model_deepNN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "model_deepNN.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/basselkassem/predict_income_potential/blob/master/model_deepNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gaa7bZWSMPMW",
        "colab_type": "code",
        "outputId": "724df5e3-85d1-43c4-d88b-a6e1301059b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "data_path = \"/content/gdrive/My Drive/Colab Notebooks/predict_income_potential/data/\"\n",
        "model_path = '/content/gdrive/My Drive/Colab Notebooks/predict_income_potential/models/'"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hi4SBbUMCZhe",
        "colab_type": "text"
      },
      "source": [
        "# Import Libs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rwv-DWNsLn2H",
        "colab_type": "code",
        "outputId": "529b0a83-df83-4d27-ad52-e2db098c72bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "import keras\n",
        "from keras.layers import *\n",
        "from keras.models import *\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from sklearn.metrics import f1_score, accuracy_score, roc_auc_score"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MHUJRmBeCdre",
        "colab_type": "text"
      },
      "source": [
        "# Load Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "puaFgVFFL2Gi",
        "colab_type": "code",
        "outputId": "f4c0b313-ade2-4a55-dec7-036c77d0e59d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "data = pd.read_pickle(data_path + 'data_cont_cat_lb.pkl')\n",
        "features = list(np.load(data_path + 'features.pkl.npy'))\n",
        "print('data shape: ', data.shape)\n",
        "print('features lenght: ', len(features))"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "data shape:  (48813, 39)\n",
            "features lenght:  31\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gRcc1jloOCEA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = data[features + ['target']]\n",
        "X, y = data.drop(columns = 'target'), data['target']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UP4PsMn54-xc",
        "colab_type": "text"
      },
      "source": [
        "# Model definition"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4DKqpYNVFI8S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.layers.merge import concatenate"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-VI2wLUE5f9-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "outputId": "9fc7a356-eab9-47b9-e15f-da7ec7f0927e"
      },
      "source": [
        "cat_features = ['workclass', 'education_duration',\n",
        "       'marital_status', 'occupation', 'relationship', 'race', 'sex', 'native_country',\n",
        "       'has_missing_values', 'com_workclass', 'com_marital_status',\n",
        "       'com_native_country']\n",
        "\n",
        "input_models=[]\n",
        "output_embeddings=[]\n",
        "numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
        "\n",
        "for categorical_var in cat_features:\n",
        "    \n",
        "    #Name of the categorical variable that will be used in the Keras Embedding layer\n",
        "    cat_emb_name= categorical_var.replace(\" \", \"\")+'_Embedding'\n",
        "  \n",
