vMq8y3y+PDK74HuHu3B63/qTuku6CjnKuYw5TbkM+MH4vjgKeBY35Pe4d0t3aDcJtxz283adNon2h3aa9qv2jDb6ttQ3LPcQt163bDdXt7Z3gTffN8A4HDgHOHX4Y7iYeMQ5JnkXeU65g/nU+j66cTroO197zXysfaG/N8CrQlOEJcVcRm7G9Mcsh2nHvYfhiEUI2gklCXHJrIniygUKeso4iciJbsgLhv+FHgOBghkAsT9vfr4+JH3Z/Yx9VXzSvCX7Lboq+Qy4drent1m3QTeXd+M4VjkSedz6hbuq/Hy9P/3YfpP/B/++/9NAhYF0gcpCnEMLA4ND4sPmQ+MD9MPHRAAEHcPmA51DQ4MOAoCCLEFggOWAb3/+v2g/O77APxn/Kn8Gv3+/QD/KAAaAXwBrAHPAQsCoAK5AwgFnQa4CKoKNgwyDZUN5g1ODpcOUA6pDYIM8QpbCU4HGQVAA8gBpgC9/+f+6P0C/VX8bftw+m35bvjO93H3P/dV9wn4KfnN+iz9n/81AjMFBAh0CtkM2A5JENkRbxPFFGgWKBiNGfEaHRy6HAMd8hyHHCkcqBuuGo8ZWxjUFksVvxPqETEQjg6nDLkKAwmDB00GpQVCBQsFPgWKBdUFDwYgBkIGoQYeB4oHQQhsCQsLVQ3eDysSSRQVFh8XdxdxF/wWXBYZFgoWCRb8FZoVPBXxFHIUqhNkEqoQsQ6ADOsJUAf8BMECFgEQABL/Mv7K/ZP9c/1c/ev8aPw+/EX8e/wJ/ez9Uf8OAbICNgSQBbUG0gcJCUYKPwsCDK4MMA2QDcENtQ2FDWoNMQ2yDBUMKwsBCuII4AfEBm4F/wOXAlQBCQB8/tX8Xvsa+vP42PfD9t31NvW09EL01vNY8+ryvfK48sXy4fIn85Dz9vNk9ND0NPWu9WH2KvfH90j4rfjq+A/5HfkM+cv4b/gX+Hb3nfbi9Q31DfQH897xlvBN79HtG+xu6sboIueh5TjkCOP84QPhPOBe327ekd223PHbStu82k7aONpN2lTagtrQ2jzbydt/3EPdy90o3lvee9623vPeV98Q4A/hG+L74objouOe47Tjt+Oy4+jjNuSd5HLlXuYw52fo7Olz6xTt0e5A8UT1bvozAL8GAA0iEnAWhxmGGx0dYB5ZH1YgTSEPIjYjliT9JeUnkSk0KkQpZya+IQMcqhWLDsYHDAK5/Tb7ufm7+AP4bPcp9pfzHvDW62vnxOPa4Mve992Z3rXgB+Tp55/rO++w8qf1QfgM+iD7Rfzr/UcACQMQBvIIugt4DhcQnRBqEMoPfA9sDyEPgw7ZDTUNqgzmCzcKFwj7Bd0D3AGx/8X9y/zk/Mn9xf6S/3UAlAHPAusDfwSqBOMEYwVBBmUH0gh7Cn0MoQ6AELgRIRJJElsSTxLiEQMR5w95DgUNSAsVCeMG8QRUA+MBgQAV/9/9D/1K/Hn7lvq4+Rb5g/jp93n3f/c8+L756fs5/sAAowNkBh0JxQviDZMPMhGHEpsTzhT+FV4XGBmiGsIbkRwHHSId9hxMHB4b0BlXGL4WIxVyE8YRYhAxD+UNkgwAC04JFghJB4kG6gWtBawF4gU/BncGswYKB2EHxAc4CLgIWwlmCrcLGA2wDiMQMBEBEpoSJxMBFPcUrxVMFo8WZhb/FUcVrxSEFDkUrRM8E1ASzxAhDzINVAvQCV8IzQZmBQUEkgJjAUYAW//W/oL+jv4K/57/NgAIAdUBMAJaAqMCEAPSA+AEAQZVB9QIEgoOC9QLNQx2DMQM4AzVDLYMQQyiCw4LUQp3Cb4IFgg/B0YGAwV4AxECygCb/57+iP1e/Ij7xfrX+fj4Avgh97f2PvaH9Q/1wfSl9Or0JPUp9Tr1ZvW19R32dvbn9mX3xPcO+Cj4JfhI+Hr4ePhG+Of3aPfX9gb2HPVC9E3zTvI58cfvGe5n7K/qB+ly56vl/+Oz4nvhauB533rel9323Ffcl9vn2kja1dl92UjZXdl92bfZUNoD26fbn9zI3d/e199k4Ifgl+Cz4Mjg6uAo4ZbhRuLr4mfjv+Pt4xzkXeQz5HPjpOL04Z3h7+Gq4sfjVeUm5wXpyOrU7PTvovSR+voAHQeoDHwRTBU9GJManRzKHtQgUCKDI4YkPCVhJiIomylJKtYpHyhYJRQhIRtjFIQNywYGAYf8LvlM92r2pfWi9LTyjO+969nnJuS84ODd/tuO27vcNN+d4p/m6upU77fzufe1+of85v11/2gBiAPMBYcIyAsdD7ERLhPbEzMUihSGFPcTBxO0EVQQZA9wDgMNZguZCccHJQY4BDEC0ABDAGQA1AAwAYUBOAJcA5wEewWgBYkF7gXXBtoH6QgqCq4Ldg0kD0UQzxAlEWARSxG8EK8PcA4iDbQLEApECHAGtAQwA88BXgAG/9r90/wZ/Hf7r/rv+XH5FfnH+KD4p/hX+fP6E/1c/8EBbgQ+BwsKxAwiDwcRcBKUE5oUbhU7Fk4XpRj4GT4bTRzeHBEdsByuG2EaxBjaFv4UcxMWEtYQzA/jDt4NpgxDC8gJbwhABx4GQAW6BFsERwRyBNAEbwXzBWsG+QZgB7IHIwi3CGoJYwqEC7MMBw4ZD8QPUxDGEBcRehEuEhoT+hOBFKAUjBTtE7ISbxEuEPMO8w3aDJcLiwqkCeMIbgjrBzkHagZ8BaEE3QMXA3cCJQIaAloCEAM4BJgF9AYTCMMI/wgNCfkIvwi5CN0IDwmBCSAKpAoDCy4LGwvvCm8KrQkDCUsIiAfhBioGUQWGBL8D4AISAk0BWABm/6T+6P1c/RT9vPxG/NL7Mft4+rz5xvjN9+D27vVF9e/0ufS39OH0/PQO9QT1yvSR9FP0FvTe86LzhfN281jzVPNa8zjz8vKY8iLyifHD8Lrvve7L7bXsmetq6i3p8ee+5m/lBuTF4r7h9eA+4H/fwN4G3mrd09wq3Inb+dp52gzarNl/2bjZMNqY2grbkNvy20XcpdwH3VTdkN3D3ezdGN5N3qjeEN+V32DgAuFa4cjhNuJ34s/iCePL4pTiyOJH4yvkZOXT5pvoa+rt667trPCG9bj7NQJ0CE4OXRMVF64Z2hvfHZIfACE6Iioj6iOQJDIlKiY9J4En5SZjJXoiVx4YGc8S8wsTBfn+n/rw9/L1uPQ99KXzb/JI8E7tOupY5yzkN+GW30HfUODj4l7mRupv7sryKfdB+2n+RwClAfYCVwT4BfAHewp3DV0QgRLuE94UHRUdFRIVXRT1EmMR+Q8GD1gOIA1uC+8JZAh8BoMEoQI8AbMAiwCcAAwBkgFjAsMD/ARxBaUF0QUJBrUGfAdJCIsJJQvDDEoOlg9UEK0QwxCBEOIPvw5pDUAMCAtmCZYH+QV3BB8D5QFxAC//Wv6c/f38k/wr/Lj7q/u9+3b7Ovta+yj8lf0//xcBUAP2Bb4IfAvwDfgPsBHpEqUTURT4FIoVVRZaF1UYKRnBGRYaRBoOGjwZCBiVFvsUbxPuEXQQWg+cDt4NDw0JDLoKiAmeCM8HEgd4BhkGAQY2BogGzAYzB60HKwiyCCwJlAkkCv4KAQxKDa0O0w/MEIoR1xHSEZgRKRG3EEgQ1Q9sDwUPow5JDtENIw07DBkL9wnlCMkHtwbbBTMFpQRJBEIEXgSABNMEOQWCBa0FyQXgBQ8GeAb8BqsHhQhZCRUKsgo3C4ILtwvjC/YLCAzuC8ALrAuACzoLCQvYCpAKSwrkCUgJvQg0CJYHCQd+BsIF5QQGBBgDPgJiAVUAU/94/qb9Av11/Lf7+PpQ+n/5nfjU99L2vPUG9Xz09vOV80zz/vLQ8sXylvJT8iryFfIG8vTx6/Hu8fjxC/Is8i7y+vGx8U7xxvAg8F3vnO7s7VTtp+yq66rq0+nj6NDnkuYb5bPjkeKE4Yrgvd/23lLe/d2y3UDdxNw/3L3bWdsI29Dastq12unaStu12yLcsNxn3Qred97h3kPfn98M4GbgveA94cjhQ+Kh4sXiyeK64oniS+IN4qvhY+F34ajh6eFg4jvjbOS65Sfnmugh6lDs4O/K9IT6sQDbBpoMrxHkFTcZCxynHpggciGwIdgh2CENIuMi+CPTJFAlBSWEI64gWhzNFoYQ+wmsA07+aPrX92X2nPUQ9Yv0jPPq8dfvkO326h3o6uXv5FrlYefV6grvofNR+I38WQCaA70FAgf4B9cIKwoHDBwOrhCNE5YVehbrFrIW3RXyFGMTGRHmDggNfwuaCuYJrwhXBwEGVwRzAmMAdv5P/eb85vxn/UP+X//9AMgCMwRuBXkGSwdTCJoJ0AryC0YN2w52ENARxxJnE64TixP4EgoS7hDTD6UOTw3pC0sKgQjSBi0FcgOrAfv/qf7Q/TD9vPx7/E78M/xS/JD8x/xK/U3+kf8AAbsCnATVBm8J5AsVDvAPcxGfEnAT+RNrFOwUZRURFsgWNRdmF08X7BZMFmMVJBTYEqMRURD/Ds0NxQzjCwQLHwo1CWAImwcDB7MGZQYqBjAGXQa4BjQHrgc+CN8IZAnnCXIK8wqBCzYMGQ3tDZYOFw9RD24Pqw/3D0EQoxDoEPgQ8BCqEB0Qeg/WDhkOVA15DHELbwqyCTMJ5AjrCC8JhQnYCeQJlwkcCZEI+weMB24HiQfbB48IhAl3CkkL4gtKDHUMcAwiDIwL2gorCqgJSwk2CVQJbQmkCcQJjwkdCY8IBAh2B9UGLgaYBSgF1gRwBNwDNwOXAt8BNAGSANL/Gv+C/vD9VP3H/CP8X/uo+tH5wvhm9+b1pPSg89PyPfLR8ZTxifGk8Z/xavEu8efwmvA88NLvi+9s73Lvh++o79Hv9+/178bvkO8e72zur+3l7Prr9erk6cXos+eo5oblT+Qa4wPiGuE/4Fbfg97Q3S3dltwQ3JjbPdvz2p3aZtpk2o7a7NpR28LbTdzT3FLdvd0X3nfe0d773iffd9+w3//fYuCk4MrgyeC04KDgm+Cm4Lzgz+DH4Kzgo+CV4FDgSOC/4FHhI+JK45XkUOZi6Jfq2O238or4yv4kBRwLjhB/FZ8Z1ByHH7whHSPMIxUkGSRSJAIlwyVfJqImBiZxJM4hjh1GGMQSwwxvBq8AG/y8+JT2UfWX9Ir0rfQn9N7yPPFw72TtPOtv6fPoZepa7SPxJ/U3+aH9FAKwBRsIpgnBCsILzQzADbMO/w+HEf8S3xPxE68TQhOUEosR9Q+zDY8LLgovCUAI3AYIBZ4DtwKhASQA0P5E/pD+Qv/x/8YAOgI5BCkGnQfKCA4KcAurDIgNbQ6kDywRuxLaE5oUCxUuFe4UHxTnEqwRiRBCD+4NjQzdCukI1QavBJkCmACz/iP9K/yW+0j7avvE+0j8z/wm/Xb9+/22/nr/bQClAUMDbgX5B58KFw07DwcRfBJtE+ATFxQhFAYU6BPaE/kTRBR0FG4UMxS9EwkT8hGJEBMPsg1hDFcLoArtCVMJ/Ai0CG8ISwg6CDUIRghrCJUI0wg3CZgJAAqSCjUL1wtnDOgMfQ37DTYOaw6fDsEOEA9gD3MPfg+qD/EPKhA8EEkQYhByEEUQvw/vDg4OWQ3HDE0M+QvVC9QL9wtBDIoMngyCDCwMcguXCtkJKAmaCGMIZQiMCBcJ6wmhCgcLKgv+CpQKFApzCdUIYwggCBkIUAipCB0JrgkSChkK3QlrCdEIPAinB+8GWQYRBusFxAWQBVMFBgWWBOoD8AK2AXsAXf9T/lj9Z/ya++v6LfpW+YL4kPdy9l/1OfQL8xrybPHQ8E/wGPD47+7vAfAA8OTvx++476Tvi++L75rvsO/Q79rv3u/g77PvVe/d7j7uc+287APsG+sf6iHpGugJ5w/m9uTH49niFOJg4afg398Z31zeut0U3Xbc+tuP2zPbBdsS207buNsr3Ivc4Nwe3UrdgN2i3Yvdbt143aXd8d1s3t3eNN+33yXgSuBN4D7gHuAT4DHgQuBf4ILglOCq4M7gCOEj4W/h+uGJ4pDjE+Xm5gbpTOvT7VbxUvbn+4ABPAfJDOARpRbiGtkdDiDpIbIi2SK4Iisi2CEUInkiiSIBIskg5R6GHDEZkhRyDysKxATz///7gPhN9r/1ofXM9Tj2cvZ29kP2pvVT9JvyQvHM8GfxR/M79sr51P0dAj4GmQnQCxYNnw3LDcwNbA3/DAsNkw1bDtgOyg6/DrkOPw5tDSwMQwpQCNMGiAVCBB4DFgI6ARoBkAHtAVYC9gLkAxkFIgbABmAHVwiHCQILZQytDTgPwxANEgMT6hOTFMIUoBQTFCcTIhI1ES0QCw/9DeAMuQuCCkQJ8QdQBocEoQKuAOj+WP0k/J37xPth/EX9Yf6n//AABgLAAk0D1gNxBCoFDAYvB6UIhwqCDC8Org8KEfERUBJeEhYSjREEEYsQHhC8D3UPNA/NDmcOGA6mDQINXwzUC0oLyApwCjwKKwpQCn4KpArlCkgL0gttDPAMXw21DfUNVw7WDkAPkw/BD9UP7w8VEDoQNhD1D7cPqw+eD5MPgw88D98Oow55DjgO/g3jDcYNvA3JDbINiA1pDSYNswwnDHgLvAofCsAJmgmkCdoJDQoSCuoJowkbCW0IwAf1BiUGhwVFBUkFkAUpBuYGugdmCP4IuQkyCh4K0wnGCZcJMQnsCMcI+AgtCToJXQmfCdQJngkqCWwIfQd/BkoFHwQHA/0BAQE5AJ3/6f4i/m395/w8/EP7PPpK+Wj4bfdU9j31PfRP85HyOfIM8rbxcfFt8X3xWfHp8Ffw5e+H7xXvqe5X7iruJO5F7n/uqe6p7oHuTO4J7o7t2+wH7EXrrOr56RTpLehd55bmu+XT5PjjOuOT4vDhROG44ELgyt913znfDN/h3s/e4t4I3zjfZ9+c383f/98z4Hzg0OAI4UPhjOG54b/ht+Gf4YHhauFQ4SLh4uCj4HbgX+BK4C/gE+AT4A3g6t/j39TfoN+B30nf/N7x3vjeRd8q4Erhv+LL5APnDelC68HtffDt8yv40/y1AfQGUQylEckW7xrnHeAfqiA+IA4fpB1fHNIb/xtZHLIc7xyyHKIbpRl1FgkS/Qy5B9oC8P7p+9j5JPnI+e/6Gfxz/Z7+Lf9y/0D/Lv7S/Nj7e/sc/LD9l//0AeUEvwdmCoMMgg2wDaUNYA2MDGwLIwoICY0ITQi4B9EGMwavBQsFbASYA7UCRwJtAo4CiQI4AocBJAFaAfQBkgI5AycE3gUuCEQKEgyrDSwPdRCDERwSMRKIEiAT1ROKFCUVgxV+FV0V1hTfE4IS0xA3D6ENMgzFCoMJpAjbBy8HPQYbBRsEFQMkAk8BlgAKAOL/RQDcALMB+wJ5BOYF8QazBxoIVAixCMsIzAjqCHAJdAp+C3kMJw2pDRoOUA43DqMN3wwHDGsLMgv/Cu8KBQtsC/ULHAwwDFAMiQzpDFwNyQ0PDpgOTQ/fD1QQfhCdENEQBRErESkRJRE0EXURuRGSET4R3hB7EDIQzw9AD5UOBw6VDS0N5AyZDGkMcwyFDHQMcgytDAQNXA2TDasNxg3NDbgNhg0uDdgMrQysDMgM2gytDHEMOgzYCzoLVgpVCWEIfwe8BioGxQVcBTMFVQVjBWgFfAWFBXcFeAVoBSAF6QTQBNEE9AQwBXEFowXjBRUGNwZGBhoG6QXLBacFVAXGBCgEpQM+A74CLgKkAQMBbwAKAJX/4/4W/mD9rvzL+9j63fng+AP4VffF9jD2mfUi9cb0Y/Tn80fzjfLM8QPxL/BM74nuAe6G7Rft2uyv7InseuxQ7AjssutI68XqJ+qh6R7phugY6MTnZecF583mqeZu5j3mDubL5WPl9eSL5CDkuONT4xTj4eLF4sfi0uL44hXj+eK/4pbiYuII4pfhHOHA4IPgaeB24IngmuCv4KzgnOCS4G3gKODb36jfnt+a337fid/L3wHgP+Bs4Jvg3+Ak4Xvhl+G34ebhEeKC4u/imuN85JjlGOd/6DrqQeyn7uTxBPae+rD+FwPfB3sMLhHQFFcXMxlYGq4aMxpYGecXJxdYF4sXHhhuGLQY6BhzGP8WABRjEGAMyQg1BsgD5AHXACIBNAJGA4UEjAVwBhoHpQdPB/AF/ARXBDgEcwSUBAUFvwXuBkwIpwmDCtMKVgt0C90K0AleCMwGlAW+BKADaAKXARoBEAFcAZsBwwEJApcCSgPxA04EcgS2BGkFWgYcB9sHhAiACQALYgzYDV0PrxD+EWcTjRQwFYMVcBVWFVIV6RQ4FHkTBRPVEokS9BHaELgPow56DYYMSQvvCfAIKghaB6sGJwZtBfMEwgR0BCIE2QOpA6QD/AOGBPcEdQX9BWgG2wYpB2gHsAe2B7MHxAf+B1YIpgjhCOkIAAkbCT4JcgmgCQMKTwqaCvoKJAs0Cz4LYgtjC1gLoQsEDKIMSw3NDWIOCg/DDzYQZxCAEG0QYRBVEFsQTBAMEPwP2Q+pD4wPVw8uDwEP1A6iDlgO/Q2CDRINmQwjDNELdQsjC9wKtQrACvkKXguxCx0MkAzvDGUNwQ0JDjAOGQ7KDUoNnAzkC0ELoAoSCpsJVAkzCf4IrQguCIMHnwaiBZYEVgM4AmkB/AD0ADUBpwEdAsACQQN9A74DpwNxA1IDHQMMAzADhQPvA1IEpwTgBB0FIwUFBecEmARFBM4DRAO4Ag0CbgG6ACMAff+5/jH+p/00/cT8P/zE+yL7ZPp0+Vb4Lfcg9l31sfQu9NLzevM88+3ygfL48VzxvvAb8Ifv9e587jvuEu7X7YjtOe3e7HXsNOz6663rcetL6zHr/uqu6kXqyulG6aroB+h159/mX+YP5tDloeVv5Tnl9+SW5DHk1OON41vjNOMd4yfjTONo44fjrePM4/fjEuQm5FDkaOR/5KXkw+Sy5I7kbOQs5AXk++P14/XjEuQ/5EXkV+Rc5DzkLeQB5MfjmeOH46PjreO246rjhON7417jJOMF4yvjfeMQ5BXlSOaM5xXp1+qk7JPu0vAs82f1//ft+hb+tAFEBWgICgspDVMOgA7xDZsMUwt3CiUKYwpGCwgNtg77D70QZRBCD6QNygvdCRYI/wZuBq0GvAf3CD4KeAtsDMsM8gxeDCALSgqMCQoJ1gjOCPcIMAmdCeAJ6AnzCcsJmglmCf4Idwj7B64HTgf0BmsG0wWVBTEF7QT0BEUF0gV1BnMHiQhoCfgJdgrSCtEK2Ar+CigLZQvAC0cM7gy2DWUO5Q5+DxEQlRAMEQcRvRCGEDMQvA9ED60OCA6+DaQNiw16DWsNcA2TDbENZQ3VDCwMbQu2Cv8JdQkiCfwIEAlTCYgJmgmvCbkJwgnICZ4JawlJCSsJJAn9CLMIfQhECBQI/AcPCDwIgwjsCC8Jcwm3CdEJygmVCVQJ8AiWCGcIVAh5CLQIFgmCCeEJTAqRCs4K6grkCt0KywrBCq0KsAqlCn4KZgpJClQKYQpvCpEKsQrpChQLMAtCCz8LKQsRCygLOwsyC0QLbgutCwUMbQzPDAwNNw1ODUgNJw3oDKAMZgxHDCMM+wvkC74LkgtdCyYL/ArQCqgKfwpjCjoK6wmbCUUJwwggCIAH1gYiBnQFxAQbBHsDAgO5AooCdQJdAjwCIAIJAvQBxgGOAXUBiwG3AekBGwJGAnYCjwKRAo8ChQJsAkoCMwIVAtYBkwFmAUcBKwH/AMQAgwA8AP3/1P+u/4H/Wf8w/w3/4P6z/or+Wf4p/ur9q/17/U79G/3y/OD8yPyk/Hb8Ofz2+5X7GvuC+sP57/gI+Dv3efbB9R71cfTd81/z3fJC8p3x/PBA8JDv9u5i7svtQu3v7LTsfuxR7Bbs1euR60Xr/erY6tPq2+oH6zHrUuuH66rryOv46yTsNOwz7DzsR+w/7CLs/+va66LrXOsL66PqLeqj6RLph+gD6Hzn5uZb5tTlTuXF5EHkzONJ493imOJv4lXiO+It4ifiH+IR4gziB+Lz4e3h/OEX4k3iluLl4iHjVuOc4/TjUOS25FvlNOY453Hon+mt6qnrmexz7U7uae+r8PLxR/Oc9L/1e/a79oz2GfaH9RL15vQJ9Y31XvZf93b4hfmD+lz7AfyL/Br9sP1f/j3/QwBsAboCBwRDBVkGJQfGB2sIFwm1CXgKaAtIDAQNpA01DqgO/w49D38Pzw8aEHoQ6RBUEbcRGRJ3EqwS1hIaE2UTvRNDFPAUtBWCFkUX/xe2GEoZsBkCGlsazBpPG9gbZBz7HIcd3h32HcAdQR2rHAkcYxvVGn8aTBofGhAa6xmXGRoZYBh4F3QWbRVyFLoTWRMeEw0TBhPiEpkSLhKpEQMRbhACELoPrg+0D7IPrQ+lD2sP8w52DuUNNQ2ODPcLggsqC9QKeQodCs4JcQntCGEI4wdtBwAHugZ8BkcGMAYLBtIFjgVBBeoEgQQbBNMDqAOPA48DoAOqA5kDbgM6A/oCwQKRAmoCZwKPAswCAwNDA44D3AMfBE4EiwTIBAEFUwWyBRwGjQYIB3sHygcVCFgIeQiGCJwIrwi6COIIDAkxCV4JfAmQCZIJgwliCSYJ5QitCHcIQQgLCNcHmwdUBwkHuQZxBj4GHgYABvgFDwYnBlAGigamBpAGcAZXBjEGBQbsBfgFIAZSBoEGrgbOBskGvgapBngGXAZFBiAGAQbcBa8FagUMBZ4EJQSvAzoDyAJYAv8BtQFbAQsBwABeAOL/XP/x/p7+VP4b/gT+G/5D/mP+b/5a/hz+uv1N/cb8Ovy4+yP7kvr++Vr5nPjF9+z2AvYC9Q30I/M+8njx0PAt8J/vOO/O7lLu1+1f7QPtxuyg7IvsmOy97Nrs+uwf7TXtPe057TjtL+0V7QHt9Ozy7PvsBu0O7Q7t9+y+7ILsMezE61vr6+p06vTpaeni6Gno7Oda58LmMeak5RXlj+Qr5NLjiONc4zDj+eLH4p3ifOJ14pDizuIS40njgeOj47DjsuOV41rjFOPd4rfiluKP4rPi6OIw44fj4eNQ5LPkAuVd5b7lIeae5krnT+jC6WfrG+3I7jPwL/F58Sfxj/DA7wbvre6t7jrvTfCI8cvy8PPr9PP14PaV91v4IPnt+f/6OfyR/fz+XQCwAcsCsAOCBBAFgAUZBtEGrQegCHsJSwoKC5ULFgyUDPQMXw3eDVsO3A5qDwgQihD9EJ4RPBLCEmUTMhQdFSQWMBchGPEYtRloGu4aTRuoGyAcyRyVHVceDB+vHxogPyABIGofih6EHa8cGhy+G50bhxuKG7MbnBsgG3MaihmJGK0X7BZoFjcWNxZ6FssW8hbxFqsWJhZyFasUFxTXE74T1RMuFJUU0hTRFJkUBxQWEwcS+RDnD/IOPA60DTsNvAwzDIsLuQrUCd0I5QccB3IG6AWdBXYFUQU1BREFywRaBMYDQwPdAo0CZgJuAqEC8QI3A3MDnwOQA2EDOAMFA9sCxQK6AsYC5wIcA0wDZQOFA4YDbwOGA60DwwPfAwsEKgRCBEgEQgRNBFEEawSfBMIE8QQzBWkFigWhBaAFggVhBUoFLgUfBS4FTQV/BbgF6QURBjsGZgaABoMGgQaSBqwG2QYuB5MH4gcJCDQIYghUCCYIAgjMB6IHqwfDB+UHHQhTCIcIogiaCHcIMgjRB2wHBwemBlcGEwbaBZoFTAUGBbMEYwQcBL4DXQMZA9UCdgIfAtcBhwE7Af4AxACTAGcAQwAvABoA9P+x/1X/5/5x/vD9cv0F/Z38Lvy++1D71vpF+pv53/gN+D33iPbV9S31svRh9Az0tvNY8+vyhfIV8rTxd/FR8UbxW/GO8c/xDfI08knyUfI+8hXy3fGf8WbxHPHL8HnwFvCx70jv1O5l7vztju0X7aTsKuyi6x3rler36Vjpxegi6Hvn8uaD5ibm4uW55ZLlXOU25TTlNuUm5Qzl++T35PPk9eQH5RjlHeUg5RXlBeX55NHkk+RW5CDk+OPQ46vjkeNw40HjCuPi4sfiq+KW4pHipeLY4iXjlOMe5KnkO+Xe5YDmOucj6DjpkOoW7KftTO/P8OrxkPK28l/yqvHl8FvwI/Bj8CPxMvJx8670o/Vw9j337/eC+CP58/n++kT8qv0e/3UArwHGAp4DVQTiBFIF0AV5BkgHQAhDCSgK7wqDC/wLcwzgDFENxg08Dr8OXg8JEJ8QHRGhESoSqRIdE48TGhTGFJgVeRZWFyAYtRgeGWAZixm3GegZKBp7Gt4aSxvAGy4ccBxfHAwchBvVGkQa5BmXGV4ZThl+GcMZ6BnvGbgZThnZGGcYERjaF8AX0xcTGFkYhhiBGDMYoRfWFvwVOxWgFDMU9hPlEwQUGRT7E6MTBBMfEuoQkw9RDiwNLgx3C/oKhgokCs4JaAnxCG0I8QeRB1oHTgdTB1wHbgeHB5QHigdZBwYHtwZ6BlUGTgZeBncGjQarBsEGrgaABj8G2gVmBfgElgQ5BPQD1wPnAxUEOwRIBC0E/gPMA5cDdgNmA28DjQOuA94DFQQ9BFYEXwRVBEMENQRDBHkEwwQcBYMF2QUNBhwGCgbbBZoFZgVYBWcFjQXGBQgGUQaFBpMGiwZoBiwG8gXJBcEF1QXlBfEF6wXKBaIFZwUYBcwEiwRkBF4EcQSQBKsEwATMBL8EqAR9BDUE9QPDA5gDeQNaAykD4gJ+AgYCjQEEAXkACwCw/1v/I/8E/+v+1v6y/oX+SP7+/bf9cv1B/Sb9Fv0M/QP99PzR/Jn8UPz8+6f7VPsC+7/6gPpE+gP6vPl1+Rj5rPg9+Mb3YfcX99j2oPZ29lT2I/bu9cH1hfU19fL0tvRz9DT0/vPO86DzavMp893ycfLp8UzxpfD+71Lvsu4W7nzt6Oxa7NfrXOv16pbqP+oB6trpuemf6ZDpg+lt6VXpN+kN6ezoyOil6JfojOh96HPofOh66FXoHOjH503nyOZN5s3lUuX45LXkdeQ45Ajk0OOE4y3j3eKX4kziEeLx4enh/OEi4ljinOLZ4hLjYeO14xTkmeRL5Rrm/uYJ6Bvp++mw6lzrGewB7RzuSe9+8MLx5PKg89jzn/MJ8zDyY/Hu8OLwPfEC8v/y9PPW9J71YPYW97H3Yvgx+Sv6YvvB/Cz+jv/YAAcCCgPJA1kE1QRUBe8FsAanB8MI3gnaCqsLVQzhDFMNpg3rDSwOaw7CDjUPsA8eEJYQIxGcEf8RaxLpEnQTDBSxFGEVEBaqFioXkhfMF+YXGRhTGH8YuBgKGXcZ6hlBGnMaaBolGtYZihlNGREZ3BjHGNUY7BjzGO0YzBiPGEQYCRj0F/IX+hcLGCYYRBhKGCEYwhcnF2cWqhUGFXsUBRS2E5gTkhNvEyoTvhINEi4RMxAjDwUOBg1HDLkLWwsXC9IKiQpICgcKsQlvCVUJQwk2CT4JVglrCYIJoAm/CccJuQmmCXsJQAkGCd8IvQicCI8IkwiZCJMIfQhHCO0HdQfwBmYG1gVWBf0E3QTpBAQFKgVVBXIFgwWDBWsFTgU6BTMFOwVRBXMFkwW9BeEF9AUVBjAGMwY5BkEGOgYoBhUG/AXVBbkFpQWHBXcFgAWMBZ0FvgXcBeYF4QXQBZoFRwXzBKsEcARBBCgEKwRNBJAE3gQkBVkFcwVrBToF8QSgBEwEEQQDBBsERARtBIUEbgQ0BOoDjwMsA9QCkgJoAk8CTAJJAhUCwwFNAbQAFQCA/wz/tv6L/p7+1v73/vD+zv6M/ib+s/1R/fn8wvzF/AL9VP2W/bn9p/1X/dX8QPyj+xT7tPqC+nn6kvqj+ov6UPrl+Uv5j/jM9yD3k/Y19gX2/PX89eX1tPVm9fD0VvS98z7z3vKf8n7yXvIu8vXxmfEU8XLwxe8V72/u8u2f7W7tUO057Rrt2+x37PzrcOvv6pDqROoY6iTqQepJ6kHqJOrd6Wnp2Og56JrnDueV5jPm9eXD5X/lNeXn5IbkFuS643rjQeMg4yTjOeNM41/jceN743rjZONR40XjQONZ45bj7eNg5N3kU+XA5RTmV+aa5tjmGOdx5+LnZ+gB6aTpRurg6ofrUuwz7THuPO9B8Djx+fFc8k3y8/Fz8e7wlvCH8M7wavFR8l7zbvSG9a/29vdE+Y361fsl/ZD+BwB0AdICHQRQBV0GQQfzB4oIEQmQCSkK3gqkC2UMEA2VDeMNFg42DkAOPQ45DkIOUg54DscOFg9gD7wPLBCyEDsRzxF3EjET/RPPFIoVMBbJFkEXihfBFxAYfxgIGaAZPRrpGpYbDhxAHDEc1RtHG7saRxrgGZoZdxl3GZwZyRneGcQZlRlVGfkYnhhNGAAYvBeGF2AXORfuFmwWyBUEFS8UchPaEm0SJRIAEvYR6RG7EVMRtBD6DzEPVw6FDeQMdAwqDPMLwQuXC2cLMQv3CrMKZQoeCvUJ5wnlCeEJ5QnrCeMJxQmLCTUJxAhTCOsHlwdkB0cHLQcTB/AGqQY/Br4FMwWsBDIE3wOvA5wDogO6A+EDAQQWBB4EFgQRBBoELgRLBHsEugT0BCcFWAWABZUFlgV5BVAFIQXxBNkE1ATWBNQE0AS8BJcEbgQ1BPADswOPA3wDigO0A+QDIQRtBLkE6wQIBQ4F9QTGBKgEqgTBBOcEGAVOBXMFewVnBUoFJQX+BOcE5ATnBOQE6gTaBKMEUwTsA28D3wJXAuMBfQEtAfcAzACfAIAAYgAoANf/hP87//L+tP6Q/oj+nP7C/uv+Cv8e/yP/Hv8U/wb/8f7m/t/+2P7E/pv+Y/4t/gH+uv1Y/er8dPzv+2H72vpS+tv5cPkS+bf4VPjr93j3Cfef9j326fWm9Wj1KfX39NL0svSV9IH0bPRQ9DP0DPTd85jzOvPL8lXy1vFL8cDwOfC970jv2O5m7vbtju0s7b/sR+zP61Xr2upb6tfpY+kD6a/oZega6NnnqOd15znnB+fY5pzmYeYm5unltuWc5Y7lkuWp5bnlw+XJ5cblw+W85a7lpOWl5bvl2eXz5QjmHOYh5hLmA+br5dfly+W+5cfl7uUj5kzmeeah5rrm3uYO50DnhOfy54boJOmt6S3qpeoo69jruuzX7SHvk/AF8jXzEvR/9Hj0GPSd8z/zF/NP8+zz4PQM9kL3cfiG+Yj6gftm/Er9QP5O/3YApwHLAs4DswR2BQYGZgamBtsGDAdbB+MHnwhvCSsKywpPC7UL8wsmDGAMqgwYDbENZw4dD+MPrxBpEQ8StBJhExQU2BS0FasWrBepGIQZHxp5GpAacRo7GhMaExowGmgayBpCG64b5hvdG5MbDBtbGp8Z9BhpGAsY5xfyFxEYMhhEGCsY8BemF1UXCRe8FoQWaRZiFlkWLBbAFR8VUxRoE4ISvBErEdgQyxDkEP0Q9hC/EEQQgg+VDoENbQxzC7EKKgrQCagJpAmzCbAJhwk4CcoISwjcB4wHWAc1BysHNQc7By0H8AaJBvsFYwXoBIsESwQ1BEIEXwR8BIsEeAQuBNADdwMkA+ECvgLEAucCFgNXA6wD+wMuBEoEbASMBJMEiASABIEEhASNBJIElASSBIUEcwRZBEMERQRMBEoEPwQ8BDwELAQSBPQDywOeA4IDgwOpA90DBAQtBGsExAQuBaEFGgaOBvkGQwdeB08HHgfeBp8GhAaFBpAGqQatBoUGKAagBQ8FjAQlBO4DBQRnBOgEUAWJBYIFLQWxBCoEowNAAxgDJgNoA7kD8QP6A8EDRgORAr8B8QBHANz/vv/k/yQAZQCLAGQA6P9K/6j+D/6Q/UX9P/1w/bn96/3r/az9Mf2L/NX7KfuM+hz69vkQ+kD6a/p1+kf64vlF+YX4zPc499f2tfbF9vD2I/c49xP3pfb09Rv1NfRm88byWfIf8gTy6fGu8UPxmfCv75Tuae1a7HXrwOpP6h3qFeoe6iHqAeqw6UDpwuhR6Ano6uf15zbomOj26DLpOukH6aToNOi551LnLOc+53Tnv+cT6E3oV+gp6MXnSOe/5lXmF+b+5RbmTeab5urmHect5xzn9ObB5pfmfuZ05njmjuao5sDmz+a75pjmeuZn5mzmkubu5nDn/+ew6HfpPOr56qjrS+wI7f7tH+9W8Jvx4PLy87j0HPUb9dv0hvRK9EH0g/Qv9S32R/dU+EP5Dvq3+kj70/ts/Cv9F/46/4oA4gEeAykEAgWgBQoGSQZuBqcGEQe2B5IIiAmMCokLWgz8DH4N7w1PDpoO2Q41D9kPmxBWEQYStxJ0EycUxRRlFQ8WxxaPF0kYABm2GUIamBq0GqcalhqUGpYamRquGuMaNBuCG6gbpxtwGxMbnRoZGrYZchk3GRMZJhlUGYAZmRmOGXUZXxk+GRUZ/Rj2GPYY9RjlGMEYchjkFxkXJhY3FVgUixPjEnMSMhL1EaURNRGTEL0PtA6IDV4MVguACuAJYgkVCeMIpAhsCDQI5weUB0kHFAf0BvIGEAc5B2kHoQfUB+EHxAeDBy8H4QakBoQGdgZ7BpcGtwbMBsIGlAZGBt4FYQXnBH8EFgTBA4kDXgNCAykDDwPvAs8CvQKrApQCgQJ6An0CggJ+AnYCfgKHAoICcgJmAmQCaQJ+AqcC2QIPA0EDaQOLA54DogOPA3wDgwOLA6EDzwMZBHoE5ARNBZ4F1gXrBe0F5wXeBdsF3QXzBQoGFAb/BbMFOQWkBBUEnQNYA1UDlgMQBJoEDgVSBWQFRAX3BKgEaQRNBF0EfwSvBOME9wTXBIMEAQRbA6YC/AF2AS8BIAEuAUQBVQFMAR0BywBvABUAyv+q/7P/2f8MADwAVwBUAEQAEgDA/2L/Cv/K/pn+d/5e/jf+9v2c/R79ffzO+xj7dvr3+ZH5QfkC+cn4hPgl+Kv3F/dt9rr1E/WH9Bz0y/OQ81fzEfO48j/yn/Hr8ELwle/u7mLu7e2U7U7tHu3k7IjsG+yu60Xr4uqa6oDqourv6lbrxOsQ7DHsJ+z7663rRuvd6nzqKer96fHp7+ns6c7pnelZ6f/ol+gn6MTnbucm5/Dm1ubA5pnmeOZY5jnmG+YJ5g3mHeZD5m7mm+bM5u7m+ebv5uHm0ebK5s/m4eb75hTnPOdy57znFeiC6Azpl+ka6pPqB+t76//rt+yr7cfuA/BX8ZLyivMb9ED0FvSq8yzz2fLQ8hjztPOZ9KP1rfab94H4aPlC+hL76/vg/O39HP9YAIoBrAKsA4AEGQWNBeIFLAaaBi4H9QfuCPQJ8wrUC4sMFg1qDZ4N0Q0aDnQO5w6ADzUQCxHSEYQSMBPCE0AUvhRUFQQWzxauF4cYRhnjGUMaaBpvGlgaShpoGqcaCBuCGxUcpRwGHSodAB2MHPAbTRuuGjEa5hm+GcIZ5BkHGh8aDhrPGW8Z7BhZGMgXRxfnFqsWgRZNFgMWlhUKFVcUfROqEvQRZxEcEfcQ5RDfEL0QahDoD0MPhA6tDdEMFgyOCzUL9grTCsYKwgq6CokKPQrnCYsJLAnECGkIJwj/B9kHqQdnBxIHqAYsBqgFMgXZBIgENAT1A8UDjANGA+gCggIdArUBWAEKAdkAxwDIAOAADAEnATYBQQE/AS0BEwH3AOAA1ADQANIAzgDJAK4AdQA2AOP/ff8b/8/+nf6H/pD+pv67/sX+w/6y/pf+df5l/n7+uf4L/3D/4/9eANkAOAF5AaYBvQHUAQsCegIwAyQEOgVMBj4H3QftB2MHVAbkBFYD+gEAAYQAnQAjAcEBSgKhArgCkwJLAvkBzgH/AYgCTwMmBOYEXwV1BSsFiwSvA8oCFQK/AeIBZwImA+MDbgSdBFQEsAPgAhQCdgE1AVcBzwF0AgQDVQNLA+sCSAJyAZ0A7f9x/z7/VP9//4L/OP+T/oz9N/zF+mX5X/jO96P31PdC+Lz4DPkB+Xj4fvdH9hT1C/RT8wvzOfPC83T0GvVt9V/19/Q/9F7zePKy8SXx3vDH8LPwe/AS8Gbvce5V7S/sIutZ6vjp/OlY6ujqbeu367XrXOuv6tPp+uhO6M3nkuex5w7od+jC6OPoxOhw6PHnW+fU5mjmOeZK5obmz+YN5zHnMOcS59fmjOZL5jDmPOZo5rbmF+dx56rnveel51/n9uaS5kvmIOYd5jTmVOaL5s3m6Obo5vbmDec153/n7+d76CHp5Om76qHrpOzY7SrvdPCi8afyYfO8887zqfNg8ybzMfOI8yX09/Tj9d/22Pe0+Iz5g/p8+3j8j/2z/ub/HgFBAi0D3AN9BBEFfgXrBW8GCwffB+II4wnUCpcLJwyaDOsMLg12DdwNaQ4dD98PoxBxEScSsxIQE2ETwxMoFKIUOhXoFbMWlRdmGP4YYBmNGYUZXhkvGSQZSxmWGQIalho2G78bDBwEHLYbMxugGgsahBkcGecY5RgTGWUZqBnBGacZZhkOGacYMBjIF4QXWxdGFywXAReyFioWbBWNFKwT4BI1ErgRXxEzESQRARG+EE4Qsg/jDvwNGg1SDKsLIQvCCoQKYwpFChUKzQlzCRUJtAhcCBQI3we0B44HdAdiBzsH8AaEBgIGgAUJBZwEOgTzA8UDnwN1A0kDDAOwAkQC1QFeAfUAqQBoADgAIAAhADMASQBNADoAEgDc/6f/eP9Y/0v/VP91/5//xv/r/wEA9f/c/87/xv/L/9//+f8gAFUAkQDIAN8A3wDZANUA4wAKAUABgwHZASUCawKiAr8CzALOAukCGwNpA9kDTgSoBM8EsQRRBMUDKAOkAlUCTwKjAi8DwANFBJ4EvgTBBLMEpQSfBKMEvgT/BGAFuAXmBe4F3AWjBVkFBAWvBHYEYQRlBG4EcgRUBP4DagPCAh0CgQEOAcMAowCoAMwA7AD3AOQAoQBAANr/fv82/xT/IP8//2D/ev9z/zL/wv44/pb9Bf2m/HD8Yfxq/Hf8dvxQ/PP7T/tw+nP5hfjC9y33z/aW9m32TPYg9sT1KvV09LXz+PJP8sjxZ/Ej8evwwfCN8Djwye8375bu/u2K7UrtN+1I7X/ty+337QLu5O2c7Tjt0ex47D/sL+w07E/scOyC7HTsMOzA6zzrpeoL6o/pL+nn6Lbokuhk6DroE+jc55fnXec15wnn3+bE5rLmo+af5qHmouaY5njmVuY05hfm/+X55QTmE+Yp5knmbeaF5qDm0+YR52fn4+dz6A7pt+le6vbqgusP7Kzsbu1t7qPv6/A28nDzYfTn9Av14PR39Az00vPa80f0EfUa9k/3k/jG+d360vuk/F/9Gv7m/sP/uAC+AcsCzAOyBHoFHgabBu0GOQehBzMI6wi9CbgKvQueDGINEQ6kDhAPYQ+xDwwQgRAVEbcRWRIDE7wTcxQZFa4VNRa0FjkXxxdTGNoYTRmOGbEZyxnhGewZ8hkUGlMapRoEG2kbsBvLG80boBtDG+IahhonGtgZtxnCGeYZERouGjEaFBrYGYoZIxmiGBwYpxdAF9sWehYdFqYVFhV/FN4TMxOREgkSnhFaES0RBxHZEI4QHBCAD8AO6A38DA8MQgurCkwKGAr6CdwJswl9CTYJ2ghwCAYInwc+B+0GrQZyBiQGvgVZBeoEbwT3A4ADEgO4AngCSAIhAvwByQGJAT0B9QC4AIUAYABGAD8ASABgAHIAcQB6AIgAiwCRAJsAmwCUAJAAmQCgAKAAnwCVAJYAowCzAL8AwwC7AJwAeABgAE4AQQBIAGIAhACwAOUAGQE+AVIBbgGUAbsB5wEZAk8CmALuAkQDjgO7A9cD3QPQA7IDkAOHA4wDnwPKAwIEPwSDBKkEogR5BEEEKgQvBEwEewS0BP4ENQUzBfwEsAReBBUE6gPiA/0DFAQxBE4ERQQ2BCUE7AOUA0IDBQPhAtgC3wLzAggDGwMWA+UCiwIeAskBpAGiAakBsgG4AacBiAFOAeAAZwD+/5n/NP/u/sz+t/6h/n/+Rv7s/XX94/w1/JX7F/uw+mf6Pvoi+vj5u/lk+fD4cfjf9zz3s/ZQ9g326/XL9ZP1TfXp9Gj03/NP88byTPLi8ZfxZfEs8enwnfBG8O/vje8T75zuN+7i7aLtg+147WXtPu0O7dvslewz7NXrgOsv6/Hquep+6kDqAerK6YjpR+kK6dPoq+iI6G7oXehQ6C/o+efJ55fnY+cm5/bm1+ax5pfmjuaf5sDm4+YS50XnY+do523nYOdD5zDnKOch5xznJOcm5zPnYOep5wnoe+gA6YfpD+qW6hPrhuv764/sUe1A7k/vbvCQ8Z7ybvPo8wj03/ON80vzTvOU8yT0BvUi9kf3U/hC+Rb62fqN+1D8N/04/l7/qQDwARMDFATxBKEFDwZSBqIGEgekB1QIKgkbCvwKuQtKDK4M8AwoDWYNqw0JDpoOTQ8NENIQjxE9EtsSaRPxE4QUHxXCFYoWbhdOGBMZvBktGlgaYxpRGikaHBo1GmQaqRoJG3ob3BsLHAMcxxtqG/wajRoxGvQZ2hnkGQsaKxouGioaARqxGWMZHBneGKwYkhiHGHkYUxj/F4UX3xYTFjIVUBSXEwgTkBI5EgMSwRFiEd4QMBBtD40OnQ3KDBgMgQscC9wKpAp4CkcK+QmaCTAJughRCPsHuweVB3sHXwdDBx4H4QaSBi4GtwVBBc4EYwQNBM8DrAOQA3MDXQMrA80CUwLTAVQB5gCcAG8AZgB9AKMAwADFAL0ApgB4AEcAIAABAPv/BgAYAC8AQgBPAE8AQgAkAAoAAwAFABQAOQBzALwABQFVAaoB+gFMAqYC+wJOA6cD5gMCBAgE6gOiA0ID2gJ4AiwCEAIiAkwCiQLRAiUDcAOdA7MDvQO5A6kDoAOiA6oDuQPEA7oDowN+A0ID7gKXAlQCHQL+AQACDAIaAiQCIgIRAu0BvQGOAWIBQgFCAWQBlAHGAfIBFwIwAi0CHAIHAuYBvwGRAV0BHwHMAGsA/f9//wn/sf5l/h/+7/3R/az9f/1K/fH8gPz6+3H7A/uv+n36YfpZ+m76hvqF+mT6I/q8+T/5w/hD+M33b/cX9732YvYE9pb1GvWX9BL0o/NL8w3z3vK58p7yfPJI8gXyvvFf8fHwhvAl8NDve+8+7xLv6e7R7r7uqe6R7nLuUu4z7gzu4e237YrtXO007RTt/ezx7PLs/ewH7Qnt/+zp7MLshuxM7BPs0uuY62frMOvw6rvqjepe6jDqAOrd6cjptemd6YXpaelD6Rfp9ujJ6JPoauhK6DDoG+gc6B3oH+g16Fzon+jx6Fvp1+lc6u7qeesA7Hfs4OxP7eftse6P75DwlPGH8lnz3/MP9P3zy/OP82/zdPOz8zL02/Si9Wz2M/cG+OD4pPlW+gf7xfue/Ij9dv5s/24AYgE8AvEChgMJBIMEBgWfBUkGCAfUB5cIVQkICrgKXgv0C3oM9QyEDRsOtw5YD/oPkxA2EecRfxIaE8ITbhQgFc0VaxboFkcXiRenF68XuRfUF/0XLxh1GM8YOhmjGeYZBBoCGuIZqhlrGTYZDxkNGSgZWxmhGecZHhooGg4a1hmHGSMZthhQGOUXjhdQFxMXyhZnFt8VMhV9FLwT8hJEErQRQhHnEKcQehBAEPIPjQ8UD5EOCQ59DfoMjQw3DPILwAuaC2ULJgveCocKLgrLCV4J4whpCP0HmgdBB+MGiQY1Bs4FYgX1BIcEIQS5A2cDMwMOA+kCxwKyApACZAJLAjECCwLjAcIBrQGjAa0BtAG2AbkBrwGoAZkBfgFVASIB8QDJALgAtQCvAKEAkwCIAHcAXgAyAPf/z/++/7r/yv/o//z/CwAeACYALQA1AFIAkgD1AIUBJAK9Aj0DlgO0A5IDTgPvApoCawJTAk8CWQJPAhoCzgF7AS4BDwEpAWsB3QFqAvACWwOqA9kD1gOyA4QDWgNGAz0DQwNeA4EDnwOtA5gDUQPvAocCLQLwAbsBiwFjAUQBHgHwAMMAiABQADcAMwAzAEYAYgBsAG4AaABNABgAzv9z/xH/wf6F/kP+A/7A/XH9Ev2l/DH8q/sl+6v6Q/ru+Z/5Yfku+en4k/gt+ML3Vffs9pT2TfYj9hH2Avbs9bz1cvUX9bL0PfS48zTzsfIy8tPxkPFK8Qfx0PCX8FDwEPDT74zvVO9B70zvXe9g707vLO/47qvuUO797bPtcO1I7UftVu1c7VPtN+317JnsO+za63rrJ+vr6sfqs+qg6ojqaOoz6v/p1+mv6ZHpf+mA6ZXpsenJ6dPpyumu6YDpSukf6QPp9ejv6O/o8Ojp6NvowuiT6GPoROgs6CHoKuhI6Ifo7Ohm6drpUOrA6hLrVOuX69/rRuzb7KLtju6M75vwlPFW8s3yAfP/8uDy1PLm8iXzpPNj9EX1MfYk9wb40fib+Wn6OPsM/Ob8yv2+/sD/sACMAV8CBwN/A9UDHwRqBMgETQXvBZ8GUwcPCLwIVgntCX0KCguSCyAMtwxNDekNjQ45D98PiBAzEd4RhBIoE9ATfBQzFdMVTBauFvoWMhdWF3MXkxfFFxEYZRjCGCEZbBmjGcAZthmLGVsZMBkKGfMY9hgWGTQZShlhGWgZXBk9GR4ZBhnrGMkYoxiQGHkYThgRGL0XVxfNFiwWkhUMFZAUKBTfE50TXxMkE9ESaBL+EY4RExGUEBYQmg8gD7AORQ7SDWgNFA3DDHkMQwwRDOALqgtmCxELpwo1CsUJWQnuCIUILAjmB6AHWQcYB9cGkAY/BuAFfAUZBbYEVAQFBM8DnwNyA0UDFgPpAsgCrwKSAnsCbQJlAlwCSQI0AhYC5gGhAVgBCwG4AGcALQAVAAgABgAKAB0APQBUAF8AaAByAHgAgwCMAJQApwDEAN8A5gDmAOoAAwE9AZIBAwKIAgIDYQObA6UDewMxA+cCrAKLAo4CtwLnAg4DIgMdAw8D/QLXAqkClAKRAqoC4AIUAzADLQMUA+IClgJEAgACyQGkAZsBowGtAaEBewE7AeIAhgAxAO7/u/+f/5f/qf/O/93/0f+v/4f/Zv9J/zT/If8n/zH/J/8X//T+sP5O/tv9Yv3x/JX8S/wK/N/7vvuE+zv76Pp8+v35ifkq+dv4qPiI+Gj4Rfgp+Pb3o/dK9+L2bfYU9tr1sfWi9aL1nfWR9Xz1SfXt9Iz0LfTM83vzQ/MQ8+Pyx/Kh8mjyHPK98U7x3/CG8ETwIfAc8B3wH/Aq8CfwAvDK73/vLe/t7s3uue6s7qruqu6p7p/uh+5e7iPu4+2j7WftN+0T7e3szOy27KTsiuxh7DjsE+zm68DrqeuO62/rW+tJ6zLrE+vv6sDqguo/6gPq4enY6drp4Onx6Q7qJuo16kDqSepX6njqteoS64Hr8utl7NfsS+297UHu5u6c713wK/H98a3yMfOH86nzqvOg86XzyPMR9Hv0APWl9WT2Lff798/4jvk4+t36j/tL/An93v21/nv/NwDrAI0BHQKkAikDuANcBBMF0AWIBjIH0wdsCAAJjAkOCoMK8wpmC94LaQwGDaoNVQ4DD60PUxDyEIURFRKZEg4TjRMcFJ8UCxVuFcIVEhZiFrgWExdxF+AXTRitGBEZZhmRGZ8ZlRl7GW8ZXhlLGT8ZOhlSGXIZihmmGasZoBmJGV0ZLRkIGeIYqxh5GEMYAxi2F0wXzRY+FrsVSRXlFJgUXxQ1FAcU1BOZE1IT+BKFEggSlREpEcMQZhAIEKwPUA/7Dq4OaQ4hDtINiQ0/DfkMtAxgDAgMrwteCw4LugpoCh0KyQlmCQYJqQhCCMwHZwcTB7oGZgYkBu4FsQVtBSEFzARuBBAExgOJA1kDPQMqAxID+QLXAp8CVAL/AbkBhwFtAWEBXAFaAVIBSQE2AQ8B4gC8AKMAoQDBAPIAIQFPAYQBsAHAAbYBogGKAW8BYAF2AbQBBAJTAo4CpwKeAmMC+gF2AfAAigBUAFkAkgDxAE0BhQGcAYwBUwH6AJUARAAcACAAVQC0ABwBeAGzAccBrQFpAQkBrQBzAF8AdgCmANYA7wDlAK4AQgC6/zT/zv6H/nD+kP7B/vf+Iv80/x7/3/6G/iP+zf2b/YX9kP3G/fv9GP4j/hX+x/1Q/eb8f/wp/PT74vvb+9P7yPug+1D74vpm+ur5g/k/+SD5JflC+Wj5fPlm+Sz5y/hM+Lv3Lve+9mz2PvYs9iL2Efbk9Zf1MvWy9B30kfMi89Lyo/KW8qLyqvKf8oryY/Ie8srxfPE+8RnxEPEe8UHxXvFj8VLxJfHY8HfwFfC872jvKe/67snuo+597kfu/+2z7WLtBe207HfsQewc7BHsE+wR7Azs/uvf67LrcOsu6/vqy+qh6nvqXepN6kPqN+ol6gnq7unV6b3pqumk6bHpvOnI6djp3eng6d3pzunL6eXpH+p26uDqXuvb60TssOwY7Xjt3+1U7uHuje9U8Azxr/E+8pvyy/Lm8gLzJPNS85zzBfSP9DH13vWG9if3yPdm+AL5pflc+h/77vvH/KH9cf4o/87/XgDjAGwBAAKmAlkDHATrBMQFmgZoBy0I4wiECREKpwpBC8cLVQz4DJENHQ6vDj4Pvw85EKgQFxGPERgSrxI/E9ITYxToFGcV2hUxFncWxhYLF08XpRcGGGMYpxjcGP8YFRkSGf0Y9hj8GAAZChkkGUkZaxlwGWYZXhlOGT0ZKBkPGf0Y+xgHGQgZ7RjAGH4YHRiqFzYXxxZmFhQW1RWpFXoVRRX/FKkUPhTME1UT1hJjEvoRmRE0EdwQkRA6ENoPeQ8VD64OQw7TDWIN9gyfDFcMGAzRC4ILNwvVClEKvwk1CagIHwi1B2YHKQf5BskGnQZ5BlsGNAb6BbsFcwUqBegEtASKBG8EaQReBE8EOwQbBOYDnQNbAywDBwPvAuICzgKvAooCagI+Av8BtwF1ATwBEwH8AO0A6wDpAN4A2wDbAMoAqACMAHkAcwCNAL4AAQFOAZgB0QH2AQQC2wGGAR4BnwAfALz/c/9B/zP/Rf9p/43/rP+t/5P/dv9b/1f/aP+X/9D///8eACAA9v+W/yv/v/5m/jP+If4r/j/+Xf5k/k3+G/7E/Vf96fyQ/F/8XfyE/MX8A/07/Vr9Tv0k/dn8fvwo/Or7xPuw+637ovuM+2r7Jfu9+k/64vl6+TP5G/kn+T/5WPlv+Wz5PPnv+JH4Fvil91X3K/cr90b3d/ec96/3sfeA9yL3s/Y49sf1e/VP9Tv1PvVB9S31A/W+9FH0xfM788XybPJB8jryTPJy8pXynfKI8lLy/vGd8UPxBfHm8PHwDvEr8UPxRPEl8eTwk/Aw8MTvYu8e7/3u7O7g7tDuwO6a7mDuFO647V/tE+3a7K/sneyb7Jjsj+x77GHsQ+wZ7OXrweu167Xrv+vP69nr0+vA65/rces+6wfrxuqZ6pfqsOrm6j/rtOs17K7sHO1w7Z/tue3C7cjt8O1E7sTubO8w8OnwkPEs8qny9PIa8zLzRvN388zzOfS/9Fr1/PWY9ir3qvch+Jn4F/m1+X/6Zvtf/E79HP7N/mj/5/9KAKIA/wBzAQYCugJ3Ay8E3ARtBfEFbQbmBmgH5AdZCN4IegkfCsQKSwu0C/8LPgydDBUNmQ0uDucOtw+dEH0RKxK2Eh0TXhOQE9YTKxSNFPkUYBXSFUYWrBbtFv0W8hbsFvAW+xYfF0cXeRevF9UX9xcLGAMY5xfKF7gXyBf0Fx4YRRhgGGoYYRhIGB4YzxdsF/sWkRZKFg8WxhVyFSkV5BSZFFYUCxSoEz0T1xJ6EisS5BGUETUR0BB2ECwQ6A+WDzwP8g66DoUOUQ4mDu0NpQ1WDf8MrQxgDBwM3QurC5ALdgteCzwLAQusCk8K8gmgCVoJGwnqCLsIiQhTCA4IuAdAB7oGTQbqBY8FTQUaBfoE6gTWBLEEiARbBBwE3gOuA4kDaQNOAysD9QLFApQCUwICArUBbwExAfcAwACWAHkAbwBcAEEAKAAFAN3/sv+X/4j/hf+I/4r/kv+V/4L/VP8o//b+wv6O/lf+Nf4Z/v/99P35/Qb+Dv4Y/hr+E/4E/uj9uf14/TL93vyF/Dv8Cfz0+/D7+/sW/ED8Zfx3/Hv8gPyN/Iz8iPyP/I38c/xL/Bf8z/uH+0T7D/vq+tP6y/rL+sv6w/q2+pf6afpC+hv6/fnx+fb5DPom+jf6R/pL+jb6DfrQ+ZX5ZPk4+RP59/jX+LH4h/hG+PP3mPc99+/2rvaO9or2lfaq9sb24vbh9rz2ivZM9v31qvVz9Vb1OvU09Tr1N/Uj9fz0wPRs9BP0uvNl8xjz1fKX8lzyJPLg8ZjxT/EA8bDwafBA8DTwMPAz8D3wS/BN8Cbw6u+s71vvCu/H7pjufu5z7nDubO5f7kDuFe7Z7ZLtXu077SHtE+0g7TvtSe1M7UvtPe0g7fXsx+yh7JDskOyB7GrsXOxJ7CLs7uux63frYetw66TrAuyB7ALtie0b7pDu9+5Z77nvIvCi8Ebx//HI8nvzC/SB9M/0+fQS9Sj1N/Vd9bX1MPbJ9nn3LfjV+HL5Avpw+sv6Evtg+937ffw5/Qz+6/6v/04A1QAvAW4BrwHrAScCiQIZA78DcAQmBc0FcAYNB3UHxAcTCGYIvggvCdQJhwo8C/ELlgwmDaINDA5kDsUOMA+rDzkQvBArEY8R5REXEjoSWhJoEoASshL8ElkTyxM4FIkU2xQaFSwVJBUYFRMVIBVEFYcV5RVIFpoWyxbeFsgWixZJFgkW0hW/FcsV3xXpFd8VwBWRFVYV+RSPFC0U0xOPE2kTVxNJEz4TKxMHE9cSnRJNEvIRrBFkES4RHBEYEQER1hClEF4QCBCvD1UP8A6RDkcOFg78DdkNqw12DS0Nzwx4DCUMxgtkCw4LyQqWCm4KRgokCv8JzwmFCS8J1wh5CCII2we5B7AHrQetB58Hdwc1B+0GrQZvBikG8QXPBbEFkAV1BWMFPQUGBcYEfgQtBOUDugOUA3MDagN0A3UDXQM2A/4CtAJmAiAC3QG5AbYByAHkAf0BDAL8Ac4BdAH7AIgAKADi/8D/u//C/9T/4//a/7b/e/8w/9/+i/5C/h3+C/7+/fL94f29/Yz9R/31/KP8Vfwd/AH8/fsH/A/8FPwX/An86fu8+4X7Ufsg+/v66vro+uL62frT+r/6o/qF+ln6JPr6+df5s/mQ+W75R/kZ+fD4wfiL+FT4H/jt97/3nPeC91n3MPcJ99D2j/ZN9hH21/Wi9Yf1dvVp9W/1ePVv9WP1WfU89RH15fSw9H30WPQn9Pfz3PO985PzXvMp8/Lyt/KF8lvyQvI48jjyP/I98jXyKvIU8vXxzvGt8Z7xmfGg8arxqvGb8YTxa/FB8Q/x3PCq8IDwZfBT8DLwEPDv78Lvg+8+7wHvzO6e7nfuXe5R7kTuMu4l7hbuA+717e/t9e0B7g3uEe4W7h3uE+4I7v7t7+3b7dTt0u3G7cXtyO3J7dvtA+457n3u2O41743v2u8b8E7wdfCh8MHw6vAv8Y7x7fFA8ovyxfID8z7zbvOo8wP0cvT09Jn1Rfbx9pT3JPil+CD5lvkL+or6E/un+zz85/yT/ST+rP4l/5H/+v9sAOIAWQHdAYACNQPeA3oEDQWTBfwFVAbEBksH1wd3CDoJDgrdCpMLJgydDPcMRA2YDf8New4HD5oPOBDaEGwR6xFNEosSxBL+EjsTlxP3E1EUwRQzFZIV3xUPFhUWGhYwFkUWZBamFgkXcRfIFw4YQRhSGEAYBxi2F44XgBd4F4wXrxfPF+UX7hfaF68XcBcsF+kWrxaIFm0WVBYrFvUVpBU8FccUQBTAE0kT7RK/EqUShxJgEiYSxhFNEbsQExB0D/AOhw41DvcNxg2VDVEN9Qx/DAsMlwsQC6oKbAo4ChwKBQrcCagJaQkSCaoIQQjeB5MHZQdHBysHDQflBp8GSQbkBWUF7QSKBDEE7QPQA8cDtgOeA3EDKQPoAq0CZgIeAugBxQGuAaQBlQF6AWkBVQExAQYB6ADLAKoAnQCUAJcAmQCOAHIASgAiAPX/0f+3/6r/rv/H/+X//f8OAAMA3/+s/3b/Of/w/qH+Yv47/iD+Ev4J/gT++f3v/ef91/3G/bT9pf2P/YD9cP1Q/TP9CP3L/Iv8X/w//CT8Ifwt/Dz8S/xW/ET8F/za+4z7QPv4+sD6kvpu+lv6RPom+gz66vm/+ZT5afk8+Rb59PjV+LT4k/h4+FX4Jvjz98D3nPd/92L3UvdT91X3Svc09xH35faq9mf2Jfbg9Zz1YPU79R71BvX89Pn07fTc9MD0m/R19Eb0EvTl89XzyfOz857zh/Nu81HzL/MJ8+jyx/Kz8qnyofKe8pfyk/J88lvySvI18hjy9PHW8czxyfHQ8dnx3PHY8b/xmvF38UfxD/HX8LLwrvCz8LrwwfC98K3wqPCm8JTwiPCQ8J3wqPC78N7wDfE38VTxc/Gb8bPxr/Gn8ZvxifF/8YTxj/GY8ajxs/G48cDxyfHJ8cfx1/H38SrygPL58nbz8/Nx9OX0QPWJ9c31BvZE9pT2B/eW9yz4wvg8+aL58/kY+if6OPpM+nf6yvo7+7b7Nvy4/CT9c/2y/ef9If5r/s3+RP/U/3oAIQHBAUcCpALgAgMDIgNGA4AD2wNYBPIElwUjBoYGwQbLBrwGrQayBtEGHgemB0sI+gicCS0KmQrKCtMK1ArgCggLZAvjC3cMBw13DcAN4Q3kDb0Nmg2dDc0NNQ7GDmQP9w96EMoQ3hDDEJEQUxAWEA0QPRCeECIRqxEFEjUSQRIbEuERpBF1EUURQhFmEYoRpxG2EbgRlhFqETcRDhHtENcQ5RD9ECIROxFQEUYRBRG5EHkQURAsEC8QWhCJELcQ0hDgENEQnhBWEAwQxw+GD1gPOw8jDwoP8g7QDqgOdA4yDvANuA2aDZANkw2cDaUNqQ2VDWkNLA3mDJ0MYQw6DB8MGQwuDEkMRAwyDBEMygt2CxoLwQp0Cj8KIAoQCgkK/gntCdYJwgmeCXcJWQkmCfMIywifCGIIGwjfB5kHRAfyBq8GeQZABgwG7gXSBaoFcwU5BfEEhAQNBKQDPgPSAngCMgL4AdABswGZAXgBRwEIAccAggAzAOT/nf9o/zP/9/7E/oX+Of7k/Yn9Lv3V/I78V/wg/Ob7tfuK+0/7A/uq+lL6BPrD+ZH5bvlk+V75Q/ko+RD53viZ+Fn4HPjY95n3avdE9yf3Effw9sX2kfZV9h/25fWm9Xb1VvU59Rf1/PTe9Kj0ZPQe9NDzhfNL8xjz9PLj8uTy7vIA8w/zDvME8/Dy1fK88qHyifJ08mnya/Jz8nfyb/Jh8k3yNPIX8vnx7fHp8ejx8vH/8Qfy//Hs8dPxpPF38V3xUfFG8TzxQPFD8TjxK/Ei8Q3x6vDQ8L/wuPCy8KPwn/Cq8KXwhfBn8EjwFPDf77fvl++C74Xvl++q78Pv3u/u7/PvAPAH8Prv8e/87wzwHfA78FPwavCH8JrwqPC68Njw8fAO8TfxafGj8d7xG/JX8pbyy/IF80nzjvPV8yP0fvTb9D71mPXm9Sr2Zvag9tz2Kvd699H3Nfii+Br5iPnr+VH6rPrp+iz7dfvB+xb8dPzk/FP9wv0t/o3+5P46/5T/BwCfAEYB+wG8AnADGAS5BDAFhwXoBT8GdwavBgoHbwfBBxsIdwi5CPEIMglnCZEJvwn8CWEK0gpSC88LUgzgDFwNwg0YDnwOwg77DjwPjA/4D1YQrBDnECURXRF3EYQRjxGYEYcRmxG7EdsR+REDEg8S/RHwEdoRuRGSEX0RihGTEbYR3RH3EfER2BG/EZ8RfhFdEVoRVhFREU8RPBEREdQQkBBDEAQQvg98D0YPCw/UDpgOXQ4kDuoNsQ19DVQNLg37DMIMkAxWDCAM6wuuC3QLKwvhCp8KXAoNCrMJZwkcCcUIfghVCCkI9gfYB9MHywe7B6EHcwc+BwkH4wbBBqsGpAaLBnQGWgYzBv4FuAV1BTwFDQXvBOAE1ATSBMYEqwSRBGoENwQBBN0DxgO9A8UDygPJA84D0AO2A4wDXgMzAxQDCwMRAxYDGQMXAwgD4AKhAlMC8wGUAVEBLAEZARcBGQEWARQBBgHqAMgAqACJAHcAgACOAJ0AnACLAHwAVgAiAOn/qf9n/yv/9P7K/qv+g/5a/if+7v2v/Wz9I/3X/K78o/yW/IT8e/xw/Fb8Lvz8+8/7mvth+yz7+/rO+qD6ZPou+v35v/mB+Un5DPnG+ID4PfgE+N33tfeB91v3QPcM99D2nvZw9kH2F/b99ej12/Xa9dH1vfWw9a/1rfWg9Y/1e/VY9TH1DPXm9LX0ePQ/9BH05vOx84XzZfNB8ynzIPMb8xHzB/P88uXyx/Kw8qLyiPJ18m/ybPJh8lbyRvIi8v3x5PHI8aXxh/Fu8WHxT/E98TvxOvEx8S7xPPE/8THxLvEw8S3xN/FM8WnxlvHK8ffxEPIq8kryXfJv8ofylvKh8rXywfLC8szy0PLY8uPy6vL48hHzMPNF82Lzk/PH8/PzHfRM9Hn0qfTj9B71VfWG9br18PUj9lD2dfae9sf28PYp92f3m/fT9xv4bPjG+CP5e/nK+Q36R/qG+sn6CPtM+5n79vtd/Mb8KP2F/eX9Nv6B/sz+C/85/2b/of/X/xUAZAC0AAMBRgGMAeQBQQKiAgoDgQP/A38ECQWGBeEFHgZPBnwGqwboBisHfwfvB2gI2ghDCZoJyQnmCf8JIgpcCqMK/QpZC8ELLQx6DKEMkwx2DFsMUgxuDLAMEQ1uDd0NRA6TDskOzg6+DrQOyQ72DkwPtg8XEHUQsRDXENIQrRCBEE8QMBA0EF0QgxC1EMsQwhCsEHUQQRD8D7oPkQ+GD44PmQ+xD7EPlg9gDxkPzQ52Di8O+w3mDfINEQ4wDjQOMg4VDtwNmQ1QDRENxwyVDIQMcQxcDEgMKQz6C8cLigtKC/oKsAqFCl0KQgo6CjkKLgoPCtkJpAliCQcJsQhvCDwIHQgXCAUI6wfNB6IHYwcXB9wGmgZmBlIGUAZTBlQGTwYoBvQFwgWBBT4FAQXXBL0EswS1BLwExAS4BI4EUAQVBMkDcAMtA/cC0wLBArQCnAJ8AlsCKgLvAbIBhgFpAVQBUAFQAU8BRwEtAQAByQCNAEgA9/+u/4P/Zf9F/zD/I/8I/93+qv5x/jH+7f2r/Xz9YP1Q/Ur9R/05/Rb98/zO/JP8TvwY/OD7o/uA+2j7R/sm+wr74fqu+n/6QfoI+tr5svmT+Yr5hflk+Un5N/kQ+db4mvhl+DH4+/fO96f3fPdb9z33Fffv9sz2qvaH9mD2SfY39ib2E/bv9cr1pvVz9TP19fTA9IP0RvQf9AT08PPk89rz0fPO88zzx/PB87jzsPOm86HzpPOl86fzrPOo853zmvOb85vzmvOh86/zr/Oi85fzk/N581jzSfNA8zLzJfM381nzevOm89jzCfQ19Fz0ffSa9K/0svSy9Lj0uPS79Mr04PT69B71TfV69Z/1zPX39Rz2O/ZV9nP2h/aZ9rD2zPb59iT3Rfdm94/3wvf19yn4ZPip+Pr4QvmF+b353/nz+QX6G/o++mj6l/rZ+iT7e/vX+yT8aPyj/Nv8+fwP/Sr9Of1K/Vr9df2m/d39Gf5g/qr++P5S/6b/+/9SAKkA9wAqAVEBaAF3AYQBkQGxAesBJwJcAqMC4AIQAzwDWQNwA48DtAPXAwgEQgR6BKQE0QQCBSQFRAVtBZ8F0QUSBmQGrAbhBv4GGAcpBygHHwclBzsHUgd/B8MHCwg+CGcIngjOCO8IEAlACWsJhQmqCdwJAQoHCgMKBgoDCgIKBQoSCiYKOwpXCncKiQqLCngKWwpLCj4KPQpJClwKcAqNCrgK1griCtUK0wrUCsEKvgrACsgKugquCr4KygrECrUKsgqnCp4KnwqeCp8KmQqMCnwKawpBCg4K4gmxCXoJRgkpCQ4J9QjoCOoI6QjdCNAIvAixCKgIngihCLIIvQi8CLIIngiECEkIBwjRB5AHWAc2Bx4HBgf6BvkG9QbvBuIGxQajBoQGaQZVBk8GUQZNBksGSgY7BhkG8AW4BXUFQAUWBfUE3ATHBKsEjQRrBDsEEgTqA70DnAOKA4cDgQN3A28DYgNMAy4DCAPVApsCbQJIAiAC/wHpAdEBrQF/AVUBKQH2ALYAggBYAC0ACADe/7z/n/99/1f/K/8A/9T+p/55/lf+Q/4z/h7+E/4I/u/9zP2e/Wn9K/3w/Lj8evxE/Bv88/vE+5v7c/tH+xn74vqx+on6aPpE+h/6Avrm+cH5kvlj+TH5BfnV+Kb4f/hb+Db4Cfjf97X3hvdJ9wn31fau9ov2afZU9kD2KPYS9gb29/Xe9b31ofWK9XL1XfVF9S31DvX79PP06/Th9M70vvSz9Kb0l/SO9IH0cvRm9GX0cfSB9I30kfSN9Hz0aPRS9Dz0KvQX9Bz0MvRI9F/0dfSC9Ij0iPSE9IP0hvSQ9KL0v/Ta9Oz09/Tr9OD00/TK9Mz01fTy9BX1Q/V19aH10fXw9f/1EfYk9jX2TPZl9o72wvbz9ib3U/d895/3xff09yr4afiq+PX4RfmT+dr5HfpV+n76ovrE+ur6E/tC+3f7tfv2+zH8bfym/Nr8Af0p/WD9nf3g/Sb+bv6y/vT+Lv9k/6X/5P8WAEoAgwC3AOcAHAFYAZIBxwECAjYCZwKUAroC3gL6AhoDRQN5A8ADCQRWBJoE0wQHBTQFXgV+BaYF6AU5BosG1wYkB2kHjQeiB7oHyAfOB94H/gcjCE4IhAi/CPcILwlTCWMJdgl9CXcJdQl3CYEJjwmiCb0J2gnuCQMKDwoWCh8KIQotCkMKWgp0CpMKsgrCCscKzgrGCrIKpwqkCqsKrwrACtcK4grfCsoKtwqZCnkKXwpUClQKSwpQClIKSwozChMK8AnKCawJmAmLCXsJegl0CW4JZQlUCUMJKAkICdkItgiZCIgIfghxCHUIcQhyCGMIPwgQCNsHqQd6B1oHQwdBB0gHTAdKB0YHNwcOB+AGsAaFBmEGSQY9BjQGMwYvBiQGCQbeBagFZwUsBf4E3ATHBMEEyATIBMcEwQSlBIcEbQRSBDsELQQvBD4EQgQ0BCEEAwTTA5MDSwMNA9kCqAKDAnACYgJLAjQCGwLpAbQBgQFKARUB6gDSALwArgCiAIsAaQBAAAoA0P+f/2//UP9A/z7/Ov8s/xP/8f7G/pL+Yv4x/gr+5/3G/bD9jv1m/UP9If3z/Ln8e/xL/Bv84vu5+5j7evti+0X7IPv/+tz6u/qh+on6evpp+lf6P/oh+v751vmh+W35P/kQ+d74rviG+Fn4Nfgb+AH46/fc9833uvex97b3tfel95/3m/eX95H3gPdr91D3MfcQ9/n25PbU9sn2v/bA9sT2xfbI9sr2x/a89qr2m/aM9n/2cfZl9mP2YvZi9lv2UfZP9lr2a/Z59o32p/a59rv2ufbA9sz20PbU9tn21PbG9rP2ofaS9oj2efZu9nP2e/aH9pz2yfb+9iz3YveK96v3zPfm9//3Ffgr+EP4UPhb+G74gfiW+Kn4vfjS+Ob4/fgQ+ST5RPlw+aT54fkZ+lH6h/q0+uX6Dfs1+1f7c/uU+7b71vv2+y38Zfyd/Nb8C/1J/X/9uf35/TH+aP6Z/sn+8v4Q/yP/MP89/0z/af+S/73/9P85AIIAzgAdAW0BuAH4ATQCdQK2AvACJgNeA48DugPjA/8DHQQ8BFAEbgSUBMQEAgU9BXQFogXXBRIGRQZ8Br0G+AYnB10HkQe6B9MH5wcGCCUIRghXCGkIeQhyCGcIXwhkCGcIhAioCMYI8wgcCUEJVQliCW4JdwmBCYcJkQmcCagJqgmiCZkJiAl2CWwJaQllCXIJlAmuCbgJvgm7CaoJjAlcCS0JDwn1COAI1gjUCMcIrwiVCGsIRgg0CCAIFwgYCCEILAgrCC0IJwgdCBMI/gfqB9oHyAesB40HbQdLBycH/gbUBqMGgAZnBk8GQwY/Bj8GMAYgBhIG+wXmBcsFqwWKBWYFOAUKBeQEwASZBHUEYQRUBE8EUgRPBD4EKgQOBOsDzAOhA30DXwNHAzYDIQMSA/cCzwKqAocCYQI+Ah4C/QHfAcUBtgGfAXsBXQFGASwBEAH4AOUA1gDBAKYAigBuAFcAOgAgAAMA5v/N/7X/p/+X/5P/jv+A/33/dP9a/zr/FP/f/qj+cv5C/hP+3/23/Zf9eP1h/VP9Qv0w/R/9BP3r/Nv8xvyw/KD8h/xl/Ej8L/wS/PP71Pu5+6f7lfuC+3X7Z/tU+zX7F/v9+uP6y/q0+qT6m/qe+qP6ofqg+p/6mfqH+nn6ZPpH+i36E/r++e/54/nN+bH5jvlx+V75Q/k6+T35Nfks+SD5Dvn++O74zvis+Jf4ifh/+Hv4dvhr+F/4YPht+HT4evh2+GT4Uvg1+Bv4Cvj+9/D35vff99f32PfX99D3v/eq96v3qPeY94z3fPdy93L3ePeA94T3iPeF94L3hveJ95D3lPeV95r3mvec95/3oPei96b3s/e/98f3zvfM98z3z/ff9wH4Gfgs+EL4U/hl+Hb4hPia+Lr44fgT+U/5jvnF+fr5LPpg+pX6y/oL+0n7g/u4++f7E/w//Gz8mfzO/An9QP1z/aL9z/0E/jf+bf6i/s3+//4v/1r/jP/D/wAAQACKANAADQFKAX4BpwHYAQ8CRwKHAsUCAQNBA4IDwAP2Ay4EZASSBL8E6gQKBSwFWAV6BZsFwAXiBQYGIAYzBk4GbwaSBrsG6AYMBy8HUwdwB4kHnge4B8wH3wf/BxYIKgg9CEwIXghqCHkIlAiyCMwI4wj+CA8JFAkSCQYJ+QjyCOoI4QjgCN4I2AjRCMAIqAiQCH8IbwhoCGYIXAhUCEwIQAgsCBYI/AfaB8gHwAfCB80H1QfaB9cHyAezB6AHiwd6B20HXwdWB0sHQgc3BxcH5QaxBoYGXQY1Bh8GCgbxBdwFxQWyBaAFigVqBUYFJwUKBfUE6gTkBN0E1ATJBMEEswScBHUESgQkBP4D4APJA7QDnwOHA2wDTgMtAwoD5wLAAqUCnAKTAosCgwJwAlYCMwIGAtkBtQGPAWoBWQFGATYBLQEcAQYB6ADDAJoAeABXAD8AMgApAC8ALwAlABoA/v/b/7T/iP9g/zr/Gv8G//j+6/7b/sn+qP6E/mH+Nv4H/tf9r/2O/YH9eP1w/Wb9U/08/Rj99vzZ/MT8tfyn/Kb8qvy0/LX8s/yr/Jr8jvx7/G/8aPxW/Er8O/wp/Br8C/z/++r72fvM+7b7n/uG+3P7ZPtg+2L7Yvtm+2v7a/tr+2/7cvtv+2f7XftS+037Qvst+xr7Cfv1+tr6yfq2+qf6ofqg+q36tfq7+rr6tvqr+pz6lPqP+pX6n/qu+rv6xPrI+sf6xfrA+r76w/rN+s76y/q9+rD6ofqE+mb6Pvod+gT67fni+eP58fn/+Q36Gfod+hv6E/oB+ur53/nl+e/5/vkL+gn6DPoF+vD55fng+dv51Pnf+e758fn0+ef52fnJ+bv5uPm1+cD50Pni+f75F/oq+j76TfpQ+lj6a/qB+pf6pvq6+sb6zPri+vP6Cvsf+y/7SPtd+3v7nPvD++37EPwy/E78bPyF/Jr8u/zb/AD9MP1W/YD9s/3h/RP+Rv54/qf+1/4I/zH/W/+J/6//0f/1/x0ASAB0AKQA1wAIATsBcAGpAeEBEwJKAn8CtwLyAiADRQNnA4ADjwOdA6wDwAPeAwUELwRkBJkEvgTkBPwEDQUtBUgFaQWMBasFyAXeBfAFAgYbBi4GOwZJBlQGXAZrBnkGggaPBpUGoAa1BsMGyQbOBtYG2wbkBvUGCgckBzMHNwc6BzcHMgcgBw4HCAcCB/0G/gb4Bu8G7AbrBvEG+Ab/BgcHDAcNBwoHBAf3BuYG0AazBpoGhQZ/BogGiAaBBnsGbgZfBkYGJwYIBuYFxAWpBZUFiwWIBXwFYgVEBSAF9wTVBL4ErwSmBKMEowSmBKYEoQSSBHYEWQQ8BBwE/QPqA9oDywO7A6EDjgNzA1EDLgMMA+sCzAK4AqcCjAJsAlECNgIbAgkC+wHmAc8BugGgAYcBcgFdAUkBLgEZAQgBAQH+AOcAygCuAJQAcgBJACYAAQDn/9L/sf+Q/27/Qf8X//D+zP63/qX+m/6V/pH+mP6a/pf+jv6A/mz+W/5S/kX+Mv4Z/v395f3I/aj9iv1z/V39Rv06/TT9Nv1G/Vr9Yf1k/Wz9cf1z/Wz9Z/1o/WP9YP1Y/Uf9Nf0n/Rb9AP3p/Nf8x/y1/KX8mPyW/Kn8wvzS/N384fzm/O786fzi/Nr80fzP/NL8z/zS/Nz84Pzj/N380/zP/Mz8yfzE/Mr81fzi/Pf8AP0B/Qb9Cf0J/Qf9+/zo/OD83fzb/Nz82PzS/NL80fzR/Mz8zfzW/OP89fwK/SL9Lf0t/Rr9+Pzd/ML8p/yP/HP8XPxQ/Ef8RvxL/FP8W/xc/Fj8VfxZ/Fj8V/xc/Fb8UvxM/Dv8L/wb/AX8APz4++378vvz+/T7//sE/An8D/wT/BP8Bvz4++P70vvS+9b76vsJ/CP8O/xK/E38Sfw4/Cv8Kfwu/En8ZvyE/KL8tvzF/Mf8zfzO/NH85fzx/Aj9LP1H/WD9d/18/X39g/1+/YX9nf2//eX9D/5D/nX+pP7I/ub+Cf8v/0//bv+W/7D/y//n//3/EgAlAD4AWgBsAHUAiwCqAMsA8wAbATwBWgFwAYgBnAGwAdEB8gEcAjwCWgJ4AooCpwK/AuECDANBA3oDpQPWA/4DGAQnBDQEQQRKBE4EVQRmBHoEkgSfBK8EwATFBMIEygTbBOQE9gQEBRIFJAUzBUMFSQVZBWsFgAWYBacFtwW1BasFpwWqBawFqQWmBaIFoQWXBYoFcwVSBTYFFAX8BPwEAwUTBSgFLgUoBRgFAwXnBM0EuQSnBJwEmwSgBKMEmwSBBGYEQQQiBA8E+gPzA+4D4gPKA6sDjQNnA0IDGAP2AuAC1gLUAtYC2gLdAtkCxgKuAo4CXQI1AhoCAwL4AfEB6QHeAcIBlQFdASsB+wDcANQAzQDZAOcA8wD8AO8A4wDRAKkAeABMACgAFQD//+r/6P/g/87/tP+Z/4D/YP9A/yT/Ev/+/uv+5f7d/tD+uP6j/pj+kv6I/nj+c/5q/ln+U/5R/lP+VP5T/lL+Tv5V/lP+Q/42/ir+Hv4U/hT+EP4R/hb+Df77/fP97v3f/dr92P3T/dH92v3m/ez98f30/fT98v3//Qb+Bf4M/gv+C/4L/v396/3Y/cL9r/2j/aH9of2g/Zn9j/2P/ZT9mf2h/a79uv3G/dH94v36/Qr+Ff4k/i/+M/42/jj+Ov4x/iT+Fv4P/hH+DP4U/iL+Mv5M/mr+fv6F/oz+kf6S/pL+mv6p/rj+wf7L/tb+1v7S/sr+vP6v/qn+rf7E/tb+3f7h/uT+5v7k/uT+4f7d/tv+2P7T/tL+3f7j/tv+1f7P/sH+tP6k/p7+ov6o/rz+0v7i/ub+7P7t/tr+xP6h/on+gv5//ob+jP6R/o/+iP59/mT+Tv47/in+Jf4l/i/+Ov5E/k3+Tv5M/j7+Mv4c/vz98P3u/fT9/v3//fj97P3c/dL9yv3A/b79u/27/cb9y/3M/dT91P3V/dn91P3O/cn9xP3E/cb9xf3Q/ef9+v0E/g3+Gv4k/i3+Of5P/mv+fP6F/pf+tf7L/tb+3f7g/uX+7f7s/vD+Cv8e/yn/Q/9m/4j/uf/n/w0ANABRAGoAiACkALgAygDgAPMAAQEaATsBWAFqAXoBjwGfAbUBzAHcAfMBCQIUAioCTQJqAosCqAK8AtQC7gINAygDOAM2AzUDPANFA1UDaQOEA5oDrAPDA9YD7AP9AwUEDgQUBBsELwRABEkETQRDBDoENAQuBCgEJgQoBCgELQQ2BEMEWARkBGQEYgRhBFsETgRBBC4EGAQMBAIE9wPtA98DzwO7A6EDhQNsA1oDUQNNA04DWANhA2kDZANNAy8DDgPzAtsCzALAArECogKNAoQCegJeAkUCIwIDAuYBxwHAAboBqAGOAXEBWAEzARgBCwH8APIA6QDfANwA1gDHALAAmwB/AFYAOAATAO//zf+q/5L/e/9w/2T/Vf88/xr/Av/y/uj+3P7M/rr+rv6h/pT+jv6F/nf+b/5q/mr+bv5u/nH+dP5v/mn+VP47/iX+DP74/eT9yf2z/Z79hv1v/WX9Y/1f/Vv9Vv1O/Uf9Qf1D/UX9RP1E/UP9Q/1B/T/9Qv1H/U79Tf1F/UX9Pf0w/S39MP02/T79PP08/UT9UP1h/XH9fP2A/YH9gf2C/YX9hv2O/Zv9p/2t/bL9uP25/cH9wv28/cT9yv3T/eD94v3m/en98f0B/g/+H/41/lH+bP6O/qn+t/7H/tH+2f7p/gT/Ff8a/y7/Nv8z/zL/Lv84/1D/bP+E/47/k/+a/6n/wv/Y/+X/8/8BAAsAEgAeACoAOABGAFQAZwB2AIIAiwCKAIQAgwCHAJAAogCwALQAtgC6AL0AuQC1AKsAmgCQAH4AdgB7AHwAfwCCAIIAiACIAHkAawBaAFAAVgBYAFYATQBHAEsAUABSAFAASgA7ACcAEgD5/+3/4//S/8f/vf+w/63/pf+W/5P/jv+O/5z/pf+q/6z/nf+P/4j/e/9x/2n/V/9F/zz/OP8u/x7/E/8L/wL/+v4C/xP/If8s/zb/Pv9D/0r/Sv9I/0L/PP8+/0X/Sf9O/1P/Vf9f/23/ev+F/4//nP+o/7b/xf/X/+v/9f/+/wUAAAD7//n//f8BAAwAGgAnADMARABbAGsAhACaAKgAtAC9AMwA3gDyAAMBGgEwAT0BRgE+ATIBLQEtATIBQQFSAV0BaAFyAYIBlgGsAbsBwwHGAc4B3QHkAfcBCgIZAicCMAI2AjgCPQI+AjkCMgIxAjQCMwI1AjUCLAIdAhMCEAIRAhQCHQIwAjwCQAJMAlUCXAJqAmsCaAJvAnACbgJmAlkCSwI/AjYCKgIgAhACBAL/AfIB8gH7Af4BAwL6AesB4AHdAd8B2wHRAcMBtwGvAaIBlQGHAX8BeQFrAWEBVAFFATYBIgELAfEA3ADKALkAqACaAJMAhQB6AG8AWwBDAC4AIgAXABYAGQAZABAA/P/s/9r/xv+u/5j/iv+A/3n/e/98/3H/ZP9T/z7/M/8v/yn/JP8i/xj/AP/n/tP+x/61/qj+q/6p/qn+ov6Z/pb+i/6B/nT+Zv5d/lD+Qv46/jT+Kv4d/g7++P3v/ez94f3f/eD95v3x/fT96/3f/dL9v/2u/Z39nf2p/bb9wf3J/c390f3P/c790P3P/dD9xP23/bf9wv3Y/fL9D/4o/j/+Rv5G/kf+R/5T/mD+df6U/q3+yf7h/u/+8v7r/uP+4v7j/ub+8v4M/yH/K/84/0f/VP9d/2z/bv9m/2//d/+R/6z/vP/F/83/4v/l/+j/+f8DAAYAEAAlAD4AXwCCAJgAnwCuAMAAxwDQANEAxwDHAMQAxADQANgA3wDiANcAxgC+AL0AugC6AMMAyQDMANMA4ADqAPIA+wAEAQYB9gDqANwA0gDHAK4ApgCYAI0AlQCYAJwAoQCeAJYAkQCNAIEAeAB1AGgAaQBrAFwAXwBdAEcAMAAeABUACAD+//7//v/1//P/BQAIAAoADQAFAP7/6v/k/+v/8f/7//v//v/3//H/6v/b/9T/y/+6/6b/oP+g/6P/pv+b/4j/df9t/2r/Zv9k/2X/av9y/3H/ZP9X/1D/Sv9A/zz/P/9K/1H/Uf9V/1j/YP90/33/g/+L/4//nP+p/7f/vv+9/73/uf+5/7n/uP+8/7v/sv+w/7P/s/+z/7r/xP/J/9T/3f/j//T/CQAaAC0AQgBWAG4AggCSAKYArgCvALEAtQDGAMEAvwDKAMoA1ADaAOQA8wDuAOoA4wDbANYA0wDfAOcA6gD8ABEBIwEzAUEBSAFEAUIBQQE7AT8BQAE5AUABRwFLAWYBfAF6AXkBbAFXAU4BQAE6ATgBNgEyASoBJwEgASMBJwElASIBHgEbAQ8B/wD4APYAAgEMAQkBAgH3APcA9gDwAO8A5wDdANQAyQC/ALEAoQCZAJEAgAB1AHQAdABnAFcASAA3ADQANwA5ADwAOwAzACIADQD4/+D/0P+4/5T/cv9P/zT/If8S/wr//f76/gP/Av8F/wv/Df8L/wb/B////vL+6P7W/s/+yf68/qz+l/6C/mv+Wv5K/jj+KP4U/gv+Bv4H/hL+E/4W/hP+Cv4M/gv+CP4I/hT+I/4Z/gz+BP76/f/9/v33/f79CP4T/hz+IP4Y/hX+Hf4d/hj+E/4Y/iX+Mf48/kj+Sv5K/kf+Of4w/jX+Sf5Z/mz+hP6S/qP+uP7I/tv+9/4P/xj/GP8f/yb/Kv9B/1L/Vv9k/3L/ev99/4D/g/+L/5z/sf/I/+D/9/8UADUAUwBeAFcASwBAADcAOQBJAFwAdwCXAKwAtwCzAKkAnwCRAIgAiQCQAKEAvgDWAO4A+ADsAN8AzQDIANcA6gD8AAUBDgEfATEBQgFOAVcBYQFmAWwBawFlAWwBbwFwAW8BZwFYAUoBRgE8ATkBRgFQAV4BaAFyAX0BhQGHAYMBgAF2AWQBVwFRAUMBMwEoASgBKwEtATABLQEfAQwBAAHzAOwA6wDdANQA2ADjAO0A8ADhAMQAsQCiAJEAiACHAI4AlQCSAIUAfABsAFYAOgAeAA0A///2//X/+/8BAAsADwAPAA4A/f/n/8b/p/+c/57/rv+9/8b/x/++/7X/rv+m/5//nv+b/53/ov+k/6H/of+f/47/fP9n/0r/Rf9J/0b/R/9F/0j/Uv9c/2P/Yv9l/2b/Yf9j/2T/Zv9u/3X/gv+Q/6H/r/+t/7H/u/+8/7j/vf/D/8T/xv++/7r/wv/I/9D/1//Z/9v/1P/R/9b/4P/3/w4AHwAoACYAIQAaABsAJAA7AFcAbgCEAJQAoQCvALkAxADOAMwAyQDJAM4AzADLANQA1QDZANkA0QDIALsAsgC1AMAA2ADwAAcBJgE1AUMBVgFjAWsBbgFvAWoBZwFlAWUBXgFXAVgBUwFGATMBJQEYAQwBBAEBAQUBCQEHAQoBDgEUARsBGAERAf0A6gDmAOoA8wD3APsA8QDfANMAwQC0AKIAhgBsAE8AOwA3ADkARABOAEQAOQAnAA0A+P/q/9z/1P/X/9L/0v/R/8n/yP+6/63/n/+X/5f/i/95/1//Rf8r/wz/8P7a/sv+xP7G/sL+w/7J/sz+0/7b/tz+1/7Z/tz+1f7M/sv+w/6z/qL+kf6D/nf+bv5j/l7+YP5i/mj+bv52/nz+hv6W/qH+pP6h/pn+kf6N/o7+kv6W/qL+qf6z/rz+s/6p/p7+lP6Z/q3+xv7c/uX+4P7j/uv+9P79/vj+7P7o/vH+//4U/zH/Q/9S/1//Zf9t/3v/kP+q/7j/yf/a/93/4f/f/+L/8f/+/xUAJAAsADcANgA7AEcASwBaAGcAdgCJAJQAngCvAL8AygDXAOYA8wD2APkA/QDvAN0A1ADXAOcA9AADARgBMAFEAVQBYQFpAXIBewF7AX0BgwGOAZoBlAGJAYoBiwGPAY8BkQGYAZoBqwHAAc0B2AHZAdwB1QHLAcABtgG7AckB1gHaAdwB3AHUAb0BngF8AV8BVAFPAVABWgFkAWYBagFpAWIBWQFVAVUBSAE/ATABHwEcARIBCgEDAfwA/QD1APEA6ADaANoA0gDHALoArACjAJ4AmgCLAH4AdABtAGoAYABVAE0ARQA+ADAAJAAhABoAEgAHAPz/9P/v/+j/4//e/87/v/+r/5j/iv94/2//Zv9Z/0r/Nf8o/xX/Av/6/vP+7v74/gb/DP8b/yb/KP8q/yP/Fv8A/+7+7f7o/uv+9/79/gL/CP8R/xf/Ev8K/wH/9/7y/vX+8v7t/vH+7v7v/vL+8f7y/vL+9P77/gn/G/8m/yz/K/8j/yH/Jv8i/xv/G/8Z/xb/GP8g/yj/Kf8g/yD/KP8y/z7/SP9U/17/a/93/4r/p/+4/8b/z//S/9X/0//a/+f/7//6/wIAAgABAPn/8//1//v/AQAGAAcABQALABUAHgAyAFIAbACDAJMAkQCKAHsAbgBqAGQAZQBtAHUAdgB2AHMAbABpAGQAYwBjAF4AWwBUAFQAWgBaAF4AYQBYAE4ASgBEAD8APQA5ADgAPgBEAEcAQgA0ACcAGgAKAPf/6//k/9X/yv/I/8r/2f/i/9z/1//N/87/2f/Z/9j/1P/H/7//vP+z/6r/nv+T/4L/av9b/0X/NP8j/w7/+f7w/u7+4f7a/tb+zv7K/sj+xv7F/sT+xf7M/sv+yv7I/sL+v/69/rT+sP66/rX+r/6v/qj+ov6c/pj+mP6a/qL+rf6w/rL+uP7D/sn+0P7S/s/+1/7e/uj+7P7s/u7+8/78/gD/Bf8F/wD/A/8K/xr/L/89/1D/Yv9v/3X/e/+L/5n/pv+t/7b/wv++/7v/uv+z/7L/qf+r/8L/0v/o//3/AwAHABIAGwAbACAAKgAyADwATwBiAHEAggCJAJEAoACwAL8AzwDWANUA2wDsAP8AFAExAUMBUgFjAXABfgGKAZYBkwGCAXEBWwFCATEBJQEcAREB/QDZAJkARgDx/5z/UP8d/wP/Ev9N/7L/MwC0ACMBbQGPAY0BdwFUATcBNQFLAYEB0gEuAnUCmAKPAk8C6AFlAecAeAAgAOT/vf+z/7v/vv+u/5H/Xf8b/+H+s/6i/rj+7v48/6L/CgBqAKQAsgCgAHcATwAzACkAKgBDAFUAXwBxAGwAUgAYAL3/Vv/z/qD+df5o/nv+sf7g/g7/N/8//y7/E//w/tT+2f7t/hL/Tf+A/53/qv+Y/2r/Mf/v/qr+eP5d/lP+XP5t/n7+i/6C/nD+Wv48/ib+Jf4z/mL+tv4U/2//rv/G/7f/i/9b/y3/DP8C//7+/v4C/wH/+f7t/t/+w/6Q/lP+G/70/eX96v0R/kn+gv62/s/+5f7x/u3+9/4D/xr/Ov9l/5f/wv/9/zAAUABTADUADwDj/7r/pv+t/8f/6/8RADQATABSAD8AGQDv/8H/p/+n/77/7P8kAF8AhQCTAI4AaQA9ABsAAgD+/woAIAA1AEsAZwBzAHQAbgBaADMAEgALAB8AVgCXANEADAEwAUEBTAFMAUEBNQEwASgBGwEXAScBOgFGAVMBSgEqAfwA0QCmAIEAdwBzAH8AjwCUAJEAhAB+AHIAYABSAEAALQAlADAAQgBYAHIAgACDAH8AdQBkAFIASwBFAD4AQgBMAFsAaABwAG4AYgBQADYAGAD8//H/9/8HACIANwA+ADoAKAAZAAwA/v/t/9T/yP/B/73/w//M/9D/xf+2/5v/hf99/3v/f/+I/5//uf/E/8n/zf/S/9b/3v/m/+f/6//t//H///8EAAAA/P/x/9z/0v/P/8v/z//X/+T/6f/u//b/+f/4//j/9f/v/+v/4//l//T/BQAcADQAPwBIAFEAVQBfAGYAcgCHAJcAqAC6AMgA0ADeAOYA5QDiAOEA3wDZANsA3QDiAOoA9QD9APwA9ADuAPUA+QD8AAcBEwEiAS0BNwE/AT8BOQEqASMBJAEjASoBPwFVAWMBawFyAW0BaAFdAVIBWQFbAWIBcAF+AYkBjAGOAY4BjAGIAYEBdQFyAXUBeAGCAX8BfQF+AXkBcgFtAWgBXQFXAUsBQAE5ATQBNwFBAU4BSgE6ASMBGgEQAf0A+ADyAPQA/wAGARgBIwEiASEBGQERAQoBAwEDAQAB/gD5AOcA1wDLALkAqgCdAJAAiAB4AGYAXABbAFsAUwBDAC8AGgAEAO//3//V/9L/1f/e/+f/9f8EAA4ADgAKAAgAAQDz/+j/4P/Z/8//v/+v/5z/ef9Z/0P/L/8h/xz/Ef8F///++v73/vr+A/8K/w3/FP8b/yL/IP8d/xb/Ef8X/xn/FP8R/xD/Av/w/ur+5P7i/uH+3f7e/uf+8v4B/xT/Gv8c/yL/I/8l/yb/Lf8y/zD/M/8z/yn/H/8Z/w//C/8N/w//GP8Y/xT/Ff8N/wz/F/8j/zf/Sf9b/2r/cf99/4r/kf+a/6j/tf/E/9f/5P/r//X/AgAEAP7///8AAAEABAABAP//BgAJAAoADgALAAYABAAFAAUACQANAAgAAgABAAcADAATABkAFwANAAYABgACAAUAAgAEAA0ADwATAA4ABAD6/+7/5v/r//f/BgAVABwAIgAiABgACQD9//f/+P/6//7/BQANABwAJAAkACEAFgAKAP3/8//v/+3/8P/3//f//P////r/+P/w/+X/2v/Q/8P/vP+8/73/vv+6/6//nf+R/4f/gP99/3f/bv9v/4H/jv+V/6H/of+W/5L/jf+N/5D/iP+B/37/g/+J/4//l/+W/5f/k/+K/4n/iv+O/4r/hP+H/4n/k/+e/6P/qP+j/5X/kv+S/47/kv+a/6D/ov+f/6D/pf+q/7D/uv/D/8b/x//K/8v/xv/B/7z/uv+9/7//wv+8/7b/rP+b/47/gv95/3X/b/9t/3f/gf+O/6L/s//C/9L/7P8CAA8AGwAoAC4ALAArAC4ANwBAAEoAUABXAF4AWQBUAE4ASABGAEUASgBHAEgATwBTAFsAYwBqAG4AaABkAGYAcAB/AIYAgQB1AGkAZgBtAHEAdwB6AHYAcABlAGIAYgBeAFsAXABjAGsAcgB6AH0AegB1AHEAcQB7AH8AhwCRAJUAngCdAJ0AmACSAJQAlACWAJMAigCDAIAAfgCDAIkAhwCEAHQAbABfAEUAMwAoACAAEAAGAPr/4v/P/73/sv+s/6//uv+7/8P/y//M/9P/1//c/+D/3P/W/87/y//E/77/vv+9/7D/oP+Y/4z/fv94/3H/Yv9W/07/Rv9D/0X/RP8+/zL/J/8m/yP/If8i/xj/Cv8D//v+//4G/wr/Dv8I/wn/EP8f/zD/Of9C/0L/Rv9J/0j/SP8+/y3/H/8X/w7/Av/2/ur+3/7Z/tL+zP7L/sX+wv7E/sr+0v7X/tr+3v7n/vj+Bv8U/yn/Ov9B/0X/Tv9W/1j/X/9l/2b/Zv9j/1f/UP9O/1D/W/9m/23/cv98/4n/kP+a/6H/pP+i/57/pP+t/7P/uP+2/7X/tv+z/7H/sP+r/6r/t//M/97/5v/n/+j/6v/1/wIADAARABEAEQANAAwAEgAdACQAJwAuADcAQQBKAFAAWABWAFsAZQBoAG8AdgB+AIQAiwCXAKgAuAC7ALgArACnAKkAqACnAKMAowCgAJ8AqACtAK8AtQC9AL4AuQC8AMAAwgC+AL4AwQC8ALsAugC2ALEApACgAKMAoACiAKYAsAC6AMMA1wDsAPoAAwEMAQgBAgH8APMA7ADoAOcA2QDLAMMAvAC4ALMAqgCmAKgApwCmAKUAqQCsALIAugDBAMoAygDKAL8ArACdAJMAkQCOAIoAhQCAAHMAbQBrAFwAUwBUAEsAPAA0ADIALAAkACUAIwAdAB8AIgAfABoAFgAPAA0ADwAQABAAFQAdACIALAA0ADsAOwA6AEEASgBRAFkAYABcAFcATgBDAD8AQABEAEcATgBYAGMAbAB3AH0AeAB3