macro_feature_selection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pylab as plt\n",
    "import matplotlib.pyplot as plt\n",
    "##import xgboost as xgb\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "color = sns.color_palette()\n",
    "from sklearn import model_selection, preprocessing\n",
    "import datetime\n",
    "import scipy as sp\n",
    "# Get ready for lots of annoying deprecation warnings\n",
    "import statsmodels.api as sm\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "micro_humility_factor = 1\n",
    "macro_humility_factor = 0.96\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Functions to use in data adjustment\n",
    "\n",
    "def scale_miss(   # Scale shifted logs and compare raw stdev to old raw stdev\n",
    "        alpha,\n",
    "        shifted_logs,\n",
    "        oldstd,\n",
    "        new_logmean\n",
    "        ):\n",
    "    newlogs = new_logmean + alpha*(shifted_logs - new_logmean)\n",
    "    newstd = np.std(np.exp(newlogs))\n",
    "    return (newstd-oldstd)**2\n",
    "    \n",
    "\n",
    "def shift_logmean_but_keep_scale(  # Or change the scale, but relative to the old scale\n",
    "        data,\n",
    "        new_logmean,\n",
    "        rescaler\n",
    "        ):\n",
    "    logdata = np.log(data)\n",
    "    oldstd = data.std()\n",
    "    shift = new_logmean - logdata.mean()\n",
    "    shifted_logs = logdata + shift\n",
    "    scale = sp.optimize.leastsq( scale_miss, 1, args=(shifted_logs, oldstd, new_logmean) )\n",
    "    alpha = scale[0][0]\n",
    "    newlogs = new_logmean + rescaler*alpha*(shifted_logs - new_logmean)\n",
    "    return np.exp(newlogs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit macro model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read data\n",
    "macro = pd.read_csv('./data/input/macro.csv')\n",
    "train = pd.read_csv('./data/input/train.csv')\n",
    "test = pd.read_csv('./data/input/test.csv')\n",
    "\n",
    "# Macro data monthly medians\n",
    "macro[\"timestamp\"] = pd.to_datetime(macro[\"timestamp\"])\n",
    "macro[\"year\"]  = macro[\"timestamp\"].dt.year\n",
    "macro[\"month\"] = macro[\"timestamp\"].dt.month\n",
    "macro[\"yearmonth\"] = 100*macro.year + macro.month\n",
    "macmeds = macro.groupby(\"yearmonth\").median()cx\n",
    "\n",
    "# Price data monthly medians\n",
    "train[\"timestamp\"] = pd.to_datetime(train[\"timestamp\"])\n",
    "train[\"year\"]  = train[\"timestamp\"].dt.year\n",
    "train[\"month\"] = train[\"timestamp\"].dt.month\n",
    "train[\"yearmonth\"] = 100*train.year + train.month\n",
    "prices = train[[\"yearmonth\",\"price_doc\"]]\n",
    "p = prices.groupby(\"yearmonth\").median()\n",
    "\n",
    "# Join monthly prices to macro data\n",
    "df = macmeds.join(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to process Almon lags\n",
    "\n",
    "import numpy.matlib as ml\n",
    " \n",
    "def almonZmatrix(X, maxlag, maxdeg):\n",
    "    \"\"\"\n",
    "    Creates the Z matrix corresponding to vector X.\n",
    "    \"\"\"\n",
    "    n = len(X)\n",
    "    Z = ml.zeros((len(X)-maxlag, maxdeg+1))\n",
    "    for t in range(maxlag,  n):\n",
    "       #Solve for Z[t][0].\n",
    "       Z[t-maxlag,0] = sum([X[t-lag] for lag in range(maxlag+1)])\n",
    "       for j in range(1, maxdeg+1):\n",
    "             s = 0.0\n",
    "             for i in range(1, maxlag+1):       \n",
    "                s += (i)**j * X[t-i]\n",
    "             Z[t-maxlag,j] = s\n",
    "    return Z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6593788.9177278513"
      ]
     },
     "execution_count": 446,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prepare data for macro model\n",
    "y = df.price_doc.div(df.cpi).apply(np.log).loc[201108:201506]\n",
    "lncpi = df.cpi.apply(np.log)\n",
    "tblags = 5    # Number of lags used on PDL for Trade Balance\n",
    "mrlags = 5    # Number of lags used on PDL for Mortgage Rate\n",
    "cplags = 5    # Number of lags used on PDL for CPI\n",
    "ztb = almonZmatrix(df.balance_trade.loc[201103:201506].as_matrix(), tblags, 1)\n",
    "zmr = almonZmatrix(df.mortgage_rate.loc[201103:201506].as_matrix(), mrlags, 1)\n",
    "zcp = almonZmatrix(lncpi.loc[201103:201506].as_matrix(), cplags, 1)\n",
    "columns = ['tb0', 'tb1', 'mr0', 'mr1', 'cp0', 'cp1']\n",
    "z = pd.DataFrame( np.concatenate( (ztb, zmr, zcp), axis=1), y.index.values, columns )\n",
    "X = sm.add_constant( z )\n",
    "\n",
    "# Fit macro model\n",
    "eq = sm.OLS(y, X)\n",
    "fit = eq.fit()\n",
    "\n",
    "# Predict with macro model\n",
    "test_cpi = df.cpi.loc[201507:201605]\n",
    "test_index = test_cpi.index\n",
    "ztb_test = almonZmatrix(df.balance_trade.loc[201502:201605].as_matrix(), tblags, 1)\n",
    "zmr_test = almonZmatrix(df.mortgage_rate.loc[201502:201605].as_matrix(), mrlags, 1)\n",
    "zcp_test = almonZmatrix(lncpi.loc[201502:201605].as_matrix(), cplags, 1)\n",
    "z_test = pd.DataFrame( np.concatenate( (ztb_test, zmr_test, zcp_test), axis=1), \n",
    "                       test_index, columns )\n",
    "X_test = sm.add_constant( z_test )\n",
    "pred_lnrp = fit.predict( X_test )\n",
    "pred_p = np.exp(pred_lnrp) * test_cpi\n",
    "\n",
    "# Merge with test cases and compute mean for macro prediction\n",
    "test[\"timestamp\"] = pd.to_datetime(test[\"timestamp\"])\n",
    "test[\"year\"]  = test[\"timestamp\"].dt.year\n",
    "test[\"month\"] = test[\"timestamp\"].dt.month\n",
    "test[\"yearmonth\"] = 100*test.year + test.month\n",
    "test_ids = test[[\"yearmonth\",\"id\"]]\n",
    "monthprices = pd.DataFrame({\"yearmonth\":pred_p.index.values,\"monthprice\":pred_p.values})\n",
    "macro_mean = np.exp(test_ids.merge(monthprices, on=\"yearmonth\").monthprice.apply(np.log).mean())\n",
    "macro_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7773440.7512406502"
      ]
     },
     "execution_count": 447,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Naive macro model assumes housing prices will simply follow CPI\n",
    "naive_pred_lnrp = y.mean()\n",
    "naive_pred_p = np.exp(naive_pred_lnrp) * test_cpi\n",
    "monthnaive = pd.DataFrame({\"yearmonth\":pred_p.index.values, \"monthprice\":naive_pred_p.values})\n",
    "macro_naive = np.exp(test_ids.merge(monthnaive, on=\"yearmonth\").monthprice.apply(np.log).mean())\n",
    "macro_naive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6637341.6089051161"
      ]
     },
     "execution_count": 450,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine naive and substantive macro models\n",
    "macro_mean = macro_naive * (macro_mean/macro_naive) ** macro_humility_factor\n",
    "macro_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2484, 100)"
      ]
     },
     "execution_count": 451,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macro = pd.read_csv('./data/input/macro.csv',parse_dates=['timestamp'],\n",
    "                   )\n",
    "macro.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/whalejasmine/anaconda/envs/python2/lib/python2.7/site-packages/ipykernel_launcher.py:3: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10862e410>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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15z5Sc2515/eJSonpoPN7b7K7/beAJ8ig7K7AyZJmB9Yju8n3ZXzndxh/31uS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1039965d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "missing_df = macro.isnull().sum(axis=0).reset_index()\n",
    "missing_df.columns = ['column_name', 'missing_count']\n",
    "missing_df = missing_df.ix[missing_df['missing_count']>0]\n",
    "\n",
    "ind1 = np.arange(missing_df.shape[0])\n",
    "\n",
    "width = 0.9\n",
    "fig,ax1 = plt.subplots()\n",
    "fig.set_size_inches(20,12)\n",
    "rects = ax1.barh(ind1, missing_df.missing_count.values, color='y')\n",
    "ax1.set_yticks(ind1)\n",
    "ax1.set_yticklabels(missing_df.column_name.values, rotation='horizontal')\n",
    "ax1.set_xlabel(\"Count of missing values\")\n",
    "ax1.set_title(\"Number of missing values in each column\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def clean_data_macro(df):\n",
    "    # convert object column to numeric\n",
    "    char_columns = (df.dtypes[df.dtypes.map(lambda x : x =='object')]).index\n",
    "    float_columns = (df.dtypes[df.dtypes.map(lambda x : x =='float64')]).index\n",
    "    le=LabelEncoder()\n",
    "    for feature in char_columns:\n",
    "        df[feature]=le.fit_transform(df[feature])\n",
    "    # cvert float64 to float32\n",
    "    df[float_columns]=df[float_columns].astype(np.float32)\n",
    "    df.fillna(df.median(),inplace = True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2484, 99)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macros_clean = clean_data(macro.drop('timestamp',axis=1))\n",
    "macros_clean.to_csv('./data/output/macro_corr.csv', index=False)\n",
    "macros_clean.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2484, 21)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import macro features\n",
    "macros_feature = pd.read_csv(\"./data/output/macro_re_corr.csv\")\n",
    "macros_feature.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'balance_trade_growth', u'net_capital_export',\n",
       "       u'average_provision_of_build_contract_moscow', u'micex',\n",
