pfe_keras.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "pfe_keras.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "6vlsWcrszHQ3",
        "S9tzMVFa41tc"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fhl5H9XW5HgY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        " !nvidia-smi"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FZWzPqTNC8hN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "################################ Data set: x_train from git #############################\n",
        "###Data-set : v3457\n",
        "import pandas as pd\n",
        "url = 'https://raw.githubusercontent.com/DinaTaklit/Loop-Unrolling-Tiramisu-Vf/master/x_train_v3457.csv?token=AE4WVKDPGVM56QVCI5J6INS5EUQV4'\n",
        "x_train_v3457 = pd.read_csv(url, delimiter=';', skiprows=1, usecols=range(23), header=None)\n",
        "#x_train1 = pd.read_csv(url, skiprows=1, header=None)\n",
        "################################ Data set: y_train #############################\n",
        "###Data-set : v3457\n",
        "url = 'https://raw.githubusercontent.com/DinaTaklit/Loop-Unrolling-Tiramisu-Vf/master/y_train_v3457.csv?token=AE4WVKFHICWRRBDPCAJL6MK5EUQYW'\n",
        "y_train_v3457 = pd.read_csv(url, skiprows=1, header=None)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dzFTeMMPj1kH",
        "colab_type": "code",
        "outputId": "bda62b30-da6d-4987-c444-9388add1e688",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Balance classes distribution\n",
        "x_train2 = x_train_v3457.copy()\n",
        "y_train2 = y_train_v3457.copy()\n",
        "x_train2['Unroll'] = y_train2 \n",
        "class0 = x_train2[x_train2['Unroll'] == 0]\n",
        "class1 = x_train2[x_train2['Unroll'] == 1]\n",
        "class2 = x_train2[x_train2['Unroll'] == 2]\n",
        "class3 = x_train2[x_train2['Unroll'] == 3]\n",
        "class4 = x_train2[x_train2['Unroll'] == 4]\n",
        "class5 = x_train2[x_train2['Unroll'] == 5]\n",
        "class6 = x_train2[x_train2['Unroll'] == 6]\n",
        "### get the same number in each class \n",
        "class0 = class0[0:len(class2)]\n",
        "class1 = class1[0:len(class2)]\n",
        "class2 = class2[0:len(class2)]\n",
        "class3 = class3[0:len(class2)]\n",
        "class4 = class4[0:len(class2)]\n",
        "class5 = class5[0:len(class2)]\n",
        "class6 = class6[0:len(class2)]\n",
        "# Build the new x_train\n",
        "x_train = class0\n",
        "x_train = x_train.append(class1)\n",
        "x_train = x_train.append(class2)\n",
        "x_train = x_train.append(class3)\n",
        "x_train = x_train.append(class4)\n",
        "x_train = x_train.append(class5)\n",
        "x_train = x_train.append(class6)\n",
        "len(x_train)\n",
        "#Random inputs \n",
        "from sklearn.utils import shuffle\n",
        "x_train = shuffle(x_train)\n",
        "#Build new y_train \n",
        "y_train = x_train['Unroll']\n",
        "#Drtop the added value from y_train\n",
        "x_train.drop('Unroll', axis=1, inplace=True)\n",
        "print(\"x_train len: \", len(x_train))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x_train len:  26670\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-5XbC3-tsSa9",
        "colab_type": "code",
        "outputId": "76e4a899-4a1c-44ca-afe5-54000eb262a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from __future__ import print_function\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Activation, Dense, Dropout\n",
        "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "import tensorboardcolab as tb\n",
        "import numpy as np\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from keras.wrappers.scikit_learn import KerasClassifier\n",
        "\n",
        "class MLP():\n",
        "  \n",
        "    def __init__(self, x_data, y_data ,optmizer, kernel_initialization, kernel_regularizer,activation='relu'):\n",
        "      \n",
        "        self.batch_size = 100\n",
        "        self.num_classes = 7\n",
        "        self.epochs = 100000000\n",
        "        #self.epochs = 150\n",
        "        self.patience = 5\n",
        "      \n",
        "        self.x_data = x_data.copy()\n",
        "        self.y_data = y_data.copy()\n",
        "        \n",
        "        self.optimizer = optmizer \n",
        "        self.kernel_initialization = kernel_initialization\n",
        "        self.kernel_regularizer = kernel_regularizer\n",
        "        self.activation = activation \n",
        "\n",
        "        \n",
        "    def preprossing(self,x_train, y_train):\n",
        "      # delete unimportant columns\n",
        "      cols = [0,3,11,4]\n",
        "      x_train.drop(x_train.columns[cols],axis=1,inplace=True)\n",
        "      x_train.fillna(0, inplace=True)\n",
        "    \n",
        "      x_train = self.standarization(x_train)\n",
        "      x_train, y_train, x_test, y_test = self.split_data(x_train, y_train)      \n",
        "      y_train = np.array(y_train).flatten()\n",
        "      y_test= np.array(y_test).flatten()\n",
        "      x_train = x_train.astype('float32')\n",
        "      x_test = x_test.astype('float32')\n",
        "        # convert class vectors to binary class matrices\n",
        "      y_train = keras.utils.to_categorical(y_train, self.num_classes)\n",
        "      y_test = keras.utils.to_categorical(y_test, self.num_classes)\n",
        "      \n",
        "      return x_train, y_train, x_test, y_test\n",
        "\n",
        "    def data_augmentation(self, df,fac):\n",
        "      df.loc[:,6] *= fac\n",
        "      df.loc[:,8] *= fac\n",
        "      df.loc[:,10] *= fac\n",
        "      df.loc[:,12] *= fac\n",
        "      df.loc[:,16] *= fac\n",
        "      return df \n",
        "\n",
        "    \n",
        "    def standarization(self,x_train):\n",
        "      from sklearn import preprocessing\n",
        "      #Standarization: \n",
        "      x_scaler = preprocessing.StandardScaler().fit(x_train)\n",
        "      x_train = x_scaler.transform(x_train)\n",
        "      return x_train\n",
        "  \n",
        "    def split_data(self, x_train, y_train):\n",
        "      import numpy as np\n",
        "      #y_train = np.array(y_train).flatten()\n",
        "\n",
        "      from sklearn.model_selection import train_test_split\n",
        "      x_train, x_test, y_train, y_test = train_test_split(x_train,y_train,test_size=0.2,train_size=0.8,random_state=9)\n",
        "      return x_train, y_train , x_test, y_test \n",
        "   \n",
        "\n",
        "    def multilayer_perceptron(self): \n",
        "\n",
        "      model = Sequential()\n",
        "      model.add(Dense(units=500, input_dim=self.x_train.shape[1], activation=self.activation, kernel_initializer=self.kernel_initialization,\n",
        "                      bias_initializer=\"zeros\", kernel_regularizer= self.kernel_regularizer))\n",
        "      model.add(BatchNormalization())\n",
        "      model.add(Dropout(0.12))\n",
        "\n",
        "      model.add(Dense(units=400, activation=self.activation , kernel_initializer= self.kernel_initialization, bias_initializer=\"zeros\",\n",
        "                      kernel_regularizer= self.kernel_regularizer))\n",
        "      model.add(BatchNormalization())\n",
        "      model.add(Dropout(0.1))\n",
        "\n",
        "      model.add(Dense(units=250, activation=self.activation , kernel_initializer= self.kernel_initialization, bias_initializer=\"zeros\", \n",
        "                      kernel_regularizer= self.kernel_regularizer))\n",
        "      model.add(BatchNormalization())\n",
        "      model.add(Dropout(0.07))\n",
        "\n",
        "      model.add(Dense(units=100, activation=self.activation , kernel_initializer= self.kernel_initialization, bias_initializer=\"zeros\", \n",
        "                     kernel_regularizer= self.kernel_regularizer))\n",
        "      model.add(BatchNormalization())\n",
        "      model.add(Dropout(0.04))\n",
        "\n",
        "      model.add(Dense(units= self.num_classes , activation=\"softmax\"))\n",
        "      \n",
        "      model.compile(loss='categorical_crossentropy',\n",
        "              optimizer=  self.optimizer,\n",
        "              metrics=['accuracy'])\n",
        "      \n",
        "      return model\n",
        "    \n",
        "    def train(self):\n",
        "      self.x_train, self.y_train, self.x_test, self.y_test = self.preprossing(self.x_data,self.y_data)\n",
        "      self.model = self.multilayer_perceptron()\n",
        "      \n",
        "\n",
        "      #Set callback functions to early stop training and save the best model so far\n",
        "      early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=self.patience, verbose=0, mode='auto')\n",
        "      \n",
        "      self.history = self.model.fit(self.x_train,# Features\n",
        "                          self.y_train, # Target vector\n",
        "                          batch_size= self.batch_size, # Number of observations per batch\n",
        "                          epochs=self.epochs, # Number of epochs\n",
        "                          callbacks=[early_stopping], # Early stopping\n",
        "                          verbose=1, # Print description after each epoch\n",
        "                          validation_split=0.2, #used for validation set every each epoch\n",
        "                          validation_data=(self.x_test, self.y_test) # Test data-set to evaluate the model in the end of training\n",
        "                          ) \n",
        "     \n",
        "      self.score = self.model.evaluate(self.x_test, self.y_test, verbose=0)\n",
        "      print('Test loss:', self.score[0])\n",
        "      print('Test accuracy:', self.score[1])\n",
        "      \n",
        "      from sklearn.metrics import classification_report\n",
        "      import numpy as np\n",
        "\n",
        "      Y_test = np.argmax(self.y_test, axis=1) # Convert one-hot to index\n",
        "      y_pred = self.model.predict_classes(self.x_test)\n",
        "      print(classification_report(Y_test, y_pred))\n",
        "     \n",
        "      ## Save the model \n",
        "      self.model.save('my_model.h5')  # creates a HDF5 file 'my_model.h5'\n",
        "      del self.model  # deletes the existing model\n",
        "      \n",
        "                                         \n",
        "      #return self.history.history['loss'], self.history.history['val_loss']\n",
        "      #return self.history.history['loss'],self.history.history['val_loss'], self.history.history['acc'], self.history.history['val_acc']      "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WmBEblUqUA1u",
        "colab_type": "code",
        "outputId": "3a9a333a-f9e9-4f1d-c781-8ef04fe27238",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#Lunch the training and save the model\n",
        "if __name__=='__main__':   \n",
        "  x_data = x_train.copy()\n",
        "  y_data = y_train.copy()\n",
        "  optimizer = keras.optimizers.Adam()\n",
        "  kernel_initialization = 'random_uniform'\n",
        "  kernel_regularizer= None\n",
        "  mlp_without = MLP(x_data, y_data,optimizer, kernel_initialization,kernel_regularizer)\n",
        "  #loss, val_loss, acc, val_acc = mlp_without.train()\n",
        "  mlp_without.train()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0703 03:43:56.371192 140348943005568 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "W0703 03:43:56.372872 140348943005568 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "W0703 03:43:56.379019 140348943005568 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "W0703 03:43:56.465621 140348943005568 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
            "\n",
            "W0703 03:43:56.489507 140348943005568 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "W0703 03:43:56.898331 140348943005568 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "W0703 03:43:56.907197 140348943005568 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n",
            "W0703 03:43:57.004951 140348943005568 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train on 21336 samples, validate on 5334 samples\n",
            "Epoch 1/100000000\n",
            "21336/21336 [==============================] - 6s 300us/step - loss: 2.0633 - acc: 0.1543 - val_loss: 1.9734 - val_acc: 0.1734\n",
            "Epoch 2/100000000\n",
            "21336/21336 [==============================] - 2s 95us/step - loss: 1.9680 - acc: 0.1723 - val_loss: 1.9556 - val_acc: 0.1691\n",
            "Epoch 3/100000000\n",
            "21336/21336 [==============================] - 2s 94us/step - loss: 1.9526 - acc: 0.1841 - val_loss: 1.9455 - val_acc: 0.1839\n",
            "Epoch 4/100000000\n",
            "21336/21336 [==============================] - 2s 98us/step - loss: 1.9442 - acc: 0.1886 - val_loss: 1.9391 - val_acc: 0.1785\n",
            "Epoch 5/100000000\n",
            "21336/21336 [==============================] - 2s 96us/step - loss: 1.9364 - acc: 0.1926 - val_loss: 1.9395 - val_acc: 0.1916\n",
            "Epoch 6/100000000\n",
            "21336/21336 [==============================] - 2s 98us/step - loss: 1.9285 - acc: 0.1984 - val_loss: 1.9270 - val_acc: 0.1954\n",
            "Epoch 7/100000000\n",
            "21336/21336 [==============================] - 2s 97us/step - loss: 1.9164 - acc: 0.2055 - val_loss: 1.9317 - val_acc: 0.1959\n",
            "Epoch 8/100000000\n",
            "21336/21336 [==============================] - 2s 96us/step - loss: 1.9139 - acc: 0.2048 - val_loss: 1.9247 - val_acc: 0.2032\n",
            "Epoch 9/100000000\n",
            "21336/21336 [==============================] - 2s 96us/step - loss: 1.9105 - acc: 0.2085 - val_loss: 1.9324 - val_acc: 0.1899\n",
            "Epoch 10/100000000\n",
            "21336/21336 [==============================] - 2s 94us/step - loss: 1.9085 - acc: 0.2123 - val_loss: 1.9173 - val_acc: 0.2045\n",
            "Epoch 11/100000000\n",
            "21336/21336 [==============================] - 2s 94us/step - loss: 1.9001 - acc: 0.2180 - val_loss: 1.9235 - val_acc: 0.2092\n",
            "Epoch 12/100000000\n",
            "21336/21336 [==============================] - 2s 95us/step - loss: 1.8968 - acc: 0.2229 - val_loss: 1.9190 - val_acc: 0.2062\n",
            "Epoch 13/100000000\n",
            "21336/21336 [==============================] - 2s 94us/step - loss: 1.8932 - acc: 0.2211 - val_loss: 1.9250 - val_acc: 0.1985\n",
            "Epoch 14/100000000\n",
            "21336/21336 [==============================] - 2s 95us/step - loss: 1.8853 - acc: 0.2266 - val_loss: 1.9236 - val_acc: 0.1989\n",
            "Epoch 15/100000000\n",
            "21336/21336 [==============================] - 2s 94us/step - loss: 1.8838 - acc: 0.2238 - val_loss: 1.9238 - val_acc: 0.2047\n",
            "Test loss: 1.923809233836391\n",
            "Test accuracy: 0.20472440947675508\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.23      0.13      0.17       776\n",
            "           1       0.17      0.11      0.14       725\n",
            "           2       0.18      0.35      0.24       751\n",
            "           3       0.12      0.05      0.07       817\n",
            "           4       0.14      0.08      0.10       755\n",
            "           5       0.21      0.19      0.20       769\n",
            "           6       0.27      0.54      0.36       741\n",
            "\n",
            "    accuracy                           0.20      5334\n",
            "   macro avg       0.19      0.21      0.18      5334\n",
            "weighted avg       0.19      0.20      0.18      5334\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V4xUId6ydJpg",
        "colab_type": "code",
        "outputId": "d62b8269-febc-4516-8fd3-21cea7f36f89",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "################################ Test benchmarks : Data #############################\n",
        "import pandas as pd\n",
        "from google.colab import files\n",
        "import io\n",
        "uploaded1 = files.upload()\n",
