solve_imbalanced_classes_credit_card_fraud_prediction.ipynb

{"cells":[{"cell_type":"code","execution_count":null,"id":"empirical-personal","metadata":{"id":"empirical-personal"},"outputs":[],"source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","from scipy.stats import t\n","from sklearn.metrics import confusion_matrix , precision_score , recall_score , f1_score\n","from sklearn.model_selection import train_test_split\n"]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ay5xyFvbJ3T3","executionInfo":{"status":"ok","timestamp":1653814393435,"user_tz":-60,"elapsed":32762,"user":{"displayName":"Hortence Phalonne Nana","userId":"07776955753800361573"}},"outputId":"dd9d6e1a-8fe6-43ca-a8f7-84d2499b5b9a"},"id":"Ay5xyFvbJ3T3","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","execution_count":null,"id":"deluxe-fraud","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":299},"id":"deluxe-fraud","executionInfo":{"status":"ok","timestamp":1653814404197,"user_tz":-60,"elapsed":5149,"user":{"displayName":"Hortence Phalonne Nana","userId":"07776955753800361573"}},"outputId":"b3020149-e530-4939-b5e8-717351a3c112"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["         V1        V2        V3        V4        V5        V6        V7  \\\n","0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n","1  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n","2 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n","3 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n","4 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n","\n","         V8        V9       V10  ...       V21       V22       V23       V24  \\\n","0  0.098698  0.363787  0.090794  ... -0.018307  0.277838 -0.110474  0.066928   \n","1  0.085102 -0.255425 -0.166974  ... -0.225775 -0.638672  0.101288 -0.339846   \n","2  0.247676 -1.514654  0.207643  ...  0.247998  0.771679  0.909412 -0.689281   \n","3  0.377436 -1.387024 -0.054952  ... -0.108300  0.005274 -0.190321 -1.175575   \n","4 -0.270533  0.817739  0.753074  ... -0.009431  0.798278 -0.137458  0.141267   \n","\n","        V25       V26       V27       V28  Amount  Class  \n","0  0.128539 -0.189115  0.133558 -0.021053  149.62      0  \n","1  0.167170  0.125895 -0.008983  0.014724    2.69      0  \n","2 -0.327642 -0.139097 -0.055353 -0.059752  378.66      0  \n","3  0.647376 -0.221929  0.062723  0.061458  123.50      0  \n","4 -0.206010  0.502292  0.219422  0.215153   69.99      0  \n","\n","[5 rows x 30 columns]"],"text/html":["\n","  <div id=\"df-1a41b565-8200-4b87-98da-edde27c0b3f8\">\n","    <div class=\"colab-df-container\">\n","      <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>...</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>-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>...</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>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>...</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>-1.358354</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>...</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>-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>...</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>-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>...</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","<p>5 rows × 30 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1a41b565-8200-4b87-98da-edde27c0b3f8')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":3}],"source":["\n","#your dataset\n","link = '/content/drive/MyDrive/dataset/creditcard.csv'\n","df_inOut = pd.read_csv(link)\n","df_inOut.drop('Time' , axis = 1 , inplace=True )\n","df_inOut.head()"]},{"cell_type":"code","execution_count":null,"id":"dependent-vienna","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":282},"id":"dependent-vienna","executionInfo":{"status":"ok","timestamp":1653814405904,"user_tz":-60,"elapsed":20,"user":{"displayName":"Hortence Phalonne Nana","userId":"07776955753800361573"}},"outputId":"d279c3a5-a1ea-45cf-f833-a5c3692b440e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7f6d75a4c850>]"]},"metadata":{},"execution_count":4},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["#before re scaling\n","plt.plot(df_inOut['Amount'])"]},{"cell_type":"code","execution_count":null,"id":"boolean-equity","metadata":{"id":"boolean-equity"},"outputs":[],"source":["# 1. If you want to add the amount column, normamlize it first between -1 and 1 before you add it , like below\n","def ReScale_centered(data, a):\n","    data_avg = data.mean()\n","    scaled_data = a*(data - data_avg)\n","    return scaled_data "]},{"cell_type":"code","execution_count":null,"id":"forced-flight","metadata":{"id":"forced-flight"},"outputs":[],"source":["df_inOut['Amount'] =  ReScale_centered(df_inOut['Amount'], 0.00001)"]},{"cell_type":"code","execution_count":null,"id":"turned-inspection","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"turned-inspection","executionInfo":{"status":"ok","timestamp":1653766496372,"user_tz":-60,"elapsed":50,"user":{"displayName":"Hortence Phalonne Nana","userId":"07776955753800361573"}},"outputId":"6c876030-b1a9-43af-c4d0-2cffefbca4eb"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(284807, 30)"]},"metadata":{},"execution_count":11}],"source":["df_inOut.shape"]},{"cell_type":"code","execution_count":null,"id":"organizational-genesis","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":282},"id":"organizational-genesis","executionInfo":{"status":"ok","timestamp":1653814411347,"user_tz":-60,"elapsed":927,"user":{"displayName":"Hortence Phalonne Nana","userId":"07776955753800361573"}},"outputId":"f18bf2b9-9d27-4f19-d928-615c5c63d526"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7f6d75536e50>]"]},"metadata":{},"execution_count":7},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["#after re scaling\n","\n","plt.plot(df_inOut['Amount'])"]},{"cell_type":"code","execution_count":null,"id":"postal-silly","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":112},"id":"postal-silly","executionInfo":{"status":"ok","timestamp":1653814417290,"user_tz":-60,"elapsed":8,"user":{"displayName":"Hortence Phalonne Nana","userId":"07776955753800361573"}},"outputId":"fe612571-b2a5-4f56-e711-c197af90dc55"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["    Class\n","0  284315\n","1     492"],"text/html":["\n","  <div id=\"df-a2d59044-3f54-4cf8-8110-93f8a1f7623f\">\n","    <div class=\"colab-df-container\">\n","      <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","  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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":8}],"source":["df_prop  =  pd.DataFrame(df_inOut['Class'].value_counts())\n","df_prop"]},{"cell_type":"code","execution_count":null,"id":"featured-mixer","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":112},"id":"featured-mixer","executionInfo":{"status":"ok","timestamp":1653814419713,"user_tz":-60,"elapsed":8,"user":{"displayName":"Hortence Phalonne Nana","userId":"07776955753800361573"}},"outputId":"84125697-c522-41ee-9bd0-c9e78c6d4a5a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["    Class  perctage\n","0  284315  0.998273\n","1     492  0.001727"],"text/html":["\n","  <div id=\"df-b4d21064-33d1-473a-83cc-c5c792f5b080\">\n","    <div class=\"colab-df-container\">\n","      <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>Class</th>\n","      <th>perctage</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>284315</td>\n","      <td>0.998273</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>492</td>\n","      <td>0.001727</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b4d21064-33d1-473a-83cc-c5c792f5b080')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-b4d21064-33d1-473a-83cc-c5c792f5b080 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-b4d21064-33d1-473a-83cc-c5c792f5b080');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":9}],"source":["df_prop['perctage'] =  round(df_prop['Class']/sum(df_prop['Class']), 9)\n","df_prop"]},{"cell_type":"code","execution_count":null,"id":"suffering-halifax","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"suffering-halifax","executionInfo":{"status":"ok","timestamp":1653766528663,"user_tz":-60,"elapsed":1222,"user":{"displayName":"Hortence Phalonne Nana","userId":"07776955753800361573"}},"outputId":"8e9fb2d7-1f08-422d-edc2-cc7cb8663b88"},"outputs":[{"output_type":"stream","name":"stdout","text":["the ratio of imbalance is  1 over 577\n"]}],"source":["# the ratio is \n","\n","print( \"the ratio of imbalance is  1 over\" , 284315//492)\n"]},{"cell_type":"code","execution_count":null,"id":"naked-titanium","metadata":{"id":"naked-titanium"},"outputs":[],"source":[""]},{"cell_type":"code","execution_count":null,"id":"informative-kazakhstan","metadata":{"id":"informative-kazakhstan"},"outputs":[],"source":["#to divide your data into while keeping a certain ratio in the training set\n","# def data_smart_divider(df_inOut, sample_minority , mult_factor): \n","#     df_class0 =  df_inOut[df_inOut['Class'] == 0]\n","#     df_class1 = df_inOut[df_inOut['Class'] == 1]\n","#     df_train = pd.concat([df_class1.head(sample_minority) , df_class0.head(sample_minority*mult_factor)] , axis = 0)\n","#     df_val_test = pd.concat([df_class1.tail(len(df_class1) -sample_minority) , df_class0.tail(len(df_class0)-sample_minority*mult_factor)] , axis = 0)\n","#     return df_train.drop('Class' , axis=1).values , df_train['Class'].values  , df_val_test.drop('Class' , axis=1 ).values , df_val_test['Class'].values "]},{"cell_type":"code","execution_count":null,"id":"extraordinary-editor","metadata":{"id":"extraordinary-editor"},"outputs":[],"source":["#to try, run this \n","#X_train, y_train , X_test , y_test =  data_smart_divider( df_inOut , 100 , 1000)"]},{"cell_type":"code","execution_count":null,"id":"reliable-webster","metadata":{"id":"reliable-webster","outputId":"af8d9fc3-1608-44f7-c735-4a6725b826eb"},"outputs":[{"name":"stdout","output_type":"stream","text":["(100100, 28) (100100,) (184707, 28) (184707,)\n"]}],"source":["#print(X_train.shape, y_train.shape , X_test.shape , y_test.shape)"]},{"cell_type":"code","execution_count":null,"id":"expired-reynolds","metadata":{"id":"expired-reynolds"},"outputs":[],"source":[""]},{"cell_type":"code","execution_count":null,"id":"norwegian-paradise","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"norwegian-paradise","executionInfo":{"status":"ok","timestamp":1653814426631,"user_tz":-60,"elapsed":620,"user":{"displayName":"Hortence Phalonne Nana","userId":"07776955753800361573"}},"outputId":"191dd712-16b1-49e1-fc6d-64a258fa3491"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0    284315\n","1       492\n","Name: Class, dtype: int64"]},"metadata":{},"execution_count":10}],"source":["df_inOut['Class'].value_counts()"]},{"cell_type":"code","execution_count":null,"id":"arbitrary-agent","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"arbitrary-agent","executionInfo":{"status":"ok","timestamp":1653814429508,"user_tz":-60,"elapsed":17,"user":{"displayName":"Hortence Phalonne Nana","userId":"07776955753800361573"}},"outputId":"59062466-7274-4b19-e6c5-11799acefa86"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(500, 30)"]},"metadata":{},"execution_count":11}],"source":["df_inOut[df_inOut['Class'] == 0].tail(500).shape"]},{"cell_type":"code","execution_count":null,"id":"floating-establishment","metadata":{"id":"floating-establishment"},"outputs":[],"source":["def data_smart_divider_TrainValidTest(df_inOut, sample_minority , mult_factor , n_valid_samples, n_test_samples): \n","    df_class0 =  df_inOut[df_inOut['Class'] == 0]\n","    df_class1 = df_inOut[df_inOut['Class'] == 1]\n","    \n","    df_test_0 = df_inOut[df_inOut['Class'] == 0].tail(n_test_samples)\n","    df_test_1 = df_inOut[df_inOut['Class'] == 1].tail(n_test_samples)\n","    df_test =  pd.concat([df_test_0 , df_test_1] , axis = 0)\n","    \n","    df_all_withoutTest_0 = df_class0.head(len(df_class0) - n_test_samples)\n","    df_all_withoutTest_1 = df_class1.head(len(df_class1) - n_test_samples) \n","    df_all_withoutTest =  pd.concat([df_all_withoutTest_0 , df_all_withoutTest_1] , axis = 0)\n","    \n","    df_valid_0 = df_all_withoutTest[df_all_withoutTest['Class'] == 0].tail(n_valid_samples)\n","    df_valid_1 = df_all_withoutTest[df_all_withoutTest['Class'] == 1].tail(n_valid_samples)\n","    df_valid =  pd.concat([df_valid_0 , df_valid_1] , axis = 0)\n","    \n","    df_all_withoutTestValid_0  = df_all_withoutTest_0.head(len(df_all_withoutTest_0) - n_valid_samples)\n","    df_all_withoutTestValid_1  = df_all_withoutTest_1.head(len(df_all_withoutTest_1) - n_valid_samples)\n","    \n","    df_train = pd.concat([df_all_withoutTestValid_1.head(sample_minority) , df_all_withoutTestValid_0.head(sample_minority*mult_factor)] , axis = 0)\n","    \n","    x_train , y_train = df_train.drop('Class' , axis=1).values, df_train['Class'].values\n","    x_valid , y_valid = df_valid.drop('Class' , axis=1 ).values, df_valid['Class'].values\n","    x_test , y_test   = df_test.drop('Class' , axis=1 ).values , df_test['Class'].values\n","    \n","    return   x_train , y_train   ,  x_valid , y_valid   , x_test , y_test "]},{"cell_type":"code","execution_count":null,"id":"offshore-iraqi","metadata":{"id":"offshore-iraqi"},"outputs":[],"source":["x_train , y_train   ,  x_valid , y_valid   , x_test , y_test  = data_smart_divider_TrainValidTest(df_inOut, 300 , 50 , 92, 100)"]},{"cell_type":"code","execution_count":null,"id":"running-creature","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"running-creature","executionInfo":{"status":"ok","timestamp":1653819601707,"user_tz":-60,"elapsed":22,"user":{"displayName":"Hortence Phalonne Nana","userId":"07776955753800361573"}},"outputId":"067dca2f-e4ca-4bda-bbd1-914caf57bde0"},"outputs":[{"output_type":"stream","name":"stdout","text":["(15300, 29) (15300,) (184, 29) (184,) (200, 29) (200,)\n"]}],"source":["print(x_train.shape , y_train.shape   ,  x_valid.shape , y_valid.shape   , x_test.shape , y_test.shape )"]},{"cell_type":"code","execution_count":null,"id":"central-junior","metadata":{"id":"central-junior"},"outputs":[],"source":["# def data_smart_divider(df_inOut, sample_minority , mult_factor): \n","#     df_class0 =  df_inOut[df_inOut['Class'] == 0]\n","#     df_class1 = df_inOut[df_inOut['Class'] == 1]\n","#     df_train = pd.concat([df_class1.head(sample_minority) , df_class0.head(sample_minority*mult_factor)] , axis = 0)\n","#     df_val_test = pd.concat([df_class1.tail(len(df_class1) -sample_minority) , df_class0.tail(len(df_class0)-sample_minority*mult_factor)] , axis = 0)\n","#     return df_train.drop('Class' , axis=1).values , df_train['Class'].values  , df_val_test.drop('Class' , axis=1 ).values , df_val_test['Class'].values "]},{"cell_type":"code","execution_count":null,"id":"peripheral-indicator","metadata":{"id":"peripheral-indicator"},"outputs":[],"source":[""]},{"cell_type":"code","execution_count":null,"id":"infinite-hacker","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"infinite-hacker","executionInfo":{"status":"ok","timestamp":1653818973727,"user_tz":-60,"elapsed":958,"user":{"displayName":"Hortence Phalonne Nana","userId":"07776955753800361573"}},"outputId":"befa8aad-1ff8-445b-b07c-f037c700997e"},"outputs":[{"output_type":"stream","name":"stdout","text":["NanaNet(\n","  (fc1): Linear(in_features=29, out_features=150, bias=False)\n","  (fc2): Linear(in_features=150, out_features=160, bias=False)\n","  (fc3): Linear(in_features=160, out_features=160, bias=False)\n","  (bnn1): BatchNorm1d(150, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (bnn2): BatchNorm1d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (fc4): Linear(in_features=160, out_features=2, bias=False)\n","  (dropout): Dropout(p=0.25, inplace=False)\n",")\n"]}],"source":["\n","# our network\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","\n","\n","class NanaNet(nn.Module):\n","\n","    def __init__(self, n_neurons_1layer , n_neurons_2layer):\n","        super(NanaNet, self).__init__()\n","        self.fc1 = nn.Linear( 29, n_neurons_1layer , bias =  False) \n","        self.fc2 = nn.Linear(n_neurons_1layer,n_neurons_2layer,bias =  False)\n","        self.fc3 = nn.Linear(n_neurons_2layer,n_neurons_2layer,bias =  False)\n","        self.bnn1 = nn.BatchNorm1d(n_neurons_1layer)\n","        self.bnn2 = nn.BatchNorm1d(n_neurons_2layer)\n","        self.fc4 = nn.Linear(n_neurons_2layer , 2,bias =  False ) \n","        self.dropout = nn.Dropout(0.25)\n","    def forward(self, x):\n","        x = torch.relu(self.fc1(x)) #dense( 28 , 40)\n","        x = self.bnn1(x) #batchnorm(40)\n","        x =  self.dropout(x) #dropout(40)\n","        x = torch.relu(self.fc2(x)) #dense(40, 50)\n","        x = self.bnn2(x)#batchnorm(50)\n","        x =  self.dropout(x)  #dropout(50)\n","        x = torch.relu(self.fc3(x)) #dense(50, 50)\n","        x = self.bnn2(x) #batchnorm(50)\n","        x =  self.dropout(x)   #dropout(50)\n","        #out_ = torch.sigmoid(self.fc3(x))\n","    \n","        out_ = torch.sigmoid(self.fc4(x))  #dense(50, 2)\n","        output =  torch.softmax(out_, dim=1)\n","        return output #out_ \n","\n","    \n","n_neurons_1layer , n_neurons_2layer = 40 , 60\n","\n","nana_reseau = NanaNet(n_neurons_1layer , n_neurons_2layer)\n","\n","print(nana_reseau)"]},{"cell_type":"code","execution_count":null,"id":"anticipated-recipe","metadata":{"id":"anticipated-recipe"},"outputs":[],"source":["#the BCE Loss\n","def my_cross_entropy(y_pred, y_true):\n","    log_loss=torch.mean(-1*(y_true*torch.log(y_pred[:,1]) + (1-y_true)*torch.log(1-y_pred[:,1])))\n","    return log_loss"]},{"cell_type":"markdown","id":"dutch-chess","metadata":{"id":"dutch-chess"},"source":["### # be careful on which loss to choose\n"]},{"cell_type":"code","execution_count":null,"id":"patent-ribbon","metadata":{"id":"patent-ribbon"},"outputs":[],"source":[""]},{"cell_type":"code","execution_count":null,"id":"creative-czech","metadata":{"id":"creative-czech"},"outputs":[],"source":["#this loss has the absolute value\n","def KD_fromStudent_loss(y_pred, y_true, degree_freedom):\n","    #to set the KD_Loss \n","    kl_loss_formula = nn.KLDivLoss(reduction=\"sum\")\n","    \n","    #to transformed the distribution and fit the student t\n","    pdf_y_pred_dist_0 = torch.tensor(t.pdf(y_pred[:,0].detach().numpy(), df=degree_freedom))\n","    pdf_y_pred_dist_1 = torch.tensor(t.pdf(y_pred[:,1].detach().numpy(), df=degree_freedom))\n","    \n","    #to  compute the  two KD_Loss\n","    kd_loss_p1_on_p0 = torch.abs(kl_loss_formula(pdf_y_pred_dist_0, torch.abs(pdf_y_pred_dist_1)))\n","    