Sklearn_SGD_Classifier_MNISt.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNSK+93oXvtYAOszHJ/MS/p",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Ogunfool/Multi-class-logistic-regression-with-Python./blob/main/Sklearn_SGD_Classifier_MNISt.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using Scikit learn SGDClassifier Class (Logistic regression + Log Loss)."
      ],
      "metadata": {
        "id": "YtSKQFbR1qI2"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "CnuQpDOqCfVI"
      },
      "outputs": [],
      "source": [
        "from google.colab import files\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, log_loss\n",
        "from sklearn.linear_model import LogisticRegression, SGDClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import train_test_split, KFold, cross_val_score, GridSearchCV, RandomizedSearchCV, StratifiedKFold\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.tree import DecisionTreeClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SfHoYg9sAdWH"
      },
      "outputs": [],
      "source": [
        "%%time\n",
        "from sklearn.datasets import fetch_openml\n",
        "mnist = fetch_openml(data_id=554) # https://www.openml.org/d/554"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YFTaOWMnA_Mx",
        "outputId": "4817fc0b-b2d0-4f56-f65d-81bf684256c2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(pandas.core.frame.DataFrame, NoneType, list, pandas.core.series.Series)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "type(mnist.data), type(mnist.categories), type(mnist.feature_names), type(mnist.target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lqctiSsnBBG1",
        "outputId": "d068ee97-5036-4f9c-8bc4-df164733f7e5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((70000, 784), (70000,))"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "mnist.data.shape, mnist.target.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "EFXtOOx8Btiy"
      },
      "outputs": [],
      "source": [
        "Xtemp_train, X_test, ytemp_train, y_test = train_test_split(mnist.data, \n",
        "                                                    mnist.target.astype('int'), #targets str to int convert\n",
        "                                                   test_size=1/7.0,\n",
        "                                                   random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "DgDvX1tdBfaQ"
      },
      "outputs": [],
      "source": [
        "# Set shuffle to true to shuffle before split\n",
        "X_train, X_val, y_train, y_val = train_test_split(Xtemp_train, ytemp_train, test_size = 0.2, random_state = 42, shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "YKtL-RA4ZR_d",
        "outputId": "0cf86e79-570b-42d0-a8f9-9407454ed551"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "plt.subplot(1,3,1)\n",
        "plt.hist(y_train);\n",
        "plt.title('Frequency of different classes - Training data');\n",
        "\n",
        "plt.subplot(1,3,2)\n",
        "plt.hist(y_val);\n",
        "plt.title('Frequency of different classes - Validation data');\n",
        "\n",
        "plt.subplot(1,3,3)\n",
        "plt.hist(y_test);\n",
        "plt.title('Frequency of different classes - Test data');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "M9bmzQZa0B53"
      },
      "outputs": [],
      "source": [
        "# Let's normalize data - StandardScaler\n",
        "# Fit t0 training set and use mean and std to transform X_train and X_test\n",
        "scaler = StandardScaler().fit(X_train)\n",
        "X_train_scaled = scaler.transform(X_train)\n",
        "X_val_scaled = scaler.transform(X_val)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nyn2vAG-NoZ7",
        "outputId": "2032d328-e610-4c3e-9b6d-801229866863"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(48000, 784) (12000, 784) (10000, 784)\n"
          ]
        }
      ],
      "source": [
        "print(X_train_scaled.shape, X_val_scaled.shape, X_test_scaled.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sULERcxRbbaF"
      },
      "source": [
        "Let's select our model using cross validation and Gridsearch - GridCV in Sklearn. Note that after choosing the best hyperparameters, the CV models are discarded and the final model is trained with those hyperparameters.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rTe5IlabqIMt"
      },
      "source": [
        "Use SGD Classifier to compare results with mine."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "MU7AbvVCqUFV"
      },
      "outputs": [],
      "source": [
        "KF = StratifiedKFold(n_splits = 3, random_state=42, shuffle=True)\n",
        "param_grid = {'eta0': [0.001, 0.003, 0.01, 0.03, 0.1, 0.3],\n",
        "              'penalty':['l2'],\n",
        "              'alpha': [0.001, 0.003, 0.01, 0.03, 0.1, 0.3]}\n",
        "SGD_classifier = SGDClassifier(fit_intercept=True,\n",
        "                        max_iter=200,\n",
        "                        verbose=2, # output progress\n",
        "                        n_jobs=5, # parallelize over 5 processes\n",
        "                        tol=0.001\n",
        "                         )\n",
        "\n",
        "SGD_classifierGridCV = GridSearchCV(SGD_classifier, param_grid, cv= KF)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NW7Ix8p8rpL6"
      },
      "outputs": [],
      "source": [
        "%%time\n",
        "SGD_classifierGridCV.fit(X_train_scaled, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jViLvHH4ruCx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1cf3069f-61a8-4483-fb4c-00d093a3f0c2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'alpha': 0.001, 'eta0': 0.003, 'penalty': 'l2'}"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ],
      "source": [
        "# klearn got the same best hyperparameters as our OOP-python based model\n",
        "SGD_classifierGridCV.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gt_QGd31r0hC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "34133323-4c4f-43f6-c2e7-b9c1e603ccd0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9080833333333334"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "SGD_classifierGridCV.best_score_"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I want to compare these results with mine.\n",
        "I got 83% accuracy while sklearn got 91% - Using the SGDClassifier, log loss function."
      ],
      "metadata": {
        "id": "DiCDqtCFM2Jt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SGD_classifier_final = SGDClassifier(fit_intercept=True,\n",
        "                        loss = 'log',\n",
        "                        eta0=0.003,\n",
        "                        alpha=0.001,\n",
        "                        max_iter=200,\n",
        "                        verbose=2, # output progress\n",
        "                        n_jobs=5, # parallelize over 5 processes\n",
        "                        tol=0.001\n",
        "                         )"
      ],
      "metadata": {
        "id": "v0vDbWQ-Jjv-"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "SGD_classifier_final.fit(X_train_scaled, y_train)"
      ],
      "metadata": {
        "id": "m0kIjwpWKYJY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "SGD_classifier_final.score(X_val_scaled, y_val)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LT3Z-I0PKrMy",
        "outputId": "50ded32e-2379-4000-f291-862a8ca96b32"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9081666666666667"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "SGD_classifier_final.intercept_.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "10S7M7LUfwvA",
        "outputId": "361b63f6-e4c5-4a1f-fed1-8c0ea0a3dcad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10,)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "SGD_classifier_final.coef_.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gEUNp8hvgg6T",
        "outputId": "2584648d-c936-47d8-dbc3-790e9925959a"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10, 784)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    }
  ]
}