02_end_to_end_machine_learning_project.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"anaconda-cloud":{},"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":"3.8.8"},"colab":{"name":"02_end_to_end_machine_learning_project.ipynb","provenance":[],"collapsed_sections":["1DVQ21noa_4y"]}},"cells":[{"cell_type":"markdown","metadata":{"id":"01I70uB-TItk"},"source":["# Author's Notes\n","\n","As of May 2021, there is no support for XGBoost on Mac (using M1 chip). You will encounter an error when training a model using XGBoost, but Logistic Regression, Random Forests, and LightGB will work successfully.\n","\n","To improve the user experience, I have commented out code blocks related to XGBoost. To uncomment the code blocks, highlight the code and use the \"CTRL\" + \"/\" key combination.\n","\n","If you have any issues or suggestions, please email me at ankur@unsupervisedlearningbook.com.\n","\n","From [Hands-on Unsupervised Learning using Python](https://amzn.to/2tVABNx) - Chapter 2 - End-to-End Machine Learning Project"]},{"cell_type":"markdown","metadata":{"id":"1DVQ21noa_4y"},"source":["# Google Colab Setup\n","**Only for users on Google Colab**"]},{"cell_type":"code","metadata":{"id":"ead7Cv39bCA-"},"source":["# Define functions to connect to Google and change directories\n","def connectDrive():\n","    from google.colab import drive\n","    drive.mount('/content/drive', force_remount=True)\n","\n","def changeDirectory(path):\n","    import os\n","    original_path = os.getcwd()\n","    os.chdir(path)\n","    new_path = os.getcwd()\n","    print(\"Original path: \",original_path)\n","    print(\"New path: \",new_path)\n","\n","# Connect to Google Drive\n","connectDrive()\n","\n","# Change path\n","changeDirectory(\"/content/drive/My Drive/github/handson-unsupervised-learning/\")"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"7DF79_LnTItm"},"source":["# Import Libraries"]},{"cell_type":"code","metadata":{"id":"ZhKATSACTItn"},"source":["'''Main'''\n","import numpy as np\n","import pandas as pd\n","import os\n","\n","'''Data Viz'''\n","import matplotlib.pyplot as plt\n","import matplotlib as mpl\n","import seaborn as sns\n","color = sns.color_palette()\n","\n","%matplotlib inline\n","\n","'''Data Prep'''\n","from sklearn import preprocessing as pp \n","from scipy.stats import pearsonr \n","from sklearn.model_selection import train_test_split\n","from sklearn.model_selection import StratifiedKFold \n","from sklearn.metrics import log_loss \n","from sklearn.metrics import precision_recall_curve, average_precision_score \n","from sklearn.metrics import roc_curve, auc, roc_auc_score\n","from sklearn.metrics import confusion_matrix, classification_report \n","\n","'''Algos'''\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.ensemble import RandomForestClassifier\n","# import xgboost as xgb\n","import lightgbm as lgb"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"collapsed":true,"id":"ZCXzBIX5TIto"},"source":["# Data Preparation"]},{"cell_type":"code","metadata":{"id":"uOwVoTcLTIto"},"source":["# Acquire Data\n","current_path = os.getcwd()\n","file = os.path.sep.join(['', 'datasets', 'credit_card_data', 'credit_card.csv'])\n","data = pd.read_csv(current_path + file)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bM82J7FvTItp","executionInfo":{"status":"ok","timestamp":1620233543040,"user_tz":240,"elapsed":4476,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"4d0b5e67-0bd7-4f75-8a10-3b83948b2f6d"},"source":["# Explore Data\n","data.shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(284807, 31)"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"code","metadata":{"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":228},"id":"LVQHLpBkTItp","executionInfo":{"status":"ok","timestamp":1620233545532,"user_tz":240,"elapsed":516,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"bce1ae8b-00d3-458a-968c-1e1fa6f27de8"},"source":["data.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Time</th>\n","      <th>V1</th>\n","      <th>V2</th>\n","      <th>V3</th>\n","      <th>V4</th>\n","      <th>V5</th>\n","      <th>V6</th>\n","      <th>V7</th>\n","      <th>V8</th>\n","      <th>V9</th>\n","      <th>V10</th>\n","      <th>V11</th>\n","      <th>V12</th>\n","      <th>V13</th>\n","      <th>V14</th>\n","      <th>V15</th>\n","      <th>V16</th>\n","      <th>V17</th>\n","      <th>V18</th>\n","      <th>V19</th>\n","      <th>V20</th>\n","      <th>V21</th>\n","      <th>V22</th>\n","      <th>V23</th>\n","      <th>V24</th>\n","      <th>V25</th>\n","      <th>V26</th>\n","      <th>V27</th>\n","      <th>V28</th>\n","      <th>Amount</th>\n","      <th>Class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.0</td>\n","      <td>-1.359807</td>\n","      <td>-0.072781</td>\n","      <td>2.536347</td>\n","      <td>1.378155</td>\n","      <td>-0.338321</td>\n","      <td>0.462388</td>\n","      <td>0.239599</td>\n","      <td>0.098698</td>\n","      <td>0.363787</td>\n","      <td>0.090794</td>\n","      <td>-0.551600</td>\n","      <td>-0.617801</td>\n","      <td>-0.991390</td>\n","      <td>-0.311169</td>\n","      <td>1.468177</td>\n","      <td>-0.470401</td>\n","      <td>0.207971</td>\n","      <td>0.025791</td>\n","      <td>0.403993</td>\n","      <td>0.251412</td>\n","      <td>-0.018307</td>\n","      <td>0.277838</td>\n","      <td>-0.110474</td>\n","      <td>0.066928</td>\n","      <td>0.128539</td>\n","      <td>-0.189115</td>\n","      <td>0.133558</td>\n","      <td>-0.021053</td>\n","      <td>149.62</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.0</td>\n","      <td>1.191857</td>\n","      <td>0.266151</td>\n","      <td>0.166480</td>\n","      <td>0.448154</td>\n","      <td>0.060018</td>\n","      <td>-0.082361</td>\n","      <td>-0.078803</td>\n","      <td>0.085102</td>\n","      <td>-0.255425</td>\n","      <td>-0.166974</td>\n","      <td>1.612727</td>\n","      <td>1.065235</td>\n","      <td>0.489095</td>\n","      <td>-0.143772</td>\n","      <td>0.635558</td>\n","      <td>0.463917</td>\n","      <td>-0.114805</td>\n","      <td>-0.183361</td>\n","      <td>-0.145783</td>\n","      <td>-0.069083</td>\n","      <td>-0.225775</td>\n","      <td>-0.638672</td>\n","      <td>0.101288</td>\n","      <td>-0.339846</td>\n","      <td>0.167170</td>\n","      <td>0.125895</td>\n","      <td>-0.008983</td>\n","      <td>0.014724</td>\n","      <td>2.69</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1.0</td>\n","      <td>-1.358354</td>\n","      <td>-1.340163</td>\n","      <td>1.773209</td>\n","      <td>0.379780</td>\n","      <td>-0.503198</td>\n","      <td>1.800499</td>\n","      <td>0.791461</td>\n","      <td>0.247676</td>\n","      <td>-1.514654</td>\n","      <td>0.207643</td>\n","      <td>0.624501</td>\n","      <td>0.066084</td>\n","      <td>0.717293</td>\n","      <td>-0.165946</td>\n","      <td>2.345865</td>\n","      <td>-2.890083</td>\n","      <td>1.109969</td>\n","      <td>-0.121359</td>\n","      <td>-2.261857</td>\n","      <td>0.524980</td>\n","      <td>0.247998</td>\n","      <td>0.771679</td>\n","      <td>0.909412</td>\n","      <td>-0.689281</td>\n","      <td>-0.327642</td>\n","      <td>-0.139097</td>\n","      <td>-0.055353</td>\n","      <td>-0.059752</td>\n","      <td>378.66</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1.0</td>\n","      <td>-0.966272</td>\n","      <td>-0.185226</td>\n","      <td>1.792993</td>\n","      <td>-0.863291</td>\n","      <td>-0.010309</td>\n","      <td>1.247203</td>\n","      <td>0.237609</td>\n","      <td>0.377436</td>\n","      <td>-1.387024</td>\n","      <td>-0.054952</td>\n","      <td>-0.226487</td>\n","      <td>0.178228</td>\n","      <td>0.507757</td>\n","      <td>-0.287924</td>\n","      <td>-0.631418</td>\n","      <td>-1.059647</td>\n","      <td>-0.684093</td>\n","      <td>1.965775</td>\n","      <td>-1.232622</td>\n","      <td>-0.208038</td>\n","      <td>-0.108300</td>\n","      <td>0.005274</td>\n","      <td>-0.190321</td>\n","      <td>-1.175575</td>\n","      <td>0.647376</td>\n","      <td>-0.221929</td>\n","      <td>0.062723</td>\n","      <td>0.061458</td>\n","      <td>123.50</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2.0</td>\n","      <td>-1.158233</td>\n","      <td>0.877737</td>\n","      <td>1.548718</td>\n","      <td>0.403034</td>\n","      <td>-0.407193</td>\n","      <td>0.095921</td>\n","      <td>0.592941</td>\n","      <td>-0.270533</td>\n","      <td>0.817739</td>\n","      <td>0.753074</td>\n","      <td>-0.822843</td>\n","      <td>0.538196</td>\n","      <td>1.345852</td>\n","      <td>-1.119670</td>\n","      <td>0.175121</td>\n","      <td>-0.451449</td>\n","      <td>-0.237033</td>\n","      <td>-0.038195</td>\n","      <td>0.803487</td>\n","      <td>0.408542</td>\n","      <td>-0.009431</td>\n","      <td>0.798278</td>\n","      <td>-0.137458</td>\n","      <td>0.141267</td>\n","      <td>-0.206010</td>\n","      <td>0.502292</td>\n","      <td>0.219422</td>\n","      <td>0.215153</td>\n","      <td>69.99</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Time        V1        V2        V3  ...       V27       V28  Amount  Class\n","0   0.0 -1.359807 -0.072781  2.536347  ...  0.133558 -0.021053  149.62      0\n","1   0.0  1.191857  0.266151  0.166480  ... -0.008983  0.014724    2.69      0\n","2   1.0 -1.358354 -1.340163  1.773209  ... -0.055353 -0.059752  378.66      0\n","3   1.0 -0.966272 -0.185226  1.792993  ...  0.062723  0.061458  123.50      0\n","4   2.0 -1.158233  0.877737  1.548718  ...  0.219422  0.215153   69.99      0\n","\n","[5 rows x 31 columns]"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":323},"id":"GEiobod5TItq","executionInfo":{"status":"ok","timestamp":1620233546311,"user_tz":240,"elapsed":823,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"e4f817a3-afdd-4463-da1e-a95bf3f66e2f"},"source":["data.describe()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Time</th>\n","      <th>V1</th>\n","      <th>V2</th>\n","      <th>V3</th>\n","      <th>V4</th>\n","      <th>V5</th>\n","      <th>V6</th>\n","      <th>V7</th>\n","      <th>V8</th>\n","      <th>V9</th>\n","      <th>V10</th>\n","      <th>V11</th>\n","      <th>V12</th>\n","      <th>V13</th>\n","      <th>V14</th>\n","      <th>V15</th>\n","      <th>V16</th>\n","      <th>V17</th>\n","      <th>V18</th>\n","      <th>V19</th>\n","      <th>V20</th>\n","      <th>V21</th>\n","      <th>V22</th>\n","      <th>V23</th>\n","      <th>V24</th>\n","      <th>V25</th>\n","      <th>V26</th>\n","      <th>V27</th>\n","      <th>V28</th>\n","      <th>Amount</th>\n","      <th>Class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>284807.000000</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>284807.000000</td>\n","      <td>284807.