       "       u'deposits_growth', u'mortgage_value', u'mortgage_rate',\n",
       "       u'income_per_cap', u'unemployment', u'electric_stove_share',\n",
       "       u'rent_price_4+room_bus', u'rent_price_3room_bus',\n",
       "       u'rent_price_1room_bus', u'rent_price_3room_eco',\n",
       "       u'rent_price_2room_eco', u'rent_price_1room_eco',\n",
       "       u'students_state_oneshift', u'old_education_build_share',\n",
       "       u'provision_doctors', u'students_reg_sports_share',\n",
       "       u'apartment_fund_sqm'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macros_feature.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2484, 14)"
      ]
     },
     "execution_count": 483,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macro_cols = [\"balance_trade\", \"balance_trade_growth\", \"eurrub\", \"average_provision_of_build_contract\",\n",
    "\"micex_rgbi_tr\", \"micex_cbi_tr\", \"deposits_rate\", \"mortgage_value\", \"mortgage_rate\",\n",
    "\"income_per_cap\", \"rent_price_4+room_bus\", \"museum_visitis_per_100_cap\", \"apartment_build\"]\n",
    "#macro = pd.read_csv('./data/input/macro.csv',parse_dates=['timestamp'],\n",
    "                   #usecols=['timestamp']+macros_feature.columns.tolist())\n",
    "macro = pd.read_csv('./data/input/macro.csv',parse_dates=['timestamp'],\n",
    "                   usecols=['timestamp']+macro_cols)\n",
    "macro.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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>timestamp</th>\n",
       "      <th>balance_trade</th>\n",
       "      <th>balance_trade_growth</th>\n",
       "      <th>eurrub</th>\n",
       "      <th>average_provision_of_build_contract</th>\n",
       "      <th>micex_rgbi_tr</th>\n",
       "      <th>micex_cbi_tr</th>\n",
       "      <th>deposits_rate</th>\n",
       "      <th>mortgage_value</th>\n",
       "      <th>mortgage_rate</th>\n",
       "      <th>income_per_cap</th>\n",
       "      <th>rent_price_4+room_bus</th>\n",
       "      <th>museum_visitis_per_100_cap</th>\n",
       "      <th>apartment_build</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>16.041</td>\n",
       "      <td>10.5</td>\n",
       "      <td>42.862202</td>\n",
       "      <td>5.0</td>\n",
       "      <td>131.279999</td>\n",
       "      <td>233.000000</td>\n",
       "      <td>5.7</td>\n",
       "      <td>142968</td>\n",
       "      <td>13.72</td>\n",
       "      <td>30789.199219</td>\n",
       "      <td>162.0</td>\n",
       "      <td>993.0</td>\n",
       "      <td>22825.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>16.041</td>\n",
       "      <td>10.5</td>\n",
       "      <td>42.862202</td>\n",
       "      <td>5.0</td>\n",
       "      <td>131.279999</td>\n",
       "      <td>175.149994</td>\n",
       "      <td>5.7</td>\n",
       "      <td>142968</td>\n",
       "      <td>13.72</td>\n",
       "      <td>30789.199219</td>\n",
       "      <td>162.0</td>\n",
       "      <td>993.0</td>\n",
       "      <td>22825.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-03</td>\n",
       "      <td>16.041</td>\n",
       "      <td>10.5</td>\n",
       "      <td>42.862202</td>\n",
       "      <td>5.0</td>\n",
       "      <td>131.279999</td>\n",
       "      <td>178.660004</td>\n",
       "      <td>5.7</td>\n",
       "      <td>142968</td>\n",
       "      <td>13.72</td>\n",
       "      <td>30789.199219</td>\n",
       "      <td>162.0</td>\n",
       "      <td>993.0</td>\n",
       "      <td>22825.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>16.041</td>\n",
       "      <td>10.5</td>\n",
       "      <td>43.405399</td>\n",
       "      <td>5.0</td>\n",
       "      <td>131.279999</td>\n",
       "      <td>183.440002</td>\n",
       "      <td>5.7</td>\n",
       "      <td>142968</td>\n",
       "      <td>13.72</td>\n",
       "      <td>30789.199219</td>\n",
       "      <td>162.0</td>\n",
       "      <td>993.0</td>\n",
       "      <td>22825.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>16.041</td>\n",
       "      <td>10.5</td>\n",
       "      <td>42.959999</td>\n",
       "      <td>5.0</td>\n",
       "      <td>131.279999</td>\n",
       "      <td>183.440002</td>\n",
       "      <td>5.7</td>\n",
       "      <td>142968</td>\n",
       "      <td>13.72</td>\n",
       "      <td>30789.199219</td>\n",
       "      <td>162.0</td>\n",
       "      <td>993.0</td>\n",
       "      <td>22825.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   timestamp  balance_trade  balance_trade_growth     eurrub  \\\n",
       "0 2010-01-01         16.041                  10.5  42.862202   \n",
       "1 2010-01-02         16.041                  10.5  42.862202   \n",
       "2 2010-01-03         16.041                  10.5  42.862202   \n",
       "3 2010-01-04         16.041                  10.5  43.405399   \n",
       "4 2010-01-05         16.041                  10.5  42.959999   \n",
       "\n",
       "   average_provision_of_build_contract  micex_rgbi_tr  micex_cbi_tr  \\\n",
       "0                                  5.0     131.279999    233.000000   \n",
       "1                                  5.0     131.279999    175.149994   \n",
       "2                                  5.0     131.279999    178.660004   \n",
       "3                                  5.0     131.279999    183.440002   \n",
       "4                                  5.0     131.279999    183.440002   \n",
       "\n",
       "   deposits_rate  mortgage_value  mortgage_rate  income_per_cap  \\\n",
       "0            5.7          142968          13.72    30789.199219   \n",
       "1            5.7          142968          13.72    30789.199219   \n",
       "2            5.7          142968          13.72    30789.199219   \n",
       "3            5.7          142968          13.72    30789.199219   \n",
       "4            5.7          142968          13.72    30789.199219   \n",
       "\n",
       "   rent_price_4+room_bus  museum_visitis_per_100_cap  apartment_build  \n",
       "0                  162.0                       993.0          22825.0  \n",
       "1                  162.0                       993.0          22825.0  \n",
       "2                  162.0                       993.0          22825.0  \n",
       "3                  162.0                       993.0          22825.0  \n",
       "4                  162.0                       993.0          22825.0  "
      ]
     },
     "execution_count": 484,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macros = clean_data_macro(macro)\n",
    "macros.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary Clean for train set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# drop missing value >0.35 of total observations\n",
    "# keep state and build_year for training data\n",
    "def drop_high_volumn_missing(df):\n",
    "    not_move = [\"build_year\",\"state\"]\n",
    "    high_missing_train_feature= df.columns[df.isnull().sum()>0.35*len(df)].tolist()\n",
    "    \n",
    "    for g in not_move:\n",
    "        if g in high_missing_train_feature:\n",
    "            high_missing_train_feature.remove(g)\n",
    "    df.drop(high_missing_train_feature,axis = 1, inplace=True)    \n",
    "    return df\n",
    "\n",
    "## drop build_ columns: drop na obs or drop columns\n",
    "def drop_build_function(df):\n",
    "    not_move = [\"build_year\"]\n",
    "    build_feature = df.filter(regex=r'^build_count_', axis=1).columns.tolist()\n",
    "    build_feature2 = df.filter(regex=r'^raion_build_count_', axis=1).columns.tolist()\n",
    "    for e in build_feature:\n",
    "        for g in not_move:\n",
    "            if e==g:\n",
    "                high_missing_train_feature.remove(e)\n",
    "    build_feature.extend(build_feature2)\n",
    "    df.drop(build_feature,axis = 1)\n",
    "    return df\n",
    "\n",
    "# remove outliers\n",
    "'''def reject_outliers(df, m = 1):\n",
    "    d = np.abs(df - np.median(df))\n",
    "    mdev = np.median(d)\n",
    "    s = d/mdev if mdev else 0.\n",
    "    return df[s<m]\n",
    "'''\n",
    "def reject_outliers(df, iq_range=0.5):\n",
    "    #df[np.abs(df.Data-df.Data.mean())<=(3*df.Data.std())]\n",
    "    pcnt = (1 - iq_range) / 2\n",
    "    qlow, median, qhigh = df.dropna().quantile([pcnt, 0.50, 1-pcnt])\n",
    "    iqr = qhigh - qlow\n",
    "    return df[ (df - median).abs() <= iqr]\n",
    "\n",
    "def clean_data(df):\n",
    "    #add str feature \n",
    "    #df_all.apartment_name=df_all.sub_area + df_all['metro_km_avto'].astype(str)\n",
    "\n",
    "    # convert object column to numeric\n",
    "    char_columns = (df.dtypes[df.dtypes.map(lambda x : x =='object')]).index\n",
    "    float_columns = (df.dtypes[df.dtypes.map(lambda x : x =='float64')]).index\n",
    "    le=LabelEncoder()\n",
    "    for feature in char_columns:\n",
    "        df[feature]=le.fit_transform(df[feature])\n",
    "    # cvert float64 to float32\n",
    "    df[float_columns]=df[float_columns].astype(np.float32)\n",
    "    \n",
    "    # fill build_ and cafe_, state, NA to 0\n",
    "\n",
    "    df.filter(regex=r'^cafe_', axis=1).fillna(0, inplace=True)\n",
    "    #build_feature\n",
    "    not_move = [\"build_year\"]\n",
