Neural_Networks.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "65624dda",
      "metadata": {
        "id": "65624dda"
      },
      "outputs": [],
      "source": [
        "from utils.utils import get_json_content\n",
        "from utils.learners import execute_a_series_of_multi_layer_perceptron_learnings"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f713ebd6",
      "metadata": {
        "id": "f713ebd6"
      },
      "source": [
        "# Neural Networks Training"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7ed89c78",
      "metadata": {
        "id": "7ed89c78"
      },
      "source": [
        "## File: 'headlines_001.json'"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0876c6ae",
      "metadata": {
        "id": "0876c6ae"
      },
      "source": [
        "Reads the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "30664769",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "30664769",
        "outputId": "48ed918e-4605-4481-d773-ccf973407088"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LENGTH - headline: 1000; is_clickbait: 1000\n"
          ]
        }
      ],
      "source": [
        "# Reads the data from the file\n",
        "X, Y = get_json_content(i=0)\n",
        "\n",
        "# Prints the dataset size\n",
        "print(f\"LENGTH - headline: {len(X)}; is_clickbait: {len(Y)}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1e2b2e64",
      "metadata": {
        "id": "1e2b2e64"
      },
      "source": [
        "Executes a series of learnings."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "ee427c97",
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ee427c97",
        "outputId": "5b621f37-d72a-4840-9594-925f7bac9f2e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\t--------------- TRAIN-TEST SPLIT ---------------\n",
            "\n",
            "- test_size: 0.3\n",
            "\n",
            "\t\t=============== BoW ===============\n",
            "\n",
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Multi-layer Perceptron\n",
            "- activation: relu\n",
            "- alpha: 0.0001\n",
            "- batch_size: auto\n",
            "- beta_1: 0.9\n",
            "- beta_2: 0.999\n",
            "- early_stopping: False\n",
            "- epsilon: 1e-08\n",
            "- hidden_layer_sizes: (100,)\n",
            "- learning_rate: constant\n",
            "- learning_rate_init: 0.001\n",
            "- max_fun: 15000\n",
            "- max_iter: 200\n",
            "- momentum: 0.9\n",
            "- n_iter_no_change: 10\n",
            "- nesterovs_momentum: True\n",
            "- power_t: 0.5\n",
            "- random_state: None\n",
            "- shuffle: True\n",
            "- solver: adam\n",
            "- tol: 0.0001\n",
            "- validation_fraction: 0.1\n",
            "- verbose: False\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 72.66666666666667 %\n",
            "\n",
            "Average precision score: 65.58980855855856 %\n",
            "\n",
            "Average recall score: 76.35135135135135 %\n",
            "\n",
            "Average F1-score: 73.37662337662339 %\n",
            "\n",
            "Matthews correlation coefficient: 0.4552754527854117\n",
            "\n",
            "Mean absolute error: 0.2733333333333333\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.75      0.69      0.72       152\n",
            "           1       0.71      0.76      0.73       148\n",
            "\n",
            "    accuracy                           0.73       300\n",
            "   macro avg       0.73      0.73      0.73       300\n",
            "weighted avg       0.73      0.73      0.73       300\n",
            "\n",
            "\n",
            "\n",
            "\t\t=============== Tf-idf ===============\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Multi-layer Perceptron\n",
            "- activation: logistic\n",
            "- alpha: 0.0001\n",
            "- batch_size: auto\n",
            "- beta_1: 0.9\n",
            "- beta_2: 0.999\n",
            "- early_stopping: False\n",
            "- epsilon: 1e-08\n",
            "- hidden_layer_sizes: (100,)\n",
            "- learning_rate: constant\n",
            "- learning_rate_init: 0.001\n",
            "- max_fun: 15000\n",
            "- max_iter: 200\n",
            "- momentum: 0.9\n",
            "- n_iter_no_change: 10\n",
            "- nesterovs_momentum: True\n",
            "- power_t: 0.5\n",
            "- random_state: None\n",
            "- shuffle: True\n",
            "- solver: adam\n",
            "- tol: 0.0001\n",
            "- validation_fraction: 0.1\n",
            "- verbose: False\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 76.33333333333333 %\n",
            "\n",
            "Average precision score: 69.48618817492988 %\n",
            "\n",
            "Average recall score: 77.02702702702703 %\n",
            "\n",
            "Average F1-score: 76.25418060200668 %\n",
            "\n",
            "Matthews correlation coefficient: 0.5268140916254531\n",
            "\n",
            "Mean absolute error: 0.23666666666666666\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.76      0.76       152\n",
            "           1       0.75      0.77      0.76       148\n",
            "\n",
            "    accuracy                           0.76       300\n",
            "   macro avg       0.76      0.76      0.76       300\n",
            "weighted avg       0.76      0.76      0.76       300\n",
            "\n",
            "\n",
            "\n",
            "\t\t=============== Word2Vec ===============\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Multi-layer Perceptron\n",
            "- activation: relu\n",
            "- alpha: 0.0001\n",
            "- batch_size: auto\n",
            "- beta_1: 0.9\n",
            "- beta_2: 0.999\n",
            "- early_stopping: False\n",
            "- epsilon: 1e-08\n",
            "- hidden_layer_sizes: (100,)\n",
            "- learning_rate: constant\n",
            "- learning_rate_init: 0.001\n",
            "- max_fun: 15000\n",
            "- max_iter: 200\n",
            "- momentum: 0.9\n",
            "- n_iter_no_change: 10\n",
            "- nesterovs_momentum: True\n",
            "- power_t: 0.5\n",
            "- random_state: None\n",
            "- shuffle: True\n",
            "- solver: adam\n",
