From c02f08f74b9621b9462d216678a23c022f607a51 Mon Sep 17 00:00:00 2001 From: Carlotta Castelluccio <82521518+carlotta94c@users.noreply.github.com> Date: Wed, 8 Mar 2023 12:19:03 +0000 Subject: [PATCH 1/3] Logistic regression README refactoring --- 2-Regression/4-Logistic/README.md | 224 ++++++++++------ 2-Regression/4-Logistic/images/ROC_2.png | Bin 0 -> 24803 bytes .../4-Logistic/images/pumpkins_catplot_1.png | Bin 0 -> 21873 bytes .../4-Logistic/images/pumpkins_catplot_2.png | Bin 0 -> 67274 bytes 2-Regression/4-Logistic/images/swarm_2.png | Bin 0 -> 30805 bytes 2-Regression/4-Logistic/notebook.ipynb | 249 ++++++++++++++---- .../4-Logistic/solution/notebook.ipynb | 2 +- 7 files changed, 351 insertions(+), 124 deletions(-) create mode 100644 2-Regression/4-Logistic/images/ROC_2.png create mode 100644 2-Regression/4-Logistic/images/pumpkins_catplot_1.png create mode 100644 2-Regression/4-Logistic/images/pumpkins_catplot_2.png create mode 100644 2-Regression/4-Logistic/images/swarm_2.png diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md index 1c39e9a6..5d019a9f 100644 --- a/2-Regression/4-Logistic/README.md +++ b/2-Regression/4-Logistic/README.md @@ -26,9 +26,9 @@ Let's build a logistic regression model to predict that, given some variables, _ ## Define the question -For our purposes, we will express this as a binary: 'Orange' or 'Not Orange'. There is also a 'striped' category in our dataset but there are few instances of it, so we will not use it. It disappears once we remove null values from the dataset, anyway. +For our purposes, we will express this as a binary: 'White' or 'Not White'. There is also a 'striped' category in our dataset but there are few instances of it, so we will not use it. It disappears once we remove null values from the dataset, anyway. -> 🎃 Fun fact, we sometimes call white pumpkins 'ghost' pumpkins. They aren't very easy to carve, so they aren't as popular as the orange ones but they are cool looking! +> 🎃 Fun fact, we sometimes call white pumpkins 'ghost' pumpkins. They aren't very easy to carve, so they aren't as popular as the orange ones but they are cool looking! So we could also reformulate our question as: 'Ghost' or 'Not Ghost'. 👻 ## About logistic regression @@ -50,10 +50,6 @@ There are other types of logistic regression, including multinomial and ordinal: ![Multinomial vs ordinal regression](./images/multinomial-ordinal.png) > Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded) -### It's still linear - -Even though this type of Regression is all about 'category predictions', it still works best when there is a clear linear relationship between the dependent variable (color) and the other independent variables (the rest of the dataset, like city name and size). It's good to get an idea of whether there is any linearity dividing these variables or not. - ### Variables DO NOT have to correlate Remember how linear regression worked better with more correlated variables? Logistic regression is the opposite - the variables don't have to align. That works for this data which has somewhat weak correlations. @@ -71,78 +67,143 @@ First, clean the data a bit, dropping null values and selecting only some of the 1. Add the following code: ```python - from sklearn.preprocessing import LabelEncoder - - new_columns = ['Color','Origin','Item Size','Variety','City Name','Package'] - - new_pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1) - - new_pumpkins.dropna(inplace=True) - - new_pumpkins = new_pumpkins.apply(LabelEncoder().fit_transform) + + columns_to_select = ['City Name','Package','Variety', 'Origin','Item Size', 'Color'] + pumpkins = full_pumpkins.loc[:, columns_to_select] + + pumpkins.dropna(inplace=True) ``` You can always take a peek at your new dataframe: ```python - new_pumpkins.info + pumpkins.info ``` -### Visualization - side-by-side grid +### Visualization - categorical plot By now you have loaded up the [starter notebook](./notebook.ipynb) with pumpkin data once again and cleaned it so as to preserve a dataset containing a few variables, including `Color`. Let's visualize the dataframe in the notebook using a different library: [Seaborn](https://seaborn.pydata.org/index.html), which is built on Matplotlib which we used earlier. -Seaborn offers some neat ways to visualize your data. For example, you can compare distributions of the data for each point in a side-by-side grid. +Seaborn offers some neat ways to visualize your data. For example, you can compare distributions of the data for each `Variety` and `Color` in a categorical plot. -1. Create such a grid by instantiating a `PairGrid`, using our pumpkin data `new_pumpkins`, followed by calling `map()`: +1. Create such a plot by using the `catplot` function, using our pumpkin data `pumpkins`, and specifying a color mapping for each pumpkin category (orange or white): ```python import seaborn as sns - g = sns.PairGrid(new_pumpkins) - g.map(sns.scatterplot) + palette = { + 'ORANGE': 'orange', + 'WHITE': 'wheat', + } + + sns.catplot( + data=pumpkins, y="Variety", hue="Color", kind="count", + palette=palette, + ) ``` - ![A grid of visualized data](images/grid.png) + ![A grid of visualized data](images/pumpkins_catplot_1.png) - By observing data side-by-side, you can see how the Color data relates to the other columns. + By observing the data, you can see how the Color data relates to Variety. - ✅ Given this scatterplot grid, what are some interesting explorations you can envision? + ✅ Given this categorical plot, what are some interesting explorations you can envision? -### Use a swarm plot +### Data pre-processing: feature and label encoding +Our pumpkins dataset contains string values for all its columns. Working with categorical data is intuitive for humans but not for machines. Machine learning algorithms work well with numbers. There's why encoding is a very important step in the data pre-processing phase, since it enables to turn categorical data into numerical data, without losing any information. A good encoding leads to build a good model. -Since Color is a binary category (Orange or Not), it's called 'categorical data' and needs 'a more [specialized approach](https://seaborn.pydata.org/tutorial/categorical.html?highlight=bar) to visualization'. There are other ways to visualize the relationship of this category with other variables. +For feature encoding there are two main types of encoders: -You can visualize variables side-by-side with Seaborn plots. +1. Ordinal encoder: it suits well for ordinal variables, which are categorical variables where their data follows a logical ordering, like the `Item Size` column in our dataset. It creates a mapping such that each category is represented by a number, which is the order of the category in the column. -1. Try a 'swarm' plot to show the distribution of values: + ```python + from sklearn.preprocessing import OrdinalEncoder + + item_size_categories = [['sml', 'med', 'med-lge', 'lge', 'xlge', 'jbo', 'exjbo']] + ordinal_features = ['Item Size'] + ordinal_encoder = OrdinalEncoder(categories=item_size_categories) + ``` + +2. Categorical encoder: it suits well for nominal variables, which are categorical variables where their data does not follow a logical ordering, like all the features different from `Item Size` in our dataset. It is a one-hot encoding, which means that each category is represented by a binary column: the encoded variable is equal to 1 if the pumpkin belongs to that Variety and 0 otherwise. ```python - sns.swarmplot(x="Color", y="Item Size", data=new_pumpkins) + from sklearn.preprocessing import OneHotEncoder + + categorical_features = ['City Name', 'Package', 'Variety', 'Origin'] + categorical_encoder = OneHotEncoder(sparse_output=False) ``` +Then, `ColumnTransformer` is used to combine multiple encoders into a single step and apply them to the appropriate columns. + + ```python + from sklearn.compose import ColumnTransformer + ct = ColumnTransformer(transformers=[ + ('ord', ordinal_encoder, ordinal_features), + ('cat', categorical_encoder, categorical_features) + ]) + + ct.set_output(transform='pandas') + encoded_features = ct.fit_transform(pumpkins) + ``` +On the other hand, to encode the label, we use the scikit-learn `LabelEncoder` class, which is a utility class to help normalize labels such that they contain only values between 0 and n_classes-1 (here, 0 and 1). + + ```python + from sklearn.preprocessing import LabelEncoder - ![A swarm of visualized data](images/swarm.png) + label_encoder = LabelEncoder() + encoded_label = label_encoder.fit_transform(pumpkins['Color']) + ``` +Once we have encoded the features and the label, we can merge them into a new dataframe `encoded_pumpkins`. -### Violin plot + ```python + encoded_pumpkins = encoded_features.assign(Color=encoded_label) + ``` +✅ What are the advantages of using an ordinal encoder for the `Item Size` column? -A 'violin' type plot is useful as you can easily visualize the way that data in the two categories is distributed. Violin plots don't work so well with smaller datasets as the distribution is displayed more 'smoothly'. +### Analyse relationships between variables -1. As parameters `x=Color`, `kind="violin"` and call `catplot()`: +Now that we have pre-processed our data, we can analyse the relationships between the features and the label to grasp an idea of how well the model will be able to predict the label given the features. +The best way to perform this kind of analysis is plotting the data. We'll be using again the Seaborn `catplot` function, to visualize the relationships between `Item Size`, `Variety` and `Color` in a categorical plot. To better plot the data we'll be using the encoded `Item Size` column and the unencoded `Variety` column. ```python - sns.catplot(x="Color", y="Item Size", - kind="violin", data=new_pumpkins) + palette = { + 'ORANGE': 'orange', + 'WHITE': 'wheat', + } + pumpkins['Item Size'] = encoded_pumpkins['ord__Item Size'] + + g = sns.catplot( + data=pumpkins, + x="Item Size", y="Color", row='Variety', + kind="box", orient="h", + sharex=False, margin_titles=True, + height=1.5, aspect=4, palette=palette, + ) + g.set(xlabel="Item Size", ylabel="").set(xlim=(0,6)) + g.set_titles(row_template="{row_name}") ``` + ![A catplot of visualized data](images/pumpkins_catplot_2.png) + +### Use a swarm plot + +Since Color is a binary category (White or Not), it needs 'a [specialized approach](https://seaborn.pydata.org/tutorial/categorical.html?highlight=bar) to visualization'. There are other ways to visualize the relationship of this category with other variables. + +You can visualize variables side-by-side with Seaborn plots. + +1. Try a 'swarm' plot to show the distribution of values: - ![a violin type chart](images/violin.png) + ```python + palette = { + '0': 'orange', + '1': 'wheat' + } + sns.swarmplot(x="Color", y="ord__Item Size", data=encoded_pumpkins, palette=palette) + ``` - ✅ Try creating this plot, and other Seaborn plots, using other variables. + ![A swarm of visualized data](images/swarm_2.png) -Now that we have an idea of the relationship between the binary categories of color and the larger group of sizes, let's explore logistic regression to determine a given pumpkin's likely color. > **🧮 Show Me The Math** > -> Remember how linear regression often used ordinary least squares to arrive at a value? Logistic regression relies on the concept of 'maximum likelihood' using [sigmoid functions](https://wikipedia.org/wiki/Sigmoid_function). A 'Sigmoid Function' on a plot looks like an 'S' shape. It takes a value and maps it to somewhere between 0 and 1. Its curve is also called a 'logistic curve'. Its formula looks like this: +> Logistic regression relies on the concept of 'maximum likelihood' using [sigmoid functions](https://wikipedia.org/wiki/Sigmoid_function). A 'Sigmoid Function' on a plot looks like an 'S' shape. It takes a value and maps it to somewhere between 0 and 1. Its curve is also called a 'logistic curve'. Its formula looks like this: > > ![logistic function](images/sigmoid.png) > @@ -157,49 +218,48 @@ Building a model to find these binary classification is surprisingly straightfor ```python from sklearn.model_selection import train_test_split - Selected_features = ['Origin','Item Size','Variety','City Name','Package'] - - X = new_pumpkins[Selected_features] - y = new_pumpkins['Color'] - + X = encoded_pumpkins[encoded_pumpkins.columns.difference(['Color'])] + y = encoded_pumpkins['Color'] + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) ``` -1. Now you can train your model, by calling `fit()` with your training data, and print out its result: +2. Now you can train your model, by calling `fit()` with your training data, and print out its result: ```python from sklearn.model_selection import train_test_split - from sklearn.metrics import accuracy_score, classification_report + from sklearn.metrics import f1_score, classification_report from sklearn.linear_model import LogisticRegression - + model = LogisticRegression() model.fit(X_train, y_train) predictions = model.predict(X_test) - + print(classification_report(y_test, predictions)) print('Predicted labels: ', predictions) - print('Accuracy: ', accuracy_score(y_test, predictions)) + print('F1-score: ', f1_score(y_test, predictions)) ``` - Take a look at your model's scoreboard. It's not too bad, considering you have only about 1000 rows of data: + Take a look at your model's scoreboard. It's not bad, considering you have only about 1000 rows of data: ```output precision recall f1-score support - 0 0.85 0.95 0.90 166 - 1 0.38 0.15 0.22 33 + 0 0.94 0.98 0.96 166 + 1 0.85 0.67 0.75 33 - accuracy 0.82 199 - macro avg 0.62 0.55 0.56 199 - weighted avg 0.77 0.82 0.78 199 + accuracy 0.92 199 + macro avg 0.89 0.82 0.85 199 + weighted avg 0.92 0.92 0.92 199 - Predicted labels: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 - 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 - 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 - 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 - 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 - 0 0 0 1 0 1 0 0 1 0 0 0 1 0] + Predicted labels: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 + 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 + 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 + 0 0 0 1 0 0 0 0 0 0 0 0 1 1] + F1-score: 0.7457627118644068 ``` ## Better comprehension via a confusion matrix @@ -219,7 +279,7 @@ While you can get a scoreboard report [terms](https://scikit-learn.org/stable/mo ```output array([[162, 4], - [ 33, 0]]) + [ 11, 22]]) ``` In Scikit-learn, confusion matrices Rows (axis 0) are actual labels and columns (axis 1) are predicted labels. @@ -229,22 +289,22 @@ In Scikit-learn, confusion matrices Rows (axis 0) are actual labels and columns | 0 | TN | FP | | 1 | FN | TP | -What's going on here? Let's say our model is asked to classify pumpkins between two binary categories, category 'orange' and category 'not-orange'. +What's going on here? Let's say our model is asked to classify pumpkins between two binary categories, category 'white' and category 'not-white'. -- If your model predicts a pumpkin as not orange and it belongs to category 'not-orange' in reality we call it a true negative, shown by the top left number. -- If your model predicts a pumpkin as orange and it belongs to category 'not-orange' in reality we call it a false negative, shown by the bottom left number. -- If your model predicts a pumpkin as not orange and it belongs to category 'orange' in reality we call it a false positive, shown by the top right number. -- If your model predicts a pumpkin as orange and it belongs to category 'orange' in reality we call it a true positive, shown by the bottom right number. +- If your model predicts a pumpkin as not white and it belongs to category 'not-white' in reality we call it a true negative, shown by the top left number. +- If your