csv_chunksize.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(146, 81)\n",
      "(146, 81)\n",
      "(146, 81)\n",
      "(146, 81)\n",
      "(146, 81)\n",
      "(146, 81)\n",
      "(146, 81)\n",
      "(146, 81)\n",
      "(146, 81)\n",
      "(146, 81)\n"
     ]
    }
   ],
   "source": [
    "#use chunks to store large dataset to save memory\n",
    "chunks = pd.read_csv('train.csv', chunksize=146) #chunk size is the size of each chunk, not the total number of chunks\n",
    "for chunk in chunks:\n",
    "    df = chunk\n",
    "    print df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from boruta import BorutaPy\n",
    "from sklearn.ensemble import RandomForestClassifier #used to select features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('train.csv') #read in training data, boruta only accept numpy arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train = df[['BsmtQual']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = x_train[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/conebeam/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <th>12</th>\n",
       "      <td>TA</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Gd</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "      <th>14</th>\n",
       "      <td>TA</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "   BsmtQual  factor\n",
       "0        Gd       0\n",
       "1        Gd       0\n",
       "2        Gd       0\n",
       "3        TA       1\n",
       "4        Gd       0\n",
       "5        Gd       0\n",
       "6        Ex       2\n",
       "7        Gd       0\n",
       "8        TA       1\n",
       "9        TA       1\n",
       "10       TA       1\n",
       "11       Ex       2\n",
       "12       TA       1\n",
       "13       Gd       0\n",
       "14       TA       1\n",
       "15       TA       1\n",
       "16       TA       1\n",
       "17      NaN      -1\n",
       "18       TA       1\n",
       "19       TA       1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['factor'] = pd.factorize(test.BsmtQual)[0]\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['SalePrice'].values\n",
    "X = df.drop(['SalePrice','Id'], axis=1).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(n_jobs=-1, class_weight='auto',max_depth=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_selector = BorutaPy(rf, n_estimators='auto',random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_selector.fit(X,y)"
   ]
  }
 ],
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