FeatureSelection-etr.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import twosigmafunc\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn\n",
    "plt.rcParams['figure.figsize'] = (14.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.read_hdf('train.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "null_labels = [\n",
    " 'technical_21',\n",
    " 'technical_19',\n",
    " 'technical_27',\n",
    " 'technical_36',\n",
    " 'technical_35',\n",
    " 'technical_17',\n",
    " 'technical_43',\n",
    " 'technical_13',\n",
    " 'fundamental_33',\n",
    " 'technical_14',\n",
    " 'technical_33',\n",
    " 'fundamental_18',\n",
    " 'fundamental_48',\n",
    " 'fundamental_59',\n",
    " 'technical_9',\n",
    " 'technical_16',\n",
    " 'technical_42',\n",
    " 'technical_18',\n",
    " 'fundamental_42',\n",
    " 'fundamental_0',\n",
    " 'fundamental_7',\n",
    " 'fundamental_41',\n",
    " 'technical_41',\n",
    " 'fundamental_21',\n",
    " 'fundamental_19',\n",
    " 'technical_29',\n",
    " 'technical_24',\n",
    " 'derived_0',\n",
    " 'derived_1',\n",
    " 'fundamental_17',\n",
    " 'technical_3',\n",
    " 'fundamental_20',\n",
    " 'fundamental_32',\n",
    " 'fundamental_62',\n",
    " 'fundamental_25',\n",
    " 'technical_1',\n",
    " 'fundamental_58',\n",
    " 'derived_3',\n",
    " 'technical_5',\n",
    " 'fundamental_52',\n",
    " 'technical_10',\n",
    " 'technical_31',\n",
    " 'technical_25',\n",
    " 'technical_44',\n",
    " 'technical_28',\n",
    " 'fundamental_40',\n",
    " 'fundamental_27',\n",
    " 'fundamental_29',\n",
    " 'fundamental_43',\n",
    " 'fundamental_15',\n",
    " 'fundamental_30',\n",
    " 'fundamental_60',\n",
    " 'fundamental_16',\n",
    " 'fundamental_50',\n",
    " 'fundamental_44',\n",
    " 'fundamental_37',\n",
    " 'fundamental_14',\n",
    " 'fundamental_23',\n",
    " 'fundamental_55',\n",
    " 'fundamental_8',\n",
    " 'fundamental_63',\n",
    " 'fundamental_39',\n",
    " 'fundamental_54',\n",
    " 'derived_2',\n",
    " 'derived_4',\n",
    " 'fundamental_35',\n",
    " 'fundamental_34',\n",
    " 'fundamental_47',\n",
    " 'fundamental_51',\n",
    " 'fundamental_31',\n",
    " 'fundamental_49',\n",
    " 'fundamental_22',\n",
    " 'fundamental_9',\n",
    " 'fundamental_24',\n",
    " 'fundamental_57',\n",
    " 'fundamental_28',\n",
    " 'fundamental_61',\n",
    " 'fundamental_1',\n",
    " 'fundamental_6',\n",
    " 'fundamental_38',\n",
    " 'fundamental_5']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "excl = ['id', 'timestamp', 'y']\n",
    "cols_origin = [col for col in train.columns if col not in ['y']]\n",
    "feature_origin = [col for col in train.columns if col not in excl]\n",
    "feature_diff = [col + '_diff' for col in feature_origin]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add differs "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# add time series diff\n",
    "d_mean = train.median()\n",
    "train.sort_values(['id', 'timestamp'], inplace=True)\n",
    "train['id_diff'] = train.id.diff()\n",
    "d_mean['id_diff'] = 0.0\n",
    "for col in feature_origin:\n",
    "    train[col + '_diff'] = train[col].diff()\n",
    "    d_mean[col + '_diff'] = 0.0\n",
    "\n",
    "train.loc[train.id_diff!=0, feature_diff] = 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "        id  timestamp  derived_0  derived_1  derived_2  derived_3  derived_4  \\\n",
       "131062   0        167        NaN        NaN        NaN        NaN        NaN   \n",
       "131895   0        168        NaN        NaN        NaN        NaN        NaN   \n",
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       "\n",
       "        fundamental_0  fundamental_1  fundamental_2        ...          \\\n",
       "131062            NaN            NaN            NaN        ...           \n",
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       "132728            NaN            NaN            NaN        ...           \n",
       "133561      -0.240929            NaN       0.212425        ...           \n",
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       "\n",
       "        technical_35_diff  technical_36_diff  technical_37_diff  \\\n",
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       "131895                NaN                NaN                NaN   \n",
       "132728                NaN                NaN                NaN   \n",
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       "134393                0.0                0.0                0.0   \n",
       "\n",
       "        technical_38_diff  technical_39_diff  technical_40_diff  \\\n",
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       "\n",
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       "\n",
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       "132728                NaN  \n",
       "133561                NaN  \n",
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       "\n",
       "[5 rows x 220 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# add `nan` tags "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n = train.isnull().sum(axis=1)\n",
    "for c in null_labels:\n",
    "    train[c + '_nan'] = pd.isnull(train[c])\n",
    "    d_mean[c + '_nan'] = 0\n",
    "train = train.fillna(d_mean)\n",
    "train['nullcounts'] = n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "        id  timestamp  derived_0  derived_1  derived_2  derived_3  derived_4  \\\n",
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       "\n",
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       "134393      -0.240929      -0.007395       0.212425     ...       \n",
       "\n",
       "        fundamental_9_nan  fundamental_24_nan  fundamental_57_nan  \\\n",
       "131062               True                True                True   \n",
       "131895               True                True                True   \n",
       "132728               True                True                True   \n",
       "133561              False               False               False   \n",
       "134393              False               False               False   \n",
       "\n",
       "        fundamental_28_nan  fundamental_61_nan  fundamental_1_nan  \\\n",
       "131062                True                True               True   \n",
       "131895                True                True               True   \n",
       "132728                True                True               True   \n",
       "133561               False                True               True   \n",
       "134393               False                True               True   \n",