model predicts a pumpkin as white and it belongs to category 'not-white' in reality we call it a false negative, shown by the bottom left number. +- If your model predicts a pumpkin as not white and it belongs to category 'white' in reality we call it a false positive, shown by the top right number. +- If your model predicts a pumpkin as white and it belongs to category 'white' in reality we call it a true positive, shown by the bottom right number. As you might have guessed it's preferable to have a larger number of true positives and true negatives and a lower number of false positives and false negatives, which implies that the model performs better. -How does the confusion matrix relate to precision and recall? Remember, the classification report printed above showed precision (0.83) and recall (0.98). +How does the confusion matrix relate to precision and recall? Remember, the classification report printed above showed precision (0.85) and recall (0.67). -Precision = tp / (tp + fp) = 162 / (162 + 33) = 0.8307692307692308 +Precision = tp / (tp + fp) = 22 / (22 + 4) = 0.8461538461538461 -Recall = tp / (tp + fn) = 162 / (162 + 4) = 0.9759036144578314 +Recall = tp / (tp + fn) = 22 / (22 + 11) = 0.6666666666666666 -✅ Q: According to the confusion matrix, how did the model do? A: Not too bad; there are a good number of true negatives but also several false negatives. +✅ Q: According to the confusion matrix, how did the model do? A: Not bad; there are a good number of true negatives but also a few false negatives. Let's revisit the terms we saw earlier with the help of the confusion matrix's mapping of TP/TN and FP/FN: @@ -266,20 +326,26 @@ Let's revisit the terms we saw earlier with the help of the confusion matrix's m ## Visualize the ROC curve of this model -This is not a bad model; its accuracy is in the 80% range so ideally you could use it to predict the color of a pumpkin given a set of variables. - -Let's do one more visualization to see the so-called 'ROC' score: +Let's do one more visualization to see the so-called 'ROC' curve: ```python from sklearn.metrics import roc_curve, roc_auc_score +import matplotlib +import matplotlib.pyplot as plt +%matplotlib inline y_scores = model.predict_proba(X_test) -# calculate ROC curve fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1]) -sns.lineplot([0, 1], [0, 1]) -sns.lineplot(fpr, tpr) + +fig = plt.figure(figsize=(6, 6)) +plt.plot([0, 1], [0, 1], 'k--') +plt.plot(fpr, tpr) +plt.xlabel('False Positive Rate') +plt.ylabel('True Positive Rate') +plt.title('ROC Curve') +plt.show() ``` -Using Seaborn again, plot the model's [Receiving Operating Characteristic](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html?highlight=roc) or ROC. ROC curves are often used to get a view of the output of a classifier in terms of its true vs. false positives. "ROC curves typically feature true positive rate on the Y axis, and false positive rate on the X axis." Thus, the steepness of the curve and the space between the midpoint line and the curve matter: you want a curve that quickly heads up and over the line. In our case, there are false positives to start with, and then the line heads up and over properly: +Using Matplotlib, plot the model's [Receiving Operating Characteristic](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html?highlight=roc) or ROC. ROC curves are often used to get a view of the output of a classifier in terms of its true vs. false positives. "ROC curves typically feature true positive rate on the Y axis, and false positive rate on the X axis." Thus, the steepness of the curve and the space between the midpoint line and the curve matter: you want a curve that quickly heads up and over the line. In our case, there are false positives to start with, and then the line heads up and over properly: ![ROC](./images/ROC.png) @@ -289,7 +355,7 @@ Finally, use Scikit-learn's [`roc_auc_score` API](https://scikit-learn.org/stabl auc = roc_auc_score(y_test,y_scores[:,1]) print(auc) ``` -The result is `0.6976998904709748`. Given that the AUC ranges from 0 to 1, you want a big score, since a model that is 100% correct in its predictions will have an AUC of 1; in this case, the model is _pretty good_. +The result is `0.9749908725812341`. Given that the AUC ranges from 0 to 1, you want a big score, since a model that is 100% correct in its predictions will have an AUC of 1; in this case, the model is _pretty good_. In future lessons on classifications, you will learn how to iterate to improve your model's scores. But for now, congratulations! 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index c151ea78..7c212763 100644 --- a/2-Regression/4-Logistic/notebook.ipynb +++ b/2-Regression/4-Logistic/notebook.ipynb @@ -1,41 +1,15 @@ { - "metadata": { - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.0" - }, - "orig_nbformat": 2, - "kernelspec": { - "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7", - "display_name": "Python 3.7.0 64-bit ('3.7')" - }, - "metadata": { - "interpreter": { - "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2, "cells": [ { + "cell_type": "markdown", + "metadata": {}, "source": [ "## Pumpkin Varieties and Color\n", "\n", "Load up required libraries and dataset. Convert the data to a dataframe containing a subset of the data: \n", "\n", "Let's look at the relationship between color and variety" - ], - "cell_type": "markdown", - "metadata": {} + ] }, { "cell_type": "code", @@ -43,8 +17,175 @@ "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { + "text/html": [ + "
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" + ] }, + "execution_count": 1, "metadata": {}, - "execution_count": 1 + "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", - "pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n", + "full_pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n", "\n", - "pumpkins.head()\n" + "full_pumpkins.head()\n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } - ] -} \ No newline at end of file + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.1" + }, + "metadata": { + "interpreter": { + "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d" + } + }, + "orig_nbformat": 2 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/2-Regression/4-Logistic/solution/notebook.ipynb b/2-Regression/4-Logistic/solution/notebook.ipynb index 13972c42..08684587 100644 --- a/2-Regression/4-Logistic/solution/notebook.ipynb +++ b/2-Regression/4-Logistic/solution/notebook.ipynb @@ -218,7 +218,7 @@ "import pandas as pd\n", "import numpy as np\n", "\n", - "full_pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n", + "full_pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n", "\n", "full_pumpkins.head()\n" ] From b18b26027a7bfe6a309672e32b3cccf26808747c Mon Sep 17 00:00:00 2001 From: Carlotta Castelluccio <82521518+carlotta94c@users.noreply.github.com> Date: Wed, 8 Mar 2023 13:49:20 +0000 Subject: [PATCH 2/3] Fix python snippets formatting & update ROC image --- 2-Regression/4-Logistic/README.md | 21 +++++++++++---------- 1 file changed, 11 insertions(+), 10 deletions(-) diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md index 5d019a9f..f99b9dd2 100644 --- a/2-Regression/4-Logistic/README.md +++ b/2-Regression/4-Logistic/README.md @@ -133,8 +133,9 @@ For feature encoding there are two main types of encoders: ``` Then, `ColumnTransformer` is used to combine multiple encoders into a single step and apply them to the appropriate columns. - ```python +```python from sklearn.compose import ColumnTransformer + ct = ColumnTransformer(transformers=[ ('ord', ordinal_encoder, ordinal_features), ('cat', categorical_encoder, categorical_features) @@ -142,20 +143,20 @@ Then, `ColumnTransformer` is used to combine multiple encoders into a single ste ct.set_output(transform='pandas') encoded_features = ct.fit_transform(pumpkins) - ``` +``` On the other hand, to encode the label, we use the scikit-learn `LabelEncoder` class, which is a utility class