        "x_data= pd.read_csv(io.StringIO(uploaded1['benchmarks_data.csv'].decode('utf-8')),  delimiter=',', skiprows=1, usecols=range(23), header=None)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-ff823832-9a27-4454-9622-06b5e40e7730\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-ff823832-9a27-4454-9622-06b5e40e7730\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving benchmarks_data.csv to benchmarks_data.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "412_x2mIdKuN",
        "colab_type": "code",
        "outputId": "c7ba49d9-1c22-4dda-977a-9801985a08fe",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "Ly8gQ29weXJpZ2h0IDIwMTcgR29vZ2xlIExMQwovLwovLyBMaWNlbnNlZCB1bmRlciB0aGUgQXBhY2hlIExpY2Vuc2UsIFZlcnNpb24gMi4wICh0aGUgIkxpY2Vuc2UiKTsKLy8geW91IG1heSBub3QgdXNlIHRoaXMgZmlsZSBleGNlcHQgaW4gY29tcGxpYW5jZSB3aXRoIHRoZSBMaWNlbnNlLgovLyBZb3UgbWF5IG9idGFpbiBhIGNvcHkgb2YgdGhlIExpY2Vuc2UgYXQKLy8KLy8gICAgICBodHRwOi8vd3d3LmFwYWNoZS5vcmcvbGljZW5zZXMvTElDRU5TRS0yLjAKLy8KLy8gVW5sZXNzIHJlcXVpcmVkIGJ5IGFwcGxpY2FibGUgbGF3IG9yIGFncmVlZCB0byBpbiB3cml0aW5nLCBzb2Z0d2FyZQovLyBkaXN0cmlidXRlZCB1bmRlciB0aGUgTGljZW5zZSBpcyBkaXN0cmlidXRlZCBvbiBhbiAiQVMgSVMiIEJBU0lTLAovLyBXSVRIT1VUIFdBUlJBTlRJRVMgT1IgQ09ORElUSU9OUyBPRiBBTlkgS0lORCwgZWl0aGVyIGV4cHJlc3Mgb3IgaW1wbGllZC4KLy8gU2VlIHRoZSBMaWNlbnNlIGZvciB0aGUgc3BlY2lmaWMgbGFuZ3VhZ2UgZ292ZXJuaW5nIHBlcm1pc3Npb25zIGFuZAovLyBsaW1pdGF0aW9ucyB1bmRlciB0aGUgTGljZW5zZS4KCi8qKgogKiBAZmlsZW92ZXJ2aWV3IEhlbHBlcnMgZm9yIGdvb2dsZS5jb2xhYiBQeXRob24gbW9kdWxlLgogKi8KKGZ1bmN0aW9uKHNjb3BlKSB7CmZ1bmN0aW9uIHNwYW4odGV4dCwgc3R5bGVBdHRyaWJ1dGVzID0ge30pIHsKICBjb25zdCBlbGVtZW50ID0gZG9jdW1lbnQuY3JlYXRlRWxlbWVudCgnc3BhbicpOwogIGVsZW1lbnQudGV4dENvbnRlbnQgPSB0ZXh0OwogIGZvciAoY29uc3Qga2V5IG9mIE9iamVjdC5rZXlzKHN0eWxlQXR0cmlidXRlcykpIHsKICAgIGVsZW1lbnQuc3R5bGVba2V5XSA9IHN0eWxlQXR0cmlidXRlc1trZXldOwogIH0KICByZXR1cm4gZWxlbWVudDsKfQoKLy8gTWF4IG51bWJlciBvZiBieXRlcyB3aGljaCB3aWxsIGJlIHVwbG9hZGVkIGF0IGEgdGltZS4KY29uc3QgTUFYX1BBWUxPQURfU0laRSA9IDEwMCAqIDEwMjQ7Ci8vIE1heCBhbW91bnQgb2YgdGltZSB0byBibG9jayB3YWl0aW5nIGZvciB0aGUgdXNlci4KY29uc3QgRklMRV9DSEFOR0VfVElNRU9VVF9NUyA9IDMwICogMTAwMDsKCmZ1bmN0aW9uIF91cGxvYWRGaWxlcyhpbnB1dElkLCBvdXRwdXRJZCkgewogIGNvbnN0IHN0ZXBzID0gdXBsb2FkRmlsZXNTdGVwKGlucHV0SWQsIG91dHB1dElkKTsKICBjb25zdCBvdXRwdXRFbGVtZW50ID0gZG9jdW1lbnQuZ2V0RWxlbWVudEJ5SWQob3V0cHV0SWQpOwogIC8vIENhY2hlIHN0ZXBzIG9uIHRoZSBvdXRwdXRFbGVtZW50IHRvIG1ha2UgaXQgYXZhaWxhYmxlIGZvciB0aGUgbmV4dCBjYWxsCiAgLy8gdG8gdXBsb2FkRmlsZXNDb250aW51ZSBmcm9tIFB5dGhvbi4KICBvdXRwdXRFbGVtZW50LnN0ZXBzID0gc3RlcHM7CgogIHJldHVybiBfdXBsb2FkRmlsZXNDb250aW51ZShvdXRwdXRJZCk7Cn0KCi8vIFRoaXMgaXMgcm91Z2hseSBhbiBhc3luYyBnZW5lcmF0b3IgKG5vdCBzdXBwb3J0ZWQgaW4gdGhlIGJyb3dzZXIgeWV0KSwKLy8gd2hlcmUgdGhlcmUgYXJlIG11bHRpcGxlIGFzeW5jaHJvbm91cyBzdGVwcyBhbmQgdGhlIFB5dGhvbiBzaWRlIGlzIGdvaW5nCi8vIHRvIHBvbGwgZm9yIGNvbXBsZXRpb24gb2YgZWFjaCBzdGVwLgovLyBUaGlzIHVzZXMgYSBQcm9taXNlIHRvIGJsb2NrIHRoZSBweXRob24gc2lkZSBvbiBjb21wbGV0aW9uIG9mIGVhY2ggc3RlcCwKLy8gdGhlbiBwYXNzZXMgdGhlIHJlc3VsdCBvZiB0aGUgcHJldmlvdXMgc3RlcCBhcyB0aGUgaW5wdXQgdG8gdGhlIG5leHQgc3RlcC4KZnVuY3Rpb24gX3VwbG9hZEZpbGVzQ29udGludWUob3V0cHV0SWQpIHsKICBjb25zdCBvdXRwdXRFbGVtZW50ID0gZG9jdW1lbnQuZ2V0RWxlbWVudEJ5SWQob3V0cHV0SWQpOwogIGNvbnN0IHN0ZXBzID0gb3V0cHV0RWxlbWVudC5zdGVwczsKCiAgY29uc3QgbmV4dCA9IHN0ZXBzLm5leHQob3V0cHV0RWxlbWVudC5sYXN0UHJvbWlzZVZhbHVlKTsKICByZXR1cm4gUHJvbWlzZS5yZXNvbHZlKG5leHQudmFsdWUucHJvbWlzZSkudGhlbigodmFsdWUpID0+IHsKICAgIC8vIENhY2hlIHRoZSBsYXN0IHByb21pc2UgdmFsdWUgdG8gbWFrZSBpdCBhdmFpbGFibGUgdG8gdGhlIG5leHQKICAgIC8vIHN0ZXAgb2YgdGhlIGdlbmVyYXRvci4KICAgIG91dHB1dEVsZW1lbnQubGFzdFByb21pc2VWYWx1ZSA9IHZhbHVlOwogICAgcmV0dXJuIG5leHQudmFsdWUucmVzcG9uc2U7CiAgfSk7Cn0KCi8qKgogKiBHZW5lcmF0b3IgZnVuY3Rpb24gd2hpY2ggaXMgY2FsbGVkIGJldHdlZW4gZWFjaCBhc3luYyBzdGVwIG9mIHRoZSB1cGxvYWQKICogcHJvY2Vzcy4KICogQHBhcmFtIHtzdHJpbmd9IGlucHV0SWQgRWxlbWVudCBJRCBvZiB0aGUgaW5wdXQgZmlsZSBwaWNrZXIgZWxlbWVudC4KICogQHBhcmFtIHtzdHJpbmd9IG91dHB1dElkIEVsZW1lbnQgSUQgb2YgdGhlIG91dHB1dCBkaXNwbGF5LgogKiBAcmV0dXJuIHshSXRlcmFibGU8IU9iamVjdD59IEl0ZXJhYmxlIG9mIG5leHQgc3RlcHMuCiAqLwpmdW5jdGlvbiogdXBsb2FkRmlsZXNTdGVwKGlucHV0SWQsIG91dHB1dElkKSB7CiAgY29uc3QgaW5wdXRFbGVtZW50ID0gZG9jdW1lbnQuZ2V0RWxlbWVudEJ5SWQoaW5wdXRJZCk7CiAgaW5wdXRFbGVtZW50LmRpc2FibGVkID0gZmFsc2U7CgogIGNvbnN0IG91dHB1dEVsZW1lbnQgPSBkb2N1bWVudC5nZXRFbGVtZW50QnlJZChvdXRwdXRJZCk7CiAgb3V0cHV0RWxlbWVudC5pbm5lckhUTUwgPSAnJzsKCiAgY29uc3QgcGlja2VkUHJvbWlzZSA9IG5ldyBQcm9taXNlKChyZXNvbHZlKSA9PiB7CiAgICBpbnB1dEVsZW1lbnQuYWRkRXZlbnRMaXN0ZW5lcignY2hhbmdlJywgKGUpID0+IHsKICAgICAgcmVzb2x2ZShlLnRhcmdldC5maWxlcyk7CiAgICB9KTsKICB9KTsKCiAgY29uc3QgY2FuY2VsID0gZG9jdW1lbnQuY3JlYXRlRWxlbWVudCgnYnV0dG9uJyk7CiAgaW5wdXRFbGVtZW50LnBhcmVudEVsZW1lbnQuYXBwZW5kQ2hpbGQoY2FuY2VsKTsKICBjYW5jZWwudGV4dENvbnRlbnQgPSAnQ2FuY2VsIHVwbG9hZCc7CiAgY29uc3QgY2FuY2VsUHJvbWlzZSA9IG5ldyBQcm9taXNlKChyZXNvbHZlKSA9PiB7CiAgICBjYW5jZWwub25jbGljayA9ICgpID0+IHsKICAgICAgcmVzb2x2ZShudWxsKTsKICAgIH07CiAgfSk7CgogIC8vIENhbmNlbCB1cGxvYWQgaWYgdXNlciBoYXNuJ3QgcGlja2VkIGFueXRoaW5nIGluIHRpbWVvdXQuCiAgY29uc3QgdGltZW91dFByb21pc2UgPSBuZXcgUHJvbWlzZSgocmVzb2x2ZSkgPT4gewogICAgc2V0VGltZW91dCgoKSA9PiB7CiAgICAgIHJlc29sdmUobnVsbCk7CiAgICB9LCBGSUxFX0NIQU5HRV9USU1FT1VUX01TKTsKICB9KTsKCiAgLy8gV2FpdCBmb3IgdGhlIHVzZXIgdG8gcGljayB0aGUgZmlsZXMuCiAgY29uc3QgZmlsZXMgPSB5aWVsZCB7CiAgICBwcm9taXNlOiBQcm9taXNlLnJhY2UoW3BpY2tlZFByb21pc2UsIHRpbWVvdXRQcm9taXNlLCBjYW5jZWxQcm9taXNlXSksCiAgICByZXNwb25zZTogewogICAgICBhY3Rpb246ICdzdGFydGluZycsCiAgICB9CiAgfTsKCiAgaWYgKCFmaWxlcykgewogICAgcmV0dXJuIHsKICAgICAgcmVzcG9uc2U6IHsKICAgICAgICBhY3Rpb246ICdjb21wbGV0ZScsCiAgICAgIH0KICAgIH07CiAgfQoKICBjYW5jZWwucmVtb3ZlKCk7CgogIC8vIERpc2FibGUgdGhlIGlucHV0IGVsZW1lbnQgc2luY2UgZnVydGhlciBwaWNrcyBhcmUgbm90IGFsbG93ZWQuCiAgaW5wdXRFbGVtZW50LmRpc2FibGVkID0gdHJ1ZTsKCiAgZm9yIChjb25zdCBmaWxlIG9mIGZpbGVzKSB7CiAgICBjb25zdCBsaSA9IGRvY3VtZW50LmNyZWF0ZUVsZW1lbnQoJ2xpJyk7CiAgICBsaS5hcHBlbmQoc3BhbihmaWxlLm5hbWUsIHtmb250V2VpZ2h0OiAnYm9sZCd9KSk7CiAgICBsaS5hcHBlbmQoc3BhbigKICAgICAgICBgKCR7ZmlsZS50eXBlIHx8ICduL2EnfSkgLSAke2ZpbGUuc2l6ZX0gYnl0ZXMsIGAgKwogICAgICAgIGBsYXN0IG1vZGlmaWVkOiAkewogICAgICAgICAgICBmaWxlLmxhc3RNb2RpZmllZERhdGUgPyBmaWxlLmxhc3RNb2RpZmllZERhdGUudG9Mb2NhbGVEYXRlU3RyaW5nKCkgOgogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAnbi9hJ30gLSBgKSk7CiAgICBjb25zdCBwZXJjZW50ID0gc3BhbignMCUgZG9uZScpOwogICAgbGkuYXBwZW5kQ2hpbGQocGVyY2VudCk7CgogICAgb3V0cHV0RWxlbWVudC5hcHBlbmRDaGlsZChsaSk7CgogICAgY29uc3QgZmlsZURhdGFQcm9taXNlID0gbmV3IFByb21pc2UoKHJlc29sdmUpID0+IHsKICAgICAgY29uc3QgcmVhZGVyID0gbmV3IEZpbGVSZWFkZXIoKTsKICAgICAgcmVhZGVyLm9ubG9hZCA9IChlKSA9PiB7CiAgICAgICAgcmVzb2x2ZShlLnRhcmdldC5yZXN1bHQpOwogICAgICB9OwogICAgICByZWFkZXIucmVhZEFzQXJyYXlCdWZmZXIoZmlsZSk7CiAgICB9KTsKICAgIC8vIFdhaXQgZm9yIHRoZSBkYXRhIHRvIGJlIHJlYWR5LgogICAgbGV0IGZpbGVEYXRhID0geWllbGQgewogICAgICBwcm9taXNlOiBmaWxlRGF0YVByb21pc2UsCiAgICAgIHJlc3BvbnNlOiB7CiAgICAgICAgYWN0aW9uOiAnY29udGludWUnLAogICAgICB9CiAgICB9OwoKICAgIC8vIFVzZSBhIGNodW5rZWQgc2VuZGluZyB0byBhdm9pZCBtZXNzYWdlIHNpemUgbGltaXRzLiBTZWUgYi82MjExNTY2MC4KICAgIGxldCBwb3NpdGlvbiA9IDA7CiAgICB3aGlsZSAocG9zaXRpb24gPCBmaWxlRGF0YS5ieXRlTGVuZ3RoKSB7CiAgICAgIGNvbnN0IGxlbmd0aCA9IE1hdGgubWluKGZpbGVEYXRhLmJ5dGVMZW5ndGggLSBwb3NpdGlvbiwgTUFYX1BBWUxPQURfU0laRSk7CiAgICAgIGNvbnN0IGNodW5rID0gbmV3IFVpbnQ4QXJyYXkoZmlsZURhdGEsIHBvc2l0aW9uLCBsZW5ndGgpOwogICAgICBwb3NpdGlvbiArPSBsZW5ndGg7CgogICAgICBjb25zdCBiYXNlNjQgPSBidG9hKFN0cmluZy5mcm9tQ2hhckNvZGUuYXBwbHkobnVsbCwgY2h1bmspKTsKICAgICAgeWllbGQgewogICAgICAgIHJlc3BvbnNlOiB7CiAgICAgICAgICBhY3Rpb246ICdhcHBlbmQnLAogICAgICAgICAgZmlsZTogZmlsZS5uYW1lLAogICAgICAgICAgZGF0YTogYmFzZTY0LAogICAgICAgIH0sCiAgICAgIH07CiAgICAgIHBlcmNlbnQudGV4dENvbnRlbnQgPQogICAgICAgICAgYCR7TWF0aC5yb3VuZCgocG9zaXRpb24gLyBmaWxlRGF0YS5ieXRlTGVuZ3RoKSAqIDEwMCl9JSBkb25lYDsKICAgIH0KICB9CgogIC8vIEFsbCBkb25lLgogIHlpZWxkIHsKICAgIHJlc3BvbnNlOiB7CiAgICAgIGFjdGlvbjogJ2NvbXBsZXRlJywKICAgIH0KICB9Owp9CgpzY29wZS5nb29nbGUgPSBzY29wZS5nb29nbGUgfHwge307CnNjb3BlLmdvb2dsZS5jb2xhYiA9IHNjb3BlLmdvb2dsZS5jb2xhYiB8fCB7fTsKc2NvcGUuZ29vZ2xlLmNvbGFiLl9maWxlcyA9IHsKICBfdXBsb2FkRmlsZXMsCiAgX3VwbG9hZEZpbGVzQ29udGludWUsCn07Cn0pKHNlbGYpOwo=",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "################################ Test benchmarks : Real labels #############################\n",
        "import pandas as pd\n",
        "from google.colab import files\n",
        "import io\n",
        "uploaded1 = files.upload()\n",
        "y_data = pd.read_csv(io.StringIO(uploaded1['becnhmarks_lables.csv'].decode('utf-8')), skiprows=1, header=None)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-d6d14e22-6702-4cb1-b461-d48c5e3bd489\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-d6d14e22-6702-4cb1-b461-d48c5e3bd489\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving becnhmarks_lables.csv to becnhmarks_lables.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nJPTgv2BTetX",
        "colab_type": "code",
        "outputId": "257d1f59-7424-4b92-af7b-6ad0553afcfc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Predicted the factors of the entire benchmarks\n",
        "cols = [0,3,11,4]\n",
        "x_data.drop(x_data.columns[cols],axis=1,inplace=True)\n",
        "x_data.fillna(0, inplace=True)\n",
        "import numpy as np\n",
        "y_data= np.array(y_data).flatten()\n",
        "from keras.models import load_model\n",
        "model = load_model('my_model.h5')\n",
        "y_pred = model.predict_classes(x_data)\n",
        "print(y_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[5 5 5 0 0 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zP7-7KX0iOjr",
        "colab_type": "code",
        "outputId": "efce72a9-d964-4b58-c8da-9e29a773b0e8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import sklearn\n",
        "acc = sklearn.metrics.accuracy_score(np.array(y_pred), np.array(y_data))\n",
        "print(acc)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jiM5gj7dirj0",
        "colab_type": "code",
        "outputId": "1b4011fd-25cc-4428-bbf1-de7d1b97daed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(y_data)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1 4 0 3 1 2]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-2KfrPZCeTDx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "print(y_pred)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pRlTnxjhwFlN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "if __name__=='__main__':\n",
        "  \n",
        "     \n",
        "  x_data = x_train_v3457.copy()\n",
        "  y_data = y_train_v3457.copy()\n",
        "\n",
        "  optimizer = keras.optimizers.Adam()\n",
        "  kernel_initialization = 'random_uniform'\n",
        "  kernel_regularizer= None\n",
        "  mlp_without = MLP(x_data, y_data,optimizer, kernel_initialization,kernel_regularizer)\n",
        "  #loss, val_loss, acc, val_acc = mlp_without.train()\n",
        "  mlp_without.train()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hrc8NDGeVG9G",
        "colab_type": "code",
        "outputId": "f24147ca-6359-41e1-de39-5b051e802896",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train = x_train1.copy()\n",
        "y_train = y_train1.copy()\n",
        "cols = [0,3,11,4]\n",
        "x_train.drop(x_train.columns[cols],axis=1,inplace=True)\n",
        "x_train.fillna(0, inplace=True)\n",
        "x_train, x_test, y_train, y_test = train_test_split(x_train,y_train,test_size=0.02,train_size=0.8,random_state=9)\n",
        "len(x_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1539"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i8KkZWfFcEcn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.models import load_model\n",
        "\n",
        "#model.save('my_model.h5')  # creates a HDF5 file 'my_model.h5'\n",
        "#del model  # deletes the existing model\n",
        "\n",
        "# returns a compiled model\n",
        "# identical to the previous one\n",
        "model = load_model('my_model.h5')\n",
        "y_pred = model.predict_classes(x_test)\n",
        "\n",
        "import sklearn\n",
        "acc = sklearn.metrics.accuracy_score(np.array(y_pred), \n",
        "                                     np.array(y_test))\n",
        "print(acc)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bl-C1raXdCYs",
        "colab_type": "code",
        "outputId": "1e1b5600-4171-4e44-ca78-cdd7d16ab222",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import sklearn\n",
        "acc = sklearn.metrics.accuracy_score(np.array(y_pred), \n",
        "                                     np.array(y_test))\n",
        "print(acc)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.1572449642625081\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MF1Z8gVk_TVz",
        "colab_type": "code",
        "outputId": "3d78f410-0fd8-4c2f-d84f-6d8238847b3d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "x_train1 = x_train.copy()\n",
        "y_train1 = y_train.copy()\n",
        "x_train1.fillna(0, inplace=True)\n",
        "cols = [0,3,11,4]\n",
        "x_train1.drop(x_train1.columns[cols],axis=1,inplace=True)\n",
        "x_train1.fillna(0, inplace=True)\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train1, x_test1, y_train1, y_test1 = train_test_split(x_train1,y_train1,test_size=0.2,train_size=0.8,random_state=9)\n",
        "#Trying with decesion tree \n",
        "from sklearn.tree import DecisionTreeClassifier  \n",
        "classifier = DecisionTreeClassifier()  \n",
        "classifier.fit(x_train1, y_train1)\n",
        "y_pred = classifier.predict(x_test1) \n",
        "\n",
        "from sklearn.metrics import classification_report, confusion_matrix  \n",
        "print(confusion_matrix(y_test1, y_pred))  \n",
        "print(classification_report(y_test1, y_pred)) "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[654 548 560 497]\n",
            " [625 586 590 404]\n",
            " [640 549 644 406]\n",
            " [460 445 402 888]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.27      0.29      0.28      2259\n",
            "           4       0.28      0.27      0.27      2205\n",
            "           5       0.29      0.29      0.29      2239\n",
            "           6       0.40      0.40      0.40      2195\n",
            "\n",
            "    accuracy                           0.31      8898\n",
            "   macro avg       0.31      0.31      0.31      8898\n",
            "weighted avg       0.31      0.31      0.31      8898\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eLLUN0wyO3b4",
        "colab_type": "code",
        "outputId": "fe872bdf-dff8-4859-fb91-59b95547d4c4",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "################################ Data set: y_train #############################\n",
        "import pandas as pd\n",
        "from google.colab import files\n",
        "import io\n",
        "uploaded1 = files.upload()\n",
        "y_train1 = pd.read_csv(io.StringIO(uploaded1['y_train.csv'].decode('utf-8')), skiprows=1, header=None)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-f2f3fccd-e02d-48e3-8cda-d166d9f441f3\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-f2f3fccd-e02d-48e3-8cda-d166d9f441f3\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving y_train.csv to y_train (1).csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7oYMfdHPQAZN",
        "colab_type": "code",
        "outputId": "486c1eb4-cc33-4169-9e56-cfa7ab266d32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        }
      },
      "source": [
        "y_train1.hist(bins=20, grid=False, xlabelsize=12, ylabelsize=12)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f98044df1d0>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kxlCQJDWGgiSpMRQkSU3voZDkkiQPJTme5FCS9/TdkySdr1b13QDwKeAlYBNwDfBIkgNV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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jDeNiPecPkpm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_train1.hist(bins=20, grid=False, xlabelsize=12, ylabelsize=12)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lTWmnFeDF0kB",
        "colab_type": "code",
        "outputId": "80da4f83-0d54-4e89-c628-46796bcea2e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        }
      },
      "source": [
        "## Distribution de y\n",
        "#from matplotlib import pyplot as plt\n",
        "y_train1.hist(bins=20, grid=False, xlabelsize=12, ylabelsize=12)\n",
        "#plt.title('Distribution des données dans les 64 classes', fontsize=16)\n",
        "#plt.xlabel(\"Classes\", fontsize=15)\n",
        "#plt.ylabel(\"Nombre de données\",fontsize=15)\n",