    "    build_feature = df.filter(regex=r'^build_count_', axis=1).columns.tolist()\n",
    "    build_feature2 = df.filter(regex=r'^raion_build_count_', axis=1).columns.tolist()\n",
    "    for e in build_feature:\n",
    "        for g in not_move:\n",
    "            if e==g:\n",
    "                high_missing_train_feature.remove(e)\n",
    "    build_feature.extend(build_feature2)\n",
    "    #build fill median or 0?\n",
    "    df[build_feature].fillna(df[build_feature].median(),inplace=True)\n",
    "    #fill state\n",
    "    df['state'].fillna(0,inplace=True) \n",
    "    df.fillna(df.median(), inplace = True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 614,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/whalejasmine/anaconda/envs/python2/lib/python2.7/site-packages/ipykernel_launcher.py:9: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix\n",
      "  if __name__ == '__main__':\n",
      "/Users/whalejasmine/anaconda/envs/python2/lib/python2.7/site-packages/ipykernel_launcher.py:11: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    }
   ],
   "source": [
    "#load files\n",
    "train = pd.read_csv('./data/input/train.csv', parse_dates=['timestamp'])\n",
    "test = pd.read_csv('./data/input/test.csv', parse_dates=['timestamp'])\n",
    "#macro = pd.read_csv('../input/macro.csv', parse_dates=['timestamp'])\n",
    "#id_test = test.id\n",
    "\n",
    "#clean data\n",
    "bad_index = train[train.life_sq > train.full_sq].index\n",
    "train.ix[bad_index, \"life_sq\"] = np.NaN\n",
    "equal_index = [601,1896,2791]\n",
    "test.ix[equal_index, \"life_sq\"] = test.ix[equal_index, \"full_sq\"]\n",
    "bad_index = test[test.life_sq > test.full_sq].index\n",
    "test.ix[bad_index, \"life_sq\"] = np.NaN\n",
    "bad_index = train[train.life_sq < 5].index\n",
    "train.ix[bad_index, \"life_sq\"] = np.NaN\n",
    "bad_index = test[test.life_sq < 5].index\n",
    "test.ix[bad_index, \"life_sq\"] = np.NaN\n",
    "bad_index = train[train.full_sq < 5].index\n",
    "train.ix[bad_index, \"full_sq\"] = np.NaN\n",
    "bad_index = test[test.full_sq < 5].index\n",
    "test.ix[bad_index, \"full_sq\"] = np.NaN\n",
    "kitch_is_build_year = [13117]\n",
    "train.ix[kitch_is_build_year, \"build_year\"] = train.ix[kitch_is_build_year, \"kitch_sq\"]\n",
    "bad_index = train[train.kitch_sq >= train.life_sq].index\n",
    "train.ix[bad_index, \"kitch_sq\"] = np.NaN\n",
    "bad_index = test[test.kitch_sq >= test.life_sq].index\n",
    "test.ix[bad_index, \"kitch_sq\"] = np.NaN\n",
    "bad_index = train[(train.kitch_sq == 0).values + (train.kitch_sq == 1).values].index\n",
    "train.ix[bad_index, \"kitch_sq\"] = np.NaN\n",
    "bad_index = test[(test.kitch_sq == 0).values + (test.kitch_sq == 1).values].index\n",
    "test.ix[bad_index, \"kitch_sq\"] = np.NaN\n",
    "bad_index = train[(train.full_sq > 210) & (train.life_sq / train.full_sq < 0.3)].index\n",
    "train.ix[bad_index, \"full_sq\"] = np.NaN\n",
    "bad_index = test[(test.full_sq > 150) & (test.life_sq / test.full_sq < 0.3)].index\n",
    "test.ix[bad_index, \"full_sq\"] = np.NaN\n",
    "bad_index = train[train.life_sq > 300].index\n",
    "train.ix[bad_index, [\"life_sq\", \"full_sq\"]] = np.NaN\n",
    "bad_index = test[test.life_sq > 200].index\n",
    "test.ix[bad_index, [\"life_sq\", \"full_sq\"]] = np.NaN\n",
    "train.product_type.value_counts(normalize= True)\n",
    "test.product_type.value_counts(normalize= True)\n",
    "bad_index = train[train.build_year < 1500].index\n",
    "train.ix[bad_index, \"build_year\"] = np.NaN\n",
    "bad_index = test[test.build_year < 1500].index\n",
    "test.ix[bad_index, \"build_year\"] = np.NaN\n",
    "bad_index = train[train.num_room == 0].index \n",
    "train.ix[bad_index, \"num_room\"] = np.NaN\n",
    "bad_index = test[test.num_room == 0].index \n",
    "test.ix[bad_index, \"num_room\"] = np.NaN\n",
    "bad_index = [10076, 11621, 17764, 19390, 24007, 26713, 29172]\n",
    "train.ix[bad_index, \"num_room\"] = np.NaN\n",
    "bad_index = [3174, 7313]\n",
    "test.ix[bad_index, \"num_room\"] = np.NaN\n",
    "bad_index = train[(train.floor == 0).values * (train.max_floor == 0).values].index\n",
    "train.ix[bad_index, [\"max_floor\", \"floor\"]] = np.NaN\n",
    "bad_index = train[train.floor == 0].index\n",
    "train.ix[bad_index, \"floor\"] = np.NaN\n",
    "bad_index = train[train.max_floor == 0].index\n",
    "train.ix[bad_index, \"max_floor\"] = np.NaN\n",
    "bad_index = test[test.max_floor == 0].index\n",
    "test.ix[bad_index, \"max_floor\"] = np.NaN\n",
    "bad_index = train[train.floor > train.max_floor].index\n",
    "train.ix[bad_index, \"max_floor\"] = np.NaN\n",
    "bad_index = test[test.floor > test.max_floor].index\n",
    "test.ix[bad_index, \"max_floor\"] = np.NaN\n",
    "train.floor.describe(percentiles= [0.9999])\n",
    "bad_index = [23584]\n",
    "train.ix[bad_index, \"floor\"] = np.NaN\n",
    "train.material.value_counts()\n",
    "test.material.value_counts()\n",
    "train.state.value_counts()\n",
    "bad_index = train[train.state == 33].index\n",
    "train.ix[bad_index, \"state\"] = np.NaN\n",
    "test.state.value_counts()\n",
    "\n",
    "# brings error down a lot by removing extreme price per sqm\n",
    "train.loc[train.full_sq == 0, 'full_sq'] = 50\n",
    "train = train[train.price_doc/train.full_sq <= 600000]\n",
    "train = train[train.price_doc/train.full_sq >= 10000]\n",
    "\n",
    "#export clean data \n",
    "train.to_csv(\"./data/output/train_clean.csv\",index=False)\n",
    "test.to_csv(\"./data/output/test_clean.csv\",index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 690,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30396, 292)"
      ]
     },
     "execution_count": 690,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = clean_data(train)\n",
    "df_train.fillna(df_train.median(),inplace = True)\n",
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 691,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7662, 291)"
      ]
     },
     "execution_count": 691,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = clean_data(test)\n",
    "df_test.fillna(df_test.median(),inplace = True)\n",
    "df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 692,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30396, 305)"
      ]
     },
     "execution_count": 692,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.merge_ordered(df_train, macros, on='timestamp', how='left')\n",
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 693,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7662, 304)"
      ]
     },
     "execution_count": 693,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.merge_ordered(df_test, macros, on='timestamp', how='left')\n",
    "df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 694,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# add month year week and other feature\n",
    "def add_feature(df_all):\n",
    "    month_year = (df_all.timestamp.dt.month + df_all.timestamp.dt.year * 100)\n",
    "    month_year_cnt_map = month_year.value_counts().to_dict()\n",
    "    df_all['month_year_cnt'] = month_year.map(month_year_cnt_map)\n",
    "\n",
    "    week_year = (df_all.timestamp.dt.weekofyear + df_all.timestamp.dt.year * 100)\n",
    "    week_year_cnt_map = week_year.value_counts().to_dict()\n",
    "    df_all['week_year_cnt'] = week_year.map(week_year_cnt_map)\n",
    "\n",
    "    df_all['month'] = df_all.timestamp.dt.month\n",
    "    df_all['dow'] = df_all.timestamp.dt.dayofweek\n",
    "\n",
    "    # Other feature engineering\n",
    "    df_all['rel_floor'] = df_all['floor'] / df_all['max_floor'].astype(float)\n",
    "    df_all['rel_kitch_sq'] = df_all['kitch_sq'] / df_all['full_sq'].astype(float)\n",
    "    \n",
    "    df_all['rel_kitch_sq'].fillna(0,inplace=True)\n",
    "    df_all['rel_floor'].fillna(0,inplace=True)\n",
    "    \n",
    "    df_all['room_size'] = df_all['life_sq'] / df_all['num_room'].astype(float)\n",
    "   # df_all.replace(np.inf, np.nan)\n",
    "    ##trainBuild = df_train.dropna()\n",
    "    df_all[\"yearbuilt\"] = pd.to_numeric(df_all[\"build_year\"].map(lambda x:str(x).split(\".\")[0]),errors='coerce')\n",
    "    # Remove timestamp buildyear column (may overfit the model in train)\n",
    "   # df_all.drop(['timestamp',\"id\"], axis=1, inplace=True)\n",
    "    return df_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 695,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30396, 313)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(7662, 312)"
      ]
     },
     "execution_count": 695,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtrain=add_feature(df_train)\n",
    "print dtrain.shape\n",
    "dtest=add_feature(df_test)\n",
    "dtest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 569,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'all_x=add_feature(df_all)\\nprint all_x.shape'"
      ]
     },
     "execution_count": 569,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''all_x=add_feature(df_all)\n",
    "print all_x.shape'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#train with macros features to remove correlations\n",
    "train.to_csv(\"./data/output/train_with_macros.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#train with macros features to remove correlations\n",
    "\n",