            "- tol: 0.0001\n",
            "- validation_fraction: 0.1\n",
            "- verbose: False\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 49.333333333333336 %\n",
            "\n",
            "Average precision score: 49.333333333333336 %\n",
            "\n",
            "Average recall score: 100.0 %\n",
            "\n",
            "Average F1-score: 66.07142857142857 %\n",
            "\n",
            "Matthews correlation coefficient: 0.0\n",
            "\n",
            "Mean absolute error: 0.5066666666666667\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00       152\n",
            "           1       0.49      1.00      0.66       148\n",
            "\n",
            "    accuracy                           0.49       300\n",
            "   macro avg       0.25      0.50      0.33       300\n",
            "weighted avg       0.24      0.49      0.33       300\n",
            "\n",
            "\n",
            "\t--------------- K-FOLD ---------------\n",
            "\n",
            "- n_folds: 10\n",
            "\n",
            "\t\t=============== BoW ===============\n",
            "\n",
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Multi-layer Perceptron\n",
            "- activation: relu\n",
            "- alpha: 0.0001\n",
            "- batch_size: auto\n",
            "- beta_1: 0.9\n",
            "- beta_2: 0.999\n",
            "- early_stopping: False\n",
            "- epsilon: 1e-08\n",
            "- hidden_layer_sizes: (100,)\n",
            "- learning_rate: constant\n",
            "- learning_rate_init: 0.001\n",
            "- max_fun: 15000\n",
            "- max_iter: 200\n",
            "- momentum: 0.9\n",
            "- n_iter_no_change: 10\n",
            "- nesterovs_momentum: True\n",
            "- power_t: 0.5\n",
            "- random_state: None\n",
            "- shuffle: True\n",
            "- solver: adam\n",
            "- tol: 0.0001\n",
            "- validation_fraction: 0.1\n",
            "- verbose: False\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 78.0 %\n",
            "\n",
            "Average precision score: 71.77777777777779 %\n",
            "\n",
            "Average recall score: 78.4 %\n",
            "\n",
            "Average F1-score: 78.08764940239044 %\n",
            "\n",
            "Matthews correlation coefficient: 0.5600179208602059\n",
            "\n",
            "Mean absolute error: 0.22\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.78      0.78       500\n",
            "           1       0.78      0.78      0.78       500\n",
            "\n",
            "    accuracy                           0.78      1000\n",
            "   macro avg       0.78      0.78      0.78      1000\n",
            "weighted avg       0.78      0.78      0.78      1000\n",
            "\n",
            "\n",
            "[#2]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Multi-layer Perceptron\n",
            "- activation: logistic\n",
            "- alpha: 0.0001\n",
            "- batch_size: auto\n",
            "- beta_1: 0.9\n",
            "- beta_2: 0.999\n",
            "- early_stopping: False\n",
            "- epsilon: 1e-08\n",
            "- hidden_layer_sizes: (100,)\n",
            "- learning_rate: constant\n",
            "- learning_rate_init: 0.001\n",
            "- max_fun: 15000\n",
            "- max_iter: 200\n",
            "- momentum: 0.9\n",
            "- n_iter_no_change: 10\n",
            "- nesterovs_momentum: True\n",
            "- power_t: 0.5\n",
            "- random_state: None\n",
            "- shuffle: True\n",
            "- solver: adam\n",
            "- tol: 0.0001\n",
            "- validation_fraction: 0.1\n",
            "- verbose: False\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 78.0 %\n",
            "\n",
            "Average precision score: 71.90322580645162 %\n",
            "\n",
            "Average recall score: 77.60000000000001 %\n",
            "\n",
            "Average F1-score: 77.91164658634537 %\n",
            "\n",
            "Matthews correlation coefficient: 0.5600179208602059\n",
            "\n",
            "Mean absolute error: 0.22\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.78      0.78       500\n",
            "           1       0.78      0.78      0.78       500\n",
            "\n",
            "    accuracy                           0.78      1000\n",
            "   macro avg       0.78      0.78      0.78      1000\n",
            "weighted avg       0.78      0.78      0.78      1000\n",
            "\n",
            "\n",
            "\n",
            "\t\t=============== Tf-idf ===============\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Multi-layer Perceptron\n",
            "- activation: logistic\n",
            "- alpha: 0.0001\n",
            "- batch_size: auto\n",
            "- beta_1: 0.9\n",
            "- beta_2: 0.999\n",
            "- early_stopping: False\n",
            "- epsilon: 1e-08\n",
            "- hidden_layer_sizes: (100,)\n",
            "- learning_rate: constant\n",
            "- learning_rate_init: 0.001\n",
            "- max_fun: 15000\n",
            "- max_iter: 200\n",
            "- momentum: 0.9\n",
            "- n_iter_no_change: 10\n",
            "- nesterovs_momentum: True\n",
            "- power_t: 0.5\n",
            "- random_state: None\n",
            "- shuffle: True\n",
            "- solver: adam\n",
            "- tol: 0.0001\n",
            "- validation_fraction: 0.1\n",
            "- verbose: False\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 77.10000000000001 %\n",
            "\n",
            "Average precision score: 70.79270216962526 %\n",
            "\n",
            "Average recall score: 77.8 %\n",
            "\n",
            "Average F1-score: 77.25918570009931 %\n",
            "\n",
            "Matthews correlation coefficient: 0.5420531238093276\n",
            "\n",
