1_06_FeatureEngineering4_Classification_with_ExtractedFeatures.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification with extracted features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import re\n",
    "import random\n",
    "import scipy.stats as scs\n",
    "import feature_engineering as fe\n",
    "\n",
    "from matplotlib  import cm\n",
    "from collections import Counter\n",
    "from importlib import reload\n",
    "from nltk import PorterStemmer\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from sklearn.preprocessing import OneHotEncoder, LabelEncoder\n",
    "from sklearn.preprocessing import scale, normalize, robust_scale\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "sns.set_context(\"paper\")\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class_train = pd.read_csv('./data/train_variants')\n",
    "text_train = pd.read_csv(\"./data/train_text\", sep=\"\\|\\|\", engine='python', header=None, skiprows=1, names=[\"ID\",\"Text\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feature_matrix1 = pd.read_csv('./data/feature_matrix_pass1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<p>10 rows × 30 columns</p>\n",
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      ],
      "text/plain": [
       "      ici164  mek5  lis  cryb2  cs07  iti  ectm  drug–kinase  pkd  \\\n",
       "1670     0.0   0.0  0.0    0.0   0.0  0.0   0.0          0.0  0.0   \n",
       "773      0.0   0.0  0.0    0.0   0.0  0.0   0.0          0.0  0.0   \n",
       "2373     0.0   0.0  0.0    0.0   0.0  0.0   0.0          0.0  0.0   \n",
       "2180     0.0   0.0  0.0    0.0   0.0  0.0   0.0          0.0  0.0   \n",
       "2641     0.0   0.0  0.0    0.0   0.0  0.0   0.0          0.0  0.0   \n",
       "2394     0.0   0.0  0.0    0.0   0.0  0.0   0.0          0.0  0.0   \n",
       "2369     0.0   0.0  0.0    0.0   0.0  0.0   0.0          0.0  0.0   \n",
       "205      0.0   0.0  0.0    0.0   0.0  0.0   0.0          0.0  0.0   \n",
       "477      0.0   0.0  0.0    0.0   0.0  0.0   0.0          0.0  0.0   \n",
       "1999     0.0   0.0  0.0    0.0   0.0  0.0   0.0          0.0  0.0   \n",
       "\n",
       "      osteoclastogenesis ...   siltuximab  pc146  nmri  fosl1  xl518  mut8  \\\n",
       "1670                 0.0 ...          0.0    0.0   0.0    0.0    0.0   0.0   \n",
       "773                  0.0 ...          0.0    0.0   0.0    0.0    0.0   0.0   \n",
       "2373                 0.0 ...          0.0    0.0   0.0    0.0    0.0   0.0   \n",
       "2180                 0.0 ...          0.0    0.0   0.0    0.0    0.0   0.0   \n",
       "2641                 0.0 ...          0.0    0.0   0.0    0.0    0.0   0.0   \n",
       "2394                 0.0 ...          0.0    0.0   0.0    0.0    0.0   0.0   \n",
       "2369                 0.0 ...          0.0    0.0   0.0    0.0    0.0   0.0   \n",
       "205                  0.0 ...          0.0    0.0   0.0    0.0    0.0   0.0   \n",
       "477                  0.0 ...          0.0    0.0   0.0    0.0    0.0   0.0   \n",
       "1999                 0.0 ...          0.0    0.0   0.0    0.0    0.0   0.0   \n",
       "\n",
       "      efcab3  eft  gabra6  rp3  \n",
       "1670     0.0  0.0     0.0  0.0  \n",
       "773      0.0  0.0     0.0  0.0  \n",
       "2373     0.0  0.0     0.0  0.0  \n",
       "2180     0.0  0.0     0.0  0.0  \n",
       "2641     0.0  0.0     0.0  0.0  \n",
       "2394     0.0  0.0     0.0  0.0  \n",
       "2369     0.0  0.0     0.0  0.0  \n",
       "205      0.0  0.0     0.0  0.0  \n",
       "477      0.0  0.0     0.0  0.0  \n",
       "1999     0.0  0.0     0.0  0.0  \n",
       "\n",
       "[10 rows x 30 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_features = random.sample(list(feature_matrix1.columns), 30)\n",
    "random_rows = random.sample(range(len(feature_matrix1)), 10)\n",
    "feature_matrix1.loc[random_rows, random_features]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try RandomForest on Raw Features Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Get Gene feature from 'train_variants' data\n",
    "X_gene = np.array(class_train.Gene)\n",
    "X_gene_int = LabelEncoder().fit_transform(X_gene.ravel()).reshape(-1, 1)\n",
    "X_gene_bin = OneHotEncoder().fit_transform(X_gene_int).toarray()\n",
    "gene_table = pd.DataFrame(X_gene_bin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Get Mutation Type from 'train_variants' data\n",
    "import mskcc_functions as ski\n",
    "new_table = ski.convert_mutation_type(class_train)\n",
    "X_mtype = np.array(new_table['mutation_type'])\n",
    "X_mtype_int = LabelEncoder().fit_transform(X_mtype.ravel()).reshape(-1, 1)\n",
    "X_mtype_bin = OneHotEncoder().fit_transform(X_mtype_int).toarray()\n",
    "mtype_table = pd.DataFrame(X_mtype_bin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features1 = pd.concat([feature_matrix1, gene_table, mtype_table], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = np.array(features1).astype(float)\n",
    "y = np.array(class_train.Class).astype(int).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=55)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 23.8 s, sys: 67.8 ms, total: 23.9 s\n",
      "Wall time: 6.25 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "rfc = RandomForestClassifier(n_estimators=100, max_depth=100, n_jobs=4)\n",
    "rfc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.482965931864\n"
     ]
    }
   ],
   "source": [
    "y_pred = rfc.predict(X_test)\n",
    "print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Get rid of features with zero weights</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  5.75219498e-04],\n",
       "       [  4.51443697e-06],\n",
       "       [  1.58299195e-04],\n",
       "       ..., \n",
       "       [  2.57197956e-04],\n",
       "       [  7.08197880e-03],\n",
       "       [  8.48858709e-06]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nonzero_features_ind = np.argwhere(rfc.feature_importances_ > 0)\n",