AHwAfAB9AIEAewB4AHQAbgBmAFwAWABUAE4ASgBIAEIARgBGADwAOgA8AEsAXQBxAIIAjwCdAKIApwCwALwAwgDPANMAzgDRAMgAxwDEAL8AxADJANIA1gDWANYA1QDcAO4A+QADAREBEgEPAQcB/gD1AOgA4gDcANEAywDLAMkAxgDCAMYAyQDLAMUAwQC7ALEAqgCeAJoAlACBAHEAaQBoAGgAbABqAGUAXQBYAFQAUwBdAGYAawBxAHMAbQBnAGMAWABAADEAKQAYABIAEQAWABYAEAARAA4ADQASABYAEgAGAPv/8//y//j//v8CAP//+f/0/+j/4P/Z/83/vv+z/6z/o/+g/57/oP+d/5L/jv+O/43/iv+I/4X/fv95/3b/cv9z/3P/Z/9h/2D/Xf9j/3H/e/9//4j/kP+T/5X/lP+Q/4f/ff93/3L/df9+/43/of+k/6L/p/+j/5z/n/+j/6v/t/++/8j/0//Z/93/3v/V/8D/tf+y/67/r/+2/7n/u/+//7//vv+8/7r/t/+0/7r/wf/J/9T/2//e/93/3v/d/9f/1//a/9z/2v/U/9b/1//h//H/9//5//r/+f/7//3/BAATAB8AIQAmADIAOQA8ADcALgAmACEAJwArADAAPABBAD4AOAA0AC0AKQAqACwAMAA1AD0AQAA/ADkAMAAwADUAMQAqACYAIgAaABEAEQAZABgAFgAVABAADgASABMAEgAQAA8AFAAbACEAIwAnADEANwA7AD0ANgAtACgAGgATABgAFwAbAB0AGAASAAwADAAKAA4AEQAWABcAGAAgABoAGAAYABMAEAAFAPz/8//w/+r/4//d/9P/yv/H/8b/vv+7/77/w/+9/7X/uf+x/6f/n/+b/53/of+o/63/s/+u/6j/rf+r/6L/nf+c/5j/mf+g/53/nP+h/6D/nv+i/6P/nv+a/5H/gf98/4L/hP+H/4b/gP9//3j/b/9n/2L/Xv9T/1L/Wv9g/2f/bP9p/13/U/9S/1H/TP9K/0P/Qv9M/1X/Yf9t/3z/gv9+/4D/gf+D/4f/h/+H/4f/jf+T/5T/lf+V/4//hv+C/4L/eP9t/2P/W/9g/2b/av9r/2v/Zv9c/1r/Wv9Y/1z/Zv9q/23/eP+E/4n/iP+L/47/kf+P/5L/k/+S/5j/mv+i/6b/of+a/5H/hv+B/4v/lv+h/6f/qP+q/6r/rv+w/67/sf+7/7r/wP/T/9P/1f/f/+T/6//u/+//5//k/+z/6f/q/+3/6//g/9T/0//T/9f/1P/U/9X/1P/a/93/4//o/+3/7v/v/+z/5f/l/+j/7f/w//L/9v/8////AwAGAP//9f/x//H/6f/k/+T/5//q/+//9v/+//v/8//u/+L/2//X/9f/2//Z/97/5v/p/+f/5f/i/9j/0P/H/8H/vP+1/6//sf+6/8D/y//L/8b/wP+4/7b/qf+j/6X/q/+v/6j/pv+f/5j/lP+J/4D/gP9+/3r/ev+C/4v/lP+g/6H/m/+U/5f/k/+R/5j/l/+b/53/oP+i/5v/mv+Y/5T/lf+c/5z/mv+Z/5z/of+g/6L/o/+l/6P/of+n/63/q/+l/6r/r/+1/8H/zP/Y/+D/4//k/+j/7v/r/+3/9v/+/wYADgAcAB8AGAAYABAABwAEAAYADAAMAA4AFQAcACQAKgAvADoAQgBHAE4AVwBlAGcAaAB3AHwAegB2AHAAaABgAFcAUQBVAFYAVgBTAFMAVQBWAF0AYgBlAGUAaQBvAHgAiwCWAJ8ArgCzALYAugCyAKoAoQCdAJUAhwCFAH4AegB+AIIAhACHAIQAdwBuAGYAXwBcAGgAdgCCAJMAmQCcAJ4AmACUAJMAkACUAJcAoACsALMAugC3ALMArACiAJcAjQCPAJAAkACPAI8AkACIAHsAawBlAFsAUwBaAF8AYwBsAHcAhACQAJMAjwCKAIMAewBxAHAAcQBoAGkAawBoAGgAYwBZAFMAVABUAFsAYgBlAHEAdgB1AHEAbABnAF8AVwBNAEcARwBGAEQAOgAyACwAJgAjACAAJAAnACsAMgA0ADUALwAkABwAEwAJAP7//P/+//j/9v/8//X/8P/x/+7/7P/l/+j/7v/r/+j/6v/z//b/9//1/+3/5//f/9v/2v/d/+X/7f/z//P/7//0//P/5//m/+D/2//g/+L/5v/l/+X/7P/q/+D/2f/V/8v/wP+7/7j/tf+4/73/vf++/8X/yv/Q/9v/6v/w//b/AAACAAQABwAEAAIAAwAAAP3/9//x/+3/7P/q/+n/7v/y//P/7v/q/+f/4f/f/9//3P/b/93/3f/j/+j/7P/1//r//f8DABAAGgAdACUALwAzADcAQQBIAEsATwBVAFoAWABZAFUASgBHAEgAPgAwAC8AMAAmAB8AHAAZABQADgAPAA4ACwAQABIAFwAjACgAKgApACUAJAAmACoAKwAxADgANQAzADMAKgAbABYAHAAYABMAGgAZABIADgALAA4AFQAZABYAFQARAAUA/P/2//H/7v/v//H/8//3//v/+v/2//L/7v/r/+T/3P/d/97/1v/Q/8//0P/L/73/sf+k/6D/oP+d/6L/pv+n/6f/o/+j/6b/qP+s/7X/uP+1/7j/u//D/8z/0v/a/9n/0f/K/8L/u/+4/7D/rv+4/7n/uf+8/7n/uP+0/63/p/+q/7f/u/+9/8n/zv/Q/97/5//r//X/9//4//b/9P/2//P/9P/0//T/9P/3//z//v/+//n/9f/p/97/3P/h/+v//P8KABAAGgAgAB0AFgAUABEADgARAA0ACgAMAAgA///+/wEAAAD//wYACwAQAB4AKgAwADQAPwBGAEoAUwBUAFUAWQBZAFQAWgBgAF0AXABbAFcAUwBSAEwAQAA0ACwAKAAoACoALQAoAB8AHAAUABMAFAAVABYAFQAUAAoABQD9//P/8f/s/+r/5v/m/+j/6f/x//r/AAD//wEAAwD6//f/9//z//H/7//s/+X/2v/O/8T/uP+w/6n/ov+o/67/tv++/8H/xf/D/7f/rP+r/6b/o/+l/6T/qP+r/6v/pf+b/5L/h/9+/3n/e/+G/5L/lv+Y/5f/kP+J/4T/ev9u/27/c/9y/3b/ev95/3j/fv+B/37/gP+C/4X/i/+P/5z/rP+z/67/q/+u/7D/tP+4/7r/t/+2/7X/rP+q/7H/sv+p/6j/rv+u/67/tf+8/7z/vf/B/8b/z//M/8X/yP/H/8H/vf/B/8T/wv/B/77/vf/B/8v/1v/g/+v/6//m/+z/9f8DABEAGgAkACgAMAA4AD8APQA2ADkANQAuACcAJwAtACwAKwAqACsALQAyADIAMgAyAC4AMQAzADMANwA/AEQASABJAEgAQwBBAEAAPgBCAEUATwBUAFUAWABfAGMAWwBZAFsAZABqAHAAeQB9AIYAgAB7AHsAeAB7AHAAbwBoAFwAWwBVAFcAWQBiAGcAbAB3AHwAfQB/AIUAhgCAAHwAfAB6AHUAdgBzAGsAZABXAEsARQA/AD0APwA/ADYANQA8ADYAMAAvADEANQA3ADQAMwA3ADYAMQApACcAJQAbABwAIgAkACMAKgA3ADwAPQA+AD0ANwAqAB0AFAALAAUABgAEAAUACAAJAAYABQACAP3/+v/x//D/+P8BAAkAGwAmACwANwA6ADgAMQAwACgAGQAWABcAGgAYABoAGwAXABYAEgALAAoACAADAAcACwATABYAFwAcAB0AJAAjACAAHAAeACAAJQA2AEAASQBPAFEAUABMAEoARABJAEwATgBTAFUAWQBaAFsAWwBZAFsAWwBZAFoAXABcAFcAXgBkAFoAVgBVAFMATwBLAEgASQBHAEAAPgA4ADcAOwA+ADcAMwA0ADUAOgA6AD0AQwBGAFIAWgBeAGEAYABbAE8ATwBTAFMAWQBaAFsAXABUAEwARQA7ADYAMQAtAC4ANQA5ADQALwAsACMAHQAhACAAFwAXABkAFAAOAA8ADQAGAAAA+v/6//z/9P/x//H/7f/m/+j/6v/m/+b/5//m/+P/4//m/+X/5v/n/+7/7f/s/+3/5f/e/9T/yP/C/8j/zP/R/9j/2f/Z/9T/z//L/8r/xv/F/8j/xP/H/9D/0f/N/83/yP+//7j/rv+o/6b/q/+r/6v/tf+2/7X/tv+5/7X/qP+k/5//nf+c/5j/lv+a/53/nf+h/6D/n/+j/6n/pf+k/6r/r/+y/7r/wv/G/8r/xf+//7n/sv+s/6f/rP+u/7D/tv+5/7f/uf+7/7n/uv+2/7X/vP/E/8r/0P/Z/+L/6f/p/+v/7//t/+z/7f/v//L/9v/+/wMAAwD///j/9//5//b/9f/0//n/AQAGAA8AFgAaABgAEwAWABgAHQAiACsANQA8AEIARgBLAEoASgBVAFwAYgBqAHMAcwB0AHgAeQB5AHYAdABtAGcAZABiAGAAYgBpAG0AdAB8AIAAfQCAAIEAfwCAAHoAdwB4AHMAcABvAG0AaQBeAFkAUwBNAFIASgBFAEUAQgA/ADsANgA1ADgAOAA4ADUANQA0AC4AMAAvAC0ALwAtACwAKQAlACEAFgAJAAEA+P/u/+z/6v/n/+z/8P/r/+v/8P/v/+n/7P/z/+n/3v/U/8r/w/+//7v/sf+t/6z/oP+c/5v/kf+I/4D/d/9w/2//c/9x/3D/df92/3D/av9o/2j/ZP9j/2P/aP9v/23/av9r/27/bf9t/23/bf9r/2n/aP9i/13/Xf9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<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">show_audio</span><span class="p">(</span><span class="sa">f</span><span class="s">"</span><span class="si">{</span><span class="n">DATA_PATH</span><span class="si">}</span><span class="s">/numbers/one_5.m4a"</span><span class="p">)</span>
</code></pre></div></div>
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" 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<p>The experiment we are aiming for is to see how much better the DTW distance is compared to the euclidean distance, when it comes to clustering same words together. For that we starting by defining a couple of functions, namely a function to load one sound file at a time, as well as one function which loops through all files within one folder. Note that we are normalizing the sound sample, given that are not interested for this experiment to be influenced by how loud a word was said.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">def</span> <span class="nf">load_audio_file</span><span class="p">(</span><span class="n">audio_path</span><span class="p">):</span>
<span class="n">audio</span> <span class="o">=</span> <span class="n">AudioSegment</span><span class="p">.</span><span class="n">from_file</span><span class="p">(</span><span class="n">audio_path</span><span class="p">)</span>
<span class="n">audio</span> <span class="o">=</span> <span class="n">audio</span><span class="p">.</span><span class="n">set_frame_rate</span><span class="p">(</span><span class="n">SAMPLING_RATE</span><span class="p">)</span>
<span class="n">samples</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">array</span><span class="p">(</span><span class="n">audio</span><span class="p">.</span><span class="n">get_array_of_samples</span><span class="p">())</span>
<span class="n">normalized_samples</span> <span class="o">=</span> <span class="n">samples</span> <span class="o">/</span> <span class="nb">max</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>
<span class="k">return</span> <span class="n">normalized_samples</span>
<span class="k">def</span> <span class="nf">load_sound_data</span><span class="p">(</span><span class="n">folder_name</span><span class="p">):</span>
<span class="n">folder_path</span> <span class="o">=</span> <span class="sa">f</span><span class="s">"</span><span class="si">{</span><span class="n">DATA_PATH</span><span class="si">}</span><span class="s">/</span><span class="si">{</span><span class="n">folder_name</span><span class="si">}</span><span class="s">"</span>
<span class="n">file_names</span> <span class="o">=</span> <span class="n">os</span><span class="p">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">folder_path</span><span class="p">)</span>
<span class="n">list_audio_arrays</span> <span class="o">=</span> <span class="p">{</span><span class="n">i</span><span class="p">:</span> <span class="n">load_audio_file</span><span class="p">(</span><span class="sa">f</span><span class="s">"</span><span class="si">{</span><span class="n">folder_path</span><span class="si">}</span><span class="s">/</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s">"</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">file_names</span>
<span class="k">if</span> <span class="n">i</span><span class="p">.</span><span class="n">endswith</span><span class="p">(</span><span class="s">"m4a"</span><span class="p">)</span> <span class="ow">or</span> <span class="n">i</span><span class="p">.</span><span class="n">endswith</span><span class="p">(</span><span class="s">".mp3"</span><span class="p">)}</span>
<span class="k">return</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">list_audio_arrays</span><span class="p">.</span><span class="n">items</span><span class="p">()))</span>
</code></pre></div></div>
<h2 id="sampling-rate">Sampling rate</h2>
<p>One particularly important aspect when it comes to sound data is the <strong>sampling rate</strong>. The sampling rate defines the number of samples per second taken from the continuous signals “sound” in order to make it discrete and measurable. This rate is measured in hertz (Hz) per second. Usually audio signals have a sampling rate of 48.000, which is the standard audio sampling rate used by professional video equipment.</p>
<p>The problem that comes with a higher sampling rate is that we are getting more data. The sound is richer. Normally we would think that this is positive and desirable. This might be true when listening to music, but it is not as desirable when trying to work with sound-data. Since DTW already suffers from heavy scaling issues (which was the main motivation of <code>fastdtw</code>), we are not interested in getting so much data.</p>
<p>When processing sound-data, it is not necessary to sample anywhere close to the standard 48,000 Hz. In the following we are using a sampling rate of 100 Hz, which is the usual sampling rate for sound-data.</p>
<h2 id="visualizing-of-sound-waves-numbers">Visualizing of sound-waves: Numbers</h2>
<p>In the following we define two functions in order to find all the audio-files in a local directory, as well as loading each individual file, changing the sampling rate and standardize the resulting numpy array between zero and one. From the chart below we can nicely see that the fifth recording, which was designed to be a fast-pace version of the word, is on average much shorter than the other recordings.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1"># Load data
</span><span class="n">SAMPLING_RATE</span> <span class="o">=</span> <span class="mi">100</span>
<span class="n">numbers_sound_dict</span> <span class="o">=</span> <span class="n">load_sound_data</span><span class="p">(</span><span class="s">"numbers"</span><span class="p">)</span>
<span class="c1"># Plotting the different numbers
</span><span class="n">number_names</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">([</span><span class="n">i</span><span class="p">.</span><span class="n">split</span><span class="p">(</span><span class="s">"_"</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">numbers_sound_dict</span><span class="p">.</span><span class="n">keys</span><span class="p">()]))</span>
<span class="n">extension_names</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">([</span><span class="n">i</span><span class="p">.</span><span class="n">split</span><span class="p">(</span><span class="s">"_"</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">numbers_sound_dict</span><span class="p">.</span><span class="n">keys</span><span class="p">()]))</span>
<span class="n">NUMBER_OF_NUMBERS</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">number_names</span><span class="p">)</span>
<span class="n">NUMBER_OF_FILES_PER_NUMBER</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">extension_names</span><span class="p">)</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="n">NUMBER_OF_NUMBERS</span><span class="p">,</span> <span class="n">nrows</span><span class="o">=</span><span class="n">NUMBER_OF_FILES_PER_NUMBER</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="s">"all"</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="s">"all"</span><span class="p">,</span>
<span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">))</span>
<span class="n">color</span><span class="o">=</span> <span class="n">cm</span><span class="p">.</span><span class="n">rainbow</span><span class="p">(</span><span class="n">np</span><span class="p">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">NUMBER_OF_NUMBERS</span><span class="p">))</span>
<span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="p">(</span><span class="n">c</span><span class="p">,</span> <span class="n">number</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">color</span><span class="p">,</span> <span class="n">number_names</span><span class="p">)):</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">example</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">extension_names</span><span class="p">):</span>
<span class="n">key_name</span> <span class="o">=</span> <span class="sa">f</span><span class="s">"</span><span class="si">{</span><span class="n">number</span><span class="si">}</span><span class="s">_</span><span class="si">{</span><span class="n">example</span><span class="si">}</span><span class="s">"</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">].</span><span class="n">plot</span><span class="p">(</span><span class="n">numbers_sound_dict</span><span class="p">[</span><span class="n">key_name</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="n">c</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">].</span><span class="n">set_title</span><span class="p">(</span><span class="n">key_name</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></div></div>
<p><img src="/assets/post_images/dtw/output_37_0.png" alt="png" /></p>
<p>It is now time to compare the performance between the two distance algorithms. This is done comparing every recording against the “one_1.m4a” recording. A good performing distance algorithm would then be able to find a smaller distance between every version of “one”, compared to recordings of the words “two” or “three”.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">relative_warping_band</span> <span class="o">=</span> <span class="mi">5</span> <span class="o">/</span> <span class="mi">100</span>
<span class="n">array1</span> <span class="o">=</span> <span class="n">numbers_sound_dict</span><span class="p">[</span><span class="s">"one_1.m4a"</span><span class="p">]</span>
<span class="n">euclidean_df</span> <span class="o">=</span> <span class="n">pd</span><span class="p">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="p">.</span><span class="n">empty</span><span class="p">((</span><span class="n">NUMBER_OF_FILES_PER_NUMBER</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">NUMBER_OF_NUMBERS</span><span class="p">)),</span>
<span class="n">columns</span><span class="o">=</span><span class="n">number_names</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">NUMBER_OF_FILES_PER_NUMBER</span><span class="o">+</span><span class="mi">1</span><span class="p">))</span>
<span class="n">dtw_df</span> <span class="o">=</span> <span class="n">euclidean_df</span><span class="p">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">r</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">array1</span><span class="p">)</span> <span class="o">*</span> <span class="n">relative_warping_band</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">sample</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">NUMBER_OF_FILES_PER_NUMBER</span><span class="o">+</span><span class="mi">1</span><span class="p">))):</span>
<span class="n">list_euclidean</span><span class="p">,</span> <span class="n">list_dtw</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">number</span> <span class="ow">in</span> <span class="n">number_names</span><span class="p">:</span>
<span class="n">array2</span> <span class="o">=</span> <span class="n">numbers_sound_dict</span><span class="p">[</span><span class="sa">f</span><span class="s">"</span><span class="si">{</span><span class="n">number</span><span class="si">}</span><span class="s">_</span><span class="si">{</span><span class="n">sample</span><span class="si">}</span><span class="s">.m4a"</span><span class="p">]</span>
<span class="n">euclidean_df</span><span class="p">.</span><span class="n">loc</span><span class="p">[</span><span class="n">sample</span><span class="p">,</span> <span class="n">number</span><span class="p">]</span> <span class="o">=</span> <span class="n">euclidean_distance_calculator</span><span class="p">(</span><span class="n">array1</span><span class="p">,</span> <span class="n">array2</span><span class="p">)</span>
<span class="n">dtw_df</span><span class="p">.</span><span class="n">loc</span><span class="p">[</span><span class="n">sample</span><span class="p">,</span> <span class="n">number</span><span class="p">]</span> <span class="o">=</span> <span class="n">dtwupd</span><span class="p">(</span><span class="n">array1</span><span class="p">,</span> <span class="n">array2</span><span class="p">,</span> <span class="n">r</span><span class="p">)</span>
</code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>100%|██████████| 4/4 [00:00<00:00, 80.13it/s]
</code></pre></div></div>
<p>Before comparing the results we are scaling the distances for better readability. It is important to note that it is <strong>not</strong> possible to compare distances between algorithm. One cannot say that the distance of the euclidean distance is half the distance of the DTW and therefore shorter. One should only compare distances within the same category. Hence, we are checking in each row which is the smallest distance to the benchmark recording, and then conclude what, according to the algorithm, is the closest match.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">def</span> <span class="nf">plotting_standardized_results</span><span class="p">(</span><span class="n">dataframe</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s">""</span><span class="p">):</span>
<span class="n">standardized_df</span> <span class="o">=</span> <span class="n">dataframe</span> <span class="o">/</span> <span class="n">np</span><span class="p">.</span><span class="nb">max</span><span class="p">(</span><span class="n">np</span><span class="p">.</span><span class="nb">max</span><span class="p">(</span><span class="n">dataframe</span><span class="p">))</span>
<span class="n">cmap</span> <span class="o">=</span> <span class="n">sns</span><span class="p">.</span><span class="n">diverging_palette</span><span class="p">(</span><span class="mi">133</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">as_cmap</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">sns</span><span class="p">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">standardized_df</span><span class="p">,</span> <span class="n">annot</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">axs</span><span class="p">)</span>
<span class="n">axs</span><span class="p">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">show</span><span class="p">()</span>
<span class="n">plotting_standardized_results</span><span class="p">(</span><span class="n">dtw_df</span><span class="p">,</span> <span class="s">"DTW"</span><span class="p">)</span>
<span class="n">plotting_standardized_results</span><span class="p">(</span><span class="n">euclidean_df</span><span class="p">,</span> <span class="s">"Euclidean"</span><span class="p">)</span>
</code></pre></div></div>
<p><img src="/assets/post_images/dtw/output_41_0.png" alt="png" /></p>
<p><img src="/assets/post_images/dtw/output_41_1.png" alt="png" /></p>
<p>From the plot above we see that the DTW distance is finding pretty accurately which audio file is describing the word “one”. In <strong>every</strong> example, the DTW distance is finding the right recording. In contrast to that, the euclidean distance is doing horribly, not even finding one correct match. Let us now check how our algorithm is performing when we feed it an entire sentences instead of only one word.</p>
<h1 id="audio-files---sentences">Audio Files - Sentences</h1>
<p>For this task I recored two different sentences, which are both inspired by the usage of my Echo Dot. The first recording says: “Please, play the previous song”, whereas the second recording says: “Please, tell me the news”.</p>
<p>Then I took the first sentence, created copies and adjusted the playback speed. I created two faster version, which are played in 0.8x and 0.9x of the time, as well as creating two slower versions, which are played at 1.1x and 1.2x of the original time.</p>
<p>In the following we can take a listen of how that would sounds like:</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">show_audio</span><span class="p">(</span><span class="sa">f</span><span class="s">"</span><span class="si">{</span><span class="n">DATA_PATH</span><span class="si">}</span><span class="s">/sentences/00_sentence_1x.m4a"</span><span class="p">)</span>
</code></pre></div></div>
<audio controls="controls">
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<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">show_audio</span><span class="p">(</span><span class="sa">f</span><span class="s">"</span><span class="si">{</span><span class="n">DATA_PATH</span><span class="si">}</span><span class="s">/sentences/01_sentence_0.5x.m4a"</span><span class="p">)</span>
</code></pre></div></div>
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" 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<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">show_audio</span><span class="p">(</span><span class="sa">f</span><span class="s">"</span><span class="si">{</span><span class="n">DATA_PATH</span><span class="si">}</span><span class="s">/sentences/02_sentence_2x.m4a"</span><span class="p">)</span>
</code></pre></div></div>
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<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">show_audio</span><span class="p">(</span><span class="sa">f</span><span class="s">"</span><span class="si">{</span><span class="n">DATA_PATH</span><span class="si">}</span><span class="s">/sentences/03_sentence_1.5x.m4a"</span><span class="p">)</span>
</code></pre></div></div>
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<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">show_audio</span><span class="p">(</span><span class="sa">f</span><span class="s">"</span><span class="si">{</span><span class="n">DATA_PATH</span><span class="si">}</span><span class="s">/sentences/04_2sentence.m4a"</span><span class="p">)</span>
</code></pre></div></div>
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Your browser does not support the audio element.