        "#plt.xlim([-5,20])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f9812fd9208>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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76R1ZvKB7/Liq7uvbfkfXs1Dv4OdvTjKdZHp2dnb4HZIkHdKoTk9dCVxXVQ8OjK8E9gyM\n7QFWdbXH5qkt1HuQqtpWVRurauPU1NQipi9JajH06akkG4DfAl54iPI+YHJgbBLYS+/01Hy1hXol\nSWMwijWNlwNrge8lgd4RwookvwrcAayf2zDJ84HjgPvohcZEktOq6u+6TdYDM93zmcP0SpLGYBSh\nsQ34877Xf0QvRM4Hngv8jyQvAf4XvXWP7XML2Um2A1ckeTuwAfht4Ne79/nU4XolSUtv6DWNqtpf\nVbvmHvROKz1ZVbNVNQP8O3oB8AN66xEX9LVfADynq30GOL/roaFXkrTERnbJ7Zyq2jrw+tPAp+fZ\n9h+Asw7zXvP2SpKWnrcRkSQ1MzQkSc0MDUlSM0NDktTM0JAkNTM0JEnNDA1JUjNDQ5LUzNCQJDUz\nNCRJzQwNSVIzQ0OS1MzQkCQ1MzQkSc0MDUlSM0NDktTM0JAkNTM0JEnNDA1JUrOhQyPJcUmuS7Iz\nyd4k307y2r76K5Pcm2R/kq8mWTPQe32Sx5LsSnLRwHvP2ytJWnqjONKYAP4eeBnwj4EtwOeSrE1y\nErAduBQ4EZgGPtvXuxU4DVgDvAK4OMlrABp6JUlLbGLYN6iqx+l985/zpSTfBV4E/BNgpqpuAUiy\nFXgkyelVdS9wDnBuVe0Gdif5OHAucAfwxgV6JUlLbORrGklOBl4AzADrgB1ztS5g7gfWJVkNnNJf\n756v657P23uIz9ycZDrJ9Ozs7Gh3SJL0MyMNjSTPBj4FfLI7GlgJ7BnYbA+wqqsxUJ+rsUDvQapq\nW1VtrKqNU1NTw+2EJGleIwuNJM8CbgKeAi7shvcBkwObTgJ7uxoD9bnaQr2SpDEYSWgkCXAdcDKw\nqap+1JVmgPV9250AnEpvrWI38HB/vXs+s1DvKOYsSTpyozrS+AjwK8AbquqJvvFbgTOSbEpyPHAZ\ncHffQvaNwJYkq5OcDrwDuKGxV5K0xEbxcxprgPOADcCuJPu6x1uqahbYBFwF7AZeDJzd1345vcXt\nncDXgfdX1R0ADb2SpCU2iktudwI5TP1O4PR5ageAt3WPI+qVJC09byMiSWpmaEiSmhkakqRmhoYk\nqZmhIUlqZmhIkpoZGpKkZoaGJKmZoSFJamZoSJKaGRqSpGaGhiSpmaEhSWpmaEiSmhkakqRmhoYk\nqZmhIUlqZmhIkpoZGpKkZss6NJKcmOTWJI8n2ZnkzeOekyQ9k02MewILuBZ4CjgZ2ADcnmRHVc2M\nd1qS9My0bI80kpwAbAIurap9VfVXwBeA3xvvzCTpmStVNe45HFKSFwLfqKp/1Df2R8DLquoNA9tu\nBjZ3L38Z+D+L/NiTgEcW2bvcuC/Lz9NlP8B9Wa6G2Zc1VTW10EbL+fTUSuCxgbE9wKrBDatqG7Bt\n2A9MMl1VG4d9n+XAfVl+ni77Ae7LcrUU+7JsT08B+4DJgbFJYO8Y5iJJYnmHxn3ARJLT+sbWAy6C\nS9KYLNvQqKrHge3AFUlOSPIbwG8DNx3Fjx36FNcy4r4sP0+X/QD3Zbk66vuybBfCofdzGsD1wKuA\nR4FLqurT452VJD1zLevQkCQtL8v29JQkafkxNCRJzQwNnj73uEpyYZLpJAeS3DDu+QwjyXFJruv+\nPPYm+XaS1457XouR5OYkDyd5LMl9Sd4+7jkNK8lpSZ5McvO457JYSb7W7cO+7rHYHwpeFpKcneSe\n7vvY/UlecjQ+Zzn/cN9Serrc4+r7wHuBVwPPGfNchjUB/D3wMuB7wOuAzyX5tap6YJwTW4Q/Bv5t\nVR1IcjrwtSTfqqq7xj2xIVwLfHPckxiBC6vqz8Y9iWEleRVwNfCvgP8JnHK0PusZf6TxdLrHVVVt\nr6rb6F1pdkyrqseramtVPVBVP62qLwHfBV407rkdqaqaqaoDcy+7x6ljnNJQkpwN/BD4r+Oei37m\nPcAVVfXX3b+Xh6rqoaPxQc/40ABeAPy4qu7rG9sBrBvTfHQISU6m92d1rB39AZDkw0n2A/cCDwNf\nHvOUFiXJJHAFcNG45zIif5zkkSTfSPLycU9mMZKsADYCU0m+k+TBJNckOSpnGwyNI7jHlcYjybOB\nTwGfrKp7xz2fxaiqC+j9nXoJvR9aPXD4jmXrSuC6qnpw3BMZgXcDzweeR++H4r6Y5Fg8AjwZeDbw\nJnp/vzYALwS2HI0PMzS8x9WyluRZ9O4C8BRw4ZinM5Sq+kl3+vOfAuePez5HKskG4LeA/zzuuYxC\nVf1NVe2tqgNV9UngG/TWzo41T3Rf/7SqHq6qR4APcpT2xYXwvntcVdXfdWPe42oZSBLgOnr/k3pd\nVf1ozFMalQmOzTWNlwNrge/1/mhYCaxI8qtV9S/GOK9RKSDjnsSRqqrdSR6kN/+fDR+tz3vGH2mM\n6R5XR0WSiSTHAyvo/WM+Psmx/B+DjwC/Aryhqp5YaOPlKMlzu0shVyZZkeTVwO9ybC4ib6MXdhu6\nx0eB2+ldrXdMSfLzSV49928kyVuAlwJ3jHtui/QJ4Pe7v2+rgXcBXzoaH3Qsf0MZpQvo3ePqB/Su\nPDr/GLzcFnrnMC/ve/1WeldVbB3LbIaQZA1wHr1z/7u6/9kCnFdVnxrbxI5c0TsV9VF6/0nbCfz7\nqvrCWGe1CFW1H9g/9zrJPuDJqpod36wW7dn0Lk8/HfgJvQsUzhq4IOZYciW9X8B0H/Ak8DngqqPx\nQd57SpLU7Bl/ekqS1M7QkCQ1MzQkSc0MDUlSM0NDktTM0JAkNTM0JEnNDA1JUrP/B9j8TkhAx1DT\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2q_j2Ca1euh8",
        "colab_type": "code",
        "outputId": "6982539f-e464-45c2-d32c-7059c967f4a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np \n",
        "  \n",
        "  # Les plots \n",
        "  # Create count of the number of epochs\n",
        "epoch_count = range(1, len(loss_adam1) + 1)\n",
        "\n",
        "\n",
        "\n",
        "# y_train = np.array(y_train).flatten()\n",
        "plt.plot(epoch_count, loss_without, 'b-')\n",
        "plt.plot(epoch_count, loss_l1, 'g-')\n",
        "plt.plot(epoch_count, loss_l2, 'r-')\n",
        "\n",
        "\n",
        "   \n",
        "  \n",
        "plt.title(\"Fonction de perte du modèle en traitment en fonction de nombre d'itérations en appliquant les deux type de régularisation ou non\")\n",
        "plt.legend(['Sans' ,'L1', 'L2'])\n",
        "plt.xlabel(\"Nombre d'itérations\")\n",
        "plt.ylabel(\"Fonction de perte\")\n",
        "plt.show();\n",
        "  \n",
        "plt.plot(epoch_count, loss_without, 'b-')\n",
        "plt.plot(epoch_count, loss_l1, 'g-')\n",
        "plt.plot(epoch_count, loss_l2, 'r-')\n",
        "\n",
        "  \n",
        "  \n",
        "plt.title(\"Fonction de perte du modèle en test en fonction  de nombre d'itérations en appliquant les deux type de régularisation ou non\")\n",
        "plt.legend(['Sans' ,'L1', 'L2'])\n",
        "plt.xlabel(\"Nombre d'itérations\")\n",
        "plt.ylabel(\"Fonction de perte\")\n",
        "plt.show();\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jY+Nm3c4555wUIhGRLNMlgUV6AVVKREREJMuUlIj0AqqUiIiISJYpKRHpBaqr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\n+n9R0NCVjaDjEgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SpuRApOu2cvf58f1bwFZpTkw1ByIiIpI2JQciFeDh0aUFH19qZpPNrMnMmsp5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\nEREB4P8DyNKCv6JZKFkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0uXf9PiguQyM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "################################ Data set: y_train #############################\n",
        "import pandas as pd\n",
        "from google.colab import files\n",
        "import io\n",
        "uploaded1 = files.upload()\n",
        "y_train1 = pd.read_csv(io.StringIO(uploaded1['y_train.csv'].decode('utf-8')), skiprows=1, header=None)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EcQqvrLHutPn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "################################ Data set: x_train #############################\n",
        "import pandas as pd\n",
        "from google.colab import files\n",
        "import io\n",
        "uploaded = files.upload()\n",
        "x_train1 = pd.read_csv(io.StringIO(uploaded['x_train.csv'].decode('utf-8')), skiprows=1, usecols=range(23), header=None)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B0aGgC_4WsLj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "if __name__=='__main__':\n",
        "  x_data = x_train1.copy()\n",
        "  y_data = y_train1.copy()\n",
        "  \n",
        "  optimizer = keras.optimizers.Adam()\n",
        "  kernel_initialization = 'random_uniform'\n",
        "  kernel_regularizer= None\n",
        "  mlp_without = MLP(x_data, y_data,optimizer, kernel_initialization,kernel_regularizer)\n",
        "  loss_without, val_loss_without = mlp_without.train()\n",
        "  \n",
        "  optimizer = keras.optimizers.Adam()\n",
        "  kernel_initialization = 'random_uniform'\n",
        "  kernel_regularizer= keras.regularizers.l1()\n",
        "  mlp_l1 = MLP(x_data, y_data,optimizer, kernel_initialization,kernel_regularizer)\n",
        "  loss_l1, val_loss_l1 = mlp_l1.train()\n",
        "  \n",
        "  optimizer = keras.optimizers.Adam()\n",
        "  kernel_initialization = 'random_uniform'\n",
        "  kernel_regularizer= keras.regularizers.l2()\n",
        "  mlp_l2 = MLP(x_data, y_data,optimizer, kernel_initialization,kernel_regularizer)\n",
        "  loss_l2, val_loss_l2 = mlp_l2.train()\n",
        "\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dLZBURfmWp2Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np \n",
        "  \n",
        "  # Les plots \n",
        "  # Create count of the number of epochs\n",
        "epoch_count = range(1, len(loss_adam1) + 1)\n",
        "\n",
        "\n",
        "\n",
        "# y_train = np.array(y_train).flatten()\n",
        "plt.plot(epoch_count, loss_without, 'b-')\n",
        "plt.plot(epoch_count, loss_l1, 'g-')\n",
        "plt.plot(epoch_count, loss_l2, 'r-')\n",
        "\n",
        "\n",
        "   \n",
        "  \n",
        "plt.title(\"Fonction de perte du modèle en traitment en fonction de nombre d'itérations en appliquant les deux type de régularisation ou non\")\n",
        "plt.legend(['Sans' ,'L1', 'L2'])\n",
        "plt.xlabel(\"Nombre d'itérations\")\n",
        "plt.ylabel(\"Fonction de perte\")\n",
        "plt.show();\n",
        "  \n",
        "plt.plot(epoch_count, loss_without, 'b-')\n",
        "plt.plot(epoch_count, loss_l1, 'g-')\n",
        "plt.plot(epoch_count, loss_l2, 'r-')\n",
        "\n",
        "  \n",
        "  \n",
        "plt.title(\"Fonction de perte du modèle en test en fonction  de nombre d'itérations en appliquant les deux type de régularisation ou non\")\n",
        "plt.legend(['Sans' ,'L1', 'L2'])\n",
        "plt.xlabel(\"Nombre d'itérations\")\n",
        "plt.ylabel(\"Fonction de perte\")\n",
        "plt.show();\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EqyhFI0sK2Pf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "if __name__=='__main__':\n",
        "  x_data = x_train1.copy()\n",
        "  y_data = y_train1.copy()\n",
        "  \n",
        "  #pour tester diff diff initialization \n",
        "  optimizer = keras.optimizers.Adam()\n",
        "  kernel_initialization = 'random_normal'\n",
        "  mlp_rn = MLP(x_data, y_data,optimizer, kernel_initialization)\n",
        "  loss_rn, val_loss_rn = mlp_rn.train()\n",
        "  \n",
        "  optimizer = keras.optimizers.Adam()\n",
        "  kernel_initialization = 'random_uniform'\n",
        "  mlp_ru = MLP(x_data, y_data,optimizer, kernel_initialization)\n",
        "  loss_ru, val_loss_ru = mlp_ru.train()\n",
        "\n",
        "  \n",
        "  optimizer = keras.optimizers.Adam()\n",
        "  kernel_initialization = keras.initializers.VarianceScaling()\n",
        "  mlp_vs = MLP(x_data, y_data,optimizer, kernel_initialization)\n",
        "  loss_vs, val_loss_vs = mlp_vs.train()\n",
        "   \n",
        "    \n",
        "  optimizer = keras.optimizers.Adam()\n",
        "  kernel_initialization = keras.initializers.glorot_normal()\n",
        "  mlp_gn = MLP(x_data, y_data,optimizer, kernel_initialization)\n",
        "  loss_gn, val_loss_gn = mlp_gn.train()\n",
        "  \n",
        "      \n",
        "  optimizer = keras.optimizers.Adam()\n",
        "  kernel_initialization = keras.initializers.glorot_uniform()\n",
        "  mlp_gu = MLP(x_data, y_data,optimizer, kernel_initialization)\n",
        "  loss_gu, val_loss_gu = mlp_gu.train()\n",
        "  \n",
        "  optimizer = keras.optimizers.Adam()\n",
        "  kernel_initialization = keras.initializers.he_normal()\n",
        "  mlp_he = MLP(x_data, y_data,optimizer, kernel_initialization)\n",
        "  loss_he, val_loss_he = mlp_he.train()\n",
        "  \n",
        "  optimizer = keras.optimizers.Adam()\n",
        "  kernel_initialization = keras.initializers.he_uniform()\n",
        "  mlp_hu = MLP(x_data, y_data,optimizer, kernel_initialization)\n",
        "  loss_hu, val_loss_hu = mlp_hu.train()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vCbGNNs7Ky_c",
        "colab_type": "code",
        "outputId": "20ce0ea8-c3dc-435c-c014-7757ca2d6155",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np \n",
        "  \n",
        "  # Les plots \n",
        "  # Create count of the number of epochs\n",
        "epoch_count = range(1, len(loss_rn) + 1)\n",
        "\n",
        "\n",
        "\n",
        "# y_train = np.array(y_train).flatten()\n",
        "plt.plot(epoch_count, loss_rn, 'b-')\n",
        "plt.plot(epoch_count, loss_ru, 'g-')\n",
        "plt.plot(epoch_count, loss_vs, 'r-')\n",
        "plt.plot(epoch_count, loss_gn, 'c-')\n",
        "plt.plot(epoch_count, loss_gu, 'm-')\n",
        "plt.plot(epoch_count, loss_he, 'y-')\n",
        "plt.plot(epoch_count, loss_hu, 'k-')\n",
        "\n",
        "   \n",
        "  \n",
        "plt.title(\"Fonction de perte du modèle en traitment en fonction nombre d'itérations pour différentes valeurs d'ininitialisation du poids\")\n",
        "plt.legend(['random_normal' ,'random_uniform', 'variance_scaling', 'glorot_normal', 'glorot_uniform', 'he_normal', 'he_uniform'])\n",
        "plt.xlabel(\"Nombre d'itérations\")\n",
        "plt.ylabel(\"Fonction de perte\")\n",
        "plt.show();\n",
        "  \n",
        "plt.plot(epoch_count, val_loss_rn, 'b-')\n",
        "plt.plot(epoch_count, val_loss_ru, 'g-')\n",
        "plt.plot(epoch_count, val_loss_vs, 'r-')\n",
        "plt.plot(epoch_count, val_loss_gn, 'c-')\n",
        "plt.plot(epoch_count, val_loss_gu, 'm-')\n",
        "plt.plot(epoch_count, val_loss_he, 'y-')\n",
        "plt.plot(epoch_count, val_loss_hu, 'k-')\n",
        "\n",
        "  \n",
        "  \n",
        "plt.title(\"Fonction de perte du modèle en test en fonction nombre d'itérations pour différentes valeurs de taux d'ininitialisation du poids\")\n",
        "plt.legend(['random_normal' ,'random_uniform', 'variance_scaling', 'glorot_normal', 'glorot_uniform', 'he_normal', 'he_uniform'])\n",
        "plt.xlabel(\"Nombre d'itérations\")\n",
        "plt.ylabel(\"Fonction de perte\")\n",
        "plt.show();\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BA7Zu3UrdunXp378/48aN4/z588yfP5969eo5jXX8+PEULFiQ4cOHA1CjRg2+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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KliyZ7bwpKQXqgqsQ4ja4ODoAIYqIwcC72jhNf1ApdQSoC2y1nEhrPQuYBcYr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/vRfD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\nkUKDiIiIiIi0SKFBRERERERa9N8E8ekOdYLycwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X9a2U5AyrO_U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "  # pour tester diff learning rate \n",
        "  \n",
        "  optimizer = keras.optimizers.Adam()\n",
        "  mlp_adam = MLP(x_data, y_data,optimizer)\n",
        "  loss_adam, val_loss_adam = mlp_adam.train()\n",
        "  \n",
        "  optimizer = keras.optimizers.Adam(lr= 0.1)\n",
        "  mlp_adam1 = MLP(x_data, y_data,optimizer)\n",
        "  loss_adam1, val_loss_adam1 = mlp_adam1.train()\n",
        "  \n",
        "  optimizer = keras.optimizers.Adam(lr= 0.01)\n",
        "  mlp_adam2 = MLP(x_data, y_data,optimizer)\n",
        "  loss_adam2, val_loss_adam2 = mlp_adam2.train()\n",
        "  \n",
        "  optimizer = keras.optimizers.Adam(lr= 0.001)\n",
        "  mlp_adam3 = MLP(x_data, y_data,optimizer)\n",
        "  loss_adam3, val_loss_adam3 = mlp_adam3.train()\n",
        "  \n",
        "  optimizer = keras.optimizers.Adam(lr= 0.0001)\n",
        "  mlp_adam4 = MLP(x_data, y_data,optimizer)\n",
        "  loss_adam4, val_loss_adam4 = mlp_adam4.train()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mnfV5j4hs15m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np \n",
        "  \n",
        "  # Les plots \n",
        "  # Create count of the number of epochs\n",
        "epoch_count = range(1, len(loss_adam1) + 1)\n",
        "\n",
        "\n",
        "\n",
        "# y_train = np.array(y_train).flatten()\n",
        "plt.plot(epoch_count, loss_adam, 'k-')\n",
        "plt.plot(epoch_count, loss_adam1, 'm-')\n",
        "plt.plot(epoch_count, loss_adam2, 'y-')\n",
        "plt.plot(epoch_count, loss_adam3, 'r-')\n",
        "plt.plot(epoch_count, loss_adam4, 'c-')\n",
        "\n",
        "  \n",
        "  \n",
        " \n",
        "  \n",
        "  \n",
        "plt.title(\"Fonction de perte du modèle en traitment en fonction nombre d'itérations pour différentes valeurs de taux d'apprentissage\")\n",
        "plt.legend(['Default value' ,'Rate 0.1', 'Rate 0.01', 'Rate 0.001', 'Rate 0.0001'])\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.show();\n",
        "  \n",
        "plt.plot(epoch_count, val_loss_adam, 'k-')\n",
        "plt.plot(epoch_count, val_loss_adam1, 'm-')\n",
        "plt.plot(epoch_count, val_loss_adam2, 'y-')\n",
        "plt.plot(epoch_count, val_loss_adam3, 'r-')\n",
        "plt.plot(epoch_count, val_loss_adam4, 'c-')\n",
        "\n",
        "  \n",
        "  \n",
        "plt.title(\"Fonction de perte du modèle en test en fonction nombre d'itérations pour différentes valeurs de taux d'apprentissage\")\n",
        "plt.legend(['Default value' ,'Rate 0.1', 'Rate 0.01', 'Rate 0.001', 'Rate 0.0001'])\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.show();"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fOTO4IY7kn5Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "  #pour tester diff algo d'optimisation\n",
        "  \n",
        "  #mlp_sgd =   MLP(x_data, y_data,'sgd')\n",
        "  #loss_sgd, val_loss_sgd, acc_sgd, val_acc_sgd = mlp_sgd.train()\n",