    "rfc.feature_importances_[nonzero_features_ind]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(nonzero_features_ind)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  5.75219498e-04],\n",
       "       [  4.51443697e-06],\n",
       "       [  1.58299195e-04],\n",
       "       ..., \n",
       "       [  2.57197956e-04],\n",
       "       [  7.08197880e-03],\n",
       "       [  8.48858709e-06]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.feature_importances_[nonzero_features_ind]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of features: 22471\n",
      "Length of feature_importances_: 22471\n",
      "Length of non-zero importance features: 8294\n"
     ]
    }
   ],
   "source": [
    "print('Length of features: %d' % features1.shape[1])\n",
    "print('Length of feature_importances_: %d' % len(rfc.feature_importances_))\n",
    "print('Length of non-zero importance features: %d' % len(nonzero_features_ind))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3321, 8294)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features2 = features1.iloc[:, list(nonzero_features_ind.ravel())]\n",
    "features2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X2 = np.array(features2).astype(float)\n",
    "X_train2, X_test2, y_train2, y_test2 = train_test_split(X2, y, test_size=0.15, random_state=55)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 9.12 s, sys: 4.14 ms, total: 9.12 s\n",
      "Wall time: 2.38 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "rfc2 = RandomForestClassifier(n_estimators=100, max_depth=100, n_jobs=4)\n",
    "rfc2.fit(X_train2, y_train2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3321, 8294)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.480961923848\n"
     ]
    }
   ],
   "source": [
    "y_pred2 = rfc2.predict(X_test2)\n",
    "print(accuracy_score(y_test2, y_pred2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>SelectFromModel</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train2, X_test2, y_train2, y_test2 = train_test_split(X2, y, test_size=0.15, random_state=75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = RandomForestClassifier(n_estimators=100, max_depth=100, n_jobs=4)\n",
    "model = SelectFromModel(clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8.96 s, sys: 7.03 ms, total: 8.97 s\n",
      "Wall time: 2.37 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SelectFromModel(estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=100, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=100, n_jobs=4, oob_score=False, random_state=None,\n",
       "            verbose=0, warm_start=False),\n",
       "        prefit=False, threshold=None)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "model.fit(X_train2, y_train2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3321, 1452)\n",
      "0.000120569086086\n"
     ]
    }
   ],
   "source": [
    "X2_select = model.transform(X2)\n",
    "print(X2_select.shape)\n",
    "print(model.threshold_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use 0.0012 as threshhold for feature selection from rfc2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1438\n",
      "(3321, 1438)\n"
     ]
    }
   ],
   "source": [
    "features_ind3 = np.argwhere(rfc2.feature_importances_ >= 0.00012)\n",
    "print(len(features_ind3))\n",
    "\n",
    "features3 = features2.iloc[:, list(features_ind3.ravel())]\n",
    "print(features3.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X3 = np.array(features3).astype(float)\n",
    "X_train3, X_test3, y_train3, y_test3 = train_test_split(X3, y, test_size=0.15, random_state=75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.5 s, sys: 8.74 ms, total: 2.51 s\n",
      "Wall time: 755 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "rfc3 = RandomForestClassifier(n_estimators=100, max_depth=100, n_jobs=4)\n",
    "rfc3.fit(X_train3, y_train3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.50501002004\n"
     ]
    }
   ],
   "source": [
    "y_pred3 = rfc3.predict(X_test3)\n",
    "print(accuracy_score(y_test3, y_pred3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With robust_scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3321, 1438)\n"
     ]
    }
   ],
   "source": [
    "features3 = features2.iloc[:, list(features_ind3.ravel())]\n",
    "print(features3.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X3_scaled = robust_scale(X3)\n",
    "X_train3s, X_test3s, y_train3s, y_test3s = train_test_split(X3_scaled, y, test_size=0.1, random_state=75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.52 s, sys: 8.64 ms, total: 1.53 s\n",
      "Wall time: 431 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "rfc3s = RandomForestClassifier(n_estimators=50, max_depth=100, n_jobs=4)\n",
    "rfc3s.fit(X_train3s, y_train3s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.459459459459\n"
     ]
    }
   ],
   "source": [
    "y_pred3s = rfc3s.predict(X_test3s)\n",
    "print(accuracy_score(y_test3s, y_pred3s))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0.0\n",
       "1        0.0\n",
       "2        0.0\n",
       "3        0.0\n",
       "4        0.0\n",
       "5        0.0\n",
       "6        0.0\n",
       "7        0.0\n",
       "8        0.0\n",
       "9        0.0\n",
       "10       0.0\n",
       "11       7.0\n",
       "12       7.0\n",
       "13       0.0\n",
       "14       0.0\n",
       "15       0.0\n",
       "16       0.0\n",
       "17       0.0\n",
       "18       0.0\n",
       "19       0.0\n",
       "20       0.0\n",
       "21       0.0\n",
       "22       0.0\n",
       "23       0.0\n",
       "24       0.0\n",
       "25       0.0\n",
       "26       1.0\n",
       "27       0.0\n",
       "28       0.0\n",
       "29       0.0\n",
       "        ... \n",
       "3291     0.0\n",
       "3292     0.0\n",
       "3293     0.0\n",
       "3294     0.0\n",
       "3295     0.0\n",
       "3296     0.0\n",
       "3297     0.0\n",
       "3298     0.0\n",