</audio>
<p>To get an even better feeling of the data, we also take a look at the sound-waves. Note how faster versions of the sentence show a shorter sound-wave, and slower versions a longer wave than the original. The goal of the algorithm is now going to be to connect the identifying characteristics of the time-series, even though they are not happening at the same time anymore.</p>
<p>Just as a side note: If we were to use the standard sampling rate of 48.000 Hz, our time series of that four second long recording would have a length of over 250k observations. An incredibly high amount for such a short recording. Since we do not need that amount of detail of the sound at hand, we are using a sampling rate of 100 Hz.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1"># Load data
</span><span class="n">sentences_sound_dict</span> <span class="o">=</span> <span class="n">load_sound_data</span><span class="p">(</span><span class="s">"sentences"</span><span class="p">)</span>
<span class="c1"># Plotting the sentences
</span><span class="k">def</span> <span class="nf">plot_sound_waves</span><span class="p">(</span><span class="n">sound_dict</span><span class="p">):</span>
<span class="n">NUMBER_OF_SENTENCES</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">sound_dict</span><span class="p">.</span><span class="n">keys</span><span class="p">())</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="n">NUMBER_OF_SENTENCES</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="n">sharex</span><span class="o">=</span><span class="s">"all"</span><span class="p">)</span>
<span class="n">axs</span> <span class="o">=</span> <span class="n">axs</span><span class="p">.</span><span class="n">ravel</span><span class="p">()</span>
<span class="n">color</span><span class="o">=</span> <span class="n">cm</span><span class="p">.</span><span class="n">rainbow</span><span class="p">(</span><span class="n">np</span><span class="p">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">NUMBER_OF_SENTENCES</span><span class="p">))</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">c</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">color</span><span class="p">,</span> <span class="n">sound_dict</span><span class="p">.</span><span class="n">keys</span><span class="p">())):</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">].</span><span class="n">plot</span><span class="p">(</span><span class="n">sound_dict</span><span class="p">[</span><span class="n">name</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="n">c</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">].</span><span class="n">set_title</span><span class="p">(</span><span class="n">name</span><span class="p">.</span><span class="n">split</span><span class="p">(</span><span class="s">"."</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">plt</span><span class="p">.</span><span class="n">show</span><span class="p">()</span>
<span class="n">plot_sound_waves</span><span class="p">(</span><span class="n">sentences_sound_dict</span><span class="p">)</span>
</code></pre></div></div>
<p><img src="/assets/post_images/dtw/output_51_0.png" alt="png" /></p>
<p>Now it is time to see how well the DTW method can classify these recordings. A perfect clustering algorithm would show a relatively small distance between the speed-adjusted sentences and the original sentence, and a large distance to the second sentence.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">def</span> <span class="nf">benchmarking</span><span class="p">(</span><span class="n">sound_dict</span><span class="p">,</span> <span class="n">relative_warping_band</span><span class="p">):</span>
<span class="n">benchmark_sound</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">sound_dict</span><span class="p">.</span><span class="n">values</span><span class="p">())[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">comparison_names</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">sound_dict</span><span class="p">.</span><span class="n">keys</span><span class="p">())[</span><span class="mi">1</span><span class="p">:]</span>
<span class="n">number_of_comparisons</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">comparison_names</span><span class="p">)</span>
<span class="n">results_df</span> <span class="o">=</span> <span class="n">pd</span><span class="p">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="p">.</span><span class="n">empty</span><span class="p">((</span><span class="n">number_of_comparisons</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s">"Euclidean"</span><span class="p">,</span> <span class="s">"DTW"</span><span class="p">],</span>
<span class="n">index</span><span class="o">=</span><span class="n">comparison_names</span><span class="p">)</span>
<span class="n">r</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">benchmark_sound</span><span class="p">)</span> <span class="o">*</span> <span class="n">relative_warping_band</span><span class="p">)</span>
<span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">comparison_names</span><span class="p">):</span>
<span class="n">comparison_sound</span> <span class="o">=</span> <span class="n">sound_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span>
<span class="n">euclidean_value</span> <span class="o">=</span> <span class="n">euclidean_distance_calculator</span><span class="p">(</span><span class="n">benchmark_sound</span><span class="p">,</span> <span class="n">comparison_sound</span><span class="p">)</span>
<span class="n">dtw_value</span> <span class="o">=</span> <span class="n">dtwupd</span><span class="p">(</span><span class="n">benchmark_sound</span><span class="p">,</span> <span class="n">comparison_sound</span><span class="p">,</span> <span class="n">r</span><span class="p">)</span>
<span class="n">results_df</span><span class="p">.</span><span class="n">loc</span><span class="p">[</span><span class="n">name</span><span class="p">,</span> <span class="s">"Euclidean"</span><span class="p">]</span> <span class="o">=</span> <span class="n">euclidean_value</span>
<span class="n">results_df</span><span class="p">.</span><span class="n">loc</span><span class="p">[</span><span class="n">name</span><span class="p">,</span> <span class="s">"DTW"</span><span class="p">]</span> <span class="o">=</span> <span class="n">dtw_value</span>
<span class="n">plotting_standardized_results</span><span class="p">(</span><span class="n">results_df</span><span class="p">)</span>
</code></pre></div></div>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">relative_warping_band</span> <span class="o">=</span> <span class="mi">50</span> <span class="o">/</span> <span class="mi">100</span>
<span class="n">benchmarking</span><span class="p">(</span><span class="n">sentences_sound_dict</span><span class="p">,</span> <span class="n">relative_warping_band</span><span class="p">)</span>
</code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>100%|██████████| 5/5 [00:00<00:00, 6.79it/s]
</code></pre></div></div>
<p><img src="/assets/post_images/dtw/output_54_1.png" alt="png" /></p>
<p>From the plot above we can see that the euclidean distance is not finding any difference in any of the provided tracks. That result is in stark contrast to what the DTW finds. Here we the biggest difference is found with sentence number two (the fifth recording). This results shows impressively how well DTW works even with entire sentences.</p>
<h1 id="audio-files---songs">Audio Files - Songs</h1>
<p>After testing the DTW algorithm on words as well as entire sentences, we now try something even more ambitious. Namely, we are trying to match entire songs. For that purpose we are using two songs from the Arctic Monkeys, namely <em>Arabella</em> and <em>Do I Wanna Know</em>. For the former song we import the live as well as the studio version. Given copyright reasons we are probably better off not playing these songs here, but rather link to the youtube videos we used for the audio files.</p>
<ul>
<li><a href="https://www.youtube.com/watch?v=Jn6-TItCazo">Arabella - Studio Version</a></li>
<li><a href="https://www.youtube.com/watch?v=oJTncbC8xZs">Arabella - Live Version</a></li>
<li><a href="https://www.youtube.com/watch?v=1tWFk8ojF4M">Do I Wanna Know - Live Version</a></li>
</ul>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">song_sound_dict</span> <span class="o">=</span> <span class="n">load_sound_data</span><span class="p">(</span><span class="s">"songs"</span><span class="p">)</span>
<span class="n">plot_sound_waves</span><span class="p">(</span><span class="n">song_sound_dict</span><span class="p">)</span>
</code></pre></div></div>
<p><img src="/assets/post_images/dtw/output_57_0.png" alt="png" /></p>
<p>We are now using the live version of Arabella as the benchmark song and calculate the distance between the other two sound-files to the benchmark. As from the sound-waves above visible, even with sampling with a rate of 100Hz, the files are still quite long which will slow down our processing speed.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">relative_warping_band</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="mi">100</span>
<span class="n">benchmarking</span><span class="p">(</span><span class="n">song_sound_dict</span><span class="p">,</span> <span class="n">relative_warping_band</span><span class="p">)</span>
</code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>100%|██████████| 2/2 [06:06<00:00, 183.28s/it]
</code></pre></div></div>
<p><img src="/assets/post_images/dtw/output_59_1.png" alt="png" /></p>
<p>It is important to not get confused about the results above. Even though the euclidean distance measure is shown in green, its performance was very bad, as it shows a closer distance to the different song. In contrast to that, the DTW algorithm was able to find bigger similarities between the studio version of <em>Arabella</em> and the live performance compared to the other song.</p>
<p>Testing two songs of course is not statistically significant by any means, but it shows that we are already going in the right direction.</p>
<p>Nevertheless, all results until now show that the DTW distance measurement is showing fantastic results when it comes to audio-data. It is now time to see whether it can also provide the same benefits for tabular data.</p>
<h1 id="time-series">Time Series</h1>
<p>Last but not least we are testing the DTW framework for tabular time-series data. Here we are aiming to do time-series classification. Of course, DTW is not able to classify time-series data on its own, since it is merely a tool to assess similarities. Though, when used in combination with a K-Nearest-Neighbors (KNN) algorithm, DTW can serve as a powerful tool to identify the most relevant neighbors.</p>
<h3 id="data">Data</h3>
<p>For this challenge we are using the web traffic time series data from <a href="https://www.kaggle.com/c/web-traffic-time-series-forecasting">Kaggle</a>. This dataset describes the number of visits to a various websites (shown in the rows) over time (shown in the columns).</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">NUMBER_ROWS</span> <span class="o">=</span> <span class="mi">500</span>
<span class="n">file_path</span> <span class="o">=</span> <span class="sa">f</span><span class="s">"</span><span class="si">{</span><span class="n">DATA_PATH</span><span class="si">}</span><span class="s">/time_series/train_1.csv"</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="p">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">file_path</span><span class="p">,</span> <span class="n">nrows</span><span class="o">=</span><span class="n">NUMBER_ROWS</span><span class="p">)</span>
<span class="n">data</span><span class="p">.</span><span class="n">head</span><span class="p">()</span>
</code></pre></div></div>
<div>
<style scoped="">
.dataframe tbody tr th:only-of-type {
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.dataframe tbody tr th {
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text-align: right;
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Page</th>
<th>2015-07-01</th>
<th>2015-07-02</th>
<th>2015-07-03</th>
<th>2015-07-04</th>
<th>2015-07-05</th>
<th>2015-07-06</th>
<th>2015-07-07</th>
<th>2015-07-08</th>
<th>2015-07-09</th>
<th>...</th>
<th>2016-12-22</th>
<th>2016-12-23</th>
<th>2016-12-24</th>
<th>2016-12-25</th>
<th>2016-12-26</th>
<th>2016-12-27</th>
<th>2016-12-28</th>
<th>2016-12-29</th>
<th>2016-12-30</th>
<th>2016-12-31</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>2NE1_zh.wikipedia.org_all-access_spider</td>
<td>18.0</td>
<td>11.0</td>
<td>5.0</td>
<td>13.0</td>
<td>14.0</td>
<td>9.0</td>
<td>9.0</td>
<td>22.0</td>
<td>26.0</td>
<td>...</td>
<td>32.0</td>
<td>63.0</td>
<td>15.0</td>
<td>26.0</td>
<td>14.0</td>
<td>20.0</td>
<td>22.0</td>
<td>19.0</td>
<td>18.0</td>
<td>20.0</td>
</tr>
<tr>
<th>1</th>
<td>2PM_zh.wikipedia.org_all-access_spider</td>
<td>11.0</td>
<td>14.0</td>
<td>15.0</td>
<td>18.0</td>
<td>11.0</td>
<td>13.0</td>
<td>22.0</td>
<td>11.0</td>
<td>10.0</td>
<td>...</td>
<td>17.0</td>
<td>42.0</td>
<td>28.0</td>
<td>15.0</td>
<td>9.0</td>
<td>30.0</td>
<td>52.0</td>
<td>45.0</td>
<td>26.0</td>
<td>20.0</td>
</tr>
<tr>
<th>2</th>
<td>3C_zh.wikipedia.org_all-access_spider</td>
<td>1.0</td>
<td>0.0</td>
<td>1.0</td>
<td>1.0</td>
<td>0.0</td>
<td>4.0</td>
<td>0.0</td>
<td>3.0</td>
<td>4.0</td>
<td>...</td>
<td>3.0</td>
<td>1.0</td>
<td>1.0</td>
<td>7.0</td>
<td>4.0</td>
<td>4.0</td>
<td>6.0</td>
<td>3.0</td>
<td>4.0</td>
<td>17.0</td>
</tr>
<tr>
<th>3</th>
<td>4minute_zh.wikipedia.org_all-access_spider</td>
<td>35.0</td>
<td>13.0</td>
<td>10.0</td>
<td>94.0</td>
<td>4.0</td>
<td>26.0</td>
<td>14.0</td>
<td>9.0</td>
<td>11.0</td>
<td>...</td>
<td>32.0</td>
<td>10.0</td>
<td>26.0</td>
<td>27.0</td>
<td>16.0</td>
<td>11.0</td>
<td>17.0</td>
<td>19.0</td>
<td>10.0</td>
<td>11.0</td>
</tr>
<tr>
<th>4</th>
<td>52_Hz_I_Love_You_zh.wikipedia.org_all-access_s...</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>...</td>
<td>48.0</td>
<td>9.0</td>
<td>25.0</td>
<td>13.0</td>
<td>3.0</td>
<td>11.0</td>
<td>27.0</td>
<td>13.0</td>
<td>36.0</td>
<td>10.0</td>
</tr>
</tbody>
</table>
<p>5 rows × 551 columns</p>
</div>
<p>Given that we are only show-casing DTW, and considering how computational expensive DTW is, there is no reason for importing more than 500 observations. We start by replacing missing observations by a zero, as it is suggested on the Kaggle website. Afterwards we drop the page name, since it does not contain any meaning to us.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">data</span><span class="p">.</span><span class="n">fillna</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">data</span><span class="p">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s">"Page"</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
</code></pre></div></div>
<p>Quite importantly for our example is the creation of a <strong>binary target variable</strong>, since as of now, every cell contains a integer variable. Given that we would like to show an example of binary classification, we take the last column (which is also the last date) and turn in into a binary variable. This is done by replacing every number above the median number of hits of that day with <code>True</code> and everything below the median with <code>False</code>.</p>
<p>Last, but not least we are dividing the data into train and test, in order to find a representative performance measure. We are holding out 20% test-data as well as setting the random-state to 42 in order to ensure the reproducibility of our results.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">last_column_name</span> <span class="o">=</span> <span class="n">data</span><span class="p">.</span><span class="n">columns</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">data</span><span class="p">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="n">last_column_name</span><span class="p">),</span> <span class="n">data</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="n">last_column_name</span><span class="p">]</span>
<span class="n">bool_y</span> <span class="o">=</span> <span class="n">y</span> <span class="o">></span> <span class="n">np</span><span class="p">.</span><span class="n">median</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">bool_y</span><span class="p">,</span> <span class="n">train_size</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
</code></pre></div></div>
<p>We are then trying to classify this binary target variable through a KNearestNeighbor model, which uses the DTW distance to find surrounding neighbors. For comparison reasons, we are benchmarking the performance of using the DTW distance, by including the euclidean distance as well.</p>
<p>For doing that we are using the handy implementation from <code>tslearn</code>, which provides us with the class <code>KNeighborsTimeSeriesClassifier</code>. This class has the option to specify which kind of distance we would like to use for finding the nearest neighbor.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s">"n_neighbors"</span><span class="p">:</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">50</span><span class="p">]}</span>
<span class="n">pred_dict</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">metrics</span> <span class="o">=</span> <span class="p">[</span><span class="s">"dtw"</span><span class="p">,</span> <span class="s">"euclidean"</span><span class="p">]</span>
<span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="n">metrics</span><span class="p">:</span>
<span class="n">estimator</span> <span class="o">=</span> <span class="n">clone</span><span class="p">(</span><span class="n">KNeighborsTimeSeriesClassifier</span><span class="p">(</span><span class="n">metric</span><span class="o">=</span><span class="n">metric</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">estimator</span><span class="o">=</span><span class="n">estimator</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">params</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
<span class="n">clf</span><span class="p">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">y_pred</span> <span class="o">=</span> <span class="n">clf</span><span class="p">.</span><span class="n">best_estimator_</span><span class="p">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="n">pred_dict</span><span class="p">[</span><span class="n">metric</span><span class="p">]</span> <span class="o">=</span> <span class="n">y_pred</span>
</code></pre></div></div>
<p>Before looking at the results of our classifiers, we also prepare a more challenging competitor to the DTW algorithm, namely <code>GradientBoostingClassifier</code>.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">metrics</span><span class="p">.</span><span class="n">append</span><span class="p">(</span><span class="s">"gb"</span><span class="p">)</span>
<span class="n">gb_model</span> <span class="o">=</span> <span class="n">GradientBoostingClassifier</span><span class="p">()</span>
<span class="n">gb_model</span><span class="p">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">pred_dict</span><span class="p">[</span><span class="s">"gb"</span><span class="p">]</span> <span class="o">=</span> <span class="n">gb_model</span><span class="p">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
</code></pre></div></div>
<p>To get a good overview about the performance of all classification methods, we are using a bunch of performance metrics.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">performance_evaluation_dict</span> <span class="o">=</span> <span class="p">{</span>
<span class="s">"accuracy"</span><span class="p">:</span> <span class="n">accuracy_score</span><span class="p">,</span>
<span class="s">"precision"</span><span class="p">:</span> <span class="n">precision_score</span><span class="p">,</span>
<span class="s">"recall"</span><span class="p">:</span> <span class="n">recall_score</span><span class="p">,</span>
<span class="s">"f1"</span><span class="p">:</span> <span class="n">f1_score</span>
<span class="p">}</span>
<span class="n">evaluation_df</span> <span class="o">=</span> <span class="n">pd</span><span class="p">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="p">.</span><span class="n">empty</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">performance_evaluation_dict</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">metrics</span><span class="p">))),</span> <span class="n">columns</span><span class="o">=</span><span class="n">metrics</span><span class="p">,</span>
<span class="n">index</span><span class="o">=</span><span class="n">performance_evaluation_dict</span><span class="p">.</span><span class="n">keys</span><span class="p">())</span>
<span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="n">metrics</span><span class="p">:</span>
<span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">performance_evaluation_dict</span><span class="p">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">score</span> <span class="o">=</span> <span class="n">value</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">pred_dict</span><span class="p">[</span><span class="n">metric</span><span class="p">])</span>
<span class="n">evaluation_df</span><span class="p">.</span><span class="n">loc</span><span class="p">[</span><span class="n">key</span><span class="p">,</span> <span class="n">metric</span><span class="p">]</span> <span class="o">=</span> <span class="n">score</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">sns</span><span class="p">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">evaluation_df</span><span class="p">,</span> <span class="n">annot</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">axs</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></div></div>
<p><img src="/assets/post_images/dtw/output_72_0.png" alt="png" /></p>
<p>As can be seen from the chart above, the DTW nearest neighbor shows an impressive performance. Not only does it beat the euclidean distance measure in every performance metric, it even beats the GradientBoostingClassifier in half of all categories. That is a big deal, given that in the end it is still K-Nearest-Neighbors what is run under the hood. The neighbors which are chosen through the DTW method, though seem to quite helpful to figure out the structure of the target variable.</p>
<p>All in all the DTW distance measurement is a powerful tool in order to enhance time-series classification in all areas. One has to be aware of the bad scaling issues, as this algorithm can become computationally expensive real quick.</p>
<h1 id="appendix">Appendix</h1>
<ul>
<li>[1] Sakoe, Hiroaki, and Seibi Chiba. “Dynamic programming algorithm optimization for spoken word recognition.” IEEE transactions on acoustics, speech, and signal processing 26.1 (1978): 43-49.</li>
<li>[2] Salvador, Stan, and Philip Chan. “Toward accurate dynamic time warping in linear time and space.” Intelligent Data Analysis 11.5 (2007): 561-580.</li>
<li>[3] Wu, Renjie, and Eamonn J. Keogh. “FastDTW is approximate and Generally Slower than the Algorithm it Approximates.” IEEE Transactions on Knowledge and Data Engineering (2020).</li>
</ul>Paul MoraExplaining the concept of Dynamic-Time-Warping and testing it extensively on sound and tabular data.DengAI Challenge Second Attempt: Forecasting - Ensemble Model2021-04-13T00:00:00+00:002021-04-13T00:00:00+00:00https://paul-mora.com/time%20series/python/classification/regression/smoother/DengAI2-Second-Attempt-Forecasting<p><em>Using a combination of regression, classification and smoother for our second take on the DengAI challenge.</em></p>
<h2 id="background-information">Background Information</h2>
<p>This post represents one out of two posts in which we are discussing our second attempt on the Data Science Challenge: DengAI from Drivendata.org, which can be found <a href="https://www.drivendata.org/competitions/44/dengai-predicting-disease-spread/">here</a>.</p>
<p>In this challenge we are asked to predict the number of cases of dengue fever in the two cities San Juan and Iquitos given time-series data of several feature variables with around ~900 and ~500 observations for each city respectively. On the website we also find some more information about the disease:</p>
<p><em>‘Dengue fever is a mosquito-borne disease that occurs in tropical and sub-tropical parts of the world. In mild cases, symptoms are similar to the flu: fever, rash, and muscle and joint pain. In severe cases, dengue fever can cause severe bleeding, low blood pressure, and even death.’</em></p>
<p>Particularly challenging for this challenge is the the fact that we are asked to predict around 250 time-steps into the future. Predicting that many periods comes especially challenging when having not that many observations for training purposes.</p>
<p>The DengAI challenge is likely to be the most popular prediction challenge on DrivenData.org, having more than 10.000 participating teams as of April 2021.</p>
<h2 id="structure-of-this-post">Structure of this post</h2>
<p>We begin this post by taking a look at the target variable and discuss what makes the prediction of that time-series so difficult. Afterwards we discuss our key learnings from our last attempt on this challenge and what we aim to improve this time.</p>
<p>The second part of this post then discussing the prediction methodology and shows its implementation into code. In the end we show our results, and the score we got for our prediction.</p>
<h3 id="target-variable">Target variable</h3>
<p>One of the first steps before deciding on the strategy of the forecasting methodology is to take a thorough look at the target variable. As mentioned already earlier, we are facing a panel dataset in this challenge. <em>Panel data</em> means we have time-series data for multiple entities, namely for the two cities San Juan and Iquitos.
Below we find plots of the number of dengue fever cases for the two cities San Juan (left) and Iquitos (right).</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">import</span> <span class="nn">os</span>
<span class="n">os</span><span class="p">.</span><span class="n">getcwd</span><span class="p">()</span>
</code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>'/Users/paulmora/Documents/projects/dengai_ensemble/notebooks'
</code></pre></div></div>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="n">pd</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="n">plt</span>
<span class="n">os</span><span class="p">.</span><span class="n">chdir</span><span class="p">(</span><span class="sa">r</span><span class="s">"../"</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">src._config</span> <span class="c1"># Contains matplotlib settings to be applied to all graphs
</span>
<span class="n">target_data</span> <span class="o">=</span> <span class="n">pd</span><span class="p">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s">"data/dengue_labels_train.csv"</span><span class="p">)</span>
<span class="n">city_names</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">target_data</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s">"city"</span><span class="p">])</span>
<span class="n">n_cities</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">city_names</span><span class="p">)</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="o">*</span><span class="n">n_cities</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="n">ncols</span><span class="o">=</span><span class="n">n_cities</span><span class="p">)</span>
<span class="n">axs</span> <span class="o">=</span> <span class="n">axs</span><span class="p">.</span><span class="n">ravel</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">city</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">city_names</span><span class="p">):</span>
<span class="n">city_data</span> <span class="o">=</span> <span class="n">target_data</span><span class="p">.</span><span class="n">query</span><span class="p">(</span><span class="s">"city==@city"</span><span class="p">).</span><span class="n">loc</span><span class="p">[:,</span> <span class="s">"total_cases"</span><span class="p">]</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">].</span><span class="n">plot</span><span class="p">(</span><span class="n">city_data</span><span class="p">)</span>
</code></pre></div></div>
<p><img src="/assets/article_images/dengai/0413/output_2_0.png" alt="png" /></p>
<p>From the chart above we can nicely see that both time series follow a somewhat yearly seasonality. That would make sense as we would assume that the mosquito population is peaking every summer and nearly go extinct in the winter times. What is even more apparent are the massive spikes in the time series. Ar around observation number 1150 for Iquitos and around 200 for San Juan we see unprecedented level of dengue fever cases. These values are going to be difficult to deal with, as they are so prominent that they could badly affect our model performance.</p>
<h2 id="one-model-for-both-cities-vs-one-model-for-each-city">One model for both cities vs. One model for each city</h2>
<p>When facing panel data, the question arises whether we build one model for each entity, or whether we use one model for both entities (a so-called pooled model). Training one model for both entities would potentially come with the benefit of having cross-learning opportunities. Meaning that the prediction for one entity would be enhanced when using the trainings data from another entity. This is only possible though if the data generating process is the same or at least not significantly different from one another.</p>
<p>If the distribution between entities is fundamentally different, then any prediction model will have a hard time trying to predict to significantly different time-series. For the purpose of testing whether the two distributions are fundamentally different we are using a Kolmogrov Smirnov test. From the strongly significant p-value we conclude that the data generating process is not the same and therefore decide to train two individual prediction models.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">stats</span>
<span class="n">san_juan</span> <span class="o">=</span> <span class="n">target_data</span><span class="p">.</span><span class="n">query</span><span class="p">(</span><span class="s">'city=="sj"'</span><span class="p">).</span><span class="n">loc</span><span class="p">[:,</span> <span class="s">"total_cases"</span><span class="p">]</span>
<span class="n">iquitos</span> <span class="o">=</span> <span class="n">target_data</span><span class="p">.</span><span class="n">query</span><span class="p">(</span><span class="s">'city=="iq"'</span><span class="p">).</span><span class="n">loc</span><span class="p">[:,</span> <span class="s">"total_cases"</span><span class="p">]</span>
<span class="n">stats</span><span class="p">.</span><span class="n">ks_2samp</span><span class="p">(</span><span class="n">san_juan</span><span class="p">,</span> <span class="n">iquitos</span><span class="p">)</span>
</code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>Ks_2sampResult(statistic=0.5068376068376068, pvalue=4.7991921470916946e-76)
</code></pre></div></div>
<h2 id="learnings-from-previous-attempt">Learnings from previous attempt</h2>
<p>Given that we already tried ourselves on the DengAI challenge in the past, it is worthwhile to talk about what has been tried, what went wrong and what could be improved for the next attempt.</p>
<p>During our last attempt we build a <a href="https://www.statsmodels.org/stable/generated/statsmodels.tsa.forecasting.stl.STLForecast.html">STL Forecasting model</a> with an underlying ARIMA model. The ARIMA was parameterized using the Box-Jenkins methodology, which is the go-to method when doing that.</p>
<p>The flaw of this model, or any ARIMA model for that matter, is that those models are not designed for long-term forecasts. That is because ARIMA models are mean-reverting and therefore useless when it comes to forecasts longer than a few periods. Especially with time-series which are as bumpy as the ones we are trying to predict, this forecasting method was wrongly placed.</p>
<p>Below we see that out-of-sample predictions from our model for the city San Juan. The out-of-sample prediction were produced by using the first 80% of the data for training purposes and applying the parameterized model then on the remaining 20%.</p>
<p>As we can see, the MAE of around 15.7 is actually not too bad, though we are failing to accurately predict the spikes.</p>
<p><img src="/assets/article_images/dengai/0413/last_attempt_forecast.png" alt="Forecasting for out-of-sample San Juan" /></p>
<p>One positive aspect of our last attempt was to for allow multiple seasonalities through the STL part of the model. STL or <em>seasonal-trend decomposition using LOESS</em> is normally used to decompose a time series into its three components: seasonality, trend and residual. Splitting a time-series into its components comes handy when trying to make the series stationary, which normally would involve removing the trend and potentially even it’s seasonality.</p>
<p>The benefit of allowing for multiple seasonalities becomes particularly apparent when plotting a <strong>power-spectrum</strong> for both time-series. A power-spectrum gives us an idea what the driving frequencies of a series are. This concept is easier understood when imaging that we are not dealing with dengue cases over time, but rather with a sound wave of a song. All (interesting) songs consist out of multiple different sounds at the same time. The “sum” of all these sounds then result in the song as we hear it. Looking at the sound-wave of a song, it is difficult to tell which individual sounds are more prominent than others. In order to split the sound wave into its frequency components we are using a so-called <a href="https://en.wikipedia.org/wiki/Fourier_transform">Fourier Transform</a>. To then find out what the dominant frequencies are, we set a so-called <em>prominence level</em>. This prominence level serves us as a threshold, in order to find out what the driving frequencies are.</p>
<p>This concept is also applicable to time-series problems in order to find out which seasonality is dominant within the series. The following code plots the power-spectrum for the two time-series.</p>
<p>We set the threshold, which divides significant frequencies from insignificant ones, to two standard deviations above the median. We chose the median in contrast to the mean, given the heavy skewness the power-plots suffer from. These thresholds are always tricky to set, and will always end up being subjective.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="n">np</span>
<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">signal</span> <span class="k">as</span> <span class="n">sig</span>
<span class="n">city_data</span> <span class="o">=</span> <span class="n">target_data</span><span class="p">.</span><span class="n">query</span><span class="p">(</span><span class="s">"city==@city"</span><span class="p">).</span><span class="n">loc</span><span class="p">[:,</span> <span class="s">"total_cases"</span><span class="p">]</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="o">*</span><span class="n">n_cities</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="n">ncols</span><span class="o">=</span><span class="n">n_cities</span><span class="p">)</span>
<span class="n">axs</span> <span class="o">=</span> <span class="n">axs</span><span class="p">.</span><span class="n">ravel</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">city</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">city_names</span><span class="p">):</span>
<span class="n">signal</span> <span class="o">=</span> <span class="n">target_data</span><span class="p">.</span><span class="n">query</span><span class="p">(</span><span class="s">"city==@city"</span><span class="p">).</span><span class="n">loc</span><span class="p">[:,</span> <span class="s">"total_cases"</span><span class="p">]</span>
<span class="n">fft</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">fft</span><span class="p">.</span><span class="n">fft</span><span class="p">(</span><span class="n">signal</span><span class="p">)</span>
<span class="n">magnitude</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="nb">abs</span><span class="p">(</span><span class="n">fft</span><span class="p">)</span>
<span class="n">frequency</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">fft</span><span class="p">.</span><span class="n">fftfreq</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">signal</span><span class="p">))</span>
<span class="n">left_magnitude</span> <span class="o">=</span> <span class="n">magnitude</span><span class="p">[:</span><span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">magnitude</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">)]</span>
<span class="n">left_frequency</span> <span class="o">=</span> <span class="n">frequency</span><span class="p">[:</span><span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">frequency</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">)]</span>
<span class="n">prominence</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">median</span><span class="p">(</span><span class="n">left_magnitude</span><span class="p">)</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="p">.</span><span class="n">std</span><span class="p">(</span><span class="n">left_magnitude</span><span class="p">)</span>
<span class="n">peaks</span> <span class="o">=</span> <span class="n">sig</span><span class="p">.</span><span class="n">find_peaks</span><span class="p">(</span><span class="n">left_magnitude</span><span class="p">[</span><span class="n">left_frequency</span> <span class="o">>=</span> <span class="mi">0</span><span class="p">],</span> <span class="n">prominence</span><span class="o">=</span><span class="n">prominence</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">peak_freq</span> <span class="o">=</span> <span class="n">left_frequency</span><span class="p">[</span><span class="n">peaks</span><span class="p">]</span>
<span class="n">list_frequencies</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="n">peak_freq</span><span class="p">).</span><span class="n">tolist</span><span class="p">()</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">].</span><span class="n">plot</span><span class="p">(</span><span class="n">left_frequency</span><span class="p">,</span> <span class="n">left_magnitude</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">].</span><span class="n">set_title</span><span class="p">(</span><span class="n">city</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="sa">f</span><span class="s">"The relevant frequencies for </span><span class="si">{</span><span class="n">city</span><span class="si">}</span><span class="s"> is/are: </span><span class="si">{</span><span class="n">list_frequencies</span><span class="si">}</span><span class="s">"</span><span class="p">)</span>
</code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>The relevant frequencies for iq is/are: [52.0]
The relevant frequencies for sj is/are: [187.2, 93.6, 52.0, 33.42857142857142]
</code></pre></div></div>
<p><img src="/assets/article_images/dengai/0413/output_8_1.png" alt="png" /></p>
<p>From the print statement above we see that we find yearly seasonality for Iquitos, and four different and prominent frequencies for San Juan. This finding supports our hypothesis of the benefit of a model which allows for multiple seasonalities.</p>
<h2 id="forecasting-methodology">Forecasting Methodology</h2>
<p>The inspiration for our new modeling approach came during one of the brain-storming sessions at work. At the time we were working for a large insurance company and we were building a claims forecasting model.</p>
<p>The business model of an insurer relies on the foundations that most people do not have to make use of their insurance most of the time. That means that in most months the customers of an insurer simply pays into the insurance without filling any claims. That means that most entries of the claims data of an insurance are zero. A phenomena which Data Scientists would describe as <em>‘sparse’</em> data.</p>
<p>That sparsity makes it significantly more difficult to predict the claims variable. Academia provides us with several suggestions on how to tackle this problem. One potential approach to model that behavior is suggested in the paper of Andrea Andrea Dal Pozzolo 2010 [1]. Herein the author splits the forecasting problem into two different parts. Firstly, a classification problems in which we classify whether the claim is having a payout or not. In the second step we apply a regression model to the observations which are non-zero.</p>
<p>The benefit of this approach is that the regression model could focus more on observations which are non-zero and therefore is able to better cope with the spikes within the target.</p>
<p>Applying this approach on our data is not as straightforward. That is mainly because of two reasons:</p>
<ol>
<li>Classification Problem: In contrast to the insurance claims data we do not find many (if any at all) observations which have a target value of zero</li>
<li>Time-series Problem: In contrast to the outlined paper we face time-series data instead of cross-sectional data</li>
<li>Imbalancement Problem: Given the nature of <em>outliers</em>, how are dealing with the fact that our classifier sees mostly <em>non-outliers</em> while training</li>
</ol>
<p>The following subsections elaborate on how we are dealing with these problems.</p>
<h3 id="1-classification-problem">1. Classification Problem</h3>
<p>To address the first problem, namely that our data is not as easily divided into observations which are either zero or non-zero, the following idea was drafted. Instead of trying to figure out which observations are non-zero, we try in a first instance to figure out which observation belongs to an unusual high level within the time-series, a.k.a. dengue fever outbursts.</p>
<p>We do that by separating the target variable into <em>outliers</em> and <em>non-outliers</em>. For example, we define every observation above the e.g. 90th percentile as <em>outliers</em> and all of the other observations as <em>non-outliers</em>. By doing that we turned the target variable from an integer to a binary, which allows us to apply a classification model. Afterwards, we apply a regression model on only those observations which are predicted to belong to the <em>outliers</em> class.</p>
<p>Below we show how exactly that separation would look like when using the 90th percentile as the threshold between <em>outliers</em> and <em>non-outliers</em>.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="n">np</span>
<span class="n">THRESHOLD</span> <span class="o">=</span> <span class="mi">90</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="o">*</span><span class="n">n_cities</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="n">ncols</span><span class="o">=</span><span class="n">n_cities</span><span class="p">)</span>
<span class="n">axs</span> <span class="o">=</span> <span class="n">axs</span><span class="p">.</span><span class="n">ravel</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">city</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">city_names</span><span class="p">):</span>
<span class="n">city_data</span> <span class="o">=</span> <span class="n">target_data</span><span class="p">.</span><span class="n">query</span><span class="p">(</span><span class="s">"city==@city"</span><span class="p">).</span><span class="n">loc</span><span class="p">[:,</span> <span class="s">"total_cases"</span><span class="p">]</span>
<span class="n">level_threshold</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">city_data</span><span class="p">,</span> <span class="n">THRESHOLD</span><span class="p">)</span>
<span class="n">bool_threshold</span> <span class="o">=</span> <span class="n">city_data</span> <span class="o">></span> <span class="n">level_threshold</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">].</span><span class="n">plot</span><span class="p">(</span><span class="n">city_data</span><span class="p">[</span><span class="n">bool_threshold</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s">"red"</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">"Outlier"</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s">"None"</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s">"o"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">].</span><span class="n">plot</span><span class="p">(</span><span class="n">city_data</span><span class="p">[</span><span class="o">~</span><span class="n">bool_threshold</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s">"blue"</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">"No Outlier"</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s">"None"</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s">"o"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">].</span><span class="n">legend</span><span class="p">()</span>
</code></pre></div></div>
<p><img src="/assets/article_images/dengai/0413/output_11_0.png" alt="png" /></p>
<h3 id="2-time-series-problem">2. Time-Series Problem</h3>
<p>The other problem we are facing is that we are dealing with a time-series problem in contrast to a cross-sectional problem we had in the insurance context.</p>
<p>When predicting which observation belongs to the <em>outliers</em> group, we are treating the problem as if it were a cross-sectional problem. Hence, ignoring the time component altogether.</p>
<p>Afterwards, we are predicting the <em>outliers</em> group through a regression model. For the <em>non-outliers</em> group, which will represent the majority of the observations, we will use a time-series prediction model. In order to be able to do that, we have to have a time-series with no missing observations. Though, in order for the <em>outliers</em> not to affect the <em>non-outliers</em> prediction method, we have to replace the extreme values. This is done through a method called <a href="https://en.wikipedia.org/wiki/Winsorizing"><strong>winsorizing</strong></a>.</p>
<p>What this method does is that it cuts off everything above the Xth percentile and replaces the values with the value of exactly this Xth percentile. In the image below we show an example how the time-series of the <em>non-outliers</em> would look like if we winsorize at the 90th percentile level.</p>
<p>The orange line shows nicely how everything above a certain absolute level is cut-off and replaced. This newly created line is then separately predicted using an exponential smoother called TBATS. This smoother has the benefit of being able to deal with multiple seasonalities.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">from</span> <span class="nn">scipy.stats.mstats</span> <span class="kn">import</span> <span class="n">winsorize</span>
<span class="k">def</span> <span class="nf">winsorize_data</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">upper_threshold</span><span class="p">):</span>
<span class="s">"""This function cuts the data at the upper threshold"""</span>
<span class="n">one_minus_upper_bound</span> <span class="o">=</span> <span class="mi">1</span><span class="o">-</span><span class="p">(</span><span class="n">upper_threshold</span><span class="o">/</span><span class="mi">100</span><span class="p">)</span>
<span class="n">cutted_data</span> <span class="o">=</span> <span class="n">winsorize</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">limits</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">one_minus_upper_bound</span><span class="p">],</span> <span class="n">inclusive</span><span class="o">=</span><span class="p">(</span><span class="bp">True</span><span class="p">,</span> <span class="bp">False</span><span class="p">)).</span><span class="n">data</span>
<span class="k">return</span> <span class="n">cutted_data</span>
<span class="n">THRESHOLD</span> <span class="o">=</span> <span class="mi">90</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="o">*</span><span class="n">n_cities</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="n">ncols</span><span class="o">=</span><span class="n">n_cities</span><span class="p">)</span>
<span class="n">axs</span> <span class="o">=</span> <span class="n">axs</span><span class="p">.</span><span class="n">ravel</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">city</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">city_names</span><span class="p">):</span>
<span class="n">city_data</span> <span class="o">=</span> <span class="n">target_data</span><span class="p">.</span><span class="n">query</span><span class="p">(</span><span class="s">"city==@city"</span><span class="p">).</span><span class="n">loc</span><span class="p">[:,</span> <span class="s">"total_cases"</span><span class="p">]</span>
<span class="n">winsorized_data</span> <span class="o">=</span> <span class="n">winsorize_data</span><span class="p">(</span><span class="n">city_data</span><span class="p">,</span> <span class="n">THRESHOLD</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">].</span><span class="n">plot</span><span class="p">(</span><span class="n">city_data</span><span class="p">.</span><span class="n">values</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">"Real"</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s">"None"</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s">"o"</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">].</span><span class="n">plot</span><span class="p">(</span><span class="n">winsorized_data</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">"Winsorized"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">].</span><span class="n">legend</span><span class="p">()</span>
</code></pre></div></div>
<p><img src="/assets/article_images/dengai/0413/output_13_0.png" alt="png" /></p>
<h3 id="3-imbalancement-problem-oversampling">3. Imbalancement Problem: Oversampling</h3>
<p>After dealing with the time-series problems, it is now time to address a problem occurring within the classification of <em>outliers</em> and <em>non-outliers</em>, namely the heavy imbalanced data. Imbalanced data describes the situation within classification problems, in which we have significantly more observations from one class compared to another class. This would impact our model-performance, as the model does not have a sufficient amount of <em>outliers</em> observations to properly learn which statistical properties constitute the concept of <em>outliers</em>.</p>
<p>In order to combat that problem, there are multiple approaches of how to tackle that. We could for instance undersample the majority class (i.e. <em>non-outliers</em>), or alternatively, oversample the minority class. We decided to go with the later approach, but use a synthetic oversampler instead of using random sampling.</p>
<p>Synthetic minority oversampling describes a technique in which new observations of the minority classes are created by finding observations which are “like” the existing ones, but are not completely identical. One of the most popular methods is called <strong>SMOTE</strong>, which stands for Synthetic Minority Oversampling Technique.</p>
<p>The exact workings and visualization of how SMOTE works will be outlined in a separate post, but the basic idea is quickly understood: One imagines that we only have two features and we plot all observations on a scatter plot, having each feature on one axes. The next step is then to draw connecting lines between every observation. The newly created values would then be created on these connection lines. This strategy is only one of many potential ways of how to use the SMOTE oversampling technique.</p>
<p>One quite important parameter within all oversample techniques is called the <strong>sampling_strategy</strong>. This parameter describes how big the minority class should grow in compared to the majority class. If we for example have a minority class which represents 10% of the amount of the majority class and we specify a sampling_strategy of 0.5, then we would create 40 percentage points more of the minority class.</p>
<p>In the original paper of SMOTE it is suggested that a combination of oversampling of the minority class and undersampling the majority class leads to favorable results. For now we only use 100% oversampling of the minority class, though gridsearching over a range of oversampling values would be favored.</p>
<h2 id="code-implementation">Code Implementation</h2>
<p>After covering the overall concept, it is now time to see how the high level code implementation looks like.</p>
<p>In the following we will import quite a few self written pipeline packages as well as functions. Those are not going to elaborated on, given that it would be quite a bit of code to show. Though, in order to give a better overview what is happening in the back, we published another post about the workings of the feature engineering pipeline.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="n">np</span>
<span class="kn">from</span> <span class="nn">imblearn.over_sampling</span> <span class="kn">import</span> <span class="n">SMOTE</span>
<span class="kn">from</span> <span class="nn">imblearn.pipeline</span> <span class="kn">import</span> <span class="n">make_pipeline</span> <span class="k">as</span> <span class="n">imblearn_make_pipeline</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span><span class="p">,</span> <span class="n">LinearRegression</span>
<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="p">(</span><span class="n">RandomForestClassifier</span><span class="p">,</span> <span class="n">GradientBoostingClassifier</span><span class="p">,</span>
<span class="n">RandomForestRegressor</span><span class="p">,</span> <span class="n">GradientBoostingRegressor</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">svm</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">mean_absolute_error</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">GridSearchCV</span><span class="p">,</span> <span class="n">train_test_split</span>
<span class="kn">from</span> <span class="nn">src._classes</span> <span class="kn">import</span> <span class="p">(</span><span class="n">Encoding</span><span class="p">,</span> <span class="n">ColumnCorrection</span><span class="p">,</span> <span class="n">Transformer</span><span class="p">,</span> <span class="n">Stationarity</span><span class="p">,</span> <span class="n">Imputer</span><span class="p">,</span> <span class="n">FeatureCreation</span><span class="p">,</span>
<span class="n">FeatureSelection</span><span class="p">,</span> <span class="n">TBATSWrapper</span><span class="p">,</span> <span class="n">ModelSwitcher</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">src._functions</span> <span class="kn">import</span> <span class="p">(</span><span class="n">city_query</span><span class="p">,</span> <span class="n">winsorize_data</span><span class="p">,</span> <span class="n">combining_models</span><span class="p">,</span> <span class="n">plotting_predictions</span><span class="p">,</span> <span class="n">save_prediction_results</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">src._config</span>
</code></pre></div></div>
<p>Potentially worth highlighting is that we are not using the <em>make_pipeline</em> function from scikit-learn, but rather the version from the <em>imblearn</em> package. The reason for that is that when using any over-/downsampling algorithm from imblearn, using <em>make_pipeline</em> from scikit-learn does not work anymore. In order to fix that, the guys from imblearn came up with their own version of pipeline function, which works equally in all respects but allows for over-/downsampling methods.</p>
