sampleImplementation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.4150</td>\n",
       "      <td>-0.3910</td>\n",
       "      <td>-0.760</td>\n",
       "      <td>-0.1190</td>\n",
       "      <td>0.17800</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>0.8090</td>\n",
       "      <td>-0.849</td>\n",
       "      <td>0.181</td>\n",
       "      <td>-0.0491</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.279</td>\n",
       "      <td>-0.02620</td>\n",
       "      <td>-0.123</td>\n",
       "      <td>-0.996</td>\n",
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       "      <td>-0.1170</td>\n",
       "      <td>-0.483</td>\n",
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       "      <td>0.182</td>\n",
       "      <td>-0.0477</td>\n",
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       "      <th>4</th>\n",
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       "      <td>-0.115</td>\n",
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       "      <td>-0.848</td>\n",
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       "      <td>-0.880</td>\n",
       "      <td>-0.1900</td>\n",
       "      <td>0.83000</td>\n",
       "      <td>0.2070</td>\n",
       "      <td>-0.4260</td>\n",
       "      <td>-0.792</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.0498</td>\n",
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       "    <tr>\n",
       "      <th>7348</th>\n",
       "      <td>0.274</td>\n",
       "      <td>-0.00775</td>\n",
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       "      <td>-0.785</td>\n",
       "      <td>0.246</td>\n",
       "      <td>0.0253</td>\n",
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       "      <td>-0.6170</td>\n",
       "      <td>-0.783</td>\n",
       "      <td>0.247</td>\n",
       "      <td>0.0367</td>\n",
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       "        0        1      2      3        4       5      6       7       8    \\\n",
       "0     0.289 -0.02030 -0.133 -0.995 -0.98300 -0.9140 -0.995 -0.9830 -0.9240   \n",
       "1     0.278 -0.01640 -0.124 -0.998 -0.97500 -0.9600 -0.999 -0.9750 -0.9580   \n",
       "2     0.280 -0.01950 -0.113 -0.995 -0.96700 -0.9790 -0.997 -0.9640 -0.9770   \n",
       "3     0.279 -0.02620 -0.123 -0.996 -0.98300 -0.9910 -0.997 -0.9830 -0.9890   \n",
       "4     0.277 -0.01660 -0.115 -0.998 -0.98100 -0.9900 -0.998 -0.9800 -0.9900   \n",
       "...     ...      ...    ...    ...      ...     ...    ...     ...     ...   \n",
       "7347  0.300 -0.05720 -0.181 -0.195  0.03990  0.0771 -0.282  0.0436  0.0604   \n",
       "7348  0.274 -0.00775 -0.147 -0.235  0.00482  0.0593 -0.323 -0.0295  0.0806   \n",
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       "7350  0.290 -0.01880 -0.158 -0.219 -0.11100  0.2690 -0.310 -0.0682  0.3190   \n",
       "7351  0.352 -0.01240 -0.204 -0.269 -0.08720  0.1770 -0.377 -0.0387  0.2290   \n",
       "\n",
       "        9    ...     551     552    553     554      555     556     557  \\\n",
       "0    -0.935  ... -0.0743 -0.2990 -0.710 -0.1130  0.03040 -0.4650 -0.0184   \n",
       "1    -0.943  ...  0.1580 -0.5950 -0.861  0.0535 -0.00743 -0.7330  0.7040   \n",
       "2    -0.939  ...  0.4150 -0.3910 -0.760 -0.1190  0.17800  0.1010  0.8090   \n",
       "3    -0.939  ...  0.4050 -0.1170 -0.483 -0.0368 -0.01290  0.6400 -0.4850   \n",
       "4    -0.942  ...  0.0878 -0.3510 -0.699  0.1230  0.12300  0.6940 -0.6160   \n",
       "...     ...  ...     ...     ...    ...     ...      ...     ...     ...   \n",
       "7347  0.211  ... -0.0702 -0.5880 -0.880 -0.1900  0.83000  0.2070 -0.4260   \n",
       "7348  0.117  ...  0.1650 -0.3910 -0.681  0.0649  0.87600 -0.8790  0.4000   \n",
       "7349  0.044  ...  0.1950  0.0251 -0.304  0.0528 -0.26700  0.8640  0.7010   \n",
       "7350  0.102  ...  0.0139  0.0639 -0.344 -0.1010  0.70100  0.9370 -0.5890   \n",
       "7351  0.269  ... -0.0584 -0.3870 -0.741 -0.2800 -0.00774 -0.0561 -0.6170   \n",
       "\n",
       "        558    559     560  \n",
       "0    -0.841  0.180 -0.0586  \n",
       "1    -0.845  0.180 -0.0543  \n",
       "2    -0.849  0.181 -0.0491  \n",
       "3    -0.849  0.182 -0.0477  \n",
       "4    -0.848  0.185 -0.0439  \n",
       "...     ...    ...     ...  \n",
       "7347 -0.792  0.239  0.0498  \n",
       "7348 -0.772  0.253  0.0501  \n",
       "7349 -0.779  0.249  0.0408  \n",
       "7350 -0.785  0.246  0.0253  \n",
       "7351 -0.783  0.247  0.0367  \n",
       "\n",
       "[7352 rows x 561 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = pd.read_csv(\"X_train.csv\", header = None) \n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>7347</th>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
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       "      <th>7349</th>\n",
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       "      <th>7350</th>\n",
       "      <td>2</td>\n",
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       "      <th>7351</th>\n",
       "      <td>2</td>\n",
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       "<p>7352 rows × 1 columns</p>\n",
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      ],
      "text/plain": [
       "      0\n",
       "0     5\n",
       "1     5\n",
       "2     5\n",
       "3     5\n",
       "4     5\n",
       "...  ..\n",
       "7347  2\n",
       "7348  2\n",
       "7349  2\n",
       "7350  2\n",
       "7351  2\n",
       "\n",
       "[7352 rows x 1 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train = pd.read_csv('y_train.csv', header = None)\n",
    "Y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = pd.read_csv(\"X_test.csv\", header = None) \n",