        "    # Define the embedding_size\n",
        "    no_of_unique_cat  = X[categorical_var].nunique()\n",
        "    embedding_size = int(min(np.ceil((no_of_unique_cat)/2), 50 ))\n",
        "  \n",
        "    #One Embedding Layer for each categorical variable\n",
        "    input_model = Input(shape=(1,))\n",
        "    output_model = Embedding(no_of_unique_cat, embedding_size, name=cat_emb_name)(input_model)\n",
        "    output_model = Reshape(target_shape=(embedding_size,))(output_model)    \n",
        "  \n",
        "    #Appending all the categorical inputs\n",
        "    input_models.append(input_model)\n",
        "  \n",
        "    #Appending all the embeddings\n",
        "    output_embeddings.append(output_model)\n",
        "  \n",
        "#Other non-categorical data columns (numerical). \n",
        "#I define single another network for the other columns and add them to our models list.\n",
        "input_numeric = Input(shape=(X.shape[1] - len(cat_features),))\n",
        "embedding_numeric = Dense(64)(input_numeric) \n",
        "\n",
        "input_models.append(input_numeric)\n",
        "output_embeddings.append(embedding_numeric)\n",
        "\n",
        "#At the end we concatenate altogther and add other Dense layers\n",
        "output = Concatenate()(output_embeddings)\n",
        "\n",
        "output = Dense(128, kernel_initializer = \"uniform\")(output)\n",
        "output = Activation('relu')(output)\n",
        "\n",
        "layers_num = 1\n",
        "neurons_num = 64\n",
        "for layer in range(layers_num):\n",
        "  output = Dense(neurons_num, kernel_initializer = \"uniform\")(output)\n",
        "  output = Activation('relu')(output)\n",
        "  #output= Dropout(0.3)(output)\n",
        "\n",
        "layers_num = 1\n",
        "neurons_num = 32\n",
        "for layer in range(layers_num):\n",
        "  output = Dense(neurons_num, kernel_initializer = \"uniform\")(output)\n",
        "  output = Activation('relu')(output)\n",
        "  #output= Dropout(0.2)(output)\n",
        "\n",
        "\n",
        "output = Dense(1, activation='sigmoid')(output)\n",
        "\n",
        "model = Model(inputs=input_models, outputs=output)\n",
        "\n",
        "optimizer = keras.optimizers.Adam()\n",
        "model.compile(loss = 'binary_crossentropy',\n",
        "              optimizer = optimizer,\n",
        "              metrics = ['accuracy']\n",
        ")\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3657: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/nn_impl.py:183: where (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": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ul7IK4HlDdj_",
        "colab_type": "text"
      },
      "source": [
        "# Model Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MwwgTUgPQPSR",
        "colab_type": "code",
        "outputId": "17cfa540-2824-4393-fd33-0f57e64a0383",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "def preproc(X_train, X_test):\n",
        "\n",
        "    input_list_train = []\n",
        "    #input_list_val = []\n",
        "    input_list_test = []\n",
        "    \n",
        "    #the cols to be embedded: rescaling to range [0, # values)\n",
        "    for c in cat_features:\n",
