Feature_selection_using_Lasso.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Feature selection using Lasso.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNETsknyq+foF/rAVxKZHsE"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "DQro-T7hkBcH"
      },
      "source": [
        "import numpy as np\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.linear_model import Lasso"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZmOHTtb0kTbJ"
      },
      "source": [
        "from sklearn.datasets import load_diabetes"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yhW-ptWnkVWj"
      },
      "source": [
        "X,y = load_diabetes(return_X_y=True)\n",
        "\n",
        "features = load_diabetes()['feature_names']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L7O3uiu7ka3z"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tOWTOEQFkfuV"
      },
      "source": [
        "pipeline = Pipeline([\n",
        "                     ('scaler',StandardScaler()),\n",
        "                     ('model',Lasso())\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "shBc5miaklZN"
      },
      "source": [
        "search = GridSearchCV(pipeline,\n",
        "                      {'model__alpha':np.arange(0.1,10,0.1)},\n",
        "                      cv = 5, scoring=\"neg_mean_squared_error\",verbose=3\n",
        "                      )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ueykK8CXkx4E",
        "outputId": "978b2516-958f-4f1e-df70-3dc4443960f5"
      },
      "source": [
        "search.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 99 candidates, totalling 495 fits\n",
            "[CV] model__alpha=0.1 ................................................\n",
            "[CV] ................ model__alpha=0.1, score=-3051.717, total=   0.0s\n",
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            "[CV] ................ model__alpha=0.1, score=-2593.336, total=   0.0s\n",
            "[CV] model__alpha=0.1 ................................................\n",
            "[CV] ................ model__alpha=0.1, score=-3309.657, total=   0.0s\n",
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            "[CV] ................ model__alpha=0.1, score=-3512.096, total=   0.0s\n",
            "[CV] model__alpha=0.2 ................................................\n",
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            "[CV] ................ model__alpha=0.2, score=-3413.510, total=   0.0s\n",
            "[CV] model__alpha=0.2 ................................................\n",
            "[CV] ................ model__alpha=0.2, score=-2606.352, total=   0.0s\n",
            "[CV] model__alpha=0.2 ................................................\n",
            "[CV] ................ model__alpha=0.2, score=-3317.452, total=   0.0s\n",
            "[CV] model__alpha=0.2 ................................................\n",
            "[CV] ................ model__alpha=0.2, score=-3502.683, total=   0.0s\n",
            "[CV] model__alpha=0.30000000000000004 ................................\n",
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            "[CV] model__alpha=1.1 ................................................\n",
            "[CV] ................ model__alpha=1.1, score=-3050.246, total=   0.0s\n",
            "[CV] model__alpha=1.1 ................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ................ model__alpha=1.1, score=-3440.874, total=   0.0s\n",
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            "[CV] model__alpha=8.7 ................................................\n",
            "[CV] ................ model__alpha=8.7, score=-3503.522, total=   0.0s\n",
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            "[CV] model__alpha=8.7 ................................................\n",
            "[CV] ................ model__alpha=8.7, score=-2720.105, total=   0.0s\n",
            "[CV] model__alpha=8.7 ................................................\n",
            "[CV] ................ model__alpha=8.7, score=-3758.988, total=   0.0s\n",
            "[CV] model__alpha=8.7 ................................................\n",
            "[CV] ................ model__alpha=8.7, score=-3351.507, total=   0.0s\n",
            "[CV] model__alpha=8.8 ................................................\n",
            "[CV] ................ model__alpha=8.8, score=-3509.076, total=   0.0s\n",
            "[CV] model__alpha=8.8 ................................................\n",
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            "[CV] model__alpha=8.8 ................................................\n",
            "[CV] ................ model__alpha=8.8, score=-2724.596, total=   0.0s\n",
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            "[CV] model__alpha=8.8 ................................................\n",
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            "[CV] model__alpha=8.9 ................................................\n",
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            "[CV] ................ model__alpha=8.9, score=-3353.887, total=   0.0s\n",
            "[CV] model__alpha=9.0 ................................................\n",
