final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import twosigmafunc\n",
    "import pandas as pd\n",
    "import numpy as np\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'\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train, origin_features = twosigmafunc.preprocess()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "etr_features = [\n",
    "    'tec20-30',\n",
    "    'technical_30',\n",
    "    'technical_43',\n",
    "    'technical_43_diff',\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",
    "    'null_count',\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'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "seed = 17\n",
    "excl = twosigmafunc.excl_features()\n",
    "origin_features = [c for c in train.columns if c not in excl]\n",
    "# origin_features_exclude_y = [c for c in observation.train.columns if c not in ['y']]\n",
    "to_add_diff = [feature[:-5] for feature in etr_features if feature.endswith('_diff')]\n",
    "to_add_nan = [feature[:-4] for feature in etr_features if feature.endswith('_nan')]\n",
    "diff_features = [feature + '_diff' for feature in to_add_diff]\n",
    "linear_features = ['technical_20_diff', 'tec20-30']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sucessfully add 20 diff features\n"
     ]
    }
   ],
   "source": [
    "twosigmafunc.add_diffs(train, to_add_diff, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sucessfully add 28 nan features\n"
     ]
    }
   ],
   "source": [
    "twosigmafunc.add_nans(train, to_add_nan, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = train.fillna(train.median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                   0\n",
       "technical_37         0\n",
       "technical_38         0\n",
       "technical_39         0\n",
       "technical_40         0\n",
       "technical_41         0\n",
       "technical_42         0\n",
       "technical_43         0\n",
       "technical_36         0\n",
       "technical_44         0\n",
       "id_diff              0\n",
       "technical_43_diff    0\n",
       "technical_11_diff    0\n",
       "technical_2_diff     0\n",
       "technical_14_diff    0\n",
       "technical_21_diff    0\n",
       "technical_30_diff    0\n",
       "y                    0\n",
       "technical_6_diff     0\n",
       "technical_35         0\n",
       "technical_33         0\n",
       "technical_14         0\n",
       "technical_16         0\n",
       "technical_17         0\n",
       "technical_18         0\n",
       "technical_19         0\n",
       "technical_20         0\n",
       "technical_21         0\n",
       "technical_34         0\n",
       "technical_22         0\n",
       "                    ..\n",
       "technical_1          0\n",
       "technical_2          0\n",
       "technical_3          0\n",
       "technical_5          0\n",
       "technical_6          0\n",
       "technical_7          0\n",
       "technical_9          0\n",
       "technical_0          0\n",
       "fundamental_54       0\n",
       "fundamental_53       0\n",
       "fundamental_52       0\n",
       "fundamental_35       0\n",
       "fundamental_36       0\n",
       "fundamental_37       0\n",
       "fundamental_38       0\n",
       "fundamental_39       0\n",
       "fundamental_40       0\n",
       "fundamental_41       0\n",
       "fundamental_42       0\n",
       "fundamental_43       0\n",
       "fundamental_44       0\n",
       "fundamental_45       0\n",
       "fundamental_46       0\n",
       "fundamental_47       0\n",
       "fundamental_48       0\n",
       "fundamental_49       0\n",
       "fundamental_50       0\n",
       "fundamental_51       0\n",
       "technical_11         0\n",
       "null_count           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train['tec20-30'] = train['technical_20'] - train['technical_30']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, y_train, X_test, y_test = twosigmafunc.split_data(train, etr_features + ['timestamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "low_y_cut = -0.075\n",
    "high_y_cut = 0.075\n",
    "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge = Ridge()\n",
    "ridge.fit(X_train.loc[y_is_within_cut, ['tec20-30', 'technical_20_diff']], y_train[y_is_within_cut])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred = ridge.predict(X_test[['tec20-30', 'technical_20_diff']]).clip(low_y_cut, high_y_cut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02648156394967555"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twosigmafunc.R_score(pred, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train['r'] = np.abs(ridge.predict(X_train[['tec20-30', 'technical_20_diff']]).clip(low_y_cut, high_y_cut) - y_train)"