       "\n",
       "        fundamental_6_nan  fundamental_38_nan  fundamental_5_nan  nullcounts  \n",
       "131062               True                True               True         107  \n",
       "131895               True                True               True         212  \n",
       "132728               True                True               True         212  \n",
       "133561              False               False              False         125  \n",
       "134393              False               False              False          38  \n",
       "\n",
       "[5 rows x 302 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add special features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train['tec20-30'] = train.technical_20 - train.technical_30\n",
    "train['tec123'] = train['tec20-30'] + train.technical_13\n",
    "train['y_past'] = train.y.shift()\n",
    "train['tec123_past'] = train.tec123.shift()\n",
    "train.loc[train.id_diff != 0, ['y_past', 'tec123_past']] = 0\n",
    "train.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train['y_past_p'] = twosigmafunc.predict_y_past(train[['tec123_past', 'tec123']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>derived_0</th>\n",
       "      <th>derived_1</th>\n",
       "      <th>derived_2</th>\n",
       "      <th>derived_3</th>\n",
       "      <th>derived_4</th>\n",
       "      <th>fundamental_0</th>\n",
       "      <th>fundamental_1</th>\n",
       "      <th>fundamental_2</th>\n",
       "      <th>...</th>\n",
       "      <th>fundamental_1_nan</th>\n",
       "      <th>fundamental_6_nan</th>\n",
       "      <th>fundamental_38_nan</th>\n",
       "      <th>fundamental_5_nan</th>\n",
       "      <th>nullcounts</th>\n",
       "      <th>tec20-30</th>\n",
       "      <th>tec123</th>\n",
       "      <th>y_past</th>\n",
       "      <th>tec123_past</th>\n",
       "      <th>y_past_p</th>\n",
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       "<p>5 rows × 307 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  timestamp  derived_0  derived_1  derived_2  derived_3  derived_4  \\\n",
       "131062   0        167  -0.000837   0.005523   0.021095   0.002476   0.011752   \n",
       "131895   0        168  -0.000837   0.005523   0.021095   0.002476   0.011752   \n",
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       "134393   0        171  -0.230583   0.488096   0.935920   0.028222  -0.083071   \n",
       "\n",
       "        fundamental_0  fundamental_1  fundamental_2    ...     \\\n",
       "131062      -0.040645      -0.007395      -0.030291    ...      \n",
       "131895      -0.040645      -0.007395      -0.030291    ...      \n",
       "132728      -0.040645      -0.007395      -0.030291    ...      \n",
       "133561      -0.240929      -0.007395       0.212425    ...      \n",
       "134393      -0.240929      -0.007395       0.212425    ...      \n",
       "\n",
       "        fundamental_1_nan  fundamental_6_nan  fundamental_38_nan  \\\n",
       "131062               True               True                True   \n",
       "131895               True               True                True   \n",
       "132728               True               True                True   \n",
       "133561               True              False               False   \n",
       "134393               True              False               False   \n",
       "\n",
       "        fundamental_5_nan  nullcounts  tec20-30  tec123    y_past  \\\n",
       "131062               True         107       0.0     0.0  0.000000   \n",
       "131895               True         212       0.0     0.0 -0.007108   \n",
       "132728               True         212       0.0     0.0  0.001950   \n",
       "133561              False         125       0.0     0.0  0.017724   \n",
       "134393              False          38       0.0     0.0  0.012934   \n",
       "\n",
       "        tec123_past  y_past_p  \n",
       "131062          0.0 -0.000266  \n",
       "131895          0.0 -0.000266  \n",
       "132728          0.0 -0.000266  \n",
       "133561          0.0 -0.000266  \n",
       "134393          0.0 -0.000266  \n",
       "\n",
       "[5 rows x 307 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import json\n",
    "try:\n",
    "    with open('features.json', 'r') as js:\n",
    "        features = json.load(js)\n",
    "except IOError as err:\n",
    "    raise err"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "etr_features = features['etr_features']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_train, y_train, X_test, y_test = twosigmafunc.split_data(train, etr_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>technical_11_diff</th>\n",
       "      <th>tec123</th>\n",
       "      <th>technical_43</th>\n",
       "      <th>tec123_past</th>\n",
       "      <th>technical_30</th>\n",
       "      <th>technical_2</th>\n",
       "      <th>technical_43_diff</th>\n",
       "      <th>technical_11</th>\n",
       "      <th>...</th>\n",
       "      <th>technical_18_nan</th>\n",
       "      <th>fundamental_17_nan</th>\n",
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       "      <th>fundamental_58</th>\n",
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       "      <th>fundamental_47_nan</th>\n",
       "      <th>technical_41_nan</th>\n",
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       "      <td>True</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          y_past  tec20-30  technical_11_diff  tec123  technical_43  \\\n",
       "131062  0.000000       0.0                0.0     0.0     -0.659754   \n",
       "131895 -0.007108       0.0                0.0     0.0     -0.659754   \n",
       "132728  0.001950       0.0                0.0     0.0     -0.659754   \n",
       "133561  0.017724       0.0                0.0     0.0      0.000000   \n",
       "134393  0.012934       0.0                0.0     0.0      0.000000   \n",
       "\n",
       "        tec123_past  technical_30  technical_2  technical_43_diff  \\\n",
       "131062          0.0           0.0    -0.659754                0.0   \n",
       "131895          0.0           0.0    -0.659754                0.0   \n",
       "132728          0.0           0.0    -0.659754                0.0   \n",
       "133561          0.0           0.0     0.000000                0.0   \n",
       "134393          0.0           0.0     0.000000                0.0   \n",
       "\n",
       "        technical_11      ...       technical_18_nan fundamental_17_nan  \\\n",
       "131062     -0.659749      ...                   True               True   \n",