        "  \n",
        "  #mlp_adagard = MLP(x_data, y_data,'adagrad')\n",
        "  #loss_adagard, val_loss_adagard, acc_adagard, val_acc_adagard = mlp_adagard.train()\n",
        "  \n",
        "  #mlp_adadelta = MLP(x_data, y_data,'adadelta')\n",
        "  #loss_adadelta, val_loss_adadelta, acc_adadelta, val_acc_adadelta = mlp_adadelta.train()\n",
        "    \n",
        "  #mlp_adamax = MLP(x_data, y_data,'adamax')\n",
        "  #loss_adamax, val_loss_adamax, acc_adamax, val_acc_adamax = mlp_adamax.train()\n",
        "    \n",
        "  #mlp_adam = MLP(x_data, y_data,'adam')\n",
        "  #loss_adam, val_loss_adam, acc_adam, val_acc_adam = mlp_adam.train()\n",
        "    \n",
        "  #mlp_nadam = MLP(x_data, y_data,'nadam')\n",
        "  #loss_nadam, val_loss_nadam, acc_nadam, val_acc_nadam = mlp_nadam.train()\n",
        "    \n",
        "  #mlp_rmsprop = MLP(x_data, y_data,'rmsprop')\n",
        "  #loss_rmsprop, val_loss_rmsprop, acc_rmsprop, val_acc_rmsprop = mlp_rmsprop.train()  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hREp0FRnoERz",
        "colab_type": "code",
        "outputId": "ff795e10-88da-4b66-d425-18f33f8626b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        }
      },
      "source": [
        "#plot de diff algo d'optimisation\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np \n",
        "  \n",
        "  # Les plots \n",
        "  # Create count of the number of epochs\n",
        "epoch_count = range(1, len(loss_sgd) + 1)\n",
        "\n",
        "\n",
        "\n",
        "# y_train = np.array(y_train).flatten()\n",
        "\n",
        "plt.plot(epoch_count, loss_sgd, 'b-')\n",
        "plt.plot(epoch_count, loss_adagard, 'g-')\n",
        "plt.plot(epoch_count, loss_adadelta, 'r-')\n",
        "plt.plot(epoch_count, loss_adamax, 'c-')\n",
        "plt.plot(epoch_count, loss_adam, 'm-')\n",
        "plt.plot(epoch_count, loss_nadam, 'y-')\n",
        "plt.plot(epoch_count, loss_rmsprop, 'k-')\n",
        "  \n",
        "  \n",
        "plt.title(\"Comparaison l'erreur du traitement de différents algorithmes d'optimisation\")\n",
        "plt.legend(['sgd', 'adagard', 'adadelta', 'adamax', 'adam', 'nadam', 'rmsprop'])\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.show();\n",
        "  \n",
        "  \n",
        "  \n",
        "plt.plot(epoch_count, val_loss_sgd, 'b-')\n",
        "plt.plot(epoch_count, val_loss_adagard, 'g-')\n",
        "plt.plot(epoch_count, val_loss_adadelta, 'r-')\n",
        "plt.plot(epoch_count, val_loss_adamax, 'c-')\n",
        "plt.plot(epoch_count, val_loss_adam, 'm-')\n",
        "plt.plot(epoch_count, val_loss_nadam, 'y-')\n",
        "plt.plot(epoch_count, val_loss_rmsprop, 'k-')\n",
        "  \n",
        "  \n",
        "plt.title(\"Comparaison de l'erreur du test de différents algorithmes d'optimisation\")\n",
        "plt.legend(['sgd', 'adagard', 'adadelta', 'adamax', 'adam', 'nadam', 'rmsprop'])\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.show();\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1DhEMhrSKUbapkPS5s5H9WjOaNYvk6s6dXLl+na9/+eU/4bzSefHL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\nd1MAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6rfm81cudxLh4lsTvYsk3/SB3bggKynTfiz4+sGABbNgAhw6p3SEtW6p5yGHD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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MRbWhQTsHqtT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "plt.plot(epoch_count, acc_sgd, 'b-')\n",
        "plt.plot(epoch_count, acc_adagard, 'g-')\n",
        "plt.plot(epoch_count, acc_adadelta, 'r-')\n",
        "plt.plot(epoch_count, acc_adamax, 'c-')\n",
        "plt.plot(epoch_count, acc_adam, 'm-')\n",
        "#plt.plot(epoch_count, acc_nadam, 'y-')\n",
        "#plt.plot(epoch_count, acc_rmsprop, 'k-')\n",
        "  \n",
        "  \n",
        "plt.title(\"Comparaison de la précision du traitement de différentes algorithmes d'optimisation\")\n",
        "#plt.legend(['sgd', 'adagard', 'adadelta', 'adamax', 'adam', 'nadam', 'rmsprop'])\n",
        "plt.legend(['sgd', 'adagard', 'adadelta', 'adamax', 'adam', 'nadam'])\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.show();\n",
        "  \n",
        "  \n",
        "plt.plot(epoch_count, val_acc_sgd, 'b-')\n",
        "plt.plot(epoch_count, val_acc_adagard, 'g-')\n",
        "plt.plot(epoch_count, val_acc_adadelta, 'r-')\n",
        "plt.plot(epoch_count, val_acc_adamax, 'c-')\n",
        "plt.plot(epoch_count, val_acc_adam, 'm-')\n",
        "#plt.plot(epoch_count, val_acc_nadam, 'y-')\n",
        "#plt.plot(epoch_count, val_acc_rmsprop, 'k-')\n",
        "  \n",
        "  \n",
        "plt.title(\"Comparaison de la précision du test de différentes algorithmes d'optimisation\")\n",
        "#plt.legend(['sgd', 'adagard', 'adadelta', 'adamax', 'adam', 'nadam', 'rmsprop'])\n",
        "plt.legend(['sgd', 'adagard', 'adadelta', 'adamax', 'adam' , 'nadam'])\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.show(); "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l_e-N8YkHfyN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loss_sgd = np.array(loss_sgd[0:176]).flatten()\n",
        "loss_adagard = np.array(loss_adagard[0:176]).flatten()\n",
        "loss_adadelta= np.array(loss_adadelta[0:176]).flatten()\n",
        "loss_adamax = np.array(loss_adamax[0:176]).flatten()\n",
        "loss_adam = np.array(loss_adam[0:176]).flatten()\n",
        "loss_nadam = np.array(loss_nadam[0:176]).flatten()\n",
        "\n",
        "val_loss_sgd = np.array(val_loss_sgd[0:176]).flatten()\n",
        "val_loss_adagard = np.array(val_loss_adagard[0:176]).flatten()\n",
        "val_loss_adadelta= np.array(val_loss_adadelta[0:176]).flatten()\n",
        "val_loss_adamax = np.array(val_loss_adamax[0:176]).flatten()\n",
        "val_loss_adam = np.array(val_loss_adam[0:176]).flatten()\n",
        "val_loss_nadam = np.array(val_loss_nadam[0:176]).flatten()\n",
        "\n",
        "\n",
        "acc_sgd = np.array(val_loss_sgd[0:176]).flatten()\n",
        "acc_adagard = np.array(acc_adagard[0:176]).flatten()\n",
        "acc_adadelta= np.array(acc_adadelta[0:176]).flatten()\n",
        "acc_adamax = np.array(acc_adamax[0:176]).flatten()\n",
        "acc_adam = np.array(acc_adam[0:176]).flatten()\n",
        "acc_nadam = np.array(acc_nadam[0:176]).flatten()\n",
        "\n",
        "val_acc_sgd = np.array(val_acc_sgd[0:176]).flatten()\n",
        "val_acc_adagard = np.array(val_acc_adagard[0:176]).flatten()\n",
        "val_acc_adadelta= np.array(val_acc_adadelta[0:176]).flatten()\n",
        "val_acc_adamax = np.array(val_acc_adamax[0:176]).flatten()\n",
        "val_acc_adam = np.array(val_acc_adam[0:176]).flatten()\n",
        "val_acc_nadam = np.array(val_acc_nadam[0:176]).flatten()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8_0W5bjsB3ts",
        "colab_type": "text"
      },
      "source": [
        "# Other things"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R4ugQcvtkrsn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Normalization \n",
        "from sklearn import preprocessing\n",
        "x_normalizer = preprocessing.Normalizer().fit(x_train)  # fit does nothing\n",
        "x_normalizer\n",
        "x_train = x_normalizer.transform(x_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FWH9jzqSpycT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "y.hist(bins=20, grid=False, xlabelsize=12, ylabelsize=12)\n",
        "#plt.title('Distribution des données dans les 64 classes', fontsize=16)\n",
        "#plt.xlabel(\"Classes\", fontsize=15)\n",
        "#plt.ylabel(\"Nombre de données\",fontsize=15)\n",
        "#plt.xlim([-5,20])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vP1eFZLGhMuv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def data_augmentation_2(df,new_span):\n",
        "  prev_span = df.loc[:,16] \n",
        "  df.loc[:,6] *= new_span \n",
        "  df.loc[:,6] /= prev_span\n",
        "  \n",
        "  df.loc[:,8] *= new_span\n",
        "  df.loc[:,8] /= prev_span\n",
        "  df.loc[:,10] *= new_span \n",
        "  df.loc[:,10] /= prev_span\n",
        "  df.loc[:,12] *= new_span \n",
        "  df.loc[:,12] /= prev_span \n",
        "  df.loc[:,16] *= new_span\n",
        "  df.loc[:,16] /= prev_span "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GutMukNkptIB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import files\n",
        "src = list(files.upload().values())[0]\n",
        "open('mylib.py','wb').write(src)\n",
        "import mylib"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AwibRdZTkzbs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#MinMaxScaler\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "# All data needs to be scaled to a small range like 0 to 1 for the neural network to work well. \n",
        "# Create scalers for the inputs \n",
        "X_scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "\n",
        "# Scale the training inputs \n",
        "x_treain = X_scaler.fit_transform(x_train)\n",
        "print(x_train.shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5qOC4hTyeSP_",
        "colab_type": "text"
      },
      "source": [
        "# Feature Selctor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T5UkPRx6bPxV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from feature_selector import FeatureSelector\n",
        "# Features are in train and labels are in train_labels\n",
        "fs = FeatureSelector(data = x_train1, labels = y_train1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ADz7_Xu1t8KC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZNyLBKWHrcAj",
        "colab_type": "code",
        "outputId": "08e5eeb3-eae0-482f-f59f-d9c0a1914642",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "fs.identify_missing(missing_threshold = 0.6)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2 features with greater than 0.60 missing values.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SJCBBw9Orlgg",
        "colab_type": "code",
        "outputId": "ca44c617-1c4c-4122-e832-c84ff06d42d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "fs.missing_stats.head()"
      ],
      "execution_count": 0,
      "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>missing_fraction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.811669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.671934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.495571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.365027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.347449</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    missing_fraction\n",
              "22          0.811669\n",
              "15          0.671934\n",
              "21          0.495571\n",
              "20          0.365027\n",
              "14          0.347449"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NE9YWmNEr-Rg",
        "colab_type": "code",
        "outputId": "7c513bed-8d2a-4a5e-aceb-5c466c3c9cc9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "missing_features = fs.ops['missing']\n",
        "missing_features[:22]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[15, 22]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DkY7BweOsHb7",
        "colab_type": "code",
        "outputId": "a191546f-5e94-40bf-ecdc-d464d36fe72e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        }
      },
      "source": [
        "fs.plot_missing()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wr/j4eB04cECbN29O1RcbG6vz58/LZDI9lL2KyDkIaACQhhEjRuj111+33IYp\nxe7du7Vv3z498sgjGb5FGP7Z7ty5o86dO6tPnz6Wuwqk+PLLL5WYmKhq1ao5/OQh/LNwiBMA0rBv\n3z517dpVCQkJatKkicqVK6fffvtNK1asUEJCgoYPH64XX3zxYZcJJ/Xll1/qo48+Uv78+fXcc8+p\nQIEC2r9/v7Zt2yYPDw999dVXCggIeNhlwokR0AAgHceOHdP06dN14MABXblyRR4eHgoICFDnzp1T\n3UQd+Lu1a9fqq6++0smTJ3Xz5k35+vqqRo0a6tWrl+VscSA9BDQAAAAnwxw0AAAAJ+PS10GrVq2a\n4uPjVbhw4YddCgAAcAFXr16Vu7t7mrcOu5dLB7S7d++mujcjAABAVklMTJQts8tcOqCl3P9u/fr1\nD7kSAADgCtK66X1amIMGAADgZAhoAAAAToaABgAA4GQIaAAAAE6GgAYAAOBkCGgAAABOhoAGAADg\nZAhoAAAAToaABgAA4GQIaAAAAE6GgAYAAOBkCGgAAABOhoAGAADgZAhoAAAATsbtYRfwT2YYhuLi\n4h52GQ7j4eEhk8n0sMsAAOAfj4CWheLi4uTl5fWwy3CYmJgYeXp6PuwyAAD4x+MQJwAAgJNhD1o2\nuSwpJ+57ipVU9GEXAQCAiyGgZRNP5cyABgAAsh+HOAEAAJwMAQ0AAMDJENAAAACcjFPNQduyZYve\nfPNN1axZU//9738t7cOGDdOyZcusxiYlJalFixYaM2ZMquU0bNhQV65csbpmV506dTR16tSsKx4A\nAMBBnCagzZgxQ5GRkSpTpkyqvtGjR2v06NGWx4mJiWrZsqWaNm2a7vK+/PJL1axZM0tqBQAAyEpO\nc4gzb9686Qa0v5szZ45KlCih+vXrZ0NlAAAA2ctpAlrnzp3l7e39wHG3bt3S1KlT9Z///Oe+4+bO\nnavQ0FBVrVpV/fr107Vr1xxVKgAAQJZymoBmq3nz5ql69eoqX758umMqVqyoypUra9myZVq5cqWi\no6PVv3//bKwSAAAg45xmDpotkpKS9PXXX2v8+PH3HTdp0iTL956enhoxYoTCwsL022+/6V//+ldW\nlwkAAJApOWoP2u7duxUfH69q1arZ9bySJUtKkq5cuZIVZQEAADhUjgpo69evV61ateTmlv6Ov/Pn\nz2vEiBGKj4+3tJ08eVKSVLp06SyvEQAAILNyVEA7evSoSpUqlap97dq16tChgySpYMGC2rBhgz78\n8EPFxcXp8uXLGjNmjBo0aKCiRbntNwAAcH5OMwctMDBQ0l/XOJOkdevWSZKioqIsY65evapChQql\neu7t27d19uxZSVK+fPn0xRcEFHYWAAAgAElEQVRf6MMPP1S9evUkSY0aNdJbb72VpfUDAAA4iskw\nDONhF/GwhISESPrr0GlWiI2NlZeXlyQpRpJnlqwla8VK8vrf9zExMfL0zImvAgAA52Br9shRhzgB\nAABcAQENAADAyRDQAAAAnAwBDQAAwMkQ0AAAAJwMAQ0AAMDJENAAAACcDAENAADAyRDQAAAAnAwB\nDQAAwMkQ0AAAAJwMAQ0AAMDJENAAAACcDAENAADAyRDQAAAAnAwBDQAAwMkQ0AAAAJwMAQ0AAMDJ\nENAAAACcDAENAADAyRDQAAAAnAwBDQAAwMkQ0AAAAJwMAQ0AAMDJENAAAACcDAENAADAyRDQAAAA\nnAwBDQAAwMkQ0AAAAJwMAQ0AAMDJENAAAACcDAENAADAyRDQAAAAnAwBDQAAwMkQ0AAAAJwMAQ0A\nAMDJENAAAACcDAENAADAyRDQAAAAnIxTBbQtW7YoKChIr7/+ulX74sWLVaFCBQUGBlp9HTp0KM3l\nREdHa8CAAQoKClLdunX19ttv688//8yOlwAAAJBpbg+7gBQzZsxQZGSkypQpk2Z/9erV9dVXX9m0\nrOHDhys+Pl7Lly9XQkKC+vfvr3HjxmnYsGGOLBkAACBLOM0etLx58943oNnqjz/+0Lp16/T666/L\n19dXRYsWVe/evbVo0SIlJCQ4qFoAAICs4zQBrXPnzvL29k63/+LFi+ratauqV6+ukJAQLVu2LM1x\nR48eVe7cueXn52dp8/f3V1xcnE6dOuXwugEAABzNaQ5x3o+vr6/Kli2rgQMHqly5clq7dq0GDx6s\nIkWKqHbt2lZjo6Oj5eXlJZPJZGnz8fGRJN24cSNb6wYAAMiIHBHQgoODFRwcbHncrFkzrV27VosX\nL04V0CTJMIxsrA4AAMCxnOYQp71KliypK1eupGr39fVVTEyMkpKSLG3R0dGSpIIFC2ZbfQAAABmV\nIwJaRESEVq5cadV28uRJlS5dOtXYihUryjAMHTt2zNIWFRWl/Pnz67HHHsvyWgEAADIrRwS0+Ph4\nvffee4qKilJCQoKWL1+uH3/8Ue3atZMkrV27Vh06dJD01x60Jk2a6JNPPtH169d16dIlTZo0Sa1b\nt5abW444ogsAAFyc0ySWwMBASVJiYqIkad26dZL+2vvVuXNnxcbGqn///rp69apKlSqlSZMmKSAg\nQJJ0+/ZtnT171rKsUaNGacSIEQoJCVGePHn07LPPprr4LQAAgLMyGS48oz4kJESStH79+ixZfmxs\nrLy8vCRJMZI8s2QtWStWktf/vo+JiZGnZ058FQAAOAdbs0eOOMQJAADgSghoAAAAToaABgAA4GQI\naAAAAE6GgAYAAOBkCGgAAABOhoAGAADgZAhoAAAAToaABgAA4GQIaAAAAE6GgAYAAOBkCGgAAABO\nhoAGAADgZAhoAAAAToaABgAA4GQIaAAAAE6GgAYAAOBkCGgAAABOhoAGAADgZAhoAAAAToaABgAA\n4GQIaAAAAE6GgAYAAOBkCGgAAABOhoAGAADgZAhoAAAAToaABgAA4GTcMvKko0ePyjAMVapUSZL0\n22+/acqUKYqOjlaLFi3UtGlThxYJAADgSuwOaHv27FHXrl3Vu3dvVapUSXfu3FHnzp116dIleXp6\natOmTXJ3d1fDhg2zol4AAIB/PLsPcU6fPl3+/v7q2LGjJOm7777TpUuXNHXqVO3du1dhYWGaM2eO\nwwsFAABwFXYHtMOHD6tr167Knz+/JGnz5s16/PHHFRwcLEl6/vnnderUKYcWCQAA4ErsDmi3bt1S\nsWLFLI/37t2rOnXqWB57e3srOjraMdUBAAC4ILsDWsGCBXX16lVJ0sGDB3Xz5k3VqlXL0n/16lV5\ne3s7rkIAAAAXY/dJAlWrVtW0adNkMpn0xRdfyMfHR0FBQZb+RYsWqWLFig4tEgAAwJXYvQetV69e\n+u2339SnTx/t379fgwYN0iOPPCJJGjVqlDZt2qTOnTs7vFAAAABXYfceND8/P61atUr79u1TsWLF\nFBAQYOl76qmnVKtWLdWvX9+hRQIAALiSDF2o1tfXV6Ghoanan3322UwXBAAA4OoyFNAMw9CqVau0\nd+9eXbx4UW+99ZZKly6tM2fOqECBAvLx8XF0nQAAAC7D7oB2584d9ejRQ/v27ZNhGDKZTOrXr58k\naebMmdq2bZvmz5+vwoULO7xYAAAAV2D3SQLTpk3Tzz//rP/85z9at26dDMOw9HXo0EF3797VxIkT\nM1TMli1bFBQUpNdffz1V35o1a9S8eXNVrVpVTZo00bfffpvucjp16iR/f38FBgZavpo3b56hmgAA\nALKb3XvQVq1apZ49e6pbt26p+ipUqKDevXtrxowZdhcyY8YMRUZGqkyZMqn6Dh06pEGDBmnChAkK\nDg7Wtm3b1KdPHz3++OOqVq1amst77733FB4ebncdAAAAD5vde9AuXryoGjVqpNtfoUIFy4Vs7ZE3\nb950A1p0dLReeeUVhYaGys3NTfXr15fZbNaePXvsXg8AAICzszug5c2bV7du3Uq3//r16/Lw8LC7\nkM6dO6d7B4J69eqpT58+lseJiYm6evWqihYtmu7yVq5cqbCwMFWtWlVdunTRb7/9ZndNAAAAD4Pd\nAS0wMFBz5sxRfHx8qr64uDh99tlnCgwMdEhx6Rk3bpw8PDwUFhaWZv8TTzyh8uXL65tvvtH69evl\n6+urHj16pFkzAACAs7F7Dlq3bt3Us2dPhYeHq0mTJjKZTFqyZIkSExO1cuVK3bp1S2+//XZW1CrD\nMDRu3DgtX75cc+fOVd68edMcN3LkSKvHo0aNUs2aNbV3717Vrl07S2oDAABwFLv3oNWtW1fjxo1T\ndHS0Jk2aJMMwNGfOHH399ddyc3PTuHHjVLNmTYcXmpycrCFDhmjDhg2KiIjQ448/bvNzvby85OPj\no8uXLzu8LgAAAEfL0IVqmzVrpiZNmigqKkoXL16UJJUqVUqVKlWSm1uGFvlAH3zwgU6cOKGIiAg9\n+uij6Y6LiYnRuHHj9Oqrr1rmqF2/fl3Xr19X6dKls6Q2AAAAR7J7D9rmzZt148YNubm5qWrVqgoL\nC1NYWJgqV66cZeFs7969+u677zR9+vQ0w9mhQ4fUtGlTxcfHy8vLSwcPHtTo0aMVHR2tmzdv6t13\n35Wfn5+qVq2aJfUBAAA4kt2JasCAAZozZ44KFCjg0EJSTixITEyUJK1bt06SFBUVpUWLFun27dtq\n0KCB1XOqV6+umTNn6s6dOzp9+rTlormTJk3SBx98oCZNmig+Pl61a9fW9OnTlSuX3XkUAAAg25mM\ne28