    "Y_test = pd.read_csv(\"y_test.csv\", header = None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNN Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/ipykernel_launcher.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=3, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = KNeighborsClassifier(n_neighbors=3)\n",
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8907363420427553\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\",metrics.accuracy_score(Y_test, predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-Score: [0.90583804 0.89596603 0.85122898 0.82188841 0.85354897 0.99813433]\n"
     ]
    }
   ],
   "source": [
    "print(\"F1-Score:\",metrics.f1_score(Y_test, predicted, average = None))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ensemble Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import BaggingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dheeraj/.local/lib/python3.6/site-packages/sklearn/ensemble/_bagging.py:645: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "clf = BaggingClassifier(base_estimator=SVC(), n_estimators=10, random_state=0).fit(X_train, Y_train)\n",
    "predicted = clf.predict(X_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9514760773668137\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\",metrics.accuracy_score(Y_test, predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-Score: [0.9635468  0.94648478 0.9529703  0.9157128  0.92705448 1.        ]\n"
     ]
    }
   ],
   "source": [
    "### Measures how accurate the test was by looking at the precision and recall of the test\n",
    "\n",
    "print(\"F1-Score:\",metrics.f1_score(Y_test, predicted, average = None))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HMM Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hmmlearn import hmm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-7a3c44c83c81>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhmm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGaussianHMM\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_components\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcovariance_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"full\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mpredicted\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/dheeraj/.local/lib/python3.6/site-packages/hmmlearn/base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, lengths)\u001b[0m\n\u001b[1;32m    475\u001b[0m                 self._accumulate_sufficient_statistics(\n\u001b[1;32m    476\u001b[0m                     \u001b[0mstats\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframelogprob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mposteriors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfwdlattice\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 477\u001b[0;31m                     bwdlattice)\n\u001b[0m\u001b[1;32m    478\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    479\u001b[0m             \u001b[0;31m# XXX must be before convergence check, because otherwise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/dheeraj/.local/lib/python3.6/site-packages/hmmlearn/hmm.py\u001b[0m in \u001b[0;36m_accumulate_sufficient_statistics\u001b[0;34m(self, stats, obs, framelogprob, posteriors, fwdlattice, bwdlattice)\u001b[0m\n\u001b[1;32m    261\u001b[0m                 \u001b[0;31m# -> (nc, nf, nf)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    262\u001b[0m                 stats['obs*obs.T'] += np.einsum(\n\u001b[0;32m--> 263\u001b[0;31m                     'ij,ik,il->jkl', posteriors, obs, obs)\n\u001b[0m\u001b[1;32m    264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    265\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_do_mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstats\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36meinsum\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m/home/dheeraj/.local/lib/python3.6/site-packages/numpy/core/einsumfunc.py\u001b[0m in \u001b[0;36meinsum\u001b[0;34m(*operands, **kwargs)\u001b[0m\n\u001b[1;32m   1354\u001b[0m     \u001b[0;31m# If no optimization, run pure einsum\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1355\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0moptimize_arg\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1356\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mc_einsum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1357\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1358\u001b[0m     \u001b[0mvalid_einsum_kwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'out'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'dtype'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'order'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'casting'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#Viterbi algorithm\n",
    "# http://www.adeveloperdiary.com/data-science/machine-learning/implement-viterbi-algorithm-in-hidden-markov-model-using-python-and-r/\n",
    "\n",
    "model = hmm.GaussianHMM(n_components=3, covariance_type=\"full\")\n",
    "\n",
    "model.startprob_ = np.array([0.6, 0.3, 0.1])\n",
    "model.transmat_ = np.array([[0.7, 0.2, 0.1],\n",
    "                            [0.3, 0.5, 0.2],\n",
    "                            [0.3, 0.3, 0.4]])\n",
    "\n",
    "model.means_ = np.array([[0.0, 0.0], [3.0, -3.0], [5.0, 10.0]])\n",
    "model.covars_ = np.tile(np.identity(2), (3, 1, 1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(X_train)\n",
    "predicted = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "####\n",
    "# Meeting 1 Notes\n",
    "####\n",
    "# F1-score needs to be used, for imbalanced datasets\n",
    "# Plot distribution of labels to see if it is imbalanced (Needs to be oversampled)\n",
    "# Dataset find mean, median, kurtosis\n",
    "# Pearson's R between features\n",
    "\n",
    "# Implement basic feature generation\n",
    "# Neural Network\n",
