pickle_digits.ipynb

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  "metadata": {
    "colab": {
      "name": "pickle_digits.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/noahgift/boston_housing_pickle/blob/master/pickle_digits.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wS4_tczAc6cz",
        "colab_type": "text"
      },
      "source": [
        "# Pickle Digits PCA + Logistic Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G9Ue6uN3cqw9",
        "colab_type": "text"
      },
      "source": [
        "## Modeling\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rFjPFTbCctyO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn import linear_model, decomposition, datasets\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.externals import joblib\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AXniYumjdUFW",
        "colab_type": "text"
      },
      "source": [
        "### Setup PCA to Logistic Regression Pipeline and Fit"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dXLK8ixBdeXT",
        "colab_type": "text"
      },
      "source": [
        "#### PCA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vpgHhyWudNdn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "d8b3556e-e6d0-4fe6-dc1b-b690b928787e"
      },
      "source": [
        "logistic = linear_model.LogisticRegression()\n",
        "pca = decomposition.PCA()\n",
        "pipe = Pipeline(steps=[('pca', pca), ('logistic', logistic)])\n",
        "digits = datasets.load_digits()\n",
        "X_digits = digits.data\n",
        "y_digits = digits.target\n",
        "pca.fit(X_digits)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,\n",
              "  svd_solver='auto', tol=0.0, whiten=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j1uXCUYodjFN",
        "colab_type": "text"
      },
      "source": [
        "#### Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sSpH9ZY3dlG3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "1d44ddba-3d5f-492d-d7c5-ed66e53f45d6"
      },
      "source": [
        "n_components = [20, 40, 64]\n",
        "Cs = np.logspace(-4, 4, 3)\n",
        "\n",
        "#Parameters of pipelines can be set using ‘__’ separated parameter names:\n",
        "\n",
        "estimator = GridSearchCV(pipe,\n",
        "                         dict(pca__n_components=n_components,\n",
        "                              logistic__C=Cs))\n",
        "estimator.fit(X_digits, y_digits)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv='warn', error_score='raise-deprecating',\n",
              "       estimator=Pipeline(memory=None,\n",
              "     steps=[('pca', PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,\n",
              "  svd_solver='auto', tol=0.0, whiten=False)), ('logistic', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
              "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
              "          tol=0.0001, verbose=0, warm_start=False))]),\n",
              "       fit_params=None, iid='warn', n_jobs=None,\n",
              "       param_grid={'pca__n_components': [20, 40, 64], 'logistic__C': array([1.e-04, 1.e+00, 1.e+04])},\n",
              "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
              "       scoring=None, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u2bCmwXyeDhZ",
        "colab_type": "text"
      },
      "source": [
        "### Predict"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "brqCrvPIdn0f",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ae52f60f-2549-4984-90c7-31ab8d213714"
      },
      "source": [
        "estimator.predict(X_digits)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, ..., 8, 9, 8])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t3qMS569eWs_",
        "colab_type": "text"
      },
      "source": [
        "### Pickle out Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "19SYsuUGeYRq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c5699332-5cbf-4e22-c33c-bbd72ea6ca1c"
      },
      "source": [
        "joblib.dump(estimator, 'digits_prediction.joblib')"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['digits_prediction.joblib']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z57he0DvfRPd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}