       "3299     0.0\n",
       "3300     0.0\n",
       "3301     0.0\n",
       "3302     0.0\n",
       "3303     0.0\n",
       "3304     0.0\n",
       "3305     0.0\n",
       "3306     0.0\n",
       "3307     1.0\n",
       "3308     0.0\n",
       "3309     1.0\n",
       "3310     1.0\n",
       "3311     1.0\n",
       "3312     0.0\n",
       "3313     0.0\n",
       "3314     0.0\n",
       "3315     0.0\n",
       "3316     0.0\n",
       "3317     7.0\n",
       "3318     0.0\n",
       "3319     1.0\n",
       "3320    34.0\n",
       "Name: p53, Length: 3321, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features3['p53']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x7f582e564208>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IPEZWqrodx+v1Ynp6GkePHsX29rbmBqNGBp2DmqcqzPLZMIKtQ9X3fu/34itf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\nERGJgEUVERERkQj+H26JlNDFhu+9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f581dd21208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a new table with ID as index\n",
    "pca_table4 = pd.DataFrame(index=class_train.ID)\n",
    "pca = PCA(n_components=3)\n",
    "pca.fit(X3)\n",
    "pca_table4['x'] = pca.fit_transform(X3).T[0]\n",
    "pca_table4['y'] = pca.fit_transform(X3).T[1]\n",
    "pca_table4['z'] = pca.fit_transform(X3).T[2]\n",
    "\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "fig = plt.figure(1, figsize=(8, 8))\n",
    "plt.clf()\n",
    "ax = Axes3D(fig, elev=18, azim=55)#, rect=[0, 0, .95, 1])\n",
    "\n",
    "ax.scatter(pca_table4.x, pca_table4.y, pca_table4.z, c=class_train['Class'], marker = 'o', s=50, cmap = cm.jet)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some variance analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      1438.000000\n",
       "mean        301.032425\n",
       "std        4710.894521\n",
       "min           0.000301\n",
       "25%           0.360865\n",
       "50%           2.479377\n",
       "75%          19.249406\n",
       "90%         118.642329\n",
       "95%         310.466751\n",
       "99%        2973.438910\n",
       "max      138280.942286\n",
       "dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_var = features3.var(axis=0)\n",
    "feat_var.describe(percentiles=[0.25, 0.5, 0.75, 0.9, 0.95, 0.99])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 100)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAD9CAYAAAC2l2x5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAzQl8ABN/R/RVCkKJjx47gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f581dd23400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_var.plot(kind='box')\n",
    "plt.ylim([0, 100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "356"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(feat_var[feat_var > 20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 30)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAD9CAYAAABazssqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ndwPn7AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f582e234940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_var.plot(kind='hist', bins=100)\n",
    "#plt.xlim([0,1000])\n",
    "plt.ylim([0, 30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features4_ind = features3.columns[features3.var(axis=0) > 20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3321, 356)\n"
     ]
    }
   ],
   "source": [
    "features4 = features3[features4_ind]\n",
    "print(features4.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X4 = np.array(features4).astype(float)\n",
    "X_train4, X_test4, y_train4, y_test4 = train_test_split(X4, y, test_size=0.15, random_state=75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.27 s, sys: 4.99 ms, total: 1.27 s\n",
      "Wall time: 457 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "rfc4 = RandomForestClassifier(n_estimators=100, max_depth=100, n_jobs=4)\n",
    "rfc4.fit(X_train4, y_train4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.458917835671\n"
     ]
    }
   ],
   "source": [
    "y_pred4 = rfc4.predict(X_test4)\n",
    "print(accuracy_score(y_test4, y_pred4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x7f582dec1828>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DITcYLRaLWF1dxe3bt+H3+5FOpxEOh498LhSq9H0shmGwubkJj8cj/wJE+xBqg0KVAna7\nHdVqtee/p6eL61GMMlYjjHNYY6zX621rn4BH1adQKIRcLge73Y4LFy7g2LFjmo+xG2a5iOuJ1+tt\naTC6srKCGzduIBaLyQ1Gzc5oa6p6OVZzo1Hah1AbFKoUsNvtKBQKPf89Iy1UN8pUpRFC1aAJgtC2\nZUuj0YDdbpfvvBsbG4Pb7aaTqUaMUtHZ32D0wYMHWFhYQLVaRSKRQDqdhtPplP+8UZ5XN4y8puqo\nYzV/z/f3wGreJocClnooVCngcDhMv1DdKFOVRnhN1RqjKIpt1adyuQwA8obBkUgE4+PjtBBZI3r/\n7PWCZVkkEgkkEgk0Gg2sra3hrbfeAsMwSKVSSCaTFKoMfqzDAhZ1cVeGQpUCo9BSwQhhBTDOOHsd\nI8/zbWuf9lefIpEIVZ90wIwXIpvNhlwuh1wuJzcYPXfuHGw2m1zFNvrnbhhTclro9r2hbXLURaFK\nASV3/xkhAADGGasRxnnYyUnaMLg5PFUqFTAMQ9UnogvNDUZXVlZw584dnDlzBuFwGOl0GoFAwJAX\nYK33/tNzgKOApRyFKgWU9KkyUqXKCGM1SqgSRRE8z7etfeI4Dg6HQ64+xWIxuFwuw1cBiPkwDAO3\n241wOIzHHnsMW1tbuH//PgqFAhKJhLxfo1JafZ+1DDrDXFPVK9qHsD8UqhRwOBx9VaqMsk4JMEZY\nAfQ5zv3Vp62tLVQqFWxvb8vVp1gshsnJSU02wSVETdI6HKnBKMdx2NjYwI0bN8DzPJLJpKIGo1pN\nlellnZOej0X7EHaPQpUCo1Kp0ltY0SOp+tTcdZzjODidTnnDYJvNhkqlgpmZmWEPl6isl++IGb5P\nnZ6D1WptaTC6urqqqMGoVrSsHgHarb0b1Hq3Tl3cgYctG9xu98gHLApVCvS7pspolSojBECtwp8o\niqhWq21rn1iWlafu4vF4x+rT1tZWX33NiDGM0sXkqCqIw+HAxMQEJiYmdN9g1Ex3MjbT4nlJP79a\nreL69et48sknR36bHApVCozC3n9GCYCDeE05jmtb+8TzPJxOpxygEokEXC5XVycvI73vZLCMfhHv\n5YLdqcHo7OwsotHooQ1GKVQpN4yq2KjvQ0ihSgEllSojVH8A4wQBJRU1URRRqVRaAlSlUoHFYoHX\n64XH40EikcD09DSsVvrKEGWM8H0ahMMajErrr4bRYNTMoUor+6dQ908Rsiwrt+EwO7pCKKCkUmUU\nRgpV3YyT47i2LVt4nofL5ZKrT8lkEk6nU/X3ySivJSFHURpE9NRglEKVcofdQTlq5z0KVQr0G6qM\nxChfiP3jlKpPzQGqWq3K1Sev1yvf+q1V9ckoryXp3ai9r2oGkf0NRldWVnDu3Dk4nU40Go2BNxil\nUKVcN+/RqLzGFKoU6Hf6z0iMEAQajQZKpRJKpZLcfVwQBLn65PP55KmFUfliE+11+9mii/jBXC4X\npqenMTU1hc3NTVy/fn3gDUbp/VBO6zso9YxClQJUqdKWKIptW7bUajVYrVZ5QWQ6nYbH49Hlrdt6\nei0JUWLQQYRhGPkXotOnT8sNRovFIuLxONLpNNxutyrHolClnBm2K1ILhSoFRiFUDevuv0aj0bb2\nSRRFufoUCASQyWTgcDjAMAw2NjZQLpfh9/s1H2u36MRtXr1+R4z+WdAiiEjHYFkWsVgMsVhMbjB6\n/fp18Dwvr79SsnUThSrltOxKr3cUqhRQMv1nlIrFoPtUCYLQtvZJqj5Ja58ymcyR1SejVIGMMEbS\nn16m/0h/OjUYvXjxIhwOB9LpNGKxWM9VagpVyh01/TdKry+FKgVsNpvpK1VqhpV6vd4SnsrlMkRR\nhNvthsfjQTAYRDabhd1u72sjUL1frIwwRkK6oWWl6iD7G4yurKzg1q1bCAQCSKVSXTUY7eY45Gg0\n/fcIhSoFlFwkjfIl7uc5CoLQtvapXq/DZrPJ1adcLge3263a2icKLIRoRw+hqpnX68Xx48dx7Ngx\n7OzsdN1gtNfjKKHl+UnrcyFN/z1CoYoc6qiwsr/6VCqVAAButxterxehUAi5XK6v6pOa49QDI4yR\n9EcURdTrdayvr6NQKMi/SLAsi2QyiXQ6LTe2NEtlRE+hSsIwDMLhMMLhMHieP7LBaL/H6YdRN1Pu\nBt399wiFqiEywslVCgKCIMgdx6WNgxuNBmw2G3w+HzweD3K5HDwez1B+Y6HAQrTSvHl2cw80m80G\nq9Xa9ovE+vo6rl69CovFgkwmg1AoNOynoJhW+2wqOT9aLBYkk0kkk0nU63Wsra3h6tWrYFkW6XQa\niUQCVqtVsyqLllNkWl9bqFL1CIWqIZEWgOvt1n/pN27pYrG7u4t8Po+LFy/C4/HA6/UiHA5jbGxM\n0R03o4iCn/FI3wUpQJXLZTAMA4/HA5/Ph2QyCY/HgwcPHoDjOORyuZa/39zYslQqYXl5Gbdu3UKj\n0UA+nx9I3yUt6G367yh2ux1jY2MYGxtDuVzG6uoqzp07J98AEwwGVTnOYbQMOlqvcaI1VY9QqFKo\n3y+JHjYq5nm+be1To9GA3W5vWfskCAJOnz491LEexQiBxQhjHFX7O/AXCgXUajV5HaDP50MkEoHb\n7e7p4tF8fvB4PDh+/DjGx8dx4cIF3Lt3D6VSqW16UI3nogUjhapmbrdbbjC6t7eH2dlZbG5uolAo\nIJ1Ow+/3D+S4Zp/+01uBYFgoVA2JlhfY5upToVCQO48zDCOvfYpEIhgfH2+rPjUaDUMEAQoso0GN\n91iaypaqT4VCoWX/R7/fj3Q6LfdAU3usDMPAbrfjiSeekPe9a54ejMfjur9AGWH67ygMwyAQCCAe\nj8PhcMBms+Hu3bsolUpIJBJIpVKqNRgFtJ0i07pyRNN/j1CoGhKWZQfS/0la79FcfeI4Dg6HQ64+\nxWIxuFyurr4ERgkrRhinEcaoZ/1cYKUmslKAkm6kkKbvYrEYJicnYbPZFI2t0/vazXgPmh4MhULI\nZDJ9TQ8abWpu2KQA0txgdH19XdUGo9JxzFypolD1EIWqIVF6gRVFEbVarSU8VSoVeb2HFJ6UXjCM\nEgSMMk4yGKIoolqttkzfVatVuYmsz+cb+I0USvf+k6YHjx07hq2trYFND6pBi8rEsO7Ks1qtyGQy\nyGQyqFarqjQY7XScQRrGmiqzhGylKFQpxLIseJ7v+UvWS6Wq2+qT2+1W/YNtlLBihHEaYYxGIPVB\na56+4zgOTqdT/j4kk0nDbqDNMAyi0Sii0WjL9KB0sT/qom6WKpIeWh04nU5MTk5icnIShUIBq6ur\ncoPRdDqNUCiky420qVI1PBSqFJK2quk1VHW6wO7/bVuqPrEs2xKe1JiuUDJOPTLCOM1wodMax3Hy\nd2Frawt7e3vY3d2F2+2WF493WgtoFs3Tg1LX8IWFBYTDYWQymYEtqj6KmaYYuz2Oz+eDz+draTB6\n48YNxGIxpNNpeL3eI49Da6rMj0KVQtKmyr2W5kVRxN7eHvL5vHzR4Hm+5bftRCIBl8s11IuxUYKA\nUcap9+AHDKfSId1M0Vx9qlQqsFgs8vchGo3C6XTixIkTmo5Nbf2+vs1dw7e2tuRF1alUqqWppVkC\nj95ClaRTg9GbN2+iVqvJ6686XQ/MvqbKKOfgQaNQpdBRmyofVH2q1+sQRRGhUAiJRALT09OwWvX5\ndhghCAD6H6cRqmlaEEWxbfpOauXh8/ng9XoRj8fbfqHI5/PyQnO90fJ9PWp6MBwOazaWQdJrqGp2\nUINRi8WCVColNxhVepxe6a1P1SgFLn1exQ1EqlQBD+80ki4W0hqo5lu1m6tP8/PzSCQSCAQCQ34G\n5kCBRZ94nm9ZPF4sFiEIQts2Rg6HY9hDVWwYF47904PLy8tYWFhAo9HA7u6u4Xsu6T1UNdvfYHRl\nZUVuMJpOp2G1Wk1bqaLpv0dMFapWVlbwiU98AtevX0exWITVasWXvvQlXLhwAU8++ST+6I/+CAC6\nfuwgPM/j9u3buHr1KpaWlvAzP/MzWF9fx+c+9zk8//zz8kJZj8dzYPVJ6qhuBEb4LcMIocpIY+zn\nPd8/fVcul1vWA6ZSqUO/E6NiUBc8r9eLEydOYHJyEufPn8edO3dQLpeRSqXkvltq0eKirdWU0iCe\ni9vtxszMDKanp7G3t4eVlRVsbGzAarUONOxKtA45NP33iKnObuFwGN/+9rfxmc98BgBw6dIlFItF\nvPrqq/jFX/xFnD9/HhaLpavHnnrqqbafv76+jk9/+tOoVquYnp7GE088gUAggF/5lV/Bhz/84Z47\nLev9Amsk9Hpqp7n7uBSipO7j0vRdNBrtuhcaUZ/dbsfp06fRaDSwurqKK1euyNOD8Xic3pcmgwyI\nUoPRQCCAaDSKxcXFgTYYlWgdcqhS9YipQpXT6WxZIHj27Fl85CMfAQA8//zzeOONN2C1Wrt6rFOo\nisVieOWVV+DxeOTHbt68iVQq1fMHalDNP0eVEUKVEca4X3M7DylANU9pBwIBZDIZxd3HyWDYbDZ5\nSqp5elDp3YOjvFC9X1IPwRMnTsgNRq9duwZBEJBOp5FMJlW7q5taKgyPqULVfvl8HlNTUwCAQCCA\na9euwWq1dvVYJyzLtgQqoHVNVS+MeIHVMyO8nnofY6PRAM/zuH//PsrlsrwovHk9oJ5vqBimXi5i\ng77gHfTzpenB48ePY3NzU9H0oNlClRaBoPn5dGoweuHCBTidTqRSKcXVRGr+OTymPjsGAgHs7e0B\nAPb29hAMBmGxWLp6rFs2m62vUEWVKnXpPbDoSfMdqVL1Seo+3mg05MXPg+w+bkZ6uqgcNhaGYeQt\nWaTpwcuXL8Nms/U0PWimUDXM4+xvMLqystJ3g9GjjjUoRwVTPX03Bs3UZ8xnn30W3/72twEAr7zy\nCp555pmuH+vWUS0VDkIhYPQM4z0XBEHuBD0/P49Lly7h/PnzmJ+fR6FQkJsZPvXUU3jyySfhcrmQ\nTCbh8/koUBlUL58xaXrwmWeewYkTJ7C7u4szZ87g+vXr2N3dPfBnmWFDZYlWv9x2Uz3y+Xw4ceIE\n3ve+9yGVSmFlZQVnzpzBzZs3USwWVT2Wmmj67xFTVaoajQY+9rGP4erVq/jRH/1RfPWrX4XT6cRz\nzz2H06dP473vfS8AdP1YN/qd/qNKlboopD7qPi5Vn6STsLQXZDQaxcTExJHrNkb9dRw0vW4jI00P\nSs1FD5seNNP0HwDNp/+OwjAMIpEIIpFIxwajqVTq0Olaav45PKYKVTabDa+88krLY08//XTbn+vU\nMuGoNgoHoTVV+mCE11OtMTZvpi0FKKn7uHT3XSaTgcfj6Wv7JNI7PX32lF5QWZY9cnoQ0Gb6TwvD\nnv47SqcGo1euXIHFYkE6nUY8Hm9b5ziMbWro3PGQqULVMCgJVUaqVOn9S2OEUNUPQRBQqVTauo9L\nm2n7fD5dbGdE9BVI1RrLQXcPMgwDl8uFcDg80Oet1fSfUY5zUINRr9eLdDqNcDgMlmWH0uJAT5//\nYaJQpZCS6T+O4wYwIvUpaQipFT2PTXLUGDmOQ6lUapm+E0Wxpfv42NiYaTcPHhWDDv+D+vnN04NX\nrlzB2toalpaWBtJcFBjOXXmDPo6az6e5weju7i5WVlYwOzuLSCQCAPD7/aodi3SPQpVCShaqG6VS\nZdYq0DBJ3celANXcfdzn8yGVSsHr9fY8facUvdfaGPRFfJA/n2VZ+dZ/j8eDtbW1vu4ePIrep+X0\nchyGYRAMBhEMBiEIAjY3NzE/P4+NjQ1Uq1Wk02m4XC7Vj0s6o1ClkJJKlVEuXlIA1PoCbwZS9/Hm\n8HTu3DnY7Xa5/1MsFoPb7TZEtW2Y6PXpjpbnlebpKDWbiwLGDzudjjPoyhvLsojH49jb24Pb7YYg\nCHjnnXcgiiJSqZSqDUZ7MUrfXQpVCtntdlQqlZ7/npEqAkYa6zBJ3cebp++k7uM+nw+BQACbm5t4\n6qmnRuokMwp6+X7ofSq9G52eQy93D/Z7jEHQsnWDVoFGFEVYrVbE43Fks9m2BqPpdBqxWEyz6dVR\nQqFKIbvdjkKh0PPfM1JLBSNV1bTSaDTaNg+WtqGQFo936j5+584dXV9QKUD3b//7Oqz3edjtDprv\nHpTuVrvrTgduAAAgAElEQVR8+TLsdjsymUzXF3OzhSotw/T+RfGdGowuLCwgGAwinU4jGAzq+rxk\nJBSqFHI4HKZvqWCksapN6j7eXH2Suo9L7QvGx8fhdrup+R3RDb1cIJunB6WL+fz8PCKRCDKZDHw+\n34FjNVvY0TJUHTbVKDUYPX78OLa3t7G0tITr168jHo/LazlJ/yhUKTQqa6qMMlYlBEFo2zyY4zg4\nnU74fD74fD55GkMvF61RMwqfQ6X02u1cuphL04O3b99GpVI5sJml2cKOlm0OunlO+xuMbmxsYG5u\nDo1GA8lk8sgGo70YpfMlhSqF6O4/Y2o0GnLlqVAoyJsHS9N3sVgMk5OTQ1nUOUxmfK/1xqxrqrq1\nf3pQai7a6/SgmozSp6qXY/XyGlosFjncSu9Jc4PRRCJx4I1KZvg8q4lClUKj0PzTyBdaqft48/Rd\npVKB1WqV776jzYOJUnr6fhjpIme32zE+Po7x8XEUCgUsLy/L04Mcx/W0ub3eaT391++xmt+TUqmE\n1dVVvPnmm3KD0Ugk0vKzuwlwRvk8qoFClUIOh6OvSpXRpv+MEABFUWypPhWLRbn7uLT+ibqPk0Hp\n9jOlxcXViPvy+Xw+nDx5Uu61NDs7i3w+j1KppOpU1LBovVBdjV8SPR5Pxwaj0WgU6XQaPp+P9v3b\nh0KVQqOw958eA6C0eXDz9F25XMa9e/fg9XoRiUQwPj5O3cd7ZKTPJelMr2uquiX1Wtre3kYoFEK1\nWh369KAa9LamqhedGozevn0b5XIZ0WiUzhlNKFQp1O+aKiO1VBj2hXb/5sHlchkWi0WevpM2D750\n6RLe9a53DW2cZHSN4kVFi2qYzWZDIpHoOD0oNRc1CiNWqjqRQm88Hkej0cDS0hKKxSLOnTsnr78a\ntbWozShUKTQKlSqtxiqKIsrlckuAqtfrcvdxaQE5dR8neqSXz6RWfaoGbf/z2D89eOvWLVSrVbm5\nqN6r0lpOk2kV4KTQm8/ncfLkyaE1GNUTClUKUaWqPwd1H2/ePDiXyxl+HYXRGCns650oih0vbrSm\nStkxmisl0p1qFy9ehMPh0PX0oFkqVQcdy+VyYWpqClNTUy0NRsPhME6fPq3JWPSAQpVCVKk6Wr1e\nb5u+a+4+nkwm4fF42rqPE2IU0h6PKysr8med53n5VnUtKylanVf0ENwOu3tQb9ODRrn7T41jNTcY\nLRaLmoxDL+gqphC1VHhEurA0B6harQabzSZP30UikYF2HzfS7eTEmJqrrNLnvFqtwuVyIR6PIxaL\nYWpqCjabDaIoYmVlBRcuXIDH40E2mwXLsqaoVOmN3qcHh1E9GvaxGIaBz+fTZBx6QaFKISXTf3o8\nMXXSKQBK3ceb97+TNg/2er0IBALIZDKadh+Xwh+FKmWM8rnUAs/z8udb+qwDkH9JkLb1uH//Pjwe\nD+LxeMvft9vtmJiYwPj4OHZ3d7G0tITt7W3YbDZUq1U4nU7Vx2z0u/+UHuOg6UGn04