Grid_search_and_random_search.ipynb

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  "metadata": {
    "colab": {
      "name": "Grid search and random search.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOQO6uVD2Sp+a9ND0Th2m6s",
      "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/gianlucamalato/machinelearning/blob/master/Grid_search_and_random_search.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "STKneFuLKwr9"
      },
      "source": [
        "from sklearn.datasets import load_diabetes\n",
        "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV, train_test_split\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "import numpy as np"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IZtjhe61K_su"
      },
      "source": [
        "X,y = load_diabetes(return_X_y=True)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "21FNqq7oLBf2"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TQHJqViqLEn3",
        "outputId": "83e6de7c-84b1-4683-aafd-b8e49610c889"
      },
      "source": [
        "grid_search = GridSearchCV(RandomForestRegressor(random_state=0),\n",
        "                           {\n",
        "                              'n_estimators':np.arange(5,100,5),\n",
        "                              'max_features':np.arange(0.1,1.0,0.05),\n",
        "                            \n",
        "                            },cv=5, scoring=\"r2\",verbose=1,n_jobs=-1\n",
        "                           )\n",
        "grid_search.fit(X_train,y_train)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 342 candidates, totalling 1710 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done 128 tasks      | elapsed:    7.6s\n",
            "[Parallel(n_jobs=-1)]: Done 728 tasks      | elapsed:   46.5s\n",
            "[Parallel(n_jobs=-1)]: Done 1710 out of 1710 | elapsed:  2.0min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score=nan,\n",
              "             estimator=RandomForestRegressor(bootstrap=True, ccp_alpha=0.0,\n",
              "                                             criterion='mse', max_depth=None,\n",
              "                                             max_features='auto',\n",
              "                                             max_leaf_nodes=None,\n",
              "                                             max_samples=None,\n",
              "                                             min_impurity_decrease=0.0,\n",
              "                                             min_impurity_split=None,\n",
              "                                             min_samples_leaf=1,\n",
              "                                             min_samples_split=2,\n",
              "                                             min_weight_fraction_leaf=0.0,\n",
              "                                             n_estimators=100, n_jobs=None,\n",
              "                                             oob_score=False, random_state=0,\n",
              "                                             verbose=0, warm_start=False),\n",
              "             iid='deprecated', n_jobs=-1,\n",
              "             param_grid={'max_features': array([0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 , 0.55, 0.6 ,\n",
              "       0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95]),\n",
              "                         'n_estimators': array([ 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85,\n",
              "       90, 95])},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='r2', verbose=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Mf5r9tRL6DT",
        "outputId": "e6f81ce7-cb95-4489-99e8-160643eb5d61"
      },
      "source": [
        "grid_search.best_params_"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_features': 0.5000000000000001, 'n_estimators': 90}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fTVYMK4PL8RS",
        "outputId": "ec4da40f-a6e6-4733-f5fb-4a794a3c9340"
      },
      "source": [
        "grid_search.best_score_"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4153440140065655"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hegQ2NlZLlp8",
        "outputId": "b60f0b43-8440-46f1-d0c7-0216105b9566"
      },
      "source": [
        "random_search = RandomizedSearchCV(RandomForestRegressor(random_state=0),\n",
        "                           {\n",
        "                              'n_estimators':np.arange(5,100,5),\n",
        "                              'max_features':np.arange(0.1,1.0,0.05),\n",
        "                            },cv=5, scoring=\"r2\",verbose=1,n_jobs=-1, n_iter=50, random_state = 0\n",
        "                           )\n",
        "random_search.fit(X_train,y_train)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  54 tasks      | elapsed:    4.8s\n",
            "[Parallel(n_jobs=-1)]: Done 250 out of 250 | elapsed:   20.3s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=5, error_score=nan,\n",
              "                   estimator=RandomForestRegressor(bootstrap=True,\n",
              "                                                   ccp_alpha=0.0,\n",
              "                                                   criterion='mse',\n",
              "                                                   max_depth=None,\n",
              "                                                   max_features='auto',\n",
              "                                                   max_leaf_nodes=None,\n",
              "                                                   max_samples=None,\n",
              "                                                   min_impurity_decrease=0.0,\n",
              "                                                   min_impurity_split=None,\n",
              "                                                   min_samples_leaf=1,\n",
              "                                                   min_samples_split=2,\n",
              "                                                   min_weight_fraction_leaf=0.0,\n",
              "                                                   n_estimators=100,\n",
              "                                                   n_jobs=None, oob_score=Fals...\n",
              "                                                   warm_start=False),\n",
              "                   iid='deprecated', n_iter=50, n_jobs=-1,\n",
              "                   param_distributions={'max_features': array([0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 , 0.55, 0.6 ,\n",
              "       0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95]),\n",
              "                                        'n_estimators': array([ 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85,\n",
              "       90, 95])},\n",
              "                   pre_dispatch='2*n_jobs', random_state=0, refit=True,\n",
              "                   return_train_score=False, scoring='r2', verbose=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UYP8IsUbL_se",
        "outputId": "7cb253d5-cf6e-4319-e62b-48eaa74851f8"
      },
      "source": [
        "random_search.best_params_"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_features': 0.5500000000000002, 'n_estimators': 95}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "anPLW1r6MCaG",
        "outputId": "d0920356-253c-49d2-b350-84ebc4134d03"
      },
      "source": [
        "random_search.best_score_"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.41479921526675173"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M0CNcevAMDxm"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
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  ]
}