FYIPu3burcuXK6t+/f1bVlG1CQkIkSevXr8+S5cfGxsrLy0uSFCPJM0vWkrViJXn97/uYmBh5\neubEVwEAgHOwNXvYvQft5Zdf1ieffKJjx46pXr16Kly4sPLkyZNqXP369e1dNAAAAJSBgPbSSy9Z\nvt+0aVOq/pQbqB89ejRThQEAALgquwNanz59ZDKZsqIWAAAAKAMB7bXXXsuKOgAAAPA/nNYIAADg\nZOzeg9a6desHjjGZTFq4cGGGCgIAAHB1dge0n3/++b79vr6+aZ7VCQAAANvYHdCOHTuWZvvly5e1\natUqrVixQtOmTct0YQAAAK7KYXPQihYtqi5duig4OFgffvihoxYLAADgchx+kkCNGjW0ZcsWRy8W\nAADAZTg8oF2+fFl379519GIBAABcht1z0DZv3pxme0JCgn777TfNmjVLZcuWzWxdAAAALsvugPbK\nK6+keycBwzCUJ08ejRo1KtOFAQAAuCqH3erJZDLp0UcfVXBwsEqWLOmQ4gAAAFyRw2/1lJycrPj4\neLm7u2e4KAAAAFdm90kCISEhOnHiRLr9q1evVqNGjTJVFAAAgCuzO6CdP39eCQkJafYlJyfr5MmT\nunbtWqYLAwAAcFU2H+KsUKGCTCaTTCaTWrVqdd+x5cqVy3RhAAAArsrmgDZ58mTt2LFDc+fOVaVK\nleTp6ZlqjMlkUtGiRdWzZ0+HFgkAAOBKbA5oDRs2VMOGDbVu3TqNHTtW5cuXz8q6AAAAXJbdc9A2\nbNhw33B27Ngx9qABAABkgt2X2ZD+uiDtqVOndP78eRmGYWlPTEzUypUrtWvXLocVCAAA4GrsDmjX\nr19X7969dfDgwTT7DcNQ9erVM10YAACAq7L7EOekSZN06NAhNW7cWK+88ooMw1Dbtm3VsmVLPfLI\nI+rbt69mzJiRFbUCAAC4BLsD2pYtWzRgwAB9+umnGjBggCSpffv2GjNmjJYtW6YlS5bo999/d3ih\nAAAArsLugHbp0iU9/fTTVm3JycmSpNKlS6tLly4aP368Y6oDAABwQXYHNHd3d8XExFgee3t7W905\nwN/fX/v373dMdQAAAC7I7oAWEBCgWbNm6fr165KkUqVKafXq1Zb+M2fOKCkpyXEVAgAAuBi7z+J8\n6aWX1Lt3b7355puaMWOGQkNDNXHiREVHR8vX11crVqyQv79/VtQKAADgEuwOaA0aNNDnn3+u8+fP\nS5K6dOmiLVu2aN26dZKkYsWKaejQoY6tEgAAwIVk6EK1oaGhlu89PT01f/58nThxQklJSXr88cfl\n7u7usAIBAABcTYYCWlq4NycAAIBj2H2SgCRdvnxZI0eO1HPPPafq1avr2LFjkqRt27Zp3759Di0Q\nAADA1dgd0M6fP6/w8HDNnz9fsbGxVpfcWLFihbp166aoqCiHFgkAAOBK7A5oEydOlJubmyIjI7Vh\nwwarm6W/8847euKJJzRt2jSHFgkAAOBK7A5oW7duVd++fRUQEJCqL1++fOrevbv27t3rkOIAAABc\nkd0B7caNG3r88cfT7S9evLhu376dqaIAAABcmd0B7dFHH9XZs2fT7f/1119VoECBTBUFAADgyuwO\naDVr1tTkyZOtQprJZJIkHThwQOPGjVNQUJDjKgQAAHAxdge01157TdHR0XruuefUqVMnmUwmjRo1\nSs8995zat28vSerbt2+GitmyZYuCgoL0+uuvp+pbuXKlnnvuOVWtWlXh4eHaunVrusuJjo7WgAED\nFBQUpLp16+rtt9/Wn3/+maGaAAAAspvdAa1s2bJauHCh6tWrp6ioKBmGob179+rcuXNq3Lixvv32\nW5UuXdruQmbMmKHRo0erTJkyqfqOHj2qN998U4MGDdKOHTvUpUsX9e3bV5cuXUpzWcOHD9edO3e0\nfPlyLVq0SCdPntS4cePsrgkAAOBhyNCFah977DFNnDhRe/fu1datW7Vt2zbt3btXn376aZoByxZ5\n8+ZVZGRkms9fuHCh6tevr/r16ytv3rxq3ry5zGazvvvuu1Rj//jjD61bt06vv/66fH19VbRoUfXu\n3VuLFi1SQkJChmoDAADITjYFtJ9++klxcXGp2pOSklSoUCEVLFhQuXJlKOtZdO7cWd7e3mn2HT58\nWJUqVbJqq1SpUpoXxD169Khy584tPz8/S5u/v7/i4uJ06tSpTNUIAACQHWxKVd26ddPp06dTtVep\nUsVym6esFB0dLR8fH6s2Hx8f3bhxI82xXl5elhMXUsZKSnM8AACAs7EpoN17twBb2rOCPevKzroA\nAAAcLXPHJbNJgQIFFB0dbdUWHR0tX1/fVGN9fX0VExOjpKQkq7GSVLBgwawtFAAAwAFyREALCAjQ\nzz//bNUWFRWlKlWqpBpbsWJFGYZhdeg1KipK+fPn12OPPZbltQIAAGRWjghobdu21fbt27Vp0ybd\nvXtXkZGROnPmjJo3by5JWrt2rTp06CDprz1oTZo00SeffKLr16/r0qVLmjRpklq3bi03N7eH+TIA\nAABs4jSJJTAwUJKUmJgoSVq3bp2kv/Z+mc1mjRs3TmPGjNH58+dVrlw5TZs2TYULF5Yk3b592+rO\nBqNGjdKIESMUEhKiPHny6Nlnn03z4rcAAADOyGkCWlqXzLhX48aN1bhx4zT7wsPDFR4ebnns7e2t\nCRMmOLQ+AACA7GJzQJs7d26qSfYmk0lz5sxJdXN0k8mk//znP46pEAAAwMXYHNCWLVuWZvuSJUtS\ntRHQAAAAMs6mgDZ37tysrgMAAAD/Y1NAq1GjRlbXAQAAgP/JEZfZAAAAcCUENAAAACdDQAMAAHAy\nBDQAAAAnQ0ADAABwMjYFtF69eunEiROSpM6dO+vMmTNZWRMAAIBLsymgbdmyRRcvXpQk7dq1S3Fx\ncVlaFAAAgCuz6TpoxYoV0+DBg1W+fHlJ0vDhw+Xp6Znu+JRbQAEAAMB+NgW0t956SyNGjNDu3btl\nMpl0+PDh+443mUwOKQ4AAMAV2RTQQkNDFRoaKkmqUKGCFi1aJH9//ywtDAAAwFXZfRbnmDFjVKpU\nqayoBQAAALJxD9q9nn/+eUnSmTNntGPHDl2+fFkmk0nFixdXnTp1VKJECYcXCQAA4ErsDmjJycka\nMWKEIiMjJUmGYVj6cufOrW7duumNN95wXIUAAAAuxu6ANnv2bC1cuFDBwcFq3LixihYtKkm6ePGi\nVq9erS+++EKPPfaYwsPDHV4sAACAK7A7oC1btkwdO3bU8OHDU/W1bt1ab7/9tiIiIghoAAAAGWT3\nSQJnz55Vw4YN0+1v3Lixfv3110wVBQAA4MoydC/Oe+ed/Z27u7uSk5MzXBAAAICrszuglSlTRps2\nbUq3f8OGDSpTpkxmagIAAHBpds9Ba968uT7++GPFxMSoSZMmKl68uAzD0IULF7Ry5UqtXLlSQ4YM\nyYpaAQAAXILdAa1r1646evSoli5dqmXLlln1GYahtm3b6qWXXnJYgQAAAK7G7oCWK1cujRs3Ti+9\n9JK2bt2qS5cuyWQyqUSJEqpfv778/Pyyok4AAACXYXdASxEYGKjAwEBH1gIAAABl8CxOAAAAZB0C\nGgAAgJMhoAEAADgZAhoAAICTIaABAAA4GbsDWufOnXXmzJl0+9etW8eN0gEAADLB7oC2a9cuxcXF\npdt/5coVHT9+PFNFAQAAuDKbr4PWsGFDmUwmSVKvXr2UJ0+eVGOSkpJ05coVFS9e3HEVAgAAuBib\n96B17dpVFSpUkMlkkmEYaX7lzp1bTz75pN5///2srBkAAOAfzeY9aJ06dVKnTp1UoUIFTZ06Vf7+\n/llZFwAAgMuy+1ZPx44dy4o6AAAA8D8ZuhfnzZs3tX37dkVHRys5OTnNMR07dsxUYQAAAK7K7oC2\na9cu9e3bV7dv35ZhGGmOMZlMBDQAAIAMsjugTZgwQYZhqGvXripbtmyaZ3M62u7du9WtWzerNsMw\nlJCQoF9++cWq/fPPP9fkyZPl5mb90jZu3KhChQplea0AAACZZXdAO3HihIYOHapWrVplRT1pql69\nuqKioqzapk6dmu58uBYtWujDDz/MjtIAAAAczu6A5u7urjJlymRFLTa7cOGCZs2apSVLljzUOgAA\nALKC3XcSqF69ug4cOJAVtdjs008/VatWrVSiRIk0+3/55Re1a9dOTz31lJo1a6atW7dmc4UAAAAZ\nZ3dAGzJkiJYtW6bvvvtO8fHxWVHTfZ07d05r1qxR165d0+wvVqyYSpcurbFjx2rbtm1q06aNevXq\npVOnTmVzpQAAABljMtI7FTMdzz//vGJjY/X777/LZDIpf/78lltAWRZqMmn79u0OLTTF2LFjdf36\ndY0dO9bm57Rp00Z16tTRgAEDrNpDQkIkSevXr3dojSliY2Pl5eUlSYqR5Jkla8lasZK8/vd9TEyM\nPD1z4qsAAMA52Jo97J6DdvPmTUl6aPfbXL16td588027nlOyZElduXIliyoCAABwLLsD2oYNG7Ki\nDpscPXpU58+fV506ddIdM3nyZFWtWlW1a9e2tJ08eVJhYWHZUSIAAECm2T0H7WE6cuSIHn30Ucth\nwxRNmzbVnj17JEnR0dF69913derUKd29e1czZ87Ub7/9pueff/5hlAwAAGA3u/egff311zaNy4o7\nCfzxxx8qXLhwqvbTp08rLi5OkvTGG29Ikrp06aLo6GiVK1dOs2fPVrFixRxeDwAAQFaw+ySBChUq\nyGQypbrNU8qJAoZhyGQy6ejRo46rMotwksCDcZIAAACOk2UnCYwZMyZVm2EYunz5stauXSsfHx+9\n/PLL9i4WAAAA/2N3QLvfXK5evXqpV69e+vXXX60m6QMAAMB2Dj1JwGQyqX379po3b54jFwsAAOBS\nHH4WZ968eXXp0iVHLxYAAMBlODSg3bhxQ3PmzFGhQoUcuVgAAACXYvcctPTmliUkJCg2NlaS0r1P\nJgAAAB7M7oD2yCOPpNnu6empsmXLqlGjRgQ0AACATMhRt3oCAABwBZmeg5acnJzqorUAAADIOLv3\noEnSqVOnNGnSJO3cuVPXrl2TJBUpUkT16tVT7969Vbx4cYcWCQAA4Ers3oN2/PhxtW7dWitWrJC7\nu7uqVKmiKlWqyGQyaeHChWrdurXOnz+fFbUCAAC4BLv3oE2aNEm+vr766quv5O/vb9V36NAh9e/f\nX5MmTdIHH3zgsCIBAABcid170Pbs2aO+ffumCmeSVLlyZfXu3Vtbt251SHEAAACuyO6AdvPmTZUq\nVSrd/scee0zXr1/PVFEAAACuzO6Alj9/fp07dy7d/gsXLih//vyZKgoAAMCV2R3QqlWrpilTpuj3\n339P1Zdydmf16tUdUhwAAIArsvskgb59++qFF15Q06ZN5efnp+LFi8swDF28eFG//PKLPDw81K9f\nv6yoFQAAwCXYHdDMZrMiIiI0YcIE7dy5U0eOHJH01y2gQkNDNWDAAD3++OMOLxQAAMBVZOhCtRUq\nVND06dOVlJSkGzduyGQyqUCBAsqVK9M3JgAAAHB5dgW0pKQk5c6d2/I4d+7cKlSokCTp9u3b8vT0\nJKQBAABkks1p6uTJk2rVqpUOHjyYZv+4cePUoUMHy62fAAAAkDE2BbSYmBi9+uqrOnHiRLqX2ChT\npoyOHDmifv36KSkpyaFFAgAAuBKbAlpkZKQuXLigKVOmqFmzZmmO6datmz755BPt379fq1atcmiR\nAAAArsSmgLZ69WqFhYWpXr169x3XsGFDPfPMM1q0aJFDigMAAHBFNgW033//XQ0aNLBpgY0aNdKZ\nM2cyUxMAAIBLsymgRUdHq1ixYjYtsGjRovrjjz8yVRQAAIArsymgeXl56caNGzYt8OrVq/Ly8spU\nUQAAAK7MpoDm5+enzZs327TANWvWyM/PL1NFAQAAuDKbAlrjxo21ZMkSHTp06L7jNm7cqJUrV6pp\n06YOKQ4AAMAV2RTQ2rRpo5IlS6pLly6aPXu2YmJirPovX76s8ePH67XXXlO5cuXUunXrLCkWAADA\nFdh0qyd3d3dNnTpVL7/8sj788EN9/PHHKlmypDw9PXXz5k1dvHhRhmHIz89PU6dOlZtbhm7xCQAA\nANlxL84yZcpo2bJlmj9/vtasWaOTJ0/qwoUL8vb2Vu3atfXMM8+oRYsWcnd3z8p6AQAA/vHs2tWV\nL18+denSRV26dMmicgAAAGDzzdIBAACQPQhoAAAAToaABgAA4GQIaAAAAE6GgAYAAOBkcswFy/z8\n/JQnTx6ZTCZLW9u2bTV8+PBUY+fOnauvv/5aV69elZ+fn95++20FBARkZ7kAAAAZlmMCmiT98MMP\nKlWq1H3HbNiwQZ9//rm++OIL+fn5ae7cuerVq5fWrFkjDw+PbKoUAAAg4/5xhzgXLFig8PBwValS\nRfny5VOPHj0k/XWfUAAAgJwgRwW08ePHKzg4WNWqVdPw4cMVGxubaszhw4dVqVIly+NcuXKpYsWK\nioqKys5SAQAAMizHBLQnn3xSQUFBWrNmjRYsWKADBw7o3XffTTUuOjpaPj4+Vm0+Pj66ceNGdpUK\nAACQKTkmoC1YsEBt2rSRu7u7nnjiCQ0aNEjLly9XfHx8qrGGYTyECgEAABwjxwS0vytVqpSSkpJ0\n7do1q/YCBQooOjraqi06Olq+vr7ZWR4AAECG5YiAduTIEX344YdWbSdPnpS7u7uKFCli1R4QEKDD\nhw9bHiclJenIkSOqUqVKttQKAACQWTkioBUsWFALFizQ9OnTFR8fr9OnT+vTTz/VCy+8oNy5c6tp\n06bas2ePJKl9+/ZaunSpDhw4oDt37mjKlClyd3dXcHDww30RAAAANsoR10ErWrSopk+frvHjx1sC\n1/PPP6/XX39dknT69GnFxcVJkurVq6eBAwdqwIABunbtmgIDAzV9+nTly5fvYb4EAAAAm5kMF55R\nHxISIklav359liw/NjZWXl5ekqQYSZ5ZspasFSvJ63/fx8TEyNMzJ74KAACcg63ZI0cc4gQAAHAl\nBDQAAAAnQ0ADAABwMgQ0AAAAJ0NAAwAAcDIENAAAACdDQAMAAHAyBDQAAAAnQ0ADAABwMgQ0AAAA\nJ0NAAwAAcDIENAAAACdDQAMAAHAyBDQAAAAnQ0ADAABwMgQ0AAAAJ0NAAwAAcDIENAAAACdDQAMA\nAHAyBDQAAAAnQ0ADAABwMgQ0AAAAJ0NAAwAAcDIENAAAACdDQAMAAHAyBDQAAAAnQ0ADAABwMgQ0\nAAAAJ0NAAwAAcDIENAAAACdDQAMAAHAyBDQAAAAnQ0ADAABwMgQ0AAAAJ0NAAwAAcDIENAAAACdD\nQAMAAHAyBDQAAAAnQ0ADAABwMjkmoJ0/f159+vRRzZo1FRQUpCFDhujWrVupxi1evFgVKlRQYGCg\n1dehQ4ceQtUAAAD2yzEBrVevXsqfP782bNigxYsX68SJExo7dmyaY6tXr66oqCirr8qVK2dzxQAA\nABmTIwLarVu3FBAQoDfeeEOenp4qVqyYnn/+ee3Zs+dhlwYAAOBwOSKg5c+fX2PGjFGhQoUsbRcv\nXlSRIkXSHH/x4kV17dpV1atXV0hIiJYtW5ZdpQIAAGSa28MuICOioqI0b948TZkyJVWfr6+vypYt\nq4EDB6pcuXJau3atBiMJ1jQAABsGSURBVA8erCJFiqh27doPoVoAAAD75LiAtnfvXr366qt64403\nFBQUlKo/ODhYwcHBlsfNmjXT2rVrtXjxYgIaAADIEXLEIc4UGzZs0Msvv6yhQ4eqc+fONj+vZMmS\nunLlShZWBgAA4Dg5Zg/avn379Oabb+rTTz9V3bp10x0XEREhHx8fhYWFWdpOnjyp0qVLZ0eZAAAA\nmZYj9qAlJiZq2LBhGjRoUJrh7KWXXtLKlSslSfHx8XrvvfcUFRWlhIQELV++XD/++KPatWuX3WUD\nAAD8X3t3HhbFff8B/L1I1AgqXsHEOxoBWU4FFayyYAFBxVtpGrQa0VYJxngm5BfvaNSqCGIhVVHR\noISsiVeroIko2th6RdEWtEoV8ERkARH4/v6wbN0swo7I7mDer+fhkZ2dnX1/ZnbZj/OdnXkh9WIP\n2tmzZ5GVlYUlS5ZgyZIlOvcdPHgQ2dnZePjwIQAgJCQEGo0G4eHhuHPnDtq3b4/o6GgolUpTRCci\nIiKSTCGEEKYOYSo+Pj4AgJSUlDpZvkajgaWlJQCgEIBFnTxL3dIAsPzv74WFhbCwqI9VEBERyYOh\nvUe9GOIkIiIi+iVhg0ZEREQkM/XiGDQiAoQQKCoqMnWMl6ZJkyZQKBSmjkFEJEts0IjqiaKiIu0x\nja8CHtNIRPR8HOIkIiIikhnuQSOqh/JQf78VbG3qEERE9QAbNKJ6yAL1s0EjIiLDcIiTiIiISGbY\noBERERHJDBs0IiIiIplhg0ZEREQkM2zQiIiIiGSGDRoRERGRzLBBIyIiIpIZNmhEREREMsMGjYiI\niEhm2KARERERyQwbNCIiIiKZYYNGREREJDNs0IiIiIhkhg0aERERkcywQSMiIiKSGXNTByCiXw7x\nzO8ajcZkOV6mJk2aQKFQmDqGZEIIFBUVmTrGS1NftwPAbUFVY4NGREbz7EeQtbW1yXK8TIWFhbCw\nsDB1DMmKiopgaWlp6hgvTX3dDgC3BVWNQ5xEREREMsM9aERkEnkA6uv/sTUAXo39f0/V123xqm0H\ngNuC/ocNGhGZhAXq5wfRq4jbQj64LagShziJiIiIZIYNGhEREZHMsEEjIiIikhk2aEREREQywwaN\niIiISGbYoBERERHJDBs0IiIiIplhg0ZEREQkM2zQiIiIiGSGDRoRERGRzLBBIyIiIpIZNmhERERE\nMlNvGrSbN28iNDQUvXv3hkqlwsqVK1FRUVHlvFu3boWfnx9cXV0RHByMn376ychpiYiIiF5cvWnQ\nwsLCYG1tjcOHD2Pz5s04fPgw4uPj9eZLTU3F+vXr8cUXX+DEiRNQqVSYOnUqioqKTJCaiIiISLp6\n0aBduHABly9fxqxZs9C0aVN07twZEyZMQGJiot68iYmJGDFiBJycnNC4cWO8//77AIAjR44YOzYR\nERHRCzE3dQBDXLx4Ee3atUPz5s210+zt7XHt2jUUFhbC0tJSZ96AgADtbTMzM9jZ2eHChQsIDAw0\nau5naUz2zLXzbG6Npr5W8Wp4dv3X1y2hec7v9c2r8L545V5P9XQ7ANwWcmNhYWHqCADqSYOWn5+P\nZs2a6UyrbNYePHig06Dl5+frNHKV8z548EBvubdv30Z5eTl8fHzqIDUghEDnzp0BAL3r5BmMo/N/\n/+3duz5X8Wp4pV5PpgzxEnT+77/1+X3xSr2e6vF2ALgt5OTtt9+u0+Xn5OSgQYMGNc5XLxo04Gmz\n87LnbdSoEUpLS180Uo0UCkWdb2giIiKqP8zNzdGwYcOa5zNCllpr2bIl8vPzdabl5+dDoVCgZcuW\nOtNbtGhR5bzvvPOO3nJPnz798sMSERER1VK9+JKAUqlETk4O7t+/r5124cIFdOvWTW+sWKlU4uLF\ni9rb5eXluHTpEpycnIyWl4iIiKg26kWD1qNHDzg4OGD16tUoLCxEVlYWNm/ejODgYACAv7+/dm9Y\ncHAw1Go1zp49i+LiYsTExKBhw4bw8vIyYQVEREREhqsXQ5wAEBkZiU8//RSenp6wtLTEuHHj8Jvf\n/AYAcO3aNe15zvr374+ZM2dixowZuHfvHhwcHBAbG4vGjRubMj4RERGRwRRCytH3RERERFTn6sUQ\np9y9CpehklKDRqPBrFmzYGNjg6ysLCMnrZ6UOnbu3Ak/Pz+4uLggKCgIhw8fNnLaqhlagxACUVFR\nUKlUcHFxQWBgINRqtQkS65OyHSrl5eXBxcUF69evN1LKmhlax/r162FnZwcHBwedn7t375ogtS4p\n2yIrKwvvvfcenJycMGDAAGzZssW4YZ/D0BomTpyotw3s7OwQFRVlgtS6DK2hoqICkZGR8Pb2houL\nC4YMGYL9+/ebIHHVDK3jyZMnWLduHXx8fODs7IyQkBBkZ2ebIHHVjh07Bg8PD3z44YfVzldRUYE1\na9bAx8cHbm5umDRpkvHqEFRrw4cPFxEREaKgoEBcu3ZN+Pr6ik2bNunNl5KSInr16iXOnj0riouL\nxZ/+9Cfh6ekpNBqNCVLrMrSG3Nxc4evrK+bMmSO6d+8uMjMzTZD2+Qyt4+DBg6Jnz57i9OnTorS0\nVOzatUvY29uLGzdumCC1LkNr2Lx5s/Dx8RFZWVmirKxMHDhwQNja2oqLFy+aILUuQ2t41vTp00XP\nnj1FZGSkkVLWzNA6IiMjxdy5c02QsGaG1lBcXCy8vLxEXFycKCoqEufOnROBgYGyeI+/yOtJCCEe\nPnwoPD09xeXLl42QsnqG1rB9+3bRr18/7fs6NTVV9OjRQ2RkZJggtT5D64iKihJeXl4iIyNDFBcX\ni7Vr14rBgweL8vJyE6TWFRsbK3x9fcW4cePEjBkzqp1369atQqVSiczMTPHo0SOxaNEiMWTIEFFR\nUVHnOdmg1dL58+eFnZ2dyM/P107bsWOH8PPz05s3NDRULFu2THu7vLxceHp6ir179xol6/NIqSEj\nI0McOnRIZGdny65Bk1KHWq0WCQkJOtPc3d3Ft99+W+c5qyOlhvT0dHH27FmdaW5ubmLPnj11nrM6\nUmqodPToUeHv7y8++ugj2TRoUuqQa4MmpYbk5GQxePBgY8YzyIu8niotXLhQLFiwoC7jGURKDfPn\nzxfh4eE60zw8PIRara7znDWRUseIESNEdHS09nZ5ebnw8PAQ//jHP4yStTrx8fGioKBAzJ07t8YG\nLTAwUMTHx2tvP3r0SPTo0UOcOXOmrmMKDnHWUk2Xofr5vD169NDefvYyVKYkpQZbW1sMHDjQ2BEN\nIqWOoKAg7ZdMAKCgoAAajQbW1tZGy1sVKTX06dNHe/qYkpISbN++HWZmZujbt69RM/+clBqAp9kX\nLVqEzz77DObm8vnektQ6rly5gnHjxsHV1RWBgYFIS0szZtwqSanh73//O7p374758+ejV69e8Pf3\nx7fffmvsyHqkbodK169fh1qtRlhYmDFiVktKDV5eXvjb3/6GjIwMlJaWIiUlBcXFxXB3dzd2bD1S\nt4VCodD+bmZmBktLS2RkZBgla3VCQkLQtGnTGucrKSlBZmamzue2paUlOnXqZJTPbTZotVTTZah+\nPq+hl6EyJik1yNmL1iGEQEREBJycnEz+R/BFaoiIiICzszM2bdqE6OhotGnTps5zVkdqDdHR0XB2\ndkafPn2Mks9QUupo27YtOnTogBUrVuD48eMYPXo0pk6diqtXrxotb1Wk1JCbm4uUlBR4eHjg2LFj\nmDJlCubOnYtLly4ZLW9VXvR9HRsbi5EjR+qdzNwUpNTg6+uLsWPHYtiwYXBwcMBHH32Ezz//HG++\n+abR8j6PlDpUKhUSExNx5coVlJaWIiEhAbm5uXj48KHR8tbWw4cPIYQw2ec2G7SXQNTBZaiMTa65\npJJax5MnTzBr1ixkZmZi3bp1dZRKGqk1LFmyBGfPnsW0adMwdepUk3+gAobXkJmZid27d2PevHl1\nnOjFGFrH6NGjERkZiU6dOuH111/HhAkTYGdnJ4s9UIbWIISAvb09hgwZgtdffx3Dhw+Ho6MjDh48\nWMcJDcsmRX5+Pvbs2YOQkJA6SiSdoTWo1Wqo1Wrs3r0b58+fx9q1a/Hxxx/j/PnzdZzQMIbWMXny\nZAwcOBCTJk2Cl5cX8vLy4ObmZtA1KOXGVJ+PbNBq6WVchsrU/8OTUoOcSa2jpKQEU6ZMwa1bt5CQ\nkIDWrVsbK+pzvei2aNy4MUaOHAlHR0ckJSXVdcxqGVqDEAILFixAWFiYyff6VaW274t27drh9u3b\ndRXPIFJqaNOmjd6wT7t27XDnzp06z1mdF9kOKSkp6NKlCzp06GCMiDWSUsP27dsxduxYODo6olGj\nRvDy8kKfPn1k0exLqaNRo0aIiIhAWloaTpw4gZkzZyIvL8/kh5FIYWVlBTMzsyprbtWqVZ0/Pxu0\nWnoVLkMlpQY5k1KHEAIffvghzM3NsWXLFrRo0cLYcaskpYapU6ciISFBZ5pCoTD5cVyG1nDr1i38\n+OOPiIyMRO/evdG7d2/s27cPX375JYYPH26K6DqkbIsNGzYgPT1dZ1pWVpbJGwQpNXTt2hX//Oc/\ndfYW3Lx5E+3atTNa3qq8yN+nlJQUeHp6GitijaTUUFFRgfLycp1ppaWlRslZEyl1XLx4Uec9kZeX\nh8zMTLi6uhotb201atQI77zzjs7ndkFBAW7cuAFHR8c6f342aLX0KlyGSkoNcialju+++047rNmo\nUSNTxtYhpQZXV1fExsbi0qVLKCsrQ2pqKtLT06FSqUxZgsE1tG3bFt9//z327Nmj/fH29sa4ceMQ\nGxtr0hoAadsiPz8fCxcuxNWrV/H48WNs2rQJN27cMHmjKaWGoUOH4sGDB9i4cSNKSkqwd+9eXLx4\nEUOHDjVlCS/09ykjIwPt27c3RdwqSanB29sbSUlJuHz5MsrKypCWlob09HT4+PiYsgQA0uq4cuUK\nZs2ahevXr6OwsBALFiyAj4+Pyf/TUpO8vDz4+/trz3UWHByMrVu3IisrC4WFhVi1apX2nId1rs6/\nJ/oLkJOTI95//33h6OgoPDw8RGRkpPYcKd27dxfff/+9dt6EhAQxYMAAoVQqRXBwsLhy5YqpYusw\ntIbo6GihVCqFvb296N69u7C3txdKpVLn69SmZGgdISEhws7OTiiVSp2fTz75xJTxhRCG11BWViai\noqKEp6encHR0FAEBASI5OdmU0bWkvCeeNXfuXNmcZkMIw+soKSkRS5cuFb/61a+Eg4ODGD58uCxO\nJyCEtG1x6tQpMXToUKFUKoWvr684evSoqWLrkPp6sre3FwcOHDBF1OcytIbS0lKxZs0a4e3tLZyd\nnUVgYKD45ptvTBldh6F1VFRUiM8//1y4u7sLV1dXMXPmTFFQUGDK6FqVf+9tbW2Fra2t9rYQQu8U\nUhUVFWLdunWib9++wtHRUUyePFnk5OQYJScv9UREREQkMxziJCIiIpIZNmhEREREMsMGjYiIiEhm\n2KARERERyQwbNCIiIiKZYYNGREREJDNs0IiIiIhkhg0aEVUrOTkZNjY2sLGxwY8//vjc+crKytCv\nXz/Y2NjoXPx8/fr1sLGxwalTp+o0X3Jycp0svyanTp3Srp/qfk6cOGGSfFWxsbHBe++9Z+oYRFQN\n0160j4jqjYYNGyIxMRFubm5V3n/06FE8fPhQb/qYMWPg5eWFLl261EkulUqFpKQkk1/aZ+jQoQgJ\nCXnu/XVVf01GjBgBlUqFsLAw7bSkpKR6dZ1dol8iNmhEZJC+ffvir3/9K/Lz82FlZaV3f1JSEtzd\n3ZGWlqYz3draGtbW1nWWq0WLFrK42H3r1q2Nc30+CR4+fIiMjAy967PKLScR6eMQJxEZ5Ne//jUe\nP34MtVqtd19eXh5++OEHDBw4UO++qoY4L1++jBkzZmDAgAFQKpXw8PDA5MmT9YZQT58+jdDQUPTr\n1w9KpRL9+vXDBx98gCtXrmjnqWqI09vbG0FBQbh58yY++OAD9OnTB66urhg7dqzeUGtZWRkiIyPh\n7e0NBwcH+Pv746uvvsKRI0dgY2OD7du3v/A6q8q8efNgY2ODjIwMjBs3Dg4ODtp6ysvLER8fj6Cg\nIDg6OsLFxQVDhgzBn//8Zzx58kRvWWq1GmPGjIGLiwv69u2LSZMm4cyZM9r14u7ujoqKCkRFRems\no6qGOG/evIn58+ejf//+UCqV6N27NyZPnqx3IfLK/Ldv38by5cvh5eUFJycnDBo0CDt27Hip64ro\nl4wNGhEZpGPHjnBycsLu3bv17ktOToaZmRn8/f1rXM69e/cwfvx4ZGdn4+OPP8a2bdvw6aef4tGj\nR/jd736HixcvAgD+9a9/YeLEiSgrK8PixYuxbds2zJ49G5mZmQgODkZeXl61z6PRaBAaGgqlUonI\nyEjMnz8fWVlZmDZtGh48eKCdb/ny5YiOjoazszOioqIQHh6OXbt2YdOmTRLXkDSLFy/GoEGDsG3b\nNrRr1w4AsGLFCixbtgzOzs6IjY3Fhg0bYGtriy+++AJ//OMfdR4fExODuXPnwtHREbGxsVi2bBk0\nGg1CQkJw5swZqFQqLFy4EMDTYeakpCS9PWmV8vLyMHr0aPzwww+YMmUKNm/ejM8++wx3797F+PHj\ncfLkSb3HzJkzB0VFRVi6dClWr16NBg0aYOHChbI61o6oPuMQJxEZbNSoUfj0009x+vRp9OrVCwAg\nhMDXX38NHx8fg4Yaz5w5g/z8fCxdulRnj1vfvn2RkJAAIQQA4Pjx43j8+DFmz54NOzs7AICLiwtc\nXFxw4MABFBcXV/s82dnZWLFiBYYNGwYAcHd3x9WrV7Fp0yakp6cjICAAjx49wldffYVu3bph9erV\nUCgUAAAPDw+Dms3asLOzw/jx43WmaTQaDBkyRNtYAYCbmxvS09OhVqsxd+5cAE+HLjds2ABvb29E\nRERo563ck5WUlISlS5dqj3t74403qh3W3LhxI+7du4ctW7agb9++2um9evWCt7c31qxZg8TERJ3H\nvPnmm1i0aJH2trm5OaZMmYK//OUv8PDweIE1QkTP4h40IjJYQEAAmjRpgl27dmmnnTx5EtnZ2Rg5\ncqRBy6g8Hu3LL7/E2bNntQ2ZlZUVpk2bBqVSCeBpAwAAkZGROkOaHTt2xJQpU9C5c+dqn0ehUOg1\nWZWPqdyD9tNPP+HJkyfo37+/tjkDgObNmyMwMNCgeipt2rSp2m9xPn78WGf+/v376y1j6dKlWLVq\nlc40c3NzdOzYEffv39c2pcePH0dpaSkGDBigM2/Lli1x6tQpLF26VFL2tLQ0WFlZ6TRnwNPGztnZ\nGefPn4dGo9G5b9CgQTq3O3XqBADIz8+X9NxEVDXuQSMig1laWmLQoEHYt28fIiIi0KxZM+zevRtt\n27ZFv379DFqGg4MDFi9ejJUrV2Ls2LGwsrKCq6srBgwYgKFDh6JJkyYAAD8/P4SFhSEuLg6pqalo\n06YNevXqBZVKhYCAALz22mvVPk/z5s3RuHFjnWmVj6lsCu/evQsAVX6JoWvXrgbVU2nYsGGYMGHC\nc+9v2LChzu02bdrozXPjxg1s2bIFx44dw+3bt1FSUqJzf0VFBQAgNzcXwNMvJrwMubm5z/2WqbW1\nNSoqKnD37l2db37+fJ39fN0SUe2wQSMiSUaPHo2vv/4ae/bswZAhQ3Do0CFMnDgRZmaG75AfM2YM\nAgMDkZ6ejpMnTyItLQ2pqamIiYlBfHy8dk/X9OnT8dvf/hbHjx9Heno6jh8/jgMHDiAuLg5bt25F\ny5Yta1VLZTPx7N6zSlVNq07Lli21Q7GGMDfX/fN7+/ZtjB49GsXFxQgNDYWbmxuaNm0KhUKBTz75\nRHtsHgDtui4tLZWU8XkUCkWNjdXP14fU9UNE0rBBIyJJXFxc0K1bN+zbtw/m5uZ48uSJwcObz7Kw\nsMDAgQO1x6Glpqbi97//PTZu3Ijly5dr57OyskJgYCACAwMhhMCOHTuwaNEiJCQk6Jzb60VUNnj3\n79/Xu+/f//53rZYtVeUpTObPn6+3J+7n55d76623AAA5OTl6yykoKEBFRUWVp0J5nrfeegu5ubkQ\nQug1Xjk5OTA3N8cbb7xh8PKIqPZ4DBoRSTZq1CicO3cO33zzDdzc3NCxY0eDH/vdd99h4cKFents\nVCoVLCwstMcwbdu2DWvXrtWZR6FQaI99ehnHOtnZ2cHMzAzHjx/XmV5YWIi9e/fWevlSlJWVAYDe\nXsH9+/fjP//5D4Cnp+EAAFdXV7z22mvYv3+/dtgTeJq7f//+mDFjBoD/7eWqXPbzeHl5oaCgAMeO\nHdOZnpubi/Pnz6NXr156w8VEVLe4B42IJAsKCsLq1atx7tw5rFixQtJjGzZsiJ07dyInJwejRo1C\n69at8ejRI6jVamg0GgwdOhTA02YkJiYGeXl58Pf3R/PmzXH//n3s2LEDDRo0QEBAQK3raNWqlfaY\nuv/7v/+Dn58fCgsLERcXBxsbG9y5c6fWz2GoPn36oEGDBoiJiUHTpk1hYWGBI0eO4OTJkwgKCsKe\nPXuwe/du+Pr6okOHDpgyZQqioqIwY8YMvPvuuyguLkZcXBzKysowffp0AP87Tiw1NRU9evRAu3bt\ntF/CeNbkyZOxf/9+zJs3D+Hh4ejatStu3bqFuLg4NGjQALNmzTLaeiCip9igEZFkLVu2hI+PD44d\nOwY/Pz9Jj/Xz88PGjRuxdetWREREoLCwEFZWVujSpQuio6O1Q54TJkyAlZUVEhMTMXv2bBQVFaFV\nq1bo3r074uPj0bNnz5dSy+LFi2FpaYlDhw5BrVbj7bffRmhoKEpKSpCWlma0Y61sbW2xatUqbNiw\nAeHh4WjWrBlUKhU2b96MvLw8nDlzBmvXrsXjx4/xhz/8AWFhYbC2tsbOnTsRGhoKhUIBJycnbN++\nHc7OzgCefrNywoQJ2LVrF+bMmYOwsLAqG7RWrVph165dWLduHdavX48HDx6gadOmcHd3x8qVK2Fr\na2uUdUBE/6MQ/MoNEZGe+Ph4LFu2TOdcakRExsJj0IjoFy0mJgbh4eF6x2kdOXIEAK9bSUSmwSFO\nIvpFa9y4MQ4ePIjHjx/j3XffRYMGDXDgwAGkp6fDz89P8vnQiIheBg5xEtEvXnJyMhISEnD9+nWU\nlJSgffv2GDx4MEJDQ/VOMEtEZAxs0IiIiIhkhsegEREREckMGzQiIiIimWGDRkRERCQzbNCIiIiI\nZIYNGhEREZHMsEEjIiIikhk2aEREREQywwaNiIiISGbYoBERERHJzP8Dn/RlsICMdbEAAAAASUVO\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b6XGBQnvsQB9",
        "colab_type": "code",
        "outputId": "59d8021f-777f-4e8e-ada3-24ae3a2a6ca6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "fs.missing_stats.head(10)"
      ],
      "execution_count": 0,
      "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>missing_fraction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.812695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.673892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.492684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.368649</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.351646</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.044235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.035835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    missing_fraction\n",
              "22          0.812695\n",
              "15          0.673892\n",
              "21          0.492684\n",
              "20          0.368649\n",
              "14          0.351646\n",
              "19          0.044235\n",
              "18          0.035835\n",
              "12          0.000000\n",
              "17          0.000000\n",
              "16          0.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3MUnzi0IscCu",
        "colab_type": "code",
        "outputId": "1cb318cf-b930-4dc7-d6aa-a73e3549d27d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "fs.identify_single_unique()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1 features with a single unique value.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O7zJHo7IskeB",
        "colab_type": "code",
        "outputId": "4a8dd79f-cc48-4bb8-de7f-32fd2fb22fd4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "single_unique = fs.ops['single_unique']\n",
        "single_unique"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PLfZ0zarsqsS",
        "colab_type": "code",
        "outputId": "c39caec6-3043-4a1b-f7d6-6aca8d13835f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        }
      },
      "source": [
        "fs.plot_unique()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8qLlz52ro0KH64YcfVLduXUnS/fffr7Vr1+r9999XQECApk+frlGjRunbb791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\niIgsCIs7IiIiIgvyf6uTMiN8CbPWAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OL714Yj0s23p",
        "colab_type": "code",
        "outputId": "ffcc56cf-2e92-444c-fb1d-77e1d2d4ba8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        }
      },
      "source": [
        "fs.unique_stats.sample(22)"
      ],
      "execution_count": 0,
      "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>nunique</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>127</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    nunique\n",
              "2        25\n",
              "13      703\n",
              "0         6\n",
              "10       17\n",
              "3         1\n",
              "4         2\n",
              "14      127\n",
              "7         8\n",
              "17       22\n",
              "18       13\n",
              "21        4\n",
              "20       10\n",
              "22        4\n",
              "12      694\n",
              "5         8\n",
              "15       16\n",
              "9         9\n",
              "11       10\n",
              "19        8\n",
              "1        10\n",
              "16       22\n",
              "8        16"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6vlsWcrszHQ3",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "# 3. Collinear (highly correlated) Features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bq6gTpkVzISE",
        "colab_type": "code",
        "outputId": "8c23357e-ea01-4f2b-af1a-a7df25efdba5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "fs.identify_collinear(correlation_threshold=0.975)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1 features with a correlation magnitude greater than 0.97.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xDcnM1KUzceu",
        "colab_type": "code",
        "outputId": "e044f76d-a891-4730-d0d2-b42ad3b206f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "correlated_features = fs.ops['collinear']\n",
        "correlated_features[:5]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[11]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ug3rzxyXzxDN",
        "colab_type": "code",
        "outputId": "a063adf6-2b5f-45c3-b8c1-3b575e1fcaa8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 888
        }
      },
      "source": [
        "fs.plot_collinear()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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fOHFi3HbbbXH77bfHDjvsUHXucJs2bWLIkCHrvB0RDAAAwFoVFRV9a9HbunXr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\n",
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G7YgUfsn0FGZ",
        "colab_type": "code",
        "outputId": "3d988b60-10ba-4fe2-972a-3c1bf38eb6cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 683
        }
      },
      "source": [
        "fs.plot_collinear(plot_all=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+ly9fLkk6cuSIRowYoS5duig5OVl79+712+AAAAAA+I7Hxf9LL70kSRoxYoTu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\n0PwDAAAAYYLmHwAAAAgT/w8EJ/pnXp1TcgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-vrjVrDr0Y_v",
        "colab_type": "code",
        "outputId": "f1bdb824-b6c8-4e29-9429-25216f0284ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 922
        }
      },
      "source": [
        "fs.identify_collinear(correlation_threshold=0.98)\n",
        "fs.plot_collinear()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1 features with a correlation magnitude greater than 0.98.\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fOHFi3HbbbXH77bfHDjvsUHXucJs2bWLIkCHrvB0RDAAAwFoVFRV9a9HbunXr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\n",
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aPchBx9q0lVg",
        "colab_type": "code",
        "outputId": "aba6de70-b7f5-4eac-8d32-1d1e41f09c7e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        }
      },
      "source": [
        "fs.record_collinear.head()"
      ],
      "execution_count": 0,
      "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>drop_feature</th>\n",
              "      <th>corr_feature</th>\n",
              "      <th>corr_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  drop_feature corr_feature  corr_value\n",
              "0           11            1         1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L4sF0_GM0snW",
        "colab_type": "text"
      },
      "source": [
        "# 4. Zero Importance Features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t1gs9Xbf0t8M",
        "colab_type": "code",
        "outputId": "f866fbf1-5cbd-4b97-b467-172ed73675e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1207
        }
      },
      "source": [
        "fs.identify_zero_importance(task = 'classification', eval_metric = 'multi_logloss', n_iterations = 10, early_stopping = True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Gradient Boosting Model\n",
            "\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "Early stopping, best iteration is:\n",
            "[94]\tvalid_0's multi_logloss: 1.8649\tvalid_0's multi_logloss: 1.8649\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "Early stopping, best iteration is:\n",
            "[111]\tvalid_0's multi_logloss: 1.85763\tvalid_0's multi_logloss: 1.85763\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "Early stopping, best iteration is:\n",
            "[109]\tvalid_0's multi_logloss: 1.86262\tvalid_0's multi_logloss: 1.86262\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "Early stopping, best iteration is:\n",
            "[90]\tvalid_0's multi_logloss: 1.86643\tvalid_0's multi_logloss: 1.86643\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "Early stopping, best iteration is:\n",
            "[107]\tvalid_0's multi_logloss: 1.86736\tvalid_0's multi_logloss: 1.86736\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "Early stopping, best iteration is:\n",
            "[113]\tvalid_0's multi_logloss: 1.86806\tvalid_0's multi_logloss: 1.86806\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "Early stopping, best iteration is:\n",
            "[100]\tvalid_0's multi_logloss: 1.86649\tvalid_0's multi_logloss: 1.86649\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "Early stopping, best iteration is:\n",
            "[98]\tvalid_0's multi_logloss: 1.85982\tvalid_0's multi_logloss: 1.85982\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "Early stopping, best iteration is:\n",
            "[88]\tvalid_0's multi_logloss: 1.86618\tvalid_0's multi_logloss: 1.86618\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "Early stopping, best iteration is:\n",
            "[81]\tvalid_0's multi_logloss: 1.87025\tvalid_0's multi_logloss: 1.87025\n",
            "\n",
            "2 features with zero importance after one-hot encoding.\n",
            "\n",
            "Training Gradient Boosting Model\n",
            "\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "Early stopping, best iteration is:\n",
            "[93]\tvalid_0's multi_logloss: 1.85974\tvalid_0's multi_logloss: 1.85974\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "Early stopping, best iteration is:\n",
            "[96]\tvalid_0's multi_logloss: 1.86416\tvalid_0's multi_logloss: 1.86416\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "Early stopping, best iteration is:\n",
            "[93]\tvalid_0's multi_logloss: 1.86901\tvalid_0's multi_logloss: 1.86901\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "Early stopping, best iteration is:\n",
            "[113]\tvalid_0's multi_logloss: 1.86118\tvalid_0's multi_logloss: 1.86118\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "Early stopping, best iteration is:\n",
            "[95]\tvalid_0's multi_logloss: 1.86409\tvalid_0's multi_logloss: 1.86409\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "Early stopping, best iteration is:\n",
            "[99]\tvalid_0's multi_logloss: 1.86201\tvalid_0's multi_logloss: 1.86201\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "Early stopping, best iteration is:\n",
            "[110]\tvalid_0's multi_logloss: 1.86132\tvalid_0's multi_logloss: 1.86132\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "Early stopping, best iteration is:\n",
            "[88]\tvalid_0's multi_logloss: 1.8609\tvalid_0's multi_logloss: 1.8609\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "Early stopping, best iteration is:\n",
            "[94]\tvalid_0's multi_logloss: 1.85926\tvalid_0's multi_logloss: 1.85926\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "Early stopping, best iteration is:\n",
            "[115]\tvalid_0's multi_logloss: 1.85817\tvalid_0's multi_logloss: 1.85817\n",
            "\n",
            "2 features with zero importance after one-hot encoding.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mSteJOsp2o26",
        "colab_type": "code",
        "outputId": "f2f1e1cd-30bb-4f68-f65d-e1ab277c5ead",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "one_hot_features = fs.one_hot_features\n",
        "base_features = fs.base_features\n",
        "print('There are %d original features' % len(base_features))\n",
        "print('There are %d one-hot features' % len(one_hot_features))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "There are 23 original features\n",
            "There are 0 one-hot features\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D7IY9iEp2wdO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fs.data_all.head(10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5BxNS9Ed23On",
        "colab_type": "code",
        "outputId": "6db49671-1636-457c-83fe-738c3e3af853",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "zero_importance_features = fs.ops['zero_importance']\n",
        "zero_importance_features[0:22]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3, 11]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fU4FrBRf3DQF",
        "colab_type": "code",
        "outputId": "787684cf-4ffc-4367-cd28-b593514d93ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 977
        }
      },
      "source": [
        "fs.plot_feature_importances(threshold = 0.99, plot_n = 22)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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a0JOTk8NMDwC0c/wrNwDADF4TehITEz1dAgDAZEHBnXToYC7BBwDQqrwm9HQd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\n",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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p0wfnzp3DzJkzcx339PSEr68vvvvuO4MESURERZCdDRw8KGequLjILpWICJl8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\nA4rW4C/gYTtkAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "20 features required for 0.99 of cumulative importance\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gmeSBmFa3xzL",
        "colab_type": "code",
        "outputId": "e594b35b-a11a-4109-cf84-6e6a8426cdc2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        }
      },
      "source": [
        "fs.feature_importances.head(22)"
      ],
      "execution_count": 0,
      "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>feature</th>\n",
              "      <th>importance</th>\n",
              "      <th>normalized_importance</th>\n",
              "      <th>cumulative_importance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12</td>\n",
              "      <td>2729.6</td>\n",
              "      <td>0.130503</td>\n",
              "      <td>0.130503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13</td>\n",
              "      <td>2433.5</td>\n",
              "      <td>0.116346</td>\n",
              "      <td>0.246849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>14</td>\n",
              "      <td>2117.9</td>\n",
              "      <td>0.101257</td>\n",
              "      <td>0.348107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>16</td>\n",
              "      <td>1404.7</td>\n",
              "      <td>0.067159</td>\n",
              "      <td>0.415266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>1107.8</td>\n",
              "      <td>0.052964</td>\n",
              "      <td>0.468230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>19</td>\n",
              "      <td>1073.4</td>\n",
              "      <td>0.051320</td>\n",
              "      <td>0.519550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>15</td>\n",
              "      <td>1051.4</td>\n",
              "      <td>0.050268</td>\n",
              "      <td>0.569817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>18</td>\n",
              "      <td>945.2</td>\n",
              "      <td>0.045190</td>\n",
              "      <td>0.615008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>17</td>\n",
              "      <td>905.6</td>\n",
              "      <td>0.043297</td>\n",
              "      <td>0.658305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>20</td>\n",
              "      <td>838.7</td>\n",
              "      <td>0.040098</td>\n",
              "      <td>0.698403</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>21</td>\n",
              "      <td>817.0</td>\n",
              "      <td>0.039061</td>\n",
