Week7 - Softmax regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Softmax regression on MNIST dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "\n",
    "import numpy as np\n",
    "from pprint import pprint\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "from sklearn.datasets import fetch_openml\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression, LogisticRegressionCV\n",
    "\n",
    "\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import make_scorer\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "mpl.rc('figure', figsize=(8,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = fetch_openml('mnist_784', version=1, return_X_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.to_numpy()\n",
    "y = y.to_numpy()\n",
    "x_train, y_train, x_test, y_test = X[:60000], y[:60000], X[60000:], y[60000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;logreg&#x27;,\n",
       "                 LogisticRegression(multi_class=&#x27;multinomial&#x27;, solver=&#x27;sag&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;logreg&#x27;,\n",
       "                 LogisticRegression(multi_class=&#x27;multinomial&#x27;, solver=&#x27;sag&#x27;))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(multi_class=&#x27;multinomial&#x27;, solver=&#x27;sag&#x27;)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('logreg',\n",
       "                 LogisticRegression(multi_class='multinomial', solver='sag'))])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe = Pipeline(steps=[('scaler', StandardScaler()),\n",
    "                        ('logreg', LogisticRegression(multi_class='multinomial',\n",
    "                                                         solver='sag'))])\n",
    "\n",
    "                                \n",
    "pipe.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 784)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe[-1].coef_.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10,)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe[-1].intercept_.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe[-1].classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.98      0.97       980\n",
      "           1       0.96      0.98      0.97      1135\n",
      "           2       0.94      0.90      0.92      1032\n",
      "           3       0.91      0.91      0.91      1010\n",
      "           4       0.92      0.94      0.93       982\n",
      "           5       0.91      0.87      0.89       892\n",
      "           6       0.93      0.95      0.94       958\n",
      "           7       0.92      0.93      0.92      1028\n",
      "           8       0.88      0.88      0.88       974\n",
      "           9       0.91      0.91      0.91      1009\n",
      "\n",
      "    accuracy                           0.92     10000\n",
      "   macro avg       0.92      0.92      0.92     10000\n",
      "weighted avg       0.92      0.92      0.92     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pipe.predict(x_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1c1cb23fcd0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay.from_estimator(pipe, x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "c:\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "c:\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "c:\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "c:\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "c:\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "c:\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "c:\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "c:\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "c:\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "c:\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "scorer = make_scorer(f1_score, average='micro')\n",
    "pipe = Pipeline(steps=[('scaler', StandardScaler()),\n",
    "                        ('logreg', LogisticRegressionCV(cv = 5,\n",
    "                                                        multi_class='multinomial',\n",
    "                                                        solver='sag',\n",
    "                                                        scoring=scorer,\n",
    "                                                        max_iter = 100,\n",
    "                                                        random_state=1729))])\n",
    "\n",
    "pipe.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe[-1].C_ "
   ]
  }
 ],
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