kd_loss_p0_on_p1 =  torch.abs(kl_loss_formula(torch.abs(pdf_y_pred_dist_1), torch.abs(pdf_y_pred_dist_0)))\n","    #the mean divergence (if it does not help in the learning, you can cancel it)\n","    \n","    #the BCE Loss\n","    log_loss = torch.mean(-1*(y_true*torch.log(y_pred[:,1]) + (1-y_true)*torch.log(1-y_pred[:,1])))\n","    \n","    result =  (0.5/2)*(kd_loss_p1_on_p0 + kd_loss_p0_on_p1) + log_loss\n","    return result  , pdf_y_pred_dist_0 , pdf_y_pred_dist_1"]},{"cell_type":"code","execution_count":null,"id":"identified-demonstration","metadata":{"id":"identified-demonstration"},"outputs":[],"source":["#this loss does not have the absolute value\n","\n","def KD_fromStudent_loss(y_pred, y_true, degree_freedom):\n","    kl_loss_formula = nn.KLDivLoss(reduction=\"batchmean\")\n","    \n","    pdf_y_pred_dist_0 = torch.tensor(t.pdf(y_pred[:,0].detach().numpy(), df=degree_freedom))\n","    pdf_y_pred_dist_1 = torch.tensor(t.pdf(y_pred[:,1].detach().numpy(), df=degree_freedom))\n","    \n","    kd_loss_p1_on_p0 =kl_loss_formula(pdf_y_pred_dist_0, torch.abs(pdf_y_pred_dist_1))\n","    kd_loss_p0_on_p1 =  kl_loss_formula(torch.abs(pdf_y_pred_dist_1), torch.abs(pdf_y_pred_dist_0))\n","    \n","    log_loss = torch.mean(-1*(y_true*torch.log(y_pred[:,1]) + (1-y_true)*torch.log(1-y_pred[:,1])))\n","    result =  (0.5/2)*(kd_loss_p1_on_p0 + kd_loss_p0_on_p1) + log_loss\n","    return result  , pdf_y_pred_dist_0 , pdf_y_pred_dist_1"]},{"cell_type":"code","execution_count":null,"id":"renewable-riding","metadata":{"id":"renewable-riding"},"outputs":[],"source":["def KD_fromStudent_loss_1(y_pred, y_true, degree_freedom_0 ,degree_freedom_1 ):\n","    #to set the KD_Loss \n","    lambda_md  =  0.001\n","    lambda_kl = 0.001\n","    kl_loss_formula = nn.KLDivLoss(reduction=\"batchmean\")\n","    \n","    #to transformed the distribution and fit the student t\n","    pdf_y_pred_dist_0 = torch.tensor(t.pdf(y_pred[:,0].detach().cpu().numpy(), df=degree_freedom_0)) #permute 0 par 1 et vice versa\n","    pdf_y_pred_dist_1 = torch.tensor(t.pdf(y_pred[:,1].detach().cpu().numpy(), df=degree_freedom_1))\n","    \n","    #to  compute the  two KD_Loss\n","    kd_loss_p1_on_p0 = torch.abs(kl_loss_formula(pdf_y_pred_dist_0, torch.abs(pdf_y_pred_dist_1)))\n","    kd_loss_p0_on_p1 =  torch.abs(kl_loss_formula(torch.abs(pdf_y_pred_dist_1), torch.abs(pdf_y_pred_dist_0)))\n","    #the mean divergence (if it does not help in the learning, you can cancel it)\n","    mean_divergence_term = lambda_md * torch.abs(  1/2  - torch.mean(y_pred[:,1]))**2\n","    #the BCE Loss\n","    log_loss =  torch.mean(-1*(y_true*torch.log(y_pred[:,1]) + (1-y_true)*torch.log(1-y_pred[:,1])))\n","    \n","    result =  (lambda_kl/2)*(kd_loss_p1_on_p0 + kd_loss_p0_on_p1) + log_loss + (lambda_md/2)*mean_divergence_term\n","    return result  , pdf_y_pred_dist_0 , pdf_y_pred_dist_1 "]},{"cell_type":"code","execution_count":null,"id":"awful-remove","metadata":{"collapsed":true,"id":"awful-remove","outputId":"8fe69da5-9a59-466c-ea58-d7ae21d93698"},"outputs":[{"name":"stdout","output_type":"stream","text":["df =  0 tensor(1.7244, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  1 tensor(1.8121, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  2 tensor(1.8458, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  3 tensor(1.8635, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  4 tensor(1.8745, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  5 tensor(1.8819, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  6 tensor(1.8873, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  7 tensor(1.8913, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  8 tensor(1.8945, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  9 tensor(1.8970, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  10 tensor(1.8991, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  11 tensor(1.9008, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  12 tensor(1.9023, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  13 tensor(1.9036, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  14 tensor(1.9047, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  15 tensor(1.9057, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  16 tensor(1.9065, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  17 tensor(1.9073, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  18 tensor(1.9080, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  19 tensor(1.9086, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  20 tensor(1.9091, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  21 tensor(1.9096, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  22 tensor(1.9101, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  23 tensor(1.9105, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  24 tensor(1.9109, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  25 tensor(1.9113, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  26 tensor(1.9116, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  27 tensor(1.9119, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  28 tensor(1.9122, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  29 tensor(1.9125, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  30 tensor(1.9127, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  31 tensor(1.9130, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  32 tensor(1.9132, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  33 tensor(1.9134, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  34 tensor(1.9136, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  35 tensor(1.9138, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  36 tensor(1.9140, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  37 tensor(1.9141, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  38 tensor(1.9143, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  39 tensor(1.9144, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  40 tensor(1.9146, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  41 tensor(1.9147, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  42 tensor(1.9148, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  43 tensor(1.9150, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  44 tensor(1.9151, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  45 tensor(1.9152, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  46 tensor(1.9153, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  47 tensor(1.9154, dtype=torch.float64, grad_fn=<AddBackward0>)\n","df =  48 tensor(1.9155, dtype=torch.float64, grad_fn=<AddBackward0>)\n"]}],"source":["#if you testyout code just for one sample . you can play around with the degree of freedom adn see how it helps or not\n","# for i , j in enumerate(range(1,50)):\n","#     print(\"df = \", i , KD_fromStudent_loss(out_ , label.float() , j))"]},{"cell_type":"code","execution_count":null,"id":"numerous-juice","metadata":{"id":"numerous-juice","outputId":"3986faae-d208-44c4-cf2e-eadfea3c0fd0"},"outputs":[{"data":{"text/plain":["((30300, 29), (30300,), (184, 29), (184,), (200, 29), (200,))"]},"execution_count":396,"metadata":{},"output_type":"execute_result"}],"source":["X_train.shape , y_train.shape   ,  X_valid.shape , y_valid.shape   , X_test.shape , y_test.shape"]},{"cell_type":"code","execution_count":null,"id":"german-treasury","metadata":{"id":"german-treasury"},"outputs":[],"source":["import torch\n","import torch.nn as nn\n","import argparse\n","import numpy as num\n","import pandas as pds\n","from sklearn.model_selection import train_test_split\n","from torch.autograd import Variable\n","\n","#X_train, X_test, y_train, y_test = train_test_split(X, Y,test_size = 0.2,random_state = 42)\n","# X_train, y_train , X_test , y_test =  data_smart_divider( df_inOut , 100 , 100)\n","# X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train ,test_size = 0.2,random_state = 42)\n","X_train , y_train   ,  X_valid , y_valid   , X_test , y_test  = data_smart_divider_TrainValidTest(df_inOut, 300 , 50 , 92, 100)                                                                            \n","# create feature and targets tensor for train set\n","featuresTrain = torch.from_numpy(X_train)\n","targetsTrain = torch.from_numpy(y_train)#.type(torch.LongTensor) \n","                                                                            \n","# create feature and targets tensor for valid set\n","featuresValid = torch.from_numpy(X_valid)\n","targetsValid= torch.from_numpy(y_valid)#.type(torch.LongTensor) \n","\n","# create feature and targets tensor for test set.