<p>Next up would be to specify the parameters which feed into the pipeline. Here we will define for example how many cross validations are conducted, what the maximum lag within the feature creation is going to be and so on.</p>
<p>Note that we are choosing <em>precision</em> as the classification scoring method since we are more worried about False Positives than False Negatives. This is because of theoretical as well as empirical reasons. When experimenting with different classification models we note that the model labels significantly more observations as <em>outliers</em> even though they are not, than the other way around. Given this behavior of being more prone towards to False Positive mistakes it would make sense to fight against that, using the <em>precision</em> scoring function. Secondly, labeling an observation as an <em>outlier</em> even though it is none would trigger a spike, which is much riskier compared to having a moderate value when it should be a spike.</p>
<p>When it comes to the error metric for the regression problem we decided to go for the very same metric as the entire Data Challenge DrivenData.org applies, namely <em>Mean Absolute Error</em>.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1"># Pipeline settings
</span><span class="n">cv</span> <span class="o">=</span> <span class="mi">2</span>
<span class="n">N_NEIGHBOURS</span> <span class="o">=</span> <span class="mi">10</span>
<span class="n">DEGREE</span> <span class="o">=</span> <span class="mi">2</span>
<span class="n">MAX_LAG</span> <span class="o">=</span> <span class="mi">4</span>
<span class="n">SIGNIFICANCE_LEVEL</span> <span class="o">=</span> <span class="mi">5</span> <span class="o">/</span> <span class="mi">100</span>
<span class="n">RS</span> <span class="o">=</span> <span class="mi">42</span>
<span class="n">e_list</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">]</span>
<span class="n">clf_scoring</span> <span class="o">=</span> <span class="s">"precision"</span>
<span class="n">reg_scoring</span> <span class="o">=</span> <span class="s">"neg_mean_absolute_error"</span>
</code></pre></div></div>
<h3 id="classification-pipeline">Classification Pipeline</h3>
<p>The next step is now to set up the first step of the forecasting methodology, which is the pipeline for predicting whether the observation belongs to the <em>outlier</em> class.</p>
<p>Therefore, when setting up the pipeline we have to be aware of two things. Firstly, we have to specify that we are facing a classification problem within the <strong>FeatureSelection</strong>. Given that our feature selection is model based, that argument will trigger classification models, rather than regression models. Furthermore, we are specifying an oversampling algorithm SMOTE into the pipeline, which was already elaborated on above.</p>
<p>Given that we would like to try multiple forecasting models with the pipeline, we apply the trick of creating a <strong>ModelSwitcher</strong> class which can intake different models as hyper-parameters within a gridsearch operation. This concept is further elaborated on within the other post of this series.</p>
<p>The range of models we would like to try are stated below as well. Given that the feature-selection class needs a prediction model specified as well, in order to find the optimal value for <em>e</em> (the purpose of which explained in the pipeline post) we have to specify the model twice. Given run-time considerations we only allow for no hyperparameter-gridsearching.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1"># Classification Pipeline
</span><span class="n">clf_pipeline</span> <span class="o">=</span> <span class="n">imblearn_make_pipeline</span><span class="p">(</span>
<span class="n">Encoding</span><span class="p">(),</span>
<span class="n">ColumnCorrection</span><span class="p">(),</span>
<span class="n">Stationarity</span><span class="p">(</span><span class="n">SIGNIFICANCE_LEVEL</span><span class="o">=</span><span class="n">SIGNIFICANCE_LEVEL</span><span class="p">),</span>
<span class="n">Transformer</span><span class="p">(),</span>
<span class="n">Imputer</span><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="n">N_NEIGHBOURS</span><span class="p">),</span>
<span class="n">FeatureCreation</span><span class="p">(</span><span class="n">degree</span><span class="o">=</span><span class="n">DEGREE</span><span class="p">,</span> <span class="n">max_lag</span><span class="o">=</span><span class="n">MAX_LAG</span><span class="p">),</span>
<span class="n">FeatureSelection</span><span class="p">(</span><span class="n">e_list</span><span class="o">=</span><span class="n">e_list</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="n">clf_scoring</span><span class="p">,</span> <span class="n">clf</span><span class="o">=</span><span class="bp">True</span><span class="p">),</span>
<span class="n">SMOTE</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="n">RS</span><span class="p">),</span>
<span class="n">ModelSwitcher</span><span class="p">()</span>
<span class="p">)</span>
<span class="n">clf_parameters</span> <span class="o">=</span> <span class="p">[</span>
<span class="p">{</span><span class="s">"modelswitcher__estimator"</span><span class="p">:</span> <span class="p">[</span><span class="n">svm</span><span class="p">.</span><span class="n">SVC</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="n">RS</span><span class="p">)],</span>
<span class="s">"featureselection__estimator"</span><span class="p">:</span> <span class="p">[</span><span class="n">svm</span><span class="p">.</span><span class="n">SVC</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="n">RS</span><span class="p">)]},</span>
<span class="p">{</span><span class="s">"modelswitcher__estimator"</span><span class="p">:</span> <span class="p">[</span><span class="n">LogisticRegression</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="n">RS</span><span class="p">)],</span>
<span class="s">"featureselection__estimator"</span><span class="p">:</span> <span class="p">[</span><span class="n">LogisticRegression</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="n">RS</span><span class="p">)]},</span>
<span class="p">{</span><span class="s">"modelswitcher__estimator"</span><span class="p">:</span> <span class="p">[</span><span class="n">RandomForestClassifier</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="n">RS</span><span class="p">)],</span>
<span class="s">"featureselection__estimator"</span><span class="p">:</span> <span class="p">[</span><span class="n">RandomForestClassifier</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="n">RS</span><span class="p">)]},</span>
<span class="p">{</span><span class="s">"modelswitcher__estimator"</span><span class="p">:</span> <span class="p">[</span><span class="n">GradientBoostingClassifier</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="n">RS</span><span class="p">)],</span>
<span class="s">"featureselection__estimator"</span><span class="p">:</span> <span class="p">[</span><span class="n">GradientBoostingClassifier</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="n">RS</span><span class="p">)]},</span>
<span class="p">]</span>
<span class="n">clf_gscv</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">estimator</span><span class="o">=</span><span class="n">clf_pipeline</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">clf_parameters</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="n">clf_scoring</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="n">cv</span><span class="p">)</span>
</code></pre></div></div>
<h3 id="regression-pipeline">Regression Pipeline</h3>
<p>The pipeline for the regression part of this forecasting is pretty similar to the pipeline used for classification purposes. The main differences being only that we do not need any oversampling method and that we are now initializing regression instead of classification models.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1"># Regression Pipeline
</span><span class="n">reg_pipeline</span> <span class="o">=</span> <span class="n">imblearn_make_pipeline</span><span class="p">(</span>
<span class="n">Encoding</span><span class="p">(),</span>
<span class="n">ColumnCorrection</span><span class="p">(),</span>
<span class="n">Stationarity</span><span class="p">(</span><span class="n">SIGNIFICANCE_LEVEL</span><span class="o">=</span><span class="n">SIGNIFICANCE_LEVEL</span><span class="p">),</span>
<span class="n">Transformer</span><span class="p">(),</span>
<span class="n">Imputer</span><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="n">N_NEIGHBOURS</span><span class="p">),</span>
<span class="n">FeatureCreation</span><span class="p">(</span><span class="n">degree</span><span class="o">=</span><span class="n">DEGREE</span><span class="p">,</span> <span class="n">max_lag</span><span class="o">=</span><span class="n">MAX_LAG</span><span class="p">,</span> <span class="n">lagged_features</span><span class="o">=</span><span class="bp">False</span><span class="p">),</span>
<span class="n">FeatureSelection</span><span class="p">(</span><span class="n">e_list</span><span class="o">=</span><span class="n">e_list</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="n">reg_scoring</span><span class="p">,</span> <span class="n">clf</span><span class="o">=</span><span class="bp">False</span><span class="p">),</span>
<span class="n">ModelSwitcher</span><span class="p">()</span>
<span class="p">)</span>
<span class="n">reg_parameters</span> <span class="o">=</span> <span class="p">[</span>
<span class="p">{</span><span class="s">"modelswitcher__estimator"</span><span class="p">:</span> <span class="p">[</span><span class="n">LinearRegression</span><span class="p">()],</span>
<span class="s">"featureselection__estimator"</span><span class="p">:</span> <span class="p">[</span><span class="n">LinearRegression</span><span class="p">()]},</span>
<span class="p">{</span><span class="s">"modelswitcher__estimator"</span><span class="p">:</span> <span class="p">[</span><span class="n">RandomForestRegressor</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="n">RS</span><span class="p">)],</span>
<span class="s">"featureselection__estimator"</span><span class="p">:</span> <span class="p">[</span><span class="n">RandomForestRegressor</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="n">RS</span><span class="p">)]},</span>
<span class="p">{</span><span class="s">"modelswitcher__estimator"</span><span class="p">:</span> <span class="p">[</span><span class="n">GradientBoostingRegressor</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="n">RS</span><span class="p">)],</span>
<span class="s">"featureselection__estimator"</span><span class="p">:</span> <span class="p">[</span><span class="n">GradientBoostingRegressor</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="n">RS</span><span class="p">)]}</span>
<span class="p">]</span>
<span class="n">reg_gscv</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">estimator</span><span class="o">=</span><span class="n">reg_pipeline</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">reg_parameters</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="n">reg_scoring</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="n">cv</span><span class="p">)</span>
</code></pre></div></div>
<p>Lastly, we implement the entire forecasting methodology in one function.</p>
<p>The functions starts by turning the target variable into a binary variable by separating the observations into <em>outliers</em> and <em>non-outliers</em> by using a certain percentile level. This is done by the function called <em>find_top_n_obs</em>.</p>
<p>Then it is time to fit the classification pipeline on the data and determine the best model. After the fitting procedure we then use the chosen and fitted model to make in-sample predictions, as well as apply the model on the test data. In order to have an idea about the performance of the model, we also show a confusion matrix below.</p>
<p>After splitting the observations into either the <em>outliers</em> or <em>non-outliers</em> class, we then apply the regression pipeline on the former group and the exponential smoother on the latter.</p>
<p>When predicting the <em>outliers</em> we have to make sure that our function can deal with the case in which no observation within the test data has been classified as as an <em>outlier</em></p>
<p>The <em>non-outliers</em> are then inputted in the TBATS smoothing model, which uses the aforementioned winsorization technique. It is noteworthy that the TBATS model comes from <strong>statsmodels</strong> and therefore does not natively support any pipeline application. We therefore build a wrapper around the TBATS in order to still use the <em>fit</em> and <em>predict</em> commands we grown used to from scikit-learn.</p>
<p>As the last step we then combine the results from all models. This is done by taking the predicted time-series from TBATS and replace those values which are predicted to be <em>outliers</em> with the values we got from the regression model.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">def</span> <span class="nf">combination_model</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">threshold</span><span class="p">):</span>
<span class="c1"># Classification
</span> <span class="n">binary_target</span> <span class="o">=</span> <span class="n">y_train</span> <span class="o">>=</span> <span class="n">np</span><span class="p">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">threshold</span><span class="p">)</span>
<span class="n">clf_gscv</span><span class="p">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">binary_target</span><span class="p">)</span>
<span class="n">clf_pred_train</span> <span class="o">=</span> <span class="n">clf_gscv</span><span class="p">.</span><span class="n">best_estimator_</span><span class="p">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="n">clf_pred_test</span> <span class="o">=</span> <span class="n">clf_gscv</span><span class="p">.</span><span class="n">best_estimator_</span><span class="p">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="c1"># Regression
</span> <span class="n">subset_x_train</span><span class="p">,</span> <span class="n">subset_y_train</span> <span class="o">=</span> <span class="n">X_train</span><span class="p">.</span><span class="n">loc</span><span class="p">[</span><span class="n">clf_pred_train</span><span class="p">,</span> <span class="p">:],</span> <span class="n">y_train</span><span class="p">[</span><span class="n">clf_pred_train</span><span class="p">]</span>
<span class="n">subset_x_test</span> <span class="o">=</span> <span class="n">X_test</span><span class="p">.</span><span class="n">loc</span><span class="p">[</span><span class="n">clf_pred_test</span><span class="p">,</span> <span class="p">:]</span>
<span class="n">reg_gscv</span><span class="p">.</span><span class="n">fit</span><span class="p">(</span><span class="n">subset_x_train</span><span class="p">,</span> <span class="n">subset_y_train</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">sum</span><span class="p">(</span><span class="n">clf_pred_test</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">reg_y_pred_test</span> <span class="o">=</span> <span class="n">reg_gscv</span><span class="p">.</span><span class="n">best_estimator_</span><span class="p">.</span><span class="n">predict</span><span class="p">(</span><span class="n">subset_x_test</span><span class="p">).</span><span class="nb">round</span><span class="p">().</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">reg_y_pred_test</span> <span class="o">=</span> <span class="p">[]</span>
<span class="c1"># Smoother
</span> <span class="n">winsorized_y_train</span> <span class="o">=</span> <span class="n">winsorize_data</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">y_train</span><span class="p">,</span> <span class="n">upper_threshold</span><span class="o">=</span><span class="n">threshold</span><span class="p">)</span>
<span class="n">smt</span> <span class="o">=</span> <span class="n">TBATSWrapper</span><span class="p">().</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">winsorized_y_train</span><span class="p">)</span>
<span class="n">smt_pred_test</span> <span class="o">=</span> <span class="n">smt</span><span class="p">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">X_test</span><span class="p">)</span>
<span class="c1"># Combination models
</span> <span class="n">total_pred_test</span> <span class="o">=</span> <span class="n">combining_models</span><span class="p">(</span><span class="n">clf</span><span class="o">=</span><span class="n">clf_pred_test</span><span class="p">,</span> <span class="n">reg</span><span class="o">=</span><span class="n">reg_y_pred_test</span><span class="p">,</span> <span class="n">smt</span><span class="o">=</span><span class="n">smt_pred_test</span><span class="p">,</span> <span class="n">index_df</span><span class="o">=</span><span class="n">X_test</span><span class="p">)</span>
<span class="k">return</span> <span class="n">total_pred_test</span>
</code></pre></div></div>
<p>In order to have an idea of how good a classification model performs, we plot a confusion matrix of the best performing hyper-parameter version. Below we give an example of the best model for the classification of <em>outliers</em> for the city of San Juan when classifying the 95th percentile as the threshold between the <em>outliers</em> and <em>non-outliers</em> class.</p>
<p>As already mentioned earlier, the classification model tend to have more False Positives than False Negatives, which is nicely visible through the confusion matrix below. The relatively poor performance is also likely the reason why this approach does not work as well as we hoped it would. It seems that the high spikes in values are not predictable with the features at hand.</p>
<p><img src="/assets/article_images/dengai/0413/sj_95.png" alt="Forecasting for out-of-sample San Juan" /></p>
<h2 id="predictions">Predictions</h2>
<p>After setting all the pipelines up, we can now loop over the two cities San Juan and Iquitos, as well as over the different percentiles from which we divide observations into <em>outliers</em> and <em>non-outliers</em>. We decide to go for three levels, namely 75, 85 and 95. Since we are only interested in defining the out of the ordinary spikes, it makes sense to set the separation level relatively high.</p>
<p>In order to get an idea how the model performs out-of-sample, we divide the dataset into train and test, using 80% of the data as the trainings data and the remaining 20% to assess our predictive power, using <strong>Mean Absolute Error</strong>.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">test_pred_results</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">threshold_list</span> <span class="o">=</span> <span class="p">[</span><span class="mi">75</span><span class="p">,</span> <span class="mi">85</span><span class="p">,</span> <span class="mi">95</span><span class="p">]</span>
<span class="k">for</span> <span class="n">city</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">([</span><span class="s">"iq"</span><span class="p">,</span> <span class="s">"sj"</span><span class="p">]):</span>
<span class="n">mae_list</span><span class="p">,</span> <span class="n">y_pred_list</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">threshold</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">threshold_list</span><span class="p">):</span>
<span class="c1"># Load data
</span> <span class="n">X_train_total</span><span class="p">,</span> <span class="n">X_test_total</span><span class="p">,</span> <span class="n">y_train_total</span> <span class="o">=</span> <span class="n">city_query</span><span class="p">(</span><span class="n">city</span><span class="p">)</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X_train_total</span><span class="p">,</span> <span class="n">y_train_total</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
<span class="c1"># Predictions
</span> <span class="n">y_pred</span> <span class="o">=</span> <span class="n">combination_model</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">threshold</span><span class="p">)</span>
<span class="n">mae</span> <span class="o">=</span> <span class="n">mean_absolute_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
<span class="n">mae_list</span><span class="p">.</span><span class="n">append</span><span class="p">(</span><span class="n">mae</span><span class="p">)</span>
<span class="n">y_pred_list</span><span class="p">.</span><span class="n">append</span><span class="p">(</span><span class="n">y_pred</span><span class="p">)</span>
<span class="n">plotting_predictions</span><span class="p">(</span><span class="n">y_pred_list</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">threshold_list</span><span class="p">,</span> <span class="n">mae_list</span><span class="p">,</span> <span class="n">city</span><span class="p">)</span>
<span class="n">threshold_level</span> <span class="o">=</span> <span class="n">threshold_list</span><span class="p">[</span><span class="n">np</span><span class="p">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">mae_list</span><span class="p">)]</span>
<span class="n">test_pred_results</span><span class="p">[</span><span class="n">city</span><span class="p">]</span> <span class="o">=</span> <span class="n">combination_model</span><span class="p">(</span><span class="n">X_train_total</span><span class="p">,</span> <span class="n">X_test_total</span><span class="p">,</span> <span class="n">y_train_total</span><span class="p">,</span> <span class="n">threshold_level</span><span class="p">)</span>
<span class="n">save_prediction_results</span><span class="p">(</span><span class="n">test_pred_results</span><span class="p">)</span>
</code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code> 0%| | 0/2 [00:00<?, ?it/s]
0%| | 0/3 [00:00<?, ?it/s][A
33%|███▎ | 1/3 [01:08<02:16, 68.17s/it][A
67%|██████▋ | 2/3 [02:02<00:59, 59.93s/it][A
100%|██████████| 3/3 [03:48<00:00, 76.19s/it][A
</code></pre></div></div>
<p><img src="/assets/article_images/dengai/0413/output_26_1.png" alt="png" /></p>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code> 50%|█████ | 1/2 [04:43<04:43, 283.97s/it]
0%| | 0/3 [00:00<?, ?it/s][A
33%|███▎ | 1/3 [01:37<03:15, 97.84s/it][A
67%|██████▋ | 2/3 [02:45<01:20, 80.31s/it][A
100%|██████████| 3/3 [05:39<00:00, 113.04s/it][A
</code></pre></div></div>
<p><img src="/assets/article_images/dengai/0413/output_26_3.png" alt="png" /></p>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>100%|██████████| 2/2 [12:05<00:00, 362.89s/it]
</code></pre></div></div>
<h2 id="results">Results</h2>
<p>The images above show nicely what difference the respective threshold made. The highest threshold (95%) has the fewest spikes, which makes sense, as it is trained to only react to more prominent spikes. As we can see from the second images, this comes with a cost, as it fails to detect spikes when it should, and even finds spikes where in reality there isn’t one.</p>
<p>Given that the 75% threshold sees more data when training the regression model, the regressed values tend also to be more moderate, which explains its relatively strong performance.</p>
<p>The two images though, also clearly show that the overall method does not seem suitable for the job, as it fails to accurately detect when spikes are occurring. This failure within the classification model then fatally hurts the overall prediction performance. Interestingly it also seems that the seasonality of all three models is off by some periods. This is visible by the fact that all model seem to have upticks a couple of periods later, then the actual data suggests.</p>
<p>The achieved performance by the model after submitting our result on DrivenData.org also reflects our moderate success on predicting the target variable. Nevertheless, the journey taught us multiple lessons, and it will not be the last time we will work on the DengAI challenge.</p>
<p><img src="/assets/article_images/dengai/0413/score.png" alt="Submitted predictions on DrivenData.org" /></p>
<h2 id="appendix">Appendix</h2>
<p>[1] Andrea Dal Pozzolo. (2010). Comparison of Data Mining Techniques for Insurance Claim Prediction. https://dalpozz.github.io/static/pdf/Claim_prediction.pdf</p>Paul MoraUsing a combination of regression, classification and smoother for our second take on the DengAI challenge.DengAI Challenge Second Attempt: Building a scikit-learn pipeline2021-03-11T00:00:00+00:002021-03-11T00:00:00+00:00https://paul-mora.com/time%20series/python/DengAI2-Second-Attempt-Pipeline<p><em>The workings of the underlying prediction pipeline</em></p>
<h2 id="background-on-the-dengai-challenge">Background on the DengAI Challenge</h2>
<p>This post represents the second part out of a series of two blogpost discussing our second attempt of the DengAI forecasting challenge hosted by DrivenData.org. Within this challenge we are tasked to predict the number of dengue fever cases in two different cities, San Juan as well as Iquitos. What makes this challenge so difficult is that we are asked to forecast multiple years into the feature, even though we do not have very many observations for each city to begin with.</p>
<p>Whenever facing a data science challenge, we often run into the issue of <strong>data leakage</strong>. Data leakage describes the problem of having information which belongs to the trainings set within the test data. For example, when fitting a scaler (e.g. a MinMaxScaler from scikit-learn) and we apply this scaler before train-test-splitting our data, we accidentally scaled the trainings data using information from the test data. Examples like this usually result in a superior prediction performance than would have been the case if we would have separated train and test data properly.</p>
<p>Even when properly splitting train and test data in the beginning of our code, applying every single step we used for the trainings data and apply it on the test data is cumbersome and prone to errors. What we would rather like to do is to combine all feature engineering steps we apply to the trainings data and fit this combinations of steps on the trainings data, before applying all steps on the test data. The solution to this problem is called a <strong>pipeline</strong>.</p>
<h2 id="benefits-of-using-a-pipeline">Benefits of using a pipeline</h2>
<p>In order to make the benefits of using a pipeline even clearer, let us consider the following simple example, in which we would like to scale the data, impute it and afterwards fit a LinearRegression.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="n">np</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="n">np</span><span class="p">.</span><span class="n">nan</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">]]</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">np</span><span class="p">.</span><span class="n">nan</span><span class="p">]]</span>
<span class="n">y_train</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">4</span><span class="p">]</span>
</code></pre></div></div>
<p>In the case without using a pipeline we have to apply the scaler and imputation method first on the trainings data, save the instance and afterwards apply to the test data in order to get a prediction:</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span>
<span class="kn">from</span> <span class="nn">sklearn.impute</span> <span class="kn">import</span> <span class="n">KNNImputer</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
<span class="c1"># Fitting everything on the trainings data
</span><span class="n">scaler</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">().</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="n">scaled_data</span> <span class="o">=</span> <span class="n">scaler</span><span class="p">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="n">imputer</span> <span class="o">=</span> <span class="n">KNNImputer</span><span class="p">().</span><span class="n">fit</span><span class="p">(</span><span class="n">scaled_data</span><span class="p">)</span>
<span class="n">imputed_data</span> <span class="o">=</span> <span class="n">imputer</span><span class="p">.</span><span class="n">transform</span><span class="p">(</span><span class="n">scaled_data</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">().</span><span class="n">fit</span><span class="p">(</span><span class="n">imputed_data</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># Applying all steps on the test data
</span><span class="n">scaled_data_test</span> <span class="o">=</span> <span class="n">scaler</span><span class="p">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="n">imputed_data_test</span> <span class="o">=</span> <span class="n">imputer</span><span class="p">.</span><span class="n">transform</span><span class="p">(</span><span class="n">scaled_data_test</span><span class="p">)</span>
<span class="n">y_pred_test</span> <span class="o">=</span> <span class="n">model</span><span class="p">.</span><span class="n">predict</span><span class="p">(</span><span class="n">imputed_data_test</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">y_pred_test</span><span class="p">)</span>
</code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>[4.97222222]
</code></pre></div></div>
<p>As we can see from the example above that even a simple prediction method becomes quite complex when not using pipelines. Not only do we always have to remember what the output from the prior step was saved as in order to input it into the next step, but also we have to remember what all the instances from our preprocessing steps are called. When doing exactly the same but with using pipelines, the benefits become clear immediately.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">make_pipeline</span>
<span class="n">pipeline</span> <span class="o">=</span> <span class="n">make_pipeline</span><span class="p">(</span>
<span class="n">MinMaxScaler</span><span class="p">(),</span>
<span class="n">KNNImputer</span><span class="p">(),</span>
<span class="n">LinearRegression</span><span class="p">()</span>
<span class="p">)</span>
<span class="n">pipeline</span><span class="p">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">y_pred</span> <span class="o">=</span> <span class="n">pipeline</span><span class="p">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">y_pred</span><span class="p">)</span>
</code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>[4.97222222]
</code></pre></div></div>
<p>Not only did we need significantly less code, there is no unnecessary data assigning and constant fitting and transforming of instances. Furthermore, the defined pipeline is easy to apply to other data. One other benefit is that it is possible to write custom classes which are then possible to be inserted into the pipeline.</p>
<p>Because of all of these benefits we decided to go with this technique for our challenge. The purpose of this post is to outline how our pipeline we used for this data challenge works under the hood.</p>
<h2 id="structure-of-this-post">Structure of this post</h2>
<p>In order to give the reader a solid understanding how our prediction pipeline-powered prediction model works, we show all classes within the pipeline and explain our steps. On a high-level the pipeline has nine different steps.</p>
<p>The pipeline starts with the <strong>Encoding</strong> of all variables which are not in a numeric format. That applies especially to date variables, as well as variables which are in an ordinal form (date of the month).</p>
<p>Since dummy variables are created, we have to make sure that we do not accidentally end up with having categories within the test data, which do not show up in the trainings data. Since that would break the code we have the following class called <strong>ColumnCorrection</strong> which deals with exactly that.</p>
<p>Afterwards we make sure that the features we are using fulfill the stationarity criteria, since any form of prediction model would have a hard-time when facing a unit-root in the data. Therefore we defined the <strong>Stationarity</strong> class, which tests for stationarity and applies potentially necessary corrections.</p>
<p>The next step is then to transform the data as well as to impute missing values, which is outlined in the two classes <strong>Transformer</strong> as well <strong>Imputer</strong>. Following that we create additional features and select those which are relevant within the two classes <strong>FeatureCreation</strong> as well as <strong>FeatureSelection</strong>.</p>
<p>Given the nature of our prediction methodology for this data challenge, we also included a oversampling method called SMOTE. The need of this class is elaborated on in the section <strong>SMOTE</strong>. Lastly we show a possible way of using docking different prediction models to our prediction model using the self-written class called <strong>ModelSwitcher</strong>.</p>
<h2 id="importing-relevant-packages">Importing relevant packages</h2>
<p>We begin by showing the relevant packages for our work. As we can see most of them are from scikit-learn, which comes not too big of a surprise, given that we are using pipelines from the same package. Sklearn pipeline are very articulate in that they require every class that is used within it has a fit and transform/ predict method.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">import</span> <span class="nn">warnings</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="n">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="n">pd</span>
<span class="kn">from</span> <span class="nn">tbats</span> <span class="kn">import</span> <span class="n">TBATS</span>
<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">signal</span> <span class="k">as</span> <span class="n">sig</span>
<span class="kn">from</span> <span class="nn">statsmodels.tsa.stattools</span> <span class="kn">import</span> <span class="n">adfuller</span>
<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">TransformerMixin</span><span class="p">,</span> <span class="n">RegressorMixin</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span>
<span class="kn">from</span> <span class="nn">sklearn.impute</span> <span class="kn">import</span> <span class="n">KNNImputer</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">PolynomialFeatures</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">svm</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span><span class="p">,</span> <span class="n">LinearRegression</span>
<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span><span class="p">,</span> <span class="n">RandomForestRegressor</span>
<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">AffinityPropagation</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">cross_val_score</span>
<span class="kn">from</span> <span class="nn">sklearn.exceptions</span> <span class="kn">import</span> <span class="n">ConvergenceWarning</span>
<span class="kn">from</span> <span class="nn">warnings</span> <span class="kn">import</span> <span class="n">simplefilter</span>
<span class="n">simplefilter</span><span class="p">(</span><span class="s">"ignore"</span><span class="p">,</span> <span class="n">category</span><span class="o">=</span><span class="n">ConvergenceWarning</span><span class="p">)</span>
<span class="n">warnings</span><span class="p">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s">"ignore"</span><span class="p">)</span>
<span class="n">pd</span><span class="p">.</span><span class="n">options</span><span class="p">.</span><span class="n">mode</span><span class="p">.</span><span class="n">chained_assignment</span> <span class="o">=</span> <span class="bp">None</span>
</code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>/Users/PM/.local/share/virtualenvs/dengai_ensemble-MOIVDowa/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
import pandas.util.testing as tm
</code></pre></div></div>
<h2 id="data-overview">Data Overview</h2>
<p>Before we start with the individual preprocessing classes we take a quick look into what kind of datatypes the data contains. That initial analysis is important in order to know what kind of processing is necessary. From the data below, we see that most of the variables are already in a float format. There are only four variables which come either in an integer or object format.</p>
<ul>
<li>city</li>
<li>year</li>
<li>weekofyear</li>
<li>week_start_date</li>
</ul>
<p>The variables <em>year</em> and <em>weekofyear</em> are already in numeric format and therefore do not necessarily have to be transformed. The other two variables <em>city</em> and <em>week_start_date</em> are currently in an string format and therefore have to be adjusted in order for a machine to be able to read them in. The following paragraph <strong>Encoding</strong> will elaborate on how to proceed with these variables.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="n">pd</span>
<span class="n">os</span><span class="p">.</span><span class="n">chdir</span><span class="p">(</span><span class="sa">r</span><span class="s">"/Users/PM/Documents/projects/dengai_ensemble/"</span><span class="p">)</span>
<span class="n">feature_data</span> <span class="o">=</span> <span class="n">pd</span><span class="p">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s">"data/dengue_features_train.csv"</span><span class="p">)</span>
<span class="n">feature_data</span><span class="p">.</span><span class="n">dtypes</span>
</code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>city object
year int64
weekofyear int64
week_start_date object
ndvi_ne float64
ndvi_nw float64
ndvi_se float64
ndvi_sw float64
precipitation_amt_mm float64
reanalysis_air_temp_k float64
reanalysis_avg_temp_k float64
reanalysis_dew_point_temp_k float64
reanalysis_max_air_temp_k float64
reanalysis_min_air_temp_k float64
reanalysis_precip_amt_kg_per_m2 float64
reanalysis_relative_humidity_percent float64
reanalysis_sat_precip_amt_mm float64
reanalysis_specific_humidity_g_per_kg float64
reanalysis_tdtr_k float64
station_avg_temp_c float64
station_diur_temp_rng_c float64
station_max_temp_c float64
station_min_temp_c float64
station_precip_mm float64
dtype: object
</code></pre></div></div>
<h2 id="encoding">Encoding</h2>
<p>Encoding describes the process of turning a categorical columns into a machine-readable numeric format. A categorical column usually refers to a column of the type “object”, meaning that the content of the variable is in a string format. Though we would also apply encoding whenever we face a variable of discrete nature. The purpose of the Encoding class is to take variables which are currently in an unusable or unfortunate format and turn them into a numeric format a machine would have an easier time to understand.</p>
<p>From the section above we know that there are four variables we should take a closer look at, namely <em>city</em>, <em>year</em>, <em>weakofyear</em> and <em>year</em>. In the following we discuss what is done to all of these variables in order to turn them into a better usable format.</p>
<p>Given that the data generating process of the number of dengue fever cases is different for these two cities, we decided to build one prediction model per city. Through that decision the variable <em>city</em> falls away automatically.</p>
<p>The variables <em>year</em> and <em>weekofyear</em> are currently both of type “integer”. There are multiple reasons on why the <em>year</em> variable should not be included within any forecasting model. The arguably strongest argument is that the year variable is by definition non-stationary, as it is has a deterministic trend. If we then even would try toe remove that trend, we would end up with a constant variable with no variation. It should therefore be clear that we drop this feature.</p>
<p>Dealing with the <em>weekofyear</em> feature is not as straightforward. To understand why that is the case we first have to explain what the problem is, given that the variable is already in a numeric format and therefore possible to simply leave the feature as it is right now.</p>
<p>The problem is that in its current representation only linear effects are possible to be captured. The algorithm has basically no chance to understand that week 52 and week 1 are actually separated by only 1 week. The algorithm thinks instead that these two weeks are 51 weeks apart.</p>
<p>That is the point where encoding comes in. Encoding helps us to change the feature representation and make the information content of the feature clearer. The two most famous encoding algortihms are arguably the creation of <a href="https://en.wikipedia.org/wiki/Dummy_variable_(statistics)">dummy-variables</a> and <a href="https://www.geeksforgeeks.org/mean-encoding-machine-learning/">mean-encoding</a>. Deciding between these two is rather an art than science. In this instance we decide against the dummy-variable creation given the amount of categories within the <em>weekofyear</em> variable, since it would create over 50 features. We therefore decide to mean-encode this feature. Note that when doing this we have to prepare the mean-encoding dictionary within the fit statement, in order to apply it in the transform statement. Furthermore, we will have to make sure that we have an answer in the case that a week turns up in the test data that we have not seen in the trainings data. This worry is the explanation of the additional code in the transform statement.</p>
<p>Lastly there is the variable <em>week_start_date</em>. This variable is particularly interesting as there are so many different ways how we can transform this variable, as it contains day of the month, weekday and month information. We deemed the information about which month the observation is in as the most useful and therefore extracted that piece of information. Afterwards we decided to apply dummy-variable encoding. Doing that, we have to be aware that when using a prediction model which includes an intercept (e.g. LogisticRegression or LinearRegression) that we have to drop one of the categories, in order to not fall into the so-called <a href="https://www.algosome.com/articles/dummy-variable-trap-regression.html">dummy-variable trap</a>. That is because if we would not drop any category and add all dummy-variable columns together we would end up with a column full of ones. A column full of ones is though already what an intercept actually represents. Having two columns full of ones would lead us to being unable to solve for the coefficient values of our features. Luckily, Pandas provides us with an option to drop one of the categories when creating the dummy-encoding, as can be seen below.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">class</span> <span class="nc">Encoding</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">TransformerMixin</span><span class="p">):</span>
<span class="s">"""This class is creating dummy variables out of the month variable. Further, it is dropping the time variable
week start date and the year. The year is dropped given that this variable is not useful and will
not be stationary. Furthermore, we mean encode the weekofyear variable."""</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="c1"># Get mean number of cases for weekofyear
</span> <span class="n">weekofyear_df</span> <span class="o">=</span> <span class="n">X</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s">"weekofyear"</span><span class="p">]].</span><span class="n">copy</span><span class="p">()</span>
<span class="n">weekofyear_df</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s">"target"</span><span class="p">]</span> <span class="o">=</span> <span class="n">y</span><span class="p">.</span><span class="n">values</span>
<span class="bp">self</span><span class="p">.</span><span class="n">weekofyear_dict</span> <span class="o">=</span> <span class="n">weekofyear_df</span><span class="p">.</span><span class="n">groupby</span><span class="p">(</span><span class="s">"weekofyear"</span><span class="p">)[</span><span class="s">"target"</span><span class="p">].</span><span class="n">mean</span><span class="p">().</span><span class="n">to_dict</span><span class="p">()</span>
<span class="c1"># Making sure that we have all weeks of the year within the data
</span> <span class="n">week_within_a_year</span> <span class="o">=</span> <span class="mi">53</span>
<span class="n">keys_present</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">weekofyear_dict</span><span class="p">.</span><span class="n">keys</span><span class="p">()</span>
<span class="n">overall_average</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">mean</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">weekofyear_dict</span><span class="p">.</span><span class="n">values</span><span class="p">()))</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="p">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">week_within_a_year</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
<span class="k">if</span> <span class="n">i</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">keys_present</span><span class="p">:</span>
<span class="bp">self</span><span class="p">.</span><span class="n">weekofyear_dict</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span> <span class="n">overall_average</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="c1"># Creating monthly dummy variables from week start date
</span> <span class="n">week_start_date</span> <span class="o">=</span> <span class="n">pd</span><span class="p">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">X</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s">"week_start_date"</span><span class="p">])</span>
<span class="n">X</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s">"month"</span><span class="p">]</span> <span class="o">=</span> <span class="n">week_start_date</span><span class="p">.</span><span class="n">dt</span><span class="p">.</span><span class="n">month</span><span class="p">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">object</span><span class="p">)</span>
<span class="n">subset_X</span> <span class="o">=</span> <span class="n">X</span><span class="p">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s">"week_start_date"</span><span class="p">,</span> <span class="s">"year"</span><span class="p">])</span>
<span class="n">dummy_X</span> <span class="o">=</span> <span class="n">pd</span><span class="p">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">subset_X</span><span class="p">,</span> <span class="n">drop_first</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="c1"># Applying the mean encoding for the weekofyear variable
</span> <span class="n">dummy_X</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s">"weekofyear"</span><span class="p">]</span> <span class="o">=</span> <span class="n">X</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s">"weekofyear"</span><span class="p">].</span><span class="nb">map</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">weekofyear_dict</span><span class="p">)</span>
<span class="k">return</span> <span class="n">dummy_X</span>
</code></pre></div></div>
<h2 id="columncorrection">ColumnCorrection</h2>
<p>This class may arguably not be needed in this very example, though it would be important to include if applying this pipeline to other problems in the future. The intend of this class is to make sure that we do not run into the case where we find an encoded category which was…</p>
<ol>
<li>present in the training data, but does not show up in the test data</li>
<li>present in the test data, but does not show up in the trainings data</li>
</ol>
<p>In the following we discuss why that is a problem and how we tackle the problem.</p>
<p>Imagine for example that in our training data we do not have any observation recorded in the month of <em>December</em>. Given the workings of the prior <strong>Encoding</strong> class, we would therefore not create a dummy-variable for December. If this category though then appears in the test data our algorithm will result in an error since it contains a feature for which the training model has not been adjusted for. In order to prevent that we would have to find all the columns names which were created in the training data and subset the test data so it only continues with these exact columns.</p>
<p>In the other scenario we would find the month of December only in the training data but not in the test data. The problem with that is that the final prediction model will then find not enough features to work with. In order to prevent that we have to create all the missing columns and fill them with zeros.</p>
<p>The reason why this problem is rather unlikely to happen in our example is that we only have eleven dummy columns to worry about, and it is rather unlikely that the trainings or testing data would contain fewer than one observation from each month.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">class</span> <span class="nc">ColumnCorrection</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">TransformerMixin</span><span class="p">):</span>
<span class="s">"""This class is necessary to ensure that all columns we created in train are also showing up in the test data."""</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="bp">self</span><span class="p">.</span><span class="n">train_columns</span> <span class="o">=</span> <span class="n">X</span><span class="p">.</span><span class="n">columns</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="n">in_train_but_not_in_test</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">train_columns</span><span class="p">)</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">X</span><span class="p">.</span><span class="n">columns</span><span class="p">))</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">in_train_but_not_in_test</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">in_train_but_not_in_test</span><span class="p">:</span>
<span class="n">X</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="bp">self</span><span class="p">.</span><span class="n">train_columns</span><span class="p">]</span>
<span class="k">return</span> <span class="n">X</span>
</code></pre></div></div>
<h2 id="stationarity">Stationarity</h2>
<p>The next concept is particularly important for time-series. In fact, without ensuring stationarity, the prediction performance of the model could be seriously worsened. A time-series would be described as stationary, if its statistical properties do not change over time. The absence of stationarity would therefore make it difficult or even impossible for any kind of prediction model to exploit a statistical pattern within a time-series which constantly changes. Future posts will elaborate more on the mathematics behind the concept of stationarity.</p>
<p>There are many potential reasons why a time-series could be non-stationary. The most typical are:</p>
<ol>
<li>Seasonality</li>
<li>Deterministic Trend</li>
<li>Unit root</li>
<li>Structural Breaks</li>
</ol>
<p>A seasonal time-series is technically a form of non-stationarity, but not much is lost when treating such a time-series as if it were stationary [1].</p>
<p>Deterministic trends have an unconditional which depend on time, their treatment is normally to model the trend (e.g. with a LinearRegression) and remove it from the time-series.</p>
<p>The arguably most dangerous version of non-stationarity is a so called [unit-root] (https://en.wikipedia.org/wiki/Unit_root), which has an unconditional variance that grows over time. This explosive nature of the unit-root makes the time series unpredictable. The treatment for this problem is to difference the time-series. Differencing is the way to go to make a time-series which suffers from a unit-root stationary.</p>
<p>Lastly, structural breaks describe an abrupt change in the statistical properties which were constant before and after. This behavior can lead to either an unconditional dependence on time of the mean or the variance, depending on the data. When facing structural breaks, one normally differences the time-series in order to proceed.</p>
<h3 id="testing-for-stationarity">Testing for stationarity</h3>
<p>There are multiple ways to test for stationarity. The two most famous tests are the <strong>Augmented Dickey Fuller</strong> (ADF) test and the <strong>KPSS</strong> test. It is important to mention that even though these two tests are applied to check for stationarity of the time series, they technically look for slightly different things. Whereas the ADf test checks whether a unit root is present in the time-series, the KPSS is checking for stationary around a deterministic trend. This slight difference will also be elaborated more on in a future post.</p>
<p>For reasons of automation we will only use the ADF test in order to check for stationarity. Usually though one would apply both tests and would then decide on the appropriate measure to treat a non-stationary time-series.</p>
<p>It is also important to note that after any step taken to achieve stationarity (e.g. detrending or differencing) it is necessary to re-run the ADF test to ensure that the method worked. This concept explains the necessity of a while loop in the following code.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">class</span> <span class="nc">Stationarity</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">TransformerMixin</span><span class="p">):</span>
<span class="s">"""This class is checking for stationarity using the augmented dickey fuller test."""</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">SIGNIFICANCE_LEVEL</span><span class="p">):</span>
<span class="bp">self</span><span class="p">.</span><span class="n">SIGNIFICANCE_LEVEL</span> <span class="o">=</span> <span class="n">SIGNIFICANCE_LEVEL</span>
<span class="k">def</span> <span class="nf">check_stationarity</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">series</span><span class="p">):</span>
<span class="s">"""This method conducts two stationary tests, namely the ADF and KPSS test"""</span>
<span class="n">no_nan_series</span> <span class="o">=</span> <span class="n">series</span><span class="p">.</span><span class="n">dropna</span><span class="p">()</span>
<span class="n">adf_stationary</span> <span class="o">=</span> <span class="n">adfuller</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">no_nan_series</span><span class="p">,</span> <span class="n">regression</span><span class="o">=</span><span class="s">"c"</span><span class="p">,</span> <span class="n">autolag</span><span class="o">=</span><span class="s">"AIC"</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="o"><</span> <span class="bp">self</span><span class="p">.</span><span class="n">SIGNIFICANCE_LEVEL</span>
<span class="k">return</span> <span class="n">adf_stationary</span>
<span class="k">def</span> <span class="nf">adjust_series</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">series</span><span class="p">,</span> <span class="n">adf_stationary</span><span class="p">):</span>
<span class="s">"""This method takes care of any adjustments needed to be done for the different cases of nonstationarity"""</span>
<span class="n">series</span><span class="p">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">series</span> <span class="o">=</span> <span class="n">series</span><span class="p">.</span><span class="n">diff</span><span class="p">()</span>
<span class="n">adf_stationary</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">check_stationarity</span><span class="p">(</span><span class="n">series</span><span class="o">=</span><span class="n">series</span><span class="p">)</span>
<span class="k">return</span> <span class="n">series</span><span class="p">,</span> <span class="n">adf_stationary</span>
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="s">"""This method takes all columns which are not time dependent and loops over them to check for stationarity.