    "#train_re_corr = pd.read_csv(\"./data/output/train_with_macros_re_corr.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 696,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30396, 313)\n",
      "(7662, 312)\n"
     ]
    }
   ],
   "source": [
    "#split train and test set\n",
    "xtrain = clean_data_macro(dtrain)\n",
    "xtest=clean_data_macro(dtest)\n",
    "print xtrain.shape\n",
    "print xtest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 686,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30471, 191)"
      ]
     },
     "execution_count": 686,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#train low correlated feature or VIF \n",
    "#train_feature = pd.read_csv(\"./data/output/train_feature_multicoliear.csv\")\n",
    "train_feature = pd.read_csv(\"./data/output/train_vif_fetrain.csv\")# to drop\n",
    "train_feature.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 697,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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>life_sq</th>\n",
       "      <th>floor</th>\n",
       "      <th>max_floor</th>\n",
       "      <th>material</th>\n",
       "      <th>num_room</th>\n",
       "      <th>kitch_sq</th>\n",
       "      <th>state</th>\n",
       "      <th>sub_area</th>\n",
       "      <th>area_m</th>\n",
       "      <th>green_zone_part</th>\n",
       "      <th>...</th>\n",
       "      <th>museum_visitis_per_100_cap</th>\n",
       "      <th>apartment_build</th>\n",
       "      <th>month_year_cnt</th>\n",
       "      <th>week_year_cnt</th>\n",
       "      <th>month</th>\n",
       "      <th>dow</th>\n",
       "      <th>rel_floor</th>\n",
       "      <th>rel_kitch_sq</th>\n",
       "      <th>room_size</th>\n",
       "      <th>yearbuilt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "      <td>6407578.0</td>\n",
       "      <td>0.189727</td>\n",
       "      <td>...</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>23587.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.186047</td>\n",
       "      <td>13.5</td>\n",
       "      <td>1981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70</td>\n",
       "      <td>9589337.0</td>\n",
       "      <td>0.372602</td>\n",
       "      <td>...</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>23587.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>9.5</td>\n",
       "      <td>1981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>129</td>\n",
       "      <td>4808270.0</td>\n",
       "      <td>0.112560</td>\n",
       "      <td>...</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>23587.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.186047</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65</td>\n",
       "      <td>12583536.0</td>\n",
       "      <td>0.194703</td>\n",
       "      <td>...</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>23587.0</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.089888</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>77.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>8398461.0</td>\n",
       "      <td>0.015234</td>\n",
       "      <td>...</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>23587.0</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.103896</td>\n",
       "      <td>38.5</td>\n",
       "      <td>1981</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   life_sq  floor  max_floor  material  num_room  kitch_sq  state  sub_area  \\\n",
       "0     27.0    4.0       14.0       1.0       2.0       8.0    0.0         9   \n",
       "1     19.0    3.0       14.0       1.0       2.0       8.0    0.0        70   \n",
       "2     29.0    2.0       14.0       1.0       2.0       8.0    0.0       129   \n",
       "3     50.0    9.0       14.0       1.0       2.0       8.0    0.0        65   \n",
       "4     77.0    4.0       14.0       1.0       2.0       8.0    0.0         6   \n",
       "\n",
       "       area_m  green_zone_part    ...      museum_visitis_per_100_cap  \\\n",
       "0   6407578.0         0.189727    ...                          1240.0   \n",
       "1   9589337.0         0.372602    ...                          1240.0   \n",
       "2   4808270.0         0.112560    ...                          1240.0   \n",
       "3  12583536.0         0.194703    ...                          1240.0   \n",
       "4   8398461.0         0.015234    ...                          1240.0   \n",
       "\n",
       "   apartment_build  month_year_cnt  week_year_cnt  month  dow  rel_floor  \\\n",
       "0          23587.0               3              1      8    5   0.285714   \n",
       "1          23587.0               3              2      8    1   0.214286   \n",
       "2          23587.0               3              2      8    5   0.142857   \n",
       "3          23587.0              39              1      9    3   0.642857   \n",
       "4          23587.0              39              5      9    0   0.285714   \n",
       "\n",
       "   rel_kitch_sq  room_size  yearbuilt  \n",
       "0      0.186047       13.5       1981  \n",
       "1      0.235294        9.5       1981  \n",
       "2      0.186047       14.5       1981  \n",
       "3      0.089888       25.0       1981  \n",
       "4      0.103896       38.5       1981  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 697,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#readin train set\n",
    "#df_train = pd.read_csv(\"./data/input/train.csv\",parse_dates=['timestamp'],\n",
    "                      #usecols =['timestamp','price_doc','build_year']+train_feature.columns.tolist())\n",
    "#df_train = pd.read_csv(\"./data/input/train.csv\",parse_dates=['timestamp'])\n",
    "feature_drop = train_feature.columns.tolist()+[\"price_doc\"]\n",
    "y_train =np.log1p(xtrain[\"price_doc\"])\n",
    "x_train=xtrain.drop(feature_drop, axis = 1)\n",
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30396, 286)"
      ]
     },
     "execution_count": 399,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop high volumn missing\n",
    "#drop_high_volumn_missing(df_train)\n",
    "#df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 635,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#df_train.drop([\"price_doc\"],axis=1,inplace=True)\n",
    "#df_train.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 701,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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>life_sq</th>\n",
       "      <th>floor</th>\n",
       "      <th>max_floor</th>\n",
       "      <th>material</th>\n",
       "      <th>num_room</th>\n",
       "      <th>kitch_sq</th>\n",
       "      <th>state</th>\n",
       "      <th>sub_area</th>\n",
       "      <th>area_m</th>\n",
       "      <th>green_zone_part</th>\n",
       "      <th>...</th>\n",
       "      <th>museum_visitis_per_100_cap</th>\n",
       "      <th>apartment_build</th>\n",
       "      <th>month_year_cnt</th>\n",
       "      <th>week_year_cnt</th>\n",
       "      <th>month</th>\n",
       "      <th>dow</th>\n",
       "      <th>rel_floor</th>\n",
       "      <th>rel_kitch_sq</th>\n",
       "      <th>room_size</th>\n",
       "      <th>yearbuilt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20.700001</td>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>38</td>\n",
       "      <td>26155138.0</td>\n",
       "      <td>0.137846</td>\n",
       "      <td>...</td>\n",
       "      <td>1309.0</td>\n",
       "      <td>42551.0</td>\n",
       "      <td>396</td>\n",
       "      <td>59</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.228205</td>\n",
       "      <td>20.700001</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30.600000</td>\n",
       "      <td>8</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>102</td>\n",
       "      <td>25536296.0</td>\n",
       "      <td>0.496315</td>\n",
       "      <td>...</td>\n",
       "      <td>1309.0</td>\n",
       "      <td>42551.0</td>\n",
       "      <td>396</td>\n",
       "      <td>59</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.107323</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>1986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25.100000</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>84</td>\n",
       "      <td>9946335.0</td>\n",
       "      <td>0.065409</td>\n",
       "      <td>...</td>\n",
       "      <td>1309.0</td>\n",
       "      <td>42551.0</td>\n",
       "      <td>396</td>\n",
       "      <td>59</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.118519</td>\n",
       "      <td>12.550000</td>\n",
       "      <td>1960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>17</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>104</td>\n",
       "      <td>21494094.0</td>\n",
       "      <td>0.262459</td>\n",
       "      <td>...</td>\n",
       "      <td>1309.0</td>\n",
       "      <td>42551.0</td>\n",
       "      <td>396</td>\n",
       "      <td>59</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.135350</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>17</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>102</td>\n",
       "      <td>25536296.0</td>\n",
       "      <td>0.496315</td>\n",
       "      <td>...</td>\n",
       "      <td>1309.0</td>\n",
