DIGITRECOGNIZER_MLPClassiferiWITHENCODING.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<b> Digit Recognizer <b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42000, 785)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "y_train = df['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42000,)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "x_train = df.drop(['label'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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     "metadata": {},
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    "x_train.head()"
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       "std        1.75987      1.894498      0.414264       0.0       0.0       0.0   \n",
       "min        0.00000      0.000000      0.000000       0.0       0.0       0.0   \n",
       "25%        0.00000      0.000000      0.000000       0.0       0.0       0.0   \n",
       "50%        0.00000      0.000000      0.000000       0.0       0.0       0.0   \n",
       "75%        0.00000      0.000000      0.000000       0.0       0.0       0.0   \n",
       "max      253.00000    254.000000     62.000000       0.0       0.0       0.0   \n",
       "\n",
       "       pixel783  \n",
       "count   42000.0  \n",
       "mean        0.0  \n",
       "std         0.0  \n",
       "min         0.0  \n",
       "25%         0.0  \n",
       "50%         0.0  \n",
       "75%         0.0  \n",
       "max         0.0  \n",
       "\n",
       "[8 rows x 784 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.isnull().values.any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Normalizing and Reshaping the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "variance ration after PCA :\n",
      "[ 0.09748938  0.07160266  0.06145903  0.05379302  0.04894262  0.04303214\n",
      "  0.03277051  0.02892103  0.02766902  0.02348871  0.02099325  0.02059001\n",
      "  0.01702553  0.01692787  0.01581126  0.0148324   0.01319688  0.01282727\n",
      "  0.01187976  0.01152755  0.01072191  0.01015199  0.00964902  0.00912846\n",
      "  0.00887641  0.00838766  0.00811856  0.00777406  0.00740635  0.00686661\n",
      "  0.00657982  0.00638798  0.00599367  0.00588912  0.00564335  0.00540965\n",
      "  0.00509216  0.00487473  0.00475534  0.0046652   0.00452909  0.00444959\n",
      "  0.00418122  0.0039727   0.00384237  0.00374824  0.00360591  0.00347523\n",
      "  0.00335994  0.00318615]\n",
      "singular values after PCA :\n",
      "[ 118576.76864452  101621.48813486   94148.59150236   88081.34284631\n",
      "   84016.49833982   78780.26467234   68748.39258148   64584.43354872\n",
      "   63171.01859374   58203.69410309   55025.10077984   54494.07177965\n",
      "   49553.15920576   49410.82714956   47753.39575275   46251.58522966\n",
      "   43627.12360751   43011.84733355   41392.8017208    40774.57291768\n",
      "   39323.94571713   38264.55077471   37304.62041925   36284.38260016\n",
      "   35779.93981576   34780.95095818   34218.45712793   33484.57786838\n",
      "   32683.08984294   31469.67799042   30805.48141256   30353.08972573\n",
      "   29401.34906364   29143.80679607   28529.18941451   27932.23454181\n",
      "   27100.16708245   26515.2911783    26188.57576605   25939.17312224\n",
      "   25557.98792134   25332.68374167   24556.83851507   23936.67644918\n",
      "   23540.77164274   23250.61655801   22804.90832223   22387.875202\n",
      "   22013.38602717   21436.50291566]\n",
      "shape of x_train after PCA :  (42000, 50)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=50, svd_solver='auto')\n",
    "pca.fit(x_train)\n",
    "print('variance ration after PCA :')\n",
    "print(pca.explained_variance_ratio_)\n",
    "print('singular values after PCA :')\n",
    "print(pca.singular_values_)\n",
    "\n",
    "x_train= pca.fit_transform(x_train)\n",
    "print(\"shape of x_train after PCA : \", x_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "x_train = x_train/255.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "x_train = x_train.values.reshape(-1,28,28,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    1\n",
       "3    4\n",
       "4    0\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "lb = preprocessing.LabelBinarizer()\n",
    "y_train=lb.fit_transform(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 0, ..., 0, 0, 0],\n",
       "       [1, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 1, 0, ..., 0, 0, 0],\n",
       "       ..., \n",
       "       [0, 0, 0, ..., 1, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 1]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_validate, y_train, y_validate = train_test_split(x_train, y_train, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33600, 50)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8400, 50)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_validate.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33600, 10)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8400, 10)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_validate.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.50997136\n",
      "Iteration 2, loss = 0.18812697\n",
      "Iteration 3, loss = 0.13326229\n",
      "Iteration 4, loss = 0.10866065\n",
      "Iteration 5, loss = 0.09462548\n",
      "Iteration 6, loss = 0.09684530\n",