            "Mean absolute error: 0.229\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAATgAAAEGCAYAAADxD4m3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAaoUlEQVR4nO3de7wVdb3/8dd7b66CCAopISoBiohHQESNJKVOec3odLzQMSs7lGJiJ/PSKS2S0m509KcWiaHlJdQsLNPMvJsXQETBVEpJSUUUVC6iwOf3x5ptG2WvPcPea681s99PH/NgrVmzZj4LHr4f3+9cvl9FBGZmRVRX7QLMzCrFAWdmheWAM7PCcsCZWWE54MyssDpUu4DG1LFrqNM21S7DMhg+ZMdql2AZLFnyDC8vX66W7KO+x84R69em2jbWvnRLRBzckuO1RG0FXKdt6DzsuGqXYRnceef51S7BMvjgmNEt3kesX0vn3Y5Kte0b8y/q3eIDtkBNBZyZ5YFA+Ti75YAzs2wE1NVXu4pUHHBmlp1adBqvzTjgzCwjd1HNrMjcgjOzQhJuwZlZUcktODMrMF9FNbNi8kUGMysq4S6qmRWYW3BmVkzuoppZUQmo90UGMysqn4Mzs2JyF9XMiswtODMrLLfgzKyQ5Ee1zKzI/KiWmRWTLzKYWZG5i2pmheTx4MysuNxFNbMi80UGMyssn4Mzs0JSfrqo+ajSzGpLw82+zS1ld6Eukh6U9IikhZK+layfKelpSfOTZXiyXpIukLRY0gJJI5sr0y04M8tMrdNFXQeMi4hVkjoC90j6Q/LZVyPiundsfwgwOFn2BS5J/mySA87MMimNWN7ygIuIAFYlbzsmS5T5ypHAFcn37pfUU1LfiHi+qS+4i2pm2UioLt0C9JY0p9EycdNdqV7SfGAZcGtEPJB8NDXphk6T1DlZ1w94ttHXn0vWNcktODPLLEMLbnlEjGrqw4jYAAyX1BO4QdIw4CzgBaATMB04A5iyJXW6BWdmmUlKtaQVESuB24GDI+L5KFkH/BwYnWy2FOjf6Gs7Juua5IAzs8xaI+Ak9UlabkjqCvw78FdJfZN1Aj4OPJZ8ZTbw6eRq6n7Aq+XOv4G7qGaWlZKl5foCl0uqp9TYmhURv5P0Z0l9kqPMB76YbH8TcCiwGFgDfLa5AzjgzCwTka372ZSIWACM2Mz6cU1sH8CkLMdwwJlZZnV1+Ti75YAzs8xa6UbfinPAmVk2rXcOruIccGaWmVtwZlZIrXWRoS044Mwss+QxrJrngDOzbOQuqpkVmAPOzArLAWdmheSLDGZWbPnINwecmWUkP6plZgXmLqqZFVc+8s0B11KdO3Xg9xedSOeOHajvUMfs2x/lvBl/ZOzeg5gy6TDq6upYvWYdJ039FU8vfZmTjh7LcUeMZsOGjSxfuYovfWcWz764sto/o12ZfO6V3HrfQnr32pq7rjwLgNm3Pcz3Z/yBJ595kVtmfIXhu+8EwFvrN/Dl71zNo088y/oNGznqkH2YfPxHqll+TchLC66iHWlJB0t6IpnH8MxKHqta1r25niNP+SkHfGYaY4+fxof23Y1Re+zED0/7BBO/dTVjPzON6259mNM+82EAFjy1lHEn/B8fOP5HzL79Ub456bAq/4L255jD9uWaaSdusm7IwL78/LsnsP/wgZusn33bw7z51nruvPIsbp35Va74zX384/mX27LcmpN2NN9aCMGKBVwySudFlOYyHAocK2lopY5XTavXvglAxw71dOxQR0QQBFt3K00G1KN7F15Y/ioA98z7G2vXvQXAQwuX0K9Pz+oU3Y7tP2IQPXtstcm6XXfZgUE7b/+ubSWxZu061q/fwBvr3qJjx3q23qpLW5Vas/IScJXsoo4GFkfE3wEkXUNpXsNFFTxmVdTViTsuO5UB/bZjxq/vY+6iZ5l83nXM+sEJrF33Fq+vXsdHJl74ru8dd8Robr3/r1Wo2NI6Ytxwbr77UfY84uusfeMtpkweT69tulW7rKrLy7OoleyipprDUNLEhjkTY/2aCpZTORs3BmM/M409xp/LyKH92X3A9px49AEcddoMho2fylU3PcS5pxyxyXeO+shIhg/ZkQuvuqMqNVs68xYuoa5OLLjxXB66/hwuufp2nlm6vNplVV1eWnBVv5klIqZHxKiIGKUOWzX/hRr22qo3uHve3/jw/kMYNui9zF1UyvcbbnuE0cN2eXu7D44azP8cP44Jp/+cN9/aUKVqLY1f/3EO4/bbnY4d6umz7daM3nMAjzz+j2qXVV1ywMEWzGGYR9v17EaP7qVzMl06deCgfQbz5DPL6NGtCwP79wbgwH0G8+SSZQDsOfi9TDv9P5hwxkyWr1xdtbotnX479OKeuU8BsHrtOuYufIZBu7z7XF17IkBKt1RbJc/BPQQMljSAUrAdA0yo4PGqYoftenDx14+mvq6Oujpxw58f4Zb7Hmfy+ddxxdRPs3FjsPL1tZz83VkATJl0ON26dmLmuccB8NyLK5hwxswq/oL25wtnz+TeeYt5ZeUq9vrYNzj984fSs8dWfO1H1/HyylVM+MpPGbZrP2b9+CQ+9x9jmXzulRww4TtEBMccth97DHrXmZZ2pjZaZ2moNBNXhXYuHQr8GKgHLouIqeW2r+u2Q3QedlzF6rHWt+zO86tdgmXwwTGjmTd3TovSqcsOu8bOx7/7otnmPPm9g+dGxKiWHK8lKnqjb0TcRGmyVjMrihrpfqbhJxnMLBNRujUqDxxwZpaZW3BmVlh5ucjggDOzbHwOzsyKSsgDXppZceWlBZePGDazmtIaj2pJ6iLpQUmPSFoo6VvJ+gGSHkiGWfuVpE7J+s7J+8XJ57s0V6cDzsyySfmYVopW3jpgXETsBQwHDpa0H3A+MC0iBgErgBOS7U8AViTrpyXbleWAM7NMSs+itrwFFyWrkrcdkyWAccB1yfrLgY8nr49M3pN8/iE1cxAHnJlllqEF17thOLRkmbjpflQvaT6wDLgV+BuwMiLWJ5s0Hmbt7SHYks9fBbYrV6cvMphZZhmeZFhe7lnUiNgADJfUE7gBGNIK5b3NLTgzy6YC48FFxErgdmB/oKekhsZX42HW3h6CLfl8G6DsBBkOODPLpLXGg5PUJ2m5Iakr8O/A45SC7pPJZscDv01ez07ek3z+52hmOCR3Uc0so1YbD64vcHkyQVUdMCsifidpEXCNpHOBh4EZyfYzgF9IWgy8QmmMybIccGaWWWvkW0QsAEZsZv3fKU1a9c71bwD/meUYDjgzy0YeLsnMCqrhPrg8cMCZWWYOODMrrJzkmwPOzLJzC87MiskDXppZUZUGvMxHwjngzCyzupw04RxwZpZZTvLNAWdm2Ui+yGBmBZaTU3BNB5ykCymNrrlZEXFKRSoys5pXhIsMc9qsCjPLDVG6kpoHTQZcRFze+L2krSJiTeVLMrNal5MGXPMDXkraPxmf6a/J+70kXVzxysysNqUczbcWLkSkGdH3x8BHSYYGjohHgLGVLMrMalsrTRtYcamuokbEs+9I4w2VKcfMap0o1o2+z0p6PxCSOgKTKY2bbmbtVF6uoqbpon4RmERpTsJ/UpqBelIlizKz2pW2e1oLjbxmW3ARsRz4VBvUYmY5kZcuapqrqO+TdKOklyQtk/RbSe9ri+LMrDYp5VJtabqoVwGzKE3x9V7gWuDqShZlZrWtSLeJbBURv4iI9cnyS6BLpQszs9pUuoqabqm2cs+ibpu8/IOkM4FrKD2bejRwUxvUZma1SMUY8HIupUBr+CVfaPRZAGdVqigzq2210P1Mo9yzqAPashAzy4eGLmoepHqSQdIwYCiNzr1FxBWVKsrMalvuW3ANJJ0DHEgp4G4CDgHuARxwZu1UPuIt3VXUTwIfAl6IiM8CewHbVLQqM6tZEtTXKdVSbWm6qGsjYqOk9ZJ6AMuA/hWuy8xqWF66qGlacHMk9QR+RunK6jzgLxWtysxqWms8iyqpv6TbJS2StFDS5GT9NyUtlTQ/WQ5t9J2zJC2W9ISkjzZXZ5pnUU9KXv5E0s1Aj4hY0Nz3zKyYhFrrWdT1wFciYp6krYG5km5NPpsWET/Y5LjSUOAYYA9KT1X9SdKuEdHk8G3lbvQdWe6ziJiX4YeYWVG00kghEfE88Hzy+nVJj1MatagpRwLXRMQ64GlJi4HRlOlRlmvB/bBcbcC4Mp9vkRFDduTee7/f2ru1Cuq1z8nVLsEyWPfEP1plPxnOwfWW1HgCq+kRMX0z+9sFGAE8AIwBTpb0aUqTX30lIlZQCr/7G33tOcoHYtkbfQ9K+QPMrB0RUJ8+4JZHxKiy+5O6A9cDp0bEa5IuAb5NqSH1bUqNrc9tSa2e+NnMMmutO0CSUcKvB66MiF8DRMSLjT7/GfC75O1SNr2DY8dkXdN1tk6ZZtaetMZoIir1c2cAj0fEjxqt79tos/HAY8nr2cAxkjpLGgAMBh4sdwy34Mwsk9ItIK3ShBsDHAc8Kml+su5rwLGShlPqoj5DMtBHRCyUNAtYROkK7KRyV1Ah3aNaojRk+fsiYoqknYAdIqJscppZcbVGFzUi7mHzT301ORxbREwFpqY9Rpou6sXA/sCxyfvXgYvSHsDMiqcwk84A+0bESEkPA0TECkmdKlyXmdUoAR1qIb1SSBNwb0mqp9QfRlIfYGNFqzKzmpaTfEsVcBcANwDvkTSV0ugiX69oVWZWs6RWe1Sr4tI8i3qlpLmUhkwS8PGI8Mz2Zu1YTvIt1VXUnYA1wI2N10VE6zzzYWa5UwNDvaWSpov6e/41+UwXYADwBKUn+s2snRHUxGCWaaTpou7Z+H0yyshJTWxuZkVXI3OeppH5SYZk7KZ9K1GMmeWDcjIrQ5pzcP/T6G0dMBL4Z8UqMrOaVrRpA7du9Ho9pXNy11emHDPLg0IEXHKD79YRcVob1WNmOZCXSWfKDVneISLWSxrTlgWZWW0rTRtY7SrSKdeCe5DS+bb5kmYD1wKrGz5sGJzOzNqfwjzJQOnet5cpzcHQcD9cAA44s3aoKBcZ3pNcQX2MfwVbg6hoVWZW03LSgCsbcPVAdzY/IJ0DzqzdEnUFuA/u+YiY0maVmFkuiGK04HLyE8ysTQk65OQkXLmA+1CbVWFmuVGIFlxEvNKWhZhZfhTpNhEzs03kJN8ccGaWjcjPjPEOODPLRu6imllBlZ5kcMCZWUHlI94ccGa2BXLSgHPAmVlWyv94cGZmm+OrqGZWaHm5yJCXIDazWqHSkOVplrK7kfpLul3SIkkLJU1O1m8r6VZJTyV/9krWS9IFkhZLWpBMYVqWA87MMmnooqZZmrEe+EpEDAX2AyZJGgqcCdwWEYOB25L3AIcAg5NlInBJcwdwwJlZZq3RgouI5yNiXvL6deBxoB9wJHB5stnlwMeT10cCV0TJ/UBPSX3LHcMBZ2aZKeUC9JY0p9EycbP7k3YBRgAPANtHxPPJRy8A2yev+wHPNvrac8m6Jvkig5llIqA+/UWG5RExquz+pO6U5lo+NSJea9zyi4iQtMUjiLsFZ2aZSemW5vejjpTC7cpGM/W92ND1TP5clqxfCvRv9PUdk3VNcsCZWUZK/V/ZvZSaajOAxyPiR40+mg0cn7w+Hvhto/WfTq6m7ge82qgru1nuoppZZq10G9wY4DjgUUnzk3VfA84DZkk6AVgCHJV8dhNwKLAYWAN8trkDOODMLJPSbSItT7iIuIemn9t/15QJERHApCzHcMCZWTYpz6/VAgecmWWWl0e1HHBmlklpwMtqV5GOA87MMmvuCmmtcMCZWWY56aE64Frq5Cm/5JZ7HqN3r635y6/+F4Df/Gke50+/iSeeeZHbZp7GiKE7A/DKylUcf+YMHl60hGMP34/vn35UuV1bhXTu1IHfTz+Vzh07UN+hntm3Pcx5029i7D67MuWU8dTVidVr1nHSt37B088tp/8Ovbjw7P+id8/urHhtDV84+3L+uWxltX9GVeWlBVexG30lXSZpmaTHKnWMWnDs4ftx3QWbXrnefeB7ueJ7/837RwzcZH3nzh352hcPZ8rk8W1Zor3DujfXc+SJF3DAp85j7ITv8qH9hzJq2C788IxjmPiNmYz91Hlcd8scTjvhYACmTB7PNb9/kA9M+C7fu/QPnD3pY1X+BdXVcA4uzVJtlXySYSZwcAX3XxPGjBxErx5bbbJutwE7MHiX7d+1bbeundl/+EC6dOrYVuVZE1avfROAjh3q6dihnoggCLbu1gWAHt278sJLrwKw2/v6cvecJwC4e86THDJ2z+oUXSsk6lIu1VaxLmpE3JWMEGBWc+rqxB2/OIMBO/ZhxrV3MXfhEiafexWzfnwSa9e9yeur3+Ajn/shAAufXMrhBw3np9fcweEH7UWP7l3ptU03Vry6usq/onqqH13pVP1ZVEkTG4ZSeWn5S9Uux9qJjRuDsZ86jz0O+zoj99iZ3Qf25cQJB3HUqRcz7PBvcNWN93PuqZ8A4Bv/dwNjRg7izl+ewZiRg1j64go2bNhY5V9QPQ3zorbrFlxaETEdmA6w996jtnhYFLMt8dqqtdw990k+vP9Qhg3ux9yFSwC44dZ5XHvBSQC8sPxVPn36pQB069qJIw4azmur1lat5lpQ/ehKp+otOLO2tl3P7vTo3hWALp07ctDoITz5zIv06N6VgTu9B4AD9y2tA9h2m25vj0775c98lCtvvL86hdeSDCNeVlPVW3B5d8L//px75z7FyytXscdhX+fMiYfSq0c3zvjBtSxfsYqjv/wT9ty1H9dfeDIA//axs3l99Ru89dZ6brpzAddfOIkh7ys76rK1sh169+Dibx5HfV0ddXXihj/N45Z7HmPy1Ku44vzPs3HjRla+vpaTv/1LAD6w92DOnvQxIuC+hxfz1e/NqvIvqL5a6H