       "131895     -0.659749      ...                   True               True   \n",
       "132728     -0.659749      ...                   True               True   \n",
       "133561      0.000000      ...                  False              False   \n",
       "134393      0.000000      ...                  False              False   \n",
       "\n",
       "        technical_20_diff  fundamental_58 technical_22  technical_25_nan  \\\n",
       "131062                0.0       -0.030392         -0.5              True   \n",
       "131895                0.0       -0.030392         -0.5              True   \n",
       "132728                0.0       -0.030392         -0.5              True   \n",
       "133561                0.0        0.314097         -0.5              True   \n",
       "134393                0.0        0.314097         -0.5              True   \n",
       "\n",
       "        technical_29_diff  fundamental_47_nan  technical_41_nan  technical_12  \n",
       "131062                0.0                True              True     -0.000058  \n",
       "131895                0.0                True              True     -0.000058  \n",
       "132728                0.0                True              True     -0.000058  \n",
       "133561                0.0                True              True     -1.000000  \n",
       "134393                0.0                True              True     -1.000000  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# extract feature importance on 190 features  \n",
    "* Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import ExtraTreesRegressor, RandomForestRegressor\n",
    "from sklearn.model_selection import GridSearchCV, StratifiedKFold, RandomizedSearchCV\n",
    "etr = ExtraTreesRegressor(n_jobs=-1, n_estimators=60, random_state=17, verbose=0)\n",
    "rfr = RandomForestRegressor(n_jobs=-1, random_state=17, verbose=0)\n",
    "from scipy.stats import randint as sp_randint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "param_dist = {\n",
    "              \"max_depth\": [3,5, 10],\n",
    "              \"max_features\": [0.3, 0.6, 0.9],\n",
    "#                 \"min_samples_leaf\": [1, 10, 100],\n",
    "#                 \"n_estimators\": [20]\n",
    "#               \"min_samples_split\": sp_randint(1, 5),\n",
    "#               \"min_samples_leaf\": sp_randint(1, 5)\n",
    "             }\n",
    "\n",
    "grid_search_etr = GridSearchCV(etr,\n",
    "                           param_grid=param_dist,\n",
    "                           cv=5)\n",
    "grid_search_rfr = RandomizedSearchCV(rfr,\n",
    "                           param_distributions=param_dist,\n",
    "                           cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-7cfc6fb155de>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgrid_search_etr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.4/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups)\u001b[0m\n\u001b[1;32m    938\u001b[0m             \u001b[0mtrain\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mtest\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    939\u001b[0m         \"\"\"\n\u001b[0;32m--> 940\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroups\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mParameterGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_grid\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    941\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    942\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.4/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, X, y, groups, parameter_iterable)\u001b[0m\n\u001b[1;32m    560\u001b[0m                                   \u001b[0mreturn_times\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_parameters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    561\u001b[0m                                   error_score=self.error_score)\n\u001b[0;32m--> 562\u001b[0;31m           \u001b[0;32mfor\u001b[0m \u001b[0mparameters\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mparameter_iterable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    563\u001b[0m           for train, test in cv.split(X, y, groups))\n\u001b[1;32m    564\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.4/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    756\u001b[0m             \u001b[0;31m# was dispatched. In particular this covers the edge\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    757\u001b[0m             \u001b[0;31m# case of Parallel used with an exhausted iterator.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 758\u001b[0;31m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\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    759\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    760\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.4/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    606\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    607\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 608\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    609\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    610\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.4/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    569\u001b[0m         \u001b[0mdispatch_timestamp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    570\u001b[0m         \u001b[0mcb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBatchCompletionCallBack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdispatch_timestamp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 571\u001b[0;31m         \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    572\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    573\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.4/site-packages/sklearn/externals/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    107\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 109\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    110\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.4/site-packages/sklearn/externals/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    320\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    321\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 322\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\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    323\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    324\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.4/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.4/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.4/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, error_score)\u001b[0m\n\u001b[1;32m    236\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    239\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.4/site-packages/sklearn/ensemble/forest.