initial_condition.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import twosigmafunc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.read_hdf('train.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>id</th>\n",
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       "      <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",
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       "      <th>technical_38</th>\n",
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       "      <th>technical_42</th>\n",
       "      <th>technical_43</th>\n",
       "      <th>technical_44</th>\n",
       "      <th>y</th>\n",
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       "  </thead>\n",
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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",
       "132728   0        169        NaN        NaN        NaN        NaN        NaN   \n",
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       "134393   0        171  -0.230583   0.488096    0.93592   0.028222  -0.083071   \n",
       "\n",
       "        fundamental_0  fundamental_1  fundamental_2    ...     technical_36  \\\n",
       "131062            NaN            NaN            NaN    ...              NaN   \n",
       "131895            NaN            NaN            NaN    ...              NaN   \n",
       "132728            NaN            NaN            NaN    ...              NaN   \n",
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       "134393      -0.240929            NaN       0.212425    ...         0.727659   \n",
       "\n",
       "        technical_37  technical_38  technical_39  technical_40  technical_41  \\\n",
       "131062           NaN           NaN           NaN           NaN           NaN   \n",
       "131895           NaN           NaN           NaN           NaN           NaN   \n",
       "132728           NaN           NaN           NaN           NaN           NaN   \n",
       "133561           0.0           0.0           0.0     -0.160478           NaN   \n",
       "134393           0.0           0.0           0.0     -0.160478           NaN   \n",
       "\n",
       "        technical_42  technical_43  technical_44         y  \n",
       "131062           NaN           NaN           NaN -0.007108  \n",
       "131895           NaN           NaN           NaN  0.001950  \n",
       "132728           NaN           NaN           NaN  0.017724  \n",
       "133561           0.0           0.0           NaN  0.012934  \n",
       "134393           0.0           0.0           NaN -0.025229  \n",
       "\n",
       "[5 rows x 111 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.sort_values(['id', 'timestamp'], inplace=True)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "origin_features = twosigmafunc.origin_features(train)\n",
    "nan_features = twosigmafunc.addtional_features(origin_features, '_nan')\n",
    "train['id_diff'] = train.id.diff()\n",
    "initial_stage = train.loc[train.id_diff!=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "initial_stage['nan_sum'] = initial_stage.isnull().sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([107, 106,  24,  22,  28,  38,  21,  30,  60,  27,  42,  51,  54,\n",
       "        33,  26,  36,  35,  29,  23,  25,  20,  37,  47,  61,  43,  50,\n",
       "        46,  34,  40,  31,  39,  32,  44,  63,  64,  82,  88,  56,  41,\n",
       "        81,  59,  55,  52,  57,  89,  48,  58,  68,  49,  62,  66,  45,\n",
       "        69,  65,  80])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initial_stage.nan_sum.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    }
   ],
   "source": [
    "for col, nan in zip(origin_features, nan_features):\n",
    "    initial_stage[nan] = initial_stage[col].isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# del train\n",
    "inert = []\n",
    "for n in range(2,10):\n",
    "    km = KMeans(n_clusters=n, n_jobs=1, random_state=11, n_init=10)\n",
    "    km.fit(initial_stage[nan_features + ['nan_sum']])\n",
    "    score = silhouette_score(initial_stage[nan_features + ['nan_sum']], km.labels_)\n",
    "    inert.append((n,score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(2, 0.86891646043095638),\n",
       " (3, 0.82780727961783229),\n",
       " (4, 0.78999489664043965),\n",
       " (5, 0.77572947077190957),\n",
       " (6, 0.78254519477431117),\n",
       " (7, 0.77240612987586688),\n",
       " (8, 0.76355227039873197),\n",
       " (9, 0.7658268251075232)]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=5, n_init=20, n_jobs=1, precompute_distances='auto',\n",
       "    random_state=11, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km = KMeans(n_clusters=5, n_jobs=1, random_state=11, n_init=20)\n",
    "km.fit(initial_stage[nan_features + ['nan_sum']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "group_id = defaultdict(list)\n",
    "for id_, group in zip(initial_stage.id, km.labels_):\n",
    "    group_id[group].append(id_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "188\n",
      "680\n",
      "166\n",
      "250\n",
      "140\n"
     ]
    }
   ],
   "source": [
    "for key, gp in group_id.items():\n",
    "    print(len(gp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def label(id):\n",
    "    if id in group_id[0]:\n",
    "        return 'a'\n",
    "    elif id in group_id[1]:\n",
    "        return 'b'\n",
    "    elif id in group_id[2]:\n",
    "        return 'c'\n",
    "    elif id in group_id[3]:\n",
    "        return 'd'\n",
    "    elif id in group_id[4]:\n",
    "        return 'e'\n",
    "    elif id in group_id[5]:\n",
    "        return 'f'\n",
    "    elif id in group_id[6]:\n",
    "        return 'g'\n",
    "    elif id in group_id[7]:\n",
    "        return 'h'\n",
    "    elif id in group_id[8]:\n",
    "        return 'i'\n",
    "    else:\n",
    "        return 'n'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>id</th>\n",