6modID+hXYsXQ1cCDQG3gROCciZpT7zt57j4p7H5hTkXqsMnrtc3K1S7AM1j0xi41rlrUonXbfc0Rc8ds7Um07emDPuc2N6FtJlbyKemyl9m1mVZaPBpy7qGaWTen0Wj4SzgFnZtl4PDgzK7Kc5JsDzsyyan5S51rhgDOzzHKSbw44M8umRu7hTcUBZ2bZ5SThHHBmlplvEzGzwvI5ODMrJt8HZ2ZF5i6qmRWScAvOzAosJ/nmgDOzLZCThHPAmVlmeRnw0kOWm1lmrTVi+ebmT5b0TUlLJc1PlkMbfXaWpMWSnpD00eb274Azs+xab06GmWx+/uRpETE8WW4CkDQUOAbYI/nOxZLqy+3cAWdmmTQMeJnmv+ZExF3AKykPfSRwTUSsi4ingcXA6HJfcMCZWTbJjb5pFqB3w7zHyTIx5VFOlrQg6cL2Stb1A55ttM1zybomOeDMLLMMPdTlETGq0TI9xe4vAQYCw4HngR9uaZ2+impmGVV2wMuIePHtI0k/A36XvF0K9G+06Y7Juia5BWdmmWXoom7BvtV4ouDxQMMV1tnAMZI6SxoADAYeLLcvt+DMLJPWHPCy8fzJkp4DzgEOlDQcCOAZ4AsAEbFQ0ixgEbAemBQRG8rt3wFnZtm1UsI1MX9ykxPER8RUYGra/TvgzCwzjyZiZoWVkye1HHBmlpGgzgFnZsWVj4RzwJlZJh7w0swKLSf55oAzs+zcgjOzwqrko1qtyQFnZpnlI94ccGaWUUueM21rDjgzy8xPMphZceUj3xxwZpZdTvLNAWdmWSk30wY64Mwskzw9yeARfc2ssNyCM7PM8tKCc8CZWWa+TcTMisk3+ppZUeXpIoMDzswycxfVzArLLTgzK6yc5JsDzsy2QE4SzgFnZpkIcvOoliKi2jW8TdJLwJJq11EBvYHl1S7CMinqv9nOEdGnJTuQdDOlv580lkfEwS05XkvUVMAVlaQ5ETGq2nVYev43KwY/i2pmheWAM7PCcsC1jenVLsAy879ZAfgcnJkVlltwZlZYDjgzKywHXAVJOljSE5IWSzqz2vVY8yRdJmmZpMeqXYu1nAOuQiTVAxcBhwBDgWMlDa1uVZbCTKBqN6Za63LAVc5oYHFE/D0i3gSuAY6sck3WjIi4C3il2nVY63DAVU4/4NlG759L1plZG3HAmVlhOeAqZynQv9H7HZN1ZtZGHHCV8xAwWNIASZ2AY4DZVa7JrF1xwFVIRKwHTgZuAR4HZkXEwupWZc2RdDXwF2A3Sc9JOqHaNdmW86NaZlZYbsGZWWE54MyssBxwZlZYDjgzKywHnJkVlgMuRyRtkDRf0mOSrpW0VQv2NVPSJ5PXl5YbCEDSgZLevwXHeEbSu2Zfamr9O7ZZlfFY35R0WtYardgccPmyNiKGR8Qw4E3gi40/lLRF89xGxOcjYlGZTQ4EMgecWbU54PLrbmBQ0rq6W9JsYJGkeknfl/SQpAWSvgCgkv+XjE/3J+A9DTuSdIekUcnrgyXNk/SIpNsk7UIpSL+ctB4PkNRH0vXJMR6SNCb57naS/ihpoaRLSTH/uaTfSJqbfGfiOz6blqy/TVKfZN1ASTcn37lb0pDW+Mu0YvLM9jmUtNQOAW5OVo0EhkXE00lIvBoR+0jqDNwr6Y/ACGA3SmPTbQ8sAi57x377AD8Dxib72jYiXpH0E2BVRPwg2e4qYFpE3CNpJ0pPa+wOnAPcExFTJB0GpHkK4HPJMboCD0m6PiJeBroBcyLiy5LOTvZ9MqXJYL4YEU9J2he4GBi3BX+N1g444PKlq6T5yeu7gRmUuo4PRsTTyfqPAP/WcH4N2AYYDIwFro6IDcA/Jf15M/vfD7irYV8R0dS4aB8GhkpvN9B6SOqeHOMTyXd/L2lFit90iqTxyev+Sa0vAxuBXyXrfwn8OjnG+4FrGx27c4pjWDvlgMuXtRExvPGK5H/01Y1XAV+KiFvesd2hrVhHHbBfRLyxmVpSk3QgpbDcPyLWSLoD6NLE5pEcd+U7/w7MmuJzcMVzC3CipI4AknaV1A24Czg6OUfXFzhoM9+9HxgraUDy3W2T9a8DWzfa7o/AlxreSGoInLuACcm6Q4BezdS6DbAiCbchlFqQDeqAhlboBEpd39eApyX9Z3IMSdqrmWNYO+aAK55LKZ1fm5dMnPJTSi31G4Cnks+uoDRixiYi4iVgIqXu4CP8q4t4IzC+4SIDcAowKrmIsYh/Xc39FqWAXEipq/qPZmq9Gegg6XHgPEoB22A1MDr5DeOAKcn6TwEnJPUtxMPAWxkeTcTMCsstODMrLAecmRWWA87MCssBZ2aF5YAzs8JywJlZYTngzKyw/j8NgJ4BVw7g0QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.76      0.77       500\n",
            "           1       0.77      0.78      0.77       500\n",
            "\n",
            "    accuracy                           0.77      1000\n",
            "   macro avg       0.77      0.77      0.77      1000\n",
            "weighted avg       0.77      0.77      0.77      1000\n",
            "\n",
            "\n",
            "[#2]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Multi-layer Perceptron\n",
            "- activation: relu\n",
            "- alpha: 0.0001\n",
            "- batch_size: auto\n",
            "- beta_1: 0.9\n",
            "- beta_2: 0.999\n",
            "- early_stopping: False\n",
            "- epsilon: 1e-08\n",
            "- hidden_layer_sizes: (100,)\n",
            "- learning_rate: constant\n",
            "- learning_rate_init: 0.001\n",
            "- max_fun: 15000\n",
            "- max_iter: 200\n",
            "- momentum: 0.9\n",
            "- n_iter_no_change: 10\n",
            "- nesterovs_momentum: True\n",
            "- power_t: 0.5\n",
            "- random_state: None\n",
            "- shuffle: True\n",
            "- solver: adam\n",
            "- tol: 0.0001\n",
            "- validation_fraction: 0.1\n",
            "- verbose: False\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 77.0 %\n",
            "\n",
            "Average precision score: 70.81927710843374 %\n",
            "\n",
            "Average recall score: 76.8 %\n",
            "\n",
            "Average F1-score: 76.95390781563127 %\n",
            "\n",
            "Matthews correlation coefficient: 0.5400043200518407\n",
            "\n",
            "Mean absolute error: 0.23\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.77      0.77       500\n",
            "           1       0.77      0.77      0.77       500\n",
            "\n",
            "    accuracy                           0.77      1000\n",
            "   macro avg       0.77      0.77      0.77      1000\n",
            "weighted avg       0.77      0.77      0.77      1000\n",
            "\n",
            "\n",
            "[#3]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Multi-layer Perceptron\n",
            "- activation: identity\n",
            "- alpha: 0.0001\n",
            "- batch_size: auto\n",
            "- beta_1: 0.9\n",
            "- beta_2: 0.999\n",
            "- early_stopping: False\n",
            "- epsilon: 1e-08\n",
            "- hidden_layer_sizes: (100,)\n",
            "- learning_rate: constant\n",
            "- learning_rate_init: 0.001\n",
            "- max_fun: 15000\n",
            "- max_iter: 200\n",
            "- momentum: 0.9\n",
            "- n_iter_no_change: 10\n",
            "- nesterovs_momentum: True\n",
            "- power_t: 0.5\n",
            "- random_state: None\n",
            "- shuffle: True\n",
            "- solver: adam\n",
            "- tol: 0.0001\n",
            "- validation_fraction: 0.1\n",
            "- verbose: False\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 76.9 %\n",
            "\n",
            "Average precision score: 70.55815324165029 %\n",
            "\n",
            "Average recall score: 77.8 %\n",
            "\n",
            "Average F1-score: 77.10604558969277 %\n",
            "\n",
            "Matthews correlation coefficient: 0.538087177184628\n",
            "\n",
            "Mean absolute error: 0.231\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.76      0.77       500\n",
            "           1       0.76      0.78      0.77       500\n",
            "\n",
            "    accuracy                           0.77      1000\n",
            "   macro avg       0.77      0.77      0.77      1000\n",
            "weighted avg       0.77      0.77      0.77      1000\n",
            "\n",
            "\n",
            "\n",
            "\t\t=============== Word2Vec ===============\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Multi-layer Perceptron\n",
            "- activation: relu\n",