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>94813.859575</td>\n","      <td>3.919560e-15</td>\n","      <td>5.688174e-16</td>\n","      <td>-8.769071e-15</td>\n","      <td>2.782312e-15</td>\n","      <td>-1.552563e-15</td>\n","      <td>2.010663e-15</td>\n","      <td>-1.694249e-15</td>\n","      <td>-1.927028e-16</td>\n","      <td>-3.137024e-15</td>\n","      <td>1.768627e-15</td>\n","      <td>9.170318e-16</td>\n","      <td>-1.810658e-15</td>\n","      <td>1.693438e-15</td>\n","      <td>1.479045e-15</td>\n","      <td>3.482336e-15</td>\n","      <td>1.392007e-15</td>\n","      <td>-7.528491e-16</td>\n","      <td>4.328772e-16</td>\n","      <td>9.049732e-16</td>\n","      <td>5.085503e-16</td>\n","      <td>1.537294e-16</td>\n","      <td>7.959909e-16</td>\n","      <td>5.367590e-16</td>\n","      <td>4.458112e-15</td>\n","      <td>1.453003e-15</td>\n","      <td>1.699104e-15</td>\n","      <td>-3.660161e-16</td>\n","      <td>-1.206049e-16</td>\n","      <td>88.349619</td>\n","      <td>0.001727</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>47488.145955</td>\n","      <td>1.958696e+00</td>\n","      <td>1.651309e+00</td>\n","      <td>1.516255e+00</td>\n","      <td>1.415869e+00</td>\n","      <td>1.380247e+00</td>\n","      <td>1.332271e+00</td>\n","      <td>1.237094e+00</td>\n","      <td>1.194353e+00</td>\n","      <td>1.098632e+00</td>\n","      <td>1.088850e+00</td>\n","      <td>1.020713e+00</td>\n","      <td>9.992014e-01</td>\n","      <td>9.952742e-01</td>\n","      <td>9.585956e-01</td>\n","      <td>9.153160e-01</td>\n","      <td>8.762529e-01</td>\n","      <td>8.493371e-01</td>\n","      <td>8.381762e-01</td>\n","      <td>8.140405e-01</td>\n","      <td>7.709250e-01</td>\n","      <td>7.345240e-01</td>\n","      <td>7.257016e-01</td>\n","      <td>6.244603e-01</td>\n","      <td>6.056471e-01</td>\n","      <td>5.212781e-01</td>\n","      <td>4.822270e-01</td>\n","      <td>4.036325e-01</td>\n","      <td>3.300833e-01</td>\n","      <td>250.120109</td>\n","      <td>0.041527</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.000000</td>\n","      <td>-5.640751e+01</td>\n","      <td>-7.271573e+01</td>\n","      <td>-4.832559e+01</td>\n","      <td>-5.683171e+00</td>\n","      <td>-1.137433e+02</td>\n","      <td>-2.616051e+01</td>\n","      <td>-4.355724e+01</td>\n","      <td>-7.321672e+01</td>\n","      <td>-1.343407e+01</td>\n","      <td>-2.458826e+01</td>\n","      <td>-4.797473e+00</td>\n","      <td>-1.868371e+01</td>\n","      <td>-5.791881e+00</td>\n","      <td>-1.921433e+01</td>\n","      <td>-4.498945e+00</td>\n","      <td>-1.412985e+01</td>\n","      <td>-2.516280e+01</td>\n","      <td>-9.498746e+00</td>\n","      <td>-7.213527e+00</td>\n","      <td>-5.449772e+01</td>\n","      <td>-3.483038e+01</td>\n","      <td>-1.093314e+01</td>\n","      <td>-4.480774e+01</td>\n","      <td>-2.836627e+00</td>\n","      <td>-1.029540e+01</td>\n","      <td>-2.604551e+00</td>\n","      <td>-2.256568e+01</td>\n","      <td>-1.543008e+01</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>54201.500000</td>\n","      <td>-9.203734e-01</td>\n","      <td>-5.985499e-01</td>\n","      <td>-8.903648e-01</td>\n","      <td>-8.486401e-01</td>\n","      <td>-6.915971e-01</td>\n","      <td>-7.682956e-01</td>\n","      <td>-5.540759e-01</td>\n","      <td>-2.086297e-01</td>\n","      <td>-6.430976e-01</td>\n","      <td>-5.354257e-01</td>\n","      <td>-7.624942e-01</td>\n","      <td>-4.055715e-01</td>\n","      <td>-6.485393e-01</td>\n","      <td>-4.255740e-01</td>\n","      <td>-5.828843e-01</td>\n","      <td>-4.680368e-01</td>\n","      <td>-4.837483e-01</td>\n","      <td>-4.988498e-01</td>\n","      <td>-4.562989e-01</td>\n","      <td>-2.117214e-01</td>\n","      <td>-2.283949e-01</td>\n","      <td>-5.423504e-01</td>\n","      <td>-1.618463e-01</td>\n","      <td>-3.545861e-01</td>\n","      <td>-3.171451e-01</td>\n","      <td>-3.269839e-01</td>\n","      <td>-7.083953e-02</td>\n","      <td>-5.295979e-02</td>\n","      <td>5.600000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>84692.000000</td>\n","      <td>1.810880e-02</td>\n","      <td>6.548556e-02</td>\n","      <td>1.798463e-01</td>\n","      <td>-1.984653e-02</td>\n","      <td>-5.433583e-02</td>\n","      <td>-2.741871e-01</td>\n","      <td>4.010308e-02</td>\n","      <td>2.235804e-02</td>\n","      <td>-5.142873e-02</td>\n","      <td>-9.291738e-02</td>\n","      <td>-3.275735e-02</td>\n","      <td>1.400326e-01</td>\n","      <td>-1.356806e-02</td>\n","      <td>5.060132e-02</td>\n","      <td>4.807155e-02</td>\n","      <td>6.641332e-02</td>\n","      <td>-6.567575e-02</td>\n","      <td>-3.636312e-03</td>\n","      <td>3.734823e-03</td>\n","      <td>-6.248109e-02</td>\n","      <td>-2.945017e-02</td>\n","      <td>6.781943e-03</td>\n","      <td>-1.119293e-02</td>\n","      <td>4.097606e-02</td>\n","      <td>1.659350e-02</td>\n","      <td>-5.213911e-02</td>\n","      <td>1.342146e-03</td>\n","      <td>1.124383e-02</td>\n","      <td>22.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>139320.500000</td>\n","      <td>1.315642e+00</td>\n","      <td>8.037239e-01</td>\n","      <td>1.027196e+00</td>\n","      <td>7.433413e-01</td>\n","      <td>6.119264e-01</td>\n","      <td>3.985649e-01</td>\n","      <td>5.704361e-01</td>\n","      <td>3.273459e-01</td>\n","      <td>5.971390e-01</td>\n","      <td>4.539234e-01</td>\n","      <td>7.395934e-01</td>\n","      <td>6.182380e-01</td>\n","      <td>6.625050e-01</td>\n","      <td>4.931498e-01</td>\n","      <td>6.488208e-01</td>\n","      <td>5.232963e-01</td>\n","      <td>3.996750e-01</td>\n","      <td>5.008067e-01</td>\n","      <td>4.589494e-01</td>\n","      <td>1.330408e-01</td>\n","      <td>1.863772e-01</td>\n","      <td>5.285536e-01</td>\n","      <td>1.476421e-01</td>\n","      <td>4.395266e-01</td>\n","      <td>3.507156e-01</td>\n","      <td>2.409522e-01</td>\n","      <td>9.104512e-02</td>\n","      <td>7.827995e-02</td>\n","      <td>77.165000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>172792.000000</td>\n","      <td>2.454930e+00</td>\n","      <td>2.205773e+01</td>\n","      <td>9.382558e+00</td>\n","      <td>1.687534e+01</td>\n","      <td>3.480167e+01</td>\n","      <td>7.330163e+01</td>\n","      <td>1.205895e+02</td>\n","      <td>2.000721e+01</td>\n","      <td>1.559499e+01</td>\n","      <td>2.374514e+01</td>\n","      <td>1.201891e+01</td>\n","      <td>7.848392e+00</td>\n","      <td>7.126883e+00</td>\n","      <td>1.052677e+01</td>\n","      <td>8.877742e+00</td>\n","      <td>1.731511e+01</td>\n","      <td>9.253526e+00</td>\n","      <td>5.041069e+00</td>\n","      <td>5.591971e+00</td>\n","      <td>3.942090e+01</td>\n","      <td>2.720284e+01</td>\n","      <td>1.050309e+01</td>\n","      <td>2.252841e+01</td>\n","      <td>4.584549e+00</td>\n","      <td>7.519589e+00</td>\n","      <td>3.517346e+00</td>\n","      <td>3.161220e+01</td>\n","      <td>3.384781e+01</td>\n","      <td>25691.160000</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                Time            V1  ...         Amount          Class\n","count  284807.000000  2.848070e+05  ...  284807.000000  284807.000000\n","mean    94813.859575  3.919560e-15  ...      88.349619       0.001727\n","std     47488.145955  1.958696e+00  ...     250.120109       0.041527\n","min         0.000000 -5.640751e+01  ...       0.000000       0.000000\n","25%     54201.500000 -9.203734e-01  ...       5.600000       0.000000\n","50%     84692.000000  1.810880e-02  ...      22.000000       0.000000\n","75%    139320.500000  1.315642e+00  ...      77.165000       0.000000\n","max    172792.000000  2.454930e+00  ...   25691.160000       1.000000\n","\n","[8 rows x 31 columns]"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"code","metadata":{"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"4_-v2donTItq","executionInfo":{"status":"ok","timestamp":1620233546311,"user_tz":240,"elapsed":634,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"60379449-ea92-492b-bfde-6dc5811c3809"},"source":["data.columns"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n","       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n","       'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount',\n","       'Class'],\n","      dtype='object')"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nL2_naZ3TItr","executionInfo":{"status":"ok","timestamp":1620233546312,"user_tz":240,"elapsed":418,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"1fe62c93-7d69-4a59-f57d-d650c2d6e842"},"source":["print(\"Number of fraudulent transactions:\", data['Class'].sum())"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Number of fraudulent transactions: 492\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"C-mEIi1VTIts"},"source":["nanCounter = np.isnan(data).sum()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"scrolled":false,"colab":{"base_uri":"https://localhost:8080/"},"id":"wXzdwyoYTIts","executionInfo":{"status":"ok","timestamp":1620233547693,"user_tz":240,"elapsed":359,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"05b859fe-cbc9-489a-e3b2-55b8e2a636d1"},"source":["nanCounter"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Time      0\n","V1        0\n","V2        0\n","V3        0\n","V4        0\n","V5        0\n","V6        0\n","V7        0\n","V8        0\n","V9        0\n","V10       0\n","V11       0\n","V12       0\n","V13       0\n","V14       0\n","V15       0\n","V16       0\n","V17       0\n","V18       0\n","V19       0\n","V20       0\n","V21       0\n","V22       0\n","V23       0\n","V24       0\n","V25       0\n","V26       0\n","V27       0\n","V28       0\n","Amount    0\n","Class     0\n","dtype: int64"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"code","metadata":{"id":"O1u6YShwTIts"},"source":["distinctCounter = data.apply(lambda x: len(x.unique()))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XAKDf308TItt","executionInfo":{"status":"ok","timestamp":1620233548232,"user_tz":240,"elapsed":568,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"21e6b427-9a98-4205-f583-d08063ebfbb9"},"source":["distinctCounter"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Time      124592\n","V1        275663\n","V2        275663\n","V3        275663\n","V4        275663\n","V5        275663\n","V6        275663\n","V7        275663\n","V8        275663\n","V9        275663\n","V10       275663\n","V11       275663\n","V12       275663\n","V13       275663\n","V14       275663\n","V15       275663\n","V16       275663\n","V17       275663\n","V18       275663\n","V19       275663\n","V20       275663\n","V21       275663\n","V22       275663\n","V23       275663\n","V24       275663\n","V25       275663\n","V26       275663\n","V27       275663\n","V28       275663\n","Amount     32767\n","Class          2\n","dtype: int64"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"code","metadata":{"id":"9PUM83lfTItt"},"source":["# Generate feature matrix and labels array\n","dataX = data.copy().drop(['Class'],axis=1)\n","dataY = data['Class'].copy()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"scrolled":false,"colab":{"base_uri":"https://localhost:8080/","height":323},"id":"V2c4fyrgTItt","executionInfo":{"status":"ok","timestamp":1620233548785,"user_tz":240,"elapsed":856,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"25e812f3-fbe2-4b34-90aa-8a97d3b7657c"},"source":["dataX.describe()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Time</th>\n","      <th>V1</th>\n","      <th>V2</th>\n","      <th>V3</th>\n","      <th>V4</th>\n","      <th>V5</th>\n","      <th>V6</th>\n","      <th>V7</th>\n","      <th>V8</th>\n","      <th>V9</th>\n","      <th>V10</th>\n","      <th>V11</th>\n","      <th>V12</th>\n","      <th>V13</th>\n","      <th>V14</th>\n","      <th>V15</th>\n","      <th>V16</th>\n","      <th>V17</th>\n","      <th>V18</th>\n","      <th>V19</th>\n","      <th>V20</th>\n","      <th>V21</th>\n","      <th>V22</th>\n","      <th>V23</th>\n","      <th>V24</th>\n","      <th>V25</th>\n","      <th>V26</th>\n","      <th>V27</th>\n","      <th>V28</th>\n","      <th>Amount</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>284807.000000</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>2.848070e+05</td>\n","      <td>284807.