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            "[CV] model__alpha=9.0 ................................................\n",
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            "[CV] model__alpha=9.0 ................................................\n",
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            "[CV] model__alpha=9.1 ................................................\n",
            "[CV] ................ model__alpha=9.1, score=-3525.912, total=   0.0s\n",
            "[CV] model__alpha=9.1 ................................................\n",
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            "[CV] model__alpha=9.1 ................................................\n",
            "[CV] ................ model__alpha=9.1, score=-2738.408, total=   0.0s\n",
            "[CV] model__alpha=9.1 ................................................\n",
            "[CV] ................ model__alpha=9.1, score=-3786.863, total=   0.0s\n",
            "[CV] model__alpha=9.1 ................................................\n",
            "[CV] ................ model__alpha=9.1, score=-3356.405, total=   0.0s\n",
            "[CV] model__alpha=9.2 ................................................\n",
            "[CV] ................ model__alpha=9.2, score=-3531.582, total=   0.0s\n",
            "[CV] model__alpha=9.2 ................................................\n",
            "[CV] ................ model__alpha=9.2, score=-3607.285, total=   0.0s\n",
            "[CV] model__alpha=9.2 ................................................\n",
            "[CV] ................ model__alpha=9.2, score=-2743.125, total=   0.0s\n",
            "[CV] model__alpha=9.2 ................................................\n",
            "[CV] ................ model__alpha=9.2, score=-3793.979, total=   0.0s\n",
            "[CV] model__alpha=9.2 ................................................\n",
            "[CV] ................ model__alpha=9.2, score=-3357.716, total=   0.0s\n",
            "[CV] model__alpha=9.3 ................................................\n",
            "[CV] ................ model__alpha=9.3, score=-3537.282, total=   0.0s\n",
            "[CV] model__alpha=9.3 ................................................\n",
            "[CV] ................ model__alpha=9.3, score=-3607.436, total=   0.0s\n",
            "[CV] model__alpha=9.3 ................................................\n",
            "[CV] ................ model__alpha=9.3, score=-2747.899, total=   0.0s\n",
            "[CV] model__alpha=9.3 ................................................\n",
            "[CV] ................ model__alpha=9.3, score=-3801.154, total=   0.0s\n",
            "[CV] model__alpha=9.3 ................................................\n",
            "[CV] ................ model__alpha=9.3, score=-3359.062, total=   0.0s\n",
            "[CV] model__alpha=9.4 ................................................\n",
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            "[CV] model__alpha=9.4 ................................................\n",
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            "[CV] model__alpha=9.5 ................................................\n",
            "[CV] ................ model__alpha=9.5, score=-3548.769, total=   0.0s\n",
            "[CV] model__alpha=9.5 ................................................\n",
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            "[CV] model__alpha=9.5 ................................................\n",
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            "[CV] model__alpha=9.5 ................................................\n",
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            "[CV] model__alpha=9.6 ................................................\n",
            "[CV] ................ model__alpha=9.6, score=-3554.557, total=   0.0s\n",
            "[CV] model__alpha=9.6 ................................................\n",
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            "[CV] model__alpha=9.6 ................................................\n",
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            "[CV] model__alpha=9.6 ................................................\n",
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            "[CV] model__alpha=9.6 ................................................\n",
            "[CV] ................ model__alpha=9.6, score=-3363.309, total=   0.0s\n",
            "[CV] model__alpha=9.700000000000001 ..................................\n",
            "[CV] .. model__alpha=9.700000000000001, score=-3560.374, total=   0.0s\n",
            "[CV] model__alpha=9.700000000000001 ..................................\n",
            "[CV] .. model__alpha=9.700000000000001, score=-3608.384, total=   0.0s\n",
            "[CV] model__alpha=9.700000000000001 ..................................\n",
            "[CV] .. model__alpha=9.700000000000001, score=-2767.561, total=   0.0s\n",
            "[CV] model__alpha=9.700000000000001 ..................................\n",
            "[CV] .. model__alpha=9.700000000000001, score=-3830.447, total=   0.0s\n",
            "[CV] model__alpha=9.700000000000001 ..................................\n",
            "[CV] .. model__alpha=9.700000000000001, score=-3364.794, total=   0.0s\n",