        "        raw_vals = np.unique(X_train[c])\n",
        "        val_map = {}\n",
        "        for i in range(len(raw_vals)):\n",
        "            val_map[raw_vals[i]] = i       \n",
        "        input_list_train.append(X_train[c].map(val_map).values)\n",
        "        #input_list_val.append(X_val[c].map(val_map).fillna(0).values)\n",
        "        input_list_test.append(X_test[c].map(val_map).fillna(0).values)\n",
        "     \n",
        "    #the rest of the columns\n",
        "    other_cols = [c for c in X_train.columns if (not c in cat_features)]\n",
        "    input_list_train.append(X_train[other_cols].values)\n",
        "    #input_list_val.append(X_val[other_cols].values)\n",
        "    input_list_test.append(X_test[other_cols].values)\n",
        "    \n",
        "    return input_list_train, input_list_test\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state = 42)\n",
        "X_train_list, X_test_list = preproc(X_train, X_test)\n",
        "\n",
        "early_stop = keras.callbacks.EarlyStopping(monitor = 'val_loss', patience = 10)\n",
        "history = model.fit(X_train_list, y_train, epochs = 1024, batch_size = 32,\n",
        "                    validation_data = (X_test_list, y_test), callbacks = [early_stop])\n",
        "\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc ='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "Train on 32704 samples, validate on 16109 samples\n",
            "Epoch 1/1024\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n",
            "32704/32704 [==============================] - 20s 599us/step - loss: 0.3359 - acc: 0.8451 - val_loss: 0.3169 - val_acc: 0.8575\n",
            "Epoch 2/1024\n",
            "32704/32704 [==============================] - 14s 423us/step - loss: 0.3123 - acc: 0.8555 - val_loss: 0.3125 - val_acc: 0.8574\n",
            "Epoch 3/1024\n",
            "32704/32704 [==============================] - 14s 415us/step - loss: 0.3080 - acc: 0.8563 - val_loss: 0.3102 - val_acc: 0.8570\n",
            "Epoch 4/1024\n",
            "32704/32704 [==============================] - 13s 412us/step - loss: 0.3054 - acc: 0.8574 - val_loss: 0.3101 - val_acc: 0.8604\n",
            "Epoch 5/1024\n",
            "32704/32704 [==============================] - 13s 409us/step - loss: 0.3027 - acc: 0.8588 - val_loss: 0.3115 - val_acc: 0.8585\n",
            "Epoch 6/1024\n",
            "32704/32704 [==============================] - 13s 412us/step - loss: 0.3019 - acc: 0.8597 - val_loss: 0.3114 - val_acc: 0.8554\n",
            "Epoch 7/1024\n",
            "32704/32704 [==============================] - 14s 422us/step - loss: 0.2996 - acc: 0.8620 - val_loss: 0.3113 - val_acc: 0.8588\n",
            "Epoch 8/1024\n",
            "32704/32704 [==============================] - 13s 404us/step - loss: 0.2983 - acc: 0.8617 - val_loss: 0.3068 - val_acc: 0.8603\n",
            "Epoch 9/1024\n",
            "32704/32704 [==============================] - 13s 408us/step - loss: 0.2972 - acc: 0.8618 - val_loss: 0.3082 - val_acc: 0.8608\n",
            "Epoch 10/1024\n",
            "32704/32704 [==============================] - 14s 419us/step - loss: 0.2958 - acc: 0.8633 - val_loss: 0.3085 - val_acc: 0.8586\n",
            "Epoch 11/1024\n",