      "Iteration 7, loss = 0.07523888\n",
      "Iteration 8, loss = 0.06526710\n",
      "Iteration 9, loss = 0.07277739\n",
      "Iteration 10, loss = 0.05964075\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\txr5070\\AppData\\Local\\Enthought\\Canopy\\edm\\envs\\User\\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: 0.989851\n",
      "Validation set score: 0.965833\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "mlp = MLPClassifier(hidden_layer_sizes=(300,150), max_iter=10, alpha=1e-4,\n",
    "                    solver='adam', verbose=10, tol=1e-4, random_state=42,\n",
    "                    learning_rate_init=.01)\n",
    "\n",
    "mlp.fit(X_train, y_train)\n",
    "print(\"Training set score: %f\" % mlp.score(X_train, y_train))\n",
    "print(\"Validation set score: %f\" % mlp.score(X_validate, y_validate))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "y_true, y_pred = y_validate, mlp.predict(X_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "class_label = lb.classes_.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report: \n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.98      0.98      0.98       813\n",
      "          1       0.98      0.99      0.99       961\n",
      "          2       0.98      0.97      0.98       860\n",
      "          3       0.96      0.97      0.97       863\n",
      "          4       0.97      0.98      0.98       827\n",
      "          5       0.98      0.96      0.97       756\n",
      "          6       0.97      0.98      0.98       841\n",
      "          7       0.98      0.97      0.97       899\n",
      "          8       0.99      0.94      0.96       768\n",
      "          9       0.97      0.96      0.97       812\n",
      "\n",
      "avg / total       0.98      0.97      0.97      8400\n",
      "\n",
      "Accuracy Score: \n",
      "\n",
      "0.965833333333\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Classification Report: \\n\")\n",
    "print(classification_report(y_true, y_pred))\n",
    "print(\"Accuracy Score: \\n\")\n",
    "print(accuracy_score(y_true, y_pred))\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#The ROC curve for each class\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "fpr = dict()\n",
    "tpr = dict()\n",
    "roc_auc = dict()\n",
    "class_size = len(class_label)\n",
    "for i in range(class_size):\n",
    "    fpr[i], tpr[i], _ = roc_curve(y_true[:,i], y_pred[:,i])\n",
    "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
    "    \n",
    "fpr[\"micro\"], tpr[\"micro\"], _ = roc_curve(y_true.ravel(), y_pred.ravel())\n",
    "roc_auc[\"micro\"] = auc(fpr[\"micro\"], tpr[\"micro\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from scipy import interp\n",
    "all_fpr = np.unique(np.concatenate([fpr[i] for i in range(class_size)]))\n",
    "mean_tpr = np.zeros_like(all_fpr)\n",
    "for i in range(class_size):\n",
    "    mean_tpr += interp(all_fpr, fpr[i], tpr[i])\n",
    "    \n",
    "    \n",
    "mean_tpr /= class_size\n",
    "fpr[\"macro\"] = all_fpr\n",
    "tpr[\"macro\"] = mean_tpr\n",
    "roc_auc[\"macro\"] = auc(fpr[\"macro\"], tpr[\"macro\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c2a161f400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import cycle\n",
    "lw=3\n",
    "plt.figure(figsize=(25,25))\n",
    "plt.plot(fpr[\"micro\"], tpr[\"micro\"],\n",
    "         label='micro-average ROC curve (area = {0:0.2f})'\n",
    "               ''.format(roc_auc[\"micro\"]),\n",
    "         color='deeppink', linestyle=':', linewidth=4)\n",
    "\n",
    "plt.plot(fpr[\"macro\"], tpr[\"macro\"],\n",
    "         label='macro-average ROC curve (area = {0:0.2f})'\n",
    "               ''.format(roc_auc[\"macro\"]),\n",
    "         color='navy', linestyle=':', linewidth=4)\n",
    "\n",
    "colors = cycle(['aqua', 'darkorange', 'cornflowerblue'])\n",
    "for i, color in zip(range(class_size), colors):\n",
    "    plt.plot(fpr[i], tpr[i], color=color, lw=lw,\n",
    "             label='ROC curve of class {0} (area = {1:0.2f})'\n",
    "             ''.format(i, roc_auc[i]))\n",
    "\n",
    "plt.plot([0, 1], [0, 1], 'k--', lw=lw)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Some extension of Receiver operating characteristic to multi-class')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#De-encode the y_pred and y_true\n",
    "y_pred = lb.inverse_transform(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "y_true = lb.inverse_transform(y_true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Detailed confusion matrix:\n",
      "[[800   0   0   0   1   0   7   0   2   3]\n",
      " [  3 953   0   1   0   0   1   2   1   0]\n",
      " [ 16   0 837   5   0   0   0   2   0   0]\n",
      " [  7   2   2 841   0   4   0   2   1   4]\n",
      " [  6   3   1   0 808   0   2   2   1   4]\n",
      " [ 16   0   1   7   0 726   3   0   2   1]\n",
      " [  9   1   0   1   3   5 822   0   0   0]\n",
      " [ 14   2   8   3   3   0   0 868   0   1]\n",
      " [ 25   7   0   9   1   4   6   1 714   1]\n",
      " [ 13   0   1   7  13   1   2   5   0 770]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Detailed confusion matrix:\")\n",
    "print(confusion_matrix(y_true, y_pred,labels=class_label))"
   ]
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}