This can only be done if there is a minimum amount of observations which has to be calculated before."""</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">non_time_columns</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">X</span><span class="p">.</span><span class="n">columns</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">x</span><span class="p">.</span><span class="n">startswith</span><span class="p">(</span><span class="s">"month_"</span><span class="p">)</span> <span class="ow">and</span> <span class="s">"weekofyear"</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">x</span><span class="p">]</span>
<span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">non_time_columns</span><span class="p">:</span>
<span class="n">series</span> <span class="o">=</span> <span class="n">X</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="n">col</span><span class="p">].</span><span class="n">copy</span><span class="p">()</span>
<span class="n">adf_stationary</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">check_stationarity</span><span class="p">(</span><span class="n">series</span><span class="p">)</span>
<span class="k">while</span> <span class="ow">not</span> <span class="n">adf_stationary</span><span class="p">:</span>
<span class="n">series</span><span class="p">,</span> <span class="n">adf_stationary</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">adjust_series</span><span class="p">(</span><span class="n">series</span><span class="o">=</span><span class="n">series</span><span class="p">,</span> <span class="n">adf_stationary</span><span class="o">=</span><span class="n">adf_stationary</span><span class="p">)</span>
<span class="n">X</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">series</span><span class="p">.</span><span class="n">copy</span><span class="p">()</span>
<span class="k">except</span> <span class="nb">ValueError</span><span class="p">:</span>
<span class="k">pass</span>
<span class="k">return</span> <span class="n">X</span>
</code></pre></div></div>
<h2 id="transformer">Transformer</h2>
<p>The next concept is a standard step within every machine-learning project, namely scaling the features. The benefits of this step are multi-fold. One example why scaling is necessary is when considering that our imputation method works with euclidean distances between observations. Having differently scaled features would then lead to the largest features dominate the search for the closest neighboring observation. [2]</p>
<p>When scaling the features, there two popular method to do so, namely <strong>normalization</strong> and <strong>standardization</strong>. Whereas the former scales everything between 0 and 1, the latter approach basically calculates z-scores out of all observations. Even though there are some stackoverflow discussion on when to use what, from own experience both scaling methods tend to have very similar effects. For this project we will go with normalization.</p>
<p>It is important to note that we have to fit the normalization method first on the trainings data and apply only afterwards on the test data in order to avoid data leakage. Furthermore it is important to note that the output of the scaling is a numpy array. Hence, in order to still keep the data in a dataframe format, we will have to save the column names in beforehand and re-create a dataframe after the transforming.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">class</span> <span class="nc">Transformer</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">TransformerMixin</span><span class="p">):</span>
<span class="s">"""This class is MinMax scaling all variables using trainings data and applying that on the test data."""</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="bp">self</span><span class="p">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">X</span><span class="p">.</span><span class="n">columns</span>
<span class="n">scaler</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">()</span>
<span class="bp">self</span><span class="p">.</span><span class="n">fitted_scaler</span> <span class="o">=</span> <span class="n">scaler</span><span class="p">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="n">scaled_X_array</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">fitted_scaler</span><span class="p">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="n">scaled_X_df</span> <span class="o">=</span> <span class="n">pd</span><span class="p">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">scaled_X_array</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">columns</span><span class="p">)</span>
<span class="k">return</span> <span class="n">scaled_X_df</span>
</code></pre></div></div>
<h2 id="imputer">Imputer</h2>
<p>The pipeline step is a rather important one. Namely the imputation of missing observation. One very neat way to solve the problem is the K nearest neighbor imputation algorithm from scikit-learn, called <strong>KNNImputer</strong>. It is no coincidence that we are using an imputation method from scikit-learn, as they have the benefit of having a build-in fit and transform statement, which makes them perfect candidates for pipeline applications.</p>
<p>The workings of KNNImputation are given its name obviously very close to the clustering algorithm KMeans. Herein we look for an observation which has a missing features what the closest K neighbors are, based on the euclidean distance of the other non-missing features. Afterwards we take the average value of all K nearest neighbors of the feature that is missing for our initial observation.</p>
<p>Like KMeans the question of which number of neighbors is the most appropriate, remains a hyper-parameter to tune. Unlike KMeans we do not have something like the silhouette-score in order find the appropriate number, but we would have to compare the predictive power of models which were imputed with a range of different number of neighbors. Given the amount of other hyper-parameter to tune for (e.g. hyper-parameter of the prediction models), and the fact that we do not face a significant amount of missing values within this data challenge, we decide to pick the fixed value of ten neighbors.</p>
<p>As for scaling, we have to be cautious to apply the KNNImputation first on the trainings data and apply afterwards on the test. Additionally we have to make sure that the outputted numpy array is brought back into a dataframe format, in order to not lose the column name information.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">class</span> <span class="nc">Imputer</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">TransformerMixin</span><span class="p">):</span>
<span class="s">"""This class is using a KNN imputation method. In this simpler version we are choosing
a fixed number of neighbors"""</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_neighbors</span><span class="p">):</span>
<span class="bp">self</span><span class="p">.</span><span class="n">n_neighbors</span> <span class="o">=</span> <span class="n">n_neighbors</span>
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="bp">self</span><span class="p">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">X</span><span class="p">.</span><span class="n">columns</span>
<span class="n">impute</span> <span class="o">=</span> <span class="n">KNNImputer</span><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">n_neighbors</span><span class="p">)</span>
<span class="bp">self</span><span class="p">.</span><span class="n">fitted_imputer</span> <span class="o">=</span> <span class="n">impute</span><span class="p">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="n">imputed_X_array</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">fitted_imputer</span><span class="p">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="n">imputed_X_df</span> <span class="o">=</span> <span class="n">pd</span><span class="p">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">imputed_X_array</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">columns</span><span class="p">)</span>
<span class="k">return</span> <span class="n">imputed_X_df</span>
</code></pre></div></div>
<h2 id="feature-creation">Feature Creation</h2>
<p>After the data is prepared, scaled and imputed, it is now time to come to one of the most important steps within each data science challenge, namely the feature engineering step. This step is normally conducted through a mixture of creativity, common knowledge and most importantly industry expertise.</p>
<p>Our challenge deals with predicting the number of dengue fever cases. This disease is known to be transmitted by mosquitoes. That explains why all feature have something to do with the weather conditions in San Juan and Iquitos. The question that should be answered within the feature engineering step is therefore: What features are important to explain the size of the mosquitoes population at a given date?</p>
<h3 id="lagged-terms">Lagged Terms</h3>
<p>Using lagged values within time-series problems is oftentimes the way to go. In our challenge using lagged values as features is a bit problematic, given that we are dealing with long-term forecasts. The problem with longer term forecasts becomes clear when thinking about how one would use lagged values if one has to predict not only t+1 but also t+2. The feature t-1 would not exist for the prediction of t+2, since t+1 lies in the future. Therefore one has two options. One is to use the predicted values for t+1 as if it realized, a quite dangerous path to go. Alternatively, one only uses lags starting from t-2 onwards. Given these problems we are only allowing lagged values not for the target variable, but for the features.</p>
<p>It is important to note that a lag of N steps will result in us having to drop N observations. Dropping these observations can be regarded as the cost of lagging. Given that we are dealing with weekly data we will allow for a four week lag, hence a month. The value of four was chosen by trading off the benefit of having more features against the cost of having to drop more observations.</p>
<p>Of course, it does not make much sense to lag time-variables like for example a dummy variable which indicates whether the observation lies within the month of January. A lagged version of January represents nothing other than December, a dummy variable which is already present within our feature set. Therefore we exclude all time variables from the creation of lagged variables.</p>
<h3 id="lagged-variables-and-the-train-test-split">Lagged variables and the train-test-split</h3>
<p>The general gist of how to create a lagged variable is quickly understood. One takes the column one would like to lag, and shifts it one cell down (assuming the we have an ascending sorted date). That would then result into a nan value in the first cell of that column. In a non-pipeline environment the creation of a nan value is not too tragic, as we would simply drop the first row from the feature set as well as from the target. Though, when facing a pipeline environment things get considerably more complicated.</p>
<p>To understand why that is a problem one has to understand that it is not possible to drop rows from the feature set when working within a pipeline. Dropping rows from the features data would require to also drop rows from the target variable. Exactly that step is not possible though. Scikit-Learn does not allow to temper with the target variable after placing it into the pipeline.</p>
<p>Therefore, we have to find a way of how we deal with the missing rows within the train and test set. When it comes to the test set, it is possible to mitigate the creation of nan-values when creating lagged features by appending the last n-values of the trainings set to the test set. This step requires us to save the last n rows from the training data within the fit method and in the transform statement append those rows to the test data. In order to do that we also have to check within the transform statement whether we are dealing with the training or the test data.</p>
<p>The creation of nan-values within the trainings data on the other hand can not be solved by appending prior data. Here the only solution is to backfill the last n-rows. Given that this is highly undesirable given that backfilling is a horrible imputation method, we are inclined to not allow too many lagged values.</p>
<h3 id="interaction-effects">Interaction Effects</h3>
<p>Additionally to the lagged terms we created interaction effects. Interaction effects describe the product of cross-multiplying all available variables. The creation of these features can help to hint potential importance of cross-effects of features. For example if the variable precipitation is only of importance in the month of September, then neither feature standing alone would be able to clearly communicate that importance to the prediction model. In this case we would need the interaction effect of the two features. In order to not create too many features we only create the interaction effects for non-lagged features.</p>
<p>It is important to note that the <strong>PolynomialFeatures</strong> class from scikit allows us to specify whether we would like to include an intercept through their input <em>include_bias=</em>. Given that we are later on using among others a LinearRegression, we disallow the creation of an intercept at this point, given that the model would automatically create one.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">class</span> <span class="nc">FeatureCreation</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">TransformerMixin</span><span class="p">):</span>
<span class="s">"""Here we create lagged versions of the variables, polynomials and sums."""</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">degree</span><span class="p">,</span> <span class="n">max_lag</span><span class="p">,</span> <span class="n">lagged_features</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">polynomial_features</span><span class="o">=</span><span class="bp">True</span><span class="p">):</span>
<span class="bp">self</span><span class="p">.</span><span class="n">degree</span> <span class="o">=</span> <span class="n">degree</span>
<span class="bp">self</span><span class="p">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="n">max_lag</span>
<span class="bp">self</span><span class="p">.</span><span class="n">lagged_features</span> <span class="o">=</span> <span class="n">lagged_features</span>
<span class="bp">self</span><span class="p">.</span><span class="n">polynomial_features</span> <span class="o">=</span> <span class="n">polynomial_features</span>
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="c1"># Saving the last n rows of training in order to append those for the test data
</span> <span class="bp">self</span><span class="p">.</span><span class="n">last_train_rows</span> <span class="o">=</span> <span class="n">X</span><span class="p">.</span><span class="n">tail</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">max_lag</span><span class="p">)</span>
<span class="bp">self</span><span class="p">.</span><span class="n">train_last_date</span> <span class="o">=</span> <span class="n">X</span><span class="p">.</span><span class="n">index</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="c1"># Appending last training rows to test data
</span> <span class="k">if</span> <span class="n">X</span><span class="p">.</span><span class="n">index</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">></span> <span class="bp">self</span><span class="p">.</span><span class="n">train_last_date</span><span class="p">:</span>
<span class="n">X</span><span class="p">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">last_train_rows</span><span class="p">)</span>
<span class="n">non_time_columns</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">X</span><span class="p">.</span><span class="n">columns</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">x</span><span class="p">.</span><span class="n">startswith</span><span class="p">(</span><span class="s">"month_"</span><span class="p">)</span> <span class="ow">and</span> <span class="s">"weekofyear"</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">x</span><span class="p">]</span>
<span class="n">X_wo_dummy</span> <span class="o">=</span> <span class="n">X</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="n">non_time_columns</span><span class="p">]</span>
<span class="c1"># Lagged terms
</span> <span class="k">if</span> <span class="bp">self</span><span class="p">.</span><span class="n">lagged_features</span><span class="p">:</span>
<span class="n">lagged_data</span> <span class="o">=</span> <span class="n">pd</span><span class="p">.</span><span class="n">concat</span><span class="p">([</span><span class="n">X_wo_dummy</span><span class="p">.</span><span class="n">shift</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="p">.</span><span class="n">max_lag</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">lagged_columns</span> <span class="o">=</span> <span class="p">[</span><span class="sa">f</span><span class="s">"</span><span class="si">{</span><span class="n">column</span><span class="si">}</span><span class="s">_lag</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s">"</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="p">.</span><span class="n">max_lag</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">non_time_columns</span><span class="p">]</span>
<span class="n">lagged_data</span><span class="p">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">lagged_columns</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">pd</span><span class="p">.</span><span class="n">concat</span><span class="p">([</span><span class="n">X</span><span class="p">,</span> <span class="n">lagged_data</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># Interaction terms
</span> <span class="k">if</span> <span class="bp">self</span><span class="p">.</span><span class="n">polynomial_features</span><span class="p">:</span>
<span class="n">poly_data</span> <span class="o">=</span> <span class="n">X</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="n">non_time_columns</span><span class="p">].</span><span class="n">copy</span><span class="p">()</span>
<span class="n">poly</span> <span class="o">=</span> <span class="n">PolynomialFeatures</span><span class="p">(</span><span class="n">degree</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">degree</span><span class="p">,</span> <span class="n">include_bias</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">interaction_only</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
<span class="n">poly_data_array</span> <span class="o">=</span> <span class="n">poly</span><span class="p">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">poly_data</span><span class="p">)</span>
<span class="n">poly_feature_names</span> <span class="o">=</span> <span class="n">poly</span><span class="p">.</span><span class="n">get_feature_names</span><span class="p">(</span><span class="n">non_time_columns</span><span class="p">)</span>
<span class="n">poly_data_df</span> <span class="o">=</span> <span class="n">pd</span><span class="p">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">poly_data_array</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">poly_feature_names</span><span class="p">)</span>
<span class="n">poly_wo_initial_features</span> <span class="o">=</span> <span class="n">poly_data_df</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">poly_feature_names</span> <span class="k">if</span> <span class="n">x</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">non_time_columns</span><span class="p">]]</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">pd</span><span class="p">.</span><span class="n">concat</span><span class="p">([</span><span class="n">X</span><span class="p">,</span> <span class="n">poly_wo_initial_features</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">X</span><span class="p">.</span><span class="n">fillna</span><span class="p">(</span><span class="n">method</span><span class="o">=</span><span class="s">"bfill"</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="k">return</span> <span class="n">X</span>
</code></pre></div></div>
<h2 id="feature-selection">Feature Selection</h2>
<p>After creating an abundance of features within the prior step, it is now time to trim down to only include the relevant features within the model in order to avoid overfitting. This is where feature selection mechanisms come in. There are many potential algorithms for feature selection out there and instead of going with one of the more ordinary ones, we try out a selection method we recently stumbled over in a paper, called <em>“Combining Multiple Feature-Ranking Techniques and Clustering of Variables for Feature Selection”</em>. [3] The beauty of this feature selection method is twofold. For once is combines multiple feature selection techniques and therefore does not overly rely on any of them. Secondly, it tests through a clustering algorithm whether the chosen feature are significantly distinct from one another, in order to avoid having multiple features which explain the same thing.</p>
<h3 id="importance-ranking">Importance-ranking</h3>
<p>The working of the technique is quite simple and even though the paper only outlines the workings for a classification problem, we discuss how it can be easily adapted to a regression problem.</p>
<p>The approach starts by ranking all features by their relative importance at explaining the target variable, using different algorithms. In a classification problem we would measure <em>importance</em> by…</p>
<ul>
<li>absolute coefficient size of a Logistic Regression</li>
<li>absolute weight of a Support Vector Machine Classifier with a linear kernel</li>
<li>feature importance of a RandomForest Classifier</li>
</ul>
<p>The benefit of using three multiple feature ranking techniques compared to using only one is that the aforementioned methods rely on slightly disjoint assumptions and therefore diversify the risk of wrong decision-making about the selection procedure. The Logistic Regression for example assumes a linear relationship between features and the target, whereas the Random Forest considers variables which may statistically interact in their effect on the target variable. Further, even though there may be some overlap between the assumption of the SVM and the Logistic Regression, as they both assume linearity, the resulting importance shows no similarity [3].</p>
<h3 id="redundant-features">Redundant features</h3>
<p>The result of the prior step are three dataframes, all of which containing all our features ranked by their respective measure of importance. The next step within this methodology is to take care of redundant features. If for example we have two feature which are highly correlated with one another (also known as high multicollinearity) then it would be hurtful to include both of them in the model, as the prediction algorithm would have a hard time to calculate the coefficient weight of each feature as they are so much alike. This results in a relatively unstable model and hurts our prediction performance.</p>
<p>In order to take care of redundant features we feed the features of each dataframe into an Affinity Propagation clustering algorithm. In contrast to other clustering mechanisms this algorithms does not need a pre-specified number of clusters, as this happens automatically. After the algorithm allocates all features into N clusters, we pick only the highest ranked feature from each cluster. Through that method we ensure that our resulting set of feature are significantly disjunct, but still are the most important representation for explaining the target variable.</p>
<h3 id="dropping-low-ranked-features">Dropping low-ranked features</h3>
<p>Lastly we introduce a hyper-parameter to this model, which comes in right before feeding the three dataframes into the Affinity Propagation. Namely, dropping an amount of unimportant features. This is beneficial especially in cases where we have a high amount of features, as we are running the risk of starting to include distinct but unimportant features. There are multiple ways to drop low-ranked features, the method the paper suggests is to drop a range of user-defined integer values <em>e</em>. It is then suggested that the value <em>e</em> can be treated as a hyper-parameter over which we can grid-search.</p>
<p>We therefore define a range of values for the parameter <em>e</em>, run the outlined algorithm to come up with a features list and use the same prediction model we would also use for the overall prediction. We then run a cross-validation to obtain an average prediction performance score for every value of <em>e</em>.</p>
<p>Though, in contrast to what the paper suggest we do not use the simple average of the cross validation score to compare the best value for <em>e</em>, but rather apply an adjustment to that score in order to account for the amount of features used.</p>
<h3 id="adjusted-scoring-function">Adjusted scoring function</h3>
<p>In contrast to how the paper suggests, we do not compare the performance of the different models by the simple mean scoring function value, but by an adjusted version which penalizes for the amount of features used. The reasoning for that is similar to the reason why one would prefer adjusted $R^2$ compared to $R^2$. The number of features matter when looking at the performance. Having lots of features may help the prediction, but it could also causes an overfit. All else equals we would always rather have fewer features within our model if we can obtain the performance level.</p>
<p>We therefore apply the same transformation to the mean scoring function as is also applied to the $R^2$ value.</p>
\[adj. mean = 1 - \left( \frac{(1-mean) * (nobs - 1)}{nobs-nfeatures-1} \right)\]
<p>In the formula above the <em>mean</em> denotes the averaged scoring function from the cross-validation using a certain value of <em>e</em>. <em>nobs</em> stand for the number of observations which were used in the training. <em>nfeatures</em> describes the number of features which resulted from this procedure using <em>e</em>.</p>
<h3 id="applicability-to-regression">Applicability to Regression</h3>
<p>As already mentioned this feature selection technique could also be applied to a regression problem by simply replacing the initial algorithm which determine the importance of each feature. In order to stay as close to the original paper as possible we suggest the following changes:</p>
<ul>
<li>Logistic Regression –> Linear Regression</li>
<li>Support Vector Machine Classifier –> Support Vector Machine Regressor</li>
<li>Random Forest Classifier –> Random Forest Regressor</li>
</ul>
<p>Furthermore, whenever we use apply the algorithm for regression problems we change the scoring function to use R2. This is done to ensure that the scoring function produces a value which is between 0 and 1.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">class</span> <span class="nc">FeatureSelection</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">TransformerMixin</span><span class="p">):</span>
<span class="s">"""Using three selection mechanisms for regression or classification to figure out how important which
features is"""</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">e_list</span><span class="p">,</span> <span class="n">scoring</span><span class="p">,</span> <span class="n">clf</span><span class="p">,</span> <span class="n">estimator</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="bp">self</span><span class="p">.</span><span class="n">e_list</span> <span class="o">=</span> <span class="n">e_list</span>
<span class="bp">self</span><span class="p">.</span><span class="n">scoring</span> <span class="o">=</span> <span class="n">scoring</span>
<span class="bp">self</span><span class="p">.</span><span class="n">clf</span> <span class="o">=</span> <span class="n">clf</span>
<span class="bp">self</span><span class="p">.</span><span class="n">estimator</span> <span class="o">=</span> <span class="n">estimator</span>
<span class="k">def</span> <span class="nf">_importance_df_creator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">importances</span><span class="p">):</span>
<span class="s">"""This method takes the importances of each method and ranks them by their importance"""</span>
<span class="n">importances_df</span> <span class="o">=</span> <span class="n">pd</span><span class="p">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s">"feature_names"</span><span class="p">:</span> <span class="bp">self</span><span class="p">.</span><span class="n">X_columns</span><span class="p">,</span> <span class="s">"importances"</span><span class="p">:</span> <span class="n">importances</span><span class="p">})</span>
<span class="n">importances_df</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s">"rank"</span><span class="p">]</span> <span class="o">=</span> <span class="n">importances_df</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s">"importances"</span><span class="p">].</span><span class="n">rank</span><span class="p">(</span><span class="n">method</span><span class="o">=</span><span class="s">"dense"</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
<span class="n">importances_df</span><span class="p">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s">"rank"</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="k">return</span> <span class="n">importances_df</span>
<span class="k">def</span> <span class="nf">_svm_feature_selection</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">model</span><span class="p">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="n">svm_coef</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">model</span><span class="p">.</span><span class="n">coef_</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">importances_df</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">_importance_df_creator</span><span class="p">(</span><span class="n">svm_coef</span><span class="p">)</span>
<span class="n">importances_df</span><span class="p">.</span><span class="n">name</span> <span class="o">=</span> <span class="s">"SupportVectorMachine"</span>
<span class="k">return</span> <span class="n">importances_df</span>
<span class="k">def</span> <span class="nf">_linear_feature_selection</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">model</span><span class="p">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="n">lgr_coef</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">model</span><span class="p">.</span><span class="n">coef_</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">importances_df</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">_importance_df_creator</span><span class="p">(</span><span class="n">lgr_coef</span><span class="p">)</span>
<span class="n">importances_df</span><span class="p">.</span><span class="n">name</span> <span class="o">=</span> <span class="s">"LinearModel"</span>
<span class="k">return</span> <span class="n">importances_df</span>
<span class="k">def</span> <span class="nf">_rfr_feature_selection</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">model</span><span class="p">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="n">rfr_fi</span> <span class="o">=</span> <span class="n">model</span><span class="p">.</span><span class="n">feature_importances_</span>
<span class="n">importances_df</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">_importance_df_creator</span><span class="p">(</span><span class="n">rfr_fi</span><span class="p">)</span>
<span class="n">importances_df</span><span class="p">.</span><span class="n">name</span> <span class="o">=</span> <span class="s">"RandomForest"</span>
<span class="k">return</span> <span class="n">importances_df</span>
<span class="k">def</span> <span class="nf">_clustering_features</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">df_columns</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="s">"""This method takes the subset of features and uses an Affinity Propagation to assign each feature to a
cluster. In the end only the highest ranked feature of each cluster is considered and extracted."""</span>
<span class="n">relevant_feature_list</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">cluster_algo</span> <span class="o">=</span> <span class="n">AffinityPropagation</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">2_500</span><span class="p">)</span>
<span class="k">for</span> <span class="n">df</span> <span class="ow">in</span> <span class="n">df_columns</span><span class="p">:</span>
<span class="n">subset_df</span> <span class="o">=</span> <span class="n">df</span><span class="p">.</span><span class="n">iloc</span><span class="p">[:</span><span class="bp">self</span><span class="p">.</span><span class="n">n_keep</span><span class="p">,</span> <span class="p">:].</span><span class="n">copy</span><span class="p">()</span>
<span class="n">chosen_features</span> <span class="o">=</span> <span class="n">subset_df</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s">"feature_names"</span><span class="p">].</span><span class="n">tolist</span><span class="p">()</span>
<span class="n">subset_feature_data</span> <span class="o">=</span> <span class="n">X</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="n">chosen_features</span><span class="p">]</span>
<span class="n">feature_cluster</span> <span class="o">=</span> <span class="n">cluster_algo</span><span class="p">.</span><span class="n">fit_predict</span><span class="p">(</span><span class="n">subset_feature_data</span><span class="p">.</span><span class="n">T</span><span class="p">)</span>
<span class="n">subset_df</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s">"cluster"</span><span class="p">]</span> <span class="o">=</span> <span class="n">feature_cluster</span>
<span class="k">for</span> <span class="n">cluster</span> <span class="ow">in</span> <span class="nb">set</span><span class="p">(</span><span class="n">feature_cluster</span><span class="p">):</span>
<span class="n">cluster_df</span> <span class="o">=</span> <span class="n">subset_df</span><span class="p">.</span><span class="n">query</span><span class="p">(</span><span class="s">"cluster==@cluster"</span><span class="p">)</span>
<span class="n">lowest_rank</span> <span class="o">=</span> <span class="n">cluster_df</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s">"rank"</span><span class="p">].</span><span class="nb">min</span><span class="p">()</span>
<span class="n">relevant_features</span> <span class="o">=</span> <span class="n">cluster_df</span><span class="p">.</span><span class="n">query</span><span class="p">(</span><span class="s">"rank==@lowest_rank"</span><span class="p">).</span><span class="n">loc</span><span class="p">[:,</span> <span class="s">"feature_names"</span><span class="p">].</span><span class="n">tolist</span><span class="p">()</span>
<span class="n">relevant_feature_list</span><span class="p">.</span><span class="n">extend</span><span class="p">(</span><span class="n">relevant_features</span><span class="p">)</span>
<span class="n">no_duplicates_relevant_features_list</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">relevant_feature_list</span><span class="p">))</span>
<span class="k">return</span> <span class="n">no_duplicates_relevant_features_list</span>
<span class="k">def</span> <span class="nf">_adjusted_score</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">cv_scores</span><span class="p">,</span> <span class="n">nfeatures</span><span class="p">,</span> <span class="n">nobs</span><span class="p">):</span>
<span class="s">"""This function adjusts the resulting cv scores by adjusting it by the number of
observations and features used"""</span>
<span class="n">scores</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">mean</span><span class="p">(</span><span class="n">cv_scores</span><span class="p">)</span>
<span class="n">adj_score</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="p">((</span><span class="mi">1</span><span class="o">-</span><span class="n">scores</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">nobs</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">nobs</span><span class="o">-</span><span class="n">nfeatures</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span>
<span class="k">return</span> <span class="n">adj_score</span>
<span class="k">def</span> <span class="nf">_choose_best_features</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">e_list</span><span class="p">):</span>
<span class="s">"""This method initiates the calculation of the importances by each feature. Afterwards we are looping over
how many features are dropped and calculate the performance of the prediction model. This is done by using the
same estimator the final model is using as well."""</span>
<span class="n">cv</span> <span class="o">=</span> <span class="mi">5</span>
<span class="n">nobs</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="n">list_scoring_results</span><span class="p">,</span> <span class="n">list_selected_features</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
<span class="k">if</span> <span class="bp">self</span><span class="p">.</span><span class="n">clf</span><span class="p">:</span>
<span class="n">svm_model</span> <span class="o">=</span> <span class="n">svm</span><span class="p">.</span><span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s">"linear"</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
<span class="n">linear_model</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">max_iter</span><span class="o">=</span><span class="mi">5_000</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
<span class="n">rfr_model</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">svm_model</span> <span class="o">=</span> <span class="n">svm</span><span class="p">.</span><span class="n">SVR</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s">"linear"</span><span class="p">)</span>
<span class="n">linear_model</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span>
<span class="n">rfr_model</span> <span class="o">=</span> <span class="n">RandomForestRegressor</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
<span class="bp">self</span><span class="p">.</span><span class="n">scoring</span> <span class="o">=</span> <span class="s">"r2"</span>
<span class="n">svm_columns_df</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">_svm_feature_selection</span><span class="p">(</span><span class="n">svm_model</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="n">lgr_columns_df</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">_linear_feature_selection</span><span class="p">(</span><span class="n">linear_model</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="n">rfr_columns_df</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">_rfr_feature_selection</span><span class="p">(</span><span class="n">rfr_model</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="n">e_list</span><span class="p">:</span>
<span class="bp">self</span><span class="p">.</span><span class="n">n_keep</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">X_columns</span><span class="p">)</span> <span class="o">*</span> <span class="n">e</span><span class="p">)</span>
<span class="n">selected_features</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">_clustering_features</span><span class="p">(</span><span class="n">svm_columns_df</span><span class="p">,</span> <span class="n">lgr_columns_df</span><span class="p">,</span> <span class="n">rfr_columns_df</span><span class="p">,</span> <span class="n">X</span><span class="o">=</span><span class="n">X</span><span class="p">)</span>
<span class="n">list_selected_features</span><span class="p">.</span><span class="n">append</span><span class="p">(</span><span class="n">selected_features</span><span class="p">)</span>
<span class="n">subset_X</span> <span class="o">=</span> <span class="n">X</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="n">selected_features</span><span class="p">]</span>
<span class="n">cv_scores</span> <span class="o">=</span> <span class="n">cross_val_score</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">estimator</span><span class="p">,</span> <span class="n">subset_X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="n">cv</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">scoring</span><span class="p">)</span>
<span class="n">nfeatures</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">subset_X</span><span class="p">.</span><span class="n">columns</span><span class="p">)</span>
<span class="n">adj_score</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">_adjusted_score</span><span class="p">(</span><span class="n">cv_scores</span><span class="p">,</span> <span class="n">nfeatures</span><span class="p">,</span> <span class="n">nobs</span><span class="p">)</span>
<span class="n">list_scoring_results</span><span class="p">.</span><span class="n">append</span><span class="p">(</span><span class="n">adj_score</span><span class="p">)</span>
<span class="n">highest_adj_score_position</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">list_scoring_results</span><span class="p">)</span>
<span class="n">best_features</span> <span class="o">=</span> <span class="n">list_selected_features</span><span class="p">[</span><span class="n">highest_adj_score_position</span><span class="p">]</span>
<span class="k">return</span> <span class="n">best_features</span>
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="bp">self</span><span class="p">.</span><span class="n">X_columns</span> <span class="o">=</span> <span class="n">X</span><span class="p">.</span><span class="n">columns</span>
<span class="bp">self</span><span class="p">.</span><span class="n">best_features</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">_choose_best_features</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="bp">self</span><span class="p">.</span><span class="n">e_list</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="n">subset_X</span> <span class="o">=</span> <span class="n">X</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="bp">self</span><span class="p">.</span><span class="n">best_features</span><span class="p">]</span>
<span class="k">return</span> <span class="n">subset_X</span>
</code></pre></div></div>
<h2 id="smote">SMOTE</h2>
<p>The first step within our prediction methodology is to classify all observations into either <em>outliers</em> or <em>non-outliers</em>. By definition of what we define what <em>outliers</em> are, the resulting dataset will be strongly imbalanced, having the majority of our observation in the <em>non-outliers</em> category.</p>
<p>Imbalanced data makes it very difficult for any classification model to learn and understand what constitutes the minority class, as it only sees so few examples. There are multiple workarounds to this problem. The most popular methods are:</p>
<ol>
<li>Undersample the majority class</li>
<li>Oversample the minority class</li>
<li>Changing the classification threshold away from 0.5 (only possible for probabilistic model)</li>
</ol>
<p>When it comes to undersampling and oversampling, it is important to note that there are multiple ways to do that. When oversampling for example, one could simply copy paste the existing values. Alternatively, one could create synthetic observations.</p>
<p>Synthetic minority oversampling describes a technique in which new observations of the minority classes are created by finding observations which are “like” the existing ones, but are not completely identical. One of the most popular methods is called SMOTE, which stands for <em>Synthetic Minority Oversampling Technique</em>.</p>
<p>The exact workings and visualization of how SMOTE works will be outlined in a separate post, but the basic idea is quickly understood: One imagines that we only have two features and we plot all observations on a scatter plot, having each feature on one axes. The next step is then to draw connecting lines between every observation.