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    324\u001b[0m                     \u001b[0mt\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[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrees\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    325\u001b[0m                     verbose=self.verbose, class_weight=self.class_weight)\n\u001b[0;32m--> 326\u001b[0;31m                 for i, t in enumerate(trees))\n\u001b[0m\u001b[1;32m    327\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    328\u001b[0m             \u001b[0;31m# Collect newly grown trees\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.4/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    766\u001b[0m                 \u001b[0;31m# consumption.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    767\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 768\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\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    769\u001b[0m             \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    770\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.4/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    717\u001b[0m                     \u001b[0mensure_ready\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_managed_backend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    718\u001b[0m                     \u001b[0mbackend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabort_everything\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mensure_ready\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mensure_ready\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 719\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mexception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    720\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    721\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.4/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    680\u001b[0m                 \u001b[0;31m# check if timeout supported in backend future implementation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    681\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m'timeout'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgetfullargspec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 682\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\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    683\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    684\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/Cellar/python3/3.4.3_2/Frameworks/Python.framework/Versions/3.4/lib/python3.4/multiprocessing/pool.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    591\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    592\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 593\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    594\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mready\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    595\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/Cellar/python3/3.4.3_2/Frameworks/Python.framework/Versions/3.4/lib/python3.4/multiprocessing/pool.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    589\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 590\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_event\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    591\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    592\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/Cellar/python3/3.4.3_2/Frameworks/Python.framework/Versions/3.4/lib/python3.4/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    551\u001b[0m             \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flag\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    552\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 553\u001b[0;31m                 \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    554\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    555\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/Cellar/python3/3.4.3_2/Frameworks/Python.framework/Versions/3.4/lib/python3.4/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    288\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    289\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 290\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\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    291\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    292\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "grid_search_etr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grid_search_etr.best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "etr = ExtraTreesRegressor(n_jobs=-1,n_estimators=128, max_depth=5, max_features='sqrt', min_samples_leaf=10,\n",
    "                          random_state=17, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=5,\n",
       "          max_features='sqrt', max_leaf_nodes=None,\n",
       "          min_impurity_split=1e-07, min_samples_leaf=10,\n",
       "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "          n_estimators=128, n_jobs=-1, oob_score=False, random_state=17,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "etr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "imp = pd.DataFrame({'feature':feature_cols, 'important':etr.feature_importances_}).sort_values('important', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.017426062779552942"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from twosigmafunc import R_score\n",
    "y_etr = etr.predict(X_test)\n",
    "R_score(y_etr, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.036813482498809912"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_etr_t = etr.predict(X_train)\n",
    "R_score(y_etr_t, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "strong overfit here, needs:\n",
    "1. add more trees\n",
    "2. reduce max_depth\n",
    "3. reduce features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`max_depth` = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=3,\n",
       "          max_features='sqrt', max_leaf_nodes=None,\n",
       "          min_impurity_split=1e-07, min_samples_leaf=10,\n",
       "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "          n_estimators=128, n_jobs=-1, oob_score=False, random_state=17,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "etr = ExtraTreesRegressor(n_jobs=-1,n_estimators=128, max_depth=3, max_features='sqrt', min_samples_leaf=10,\n",
