Week5 - Multiclass classifier with perceptron.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiclass Classifier with Perceptron(OneVsAll)\n",
    "\n",
    "* MNIST handwritten digiot classification with perceptron\n",
    "* Approach: Since perrceptron can only handle binary classification natively, we combine multiple binary classifiers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Common Imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import io\n",
    "import warnings\n",
    "\n",
    "#skearn importts\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.metrics import hinge_loss\n",
    "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix, precision_recall_curve\n",
    "from sklearn.metrics import precision_score, recall_score, classification_report\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.model_selection import cross_validate, cross_val_predict, GridSearchCV\n",
    "from pprint import pprint\n",
    "\n",
    "# make pretty images\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data Loading\n",
    "X, y = fetch_openml('mnist_784', version=1, return_X_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#To numpy arrays\n",
    "X = X.to_numpy()\n",
    "y = y.to_numpy()\n",
    "\n",
    "#scaling\n",
    "X = MinMaxScaler().fit_transform(X)\n",
    "\n",
    "#Train-Test_Split\n",
    "x_train, x_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5:[0 0 0 0 0 1 0 0 0 0]\n",
      "0:[1 0 0 0 0 0 0 0 0 0]\n",
      "4:[0 0 0 0 1 0 0 0 0 0]\n",
      "1:[0 1 0 0 0 0 0 0 0 0]\n",
      "9:[0 0 0 0 0 0 0 0 0 1]\n",
      "2:[0 0 1 0 0 0 0 0 0 0]\n",
      "1:[0 1 0 0 0 0 0 0 0 0]\n",
      "3:[0 0 0 1 0 0 0 0 0 0]\n",
      "1:[0 1 0 0 0 0 0 0 0 0]\n",
      "4:[0 0 0 0 1 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "#Let's use LabelBinarizer just to see how the encoding works\n",
    "y_train_ovr = LabelBinarizer().fit_transform(y_train)\n",
    "\n",
    "for i in range(10):\n",
    "    print('{0}:{1}'.format(y_train[i], y_train_ovr[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 10)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_ovr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "Perceptron(random_state=1729)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = Perceptron(random_state=1729)\n",
    "clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Weight matrix:(10, 784) and bias vector:(10,)\n"
     ]
    }
   ],
   "source": [
    "print('Shape of Weight matrix:{0} and bias vector:{1}'.format(clf.coef_.shape, clf.intercept_.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-631.88492118  154.366213    -65.23234141  -90.95338716 -189.29568627\n",
      "  -137.14385236  -99.64604383 -159.09859285 -136.86391388 -199.26120723]]\n",
      "Predicted Class:  1 Actual Class:  1\n"
     ]
    }
   ],
   "source": [
    "scores = clf.decision_function(x_train[6].reshape(1,-1))\n",
    "print(scores)\n",
    "print('Predicted Class: ', np.argmax(scores), 'Actual Class: ', y_train[6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.95      0.97      5923\n",
      "           1       0.94      0.98      0.96      6742\n",
      "           2       0.89      0.90      0.90      5958\n",
      "           3       0.86      0.87      0.87      6131\n",
      "           4       0.89      0.94      0.91      5842\n",
      "           5       0.81      0.88      0.85      5421\n",
      "           6       0.92      0.97      0.94      5918\n",
      "           7       0.91      0.94      0.92      6265\n",
      "           8       0.92      0.77      0.84      5851\n",
      "           9       0.92      0.82      0.87      5949\n",
      "\n",
      "    accuracy                           0.90     60000\n",
      "   macro avg       0.90      0.90      0.90     60000\n",
      "weighted avg       0.91      0.90      0.90     60000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_hat = clf.predict(x_train)\n",
    "print(classification_report(y_train, y_hat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_display = ConfusionMatrixDisplay.from_predictions(y_train, y_hat, values_format='.5g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "369f2c481f4da34e4445cda3fffd2e751bd1c4d706f27375911949ba6bb62e1c"
  },
  "kernelspec": {
   "display_name": "Python 3.10.2 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.2"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}