mnist.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time \n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xgboost as xgb\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.utils import check_random_state\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for faster convergence\n",
    "train_samples = 5000\n",
    "\n",
    "data= pd.read_csv('mnist_784.csv')\n",
    "random_state = check_random_state(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5\n",
       "1    0\n",
       "2    4\n",
       "3    1\n",
       "4    9\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = data['class']\n",
    "X = data.iloc[:,:-1]\n",
    "X = X/255\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test,y_train, y_test = train_test_split(X,y,train_size=train_samples, test_size=10000,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Standardization of a dataset is a common requirement for many machine learning estimators:\n",
    "they might behave badly if the individual feature do not more or less look like standard normally distributed data \n",
    "(e.g. Gaussian with 0 mean and unit variance).\n",
    "\"\"\"\n",
    "scaler = StandardScaler()\n",
    "x_train = scaler.fit_transform(x_train)\n",
    "x_test = scaler.transform(x_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8343\n"
     ]
    }
   ],
   "source": [
    "t0 = time.time()\n",
    "clf = LogisticRegression(C=50/train_samples,\n",
    "                        multi_class = 'multinomial',\n",
    "                        penalty ='l1',solver = 'saga',\n",
    "                        tol = 0.1)\n",
    "clf.fit(x_train,y_train)\n",
    "y_predicted1 = clf.predict(x_test)\n",
    "accuracyscore1 = accuracy_score(y_test, y_predicted1)\n",
    "print(accuracyscore1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "runtime in 5.749 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coef1 = clf.coef_.copy()\n",
    "plt.figure(figsize=(10,5))\n",
    "scale1 = np.abs(coef1).max()\n",
    "\n",
    "for i in range(10):\n",
    "    l1_plot = plt.subplot(2,5,i+1)\n",
    "    l1_plot.imshow(coef1[i].reshape(28,28),interpolation='nearest',\n",
    "                 cmap=plt.cm.RdBu,vmin = -scale1,vmax=scale1)\n",
    "    l1_plot.set_xticks(())\n",
    "    l1_plot.set_yticks(())\n",
    "    l1_plot.set_xlabel('Class %i'% i)\n",
    "plt.suptitle('Classification vector for...')\n",
    "run_time=time.time()-t0\n",
    "print('runtime in %.3f s' % run_time)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(alpha=0.1, base_score=0.5, booster='gblinear',\n",
       "       colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=3,\n",
       "       min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,\n",
       "       nthread=None, objective='multi:softprob', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "       silent=None, subsample=1, verbosity=1)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t0 = time.time()\n",
    "modelxgb = xgb.XGBClassifier(booster='gblinear',alpha =0.1)\n",
    "modelxgb.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8562"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted2 = modelxgb.predict(x_test)\n",
    "accuracyscore2 = accuracy_score(y_test, y_predicted2)\n",
    "accuracyscore2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "runtime in 1914.610 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coef2 = modelxgb.coef_.copy()\n",
    "plt.figure(figsize=(10,5))\n",
    "scale2 = np.abs(coef2).max()\n",
    "for i in range(10):\n",
    "    l1_plot = plt.subplot(2,5,i+1)\n",
    "    l1_plot.imshow(coef[i].reshape(28,28),interpolation='nearest',\n",
    "                 cmap=plt.cm.RdBu,vmin = -scale2,vmax=scale2)\n",
    "    l1_plot.set_xticks(())\n",
    "    l1_plot.set_yticks(())\n",
    "    l1_plot.set_xlabel('Class %i'% i)\n",
    "plt.suptitle('Classification vector for...')\n",
    "run_time=time.time()-t0\n",
    "print('runtime in %.3f s' % run_time)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\xgboost\\core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-merror:0.1612\tvalidation-merror:0.2263\n",
      "Multiple eval metrics have been passed: 'validation-merror' will be used for early stopping.\n",
      "\n",
      "Will train until validation-merror hasn't improved in 50 rounds.\n",
      "[1]\ttrain-merror:0.1186\tvalidation-merror:0.1739\n",
      "[2]\ttrain-merror:0.1062\tvalidation-merror:0.1662\n",
      "[3]\ttrain-merror:0.0956\tvalidation-merror:0.153\n",
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      "[274]\ttrain-merror:0.0092\tvalidation-merror:0.081\n",
      "[275]\ttrain-merror:0.0092\tvalidation-merror:0.081\n",
      "[276]\ttrain-merror:0.0092\tvalidation-merror:0.081\n",
      "[277]\ttrain-merror:0.0092\tvalidation-merror:0.081\n",