    "# Figure out why the results are ending up the way there are"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Label Distributions and Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.181</td>\n",
       "      <td>-0.0491</td>\n",
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       "      <td>-0.323</td>\n",
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       "      <td>0.0806</td>\n",
       "      <td>0.117</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1650</td>\n",
       "      <td>-0.3910</td>\n",
       "      <td>-0.681</td>\n",
       "      <td>0.0649</td>\n",
       "      <td>0.87600</td>\n",
       "      <td>-0.8790</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>-0.772</td>\n",
       "      <td>0.253</td>\n",
       "      <td>0.0501</td>\n",
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       "    <tr>\n",
       "      <th>7349</th>\n",
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       "      <td>-0.01700</td>\n",
       "      <td>-0.045</td>\n",
       "      <td>-0.218</td>\n",
       "      <td>-0.10400</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>-0.305</td>\n",
       "      <td>-0.0989</td>\n",
       "      <td>0.3330</td>\n",
       "      <td>0.044</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1950</td>\n",
       "      <td>0.0251</td>\n",
       "      <td>-0.304</td>\n",
       "      <td>0.0528</td>\n",
       "      <td>-0.26700</td>\n",
       "      <td>0.8640</td>\n",
       "      <td>0.7010</td>\n",
       "      <td>-0.779</td>\n",
       "      <td>0.249</td>\n",
       "      <td>0.0408</td>\n",
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       "    <tr>\n",
       "      <th>7350</th>\n",
       "      <td>0.290</td>\n",
       "      <td>-0.01880</td>\n",
       "      <td>-0.158</td>\n",
       "      <td>-0.219</td>\n",
       "      <td>-0.11100</td>\n",
       "      <td>0.2690</td>\n",
       "      <td>-0.310</td>\n",
       "      <td>-0.0682</td>\n",
       "      <td>0.3190</td>\n",
       "      <td>0.102</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0139</td>\n",
       "      <td>0.0639</td>\n",
       "      <td>-0.344</td>\n",
       "      <td>-0.1010</td>\n",
       "      <td>0.70100</td>\n",
       "      <td>0.9370</td>\n",
       "      <td>-0.5890</td>\n",
       "      <td>-0.785</td>\n",
       "      <td>0.246</td>\n",
       "      <td>0.0253</td>\n",
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       "    <tr>\n",
       "      <th>7351</th>\n",
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       "      <td>-0.01240</td>\n",
       "      <td>-0.204</td>\n",
       "      <td>-0.269</td>\n",
       "      <td>-0.08720</td>\n",
       "      <td>0.1770</td>\n",
       "      <td>-0.377</td>\n",
       "      <td>-0.0387</td>\n",
       "      <td>0.2290</td>\n",
       "      <td>0.269</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0584</td>\n",
       "      <td>-0.3870</td>\n",
       "      <td>-0.741</td>\n",
       "      <td>-0.2800</td>\n",
       "      <td>-0.00774</td>\n",
       "      <td>-0.0561</td>\n",
       "      <td>-0.6170</td>\n",
       "      <td>-0.783</td>\n",
       "      <td>0.247</td>\n",
       "      <td>0.0367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7352 rows × 561 columns</p>\n",
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       "        0        1      2      3        4       5      6       7       8    \\\n",
       "0     0.289 -0.02030 -0.133 -0.995 -0.98300 -0.9140 -0.995 -0.9830 -0.9240   \n",
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       "3     0.279 -0.02620 -0.123 -0.996 -0.98300 -0.9910 -0.997 -0.9830 -0.9890   \n",
       "4     0.277 -0.01660 -0.115 -0.998 -0.98100 -0.9900 -0.998 -0.9800 -0.9900   \n",
       "...     ...      ...    ...    ...      ...     ...    ...     ...     ...   \n",
       "7347  0.300 -0.05720 -0.181 -0.195  0.03990  0.0771 -0.282  0.0436  0.0604   \n",
       "7348  0.274 -0.00775 -0.147 -0.235  0.00482  0.0593 -0.323 -0.0295  0.0806   \n",
       "7349  0.273 -0.01700 -0.045 -0.218 -0.10400  0.2750 -0.305 -0.0989  0.3330   \n",
       "7350  0.290 -0.01880 -0.158 -0.219 -0.11100  0.2690 -0.310 -0.0682  0.3190   \n",
       "7351  0.352 -0.01240 -0.204 -0.269 -0.08720  0.1770 -0.377 -0.0387  0.2290   \n",
       "\n",
       "        9    ...     551     552    553     554      555     556     557  \\\n",
       "0    -0.935  ... -0.0743 -0.2990 -0.710 -0.1130  0.03040 -0.4650 -0.0184   \n",
       "1    -0.943  ...  0.1580 -0.5950 -0.861  0.0535 -0.00743 -0.7330  0.7040   \n",
       "2    -0.939  ...  0.4150 -0.3910 -0.760 -0.1190  0.17800  0.1010  0.8090   \n",
       "3    -0.939  ...  0.4050 -0.1170 -0.483 -0.0368 -0.01290  0.6400 -0.4850   \n",
       "4    -0.942  ...  0.0878 -0.3510 -0.699  0.1230  0.12300  0.6940 -0.6160   \n",
       "...     ...  ...     ...     ...    ...     ...      ...     ...     ...   \n",
       "7347  0.211  ... -0.0702 -0.5880 -0.880 -0.1900  0.83000  0.2070 -0.4260   \n",
       "7348  0.117  ...  0.1650 -0.3910 -0.681  0.0649  0.87600 -0.8790  0.4000   \n",
       "7349  0.044  ...  0.1950  0.0251 -0.304  0.0528 -0.26700  0.8640  0.7010   \n",
       "7350  0.102  ...  0.0139  0.0639 -0.344 -0.1010  0.70100  0.9370 -0.5890   \n",
       "7351  0.269  ... -0.0584 -0.3870 -0.741 -0.2800 -0.00774 -0.0561 -0.6170   \n",
       "\n",
       "        558    559     560  \n",
       "0    -0.841  0.180 -0.0586  \n",
       "1    -0.845  0.180 -0.0543  \n",
       "2    -0.849  0.181 -0.0491  \n",
       "3    -0.849  0.182 -0.0477  \n",
       "4    -0.848  0.185 -0.0439  \n",
       "...     ...    ...     ...  \n",
       "7347 -0.792  0.239  0.0498  \n",
       "7348 -0.772  0.253  0.0501  \n",
       "7349 -0.779  0.249  0.0408  \n",
       "7350 -0.785  0.246  0.0253  \n",