    "                          random_state=17, verbose=0)\n",
    "etr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "imp = pd.DataFrame({'feature':feature_cols, 'important':etr.feature_importances_}).sort_values('important', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.013063600794493386"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_etr = etr.predict(X_test)\n",
    "R_score(y_etr, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "nope, we shall set `max_depth` = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=10,\n",
       "          max_features='sqrt', max_leaf_nodes=None,\n",
       "          min_impurity_split=1e-07, min_samples_leaf=10,\n",
       "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "          n_estimators=128, n_jobs=-1, oob_score=False, random_state=17,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "etr = ExtraTreesRegressor(n_jobs=-1,n_estimators=128, max_depth=10, max_features='sqrt', min_samples_leaf=10,\n",
    "                          random_state=17, verbose=0)\n",
    "etr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.023496240919556825"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_etr = etr.predict(X_test)\n",
    "R_score(y_etr, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>important</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>y_past</td>\n",
       "      <td>0.028259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>technical_30</td>\n",
       "      <td>0.019654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>tec123</td>\n",
       "      <td>0.018467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>tec123_past</td>\n",
       "      <td>0.018313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>tec20-30</td>\n",
       "      <td>0.017759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>technical_2</td>\n",
       "      <td>0.015560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>technical_11</td>\n",
       "      <td>0.013893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>technical_30_diff</td>\n",
       "      <td>0.013294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>technical_20</td>\n",
       "      <td>0.012659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>technical_43</td>\n",
       "      <td>0.012044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               feature  important\n",
       "301             y_past   0.028259\n",
       "93        technical_30   0.019654\n",
       "300             tec123   0.018467\n",
       "302        tec123_past   0.018313\n",
       "299           tec20-30   0.017759\n",
       "70         technical_2   0.015560\n",
       "77        technical_11   0.013893\n",
       "202  technical_30_diff   0.013294\n",
       "85        technical_20   0.012659\n",
       "106       technical_43   0.012044"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp = pd.DataFrame({'feature':feature_cols, 'important':etr.feature_importances_}).sort_values('important', ascending=False)\n",
    "imp[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.080991721094345284"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_etr_t = etr.predict(X_train)\n",
    "R_score(y_etr_t, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "set `min_samples_leaf` = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=5,\n",
       "          max_features='sqrt', max_leaf_nodes=None,\n",
       "          min_impurity_split=1e-07, min_samples_leaf=50,\n",
       "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "          n_estimators=128, n_jobs=-1, oob_score=False, random_state=17,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "etr = ExtraTreesRegressor(n_jobs=-1,n_estimators=128, max_depth=5, max_features='sqrt', min_samples_leaf=50,\n",
    "                          random_state=17, verbose=0)\n",
    "etr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.017651482292738052"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_etr = etr.predict(X_test)\n",
    "R_score(y_etr, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "add `max_features` to 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=5,\n",
       "          max_features=0.5, max_leaf_nodes=None, min_impurity_split=1e-07,\n",
       "          min_samples_leaf=10, min_samples_split=2,\n",
       "          min_weight_fraction_leaf=0.0, n_estimators=128, n_jobs=-1,\n",
       "          oob_score=False, random_state=17, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "etr = ExtraTreesRegressor(n_jobs=-1,n_estimators=128, max_depth=5, max_features=0.5, min_samples_leaf=10,\n",
    "                          random_state=17, verbose=0)\n",
    "etr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.025439703873461724"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_etr = etr.predict(X_test)\n",
    "R_score(y_etr, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.057064044119817504"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_etr_t = etr.predict(X_train)\n",
    "R_score(y_etr_t, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>important</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>y_past</td>\n",
       "      <td>0.050888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>tec20-30</td>\n",
       "      <td>0.044819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>technical_30</td>\n",
       "      <td>0.044686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>tec123</td>\n",
       "      <td>0.040665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>tec123_past</td>\n",
       "      <td>0.033899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>technical_11_diff</td>\n",
       "      <td>0.031841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>technical_11</td>\n",
       "      <td>0.031784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>technical_43</td>\n",
       "      <td>0.030474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>fundamental_11</td>\n",
       "      <td>0.030417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>technical_43_diff</td>\n",
       "      <td>0.029247</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               feature  important\n",
       "301             y_past   0.050888\n",
       "299           tec20-30   0.044819\n",
       "93        technical_30   0.044686\n",
       "300             tec123   0.040665\n",
       "302        tec123_past   0.033899\n",
       "186  technical_11_diff   0.031841\n",
       "77        technical_11   0.031784\n",
       "106       technical_43   0.030474\n",
       "15      fundamental_11   0.030417\n",