       "      <td>42551.0</td>\n",
       "      <td>396</td>\n",
       "      <td>59</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.212500</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>1986</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     life_sq  floor  max_floor  material  num_room  kitch_sq  state  sub_area  \\\n",
       "0  20.700001      2        9.0         1       1.0       8.9    3.0        38   \n",
       "1  30.600000      8       17.0         1       3.0       8.5    1.0       102   \n",
       "2  25.100000      3        5.0         2       2.0       4.8    2.0        84   \n",
       "3  36.000000     17       17.0         1       2.0       8.5    3.0       104   \n",
       "4  40.000000     17       17.0         1       1.0       8.5    1.0       102   \n",
       "\n",
       "       area_m  green_zone_part    ...      museum_visitis_per_100_cap  \\\n",
       "0  26155138.0         0.137846    ...                          1309.0   \n",
       "1  25536296.0         0.496315    ...                          1309.0   \n",
       "2   9946335.0         0.065409    ...                          1309.0   \n",
       "3  21494094.0         0.262459    ...                          1309.0   \n",
       "4  25536296.0         0.496315    ...                          1309.0   \n",
       "\n",
       "   apartment_build  month_year_cnt  week_year_cnt  month  dow  rel_floor  \\\n",
       "0          42551.0             396             59      7    2   0.222222   \n",
       "1          42551.0             396             59      7    2   0.470588   \n",
       "2          42551.0             396             59      7    2   0.600000   \n",
       "3          42551.0             396             59      7    2   1.000000   \n",
       "4          42551.0             396             59      7    2   1.000000   \n",
       "\n",
       "   rel_kitch_sq  room_size  yearbuilt  \n",
       "0      0.228205  20.700001       1998  \n",
       "1      0.107323  10.200000       1986  \n",
       "2      0.118519  12.550000       1960  \n",
       "3      0.135350  18.000000       2016  \n",
       "4      0.212500  40.000000       1986  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 701,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import test dataset\n",
    "#df_test = pd.read_csv(\"./data/input/test.csv\", parse_dates=['timestamp'],\n",
    "                      #usecols =['timestamp','build_year']+train_feature.columns.tolist())\n",
    "#df_test = pd.read_csv(\"./data/input/test.csv\", parse_dates=['timestamp'],\n",
    "                     #usecols =['timestamp']+df_train.columns.tolist())\n",
    "feature_drop = train_feature.columns.tolist()\n",
    "feature_drop.remove(\"price_doc\")\n",
    "x_test = xtest.drop(feature_drop, axis = 1)\n",
    "x_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Build df_all = (df_train+df_test).join(df_macro)\n",
    "#num_train = len(df_train)\n",
    "#df = pd.concat([df_train, df_test])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38133, 147)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 571,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "EOF while scanning triple-quoted string literal (<ipython-input-571-a94f7cc5465c>, line 5)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-571-a94f7cc5465c>\"\u001b[0;36m, line \u001b[0;32m5\u001b[0m\n\u001b[0;31m    print test.shape\u001b[0m\n\u001b[0m                    \n^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m EOF while scanning triple-quoted string literal\n"
     ]
    }
   ],
   "source": [
    "#split all_x to train and test set for impute test & train stacked dataset\n",
    "'''x_train = clean_data_macro(all_x[:num_train])\n",
    "test=clean_data_macro(all_x[num_train:])\n",
    "print x_train.shape\n",
    "print test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GBM & Tuninng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/whalejasmine/anaconda/envs/python2/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "/Users/whalejasmine/anaconda/envs/python2/lib/python2.7/site-packages/sklearn/grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n",
      "/Users/whalejasmine/anaconda/envs/python2/lib/python2.7/site-packages/sklearn/learning_curve.py:23: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the functions are moved. This module will be removed in 0.20\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# Load the packages for modeling\n",
    "from math import (sqrt,exp,expm1)\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.grid_search import GridSearchCV,  RandomizedSearchCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn import cross_validation, metrics \n",
    "#Additional scklearn functions\n",
    "%matplotlib inline\n",
    "import matplotlib.pylab as plt\n",
    "import numpy as np\n",
    "from scipy import sparse\n",
    "from sklearn.datasets import make_classification, make_blobs, load_boston, fetch_california_housing\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cross_validation import ShuffleSplit, train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.learning_curve import learning_curve\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from pprint import pprint\n",
    "import pandas as pd\n",
    "from pandas.tools.plotting import scatter_matrix\n",
    "import urllib\n",
    "import requests\n",
    "import zipfile\n",
    "import StringIO\n",
    "import seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 702,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_x, test_x, train_y, test_y = train_test_split(x_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 703,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbrt= GradientBoostingRegressor(n_estimators=100)\n",
    "gbrt.fit(train_x,train_y)\n",
    "y_pred = gbrt.predict(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 704,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Importance\n",
      "[  4.75409038e-02   2.19412040e-02   1.25210429e-02   0.00000000e+00\n",
      "   2.16825157e-02   8.63227266e-02   1.96772583e-02   2.27964707e-03\n",
      "   3.82677657e-03   2.59536595e-03   2.18398087e-02   9.65798822e-03\n",
      "   0.00000000e+00   1.05435028e-02   0.00000000e+00   0.00000000e+00\n",
      "   0.00000000e+00   8.06187687e-03   5.76562288e-03   0.00000000e+00\n",
      "   0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "   0.00000000e+00   1.12481934e-03   7.54686325e-04   5.43778739e-03\n",
      "   5.14485715e-03   8.46840726e-03   4.17679170e-03   0.00000000e+00\n",
      "   8.02374722e-03   6.32224628e-03   1.58023782e-02   2.34352613e-02\n",
      "   1.82860148e-02   9.03284750e-03   3.89001119e-03   0.00000000e+00\n",
      "   7.94740926e-03   1.45652799e-02   3.92462137e-03   0.00000000e+00\n",
      "   6.41571643e-03   2.69225953e-03   0.00000000e+00   1.33526658e-02\n",
      "   5.03388327e-03   0.00000000e+00   3.30256599e-03   5.45896594e-04\n",
      "   2.94069573e-02   8.65358822e-03   1.26135068e-02   1.05846608e-02\n",
      "   8.21831899e-03   5.31612016e-03   1.99599564e-02   1.83955270e-02\n",
      "   1.18219918e-02   4.68291033e-04   9.24122313e-03   9.82472057e-03\n",
      "   7.40692842e-03   1.12433985e-02   8.41685640e-03   2.05603280e-02\n",
      "   1.11041478e-02   1.69798828e-03   8.80204390e-05   5.95149230e-03\n",
      "   1.05818577e-03   1.09921981e-03   2.32032722e-03   4.55947568e-04\n",
      "   2.85874296e-03   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "   3.56083758e-05   9.28023398e-04   3.50511075e-04   1.20227826e-02\n",
      "   8.81315886e-03   1.92367442e-03   1.16413878e-02   0.00000000e+00\n",
      "   7.24529066e-03   0.00000000e+00   2.89091586e-03   0.00000000e+00\n",
      "   2.78056298e-04   8.43983794e-03   1.62977439e-02   0.00000000e+00\n",
      "   7.16597289e-03   7.88819982e-03   2.11163638e-03   0.00000000e+00\n",
      "   9.57321971e-03   1.30239230e-03   1.25147748e-05   1.94730375e-02\n",
      "   1.48572329e-03   2.68880814e-02   1.96121300e-02   2.88161759e-04\n",
      "   0.00000000e+00   2.33549752e-03   7.74684454e-05   0.00000000e+00\n",
      "   0.00000000e+00   0.00000000e+00   7.61134962e-03   0.00000000e+00\n",
      "   0.00000000e+00   1.03726151e-03   3.85560501e-03   1.34487249e-01\n",
      "   2.27450539e-03   2.09501622e-02]\n",
      "\n",
      "R-squared for Train: 0.44\n",
      "R-squared for Test: 0.42\n"
     ]
    }
   ],
   "source": [
    "print \"Feature Importance\"\n",
    "print gbrt.feature_importances_\n",
    "print\n",
    "\n",
    "print \"R-squared for Train: %.2f\" %gbrt.score(train_x,train_y)\n",
    "print \"R-squared for Test: %.2f\" %gbrt.score(test_x,test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def GradientBooster(param_grid, n_jobs): \n",
    "    estimator = GradientBoostingRegressor() \n",
    "    #for each iteration. There are other methods like the KFold split. \n",
    "    cv = ShuffleSplit(train_x.shape[0], n_iter=10, test_size=0.2) \n",
    "    classifier = GridSearchCV(estimator=estimator, cv=cv, param_grid=param_grid, n_jobs=n_jobs) \n",
    "    classifier.fit(train_x, train_y) \n",
    "    #Let's look at the best estimator that was found by GridSearchCV \n",
    "    print \"Best Estimator learned through GridSearch\"\n",
    "    print classifier.best_estimator_ \n",