       "7351 -0.783  0.247  0.0367  \n",
       "\n",
       "[7352 rows x 561 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "      0\n",
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       "...  ..\n",
       "7347  2\n",
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       "7349  2\n",
       "7350  2\n",
       "7351  2\n",
       "\n",
       "[7352 rows x 1 columns]"
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     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_train = Y_train.rename(columns = {0: 'label'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [X_train, Y_train]\n",
    "trainData = pd.concat(data, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0695be6eb8>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(trainData.label, hist = True)\n",
    "\n",
    "# ######\n",
    "# 1 WALKING\n",
    "# 2 WALKING_UPSTAIRS\n",
    "# 3 WALKING_DOWNSTAIRS\n",
    "# 4 SITTING\n",
    "# 5 STANDING\n",
    "# 6 LAYING\n",
    "# ######\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pearson's Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import pearsonr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Correlation Matrix\n",
    "\n",
    "corrMatrix = trainData.corr()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/home/dheeraj/.local/lib/python3.6/site-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36m_setattr_cm\u001b[0;34m(obj, **kwargs)\u001b[0m\n\u001b[1;32m   1964\u001b[0m             \u001b[0msetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1965\u001b[0;31m         \u001b[0;32myield\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1966\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/dheeraj/.local/lib/python3.6/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m   2081\u001b[0m                     bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[0;32m-> 2082\u001b[0;31m                             bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[1;32m   2083\u001b[0m                     \u001b[0mpad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"pad_inches\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/dheeraj/.local/lib/python3.6/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mget_tightbbox\u001b[0;34m(self, renderer, bbox_extra_artists)\u001b[0m\n\u001b[1;32m   2384\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2385\u001b[0;31m             \u001b[0mbbox\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2386\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbbox\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwidth\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mbbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheight\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/dheeraj/.local/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mget_tightbbox\u001b[0;34m(self, renderer, call_axes_locator, bbox_extra_artists)\u001b[0m\n\u001b[1;32m   4354\u001b[0m                     \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4355\u001b[0;31m             \u001b[0mbbox\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4356\u001b[0m             if (bbox is not None\n",
      "\u001b[0;32m/home/dheeraj/.local/lib/python3.6/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mget_tightbbox\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    281\u001b[0m         \"\"\"\n\u001b[0;32m--> 282\u001b[0;31m         \u001b[0mbbox\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_window_extent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    283\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_clip_on\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/dheeraj/.local/lib/python3.6/site-packages/matplotlib/text.py\u001b[0m in \u001b[0;36mget_window_extent\u001b[0;34m(self, renderer, dpi)\u001b[0m\n\u001b[1;32m    904\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 905\u001b[0;31m         \u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdescent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_renderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    906\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_unitless_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/dheeraj/.local/lib/python3.6/site-packages/matplotlib/text.py\u001b[0m in \u001b[0;36m_get_layout\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    292\u001b[0m             \u001b[0;34m\"lp\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fontproperties\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 293\u001b[0;31m             ismath=\"TeX\" if self.get_usetex() else False)\n\u001b[0m\u001b[1;32m    294\u001b[0m         \u001b[0mmin_dy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlp_h\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mlp_d\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_linespacing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/dheeraj/.local/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[0;34m(self, s, prop, ismath)\u001b[0m\n\u001b[1;32m    211\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 212\u001b[0;31m         \u001b[0mflags\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_hinting_flag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    213\u001b[0m         \u001b[0mfont\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_agg_font\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/dheeraj/.local/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mget_hinting_flag\u001b[0;34m()\u001b[0m\n\u001b[1;32m     63\u001b[0m         }\n\u001b[0;32m---> 64\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrcParams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'text.hinting'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-48-3abfe61c7495>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheatmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcorrMatrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mannot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/home/dheeraj/.local/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m    