\n","featuresTest = torch.from_numpy(X_test)\n","targetsTest = torch.from_numpy(y_test)#.type(torch.LongTensor) \n","\n","# batch_size, epoch and iteration\n","batch_size = 10\n","n_iters = 100\n","num_epochs = n_iters / (len(featuresTrain) / batch_size)\n","num_epochs = int(num_epochs)\n","\n","# Pytorch train and test sets\n","train = torch.utils.data.TensorDataset(featuresTrain,targetsTrain)\n","valid =  torch.utils.data.TensorDataset(featuresValid, targetsValid)\n","test = torch.utils.data.TensorDataset(featuresTest,targetsTest)\n","\n","# data loader\n","train_loader = torch.utils.data.DataLoader(train, batch_size = batch_size, shuffle = True)\n","valid_loader = torch.utils.data.DataLoader(valid, batch_size = batch_size, shuffle = True)\n","\n","test_loader = torch.utils.data.DataLoader(test, batch_size = batch_size, shuffle = False)\n"]},{"cell_type":"code","execution_count":null,"id":"looking-worcester","metadata":{"id":"looking-worcester","outputId":"9fee924c-3815-41cc-8498-53f3908c3a69"},"outputs":[{"data":{"text/plain":["19"]},"execution_count":185,"metadata":{},"output_type":"execute_result"}],"source":["len(valid_loader)"]},{"cell_type":"code","execution_count":null,"id":"cleared-privacy","metadata":{"id":"cleared-privacy","outputId":"e5410acc-5f30-4938-ba83-3575ca0e987f"},"outputs":[{"data":{"text/plain":["20"]},"execution_count":186,"metadata":{},"output_type":"execute_result"}],"source":["len(test_loader)"]},{"cell_type":"code","execution_count":null,"id":"raising-playback","metadata":{"collapsed":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"raising-playback","executionInfo":{"status":"ok","timestamp":1653819720836,"user_tz":-60,"elapsed":101867,"user":{"displayName":"Hortence Phalonne Nana","userId":"07776955753800361573"}},"outputId":"5a48ce8f-b667-4faa-fdef-7dc159971186"},"outputs":[{"output_type":"stream","name":"stdout","text":["Device state: cpu\n","[1/20, 100/1530] loss: 0.31374196\n","[1/20, 200/1530] loss: 0.31374196\n","[1/20, 300/1530] loss: 0.31374196\n","[1/20, 400/1530] loss: 0.31374196\n","[1/20, 500/1530] loss: 0.31374198\n","[1/20, 600/1530] loss: 0.31374196\n","[1/20, 700/1530] loss: 0.31374196\n","[1/20, 800/1530] loss: 0.41374198\n","[1/20, 900/1530] loss: 0.31374196\n","[1/20, 1000/1530] loss: 0.31374196\n","[1/20, 1100/1530] loss: 0.31374196\n","[1/20, 1200/1530] loss: 0.31374196\n","[1/20, 1300/1530] loss: 0.31374196\n","[1/20, 1400/1530] loss: 0.31374196\n","[1/20, 1500/1530] loss: 0.31374196\n","[1/20, 1530/1530] loss: 0.31374196\n","====================\n","tr_loss  =  0.03204\n","Accuracy =  0.99333\n","f1_score =  0.8125\n","conf_mat =  [[14977    23]\n"," [   79   221]]\n","precision_maj =  0.9985\n","precision_min =  0.7367\n","====================\n","====================\n","val_loss  =  0.04674\n","Accuracy =  0.83696\n","f1_score =  0.80519\n","conf_mat =  [[92  0]\n"," [30 62]]\n","precision_maj =  1.0\n","precision_min =  0.6739\n","====================\n","The Current train Loss: 0.032  and the val_loss 0.0467\n","trigger times: 0\n","[2/20, 100/1530] loss: 0.31374196\n","[2/20, 200/1530] loss: 0.31374196\n","[2/20, 300/1530] loss: 0.31374196\n","[2/20, 400/1530] loss: 0.31374196\n","[2/20, 500/1530] loss: 0.31374196\n","[2/20, 600/1530] loss: 0.31374196\n","[2/20, 700/1530] loss: 0.31374196\n","[2/20, 800/1530] loss: 0.31374196\n","[2/20, 900/1530] loss: 0.31374196\n","[2/20, 1000/1530] loss: 0.31374196\n","[2/20, 1100/1530] loss: 0.31374196\n","[2/20, 1200/1530] loss: 0.31374196\n","[2/20, 1300/1530] loss: 0.31374198\n","[2/20, 1400/1530] loss: 0.31374196\n","[2/20, 1500/1530] loss: 0.31374196\n","[2/20, 1530/1530] loss: 0.31374196\n","====================\n","tr_loss  =  0.03214\n","Accuracy =  0.99157\n","f1_score =  0.75048\n","conf_mat =  [[14977    23]\n"," [  106   194]]\n","precision_maj =  0.9985\n","precision_min =  0.6467\n","====================\n","====================\n","val_loss  =  0.04845\n","Accuracy =  0.81522\n","f1_score =  0.77333\n","conf_mat =  [[92  0]\n"," [34 58]]\n","precision_maj =  1.0\n","precision_min =  0.6304\n","====================\n","The Current train Loss: 0.0321  and the val_loss 0.0484\n","Trigger Times: 1\n","[3/20, 100/1530] loss: 0.31374196\n","[3/20, 200/1530] loss: 0.31374196\n","[3/20, 300/1530] loss: 0.31374196\n","[3/20, 400/1530] loss: 0.31374196\n","[3/20, 500/1530] loss: 0.31374198\n","[3/20, 600/1530] loss: 0.31374196\n","[3/20, 700/1530] loss: 0.31374196\n","[3/20, 800/1530] loss: 0.31374196\n","[3/20, 900/1530] loss: 0.31374196\n","[3/20, 1000/1530] loss: 0.31374196\n","[3/20, 1100/1530] loss: 0.31374196\n","[3/20, 1200/1530] loss: 0.31374196\n","[3/20, 1300/1530] loss: 0.31374196\n","[3/20, 1400/1530] loss: 0.31374198\n","[3/20, 1500/1530] loss: 0.413742\n","[3/20, 1530/1530] loss: 0.31374196\n","====================\n","tr_loss  =  0.03329\n","Accuracy =  0.98039\n","f1_score =  0.0\n","conf_mat =  [[15000     0]\n"," [  300     0]]\n","precision_maj =  1.0\n","precision_min =  0.0\n","====================\n","====================\n","val_loss  =  0.08133\n","Accuracy =  0.5\n","f1_score =  0.0\n","conf_mat =  [[92  0]\n"," [92  0]]\n","precision_maj =  1.0\n","precision_min =  0.0\n","====================\n","The Current train Loss: 0.0333  and the val_loss 0.0813\n","Trigger Times: 2\n","[4/20, 100/1530] loss: 0.31374196\n","[4/20, 200/1530] loss: 0.31374196\n","[4/20, 300/1530] loss: 0.31374196\n","[4/20, 400/1530] loss: 0.31374196\n","[4/20, 500/1530] loss: 0.31374196\n","[4/20, 600/1530] loss: 0.31374196\n","[4/20, 700/1530] loss: 0.41374192\n","[4/20, 800/1530] loss: 0.31374196\n","[4/20, 900/1530] loss: 0.31374196\n","[4/20, 1000/1530] loss: 0.31374196\n","[4/20, 1100/1530] loss: 0.31374198\n","[4/20, 1200/1530] loss: 0.31374196\n","[4/20, 1300/1530] loss: 0.31374196\n","[4/20, 1400/1530] loss: 0.31374196\n","[4/20, 1500/1530] loss: 0.31374196\n","[4/20, 1530/1530] loss: 0.31374196\n","====================\n","tr_loss  =  0.03329\n","Accuracy =  0.98039\n","f1_score =  0.0\n","conf_mat =  [[15000     0]\n"," [  300     0]]\n","precision_maj =  1.0\n","precision_min =  0.0\n","====================\n","====================\n","val_loss  =  0.08054\n","Accuracy =  0.5\n","f1_score =  0.0\n","conf_mat =  [[92  0]\n"," [92  0]]\n","precision_maj =  1.0\n","precision_min =  0.0\n","====================\n","The Current train Loss: 0.0333  and the val_loss 0.0805\n","trigger times: 0\n","[5/20, 100/1530] loss: 0.31374196\n","[5/20, 200/1530] loss: 0.31374196\n","[5/20, 300/1530] loss: 0.31374198\n","[5/20, 400/1530] loss: 0.31374196\n","[5/20, 500/1530] loss: 0.31374196\n","[5/20, 600/1530] loss: 0.31374196\n","[5/20, 700/1530] loss: 0.31374196\n","[5/20, 800/1530] loss: 0.41374192\n","[5/20, 900/1530] loss: 0.41374198\n","[5/20, 1000/1530] loss: 0.31374196\n","[5/20, 1100/1530] loss: 0.31374196\n","[5/20, 1200/1530] loss: 0.31374196\n","[5/20, 1300/1530] loss: 0.31374196\n","[5/20, 1400/1530] loss: 0.31374196\n","[5/20, 1500/1530] loss: 0.31374198\n","[5/20, 1530/1530] loss: 0.31374198\n","====================\n","tr_loss  =  0.03329\n","Accuracy =  0.98039\n","f1_score =  0.0\n","conf_mat =  [[15000     0]\n"," [  300     0]]\n","precision_maj =  1.0\n","precision_min =  0.0\n","====================\n","====================\n","val_loss  =  0.08054\n","Accuracy =  0.5\n","f1_score =  0.0\n","conf_mat =  [[92  0]\n"," [92  0]]\n","precision_maj =  1.0\n","precision_min =  0.0\n","====================\n","The Current train Loss: 0.0333  and the val_loss 0.0805\n","trigger times: 0\n","[6/20, 100/1530] loss: 0.31374196\n","[6/20, 200/1530] loss: 0.31374198\n","[6/20, 300/1530] loss: 0.31374196\n","[6/20, 400/1530] loss: 0.31374196\n","[6/20, 500/1530] loss: 0.31374196\n","[6/20, 600/1530] loss: 0.31374196\n","[6/20, 700/1530] loss: 0.31374196\n","[6/20, 800/1530] loss: 0.31374196\n","[6/20, 900/1530] loss: 0.31374196\n","[6/20, 1000/1530] loss: 0.31374196\n","[6/20, 1100/1530] loss: 0.31374196\n","[6/20, 1200/1530] loss: 0.31374196\n","[6/20, 1300/1530] loss: 0.31374198\n","[6/20, 1400/1530] loss: 0.31374196\n","[6/20, 1500/1530] loss: 0.31374198\n","[6/20, 1530/1530] loss: 0.31374196\n","====================\n","tr_loss  =  0.03329\n","Accuracy =  0.98039\n","f1_score =  0.0\n","conf_mat =  [[15000     0]\n"," [  300     0]]\n","precision_maj =  1.0\n","precision_min =  0.0\n","====================\n","====================\n","val_loss  =  0.08211\n","Accuracy =  0.5\n","f1_score =  0.0\n","conf_mat =  [[92  0]\n"," [92  0]]\n","precision_maj =  1.0\n","precision_min =  0.0\n","====================\n","The Current train Loss: 0.0333  and the val_loss 0.0821\n","Trigger Times: 1\n","[7/20, 100/1530] loss: 0.31374196\n","[7/20, 200/1530] loss: 0.31374196\n","[7/20, 300/1530] loss: 0.31374196\n","[7/20, 400/1530] loss: 0.31374196\n","[7/20, 500/1530] loss: 0.31374196\n","[7/20, 600/1530] loss: 0.31374196\n","[7/20, 700/1530] loss: 0.31374196\n","[7/20, 800/1530] loss: 0.31374196\n","[7/20, 900/1530] loss: 0.31374196\n","[7/20, 1000/1530] loss: 0.31374198\n","[7/20, 1100/1530] loss: 0.31374196\n","[7/20, 1200/1530] loss: 0.31374196\n","[7/20, 1300/1530] loss: 0.31374196\n","[7/20, 1400/1530] loss: 0.31374198\n","[7/20, 1500/1530] loss: 0.31374196\n","[7/20, 1530/1530] loss: 0.31374196\n","====================\n","tr_loss  =  0.03319\n","Accuracy =  0.98078\n","f1_score =  0.03922\n","conf_mat =  [[15000     0]\n"," [  294     6]]\n","precision_maj =  1.0\n","precision_min =  0.02\n","====================\n","====================\n","val_loss  =  0.07799\n","Accuracy =  0.5163\n","f1_score =  0.06316\n","conf_mat =  [[92  0]\n"," [89  3]]\n","precision_maj =  1.0\n","precision_min =  0.0326\n","====================\n","The Current train Loss: 0.0332  and the val_loss 0.078\n","trigger times: 0\n","[8/20, 100/1530] loss: 0.31374196\n","[8/20, 200/1530] loss: 0.31374196\n","[8/20, 300/1530] loss: 0.41374192\n","[8/20, 400/1530] loss: 0.31374196\n","[8/20, 500/1530] loss: 0.31374196\n","[8/20, 600/1530] loss: 0.31374198\n","[8/20, 700/1530] loss: 0.31374196\n","[8/20, 