The newly created values would then be found on these connection lines. This strategy is only one of many potential ways of how to use the SMOTE oversampling technique.</p>
<p>One quite important parameter within all oversample techniques is called the <em>sampling_strategy</em>. This parameter describes how big the minority class should grow in compared to the majority class. If we for example have a minority class which represents 10% of the amount of the majority class and we specify a sampling_strategy of 0.5, then we would create 40 percentage points more of the minority class.</p>
<p>In the original paper of SMOTE it is suggested that a combination of oversampling of the minority class and undersampling the majority class leads to favorable results. For now we only grid-search over a sampling strategy of 0.5 and 1.</p>
<p>Even though the oversampling class SMOTE is not compatible with the scikit-learn version of pipelines, the guys from <strong>imblearn</strong> came up with a pipeline class which is identical to scikit’s version in all regards, but can handle the SMOTE class.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">from</span> <span class="nn">imblearn.over_sampling</span> <span class="kn">import</span> <span class="n">SMOTE</span>
<span class="kn">from</span> <span class="nn">imblearn.pipeline</span> <span class="kn">import</span> <span class="n">make_pipeline</span>
</code></pre></div></div>
<h2 id="modelswitcher">ModelSwitcher</h2>
<p>After all feature engineering, we would now like to find out which prediction model is working the best with our data. When using pipelines, we likely stumble over the problem that we have to specify which model to use when writing down the initial pipeline. This is of course unfortunate, because we would like to try out many different models and pick the best performing one afterwards.</p>
<p>The solution to that problem is to write a custom function which takes an estimator as the input and then to use a gridsearch function and specify the models we would like to try-out as hyper-parameters. The following small snippet of code allows us to do exactly that. It is important to specify that the default estimator is set to “None” since otherwise we would have to initialize the pipeline with a model right away.</p>
<p>Furthermore, it is important that we call the second method in contrast to all prior steps “predict” instead of “transform” since at this point of the pipline we are actually interested to get predictions and not further wrangle the data. Therefore, we also specified the “RegressorMixin” at the very top of the class in order to equip our class with all the necessary parts of a prediction class.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">class</span> <span class="nc">ModelSwitcher</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">RegressorMixin</span><span class="p">):</span>
<span class="s">"""This class allows to use any kind of sklearn prediction model and simply insert the models we would like
to try out as a hpyer-parameter."""</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">estimator</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="bp">self</span><span class="p">.</span><span class="n">estimator</span> <span class="o">=</span> <span class="n">estimator</span>
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="bp">self</span><span class="p">.</span><span class="n">estimator</span><span class="p">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="n">pred</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">estimator</span><span class="p">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="k">return</span> <span class="n">pred</span>
</code></pre></div></div>
<h1 id="working-example">Working example</h1>
<p>In order to give a better idea how the pipeline works, we are showing a small demonstration of how that pipeline would look like in action. Therefore, we start by importing the data and turn the problem into a classification problem by defining all observations above the 90th percentile as an <em>outlier</em> and the remaining observations as <em>non-outliers</em>.</p>
<p>In order to visualize how that would look like, we show in the following the time-series of San Juan in which the <em>outlier</em> cases are painted red and the <em>non-outlier</em> cases are painted blue. The graph on the right also nicely visualizes the imbalance of the two classes.</p>
<h3 id="getting-example-data">Getting example data</h3>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="n">plt</span>
<span class="kn">from</span> <span class="nn">src._functions</span> <span class="kn">import</span> <span class="n">city_query</span><span class="p">,</span> <span class="n">find_top_n_obs</span>
<span class="kn">import</span> <span class="nn">src._config</span>
<span class="n">city</span> <span class="o">=</span> <span class="s">"sj"</span>
<span class="n">threshold</span> <span class="o">=</span> <span class="mi">90</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span> <span class="o">=</span> <span class="n">city_query</span><span class="p">(</span><span class="n">city</span><span class="p">)</span>
<span class="n">binary_target</span> <span class="o">=</span> <span class="n">find_top_n_obs</span><span class="p">(</span><span class="n">target</span><span class="o">=</span><span class="n">y_train</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="n">threshold</span><span class="p">,</span> <span class="n">city</span><span class="o">=</span><span class="n">city</span><span class="p">)</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">y_train</span><span class="p">[</span><span class="n">binary_target</span><span class="p">].</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">linestyle</span><span class="o">=</span><span class="s">"None"</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s">"o"</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">"red"</span><span class="p">)</span>
<span class="n">y_train</span><span class="p">[</span><span class="o">~</span><span class="n">binary_target</span><span class="p">].</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">linestyle</span><span class="o">=</span><span class="s">"None"</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s">"o"</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">"blue"</span><span class="p">)</span>
<span class="n">binary_target</span><span class="p">.</span><span class="n">value_counts</span><span class="p">().</span><span class="n">plot</span><span class="p">.</span><span class="n">bar</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
</code></pre></div></div>
<p><img src="/assets/article_images/dengai/0311/output_32_1.png" alt="png" /></p>
<p>Before defining and arranging all pipeline steps we have to define all options which are used within the pipeline itself. We also state all the prediction models and potential hyper-parameters for those models. Note that this is done by stating the name of the pipeline step in lowercase and after two underscores we define which option of that class is parameterized. If for instance we would like to grid-search the sampling strategy of the SMOTE oversampling class, we have to wrote <em>smote__sampling_strategy</em>.</p>
<p>Afterwards we fit the model and obtain in-sample predictions for the entire dataset. Furthermore, we print the best estimator in order to see which hyper-parameters were performing the best.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">GridSearchCV</span>
<span class="n">cv</span> <span class="o">=</span> <span class="mi">3</span>
<span class="n">N_NEIGHBOURS</span> <span class="o">=</span> <span class="mi">10</span>
<span class="n">DEGREE</span> <span class="o">=</span> <span class="mi">2</span>
<span class="n">MAX_LAG</span> <span class="o">=</span> <span class="mi">4</span>
<span class="n">SIGNIFICANCE_LEVEL</span> <span class="o">=</span> <span class="mi">5</span> <span class="o">/</span> <span class="mi">100</span>
<span class="n">RS</span> <span class="o">=</span> <span class="mi">42</span>
<span class="n">e_list</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">]</span>
<span class="n">clf_scoring</span> <span class="o">=</span> <span class="s">"precision"</span>
<span class="n">smote_minimum_sampling_strategy</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="p">(</span><span class="n">threshold</span> <span class="o">/</span> <span class="mi">100</span><span class="p">)</span>
<span class="n">pipeline</span> <span class="o">=</span> <span class="n">make_pipeline</span><span class="p">(</span>
<span class="n">Encoding</span><span class="p">(),</span>
<span class="n">ColumnCorrection</span><span class="p">(),</span>
<span class="n">Stationarity</span><span class="p">(</span><span class="n">SIGNIFICANCE_LEVEL</span><span class="o">=</span><span class="n">SIGNIFICANCE_LEVEL</span><span class="p">),</span>
<span class="n">Transformer</span><span class="p">(),</span>
<span class="n">Imputer</span><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="n">N_NEIGHBOURS</span><span class="p">),</span>
<span class="n">FeatureCreation</span><span class="p">(</span><span class="n">degree</span><span class="o">=</span><span class="n">DEGREE</span><span class="p">,</span> <span class="n">max_lag</span><span class="o">=</span><span class="n">MAX_LAG</span><span class="p">),</span>
<span class="n">FeatureSelection</span><span class="p">(</span><span class="n">e_list</span><span class="o">=</span><span class="n">e_list</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="n">clf_scoring</span><span class="p">,</span> <span class="n">clf</span><span class="o">=</span><span class="bp">True</span><span class="p">),</span>
<span class="n">SMOTE</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="n">RS</span><span class="p">),</span>
<span class="n">ModelSwitcher</span><span class="p">()</span>
<span class="p">)</span>
<span class="n">clf_parameters</span> <span class="o">=</span> <span class="p">[</span>
<span class="p">{</span><span class="s">"modelswitcher__estimator"</span><span class="p">:</span> <span class="p">[</span><span class="n">LogisticRegression</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="n">RS</span><span class="p">)],</span>
<span class="s">"featureselection__estimator"</span><span class="p">:</span> <span class="p">[</span><span class="n">LogisticRegression</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="n">RS</span><span class="p">)],</span>
<span class="s">"smote__sampling_strategy"</span><span class="p">:</span> <span class="p">[</span><span class="n">smote_minimum_sampling_strategy</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mi">1</span><span class="p">]},</span>
<span class="p">]</span>
<span class="n">clf_gscv</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">estimator</span><span class="o">=</span><span class="n">pipeline</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">clf_parameters</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="n">clf_scoring</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="n">cv</span><span class="p">)</span>
<span class="n">clf_gscv</span><span class="p">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">binary_target</span><span class="p">)</span>
<span class="n">y_pred</span> <span class="o">=</span> <span class="n">clf_gscv</span><span class="p">.</span><span class="n">best_estimator_</span><span class="p">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">clf_gscv</span><span class="p">.</span><span class="n">best_estimator_</span><span class="p">)</span>
</code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>Pipeline(steps=[('encoding', Encoding()),
('columncorrection', ColumnCorrection()),
('stationarity', Stationarity(SIGNIFICANCE_LEVEL=0.05)),
('transformer', Transformer()),
('imputer', Imputer(n_neighbors=10)),
('featurecreation', FeatureCreation(degree=2, max_lag=4)),
('featureselection',
FeatureSelection(clf=True, e_list=[0.01, 0.05, 0.1, 0.25, 0.5],
estimator=LogisticRegression(random_state=42),
scoring='precision')),
('smote', SMOTE(random_state=42, sampling_strategy=1)),
('modelswitcher',
ModelSwitcher(estimator=LogisticRegression(random_state=42)))])
</code></pre></div></div>
<p>Since our example deals with a classification task, we follow up and show the resulting confusion matrix when using that estimator. The graph below shows that the classifier has a lot of trouble with False Positives. That means that the classifier predicts significantly more observations as <em>outliers</em>, even though they are not. In order to counter that tendency we already specified “precision” as our performance metric.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">confusion_matrix</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="n">sns</span>
<span class="n">confusion_matrix_array</span> <span class="o">=</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_true</span><span class="o">=</span><span class="n">binary_target</span><span class="p">,</span> <span class="n">y_pred</span><span class="o">=</span><span class="n">y_pred</span><span class="p">)</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">sns</span><span class="p">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">confusion_matrix_array</span><span class="p">,</span> <span class="n">annot</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">axs</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s">"d"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s">"True Values"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s">"Predicted Values"</span><span class="p">)</span>
</code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>Text(0.5, 60.0, 'Predicted Values')
</code></pre></div></div>
<p><img src="/assets/article_images/dengai/0311/output_36_1.png" alt="png" /></p>
<h1 id="conclusion">Conclusion</h1>
<p>In this blogpost we took a look how our prediction method looks like under the hood. We saw how the different classes interact with each other and what the thought process was behind each step. Given that we introduced multiple sub-concepts, such as SMOTE, there will be several follow-up posts, covering the different aspects in a bit more depth.</p>
<h2 id="appendix">Appendix</h2>
<p>[1] Kevin Sheppard - [Financial Econometrics Notes}(https://www.kevinsheppard.com/files/teaching/mfe/notes/financial-econometrics-2020-2021.pdf)</p>
<p>[2] https://towardsdatascience.com/what-is-feature-scaling-why-is-it-important-in-machine-learning-2854ae877048</p>
<p>[3] ANWAR UL HAQ et al. - <a href="https://ieeexplore.ieee.org/abstract/document/8871132">Combining Multiple Feature-Ranking Techniques and Clustering of Variables for Feature Selection</a> 2019</p>Paul MoraThe workings of the underlying prediction pipelineClassifier Evaluation Methods - A Hands-On Explanation2020-11-16T00:00:00+00:002020-11-16T00:00:00+00:00https://paul-mora.com/classification/tutorial/python/Classifier-Evaluation-Methods-A-Hands-On-explanation<p><em> Accuracy/ Recall/ Precision/ Confusion Matrix/ ROC Curve/ AUC </em></p>
<p><a href="https://github.com/data4help/roc_curve">Github Repository</a></p>
<p>In pretty much 50% of all Data Science interviews around the world, the interviewee is asked to build and assess a binary classification model. This means classifying a certain observation to be either positive (normally denoted as the number 1) or negative (denoted as 0), given a bunch of features. A common mistake that interviewees make is to spend too much time building and tuning an overly-sophisticated model and not enough time elaborating on which classification evaluation metric is appropriate for the problem. That habit is even enforced through Kaggle, or Kaggle-like Data Challenges, in which the classifier evaluation metric is not carefully chosen by the participant, but is already set by the host.</p>
<p>This blogpost will therefore shed more light on the different classification evaluation methods commonly used, how they are derived, when to use them, and, arguably most importantly, how to implement them in Python.</p>
<p>The toy dataset we use for this post is obtained from the DrivenData challenge called: <a href="https://www.drivendata.org/competitions/57/nepal-earthquake/">Richter’s Predictor: Modeling Earthquake Damage</a>. WE are already familiar with the dataset for this project, given that we participated in this challenge which can be read up on here. As the name of the project suggests, this challenge involves predicting earthquake damages, specifically damage from the Gorkha earthquake which occurred in April 2015 and killed over 9,000 people. It represents the worst natural disaster to strike Nepal since the 1934 Nepal-Bihar earthquake.</p>
<p>The prediction task for this project is to forecast how badly an individual house is damaged, given the information about its location, secondary usage, and the materials used to build the house in the first place. The damage grade of each house is stated as an integer variable between one and three.</p>
<h2 id="preliminaries-importing-the-data-feature-engineering">Preliminaries/ Importing the data/ Feature engineering</h2>
<p>We begin by importing the relevant packages and setting all the path variables in order to better access the data. Afterwards we import the features and the label which can be downloaded from the DrivenData website. All of that is done by the following lines of code.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1"># Packages
</span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="n">pd</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="n">np</span>
<span class="kn">import</span> <span class="nn">pickle</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="n">plt</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="n">sns</span>
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span><span class="p">,</span> <span class="n">SGDClassifier</span>
<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">GradientBoostingClassifier</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="p">(</span><span class="n">accuracy_score</span><span class="p">,</span>
<span class="n">confusion_matrix</span><span class="p">,</span>
<span class="n">plot_confusion_matrix</span><span class="p">,</span>
<span class="n">recall_score</span><span class="p">,</span>
<span class="n">precision_score</span><span class="p">,</span>
<span class="n">auc</span><span class="p">,</span> <span class="n">roc_curve</span><span class="p">)</span>
<span class="c1"># Paths
</span><span class="n">MAIN_PATH</span> <span class="o">=</span> <span class="sa">r</span><span class="s">"/Users/paulmora/Documents/projects/roc_curve"</span>
<span class="n">RAW_PATH</span> <span class="o">=</span> <span class="sa">r</span><span class="s">"{}/00 Raw"</span><span class="p">.</span><span class="nb">format</span><span class="p">(</span><span class="n">MAIN_PATH</span><span class="p">)</span>
<span class="n">CODE_PATH</span> <span class="o">=</span> <span class="sa">r</span><span class="s">"{}/01 Code"</span><span class="p">.</span><span class="nb">format</span><span class="p">(</span><span class="n">MAIN_PATH</span><span class="p">)</span>
<span class="n">DATA_PATH</span> <span class="o">=</span> <span class="sa">r</span><span class="s">"{}/02 Data"</span><span class="p">.</span><span class="nb">format</span><span class="p">(</span><span class="n">MAIN_PATH</span><span class="p">)</span>
<span class="n">OUTPUT_PATH</span> <span class="o">=</span> <span class="sa">r</span><span class="s">"{}/03 Output"</span><span class="p">.</span><span class="nb">format</span><span class="p">(</span><span class="n">MAIN_PATH</span><span class="p">)</span>
<span class="c1"># Loading the data
</span><span class="n">total_labels</span> <span class="o">=</span> <span class="n">pd</span><span class="p">.</span><span class="n">read_csv</span><span class="p">(</span><span class="sa">r</span><span class="s">"{}/train_labels.csv"</span><span class="p">.</span><span class="nb">format</span><span class="p">(</span><span class="n">RAW_PATH</span><span class="p">))</span>
<span class="n">total_values</span> <span class="o">=</span> <span class="n">pd</span><span class="p">.</span><span class="n">read_csv</span><span class="p">(</span><span class="sa">r</span><span class="s">"{}/train_values.csv"</span><span class="p">.</span><span class="nb">format</span><span class="p">(</span><span class="n">RAW_PATH</span><span class="p">))</span>
</code></pre></div></div>
<p>In contrast to an usual data challenge, we do not need to import any test values, since this blogpost is elaborating on the evaluation of classification model and not on the classification model itself.
Next up is some initial data cleaning and feature engineering. Given that we are not interested in this post in explaining optimizing the actual model-performance, we will keep the data-processing work to a minimum.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">total_values</span><span class="p">.</span><span class="n">info</span><span class="p">()</span>
</code></pre></div></div>
<p>The above mentioned line of code shows us what kind of work needs to be done, as it is showing us all column names and the information what datatype the information is stored as.</p>
<p><img src="/assets/post_images/classifier/picture1.png" alt="" /></p>
<p>We can see that most variables are already specified in integer-form and therefore do not need any form of altering. The exception of that rule is the column building_id which is an identifier of the particular building and represent non-informative information and can therefore be dropped.</p>
<p>All columns which are represented as a categorical variable (dtype: “object”) will be transformed to dummy variables via one-hot-encoding. This could potentially result in a dangerously sparse dataset, but again, the model performance is not our focus.</p>
<p>It is now time to briefly look at the target variable before merging it with the features and to build our classification models. For that we consider the following line of code as well as the result given beneath it.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">total_labels</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s">"damage_grade"</span><span class="p">].</span><span class="n">value_counts</span><span class="p">()</span>
</code></pre></div></div>
<p><img src="/assets/post_images/classifier/picture2.png" alt="" /></p>
<p>Given that we would like to focus on binary classification, we see from the output above that some changes in the target variable are necessary since we see that we have more than two outcomes. We therefore drop the damage category 3 from the data. Afterwards we subtract 1 from the target variable in order to create a target variable which is either 0 or 1. This step will also alter the meaning of the problem. Whereas before the target variable was telling us how badly a house was damaged, now the target variable can be regarded as an indication whether a house experienced high damage or whether it did not.</p>
<p>The following lines of code handle the feature engineering and the altering of the target variable. Furthermore, we create a countplot using the seaborn visualization package in order to see the balance of the target variable.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">total_df</span> <span class="o">=</span> <span class="n">pd</span><span class="p">.</span><span class="n">concat</span><span class="p">([</span><span class="n">total_labels</span><span class="p">,</span> <span class="n">total_values</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">not_3_bool</span> <span class="o">=</span> <span class="n">total_df</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s">"damage_grade"</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">3</span>
<span class="n">subset_df</span> <span class="o">=</span> <span class="n">total_df</span><span class="p">.</span><span class="n">loc</span><span class="p">[</span><span class="n">not_3_bool</span><span class="p">,</span> <span class="p">:]</span>
<span class="n">subset_df</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s">"damage_grade"</span><span class="p">]</span> <span class="o">=</span> <span class="n">subset_df</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s">"damage_grade"</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span>
<span class="n">subset_df</span><span class="p">.</span><span class="n">rename</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">{</span><span class="s">"damage_grade"</span><span class="p">:</span> <span class="s">"high_damage"</span><span class="p">},</span> <span class="n">inplace</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">subset_df</span><span class="p">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s">"building_id"</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">subset_dummy_df</span> <span class="o">=</span> <span class="n">pd</span><span class="p">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">subset_df</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">rcParams</span><span class="p">.</span><span class="n">update</span><span class="p">({</span><span class="s">"font.size"</span><span class="p">:</span> <span class="mi">20</span><span class="p">})</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">sns</span><span class="p">.</span><span class="n">countplot</span><span class="p">(</span><span class="n">subset_df</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s">"high_damage"</span><span class="p">],</span> <span class="n">ax</span><span class="o">=</span><span class="n">axs</span><span class="p">)</span>
<span class="n">axs</span><span class="p">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s">"both"</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s">"major"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s">"High Grade"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s">"Count"</span><span class="p">)</span>
<span class="n">path</span> <span class="o">=</span> <span class="p">(</span><span class="sa">r</span><span class="s">"{}/int.png"</span><span class="p">.</span><span class="nb">format</span><span class="p">(</span><span class="n">OUTPUT_PATH</span><span class="p">))</span>
<span class="n">fig</span><span class="p">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s">'tight'</span><span class="p">)</span>
</code></pre></div></div>
<p><img src="/assets/post_images/classifier/picture3.png" alt="" /></p>
<p>From the chart above we can see that the majority of cases show a highly damaged house and that the data is therefore imbalanced. An imbalanced dataset describes a situation where we have a significantly inequality in the number of appearances of one or multiple classes with the target variable. For the moment we will leave this imbalance as it is, but remember it for later when talking about the model performance.</p>
<h2 id="model-training">Model Training</h2>
<p>The three models chosen for our prediction model are a Logistic Regression, Gradient Boosting Classifier and Stochastic Gradient Descent Classifier. There is no particular reason to choose these three models and any other would do as well.</p>
<p>We then split the data into train and test in order to avoid data-leakage which would result in artificial superior performance of the model given that it was trained partly with test data. Afterwards the three models are initiated and a random state is set in order to be able to reproduce our results later. The following lines code cover the aforementioned as well as fit the three models on the data.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">subset_dummy_df</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span>
<span class="n">random_state</span><span class="o">=</span><span class="mi">28</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
<span class="n">train</span><span class="p">.</span><span class="n">to_pickle</span><span class="p">(</span><span class="s">"{}/train.pickle"</span><span class="p">.</span><span class="nb">format</span><span class="p">(</span><span class="n">DATA_PATH</span><span class="p">))</span>
<span class="n">test</span><span class="p">.</span><span class="n">to_pickle</span><span class="p">(</span><span class="s">"{}/test.pickle"</span><span class="p">.</span><span class="nb">format</span><span class="p">(</span><span class="n">DATA_PATH</span><span class="p">))</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">train</span><span class="p">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="s">"high_damage"</span><span class="p">)</span>
<span class="n">y_train</span> <span class="o">=</span> <span class="n">train</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s">"high_damage"</span><span class="p">]</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">test</span><span class="p">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="s">"high_damage"</span><span class="p">)</span>
<span class="n">y_test</span> <span class="o">=</span> <span class="n">test</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s">"high_damage"</span><span class="p">]</span>
<span class="n">logreg</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">max_iter</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">28</span><span class="p">)</span>
<span class="n">logreg</span><span class="p">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">gbt</span> <span class="o">=</span> <span class="n">GradientBoostingClassifier</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">28</span><span class="p">)</span>
<span class="n">gbt</span><span class="p">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">sgdreg</span> <span class="o">=</span> <span class="n">SGDClassifier</span><span class="p">(</span><span class="n">fit_intercept</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">sgdreg</span><span class="p">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
</code></pre></div></div>
<h2 id="confusion-matrix">Confusion Matrix</h2>
<p>So far we cleaned the data, created some basic features and trained three models. It is now time to finally create some predictions and evaluate these. In the case of a binary prediction we can end up with four different possibilities between the true label and our prediction, namely:</p>
<ol>
<li>The true value is 1 and the prediction is 1 (True Positive)</li>
<li>The true value is 1 and the prediction is 0 (False Negative)</li>
<li>The true value is 0 and the prediction is 1 (False Positive)</li>
<li>The true value is 0 and the prediction is 0 (True Negative)</li>
</ol>
<p>As it can be already seen from the description in brackets behind each case, there also exist a name for each of these cases. In order to have all of that information in one big picture, we look at something called the confusion matrix, which is implemented through the following code.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1"># Predictions
</span><span class="n">logreg_pred</span> <span class="o">=</span> <span class="n">logreg</span><span class="p">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="n">gbt_pred</span> <span class="o">=</span> <span class="n">gbt</span><span class="p">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="n">sgdreg_pred</span> <span class="o">=</span> <span class="n">sgdreg</span><span class="p">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="c1"># Confusion matrix
</span><span class="n">raw_conf_logreg</span> <span class="o">=</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">logreg_pred</span><span class="p">)</span>
<span class="n">raw_conf_gbt</span> <span class="o">=</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">gbt_pred</span><span class="p">)</span>
<span class="n">raw_conf_sgd</span> <span class="o">=</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">gbt_pred</span><span class="p">)</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">axs</span> <span class="o">=</span> <span class="n">axs</span><span class="p">.</span><span class="n">ravel</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">title</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">([</span><span class="s">"Logistic Model"</span><span class="p">,</span>
<span class="s">"GradientBoosting Model"</span><span class="p">,</span>
<span class="s">"Stochastic Gradient Descent"</span><span class="p">],</span>
<span class="p">[</span><span class="n">logreg</span><span class="p">,</span> <span class="n">gbt</span><span class="p">,</span> <span class="n">sgdreg</span><span class="p">])):</span>
<span class="n">plot_confusion_matrix</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">].</span><span class="n">set_title</span><span class="p">(</span><span class="n">title</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">24</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">].</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s">"True Label"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">].</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s">"Predicted Label"</span><span class="p">)</span>
<span class="n">path</span> <span class="o">=</span> <span class="p">(</span><span class="sa">r</span><span class="s">"{}/confusion_matrices.png"</span><span class="p">.</span><span class="nb">format</span><span class="p">(</span><span class="n">OUTPUT_PATH</span><span class="p">))</span>
<span class="n">fig</span><span class="p">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s">'tight'</span><span class="p">)</span>
</code></pre></div></div>
<p><img src="/assets/post_images/classifier/picture4.png" alt="" /></p>
<p>The confusion matrices above tell us how many of each of the four aforementioned cases we have for each prediction model. In the upper left corner of each graph we see number of cases of True Negatives, i.e. where the true label and the predicted label is zero. The bottom right shows the True Positives, meaning all cases where the True and Predicted value is equal to one. The other two, False Positives (upper right) and False Negatives (bottom left) are misclassification of the model.</p>
<p>When comparing the Gradient Boosting’s and Logistic Model’s confusion matricies, we see that the number of True Positives and the number of True Negatives are higher for the Gradient Boosting Model. This finding makes it clear that the Gradient Boosting model is much better than the Logistic Regression Model.</p>
<p>Figuring out whether the Gradient Boosting or the Stochastic Gradient Descent Model is better, on the other hand, is not so straightforward. That is because the number of True Negatives might be lower for the Stochastic Gradient Descent Model, but it has a higher amount of True Positives when compared to the Gradient Boosting Model.</p>
<p>Through the confusion matrix we could tell that we always would prefer the Gradient Boosting model over the Logistic Model, but we cannot infer which model to use when comparing the Stochastic Gradient Descent Model against the Gradient Boosting Model. For that we need to know more about our underlying problem by looking at further evaluation methods.</p>
<h2 id="the-flaws-of-using-accuracy">The Flaws of using Accuracy</h2>
<p>The most common evaluation method used for classification models is arguably the Accuracy score. This score tells us out how often we were right out of all predictions. Accuracy is a very intuitive approach, but it can result in misleading results on imbalanced datasets.</p>
<p><img src="/assets/post_images/classifier/picture5.png" alt="" /></p>
<p>The misleading nature of accuracy as an evaluation method becomes evident when considering an example of a classification model which detects every second whether a house is on fire. We then consider a stupid classifier which always says the house is not on fire. Given that a house is most of the time not in a burning state, the classifier’s prediction will be correct basically all the time.</p>
<p>However, that one night when someone forgot to blow out the candle, the classification model will have False Negative errors — with deadly consequences. Fatal mistakes like this one are not reflected in the Accuracy score, given that in every moment where the house was not burning, it predicted the label correctly.</p>
<p>This great performance of this stupid classifier shows the danger of evaluating a model only by its Accuracy score, when the overall dataset is imbalanced or when we are more concerned with a certain type or errors.</p>
<h2 id="precision-and-recall">Precision and Recall</h2>
<p>Two other popular classification evaluation methods are called Precision and Recall. When to use which method depends on the use case and specifically to which error we are more prone to. To illustrate that, we consider two cases:</p>
<ol>
<li>
<p>Breast Cancer Classification: Within this scenario, it is preferable to predict that a woman has breast cancer even though she does not have any (False Positive). That is because predicting the other way around (False Negative) would leave a woman in the believe she is safe when she is not.</p>
</li>
<li>
<p>Spam Mail Classification: Here, it would be okay for us if our spam detector classifies a fishy email as non-spam (False Negative), but it would be annoying to find out that an important business email/bill was hiding in the spam folder for the last two weeks because it was incorrectly classified as spam (False Positive).</p>
</li>
</ol>
<p>The two cases above show us that the choice of the evaluation metric depends crucially on what is important to us. In the first case we are much more concerned about False Negatives, meaning that leaving a woman with cancer believing she is well is much worse than telling a woman to undergo further checks even if she does not have to. When the cost of of False Negatives are high, we use Recall, which has the following definition:</p>
<p><img src="/assets/post_images/classifier/picture6.png" alt="" /></p>
<p>In the second example we are more concerned about False Positive errors, namely saying an email is spam even though it was a harmless email from your work. Not seeing this email is much more damaging to the user than seeing one additional spam email. When er are concerned about False Positives, we use Precision, which is defined the following way:</p>
<p><img src="/assets/post_images/classifier/picture6_1.png" alt="" /></p>
<p>In our example, we are predicting whether a house is highly damaged. We furthermore assume that in the case the classifcation model says that the house is not damaged, the house-owner rents the house out to others. In this case we are much more concerned about False Negative errors. This is because a False Negative mistake would lead the house owner to believe that the house has low damage even though it is highly damaged, endangering the lives of the people living in it. In the case of a False Positive, the house owner would falsely think that the house is severly damaged and order reparations of the house when none are needed — aconsiderably better outcome compared to death.