270\u001b[0m     \"\"\"\n\u001b[1;32m    271\u001b[0m     \u001b[0;32mglobal\u001b[0m \u001b[0m_show\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_show\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    273\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m     34\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mfigure_manager\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mGcf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_all_fig_managers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m             \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0mshow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/display.py\u001b[0m in \u001b[0;36mdisplay\u001b[0;34m(*objs, **kwargs)\u001b[0m\n\u001b[1;32m    304\u001b[0m             \u001b[0mpublish_display_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    305\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 306\u001b[0;31m             \u001b[0mformat_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmd_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    307\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mformat_dict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    308\u001b[0m                 \u001b[0;31m# nothing to display (e.g. _ipython_display_ took over)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mformat\u001b[0;34m(self, obj, include, exclude)\u001b[0m\n\u001b[1;32m    171\u001b[0m             \u001b[0mmd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 173\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    174\u001b[0m             \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m                 \u001b[0;31m# FIXME: log the exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<decorator-gen-9>\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mcatch_format_error\u001b[0;34m(method, self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    215\u001b[0m     \u001b[0;34m\"\"\"show traceback on failed format call\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    216\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 217\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    218\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m         \u001b[0;31m# don't warn on NotImplementedErrors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    332\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 334\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    335\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    239\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 241\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    242\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 125\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    126\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    127\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/dheeraj/.local/lib/python3.6/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m   2111\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_edgecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morigedgecolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2112\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_canvas\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2113\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2114\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2115\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.6/contextlib.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, type, value, traceback)\u001b[0m\n\u001b[1;32m     97\u001b[0m                 \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 99\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraceback\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    100\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m                 \u001b[0;31m# Suppress StopIteration *unless* it's the same exception that\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/dheeraj/.local/lib/python3.6/site-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36m_setattr_cm\u001b[0;34m(obj, **kwargs)\u001b[0m\n\u001b[1;32m   1969\u001b[0m                 \u001b[0mdelattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1970\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1971\u001b[0;31m                 \u001b[0msetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1972\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1973\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/dheeraj/.local/lib/python3.6/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36m_set_dpi\u001b[0;34m(self, dpi, forward)\u001b[0m\n\u001b[1;32m    474\u001b[0m         \"\"\"\n\u001b[1;32m    475\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 476\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi_scale_trans\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    477\u001b[0m         \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_size_inches\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    478\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_size_inches\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/dheeraj/.local/lib/python3.6/site-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36mclear\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1919\u001b[0m         \u001b[0;31m# A bit faster than np.identity(3).