    "    return cv, classifier.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import cross_validation\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.datasets import load_digits\n",
    "from sklearn.learning_curve import learning_curve\n",
    "\n",
    "\n",
    "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n",
    "                        n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n",
    "    \"\"\"\n",
    "    Generate a simple plot of the test and traning learning curve.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    estimator : object type that implements the \"fit\" and \"predict\" methods\n",
    "        An object of that type which is cloned for each validation.\n",
    "\n",
    "    title : string\n",
    "        Title for the chart.\n",
    "\n",
    "    X : array-like, shape (n_samples, n_features)\n",
    "        Training vector, where n_samples is the number of samples and\n",
    "        n_features is the number of features.\n",
    "\n",
    "    y : array-like, shape (n_samples) or (n_samples, n_features), optional\n",
    "        Target relative to X for classification or regression;\n",
    "        None for unsupervised learning.\n",
    "\n",
    "    ylim : tuple, shape (ymin, ymax), optional\n",
    "        Defines minimum and maximum yvalues plotted.\n",
    "\n",
    "    cv : integer, cross-validation generator, optional\n",
    "        If an integer is passed, it is the number of folds (defaults to 3).\n",
    "        Specific cross-validation objects can be passed, see\n",
    "        sklearn.cross_validation module for the list of possible objects\n",
    "\n",
    "    n_jobs : integer, optional\n",
    "        Number of jobs to run in parallel (default 1).\n",
    "    \"\"\"\n",
    "    plt.figure()\n",
    "    plt.title(title)\n",
    "    if ylim is not None:\n",
    "        plt.ylim(*ylim)\n",
    "    plt.xlabel(\"Training examples\")\n",
    "    plt.ylabel(\"Score\")\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    plt.grid()\n",
    "\n",
    "    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                     color=\"r\")\n",
    "    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
    "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
    "             label=\"Training score\")\n",
    "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
    "             label=\"Cross-validation score\")\n",
    "\n",
    "    plt.legend(loc=\"best\")\n",
    "    return plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 705,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Estimator learned through GridSearch\n",
      "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
      "             learning_rate=0.02, loss='ls', max_depth=6, max_features=0.5,\n",
      "             max_leaf_nodes=None, min_impurity_split=1e-07,\n",
      "             min_samples_leaf=5, min_samples_split=2,\n",
      "             min_weight_fraction_leaf=0.0, n_estimators=300,\n",
      "             presort='auto', random_state=None, subsample=1.0, verbose=0,\n",
      "             warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "'''param_grid={'n_estimators':[100], \n",
    "            'learning_rate': [0.1],# 0.05, 0.02, 0.01],\n",
    "            'max_depth':[6],#4,6], \n",
    "            'min_samples_leaf':[3],#,5,9,17], \n",
    "            'max_features':[1.0],#,0.3]#,0.1]\n",
    "           }\n",
    "'''\n",
    "\n",
    "param_grid={'n_estimators':[300], \n",
    "            'learning_rate': [0.02],# 0.05, 0.02, 0.01],\n",
    "            'max_depth':[6],#4,6], \n",
    "            'min_samples_leaf':[5],#,5,9,17], \n",
    "            'max_features':[0.5],#,0.3]#,0.1]\n",
    "           }\n",
    "n_jobs=4\n",
    "\n",
    "#Let's fit GBRT to the digits training dataset by calling the function we just created.\n",
    "cv,best_est=GradientBooster(param_grid, n_jobs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 654,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ShuffleSplit(22797, n_iter=10, test_size=0.2, random_state=None)"
      ]
     },
     "execution_count": 654,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 641,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Jnmf6BSorsCSFhCAIrYS4gNoD8TjW2jVYrpt0CQVnfELR5Rdhl62jdtxhVFz/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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c407150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = \"Learning Curves (Gradient Boosted Regression Trees)\" \n",
    "#estimator = GradientBoostingRegressor(n_estimators=best_est.n_estimators, max_depth=best_est.max_depth,\n",
    "                                     #learning_rate=best_est.learning_rate, min_samples_leaf=best_est.min_samples_leaf,\n",
    "                                    # max_features=best_est.max_features)\n",
    "plot_learning_curve(best_est, title, train_x, train_y, cv=cv, n_jobs=n_jobs)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 706,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Estimator Parameters\n",
      "---------------------------\n",
      "n_estimators: 300\n",
      "max_depth: 6\n",
      "Learning Rate: 0.0\n",
      "min_samples_leaf: 5\n",
      "max_features: 0.5\n",
      "\n",
      "Train R-squared: 0.56\n",
      "Test R-squared: 0.44\n"
     ]
    }
   ],
   "source": [
    "best_est.fit(train_x, train_y)\n",
    "\n",
    "print \"Best Estimator Parameters\"\n",
    "print\"---------------------------\"\n",
    "print \"n_estimators: %d\" %best_est.n_estimators\n",
    "print \"max_depth: %d\" %best_est.max_depth\n",
    "print \"Learning Rate: %.1f\" %best_est.learning_rate\n",
    "print \"min_samples_leaf: %d\" %best_est.min_samples_leaf\n",
    "print \"max_features: %.1f\" %best_est.max_features\n",
    "\n",
    "print\n",
    "print \"Train R-squared: %.2f\" %best_est.score(train_x,train_y)\n",
    "print \"Test R-squared: %.2f\" %best_est.score(test_x,test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/whalejasmine/anaconda/envs/python2/lib/python2.7/site-packages/sklearn/utils/deprecation.py:70: DeprecationWarning: Function transform is deprecated; Support to use estimators as feature selectors will be removed in version 0.19. Use SelectFromModel instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/whalejasmine/anaconda/envs/python2/lib/python2.7/site-packages/sklearn/utils/deprecation.py:70: DeprecationWarning: Function transform is deprecated; Support to use estimators as feature selectors will be removed in version 0.19. Use SelectFromModel instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "data": {
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Ej9TsCR2D8LQrCL7wn1rTPevSxzTNnTyJ8PRpadMqjjiakmnTG23Lxx9/yN1334Hf7+fI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\n64kctvPhAMx6+ByWLE9nZx09fDoTd5gEvLm30b+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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c429050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = \"Learning Curves (Gradient Boosted Regression Trees drop feature)\" \n",
    "\n",
    "#Dropping all parameters except n_estimators and learning_rate since we're going to trim the features anyway.\n",
    "estimator = GradientBoostingRegressor(n_estimators=best_est.n_estimators, learning_rate=best_est.learning_rate)\n",
    "\n",
    "#Calling fit on the estimator so we can transform the X matrices.\n",
    "estimator.fit(train_x, train_y)\n",
    "\n",
    "#Trimming feature matrices to include only those features that are more important than the mean of all importances.\n",
    "X_train_trim=estimator.transform(train_x, threshold=0.01)\n",
    "\n",
    "#Trimming test as well in case we end up going with this model as final.\n",
    "X_test_trim=estimator.transform(test_x, threshold=0.01)\n",
    "\n",
    "#Re-plotting Learning cruves.\n",
    "plot_learning_curve(estimator, title, train_x, train_y, cv=cv, n_jobs=n_jobs)\n",
    "plt.show()\n",
    "# not useful/ features are important"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FBWnLvHLl1xxxxNEATJgwiTlzzKmIlp5Al7BixXLGjZuQvDf9pEk7sGzZUgBG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10da56f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = \"Learning Curves (Gradient Boosted Regression Trees, learningrate=0.05,n_estimator =500)\" \n",
    "estimator = GradientBoostingRegressor(n_estimators=best_est.n_estimators, max_depth=best_est.max_depth,\n",
    "                                      learning_rate=0.05, min_samples_leaf=best_est.min_samples_leaf,\n",
    "                                      max_features=best_est.max_features)\n",
    "plot_learning_curve(estimator, title, train_x, train_y, cv=cv, n_jobs=n_jobs)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2T7oVt28/3HDYvJhry2tmgiC0DiJMaaRdC1OcpKKw9tq15N0+mfC//omXlUXl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\nByCpD3AO8McK6q4MXXJVHjGkdCpwk6T2uaZvEF3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1097de3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#the typical recommended values of Max depth is 4 to 6, so lets leave it at 4. Let's try increasing the min\n",
    "#samples leaf parameter, this basically enforces a lower bound on the number of samples in any given leaf.\n",
    "min_samples_leaf=9\n",
    "\n",