Below we can see the different values of all aforementioned evaluation methods for all three models plus the generating code. From the highest bar within the Recall category, we can see that for our purposes the Stochastic Gradient Boosting model is the model of choice.</p>
<p><img src="/assets/post_images/classifier/picture7.png" alt="" /></p>
<p>True Positive Rate/ False Positive Rate
Closely related to Precision and Recall is the concept of True Positive Rate (TPR) and False Positive Rate (FPR), which are defined in the following way:</p>
<p><img src="/assets/post_images/classifier/picture8.png" alt="" /></p>
<p>The observant reader might have the feeling that they have seen the formula for the TPR already. That potential feeling is correct, given that the True Positive Rate is a synonym for the Recall evaluation method we encountered before. Given that we already covered the concept of Recall/ TPR, we only elaborate on the intuition of the False Positive Rate.</p>
<p>Also known as the probability of False Alarm, the False Positive Rate is defined as the probability of rejecting a null-hypothesis which is in fact true. In the area of inferential statistics, this type of error is also referred to as a Type I error (in contrast to a Type II error which describes accepting a null-hypothesis when it is in fact incorrect).</p>
<p>Below we can see a matrix of how TPR and FPR fit in the bigger picture. It is obvious that in a perfect world we would like to have a high TPR but a low FPR. That is because we would like to classify everything that is positive as positive as well as nothing that is negative as positive.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">eval_df</span> <span class="o">=</span> <span class="n">pd</span><span class="p">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s">"Method"</span><span class="p">,</span> <span class="s">"Model"</span><span class="p">,</span> <span class="s">"Score"</span><span class="p">])</span>
<span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">model</span><span class="p">,</span> <span class="n">pred</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">([</span><span class="s">"Logistic Model"</span><span class="p">,</span>
<span class="s">"GradientBoosting Model"</span><span class="p">,</span>
<span class="s">"Stochastic Gradient Descent"</span><span class="p">],</span>
<span class="p">[</span><span class="n">logreg_pred</span><span class="p">,</span> <span class="n">gbt_pred</span><span class="p">,</span> <span class="n">sgdreg_pred</span><span class="p">]):</span>
<span class="k">for</span> <span class="n">meth</span><span class="p">,</span> <span class="n">score</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">([</span><span class="s">"Accuracy"</span><span class="p">,</span> <span class="s">"Precision"</span><span class="p">,</span> <span class="s">"Recall"</span><span class="p">],</span>
<span class="p">[</span><span class="n">accuracy_score</span><span class="p">,</span> <span class="n">precision_score</span><span class="p">,</span> <span class="n">recall_score</span><span class="p">]):</span>
<span class="n">eval_df</span><span class="p">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="s">"Method"</span><span class="p">]</span> <span class="o">=</span> <span class="n">meth</span>
<span class="n">eval_df</span><span class="p">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="s">"Model"</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</span>
<span class="n">eval_df</span><span class="p">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="s">"Score"</span><span class="p">]</span> <span class="o">=</span> <span class="n">score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">pred</span><span class="p">)</span>
<span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">sns</span><span class="p">.</span><span class="n">barplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s">"Method"</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s">"Score"</span><span class="p">,</span> <span class="n">hue</span><span class="o">=</span><span class="s">"Model"</span><span class="p">,</span>
<span class="n">data</span><span class="o">=</span><span class="n">eval_df</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">axs</span><span class="p">)</span>
<span class="n">axs</span><span class="p">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s">"lower center"</span><span class="p">)</span>
<span class="n">path</span> <span class="o">=</span> <span class="p">(</span><span class="sa">r</span><span class="s">"{}/acc_prec_rec.png"</span><span class="p">.</span><span class="nb">format</span><span class="p">(</span><span class="n">OUTPUT_PATH</span><span class="p">))</span>
<span class="n">fig</span><span class="p">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s">'tight'</span><span class="p">)</span>
</code></pre></div></div>
<p><img src="/assets/post_images/classifier/picture9.png" alt="" /></p>
<p>We also notice that there is some sort of trade-off between these two rates. Consider an example where we have seven true values and three negative values. If our algorithm classifies all ten as true we would get a TPR and FPR of 1. If on the other hand we classify all ten observations as false we would get a value of zero for both rates.</p>
<p>Thinking back to the example of spam-mails and breast cancer, we know that there are cases where we are prone towards False Positive mistakes than towards False Negatives. Combining that desire with the mentioned trade-off between TPR and FPR above leads us to the next topic: specific targeting for certain error types.</p>
<h2 id="targeting-for-error-types">Targeting for Error Types</h2>
<p>If we are particularly concerned about a certain error type, many classification models allow us to increase the sensitivity towards one or the other error. In order to explain how that works, it is important to note that probabilistic classification algorithms do not assign each observation directly to either 0 or 1, but rather tocalculate a probability with which an observation belongs to either class. The default rule is then if the probability that an observation belongs to class 1 is above 50%, then the observation is assigned to class 1. It is exactly this threshold value of 50%, which we will denote as c, that we will tweak.</p>
<p>If, for example, the probability of an observation belonging to class one is 60%, or 0.6, it is assigned to class one, since this is above the threshold of 0.5. This decision rule, even though simple, is also illustrated in the graphic below.</p>
<p><img src="/assets/post_images/classifier/picture10.png" alt="" /></p>
<p>If we now tweak parameter c to be either higher or lower than 0.5, we would also alter the TPR and FPR.</p>
<p><img src="/assets/post_images/classifier/picture11.png" alt="" /></p>
<p>This is easiest explained and understood when considering an example. Below we find the probability to belong to class one for five observation, as well as their true label. We can see that when the cutoff level is at its default level of 0.5. With that level of c, we find one False Negative and 0 False Positives.</p>
<p><img src="/assets/post_images/classifier/picture12.png" alt="" /></p>
<p>If we now alter the cutoff threshold to 0.2 we can see from the graphic below that we find one False Positive and zero False Negative.</p>
<p><img src="/assets/post_images/classifier/picture13.png" alt="" /></p>
<p>We notice: When changing the threshold value c, it is possible to obtain a different TPR and FPR, which therefore allows us to target for a certain error type.</p>
<h2 id="concept-of-roc-curve-and-auc">Concept of ROC Curve and AUC</h2>
<p>Now that we’ve seen how we can target and tweak our model towards a certain type of error through adjusting the parameter c, the question might arise how we would know what the optimal value of c is for our problem. This is where the Receiver Operating Characteristc (or short ROC curve) comes into play. The ROC curve plots all combinatins of TPR and FPR at every meaningful threshold level c, as shown in the GIF below.</p>
<p><img src="/assets/post_images/classifier/picture14.gif" alt="" /></p>
<p>The off-the-shelf ROC curve implementation from sklearn does not take a random amount of different cutoffs, but loops over the probabilities of every observation for efficiency reasons. This will become clearer later when we implement the code for deriving the ROC curve ourselves.</p>
<p>Related to the concept of the ROC curve is the corresponding value under the curve, called simply area under the curve (or in short: AUC). This metric attempts to summarize the goodness-of-fit of the ROC curve in a single number. As the name implies, this is done by measuring the area under the ROC curve.</p>
<p>Given that the ideal curve hugs the upper lefthand corner as closely as possible — since that would mean that our classifier is able to identify all true positives while avoiding false positives — we know that the ideal model would have an AUC of 1. On the flipside, if your model was no better at predicting than a random guess, your TPR and FPR would increase in parallel to one another, corresponding with an AUC of 0.5.</p>
<p>When applying this concept to our house damage data, we note that not all classification methods provide the option to obtain a probability estimate. The Stochastic Gradient Descent (SGD) classification for example does not allow for probability score when using its default loss function (“hinge”). The reason for that is, that this loss function turns the SGD classifier into a Support Vector Machine, which is a non-probabilistic model.</p>
<p>The following code is therefore showing how to calculate and plot the ROC curve for the Gradient Boosting and Logistic Regression.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">pred_proba_reg</span> <span class="o">=</span> <span class="n">logreg</span><span class="p">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="n">pred_proba_gbt</span> <span class="o">=</span> <span class="n">gbt</span><span class="p">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="k">for</span> <span class="n">model</span><span class="p">,</span> <span class="n">pred</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">([</span><span class="s">"Logistic Model"</span><span class="p">,</span>
<span class="s">"GradientBoosting Model"</span><span class="p">],</span>
<span class="p">[</span><span class="n">pred_proba_reg</span><span class="p">,</span>
<span class="n">pred_proba_gbt</span><span class="p">]):</span>
<span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">pred</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">])</span>
<span class="n">auc_reg</span> <span class="o">=</span> <span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">"{} AUC:{:.2f}"</span><span class="p">.</span><span class="nb">format</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">auc_reg</span><span class="p">))</span>
<span class="n">plt</span><span class="p">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">axs</span><span class="p">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s">"False Positive Rate"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s">"True Positive Rate"</span><span class="p">)</span>
<span class="n">path</span> <span class="o">=</span> <span class="p">(</span><span class="sa">r</span><span class="s">"{}/automatic_auc_roc_curve.png"</span><span class="p">.</span><span class="nb">format</span><span class="p">(</span><span class="n">OUTPUT_PATH</span><span class="p">))</span>
<span class="n">fig</span><span class="p">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s">'tight'</span><span class="p">)</span>
</code></pre></div></div>
<p><img src="/assets/post_images/classifier/picture15.png" alt="" /></p>
<p>Given that we already know that the GradientBoosting model was superior to the Logistic Regression in terms of the number of True Positives and True Negatives, it was predictable that the GradientBoosting model will also dominate in the ROC curve space. This can be seen by noticing that the red line (GradientBoosting Model) is above the blue line (Logistic Regression) for every single threshold value c.</p>
<h2 id="manual-implementation-of-the-roc-curve">Manual Implementation of the ROC Curve</h2>
<p>Lastly, it might be interesting to see how the ROC curve is implemented when doing it by hand. The code below does not intend to be the most efficient code for implement the ROC curve (the source code from sklearn is the place to look for that), but to be very easy to understand.</p>
<p>As can be seen in line 11, we sort the data-frame, which consists of the true labels and the probability that an observation is equal to one, by their probability value. This is done in order to afterwards loop over these probabilities and use them as the cutoff value. Afterwards, we simply have to count how many True Positive and False Positives we find for all observations which have a higher probability than the cutoff level c and divide them respectively by the total number of positives and negative labels.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code>
<span class="k">def</span> <span class="nf">roc_manual</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">pred_prob</span><span class="p">):</span>
<span class="c1"># Get the data ready
</span> <span class="n">pred_prob_series</span> <span class="o">=</span> <span class="n">pd</span><span class="p">.</span><span class="n">Series</span><span class="p">(</span><span class="n">pred_prob</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">])</span>
<span class="n">pred_prob_series</span><span class="p">.</span><span class="n">name</span> <span class="o">=</span> <span class="s">"pred"</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="p">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">pd</span><span class="p">.</span><span class="n">concat</span><span class="p">([</span><span class="n">y_true</span><span class="p">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="bp">True</span><span class="p">),</span>
<span class="n">pred_prob_series</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
<span class="c1"># Sorting probabilities in order to loop in an ascending manner
</span> <span class="n">sorted_df</span> <span class="o">=</span> <span class="n">df</span><span class="p">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s">"pred"</span><span class="p">)</span>
<span class="c1"># Calculate denominators
</span> <span class="n">true_num_pos</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">y_true</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">true_num_neg</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">y_true</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span>
<span class="c1"># Create list container for results
</span> <span class="n">list_tpr</span><span class="p">,</span> <span class="n">list_fpr</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">prob</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">sorted_df</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s">"pred"</span><span class="p">]):</span>
<span class="c1"># Create a boolean to mask only the values which qualify to be positive
</span> <span class="n">bool_classified_pos</span> <span class="o">=</span> <span class="n">sorted_df</span><span class="p">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s">"pred"</span><span class="p">]</span> <span class="o">></span> <span class="n">prob</span>
<span class="c1"># Total number of positives and negative values
</span> <span class="n">tp</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">sorted_df</span><span class="p">.</span><span class="n">loc</span><span class="p">[</span><span class="n">bool_classified_pos</span><span class="p">,</span> <span class="s">"high_damage"</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">fp</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">sorted_df</span><span class="p">.</span><span class="n">loc</span><span class="p">[</span><span class="n">bool_classified_pos</span><span class="p">,</span> <span class="s">"high_damage"</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span>
<span class="c1"># Calculate the TPR and FPR
</span> <span class="n">tpr</span> <span class="o">=</span> <span class="n">tp</span> <span class="o">/</span> <span class="n">true_num_pos</span>
<span class="n">fpr</span> <span class="o">=</span> <span class="n">fp</span> <span class="o">/</span> <span class="n">true_num_neg</span>
<span class="n">list_tpr</span><span class="p">.</span><span class="n">append</span><span class="p">(</span><span class="n">tpr</span><span class="p">)</span>
<span class="n">list_fpr</span><span class="p">.</span><span class="n">append</span><span class="p">(</span><span class="n">fpr</span><span class="p">)</span>
<span class="k">return</span> <span class="n">list_tpr</span><span class="p">,</span> <span class="n">list_fpr</span>
<span class="n">gbt_list_tpr</span><span class="p">,</span> <span class="n">gbt_list_fpr</span> <span class="o">=</span> <span class="n">roc_manual</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">pred_proba_gbt</span><span class="p">)</span>
<span class="n">lgt_list_tpr</span><span class="p">,</span> <span class="n">lgt_list_fpr</span> <span class="o">=</span> <span class="n">roc_manual</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">pred_proba_reg</span><span class="p">)</span>
<span class="c1"># Manual AUC and plotting
</span><span class="n">manual_auc_reg</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">np</span><span class="p">.</span><span class="n">trapz</span><span class="p">(</span><span class="n">lgt_list_tpr</span><span class="p">,</span> <span class="n">lgt_list_fpr</span><span class="p">))</span>
<span class="n">manual_auc_gbt</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">np</span><span class="p">.</span><span class="n">trapz</span><span class="p">(</span><span class="n">gbt_list_tpr</span><span class="p">,</span> <span class="n">gbt_list_fpr</span><span class="p">))</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">plt</span><span class="p">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lgt_list_fpr</span><span class="p">,</span> <span class="n">lgt_list_tpr</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">"orange"</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="s">"Logistic Regression AUC:{:.2f}"</span><span class="p">.</span><span class="nb">format</span><span class="p">(</span><span class="n">manual_auc_reg</span><span class="p">))</span>
<span class="n">plt</span><span class="p">.</span><span class="n">plot</span><span class="p">(</span><span class="n">gbt_list_fpr</span><span class="p">,</span> <span class="n">gbt_list_tpr</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">"purple"</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="s">"GradientBoosting AUC:{:.2f}"</span><span class="p">.</span><span class="nb">format</span><span class="p">(</span><span class="n">manual_auc_gbt</span><span class="p">))</span>
<span class="n">plt</span><span class="p">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">axs</span><span class="p">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s">"False Positive Rate"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s">"True Positive Rate"</span><span class="p">)</span>
<span class="n">path</span> <span class="o">=</span> <span class="p">(</span><span class="sa">r</span><span class="s">"{}/manual_auc_roc_curve.png"</span><span class="p">.</span><span class="nb">format</span><span class="p">(</span><span class="n">OUTPUT_PATH</span><span class="p">))</span>
<span class="n">fig</span><span class="p">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s">'tight'</span><span class="p">)</span>
</code></pre></div></div>
<p><img src="/assets/post_images/classifier/picture16.png" alt="" /></p>
<p>From the chart above we see that our manual implementation went correctly and that it perfectly matches the result of the sklearn-algorithm.</p>
<h2 id="summary">Summary</h2>
<p>In this post, we’ve seen how the go-to evaluation metric for classificaiton models, accuracy, can fail to show goodness-of-fit for certain cases, such as cases of imbalanced classes. We then explored additional metrics that give more insight into model perfomance on imbalanced data, such as precision and recall. Finally, we dervied our own ROC curve and showed how it can be used to determine which model is best for the prediction task. These additional metrics extend your toolkit for understanding your models — helping you ultimately choose the right model for the job.</p>Paul MoraAccuracy/ Recall/ Precision/ Confusion Matrix/ ROC Curve/ AUCHow the People Really Voted2020-11-13T00:00:00+00:002020-11-13T00:00:00+00:00https://paul-mora.com/data%20journalism/web%20scraping/r/How-the-People-Really-Voted<p><em> Why geographically correct maps show elections results inaccurately </em></p>
<p><a href="https://github.com/data4help/cartogram_usa">Github Repository</a></p>
<p>The presidential election of 2020 was said by many to be the most important election ever held. The voting turnout was higher than ever before, and the arguably most controversial president in history, Donald Trump, lost to his democratic opponent Joe Biden.</p>
<p>The unprecedented amount of mail-in ballots took its toll on the patience of the nation and the world. On election day, the 3rd of November, it was anything but clear who won the election. The polls before election turned out not to be very representative of true sentiments, a similar flaw as was seen in 2016. Donald Trump’s performance and his backing were grossly underestimated.</p>
<p>On Saturday the 7th of November, the United States of America finally had a new president-elect, assuming of course that the Trump campaign is not able to successfully prove voter fraud in multiple states; a claim which at this point in time seems unfounded and not backed by hard evidence.</p>
<p><img src="/assets/post_images/polarization/picture3_1.png" alt="https://edition.cnn.com/election/2020/results/president" /></p>
<p>Throughout the long wait before the announcement of the president-elect, all major news sources showed a map of the United States in which every states was colored either in blue for the Democrats or red for the Republicans. This blogpost elaborates on why this map is a misleading representation of the relative importance of each state in determining who wins the presidency, and might convey the wrong feeling as to which candidate had the strength of numbers behind him in this election.</p>
<h2 id="the-problems-of-the-actual-map">The problems of the actual map</h2>
<p>The question might arise why in the United States each state is colored in the first place. The reason for that the different states are coloured in the first place is because of the Electoral College, which is a group of presidential electors whose sole purpose it is to elect the president. Every state is assigned a certain number of electors proportional to their population. Therefore, whichever candidate gets the majority within a state, gets all electors from one state (with the exception of Nebraska and Maine, where electors can be split between different candidates).</p>
<p><img src="/assets/post_images/polarization/picture3_2.png" alt="" /></p>
<p>Below we can see the map of the United States already coloured by the party which won in the 2020 election. Even though the map is geographically correct, the large amount of red might convey the feeling that the Republican party was the predominant force. Considering the system of the electoral college, which was briefly discussed above, tells us that the number of electoral votes and not the size of the state is the decisive factor.</p>
<p>In order to give a better feeling how the people voted, or which state’s decision carried bigger importance, a geographically correct map is not the right tool to use. A significantly better method would be to use a cartogram.</p>
<h2 id="what-is-a-cartogram">What is a Cartogram</h2>
<p>During the long period of waiting for results, all major news-sources used a country map of the United States, with states colored either in blue, red, or grey to indicate whether a state was called for the Democratic Party, the Republican Party or was not called yet, respectively. This map, despite being geographically accurate, does not really reflect the vote of the people.</p>
<p>That is because in the US there are many small states with a relatively large population, like New York or Massachussets. At the same time we also have large states which are home to only a few residents like Wyoming or North Dakota. All of this information remains hidden when looking on the map of the United States.</p>
<p>In order to give a better idea of how the people of the United States really voted in this election, the better choice would be to use a cartogram. A cartogram is a visualization based on maps, where features of the map like land mass size are distorted based on values of a variable.</p>
<p>To better explain the workings of such a map, we use the example from the R Graph gallery. Herein, the countries within the African continent are distorted based on their respective population size. Since Nigeria has the largest population of any country in Africa, it appears much larger than normal, as does Egypt. Other countries like Libya with very small populations appear smaller than their normal size.</p>
<p><img src="/assets/post_images/polarization/picture3_3.png" alt="Cartogram from the R graph gallery: https://www.r-graph-gallery.com/331-basic-cartogram.html" /></p>
<p>Looking at this map gives us a much better idea of the relative population by country. The same concept is now applied to better visualize how the people in each state voted.</p>
<h2 id="a-cartogram-for-the-united-states-election">A cartogram for the United States election</h2>
<p>When applying the methodology of the cartograms outlined above now to the map of the United States we get the following image.</p>
<p><img src="/assets/post_images/polarization/picture3_4.png" alt="" /></p>
<p>Even the map looks very different from the geographically correct map we are accustomed to seeing, it is still possible to identify the states. Especially remarkable are the changes in size of the states on the East and West Coasts. California, an already larger state, has here more than doubled in size. This result may have been expected for those who know that California is home to about 40 million Americans. Even more drastic are the changes in the East Coast states, where relatively small states like New Jersey and Massachusetts now represent one of the biggest states there are in the US.</p>
<p>The cartogram above results in a graphical representation of the United States as we have not seen it before</p>
<p>Using electoral votes instead of actual population</p>
<p>It is important to note though that because of the electoral college, the cartogram which is based on the actual population by state might not be the optimal visualization to use. That is because electoral college delegates are not only distributed amongst the states entirely based on how many people live in each state — even the states with the smallest populations still get the same minimum number of 3 electors. This means that they receive relatively more electors per person in their state than states with larger populations.</p>
<p>The cartogram below shows, in contrast to the cartograms before, how the cartogram would look like if not the population but the number of electoral votes is used to determine the distortion of each state. We can see that states like California and basically the entire east coast still looks much bigger than the original map, even though the distortion magnitude decreased.</p>
<p><img src="/assets/post_images/polarization/picture3_5.png" alt="" /></p>
<p>Whether it be by population, or by electoral votes, when looking at the colored map of the United States during election time, it is important to note that a purely geographical representation underestimates the importance of high-population, small-land-mass states.</p>
<p>The electoral system of the United States is drastically different to that of other countries. Therefore, a simple glance at a geographically correct map should always be taken with a grain of salt. To see the truth, one has to dig a bit deeper.</p>Paul MoraWhy geographically correct maps show elections results inaccuratelyDengAI: Predicting Disease Spread — STL Forecasting/ ARIMA/ Box-Jenkins2020-11-06T00:00:00+00:002020-11-06T00:00:00+00:00https://paul-mora.com/time%20series/python/DengAI-Predicting-Disease-Spread-STL-Forecasting:%20ARIMA:-Box-Jenkins<p>Using the STL Forecasting Method with an ARIMA model, which is parameterized through the Box-Jenkins Method.</p>
<p>This post builds on our first blogpost which dealt with the initial data-transformation of the exogenous variables. Now we build a first model using only target variable itself. This is done by using the <a href="https://www.statsmodels.org/stable/examples/notebooks/generated/stl_decomposition.html">STL Forecasting method</a>. This allows us to model time series which are affected by seasonal effects, by first removing the specified seasonality through a STL decomposition and then modeling the deseasonalized time series with a model of our choice — which is an Autoregressive Integrated Moving Average Model (ARIMA).</p>
<p>For those not familiar with the <a href="https://www.drivendata.org/competitions/44/dengai-predicting-disease-spread/page/82/">forecasting challenge</a>, this competition deals with the prediction of dengue fever in two cities, or in the words from DrivenData themselves:</p>
<p><em>
Your goal is to predict the total_cases label for each (city, year, weekofyear) in the test set. There are two cities, San Juan and Iquitos, with test data for each city spanning 5 and 3 years respectively. You will make one submission that contains predictions for both cities.
</em></p>
<p>In order for better readability, this blogpost only shows and discusses graphs from the city San Juan. Further, the related code for every graph is found directly below each graph. Additionally the entire code (for both cities) is found at the bottom of this blogpost, or <a href="https://github.com/data4help/dengai">on GitHub</a>.</p>
<p>In the center of our prediction approach is the aforementioned so-called STL method. STL stands for Seasonal-Trend decomposition method using LOESS. This method decomposes a time series into three components, namely its trend, its seasonality and its residuals. LOESS stands for locally estimated scatterplot smoothing and is extracting smooth estimates of the three aforementioned components.</p>
<h2 id="structural-changes">Structural Changes</h2>
<p>Looking at the time series of dengue fever in San Juan, several things are worth pointing out. For once there is a strong seasonal component visible. In the first half of the data it looks like the seasonal pattern is yearly (52 weeks), whereas in the second half of the time series the pattern somewhat switched to a two-yearly pattern. The other predominant characteristic of the time series are the few but stark spikes. Especially in the first half of the time series we see two outbursts in the target variable of a magnitude which is unparalleled in the second half.</p>
<p><img src="/assets/post_images/dengai/picture2_1.png" alt="" /></p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">def</span> <span class="nf">plot_cutoff_comparison</span><span class="p">(</span><span class="n">time_series</span><span class="p">,</span> <span class="n">cutoff</span><span class="p">,</span> <span class="n">city_name</span><span class="p">):</span>
<span class="n">first_half</span> <span class="o">=</span> <span class="n">time_series</span><span class="p">[:</span><span class="n">cutoff</span><span class="p">]</span>
<span class="n">first_half</span><span class="p">.</span><span class="n">name</span> <span class="o">=</span> <span class="s">"First Half"</span>
<span class="n">second_half</span> <span class="o">=</span> <span class="n">time_series</span><span class="p">[</span><span class="n">cutoff</span><span class="p">:]</span>
<span class="n">second_half</span><span class="p">.</span><span class="n">name</span> <span class="o">=</span> <span class="s">"Second Half"</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">].</span><span class="n">plot</span><span class="p">(</span><span class="n">time_series</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">"Complete Series"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">].</span><span class="n">axvline</span><span class="p">(</span><span class="n">cutoff</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">"r"</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s">"--"</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">"Cutoff"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">].</span><span class="n">plot</span><span class="p">(</span><span class="n">second_half</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="s">"Series from observation {} onwards"</span><span class="p">.</span><span class="nb">format</span><span class="p">(</span><span class="n">cutoff</span><span class="p">))</span>
<span class="n">axs</span> <span class="o">=</span> <span class="n">axs</span><span class="p">.</span><span class="n">ravel</span><span class="p">()</span>
<span class="k">for</span> <span class="n">axes</span> <span class="ow">in</span> <span class="n">axs</span><span class="p">.</span><span class="n">ravel</span><span class="p">():</span>
<span class="n">axes</span><span class="p">.</span><span class="n">legend</span><span class="p">(</span><span class="n">prop</span><span class="o">=</span><span class="p">{</span><span class="s">"size"</span><span class="p">:</span> <span class="mi">16</span><span class="p">},</span> <span class="n">loc</span><span class="o">=</span><span class="s">"upper right"</span><span class="p">)</span>
<span class="n">axes</span><span class="p">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s">"both"</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
<span class="n">fig</span><span class="p">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">fig</span><span class="p">.</span><span class="n">savefig</span><span class="p">(</span><span class="sa">r</span><span class="s">"{}/{}/{}_cutoff_plot.png"</span><span class="p">.</span><span class="nb">format</span><span class="p">(</span><span class="n">output_path</span><span class="p">,</span>
<span class="n">approach_method</span><span class="p">,</span>
<span class="n">city_name</span><span class="p">),</span>
<span class="n">bbox_inches</span><span class="o">=</span><span class="s">"tight"</span><span class="p">)</span>
<span class="k">return</span> <span class="n">first_half</span><span class="p">,</span> <span class="n">second_half</span>
</code></pre></div></div>
<p>At this point it is important to stress the higher importance of newer data compared to older data within time series problems. Normally within data science projects, we are very always interested in gathering more data, as this would in general increase the robustness and accuracy of our model. Though, when it comes to time series data, we have to weight the importance of new data on when exactly the data was sampled. If we are for example interested in predicting the stock price for a certain company, the company’s financial statements of the last couple of years, are undoubtely more important than the company’s performance 100 years ago. Why is that so? Because it could be possible that underlying data generating process changed over time. That would imply that older data is not giving us any information about future data and is therefore not only incorrect, but could also hurt our model performance.</p>
<p>The three charts below visualize the differences between the first and second half of the time series.</p>
<p><img src="/assets/post_images/dengai/picture2_2.png" alt="" /></p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">def</span> <span class="nf">difference_in_distribution</span><span class="p">(</span><span class="n">series1</span><span class="p">,</span> <span class="n">series2</span><span class="p">,</span> <span class="n">city_name</span><span class="p">):</span>
<span class="c1"># CDF
</span> <span class="k">def</span> <span class="nf">ecdf</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
<span class="s">""" Compute ECDF """</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">sort</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="n">n</span> <span class="o">=</span> <span class="n">x</span><span class="p">.</span><span class="n">size</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">n</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="n">n</span>
<span class="k">return</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span>
<span class="n">test_results</span> <span class="o">=</span> <span class="n">pd</span><span class="p">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s">"KS 2 Sample Test"</span><span class="p">,</span> <span class="s">"ANOVA"</span><span class="p">],</span>
<span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s">"Statistic"</span><span class="p">,</span> <span class="s">"P Value"</span><span class="p">])</span>
<span class="n">test_results</span><span class="p">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">st</span><span class="p">.</span><span class="n">ks_2samp</span><span class="p">(</span><span class="n">series1</span><span class="p">,</span> <span class="n">series2</span><span class="p">)</span>
<span class="n">test_results</span><span class="p">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">st</span><span class="p">.</span><span class="n">f_oneway</span><span class="p">(</span><span class="n">series1</span><span class="p">,</span> <span class="n">series2</span><span class="p">)</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">40</span><span class="p">,</span> <span class="mi">15</span><span class="p">))</span>
<span class="c1"># Time series
</span> <span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">].</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="p">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">series1</span><span class="p">)),</span> <span class="n">series1</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">"b"</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="n">series1</span><span class="p">.</span><span class="n">name</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">].</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="p">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">series2</span><span class="p">)),</span> <span class="n">series2</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">"r"</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="n">series2</span><span class="p">.</span><span class="n">name</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">].</span><span class="n">set_title</span><span class="p">(</span><span class="s">"Level Data"</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">40</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">].</span><span class="n">legend</span><span class="p">(</span><span class="n">prop</span><span class="o">=</span><span class="p">{</span><span class="s">"size"</span><span class="p">:</span> <span class="mi">30</span><span class="p">})</span>
<span class="c1"># Boxplots
</span> <span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">].</span><span class="n">boxplot</span><span class="p">([</span><span class="n">series1</span><span class="p">,</span> <span class="n">series2</span><span class="p">])</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">].</span><span class="n">set_title</span><span class="p">(</span><span class="s">"Boxplots"</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">40</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">].</span><span class="n">set_xticklabels</span><span class="p">([</span><span class="n">series1</span><span class="p">.</span><span class="n">name</span><span class="p">,</span> <span class="n">series2</span><span class="p">.</span><span class="n">name</span><span class="p">],</span>
<span class="n">fontsize</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span>
<span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
<span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">ecdf</span><span class="p">(</span><span class="n">series1</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">2</span><span class="p">].</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">"b"</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">series1</span><span class="p">.</span><span class="n">name</span><span class="p">)</span>
<span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">ecdf</span><span class="p">(</span><span class="n">series2</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">2</span><span class="p">].</span><span class="n">set_title</span><span class="p">(</span><span class="s">"Empirical Cumulative Distribution Function"</span><span class="p">,</span>
<span class="n">fontsize</span><span class="o">=</span><span class="mi">40</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">2</span><span class="p">].</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">"r"</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">series2</span><span class="p">.</span><span class="n">name</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">2</span><span class="p">].</span><span class="n">legend</span><span class="p">(</span><span class="n">prop</span><span class="o">=</span><span class="p">{</span><span class="s">"size"</span><span class="p">:</span> <span class="mi">30</span><span class="p">})</span>
<span class="k">for</span> <span class="n">ax</span> <span class="ow">in</span> <span class="n">axs</span><span class="p">.</span><span class="n">ravel</span><span class="p">():</span>
<span class="n">ax</span><span class="p">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s">"both"</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
<span class="n">fig</span><span class="p">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">fig</span><span class="p">.</span><span class="n">savefig</span><span class="p">(</span><span class="sa">r</span><span class="s">"{}/{}/{}_cutoff_plot.png"</span><span class="p">.</span><span class="nb">format</span><span class="p">(</span><span class="n">output_path</span><span class="p">,</span>
<span class="n">approach_method</span><span class="p">,</span>
<span class="n">city_name</span><span class="p">),</span>
<span class="n">bbox_inches</span><span class="o">=</span><span class="s">"tight"</span><span class="p">)</span>
<span class="k">return</span> <span class="n">test_results</span>
</code></pre></div></div>
<p>Next to individual visual judgement, there are also several tests to formally check whether the distribution of the data is different, namely the Chow Test, the Kolmogrov Smirnov 2-Sample test, and an Analysis of Variance (ANOVA). For the latter two we find the test results between the first half and the second half of the data below.</p>
<p><img src="/assets/post_images/dengai/picture2_3.png" alt="" /></p>
<p>The significance test results confirm our initial hypothesis of a structural difference in the data generating process between the first and second half of the data.</p>
<p>Though it is important to note that only because the data is found to be different, we do not necessarily have to discard all observations from the first half. Instead we will winsorize and use the seasonality found in the second half of the data.</p>
<h2 id="winsorizing">Winsorizing</h2>
<p>Winsorizing presents a valid alternative compared to simply dropping the data. This method is used when instead of throwing out potential outliers, we would like to keep them but alter their magnitude. This is done by specifying by much a potential outlier should be adjusted. A winsorizing value of X% for example means that all values which are higher than the (100-X) percentile are set to the value of the (100-X) percentile. A more thorough explanation of the process can be found here.</p>
<p>The chart below shows the impact of winsorizing our data to the 2.5% level (a value that is chosen to equalize the outbursts in the first half of the data). When looking at the scale of the y-axis the effect of the measure becomes apparent, the magnitude of the outliers has dampened.</p>
<p><img src="/assets/post_images/dengai/picture2_4.png" alt="" /></p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">def</span> <span class="nf">winsorizer</span><span class="p">(</span><span class="n">time_series</span><span class="p">,</span> <span class="n">level</span><span class="p">,</span> <span class="n">city_name</span><span class="p">):</span>
<span class="n">decimal_level</span> <span class="o">=</span> <span class="n">level</span> <span class="o">/</span> <span class="mi">100</span>
<span class="n">wind_series</span> <span class="o">=</span> <span class="n">st</span><span class="p">.</span><span class="n">mstats</span><span class="p">.</span><span class="n">winsorize</span><span class="p">(</span><span class="n">time_series</span><span class="p">,</span> <span class="n">limits</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">decimal_level</span><span class="p">])</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">].</span><span class="n">plot</span><span class="p">(</span><span class="n">time_series</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">"Original Time Series"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">].</span><span class="n">plot</span><span class="p">(</span><span class="n">wind_series</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="s">"Winsorized at the {}% level"</span><span class="p">.</span><span class="nb">format</span><span class="p">(</span><span class="n">level</span><span class="p">))</span>
<span class="n">axs</span> <span class="o">=</span> <span class="n">axs</span><span class="p">.</span><span class="n">ravel</span><span class="p">()</span>
<span class="k">for</span> <span class="n">axes</span> <span class="ow">in</span> <span class="n">axs</span><span class="p">.</span><span class="n">ravel</span><span class="p">():</span>
<span class="n">axes</span><span class="p">.</span><span class="n">legend</span><span class="p">(</span><span class="n">prop</span><span class="o">=</span><span class="p">{</span><span class="s">"size"</span><span class="p">:</span> <span class="mi">16</span><span class="p">},</span> <span class="n">loc</span><span class="o">=</span><span class="s">"upper right"</span><span class="p">)</span>
<span class="n">axes</span><span class="p">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s">"both"</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
<span class="n">fig</span><span class="p">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">fig</span><span class="p">.</span><span class="n">savefig</span><span class="p">(</span><span class="sa">r</span><span class="s">"{}/{}/{}_win.png"</span><span class="p">.</span><span class="nb">format</span><span class="p">(</span><span class="n">output_path</span><span class="p">,</span>
<span class="n">approach_method</span><span class="p">,</span>
<span class="n">city_name</span><span class="p">),</span>
<span class="n">bbox_inches</span><span class="o">=</span><span class="s">"tight"</span><span class="p">)</span>
<span class="k">return</span> <span class="n">wind_series</span>
</code></pre></div></div>
<p>Of course this does measure does not come without any risks. Winsorizing, or outlier altering in general, implies that we do not expect to see levels as high as the altered values. Given that the second half of the time series, which spans eight years of data, does not exhibit outbursts in any comparable magnitude, we feel confident to proceed with the winsorization.</p>
<h2 id="seasonality-detection">Seasonality Detection</h2>
<p>When using a STL decomposition, we need to specify which periodicity the seasonality is supposed to have. In order to find that out, we can either specify an appropriate time index and let the frequency be inferred automatically, or we can look at the autocorrelation function (ACF) of the time series. The ACF shows us the correlation between time series observations and observations with various lagged version of the very same time series. A high autocorrelation with the first lag would for example describe a process where the value today is very much alike the value yesterday. Following the same logic, it is also possible to spot a potential seasonality in the data. Namely, by finding a timely reoccurring amplitude in the ACF of a time series.</p>
<p><img src="/assets/post_images/dengai/picture2_5.png" alt="Source: https://www.quora.com/What-is-the-SI-unit-of-amplitude" /></p>
<p>Below we can see in the upper plot the actual target variable, namely the number of Dengue Fever cases in San Juan. The plot underneath shows the discussed autocorrelation function of that time series. The ACF plot indicates that initially (up until lag 400) we find a yearly pattern. That means that the amplitude is occurring at multiple of 52 (52 weeks equal a year). Beyond lag 400 we find a different pattern though. Something that resembles rather a two-year seasonality. Given the aforementioned higher importance of more recent data, we will therefore continue with a 104 week (or 2 year) seasonality.</p>
<p><img src="/assets/post_images/dengai/picture2_6.png" alt="" /></p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">def</span> <span class="nf">acf_plots</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">max_lags</span><span class="p">,</span> <span class="n">city_name</span><span class="p">):</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">sm</span><span class="p">.</span><span class="n">graphics</span><span class="p">.</span><span class="n">tsa</span><span class="p">.</span><span class="n">plot_acf</span><span class="p">(</span><span class="n">y</span><span class="p">.</span><span class="n">values</span><span class="p">.</span><span class="n">squeeze</span><span class="p">(),</span>
<span class="n">lags</span><span class="o">=</span><span class="n">max_lags</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">missing</span><span class="o">=</span><span class="s">"drop"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">].</span><span class="n">set_title</span><span class="p">(</span><span class="s">"Autocorrelation Plot"</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">18</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">].</span><span class="n">set_title</span><span class="p">(</span><span class="s">"Original Time Series"</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">18</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">].</span><span class="n">plot</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="k">for</span> <span class="n">ax</span> <span class="ow">in</span> <span class="n">axs</span><span class="p">.</span><span class="n">ravel</span><span class="p">():</span>
<span class="n">ax</span><span class="p">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s">"both"</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
<span class="n">fig</span><span class="p">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">fig</span><span class="p">.</span><span class="n">savefig</span><span class="p">(</span><span class="sa">r</span><span class="s">"{}/{}/{}_raw_acf.png"</span><span class="p">.</span><span class="nb">format</span><span class="p">(</span><span class="n">output_path</span><span class="p">,</span> <span class="c1"># Individual output path
</span> <span class="n">approach_method</span><span class="p">,</span> <span class="c1"># Individual folder
</span> <span class="n">city_name</span><span class="p">),</span>
<span class="n">bbox_inches</span><span class="o">=</span><span class="s">"tight"</span><span class="p">)</span>
</code></pre></div></div>
<h2 id="autoregressive-integrated-moving-average-model">Autoregressive Integrated Moving Average Model</h2>
<p>Next in line is the parameterization of the ARIMA model. For that we have to remember that the STL Forecasting function is applying the prediction model on the deasonalized time series data, and not on the raw data. We therefore have to deseasonalize our data before examining it. This is by using the STL decomposition, specifying aforementioned 14 as our period.</p>
<p>In the image below we can see the original series at the very top, followed by the seasonality and the difference between the original series and the seasonality. It is the latter time series we will use to identify the appropriate paramerterization of the ARIMA model.</p>
<p><img src="/assets/post_images/dengai/picture2_7.png" alt="" /></p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">def</span> <span class="nf">stl_decomposing</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">period</span><span class="p">,</span> <span class="n">city_name</span><span class="p">):</span>
<span class="n">time_series</span> <span class="o">=</span> <span class="n">y</span><span class="p">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">STL</span><span class="p">(</span><span class="n">time_series</span><span class="p">,</span> <span class="n">period</span><span class="o">=</span><span class="n">period</span><span class="p">,</span> <span class="n">robust</span><span class="o">=</span><span class="bp">True</span><span class="p">).</span><span class="n">fit</span><span class="p">()</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">20</span><span class="p">))</span>
<span class="n">time_series</span><span class="p">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s">"Original Series"</span><span class="p">)</span>
<span class="n">time_series_wo_season</span> <span class="o">=</span> <span class="n">time_series</span> <span class="o">-</span> <span class="n">res</span><span class="p">.</span><span class="n">seasonal</span>
<span class="n">res</span><span class="p">.</span><span class="n">seasonal</span><span class="p">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="n">time_series_wo_season</span><span class="p">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">].</span><span class="n">set_title</span><span class="p">(</span><span class="s">"Original Series"</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">].</span><span class="n">set_title</span><span class="p">(</span><span class="s">"Seasonality"</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">2</span><span class="p">].</span><span class="n">set_title</span><span class="p">(</span><span class="s">"Original Series Minus Seasonality"</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
<span class="k">for</span> <span class="n">ax</span> <span class="ow">in</span> <span class="n">axs</span><span class="p">.</span><span class="n">ravel</span><span class="p">():</span>
<span class="n">ax</span><span class="p">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s">"both"</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">25</span><span class="p">)</span>
<span class="n">fig</span><span class="p">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">fig</span><span class="p">.</span><span class="n">savefig</span><span class="p">(</span><span class="sa">r</span><span class="s">"{}/{}/{}_decomposed_acf.png"</span><span class="p">.</span><span class="nb">format</span><span class="p">(</span><span class="n">output_path</span><span class="p">,</span>
<span class="n">approach_method</span><span class="p">,</span>
<span class="n">city_name</span><span class="p">),</span>
<span class="n">bbox_inches</span><span class="o">=</span><span class="s">"tight"</span><span class="p">)</span>
<span class="k">return</span> <span class="n">time_series_wo_season</span>
</code></pre></div></div>
<p>Given that the deasonalized time series does not suffer from any kind of non-stationarity, we do not have to worry about the number of integration within the ARIMA model, since none is necessary. The bigger question is how many autoregressive (AR) and moving averages (MA) lags are necessary. In practice the lag length of the AR and MA processes are denoted by the letter p and q respectively.</p>
<p>One well-known approach for specifying the optimal lag length of AR and MA processes is the so-called Box-Jenkins method. This method involves three steps:</p>
<ol>
<li>After ensuring stationarity, use plots of the autocorrelation function (ACF) and the partial autocorrelation function (PACF) to make an initial guess of the lag length for the AR and MA process</li>
<li>Use the parameters gauged from the first step and specify a model</li>
<li>Lastly check the residuals of the fitted model for serial correlation (independence of each other) and for stationarity. The former can be achieved using a Ljung-Box test. If the specified model fails these tests, go back to step 2 and use slightly different parameter for p and/or q</li>
</ol>
<p>Following the three steps outlined above, we start by plotting the autocorrelation function and the partial autocorrelation function. These can be seen in the graph below.</p>
<p>A partial autocorrelation is the amount of correlation between a variable and a lag of itself that is not explained by correlations at all lower-order-lags. That is done by regressing the time series with all n-1 lags and therefore controlling for them when assesing the n-th partial correlation. It contrasts with the autocorrelation function, which does not control for other lags.</p>
<p><img src="/assets/post_images/dengai/picture2_8.png" alt="" /></p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">def</span> <span class="nf">acf_pacf_plots</span><span class="p">(</span><span class="n">time_series</span><span class="p">,</span> <span class="n">nlags</span><span class="p">,</span> <span class="n">city_name</span><span class="p">):</span>
<span class="c1"># Plotting results
</span> <span class="n">no_nan_time_series</span> <span class="o">=</span> <span class="n">time_series</span><span class="p">.</span><span class="n">dropna</span><span class="p">()</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="n">sharey</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">sm</span><span class="p">.</span><span class="n">graphics</span><span class="p">.</span><span class="n">tsa</span><span class="p">.</span><span class="n">plot_acf</span><span class="p">(</span><span class="n">no_nan_time_series</span><span class="p">,</span>
<span class="n">lags</span><span class="o">=</span><span class="n">nlags</span><span class="p">,</span> <span class="n">fft</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">sm</span><span class="p">.</span><span class="n">graphics</span><span class="p">.</span><span class="n">tsa</span><span class="p">.</span><span class="n">plot_pacf</span><span class="p">(</span><span class="n">no_nan_time_series</span><span class="p">,</span>
<span class="n">lags</span><span class="o">=</span><span class="n">nlags</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">title</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">axs</span><span class="p">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="p">[</span><span class="s">"ACF"</span><span class="p">,</span> <span class="s">"PACF"</span><span class="p">]):</span>
<span class="n">ax</span><span class="p">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s">"both"</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
<span class="n">ax</span><span class="p">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">title</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">18</span><span class="p">)</span>
<span class="n">ax</span><span class="p">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">title</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">18</span><span class="p">)</span>
<span class="n">fig</span><span class="p">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">fig</span><span class="p">.</span><span class="n">savefig</span><span class="p">(</span><span class="sa">r</span><span class="s">"{}/{}/{}_acf_pacf.png"</span><span class="p">.</span><span class="nb">format</span><span class="p">(</span><span class="n">output_path</span><span class="p">,</span>
<span class="n">approach_method</span><span class="p">,</span>
<span class="n">city_name</span><span class="p">),</span>
<span class="n">bbox_inches</span><span class="o">=</span><span class="s">"tight"</span><span class="p">)</span>
</code></pre></div></div>
<p>The way of how to identify the appropriate lag length from the two plots above is summarized in the following table:</p>
<p><img src="/assets/post_images/dengai/picture2_9.png" alt="Source: https://www.kevinsheppard.com/files/teaching/mfe/notes/financial-econometrics-2019-2020.pdf" /></p>
<p>From that we can see that the appropriate lag length of the AR process is the number of initial non-zero lags in the partial autocorrelation function. Looking at the plot above, we can see that this should be around 6, given that the seventh lag lies within the boundaries of the 95% confidence interval and is therefore not significant. It is to be noted that the very first vertical line represent the partial autocorrelation with itself and should be not counted.</p>
<p>The number of MA processes is taken by counting the number of initial non-zero terms within the autocorrelation function. In our example, we find rather many, namely around 20. It is important to note that the exact number is not too important as will empirically check a multitude of models before deciding for one.</p>
<p>It is important to stress that the Box-Jenkins method builds on the parsimony principal. Quoting the Oxford econometrician Kevin Sheppard:</p>
<p><em>
Parsimony is a property of a model where the specification with the fewest parameters capable of capturing the dynamics of a time series is preferred to other representations equally capable of capturing the same dynamics.
</em></p>
<p>That means that if we find multiple models which meet our conditions regarding serial correlation, we will choose the model with the smallest amount of lags. Therefore we check the results of the Ljung-Box serial correlation test not only for the lag lengths 6 and 20 (which is already way too many), but for all smaller lengths as well. The p-values of the Ljung-Box tests are captured in the image below.</p>
<p>It is visible that nearly all model parameterization find a significant Ljung-Box test, signaling serious serial correlation. Luckily, we also find some model parameterizations with non-significant serial correlation within the residuals. Applying the parsimony principle leads us then to take the AR-2 MA-2 model.</p>
<p><img src="/assets/post_images/dengai/picture2_10.png" alt="" /></p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">def</span> <span class="nf">box_jenkins_lb_finder</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">q</span><span class="p">,</span> <span class="n">year_in_weeks</span><span class="p">,</span> <span class="n">city_name</span><span class="p">):</span>
<span class="n">lb_df</span> <span class="o">=</span> <span class="n">pd</span><span class="p">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s">"MA_{}"</span><span class="p">.</span><span class="nb">format</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">q</span><span class="p">)],</span>
<span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s">"AR_{}"</span><span class="p">.</span><span class="nb">format</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="p">)])</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">q</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
<span class="n">stlf</span> <span class="o">=</span> <span class="n">STLForecast</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">ARIMA</span><span class="p">,</span> <span class="n">period</span><span class="o">=</span><span class="n">year_in_weeks</span><span class="p">,</span> <span class="n">robust</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
<span class="n">model_kwargs</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">j</span><span class="p">),</span> <span class="n">trend</span><span class="o">=</span><span class="s">"c"</span><span class="p">))</span>
<span class="n">stlf_res</span> <span class="o">=</span> <span class="n">stlf</span><span class="p">.</span><span class="n">fit</span><span class="p">()</span>
<span class="n">results_as_html</span> <span class="o">=</span> <span class="n">stlf_res</span><span class="p">.</span><span class="n">summary</span><span class="p">().</span><span class="n">tables</span><span class="p">[</span><span class="mi">2</span><span class="p">].</span><span class="n">as_html</span><span class="p">()</span>
<span class="n">results_df</span> <span class="o">=</span> <span class="n">pd</span><span class="p">.</span><span class="n">read_html</span><span class="p">(</span><span class="n">results_as_html</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">lb_df</span><span class="p">.</span><span class="n">loc</span><span class="p">[</span><span class="s">"AR_{}"</span><span class="p">.</span><span class="nb">format</span><span class="p">(</span><span class="n">i</span><span class="p">),</span>
<span class="s">"MA_{}"</span><span class="p">.</span><span class="nb">format</span><span class="p">(</span><span class="n">j</span><span class="p">)]</span> <span class="o">=</span> <span class="n">results_df</span><span class="p">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
<span class="c1"># Create heatmaps out of all dataframes
</span> <span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">sns</span><span class="p">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">lb_df</span><span class="p">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">),</span> <span class="n">annot</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s">".2f"</span><span class="p">,</span>
<span class="n">ax</span><span class="o">=</span><span class="n">axs</span><span class="p">,</span> <span class="n">annot_kws</span><span class="o">=</span><span class="p">{</span><span class="s">"size"</span><span class="p">:</span> <span class="mi">18</span><span class="p">},</span>
<span class="n">vmin</span><span class="o">=</span><span class="n">np</span><span class="p">.</span><span class="nb">min</span><span class="p">(</span><span class="n">lb_df</span><span class="p">.</span><span class="n">values</span><span class="p">),</span>
<span class="n">vmax</span><span class="o">=</span><span class="n">np</span><span class="p">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">lb_df</span><span class="p">.</span><span class="n">values</span><span class="p">,</span> <span class="mi">25</span><span class="p">))</span>
<span class="n">axs</span><span class="p">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s">"MA Terms"</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
<span class="n">axs</span><span class="p">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s">"AR Terms"</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
<span class="n">axs</span><span class="p">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s">"both"</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
<span class="n">fig</span><span class="p">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">fig</span><span class="p">.</span><span class="n">savefig</span><span class="p">(</span><span class="sa">r</span><span class="s">"{}/{}/{}_lb_comp.png"</span><span class="p">.</span><span class="nb">format</span><span class="p">(</span><span class="n">output_path</span><span class="p">,</span> <span class="n">approach_method</span><span class="p">,</span>
<span class="n">city_name</span><span class="p">),</span>
<span class="n">bbox_inches</span><span class="o">=</span><span class="s">"tight"</span><span class="p">)</span>
</code></pre></div></div>
<h2 id="in-sample-check-and-forecasting">In-Sample Check and Forecasting</h2>
<p>Finally it is time to look at some predictions from our model. It is important to note that in this forecasting challenge we are not interested in one-step ahead predictions, but a 260-step ahead forecast. In order to validate our performance we therefore need to specify a dynamic prediction method. That means that the model does not treat prior values as realized from a defined point onward, but is aware that these are predictions themselves.The graph below shows predictions of 260 values with the aforementioned dynamic forecasting method. The mean absolute error of 15.7 still represents an in-sample value and can therefore not be taken as representative for out-of-sample performance.</p>
<p><img src="/assets/post_images/dengai/picture2_11.png" alt="" /></p>
<p>After applying the same steps for the other city in our data, we can then hand in our prediction results. From the picture below we can see that our predictions are far away from being competitive. Though, it has to be said that this performance, which is more or less equally good compared to the benchmark model provided by DrivenData, did not make use of any exogenous variable, meaning that there is still a lot improvement potential.</p>
<p><img src="/assets/post_images/dengai/picture2_12.png" alt="" /></p>Paul MoraUsing the STL Forecasting Method with an ARIMA model, which is parameterized through the Box-Jenkins Method.