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>94813.859575</td>\n","      <td>3.919560e-15</td>\n","      <td>5.688174e-16</td>\n","      <td>-8.769071e-15</td>\n","      <td>2.782312e-15</td>\n","      <td>-1.552563e-15</td>\n","      <td>2.010663e-15</td>\n","      <td>-1.694249e-15</td>\n","      <td>-1.927028e-16</td>\n","      <td>-3.137024e-15</td>\n","      <td>1.768627e-15</td>\n","      <td>9.170318e-16</td>\n","      <td>-1.810658e-15</td>\n","      <td>1.693438e-15</td>\n","      <td>1.479045e-15</td>\n","      <td>3.482336e-15</td>\n","      <td>1.392007e-15</td>\n","      <td>-7.528491e-16</td>\n","      <td>4.328772e-16</td>\n","      <td>9.049732e-16</td>\n","      <td>5.085503e-16</td>\n","      <td>1.537294e-16</td>\n","      <td>7.959909e-16</td>\n","      <td>5.367590e-16</td>\n","      <td>4.458112e-15</td>\n","      <td>1.453003e-15</td>\n","      <td>1.699104e-15</td>\n","      <td>-3.660161e-16</td>\n","      <td>-1.206049e-16</td>\n","      <td>88.349619</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>47488.145955</td>\n","      <td>1.958696e+00</td>\n","      <td>1.651309e+00</td>\n","      <td>1.516255e+00</td>\n","      <td>1.415869e+00</td>\n","      <td>1.380247e+00</td>\n","      <td>1.332271e+00</td>\n","      <td>1.237094e+00</td>\n","      <td>1.194353e+00</td>\n","      <td>1.098632e+00</td>\n","      <td>1.088850e+00</td>\n","      <td>1.020713e+00</td>\n","      <td>9.992014e-01</td>\n","      <td>9.952742e-01</td>\n","      <td>9.585956e-01</td>\n","      <td>9.153160e-01</td>\n","      <td>8.762529e-01</td>\n","      <td>8.493371e-01</td>\n","      <td>8.381762e-01</td>\n","      <td>8.140405e-01</td>\n","      <td>7.709250e-01</td>\n","      <td>7.345240e-01</td>\n","      <td>7.257016e-01</td>\n","      <td>6.244603e-01</td>\n","      <td>6.056471e-01</td>\n","      <td>5.212781e-01</td>\n","      <td>4.822270e-01</td>\n","      <td>4.036325e-01</td>\n","      <td>3.300833e-01</td>\n","      <td>250.120109</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.000000</td>\n","      <td>-5.640751e+01</td>\n","      <td>-7.271573e+01</td>\n","      <td>-4.832559e+01</td>\n","      <td>-5.683171e+00</td>\n","      <td>-1.137433e+02</td>\n","      <td>-2.616051e+01</td>\n","      <td>-4.355724e+01</td>\n","      <td>-7.321672e+01</td>\n","      <td>-1.343407e+01</td>\n","      <td>-2.458826e+01</td>\n","      <td>-4.797473e+00</td>\n","      <td>-1.868371e+01</td>\n","      <td>-5.791881e+00</td>\n","      <td>-1.921433e+01</td>\n","      <td>-4.498945e+00</td>\n","      <td>-1.412985e+01</td>\n","      <td>-2.516280e+01</td>\n","      <td>-9.498746e+00</td>\n","      <td>-7.213527e+00</td>\n","      <td>-5.449772e+01</td>\n","      <td>-3.483038e+01</td>\n","      <td>-1.093314e+01</td>\n","      <td>-4.480774e+01</td>\n","      <td>-2.836627e+00</td>\n","      <td>-1.029540e+01</td>\n","      <td>-2.604551e+00</td>\n","      <td>-2.256568e+01</td>\n","      <td>-1.543008e+01</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>54201.500000</td>\n","      <td>-9.203734e-01</td>\n","      <td>-5.985499e-01</td>\n","      <td>-8.903648e-01</td>\n","      <td>-8.486401e-01</td>\n","      <td>-6.915971e-01</td>\n","      <td>-7.682956e-01</td>\n","      <td>-5.540759e-01</td>\n","      <td>-2.086297e-01</td>\n","      <td>-6.430976e-01</td>\n","      <td>-5.354257e-01</td>\n","      <td>-7.624942e-01</td>\n","      <td>-4.055715e-01</td>\n","      <td>-6.485393e-01</td>\n","      <td>-4.255740e-01</td>\n","      <td>-5.828843e-01</td>\n","      <td>-4.680368e-01</td>\n","      <td>-4.837483e-01</td>\n","      <td>-4.988498e-01</td>\n","      <td>-4.562989e-01</td>\n","      <td>-2.117214e-01</td>\n","      <td>-2.283949e-01</td>\n","      <td>-5.423504e-01</td>\n","      <td>-1.618463e-01</td>\n","      <td>-3.545861e-01</td>\n","      <td>-3.171451e-01</td>\n","      <td>-3.269839e-01</td>\n","      <td>-7.083953e-02</td>\n","      <td>-5.295979e-02</td>\n","      <td>5.600000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>84692.000000</td>\n","      <td>1.810880e-02</td>\n","      <td>6.548556e-02</td>\n","      <td>1.798463e-01</td>\n","      <td>-1.984653e-02</td>\n","      <td>-5.433583e-02</td>\n","      <td>-2.741871e-01</td>\n","      <td>4.010308e-02</td>\n","      <td>2.235804e-02</td>\n","      <td>-5.142873e-02</td>\n","      <td>-9.291738e-02</td>\n","      <td>-3.275735e-02</td>\n","      <td>1.400326e-01</td>\n","      <td>-1.356806e-02</td>\n","      <td>5.060132e-02</td>\n","      <td>4.807155e-02</td>\n","      <td>6.641332e-02</td>\n","      <td>-6.567575e-02</td>\n","      <td>-3.636312e-03</td>\n","      <td>3.734823e-03</td>\n","      <td>-6.248109e-02</td>\n","      <td>-2.945017e-02</td>\n","      <td>6.781943e-03</td>\n","      <td>-1.119293e-02</td>\n","      <td>4.097606e-02</td>\n","      <td>1.659350e-02</td>\n","      <td>-5.213911e-02</td>\n","      <td>1.342146e-03</td>\n","      <td>1.124383e-02</td>\n","      <td>22.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>139320.500000</td>\n","      <td>1.315642e+00</td>\n","      <td>8.037239e-01</td>\n","      <td>1.027196e+00</td>\n","      <td>7.433413e-01</td>\n","      <td>6.119264e-01</td>\n","      <td>3.985649e-01</td>\n","      <td>5.704361e-01</td>\n","      <td>3.273459e-01</td>\n","      <td>5.971390e-01</td>\n","      <td>4.539234e-01</td>\n","      <td>7.395934e-01</td>\n","      <td>6.182380e-01</td>\n","      <td>6.625050e-01</td>\n","      <td>4.931498e-01</td>\n","      <td>6.488208e-01</td>\n","      <td>5.232963e-01</td>\n","      <td>3.996750e-01</td>\n","      <td>5.008067e-01</td>\n","      <td>4.589494e-01</td>\n","      <td>1.330408e-01</td>\n","      <td>1.863772e-01</td>\n","      <td>5.285536e-01</td>\n","      <td>1.476421e-01</td>\n","      <td>4.395266e-01</td>\n","      <td>3.507156e-01</td>\n","      <td>2.409522e-01</td>\n","      <td>9.104512e-02</td>\n","      <td>7.827995e-02</td>\n","      <td>77.165000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>172792.000000</td>\n","      <td>2.454930e+00</td>\n","      <td>2.205773e+01</td>\n","      <td>9.382558e+00</td>\n","      <td>1.687534e+01</td>\n","      <td>3.480167e+01</td>\n","      <td>7.330163e+01</td>\n","      <td>1.205895e+02</td>\n","      <td>2.000721e+01</td>\n","      <td>1.559499e+01</td>\n","      <td>2.374514e+01</td>\n","      <td>1.201891e+01</td>\n","      <td>7.848392e+00</td>\n","      <td>7.126883e+00</td>\n","      <td>1.052677e+01</td>\n","      <td>8.877742e+00</td>\n","      <td>1.731511e+01</td>\n","      <td>9.253526e+00</td>\n","      <td>5.041069e+00</td>\n","      <td>5.591971e+00</td>\n","      <td>3.942090e+01</td>\n","      <td>2.720284e+01</td>\n","      <td>1.050309e+01</td>\n","      <td>2.252841e+01</td>\n","      <td>4.584549e+00</td>\n","      <td>7.519589e+00</td>\n","      <td>3.517346e+00</td>\n","      <td>3.161220e+01</td>\n","      <td>3.384781e+01</td>\n","      <td>25691.160000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                Time            V1  ...           V28         Amount\n","count  284807.000000  2.848070e+05  ...  2.848070e+05  284807.000000\n","mean    94813.859575  3.919560e-15  ... -1.206049e-16      88.349619\n","std     47488.145955  1.958696e+00  ...  3.300833e-01     250.120109\n","min         0.000000 -5.640751e+01  ... -1.543008e+01       0.000000\n","25%     54201.500000 -9.203734e-01  ... -5.295979e-02       5.600000\n","50%     84692.000000  1.810880e-02  ...  1.124383e-02      22.000000\n","75%    139320.500000  1.315642e+00  ...  7.827995e-02      77.165000\n","max    172792.000000  2.454930e+00  ...  3.384781e+01   25691.160000\n","\n","[8 rows x 30 columns]"]},"metadata":{"tags":[]},"execution_count":15}]},{"cell_type":"code","metadata":{"id":"2DvHeK3CTItu"},"source":["correlationMatrix = pd.DataFrame(data=[],index=dataX.columns,columns=dataX.columns)\n","for i in dataX.columns:\n","    for j in dataX.columns:\n","        correlationMatrix.loc[i,j] = np.round(pearsonr(dataX.loc[:,i],dataX.loc[:,j])[0],2)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"1ufgZxWlTItu"},"source":["correlation_file = os.path.sep.join(['', 'datasets', 'credit_card_data', 'correlationMatrix.csv'])\n","correlationMatrix.to_csv(current_path+correlation_file)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":312},"id":"iTRIcLwPTItu","executionInfo":{"status":"ok","timestamp":1620233555104,"user_tz":240,"elapsed":6752,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"e85703c0-5832-4435-f761-8f69582d7607"},"source":["count_classes = pd.value_counts(data['Class'],sort=True).sort_index()\n","ax = sns.barplot(x=count_classes.index, y=[tuple(count_classes/len(data))[0],tuple(count_classes/len(data))[1]])\n","ax.set_title('Frequency Percentage by Class')\n","ax.set_xlabel('Class')\n","ax.set_ylabel('Frequency Percentage')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0, 0.5, 'Frequency Percentage')"]},"metadata":{"tags":[]},"execution_count":18},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"collapsed":true,"id":"nrNRnb6-TItv"},"source":["# Model Preparation"]},{"cell_type":"code","metadata":{"id":"kmvUrdcQTItv"},"source":["X_train, X_test, y_train, y_test = train_test_split(dataX, \n","                                    dataY, test_size=0.33, \n","                                    random_state=2018, stratify=dataY)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Sy9hhtzSTItv","executionInfo":{"status":"ok","timestamp":1620233555309,"user_tz":240,"elapsed":6554,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"8ed2b6af-d4e7-471b-96e8-3cb0f4a9b9b8"},"source":["len(X_train)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["190820"]},"metadata":{"tags":[]},"execution_count":20}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"w2c-t0jcTItv","executionInfo":{"status":"ok","timestamp":1620233555309,"user_tz":240,"elapsed":6419,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"9f8e5dae-af80-4af3-9de0-1231a554b5b9"},"source":["len(X_test)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["93987"]},"metadata":{"tags":[]},"execution_count":21}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JyGyMORCTItw","executionInfo":{"status":"ok","timestamp":1620233555310,"user_tz":240,"elapsed":6293,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"1ff156dc-8e8f-4e24-beb3-522e1cd66b15"},"source":["y_train.sum()/len(y_train)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.0017293784718582959"]},"metadata":{"tags":[]},"execution_count":22}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HvmqvAcVTItw","executionInfo":{"status":"ok","timestamp":1620233555452,"user_tz":240,"elapsed":6021,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"8ff01637-13d7-46ed-f5e7-373e3f1dcc2b"},"source":["y_test.sum()/len(y_test)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.0017236426314277506"]},"metadata":{"tags":[]},"execution_count":23}]},{"cell_type":"code","metadata":{"id":"7S4iygapTItw"},"source":["# Feature scaling\n","featuresToScale = dataX.drop(['Time'],axis=1).columns"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qzfMKPrYTItw","executionInfo":{"status":"ok","timestamp":1620233555453,"user_tz":240,"elapsed":5687,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"60124654-a462-4d17-81ef-cd2bf11edf1e"},"source":["featuresToScale"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Index(['V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11',\n","       'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21',\n","       'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount'],\n","      dtype='object')"]},"metadata":{"tags":[]},"execution_count":25}]},{"cell_type":"code","metadata":{"id":"vlXSlwC4TItw"},"source":["# Suppress warnings\n","pd.set_option('mode.chained_assignment', None)\n","\n","sX = pp.StandardScaler(copy=True)\n","X_train.loc[:,featuresToScale] = sX.fit_transform(X_train.loc[:,featuresToScale])\n","scalingFactors = pd.DataFrame(data=[sX.mean_,sX.scale_],index=['Mean','StDev'],columns=featuresToScale)\n","\n","X_test.loc[:,featuresToScale] = sX.transform(X_test.loc[:,featuresToScale])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":133},"id":"L-hGyZKtTItx","executionInfo":{"status":"ok","timestamp":1620233555786,"user_tz":240,"elapsed":5752,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"f3f648b0-6756-4181-a248-89b03e5a3256"},"source":["scalingFactors"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>V1</th>\n","      <th>V2</th>\n","      <th>V3</th>\n","      <th>V4</th>\n","      <th>V5</th>\n","      <th>V6</th>\n","      <th>V7</th>\n","      <th>V8</th>\n","      <th>V9</th>\n","      <th>V10</th>\n","      <th>V11</th>\n","      <th>V12</th>\n","      <th>V13</th>\n","      <th>V14</th>\n","      <th>V15</th>\n","      <th>V16</th>\n","      <th>V17</th>\n","      <th>V18</th>\n","      <th>V19</th>\n","      <th>V20</th>\n","      <th>V21</th>\n","      <th>V22</th>\n","      <th>V23</th>\n","      <th>V24</th>\n","      <th>V25</th>\n","      <th>V26</th>\n","      <th>V27</th>\n","      <th>V28</th>\n","      <th>Amount</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Mean</th>\n","      <td>0.000169</td>\n","      <td>-0.001847</td>\n","      <td>-0.002083</td>\n","      <td>0.000909</td>\n","      <td>0.002961</td>\n","      <td>0.002817</td>\n","      <td>-0.000048</td>\n","      <td>-0.002589</td>\n","      <td>-0.000516</td>\n","      <td>0.000787</td>\n","      <td>0.000403</td>\n","      <td>-0.001410</td>\n","      <td>-0.000012</td>\n","      <td>0.000702</td>\n","      <td>0.000038</td>\n","      <td>0.000596</td>\n","      <td>0.001182</td>\n","      <td>0.002464</td>\n","      <td>0.000574</td>\n","      <td>-0.000925</td>\n","      <td>0.001924</td>\n","      <td>-0.000187</td>\n","      <td>0.001501</td>\n","      <td>-0.000785</td>\n","      <td>0.000352</td>\n","      <td>-0.000369</td>\n","      <td>0.000434</td>\n","      <td>0.000083</td>\n","      <td>88.324359</td>\n","    </tr>\n","    <tr>\n","      <th>StDev</th>\n","      <td>1.965201</td>\n","      <td>1.656922</td>\n","      <td>1.513847</td>\n","      <td>1.414958</td>\n","      <td>1.385615</td>\n","      <td>1.337316</td>\n","      <td>1.244564</td>\n","      <td>1.216633</td>\n","      <td>1.097482</td>\n","      <td>1.085226</td>\n","      <td>1.019253</td>\n","      <td>0.994948</td>\n","      <td>0.995768</td>\n","      <td>0.955115</td>\n","      <td>0.916130</td>\n","      <td>0.874185</td>\n","      <td>0.842864</td>\n","      <td>0.836401</td>\n","      <td>0.815053</td>\n","      <td>0.769595</td>\n","      <td>0.752358</td>\n","      <td>0.727151</td>\n","      <td>0.626533</td>\n","      <td>0.606504</td>\n","      <td>0.522020</td>\n","      <td>0.481376</td>\n","      <td>0.402544</td>\n","      <td>0.340144</td>\n","      <td>246.692699</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["             V1        V2        V3  ...       