800/1530] loss: 0.31374196\n","[8/20, 900/1530] loss: 0.31374196\n","[8/20, 1000/1530] loss: 0.31374196\n","[8/20, 1100/1530] loss: 0.31374196\n","[8/20, 1200/1530] loss: 0.31374196\n","[8/20, 1300/1530] loss: 0.31374196\n","[8/20, 1400/1530] loss: 0.31374196\n","[8/20, 1500/1530] loss: 0.31374196\n","[8/20, 1530/1530] loss: 0.31374196\n","====================\n","tr_loss  =  0.03328\n","Accuracy =  0.98039\n","f1_score =  0.0\n","conf_mat =  [[15000     0]\n"," [  300     0]]\n","precision_maj =  1.0\n","precision_min =  0.0\n","====================\n","====================\n","val_loss  =  0.08051\n","Accuracy =  0.5\n","f1_score =  0.0\n","conf_mat =  [[92  0]\n"," [92  0]]\n","precision_maj =  1.0\n","precision_min =  0.0\n","====================\n","The Current train Loss: 0.0333  and the val_loss 0.0805\n","Trigger Times: 1\n","[9/20, 100/1530] loss: 0.31374196\n","[9/20, 200/1530] loss: 0.31374196\n","[9/20, 300/1530] loss: 0.31374196\n","[9/20, 400/1530] loss: 0.31374196\n","[9/20, 500/1530] loss: 0.31374196\n","[9/20, 600/1530] loss: 0.31374196\n","[9/20, 700/1530] loss: 0.31374196\n","[9/20, 800/1530] loss: 0.31374196\n","[9/20, 900/1530] loss: 0.31374198\n","[9/20, 1000/1530] loss: 0.31374196\n","[9/20, 1100/1530] loss: 0.31374198\n","[9/20, 1200/1530] loss: 0.31374196\n","[9/20, 1300/1530] loss: 0.31374196\n","[9/20, 1400/1530] loss: 0.41374188\n","[9/20, 1500/1530] loss: 0.31374196\n","[9/20, 1530/1530] loss: 0.31374198\n","====================\n","tr_loss  =  0.03328\n","Accuracy =  0.98039\n","f1_score =  0.0\n","conf_mat =  [[15000     0]\n"," [  300     0]]\n","precision_maj =  1.0\n","precision_min =  0.0\n","====================\n","====================\n","val_loss  =  0.08115\n","Accuracy =  0.5\n","f1_score =  0.0\n","conf_mat =  [[92  0]\n"," [92  0]]\n","precision_maj =  1.0\n","precision_min =  0.0\n","====================\n","The Current train Loss: 0.0333  and the val_loss 0.0811\n","Trigger Times: 2\n","[10/20, 100/1530] loss: 0.31374196\n","[10/20, 200/1530] loss: 0.31374196\n","[10/20, 300/1530] loss: 0.31374196\n","[10/20, 400/1530] loss: 0.31374243\n","[10/20, 500/1530] loss: 0.31374196\n","[10/20, 600/1530] loss: 0.31374196\n","[10/20, 700/1530] loss: 0.31374199\n","[10/20, 800/1530] loss: 0.41374194\n","[10/20, 900/1530] loss: 0.31374196\n","[10/20, 1000/1530] loss: 0.31374196\n","[10/20, 1100/1530] loss: 0.31374196\n","[10/20, 1200/1530] loss: 0.31374196\n","[10/20, 1300/1530] loss: 0.31374202\n","[10/20, 1400/1530] loss: 0.31374198\n","[10/20, 1500/1530] loss: 0.31374196\n","[10/20, 1530/1530] loss: 0.31374196\n","====================\n","tr_loss  =  0.03255\n","Accuracy =  0.98634\n","f1_score =  0.51508\n","conf_mat =  [[14980    20]\n"," [  189   111]]\n","precision_maj =  0.9987\n","precision_min =  0.37\n","====================\n","====================\n","val_loss  =  0.05544\n","Accuracy =  0.71739\n","f1_score =  0.60606\n","conf_mat =  [[92  0]\n"," [52 40]]\n","precision_maj =  1.0\n","precision_min =  0.4348\n","====================\n","The Current train Loss: 0.0325  and the val_loss 0.0554\n","trigger times: 0\n","[11/20, 100/1530] loss: 0.31374196\n","[11/20, 200/1530] loss: 0.31374196\n","[11/20, 300/1530] loss: 0.31374196\n","[11/20, 400/1530] loss: 0.41374198\n","[11/20, 500/1530] loss: 0.31374196\n","[11/20, 600/1530] loss: 0.31374196\n","[11/20, 700/1530] loss: 0.31374196\n","[11/20, 800/1530] loss: 0.31374196\n","[11/20, 900/1530] loss: 0.31374196\n","[11/20, 1000/1530] loss: 0.31374196\n","[11/20, 1100/1530] loss: 0.31374196\n","[11/20, 1200/1530] loss: 0.31374196\n","[11/20, 1300/1530] loss: 0.31374196\n","[11/20, 1400/1530] loss: 0.31374198\n","[11/20, 1500/1530] loss: 0.41374194\n","[11/20, 1530/1530] loss: 0.31374196\n","====================\n","tr_loss  =  0.03256\n","Accuracy =  0.98614\n","f1_score =  0.49524\n","conf_mat =  [[14984    16]\n"," [  196   104]]\n","precision_maj =  0.9989\n","precision_min =  0.3467\n","====================\n","====================\n","val_loss  =  0.05507\n","Accuracy =  0.72826\n","f1_score =  0.62687\n","conf_mat =  [[92  0]\n"," [50 42]]\n","precision_maj =  1.0\n","precision_min =  0.4565\n","====================\n","The Current train Loss: 0.0326  and the val_loss 0.0551\n","trigger times: 0\n","[12/20, 100/1530] loss: 0.31374222\n","[12/20, 200/1530] loss: 0.31374196\n","[12/20, 300/1530] loss: 0.31374196\n","[12/20, 400/1530] loss: 0.31374196\n","[12/20, 500/1530] loss: 0.31374196\n","[12/20, 600/1530] loss: 0.31374196\n","[12/20, 700/1530] loss: 0.31374196\n","[12/20, 800/1530] loss: 0.31374198\n","[12/20, 900/1530] loss: 0.31374196\n","[12/20, 1000/1530] loss: 0.31374196\n","[12/20, 1100/1530] loss: 0.31374196\n","[12/20, 1200/1530] loss: 0.31374196\n","[12/20, 1300/1530] loss: 0.31374198\n","[12/20, 1400/1530] loss: 0.31374196\n","[12/20, 1500/1530] loss: 0.31374196\n","[12/20, 1530/1530] loss: 0.31374196\n","====================\n","tr_loss  =  0.03329\n","Accuracy =  0.98039\n","f1_score =  0.0\n","conf_mat =  [[15000     0]\n"," [  300     0]]\n","precision_maj =  1.0\n","precision_min =  0.0\n","====================\n","====================\n","val_loss  =  0.08133\n","Accuracy =  0.5\n","f1_score =  0.0\n","conf_mat =  [[92  0]\n"," [92  0]]\n","precision_maj =  1.0\n","precision_min =  0.0\n","====================\n","The Current train Loss: 0.0333  and the val_loss 0.0813\n","Trigger Times: 1\n","[13/20, 100/1530] loss: 0.41374192\n","[13/20, 200/1530] loss: 0.31374196\n","[13/20, 300/1530] loss: 0.31374196\n","[13/20, 400/1530] loss: 0.31374196\n","[13/20, 500/1530] loss: 0.31374196\n","[13/20, 600/1530] loss: 0.31374196\n","[13/20, 700/1530] loss: 0.31374196\n","[13/20, 800/1530] loss: 0.31374196\n","[13/20, 900/1530] loss: 0.41374192\n","[13/20, 1000/1530] loss: 0.31374196\n","[13/20, 1100/1530] loss: 0.31374196\n","[13/20, 1200/1530] loss: 0.31374196\n","[13/20, 1300/1530] loss: 0.31374196\n","[13/20, 1400/1530] loss: 0.31374196\n","[13/20, 1500/1530] loss: 0.31374196\n","[13/20, 1530/1530] loss: 0.31374196\n","====================\n","tr_loss  =  0.03329\n","Accuracy =  0.98039\n","f1_score =  0.0\n","conf_mat =  [[15000     0]\n"," [  300     0]]\n","precision_maj =  1.0\n","precision_min =  0.0\n","====================\n","====================\n","val_loss  =  0.08117\n","Accuracy =  0.5\n","f1_score =  0.0\n","conf_mat =  [[92  0]\n"," [92  0]]\n","precision_maj =  1.0\n","precision_min =  0.0\n","====================\n","The Current train Loss: 0.0333  and the val_loss 0.0812\n","trigger times: 0\n","[14/20, 100/1530] loss: 0.31374196\n","[14/20, 200/1530] loss: 0.31374196\n","[14/20, 300/1530] loss: 0.31374196\n","[14/20, 400/1530] loss: 0.31374196\n","[14/20, 500/1530] loss: 0.413742\n","[14/20, 600/1530] loss: 0.31374196\n","[14/20, 700/1530] loss: 0.31374196\n","[14/20, 800/1530] loss: 0.41374198\n","[14/20, 900/1530] loss: 0.31374196\n","[14/20, 1000/1530] loss: 0.31374196\n","[14/20, 1100/1530] loss: 0.31374196\n","[14/20, 1200/1530] loss: 0.31374196\n","[14/20, 1300/1530] loss: 0.31374196\n","[14/20, 1400/1530] loss: 0.31374196\n","[14/20, 1500/1530] loss: 0.31374196\n","[14/20, 1530/1530] loss: 0.31374196\n","====================\n","tr_loss  =  0.03319\n","Accuracy =  0.98052\n","f1_score =  0.01325\n","conf_mat =  [[15000     0]\n"," [  298     2]]\n","precision_maj =  1.0\n","precision_min =  0.0067\n","====================\n","====================\n","val_loss  =  0.07801\n","Accuracy =  0.5163\n","f1_score =  0.06316\n","conf_mat =  [[92  0]\n"," [89  3]]\n","precision_maj =  1.0\n","precision_min =  0.0326\n","====================\n","The Current train Loss: 0.0332  and the val_loss 0.078\n","trigger times: 0\n","[15/20, 100/1530] loss: 0.31374196\n","[15/20, 200/1530] loss: 0.31374196\n","[15/20, 300/1530] loss: 0.31374198\n","[15/20, 400/1530] loss: 0.31374198\n","[15/20, 500/1530] loss: 0.31374196\n","[15/20, 600/1530] loss: 0.31374196\n","[15/20, 700/1530] loss: 0.31374196\n","[15/20, 800/1530] loss: 0.31374196\n","[15/20, 900/1530] loss: 0.31374196\n","[15/20, 1000/1530] loss: 0.31374196\n","[15/20, 1100/1530] loss: 0.31374196\n","[15/20, 1200/1530] loss: 0.31374196\n","[15/20, 1300/1530] loss: 0.31374196\n","[15/20, 1400/1530] loss: 0.31374198\n","[15/20, 1500/1530] loss: 0.31374196\n","[15/20, 1530/1530] loss: 0.31374196\n","====================\n","tr_loss  =  0.03329\n","Accuracy =  0.98039\n","f1_score =  0.0\n","conf_mat =  [[15000     0]\n"," [  300     0]]\n","precision_maj =  1.0\n","precision_min =  0.0\n","====================\n","====================\n","val_loss  =  0.08132\n","Accuracy =  0.5\n","f1_score =  0.0\n","conf_mat =  [[92  0]\n"," [92  0]]\n","precision_maj =  1.0\n","precision_min =  0.0\n","====================\n","The Current train Loss: 0.0333  and the val_loss 0.0813\n","Trigger Times: 1\n","[16/20, 100/1530] loss: 0.31374196\n","[16/20, 200/1530] loss: 0.31374196\n","[16/20, 300/1530] loss: 0.31374196\n","[16/20, 400/1530] loss: 0.31374196\n","[16/20, 500/1530] loss: 0.31374198\n","[16/20, 600/1530] loss: 0.31374196\n","[16/20, 700/1530] loss: 0.41374198\n","[16/20, 800/1530] loss: 0.31374196\n","[16/20, 900/1530] loss: 0.31374196\n","[16/20, 1000/1530] loss: 0.31374198\n","[16/20, 1100/1530] loss: 0.31374196\n","[16/20, 1200/1530] loss: 0.41374198\n","[16/20, 1300/1530] loss: 0.31374196\n","[16/20, 1400/1530] loss: 