RandomForestMio.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "RandomForestMio.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMSkXKiJorNxVUuwspVeVtr",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/alessio76/Progetto-ML/blob/main/RandomForestMio.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NhfnuQ6_4Zq8"
      },
      "source": [
        "from sklearn.model_selection import cross_val_score, GridSearchCV, ParameterGrid, train_test_split\n",
        "from sklearn.datasets import load_breast_cancer\n",
        "from sklearn.neighbors import DistanceMetric\n",
        "from sklearn import metrics\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler, MaxAbsScaler\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.decomposition import PCA\n",
        "import numpy as np\n",
        "import seaborn as sns; sns.set()\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PXfSANlw5ab5"
      },
      "source": [
        "def grid_search(classifier, training_set, param_grid, n_folds, training_labels):\n",
        "  grid_search_cv = GridSearchCV(classifier, param_grid, cv=n_folds)\n",
        "  grid_search_cv.fit(training_set,training_labels)\n",
        "  print('Combinazione migliore:\\n', grid_search_cv.best_params_)\n",
        "  print('Accuratezza media della combinazione migliore: %.3f' % grid_search_cv.best_score_)\n",
        "  return grid_search_cv.best_params_"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DK4zXJa4-s1u"
      },
      "source": [
        "def scoring(classifier, pred_y_test, pred_y_train, test_y, train_y):\n",
        "  print(metrics.classification_report(pred_y_test, test_y))\n",
        "  print(metrics.classification_report(pred_y_train, train_y))\n",
        "  mat = confusion_matrix(test_y, pred_y_test)\n",
        "  sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PEbFw5tG4L71"
      },
      "source": [
        "def predict_values(classifier, test_set, training_set):\n",
        "  y_test = classifier.predict(test_set)\n",
        "  y_train = classifier.predict(training_set)\n",
        "  return y_test, y_train"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oJB9mBkC5Guk",
        "outputId": "bb545e76-6c49-46d2-ce08-1882a5f04783"
      },
      "source": [
        "dataset = load_breast_cancer()\n",
        "x,y = dataset[\"data\"], dataset[\"target\"]\n",
        "train_x, test_x, train_y, test_y = train_test_split(x, y, test_size=.3, random_state=1)\n",
        "print(train_x.shape, train_y.shape, test_x.shape, test_y.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(398, 30) (398,) (171, 30) (171,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oZ-H2kL57lhS",
        "outputId": "670e3b0b-c34d-43cc-86f7-ca4939b2a405"
      },
      "source": [
        "base_classifier = RandomForestClassifier(random_state=0)\n",
        "param_grid = {'n_estimators' : list(range(101,401,50))}\n",
        "n_folds = 5\n",
        "base_values = grid_search(base_classifier, train_x, param_grid, n_folds, train_y)\n",
        "base_classifier.set_params(**base_values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Combinazione migliore:\n",
            " {'n_estimators': 351}\n",
            "Accuratezza media della combinazione migliore: 0.947\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=351,\n",
              "                       n_jobs=None, oob_score=False, random_state=0, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7JMpmySYLcs-",
        "outputId": "ef9f58de-58d3-4ea5-99f0-db4c3368dc95"
      },
      "source": [
        "param_grid = {'max_depth': list(range(2,15))}\n",
        "base_values = {**base_values, **grid_search(base_classifier, train_x, param_grid, n_folds, train_y)}\n",
        "base_classifier.set_params(**base_values)\n",
        "print(base_values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Combinazione migliore:\n",
            " {'max_depth': 6}\n",
            "Accuratezza media della combinazione migliore: 0.947\n",
            "{'n_estimators': 351, 'max_depth': 6}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        },
        "id": "U-lI_ZDOSap0",
        "outputId": "c37722cf-5fd8-4466-f0dc-59756e18e91a"
      },
      "source": [
        "base_classifier.fit(train_x, train_y)\n",
        "pred_y_test, pred_y_train = predict_values(base_classifier, test_x, train_x)\n",
        "scoring(base_classifier, pred_y_test, pred_y_train, test_y, train_y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.94      0.93        62\n",
            "           1       0.96      0.95      0.96       109\n",
            "\n",
            "    accuracy                           0.95       171\n",
            "   macro avg       0.94      0.94      0.94       171\n",
            "weighted avg       0.95      0.95      0.95       171\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00       149\n",
            "           1       1.00      1.00      1.00       249\n",
            "\n",
            "    accuracy                           1.00       398\n",
            "   macro avg       1.00      1.00      1.00       398\n",
            "weighted avg       1.00      1.00      1.00       398\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HgAHpirAxEfj"
      },
      "source": [
        "scaler = StandardScaler()\n",
        "scaler.fit(train_x)\n",
        "scaled_train_x = scaler.transform(train_x)\n",
        "scaled_test_x = scaler.transform(test_x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GQUlCRr676rU",
        "outputId": "2a3ff056-571b-4347-973f-0a4fcfef2425"
      },
      "source": [
        "scaled_classifier = RandomForestClassifier(random_state=0)\n",