to help normalize labels such that they contain only values between 0 and n_classes-1 (here, 0 and 1). - ```python +```python from sklearn.preprocessing import LabelEncoder label_encoder = LabelEncoder() encoded_label = label_encoder.fit_transform(pumpkins['Color']) - ``` +``` Once we have encoded the features and the label, we can merge them into a new dataframe `encoded_pumpkins`. - ```python +```python encoded_pumpkins = encoded_features.assign(Color=encoded_label) - ``` +``` ✅ What are the advantages of using an ordinal encoder for the `Item Size` column? ### Analyse relationships between variables @@ -163,7 +164,7 @@ Once we have encoded the features and the label, we can merge them into a new da Now that we have pre-processed our data, we can analyse the relationships between the features and the label to grasp an idea of how well the model will be able to predict the label given the features. The best way to perform this kind of analysis is plotting the data. We'll be using again the Seaborn `catplot` function, to visualize the relationships between `Item Size`, `Variety` and `Color` in a categorical plot. To better plot the data we'll be using the encoded `Item Size` column and the unencoded `Variety` column. - ```python +```python palette = { 'ORANGE': 'orange', 'WHITE': 'wheat', @@ -179,8 +180,8 @@ The best way to perform this kind of analysis is plotting the data. We'll be usi ) g.set(xlabel="Item Size", ylabel="").set(xlim=(0,6)) g.set_titles(row_template="{row_name}") - ``` - ![A catplot of visualized data](images/pumpkins_catplot_2.png) +``` +![A catplot of visualized data](images/pumpkins_catplot_2.png) ### Use a swarm plot @@ -347,7 +348,7 @@ plt.show() ``` Using Matplotlib, plot the model's [Receiving Operating Characteristic](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html?highlight=roc) or ROC. ROC curves are often used to get a view of the output of a classifier in terms of its true vs. false positives. "ROC curves typically feature true positive rate on the Y axis, and false positive rate on the X axis." Thus, the steepness of the curve and the space between the midpoint line and the curve matter: you want a curve that quickly heads up and over the line. In our case, there are false positives to start with, and then the line heads up and over properly: -![ROC](./images/ROC.png) +![ROC](./images/ROC_2.png) Finally, use Scikit-learn's [`roc_auc_score` API](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html?highlight=roc_auc#sklearn.metrics.roc_auc_score) to compute the actual 'Area Under the Curve' (AUC): From ae407eb7711de6c65dd47d0df92ba609223bc4ba Mon Sep 17 00:00:00 2001 From: Carlotta Castelluccio <82521518+carlotta94c@users.noreply.github.com> Date: Thu, 13 Apr 2023 13:42:13 +0000 Subject: [PATCH 3/3] minor changes to avoid warnings --- 2-Regression/4-Logistic/README.md | 7 +- .../4-Logistic/solution/notebook.ipynb | 157 ++++++++---------- 2 files changed, 68 insertions(+), 96 deletions(-) diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md index f99b9dd2..a734df7a 100644 --- a/2-Regression/4-Logistic/README.md +++ b/2-Regression/4-Logistic/README.md @@ -176,7 +176,7 @@ The best way to perform this kind of analysis is plotting the data. We'll be usi x="Item Size", y="Color", row='Variety', kind="box", orient="h", sharex=False, margin_titles=True, - height=1.5, aspect=4, palette=palette, + height=1.8, aspect=4, palette=palette, ) g.set(xlabel="Item Size", ylabel="").set(xlim=(0,6)) g.set_titles(row_template="{row_name}") @@ -193,8 +193,8 @@ You can visualize variables side-by-side with Seaborn plots. ```python palette = { - '0': 'orange', - '1': 'wheat' + 0: 'orange', + 1: 'wheat' } sns.swarmplot(x="Color", y="ord__Item Size", data=encoded_pumpkins, palette=palette) ``` @@ -229,7 +229,6 @@ Building a model to find these binary classification is surprisingly straightfor 2. Now you can train your model, by calling `fit()` with your training data, and print out its result: ```python - from sklearn.model_selection import train_test_split from sklearn.metrics import f1_score, classification_report from sklearn.linear_model import LogisticRegression diff --git a/2-Regression/4-Logistic/solution/notebook.ipynb b/2-Regression/4-Logistic/solution/notebook.ipynb index 08684587..df1ecb63 100644 --- a/2-Regression/4-Logistic/solution/notebook.ipynb +++ b/2-Regression/4-Logistic/solution/notebook.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 63, "metadata": {}, "outputs": [ { @@ -185,15 +185,15 @@ "" ], "text/plain": [ - " City Name Type Package Variety Sub Variety Grade Date \\\n", - "0 BALTIMORE NaN 24 inch bins NaN NaN NaN 4/29/17 \n", + " City Name Type Package Variety Sub Variety Grade Date \n", + "0 BALTIMORE NaN 24 inch bins NaN NaN NaN 4/29/17 \\\n", "1 BALTIMORE NaN 24 inch bins NaN NaN NaN 5/6/17 \n", "2 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 9/24/16 \n", "3 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 9/24/16 \n", "4 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 11/5/16 \n", "\n", - " Low Price High Price Mostly Low ... Unit of Sale Quality Condition \\\n", - "0 270.0 280.0 270.0 ... NaN NaN NaN \n", + " Low Price High Price Mostly Low ... Unit of Sale Quality Condition \n", + "0 270.0 280.0 270.0 ... NaN NaN NaN \\\n", "1 270.0 280.0 270.0 ... NaN NaN NaN \n", "2 160.0 160.0 160.0 ... NaN NaN NaN \n", "3 160.0 160.0 160.0 ... NaN NaN NaN \n", @@ -209,7 +209,7 @@ "[5 rows x 26 columns]" ] }, - "execution_count": 1, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -225,7 +225,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -316,7 +316,7 @@ "6 BALTIMORE 36 inch bins HOWDEN TYPE MARYLAND med ORANGE" ] }, - "execution_count": 2, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } @@ -344,22 +344,22 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 3, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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o0qUL7OzssG7dOpw/fx6bN29G+/btMW7cuCeu6ezsjJiYGFnbkSNHkJqaCnNzc4NjoqOjMXnyZERHR+udS05ORsuWLbF37158/vnnSExMRGxsLLRaLUaPHm1wvvz8fAwbNgyrVq1CXFwc/Pz89PocO3ZMem42btwIADh//rzUFhkZiYiICISEhCAlJUUaN3/+fPz1119YtmyZ1Fb4PF+8eBETJkzArFmz9MLZo3MXHo6OjoafRCIiemapqroAJVKr1dBoNAAAjUaDqVOnomPHjvj333/h4OCg1//atWsYO3Ysxo4diy+//FJ2rmnTpvjwww+fuOagQYOwYMECXL9+Hc7OzgAeBpZBgwZh1apVev337duHBw8eICwsDKtWrcLvv/+O9u3bS+dHjRoFQRAQHx8vC0Q+Pj4YOnSo3nzZ2dkICgrC8ePHceDAAXh5eRms89Hrt7W1BQA4OjrCxsZGag8JCcGWLVswYsQI7NixA+fOncOMGTOwbt062NvbS/0efZ7ff/99bN68Gdu2bUNoaKjU5/G5iYjo+cQdmnLKzMzEDz/8AA8PD9jZ2Rnss3HjRuTm5mLy5MkGzwuC8MR1ateujYCAAHz33XcAgP/++w/r1q0zGD4AICoqCkFBQahRowaCgoIQFRUlnbtz5w5iY2MxevRog7s7jweEzMxMdO/eHWfPnsWhQ4eKDDMlJQgCYmJicODAAaxYsQLBwcEYOHAgXn/99WLHmZmZIScnp8zrZmdnIz09XXYQEdGzgTs0ZbBjxw5YWFgAAO7fvw8nJyfs2LEDRkaG8+GFCxdgZWUl7TYAD0POkCFDpMeHDx9GkyZNil136NChmDBhAj766CNs2LABDRo0QPPmzfX6paenY8OGDTh8+DAAYPDgwejYsSMWLlwICwsLXLp0CaIowtvbu0TXO3v2bFhaWiIpKcngDlRZuLq64quvvsLw4cNRr149/Pbbb0X2FUURe/bswa+//oqQkBDZuXr16unNe+bMGYPzhIeH45NPPtFrt+ywCFaW+sHOtoF/SS6FiIiqAe7QlIFWq0VCQgISEhIQHx+PgIAAdOvWDVevXi1yzOO7MAEBAUhISMDPP/+M+/fvIz8//4nrdu/eHZmZmdi/fz+io6OL3J1Zs2YNGjRogGbNmgEAmjdvDldXV6xbtw7Aw4BQGq+88gru37+PuXPnlmrck7zzzjtwcnJCSEgIrKys9M4XBkdTU1N069YNAwYMwKxZs2R9Dhw4IP1dJCQk4JdffilyvdDQUKSlpUnH9evXK/R6iIio6nCHpgzMzc3h4eEhPf72229hbW2NFStW4NNPP9Xr37BhQ6SlpSE1NVXapbGwsICHhwdUqpL/FahUKrz11luYOXMmjh49is2bNxvsFxUVhTNnzsjmLigoQHR0NIYNG4aGDRtCEAScO3euROt26dIFISEheOONN1BQUICFCxeWuOYnUalURT4HWq0WkZGRMDExQZ06dQz2c3d3L/E9NGq1Gmq1ujzlEhFRNcUdmgogCAKMjIzw4MEDg+f79u2LGjVqYN68eeVea+jQodi3bx/eeOMN1KpVS+98YmIijh8/Dp1OJ9u50Ol0OHz4MM6dOwdbW1sEBARgyZIluH//vt4c9+7d02t75ZVXsH37dqxYsaJENzFXhMLg6OLiUqrgR0REzx/+liiD7Oxs6bNO7t69i8WLFyMzMxM9evQw2N/FxQXz58/HmDFjcOfOHQQHB8Pd3R137tzBDz/8AAAwNjYu0dqNGjXC//73P9SsWdPg+aioKLRu3RqdOnXSO9eqVStERUXh888/x5IlS/Dyyy+jdevWCAsLQ9OmTZGXl4ddu3YhMjISSUlJeuP9/f2xY8cO9OjRAwUFBVi8eHGJaq5Mt27dQlZWlqzNzs4ONWrUqKKKiIioKnCHpgxiY2Ph5OQEJycntGnTBseOHcP69esNfi5LoZCQEPz222/4999/0bdvXzRs2BCvvvoqrly5gtjY2CfeEPwoOzs7mJmZ6bXn5OTghx9+QJ8+