Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_mldata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist = fetch_mldata('MNIST original')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'COL_NAMES': ['label', 'data'],\n",
       " 'DESCR': 'mldata.org dataset: mnist-original',\n",
       " 'data': array([[0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0]], dtype=uint8),\n",
       " 'target': array([0., 0., 0., ..., 9., 9., 9.])}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = mnist[\"data\"], mnist[\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000, 784)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000,)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/matplotlib/font_manager.py:281: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  'Matplotlib is building the font cache using fc-list. '\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "some_digit = X[2800]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "some_digit_image = some_digit.reshape(28, 28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 27.5, 27.5, -0.5)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b72fc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(some_digit_image, cmap=matplotlib.cm.binary, interpolation=\"nearest\")\n",
    "plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[2800]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([19477, 33415, 28391, ..., 30205, 34809, 18517])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shuffle_index = np.random.permutation(60000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = X_train[shuffle_index], y_train[shuffle_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_5 = (y_train == 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_5 = (y_test == 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import SGDClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "sgd_clf = SGDClassifier(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n",
       "       eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n",
       "       learning_rate='optimal', loss='hinge', max_iter=None, n_iter=None,\n",
       "       n_jobs=1, penalty='l2', power_t=0.5, random_state=42, shuffle=True,\n",
       "       tol=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.fit(X_train, y_train_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.predict([X_train[3600]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score, cross_val_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.95855, 0.9507 , 0.9304 ])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(sgd_clf, X_train, y_train_5, cv=3, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "y_train_pred = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[53233,  1346],\n",
       "       [ 1861,  3560]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.90756849, 0.91129556, 0.91128669])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train.astype(np.float64))\n",
    "cross_val_score(sgd_clf, X_train_scaled, y_train, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "y_train_pred = cross_val_predict(sgd_clf, X_train_scaled, y_train, cv=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "conf_mx = confusion_matrix(y_train, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5741,    2,   18,   12,   11,   43,   41,   11,   40,    4],\n",
       "       [   1, 6481,   45,   26,    7,   37,    7,   10,  116,   12],\n",
       "       [  60,   37, 5329,   93,   78,   22,   96,   55,  172,   16],\n",
       "       [  47,   38,  127, 5378,    3,  214,   35,   54,  134,  101],\n",
       "       [  19,   27,   32,   10, 5354,    9,   44,   34,   86,  227],\n",
       "       [  76,   40,   39,  194,   66, 4580,  103,   29,  194,  100],\n",
       "       [  35,   24,   43,    2,   42,   88, 5624,    7,   52,    1],\n",
       "       [  24,   20,   68,   34,   48,   10,    5, 5781,   16,  259],\n",
       "       [  61,  152,   76,  153,   15,  146,   58,   27, 5025,  138],\n",
       "       [  45,   30,   27,   88,  150,   29,    2,  192,   76, 5310]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_mx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1a3b31ddd8>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2527ca90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(conf_mx, cmap=plt.cm.gray)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=uint8)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}