        "param_grid = {'n_estimators' : list(range(101,401,20))}\n",
        "scaled_values = grid_search(scaled_classifier, scaled_train_x, param_grid , n_folds, train_y)\n",
        "scaled_classifier.set_params(**scaled_values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Combinazione migliore:\n",
            " {'n_estimators': 321}\n",
            "Accuratezza media della combinazione migliore: 0.947\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=321,\n",
              "                       n_jobs=None, oob_score=False, random_state=0, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CBx3t8CJrsPh",
        "outputId": "dc99f94e-8d4b-428d-ef02-cac36762f42e"
      },
      "source": [
        "param_grid = {'max_depth': list(range(2,15))}\n",
        "scaled_values = {**scaled_values, **grid_search(scaled_classifier, scaled_train_x, param_grid, n_folds, train_y)}\n",
        "scaled_classifier.set_params(**scaled_values)\n",
        "print(scaled_values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Combinazione migliore:\n",
            " {'max_depth': 7}\n",
            "Accuratezza media della combinazione migliore: 0.947\n",
            "{'n_estimators': 321, 'max_depth': 7}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        },
        "id": "gb1x_lvz8i35",
        "outputId": "d52fe085-3ebf-4459-b9a6-bff1eaf42d2c"
      },
      "source": [
        "scaled_classifier.fit(scaled_train_x, train_y)\n",
        "scaled_pred_y_test, scaled_pred_y_train = predict_values(scaled_classifier, scaled_test_x, scaled_train_x)\n",
        "scoring(scaled_classifier, scaled_pred_y_test, scaled_pred_y_train, test_y, train_y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.94      0.93        62\n",
            "           1       0.96      0.95      0.96       109\n",
            "\n",
            "    accuracy                           0.95       171\n",
            "   macro avg       0.94      0.94      0.94       171\n",
            "weighted avg       0.95      0.95      0.95       171\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00       149\n",
            "           1       1.00      1.00      1.00       249\n",
            "\n",
            "    accuracy                           1.00       398\n",
            "   macro avg       1.00      1.00      1.00       398\n",
            "weighted avg       1.00      1.00      1.00       398\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ta_1uDta9Fl4",
        "outputId": "3ae86ada-7e62-4e95-af3e-6b05894ee725"
      },
      "source": [
        "preservedInfo = .95\n",
        "pca = PCA(preservedInfo)\n",
        "pca.fit(scaled_train_x)\n",
        "reduced_train_x, reduced_test_x = pca.transform(scaled_train_x), pca.transform(scaled_test_x)\n",
        "print(reduced_train_x.shape, reduced_test_x.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(398, 10) (171, 10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wSBIP3tc-d0f",
        "outputId": "eb532fc9-5a1a-4773-baea-c59560724b9b"
      },
      "source": [
        "reduced_classifier = RandomForestClassifier(random_state=0)\n",
        "param_grid = {'n_estimators' : list(range(101,401,20))}\n",
        "reduced_values = grid_search(reduced_classifier, reduced_train_x, param_grid,n_folds,train_y)\n",
        "reduced_classifier.set_params(**reduced_values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Combinazione migliore:\n",
            " {'n_estimators': 121}\n",
            "Accuratezza media della combinazione migliore: 0.950\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=121,\n",
              "                       n_jobs=None, oob_score=False, random_state=0, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bI2OSO6z-dmG",
        "outputId": "1a11c83f-e268-4839-ae94-6961009136e4"
      },
      "source": [
        "param_grid = {'max_depth': list(range(2,15))}\n",
        "reduced_values = {**reduced_values, **grid_search(reduced_classifier, reduced_train_x, param_grid, n_folds, train_y)}\n",
        "reduced_classifier.set_params(**reduced_values)\n",
        "print(reduced_values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Combinazione migliore:\n",
            " {'max_depth': 8}\n",
            "Accuratezza media della combinazione migliore: 0.950\n",
            "{'n_estimators': 121, 'max_depth': 8}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        },
        "id": "NwkrprTq-dSD",
        "outputId": "e8c7c2ab-7bcd-4ce7-af28-050b0768a30b"
      },
      "source": [
        "reduced_classifier.fit(reduced_train_x, train_y)\n",
        "reduced_pred_y_test, reduced_pred_y_train = predict_values(reduced_classifier, reduced_test_x, reduced_train_x)\n",
        "scoring(reduced_classifier, reduced_pred_y_test, reduced_pred_y_train, test_y, train_y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.92      0.93        64\n",
            "           1       0.95      0.96      0.96       107\n",
            "\n",
            "    accuracy                           0.95       171\n",
            "   macro avg       0.95      0.94      0.94       171\n",
            "weighted avg       0.95      0.95      0.95       171\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00       149\n",
            "           1       1.00      1.00      1.00       249\n",
            "\n",
            "    accuracy                           1.00       398\n",
            "   macro avg       1.00      1.00      1.00       398\n",
            "weighted avg       1.00      1.00      1.00       398\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N7gm8Qxj_a82"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}