            "- alpha: 0.0001\n",
            "- batch_size: auto\n",
            "- beta_1: 0.9\n",
            "- beta_2: 0.999\n",
            "- early_stopping: False\n",
            "- epsilon: 1e-08\n",
            "- hidden_layer_sizes: (100,)\n",
            "- learning_rate: constant\n",
            "- learning_rate_init: 0.001\n",
            "- max_fun: 15000\n",
            "- max_iter: 200\n",
            "- momentum: 0.9\n",
            "- n_iter_no_change: 10\n",
            "- nesterovs_momentum: True\n",
            "- power_t: 0.5\n",
            "- random_state: None\n",
            "- shuffle: True\n",
            "- solver: adam\n",
            "- tol: 0.0001\n",
            "- validation_fraction: 0.1\n",
            "- verbose: False\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 63.4 %\n",
            "\n",
            "Average precision score: 58.53975409836065 %\n",
            "\n",
            "Average recall score: 62.2 %\n",
            "\n",
            "Average F1-score: 62.955465587044536 %\n",
            "\n",
            "Matthews correlation coefficient: 0.26807721735950096\n",
            "\n",
            "Mean absolute error: 0.366\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.63      0.65      0.64       500\n",
            "           1       0.64      0.62      0.63       500\n",
            "\n",
            "    accuracy                           0.63      1000\n",
            "   macro avg       0.63      0.63      0.63      1000\n",
            "weighted avg       0.63      0.63      0.63      1000\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Executes a series of learnings\n",
        "execute_a_series_of_multi_layer_perceptron_learnings(X=X, Y=Y)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1fc5f12d",
      "metadata": {
        "id": "1fc5f12d"
      },
      "source": [
        "## File: 'headlines_004.json'"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "22689972",
      "metadata": {
        "id": "22689972"
      },
      "source": [
        "Reads the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "059afe17",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "059afe17",
        "outputId": "0535ab7f-42f1-4fa7-ff01-ff987b8caeb1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LENGTH - headline: 11000; is_clickbait: 11000\n"
          ]
        }
      ],
      "source": [
        "# Reads the data from the file\n",
        "X, Y = get_json_content(i=3)\n",
        "\n",
        "# Prints the dataset size\n",
        "print(f\"LENGTH - headline: {len(X)}; is_clickbait: {len(Y)}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "27ece71a",
      "metadata": {
        "id": "27ece71a"
      },
      "source": [
        "Executes a series of learnings."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "98877a1a",
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "98877a1a",
        "outputId": "b4310817-24f9-4631-ddde-c0ffbb96118f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\t--------------- TRAIN-TEST SPLIT ---------------\n",
            "\n",
            "- test_size: 0.3\n",
            "\n",
            "\t\t=============== BoW ===============\n",
            "\n",
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Multi-layer Perceptron\n",
            "- activation: identity\n",
            "- alpha: 0.0001\n",
            "- batch_size: auto\n",
            "- beta_1: 0.9\n",
            "- beta_2: 0.999\n",
            "- early_stopping: False\n",
            "- epsilon: 1e-08\n",
            "- hidden_layer_sizes: (100,)\n",
            "- learning_rate: constant\n",
            "- learning_rate_init: 0.001\n",
            "- max_fun: 15000\n",
            "- max_iter: 200\n",
            "- momentum: 0.9\n",
            "- n_iter_no_change: 10\n",
            "- nesterovs_momentum: True\n",
            "- power_t: 0.5\n",
            "- random_state: None\n",
            "- shuffle: True\n",
            "- solver: adam\n",
            "- tol: 0.0001\n",
            "- validation_fraction: 0.1\n",
            "- verbose: False\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 98.81818181818181 %\n",
            "\n",
            "Average precision score: 98.12514886859866 %\n",
            "\n",
            "Average recall score: 98.875 %\n",
            "\n",
            "Average F1-score: 98.7823915079613 %\n",
            "\n",
            "Matthews correlation coefficient: 0.9763448329507854\n",
            "\n",
            "Mean absolute error: 0.011818181818181818\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.99      0.99      1700\n",
            "           1       0.99      0.99      0.99      1600\n",
            "\n",
            "    accuracy                           0.99      3300\n",
            "   macro avg       0.99      0.99      0.99      3300\n",
            "weighted avg       0.99      0.99      0.99      3300\n",
            "\n",
            "\n",
            "[#2]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Multi-layer Perceptron\n",
            "- activation: tanh\n",
            "- alpha: 0.0001\n",
            "- batch_size: auto\n",
            "- beta_1: 0.9\n",
            "- beta_2: 0.999\n",
            "- early_stopping: False\n",
            "- epsilon: 1e-08\n",
            "- hidden_layer_sizes: (100,)\n",
            "- learning_rate: constant\n",
            "- learning_rate_init: 0.001\n",
            "- max_fun: 15000\n",
            "- max_iter: 200\n",
            "- momentum: 0.9\n",
            "- n_iter_no_change: 10\n",
            "- nesterovs_momentum: True\n",
            "- power_t: 0.5\n",
            "- random_state: None\n",
            "- shuffle: True\n",
            "- solver: adam\n",
            "- tol: 0.0001\n",
            "- validation_fraction: 0.1\n",
            "- verbose: False\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 98.81818181818181 %\n",
            "\n",
            "Average precision score: 98.01098978612944 %\n",
            "\n",
            "Average recall score: 99.125 %\n",
            "\n",
            "Average F1-score: 98.78542510121456 %\n",
            "\n",
            "Matthews correlation coefficient: 0.9763684230346736\n",
            "\n",
            "Mean absolute error: 0.011818181818181818\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.99      0.99      1700\n",