              "      <td>0.737464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1</td>\n",
              "      <td>719.4</td>\n",
              "      <td>0.034395</td>\n",
              "      <td>0.771859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>6</td>\n",
              "      <td>658.0</td>\n",
              "      <td>0.031459</td>\n",
              "      <td>0.803318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>10</td>\n",
              "      <td>644.7</td>\n",
              "      <td>0.030823</td>\n",
              "      <td>0.834141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>8</td>\n",
              "      <td>642.8</td>\n",
              "      <td>0.030732</td>\n",
              "      <td>0.864874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>4</td>\n",
              "      <td>590.9</td>\n",
              "      <td>0.028251</td>\n",
              "      <td>0.893125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>5</td>\n",
              "      <td>536.2</td>\n",
              "      <td>0.025636</td>\n",
              "      <td>0.918761</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>22</td>\n",
              "      <td>536.0</td>\n",
              "      <td>0.025626</td>\n",
              "      <td>0.944387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>9</td>\n",
              "      <td>531.7</td>\n",
              "      <td>0.025421</td>\n",
              "      <td>0.969808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>7</td>\n",
              "      <td>484.8</td>\n",
              "      <td>0.023178</td>\n",
              "      <td>0.992986</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0</td>\n",
              "      <td>146.7</td>\n",
              "      <td>0.007014</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    feature  importance  normalized_importance  cumulative_importance\n",
              "0        12      2729.6               0.130503               0.130503\n",
              "1        13      2433.5               0.116346               0.246849\n",
              "2        14      2117.9               0.101257               0.348107\n",
              "3        16      1404.7               0.067159               0.415266\n",
              "4         2      1107.8               0.052964               0.468230\n",
              "5        19      1073.4               0.051320               0.519550\n",
              "6        15      1051.4               0.050268               0.569817\n",
              "7        18       945.2               0.045190               0.615008\n",
              "8        17       905.6               0.043297               0.658305\n",
              "9        20       838.7               0.040098               0.698403\n",
              "10       21       817.0               0.039061               0.737464\n",
              "11        1       719.4               0.034395               0.771859\n",
              "12        6       658.0               0.031459               0.803318\n",
              "13       10       644.7               0.030823               0.834141\n",
              "14        8       642.8               0.030732               0.864874\n",
              "15        4       590.9               0.028251               0.893125\n",
              "16        5       536.2               0.025636               0.918761\n",
              "17       22       536.0               0.025626               0.944387\n",
              "18        9       531.7               0.025421               0.969808\n",
              "19        7       484.8               0.023178               0.992986\n",
              "20        0       146.7               0.007014               1.000000\n",
              "21        3         0.0               0.000000               1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q7xrqP2v4Em6",
        "colab_type": "code",
        "outputId": "2cee6d11-e56d-43e1-d984-b27dc68988ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "one_hundred_features = list(fs.feature_importances.loc[:22, 'feature'])\n",
        "len(one_hundred_features)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jU5ucK614QDi",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "# 5. Low Importance Features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Za7q2mxn4Rrt",
        "colab_type": "code",
        "outputId": "8b254c2a-1c1c-45d3-8001-28d904d717ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "fs.identify_low_importance(cumulative_importance = 0.99)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "19 features required for cumulative importance of 0.99 after one hot encoding.\n",
            "4 features do not contribute to cumulative importance of 0.99.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hLTEI2z64cBW",
        "colab_type": "code",
        "outputId": "6ec395f1-61d6-485e-ca8f-25ffdb4823b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "low_importance_features = fs.ops['low_importance']\n",
        "low_importance_features[:5]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[4, 0, 3, 11]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S9tzMVFa41tc",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "# Removing Features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xp5vot6p43YP",
        "colab_type": "code",
        "outputId": "e6e70dbb-ff82-41d8-9408-cf8fc4bcfadb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_no_missing = fs.remove(methods = ['missing'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Removed 2 features.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bllfFlOs4_8g",
        "colab_type": "code",
        "outputId": "e730aaab-843c-4e55-c5b3-38467d216651",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_no_missing_zero = fs.remove(methods = ['missing', 'zero_importance'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Removed 4 features.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7f7O1lhA5GGJ",
        "colab_type": "code",
        "outputId": "dc7da17c-5b81-4ef0-e141-5655437d841d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "all_to_remove = fs.check_removal()\n",
        "all_to_remove[0:22]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total of 6 features identified for removal\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 3, 4, 11, 15, 22]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DhN_gmfx5LwW",
        "colab_type": "code",
        "outputId": "3b9cbba9-ff1c-4d13-81ad-e7210839f32c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1969
        }
      },
      "source": [
        "x_train"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>16</td>\n",
              "      <td>1.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>5</td>\n",
              "      <td>12</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>100663296</td>\n",
              "      <td>2</td>\n",
              "      <td>67108864</td>\n",
              "      <td>1</td>\n",
              "      <td>33554432</td>\n",
              "      <td>7</td>\n",
              "      <td>658432</td>\n",
              "      <td>1664</td>\n",
              "      <td>1536.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1024</td>\n",
              "      <td>2</td>\n",
              "      <td>16.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>5</td>\n",
              "      <td>12</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>100663296</td>\n",
              "      <td>2</td>\n",
              "      <td>67108864</td>\n",
              "      <td>1</td>\n",
              "      <td>33554432</td>\n",
              "      <td>7</td>\n",
              "      <td>658432</td>\n",
              "      <td>1664</td>\n",
              "      <td>1536.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1024</td>\n",
              "      <td>2</td>\n",
              "      <td>8.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>100663296</td>\n",
              "      <td>2</td>\n",
              "      <td>67108864</td>\n",
              "      <td>1</td>\n",
              "      <td>33554432</td>\n",
              "      <td>7</td>\n",
              "      <td>658432</td>\n",
              "      <td>134144</td>\n",
              "      <td>5120.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>32.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>5</td>\n",
              "      <td>12</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>100663296</td>\n",
              "      <td>2</td>\n",
              "      <td>67108864</td>\n",
              "      <td>1</td>\n",
              "      <td>33554432</td>\n",
              "      <td>7</td>\n",
              "      <td>658432</td>\n",
              "      <td>134144</td>\n",
              "      <td>5120.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>512</td>\n",
              "      <td>2</td>\n",
              "      <td>8.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>100663296</td>\n",
              "      <td>2</td>\n",
              "      <td>67108864</td>\n",
              "      <td>1</td>\n",
              "      <td>33554432</td>\n",
              "      <td>7</td>\n",
              "      <td>658432</td>\n",
              "      <td>134144</td>\n",
              "      <td>5120.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>8.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>134217728</td>\n",
              "      <td>2</td>\n",
              "      <td>134217728</td>\n",
              "      <td>5</td>\n",
              "      <td>656384</td>\n",
              "      <td>132608</td>\n",
              "      <td>2048.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>128.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>512.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>134217728</td>\n",
              "      <td>2</td>\n",
              "      <td>134217728</td>\n",
              "      <td>5</td>\n",
              "      <td>656384</td>\n",
              "      <td>525824</td>\n",
              "      <td>1536.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>256</td>\n",
              "      <td>8</td>\n",
              "      <td>16.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>134217728</td>\n",
              "      <td>2</td>\n",
              "      <td>134217728</td>\n",
              "      <td>5</td>\n",
              "      <td>656384</td>\n",
              "      <td>1792</td>\n",
              "      <td>256.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>134217728</td>\n",
              "      <td>2</td>\n",
              "      <td>134217728</td>\n",
              "      <td>5</td>\n",
              "      <td>656384</td>\n",
              "      <td>525824</td>\n",
              "      <td>1536.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>16</td>\n",
              "      <td>4.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>134217728</td>\n",
              "      <td>2</td>\n",
              "      <td>134217728</td>\n",
              "      <td>5</td>\n",
              "      <td>656384</td>\n",
              "      <td>132608</td>\n",
              "      <td>2048.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>512</td>\n",
              "      <td>2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>134217728</td>\n",
              "      <td>2</td>\n",
              "      <td>134217728</td>\n",
              "      <td>5</td>\n",
              "      <td>656384</td>\n",
              "      <td>1792</td>\n",
              "      <td>256.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>16</td>\n",
              "      <td>64.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>256.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>134217728</td>\n",
              "      <td>2</td>\n",
              "      <td>134217728</td>\n",
              "      <td>5</td>\n",
              "      <td>656384</td>\n",
              "      <td>132608</td>\n",
              "      <td>256.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>8.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>134217728</td>\n",
              "      <td>2</td>\n",
              "      <td>134217728</td>\n",
              "      <td>5</td>\n",
              "      <td>656384</td>\n",
              "      <td>1792</td>\n",
              "      <td>256.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>8.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>134217728</td>\n",
              "      <td>2</td>\n",
              "      <td>134217728</td>\n",
              "      <td>5</td>\n",
              "      <td>656384</td>\n",
              "      <td>132608</td>\n",
              "      <td>2048.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>16.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>134217728</td>\n",
              "      <td>2</td>\n",
              "      <td>134217728</td>\n",
              "      <td>5</td>\n",
              "      <td>656384</td>\n",
              "      <td>525824</td>\n",
              "      <td>1536.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>64.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>512.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9924</th>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>67108864</td>\n",
              "      <td>1</td>\n",
              "      <td>67108864</td>\n",
              "      <td>2</td>\n",
              "      <td>134217728</td>\n",
              "      <td>5</td>\n",
              "      <td>3153920</td>\n",
              "      <td>57344</td>\n",
              "      <td>448.0</td>\n",
              "      <td>192.0</td>\n",
              "      <td>64</td>\n",
              "      <td>2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9925</th>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>67108864</td>\n",
              "      <td>1</td>\n",
              "      <td>67108864</td>\n",
              "      <td>2</td>\n",
              "      <td>134217728</td>\n",
              "      <td>5</td>\n",
              "      <td>3153920</td>\n",
              "      <td>2113664</td>\n",
              "      <td>32896.0</td>\n",
              "      <td>256.0</td>\n",
              "      <td>64</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>64.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9926</th>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>67108864</td>\n",
              "      <td>1</td>\n",
              "      <td>67108864</td>\n",
              "      <td>2</td>\n",
              "      <td>134217728</td>\n",
              "      <td>5</td>\n",
              "      <td>3153920</td>\n",
              "      <td>24640</td>\n",
              "      <td>16512.0</td>\n",
              "      <td>256.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>64.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9927</th>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>67108864</td>\n",
              "      <td>1</td>\n",
              "      <td>67108864</td>\n",
              "      <td>2</td>\n",
              "      <td>134217728</td>\n",
              "      <td>5</td>\n",
              "      <td>3153920</td>\n",
              "      <td>2113664</td>\n",
              "      <td>16512.0</td>\n",
              "      <td>256.0</td>\n",
              "      <td>64</td>\n",
              "      <td>128</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9928</th>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>33554432</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>33554432</td>\n",
              "      <td>3</td>\n",
              "      <td>263168</td>\n",
              "      <td>263168</td>\n",
              "      <td>512.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>8.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9929</th>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>33554432</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>33554432</td>\n",
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              "    <tr>\n",
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              "      <td>4</td>\n",
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              "    <tr>\n",