       "215  technical_43_diff   0.029247"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp = pd.DataFrame({'feature':feature_cols, 'important':etr.feature_importances_}).sort_values('important', ascending=False)\n",
    "imp[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "conlusion:\n",
    "`max_depth`:4-7\n",
    "`max_features`:0.5-1\n",
    "`min_leaf`:11-40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=6,\n",
       "          max_features=0.5, max_leaf_nodes=None, min_impurity_split=1e-07,\n",
       "          min_samples_leaf=10, min_samples_split=2,\n",
       "          min_weight_fraction_leaf=0.0, n_estimators=128, n_jobs=-1,\n",
       "          oob_score=False, random_state=17, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "etr = ExtraTreesRegressor(n_jobs=-1,n_estimators=128, max_depth=6, max_features=0.5, min_samples_leaf=10,\n",
    "                          random_state=17, verbose=0)\n",
    "etr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.028108048001197376"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_etr = etr.predict(X_test)\n",
    "R_score(y_etr, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.069472023005379205"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_etr_t = etr.predict(X_train)\n",
    "R_score(y_etr_t, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "set `min_samples_leaf` = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=5,\n",
       "          max_features=0.5, max_leaf_nodes=None, min_impurity_split=1e-07,\n",
       "          min_samples_leaf=30, min_samples_split=2,\n",
       "          min_weight_fraction_leaf=0.0, n_estimators=128, n_jobs=-1,\n",
       "          oob_score=False, random_state=17, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "etr = ExtraTreesRegressor(n_jobs=-1,n_estimators=128, max_depth=5, max_features=0.5, min_samples_leaf=30,\n",
    "                          random_state=17, verbose=0)\n",
    "etr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.026180562830858595"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_etr = etr.predict(X_test)\n",
    "R_score(y_etr, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.057442264220322219"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_etr_t = etr.predict(X_train)\n",
    "R_score(y_etr_t, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>important</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>y_past</td>\n",
       "      <td>0.053863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>tec20-30</td>\n",
       "      <td>0.042727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>tec123</td>\n",
       "      <td>0.042613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>technical_30</td>\n",
       "      <td>0.040247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>technical_11_diff</td>\n",
       "      <td>0.036575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>tec123_past</td>\n",
       "      <td>0.035329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>technical_11</td>\n",
       "      <td>0.035016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>technical_43</td>\n",
       "      <td>0.029057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>technical_20</td>\n",
       "      <td>0.026772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>technical_43_diff</td>\n",
       "      <td>0.026645</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               feature  important\n",
       "301             y_past   0.053863\n",
       "299           tec20-30   0.042727\n",
       "300             tec123   0.042613\n",
       "93        technical_30   0.040247\n",
       "186  technical_11_diff   0.036575\n",
       "302        tec123_past   0.035329\n",
       "77        technical_11   0.035016\n",
       "106       technical_43   0.029057\n",
       "85        technical_20   0.026772\n",
       "215  technical_43_diff   0.026645"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp = pd.DataFrame({'feature':feature_cols, 'important':etr.feature_importances_}).sort_values('important', ascending=False)\n",
    "imp[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=6,\n",
       "          max_features=0.5, max_leaf_nodes=None, min_impurity_split=1e-07,\n",
       "          min_samples_leaf=30, min_samples_split=2,\n",
       "          min_weight_fraction_leaf=0.0, n_estimators=128, n_jobs=-1,\n",
       "          oob_score=False, random_state=17, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "etr = ExtraTreesRegressor(n_jobs=-1,n_estimators=128, max_depth=6, max_features=0.5, min_samples_leaf=30,\n",
    "                          random_state=17, verbose=0)\n",
    "etr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.029146165906949503"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_etr = etr.predict(X_test)\n",
    "R_score(y_etr, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.068558158687025014"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_etr_t = etr.predict(X_train)\n",
    "R_score(y_etr_t, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>important</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>y_past</td>\n",
       "      <td>0.048297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>tec20-30</td>\n",
       "      <td>0.040721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>technical_30</td>\n",
       "      <td>0.037553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>tec123</td>\n",
       "      <td>0.037264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>technical_43</td>\n",
       "      <td>0.033812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>technical_43_diff</td>\n",
       "      <td>0.033129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>tec123_past</td>\n",
       "      <td>0.029382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>technical_11_diff</td>\n",
       "      <td>0.025346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>technical_2_diff</td>\n",
       "      <td>0.025297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>technical_11</td>\n",
       "      <td>0.024707</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               feature  important\n",
       "301             y_past   0.048297\n",
       "299           tec20-30   0.040721\n",
       "93        technical_30   0.037553\n",
       "300             tec123   0.037264\n",
       "106       technical_43   0.033812\n",
       "215  technical_43_diff   0.033129\n",
       "302        tec123_past   0.029382\n",
       "186  technical_11_diff   0.025346\n",
       "179   technical_2_diff   0.025297\n",
       "77        technical_11   0.024707"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp = pd.DataFrame({'feature':feature_cols, 'important':etr.feature_importances_}).sort_values('important', ascending=False)\n",