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1920\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mtx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mIdentityTransform\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mtx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1921\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minvalidate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1922\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1923\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/dheeraj/.local/lib/python3.6/site-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36minvalidate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    132\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_affine\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m             \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mINVALID_AFFINE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 134\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalidate_internal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minvalidating_node\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_invalidate_internal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minvalidating_node\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/dheeraj/.local/lib/python3.6/site-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36m_invalidate_internal\u001b[0;34m(self, value, invalidating_node)\u001b[0m\n\u001b[1;32m    159\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mparent\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    160\u001b[0m                     parent._invalidate_internal(\n\u001b[0;32m--> 161\u001b[0;31m                         value=value, invalidating_node=self)\n\u001b[0m\u001b[1;32m    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mset_children\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/dheeraj/.local/lib/python3.6/site-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36m_invalidate_internal\u001b[0;34m(self, value, invalidating_node)\u001b[0m\n\u001b[1;32m    159\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mparent\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    160\u001b[0m                     parent._invalidate_internal(\n\u001b[0;32m--> 161\u001b[0;31m                         value=value, invalidating_node=self)\n\u001b[0m\u001b[1;32m    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mset_children\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/dheeraj/.local/lib/python3.6/site-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36m_invalidate_internal\u001b[0;34m(self, value, invalidating_node)\u001b[0m\n\u001b[1;32m    159\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mparent\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    160\u001b[0m                     parent._invalidate_internal(\n\u001b[0;32m--> 161\u001b[0;31m                         value=value, invalidating_node=self)\n\u001b[0m\u001b[1;32m    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mset_children\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/dheeraj/.local/lib/python3.6/site-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36m_invalidate_internal\u001b[0;34m(self, value, invalidating_node)\u001b[0m\n\u001b[1;32m    159\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mparent\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    160\u001b[0m                     parent._invalidate_internal(\n\u001b[0;32m--> 161\u001b[0;31m                         value=value, invalidating_node=self)\n\u001b[0m\u001b[1;32m    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mset_children\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/dheeraj/.local/lib/python3.6/site-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36m_invalidate_internal\u001b[0;34m(self, value, invalidating_node)\u001b[0m\n\u001b[1;32m    159\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mparent\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    160\u001b[0m                     parent._invalidate_internal(\n\u001b[0;32m--> 161\u001b[0;31m                         value=value, invalidating_node=self)\n\u001b[0m\u001b[1;32m    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mset_children\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/dheeraj/.local/lib/python3.6/site-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36m_invalidate_internal\u001b[0;34m(self, value, invalidating_node)\u001b[0m\n\u001b[1;32m   2331\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2332\u001b[0m         Transform._invalidate_internal(self, value=value,\n\u001b[0;32m-> 2333\u001b[0;31m                                        invalidating_node=invalidating_node)\n\u001b[0m\u001b[1;32m   2334\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2335\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__eq__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/dheeraj/.local/lib/python3.6/site-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36m_invalidate_internal\u001b[0;34m(self, value, invalidating_node)\u001b[0m\n\u001b[1;32m    159\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mparent\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    160\u001b[0m                     parent._invalidate_internal(\n\u001b[0;32m--> 161\u001b[0;31m                         value=value, invalidating_node=self)\n\u001b[0m\u001b[1;32m    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mset_children\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/dheeraj/.local/lib/python3.6/site-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36m_invalidate_internal\u001b[0;34m(self, value, invalidating_node)\u001b[0m\n\u001b[1;32m    151\u001b[0m         \u001b[0mstatus_changed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalid\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpass_through\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mstatus_changed\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    154\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "sns.heatmap(corrMatrix, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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