    "title = \"Learning Curves (Gradient Boosted Regression Trees), min_samples_leaf=9\" \n",
    "estimator = GradientBoostingRegressor(n_estimators=best_est.n_estimators, max_depth=best_est.max_depth,\n",
    "                                      learning_rate=best_est.learning_rate, min_samples_leaf=min_samples_leaf,\n",
    "                                      max_features=best_est.max_features)\n",
    "plot_learning_curve(estimator, title, train_x, train_y, cv=cv, n_jobs=n_jobs)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Estimator Parameters trim features \n",
      "---------------------------\n",
      "n_estimators: 100\n",
      "max_depth: 3\n",
      "Learning Rate: 0.1\n",
      "min_samples_leaf: 1\n",
      "\n",
      "Train R-squared: 0.45\n",
      "Test R-squared: 0.42\n"
     ]
    }
   ],
   "source": [
    "estimator.fit(train_x,train_y)\n",
    "print \" Estimator Parameters trim features \"\n",
    "print\"---------------------------\"\n",
    "print \"n_estimators: %d\" %estimator.n_estimators\n",
    "print \"max_depth: %d\" %estimator.max_depth\n",
    "print \"Learning Rate: %.1f\" %estimator.learning_rate\n",
    "print \"min_samples_leaf: %d\" %estimator.min_samples_leaf\n",
    "#print \"max_features: %.1f\" %estimator.max_features\n",
    "\n",
    "print\n",
    "print \"Train R-squared: %.2f\" %estimator.score(train_x,train_y)\n",
    "print \"Test R-squared: %.2f\" %estimator.score(test_x,test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cB/wAeIHbtdZf17a8nByYUB9lZVglxViRCP5X/0n+ww/iKt5EvEMHgpdcRvnx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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a308110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_features=0.5\n",
    "\n",
    "title = \"Learning Curves (Gradient Boosted Regression Trees), max_features=50%, 300 estimater, 0.05 lR\" \n",
    "estimator = GradientBoostingRegressor(n_estimators=300, max_depth=best_est.max_depth,\n",
    "                                      learning_rate=0.05, max_features=max_features)\n",
    "plot_learning_curve(estimator, title, train_x, train_y, cv=cv, n_jobs=n_jobs)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Estimator Parameters \n",
      "---------------------------\n",
      "n_estimators: 100\n",
      "max_depth: 6\n",
      "Learning Rate: 0.1\n",
      "min_samples_leaf: 3\n",
      "max_features: 1.0\n",
      "\n",
      "Train R-squared: 0.58\n",
      "Test R-squared: 0.41\n"
     ]
    }
   ],
   "source": [
    "estimator.fit(train_x,train_y)\n",
    "print \" Estimator Parameters \"\n",
    "print\"---------------------------\"\n",
    "print \"n_estimators: %d\" %estimator.n_estimators\n",
    "print \"max_depth: %d\" %estimator.max_depth\n",
    "print \"Learning Rate: %.1f\" %estimator.learning_rate\n",
    "print \"min_samples_leaf: %d\" %estimator.min_samples_leaf\n",
    "print \"max_features: %.1f\" %estimator.max_features\n",
    "\n",
    "print\n",
    "print \"Train R-squared: %.2f\" %estimator.score(train_x,train_y)\n",
    "print \"Test R-squared: %.2f\" %estimator.score(test_x,test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PP/8v9957J+np6YwefTyZmZm8+OI/qKysJBAI8Kc/3cKCBfOZMeMFbrjhz5x8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\n+X/4u7J2D5knVAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c518a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = \"Learning Curves (Gradient Boosted Regression Trees n_estimators =500)\" \n",
    "estimator = GradientBoostingRegressor(n_estimators=500, max_depth=best_est.max_depth,\n",
    "                                      learning_rate=best_est.learning_rate, min_samples_leaf=best_est.min_samples_leaf,\n",
    "                                      max_features=best_est.max_features)\n",
    "plot_learning_curve(estimator, title, train_x, train_y, cv=cv, n_jobs=n_jobs)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 595,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10a7d38d0>"
      ]
     },
     "execution_count": 595,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nLm9ei0gH2rc1yeeSpKq+KqvvrfTNI++PtG3bSVlaOiFJsrwOQz8Y27dvPeT+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\nzMzMzMzMzKxbHEgyMzMzMzMzM7Nu+X9690qsPU4AzgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a2a7350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = pd.DataFrame()\n",
    "features['features'] = x_train.columns\n",
    "features['importance'] = best_est.feature_importances_\n",
    "features.sort_values(by=['importance'],ascending=False,inplace=True)\n",
    "fig,ax= plt.subplots()\n",
    "fig.set_size_inches(20,10)\n",
    "plt.xticks(rotation=60)\n",
    "sns.barplot(data=features.head(30),x=\"features\",y=\"importance\",ax=ax,orient=\"v\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 708,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Estimator Parameters\n",
      "---------------------------\n",
      "n_estimators: 300\n",
      "max_depth: 6\n",
      "Learning Rate: 0.0\n",
      "min_samples_leaf: 5\n",
      "max_features: 0.5\n",
      "\n",
      "Final Train R-squared: 0.63\n",
      "Final Test R-squared: 0.44\n"
     ]
    }
   ],
   "source": [
    "'''estimator = GradientBoostingRegressor(n_estimators=best_est.n_estimators, max_depth=best_est.max_depth,\n",
    "                                      learning_rate=best_est.learning_rate, min_samples_leaf=best_est.min_samples_leaf,\n",
    "                                      max_features=best_est.max_features)\n",
    "'''\n",
    "estimator.fit(train_x, train_y)\n",
    "\n",
    "print \"Final Estimator Parameters\"\n",
    "print\"---------------------------\"\n",
    "print \"n_estimators: %d\" %best_est.n_estimators\n",
    "print \"max_depth: %d\" %best_est.max_depth\n",
    "print \"Learning Rate: %.1f\" %best_est.learning_rate\n",
    "print \"min_samples_leaf: %d\" %best_est.min_samples_leaf\n",
    "print \"max_features: %.1f\" %best_est.max_features\n",
    "print\n",
    "print \"Final Train R-squared: %.2f\" %estimator.score(train_x, train_y)\n",
    "print \"Final Test R-squared: %.2f\" %estimator.score(test_x, test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 644,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.3852753   0.40724221  0.41730449]\n",
      "[ 0.3671868   0.38750634  0.36363725]\n"
     ]
    }
   ],
   "source": [
    "cv_score_train = cross_validation.cross_val_score(estimator, train_x, train_y)\n",
    "print cv_score_train\n",
    "\n",
    "cv_score_vl = cross_validation.cross_val_score(estimator, test_x, test_y)\n",
    "print cv_score_vl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 710,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.42871803  0.39661376  0.41423573]\n",
      "[ 0.40428991  0.38200969  0.41885764]\n"
     ]
    }
   ],
   "source": [
    "cv_score_train = cross_validation.cross_val_score(best_est, train_x, train_y)\n",
    "print cv_score_train\n",
    "\n",
    "cv_score_vl = cross_validation.cross_val_score(best_est, test_x, test_y)\n",
    "print cv_score_vl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 709,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.157960696976\n",
      "0.198137585914\n"
     ]
    }
   ],
   "source": [
    "mse = mean_squared_error(train_y, best_est.predict(train_x))\n",
    "print mse\n",
    "\n",
    "mse = mean_squared_error(test_y, best_est.predict(test_x))\n",
    "print mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 587,
   "metadata": {},
   "outputs": [
    {
     "ename": "NotFittedError",
     "evalue": "Estimator not fitted, call `fit` before making predictions`.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNotFittedError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-587-ab09a0ba282a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_y\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_x\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      2\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mmse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mmse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_y\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_x\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mmse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/whalejasmine/anaconda/envs/python2/lib/python2.7/site-packages/sklearn/ensemble/gradient_boosting.pyc\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m   1850\u001b[0m         \"\"\"\n\u001b[1;32m   1851\u001b[0m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"C\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1852\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_decision_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\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   1853\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1854\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mstaged_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/whalejasmine/anaconda/envs/python2/lib/python2.7/site-packages/sklearn/ensemble/gradient_boosting.pyc\u001b[0m in \u001b[0;36m_decision_function\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m   1122\u001b[0m         \u001b[0;31m# for use in inner loop, not raveling the output in single-class case,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1123\u001b[0m         \u001b[0;31m# not doing input validation.