      "[278]\ttrain-merror:0.0092\tvalidation-merror:0.081\n",
      "[279]\ttrain-merror:0.0092\tvalidation-merror:0.081\n",
      "[280]\ttrain-merror:0.0092\tvalidation-merror:0.081\n",
      "[281]\ttrain-merror:0.0092\tvalidation-merror:0.081\n",
      "[282]\ttrain-merror:0.0092\tvalidation-merror:0.081\n",
      "[283]\ttrain-merror:0.0094\tvalidation-merror:0.081\n",
      "[284]\ttrain-merror:0.0094\tvalidation-merror:0.081\n",
      "[285]\ttrain-merror:0.0094\tvalidation-merror:0.081\n",
      "[286]\ttrain-merror:0.0094\tvalidation-merror:0.081\n",
      "[287]\ttrain-merror:0.0094\tvalidation-merror:0.0809\n",
      "[288]\ttrain-merror:0.0092\tvalidation-merror:0.081\n",
      "[289]\ttrain-merror:0.0092\tvalidation-merror:0.081\n",
      "[290]\ttrain-merror:0.0092\tvalidation-merror:0.081\n",
      "[291]\ttrain-merror:0.0092\tvalidation-merror:0.081\n",
      "[292]\ttrain-merror:0.0092\tvalidation-merror:0.081\n",
      "[293]\ttrain-merror:0.0092\tvalidation-merror:0.081\n",
      "[294]\ttrain-merror:0.0092\tvalidation-merror:0.081\n",
      "[295]\ttrain-merror:0.0092\tvalidation-merror:0.081\n",
      "[296]\ttrain-merror:0.0092\tvalidation-merror:0.0812\n",
      "[297]\ttrain-merror:0.0092\tvalidation-merror:0.0812\n",
      "[298]\ttrain-merror:0.0092\tvalidation-merror:0.0813\n",
      "[299]\ttrain-merror:0.009\tvalidation-merror:0.0812\n",
      "[300]\ttrain-merror:0.0088\tvalidation-merror:0.0812\n",
      "[301]\ttrain-merror:0.0088\tvalidation-merror:0.0812\n",
      "[302]\ttrain-merror:0.009\tvalidation-merror:0.0812\n",
      "[303]\ttrain-merror:0.009\tvalidation-merror:0.0812\n",
      "[304]\ttrain-merror:0.009\tvalidation-merror:0.0812\n",
      "[305]\ttrain-merror:0.0086\tvalidation-merror:0.0812\n",
      "[306]\ttrain-merror:0.0086\tvalidation-merror:0.0812\n",
      "Stopping. Best iteration:\n",
      "[256]\ttrain-merror:0.0094\tvalidation-merror:0.0808\n",
      "\n"
     ]
    }
   ],
   "source": [
    "param_list = [(\"eta\", 0.08), (\"max_depth\", 6), (\"subsample\", 0.8), (\"colsample_bytree\", 0.8), (\"objective\", \"multi:softmax\"), (\"eval_metric\", \"merror\"), (\"alpha\", 8), (\"lambda\", 2), (\"num_class\", 10)]\n",
    "n_rounds = 600\n",
    "early_stopping = 50\n",
    "    \n",
    "d_train = xgb.DMatrix(x_train, label=y_train)\n",
    "d_val = xgb.DMatrix(x_test, label=y_test)\n",
    "eval_list = [(d_train, \"train\"), (d_val, \"validation\")]\n",
    "bst = xgb.train(param_list, d_train, n_rounds, evals=eval_list, early_stopping_rounds=early_stopping, verbose_eval=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9188"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "d_test = xgb.DMatrix(data=x_test)\n",
    "y_pred = bst.predict(d_test)\n",
    "\n",
    "accuracyscore3 = accuracy_score(y_test, y_pred)\n",
    "accuracyscore3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e199d03208>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb.plot_importance(bst,max_num_features=10,importance_type='cover')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rescale the data, use the traditional train/test split\n",
    "X_train, X_test = X[:60000], X[60000:]\n",
    "y_train, y_test = y[:60000], y[60000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.32009978\n",
      "Iteration 2, loss = 0.15347534\n",
      "Iteration 3, loss = 0.11544755\n",
      "Iteration 4, loss = 0.09279764\n",
      "Iteration 5, loss = 0.07889367\n",
      "Iteration 6, loss = 0.07170497\n",
      "Iteration 7, loss = 0.06282111\n",
      "Iteration 8, loss = 0.05529723\n",
      "Iteration 9, loss = 0.04960484\n",
      "Iteration 10, loss = 0.04645355\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (10) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 0.986800\n",
      "Test set score: 0.970000\n"
     ]
    }
   ],
   "source": [
    "mlp = MLPClassifier(hidden_layer_sizes=(50,), max_iter=10, alpha=1e-4,\n",
    "                    solver='sgd', verbose=10, tol=1e-4, random_state=1,\n",
    "                    learning_rate_init=.1)\n",
    "mlp.fit(X_train, y_train)\n",
    "print(\"Training set score: %f\" % mlp.score(X_train, y_train))\n",
    "print(\"Test set score: %f\" % mlp.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(4, 4)\n",
    "# use global min / max to ensure all weights are shown on the same scale\n",
    "vmin, vmax = mlp.coefs_[0].min(), mlp.coefs_[0].max()\n",
    "for coef, ax in zip(mlp.coefs_[0].T, axes.ravel()):\n",
    "    ax.matshow(coef.reshape(28, 28), cmap=plt.cm.gray, vmin=.5 * vmin,\n",
    "               vmax=.5 * vmax)\n",
    "    ax.set_xticks(())\n",
    "    ax.set_yticks(())\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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