       "      <th>timestamp</th>\n",
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       "      <th>derived_2</th>\n",
       "      <th>derived_3</th>\n",
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       "      <th>fundamental_0</th>\n",
       "      <th>fundamental_1</th>\n",
       "      <th>fundamental_2</th>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 112 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        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",
       "132728   0        169        NaN        NaN        NaN        NaN        NaN   \n",
       "133561   0        170  -0.230583   0.488096    0.93592   0.028222  -0.083071   \n",
       "134393   0        171  -0.230583   0.488096    0.93592   0.028222  -0.083071   \n",
       "\n",
       "        fundamental_0  fundamental_1  fundamental_2   ...     technical_37  \\\n",
       "131062            NaN            NaN            NaN   ...              NaN   \n",
       "131895            NaN            NaN            NaN   ...              NaN   \n",
       "132728            NaN            NaN            NaN   ...              NaN   \n",
       "133561      -0.240929            NaN       0.212425   ...              0.0   \n",
       "134393      -0.240929            NaN       0.212425   ...              0.0   \n",
       "\n",
       "        technical_38  technical_39  technical_40  technical_41  technical_42  \\\n",
       "131062           NaN           NaN           NaN           NaN           NaN   \n",
       "131895           NaN           NaN           NaN           NaN           NaN   \n",
       "132728           NaN           NaN           NaN           NaN           NaN   \n",
       "133561           0.0           0.0     -0.160478           NaN           0.0   \n",
       "134393           0.0           0.0     -0.160478           NaN           0.0   \n",
       "\n",
       "        technical_43  technical_44         y  id_diff  \n",
       "131062           NaN           NaN -0.007108      NaN  \n",
       "131895           NaN           NaN  0.001950      0.0  \n",
       "132728           NaN           NaN  0.017724      0.0  \n",
       "133561           0.0           NaN  0.012934      0.0  \n",
       "134393           0.0           NaN -0.025229      0.0  \n",
       "\n",
       "[5 rows x 112 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "label = [label(id) for id in train.id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a', 'b', 'c', 'd', 'e'}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "label_dummy = pd.get_dummies(label, prefix='Label')\n",
    "train.drop(['id', 'id_diff'], axis=1, inplace=True)\n",
    "train = pd.concat([train, label_dummy], axis=1)\n",
    "median = train.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for col, nan in zip(origin_features, nan_features):\n",
    "    train[nan] = train[col].isnull()\n",
    "    median[nan] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train = train.fillna(median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>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>fundamental_3</th>\n",
       "      <th>...</th>\n",
       "      <th>technical_35_nan</th>\n",
       "      <th>technical_36_nan</th>\n",
       "      <th>technical_37_nan</th>\n",
       "      <th>technical_38_nan</th>\n",
       "      <th>technical_39_nan</th>\n",
       "      <th>technical_40_nan</th>\n",
       "      <th>technical_41_nan</th>\n",
       "      <th>technical_42_nan</th>\n",
       "      <th>technical_43_nan</th>\n",
       "      <th>technical_44_nan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.370326</td>\n",
       "      <td>-0.006316</td>\n",
       "      <td>0.222831</td>\n",
       "      <td>-0.213030</td>\n",
       "      <td>0.729277</td>\n",
       "      <td>-0.335633</td>\n",
       "      <td>0.113292</td>\n",
       "      <td>1.621238</td>\n",
       "      <td>-0.179404</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.014765</td>\n",
       "      <td>-0.038064</td>\n",
       "      <td>-0.017425</td>\n",
       "      <td>0.320652</td>\n",
       "      <td>-0.034134</td>\n",
       "      <td>0.004413</td>\n",
       "      <td>0.114285</td>\n",
       "      <td>-0.210185</td>\n",
       "      <td>0.216281</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.010622</td>\n",
       "      <td>-0.050577</td>\n",
       "      <td>3.379575</td>\n",
       "      <td>-0.157525</td>\n",
       "      <td>-0.068550</td>\n",
       "      <td>-0.155937</td>\n",
       "      <td>1.219439</td>\n",
       "      <td>-0.764516</td>\n",
       "      <td>-0.040183</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.000837</td>\n",
       "      <td>0.005523</td>\n",
       "      <td>0.021095</td>\n",
       "      <td>0.002476</td>\n",
       "      <td>0.011752</td>\n",
       "      <td>0.178495</td>\n",
       "      <td>-0.007395</td>\n",
       "      <td>-0.007262</td>\n",
       "      <td>-0.097903</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.176693</td>\n",
       "      <td>-0.025284</td>\n",
       "      <td>-0.057680</td>\n",
       "      <td>0.015100</td>\n",
       "      <td>0.180894</td>\n",
       "      <td>0.139445</td>\n",
       "      <td>-0.125687</td>\n",
       "      <td>-0.018707</td>\n",
       "      <td>0.196391</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 223 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   timestamp  derived_0  derived_1  derived_2  derived_3  derived_4  \\\n",
       "0          0   0.370326  -0.006316   0.222831  -0.213030   0.729277   \n",
       "1          0   0.014765  -0.038064  -0.017425   0.320652  -0.034134   \n",
       "2          0  -0.010622  -0.050577   3.379575  -0.157525  -0.068550   \n",
       "3          0  -0.000837   0.005523   0.021095   0.002476   0.011752   \n",
       "4          0   0.176693  -0.025284  -0.057680   0.015100   0.180894   \n",
       "\n",