    "imp[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "stick parameters here, we reduce features to 100, see what happens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "feature_100 = imp.loc[imp.important > imp.important.mean() / 2, 'feature']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=6,\n",
       "          max_features=0.5, max_leaf_nodes=None, min_impurity_split=1e-07,\n",
       "          min_samples_leaf=30, min_samples_split=2,\n",
       "          min_weight_fraction_leaf=0.0, n_estimators=128, n_jobs=-1,\n",
       "          oob_score=False, random_state=17, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "etr = ExtraTreesRegressor(n_jobs=-1,n_estimators=128, max_depth=6, max_features=0.5, min_samples_leaf=30,\n",
    "                          random_state=17, verbose=0)\n",
    "etr.fit(X_train[feature_100], y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.028795669708638838"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_etr = etr.predict(X_test[feature_100])\n",
    "R_score(y_etr, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "almost as good as 300+ features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.068178389469970527"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_etr_t = etr.predict(X_train[feature_100])\n",
    "R_score(y_etr_t, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## set 256 trees "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=6,\n",
       "          max_features=0.5, max_leaf_nodes=None, min_impurity_split=1e-07,\n",
       "          min_samples_leaf=30, min_samples_split=2,\n",
       "          min_weight_fraction_leaf=0.0, n_estimators=256, n_jobs=-1,\n",
       "          oob_score=False, random_state=17, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "etr = ExtraTreesRegressor(n_jobs=-1,n_estimators=256, max_depth=6, max_features=0.5, min_samples_leaf=30,\n",
    "                          random_state=17, verbose=0)\n",
    "etr.fit(X_train[feature_100], y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let's plot the accumulated y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "without clip\n",
      "1000 0.0114897040457\n",
      "1100 -0.00676789484353\n",
      "1200 0.0131572869728\n",
      "1300 0.0130994377995\n",
      "1400 -0.0141501658827\n",
      "1500 0.0133605055644\n",
      "1600 0.0312843211064\n",
      "1700 0.0315856902462\n",
      "1800 0.0295618129653\n"
     ]
    }
   ],
   "source": [
    "low_y_cut = -0.075\n",
    "high_y_cut = 0.075\n",
    "reward = []\n",
    "stamp = 906\n",
    "y_true_acc = []\n",
    "y_pred_acc = []\n",
    "print('without clip')\n",
    "while stamp <= train.timestamp.max():\n",
    "    x = train.loc[train.timestamp == stamp, feature_100]\n",
    "    y_pred = etr.predict(x)\n",
    "    y_true = train.loc[train.timestamp == stamp, 'y']\n",
    "    y_pred_acc.extend(list(y_pred))\n",
    "    y_true_acc.extend(list(y_true))\n",
    "    score = twosigmafunc.R_score(np.array(y_pred_acc), np.array(y_true_acc))\n",
    "    reward.append(score)\n",
    "    stamp += 1\n",
    "    if stamp % 100 == 0:\n",
    "        print(stamp, score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ridge_data = pd.read_csv('Linear_R_score.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Bi_Ridge</th>\n",
       "      <th>LR</th>\n",
       "      <th>Mixed_model</th>\n",
       "      <th>Ridge</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.199228</td>\n",
       "      <td>-0.186395</td>\n",
       "      <td>-0.189450</td>\n",
       "      <td>-0.189450</td>\n",
       "      <td>906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.130472</td>\n",
       "      <td>-0.123510</td>\n",
       "      <td>-0.125910</td>\n",
       "      <td>-0.125910</td>\n",
       "      <td>907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-0.062324</td>\n",
       "      <td>-0.075748</td>\n",
       "      <td>-0.067650</td>\n",
       "      <td>-0.067650</td>\n",
       "      <td>908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.017021</td>\n",
       "      <td>-0.045951</td>\n",
       "      <td>-0.034686</td>\n",
       "      <td>-0.034686</td>\n",
       "      <td>909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-0.027521</td>\n",
       "      <td>-0.046525</td>\n",
       "      <td>-0.036888</td>\n",
       "      <td>-0.036888</td>\n",
       "      <td>910</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Bi_Ridge        LR  Mixed_model     Ridge  timestamp\n",
       "0           0 -0.199228 -0.186395    -0.189450 -0.189450        906\n",
       "1           1 -0.130472 -0.123510    -0.125910 -0.125910        907\n",
       "2           2 -0.062324 -0.075748    -0.067650 -0.067650        908\n",
       "3           3 -0.017021 -0.045951    -0.034686 -0.034686        909\n",
       "4           4 -0.027521 -0.046525    -0.036888 -0.036888        910"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.1, 0.034)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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m4/Lm9rvvEHfyIXKdw4LW5e9Z3x7p4KEVf2dYTbRn3dCSA49Kd6xYuvu3jEwb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kZfV4rhiRIV28XUl8evgtJ1fM+te63V18ArFdGSwSwWvasbZ3wc365ekEn5ijC/VL9TyjRPKNmu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\nLTFPtQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1194a9e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Best of Best')\n",
    "plt.xlabel('timestamp')\n",
    "plt.ylabel('R score')\n",
    "plt.plot([905, 1801], [0, 0], 'k--')\n",
    "plt.plot(ridge_data.timestamp, ridge_data.Ridge, lw=1.5, label='Ridge')\n",
    "plt.plot(ridge_data.timestamp, ridge_data.Bi_Ridge, lw=1.5, label='Bi-ridge')\n",
    "# plt.plot(ridge_data.timestamp, ridge_data.Mixed_model, lw=1.5, label='Mixed-Ridge')\n",
    "plt.plot(ridge_data.timestamp, reward,lw=1.5, label='ETR')\n",
    "plt.legend(loc='best')\n",
    "plt.ylim(-0.1, 0.034)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.1, 0.034)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IF+te5xuLv8dto2+ku7GTBXXLuabmci4on82O3t20hdtpCrXgNDtZ0PgOs0tncP455xJc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\nBgZJkiRJktTg/wMs34xknVHK0gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114c45588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Best of Best')\n",
    "plt.xlabel('timestamp')\n",
    "plt.ylabel('R score')\n",
    "plt.plot([905, 1801], [0, 0], 'k--')\n",
    "plt.plot(ridge_data.timestamp, ridge_data.Ridge, lw=1.5, label='Ridge')\n",
    "plt.plot(ridge_data.timestamp, ridge_data.Bi_Ridge, lw=1.5, label='Bi-ridge')\n",