fQyO69OnD1atWoXc3FzUr18fJ06cgFarxYQJE9C4cWN07doVe/bsQWRkZJFrd+7cGT///DNWrlyJ0aNHl/p+nIrm5eUl/V0UHn/88UeV1kRERE+fIFb1bySiKpKeng5ra2tcObmF73IiIlI47tAQERGR4jHQEBERkeIx0BAREZHiMdAQERGR4jHQEBERkeIx0BAREZHiMdAQERGR4jHQEBERkeIx0BAREZHiMdAQERGR4jHQEBERkeLx27bpuWdbXwsrK6uqLoOIiMqBOzRERESkeAw0REREpHgMNERERKR4DDRERESkeAw0REREpHgMNERERKR4DDRERESkeAw0REREpHgMNERERKR4/KRgeu5dXdUElmaVl+3dhl2ptLmJiOgh7tAQERGR4jHQEBERkeIx0BAREZHiMdAQERGR4jHQEBERkeIx0BAREZHiMdAQERGR4jHQEBERkeIx0BAREZHiMdAQERGR4jHQEBERkeIx0BAREZHiMdAQERGR4jHQEBERkeIx0ChMcHAwBEHAyJEj9c6NHj0agiAgODhY6tuzZ0+9sREREbJxW7ZsgSAI0mOdTgdBEHDv3j29Nby9vaFWq5GamirrW9yh0+kwa9YsNG/eXG++5ORkCIKAhIQEg/M5ODjg1VdfRWJiosHn4fEjMDCwBM8iERE9axhoFMjZ2Rlr167FgwcPpLasrCysXr0aLi4uxY41NTXFvHnzcPfu3VKve/DgQTx48AB9+/bFd999BwBo3749UlJSpKN///4IDAyUtbVv377Ua50/fx4pKSn49ddfkZ2dje7duyMnJ0fW5/F1UlJSsGbNmlKvRUREysdAo0AtWrSAs7MzNm3aJLVt2rQJLi4uePHFF4sd6+/vD41Gg/Dw8FKvGxUVhTfffBNvvfUWoqOjAQAmJibQaDTSYWZmBrVaLWszMTEp9VqOjo7QaDRo0aIFxo4di+vXr+PcuXOyPo+vo9FoUKtWrVKvRUREysdAo1BDhw5FTEyM9Dg6OhrvvPPOE8cZGxtj7ty5+Prrr/H333+XeL2MjAysX78egwcPRteuXZGWloYDBw6UqfbSSEtLw9q1awGgTMHoUdnZ2UhPT5cdRET0bFBVdQFUNoMHD0ZoaCiuXr0KADh06BDWrl0LnU73xLG9evVC8+bNMXPmTERFRZVovbVr16Jhw4bw8fEBAAwcOBBRUVHo2LFjma+hOPXq1QMA3L9/HwDw+uuvw9vbW9Znx44dsLCwkLVNmzYN06ZNMzhneHg4PvnkE712yw6LYGVpXhFlG3Tn8u4n9rFt4F9p6xMRPQ8YaBTKwcEB3bt3x8qVKyGKIrp37w57e/sSj583bx46d+6MiRMnlqh/dHQ0Bg8eLD0ePHgwfH198fXXX8PS0rLU9T/JgQMHULNmTRw5cgRz587FsmXL9PpotVpERkbK2mxtbYucMzQ0FOPHj5cep6enw9nZueKKJiKiKsNAo2BDhw7FBx98AABYsmRJqcZ26tQJAQEBCA0Nld4VVZSzZ8/iyJEjiI+Px5QpU6T2/Px8rF27FiNGjHjielZWVkhLS9NrL3wnlbW1tazd3d0dNjY28PLywq1btzBgwADs379f1sfc3BweHh5PXLuQWq2GWq0ucX8iIlIO3kOjYIGBgcjJyUFubi4CAgJKPT4iIgLbt2/H4cOHi+0XFRWFTp064c8//0RCQoJ0jB8/vsQvWXl5eeHvv//GzZs3Ze0nTpyAqalpse/OGj16NE6fPo3NmzeXaC0iInr+cIdGwYyNjZGUlCT9ubSaNGmCQYMGYdGiRUX2yc3Nxffff4+wsDA0btxYdm748OH48ssvcebMGenemqIEBATAy8sLQUFB+PTTT6HRaHDixAl8/PHHGDNmTLH116xZEyNGjMDMmTPRs2dP6TNzsrOzpc/DKaRSqUr10hsRET0buEOjcFZWVrCysirz+LCwMBQUFBR5ftu2bbh9+zZ69eqld65Ro0Zo1KhRiXZpVCoVfvvtN7i4uCAoKAiNGzfGzJkzMWbMGMyePfuJ4z/44AMkJSVh/fr1UltsbCycnJxkR4cOHZ44FxERPXsEURTFqi6CqCqkp6fD2toaV05uqdR3OZUE3+VERFQ+3KEhIiIixWOgISIiIsVjoCEiIiLFY6AhIiIixWOgISIiIsVjoCEiIiLFY6AhIiIixWOgISIiIsVjoCEiIiLFY6AhIiIixWOgISIiIsVjoCEiIiLFU1V1AURVzba+tlzfWE5ERFWPOzRERESkeAw0REREpHgMNERERKR4DDRERESkeAw0REREpHgMNERERKR4pQ40cXFxlVEHERERUZmVOtAEBgaiQYMG+PTTT3H9+vXKqImIiIioVEodaG7cuIEPPvgAGzZsQP369REQEICffvoJOTk5lVEfUaW7uqoJkqPcq7oMIiIqh1IHGnt7e4wbNw4JCQk4evQoPD09MWrUKNSpUwcffvgh/vzzz8qok4iIiKhI5bopuEWLFggNDcUHH3yAzMxMREdHo2XLlujYsSPOnDlTUTUSERERFatMgSY3NxcbNmzAq6++CldXV/z6669YvHgxbt68iUuXLsHV1RX9+vWr6FqJiIiIDCr1l1OGhIRgzZo1EEURb731Fj777DM0btxYOm9ubo4vvvgCderUqdBCiYiIiIpS6kBz9uxZfP311+jduzfUarXBPvb29nx7NxERET01pX7JaebMmejXr59emMnLy8P+/fsBACqVCr6+vhVTIREREdETlDrQaLVa3LlzR689LS0NWq22QooiIiIiKo1SBxpRFCEIgl777du3YW5uXiFFEREREZVGie+h6d27NwBAEAQEBwfLXnLKz8/HqVOn0L59+4qvkIiIiOgJShxorK2tATzcobG0tISZmZl0zsTEBG3btsWIESMqvkIiIiKiJyhxoImJiQEAuLm5YeLEiXx5iYiIiKqNMr3LSa1WY/fu3fjmm2+QkZEBAPjnn3+QmZlZ4QUSERERPUmpP4fm6tWrCAwMxLVr15CdnY2uXbvC0tIS8+bNQ3Z2NpYtW1YZdRIREREVqdQ7NGPGjMFLL72Eu3fvyu6j6dWrF/bs2VOhxREREVHpzZo1C82bN6/qMp6qUgeaAwcO4OOPP4aJiYms3c3NDTdu3KiwwoiIiJ5XqampCAkJQf369aFWq+Hs7IwePXpw46AYpQ40BQUFyM/P12v/+++/YWlpWSFFPY+e9MPr5uaGr776Surv5uYGQRBw5MgR2Txjx46Fn5+f9HjWrFkQBAGCIEClUsHe3h6dOnXCV199hezsbNlYPz8/qe+jx8iRI6U+j7ZbWVmhVatW2Lp1q2yelStXGpzH1NRU6hMcHIyePXsW+Xw8fr1FWbNmDYyNjTF69Ogn9iUiUoLk5GS0bNkSe/fuxeeff47ExETExsZCq9U+1X/rcnNzn9paFaHUgeaVV16R/aIRBAGZmZmYOXMmXn311Yqs7blR1h9eU1NTTJky5Ynz+/j4ICUlBdeuXUNcXBz69euH8PBwtG/fXrqpu9CIESOQkpIiOz777DNZn5iYGKSkpOD48eN4+eWX0bdvXyQmJsr6WFlZ6c1z9erVUjwrJRMVFYXJkydjzZo1yMrKqvD5iYietlGjRkEQBMTHx6NPnz7w9PSEj48Pxo8fL/1P7LVr1/DGG2/AwsICVlZW6N+/P27evFnknAUFBQgLC0O9evWgVqvRvHlzxMbGSueTk5MhCALWrVsHX19fmJqa4scff6z0a61IpQ408+fPx6FDh/DCCy8gKysLb775pvRy07x58yqjxmdeSX54DXn33Xdx5MgR/PLLL8XOr1KpoNFoUKdOHTRp0gQhISHYt28fTp8+rfd3VrNmTWg0GtlhZWUl62NjYwONRgNPT0/Mnj0beXl5el9GKgiC3jy1a9cu5TNTvCtXruD333/H1KlT4enpiU2bNhXbPzs7G+np6bKDiKg6uXPnDmJjYzF69GiDH49iY2ODgoICvPHGG7hz5w727duHXbt24a+//sKAAQOKnHfhwoWYP38+vvjiC5w6dQoBAQF4/fXXcfHiRVm/qVOnYsyYMUhKSkJAQECFX19lKnWgqVevHv78809MmzYN48aNw4svvoiIiAicPHkSjo6OlVHjM60kP7xFcXd3x8iRIxEaGoqCgoJSrevt7Y1u3bo9MQQUJy8vD1FRUQCgd0/V0xATE4Pu3bvD2toagwcPlmopSnh4OKytraXD2dkZAGDZYRGs/FY8jZKJiIp16dIliKIIb2/vIvvs2bMHiYmJWL16NVq2bIk2bdpg1apV2LdvH44dO2ZwzBdffIEpU6Zg4MCB8PLywrx589C8eXO9l/bHjh2L3r17w93dHU5OThV5aZWu1IEGePh//IMHD8Znn32GpUuXYvjw4bJ3PFHJleSHtzgff/wxrly5UqatQW9vbyQnJ8vali5dCgsLC9nx+NxBQUGwsLCAWq3GuHHj4Obmhv79+8v6pKWl6c3TrVu3UtdYlIKCAqxcuRKDBw8GAAwcOBAHDx7ElStXihwTGhqKtLQ06bh+/XqF1UNEVBFEUXxin6SkJDg7O0v/UwYAL7zwAmxsbJCUlKTXPz09Hf/88w9efvllWfvLL7+s1/+ll14qY+VVr0SfQ7Nt2zZ069YNNWrUwLZt24rt+/rrr1dIYc+LkvzwFsfBwQETJ07EjBkzit1uLGrtx79odNCgQfjoo49kbY+/VLRgwQL4+/vjr7/+wrhx47Bo0SLY2trK+lhaWuLEiROytooMvbt27cL9+/el+7bs7e3RtWtXREdHY/bs2QbHqNVq2XeQERFVNw0bNoQgCDh37lyVrK/kbwEoUaDp2bMnUlNT4ejoWOw7UwRBMPgOKCpaRfzwjh8/HkuXLsXSpUtLNS4pKQnu7u6yNmtra3h4eBQ7TqPRwMPDAx4eHoiJicGrr76Ks2fPyl5yNDIyeuI85REVFYU7d+7IQlJBQQFOnTqFTz75BEZGZdp8JCKqUra2tggICMCSJUvw4Ycf6gWMe/fuoVGjRrh+/TquX78u7dKcPXsW9+7dwwsvvKA3p5WVFerUqYNDhw7B19dXaj906BBat25duRf0FJXoX/2CggLpl1VBQUGRB8NM6T36w3v//n298/fu3XviHBYWFpg+fTrmzJmj966lopw7dw6xsbHo06dPaUuWad26NVq2bIk5c+aUa57SuH37NrZu3Yq1a9ciISFBOk6ePIm7d+/it99+e2q1EBFVtCVLliA/Px+tW7fGxo0bcfHiRSQlJWHRokVo164d/P390aRJEwwaNAgnTpxAfHw83n77bfj6+hb5ktGkSZMwb948rFu3DufPn8fUqVORkJCAMWPGPOWrqzyl+uqD3NxcBAYGYtmyZWjYsGFl1fTcWbJkCV5++WW0bt0aYWFhaNq0KfLy8rBr1y5ERkYafE30ce+++y4WLFiA1atXo02bNrJzeXl5SE1NRUFBAW7fvg2dTodPP/0UzZs3x6RJk2R9//vvP6Smpsra1Go1atWqVeTaY8eORa9evTB58mTUrVsXwMOXsx6fBwAcHR2l3ZO0tDQkJCTIztvZ2Un/x3Hjxg29866urvj+++9hZ2eH/v37671k9uqrryIqKgqBgYFF1ktEVJ3Vr18fJ06cwJw5czBhwgSkpKTAwcEBLVu2RGRkJARBwNatWxESEoJOnTrByMgIgYGB+Prrr4uc88MPP0RaWhomTJiAW7du4YUXXsC2bduerd/lYinZ29uLFy5cKO0weoJ//vlHHD16tOjq6iqamJiIdevWFV9//XUxLi5OFEVRdHV1FRcsWCD1f/yxKIri6tWrRQCir6+v1DZz5kwRgAhANDY2Fm1tbcUOHTqICxYsELOysmTjfX19pb6PHgEBAVIfAOLmzZtl4woKCkRvb2/x/fffF0VRFGNiYgzOA0BMSUkRRVEUhwwZYvD8sGHDpOszdP77778XmzRpIo4aNcrg87hu3TrRxMRE/Pfff5/4nKelpYkAxCsnt4i3L+16Yn8iIqq+BFEs3V2p48aNg1qtRkRERPmSFFEVS09Ph7W1Na6c3AIrS3PYNvCv6pKIiKiMSv1t23l5eYiOjsbu3bvRsmVLvRuWvvzyyworjoiIiKgkSh1oTp8+jRYtWgAALly4IDv3+P0MRERERE9DqQPN4x9xT0RERFTV+GEdREREpHil3qEBgOPHj+Onn37CtWvXkJOTIztXnu8GIiIiIiqLUu/QrF27Fu3bt0dSUhI2b96M3NxcnDlzBnv37oW1tXVl1EhERERUrFIHmrlz52LBggXYvn07TExMsHDhQpw7dw79+/eHi4tLZdRIREREVKxSB5rLly+je/fuAAATExPcv38fgiBg3LhxWL58eYUXSERERPQkpb6HplatWtL3BdWtWxenT59GkyZNcO/ePfz3338VXiAREdHTlBzl/uROFcRt2JWnttazrsQ7NKdPnwYAdOrUCbt27QIA9OvXD2PGjMGIESMQFBSELl26VE6VREREJLl+/TqGDh2KOnXqwMTEBK6urhgzZgxu374t9fHz84MgCBAEAaampvD09ER4eDgMfUHA4cOHYWxsLL0C86jk5GQIggBHR0e9L0Bu3rw5Zs2aJWu7dOkShg4dChcXF6jVatStWxddunTBjz/+iLy8PKlfYW2PH2vXri3Tc1LiQNO0aVO0adMGTZo0Qb9+/QAAH330EcaPH4+bN2+iT58+iIqKKlMRREREVDJ//fUXXnrpJVy8eBFr1qzBpUuXsGzZMuzZswft2rXDnTt3pL4jRoxASkoKzp8/j9DQUMyYMQPLli3TmzMqKgohISHYv38//vnnH4PrZmRk4Isvvii2tvj4eLRo0QJJSUlYsmQJTp8+DZ1Oh+HDhyMyMhJnzpyR9Y+JiUFKSors6NmzZ+mfFAAl/i6nAwcOICYmBhs2bEBBQQH69OmD4cOHo2PHjmVamKiqFX6XU1paGqysrKq6HCKqJqr7S07dunXD6dOnceHCBZiZmUntqampaNCgAd5++21ERkbCz88PzZs3x1dffSX1admyJVxdXWUfsZKZmQknJyccP34cM2fORNOmTTFt2jTpfHJyMtzd3TFp0iRERkbi8uXLcHR0BPBwh6Znz56YNWsWRFGEj48Patasifj4eBgZ6e+ZiKIofauAIAjYvHlzmQPM40q8Q9OxY0dER0cjJSUFX3/9NZKTk+Hr6wtPT0/MmzcPqampFVIQERERGXbnzh38+uuvGDVqlCzMAIBGo8GgQYOwbt06vZeVRFHEgQMHcO7cOZiYmMjO/fTTT/D29oaXlxcGDx6M6Ohogy9LBQUFwcPDA2FhYQZrS0hIQFJSEiZOnGgwzACV+xVJpX6Xk7m5Od555x3s27cPFy5cQL9+/bBkyRK4uLjg9ddfr4waiYiICMDFixchiiIaNWpk8HyjRo1w9+5d/PvvvwCApUuXwsLCAmq1Gp06dUJBQQE+/PBD2ZioqCgMHjwYABAYGIi0tDTs27dPb25BEBAREYHly5fj8uXLeucLv9/Ry8tLart16xYsLCykY+nSpbIxQUFBsvMWFha4du1aKZ6R/1eurz7w8PDAtGnT8PHHH8PS0hI///xzeaYjIiKiEijh3SIYNGgQEhIScOjQIXTr1g0fffQR2rdvL50/f/484uPjERQUBABQqVQYMGBAkffEBgQEoEOHDpg+fXqJ1rezs0NCQgISEhJgY2Oj9+0CCxYskM4XHnXq1CnR3I8r01cfAMD+/fsRHR2NjRs3wsjICP3798ewYcPKOh0RERE9gYeHBwRBQFJSEnr16qV3PikpCbVq1YKDgwMAwNraGh4eHgAevrTk4eGBtm3bwt/fH8DD3Zm8vDxZiBBFEWq1GosXLzb4DQARERFo164dJk2aJGtv2LAhgIch6cUXXwQAGBsbS+urVPqRQ6PRSOfLq1Q7NP/88w/mzp0LT09P+Pn54dKlS1i0aBH++ecfrFixAm3btq2QooiIiEifnZ0dunbtiqVLl+LBgweyc6mpqfjxxx8xYMAAg/eqWFhYYMyYMZg4cSJEUUReXh5WrVqF+fPny3ZI/vzzT9SpUwdr1qwxWEPr1q3Ru3dvTJ06Vdb+4osvwtvbG1988QUKCgoq7qJLqMQ7NN26dcPu3bthb2+Pt99+G0OHDpW9TkZERESVb/HixWjfvj0CAgLw6aefwt3dHWfOnMGkSZNQt25dzJkzp8ix7733HmbPno2NGzdCpVLh7t27GDZsmN5OTOFHsYwcOdLgPHPmzIGPj49s10UQBMTExKBr1654+eWXERoaikaNGiE3Nxf79+/Hv//+C2NjY9k89+7d03tTkaWlJczNzUv7tABiCfXo0UPcsmWLmJeXV9IhRNVaWlqaCEBMS0ur6lKIiEolOTlZHDJkiFi7dm2xRo0aorOzsxgSEiL+73//k/r4+vqKY8aM0Rv73nvviT4+PuJrr70mvvrqqwbnP3r0qAhA/PPPP8UrV66IAMSTJ0/K+rz77rsiAHHmzJmy9vPnz4tDhgwR69WrJ6pUKtHa2lrs1KmT+M0334i5ublSPwAGj/Dw8DI9JyX+HBqiZw0/h4aI6NlRrnc5EREREVUHZX6XE9Gz4uqqJrA008/2/NI4IiLl4A4NERERKR4DDRERESkeAw0REREpHgMNERERKR4DDRERESkeAw0REREpHgMNERERKR4/h4aIiOgRdy7vfmpr2Tbwf2prPeu4Q0NERKQQy5Ytg6WlJfLy8qS2zMxM1KhRA35+frK+Op0OgiDg8uXLcHNzw1dffaU336xZs9C8eXODj93c3CAIQpFHcHAwABR5fu3atRV89cXjDg0REZFCaLVaZGZm4vjx42jbti0A4MCBA9BoNDh69CiysrJgamoKAIiLi4OLiwsaNGhQprWOHTuG/Px8AMDvv/+OPn364Pz589J335mZmUl9Y2JiEBgYKBtvY2NTpnXLioGGiIhIIby8vODk5ASdTicFGp1OhzfeeAN79+7FkSNHpJ0anU4HrVZb5rUcHBykP9va2gIAHB0dDQYVGxsbaDSaMq9VEfiSExERkYJotVrExcVJj+Pi4uDn5wdfX1+p/cGDBzh69Gi5Ao3SMNAQEREpiFarxaFDh5CXl4eMjAycPHkSvr6+6NSpE3Q6HQDg8OHDyM7OlgWaKVOmwMLCQnbMnTu3QmoKCgrSm/vatWsVMndJ8SUnIiIiBfHz88P9+/dx7Ngx3L17F56ennBwcICvry/eeecdZGVlQafToX79+nBxcZHGTZo0SbqRt9CiRYuwf//+cte0YMEC+PvL37FVp06dcs9bGtyheQYEBwcbvMP80qVLAIDw8HAYGxvj888/1xu7cuVK2euhK1eulMYbGRnByckJAwYMwLVr1yCKIvz9/REQEKA3z9KlS2FjYwN/f/9i74p3c3OTxpSmrpJe8+M3pRERPWs8PDxQr149xMXFIS4uDr6+vgAeBghnZ2f8/vvviIuLQ+fOnWXj7O3t4eHhITsK740pL41Goze3SvV090wYaJ4RgYGBSElJkR3u7u4AgOjoaEyePBnR0dElmsvKygopKSm4ceMGNm7ciPPnz6Nfv34QBAExMTE4evQovvnmG6n/lStXMHnyZHz99dfYuHGjrAbg4d3vhY+PHTsmjSttXSW55jVr1pRpLiIiJdFqtdDpdNDpdLK3a3fq1Ak7d+5EfHz8c3X/DMBA88xQq9XQaDSyw9jYGPv27cODBw8QFhaG9PR0/P7770+cSxAEaDQaODk5oX379hg2bBji4+ORnp4OZ2dnLFy4EBMnTsSVK1cgiiKGDRuGV155BW+99Rasra1lNQD/f/e7RqOR7povS10lueZatWqVeh4iIqXRarU4ePAgEhISpB0aAPD19cU333yDnJycpxpo7t27h9TUVNlx//79p7Y+wHtonnlRUVEICgpCjRo1EBQUhKioKLRv377E42/duoXNmzfD2NgYxsbGAIAhQ4Zg8+bNGDp0KHr37o3Tp0/jzJkzT7WussjOzkZ2drb0OD09vVLXIyJlUsKn92q1Wjx48ADe3t6oXbu21O7r64uMjAzp7d1PyzvvvKPXFh4ejqlTpz61GiCS4g0ZMkQ0NjYWzc3NpaNv375iWlqaaGZmJiYkJIiiKIonT54ULSwsxIyMDGlsTEyMaG1tLXsMQDQ3Nxdr1qwpAhABiB9++KFszZs3b4r29vaikZGRuHnz5iJrA6B3vix1leSazc3NxTlz5hQ5ZubMmdL1PHpcOblFvH1pV5HjiIio+uMOzTNCq9UiMjJSemxubo41a9agQYMGaNasGQCgefPmcHV1xbp16zBs2LAi57K0tMSJEyeQm5uLnTt34scff8ScOXNkfRwdHfHee+9hy5Yt6NmzZ6lqLWtdj3v8mgEUe4NbaGgoxo8fLz0ufAmNiIiUj4HmGWFubg4PDw9ZW1RUFM6cOSO707ygoADR0dHFBgcjIyNprkaNGuHy5ct4//338f3338v6qVSqMt3FXta6HmfomoujVquhVqtLVSsRESkDA80zKjExEcePH4dOp5PtWty5cwd+fn44d+4cvL29SzTX1KlT0aBBA4wbNw4tWrSoNnUREREVYqB5RkVFRaF169bo1KmT3rlWrVohKirK4Oe/GOLs7IxevXphxowZ2LFjx1OrKz8/HwkJCbI+arUajRo1AvDwJt/U1FTZeZVKBXt7+3LVSEREysO3bT+DcnJy8MMPP6BPnz4Gz/fp0werVq1Cbm5uieccN24cfv75Z8THxz+1ujIzM/Hiiy/Kjh49ekj9Y2Nj4eTkJDs6dOhQ5vqIiEi5BFEUxaougqgqpKenw9raGldOboGVpbki3qpJRESGcYeGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUT1XVBRBVNdv6WlhZWVV1GUREVA7coSEiIiLFY6AhIiIixWOgISIiIsVjoCEiIiLFY6AhIiIixWOgISIiIsVjoCEiIiLFY6AhIiIixWOgoefe1VVNkBzlXtVlEBFROTDQEBERkeIx0BAREZHiMdAQERGR4jHQEBERkeIx0BAREZHiMdAQERGR4jHQEBERkeIx0BAREZHiMdAQERGR4jHQEBERkeIx0BAREZHiMdAQERGR4jHQEBERkeIx0BAREZHiMdAQERGR4jHQKFRwcDAEQZAOOzs7BAYG4tSpU7J+giBgy5YteuOTk5MhCAISEhL0zvn5+WHs2LHSYzc3N9lahUdERESR8xZ3zJ49G+bm5rh06ZJs7D///INatWph8eLFeuuam5ujRYsWWL9+vdR/1qxZBuf39vYuxTNJRETPAgYaBQsMDERKSgpSUlKwZ88eqFQqvPbaa5WyVlhYmLRW4RESEqLXz9nZWdZnwoQJ8PHxkbVNnDgRAQEBCA4ORkFBgTR2xIgRaNmyJUaPHq237smTJ9GqVSsMGDAAv//+u3T+8blTUlJw8ODBSnkOiIio+lJVdQFUdmq1GhqNBgCg0WgwdepUdOzYEf/++y8cHBwqdC1LS0tpreIYGxvL+llYWEClUumN/eabb+Dj44Mvv/wSEydOxMqVK3Ho0CEkJiZCEAS9dTUaDZYsWYIffvgB27dvR/v27QHA4NxFyc7ORnZ2tvQ4PT29ROOIiKj6Y6B5RmRmZuKHH36Ah4cH7OzsqrqcJ3JwcMDy5csRFBSEZs2aYdy4cVi4cCGcnZ2LHKNSqVCjRg3k5OSUac3w8HB88skneu2ubyfCysqqTHMSEVH1wJecFGzHjh2wsLCAhYUFLC0tsW3bNqxbtw5GRhX/1zplyhRprcLjwIED5ZqzZ8+e6N+/PwIDA+Hr64shQ4YU2TcnJwfh4eFIS0tD586dpfbExES9ukaOHGlwjtDQUKSlpUnH9evXy1U/ERFVH9yhUTCtVovIyEgAwN27d7F06VJ069YN8fHxcHV1rdC1Jk2ahODgYFlb3bp1yz3v9OnTsWrVKnz88ccGz0+ZMgUff/wxsrKyYGFhgYiICHTv3l067+XlhW3btsnGFLXbolaroVary10zERFVPww0CmZubg4PDw/p8bfffgtra2usWLECn376abFjC3/pp6Wl6Z27d+8erK2tZW329vaytSqKSqWS/fdxhUHKwsICtWvXlt1fAwAmJiaVUhcRESkLX3J6hgiCACMjIzx48OCJfW1tbWFvb48//vhD1p6eno5Lly7B09OzssoslcIgpdFo9MIMERFRIe7QKFh2djZSU1MBPHzJafHixcjMzESPHj1k/a5cuaL3eTMNGzbE+PHjMXfuXNSuXRtt27bF7du3MXv2bDg4OKB3796y/hkZGdJahWrWrFnlN9Pm5eXp1SUIAmrXrl1FFRERUVVgoFGw2NhYODk5AXj49mZvb2+sX78efn5+sn7jx4/XG3vgwAFMnjwZFhYWmDdvHi5fvgxbW1u8/PLLiIuLg5mZmaz/jBkzMGPGDFnbe++9h2XLllXsRZXSmTNnpOegkFqtRlZWVhVVREREVUEQRVGs6iKIqkJ6ejqsra2RlpZW5TtNRERUPryHhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+Bhp57V1c1QXKUe1WXQURE5cBAQ0RERIrHQENERESKx0BDREREisdAQ0RERIrHQENERESKx0BDREREisdAQ0RERIrHQENERESKx0BDREREisdAQ0RERIrHQENERESKx0BDREREisdAQ0RERIrHQENERESKx0BTCsHBwejZs6esbcOGDTA1NcX8+fOlPoIgICIiQtZvy5YtEARBerxy5UrY2NgYXEcQBGzZskXWtnHjRvj5+cHa2hoWFhZo2rQpwsLCcOfOnSLnS0pKgrOzM/r164ecnJxi13y09sePwMDAIsfMmjULzZs312tPTk6GIAhISEiQPTZ0HDlyRDb2wYMHsLW1hb29PbKzs/XmdnNzk8aamZnBzc0N/fv3x969e4usk4iInm0MNOXw7bffYtCgQYiMjMSECROkdlNTU8ybNw93796tkHU++ugjDBgwAK1atcLOnTtx+vRpzJ8/H3/++Se+//57g2OOHTuGjh07IjAwEOvWrYOJiUmJ1goMDERKSorsWLNmTYVcBwDs3r1bb/6WLVvK+mzcuBE+Pj7w9vbWC3aFwsLCkJKSgvPnz2PVqlWwsbGBv78/5syZU2G1EhGRcqiqugCl+uyzzzBz5kysXbsWvXr1kp3z9/fHpUuXEB4ejs8++6xc68THx2Pu3Ln46quvMGbMGKndzc0NXbt2xb179/TG7N27F2+88QZGjRqFefPmlWo9tVoNjUZTrpqLY2dn98T5o6KiMHjwYIiiiKioKAwYMECvj6WlpTSPi4sLOnXqBCcnJ8yYMQN9+/aFl5dXpdRPRETVE3doymDKlCmYPXs2duzYoRdmAMDY2Bhz587F119/jb///rtca/3444+wsLDAqFGjDJ5//CWkzZs3o3v37vj4449LHWaqg8uXL+Pw4cPo378/+vfvjwMHDuDq1aslGjtmzBiIooitW7caPJ+dnY309HTZQUREzwYGmlLauXMnPvvsM2zduhVdunQpsl+vXr3QvHlzzJw5s1zrXbx4EfXr10eNGjWe2DczMxP9+vXDpEmTMGXKlDKtt2PHDlhYWMiOuXPnFjsmMTFRb4yPj4/Bvu3bt9fr+6jo6Gh069YNtWrVgq2tLQICAhATE1Oi2m1tbeHo6Ijk5GSD58PDw2FtbS0dzs7OAADXtxPhNuxKidYgIqLqiS85lVLTpk3xv//9DzNnzkTr1q31fiE/at68eejcuTMmTpxY5vVEUSxxXzMzM3To0AErVqxAUFAQGjVqVOr1tFotIiMjZW22trbFjvHy8sK2bdtkbTdu3ICfn59e33Xr1hVZV35+Pr777jssXLhQahs8eDAmTpyIGTNmwMjoyflbFEXZzdePCg0Nxfjx46XH6enpUqghIiJlY6Appbp162LDhg3QarUIDAzEzp07YWlpabBvp06dEBAQgNDQUAQHB8vOWVlZ4f79+ygoKJD9oi68J8ba2hoA4OnpiYMHDyI3N/eJuzTGxsbYsmULevfuDa1Wi7i4uFKHGnNzc3h4eJRqjImJid4Ylcrwj5azs3OR8//666+4ceOG3j0z+fn52LNnD7p27VpsHbdv38a///4Ld3d3g+fVajXUanWxcxARkTLxJacycHV1xb59+5CamorAwEBkZGQU2TciIgLbt2/H4cOHZe1eXl7Iy8uT3tZc6MSJEwAeBhkAePPNN5GZmYmlS5canP/xm4LVajU2bdqEVq1aQavV4uzZs6W8uqoTFRWFgQMHIiEhQXYMHDgQUVFRTxy/cOFCGBkZ6b21noiInn3coSkjZ2dn6HQ6aLVaBAQEIDY2FlZWVnr9mjRpgkGDBmHRokWydh8fH7zyyisYOnQo5s+fj/r16+P8+fMYO3YsBgwYgLp16wIA2rRpg8mTJ2PChAm4ceMGevXqhTp16uDSpUtYtmwZOnToIHv3E/Aw1GzcuBH9+vWDVqvF3r17pXta8vPz9UKUWq2WdnKys7ORmpoqO69SqWBvb1+u56vQ7du39ea3sbFBRkYGtm/fjm3btqFx48ay82+//TZ69eqFO3fuSC9/ZWRkIDU1Fbm5ubhy5Qp++OEHfPvttwgPDy/1DhMRESkfA0051KtXTxZqfv31V4P9wsLCsG7dOr32devWYebMmXjvvffwzz//oF69eujVqxemT58u6zdv3jy0bNkSS5YswbJly1BQUIAGDRqgb9++GDJkiME1TUxMsGHDBvTv318KNcDDG4dffPFFWd8GDRrg0qVLAIDY2Fg4OTnJznt5eeHcuXMle1KewN/fX69tzZo1uHHjBszNzQ3eaN2lSxeYmZnhhx9+wIcffggAmDFjBmbMmAETExNoNBq0bdsWe/bsgVarrZA6iYhIWQSxNHedEj1D0tPTYW1tjbS0NIO7a0REpBy8h4aIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUr0oDTXBwMHr27KnXrtPpIAgC7t27J7Xl5+djwYIFaNKkCUxNTVGrVi1069YNhw4dkvqcO3cOgiDgyJEjsvnatm0LU1NTZGVlSW1ZWVkwNTVFVFSUVIsgCBAEATVq1EDt2rXRtWtXREdHo6CgQDafm5ub1PfRIyIiAgCQnJwMQRDg6OiIjIwM2djmzZtj1qxZBp+P8tZfkufy0cePXrOhw83NDQDg5+dn8PzIkSMNXkdxcwqCAK1Wixo1auDgwYOycffv30f9+vUxceJEvXVNTU3xwgsvYOnSpVL/lStXGpzf1NTUYF1ERPTsUsQOjSiKGDhwIMLCwjBmzBgkJSVBp9PB2dkZfn5+2LJlCwDA29sbGo0GOp1OGpuRkYETJ07AwcFBFhQOHz6M7OxsdO7cWWoLDAxESkoKkpOTsXPnTmi1WowZMwavvfYa8vLyZDWFhYUhJSVFdoSEhMj6ZGRk4IsvvijxdZa3/tJauHChrH4AiImJkR4fO3ZM6jtixAi96/3ss88Mzvton6+++gpWVlaytu3btyMkJATBwcG4f/++NG7y5MkwMzPDp59+qrfu2bNn0b9/f4wePRpr1qyRzj8+d0pKCq5evVrm54SIiJRJEYHmp59+woYNG7Bq1SoMHz4c7u7uaNasGZYvX47XX38dw4cPl34xarVaWSA4ePAgPD090aNHD1m7TqeDq6sr3N3dpTa1Wg2NRoO6deuiRYsWmDZtGrZu3YqdO3di5cqVsposLS2h0Whkh7m5uaxPSEgIvvzyS9y6davE11qe+kvL2tpaVj8A2NjYSI8dHBykvjVr1tS7XisrK4PzPtrH2toagiDI2iwsLDB37lyYmJhgypQpAIC4uDh8++23WLVqlWyHpXDd+vXrY9asWWjYsCG2bdsmnX98bo1Gg9q1axusKzs7G+np6bKDiIieDYoINKtXr5Z+qT9uwoQJuH37Nnbt2gXgYSA4ePCgtKMSFxcHPz8/+Pr6Ii4uThoXFxcHrVb7xLU7d+6MZs2aYdOmTaWuOygoCB4eHggLCyvxmIquv7oyNTXFqlWrsHz5cmzduhVDhw7FtGnT0LJly2LHmZmZIScnp0xrhoeHw9raWjqcnZ0BAHf+isOdy7sr9CAioqerygPNjh07YGFhITu6desm63PhwgU0atTI4PjC9gsXLgB4GAju378vvVyi0+ng6+uLTp064ejRo8jKysKDBw8QHx9f4kDg7e2N5ORkWduUKVP06j5w4ICsT+F9NcuXL8fly5dLtFZ56i/Jc1lWS5cu1Zv7xx9/LNecL730EkJDQ9G7d2/Y2dnho48+KrJvfn4+fvjhB5w6dUr2MltaWlqJrzk0NBRpaWnScf369XLVT0RE1YeqqgvQarWIjIyUtR09ehSDBw+WtYmiWKL5PDw8UK9ePeh0Ovj4+ODkyZPw9fWFo6MjXFxccPjwYYiiiOzs7BIHGlEUIQiCrG3SpEkIDg6WtdWtW1dvbEBAADp06IDp06dj9erVlVp/SZ/Lshg0aJBe4CjqpZ3SmD59OsLCwjB16lSoVPo/jkuXLsW3336LnJwcGBsbY9y4cXj//fel85aWljhx4oRsjJmZmcG11Go11Gp1uWsmIqLqp8oDjbm5OTw8PGRtf//9t+yxp6cnkpKSDI4vbPf09JTa/Pz8EBcXh6ZNm6Jhw4ZwdHQEAOllG1EU4eHhIb3k8CRJSUl696rY29vr1V2UiIgItGvXDpMmTSpR/7LWX5Lnsqysra1LfL2lURhiDIUZ4P+DlJmZGZycnGBkJN9UNDIyqpS6iIhIWar8JaeSGDhwIC5evIjt27frnZs/fz7s7OzQtWtXqU2r1eL333/Hrl274OfnJ7V36tQJOp0OOp2uxLsze/fuRWJiIvr06VPm+lu3bo3evXtj6tSpJepfkfUrXWGQqlu3rl6YISIiKlTlOzQlMXDgQKxfvx5DhgzB559/ji5duiA9PR1LlizBtm3bsH79etk7jArvQ4mOjsaKFSukdl9fXwwfPhwAMGrUKL11srOzkZqaivz8fNy8eROxsbEIDw/Ha6+9hrffflvWNyMjA6mpqbK2mjVrFvnOnzlz5sDHx6fInYhHlbX+yvTff//pXa9arUatWrWeah2PE0VRry4AcHR0ZAAiInqOKOJffEEQ8NNPP2HatGlYsGABvLy80LFjR1y9ehU6nU7vA+Xc3d3h6uqKjIwM+Pr6Su0uLi6oU6cOcnJyZDsfhWJjY+Hk5AQ3NzcEBgYiLi4OixYtwtatW2FsbCzrO2PGDDg5OcmOyZMnF3kNnp6eGDp0qOzD8YpS1vor04oVK/SuNygo6KnWYEh6erpeXU5OTqV6qzwRESmfIJb0bluiZ0x6ejqsra1x5eQWWFmaP3lAKdg28K/Q+YiIqHiK2KEhIiIiKg4DDRERESkeAw0REREpHgMNERERKR4DDRERESkeAw0REREpHgMNERERKR4DDRERESkeAw0REREpHgMNERERKR4DDRERESmeIr5tm6gy2dbXFvkt6UREpAzcoSEiIiLFY6AhIiIixWOgISIiIsXjPTT03BJFEQCQnp5exZUQ0dNmaWkJQRCqugyqQAw09Ny6ffs2AMDZ2bmKKyGipy0tLY1vBnjGMNDQc8vW1hYAcO3aNVhbW1dxNSWXnp4OZ2dnXL9+XTH/ICuxZkCZdSuxZuDp121paVnpa9DTxUBDzy0jo4e3kFlbWyvqH/5CVlZWiqtbiTUDyqxbiTUDyq2bqh5vCiYiIiLFY6AhIiIixWOgoeeWWq3GzJkzoVarq7qUUlFi3UqsGVBm3UqsGVBu3VR9CGLhe1eJiIiIFIo7NERERKR4DDRERESkeAw0REREpHgMNERERKR4DDT03FqyZAnc3NxgamqKNm3aID4+vspq2b9/P3r06IE6depAEARs2bJFdl4URcyYMQNOTk4wMzODv78/Ll68KOtz584dDBo0CFZWVrCxscGwYcOQmZlZaTWHh4ejVatWsLS0hKOjI3r27Inz58/L+mRlZWH06NGws7ODhYUF+vTpg5s3b8r6XLt2Dd27d0fNmjXh6OiISZMmIS8vr9LqjoyMRNOmTaUPcGvXrh127txZrWt+XEREBARBwNixY6t13bNmzYIgCLLD29u7WtdMCiYSPYfWrl0rmpiYiNHR0eKZM2fEESNGiDY2NuLNmzerpJ5ffvlF/Oijj8RNmzaJAMTNmzfLzkdERIjW1tbili1bxD///FN8/fXXRXd3d/HBgwdSn8DAQLFZs2bikSNHxAMHDogeHh5iUFBQpdUcEBAgxsTEiKdPnxYTEhLEV199VXRxcREzMzOlPiNHjhSdnZ3FPXv2iMePHxfbtm0rtm/fXjqfl5cnNm7cWPT39xdPnjwp/vLLL6K9vb0YGhpaaXVv27ZN/Pnnn8ULFy6I58+fF6dNmybWqFFDPH36dLWt+VHx8fGim5ub2LRpU3HMmDFSe3Wse+bMmaKPj4+YkpIiHf/++2+1rpmUi4GGnkutW7cWR48eLT3Oz88X69SpI4aHh1dhVQ89HmgKCgpEjUYjfv7551LbvXv3RLVaLa5Zs0YURVE8e/asCEA8duyY1Gfnzp2iIAjijRs3nkrdt27dEgGI+/btk2qsUaOGuH79eqlPUlKSCEA8fPiwKIoPg5yRkZGYmpoq9YmMjBStrKzE7Ozsp1K3KIpirVq1xG+//bba15yRkSE2bNhQ3LVrl+jr6ysFmupa98yZM8VmzZoZPFddaybl4ktO9NzJycnBH3/8AX9/f6nNyMgI/v7+OHz4cBVWZtiVK1eQmpoqq9fa2hpt2rSR6j18+DBsbGzw0ksvSX38/f1hZGSEo0ePPpU609LSAPz/l37+8ccfyM3NldXt7e0NFxcXWd1NmjRB7dq1pT4BAQFIT0/HmTNnKr3m/Px8rF27Fvfv30e7du2qfc2jR49G9+7dZfUB1fu5vnjxIurUqYP69etj0KBBuHbtWrWvmZSJX05Jz53//e9/yM/Pl/0jCQC1a9fGuXPnqqiqoqWmpgKAwXoLz6WmpsLR0VF2XqVSwdbWVupTmQoKCjB27Fi8/PLLaNy4sVSTiYkJbGxsiq3b0HUVnqssiYmJaNeuHbKysmBhYYHNmzfjhRdeQEJCQrWtee3atThx4gSOHTumd666Ptdt2rTBypUr4eXlhZSUFHzyySfo2LEjTp8+XW1rJuVioCGichs9ejROnz6NgwcPVnUpJeLl5YWEhASkpaVhw4YNGDJkCPbt21fVZRXp+vXrGDNmDHbt2gVTU9OqLqfEunXrJv25adOmaNOmDVxdXfHTTz/BzMysCiujZxFfcqLnjr29PYyNjfXeTXHz5k1oNJoqqqpohTUVV69Go8GtW7dk5/Py8nDnzp1Kv6YPPvgAO3bsQFxcHOrVqyerOycnB/fu3Su2bkPXVXiuspiYmMDDwwMtW7ZEeHg4mjVrhoULF1bbmv/44w/cunULLVq0gEqlgkqlwr59+7Bo0SKoVCrUrl27Wtb9OBsbG3h6euLSpUvV9rkm5WKgoeeOiYkJWrZsiT179khtBQUF2LNnD9q1a1eFlRnm7u4OjUYjqzc9PR1Hjx6V6m3Xrh3u3buHP/74Q+qzd+9eFBQUoE2bNpVSlyiK+OCDD7B582bs3bsX7u7usvMtW7ZEjRo1ZHWfP38e165dk9WdmJgoC2O7du2ClZUVXnjhhUqp25CCggJkZ2dX25q7dOmCxMREJCQkSMdLL72EQYMGSX+ujnU/LjMzE5cvX4aTk1O1fa5Jwar6rmSiqrB27VpRrVaLK1euFM+ePSu+++67oo2NjezdFE9TRkaGePLkSfHkyZMiAPHLL78UT548KV69elUUxYdv27axsRG3bt0qnjp1SnzjjTcMvm37xRdfFI8ePSoePHhQbNiwYaW+bfv9998Xra2tRZ1OJ3tb7n///Sf1GTlypOji4iLu3btXPH78uNiuXTuxXbt20vnCt+W+8sorYkJCghgbGys6ODhU6ttyp06dKu7bt0+8cuWKeOrUKXHq1KmiIAjib7/9Vm1rNuTRdzlV17onTJgg6nQ68cqVK+KhQ4dEf39/0d7eXrx161a1rZmUi4GGnltff/216OLiIpqYmIitW7cWjxw5UmW1xMXFiQD0jiFDhoii+PCt29OnTxdr164tqtVqsUuXLuL58+dlc9y+fVsMCgoSLSwsRCsrK/Gdd94RMzIyKq1mQ/UCEGNiYqQ+Dx48EEeNGiXWqlVLrFmzptirVy8xJSVFNk9ycrLYrVs30czMTLS3txcnTJgg5ubmVlrdQ4cOFV1dXUUTExPRwcFB7NKlixRmqmvNhjweaKpj3QMGDBCdnJxEExMTsW7duuKAAQPES5cuVeuaSbkEURTFqtkbIiIiIqoYvIeGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiKgUkpOTIQgCEhISqroUInoEAw0REREpHgMNESlKQUEBPvvsM3h4eECtVsPFxQVz5swBACQmJqJz584wMzODnZ0d3n33XWRmZkpj/fz8MHbsWNl8PXv2RHBwsPTYzc0Nc+fOxdChQ2FpaQkXFxcsX75cOu/u7g4AePHFFyEIAvz8/CrtWomo5BhoiEhRQkNDERERgenTp+Ps2bNYvXo1ateujfv37yMgIAC1atXCsWPHsH79euzevRsffPBBqdeYP38+XnrpJZw8eRKjRo3C+++/j/PnzwMA4uPjAQC7d+9GSkoKNm3aVKHXR0Rlo6rqAoiISiojIwMLFy7E4sWLMWTIEABAgwYN0KFDB6xYsQJZWVlYtWoVzM3NAQCLFy9Gjx49MG/ePNSuXbvE67z66qsYNWoUAGDKlClYsGAB4uLi4OXlBQcHBwCAnZ0dNBpNBV8hEZUVd2iISDGSkpKQnZ2NLl26GDzXrFkzKcwAwMsvv4yCggJpd6WkmjZtKv1ZEARoNBrcunWr7IUTUaVjoCEixTAzMyvXeCMjI4iiKGvLzc3V61ejRg3ZY0EQUFBQUK61iahyMdAQkWI0bNgQZmZm2LNnj965Ro0a4c8//8T9+/eltkOHDsHIyAheXl4AAAcHB6SkpEjn8/Pzcfr06VLVYGJiIo0louqDgYaIFMPU1BRTpkzB5MmTsWrVKly+fBlHjhxBVFQUBg0aBFNTUwwZMgSnT59GXFwcQkJC8NZbb0n3z3Tu3Bk///wzfv75Z5w7dw7vv/8+7t27V6oaHB0dYWZmhtjYWNy8eRNpaWmVcKVEVFoMNESkKNOnT8eECRMwY8YMNGrUCAMGDMCtW7dQs2ZN/Prrr7hz5w5atWqFvn37okuXLli8eLE0dujQoRgyZAjefvtt+Pr6on79+tBqtaVaX6VSYdGiRfjmm29Qp04dvPHGGxV9iURUBoL4+AvKRERERArDHRoiIiJSPAYaIiIiUjwGGiIiIlI8BhoiIiJSPAYaIiIiUjwGGiIiIlI8BhoiIiJSPAYaIiIiUjwGGiIiIlI8BhoiIiJSPAYaIiIiUrz/A+sUfVTiRBWAAAAAAElFTkSuQmCC", + "image/png": 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", "text/plain": [ "
" ] @@ -394,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 66, "metadata": {}, "outputs": [ { @@ -404,7 +404,7 @@ " dtype=object)" ] }, - "execution_count": 4, + "execution_count": 66, "metadata": {}, "output_type": "execute_result" } @@ -416,7 +416,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ @@ -429,7 +429,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ @@ -441,7 +441,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 69, "metadata": {}, "outputs": [ { @@ -615,43 +615,43 @@ "" ], "text/plain": [ - " ord__Item Size cat__City Name_ATLANTA cat__City Name_BALTIMORE \\\n", - "2 1.0 0.0 1.0 \n", + " ord__Item Size cat__City Name_ATLANTA cat__City Name_BALTIMORE \n", + "2 1.0 0.0 1.0 \\\n", "3 1.0 0.0 1.0 \n", "4 3.0 0.0 1.0 \n", "5 3.0 0.0 1.0 \n", "6 1.0 0.0 1.0 \n", "\n", - " cat__City Name_BOSTON cat__City Name_CHICAGO cat__City Name_COLUMBIA \\\n", - "2 0.0 0.0 0.0 \n", + " cat__City Name_BOSTON cat__City Name_CHICAGO cat__City Name_COLUMBIA \n", + "2 0.0 0.0 0.0 \\\n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 \n", "\n", - " cat__City Name_DALLAS cat__City Name_DETROIT cat__City Name_LOS ANGELES \\\n", - "2 0.0 0.0 0.0 \n", + " cat__City Name_DALLAS cat__City Name_DETROIT cat__City Name_LOS ANGELES \n", + "2 0.0 0.0 0.0 \\\n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 \n", "\n", - " cat__City Name_MIAMI ... cat__Origin_MICHIGAN cat__Origin_NEW JERSEY \\\n", - "2 0.0 ... 0.0 0.0 \n", + " cat__City Name_MIAMI ... cat__Origin_MICHIGAN cat__Origin_NEW JERSEY \n", + "2 0.0 ... 0.0 0.0 \\\n", "3 0.0 ... 0.0 0.0 \n", "4 0.0 ... 0.0 0.0 \n", "5 0.0 ... 0.0 0.0 \n", "6 0.0 ... 