       "   fundamental_0  fundamental_1  fundamental_2  fundamental_3  \\\n",
       "0      -0.335633       0.113292       1.621238      -0.179404   \n",
       "1       0.004413       0.114285      -0.210185       0.216281   \n",
       "2      -0.155937       1.219439      -0.764516      -0.040183   \n",
       "3       0.178495      -0.007395      -0.007262      -0.097903   \n",
       "4       0.139445      -0.125687      -0.018707       0.196391   \n",
       "\n",
       "         ...         technical_35_nan  technical_36_nan  technical_37_nan  \\\n",
       "0        ...                    False             False              True   \n",
       "1        ...                    False             False              True   \n",
       "2        ...                    False             False              True   \n",
       "3        ...                    False             False              True   \n",
       "4        ...                    False             False              True   \n",
       "\n",
       "   technical_38_nan  technical_39_nan  technical_40_nan  technical_41_nan  \\\n",
       "0              True              True             False              True   \n",
       "1              True              True             False              True   \n",
       "2              True              True             False              True   \n",
       "3              True              True             False              True   \n",
       "4              True              True             False              True   \n",
       "\n",
       "   technical_42_nan  technical_43_nan  technical_44_nan  \n",
       "0              True             False              True  \n",
       "1              True             False              True  \n",
       "2              True             False              True  \n",
       "3              True             False              True  \n",
       "4              True             False              True  \n",
       "\n",
       "[5 rows x 223 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "etr =  ExtraTreesRegressor(n_estimators=60, n_jobs=-1,max_depth=4, random_state=17, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, y_train, X_test, y_test = twosigmafunc.split_data(train, origin_features + nan_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=4,\n",
       "          max_features='auto', max_leaf_nodes=None,\n",
       "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "          n_estimators=60, n_jobs=-1, oob_score=False, random_state=17,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "etr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.021350026493180539"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = etr.predict(X_test)\n",
    "twosigmafunc.R_score(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# add clusters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, y_train, X_test, y_test = twosigmafunc.split_data(train, origin_features + nan_features + ['Label_a', 'Label_b', 'Label_c', 'Label_d', 'Label_e'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=4,\n",
       "          max_features='auto', max_leaf_nodes=None,\n",
       "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "          n_estimators=60, n_jobs=-1, oob_score=False, random_state=17,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "etr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.0069139774386441271"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = etr.predict(X_test)\n",
    "twosigmafunc.R_score(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>85</th>\n",
       "      <td>technical_20</td>\n",
       "      <td>0.134829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>technical_30</td>\n",
       "      <td>0.121247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>Label_b</td>\n",
       "      <td>0.111148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>Label_c</td>\n",
       "      <td>0.104304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>Label_e</td>\n",
       "      <td>0.057408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>fundamental_11</td>\n",
       "      <td>0.042117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>technical_7</td>\n",
       "      <td>0.036381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>technical_24_nan</td>\n",
       "      <td>0.030301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>technical_40</td>\n",
       "      <td>0.024341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>technical_21</td>\n",
       "      <td>0.020578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>Label_a</td>\n",
       "      <td>0.019384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>technical_43</td>\n",
       "      <td>0.018786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>technical_22</td>\n",
       "      <td>0.017108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>technical_19</td>\n",
       "      <td>0.016069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>technical_35</td>\n",
       "      <td>0.012238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>fundamental_15</td>\n",
       "      <td>0.011546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>technical_27</td>\n",
       "      <td>0.009125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>fundamental_53</td>\n",
       "      <td>0.008934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>technical_0_nan</td>\n",
       "      <td>0.008879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>fundamental_18</td>\n",
       "      <td>0.008027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>technical_44_nan</td>\n",
       "      <td>0.007687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>technical_38_nan</td>\n",
       "      <td>0.007430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>technical_9_nan</td>\n",
       "      <td>0.006980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>fundamental_51</td>\n",