            "           1       0.98      0.99      0.99      1600\n",
            "\n",
            "    accuracy                           0.99      3300\n",
            "   macro avg       0.99      0.99      0.99      3300\n",
            "weighted avg       0.99      0.99      0.99      3300\n",
            "\n",
            "\n",
            "\n",
            "\t\t=============== Tf-idf ===============\n",
            "\n",
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Multi-layer Perceptron\n",
            "- activation: relu\n",
            "- alpha: 0.0001\n",
            "- batch_size: auto\n",
            "- beta_1: 0.9\n",
            "- beta_2: 0.999\n",
            "- early_stopping: False\n",
            "- epsilon: 1e-08\n",
            "- hidden_layer_sizes: (100,)\n",
            "- learning_rate: constant\n",
            "- learning_rate_init: 0.001\n",
            "- max_fun: 15000\n",
            "- max_iter: 200\n",
            "- momentum: 0.9\n",
            "- n_iter_no_change: 10\n",
            "- nesterovs_momentum: True\n",
            "- power_t: 0.5\n",
            "- random_state: None\n",
            "- shuffle: True\n",
            "- solver: adam\n",
            "- tol: 0.0001\n",
            "- validation_fraction: 0.1\n",
            "- verbose: False\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 99.06060606060606 %\n",
            "\n",
            "Average precision score: 98.32539637864714 %\n",
            "\n",
            "Average recall score: 99.5 %\n",
            "\n",
            "Average F1-score: 99.0357698289269 %\n",
            "\n",
            "Matthews correlation coefficient: 0.9812406369052838\n",
            "\n",
            "Mean absolute error: 0.009393939393939394\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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6Nf3MLyL26MyKmFnXIKCxBoKfFy03s9xKNbGBpFMkPSVpiqQxkrpL2lDSRElTJV0rafmUd4X0e2o63r9D99CRk82sPpUi+ElaDzgZGBwRWwKNwGHAb4FzI2JjYB5wXDrlOGBeSj835Wv/PXTkZDOrP9kU9SWbz68bsKKkbsBKwAyy8YTr0/ErgAPT/tD0m3R8L3Xg4WMxMzlL0lGSfp5+ry9pu/YWaGZdXylafhExHTgHeIUs6L1F9i7xmxHRlLJNI1s/iPT31XRuU8q/RrvvoYg8FwE7As2rLL0DXNjeAs2s68uxgFFvSZMLtuEfX0O9yFpzG5LNEr8yMKSz7qGYLzy2j4htJT0KEBHzmh9Amln9EdCt+N7m7IgY3MKxvYEXI2IWgKQbgZ2BnpK6pdZdX2B6yj+d7NPaaambvBowp313UVzL7yNJjWTv9iFpTWBRews0s66vREtXvgLsIGml9OxuL+Bp4C6yCVUAhgE3pf1x6Tfp+J1pLd92Kabl90fgr8Baks5KhZ7e3gLNrGuTSvN5W0RMlHQ98AjQBDwKjAT+AVwj6cyUNiqdMgq4StJUYC7ZyHC7FfNt758lPUwWlQUcGBHPdKRQM+vaSvWOc0SMAJb8TOgFYKlB1Yj4ADikNCUXN5np+sB84ObCtIh4pVSVMLOupcqn6itKMd3ef/DxQkbdyUZmngW2KGO9zKxKCap+otJiFNPt/XTh7zTby7dbyG5mta4LrMlbjKIWMCoUEY9I2r4clTGzrkE1sIpHMc/8vlfwswHYFijNvDhm1uXU09KVqxbsN5E9A7yhPNUxs66g5oNferl51Yj4QSfVx8y6gJqezLT58xJJO3dmhcysumVLV1a6Fh3XWsvvIbLne49JGgdcB7zXfDAibixz3cysStX0AkYFupN9PLwnH7/vF4CDn1kdqocBj7XSSO8UPg56zdr9MbGZdX010PBrNfg1AqvAMl/ocfAzq1uiocbf85sREWd0Wk3MrEsQtd/yq4HbM7OSE3SrgYd+rQW/vTqtFmbWZdR8yy8i5nZmRcys66iXV13MzD6hBmKfg5+Z5SNqY8HvWrgHM+tMyrq9xWxtXkrqKel6Sf+R9IykHSWtLmmCpOfS314pryT9UdJUSU+kuUXbzcHPzHLJvvAoTfADzgNujYjNgK2AZ4BTgTsiYgBwR/oNsB8wIG3DgYs7ch8OfmaWm4rcWr2GtBqwK2l1tohYEBFvki1kfkXKdgVwYNofClwZmQfJ1vddt7334OBnZrnlWLe3t6TJBdvwgstsCMwC/k/So5Iuk7QysHZEzEh5XgfWTvvrAa8WnD8tpbWLBzzMLCflmc9vdkQMbuFYN7KZo05Ka/iex8ddXAAiIiSV5XNat/zMLJfm0d5itjZMA6ZFxMT0+3qyYPhGc3c2/Z2Zjk8H+hWc3zeltYuDn5nlVooBj4h4HXhV0qYpaS/gaWAcMCylDQNuSvvjgKPTqO8OwFsF3ePc3O01s3xU0mnsTwL+LGl54AXgWLJG2VhJxwEvA4emvOOB/YGpwPyUt90c/Mwsl1K+5BwRjwHLeia41NwCERHAiSUq2sHPzPKr6QWMzMxa0vVDn4OfmeUkoNEtPzOrRzUQ+xz8zCwvoRro+Dr4mVlubvmZWd3JXnXp+tHPwc/M8pFbfmZWp7yGh5nVnWwy00rXouMc/MwsN4/2mlldqoFer4NfOV30lzu56m//BomBG/fhwp8fRfcVlqt0terS+T8cyr47bMLsN99jp+MuAuDHw3bn6C8MYs6b7wHwq1F3MGHicyzXrZFzv3cA22zSh0URnHrBLfzr8ZdYcYXluHzEofTv04uFi4LbHniWX176z0reVsXUQsuvbPP5SRotaaakKeUqo5q9NvNN/nTtPdx55Y944NqfsmjRIm68/eFKV6tujbntMQ4+9eql0i++/gF2HX4Juw6/hAkTnwNg2BcGAbDzNy7ioB9eyZkn7Lv4Q/7zx/6L7Y+5gN2GX8L2W6zP3ttt3Hk3USWan/kVs1Wzck5mejkwpIzXr3pNTQv54MOPaGpayPwPFrDOmqtVukp1699PvMy8t98vKu+mG6zJfY++AMDsN9/jrXc/YJtN+/D+hx9x/2MvAfBR00Ief24Gferxv2mRE5lW+4hw2YJfRNwLzC3X9atdn7V6ctJRe/HpA37GZvv9lB4rr8ieO2xe6WrZEr554Hbcf+kJnP/Doay2SncApjz/OkN22ozGhgbWX6cnW2+yLuut2eMT5/VYuTtDdtyEex55oRLVrrhSrN5WaRWfxl7S8OaVnWbNnlXp6pTMm2/PZ/y9T/LYTb/kmVvOYv4HC7h2/EOVrpYVGD1uEtscdR67DL+EN+a8y5kn7AvA1bc8ymuz3uauS4bzmxP346GnXmXhoo/X0GlsaGDU6Qfzp79O5OUZ8ypV/Yop8bq9FVPx4BcRIyNicEQMXrP3mpWuTsnc/dB/2KDPGvTutSrLdWvkgD224qEnXqx0tazArHnvsWhREBFc8Y+HGbRZtgriwkWL+OlFt7Lr8Es48mdjWG2V7jw/bc7i8/7w/QN4fvocLrnhwUpVveJK2fKT1JiWrvx7+r2hpImSpkq6Nk1xj6QV0u+p6Xj/jtxDxYNfreq7zupMfvJF5n+wgIjgnknPsumGa7d9onWatVdfZfH+F3fZnGdezBYJW3GF5VipezYqv/ugT9G0cBHPvpz1Sn769T3psXJ3Trvw1s6vcDUpbb/3u8AzBb9/C5wbERsD84DjUvpxwLyUfm7K125+1aVMBm/Zny/ttQ27H/VbGhsb+MymfRl20M6Vrlbduuz0g9l5q/6ssdpKTLn2e5x9+d18buv+fHqjdYgIXnnjTU75/c0A9O65Mjf87mssWhTMmP02x//mRgD69O7BD47ajWdfnsU9f/oWAJf+7SGuGv9Ixe6rUkrVpZXUF/gCcBbwPWXD6nsCR6QsVwC/AC4GhqZ9yJa5vECS0toe+ctu53ltX1gaA+wO9AbeAEZExKjWzhk0aHD8a+LkstTHyqPXniMqXQXL4cNHL2XRO691KHJt/ult4sqb7i4q73Yb9XwZmF2QNDIiRjb/kHQ98BtgVeAHwDHAg6l1h6R+wC0RsWV6bW5IRExLx54Hto+IwusXrWwtv4g4vFzXNrMKKz58zo6IZa3OhqQvAjMj4mFJu5eoZkVzt9fMcske55Wk27sz8CVJ+wPdgR7AeUBPSd0iognoC0xP+acD/YBpkroBqwFzlr5scTzgYWb5pPn8itlaExGnRUTfiOgPHAbcGRFHAncBB6dsw4Cb0v649Jt0/M72Pu8DBz8za4cyv+T8Y7LBj6nAGkDzWMEoYI2U/j3g1PYX4W6vmeWmki9aHhF3A3en/ReA7ZaR5wPgkFKV6eBnZrlV+ccbRXHwM7NcusJ3u8Vw8DOz/Gog+jn4mVlutTCZqYOfmeXmZ35mVn+8bq+Z1St3e82s7gi3/MysTtVA7HPwM7N2qIHo5+BnZrlV+/ocxXDwM7Pcun7oc/Azs/aogejn4GdmuZRwMtOKcvAzs3z8krOZ1asaiH0OfmaWV+knM60ET2NvZrmVYg0PSf0k3SXpaUlPSfpuSl9d0gRJz6W/vVK6JP1R0lRJT0jatiP34OBnZrkUu35HEW3DJuD7ETEQ2AE4UdJAsrU57oiIAcAdfLxWx37AgLQNJ1vIvN0c/MwsvxJEv4iYERGPpP13gGeA9YChwBUp2xXAgWl/KHBlZB4kW+Jy3fbegp/5mVluOV516S1pcsHvkRExcqnrSf2BbYCJwNoRMSMdeh1YO+2vB7xacNq0lDaDdnDwM7Pccox3zI6Iwa1fS6sANwD/LyLeLhxMiYiQ1O61eVvjbq+Z5SNoKHJr81LScmSB788RcWNKfqO5O5v+zkzp04F+Baf3TWnt4uBnZu3Q8Yd+ypp4o4BnIuL3BYfGAcPS/jDgpoL0o9Oo7w7AWwXd49zc7TWzXEo4menOwNeAJyU9ltJ+ApwNjJV0HPAycGg6Nh7YH5gKzAeO7UjhDn5mllspYl9E3N/KpfZaRv4ATixB0YCDn5m1Qw184OHgZ2b51cLnbQ5+ZpZb1w99Dn5mllMx3+12BQ5+ZpabJzM1s/rU9WOfg5+Z5VcDsc/Bz8zykpeuNLP6U8IvPCrK3/aaWV1yy8/McquFlp+Dn5nl5lddzKz++CVnM6tHtTLg4eBnZrm522tmdcktPzOrSzUQ+xz8zKwdaiD6OfiZWS6Cmvi8Tdm0+NVB0iyyBUtqTW9gdqUrYbnU6n+zDSJizY5cQNKtZP8+xZgdEUM6Ul65VFXwq1WSJre1cLNVF/83q33+ttfM6pKDn5nVJQe/zjGy0hWw3PzfrMb5mZ+Z1SW3/MysLjn4mVldcvArI0lDJD0raaqkUytdH2ubpNGSZkqaUum6WHk5+JWJpEbgQmA/YCBwuKSBla2VFeFyoCpfyrXScvArn+2AqRHxQkQsAK4Bhla4TtaGiLgXmFvpelj5OfiVz3rAqwW/p6U0M6sCDn5mVpcc/MpnOtCv4HfflGZmVcDBr3wmAQMkbShpeeAwYFyF62RmiYNfmUREE/Ad4DbgGWBsRDxV2VpZWySNAR4ANpU0TdJxla6TlYc/bzOzuuSWn5nVJQc/M6tLDn5mVpcc/MysLjn4mVldcvDrQiQtlPSYpCmSrpO0Ugeudbmkg9P+Za1NuiBpd0k7taOMlyQttcpXS+lL5Hk3Z1m/kPSDvHW0+uXg17W8HxFbR8SWwALg+MKDktq1DnNEfCMinm4ly+5A7uBnVs0c/Lqu+4CNU6vsPknjgKclNUr6H0mTJD0h6VsAylyQ5hf8J7BW84Uk3S1pcNofIukRSY9LukNSf7Ige0pqde4iaU1JN6QyJknaOZ27hqTbJT0l6TKy9a1bJelvkh5O5wxf4ti5Kf0OSWumtI0k3ZrOuU/SZqX4x7T6066WglVWauHtB9yakrYFtoyIF1MAeSsiPitpBeBfkm4HtgE2JZtbcG3gaWD0EtddE7gU2DVda/WImCvpEuDdiDgn5fsLcG5E3C9pfbKvWDYHRgD3R8QZkr4AFPN1xNdTGSsCkyTdEBFzgJWByRFxiqSfp2t/h2xhoeMj4jlJ2wMXAXu245/R6pyDX9eyoqTH0v59wCiy7uhDEfFiSt8H+Ezz8zxgNWAAsCswJiIWAq9JunMZ198BuLf5WhHR0rx2ewMDpcUNux6SVkllfDmd+w9J84q4p5MlHZT2+6W6zgEWAdem9KuBG1MZOwHXFZS9QhFlmC3Fwa9reT8iti5MSEHgvcIk4KSIuG2JfPuXsB4NwA4R8cEy6lI0SbuTBdIdI2K+pLuB7i1kj1Tum0v+G5i1h5/51Z7bgBMkLQcgaRNJKwP3Al9NzwTXBfZYxrkPArtK2jCdu3pKfwdYtSDf7cBJzT8kNQeje4EjUtp+QK826roaMC8Fvs3IWp7NGoDm1usRZN3pt4EXJR2SypCkrdoow2yZHPxqz2Vkz/MeSYvw/Imshf9X4Ll07EqymUs+ISJmAcPJupiP83G382bgoOYBD+BkYHAaUHmaj0edf0kWPJ8i6/6+0kZdbwW6SXoGOJss+DZ7D9gu3cOewBkp/UjguFS/p/DSANZOntXFzOqSW35mVpcc/MysLjn4mVldcvAzs7rk4GdmdcnBz8zqkoOfmdWl/w+vUjlNV+6GVAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.99      0.99      1700\n",
            "           1       0.99      0.99      0.99      1600\n",
            "\n",
            "    accuracy                           0.99      3300\n",
            "   macro avg       0.99      0.99      0.99      3300\n",
            "weighted avg       0.99      0.99      0.99      3300\n",
            "\n",
            "\n",
            "[#2]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Multi-layer Perceptron\n",
            "- activation: identity\n",
            "- alpha: 0.0001\n",
            "- batch_size: auto\n",
            "- beta_1: 0.9\n",
            "- beta_2: 0.999\n",
            "- early_stopping: False\n",
            "- epsilon: 1e-08\n",
            "- hidden_layer_sizes: (100,)\n",
            "- learning_rate: constant\n",
            "- learning_rate_init: 0.001\n",
            "- max_fun: 15000\n",
            "- max_iter: 200\n",
            "- momentum: 0.9\n",
            "- n_iter_no_change: 10\n",
            "- nesterovs_momentum: True\n",
            "- power_t: 0.5\n",
            "- random_state: None\n",
            "- shuffle: True\n",
            "- solver: adam\n",
            "- tol: 0.0001\n",
            "- validation_fraction: 0.1\n",
            "- verbose: False\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 99.03030303030303 %\n",
            "\n",
            "Average precision score: 98.20789842873175 %\n",
            "\n",
            "Average recall score: 99.625 %\n",
            "\n",
            "Average F1-score: 99.00621118012421 %\n",
            "\n",
            "Matthews correlation coefficient: 0.9806674931412547\n",
            "\n",
            "Mean absolute error: 0.009696969696969697\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.98      0.99      1700\n",
            "           1       0.98      1.00      0.99      1600\n",
            "\n",
            "    accuracy                           0.99      3300\n",
            "   macro avg       0.99      0.99      0.99      3300\n",
            "weighted avg       0.99      0.99      0.99      3300\n",
            "\n",
            "\n",
            "\n",
            "\t\t=============== Word2Vec ===============\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Multi-layer Perceptron\n",
            "- activation: tanh\n",
            "- alpha: 0.0001\n",
            "- batch_size: auto\n",
            "- beta_1: 0.9\n",
            "- beta_2: 0.999\n",
            "- early_stopping: False\n",
            "- epsilon: 1e-08\n",
            "- hidden_layer_sizes: (100,)\n",
            "- learning_rate: constant\n",
            "- learning_rate_init: 0.001\n",
            "- max_fun: 15000\n",
            "- max_iter: 200\n",
            "- momentum: 0.9\n",
            "- n_iter_no_change: 10\n",
            "- nesterovs_momentum: True\n",
            "- power_t: 0.5\n",
            "- random_state: None\n",
            "- shuffle: True\n",
            "- solver: adam\n",
            "- tol: 0.0001\n",
            "- validation_fraction: 0.1\n",
            "- verbose: False\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 59.696969696969695 %\n",
            "\n",
            "Average precision score: 55.695451890399326 %\n",
            "\n",
            "Average recall score: 21.8125 %\n",
            "\n",
            "Average F1-score: 34.418145956607496 %\n",
            "\n",
            "Matthews correlation coefficient: 0.25534380141222796\n",
            "\n",
            "Mean absolute error: 0.403030303030303\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.56      0.95      0.71      1700\n",
            "           1       0.82      0.22      0.34      1600\n",
            "\n",
            "    accuracy                           0.60      3300\n",
            "   macro avg       0.69      0.59      0.53      3300\n",
            "weighted avg       0.69      0.60      0.53      3300\n",
            "\n",
            "\n",
            "\t--------------- K-FOLD ---------------\n",
            "\n",
            "- n_folds: 10\n",
            "\n",
            "\t\t=============== BoW ===============\n",
            "\n",
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Multi-layer Perceptron\n",
            "- activation: identity\n",
            "- alpha: 0.0001\n",
            "- batch_size: auto\n",
            "- beta_1: 0.9\n",
            "- beta_2: 0.999\n",
            "- early_stopping: False\n",
            "- epsilon: 1e-08\n",
            "- hidden_layer_sizes: (100,)\n",
            "- learning_rate: constant\n",
            "- learning_rate_init: 0.001\n",
            "- max_fun: 15000\n",
            "- max_iter: 200\n",
            "- momentum: 0.9\n",
            "- n_iter_no_change: 10\n",
            "- nesterovs_momentum: True\n",
            "- power_t: 0.5\n",
            "- random_state: None\n",
            "- shuffle: True\n",
            "- solver: adam\n",
            "- tol: 0.0001\n",
            "- validation_fraction: 0.1\n",
            "- verbose: False\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 98.62727272727273 %\n",
            "\n",
            "Average precision score: 97.88060407699118 %\n",
            "\n",
            "Average recall score: 98.59865470852019 %\n",