0.0 0.0 \n", "\n", - " cat__Origin_NEW YORK cat__Origin_NORTH CAROLINA cat__Origin_OHIO \\\n", - "2 0.0 0.0 0.0 \n", + " cat__Origin_NEW YORK cat__Origin_NORTH CAROLINA cat__Origin_OHIO \n", + "2 0.0 0.0 0.0 \\\n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 \n", "\n", - " cat__Origin_PENNSYLVANIA cat__Origin_TENNESSEE cat__Origin_TEXAS \\\n", - "2 0.0 0.0 0.0 \n", + " cat__Origin_PENNSYLVANIA cat__Origin_TENNESSEE cat__Origin_TEXAS \n", + "2 0.0 0.0 0.0 \\\n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 \n", @@ -667,7 +667,7 @@ "[5 rows x 48 columns]" ] }, - "execution_count": 7, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } @@ -686,7 +686,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -860,43 +860,43 @@ "" ], "text/plain": [ - " ord__Item Size cat__City Name_ATLANTA cat__City Name_BALTIMORE \\\n", - "2 1.0 0.0 1.0 \n", + " ord__Item Size cat__City Name_ATLANTA cat__City Name_BALTIMORE \n", + "2 1.0 0.0 1.0 \\\n", "3 1.0 0.0 1.0 \n", "4 3.0 0.0 1.0 \n", "5 3.0 0.0 1.0 \n", "6 1.0 0.0 1.0 \n", "\n", - " cat__City Name_BOSTON cat__City Name_CHICAGO cat__City Name_COLUMBIA \\\n", - "2 0.0 0.0 0.0 \n", + " cat__City Name_BOSTON cat__City Name_CHICAGO cat__City Name_COLUMBIA \n", + "2 0.0 0.0 0.0 \\\n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 \n", "\n", - " cat__City Name_DALLAS cat__City Name_DETROIT cat__City Name_LOS ANGELES \\\n", - "2 0.0 0.0 0.0 \n", + " cat__City Name_DALLAS cat__City Name_DETROIT cat__City Name_LOS ANGELES \n", + "2 0.0 0.0 0.0 \\\n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 \n", "\n", - " cat__City Name_MIAMI ... cat__Origin_NEW JERSEY cat__Origin_NEW YORK \\\n", - "2 0.0 ... 0.0 0.0 \n", + " cat__City Name_MIAMI ... cat__Origin_NEW JERSEY cat__Origin_NEW YORK \n", + "2 0.0 ... 0.0 0.0 \\\n", "3 0.0 ... 0.0 0.0 \n", "4 0.0 ... 0.0 0.0 \n", "5 0.0 ... 0.0 0.0 \n", "6 0.0 ... 0.0 0.0 \n", "\n", - " cat__Origin_NORTH CAROLINA cat__Origin_OHIO cat__Origin_PENNSYLVANIA \\\n", - "2 0.0 0.0 0.0 \n", + " cat__Origin_NORTH CAROLINA cat__Origin_OHIO cat__Origin_PENNSYLVANIA \n", + "2 0.0 0.0 0.0 \\\n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 \n", "\n", - " cat__Origin_TENNESSEE cat__Origin_TEXAS cat__Origin_VERMONT \\\n", - "2 0.0 0.0 0.0 \n", + " cat__Origin_TENNESSEE cat__Origin_TEXAS cat__Origin_VERMONT \n", + "2 0.0 0.0 0.0 \\\n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 \n", @@ -912,7 +912,7 @@ "[5 rows x 49 columns]" ] }, - "execution_count": 14, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -928,7 +928,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 71, "metadata": {}, "outputs": [ { @@ -937,7 +937,7 @@ "['ORANGE', 'WHITE']" ] }, - "execution_count": 18, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" } @@ -957,32 +957,24 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 81, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/vscode/.local/lib/python3.11/site-packages/seaborn/axisgrid.py:118: UserWarning: Tight layout not applied. tight_layout cannot make axes height small enough to accommodate all axes decorations.\n", - " self._figure.tight_layout(*args, **kwargs)\n" - ] - }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 19, + "execution_count": 81, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, @@ -1002,7 +994,7 @@ " x=\"Item Size\", y=\"Color\", row='Variety',\n", " kind=\"box\", orient=\"h\",\n", " sharex=False, margin_titles=True,\n", - " height=1.5, aspect=4, palette=palette,\n", + " height=1.8, aspect=4, palette=palette,\n", ")\n", "# Defining axis labels \n", "g.set(xlabel=\"Item Size\", ylabel=\"\").set(xlim=(0,6))\n", @@ -1019,15 +1011,13 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 83, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_728/133969970.py:5: FutureWarning: Passing `palette` without assigning `hue` is deprecated.\n", - " sns.swarmplot(x=\"Color\", y=\"ord__Item Size\", data=encoded_pumpkins, palette=palette)\n", "/home/vscode/.local/lib/python3.11/site-packages/seaborn/categorical.py:3544: UserWarning: 63.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n", " warnings.warn(msg, UserWarning)\n", "/home/vscode/.local/lib/python3.11/site-packages/seaborn/categorical.py:3544: UserWarning: 21.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n", @@ -1040,7 +1030,7 @@ "" ] }, - "execution_count": 27, + "execution_count": 83, "metadata": {}, "output_type": "execute_result" }, @@ -1056,7 +1046,7 @@ }, { "data": { - "image/png": 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", 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"text/plain": [ "
" ] @@ -1067,10 +1057,10 @@ ], "source": [ "palette = {\n", - " '0': 'orange',\n", - " '1': 'wheat'\n", + " 0: 'orange',\n", + " 1: 'wheat'\n", "}\n", - "sns.swarmplot(x=\"Color\", y=\"ord__Item Size\", data=encoded_pumpkins, palette=palette)" + "sns.swarmplot(x=\"Color\", y=\"ord__Item Size\", hue=\"Color\", data=encoded_pumpkins, palette=palette)" ] }, { @@ -1083,7 +1073,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 74, "metadata": {}, "outputs": [], "source": [ @@ -1099,7 +1089,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 75, "metadata": {}, "outputs": [ { @@ -1123,23 +1113,6 @@ " 0 0 0 1 0 0 0 0 0 0 0 0 1 1]\n", "F1-score: 0.7457627118644068\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Bad pipe message: %s [b'', b'\\x9ay^79\\x00\\x85\\xd91\\x8bc|\\xa9+\\x16\\xac\\x00\\x00\\xa2\\xc0\\x14\\xc0\\n\\x009\\x008\\x007']\n", - "Bad pipe message: %s 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[b\">\\xd1\\xb9\\x90\\xf8\\xdc\\x19\\x1b\\x01\\xec\\\\+\\xe4*\\xaf\\xd0\\x0e:\\x00\\x00\\xf4\\xc00\\xc0,\\xc0(\\xc0$\\xc0\\x14\\xc0\\n\\x00\\xa5\\x00\\xa3\\x00\\xa1\\x00\\x9f\\x00k\\x00j\\x00i\\x00h\\x009\\x008\\x007\\x006\\x00\\x88\\x00\\x87\\x00\\x86\\x00\\x85\\xc0\\x19\\x00\\xa7\\x00m\\x00:\\x00\\x89\\xc02\\xc0.\\xc0*\\xc0&\\xc0\\x0f\\xc0\\x05\\x00\\x9d\\x00=\\x005\\x00\\x84\\xc0/\\xc0+\\xc0'\\xc0#\\xc0\\x13\\xc0\\t\\x00\\xa4\\x00\\xa2\\x00\\xa0\\x00\\x9e\\x00g\\x00@\\x00?\\x00>\\x003\\x002\\x001\\x000\\x00\\x9a\\x00\\x99\\x00\\x98\\x00\\x97\\x00E\\x00D\\x00C\\x00B\", b'\\x00\\xa6\\x00l\\x004\\x00\\x9b\\x00F\\xc01\\xc0-\\xc0)\\xc0%\\xc0\\x0e\\xc0\\x04\\x00\\x9c\\x00<\\x00/\\x00\\x96\\x00A\\x00\\x07\\xc0\\x11\\xc0\\x07\\xc0\\x16\\x00\\x18\\xc0\\x0c\\xc0\\x02\\x00\\x05\\x00\\x04\\xc0\\x12\\xc0\\x08\\x00\\x16\\x00\\x13\\x00\\x10\\x00\\r\\xc0\\x17\\x00\\x1b\\xc0\\r\\xc0\\x03\\x00\\n\\x00\\x15\\x00\\x12\\x00\\x0f\\x00\\x0c\\x00\\x1a\\x00\\t\\x00\\x14\\x00\\x11\\x00\\x19\\x00\\x08\\x00\\x06\\x00\\x17\\x00\\x03\\xc0\\x10\\xc0\\x06\\xc0\\x15\\xc0\\x0b\\xc0\\x01\\x00;\\x00\\x02\\x00\\x01\\x00\\xff\\x02\\x01\\x00\\x00g\\x00\\x00\\x00\\x0e\\x00\\x0c\\x00\\x00\\t127.0.0.1\\x00\\x0b\\x00\\x04\\x03\\x00\\x01\\x02\\x00\\n\\x00\\x1c\\x00\\x1a\\x00\\x17\\x00\\x19\\x00\\x1c\\x00\\x1b\\x00\\x18\\x00\\x1a\\x00\\x16\\x00\\x0e\\x00\\r\\x00\\x0b\\x00\\x0c\\x00\\t\\x00\\n\\x00#\\x00\\x00\\x00\\r\\x00 \\x00\\x1e\\x06\\x01']\n", - "Bad pipe message: %s [b\"\\xe0r\\xd1\\x9e\\xc7J'j\\xa5&0\\xcd\\xe6\\r\\xff\\xdf\\x06\\t\\x00\\x00\\xa2\\xc0\\x14\\xc0\\n\\x009\\x008\\x007\\x006\\x00\\x88\\x00\\x87\\x00\\x86\\x00\\x85\\xc0\\x19\\x00:\\x00\\x89\\xc0\\x0f\\xc0\\x05\\x005\\x00\\x84\\xc0\\x13\\xc0\\t\\x003\\x002\\x001\\x000\\x00\\x9a\\x00\\x99\\x00\\x98\\x00\\x97\\x00E\\x00D\\x00C\\x00B\\xc0\\x18\\x004\\x00\\x9b\\x00F\\xc0\\x0e\\xc0\\x04\\x00/\\x00\\x96\\x00A\\x00\\x07\\xc0\\x11\\xc0\\x07\\xc0\\x16\\x00\\x18\\xc0\\x0c\\xc0\\x02\\x00\\x05\\x00\\x04\\xc0\\x12\\xc0\\x08\\x00\\x16\\x00\\x13\\x00\\x10\\x00\\r\\xc0\\x17\\x00\\x1b\\xc0\\r\\xc0\\x03\\x00\\n\\x00\\x15\\x00\\x12\\x00\\x0f\\x00\\x0c\\x00\\x1a\\x00\\t\\x00\\x14\\x00\\x11\\x00\\x19\\x00\\x08\\x00\\x06\\x00\\x17\\x00\\x03\\xc0\"]\n", - "Bad pipe message: %s [b'\\x8c\\xaf\\x10\\x0c\\xb4\\xd1\\xc3l\\x8c\\xf9\\x85\\xf9\\x16\\x12\\x12']\n", - "Bad pipe message: %s [b'\\x06\\x03\\x05\\x01\\x05']\n", - "Bad pipe message: %s [b'\\x06\\xc0\\x15\\xc0\\x0b\\xc0\\x01\\x00\\x02\\x00\\x01\\x00\\xff\\x02\\x01']\n", - "Bad pipe message: %s [b'\\x03', b'\\x04\\x02\\x04', b'\\x01\\x03', b'\\x03', b'\\x02', b'\\x03']\n" - ] } ], "source": [ @@ -1160,7 +1133,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 76, "metadata": {}, "outputs": [ { @@ -1170,7 +1143,7 @@ " [ 11, 22]])" ] }, - "execution_count": 24, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" } @@ -1182,12 +1155,12 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 77, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1220,7 +1193,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 78, "metadata": {}, "outputs": [ {