lMJoNoNDqU\n6cFhV48GeSw9TrcOC4UqhZRUqoxCFEUUCgW5CqXX7uNGmlLVKyN9LtXGcVzLLwmlUgkMw8gBSrrL\n9KDO0oe9ds23pEu3o1+9ehVWqxW5XG5oF/p+6TlUNes0PXjz5k1EIhFks1lNqyhmDVXUp6oVhSqF\n+g1VeiRtHtzc/6larYLjOLjdbiQSCeo+Tkyh0WjI1adCodDSpsPn8w30cy4d59SpUygUClhaWsLN\nmzcRj8fl4KaEWaq1aj+P/dODCwsLqFarqNVq8l3Gg2TmUHXY98QMn8VeUKhSqN/pP2C40yyCIKBc\nLrf8Zi5tHixdWJLJJJxOJxYXF2G325FMJoc23m5QpYp0Uq/XWwKUtE2RtEl2NBrVtE1H8wXP5/Ph\nsccegyAIWF9fx/Xr1yGKIrLZ7KH7rR3FCFWkYR1j//Tg66+/rsn0oJbrnADtwgxN/7WiUKWQzWbT\nfaWqU/dxURThdrvlxeMTExMHTt8ZJawYZZx6ZuTXUBTFtgBVrVZhs9nkABWPxweyTZHS14xlWXlD\n23K5jOXlZZw9exahUAjZbLanu9hGfU1VL2w2GxwOB5599lns7e3J04PRaBSZTEbV6UGzVBD30zos\n6h2FKoWUfEkGcWKXLipSgKpUKi3TGketCzlonEa40BplnEQ5aap6/x140j6PzZVWLW+UUIPb7cax\nY8cwMzMj38VWq9WQTqeRTqdHpvWI1sHN7/fD7/e3TQ+m02mkUinF04NmDlVmfF79Go1v54AN40Iu\ndR9vDlDS5sHS9i1q/VZulPYPRglVZj25DorUqmN7exu7u7u4fPkyGo2GPFUdCASQzWZht9sN8bp2\n+/4zDINYLIZYLIZarYaVlRWcO3cOPp8P2WwWwWCw488xSxVpWMfYPz24srKiyvSgWSs6NP3XikKV\nAUi3dTf3fxIEQZ6+C4VCGBsbG9hCS5ZlwXHcQH62mowQqvTe9mHYr2HzLwvSPxzHweVyyVM1jz32\nWH+fdYEHGBbQ6Wt/GIfDgcnJSUxMTGBnZweLi4u4ceMGUqkUMplM2+thhsCjhaOeh9QSY2JiQvH0\noFles/2oUtWKQtUQdbp47Z++K5fLYFlWrj5JfXH6XcDaj2FfaLtllHGShw7qdXbQWr/d3V2sra31\nHKjYzYuwv/0fYNm8AJG1gk8+h/rp34DoyQ7iaQ0UwzAIh8MIh8NoNBotPZiy2Syi0ahpvgN6q4Y1\nTw8+ePAA8/Pz8rRst9ODZgwfZq3A9YtClQr6+aKIoghBELCxsdHWfdzn88Hr9SIajcLlcg39A2uU\nsGKEcRphjIMgCEJbE03pZgm/3y93IT9svVA/3zNmbwHO7/1PsBTvyY9Zbv0VLPk5lD/6D4BFnb0l\ne3lP1QoLNpsNY2NjGBsbw+7urlxF8Xq9A+8Zp7fAo+UxWJZFIpFAIpFQdXrQqARBGHqPQj2hUKUB\naVuL5uk7nudRr9dRKpV0vyZkVIPAqFL6XnfbhVyLaqt97r+0BCqJZesSrAv/L7gTn1ftWMP87gYC\nAQQCAfA8j5s3b2JjYwMXLlxANptFPB4fyEVej4Gnn2MoeW3UnB40KrNOa/aLQpXKpKaC0hooqSuz\nx+OB1+tFIpHA9PQ0rFYrLl68iLGxMU2n8vphlFBlhHHqfYy9nhyVdiEfNKZw68D/xu7e1HAk2rBY\nLAiHw3JfuaWlJSwsLCAajSKbzcLr9Q57iF3TKlSp5ajpQbOi6b9Wpg1Vf//3f4/f+Z3fAQDMzc3h\nT//0T/HTP/3T+MEf/EEAwEsvvYRwOIyvf/3r+OM//mOEw2G8+OKLXfeDEUUR9+7dw5UrV1CpVPDx\nj38cq6ur+PVf/3U88cQT8Hq9GBsbO3TzYL1fYCU0TgIMtwt5v0RH9OD/6IhpN5Amgw4L0nfA4/Hg\nxIkTOHbsGB48eIC5uTlwHIdMJoNkMqn71gxahKpBBILm6cFarSaveyuXy9jY2Bj49KDW50C6+6+V\nvr9VCnz0ox/FRz/6UQDA008/jeeffx6PP/44vvvd78p/ptFo4M/+7M/wve99D3/zN3+Dr33ta/jV\nX/3VQ3/u9evX8Uu/9EvI5/OYmJjA6dOnwbIs/vAP/xDHjx/v6cNlpFYFRhmn3kOVEcbYqYlmcxdy\nr9eLyclJTbuQ94ub+pew3fsmGK7U8rjgTqNx7KeHNKrBa35fmi/y1WoVy8vLePPNNxEMBuXGonp8\nH7WaVhrkMRwOhzw9+Oqrr2Jra2vg04NaV47o7r9Wpg1Vktu3byORSMDr9eLGjRt47rnn8P73vx+/\n/du/jfn5eTz++OOwWq14/vnn8fnPH72+YnJyUq5ySf7hH/4BExMTPX+QWZbV/QUWMM44jRBY9DbG\n/QFqZ2cH+XweTqdz4F3I+x1vL/jUB1F7z/8B2/X/CEvhzsPHQo+j9oP/HqI7MbRxDdJhY3E6nZie\nnsbU1BS2trZw584dVCoVubGonhYca1Wp0upzbbFY5C2JpOnBer0ud9JXqyWO1mucaPqvlelD1Usv\nvYTPfOYzAID5+XmEQiH8wi/8Al5++WVEo1F5ui8QCCCfzx/581wuF1wuV8tj0qbKTqezp7FRBUhd\nRhnnsHTThdzv9yOXyyEQCAx7uKppnPif0Zj5KVgW/ytgcYPPPA+w6q/x6vZCpsVF76ifzzAMotEo\notGofAfb+fPn4fF4kM1mEQ6HdRGiB20Yi6wPmh6U2mJEIhFFIUXrkHPU9N8ofI6amT5Uvfzyy3jp\npZcAQK4uffrTn8bly5fxqU99Cnt7ewCAvb09BIPBvo7R76bKVAFSlxG+vFq9llIX8uYpvG66kO/s\n7BjideyZxQl+4jPDHoUmev18SXewjY+PY3d3F0tLS5idnUUqlUI6ne75l0U1meEOw8NI04Pj4+Mo\nFApYXl7G3NwcYrEY0ul0X9ODVKkaLlOHKqlRYCQSQalUgtPphMViweuvv47HH38cx48fxzvvvAOe\n5/HKK6/gmWee6es4UqWqV0YKK0YYJ6CvaRitHNaFvJ+O+6P4GppNXz29GAbBYBDBYBAcx2FtbQ1X\nr16FzWaTG4ua7eI57FAlYRim492D9Xod6XQayWSy6+8vrakaLlOHqm9961v41Kc+BeDh1N/P/uzP\nyotsf+u3fgsWiwWf//zn8dxzzyEUCuHFF1/s6zj9hiqWZWn6T0VGGKfSMQqCgHK5jL29va66kBNt\n9PqeanH3nxJWqxXZbBbZbBaFQgFLS0u4efMm4vE4slnjdaI/iF5CVbODpgddLhcymcyR04NaPye6\n+6+VqUPVz//8z8v///Tp07h06VLbn3nhhRfwwgsvKDqOzWYzfaWKwp86ehnjYV3IfT5fV13IiXZ6\nWVNlJD6fT15gvb6+jmvXrqFUKmFlZQWJREL3ffYOo1UA6fc9P2x6MJPJdOw7RpWq4aKzsQocDkff\na6qMEFZo7dfg6aULuZFfQ/LQoIICy7LynWqvvfYaSqUS3njjDUQiEWSzWUN2D9cyVCk5TqfpwZs3\nb8rTg6lUSq5O05qq4aJQpQJaU6UPRhgnwzDgOA75fF4OUHrrQk4GSw93/ynFsiyOHTuG6elpbG5u\nyut/MpkMUqmUYaqnRglVzfZPD0p3brrdbmQyGdhsNrr7b4iM8cnXOSXTf0aoVBkhrAD6HOf+LuTb\n29uoVCrw+/267UJOjE3L7wDLsojH44jH46jValheXsa5c+fg8/mQzWYRDAZ1fVHVMlQN4jvucDgw\nOTnZsvfg5uYmLBYLisWiJtsS0fRfKwpVKqCWCvow7C/2UV3IJyYmADxsIOvxeIY61oMY5b3WGz29\nZsNafO1wODA1NYXJyUns7OxgcXERN27ckBuLqtXcUk1avVaDDh4Mw8ibam9ubuLu3buYm5tDo9Fo\nmx5UG03/taJQpQKa/tMPrXpA7Q9Q1WoVNpvtyC7kww5+ZHB6eW/N/DlgGAbhcBjhcBiNRqPl7jWp\nuaVenr8Rp/+OIi0lOHnyZMfpwWg0qupY6O6/VhSqVEAtFfRhEOOUupA3LyLf34U8mUzC6XR2daIy\nymtJlDvo86DF+6+X0GKz2TA2NoaxsTG5sejc3BwSiQQymUzb7hRaM2Ooaq6KdZoePOruQSXHIxSq\nVDEKlapRCH9HdSGXFpHv70Ku5RgHTe/jI0fT6/snTU/xPI+1tTW89dZbsFgsyGaziMfjQ6l2aDn9\np9Xz61Q5ap4eFAQBGxsbqk4PUqh6hEKVChwOh6krVWZc+6V2F3JC9LL3nx4bWjazWCzIZDLIZDIo\nFotYXl7GwsKCqtWTbmk1daXle3LUsViWRTKZRDKZ1GR6cNRQqFKBzWbra6G6UaoCRh+n1IW8OUAN\nqwu5UV5L0ht6T/vj9Xpx4sQJHDt2TK6e8DyPTCaj2fpIM07/dRsUBz09qPeAPwgUqlSgZE0Vx3ED\nGJG6jPKlYBgGPM9jb29PbqBZKBR01YXcCK8lBQTjM8LnrFlz9aRSqWB5eRmlUgnXrl1DNptFIBAY\nyHG1Wg+kp0pVJwdND3IcJzd8pa2vukOhSgV2ux2VSqXnv0dVC2X2dyHf2dmBKIoIh8PyAvKZmRnd\nNdHU83tutIuxUel9779hcrlcmJmZwfr6OhKJBO7cuYNKpSI3FlXz4m7GNVVKj9UccKvVKlZXV2l6\nsAcUqlRgt9tRKBR6/ntGWVOlBxzHtQSoTl3IpW0y9LzhK52MiNHv/tMqtDEMg2g0img0inq9Lq/9\n8Xq9yGazCIVCip+nGaf/1DyW0+kc+N2DZkOhSgX9LlSnSlVnUhdyKUSVSiVYLBY5QB3UhbxUKhli\nOlXv77mex6fXse2/kNXrdZTLZQSDQc1vdhj0a6RFQNj/HOx2u7yxcD6fx9LSEmZnZ5FMJpHJZOBw\nOPo+jtkWqguCoHp1/rDpwXq9jkajcWAFcdR+kaRQpQKzt1QYpKO6kI+Pj8Ptdnd14jPC66n3Mer5\nBKjXsTUaDdRqNayuruLevXstn+GFhQV4PB6MjY3JW7YYvVKlhYNCCMMwCIVCCIVC4DgOq6uruHz5\nMux2O7LZLKLRaE8hiSpVvds/PfjGG2/g/Pnz8Hg8yGQyumruOgwUqlSgZJsao0z/Kb0QKOlC3i29\nBxZifBzHtXyGpSoqx3Hw+/3IZrNwu91gGAYsy8JisSCfz8tbtmQyGQDGnp7TqlJ11DGsVityuRxy\nuRwKhQKWlpYwPz+PeDyOTCYDt9utynHUYKQ1Vb1wOBxwOBx49tln5enB2dlZxONxpNNpeDyekQtY\nFKpUQJWqVp26kNdqNdjt9r66kHfLCK+nEcZIHpJuhGi+m9Riscif4fHxcfmiMTs7i0gk0ranY3Nl\nRVoXdPfuXTgcDgSDQfj9/oFcdIzeB6vXY/h8Pjz22GPgeR7r6+u4du0agIfrKxOJxIEhgypVyo/F\nsmzL9CDP83jw4IE8Pfj+979fdzcLDRKFKhU4HA7TV6oO23LjqC7k6XQaDodj4F90IwQWvY9R7+Mb\nFEEQ2gKUdCOE3+8/cB2fpJvXTFoXZLFYUCwW5bvastksUqmUam0+zDC92G8wsFgs8gbO5XIZS0tL\nuH37NsLhMLLZrHwzi9Lj9EqvfaoGcSyLxSJPD9br9ZHbF5BClQpGpVLVTRfyXC7X96JRpYz2epLh\nEAQBpVIJhUIBe3t7KBaLEEVRDlDSXU29Xgx6uWh6PB489thjqNVqWF5exptvvolgMIhcLge/39/r\nU2qhRcf2QVPjObjdbhw/fhwzMzPY3NzE/Pw8Go0GMpkMkskkrFarpqFKy21qhrHPYCejuCMFhSoV\nmHFN1f4u5OVyWe5VonUX8m4ZIVQZYYxmIopiS4CSmsF6PB74fD6kUil4vd6hTU84HA5MTU1hcnIS\nm5ubWFhYQKPRQDabRTKZ1OW0idG6ZLMsi3g8jng8jmq1ipWVFbz55psIBAIQRbGtejUIWm46bNaq\nmFFQqFKB0StV0tTHYV3I8/k83vve9w57qGTA9PKZ7AZTvA/rrb+CJX8dYCzgIk9iL/MT2KuKcoji\neV4OUIlEAtPT00Prpi/p9PoyDINYLIZYLIZqtYqlpSW88cYbiEQiyOVyPfcD0mKqfZAGFQycTqcc\nYre3t3Hjxg3s7OygUqkgnU4PrLJi1qDTTQXOSAFcDRSqVNBvpYphGM0rVfu7kBeLRQCQ1z8d1IVc\nutjq+QtihEBghDEagVjegPXcv4VQ3kSD58BxHMT1BYj3zqF26jd1WUmVrK+z+Iu/CKJYtCAWF/Fv\nflnA+Pij/+50OjEzM4OpqSlsbm5idnYWgiAgl8sduuhaosXdf4M26HMNwzCIRCKIxWIIBAKo1Wq4\nePEiXC4Xstms6m0BzLpQXcsKnFFQqFKBkr3/BnmC6qYLucfj6WqKgUIVGRZRFFGr1bC9vY29vT1c\nvnwZ8Y1vIFlahtVqhdVqg8PpBMuwgLgNv20BfPifaz7Gbr4b3/se8G++kEGlwsLCiKiUOXzzbwT8\nwe8J+PSP2wGhAZQ3AGcIrNUtT1tJi65v3bqFWCyGXC53YMsALdZUGbVS1ek4NpsNyWQSY2Nj2Nvb\nw9LSEubm5uTGok6nU5XjmLGlAk3/taNQpQI9TP/124V8GGMdFBqjOoY9vlqt1rIGqlaryf1w7HY7\nTp06BR8PWJgOa2EYFuzuAviMtqGqW1/9bQuqVQGVYgPVKgOAAYoM/pdf5vFY4w9wKnMbTC0PWJ0Q\nYu+BcOJzgNXRsuh6Y2MD165dA8MwyOVyiMViml7YzBaqpNeuuS0Ax3FYX1/H1atXYbVakc1mFb3O\nZl1TpWVYNAoKVSrQeqG6ml3Iu2WEMEBjVE7rSqT0WZYCVLValfuZ+f3+lnYcUoNHh8MB0Xbw4mLR\nps/9yNbWgLt3WdQrNVQqzKPXmmFQ50Rc/M49HPv0LuxOOyAKYNfPAXwDwg98Uf4Zzd2sS6USFhcX\nMT8/j0QigWw2C5fL9f0fSWuquj1OJ1arFZlMBplMBsViUW4sGovFkM1m2/qRdXMcM1aPaPqvHYUq\nFQyyUiX91j6oLuRqjnXYjDDGUSZVU6UAValU5M+y3+/vqSEsn/owrOuvA2j9s6LFDi77sQE9A2V4\nHhBFoFZtGzXsbB0Nzo71xT3kjkUePswwYLeuQCivAe5k28/zeDw4efKk3PDyrbfegtVqldcLDYoZ\n1lRJugkgXq8XJ0+elPe8u3HjBgRBkFszdLN8wsxrqg57/UYxcFGoUoGSNVVSpWpYXci7ZYTAQmNU\nTq3xNW/nsre3h3K5LFdT1fhlQIg/jcb0T8F69xtguBoAQHQEUT/5ecAVUzz+XnXzmmUyQC4n4p2t\n1scZABHvFk6mboLj9v0cUQRTuAuxQ6iSNDe8LBQKeOutt3Dt2jVks1nV1gS1Dsk8039A9xf+5iph\npVLB8vIyzp49i1AohGw2e2h/MbNWqmj6rx2FKhX0Ov3X3IW8Wq3i8uXLaDQacDgc8kVHqy7k3Rr0\nono16D2wmBXP8y0BSlrPJ32WJycn5f3w1MRN/ytwuY/Bsv46wNrAJz8AWIbTeBbo7uL8pS9y+MWf\n48FxzadeEZ94/B/AMoDHWUXh9kUwYgOwB+FOH4fonwQArGMNC+w8dpk8rLAhJabxLuHdYPHooubz\n+RAOhxGNRlGr1XDlyhU4HA7kcrmR3+i2k36nr1wuF2ZmZjA9PY3NzU3cunUL1WoVmUwGqVSq7a5T\ns66poum/dhSqVGCz2Q6sVB3VhZxlWZw6dWpoXci7NYz2D2Zk9ODX3JJDClAMw7Tsh6f2er5D2QPg\ncx/X5lgq+LEfA/7D793D//6bARTLbjjtDUyl1pCIVJD2LyJQX340Ncg9wN78DvADIiquB7hgOQeB\nefjZ4VHDHeY2yijhaeHZtuNYrVZ5/c/u7i4WFxcxNzeHdDqNTCajqB+TmSpVSo/T3F+sVqthZWUF\n58+fh9frRTabRSgU0vzOaapUDReFKhVIX5ZarYarV68im83KFx6e5w/tQv7gwQPdByrAGGGAxqgO\naXzSdi7SGqhCoQAAXe+HRzp76v12/OVfPsDZ/7qLmzcZ2Kwiwicfh9+1B1FkwDACRDBoCG40eCt2\nXv5NrH/+X8uBSsKAwTq7jm1hG2GE5cf3f76a72hbWVnBxYsX4Xa7kcvl5It+LyhUdeZwODA5OYmJ\niQnk83ksLS1hdnYWqVQKjUbDtJUq+v63MnWounv3Lp5++mk89thjsNvt+Md//Ef8/u//Pr71rW9h\nfHwcf/7nfw6bzdbxsaPUajVcu3YNFy9exKVLl7C6uor3ve99mJ6exle+8hUkEglMTU0NvXuzWowQ\nBowwRr2StiXa29tDvV7H8vKyvJ2L3+9HKpXCsWPHdLltyqCJgoj7V+dRKRQw/fQPwOZQ1lBUFEVE\nc2H83G++W37s9kv/EY1lNzjODRYcBFiA70/ructvocDsdfxZDBhsM1sIi+HWxztcVK1WK8bGxpDL\n5ZDP57G4uIjZ2VlkMhmk02ldNUo1YqiSMAyDUCiEUCgEjuOwurqKtbU17O7ugmEYRKNRw/cRk9D0\nXztzXPEP8ZGPfAR/+Zd/CQDY2NjAd77zHbz22mv43d/9Xfzt3/4tPvjBD7Y99hM/8ROH/syXX34Z\nX/nKV/Dud78b73nPe/DCCy/gtddew5kzZ7R4SkNhhMBCY+yONCXdXIGStnMRBAGBQADj4+Om+YVA\nibuX57D5T/8JPuEWGDC4djYCcfoz+MEf/7GWP6f4PRWlNZkMBLSGG4YRYBcdqDE1oFxF+uVvwl3K\nYys5jc1//mE44eppLM0X/Xq9Lk9ZSf3sAoHAoRdKqlR1z2q1IpfLoVQqwePx4MGDB7h58ybi8Tgy\nmcyBDVyVoOafw2X6s+Z3vvMdPPfcc/jsZz+LEydO4EMf+hAA4Pnnn8fXv/51eDyetseOClWf/OQn\n8clPfrLlsX5/gx/2BbZbeggDRzHCGLXWfFOEFKI4joPb7Ybf70csFmupqC4uLsJisVCgAlAr15B/\n5Q/gxTZEPPx+O5kdiLf+H8z+9zhOfvCplj+v5OIc++GfRP2v/wIM2j+/ZedJZMUM7p75Np65+Oew\nMVUALKZvX0Thz16B68e/DTTd8NhLULDb7ZiYmMD4+Di2t7dx9+5dVCoVuXrV6XNAoaq/43g8HuRy\nObkFxrVr1wAA2Wy2q+2HejkWNf8cHlOfOVOpFG7evAmHw4FPfepTKBQKiMfjAB6uM8jn88jn8/Kt\nsNJjpJ0RAotRxjioBf9SW47mANVoNOSbIrrZD49K+Y9c+7v/Bje2sL+rFMOI2H3r28C+UNWtThc9\nXyKN29EfR3Dzr8F8f9pPhIgG44P/w/8bQo0pxC++CBtTB+S7/Vj4sYO9b/1ruD/3T32NRSL1topE\nIqjValheXsabb76JYDCIXC7X1i5Ai1ClhWGEt+YWGKVSCcvLy7h161bfm2d3ouX03yguCTiMqUOV\ntM1TQOkAACAASURBVLUFAHziE5+A3+/H8vIyAGBvbw/BYBCBQABLS0stj/XD7Bcjo7RUGBWiKKJe\nr8vhSVoL5XQ64fP5EAqFMDY2pugur1HHFXewP1BJ2MaO6seb+sl/j/vffRzC/LfANArgPDOIf/SL\n8CXSWPunryHClPAwUIlN42LhbdyEyPFgrI8ubkq+Cw6HA1NTU5icnJTbBdRqNWSzWaRSKc3OA1pU\nQIbdZNTj8cjbD21ubmJubg4cx8mNRfVeMRYEQVdr8fRA3++YQoVCAT7fw+0sXn/9dXzhC1/Aiy++\niF/7tV/DK6+8gmeeeQZPPfUU/uRP/qTlMdLOKC0VjBD8+hnj/gBVrVblACVtjm2Eu0iNxBnPgnkg\nQET7xZB3prCzA5w9a4HdDoRC6hxz7EOfBT702fb/sLf8/f/z/f0Cm7DgIYo8mO9PUar1HWhuF1Ct\nVrG0tISzZ8/C4/EMPIhoda7RyzQjy7Ly5tnValWuFAYCAWSz2SPXuQ0LTf+1M3WoevXVV/Ebv/Eb\ncDgceO655/D000/jAx/4AH74h38YY2Nj+OIXvwi73d72mJa07mHSL6NMrZlhjI1Go2URubSdi9/v\nh8/nQyqVGlhjWCO8hlp5949+EJeuvwyveK/lcQ4OnC/8FP7PLzhRrz98DwRhBr/8Sxb8yI8c/XP7\n+b4H3v/zEL/xn8Ew7e9NhYnBb3tUkRzE+cTpdGJmZgZTU1O4e/cuFhcXce7cOWSzWSSTyYFcWPUQ\ndoZxHKfTienpaUxNTWF7exv37t1DuVxGKpVCOp3WVfWZ7v5rZ+pQ9fGPfxwf/3hrY8Avf/nL+PKX\nv3zkY71iWRY8z/c1v0yhSh1GGON+HMe1BChpOxcpQGm1t6NRaPn+shYW0z/17zD30v8Nb+kSLKij\naD2G267P4XvfyYFhGEhvS7lkwf/1n9144okGBrHtniuWwbr3g4iU/qmlciaKDMozv4CDN0hRF8uy\nCAaDqNVqGB8fx9LSEs6cOdP3RsMH0TLs6HWasXmdW71ex+rqqtxjLJvNIhwOD/28QHf/tTN1qNKS\ntFVNr6HKCGuVAGMEFr2PkeO4lu76zdu5+P1+RKPRgWznYhbDeF1CqTCe+eX/FY1aA1yDg8vrwj/9\nnq3jWOp1Bv/tFQv+1b/kBzKWxAt/hdVv/lu41/8ONrGIGhtF/V2/jORzL7T9WS36ILndbnk90MbG\nBq5fvw4AyOVyiMfjii62WoYqLSgNH3a7HePj4xgbG8Pe3p7cYyyZTA5kf8duHRVKR/FcRqFKJdKm\nyr1+uPUeBCRGGKeexiht5yJVoaTtXKxWKywWi/bbuXTJKGvntGZz2OSmn8XiwReKw/6bRElgSH3m\nqwC+CgA4qMPRoL8D+8ffvNFwqVTC4uIiFhYWkEgkkM1m4XK5Dvlp3R1jUIx2HIZhWjrkr62t4erV\nq7Barchms4jFtN1MnKb/2lGoUkmvmypLWJY1xEVMT4HlIMMaoyAILfvhFYtFMAwDr9crN1SUtnPZ\n2NhAuVxW5bZpMhwT4wLm5lor0iIAQQCOzQz/uzzM5QQejwcnT56UezG9/fbbsFgsyOVyiMViXY9L\ny+dgpFDVTApS0rZoS0tLmJ+fR71el5uNDhotVG9HoUolUqWqV0YIK4Bxwt+gSfvhNQcoURTlAJXJ\nZODxeA6cBjbK+00O9slPcnjznAX5fPPaJmBmmsdzz5n/O9JNQGjuxVQoFLC4uIj5+fmup6uMsM60\nF4MOH16vFydPnoQgCHj11Vdx48YNCIIgNxYdVC8pWlPVjkKVSvoNVUYJK0YIA2qfhEVRlAOU9I8g\nCPB4PPJdeF6v13TN7/T+Pg9bPA78xr+r4xvfsOLGrAU2m4h3ndrBF77gB8s+CgsHfR4HHRj09vN9\nPh9OnTolT1dduXIFDocDuVwOkUjEVOHpIFpNkzEMA5vNhh/6oR9CpVKR22CEQiFks9m2Jq5K0fRf\nOwpVKrHZbKauVBllnP2StnNpvhPvsO1c+qX315FOkN3J5UR86UsNAA+n/N96aw1ut1b34BlT83TV\n7u4uFhcXMTc3h3Q6jUwm09IqwIyVKq2nGV0uF44dOyY3FpWauEoVRDUai1Klqh2FKpU4HI6+1lQZ\nZWGw3sNAL6TtXJqbaXIcB5fLBb/f39V2LoPGMysQmTuAGIRFPAXmgM7eRD8GetEURaBwG6jlgeAp\nwHb44m89Vao6aV5svbKyIrcKyOVyCIVCFKr61CnkNDdxrdVqWFlZwblz5+Dz+ZDNZhEMBvseG939\n145ClUqUTP8ZIawYJfztJ4oiarVay354w9zO5ahwKog8GrYXIVhuAmAgQgArRmGv/yRYJDQZI+ld\nL9/hni+wpWWwc38BtngfYCwQWRuEzI9AnPgfFI+lH2oGBKvVirGxMeRyOezu7uL+/fuYnZ2F2+1G\nIBBQ5Rh6oJd+WA6HA5OTk5iYmEA+n8fi4iJu3LghNxbtdVcGmv5rR6FKJTT9pw+CIGBzc1MOUM3b\nuUhbPgxzO5d8nsGVKy7cucvC5QSmpgSkUkChAFy7xkJw/h2C8QW4XCwiERE2GwuR2Ubd/v/BUf/C\nwCtWRnmfR4YogL3+n8DWtgD24emaETlYFv8enCMIpD4AAGhAxJtMHRsQsB60Y4wR0Xsjg+FhGAbB\nYBDBYBCNRgPvvPMO7t27h2KxiFwup9ttWrqlt35YDMMgFAohFAqh0WhgdXUVly9fhsPhQDabRTQa\n7er1pum/dhSqVEItFbRXr9dbKlDVahXVahW7u7vw+/3yb156ORmvrwNvvOFCqcTDamVRKgIbGyxO\nnORx7y4LQWAw8QPvgGUY1KrA6gqDTFaAxQKIzAYE5g4s4tSwnwbR0oOLYKsPAKbpwiWKD8PW2usQ\nUh/ANnj8F7aMbVYAAwYbIReWnTX8NGw4BvWnsAc9lWWz2RAOhxGNRuHxeHD37l1UKhVkMhnV1gIN\ng15bN9hsNoyNjcmNRZeWlnDz5k3E4/Ej+4xRS4V2xvx06hC1VBisRqPREqAqlYq8nYvf70cymYTT\n6cSFCxcwPT09tHEe5to1FgzLoPntZlngu99hkRtjwDIiWGtV/m+iCOztMgiFRQAsRCb/sCGSWkQR\nzO4cGK4MwT8D2GmhtRKCIGBvb0/+p1wuIxqNYmxsrKWFQC8XPqa61RqoqptgissAVwK7/Q5ETw5/\nN/0x7FhtchWTAdBggG8yVfyqYFW9uqnF+iDpYh0OhxEOh1Gr1eRNhoPBIHK5nOp3spmB0sqR3+/H\nqVOnWvqMsSyLbDbbsUs+Tf+1o1ClEmqpoB6O4+Q78KSLk9Vqhc/nM+x+eDwPbG13Hm+xxCKfFxEO\nMaiVU3C6l+X/Jn+kRAascEy9ARUXYbv3TTC1XYC1PGxeGTkNxvakIUL+sImiiHK5LIf8fD6Pt99+\nW95yKJfLwefzYXNzE1evXoXdbsfY2BjC4XBvx/FPAgIPsBagtg1mdwFgmIf/bnWD2X4LkcYq8AM/\nB3kjwu+/fdusgAWBG0i1SotQ1XwMh8OBqakpTE5OYmtrS76TLZvNIpVKma6tSb/UCrzNfcZKpRKW\nlpZw69YtRCIRZLNZuXkxTf+1o1ClErNXqgY1zv3buRSLRXk/PJ/Ph8nJSVPsh8fU87AIXjBgIO4r\nN1ktgDSjsbP6QSSnX5RrCxYLIEKAlX8vWPjUGYzAw3b3b8Bw5YcXZzysblg2L8HhYlB1nVLnOCZS\nq9VaqlCNRkO+WzQWi6FUKuFd73pXyw0PzRcmaRH23NwcbDYbgsFgdwcOnoAQPAZ29xZQXnsUnACI\nvnGAYRAsLCK6ewubwZmHj0OUPz+dzkjzC8DCPAObDXj8cRGJHu9/GOb5imEYRKNRRKNRVKtVuQ9T\nOBxGLpcb+Z0KBhFyPB4PTpw4gWPHjuHBgweYm5sDx3HIZDJHhjijn7f7QaFKJQ6Ho+9QNSqVKp7n\nUSqVWgIUwzBygGrezsU09m7DunkeTC2PDJfB0k4ErNh6FZuY5GG1MgAYlHdPYXXhf0Qo+RqsjjX4\nPG7YGk/AKnxAtSGxO2+DaRRbp5UAgGHh2LsO0fmYascyIqlS2jyN53A44Pf7D7zZ4d69e4f+zEAg\ngMcffxz1eh1XrlzB7Owstra2MDY2dmQQWA79Et589U1Mlr4Gr92NSERAIJWC1RkBA8DPWhEsrsih\nSuIRWZxoOsWLIvDXf83g5jwrh/izZ4EPfojHcz/c/euj1fTfUcdwOp2YmZnB9PR0y8U+l8shmUya\n6zzSpUG+NyzLIpFIIJFIoFqtYnl5GaVSCdeuXZMbizYf2wjFgkGgUKUSm83W90J1I3z4eg1V0nYu\nzQFK2s7F7/cjk8nA6/Wa+8RX2YB19btgGBawOPCed2/jwWs2OHd3AHEKIqxgWREf/ICAchl4+20L\neJ5BKX8Slb0TePxxHkELALUzd6PQHqi+jxVqff/YWo2D1crCYjHOeyrt2ygFqFKpBJZl5Wm86elp\nVaea7XY7/H4/pqamIAgCZmdnIYoixtIxJMQ7YOo7gD0AMfZDgCOA27eBl15yQxB/BC68Df7BMiqL\ngO8WkEwAE5MixgMsXnW0Tyt+hHfA2rSe6tw5YOHWo0AFABYr8N+/a8GJ4zzi8e6fhx5CVfNY4vE4\n4vG43EX8zJkziMViyGazmuyBpxdarXFyOp2Ynp7G2toakskk7ty5g0qlIldmh9njb9goVKlklNdU\nCYLQsr6kUChAFEV4PB74/X7TbudyFEv+2sNA9X0up4Afff9tvD0L2MLzcCSPY2pKhNUKRCJAJsNj\nbe3hn00mHy5iHwTRMwaIPMC0vx+8I9Lzz7t7N4+5uW2USnWwLItk0ov3vCcBm01f73dz13zpsypt\nO+T3++W1Iv0E/V5/MWoOAuUHt1C+/CdYrhTg9XoRCATAbl6AMPUv8L3vHYMgMuB54B/feRrPJb8B\nhrGgXBZRKgM3rjN4/KkYPht8L17jG3jA8PAUavgXfidOWFt7r83PMx0/UxYr8NbbDJ7/Z909By1+\nCey34iJ1EZ+ensbGxgauX78OAMjlcm0LrY3wy2yvtL4bj2EYRCIRRCL/P3tvHiTXeZ77/b7vLL3v\nPT09S8+KjSDARVxEiaJFycy1LcWSJSu5lm3dlG9FVfa1qxLHZde910ndOFWJKhXfKFU3Ttn/pBLH\n1nUsW15kO5YsU6JMigRBgiQAYh1ggNnXnt779Fm+L380ZoghBisHwADEw0Kx0Og+5zvf+c45z3nf\n533eHK7rMjc3x+HDh4nFYgwMDNB7s7nl+wAPSNU2wbZt2u32Tf/uXtFUrUfU1gW6l1fiBUGw0Q+v\nt7eX8fHxe7bseVvh1q/4SAgY6qvSu2eRoLhZeC4l9Pff/mHpxDAqMYqsX9ys0UHSzjx5U9uamanz\nzjtLGIbEtrvnfGWlxSuvzPL880PbOu7rwlnFqJxE+02wEjjR3dQ6gkqlxuRkG3ApFs1tbTt0OW6U\nBLz/eo+XXyHRk0GpFPV6nbm5OSzLItb5S+bmfgvTgqkpOLPwcULUeazwEhYd3E6AYwzxj/M/x08L\nky9qEzQcWmgwPnBlpMDzrz4+/yaC7Dsl/XctdMl9kWKxuCG0npiYoLe3d8Mm4H5zbYe7W41n2zYj\nIyMMDw9TrVaZmZkhl8t96J4FH66jvY2wbZtarXbTv9vJpGr9zb5er7OyssLa2hqVSoVoNEoikbgt\nD6a7BQ+PNbmGJiCuksTYhpSBnYBOedNHAgFaoa27Ww7uj/0cxuw/IKunIXDQ0X6C3k+gOgl0o3HD\n25mYWNsy3VetOqysNMnntzf1EoiAmqhi6xBh3rMpUGsT+FP/SLvj4rTbdFwXw3iJ0+3PcGZ6D0JG\nMA2TWg0+8QmfTGZbh3XT2HjwBR1E/SIYJlJKUqkUyWQKx2mztnSOxuIbyMQeGo0EQsDriz/Bm0uf\nZiB2hgGdQkf6GapobiRHPDSkWVi48oHr+7Bnz81H2m4ntpPwXC60XlhY4NixYxiGwcDAwH1HqnaC\nb9S6kWsqlbpjnSp2Eu79p+EOwa32/pNS4vv+bRjRzWG9H97lVgbrFU7rbuQADz/88F0e6fVxszfk\nChVmzRkk3ZvRilwlruMMBcMfyOMnyBxE1C9csQ0lTFTmLlfYSZOg9FMEpZ/a/Pny8k1tptXaOuVt\nGJJKxd02UqXRLFqLLGTmMYTEcR1UQxFaDOPU2hTq3ycWtoiEw+R7egjZNjNLMS4esYj0vEdgGw34\n7ndNvvxlnzv9LhAEMDEBJ05EqNclTzwBhtYgNhMaIbpprEh/kaefzvHqMY9abZlWM0k4EgJpseTt\npz/S/V0y1f2/QvOKcPmHvjhvmG36hMlT2qJ06Tb/iWc1Z05rKtX31qNSsG+vYmzdU9Z3oDYBoRzE\n+rY8jp2c/rsWpJQbmp9Go8HFixep1+tMTEwwODi4yUvsXsX9GH271/CAVG0T7jVLhfUS8XUC1el0\nCIfDGxVOpVJp01vGupj3foOPz5wxu0GoACSSpmiyLJcpqJtQ774f4Tx+36cwlw8jOmW0EKhQlrXU\noxTk/XHphcMmrdaVLwVBoIjHP7hYdZ3sX2hfYKYzTbPRZPL8JOFwmEgkQng0zBPBKPbsCZCb93di\nIoOpGijlbfq3IBCcOCF55JE7p2WsVOClHwpUIJift2m1bRYWBZ/8sTCZ2CCivXjFb7Sd4cd/MovR\neItnMnOcPBUwXe7hrbmniPQlmZ6xKBbhY8907x/fFg5TMqBjSJSAOan4a93h8woGMbEseOEFxdtv\nC5pNgWVp9uzVPPkEXSPYi99GLrwMgQtao5OjqN2/CJH85nHdA+m/6yEej7N7926azSbhcJi3336b\nUChEqVQil8tt677v5P19p/lGfRgJ3v1xZ98B2MltalzX3SQib7fbGyXiiUSCgYEBbNu+rt/ITk1T\nXo71cd7oxVyW5S2/KxDUqVHgA5AqgMQIfmIEvCZIA9cJ8C9c+GDbvI242fM8Oprm2LFlpNw8h7GY\nRbF4855B62t1fb2uk/36QI14LA4ahobe02oFBHQaHvYWQ262LVAdaM13RfmhTNcwU0CzedNDuyau\nt+beeFOglUCISz5SAlQgOHIEPvXkC8hzf4LQwWW/EKj+50mu/Q2fe95lZgaKBcHrry9STH6X7577\nLGvlFqYhqVRtQgguygCJ6LqgXRqLFvCG8KgcMzl0SOJ53ehUKgWf+pR+T8M39wPk7IvdqlAhQYBo\nTCFP/5+oR39zs/buDpGqO4F1t/DBwUFqtRrT09OcPn2avr4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CJjHoyhFaXhwtDEq5CrYp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54llihy6ql5zlpPMKWX2Tp+nPPaEbo/9EhdvsBf9iYYKK0zcijGM6Vt2isp1ucH2WiVmF2fpNrL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279+f9W+vv/46PvOZz7D/HwgE0NnZCYqicOTIEdx7771oaWnB008/jR07dmDv3r24/fbbl/3M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\nIiIR/D/QyGT+6diiyAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f582e0f4748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a new table with ID as index\n",
    "pca_table4 = pd.DataFrame(index=class_train.ID)\n",
    "pca = PCA(n_components=3)\n",
    "pca.fit(X4)\n",
    "pca_table4['x'] = pca.fit_transform(X4).T[0]\n",
    "pca_table4['y'] = pca.fit_transform(X4).T[1]\n",
    "pca_table4['z'] = pca.fit_transform(X4).T[2]\n",
    "\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "fig = plt.figure(1, figsize=(8, 8))\n",
    "plt.clf()\n",
    "ax = Axes3D(fig, elev=18, azim=55)#, rect=[0, 0, .95, 1])\n",
    "\n",