            "32704/32704 [==============================] - 14s 427us/step - loss: 0.2948 - acc: 0.8623 - val_loss: 0.3075 - val_acc: 0.8607\n",
            "Epoch 12/1024\n",
            "32704/32704 [==============================] - 14s 419us/step - loss: 0.2935 - acc: 0.8629 - val_loss: 0.3077 - val_acc: 0.8587\n",
            "Epoch 13/1024\n",
            "32704/32704 [==============================] - 14s 426us/step - loss: 0.2913 - acc: 0.8628 - val_loss: 0.3106 - val_acc: 0.8571\n",
            "Epoch 14/1024\n",
            "32704/32704 [==============================] - 14s 421us/step - loss: 0.2903 - acc: 0.8661 - val_loss: 0.3110 - val_acc: 0.8596\n",
            "Epoch 15/1024\n",
            "32704/32704 [==============================] - 13s 411us/step - loss: 0.2890 - acc: 0.8649 - val_loss: 0.3102 - val_acc: 0.8608\n",
            "Epoch 16/1024\n",
            "32704/32704 [==============================] - 14s 422us/step - loss: 0.2878 - acc: 0.8655 - val_loss: 0.3116 - val_acc: 0.8603\n",
            "Epoch 17/1024\n",
            "32704/32704 [==============================] - 13s 399us/step - loss: 0.2854 - acc: 0.8675 - val_loss: 0.3103 - val_acc: 0.8595\n",
            "Epoch 18/1024\n",
            "32704/32704 [==============================] - 13s 400us/step - loss: 0.2840 - acc: 0.8682 - val_loss: 0.3133 - val_acc: 0.8576\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uV1VB4XbnSm3VQbL0uRMtlKiOboCMcH66jHCe11tVVe6cYCVOd1N5iXMGc/Xt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\nnpHd4/nO2B5UVFaxJruIhVv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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "db8PQ2axDkzC",
        "colab_type": "text"
      },
      "source": [
        "# Model evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZlAT2MdtFSA0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "1ec87506-ef08-4664-af93-eaf74c202bd7"
      },
      "source": [
        "plt.plot(history.history['acc'])\n",
        "plt.plot(history.history['val_acc'])\n",
        "plt.title('model acc')\n",
        "plt.ylabel('acc')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc ='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FIsvnRGW+7GfSBNjl9jzJWebuCeBGEUnC3mXc6yxvBxgRmS8iK0TkYbdjJp3h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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r0tonkspXDIh",
        "colab_type": "code",
        "outputId": "16bd917f-0d27-4ee8-816b-9d7567ffefe2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        }
      },
      "source": [
        "y_train_hat = model.predict(X_train_list).round()\n",
        "y_test_hat = model.predict(X_test_list).round()\n",
        "\n",
        "tr_acc = accuracy_score(y_train, y_train_hat)\n",
        "tr_f1 = f1_score(y_train, y_train_hat)\n",
        "tr_auc = roc_auc_score(y_train, y_train_hat)\n",
        "\n",
        "test_acc = accuracy_score(y_test, y_test_hat)\n",
        "test_f1 = f1_score(y_test, y_test_hat)\n",
        "test_auc = roc_auc_score(y_test, y_test_hat)\n",
        "print('------train-----------')\n",
        "print('accuracy_score: ', tr_acc)\n",
        "print('f1_score: ', tr_f1)\n",
        "print('roc_auc_score: ', tr_auc)\n",
        "print('------test-------------')\n",