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              "      <td>4</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
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              "      <td>4</td>\n",
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              "    <tr>\n",
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              "      <td>4</td>\n",
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              "      <td>263168</td>\n",
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              "      <td>2</td>\n",
              "      <td>4</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
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              "      <td>4</td>\n",
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              "      <td>263168</td>\n",
              "      <td>263168</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
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              "      <td>4</td>\n",
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              "      <td>8</td>\n",
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              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
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              "      <td>512</td>\n",
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              "      <td>16</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>33554432</td>\n",
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              "      <td>1536</td>\n",
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              "      <td>4</td>\n",
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              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9938</th>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
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              "      <td>7</td>\n",
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              "      <td>2</td>\n",
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              "      <td>128.0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>9939</th>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
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              "      <td>1</td>\n",
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              "      <td>133504</td>\n",
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              "    <tr>\n",
              "      <th>9940</th>\n",
              "      <td>5</td>\n",
              "      <td>12</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
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              "      <td>6</td>\n",
              "      <td>12</td>\n",
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              "    <tr>\n",
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              "      <td>12</td>\n",
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              "    <tr>\n",
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              "      <td>12</td>\n",
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              "      <td>4</td>\n",
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              "    <tr>\n",
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              "      <td>6</td>\n",
              "      <td>12</td>\n",
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              "    <tr>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>268435456</td>\n",
              "      <td>1</td>\n",
              "      <td>67108864</td>\n",
              "      <td>3</td>\n",
              "      <td>201326592</td>\n",
              "      <td>9</td>\n",
              "      <td>3146112</td>\n",
              "      <td>3145728</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>4096</td>\n",
              "      <td>64.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9952</th>\n",
              "      <td>4</td>\n",
              "      <td>16</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>268435456</td>\n",
              "      <td>1</td>\n",
              "      <td>67108864</td>\n",
              "      <td>3</td>\n",
              "      <td>201326592</td>\n",
              "      <td>9</td>\n",
              "      <td>3146112</td>\n",
              "      <td>3145728</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>4096</td>\n",
              "      <td>32.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9953</th>\n",
              "      <td>4</td>\n",
              "      <td>16</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>268435456</td>\n",
              "      <td>1</td>\n",
              "      <td>67108864</td>\n",
              "      <td>3</td>\n",
              "      <td>201326592</td>\n",
              "      <td>9</td>\n",
              "      <td>3146112</td>\n",
              "      <td>3145728</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>8192</td>\n",
              "      <td>128.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9954 rows × 23 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      0   1   2   3   4   5   ...    17     18     19      20     21    22\n",
              "0      5  12  10   0   0   3  ...    16  128.0   64.0    64.0    NaN   NaN\n",
              "1      6  12  10   0   0   3  ...     2    8.0   64.0    32.0   64.0   NaN\n",
              "2      6  12  10   0   0   3  ...    16   32.0   32.0    32.0   32.0   NaN\n",
              "3      5  12  10   0   0   3  ...     2   64.0   32.0   512.0    NaN   NaN\n",
              "4      5  12  10   0   0   3  ...     1  128.0   64.0   512.0    NaN   NaN\n",
              "5      6  12  10   0   0   3  ...     1   16.0   64.0    64.0   64.0   NaN\n",
              "6      5  12  10   0   0   3  ...     1   64.0   64.0  1024.0    NaN   NaN\n",
              "7      6  12  10   0   0   3  ...     1   16.0   32.0    64.0   32.0   NaN\n",
              "8      6  12  10   0   0   3  ...    16   16.0   64.0    32.0   64.0   NaN\n",
              "9      6  12  10   0   0   3  ...    16   16.0   32.0    32.0   64.0   NaN\n",
              "10     5  12  10   0   0   3  ...     2   64.0   32.0   512.0    NaN   NaN\n",
              "11     5  12  10   0   0   3  ...     1  128.0   64.0  1024.0    NaN   NaN\n",
              "12     5  12  10   0   0   3  ...     1   16.0   64.0    32.0    NaN   NaN\n",
              "13     6  12  10   0   0   3  ...     4   16.0   32.0   128.0   64.0   NaN\n",
              "14     6  12  10   0   0   3  ...    16    1.0  128.0    64.0   64.0   NaN\n",
              "15     5  12  10   0   0   3  ...     2   16.0   32.0    32.0    NaN   NaN\n",
              "16     5  12  10   0   0   3  ...     2    8.0   32.0    64.0    NaN   NaN\n",
              "17     6  12  10   0   0   3  ...     1   32.0   32.0    64.0   32.0   NaN\n",
              "18     5  12  10   0   0   3  ...     2    8.0   32.0   128.0    NaN   NaN\n",
              "19     6  12  10   0   0   3  ...     1    8.0  128.0    64.0  128.0   NaN\n",
              "20     5   8   8   0   0   0  ...     2  128.0  128.0   512.0    NaN   NaN\n",
              "21     5   8   8   0   0   0  ...     8   16.0   64.0    32.0    NaN   NaN\n",
              "22     6   8   8   0   0   0  ...     4    2.0   32.0   128.0  128.0   NaN\n",
              "23     6   8   8   0   0   0  ...    16    4.0   32.0    32.0  128.0   NaN\n",
              "24     5   8   8   0   0   0  ...     2    4.0  128.0   128.0    NaN   NaN\n",
              "25     5   8   8   0   0   0  ...    16   64.0   32.0   256.0    NaN   NaN\n",
              "26     6   8   8   0   0   0  ...     8    8.0  128.0    64.0   32.0   NaN\n",
              "27     6   8   8   0   0   0  ...     4    8.0  128.0   128.0   32.0   NaN\n",
              "28     6   8   8   0   0   0  ...     2   16.0   64.0   128.0   32.0   NaN\n",
              "29     5   8   8   0   0   0  ...     8   64.0   64.0   512.0    NaN   NaN\n",
              "...   ..  ..  ..  ..  ..  ..  ...   ...    ...    ...     ...    ...   ...\n",
              "9924   6   8  14   0   1   1  ...     2    4.0   64.0    32.0   64.0   NaN\n",
              "9925   7   8  14   0   1   1  ...     1    2.0    1.0   128.0   64.0  64.0\n",
              "9926   7   8  14   0   1   1  ...     2    4.0  128.0    32.0   32.0  64.0\n",
              "9927   6   8  14   0   1   1  ...   128    4.0    1.0    32.0   64.0   NaN\n",
              "9928   6   4   4   0   1   1  ...     4    8.0   64.0   128.0   64.0   NaN\n",
              "9929   6   4   4   0   1   1  ...    16   16.0  128.0    32.0   32.0   NaN\n",
              "9930   6   4   4   0   1   1  ...     4    2.0   64.0   128.0   64.0   NaN\n",
              "9931   5   4   4   0   1   1  ...     8   32.0   64.0   128.0    NaN   NaN\n",
              "9932   6   4   4   0   1   1  ...     4    4.0  128.0   128.0   32.0   NaN\n",
              "9933   6   4   4   0   1   1  ...     4    4.0   64.0   128.0  128.0   NaN\n",
              "9934   6   4   4   0   1   1  ...     8    4.0   32.0    64.0  128.0   NaN\n",
              "9935   6   4   4   0   1   1  ...     8    1.0   32.0    64.0  128.0   NaN\n",
              "9936   5   4   4   0   1   1  ...    16   32.0   32.0   128.0    NaN   NaN\n",
              "9937   6   4   4   0   1   1  ...     4    4.0   32.0    32.0  128.0   NaN\n",
              "9938   6  12   9   0   1   0  ...     2   16.0  128.0    64.0   64.0   NaN\n",
              "9939   6  12   9   0   1   0  ...     8   16.0  128.0    64.0   64.0   NaN\n",
              "9940   5  12   9   0   1   0  ...     4   64.0  128.0   128.0    NaN   NaN\n",
              "9941   6  12   9   0   1   0  ...     4    1.0   64.0   128.0  128.0   NaN\n",
              "9942   6  12   9   0   1   0  ...     8   32.0   32.0    64.0   32.0   NaN\n",
              "9943   6  12   9   0   1   0  ...     4   32.0   64.0   128.0   32.0   NaN\n",
              "9944   6  12   9   0   1   0  ...     8   16.0   32.0   128.0   32.0   NaN\n",
              "9945   6  12   9   0   1   0  ...     4   32.0  128.0   128.0   32.0   NaN\n",
              "9946   5  12   9   0   1   0  ...     4    1.0  128.0   128.0    NaN   NaN\n",
              "9947   6  12   9   0   1   0  ...     1    8.0  128.0   128.0  128.0   NaN\n",
              "9948   4  16   9   0   1   4  ...     2   64.0   64.0     NaN    NaN   NaN\n",
              "9949   4  16   9   0   1   4  ...     1   64.0  128.0     NaN    NaN   NaN\n",
              "9950   4  16   9   0   1   4  ...  4096  128.0  128.0     NaN    NaN   NaN\n",
              "9951   4  16   9   0   1   4  ...  4096   64.0  128.0     NaN    NaN   NaN\n",
              "9952   4  16   9   0   1   4  ...  4096   32.0  128.0     NaN    NaN   NaN\n",
              "9953   4  16   9   0   1   4  ...  8192  128.0   64.0     NaN    NaN   NaN\n",
              "\n",
              "[9954 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t0W29imf23ke",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "################################ Data set: x_train from git #############################\n",
        "###Data-set : v3\n",
        "import pandas as pd\n",
        "url = 'https://raw.githubusercontent.com/DinaTaklit/Loop-Unrolling-Tiramisu-Vf/master/x_train_v3.csv?token=AE4WVKAVM6QBC7R5F2L56WK5BPLOU'\n",
        "x_train_v3 = pd.read_csv(url, delimiter=',', skiprows=1, usecols=range(23), header=None)\n",
        "#x_train1 = pd.read_csv(url, skiprows=1, header=None)\n",
        "################################ Data set: y_train #############################\n",
        "###Data-set : v3\n",
        "url = 'https://raw.githubusercontent.com/DinaTaklit/Loop-Unrolling-Tiramisu-Vf/master/y_train_v3.csv?token=AE4WVKB3GWTW4Z3P5TMQJA25BPMH6'\n",
        "y_train_v3 = pd.read_csv(url, skiprows=1, header=None)\n",
        "\n",
        "################################ Data set: x_train from git #############################\n",
        "###Data-set : v34\n",
        "import pandas as pd\n",
        "url = 'https://raw.githubusercontent.com/DinaTaklit/Loop-Unrolling-Tiramisu-Vf/master/x_train_v34.csv?token=AE4WVKBWZT7IAUMTLWCTI625BPMQ2'\n",
        "x_train_v34 = pd.read_csv(url, delimiter=';', skiprows=1, usecols=range(23), header=None)\n",
        "#x_train1 = pd.read_csv(url, skiprows=1, header=None)\n",
        "\n",
        "################################ Data set: y_train #############################\n",
        "###Data-set : v34\n",
        "url = 'https://raw.githubusercontent.com/DinaTaklit/Loop-Unrolling-Tiramisu-Vf/master/y_train_v34.csv?token=AE4WVKHDWVY5JQB22G6VZ3K5BPMUY'\n",
        "y_train_v34 = pd.read_csv(url, skiprows=1, header=None)\n",
        "################################ Data set: x_train from git #############################\n",
        "###Data-set : v345\n",
        "import pandas as pd\n",
        "url = 'https://raw.githubusercontent.com/DinaTaklit/Loop-Unrolling-Tiramisu-Vf/master/x_train_v345.csv?token=AE4WVKH4ZTRUCMMTJNG2Q6K5BPMWU'\n",
        "x_train_v345 = pd.read_csv(url, delimiter=';', skiprows=1, usecols=range(23), header=None)\n",
        "#x_train1 = pd.read_csv(url, skiprows=1, header=None)\n",
        "################################ Data set: y_train #############################\n",
        "###Data-set : v345\n",
        "url = 'https://raw.githubusercontent.com/DinaTaklit/Loop-Unrolling-Tiramisu-Vf/master/y_train_v345.csv?token=AE4WVKHNSOKT7UTB6CT7VBK5BPMYE'\n",
        "y_train_v345 = pd.read_csv(url, skiprows=1, header=None)\n",
        "################################ Data set: x_train from git #############################\n",
        "###Data-set : v34579\n",
        "import pandas as pd\n",
        "url = 'https://raw.githubusercontent.com/DinaTaklit/Loop-Unrolling-Tiramisu-Vf/master/x_train_v34579.csv?token=AE4WVKHGWOBZUEVRRBITXXK5BPNAA'\n",
        "x_train_v34579 = pd.read_csv(url, delimiter=';', skiprows=1, usecols=range(23), header=None)\n",
        "#x_train1 = pd.read_csv(url, skiprows=1, header=None)\n",
        "################################ Data set: y_train #############################\n",
        "###Data-set : v34579\n",
        "url = 'https://raw.githubusercontent.com/DinaTaklit/Loop-Unrolling-Tiramisu-Vf/master/y_train_v34579.csv?token=AE4WVKGYNUIBEQOZQG2244S5BPNCO'\n",
        "y_train_v34579 = pd.read_csv(url, skiprows=1, header=None)\n",
        "\n",
        "print('x_v3',len(x_train_v3))\n",
        "print('y_v3',len(y_train_v3))\n",
        "\n",
        "print('x_v34',len(x_train_v34))\n",
        "print('y_v34',len(y_train_v34))\n",
        "\n",
        "print('x_v345',len(x_train_v345))\n",
        "print('y_v345',len(y_train_v345))\n",
        "\n",
        "print('x_v3457',len(x_train_v3457))\n",
        "print('y_v3457',len(y_train_v3457))\n",
        "\n",
        "print('x_v34579',len(x_train_v34579))\n",
        "print('y_v34579',len(y_train_v34579))\n",
        "\n",
        "y_train_v3457.hist(bins=20, grid=False, xlabelsize=12, ylabelsize=12)\n",
        "\n",
        "x_train2 = x_train_v3457.copy()\n",
        "y_train2 = y_train_v3457.copy()\n",
        "\n",
        "x_train2['Unroll'] = y_train2 \n",
        "class0 = x_train2[x_train2['Unroll'] == 0]\n",
        "class1 = x_train2[x_train2['Unroll'] == 1]\n",
        "class2 = x_train2[x_train2['Unroll'] == 2]\n",
        "class3 = x_train2[x_train2['Unroll'] == 3]\n",
        "class4 = x_train2[x_train2['Unroll'] == 4]\n",
        "class5 = x_train2[x_train2['Unroll'] == 5]\n",
        "class6 = x_train2[x_train2['Unroll'] == 6]\n",
        "print(\"0: \",len(class0))\n",
        "print(\"1: \",len(class1))\n",
        "print(\"2: \",len(class2))\n",
        "print(\"3: \",len(class3))\n",
        "print(\"4: \",len(class4))\n",
        "print(\"5: \",len(class5))\n",
        "print(\"6: \",len(class6))\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train = x_train.copy()\n",
        "y_train = y_train.copy()\n",
        "cols = [0,3,11,4]\n",
        "x_train.drop(x_train.columns[cols],axis=1,inplace=True)\n",
        "x_train.fillna(0, inplace=True)\n",
        "x_train, x_test, y_train, y_test = train_test_split(x_train,y_train,test_size=0.02,train_size=0.8,random_state=9)\n",
        "len(x_test)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}