0.31374198\n","[16/20, 1500/1530] loss: 0.31374196\n","[16/20, 1530/1530] loss: 0.31374196\n","====================\n","tr_loss  =  0.03287\n","Accuracy =  0.98353\n","f1_score =  0.31522\n","conf_mat =  [[14990    10]\n"," [  242    58]]\n","precision_maj =  0.9993\n","precision_min =  0.1933\n","====================\n","====================\n","val_loss  =  0.06479\n","Accuracy =  0.65761\n","f1_score =  0.47934\n","conf_mat =  [[92  0]\n"," [63 29]]\n","precision_maj =  1.0\n","precision_min =  0.3152\n","====================\n","The Current train Loss: 0.0329  and the val_loss 0.0648\n","trigger times: 0\n","[17/20, 100/1530] loss: 0.31374196\n","[17/20, 200/1530] loss: 0.31374196\n","[17/20, 300/1530] loss: 0.31374196\n","[17/20, 400/1530] loss: 0.31374196\n","[17/20, 500/1530] loss: 0.31374196\n","[17/20, 600/1530] loss: 0.31374196\n","[17/20, 700/1530] loss: 0.31374196\n","[17/20, 800/1530] loss: 0.313742\n","[17/20, 900/1530] loss: 0.31374198\n","[17/20, 1000/1530] loss: 0.31374196\n","[17/20, 1100/1530] loss: 0.413742\n","[17/20, 1200/1530] loss: 0.31374198\n","[17/20, 1300/1530] loss: 0.31374198\n","[17/20, 1400/1530] loss: 0.31374196\n","[17/20, 1500/1530] loss: 0.31374196\n","[17/20, 1530/1530] loss: 0.31374196\n","====================\n","tr_loss  =  0.03188\n","Accuracy =  0.99451\n","f1_score =  0.85159\n","conf_mat =  [[14975    25]\n"," [   59   241]]\n","precision_maj =  0.9983\n","precision_min =  0.8033\n","====================\n","====================\n","val_loss  =  0.03922\n","Accuracy =  0.91848\n","f1_score =  0.91124\n","conf_mat =  [[92  0]\n"," [15 77]]\n","precision_maj =  1.0\n","precision_min =  0.837\n","====================\n","The Current train Loss: 0.0319  and the val_loss 0.0392\n","trigger times: 0\n","[18/20, 100/1530] loss: 0.31374196\n","[18/20, 200/1530] loss: 0.31374196\n","[18/20, 300/1530] loss: 0.31374196\n","[18/20, 400/1530] loss: 0.31374196\n","[18/20, 500/1530] loss: 0.31374198\n","[18/20, 600/1530] loss: 0.31374198\n","[18/20, 700/1530] loss: 0.31374196\n","[18/20, 800/1530] loss: 0.31374196\n","[18/20, 900/1530] loss: 0.31374196\n","[18/20, 1000/1530] loss: 0.31374196\n","[18/20, 1100/1530] loss: 0.31374196\n","[18/20, 1200/1530] loss: 0.31374196\n","[18/20, 1300/1530] loss: 0.31374196\n","[18/20, 1400/1530] loss: 0.31374196\n","[18/20, 1500/1530] loss: 0.31374196\n","[18/20, 1530/1530] loss: 0.31374196\n","====================\n","tr_loss  =  0.03188\n","Accuracy =  0.99451\n","f1_score =  0.85159\n","conf_mat =  [[14975    25]\n"," [   59   241]]\n","precision_maj =  0.9983\n","precision_min =  0.8033\n","====================\n","====================\n","val_loss  =  0.03922\n","Accuracy =  0.91848\n","f1_score =  0.91124\n","conf_mat =  [[92  0]\n"," [15 77]]\n","precision_maj =  1.0\n","precision_min =  0.837\n","====================\n","The Current train Loss: 0.0319  and the val_loss 0.0392\n","Trigger Times: 1\n","[19/20, 100/1530] loss: 0.31374196\n","[19/20, 200/1530] loss: 0.31374196\n","[19/20, 300/1530] loss: 0.31374198\n","[19/20, 400/1530] loss: 0.31374196\n","[19/20, 500/1530] loss: 0.31374196\n","[19/20, 600/1530] loss: 0.31374196\n","[19/20, 700/1530] loss: 0.31374196\n","[19/20, 800/1530] loss: 0.31374196\n","[19/20, 900/1530] loss: 0.31374196\n","[19/20, 1000/1530] loss: 0.31374198\n","[19/20, 1100/1530] loss: 0.31374196\n","[19/20, 1200/1530] loss: 0.31374196\n","[19/20, 1300/1530] loss: 0.31374196\n","[19/20, 1400/1530] loss: 0.31374196\n","[19/20, 1500/1530] loss: 0.31374196\n","[19/20, 1530/1530] loss: 0.31374196\n","====================\n","tr_loss  =  0.03188\n","Accuracy =  0.99451\n","f1_score =  0.85159\n","conf_mat =  [[14975    25]\n"," [   59   241]]\n","precision_maj =  0.9983\n","precision_min =  0.8033\n","====================\n","====================\n","val_loss  =  0.03922\n","Accuracy =  0.91848\n","f1_score =  0.91124\n","conf_mat =  [[92  0]\n"," [15 77]]\n","precision_maj =  1.0\n","precision_min =  0.837\n","====================\n","The Current train Loss: 0.0319  and the val_loss 0.0392\n","trigger times: 0\n","[20/20, 100/1530] loss: 0.31374198\n","[20/20, 200/1530] loss: 0.41374198\n","[20/20, 300/1530] loss: 0.31374198\n","[20/20, 400/1530] loss: 0.31374196\n","[20/20, 500/1530] loss: 0.31374196\n","[20/20, 600/1530] loss: 0.31374196\n","[20/20, 700/1530] loss: 0.31374196\n","[20/20, 800/1530] loss: 0.31374196\n","[20/20, 900/1530] loss: 0.31374196\n","[20/20, 1000/1530] loss: 0.31374196\n","[20/20, 1100/1530] loss: 0.31374196\n","[20/20, 1200/1530] loss: 0.31374224\n","[20/20, 1300/1530] loss: 0.31374196\n","[20/20, 1400/1530] loss: 0.31374196\n","[20/20, 1500/1530] loss: 0.31374196\n","[20/20, 1530/1530] loss: 0.31374196\n","====================\n","tr_loss  =  0.03192\n","Accuracy =  0.99399\n","f1_score =  0.83453\n","conf_mat =  [[14976    24]\n"," [   68   232]]\n","precision_maj =  0.9984\n","precision_min =  0.7733\n","====================\n","====================\n","val_loss  =  0.04463\n","Accuracy =  0.86957\n","f1_score =  0.85\n","conf_mat =  [[92  0]\n"," [24 68]]\n","precision_maj =  1.0\n","precision_min =  0.7391\n","====================\n","The Current train Loss: 0.0319  and the val_loss 0.0446\n","Trigger Times: 1\n","====================\n","test_loss  =  0.04578\n","Accuracy =  0.86\n","f1_score =  0.83721\n","conf_mat =  [[100   0]\n"," [ 28  72]]\n","precision_maj =  1.0\n","precision_min =  0.72\n","====================\n"]}],"source":["def traindata(device, model, epochs, optimizer, loss_function, train_loader, valid_loader , test_loader , batch_size):\n","    # Early stopping\n","    last_loss = 100\n","    patience = epochs//2\n","    triggertimes = 0\n","    \n","    prec_min_train,prec_max_train = [],[] \n","    prec_min_val, prec_max_val = [],[] \n","    dic_scores = dict()\n","    #dic_prec_val = dict()\n","    \n","    f1_score_train, f1_score_val = [],[] \n","    \n","    \n","    train_losses = []\n","    val_losses  = []\n","    \n","    \n","    distribs_0  = []\n","    distribs_1 = []\n","    \n","    tr_accies = []\n","    val_accies = []\n","    \n","    #loss_total = 0\n","    for epoch in range(1, epochs+1):\n","        model.train()\n","\n","        for times, data in enumerate(train_loader, 1):\n","            input = data[0].to(device)\n","            label = data[1].to(device)\n","\n","            # Zero the gradients\n","            optimizer.zero_grad()\n","\n","            # Forward and backward propagation\n","            #output = model(input.view(input.shape[0], -1))\n","#             out_ = model(input.float())\n","#             output = torch.softmax(out_, dim=1)\n","            output = model(input.float())\n","            \n","            #loss = loss_function(output[:,0], label.float())\n","            \n","            # ==================  uncomment it for modified loss\n","            \n","            loss , dist_0 , dist_1 = loss_function(output, label.float() , 40 , 60)#, label.float())\n","            \n","            # ==================  uncomment it for normal loss\n","            #loss  = loss_function(output[:,1], label.float())\n","            #loss_total += loss.item()\n","            \n","            loss.backward()\n","            optimizer.step()\n","\n","            # Show progress\n","            if times % 100 == 0 or times == len(train_loader):\n","                print('[{}/{}, {}/{}] loss: {:.8}'.format(epoch, epochs, times, len(train_loader), loss.item()))\n","\n","        # Early stopping\n","        # ==================  uncomment it for modified loss\n","        for class_0 , class_1  in zip(dist_0.tolist() ,dist_1.tolist()) :\n","                distribs_0.append(class_0)\n","                distribs_1.append(class_1)\n","        #tr_loss =  loss_total / (len(train_loader)*batch_size)\n","        # ====================\n","        tr_loss , tr_acc , precision_maj_train, precision_min_train , f1score_train = validation(model, device, train_loader, batch_size , \"tr_loss\")\n","        val_loss , val_acc , precision_maj_val, precision_min_val , f1score_val  = validation(model, device, valid_loader, batch_size , \"val_loss\")\n","        \n","        train_losses.append(tr_loss)\n","        val_losses.append(val_loss)\n","        \n","        tr_accies.append(tr_acc)\n","        val_accies.append(val_acc)\n","        \n","        prec_min_train.append(precision_min_train)\n","        prec_max_train.append(precision_maj_train)\n","        \n","        prec_min_val.append(precision_min_val)\n","        prec_max_val.append(precision_maj_val)\n","        \n","        \n","        f1_score_train.append(f1score_train)\n","        f1_score_val.append(f1score_val)\n","    \n","    \n","        print('The Current train Loss:', round(tr_loss,4)   , \" and the val_loss\" , round(val_loss,4))\n","\n","        if val_loss > last_loss:\n","            trigger_times += 1\n","            print('Trigger Times:', trigger_times)\n","\n","            if trigger_times >= patience:\n","                print('Early stopping!\\nStart to test process.')