        "print('accuracy_score: ', test_acc)\n",
        "print('f1_score: ', test_f1)\n",
        "print('roc_auc_score: ', test_auc)\n",
        "plt.hist(y_train_hat)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "------train-----------\n",
            "accuracy_score:  0.8708414872798435\n",
            "f1_score:  0.7083275790636652\n",
            "roc_auc_score:  0.7980713193300434\n",
            "------test-------------\n",
            "accuracy_score:  0.8575951331553789\n",
            "f1_score:  0.6827986725663716\n",
            "roc_auc_score:  0.7809807535658215\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([26002.,     0.,     0.,     0.,     0.,     0.,     0.,     0.,\n",
              "            0.,  6702.]),\n",
              " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ],\n",
              "       dtype=float32),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ooveQLUhDz6L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "outputId": "9ef359c4-5938-4ecf-f6aa-d4d347440e5f"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "labels = [0, 1]\n",
        "cm = confusion_matrix(y_test, y_test_hat, labels)\n",
        "print(cm)\n",
        "ax =  plt.subplot()\n",
        "sns.heatmap(cm, annot = True, ax = ax);\n",
        "\n",
        "ax.set_xlabel('Predicted labels');\n",
        "ax.set_ylabel('True labels'); \n",
        "ax.set_title('Confusion Matrix'); \n",
        "ax.xaxis.set_ticklabels(['<=50k', '>50k']); \n",
        "ax.yaxis.set_ticklabels(['<=50k', '>50k']);"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[11346   858]\n",
            " [ 1436  2469]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cwp4792fL7r+i547bZHXO9u3bMXfOPADmzplH+/btlu3ru19vJr42insfuZ3T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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VYXGMAGQD70m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 182
        },
        "outputId": "eca4f1a2-1bd0-49ae-fdda-421d806ad33f"
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test, y_test_hat))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.93      0.91     12204\n",
            "           1       0.74      0.63      0.68      3905\n",
            "\n",
            "    accuracy                           0.86     16109\n",
            "   macro avg       0.81      0.78      0.80     16109\n",
            "weighted avg       0.85      0.86      0.85     16109\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u7M3ys-2EAaV",
        "colab_type": "text"
      },
      "source": [
        "# Model saving"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Kz-6fYBUKY2",
        "colab_type": "code",
        "outputId": "6168e5dd-82c8-4077-aa1d-8538d0bd6f32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        }
      },
      "source": [
        "from keras.utils import plot_model\n",
        "from IPython.display import Image\n",
        "import pydot_ng as pydot\n",
        "plot_model(model, \n",
        "           show_shapes = True, \n",
        "           show_layer_names = True, \n",
        "           to_file = data_path + 'model.png')\n",