V27       V28      Amount\n","Mean   0.000169 -0.001847 -0.002083  ...  0.000434  0.000083   88.324359\n","StDev  1.965201  1.656922  1.513847  ...  0.402544  0.340144  246.692699\n","\n","[2 rows x 29 columns]"]},"metadata":{"tags":[]},"execution_count":27}]},{"cell_type":"code","metadata":{"id":"jNb6bkUXTItx"},"source":["k_fold = StratifiedKFold(n_splits=5,shuffle=True,random_state=2018)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"collapsed":true,"id":"0oPwBcTVTItx"},"source":["# Logistic Regression"]},{"cell_type":"code","metadata":{"id":"i9hcp7KqTItx"},"source":["penalty = 'l2'\n","C = 1.0\n","class_weight = 'balanced'\n","random_state = 2018\n","solver = 'liblinear'\n","n_jobs = 1\n","\n","logReg = LogisticRegression(penalty=penalty, C=C, \n","            class_weight=class_weight, random_state=random_state, \n","                            solver=solver, n_jobs=n_jobs)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_LPCSXZCTIty","executionInfo":{"status":"ok","timestamp":1620233583441,"user_tz":240,"elapsed":32729,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"3156a22c-f8e6-44bc-c5f8-e4189feb0f48"},"source":["trainingScores = []\n","cvScores = []\n","predictionsBasedOnKFolds = pd.DataFrame(data=[],\n","                                        index=y_train.index,columns=[0,1])\n","\n","model = logReg\n","\n","for train_index, cv_index in k_fold.split(np.zeros(len(X_train))\n","                                          ,y_train.ravel()):\n","    X_train_fold, X_cv_fold = X_train.iloc[train_index,:], \\\n","        X_train.iloc[cv_index,:]\n","    y_train_fold, y_cv_fold = y_train.iloc[train_index], \\\n","        y_train.iloc[cv_index]\n","    \n","    model.fit(X_train_fold, y_train_fold)\n","    loglossTraining = log_loss(y_train_fold, \n","                               model.predict_proba(X_train_fold)[:,1])\n","    trainingScores.append(loglossTraining)\n","    \n","    predictionsBasedOnKFolds.loc[X_cv_fold.index,:] = \\\n","        model.predict_proba(X_cv_fold)  \n","    loglossCV = log_loss(y_cv_fold, \n","                         predictionsBasedOnKFolds.loc[X_cv_fold.index,1])\n","    cvScores.append(loglossCV)\n","    \n","    print('Training Log Loss: ', loglossTraining)\n","    print('CV Log Loss: ', loglossCV)\n","    \n","loglossLogisticRegression = log_loss(y_train, \n","                                     predictionsBasedOnKFolds.loc[:,1])\n","print('Logistic Regression Log Loss: ', loglossLogisticRegression)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Training Log Loss:  0.10951353746607272\n","CV Log Loss:  0.10867783336234717\n","Training Log Loss:  0.10460227416593064\n","CV Log Loss:  0.10407451870069642\n","Training Log Loss:  0.1155100269830853\n","CV Log Loss:  0.11787700644717178\n","Training Log Loss:  0.11552991927823339\n","CV Log Loss:  0.11812642817093104\n","Training Log Loss:  0.09713987415636184\n","CV Log Loss:  0.09707857057711211\n","Logistic Regression Log Loss:  0.10916687145165169\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Xox-gEMZTIty"},"source":["preds = pd.concat([y_train,predictionsBasedOnKFolds.loc[:,1]], axis=1)\n","preds.columns = ['trueLabel','prediction']\n","predictionsBasedOnKFoldsLogisticRegression = preds.copy()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"RFvoXHzJTIty"},"source":["precision, recall, thresholds = precision_recall_curve(preds['trueLabel'],\n","                                                       preds['prediction'])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ce2rwVETTItz"},"source":["average_precision = average_precision_score(preds['trueLabel'],\n","                                            preds['prediction'])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":312},"id":"ZZPcrDwXTItz","executionInfo":{"status":"ok","timestamp":1620233584514,"user_tz":240,"elapsed":33227,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"3867246f-8a8e-4e0d-8db9-afe49aa53452"},"source":["plt.step(recall, precision, color='k', alpha=0.7, where='post')\n","plt.fill_between(recall, precision, step='post', alpha=0.3, color='k')\n","\n","plt.xlabel('Recall')\n","plt.ylabel('Precision')\n","plt.ylim([0.0, 1.05])\n","plt.xlim([0.0, 1.0])\n","\n","plt.title('Precision-Recall curve: Average Precision = {0:0.2f}'.format(\n","          average_precision))"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'Precision-Recall curve: Average Precision = 0.74')"]},"metadata":{"tags":[]},"execution_count":34},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"PCvcN2raTItz"},"source":["fpr, tpr, thresholds = roc_curve(preds['trueLabel'],preds['prediction'])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"WllFImwrTItz"},"source":["areaUnderROC = auc(fpr, tpr)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"S9oJE81iTIt0","executionInfo":{"status":"ok","timestamp":1620233585042,"user_tz":240,"elapsed":33317,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"79d2b14f-f050-4566-8d3b-cec523a1b906"},"source":["plt.figure()\n","plt.plot(fpr, tpr, color='r', lw=2, label='ROC curve')\n","plt.plot([0, 1], [0, 1], color='k', lw=2, linestyle='--')\n","plt.xlim([0.0, 1.0])\n","plt.ylim([0.0, 1.05])\n","plt.xlabel('False Positive Rate')\n","plt.ylabel('True Positive Rate')\n","plt.title('Receiver operating characteristic: \\\n","          Area under the curve = {0:0.2f}'.format(areaUnderROC))\n","plt.legend(loc=\"lower right\")\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"oIWu8k4fTIt0"},"source":["# Random Forests"]},{"cell_type":"code","metadata":{"id":"2lvXCdulTIt0"},"source":["n_estimators = 10\n","max_features = 'auto'\n","max_depth = None\n","min_samples_split = 2\n","min_samples_leaf = 1\n","min_weight_fraction_leaf = 0.0\n","max_leaf_nodes = None\n","bootstrap = True\n","oob_score = False\n","n_jobs = -1\n","random_state = 2018\n","class_weight = 'balanced'\n","\n","RFC = RandomForestClassifier(n_estimators=n_estimators, \n","        max_features=max_features, max_depth=max_depth,\n","        min_samples_split=min_samples_split, min_samples_leaf=min_samples_leaf,\n","        min_weight_fraction_leaf=min_weight_fraction_leaf, \n","        max_leaf_nodes=max_leaf_nodes, bootstrap=bootstrap, \n","        oob_score=oob_score, n_jobs=n_jobs, random_state=random_state, \n","        class_weight=class_weight)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZOU8_GD2TIt0","executionInfo":{"status":"ok","timestamp":1620233614834,"user_tz":240,"elapsed":62602,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"5f588960-58df-4187-ab19-6213f092be7c"},"source":["trainingScores = []\n","cvScores = []\n","predictionsBasedOnKFolds = pd.DataFrame(data=[],\n","                                        index=y_train.index,columns=[0,1])\n","\n","model = RFC\n","\n","for train_index, cv_index in k_fold.split(np.zeros(len(X_train)),\n","                                          y_train.ravel()):\n","    X_train_fold, X_cv_fold = X_train.iloc[train_index,:], \\\n","        X_train.iloc[cv_index,:]\n","    y_train_fold, y_cv_fold = y_train.iloc[train_index], \\\n","        y_train.iloc[cv_index]\n","    \n","    model.fit(X_train_fold, y_train_fold)\n","    loglossTraining = log_loss(y_train_fold, \\\n","                                model.predict_proba(X_train_fold)[:,1])\n","    trainingScores.append(loglossTraining)\n","    \n","    predictionsBasedOnKFolds.loc[X_cv_fold.index,:] = \\\n","        model.predict_proba(X_cv_fold)  \n","    loglossCV = log_loss(y_cv_fold, \\\n","        predictionsBasedOnKFolds.loc[X_cv_fold.index,1])\n","    cvScores.append(loglossCV)\n","    \n","    print('Training Log Loss: ', loglossTraining)\n","    print('CV Log Loss: ', loglossCV)\n","    \n","loglossRandomForestsClassifier = log_loss(y_train, \n","                                          predictionsBasedOnKFolds.loc[:,1])\n","print('Random Forests Log Loss: ', loglossRandomForestsClassifier)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Training Log Loss:  0.0004717456539906082\n","CV Log Loss:  0.009958407581679842\n","Training Log Loss:  0.00039391696555908875\n","CV Log Loss:  0.012614958898596576\n","Training Log Loss:  0.00039619886266122875\n","CV Log Loss:  0.008321682857781797\n","Training Log Loss:  0.0004174848852259459\n","CV Log Loss:  0.009209166975528824\n","Training Log Loss:  0.00042710655140527107\n","CV Log Loss:  0.014292100977712807\n","Random Forests Log Loss:  0.010879263458259968\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":572},"id":"npXdbBNATIt1","executionInfo":{"status":"ok","timestamp":1620233615678,"user_tz":240,"elapsed":63293,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"fb337c3f-86f0-4f9d-de63-d0e9775b3880"},"source":["preds = pd.concat([y_train,predictionsBasedOnKFolds.loc[:,1]], axis=1)\n","preds.columns = ['trueLabel','prediction']\n","predictionsBasedOnKFoldsRandomForests = preds.copy()\n","\n","precision, recall, thresholds = precision_recall_curve(preds['trueLabel'],\n","                                                       preds['prediction'])\n","average_precision = average_precision_score(preds['trueLabel'],\n","                                            preds['prediction'])\n","\n","plt.step(recall, precision, color='k', alpha=0.7, where='post')\n","plt.fill_between(recall, precision, step='post', alpha=0.3, color='k')\n","\n","plt.xlabel('Recall')\n","plt.ylabel('Precision')\n","plt.ylim([0.0, 1.05])\n","plt.xlim([0.0, 1.0])\n","\n","plt.title('Precision-Recall curve: Average Precision = {0:0.2f}'.format(\n","          average_precision))\n","\n","fpr, tpr, thresholds = roc_curve(preds['trueLabel'],preds['prediction'])\n","areaUnderROC = auc(fpr, tpr)\n","\n","plt.figure()\n","plt.plot(fpr, tpr, color='r', lw=2, label='ROC curve')\n","plt.plot([0, 1], [0, 1], color='k', lw=2, linestyle='--')\n","plt.xlim([0.0, 1.0])\n","plt.ylim([0.0, 1.05])\n","plt.xlabel('False Positive Rate')\n","plt.ylabel('True Positive Rate')\n","plt.title('Receiver operating characteristic: \\\n","          Area under the curve = {0:0.2f}'.format(\n","          areaUnderROC))\n","plt.legend(loc=\"lower right\")\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"-Oqu_2_-TIt1"},"source":["# XGBoost\n","\n","To improve the user experience, I have commented out code blocks related to XGBoost. To uncomment the code blocks, highlight the code and use the \"CTRL\" + \"/\" key combination."]