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1124\u001b[0;31m         \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_decision_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1125\u001b[0m         \u001b[0mpredict_stages\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimators_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearning_rate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscore\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1126\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mscore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/whalejasmine/anaconda/envs/python2/lib/python2.7/site-packages/sklearn/ensemble/gradient_boosting.pyc\u001b[0m in \u001b[0;36m_init_decision_function\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m   1111\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_init_decision_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1112\u001b[0m         \u001b[0;34m\"\"\"Check input and compute prediction of ``init``. \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1113\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_initialized\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   1114\u001b[0m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimators_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1115\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_features\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/whalejasmine/anaconda/envs/python2/lib/python2.7/site-packages/sklearn/ensemble/gradient_boosting.pyc\u001b[0m in \u001b[0;36m_check_initialized\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    928\u001b[0m         \u001b[0;34m\"\"\"Check that the estimator is initialized, raising an error if not.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    929\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimators_\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimators_\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 930\u001b[0;31m             raise NotFittedError(\"Estimator not fitted, call `fit`\"\n\u001b[0m\u001b[1;32m    931\u001b[0m                                  \" before making predictions`.\")\n\u001b[1;32m    932\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNotFittedError\u001b[0m: Estimator not fitted, call `fit` before making predictions`."
     ]
    }
   ],
   "source": [
    "mse = mean_squared_error(train_y, estimator.predict(train_x))\n",
    "print mse\n",
    "\n",
    "mse = mean_squared_error(test_y, estimator.predict(test_x))\n",
    "print mse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 711,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"Index([u'balance_trade', u'balance_trade_growth', u'eurrub',\\n       u'average_provision_of_build_contract', u'micex_rgbi_tr',\\n       u'micex_cbi_tr', u'deposits_rate', u'mortgage_value', u'mortgage_rate',\\n       u'income_per_cap', u'rent_price_4+room_bus',\\n       u'museum_visitis_per_100_cap', u'apartment_build', u'month_year_cnt',\\n       u'week_year_cnt', u'month', u'dow', u'rel_floor', u'rel_kitch_sq',\\n       u'room_size', u'yearbuilt'],\\n      dtype='object') not in index\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-711-7836b2038a4c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdf_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"./data/input/test.csv\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0musecols\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0my_predict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbest_est\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\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      4\u001b[0m \u001b[0mp_y\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_predict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mid_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/whalejasmine/anaconda/envs/python2/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1956\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\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   1957\u001b[0m             \u001b[0;31m# either boolean or fancy integer index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1958\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1959\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1960\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/whalejasmine/anaconda/envs/python2/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_getitem_array\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2000\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2001\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2002\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_to_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2003\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2004\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/whalejasmine/anaconda/envs/python2/lib/python2.7/site-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m_convert_to_indexer\u001b[0;34m(self, obj, axis, is_setter)\u001b[0m\n\u001b[1;32m   1229\u001b[0m                 \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1230\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\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-> 1231\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'%s not in index'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mobjarr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\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   1232\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1233\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0m_values_from_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"Index([u'balance_trade', u'balance_trade_growth', u'eurrub',\\n       u'average_provision_of_build_contract', u'micex_rgbi_tr',\\n       u'micex_cbi_tr', u'deposits_rate', u'mortgage_value', u'mortgage_rate',\\n       u'income_per_cap', u'rent_price_4+room_bus',\\n       u'museum_visitis_per_100_cap', u'apartment_build', u'month_year_cnt',\\n       u'week_year_cnt', u'month', u'dow', u'rel_floor', u'rel_kitch_sq',\\n       u'room_size', u'yearbuilt'],\\n      dtype='object') not in index\""
     ]
    }
   ],
   "source": [
    "df_test = pd.read_csv(\"./data/input/test.csv\", usecols =['id'])\n",
    "\n",
    "y_predict = best_est.predict(test[x_train.columns])\n",
    "p_y =np.exp(y_predict)-1\n",
    "id_test = df_test.id\n",
    "output = pd.DataFrame({'id': id_test, 'price_doc': p_y})\n",
    "output.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 712,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>price_doc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30474</td>\n",
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       "      <th>2</th>\n",
       "      <td>30476</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30477</td>\n",
       "      <td>5.088585e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30478</td>\n",
       "      <td>5.399556e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id     price_doc\n",
       "0  30474  5.535769e+06\n",
       "1  30475  8.401518e+06\n",
       "2  30476  5.226902e+06\n",
       "3  30477  5.088585e+06\n",
       "4  30478  5.399556e+06"
      ]
     },
     "execution_count": 712,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#tuning parameter \n",
    "\n",
    "n_jobs=4\n",
    "df_test = pd.read_csv(\"./data/input/test.csv\", usecols =['id'])\n",
    "\n",
    "y_predict = best_est.predict(x_test)\n",
    "p_y =np.exp(y_predict)-1\n",
    "id_test = df_test.id\n",
    "output = pd.DataFrame({'id': id_test, 'price_doc': p_y})\n",
    "output.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.708222081182461"
      ]
     },
     "execution_count": 472,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(output.price_doc.apply(np.log).mean())\n",
    "lnm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 714,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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>id</th>\n",
       "      <th>price_doc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30474</td>\n",
       "      <td>5.761487e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30475</td>\n",
       "      <td>8.656224e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30476</td>\n",
       "      <td>5.447592e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30477</td>\n",
       "      <td>5.306879e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30478</td>\n",
       "      <td>5.623110e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id     price_doc\n",
       "0  30474  5.761487e+06\n",
       "1  30475  8.656224e+06\n",
       "2  30476  5.447592e+06\n",
       "3  30477  5.306879e+06\n",
       "4  30478  5.623110e+06"
      ]
     },
     "execution_count": 714,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##\n",
    "lnm = np.log(macro_mean)\n",
    "p_y = shift_logmean_but_keep_scale(p_y, lnm, micro_humility_factor )\n",
    "\n",
    "adjusted_output = pd.DataFrame({'id': id_test, 'price_doc': p_y})\n",
    "adjusted_output.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 713,
   "metadata": {},
   "outputs": [],
   "source": [
    "output.to_csv('./data/output/GBM_cleandata_adjust_feaure_vif.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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