\n","                return model\n","\n","        else:\n","            print('trigger times: 0')\n","            trigger_times = 0\n","\n","        last_loss = val_loss\n","        \n","    te_loss, test_acc , precision_maj_test, precision_min_test, f1score_test =  validation(model, device, test_loader, batch_size, \"test_loss\")\n","    #test_accies.append(test_acc)\n","    #test_losses.append(te_loss)\n","    dic_scores['prec_min_train'] , dic_scores['prec_max_train'] = prec_min_train,prec_max_train\n","    dic_scores['prec_val_min'] , dic_scores['prec_val_max'] = prec_min_val,prec_max_val\n","    dic_scores['f1score_train'] , dic_scores['f1score_val'] = f1_score_train,f1_score_val\n","    dic_scores['precision_maj_test'] , dic_scores['precision_min_test'] = precision_maj_test,precision_min_test\n","    dic_scores['f1score_test'] = f1score_test\n","    return model , train_losses, val_losses ,te_loss, distribs_0 , distribs_1 , tr_accies, val_accies ,test_acc , dic_scores \n","\n","\n","def validation(model, device, data__loader, batch_size , which_data):\n","    total = 0\n","    correct = 0\n","    model.eval()\n","    loss_total = 0\n","    preds , labs = [] , []\n","    \n","    # Test validation data\n","    with torch.no_grad():\n","        for data in data__loader:\n","            input = data[0].to(device)\n","            label = data[1].to(device)\n","\n","            #output = model(input.view(input.shape[0], -1))\n","            output = model(input.float())\n","            #output = torch.softmax(out_, dim=1)\n","            loss = nn.BCELoss(reduction = 'mean')(output[:,1], label.float())\n","            #loss = loss_function(output[:,0], label.float())\n","            #loss = loss_function(output, label.float() , 5)#, label.float())\n","            #loss.requires_grad= True\n","            loss_total += loss.item()\n","            \n","            _, predicted = torch.max(output.data, 1)\n","\n","            total += label.size(0)\n","            correct += (predicted == label).sum().item()\n","            \n","            #for i in range(3):\n","            for pred_ , obs_  in zip(predicted.tolist() ,label.tolist()) :\n","                preds.append(pred_)\n","                labs.append(obs_)\n","    f1score =  f1_score(np.array(labs), np.array(preds) )\n","    conf_mat =  confusion_matrix(np.array(labs), np.array(preds) )\n","    loss__ =  loss_total / (len(data__loader)*batch_size)\n","    precision_maj = conf_mat[0][0] / conf_mat[0].sum()\n","    precision_min = conf_mat[1][1] / conf_mat[1].sum()\n","    print(\"====================\")\n","    print(which_data ,\" = \" , round(loss__,5) )\n","    print('Accuracy = ', round(correct / total , 5))\n","    print('f1_score = ', round(f1score , 5))\n","    print('conf_mat = ', conf_mat)\n","    print(\"precision_maj = \",round(precision_maj , 4))\n","    print(\"precision_min = \",round(precision_min , 4))\n","    print(\"====================\")\n","    \n","    return loss__ , correct / total , precision_maj, precision_min , f1score\n","\n","\n","\n","\n","def main(epochs, batch_size):\n","    # GPU device\n","    device = 'cuda:0' if torch.cuda.is_available() else 'cpu'\n","    print('Device state:', device)\n","\n","    \n","    batch_size = batch_size\n","    lr = 0.001\n","    #loss_function = nn.NLLLoss()\n","    #loss_function = nn.BCELoss()\n","    loss_function =  KD_fromStudent_loss_1\n","    model = nana_reseau.to(device)\n","    optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n","    model , tr_losses ,val_losses, test_loss, distribs_0 , distribs_1 , tr_accies, val_accies ,test_accies , dic_scores = traindata(device, model, epochs, optimizer, loss_function, train_loader, valid_loader, test_loader , batch_size)\n","    #t_loss = test(device, model, test_loader)\n","    return tr_losses ,val_losses, test_loss, distribs_0 , distribs_1, tr_accies, val_accies , test_accies ,dic_scores#, t_loss\n","\n","# if __name__ == '__main__':\n","#     main()\n","## Mention the epochs here below\n","epochs = 20\n","\n","train_losses ,  val_losses , test_loss, distribs_0 , distribs_1 , tr_accies , val_accies ,test_accies , dic_scores =  main(epochs, batch_size)"]},{"cell_type":"code","execution_count":null,"id":"atomic-floor","metadata":{"id":"atomic-floor","outputId":"38401721-9ac9-4cd0-962b-cffac2a91acb"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 720x360 with 4 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["colors = ['r' , 'g' , 'b' , 'y']\n","plt.figure(figsize=(10,5))\n","for i , j ,col in  zip(range(4) , list(dic_scores.keys())[:4] , colors):\n","    plt.subplot(2,2,i+1)\n","    plt.plot(range(1, epochs+1) ,dic_scores[j] , col)\n","    plt.xlabel('epochs')\n","    plt.ylabel('score')\n","    plt.title(j )#\"precision \"+ j[5:8]+ ' class '+ j[-5:] )\n","\n","plt.subplots_adjust(left=0.1,bottom=0.1, right=0.9,top=0.9,wspace=0.4,hspace=0.4)\n","plt.show()"]},{"cell_type":"code","execution_count":null,"id":"suitable-saver","metadata":{"id":"suitable-saver","outputId":"12f9e0de-f75e-4abd-c797-6d8d9afde353"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 720x144 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["fscores = [\"f1score_train\" , \"f1score_val\"]\n","colors = ['r' , 'g']\n","plt.figure(figsize=(10,2))\n","for i , j ,col in  zip(range(2) , fscores , colors):\n","    plt.subplot(1,2,i+1)\n","    plt.plot(range(1, epochs+1) ,dic_scores[j] , col)\n","    plt.xlabel('epochs')\n","    plt.ylabel('score')\n","    plt.title(j )#\"precision \"+ j[5:8]+ ' class '+ j[-5:] )\n","\n","#plt.subplots_adjust(left=0.1,bottom=0.1, right=0.9,top=0.9,wspace=0.4,hspace=0.4)\n","plt.show()"]},{"cell_type":"code","execution_count":null,"id":"running-bulgarian","metadata":{"id":"running-bulgarian","outputId":"04bc25f0-14cd-4270-9e87-76a69127780a"},"outputs":[{"data":{"text/plain":["2"]},"execution_count":129,"metadata":{},"output_type":"execute_result"}],"source":["vv[0].sum()"]},{"cell_type":"code","execution_count":null,"id":"comfortable-antique","metadata":{"id":"comfortable-antique","outputId":"75138f73-2641-498a-82bd-f9792fa8dcb5"},"outputs":[{"data":{"text/plain":["[0.9912820512820513,\n"," 0.9921794871794872,\n"," 0.9917948717948718,\n"," 0.992051282051282,\n"," 0.992051282051282,\n"," 0.9921794871794872,\n"," 0.9921794871794872,\n"," 0.992051282051282,\n"," 0.9917948717948718,\n"," 0.9921794871794872]"]},"execution_count":101,"metadata":{},"output_type":"execute_result"}],"source":["tr_accies"]},{"cell_type":"code","execution_count":null,"id":"celtic-architecture","metadata":{"id":"celtic-architecture"},"outputs":[],"source":["for data in train_loader:\n","            input = data[0]#.to(device)\n","            label = data[1]#.to(device)"]},{"cell_type":"code","execution_count":null,"id":"progressive-federation","metadata":{"id":"progressive-federation","outputId":"778a3241-583b-4e58-cff3-591079abd3aa"},"outputs":[{"data":{"text/plain":["tensor([0, 0, 0, 0, 0, 0, 0, 1, 0, 0])"]},"execution_count":40,"metadata":{},"output_type":"execute_result"}],"source":["label"]},{"cell_type":"code","execution_count":null,"id":"banned-playback","metadata":{"id":"banned-playback"},"outputs":[],"source":["out_ = nana_reseau(input.float())\n","#out_ = model(input.float())\n","output = torch.softmax(out_, dim=1)\n"]},{"cell_type":"code","execution_count":null,"id":"spiritual-chance","metadata":{"id":"spiritual-chance","outputId":"a360adff-5311-4523-809b-227743e085a8"},"outputs":[{"data":{"text/plain":["tensor([[1.0000e+00, 1.8938e-16],\n","        [1.0000e+00, 1.0066e-17],\n","        [1.0000e+00, 2.9741e-18],\n","        [1.0000e+00, 7.5970e-15],\n","        [1.0000e+00, 9.4894e-14],\n","        [1.0000e+00, 5.4042e-16],\n","        [1.0000e+00, 1.0687e-14],\n","        [1.0000e+00, 7.6077e-16],\n","        [1.0000e+00, 7.6991e-14],\n","        [1.0000e+00, 4.5149e-15]], grad_fn=<SigmoidBackward0>)"]},"execution_count":47,"metadata":{},"output_type":"execute_result"}],"source":["out_"]},{"cell_type":"code","execution_count":null,"id":"anonymous-civilization","metadata":{"id":"anonymous-civilization","outputId":"c017718e-48c3-4730-8784-26f3e9a57315"},"outputs":[{"data":{"text/plain":["tensor([[0.7311, 0.2689],\n","        [0.7311, 0.2689],\n","        [0.7311, 0.2689],\n","        [0.7311, 0.2689],\n","        [0.7311, 0.2689],\n","        [0.7311, 0.2689],\n","        [0.7311, 0.2689],\n","        [0.7311, 0.2689],\n","        [0.7311, 0.2689],\n","        [0.7311, 0.2689]], grad_fn=<SoftmaxBackward0>)"]},"execution_count":48,"metadata":{},"output_type":"execute_result"}],"source":["output"]},{"cell_type":"code","execution_count":null,"id":"polish-collector","metadata":{"id":"polish-collector"},"outputs":[],"source":["loss = nn.BCELoss(reduction = 'mean')(output[:,1], label.float())"]},{"cell_type":"code","execution_count":null,"id":"involved-russell","metadata":{"id":"involved-russell","outputId":"94674187-55aa-434b-a0be-9f9781b37fa7"},"outputs":[{"data":{"text/plain":["torch.Size([10, 29])"]},"execution_count":380,"metadata":{},"output_type":"execute_result"}],"source":["input.shape"]},{"cell_type":"code","execution_count":null,"id":"sought-calcium","metadata":{"id":"sought-calcium","outputId":"29e31430-59c7-43aa-c815-0edb97d102eb"},"outputs":[{"data":{"text/plain":["tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"]},"execution_count":381,"metadata":{},"output_type":"execute_result"}],"source":["label.float()"]},{"cell_type":"code","execution_count":null,"id":"transparent-castle","metadata":{"scrolled":true,"id":"transparent-castle","outputId":"aee84415-9d42-49f5-e836-440939c7675c"},"outputs":[{"data":{"text/plain":["tensor(0.7168, grad_fn=<BinaryCrossEntropyBackward0>)"]},"execution_count":383,"metadata":{},"output_type":"execute_result"}],"source":["nn.BCELoss()(output[:,1] , label.float())"]},{"cell_type":"code","execution_count":null,"id":"earlier-trigger","metadata":{"id":"earlier-trigger","outputId":"f3146bce-e83b-4e57-d9e1-dd0d98dfe181"},"outputs":[{"data":{"text/plain":["tensor([[9.9996e-01, 