},{"cell_type":"code","metadata":{"id":"d-cWOP0OTIt1"},"source":["# params_xGB = {\n","#     'nthread':16, #number of cores\n","#     'learning rate': 0.3, #range 0 to 1, default 0.3\n","#     'gamma': 0, #range 0 to infinity, default 0 \n","#         # increase to reduce complexity (increase bias, reduce variance)\n","#     'max_depth': 6, #range 1 to infinity, default 6\n","#     'min_child_weight': 1, #range 0 to infinity, default 1\n","#     'max_delta_step': 0, #range 0 to infinity, default 0\n","#     'subsample': 1.0, #range 0 to 1, default 1\n","#         # subsample ratio of the training examples\n","#     'colsample_bytree': 1.0, #range 0 to 1, default 1 \n","#         # subsample ratio of features\n","#     'objective':'binary:logistic',\n","#     'num_class':1,\n","#     'eval_metric':'logloss',\n","#     'seed':2018,\n","#     'silent':1\n","# }"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"xylVyyVaTIt1"},"source":["# trainingScores = []\n","# cvScores = []\n","# predictionsBasedOnKFolds = pd.DataFrame(data=[],\n","#                                     index=y_train.index,columns=['prediction'])\n","\n","# for train_index, cv_index in k_fold.split(np.zeros(len(X_train)),\n","#                                           y_train.ravel()):\n","#     X_train_fold, X_cv_fold = X_train.iloc[train_index,:], \\\n","#         X_train.iloc[cv_index,:]\n","#     y_train_fold, y_cv_fold = y_train.iloc[train_index], \\\n","#         y_train.iloc[cv_index]\n","    \n","#     dtrain = xgb.DMatrix(data=X_train_fold, label=y_train_fold)\n","#     dCV = xgb.DMatrix(data=X_cv_fold)\n","    \n","#     bst = xgb.cv(params_xGB, dtrain, num_boost_round=2000, \n","#                  nfold=5, early_stopping_rounds=200, verbose_eval=50)\n","    \n","#     best_rounds = np.argmin(bst['test-logloss-mean'])\n","#     bst = xgb.train(params_xGB, dtrain, best_rounds)\n","    \n","#     loglossTraining = log_loss(y_train_fold, bst.predict(dtrain))\n","#     trainingScores.append(loglossTraining)\n","    \n","#     predictionsBasedOnKFolds.loc[X_cv_fold.index,'prediction'] = \\\n","#         bst.predict(dCV)\n","#     loglossCV = log_loss(y_cv_fold, \\\n","#         predictionsBasedOnKFolds.loc[X_cv_fold.index,'prediction'])\n","#     cvScores.append(loglossCV)\n","    \n","#     print('Training Log Loss: ', loglossTraining)\n","#     print('CV Log Loss: ', loglossCV)\n","    \n","# loglossXGBoostGradientBoosting = \\\n","#     log_loss(y_train, predictionsBasedOnKFolds.loc[:,'prediction'])\n","# print('XGBoost Gradient Boosting Log Loss: ', loglossXGBoostGradientBoosting)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"BKSb8fqGTIt2"},"source":["# preds = pd.concat([y_train,predictionsBasedOnKFolds.loc[:,'prediction']], axis=1)\n","# preds.columns = ['trueLabel','prediction']\n","# predictionsBasedOnKFoldsXGBoostGradientBoosting = preds.copy()\n","\n","# precision, recall, thresholds = \\\n","#     precision_recall_curve(preds['trueLabel'],preds['prediction'])\n","# average_precision = \\\n","#     average_precision_score(preds['trueLabel'],preds['prediction'])\n","\n","# plt.step(recall, precision, color='k', alpha=0.7, where='post')\n","# plt.fill_between(recall, precision, step='post', alpha=0.3, color='k')\n","\n","# plt.xlabel('Recall')\n","# plt.ylabel('Precision')\n","# plt.ylim([0.0, 1.05])\n","# plt.xlim([0.0, 1.0])\n","\n","# plt.title('Precision-Recall curve: Average Precision = {0:0.2f}'.format(\n","#           average_precision))\n","\n","# fpr, tpr, thresholds = roc_curve(preds['trueLabel'],preds['prediction'])\n","# areaUnderROC = auc(fpr, tpr)\n","\n","# plt.figure()\n","# plt.plot(fpr, tpr, color='r', lw=2, label='ROC curve')\n","# plt.plot([0, 1], [0, 1], color='k', lw=2, linestyle='--')\n","# plt.xlim([0.0, 1.0])\n","# plt.ylim([0.0, 1.05])\n","# plt.xlabel('False Positive Rate')\n","# plt.ylabel('True Positive Rate')\n","# plt.title('Receiver operating characteristic: \\\n","#         Area under the curve = {0:0.2f}'.format(areaUnderROC))\n","# plt.legend(loc=\"lower right\")\n","# plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"qy3f4QOOTIt2"},"source":["# LightGBM"]},{"cell_type":"code","metadata":{"id":"Am6NErSjTIt2"},"source":["params_lightGB = {\n","    'task': 'train',\n","    'num_class':1,\n","    'boosting': 'gbdt',\n","    'objective': 'binary',\n","    'metric': 'binary_logloss',\n","    'metric_freq':50,\n","    'is_training_metric':False,\n","    'max_depth':4,\n","    'num_leaves': 31,\n","    'learning_rate': 0.01,\n","    'feature_fraction': 1.0,\n","    'bagging_fraction': 1.0,\n","    'bagging_freq': 0,\n","    'bagging_seed': 2018,\n","    'verbose': -1,\n","    'num_threads':16\n","}"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LrKEYNIOTIt2","executionInfo":{"status":"ok","timestamp":1620233773755,"user_tz":240,"elapsed":220235,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"13fa19af-9a79-4159-e9ea-5b835e37fb04"},"source":["trainingScores = []\n","cvScores = []\n","predictionsBasedOnKFolds = pd.DataFrame(data=[],\n","                                index=y_train.index,columns=['prediction'])\n","\n","for train_index, cv_index in k_fold.split(np.zeros(len(X_train)),\n","                                          y_train.ravel()):\n","    X_train_fold, X_cv_fold = X_train.iloc[train_index,:], \\\n","        X_train.iloc[cv_index,:]\n","    y_train_fold, y_cv_fold = y_train.iloc[train_index], \\\n","        y_train.iloc[cv_index]\n","    \n","    lgb_train = lgb.Dataset(X_train_fold, y_train_fold)\n","    lgb_eval = lgb.Dataset(X_cv_fold, y_cv_fold, reference=lgb_train)\n","    gbm = lgb.train(params_lightGB, lgb_train, num_boost_round=2000,\n","                   valid_sets=lgb_eval, early_stopping_rounds=200)\n","    \n","    loglossTraining = log_loss(y_train_fold, \\\n","                gbm.predict(X_train_fold, num_iteration=gbm.best_iteration))\n","    trainingScores.append(loglossTraining)\n","    \n","    predictionsBasedOnKFolds.loc[X_cv_fold.index,'prediction'] = \\\n","        gbm.predict(X_cv_fold, num_iteration=gbm.best_iteration) \n","    loglossCV = log_loss(y_cv_fold, \\\n","        predictionsBasedOnKFolds.loc[X_cv_fold.index,'prediction'])\n","    cvScores.append(loglossCV)\n","    \n","    print('Training Log Loss: ', loglossTraining)\n","    print('CV Log Loss: ', loglossCV)\n","    \n","loglossLightGBMGradientBoosting = \\\n","    log_loss(y_train, predictionsBasedOnKFolds.loc[:,'prediction'])\n","print('LightGBM Gradient Boosting Log Loss: ', loglossLightGBMGradientBoosting)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[1]\tvalid_0's binary_logloss: 0.00626392\n","Training until validation scores don't improve for 200 rounds.\n","[2]\tvalid_0's binary_logloss: 0.00619722\n","[3]\tvalid_0's binary_logloss: 0.00612709\n","[4]\tvalid_0's binary_logloss: 0.00607428\n","[5]\tvalid_0's binary_logloss: 0.0060139\n","[6]\tvalid_0's binary_logloss: 0.005965\n","[7]\tvalid_0's binary_logloss: 0.00591106\n","[8]\tvalid_0's binary_logloss: 0.00585687\n","[9]\tvalid_0's binary_logloss: 0.00580189\n","[10]\tvalid_0's binary_logloss: 0.00575339\n","[11]\tvalid_0's binary_logloss: 0.00570291\n","[12]\tvalid_0's binary_logloss: 0.00565458\n","[13]\tvalid_0's binary_logloss: 0.00560416\n","[14]\tvalid_0's binary_logloss: 0.00555768\n","[15]\tvalid_0's binary_logloss: 0.00551172\n","[16]\tvalid_0's binary_logloss: 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0.0007675505867374159\n","CV Log Loss:  0.002721999440091275\n","[1]\tvalid_0's binary_logloss: 0.00656941\n","Training until validation scores don't improve for 200 rounds.\n","[2]\tvalid_0's binary_logloss: 0.00649395\n","[3]\tvalid_0's binary_logloss: 0.00642411\n","[4]\tvalid_0's binary_logloss: 0.0063566\n","[5]\tvalid_0's binary_logloss: 0.0063065\n","[6]\tvalid_0's binary_logloss: 0.0062637\n","[7]\tvalid_0's binary_logloss: 0.00621532\n","[8]\tvalid_0's binary_logloss: 0.00617728\n","[9]\tvalid_0's binary_logloss: 0.00613585\n","[10]\tvalid_0's binary_logloss: 0.00609336\n","[11]\tvalid_0's binary_logloss: 0.00605426\n","[12]\tvalid_0's binary_logloss: 0.00601431\n","[13]\tvalid_0's binary_logloss: 0.00597539\n","[14]\tvalid_0's binary_logloss: 0.00594029\n","[15]\tvalid_0's binary_logloss: 0.00590104\n","[16]\tvalid_0's binary_logloss: 0.00586497\n","[17]\tvalid_0's binary_logloss: 0.00582927\n","[18]\tvalid_0's binary_logloss: 0.00579455\n","[19]\tvalid_0's binary_logloss: 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0.00361669\n","[719]\tvalid_0's binary_logloss: 0.00361644\n","[720]\tvalid_0's binary_logloss: 0.00361593\n","[721]\tvalid_0's binary_logloss: 0.00361726\n","[722]\tvalid_0's binary_logloss: 0.00361736\n","[723]\tvalid_0's binary_logloss: 0.00361618\n","[724]\tvalid_0's binary_logloss: 0.00361774\n","[725]\tvalid_0's binary_logloss: 0.00361911\n","Early stopping, best iteration is:\n","[525]\tvalid_0's binary_logloss: 0.00359124\n","Training Log Loss:  0.0006913932197668345\n","CV Log Loss:  0.0035912365769961265\n","LightGBM Gradient Boosting Log Loss:  0.0028126020210081175\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":572},"id":"D3wmp-KDTIt3","executionInfo":{"status":"ok","timestamp":1620233774982,"user_tz":240,"elapsed":221240,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"9667d2a3-2f68-4d58-861a-a5ddb735f684"},"source":["preds = pd.concat([y_train,predictionsBasedOnKFolds.loc[:,'prediction']], axis=1)\n","preds.columns = ['trueLabel','prediction']\n","predictionsBasedOnKFoldsLightGBMGradientBoosting = preds.copy()\n","\n","precision, recall, thresholds = \\\n","    precision_recall_curve(preds['trueLabel'],preds['prediction'])\n","average_precision = \\\n","    average_precision_score(preds['trueLabel'],preds['prediction'])\n","\n","plt.step(recall, precision, color='k', alpha=0.7, where='post')\n","plt.fill_between(recall, precision, step='post', alpha=0.3, color='k')\n","\n","plt.xlabel('Recall')\n","plt.ylabel('Precision')\n","plt.ylim([0.0, 1.05])\n","plt.xlim([0.0, 1.0])\n","\n","plt.title('Precision-Recall curve: Average Precision = {0:0.2f}'.format(\n","          average_precision))\n","\n","fpr, tpr, thresholds = roc_curve(preds['trueLabel'],preds['prediction'])\n","areaUnderROC = auc(fpr, tpr)\n","\n","plt.figure()\n","plt.plot(fpr, tpr, color='r', lw=2, label='ROC curve')\n","plt.plot([0, 1], [0, 1], color='k', lw=2, linestyle='--')\n","plt.xlim([0.0, 1.0])\n","plt.ylim([0.0, 1.05])\n","plt.xlabel('False Positive Rate')\n","plt.ylabel('True Positive Rate')\n","plt.title('Receiver operating characteristic: \\\n","Area under the curve = {0:0.2f}'.format(areaUnderROC))\n","plt.legend(loc=\"lower right\")\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"collapsed":true,"id":"qjY_XWA7TIt3"},"source":["# Test Set Evaluation"]},{"cell_type":"code","metadata":{"id":"oHvKY70eTIt3"},"source":["predictionsTestSetLogisticRegression = \\\n","    pd.DataFrame(data=[],index=y_test.index,columns=['prediction'])\n","predictionsTestSetLogisticRegression.loc[:,'prediction'] = \\\n","    logReg.predict_proba(X_test)[:,1]\n","logLossTestSetLogisticRegression = \\\n","    log_loss(y_test, predictionsTestSetLogisticRegression)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"eq8peCvUTIt3"},"source":["predictionsTestSetRandomForests = \\\n","    pd.DataFrame(data=[],index=y_test.index,columns=['prediction'])\n","predictionsTestSetRandomForests.loc[:,'prediction'] = \\\n","    RFC.predict_proba(X_test)[:,1]\n","logLossTestSetRandomForests = \\\n","    log_loss(y_test, predictionsTestSetRandomForests)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"wij9ICACTIt3"},"source":["# predictionsTestSetXGBoostGradientBoosting = \\\n","#     pd.DataFrame(data=[],index=y_test.index,columns=['prediction'])\n","# dtest = xgb.DMatrix(data=X_test)\n","# predictionsTestSetXGBoostGradientBoosting.loc[:,'prediction'] = \\\n","#     bst.predict(dtest)\n","# logLossTestSetXGBoostGradientBoosting = \\\n","#     log_loss(y_test, predictionsTestSetXGBoostGradientBoosting)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"O1_tSOZwTIt4"},"source":["predictionsTestSetLightGBMGradientBoosting = \\\n","    pd.DataFrame(data=[],index=y_test.index,columns=['prediction'])\n","predictionsTestSetLightGBMGradientBoosting.loc[:,'prediction'] = \\\n","    gbm.predict(X_test, num_iteration=gbm.best_iteration)\n","logLossTestSetLightGBMGradientBoosting = \\\n","    log_loss(y_test, predictionsTestSetLightGBMGradientBoosting)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eIN8fLYxTIt4","executionInfo":{"status":"ok","timestamp":1620233776804,"user_tz":240,"elapsed":221910,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"010a3f88-4bdf-450c-d71d-b1c29f8bcc8f"},"source":["print(\"Log