            "\n",
            "Average F1-score: 98.5894441849603 %\n",
            "\n",
            "Matthews correlation coefficient: 0.9725257109414902\n",
            "\n",
            "Mean absolute error: 0.013727272727272727\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.99      0.99      5648\n",
            "           1       0.99      0.99      0.99      5352\n",
            "\n",
            "    accuracy                           0.99     11000\n",
            "   macro avg       0.99      0.99      0.99     11000\n",
            "weighted avg       0.99      0.99      0.99     11000\n",
            "\n",
            "\n",
            "[#2]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Multi-layer Perceptron\n",
            "- activation: tanh\n",
            "- alpha: 0.0001\n",
            "- batch_size: auto\n",
            "- beta_1: 0.9\n",
            "- beta_2: 0.999\n",
            "- early_stopping: False\n",
            "- epsilon: 1e-08\n",
            "- hidden_layer_sizes: (100,)\n",
            "- learning_rate: constant\n",
            "- learning_rate_init: 0.001\n",
            "- max_fun: 15000\n",
            "- max_iter: 200\n",
            "- momentum: 0.9\n",
            "- n_iter_no_change: 10\n",
            "- nesterovs_momentum: True\n",
            "- power_t: 0.5\n",
            "- random_state: None\n",
            "- shuffle: True\n",
            "- solver: adam\n",
            "- tol: 0.0001\n",
            "- validation_fraction: 0.1\n",
            "- verbose: False\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 98.62727272727273 %\n",
            "\n",
            "Average precision score: 97.86348147360661 %\n",
            "\n",
            "Average recall score: 98.63602391629297 %\n",
            "\n",
            "Average F1-score: 98.58997105238583 %\n",
            "\n",
            "Matthews correlation coefficient: 0.9725266353324554\n",
            "\n",
            "Mean absolute error: 0.013727272727272727\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.99      0.99      5648\n",
            "           1       0.99      0.99      0.99      5352\n",
            "\n",
            "    accuracy                           0.99     11000\n",
            "   macro avg       0.99      0.99      0.99     11000\n",
            "weighted avg       0.99      0.99      0.99     11000\n",
            "\n",
            "\n",
            "\n",
            "\t\t=============== Tf-idf ===============\n",
            "\n",
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Multi-layer Perceptron\n",
            "- activation: relu\n",
            "- alpha: 0.0001\n",
            "- batch_size: auto\n",
            "- beta_1: 0.9\n",
            "- beta_2: 0.999\n",
            "- early_stopping: False\n",
            "- epsilon: 1e-08\n",
            "- hidden_layer_sizes: (100,)\n",
            "- learning_rate: constant\n",
            "- learning_rate_init: 0.001\n",
            "- max_fun: 15000\n",
            "- max_iter: 200\n",
            "- momentum: 0.9\n",
            "- n_iter_no_change: 10\n",
            "- nesterovs_momentum: True\n",
            "- power_t: 0.5\n",
            "- random_state: None\n",
            "- shuffle: True\n",
            "- solver: adam\n",
            "- tol: 0.0001\n",
            "- validation_fraction: 0.1\n",
            "- verbose: False\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 98.89090909090909 %\n",
            "\n",
            "Average precision score: 98.24474829771226 %\n",
            "\n",
            "Average recall score: 98.95366218236174 %\n",
            "\n",
            "Average F1-score: 98.86130296807916 %\n",
            "\n",
            "Matthews correlation coefficient: 0.977804813514846\n",
            "\n",
            "Mean absolute error: 0.01109090909090909\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.99      0.99      5648\n",
            "           1       0.99      0.99      0.99      5352\n",
            "\n",
            "    accuracy                           0.99     11000\n",
            "   macro avg       0.99      0.99      0.99     11000\n",
            "weighted avg       0.99      0.99      0.99     11000\n",
            "\n",
            "\n",
            "\n",
            "\t\t=============== Word2Vec ===============\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Multi-layer Perceptron\n",
            "- activation: relu\n",
            "- alpha: 0.0001\n",
            "- batch_size: auto\n",
            "- beta_1: 0.9\n",
            "- beta_2: 0.999\n",
            "- early_stopping: False\n",
            "- epsilon: 1e-08\n",
            "- hidden_layer_sizes: (100,)\n",
            "- learning_rate: constant\n",
            "- learning_rate_init: 0.001\n",
            "- max_fun: 15000\n",
            "- max_iter: 200\n",
            "- momentum: 0.9\n",
            "- n_iter_no_change: 10\n",
            "- nesterovs_momentum: True\n",
            "- power_t: 0.5\n",
            "- random_state: None\n",
            "- shuffle: True\n",
            "- solver: adam\n",
            "- tol: 0.0001\n",
            "- validation_fraction: 0.1\n",
            "- verbose: False\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 96.0 %\n",
            "\n",
            "Average precision score: 93.94013066113611 %\n",
            "\n",
            "Average recall score: 95.90807174887892 %\n",
            "\n",
            "Average F1-score: 95.89015505324117 %\n",
            "\n",
            "Matthews correlation coefficient: 0.9199428749369468\n",
            "\n",
            "Mean absolute error: 0.04\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.96      0.96      5648\n",
            "           1       0.96      0.96      0.96      5352\n",
            "\n",
            "    accuracy                           0.96     11000\n",
            "   macro avg       0.96      0.96      0.96     11000\n",
            "weighted avg       0.96      0.96      0.96     11000\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Executes a series of learnings\n",
        "execute_a_series_of_multi_layer_perceptron_learnings(X=X, Y=Y)"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    },
    "colab": {
      "name": "Neural_Networks.ipynb",
      "provenance": []
    }
  },
  "nbformat": 4,
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