            "[CV] model__alpha=9.8 ................................................\n",
            "[CV] ................ model__alpha=9.8, score=-3566.220, total=   0.0s\n",
            "[CV] model__alpha=9.8 ................................................\n",
            "[CV] ................ model__alpha=9.8, score=-3608.707, total=   0.0s\n",
            "[CV] model__alpha=9.8 ................................................\n",
            "[CV] ................ model__alpha=9.8, score=-2772.618, total=   0.0s\n",
            "[CV] model__alpha=9.8 ................................................\n",
            "[CV] ................ model__alpha=9.8, score=-3837.918, total=   0.0s\n",
            "[CV] model__alpha=9.8 ................................................\n",
            "[CV] ................ model__alpha=9.8, score=-3366.313, total=   0.0s\n",
            "[CV] model__alpha=9.9 ................................................\n",
            "[CV] ................ model__alpha=9.9, score=-3572.095, total=   0.0s\n",
            "[CV] model__alpha=9.9 ................................................\n",
            "[CV] ................ model__alpha=9.9, score=-3609.064, total=   0.0s\n",
            "[CV] model__alpha=9.9 ................................................\n",
            "[CV] ................ model__alpha=9.9, score=-2777.731, total=   0.0s\n",
            "[CV] model__alpha=9.9 ................................................\n",
            "[CV] ................ model__alpha=9.9, score=-3845.449, total=   0.0s\n",
            "[CV] model__alpha=9.9 ................................................\n",
            "[CV] ................ model__alpha=9.9, score=-3367.868, total=   0.0s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done 495 out of 495 | elapsed:    2.0s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score=nan,\n",
              "             estimator=Pipeline(memory=None,\n",
              "                                steps=[('scaler',\n",
              "                                        StandardScaler(copy=True,\n",
              "                                                       with_mean=True,\n",
              "                                                       with_std=True)),\n",
              "                                       ('model',\n",
              "                                        Lasso(alpha=1.0, copy_X=True,\n",
              "                                              fit_intercept=True, max_iter=1000,\n",
              "                                              normalize=False, positive=False,\n",
              "                                              precompute=False,\n",
              "                                              random_state=None,\n",
              "                                              selection='cyclic', tol=0.0001,\n",
              "                                              warm_start=False))],\n",
              "                                verbose=False),\n",
              "             iid='deprec...\n",
              "       2.7, 2.8, 2.9, 3. , 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9,\n",
              "       4. , 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5. , 5.1, 5.2,\n",
              "       5.3, 5.4, 5.5, 5.6, 5.7, 5.8, 5.9, 6. , 6.1, 6.2, 6.3, 6.4, 6.5,\n",
              "       6.6, 6.7, 6.8, 6.9, 7. , 7.1, 7.2, 7.3, 7.4, 7.5, 7.6, 7.7, 7.8,\n",
              "       7.9, 8. , 8.1, 8.2, 8.3, 8.4, 8.5, 8.6, 8.7, 8.8, 8.9, 9. , 9.1,\n",
              "       9.2, 9.3, 9.4, 9.5, 9.6, 9.7, 9.8, 9.9])},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='neg_mean_squared_error', verbose=3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aZs17eWTk0yc",
        "outputId": "9efff6e8-15a7-4c0c-abb7-c7a0dd93f711"
      },
      "source": [
        "search.best_params_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model__alpha': 1.2000000000000002}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1C8CmuWwk8I0"
      },
      "source": [
        "coefficients = search.best_estimator_.named_steps['model'].coef_"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8XZYJeb8lE6-"
      },
      "source": [
        "importance = np.abs(coefficients)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cCqsfCAalIPj",
        "outputId": "4d1f9dda-4147-4e17-99c6-02181ca7519e"
      },
      "source": [
        "importance"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.15096441,  9.00511695, 26.90181607, 18.04877039,  5.41870204,\n",
              "        0.        , 12.27908758,  0.        , 19.4892079 ,  0.        ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SHuOOT6wlKEI",
        "outputId": "1e01c6bf-7106-4211-b802-c9c93fac7990"
      },
      "source": [
        "np.array(features)[importance > 0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['age', 'sex', 'bmi', 'bp', 's1', 's3', 's5'], dtype='<U3')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1lpWGv3ylkuw",
        "outputId": "7c929df6-b5a4-418d-d3ab-d2ffeed24b26"
      },
      "source": [
        "np.array(features)[importance == 0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['s2', 's4', 's6'], dtype='<U3')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    }
  ]
}