        "Image(retina = True, filename= data_path + 'model.png')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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BBx6ochtzlDkKAKjkpRZo1apV0Avc7Nu3r+Jj59ECtAAAAOd5pYWSks69vEh6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\ncXGxSktL9fHHH+t3v/udMjM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0jRs3Lh1wwAHpySef7Dym\nnL7629/+lv7f//t/acqUKWnIkCFpvfXWS9OnT0+PPfZYSimlAw44IEVEOuOMM3r9Oe+99960yy67\npEmTJqWISBGRJk6cmHbeeef061//utvxe++9d1p//fXTihUrSnshyzCQ/blSNf7OAKidalzXNVpe\nSmng6p+8NPh5KaWBzzPmTwC6q8b1aqNlkIGsJzLI4GeQwcgHgzEvBDDQrPOUzzpPvjKDdR6A8ln3\nqIx1j/zkh5QGvr7LD9A4XJeXz3V5vurqQF+Xm8eHfFI/y6d+qp9ZOj9Anpk/r8xgz4fKL7WZV1/T\nQL+/z+cLAPLH5yPK5/MR+cpU8hNAfTA/VBnvr8xPZknJ+ysBKM78TvnM7+QrK5nfAaAazD9VxvxT\nfjJVSuafoBrUk8qUOz6oE13rxB577JHGjh2bhgwZksaNG5f23nvvdP/99xc9b6X1pJff7xtsBgbU\nnSyE02OOOSatvfbaNW1DOQbyw5FPPfVUGjJkSLrmmmsG5PwDrb29Pe26667pqquuqto5X3311dTS\n0pK+853vVO2cq3OTSAD6koXrunrLSykNXP2Tl7ob6LyUks3AAGohCzdnqrcMMpD1RAbpbqAziM3A\nAEqThfnnessM1nmKq8fMYJ0HoHxZmI+tt/yQknWPYuoxP6TkwwJA9WTh773e6qrr8uLqsa7azASo\nhPpZPvWzOPVz8M8PkGfmzysz2PlOfuluMObV1+RmhwCUKwvXq/WWtXw+orh6zFTyE0B9yMJ4Wm+Z\nJSXvryymHjNLSt5fCUBx5nfKZ36nuHrMSuZ3AKiGLIz39ZapUjL/VEw9ZqqUzD9BNagnlSl3fFAn\nKtefetLbZmBNAUBF2tvba92ETNhkk03irLPOirPOOivefvvtWjenLO3t7XHLLbfEW2+9FbNmzara\nec8888x4//vfH8cee2zVzgkA9Uhe6iAvdScvATCQZJAOMkh3MggAq5MZOsgM3ckMABQjP3SQH7qT\nHwDKp652UFe7U1cBilM/O6if3amfANSanNI7+aU7+QUASidrdZCpupOpAMgSmaWDzNKdzAIAstJK\nslJ3shIAlE6m6iBTdSdTAeXIez1RJyo3UPWkbjcD+9a3vhVjxoyJQqEQDz/8cMXn+c53vhPjx4+P\nQqEQl112WRVbWLmbbroppk6dGoVCoei/jTbaqCrfa4cddojm5uZ4//vfX5Xzre7oo4+OUaNG9dlH\nxY674447YsyYMfGzn/2s6m0rh/7okJX+IJtOPfXUmDlzZsyaNSsWL15c6+aU7O67746bbropfvGL\nX0Rra2tVznnBBRfEww8/HHfccUcMHTq0KufMo1LHpMGU5XEuS69XVl4n9bmD/qiuvPQH2SQvrSIv\nDa5qjE1ZnDupFTWvQ1Zqnv7okJX+IJtkkFVkkMaQlTFRjeqgP6orL/1BNskMq8gMpavmfEEtx6ij\njjoqWlpaolAoxDvvvNPrsQPdTjWrQ1Zqlv7okJX+IHvkh1Xkh+wxhnfIyhiuPzpkpT/IJnV1FXW1\ntozZHbIyZuuPDlnpD7JH/VxF/aw+Y0/2qIsdsvK7qT86ZKU/qB/yyyryS4dy1qqzrppjYt7G16x/\nFkNd75CV3zv90SEr/UE2yVSryFT0xL24NqrK91In+6Y/OmSlP8gemWUVmYWeuP9FdanLHbJSl/VH\nh6z0B9kkK60iKzU2Y2X2qOMdsvK7qT86ZKU/yB6ZapVGzVR5Gx9WrFgRF154Yey88849Pn/eeefF\nFltsESNGjIiRI0fGFltsEV//+tfjzTffLPt7/c///E9sueWW0dTUFIVCISZMmBDnnHNOf3+Eqlqz\nDk6cODFmz55d62ZRR9SJ8g1kPanbzcBOPfXUuPzyy/t9npNPPjl++9vfVqFF1TN9+vT485//HNOm\nTYsxY8ZESilSSrF8+fJoa2uLl156qWq/hPfff3/stttuVTnXmr7//e/HlVdeWfFxKaWBaFbZ9EeH\nrPRHFpx22mlx9dVXx+LFi2PjjTeOG2+8sdZNyoRzzz03jj322PjWt75V66aUbPfdd4+f/OQnMXHi\nxKqc79Zbb42//e1vcffdd8e4ceOqcs68KnVMGkxZHuey9Hpl5XVSnzvoj+rKS39kgbzUM3lJXqqF\naoxNWZw7qRU1r0NWap7+6JCV/sgCGaRnMogM0kiyMiaqUR30R3XlpT+yQGbomcwgM5SrmvMFtRyj\nrr766jj55JNLOnag26lmdchKzdIfHbLSH7UmP/RMfpAfssoY3iErY7j+6JCV/sgCdbVn6qq6mgXG\n7A5ZGbP1R4es9EetqZ89Uz/Vz4Fi7MkedbFDVn439UeHrPRHrckp5ZFf5JfVlbNWnXXVHBPzNr5m\n/bMY6nqHrPze6Y8OWemPLJC1eiZTyVQU515c6uRg0R8dstIftSaz9ExmkVkozv0vqktd7pCVuqw/\nOmSlP7JAVuqZrCQrYazMInW8Q1Z+N/VHh6z0R63JVD2TqRo7U+VpfHjqqafiIx/5SJx44onR1tbW\n4zG/+c1v4rOf/Ww8//zz8dJLL8XZZ58d5513XsyYMaPs77fjjjvG448/HnvuuWdERDz55JNx+umn\n9+tnqLY16+CLL74Y1157ba2bVfcarZ6oE6Ub6HoypOpnZMA0NzfHiBEjYsSIEbHZZptV9dyFQqGq\n56uGvffeO9M7BuqPxjVnzpyYM2dOrZuRSXvuuWdnkG1E++23X+y33361bgYlWLp0aey+++5dFvWN\nc93V4+ukPmeL/mhc8lJx8pK8NNh6Gpt6qvG1UklbstT+ldS8bNEfjUsGKU4GkUHyyLxFV2pU+fRH\n45IZipMZZIbBUI81fKVatVPNyhb90Zjkh+LkB/mhnhjDs0V/NC51tTh1VV3NKmN2tuiPxqR+Fqd+\nqp/9Vc9z1rWQtffuqYvZoj8ak5xSPvlFfsmjao6JxtdsUNezRX80LlmrOJlKpoKV1Mls0R+NSWYp\nTmaRWeiZ+18MDnU5W/RH45KVipOVZKVG4v1J5clatlLHs0V/NCaZqjiZqnEzVZbGh/7U7kceeSTO\nOuus+MIXvhBLliwpusnZsGHD4ktf+lK0tLRERMTMmTNj/vz5MX/+/PjrX/8akyZN6tfPUGtZyz95\n1Yj1pNHrRKkGup40DdiZGVC33HJLVc83dOjQqp5vpVIvZAbjgielFPPnz48rrrii6ufWH+UbyP4A\n6kMtJ7uuuuqqePnll2v2/StRi9erHl+n1anP5ZOXGqc/gMaWpRpfSVuy1P6eqHnlk0Eapz8Aqinr\nmaAvalT5ZIbG6Q8g37Jcw7P4Ru01qVnlkyEapz8AymEML5+a2jj9AdAbY3b51NDG6Q+gPmV5zjqL\nsvx6qYvlk1Mapz+A7KuHtep6ZXztP3W9fHJW4/QHANmiTpZPbmmc/gDIuiytQbn/Rd/U5f7RH+WT\nk4B6l/WskDVZfr3U8fLJVY3TH0Dj6k/t3nbbbeOmm26Kww47LIYPH170uJtvvrlzI7CV1l9//YiI\nePvttyv63lmS5fwD9F+/NwPbfvvto1AoRKFQiG222SZeeOGFHo8788wzY+21146WlpY455xzIqIj\nAF5wwQWx5ZZbxvDhw2PcuHGx//77xxNPPNH5dd/+9rejtbU1Ro0aFS+//HKcdNJJsf7668eTTz7Z\n7Xu89NJLsdFGG8WQIUNir7326vLcNddcE9tvv320tLTEyJEjY6ONNoqzzz676M/1m9/8JrbaaqsY\nM2ZMtLS0xNZbbx2/+tWvOp//9a9/HR/84AejtbU1Ro8eHVtvvXW8+eabfT43EObOnRsjR46Mpqam\n+MAHPhATJkyIoUOHxsiRI2O77baLXXfdNTbYYINoaWmJsWPHxle/+tVu53j66adjiy22iJEjR8aI\nESNi1113jXvuuafLMe3t7XHGGWfElClTYsSIEbHNNtvEvHnzOp9PKcX5558fm2++eQwfPjzGjBkT\nX/nKV7p9r1KOu+eee2LKlClRKBTie9/7XkREXHrppTFy5MhobW2NW2+9NT75yU/G6NGjY/LkyXHd\nddd1a+ucOXNi8803jxEjRsS6664bG2+8ccyZMycOOuigzuN++ctfxujRo+Pcc88t/4UvQn9U3h9A\n/arGmHTsscfGsGHDYuLEiZ2PfelLX4qRI0dGoVCIV199tcvxvdX23ur48ccfHyeddFIsWLAgCoVC\nbLLJJj2Ocyvb3VdWKWc8LFU1X6/eslS9v07lUJ/lpbz0B1C/+jv29Va3i9X7q666qtvY1FON7+v8\n/VVsnqSStvT0NeXkSHM29VPzZJBs9QdQ30q5bl+pr7WUYs+XWo8vuuiiaGlpifHjx8fnP//5mDRp\nUrS0tMTOO+8c9913X5e2mLdQo2SG+usPoD71to7Q11jVk6zW8L7mBJqamuL222+PT37ykzFmzJiY\nNGlS/OAHP+h8vqd2lpNtBoKaJUPkpT+A+lPp+xBW6m3+oZL80V/GcDU1L/0B1K/erll7G69++MMf\nxlprrRWFQiHGjRsXt9xySzzwwAOx4YYbRnNzcxx66KED2m5jthqal/4A6k85Y0E15qy33HLLKBQK\nnWNsW1tbRER89atf7TzvD3/4w4io7nVtb9fPpcyhl7qOX+rr2dPrVWyOYNddd+38vOm0adPioYce\nioiIo446KlpbW2PMmDFx2223VfS6lEJdlFPy0h9AY+hrrTqif/c8KEV/xupia/6V3IchS+NrORmw\n3M9ilPP5h77qm89H1E9dl7Oy1R9AfevvnEk1xs++uBeXe3GtVI91Um7JVn8A9aseMov7X7j/hbq8\nqq1yUv31B1Df6iEreX+S9yf1RR2Xq/LSH0D96c/4UOr9MUqt8cXmUgbDU089FWP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fdeLVmyxHQsAADgRI8//rhGjRql0NBQrV+/3nQcAACA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\npaamGkgFAEDhWbJkibp06ZLjnLfFYtHixYvVqVMnQ8kAACgZs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dLdS3CMAwBK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\nevToYX1za1RUlPXf3bt3R/fu3RV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xrhSDBgAAxqMGd5ltmUym4x2CXXd75ayh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\neHjYcQ/c3d1VOp3uuTfu7u7q/v37PcUCBt0Xl5aWlEgketYnEgmeTwPACOX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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 3457,
              "height": 590
            }
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VmZ9CrDqEs6g",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "195e480f-7ec8-49bd-af50-ff303bcc56be"
      },
      "source": [
        "model.save(model_path + \"nn_model_1.h5\") \n",
        "model_json = model.to_json()\n",
        "with open(model_path + \"nn_model.json\", \"w\") as json_file:\n",
        "    json_file.write(model_json)\n",
        "model.save_weights(model_path + \"nn_model.h5\")\n",
        "print(\"Saved model to cloud\")"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saved model to cloud\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}