    "ax.scatter(pca_table4.x, pca_table4.y, pca_table4.z, c=class_train['Class'], marker = 'o', s=50, cmap = cm.jet)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<b>Hmm... back to exploring feature space</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3321, 22472)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes = class_train.Class\n",
    "table = pd.concat([features1, classes], axis=1)\n",
    "table.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class_feature_counts = []\n",
    "for i in range(9):\n",
    "    class_feature_counts.append(table[table['Class'] == (i+1)].sum(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "feature_counts_by_class = pd.concat(class_feature_counts, axis=1).transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     567.0\n",
       "1     902.0\n",
       "2     264.0\n",
       "3    2740.0\n",
       "4    1205.0\n",
       "5    1644.0\n",
       "6    6664.0\n",
       "7     144.0\n",
       "8     324.0\n",
       "Name: Class, dtype: float64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_counts_by_class['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aa</th>\n",
       "      <th>aa0869</th>\n",
       "      <th>aa1077</th>\n",
       "      <th>aa1370</th>\n",
       "      <th>aa2800</th>\n",
       "      <th>aa491</th>\n",
       "      <th>aa499</th>\n",
       "      <th>aa8</th>\n",
       "      <th>aaa</th>\n",
       "      <th>aaalac</th>\n",
       "      <th>...</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>483.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>403.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>194.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>641.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>630.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>538.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22471 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      aa  aa0869  aa1077  aa1370  aa2800  aa491  aa499   aa8   aaa  aaalac  \\\n",
       "0  483.0     1.0     8.0     0.0     1.0    0.0    0.0   0.0  53.0     0.0   \n",
       "1  194.0     3.0    24.0     1.0     0.0    0.0    0.0  19.0  51.0     0.0   \n",
       "2  110.0     0.0     0.0     0.0     0.0    0.0    0.0   0.0   3.0     0.0   \n",
       "3  641.0     1.0     8.0     0.0     0.0    0.0    0.0   0.0  41.0     0.0   \n",
       "4  538.0     0.0     0.0     0.0     0.0    0.0    0.0   0.0  10.0     0.0   \n",
       "\n",
       "  ...      7    8    9   10   11     12   13   14    15   16  \n",
       "0 ...    1.0  0.0  1.0  0.0  0.0  403.0  0.0  2.0  91.0  0.0  \n",
       "1 ...   76.0  1.0  3.0  1.0  2.0  303.0  0.0  0.0   3.0  0.0  \n",
       "2 ...    0.0  0.0  0.0  0.0  0.0   87.0  0.0  0.0   0.0  1.0  \n",
       "3 ...    3.0  2.0  2.0  0.0  0.0  630.0  0.0  2.0   1.0  0.0  \n",
       "4 ...    0.0  0.0  0.0  0.0  0.0  238.0  0.0  0.0   0.0  0.0  \n",
       "\n",
       "[5 rows x 22471 columns]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del feature_counts_by_class['Class']\n",
    "feature_counts_by_class.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18693\n",
      "6302\n",
      "2693\n"
     ]
    }
   ],
   "source": [
    "feature_uniqueness = feature_counts_by_class.astype(bool).sum(axis=0)\n",
    "feature_total_counts = feature_counts_by_class.sum(axis=0)\n",
    "\n",
    "discriminating_feature_ind = list(np.argwhere(feature_uniqueness <= 5).ravel())\n",
    "minimum_appearance_feature_ind = list(np.argwhere(feature_total_counts >= 20).ravel())\n",
    "print(len(discriminating_feature_ind))\n",
    "print(len(minimum_appearance_feature_ind))\n",
    "\n",
    "feature_meeting_conditions = set(discriminating_feature_ind).intersection(set(minimum_appearance_feature_ind))\n",
    "useful_feature_ind = sorted(list(feature_meeting_conditions))\n",
    "print(len(useful_feature_ind))\n",
    "\n",
    "X5 = X[:, useful_feature_ind]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3321, 2693)"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X5.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x7f582cf02f98>"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rnzhxAqlUCplMxtIV9K2OQtWQaPnvcKyPzyyNRqNt83ipVALHceoG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\nAAADCFUAAAAGEKoAAAAMIFQBAAAY8B+WDx51DKk87wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f582c321400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a new table with ID as index\n",
    "pca_table4 = pd.DataFrame(index=class_train.ID)\n",
    "pca = PCA(n_components=3)\n",
    "pca.fit(X5)\n",
    "pca_table4['x'] = pca.fit_transform(X5).T[0]\n",
    "pca_table4['y'] = pca.fit_transform(X5).T[1]\n",
    "pca_table4['z'] = pca.fit_transform(X5).T[2]\n",
    "\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "fig = plt.figure(1, figsize=(8, 8))\n",
    "plt.clf()\n",
    "ax = Axes3D(fig, elev=18, azim=55)#, rect=[0, 0, .95, 1])\n",
    "\n",
    "ax.scatter(pca_table4.x, pca_table4.y, pca_table4.z, c=class_train['Class'], marker = 'o', s=50, cmap = cm.jet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train5, X_test5, y_train5, y_test5 = train_test_split(X5, y, test_size=0.15, random_state=75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 797 ms, sys: 7.75 ms, total: 804 ms\n",
      "Wall time: 321 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "rfc5 = RandomForestClassifier(n_jobs=4)\n",
    "rfc5.fit(X_train5, y_train5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.464929859719\n"
     ]
    }
   ],
   "source": [
    "y_pred5 = rfc5.predict(X_test5)\n",
    "print(accuracy_score(y_test5, y_pred5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Try Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb = MultinomialNB()\n",
    "nb.fit(X_train5, y_train5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.338677354709\n"
     ]
    }
   ],
   "source": [
    "y_pred5nb = nb.predict(X_test5)\n",
    "print(accuracy_score(y_test5, y_pred5nb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}