    "# plt.plot(ridge_data.timestamp, ridge_data.Mixed_model, lw=1.5, label='Mixed-Ridge')\n",
    "plt.plot(ridge_data.timestamp, reward,lw=1.5, label='ETR-256trees')\n",
    "plt.legend(loc='best')\n",
    "plt.ylim(-0.1, 0.034)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "seems not better than 128 trees, we could save time for xgb?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### how we combine it with ridge?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "ridge = Ridge()\n",
    "ridge2 = Ridge()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_is_above_cut = (y_train > high_y_cut)\n",
    "y_is_below_cut = (y_train < low_y_cut)\n",
    "y_is_within_cut = (~y_is_above_cut & ~y_is_below_cut)\n",
    "ridge.fit(np.array(X_train.loc[y_is_within_cut, 'technical_20_diff']).reshape(-1,1), y_train.loc[y_is_within_cut])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.021085779479170109"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_lr_1 = ridge.predict(np.array(X_test['technical_20_diff']).reshape(-1, 1))\n",
    "twosigmafunc.R_score(y_lr_1, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge2.fit(X_train.loc[y_is_within_cut, ['technical_20_diff', 'tec20-30']], y_train[y_is_within_cut])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02648156394967555"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_lr_2 = ridge2.predict(X_test[['technical_20_diff', 'tec20-30']])\n",
    "twosigmafunc.R_score(y_lr_2, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_etr = etr.predict(X_test[feature_100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.029310961622725951"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R_score(y_etr, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 0.0293109616227\n",
      "0.125 0.0294862292046\n",
      "0.25 0.0295154327325\n",
      "0.375 0.0293990074883\n",
      "0.5 0.0291352077122\n",
      "0.625 0.0287199726809\n",
      "0.75 0.0281466010093\n",
      "0.875 0.0274051688772\n",
      "1.0 0.0264815639497\n"
     ]
    }
   ],
   "source": [
    "for a in np.linspace(0, 1, 9):\n",
    "    y = y_lr_2 * a + y_etr * (1 - a)\n",
    "    print(a, R_score(y, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a=0.1500, score 0.0295037\n",
      "a=0.1625, score 0.0295103\n",
      "a=0.1750, score 0.0295154\n",
      "a=0.1875, score 0.0295190\n",
      "a=0.2000, score 0.0295212\n",
      "a=0.2125, score 0.0295219\n",
      "a=0.2250, score 0.0295212\n",
      "a=0.2375, score 0.0295191\n",
      "a=0.2500, score 0.0295154\n"
     ]
    }
   ],
   "source": [
    "for a in np.linspace(0.15, 0.25, 9):\n",
    "    y = y_lr_2 * a + y_etr * (1 - a)\n",
    "    print('a={a:.4f}, score {b:.7f}'.format(a=a, b=R_score(y, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = 0.2125\n",
    "y_1 = y_lr_2 * a + y_etr * (1 - a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a=0.0000, score 0.0295219\n",
      "a=0.0250, score 0.0294829\n",
      "a=0.0500, score 0.0294365\n",
      "a=0.0750, score 0.0293828\n",
      "a=0.1000, score 0.0293216\n",
      "a=0.1250, score 0.0292530\n",
      "a=0.1500, score 0.0291769\n",
      "a=0.1750, score 0.0290931\n",
      "a=0.2000, score 0.0290018\n"
     ]
    }
   ],
   "source": [
    "for a in np.linspace(0, 0.2, 9):\n",
    "    y = y_1 * (1-a) + y_lr_1 * a\n",
    "    print('a={a:.4f}, score {b:.7f}'.format(a=a, b=R_score(y, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['y_past',\n",
       " 'tec20-30',\n",
       " 'technical_30',\n",
       " 'tec123',\n",
       " 'technical_43',\n",
       " 'technical_43_diff',\n",
       " 'tec123_past',\n",
       " 'technical_11_diff',\n",
       " 'technical_2_diff',\n",
       " 'technical_11',\n",
       " 'technical_20',\n",
       " 'technical_2',\n",
       " 'fundamental_25_nan',\n",
       " 'technical_14_diff',\n",
       " 'technical_21_diff',\n",
       " 'technical_9_nan',\n",
       " 'technical_40',\n",
       " 'technical_30_diff',\n",
       " 'technical_6_diff',\n",
       " 'technical_6',\n",
       " 'technical_17_diff',\n",
       " 'technical_17',\n",
       " 'technical_14',\n",
       " 'technical_7',\n",
       " 'technical_19',\n",
       " 'technical_44_nan',\n",
       " 'fundamental_27_nan',\n",
       " 'technical_18_nan',\n",
       " 'technical_28_nan',\n",
       " 'technical_21',\n",
       " 'technical_42_nan',\n",
       " 'technical_29_diff',\n",
       " 'technical_20_diff',\n",
       " 'technical_31_nan',\n",
       " 'fundamental_53',\n",
       " 'technical_24_nan',\n",
       " 'technical_36',\n",
       " 'technical_19_diff',\n",
       " 'technical_27',\n",
       " 'technical_29',\n",
       " 'technical_35',\n",
       " 'technical_22',\n",
       " 'technical_41_nan',\n",
       " 'fundamental_8',\n",
       " 'fundamental_21',\n",
       " 'fundamental_17_nan',\n",
       " 'technical_34',\n",
       " 'technical_16_nan',\n",
       " 'technical_27_diff',\n",
       " 'fundamental_33_nan',\n",
       " 'fundamental_58',\n",
       " 'derived_1_nan',\n",
       " 'technical_10',\n",
       " 'technical_25_nan',\n",
       " 'fundamental_18',\n",
       " 'fundamental_59',\n",
       " 'technical_40_diff',\n",
       " 'nullcounts',\n",
       " 'fundamental_5_nan',\n",
       " 'fundamental_48',\n",
       " 'fundamental_47_nan',\n",
       " 'technical_36_diff',\n",
       " 'fundamental_41_nan',\n",
       " 'fundamental_42_nan',\n",
       " 'fundamental_0_nan',\n",
       " 'fundamental_50',\n",
       " 'fundamental_40',\n",
       " 'technical_3_nan',\n",
       " 'fundamental_23',\n",
       " 'fundamental_49_nan',\n",
       " 'fundamental_36',\n",
       " 'technical_44',\n",
       " 'fundamental_2',\n",
       " 'fundamental_0',\n",
       " 'technical_41',\n",
       " 'fundamental_62_diff',\n",
       " 'technical_38_diff',\n",
       " 'fundamental_22_nan',\n",
       " 'technical_12',\n",
       " 'fundamental_62',\n",
       " 'technical_37_diff',\n",
       " 'fundamental_44',\n",
       " 'technical_29_nan',\n",
       " 'fundamental_24_nan',\n",
       " 'technical_10_nan',\n",
       " 'fundamental_46',\n",
       " 'technical_1',\n",
       " 'fundamental_54_nan',\n",
       " 'fundamental_0_diff',\n",
       " 'technical_12_diff',\n",
       " 'technical_35_diff',\n",
       " 'derived_3_nan',\n",
       " 'fundamental_63_nan',\n",
       " 'fundamental_31_nan',\n",
       " 'fundamental_40_nan',\n",
       " 'fundamental_35_nan',\n",
       " 'technical_3',\n",
       " 'fundamental_13']"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(feature_100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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