3.6853e-05],\n","        [1.0000e+00, 3.5213e-06],\n","        [9.9999e-01, 7.8410e-06],\n","        [1.0000e+00, 4.5457e-06],\n","        [9.9997e-01, 3.2269e-05]])"]},"execution_count":241,"metadata":{},"output_type":"execute_result"}],"source":["output.data"]},{"cell_type":"code","execution_count":null,"id":"secure-renewal","metadata":{"id":"secure-renewal","outputId":"614bba88-0d4f-4649-901a-631f38c65145"},"outputs":[{"data":{"text/plain":["tensor([[9.9996e-01, 3.6853e-05],\n","        [1.0000e+00, 3.5213e-06],\n","        [9.9999e-01, 7.8410e-06],\n","        [1.0000e+00, 4.5457e-06],\n","        [9.9997e-01, 3.2269e-05]], grad_fn=<SoftmaxBackward0>)"]},"execution_count":242,"metadata":{},"output_type":"execute_result"}],"source":["output"]},{"cell_type":"code","execution_count":null,"id":"likely-enterprise","metadata":{"id":"likely-enterprise","outputId":"c5cd6374-307d-4fdd-82ed-4b3ba07dcbee"},"outputs":[{"data":{"text/plain":["Text(0.5, 1.0, 'distribution class_0')"]},"execution_count":109,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["import seaborn as sns\n","sns.kdeplot( distribs_0 , color= 'green')\n","plt.title('distribution class_0')\n"]},{"cell_type":"code","execution_count":null,"id":"hungry-frontier","metadata":{"id":"hungry-frontier","outputId":"a6633551-8fee-452f-cdb5-89004ac17a7a"},"outputs":[{"data":{"text/plain":["Text(0.5, 1.0, 'distribution class_1')"]},"execution_count":110,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["sns.kdeplot(distribs_1 , color= 'red')\n","plt.title('distribution class_1')\n"]},{"cell_type":"code","execution_count":null,"id":"joined-cotton","metadata":{"id":"joined-cotton","outputId":"5b78316f-dd49-4eb6-d5f4-d8044493d597"},"outputs":[{"data":{"text/plain":["<matplotlib.legend.Legend at 0x7fdaf1a46c40>"]},"execution_count":120,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","text/plain":["<Figure size 576x432 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["plt.figure(figsize=(8,6))\n","plt.plot(train_losses ,label =  \"train_loss\")\n","plt.xlabel('epoch')\n","plt.plot(val_losses , label =  \"val_loss\")\n","plt.ylabel('loss')\n","plt.legend()"]},{"cell_type":"code","execution_count":null,"id":"stainless-hybrid","metadata":{"id":"stainless-hybrid","outputId":"1d70755f-1da1-479b-ca4c-7ccc4dc09a71"},"outputs":[{"data":{"text/plain":["<matplotlib.legend.Legend at 0x7fdaeb650e80>"]},"execution_count":123,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["plt.plot(tr_accies ,label =  \"train_accuracy\")\n","plt.xlabel('epoch')\n","plt.plot(val_accies , label =  \"val_accuracy\")\n","plt.ylabel('accuracies')\n","plt.legend()"]},{"cell_type":"code","execution_count":null,"id":"sustained-savannah","metadata":{"id":"sustained-savannah"},"outputs":[],"source":[" for times, data in enumerate(train_loader, 1):\n","            input = data[0]#.to(device)\n","            label = data[1]#.to(device)"]},{"cell_type":"code","execution_count":null,"id":"after-feeling","metadata":{"scrolled":true,"id":"after-feeling","outputId":"76adb5bf-bb3f-4f73-a710-99af6d27b08c"},"outputs":[{"name":"stdout","output_type":"stream","text":["torch.Size([5, 29])\n","torch.Size([5, 29])\n","torch.Size([5, 29])\n","torch.Size([5, 29])\n","torch.Size([5, 29])\n","torch.Size([5, 29])\n","torch.Size([5, 29])\n","torch.Size([5, 29])\n","torch.Size([5, 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29])\n","torch.Size([5, 29])\n","torch.Size([5, 29])\n","torch.Size([5, 29])\n","torch.Size([5, 29])\n","torch.Size([5, 29])\n","torch.Size([5, 29])\n","torch.Size([5, 29])\n","torch.Size([5, 29])\n","torch.Size([5, 29])\n","torch.Size([5, 29])\n","torch.Size([5, 29])\n","torch.Size([5, 29])\n","torch.Size([5, 29])\n","torch.Size([5, 29])\n","torch.Size([5, 29])\n","torch.Size([5, 29])\n","torch.Size([5, 29])\n","torch.Size([5, 29])\n","torch.Size([5, 29])\n","torch.Size([5, 29])\n","torch.Size([5, 29])\n","torch.Size([5, 29])\n","torch.Size([5, 29])\n","torch.Size([5, 29])\n"]}],"source":["for times, data in enumerate(train_loader, 1):\n","    print(data[0].shape)"]},{"cell_type":"code","execution_count":null,"id":"entire-chamber","metadata":{"id":"entire-chamber","outputId":"7f9954ff-1dd6-4044-d6d2-4b2cc08433bc"},"outputs":[{"data":{"text/plain":["(torch.Size([6, 29]), torch.Size([6]))"]},"execution_count":274,"metadata":{},"output_type":"execute_result"}],"source":["input.shape , label.shape"]},{"cell_type":"code","execution_count":null,"id":"derived-murder","metadata":{"id":"derived-murder","outputId":"99224df0-53f5-45ae-b704-5f537cd2ca9c"},"outputs":[{"data":{"text/plain":["tensor([0, 0, 0, 0, 0])"]},"execution_count":302,"metadata":{},"output_type":"execute_result"}],"source":["label"]},{"cell_type":"code","execution_count":null,"id":"adaptive-nashville","metadata":{"id":"adaptive-nashville"},"outputs":[],"source":["output = nana_reseau(input.float())"]},{"cell_type":"code","execution_count":null,"id":"golden-fruit","metadata":{"id":"golden-fruit","outputId":"bb404619-8d79-468e-927d-137bb3b32c82"},"outputs":[{"data":{"text/plain":["tensor([[0.5040, 0.5598],\n","        [0.5764, 0.7851],\n","        [0.7406, 0.2858],\n","        [0.2786, 0.3149],\n","        [0.2666, 0.6240]], grad_fn=<SigmoidBackward0>)"]},"execution_count":304,"metadata":{},"output_type":"execute_result"}],"source":["output"]},{"cell_type":"code","execution_count":null,"id":"weekly-learning","metadata":{"id":"weekly-learning","outputId":"17421807-373b-430a-dce0-43e9fa8af139"},"outputs":[{"data":{"text/plain":["torch.Size([6, 2])"]},"execution_count":289,"metadata":{},"output_type":"execute_result"}],"source":["output.shape"]},{"cell_type":"code","execution_count":null,"id":"attractive-advisory","metadata":{"id":"attractive-advisory","outputId":"40b6ec26-edc0-42eb-8fc8-d7b1a88ee58d"},"outputs":[{"data":{"text/plain":["tensor([[0.1448, 0.3461, 0.8718, 0.2634],\n","        [0.0283, 0.9726, 0.1090, 0.4438]])"]},"execution_count":290,"metadata":{},"output_type":"execute_result"}],"source":["xx = torch.rand(2,4)\n","xx"]},{"cell_type":"code","execution_count":null,"id":"ignored-probability","metadata":{"id":"ignored-probability"},"outputs":[],"source":["a, b = torch.max(output.data, 1)"]},{"cell_type":"code","execution_count":null,"id":"graphic-marking","metadata":{"id":"graphic-marking","outputId":"cd31023d-2b23-4634-afeb-6ab36f6b1859"},"outputs":[{"data":{"text/plain":["tensor([0, 0, 0, 0, 1, 0])"]},"execution_count":283,"metadata":{},"output_type":"execute_result"}],"source":["b"]},{"cell_type":"code","execution_count":null,"id":"reported-cliff","metadata":{"id":"reported-cliff","outputId":"9bb54102-fd97-4f52-f2be-e23a4037bfd7"},"outputs":[{"data":{"text/plain":["torch.return_types.max(\n","values=tensor([0.5380, 0.5257, 0.5939, 0.5149, 0.5627, 0.5323]),\n","indices=tensor([0, 0, 1, 1, 0, 1]))"]},"execution_count":291,"metadata":{},"output_type":"execute_result"}],"source":["torch.max(output.data, 1)"]},{"cell_type":"code","execution_count":null,"id":"reverse-major","metadata":{"id":"reverse-major"},"outputs":[],"source":["torch.max?"]},{"cell_type":"code","execution_count":null,"id":"integrated-triple","metadata":{"id":"integrated-triple","outputId":"951e246e-95b1-47da-9da7-1806165eff7e"},"outputs":[{"data":{"text/plain":["tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000], grad_fn=<SumBackward1>)"]},"execution_count":245,"metadata":{},"output_type":"execute_result"}],"source":["torch.sum(output , 1)"]},{"cell_type":"code","execution_count":null,"id":"missing-collective","metadata":{"id":"missing-collective","outputId":"43299366-04c2-4513-df27-35e2f9ed523c"},"outputs":[{"data":{"text/plain":["tensor(1., grad_fn=<SumBackward0>)"]},"execution_count":223,"metadata":{},"output_type":"execute_result"}],"source":["torch.sum(output[0, :])"]},{"cell_type":"code","execution_count":null,"id":"informed-bidding","metadata":{"id":"informed-bidding","outputId":"6cf25a78-9027-4ad4-da24-829394b44631"},"outputs":[{"data":{"text/plain":["tensor(1.6999e-05, grad_fn=<MeanBackward0>)"]},"execution_count":224,"metadata":{},"output_type":"execute_result"}],"source":["log_loss =  torch.mean(-1*(label*torch.log(output[:,1]) + (1-label)*torch.log(1-output[:,1])))\n","log_loss"]},{"cell_type":"code","execution_count":null,"id":"intense-college","metadata":{"id":"intense-college","outputId":"a900cf14-6e96-43b0-aca8-298354e6ba48"},"outputs":[{"data":{"text/plain":["tensor(1.6999e-05, grad_fn=<BinaryCrossEntropyBackward0>)"]},"execution_count":225,"metadata":{},"output_type":"execute_result"}],"source":["nn.BCELoss()(output[:,1] , label.float())"]},{"cell_type":"code","execution_count":null,"id":"positive-butler","metadata":{"id":"positive-butler"},"outputs":[],"source":[""]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.2"},"colab":{"name":"kullback_divergence_CCard_Fraud_Detection_v05.ipynb","provenance":[],"collapsed_sections":[]}},"nbformat":4,"nbformat_minor":5}