Loss of Logistic Regression on Test Set: \", \\\n","          logLossTestSetLogisticRegression)\n","print(\"Log Loss of Random Forests on Test Set: \", \\\n","          logLossTestSetRandomForests)\n","# print(\"Log Loss of XGBoost Gradient Boosting on Test Set: \", \\\n","#           logLossTestSetXGBoostGradientBoosting)\n","print(\"Log Loss of LightGBM Gradient Boosting on Test Set: \", \\\n","          logLossTestSetLightGBMGradientBoosting)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Log Loss of Logistic Regression on Test Set:  0.09686247466781985\n","Log Loss of Random Forests on Test Set:  0.009037864825132656\n","Log Loss of LightGBM Gradient Boosting on Test Set:  0.002538541967525037\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":572},"id":"dSzHJ5YFTIt4","executionInfo":{"status":"ok","timestamp":1620233777357,"user_tz":240,"elapsed":222297,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"85786954-fa58-409c-ee46-d672d56dacb2"},"source":["precision, recall, thresholds = \\\n","    precision_recall_curve(y_test,predictionsTestSetLogisticRegression)\n","average_precision = \\\n","    average_precision_score(y_test,predictionsTestSetLogisticRegression)\n","\n","plt.step(recall, precision, color='k', alpha=0.7, where='post')\n","plt.fill_between(recall, precision, step='post', alpha=0.3, color='k')\n","\n","plt.xlabel('Recall')\n","plt.ylabel('Precision')\n","plt.ylim([0.0, 1.05])\n","plt.xlim([0.0, 1.0])\n","\n","plt.title('Precision-Recall curve: Average Precision = {0:0.2f}'.format(\n","          average_precision))\n","\n","fpr, tpr, thresholds = roc_curve(y_test,predictionsTestSetLogisticRegression)\n","areaUnderROC = auc(fpr, tpr)\n","\n","plt.figure()\n","plt.plot(fpr, tpr, color='r', lw=2, label='ROC curve')\n","plt.plot([0, 1], [0, 1], color='k', lw=2, linestyle='--')\n","plt.xlim([0.0, 1.0])\n","plt.ylim([0.0, 1.05])\n","plt.xlabel('False Positive Rate')\n","plt.ylabel('True Positive Rate')\n","plt.title('Receiver operating characteristic: \\\n","Area under the curve = {0:0.2f}'.format(areaUnderROC))\n","plt.legend(loc=\"lower right\")\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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piACFiCEZHXRGSHiGTbnbPb99U4EVkvIstE5MJAxWKMMSb4AnkfzBs4XdK/lcP4a3C6sa6D0430JPLRbbcxppDbtg2WLPE6ikLheEqKJ8sNWIJR1QUiUjOXSW4A3nKf8LZQRCqISFX3AVHGFF2qzl9R16IFbN7sdRRh7xHAqzTt5Z381Tj5oVNJ7rC/JRgR6Qn0BDjnnHOCEpwxnkhNhebN7cjd12WXQYl8P7DVuOonJzNuxQpPDlrCoqsYVZ0KTAWIj4+3QzsTnv73P3grpxpj159/wqpVzmuRwMcU6q68EubOtbLIh1WrVrF48WLuuOMOAO5Upc0ff1CrVq2gx+JlgkkGzvZ5X90dZkz2vvoKEhO9juLUPfyw/9M2awa//BK4WEyhc/jwYUaOHMlzzz1HREQELVu25LzzzkNE8KqDYC8TzCygr4hMx2nc32ftL0XEnj3OkXp+7N8PV1wRmHiC7a234B//yH2aZs2CE4spFObMmUOfPn3YuHEjAD169CAmJsbjqAKYYETkXaAtUFlEkoAngOIAqjoZ+AzoAKwHDgPdAxWLCSE7d0KNGnDkyKnPY+DAgosn2Bo2hK5dvY7CFBLJycn079+fDz74AICGDRsyefJkLrroIo8jcwTyKrIueYxXoE+gll+k7d4N11wDySFY4+h75nLBBfn/fNeuMHhwwcVjTBjr06cPM2fOpFSpUowYMYIHH3yQyMjQaVoPnUhM/u3aBU89BX/9dfLwOXOcewhC2T33wMsvex2FMWEnNTU1M4k888wzFC9enBdeeCEkr7C1BBOqVOHHH3M/C5kwARYsyHl8u3bOlUuhplixvNsgjDEn2bdvH0OHDmXdunXMnTsXEaFu3bq8//77XoeWI0swwZCWBgcP5u8zS5Y4CcIfZ5wBo0efPCwyEjp0gMqV87dcY0xIUVXef/99+vfvz9atW4mIiCAxMZEmTZp4HVqeLMEEWloaNGoEK1ee+jxuuSXncaVKwdChcN55pz5/Y0xI+v333+nbty9z584F4KKLLmLy5Mk0bNjQ48j8YwkmEObMgVGjnLuyd++G9eud4eXK5W8+kZFOG8t99xV8jMaYkPb8888zbNgwjh49SoUKFXjmmWe45557KFYsfDrBtwRzqpKS4M034fjxv48bMeLvw5o2hYSEwMdljCkUDh8+zNGjR+natSvPP/88Z555ptch5ZslmOzs3g3r1uU+Te/eed9VPno0tGnjvG7QoGBiM8YUSjt37mTt2rVccsklAAwePJi2bdvSunVrjyM7dZZgskpJgbg42LHDv+nPOw/cPn9OctZZcPfdEBFRsPEZYwqV9PR0XnvtNQYNGkRkZCRr1qyhUqVKREVFhXVyAUswJ6SnO12RzJ9/YljLlrl/5owz4JVXIAxPXY0x3luxYgW9evXihx9+AODKK6/k8OHDVKpUyePICkbRTDCzZsG0aSd3X711K7hfMgAdO8Ls2cGPzRhT6B06dIgRI0YwZswYUlNTqVKlCv/973/p3LkzUoh6jg7/BJOeDp99lr871++9N+dxtWo5bStly55+bMYYk42bb74582bJ3r17M2rUKCpUqOB1WAUu/BPM99/Ddded2mefe87peNFXq1b5v5zYGGPyYfDgwWzfvp1JkybRokXhfVJ8+CeYqVNPvO7Rw//PXXABDBhQ8PEYY4yP1NRUXnrpJTZt2sTYsWMBaNu2LQkJCWF1T8upCO8Ec+QIvP228/q665wGd2OMCRG//PIL9913H4nuLQ09e/bkArcX8cKeXADCew19G+HD+RkhxphC5a+//qJ37960bNmSxMREatSowSeffJKZXIqK8E0wKSnw+OPO69KloW1bT8MxxhiA6dOnExsby6RJk4iIiGDw4MGsXLmSa6+91uvQgi58q8hGjTrRx1cfe26ZMSY0fP7552zfvp1WrVoxadIkGhThXjxEfe8FCQPx8fGakJAAvteKJyc7d84bY0yQHTt2jOTkZM4991wAdu3axSeffMJdd90VUu0sIvKrqsYHc5mhs/b5kZp64vXixZZcjDGe+Prrr2nYsCEdO3bkuNvxbeXKlenevXtIJRevhF8JpKScfKNk48bexWKMKZK2b99O165dufzyy1nndoyblJTkcVShJ/wSTHIyvPGG87pu3ZOryowxJoDS09OZMmUKsbGxTJs2jejoaEaOHMnSpUszq8jMCeHXyL97t/O/enV45x1vYzHGFCk33ngjs2bNAuCqq65iwoQJ1K5d2+OoQlf4ncFkmDkTLrzQ6yiMMUXITTfdxD/+8Q/+7//+jzlz5lhyyUP4XUUmogng3MUfHe11OMaYQmzWrFkkJSXRu3dvAFSVgwcPUjYMO8P14iqy8KsiA+jUyZKLMSZgNm/ezAMPPMDMmTOJiori6quv5txzz0VEwjK5eCU8q8jq1/c6AmNMIZSSksILL7xAXFwcM2fOpGzZsjz77LPUyNrruvFLeJ7BFMLnJhhjvLVw4ULuu+8+li1bBsAtt9zCiy++SLVq1TyOLHyFZ4IpXtzrCIwxhcywYcNYtmwZtWrVYvz48XTo0MHrkMJeeFaRRYZnXjTGhA5VZf/+/Znvx48fz+OPP86KFSssuRSQ8Ewwx455HYExJoytXbuWK664gptuuomMK2nr1q3LqFGjKFWqlMfRFR7hmWDOP9/rCIwxYejo0aM88cQTNGzYkK+//prExEQ2bdrkdViFVngmGLtE2RiTT1988QUNGjRgxIgRHD9+nLvvvpu1a9dSq1Ytr0MrtAKaYETkahFZKyLrReTRbMafIyLzRWSJiCwTEav4NMYUKFXl7rvvpn379qxfv564uDgWLFjAq6++SkxMjNfhFWoBSzAiEgFMAK4B4oAuIhKXZbKhwHuq2gS4FZgYqHiMMUWTiFCzZk1KlizJ008/zZIlS7j00ku9DqtICOTlWM2B9aq6AUBEpgM3AKt8plGgnPu6PPCnX3O2HpSNMblITExk69atXHPNNQAMHjyYrl27WnVYkAWyiqwasMXnfZI7zNdw4A4RSQI+A/plNyMR6SkiCSKSEIhAjTGFw4EDBxg4cCBNmzblrrvuYs+ePQBERUVZcvGA1438XYA3VLU60AH4n4j8LSZVnaqq8cHuqM0YEx5UlRkzZhAXF8eLL74IwG233UZxuynbU4GsIksGzvZ5X90d5qsHcDWAqv4kItFAZWBHrnO2KjJjjOuPP/6gb9++zJ49G4D4+HimTJnChfY4D88F8gxmEVBHRGqJSAmcRvxZWabZDFwOICL1gGhgZwBjMsYUIqpKp06dmD17NuXKlWP8+PEsXLjQkkuICFiCUdVUoC8wD1iNc7XYShEZISLXu5M9BNwrIkuBd4FuGm4PqDHGBF16ejrgXCH2/PPP07lzZ9asWUOfPn2IiIjwODqTITwfOPbtt9C6tdehGGOCbPfu3Tz6qHNL3csvv+xxNOHFiweOed3If2qsDcaYIkVVefPNN4mNjeWVV17hrbfeIikpyeuwTB7CM8EYY4qM1atX065dO7p168auXbto27YtS5cupXr16l6HZvJgCcYYE5JUlWHDhtGoUSO+/fZbKleuzJtvvsnXX39NbGys1+EZP1iCMcaEJBEhOTmZlJQU7r33XtauXcudd96JWBV52AjPRv7vvoNLLvE6FGNMAfvzzz/ZtWsXDRs2BGDXrl2sXbuWVq1aeRxZ+LNGfmNMkZSWlsb48eOpV68et956K8ePHwegcuXKllzCmCUYY4ynFi9eTMuWLenXrx/79++ndu3aJz3K2IQvSzDGGE/s37+fBx98kGbNmpGQkED16tX56KOPmDVrFpUrV/Y6PFMA/O6LTERKqerhQAbjN2vkMyasqSqtW7dm6dKlREREMHDgQIYPH07ZsmW9Ds0UoDzPYETkYhFZBaxx3zcSEXswmDHmlIkIAwYMoHnz5iQkJPDCCy9YcimE8ryKTER+Bm4GZrlPnkREVqhq/SDE9zfxIprw/fdgDX/GhI3jx48zZswYIiIieOSRRwDnLCY9Pd36DgsSL64i86uKTFW3ZLn2PC0w4fjJqsiMCRvfffcdvXr1YtWqVURFRXHnnXdSpUoVRMSSSyHnTyP/FhG5GFARKS4iD+P0jmyMMTnatWsXd999N61bt2bVqlXUqVOH2bNnU6VKFa9DM0HiT4LpBfTBedxxMtAY6B3IoIwx4UtVef3114mNjeX111+nRIkSPPHEEyxbtowrrrjC6/BMEPlTRVZXVW/3HSAirYAfAhOSMSbcTZs2jd27d3PZZZcxceJE6tat63VIxgP+JJiXgKyPh8tuWPBYG4wxIeXw4cPs27ePqlWrIiJMnDiRRYsWcfvtt1vfYUVYjglGRC4CLgbOEJGBPqPKAdYyZ4wBYM6cOfTp04dzzz2XL774AhGhbt26dtZicm2DKQGUwUlCZX3+9uNctmyMKcKSk5O55ZZb6NChAxs3bmTnzp3s3r3b67BMCMnxDEZVvwW+FZE3VPWPIMZkjAlhaWlpTJgwgaFDh3LgwAFKly7NiBEjeOCBB4iM9LtzEFME+PNrOCwizwEXANEZA1X1soBFlRer0zXGE+np6bRp04YffnCu8fnnP//J2LFjOeecczyOzIQify5Tfhunm5hawH+ATcCiAMZkjAlRxYoVo3379px99tnMnDmTGTNmWHIxOfKnq5hfVbWpiCxT1YbusEWq2iwoEWYRL6IJP/0ELVt6sXhjihRV5b333iMyMpJOnToBcOzYMVJSUihTpozH0Zn8CNWuYlLc/1tFpCPwJ1ApcCEZY0LB77//Tu/evfn8888544wzuOyyy6hYsSJRUVFERUV5HZ4JA/4kmJEiUh54COf+l3JA/4BGlRdrgzEmYI4dO8Zzzz3HqFGjOHr0KBUrVmTUqFGUL1/e69BMmMkzwajqbPflPqAdZN7Jb4wpZL755hvuv/9+1qxZA0DXrl15/vnnOfPMMz2OzISj3G60jAD+hdMH2VxVXSEi1wKPAyWBJsEJ0RgTDGlpafTu3Zs1a9ZQt25dJk2aRLt27bwOy4Sx3M5gXgXOBn4BxonIn0A88KiqfhyM4HJkVWTGFIj09HSOHj1KqVKliIiIYNKkSSxYsIBBgwZZO4s5bbklmHigoaqmi0g0sA2orap2q64xhcDy5cvp1asXsbGxvPrqqwC0adOGNm3aeByZKSxySzDHVTUdQFWPisgGSy7GhL9Dhw4xYsQIxowZQ2pqKhs3bmTv3r1UrFjR69BMIZPbjZaxIrLM/Vvu8365iCwLVoDGmILzySefEBcXx7PPPpvZ5rJq1SpLLiYgcjuDqRe0KPLL2mCMyZfU1FQ6d+7MRx99BEDjxo2ZMmUKzZs39zgyU5jl1tmldXBpTCERGRlJ+fLlKVOmDE8++SR9+/a1jilNwOXZVcxpzVzkamAszvNjXlHV0dlM8y9gOKDAUlW9Lbd5xotowi+/QDNPeqoxJmz8/PPPALRo0QKA3bt3c+TIEapXr+5lWMYjodpVzClx76OZAFwJJAGLRGSWqq7ymaYO8BjQSlX3iojdzWXMafrrr7947LHHmDJlCrGxsSQmJlKiRAliYmK8Ds0UMf70poyIlBSR/D6erjmwXlU3qOpxYDpwQ5Zp7gUmqOpeAFXd4decrQ3GmL9RVd555x1iY2OZPHkyERERXH/99aSlpXkdmimi8kwwInIdkAjMdd83FpFZfsy7GrDF532SO8zX+cD5IvKDiCx0q9SMMfn022+/0b59e26//Xa2b99Oq1atWLJkCaNHj6ZkyZJeh2eKKH+qyIbjnI18A6CqiSJSqwCXXwdoC1QHFohIA1X9y3ciEekJ9ARoWkALNqawSElJ4bLLLiMpKYlKlSrx7LPP0r17d4oV86uCwpiA8au7flXdJydXS/lzZUAyTlczGaq7w3wlAT+ragqwUUTW4SSckx5opqpTgangNPJbFZkxTpWYiFC8eHFGjRrF/PnzefbZZznjjDO8Ds0YwL82mJUichsQISJ1ROQl4Ec/PrcIqCMitUSkBHArkLVq7WOcsxdEpDJOldkGf4M3pijavn07Xbt2ZeTIkZnD7rzzTl5//XVLLiak+JNg+gEXAMeAd3C67c/zeTCqmgr0BeYBq4H3VHWliIwQkevdyeYBu0VkFTAfeMS6ozEme+np6ZlXhk2bNo0xY8Zw4MABr8MyJkf+PDL5QlVdHKR48hQvogkJCdDUWmNM0bF06VJ69chHbhQAACAASURBVOrFwoULAbj66quZMGEC5557rseRmXDhxX0w/pzBvCAiq0XkSRGpH/CI/GFtMKaISElJ4eGHH6Zp06YsXLiQqlWr8t577/HZZ59ZcjEhL88Eo6rtcJ5kuROY4nZ2OTTgkRljiIyMZMmSJaSnp9OvXz9Wr17NLbfcgthBlgkD+eoqRkQaAIOAzqpaImBR5SJeRBN+/RUuvNCLxRsTcJs3byYtLY1atZy7AX777Tf27dtHfHxQazdMIROSVWQiUk9Ehrtd9mdcQWadGRlTwFJSUnj++eepV68e9957LxkHf3Xq1LHkYsKSP/fBvAb8H3CVqv4Z4Hj8Y9UDppD56aef6NWrF8uWOY9aqlSpEocPH6Z06dIeR2bMqcszwajqRcEIxJiiaO/evTz66KNMnToVgFq1ajFhwgSuueYajyMz5vTlmGBE5D1V/ZdbNebbUCOAqmrDgEdnTCF27NgxGjduzObNmylevDiPPPIIQ4YMoVSpUl6HZkyByO0M5kH3/7XBCMSYoiYqKooePXrw1VdfMWnSJOLi4rwOyZgC5c+Nls+o6uC8hgVLvIgmLFkCjRt7sXhjTtnRo0d5+umnqVu3Lrfd5jxXLzU1lYiICLvs2ARcSF5FhvPAsKysgtiYfPjiiy9o0KABI0aMYMCAARw5cgRw7nOx5GIKqxwTjIjc77a/1BWRZT5/G4FlwQvRmPC1bds2brvtNtq3b8/69eu54IIL+PDDD+0ZLaZIyK0N5h1gDvA08KjP8AOquiegUeXFjvhMiEtLS2PKlCk8/vjj7Nu3j5IlS/LEE08wYMAASpTw5B5lY4IutwSjqrpJRPpkHSEilTxPMsaEsLS0NF566SX27dtHhw4dGD9+fOad+cYUFXmdwVwL/IpzmbLvaYMC1tOeMT4OHDhAWloaFSpUoESJErz88sts376dm266ydpZTJGUY4JR1Wvd/3bYZUwuVJUZM2bwwAMPcNVVV/Hqq68CcMkll3gcmTHe8qcvslYiUtp9fYeIjBGRcwIfWq5Bebp4YzJs2rSJ66+/nk6dOpGcnMyKFSs4evSo12EZExL8uUx5EnBYRBoBDwG/A/8LaFTGhLiUlBSeeeYZ4uLimD17NuXKlWP8+PH8+OOPREdHex2eMSHBn84uU1VVReQGYLyqvioiPQIdmDGh6vDhw7Rs2ZLly5cDcOuttzJmzBiqVq3qcWTGhBZ/EswBEXkM6ApcKiLFgOKBDcuY0FWqVCni4+M5fPgwEydOpH379l6HZExI8ifBdAZuA+5W1W1u+8tzgQ0rD9YGY4JIVXnrrbeoXbt2ZsP9iy++SIkSJeyGSWNy4c8jk7cBbwPlReRa4KiqvhXwyIwJAatXr6Zdu3Z069aNnj17cvz4cQDKly9vycWYPPhzFdm/gF+AW4B/AT+LyM2BDswYLx05coShQ4fSqFEjvv32W8444wwee+wxihe32mFj/OVPFdkQoJmq7gAQkTOAL4EPAhmYMV6ZO3cuffr0YcOGDQDce++9jB49mkqVKnkcmTHhxZ8EUywjubh249/lzYFjbTAmQA4ePEjXrl3ZtWsX9evXZ/LkybRq1crrsIwJS/4kmLkiMg94133fGfgscCEZE1xpaWmkp6dTvHhxypQpw9ixY0lKSmLAgAFWJWbMacjzgWMAInITkNHvxXeqOiOgUeUiXkQTli+H+vW9CsEUIr/++iv33XcfN9xwA8OGDfM6HGMCxosHjuV4BiMidYDngdrAcuBhVU0OVmC5sioyc5r279/PsGHDGD9+POnp6ezfv59HH33UzliMKUC5taW8BswGOuH0qPxSUCIyJoBUlffff5/Y2FjGjRuHiDBw4EAWL15sycWYApZbG0xZVX3Zfb1WRBYHIyBjAuXAgQN07tyZOXPmANCiRQsmT55M48aNPY7MmMIptwQTLSJNOPEcmJK+71XVEo4JK2XKlOHYsWOUL1+e0aNH07NnT4oV8/aCSGMKsxwb+UVkfi6fU1W9LDAh5S5eRBNWroS4OC8Wb8LMggULqFq1KnXq1AHgjz/+IDo6mipVqngcmTHBFVKN/KraLpiBGFOQdu3axaBBg3j99de5/PLL+eKLLxARatSo4XVoxhQZVj9gCpX09HRee+016taty+uvv06JEiW49NJLSUtL8zo0Y4qcgCYYEblaRNaKyHoReTSX6TqJiIpIUE/fTOGycuVK2rZtS48ePdizZw+XX345y5cv54knniAy0p97io0xBSlgW52IRAATgCuBJGCRiMxS1VVZpisLPAj8nI+ZF2CkpjDYt28fLVu25ODBg5x55pmMGTOG2267DbHfijGeyTPBiLOF3g6cq6oj3OfB/ENVf8njo82B9aq6wZ3PdOAGYFWW6Z4EngEeyW/wxqgqIkL58uUZPHgwycnJPPXUU1SsWNHr0Iwp8vypIpsIXAR0cd8fwDkzyUs1YIvP+yR3WCYRuRA4W1U/zW1GItJTRBJEJMGP5ZoiIDk5mZtvvplp06ZlDhsyZAiTJk2y5GJMiPAnwbRQ1T7AUQBV3QuUON0Fu49eHgM8lNe0qjpVVeMzL7Gzao8iKzU1lbFjxxIbG8uHH37IE088kdmAb9VhxoQWfxJMitueopD5PJh0Pz6XDJzt8766OyxDWaA+8I2IbAJaArOsod/kZNGiRbRo0YL+/ftz8OBB/vnPf/Ltt98SERHhdWjGmGz4k2DGATOAM0VkFPA98JQfn1sE1BGRWiJSArgVmJUxUlX3qWplVa2pqjWBhcD1qmrVYOYkhw4dom/fvrRo0YLFixdzzjnnMHPmTGbMmMHZZ5+d9wyMMZ7Is5FfVd8WkV+By3G6ifmnqq7243OpItIXmAdEAK+p6koRGQEkqOqs3OdgjCMyMpIvv/ySYsWKMXDgQJ544glKly7tdVjGmDzk+TwY96qxv1HVzQGJKA/xIpqwZg3UrevF4k2Q/P7771SoUIGYmBjAqR6Ljo6mQYMGHkdmTHjyoqsYf6rIPsXptv9T4CtgAzAnkEGZouvYsWOMHDmS+vXrM3jw4MzhzZo1s+RiTJjxp4rspK3avbS4d8AiMkXWN998w/3338+aNWsA54qxtLQ0a8Q3Jkzlu6sYt5v+FgGIxRRRO3bs4K677qJdu3asWbOGunXr8vXXX/PGG29YcjEmjPlzJ/9An7fFgAuBPwMWkT/sfodCY9euXdSrV489e/YQFRXFkCFDGDRoEFFRUV6HZow5Tf70RVbW53UqTlvMh4EJxxQ1lStX5oYbbiApKYmJEydy3nnneR2SMaaA5Jpg3Bssy6rqw0GKxxRyhw4dYsSIEXTs2JHWrVsDMHHiRKKiouxOfGMKmRwTjIhEuveytApmQKbw+uSTT+jbty+bN2/m008/ZdmyZRQrVozo6GivQzPGBEBuZzC/4LS3JIrILOB94FDGSFX9KMCx5cyOdMPKli1bePDBB5kxYwYATZo0YcqUKRQrZs+7M6Yw86cNJhrYDVyG0x+ZuP+9SzAmLKSmpjJu3Dj+/e9/c+jQIcqUKcPIkSPp06ePPQDMmCIgt638TPcKshWcSCwZcr/93xhg//79PP300xw6dIhOnTrx3//+l+rVq3sdljEmSHJLMBFAGU5OLBm8TTBWRRay/vrrL0qWLElUVBSVKlViypQpREVF0bFjR69DM8YEWW4JZquqjghaJCasqSrvvvsuAwYMoG/fvgwbNgyAm266yePIjDFeyS3B2GmC8cu6devo3bs3X331FQALFizIfJSxMaboyu0ynsuDFoUJS0ePHuU///kPDRo04KuvvqJSpUq8+uqrzJs3z5KLMSbnMxhV3RPMQPLFdl6e27ZtG61bt+a3334DoFu3bjz33HNUrlzZ48iMMaHCrhU1p6RKlSqcffbZREZGMmnSJNq0aeN1SMaYEGMJxvglPT2dl19+mXbt2nH++ecjIrzzzjtUrFiREiVKeB2eMSYE2a3UJk9Lly6lVatW9OrVi969e5PxFNQqVapYcjHG5Cg8E4y1wQTFwYMHefjhh2natCkLFy7krLPOolevXl6HZYwJE1ZFZrL18ccf069fP5KSkihWrBj9+vVj5MiRlCtXzuvQjDFhwhKM+Zvk5GRuvfVWjh07RtOmTZk8eTLx8fFeh2WMCTOWYAwAKSkpREZGIiJUq1aNUaNGUaJECXr37m2PLTbGnBJrgzH8+OOPNG3alGnTpmUOe+ihh+jXr58lF2PMKQvPBGMKxJ49e7jvvvto1aoVy5cvZ+LEiZlXiBljzOmyBFMEqSr/+9//iI2NZerUqRQvXpwhQ4bw9ddfWxcvxpgCE55tMLYTPGXbt2+nS5cuzJ8/H4A2bdowadIk6tWr53FkxpjCxs5gipgKFSqwdetWKleuzBtvvMH8+fMtuRhjAiI8z2BMvnzxxRdceOGFxMTEEBUVxfvvv0/VqlWJiYnxOjRjTCFmZzCF2NatW+nSpQvt27dn8ODBmcPr169vycUYE3DhmWCsDSZXaWlpTJw4kdjYWKZPn07JkiWpW7euXSFmjAkqqyIrZBYvXkyvXr1YtGgRAB07dmT8+PHUrFnT28CMMUWOJZhCZNOmTTRv3py0tDSqVavGuHHjuPHGG+3SY2OMJwKaYETkamAsEAG8oqqjs4wfCNwDpAI7gbtV9Y9AxlSY1axZk+7du1O2bFn+85//ULZsWa9DMsYUYQFrgxGRCGACcA0QB3QRkbgsky0B4lW1IfAB8KyfMy/ASMPXpk2buO666/j2228zh02dOpUxY8ZYcjHGeC6QZzDNgfWqugFARKYDNwCrMiZQ1fk+0y8E7ghgPIVGSkoKY8aM4T//+Q9Hjhxh165d/PTTTwBWHWaMCRmBvIqsGrDF532SOywnPYA52Y0QkZ4ikiAiCQUYX1j6/vvvadKkCY8++ihHjhzh1ltv5aOPPvI6LGOM+ZuQuExZRO4A4oHnshuvqlNVNV5V490PBDG60LB3717uueceLr30UlauXEnt2rWZN28e7777LlWrVvU6PGOM+ZtAJphk4Gyf99XdYScRkSuAIcD1qnosgPGEtfT0dGbOnEnx4sUZNmwYy5cvp3379l6HZYwxOQpkG8wioI6I1MJJLLcCt/lOICJNgCnA1aq6I4CxhKU1a9ZQq1YtoqKiiImJ4e233+acc84hNjbW69CMMSZPATuDUdVUoC8wD1gNvKeqK0VkhIhc7072HFAGeF9EEkVkVqDiCSeHDx9myJAhNGzYkGefPXFhXfv27S25GGPCRkDvg1HVz4DPsgz7t8/rK05pxoW4DWbu3Ln07t2bjRs3ArBr1y6PIzLGmFNjd/KHiD///JP+/fvz/vvvA9CgQQMmT57MxRdf7HFkxhhzaizBhIB169YRHx/PgQMHKFWqFMOHD6d///4UL17c69CMMeaUWYIJAXXq1KFZs2aULl2al156iRo1angdkjHGnLaQuA8m38K8DWb//v3079+fdevWAc7d97NmzWLWrFmWXIwxhYadwQSRqvLBBx/w4IMPsnXrVtasWcPcuXMBKF26tMfRGWNMwbIEEyQbNmygb9++zJnj9IbTsmVLnnnmGY+jMsaYwAnPKrIwcvz4cZ566ikuuOAC5syZQ4UKFZg8eTI//PADjRo18jo8Y4wJmPA8gwmjNpgtW7YwYsQIjh07xu23384LL7xAlSpVvA7LGGMCLjwTTIjbu3cvFSpUQESoXbs2Y8eO5bzzzuPyyy/3OjRjjAkaqyIrQOnp6bz22mucd955TJs2LXP4fffdZ8nFGFPkhGeCCcEqspUrV9K2bVt69OjBnj17MhvzjTGmqArPBBNCDh8+zGOPPUbjxo357rvvOPPMM3n77bd5++23vQ7NGGM8ZW0wp2HdunVcddVVbNq0CRGhV69ePPXUU1SsWNHr0IwxxnOWYE5DjRo1iI6OplGjRkyePJmWLVt6HZIxIS8lJYWkpCSOHj3qdSiFUnR0NNWrVw+JvgzDM8F41AaTmprK5MmT6dKlCzExMURFRTF37lyqVatGZGR4FqUxwZaUlETZsmWpWbMmEoLtqeFMVdm9ezdJSUnUqlXL63CsDcZfv/zyC82bN6dfv34MHjw4c3iNGjUsuRiTD0ePHiUmJsaSSwCICDExMSFzdmgJJg/79u2jb9++tGzZkiVLlnDOOedwww03eB2WMWHNkkvghFLZWoLJgaoyffp0YmNjmTBhAhEREQwaNIhVq1Zx3XXXeR2eMcaEvPBMMEHI0EuXLqVLly5s27aNiy++mMWLF/PMM89Yr8fGFAIRERE0btyY+vXrc9111/HXX39ljlu5ciWXXXYZdevWpU6dOjz55JOoaub4OXPmEB8fT1xcHE2aNOGhhx7yYhXCQngmmABJS0vLfN24cWMGDBjAyy+/zHfffUeDBg08jMwYU5BKlixJYmIiK1asoFKlSkyYMAGAI0eOcP311/Poo4+ydu1ali5dyo8//sjEiRMBWLFiBX379mXatGmsWrWKhIQEzjvvvAKNLTU1tUDn5yVLMK758+dTv359FixYkDlszJgx3HPPPRQrZsVkTECIBOYvHy666CKSk5MBeOedd2jVqhXt27cHoFSpUowfP57Ro0cD8OyzzzJkyBBiY2MB50zo/vvv/9s8Dx48SPfu3WnQoAENGzbkww8/BKBMmTKZ03zwwQd069YNgG7dutGrVy9atGjBoEGDqFmz5klnVXXq1GH79u3s3LmTTp060axZM5o1a8YPP/yQr3UNtiJ/+dOOHTt45JFHeOuttwAnqbRu3drjqIwxwZCWlsZXX31Fjx49AKd6rGnTpidNU7t2bQ4ePMj+/ftZsWKFX1ViTz75JOXLl2f58uWA0wFuXpKSkvjxxx+JiIggLS2NGTNm0L17d37++Wdq1KhBlSpVuO222xgwYACXXHIJmzdv5qqrrmL16tWnsObBEZ4JpgDaYNLT03n11VcZPHgwe/fuJSoqiqFDh/LII48UQIDGGL/4tG0E05EjR2jcuDHJycnUq1ePK6+8skDn/+WXXzJ9+vTM9/707nHLLbcQEREBQOfOnRkxYgTdu3dn+vTpdO7cOXO+q1atyvzM/v37OXjw4ElnRqGkSNb9bNy4kUsvvZSePXuyd+9e2rdvz4oVKxg6dChRUVFeh2eMCbCMNpg//vgDVc1sg4mLi+PXX389adoNGzZQpkwZypUrxwUXXPC38fnhewlx1ntVfC8guuiii1i/fj07d+7k448/5qabbgKcA+OFCxeSmJhIYmIiycnJIZtcoIgmmHLlyrFu3Tr+8Y9/MH36dObOnVvgDXXGmNBXqlQpxo0bxwsvvEBqaiq3334733//PV9++SXgnOk88MADDBo0CIBHHnmEp556inXr1gHODn/y5Ml/m++VV16ZmbTgRBVZlSpVWL16Nenp6cyYMSPHuESEG2+8kYEDB1KvXj1iYmIAaN++PS+99FLmdImJiadZAoEVngnmFKrI5s2bx7FjxwCIiYlh1qxZrFmzhs6dO4fUjUnGmOBq0qQJDRs25N1336VkyZLMnDmTkSNHUrduXRo0aECzZs3o27cvAA0bNuS///0vXbp0oV69etSvX58NGzb8bZ5Dhw5l79691K9fn0aNGjF//nwARo8ezbXXXsvFF19M1apVc42rc+fOTJs2LbN6DGDcuHEkJCTQsGFD4uLisk1uoUTUozrQUxUvogk7dsAZZ/g1/ZYtW3jggQf4+OOPefLJJxk6dGiAIzTG5Gb16tXUq1fP6zAKtezKWER+VdX4YMYRnmcwfkhNTWXMmDHUq1ePjz/+mDJlylCpUiWvwzLGmCIjPK8iy8PChQvp1asXS5cuBaBTp06MHTuWatWqeRyZMcYUHeGZYHJpM/n555+5+OKLUVVq1qzJ+PHj6dixYxCDM8bkRVWt7TNAQqnZIzwTTC6aN2/OVVddRZMmTRg6dCilSpXyOiRjjI/o6Gh2795tXfYHQMbzYKKjo70OBQjXRv6dO6FyZQB+++03BgwYwJgxYzj//PMB59JB697FmNBkT7QMrJyeaOlFI3/YnsEcO3aM0aNH8/TTT3Ps2DGio6P54IMPACy5GBPCihcvHhJPWzSBF9A9sYhcLSJrRWS9iDyazfgoEfk/d/zPIlLTn/l+9d13NGzYkOHDh3Ps2DG6d+8e8teDG2NMUROwKjIRiQDWAVcCScAioIuqrvKZpjfQUFV7icitwI2q2jnbGbpiRHSP+7pevXpMnjzZOqc0xpg8FLb7YJoD61V1g6oeB6YDWZ81fAPwpvv6A+ByyaPVby9OHeNTTz1FYmKiJRdjjAlRgTyDuRm4WlXvcd93BVqoal+faVa40yS57393p9mVZV49gZ7u2/rAioAEHX4qA7vynKposLI4wcriBCuLE+qqatlgLjAsGvlVdSowFUBEEoJ9mheqrCxOsLI4wcriBCuLE0QkIdjLDGQVWTJwts/76u6wbKcRkUigPLA7gDEZY4wJkkAmmEVAHRGpJSIlgFuBWVmmmQXc5b6+Gfhaw+3GHGOMMdkKWBWZqqaKSF9gHhABvKaqK0VkBJCgqrOAV4H/ich6YA9OEsrL1EDFHIasLE6wsjjByuIEK4sTgl4WYXcnvzHGmPBgt7wbY4wJCEswxhhjAiJkE0ygupkJR36UxUARWSUiy0TkKxGp4UWcwZBXWfhM10lEVEQK7SWq/pSFiPzL/W2sFJF3gh1jsPixjZwjIvNFZIm7nXTwIs5AE5HXRGSHe49hduNFRMa55bRMRC4MaECqGnJ/OBcF/A6cC5QAlgJxWabpDUx2X98K/J/XcXtYFu2AUu7r+4tyWbjTlQUWAAuBeK/j9vB3UQdYAlR035/pddwelsVU4H73dRywyeu4A1QWrYELgRU5jO8AzAEEaAn8HMh4QvUMJiDdzISpPMtCVeer6mH37UKce44KI39+FwBPAs8Ahbk/eH/K4l5ggqruBVDVHUGOMVj8KQsFyrmvywN/BjG+oFHVBThX5ObkBuAtdSwEKohI1UDFE6oJphqwxed9kjss22lUNRXYB8QEJbrg8qcsfPXAOUIpjPIsC/eU/2xV/TSYgXnAn9/F+cD5IvKDiCwUkauDFl1w+VMWw4E7RCQJ+AzoF5zQQk5+9yenJSy6ijH+EZE7gHigjdexeEFEigFjgG4ehxIqInGqydrinNUuEJEGqvqXp1F5owvwhqq+ICIX4dx/V19V070OrDAL1TMY62bmBH/KAhG5AhgCXK+qx4IUW7DlVRZlcTpD/UZENuHUMc8qpA39/vwukoBZqpqiqhtxHp9RJ0jxBZM/ZdEDeA9AVX8ConE6wixq/NqfFJRQTTDWzcwJeZaFiDQBpuAkl8Jazw55lIWq7lPVyqpaU1Vr4rRHXa+qQe/kLwj82UY+xjl7QUQq41SZbQhmkEHiT1lsBi4HEJF6OAlmZ1CjDA2zgDvdq8laAvtUdWugFhaSVWQauG5mwo6fZfEcUAZ4373OYbOqXu9Z0AHiZ1kUCX6WxTygvYisAtKAR1S10J3l+1kWDwEvi8gAnAb/boXxgFRE3sU5qKjstjc9ARQHUNXJOO1PHYD1wGGge0DjKYRlbIwxJgSEahWZMcaYMGcJxhhjTEBYgjHGGBMQlmCMMcYEhCUYY4wxAWEJxoQkEUkTkUSfv5q5THuwAJb3hohsdJe12L3bO7/zeEVE4tzXj2cZ9+PpxujOJ6NcVojIJyJSIY/pGxfWnoNN6LPLlE1IEpGDqlqmoKfNZR5vALNV9QMRaQ88r6oNT2N+px1TXvMVkTeBdao6Kpfpu+H0KN23oGMxJi92BmPCgoiUcZ91s1hElovI33pRFpGqIrLA5wj/Und4exH5yf3s+yKS145/AXCe+9mB7rxWiEh/d1hpEflURJa6wzu7w78RkXgRGQ2UdON42x130P0/XUQ6+sT8hojcLCIRIvKciCxyn9Nxnx/F8hNuR4Ui0txdxyUi8qOI1HXvah8BdHZj6ezG/pqI/OJOm11v1MYUDC+fXWB/9pfTH86d54nu3wycXifKueMq49yJnHEGftD9/xAwxH0dgdM3WWWchFHaHT4Y+Hc2y3sDuNl9fQvwM9AUWA6UxukpYSXQBOgEvOzz2fLu/29wnz+TEZPPNBkx3gi86b4ugdOzbUmgJzDUHR4FJAC1sonzoM/6vQ9c7b4vB0S6r68APnRfdwPG+3z+KeAO93UFnP7JSnv9fdtf4fwLya5ijAGOqGrjjDciUhx4SkRaA+k4R+5VgG0+n1kEvOZO+7GqJopIG5wHTP3gdqNTAufIPzvPichQnD6qeuD0XTVDVQ+5MXwEXArMBV4QkWdwqtW+y8d6zQHGikgUcDWwQFWPuNVyDUXkZne68jgdU27M8vmSIpLorv9q4Auf6d8UkTo4XaEUz2H57YHrReRh9300cI47L2MKlCUYEy5uB84Amqpqiji9JUf7TqCqC9wE1BF4Q0TGAHuBL1S1ix/LeERVP8h4IyKXZzeRqq4T57kzHYCRIvKVqo7wZyVU9aiIfANcBXTGeTgWOE8Y7Keq8/KYxRFVbSwipXD63uoDjMN5yNp8Vb3RvSDimxw+L0AnVV3rT7zGnA5rgzHhojyww00u7YAaWScQkRrAdlV9GXgF59GxC4FWIpLRplJaRM73c5nfAf8UkVIiUhqneus7ETkLOKyq03A6Gs3uueYp7plUdv4Pp5PBjLMhcJLF/RmfEZHz3WVmS50nmD4APCQnHleR0e16N59JD+BUFWaYB/QT93ROnJ64jQkISzAmXLwNxIvIcuBOYE0207QFlorIEpyzg7GquhNnh/uuiCzDqR6L9WeBqroYp23mF5w2mVdUdQnQAPjFrap6AhiZzcenAssyGvmz+BznoXBfqvOIX3AS4ipgsYiswHn8Qq41DG4sy3AepvUs8LS77r6fmw/EZTTy45zpFHdjW+m+NyYgzfpw5wAAAD1JREFU7DJlY4wxAWFnMMYYYwLCEowxxpiAsARjjDEmICzBGGOMCQhLMMYYYwLCEowxxpiAsARjjDEmIP4f11Nhm2t7FHAAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":572},"id":"8pWwWJujTIt4","executionInfo":{"status":"ok","timestamp":1620233778115,"user_tz":240,"elapsed":222866,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"c7289b49-1507-4007-afae-a4d36099435f"},"source":["precision, recall, thresholds = \\\n","    precision_recall_curve(y_test,predictionsTestSetRandomForests)\n","average_precision = \\\n","    average_precision_score(y_test,predictionsTestSetRandomForests)\n","\n","plt.step(recall, precision, color='k', alpha=0.7, where='post')\n","plt.fill_between(recall, precision, step='post', alpha=0.3, color='k')\n","\n","plt.xlabel('Recall')\n","plt.ylabel('Precision')\n","plt.ylim([0.0, 1.05])\n","plt.xlim([0.0, 1.0])\n","\n","plt.title('Precision-Recall curve: Average Precision = {0:0.2f}'.format(\n","          average_precision))\n","\n","fpr, tpr, thresholds = roc_curve(y_test,predictionsTestSetRandomForests)\n","areaUnderROC = auc(fpr, tpr)\n","\n","plt.figure()\n","plt.plot(fpr, tpr, color='r', lw=2, label='ROC curve')\n","plt.plot([0, 1], [0, 1], color='k', lw=2, linestyle='--')\n","plt.xlim([0.0, 1.0])\n","plt.ylim([0.0, 1.05])\n","plt.xlabel('False Positive Rate')\n","plt.ylabel('True Positive Rate')\n","plt.title('Receiver operating characteristic: \\\n","Area under the curve = {0:0.2f}'.format(areaUnderROC))\n","plt.legend(loc=\"lower right\")\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"mWaGDNOyTIt4"},"source":["# precision, recall, thresholds = \\\n","#     precision_recall_curve(y_test,predictionsTestSetXGBoostGradientBoosting)\n","# average_precision = \\\n","#     average_precision_score(y_test,predictionsTestSetXGBoostGradientBoosting)\n","\n","# plt.step(recall, precision, color='k', alpha=0.7, where='post')\n","# plt.fill_between(recall, precision, step='post', alpha=0.3, color='k')\n","\n","# plt.xlabel('Recall')\n","# plt.ylabel('Precision')\n","# plt.ylim([0.0, 1.05])\n","# plt.xlim([0.0, 1.0])\n","\n","# plt.title('Precision-Recall curve: Average Precision = {0:0.2f}'.format(\n","#           average_precision))\n","\n","# fpr, tpr, thresholds = \\\n","#     roc_curve(y_test,predictionsTestSetXGBoostGradientBoosting)\n","# areaUnderROC = auc(fpr, tpr)\n","\n","# plt.figure()\n","# plt.plot(fpr, tpr, color='r', lw=2, label='ROC curve')\n","# plt.plot([0, 1], [0, 1], color='k', lw=2, linestyle='--')\n","# plt.xlim([0.0, 1.0])\n","# plt.ylim([0.0, 1.05])\n","# plt.xlabel('False Positive Rate')\n","# plt.ylabel('True Positive Rate')\n","# plt.title('Receiver operating characteristic: \\\n","# Area under the curve = {0:0.2f}'.format(areaUnderROC))\n","# plt.legend(loc=\"lower right\")\n","# plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":572},"id":"EFaq1_NeTIt5","executionInfo":{"status":"ok","timestamp":1620233778318,"user_tz":240,"elapsed":222761,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"19ca9052-88bb-48f5-9967-0fa8834555c9"},"source":["precision, recall, thresholds = \\\n","    precision_recall_curve(y_test,predictionsTestSetLightGBMGradientBoosting)\n","average_precision = \\\n","    average_precision_score(y_test,predictionsTestSetLightGBMGradientBoosting)\n","\n","plt.step(recall, precision, color='k', alpha=0.7, where='post')\n","plt.fill_between(recall, precision, step='post', alpha=0.3, color='k')\n","\n","plt.xlabel('Recall')\n","plt.ylabel('Precision')\n","plt.ylim([0.0, 1.05])\n","plt.xlim([0.0, 1.0])\n","\n","plt.title('Precision-Recall curve: Average Precision = {0:0.2f}'.format(\n","          average_precision))\n","\n","fpr, tpr, thresholds = \\\n","    roc_curve(y_test,predictionsTestSetLightGBMGradientBoosting)\n","areaUnderROC = auc(fpr, tpr)\n","\n","plt.figure()\n","plt.plot(fpr, tpr, color='r', lw=2, label='ROC curve')\n","plt.plot([0, 1], [0, 1], color='k', lw=2, linestyle='--')\n","plt.xlim([0.0, 1.0])\n","plt.ylim([0.0, 1.05])\n","plt.xlabel('False Positive Rate')\n","plt.ylabel('True Positive Rate')\n","plt.title('Receiver operating characteristic: \\\n","Area under the curve = {0:0.2f}'.format(areaUnderROC))\n","plt.legend(loc=\"lower right\")\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"collapsed":true,"id":"OzpE5NYxTIt5"},"source":["# Ensemble"]},{"cell_type":"code","metadata":{"id":"lfnDjKhhTIt5"},"source":["# Without XGBoost\n","predictionsBasedOnKFoldsFourModels = pd.DataFrame(data=[],index=y_train.index)\n","predictionsBasedOnKFoldsFourModels = predictionsBasedOnKFoldsFourModels.join(\n","    predictionsBasedOnKFoldsLogisticRegression['prediction'].astype(float), \\\n","    how='left').join(predictionsBasedOnKFoldsRandomForests['prediction'] \\\n","    .astype(float),how='left',rsuffix=\"2\").join( \\\n","    predictionsBasedOnKFoldsLightGBMGradientBoosting['prediction'].astype(float), \\\n","    how='left',rsuffix=\"4\")\n","predictionsBasedOnKFoldsFourModels.columns = \\\n","    ['predsLR','predsRF','predsLightGBM']\n","\n","# predictionsBasedOnKFoldsFourModels = pd.DataFrame(data=[],index=y_train.index)\n","# predictionsBasedOnKFoldsFourModels = predictionsBasedOnKFoldsFourModels.join(\n","#     predictionsBasedOnKFoldsLogisticRegression['prediction'].astype(float), \\\n","#     how='left').join(predictionsBasedOnKFoldsRandomForests['prediction'] \\\n","#     .astype(float),how='left',rsuffix=\"2\").join( \\\n","#     predictionsBasedOnKFoldsXGBoostGradientBoosting['prediction'].astype(float), \\\n","#     how='left',rsuffix=\"3\").join( \\\n","#     predictionsBasedOnKFoldsLightGBMGradientBoosting['prediction'].astype(float), \\\n","#     how='left',rsuffix=\"4\")\n","# predictionsBasedOnKFoldsFourModels.columns = \\\n","#     ['predsLR','predsRF','predsXGB','predsLightGBM']"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"rJFzOyTDTIt5"},"source":["X_trainWithPredictions = \\\n","    X_train.merge(predictionsBasedOnKFoldsFourModels,\n","                  left_index=True,right_index=True)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"vu22v94HTIt5"},"source":["params_lightGB = {\n","    'task': 'train',\n","    'num_class':1,\n","    'boosting': 'gbdt',\n","    'objective': 'binary',\n","    'metric': 'binary_logloss',\n","    'metric_freq':50,\n","    'is_training_metric':False,\n","    'max_depth':4,\n","    'num_leaves': 31,\n","    'learning_rate': 0.01,\n","    'feature_fraction': 1.0,\n","    'bagging_fraction': 1.0,\n","    'bagging_freq': 0,\n","    'bagging_seed': 2018,\n","    'verbose': -1,\n","    'num_threads':16\n","}"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Gp9eoeX8TIt6","executionInfo":{"status":"ok","timestamp":1620233894647,"user_tz":240,"elapsed":337246,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"658fc9f1-7aca-4bdd-b817-1e00e5c5153f"},"source":["trainingScores = []\n","cvScores = []\n","predictionsBasedOnKFoldsEnsemble = \\\n","    pd.DataFrame(data=[],index=y_train.index,columns=['prediction'])\n","\n","for train_index, cv_index in k_fold.split(np.zeros(len(X_train)), \\\n","                                          y_train.ravel()):\n","    X_train_fold, X_cv_fold = \\\n","        X_trainWithPredictions.iloc[train_index,:], \\\n","        X_trainWithPredictions.iloc[cv_index,:]\n","    y_train_fold, y_cv_fold = y_train.iloc[train_index], y_train.iloc[cv_index]\n","    \n","    lgb_train = lgb.Dataset(X_train_fold, y_train_fold)\n","    lgb_eval = lgb.Dataset(X_cv_fold, y_cv_fold, reference=lgb_train)\n","    gbm = lgb.train(params_lightGB, lgb_train, num_boost_round=2000,\n","                   valid_sets=lgb_eval, early_stopping_rounds=200)\n","    \n","    loglossTraining = log_loss(y_train_fold, \\\n","        gbm.predict(X_train_fold, num_iteration=gbm.best_iteration))\n","    trainingScores.append(loglossTraining)\n","    \n","    predictionsBasedOnKFoldsEnsemble.loc[X_cv_fold.index,'prediction'] = \\\n","        gbm.predict(X_cv_fold, num_iteration=gbm.best_iteration) \n","    loglossCV = log_loss(y_cv_fold, \\\n","        predictionsBasedOnKFoldsEnsemble.loc[X_cv_fold.index,'prediction'])\n","    cvScores.append(loglossCV)\n","    \n","    print('Training Log Loss: ', loglossTraining)\n","    print('CV Log Loss: ', loglossCV)\n","    \n","loglossEnsemble = log_loss(y_train, \\\n","        predictionsBasedOnKFoldsEnsemble.loc[:,'prediction'])\n","print('Ensemble Log Loss: ', loglossEnsemble)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[1]\tvalid_0's binary_logloss: 0.00575886\n","Training until validation scores don't improve for 200 rounds.\n","[2]\tvalid_0's 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binary_logloss: 0.00307279\n","[538]\tvalid_0's binary_logloss: 0.00307432\n","[539]\tvalid_0's binary_logloss: 0.00307417\n","Early stopping, best iteration is:\n","[339]\tvalid_0's binary_logloss: 0.00299064\n","Training Log Loss:  0.0010576114423454742\n","CV Log Loss:  0.0029906372842922377\n","[1]\tvalid_0's binary_logloss: 0.00541223\n","Training until validation scores don't improve for 200 rounds.\n","[2]\tvalid_0's binary_logloss: 0.00534835\n","[3]\tvalid_0's binary_logloss: 0.00529337\n","[4]\tvalid_0's binary_logloss: 0.00523577\n","[5]\tvalid_0's binary_logloss: 0.00518089\n","[6]\tvalid_0's binary_logloss: 0.00512847\n","[7]\tvalid_0's binary_logloss: 0.00508111\n","[8]\tvalid_0's binary_logloss: 0.00503635\n","[9]\tvalid_0's binary_logloss: 0.00498949\n","[10]\tvalid_0's binary_logloss: 0.00494422\n","[11]\tvalid_0's binary_logloss: 0.00490119\n","[12]\tvalid_0's binary_logloss: 0.00486336\n","[13]\tvalid_0's binary_logloss: 0.00482302\n","[14]\tvalid_0's binary_logloss: 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binary_logloss: 0.00347992\n","[477]\tvalid_0's binary_logloss: 0.00348044\n","[478]\tvalid_0's binary_logloss: 0.00348034\n","[479]\tvalid_0's binary_logloss: 0.00348141\n","[480]\tvalid_0's binary_logloss: 0.00348194\n","[481]\tvalid_0's binary_logloss: 0.00348324\n","[482]\tvalid_0's binary_logloss: 0.0034837\n","[483]\tvalid_0's binary_logloss: 0.00348416\n","[484]\tvalid_0's binary_logloss: 0.00348386\n","[485]\tvalid_0's binary_logloss: 0.00348387\n","[486]\tvalid_0's binary_logloss: 0.00348315\n","[487]\tvalid_0's binary_logloss: 0.00348343\n","[488]\tvalid_0's binary_logloss: 0.00348398\n","[489]\tvalid_0's binary_logloss: 0.00348514\n","[490]\tvalid_0's binary_logloss: 0.00348561\n","[491]\tvalid_0's binary_logloss: 0.00348534\n","[492]\tvalid_0's binary_logloss: 0.00348564\n","[493]\tvalid_0's binary_logloss: 0.00348612\n","[494]\tvalid_0's binary_logloss: 0.00348623\n","[495]\tvalid_0's binary_logloss: 0.0034867\n","[496]\tvalid_0's binary_logloss: 0.00348719\n","[497]\tvalid_0's binary_logloss: 0.00348784\n","[498]\tvalid_0's binary_logloss: 0.00348728\n","[499]\tvalid_0's binary_logloss: 0.0034874\n","[500]\tvalid_0's binary_logloss: 0.00348703\n","[501]\tvalid_0's binary_logloss: 0.00348754\n","[502]\tvalid_0's binary_logloss: 0.00348771\n","[503]\tvalid_0's binary_logloss: 0.00348753\n","[504]\tvalid_0's binary_logloss: 0.00348805\n","[505]\tvalid_0's binary_logloss: 0.00348858\n","[506]\tvalid_0's binary_logloss: 0.00348911\n","[507]\tvalid_0's binary_logloss: 0.00348988\n","[508]\tvalid_0's binary_logloss: 0.00349057\n","[509]\tvalid_0's binary_logloss: 0.00349053\n","[510]\tvalid_0's binary_logloss: 0.00349077\n","[511]\tvalid_0's binary_logloss: 0.00349048\n","[512]\tvalid_0's binary_logloss: 0.00349103\n","[513]\tvalid_0's binary_logloss: 0.00349148\n","[514]\tvalid_0's binary_logloss: 0.00349124\n","[515]\tvalid_0's binary_logloss: 0.00349149\n","[516]\tvalid_0's binary_logloss: 0.00349216\n","[517]\tvalid_0's binary_logloss: 0.00349291\n","[518]\tvalid_0's binary_logloss: 0.00349317\n","[519]\tvalid_0's binary_logloss: 0.00349318\n","[520]\tvalid_0's binary_logloss: 0.00349361\n","[521]\tvalid_0's binary_logloss: 0.00349433\n","[522]\tvalid_0's binary_logloss: 0.00349509\n","[523]\tvalid_0's binary_logloss: 0.00349536\n","[524]\tvalid_0's binary_logloss: 0.00349492\n","[525]\tvalid_0's binary_logloss: 0.00349484\n","[526]\tvalid_0's binary_logloss: 0.00349561\n","[527]\tvalid_0's binary_logloss: 0.00349533\n","[528]\tvalid_0's binary_logloss: 0.00349611\n","[529]\tvalid_0's binary_logloss: 0.00349669\n","[530]\tvalid_0's binary_logloss: 0.00349667\n","[531]\tvalid_0's binary_logloss: 0.00349662\n","[532]\tvalid_0's binary_logloss: 0.00349698\n","[533]\tvalid_0's binary_logloss: 0.00349765\n","[534]\tvalid_0's binary_logloss: 0.00349724\n","[535]\tvalid_0's binary_logloss: 0.00349753\n","[536]\tvalid_0's binary_logloss: 0.00349742\n","[537]\tvalid_0's binary_logloss: 0.00349811\n","[538]\tvalid_0's binary_logloss: 0.00349793\n","[539]\tvalid_0's binary_logloss: 0.00349792\n","[540]\tvalid_0's binary_logloss: 0.00349821\n","[541]\tvalid_0's binary_logloss: 0.00349851\n","[542]\tvalid_0's binary_logloss: 0.00349893\n","[543]\tvalid_0's binary_logloss: 0.00349946\n","[544]\tvalid_0's binary_logloss: 0.00349927\n","[545]\tvalid_0's binary_logloss: 0.00349998\n","[546]\tvalid_0's binary_logloss: 0.00350028\n","[547]\tvalid_0's binary_logloss: 0.00350059\n","[548]\tvalid_0's binary_logloss: 0.00350056\n","[549]\tvalid_0's binary_logloss: 0.0035009\n","[550]\tvalid_0's binary_logloss: 0.00350134\n","Early stopping, best iteration is:\n","[350]\tvalid_0's binary_logloss: 0.00345359\n","Training Log Loss:  0.000914932068081241\n","CV Log Loss:  0.0034535895589813187\n","Ensemble Log Loss:  0.0028765241424578755\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DjnPnSu6TIt7","executionInfo":{"status":"ok","timestamp":1620233894648,"user_tz":240,"elapsed":337098,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"0058dc7f-7c74-4920-e6eb-86a483cbeaa9"},"source":["print('Feature importances:', list(gbm.feature_importance()))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Feature importances: [127, 118, 64, 129, 165, 61, 68, 119, 180, 91, 182, 99, 168, 169, 362, 43, 177, 37, 67, 323, 95, 86, 146, 42, 171, 213, 248, 95, 110, 438, 186, 152, 427]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":572},"id":"3GUgbUb0TIt7","executionInfo":{"status":"ok","timestamp":1620233895712,"user_tz":240,"elapsed":337996,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"724e2818-46b0-4a17-ec11-8d3b99a5be4e"},"source":["preds = pd.concat([y_train,predictionsBasedOnKFoldsEnsemble.loc[:,'prediction']], axis=1)\n","preds.columns = ['trueLabel','prediction']\n","\n","precision, recall, thresholds = \\\n","    precision_recall_curve(preds['trueLabel'],preds['prediction'])\n","average_precision = \\\n","    average_precision_score(preds['trueLabel'],preds['prediction'])\n","\n","plt.step(recall, precision, color='k', alpha=0.7, where='post')\n","plt.fill_between(recall, precision, step='post', alpha=0.3, color='k')\n","\n","plt.xlabel('Recall')\n","plt.ylabel('Precision')\n","plt.ylim([0.0, 1.05])\n","plt.xlim([0.0, 1.0])\n","\n","plt.title('Precision-Recall curve: Average Precision = {0:0.2f}'.format(\n","          average_precision))\n","\n","fpr, tpr, thresholds = roc_curve(preds['trueLabel'],preds['prediction'])\n","areaUnderROC = auc(fpr, tpr)\n","\n","plt.figure()\n","plt.plot(fpr, tpr, color='r', lw=2, label='ROC curve')\n","plt.plot([0, 1], [0, 1], color='k', lw=2, linestyle='--')\n","plt.xlim([0.0, 1.0])\n","plt.ylim([0.0, 1.05])\n","plt.xlabel('False Positive Rate')\n","plt.ylabel('True Positive Rate')\n","plt.title('Receiver operating characteristic: \\\n","Area under the curve = {0:0.2f}'.format(areaUnderROC))\n","plt.legend(loc=\"lower right\")\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"WqzDkBVATIt7","executionInfo":{"status":"ok","timestamp":1620233896346,"user_tz":240,"elapsed":338472,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"449a5f17-e734-4094-fcd9-1665b92a8caf"},"source":["scatterData = predictionsTestSetLightGBMGradientBoosting.join(y_test,how='left')\n","scatterData.columns = ['Predicted Probability','True Label']\n","ax = sns.regplot(x=\"True Label\", y=\"Predicted Probability\", color='k', \n","                 fit_reg=False, scatter_kws={'alpha':0.1},\n","                 data=scatterData).set_title( \\\n","                'Plot of Prediction Probabilities and the True Label')"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"dhEIzJg9TIt7","executionInfo":{"status":"ok","timestamp":1620233901493,"user_tz":240,"elapsed":343475,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"c9cfa4c4-f105-4e50-9cea-412906b5aa6a"},"source":["scatterDataMelted = pd.melt(scatterData, \"True Label\", \\\n","                            var_name=\"Predicted Probability\")\n","ax = sns.stripplot(x=\"value\", y=\"Predicted Probability\", \\\n","                   hue='True Label', jitter=0.4, \\\n","                   data=scatterDataMelted).set_title( \\\n","                   'Plot of Prediction Probabilities and the True Label')"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"TOvlE1p9TIt8"},"source":["# Pipeline for New Data\n","# first, import new data into a dataframe called 'newData'\n","# second, scale data\n","# newData.loc[:,featuresToScale] = sX.transform(newData[featuresToScale])\n","# third, predict using LightGBM\n","# gbm.predict(newData, num_iteration=gbm.best_iteration)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"dHTKuHEIcit1"},"source":["# Conclusion"]}]}