       "      <td>0.006879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>technical_36</td>\n",
       "      <td>0.006421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>technical_12_nan</td>\n",
       "      <td>0.006142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>technical_32_nan</td>\n",
       "      <td>0.006090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>Label_d</td>\n",
       "      <td>0.005763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>technical_29</td>\n",
       "      <td>0.005506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>technical_37_nan</td>\n",
       "      <td>0.005366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>fundamental_15_nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>fundamental_57</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>fundamental_48_nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>fundamental_18_nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>fundamental_47_nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>fundamental_46_nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>fundamental_44_nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>fundamental_43_nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>fundamental_42_nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>fundamental_41_nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>fundamental_40_nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>fundamental_39_nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>fundamental_42</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>fundamental_37_nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>fundamental_36_nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>fundamental_34_nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>fundamental_33_nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>fundamental_32_nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>fundamental_31_nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>fundamental_30_nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>fundamental_29_nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>fundamental_47</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>fundamental_49</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>fundamental_24_nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>fundamental_23_nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>fundamental_54</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>fundamental_21_nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>fundamental_20_nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>fundamental_19_nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>fundamental_45_nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>221 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                feature  important\n",
       "85         technical_20   0.134829\n",
       "93         technical_30   0.121247\n",
       "217             Label_b   0.111148\n",
       "218             Label_c   0.104304\n",
       "220             Label_e   0.057408\n",
       "15       fundamental_11   0.042117\n",
       "74          technical_7   0.036381\n",
       "196    technical_24_nan   0.030301\n",
       "103        technical_40   0.024341\n",
       "86         technical_21   0.020578\n",
       "216             Label_a   0.019384\n",
       "106        technical_43   0.018786\n",
       "87         technical_22   0.017108\n",
       "84         technical_19   0.016069\n",
       "98         technical_35   0.012238\n",
       "19       fundamental_15   0.011546\n",
       "90         technical_27   0.009125\n",
       "57       fundamental_53   0.008934\n",
       "176     technical_0_nan   0.008879\n",
       "22       fundamental_18   0.008027\n",
       "215    technical_44_nan   0.007687\n",
       "209    technical_38_nan   0.007430\n",
       "183     technical_9_nan   0.006980\n",
       "55       fundamental_51   0.006879\n",
       "99         technical_36   0.006421\n",
       "186    technical_12_nan   0.006142\n",
       "203    technical_32_nan   0.006090\n",
       "219             Label_d   0.005763\n",
       "92         technical_29   0.005506\n",
       "208    technical_37_nan   0.005366\n",
       "..                  ...        ...\n",
       "127  fundamental_15_nan   0.000000\n",
       "61       fundamental_57   0.000000\n",
       "160  fundamental_48_nan   0.000000\n",
       "130  fundamental_18_nan   0.000000\n",
       "159  fundamental_47_nan   0.000000\n",
       "158  fundamental_46_nan   0.000000\n",
       "156  fundamental_44_nan   0.000000\n",
       "155  fundamental_43_nan   0.000000\n",
       "154  fundamental_42_nan   0.000000\n",
       "153  fundamental_41_nan   0.000000\n",
       "152  fundamental_40_nan   0.000000\n",
       "151  fundamental_39_nan   0.000000\n",
       "46       fundamental_42   0.000000\n",
       "149  fundamental_37_nan   0.000000\n",
       "148  fundamental_36_nan   0.000000\n",
       "146  fundamental_34_nan   0.000000\n",
       "145  fundamental_33_nan   0.000000\n",
       "144  fundamental_32_nan   0.000000\n",
       "143  fundamental_31_nan   0.000000\n",
       "142  fundamental_30_nan   0.000000\n",
       "141  fundamental_29_nan   0.000000\n",
       "51       fundamental_47   0.000000\n",
       "53       fundamental_49   0.000000\n",
       "136  fundamental_24_nan   0.000000\n",
       "135  fundamental_23_nan   0.000000\n",
       "58       fundamental_54   0.000000\n",
       "133  fundamental_21_nan   0.000000\n",
       "132  fundamental_20_nan   0.000000\n",
       "131  fundamental_19_nan   0.000000\n",
       "157  fundamental_45_nan   0.000000\n",
       "\n",
       "[221 rows x 2 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp = pd.DataFrame({'feature':X_train.columns, 'important':etr.feature_importances_})\n",
    "imp.sort_values('important', ascending=False)"
   ]
  }
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