   ]
  },
  {
   "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>t22_0.0</th>\n",
       "      <th>t22_0.5</th>\n",
       "      <th>t34_0.0</th>\n",
       "      <th>t34_0.5</th>\n",
       "      <th>y_lr1</th>\n",
       "      <th>y_lr2</th>\n",
       "      <th>y_past</th>\n",
       "      <th>tec20-30</th>\n",
       "      <th>technical_30</th>\n",
       "      <th>tec123</th>\n",
       "      <th>...</th>\n",
       "      <th>fundamental_24_nan</th>\n",
       "      <th>technical_10_nan</th>\n",
       "      <th>fundamental_46</th>\n",
       "      <th>technical_1</th>\n",
       "      <th>fundamental_54_nan</th>\n",
       "      <th>fundamental_0_diff</th>\n",
       "      <th>technical_12_diff</th>\n",
       "      <th>technical_35_diff</th>\n",
       "      <th>derived_3_nan</th>\n",
       "      <th>r</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>131062</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.020412</td>\n",
       "      <td>-0.000483</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.007212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131895</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.020412</td>\n",
       "      <td>-0.000483</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.741791e-11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.001846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132728</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.020412</td>\n",
       "      <td>-0.000483</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.741791e-11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.017620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133561</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.032304</td>\n",
       "      <td>-0.000483</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.741791e-11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.012831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134393</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.032304</td>\n",
       "      <td>-0.000483</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.025333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 97 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        t22_0.0  t22_0.5  t34_0.0  t34_0.5  y_lr1  y_lr2  y_past  tec20-30  \\\n",
       "131062      NaN      NaN      NaN      NaN    NaN    NaN     NaN       0.0   \n",
       "131895      NaN      NaN      NaN      NaN    NaN    NaN     NaN       0.0   \n",
       "132728      NaN      NaN      NaN      NaN    NaN    NaN     NaN       0.0   \n",
       "133561      NaN      NaN      NaN      NaN    NaN    NaN     NaN       0.0   \n",
       "134393      NaN      NaN      NaN      NaN    NaN    NaN     NaN       0.0   \n",
       "\n",
       "        technical_30  tec123    ...     fundamental_24_nan  technical_10_nan  \\\n",
       "131062           0.0     NaN    ...                      1                 1   \n",
       "131895           0.0     NaN    ...                      1                 1   \n",
       "132728           0.0     NaN    ...                      1                 1   \n",
       "133561           0.0     NaN    ...                      0                 0   \n",
       "134393           0.0     NaN    ...                      0                 0   \n",
       "\n",
       "        fundamental_46  technical_1  fundamental_54_nan  fundamental_0_diff  \\\n",
       "131062        0.020412    -0.000483                   1                 0.0   \n",
       "131895        0.020412    -0.000483                   1                 0.0   \n",
       "132728        0.020412    -0.000483                   1                 0.0   \n",
       "133561       -0.032304    -0.000483                   0                 0.0   \n",
       "134393       -0.032304    -0.000483                   0                 0.0   \n",
       "\n",
       "        technical_12_diff  technical_35_diff  derived_3_nan         r  \n",
       "131062       0.000000e+00                0.0              1  0.007212  \n",
       "131895       9.741791e-11                0.0              1  0.001846  \n",
       "132728       9.741791e-11                0.0              1  0.017620  \n",
       "133561       9.741791e-11                0.0              0  0.012831  \n",
       "134393       0.000000e+00                0.0              0  0.025333  \n",
       "\n",
       "[5 rows x 97 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "high_r = X_train.r.quantile(0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.009420854435285494"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=5,\n",
       "          max_features=0.6, max_leaf_nodes=None, min_impurity_split=1e-07,\n",
       "          min_samples_leaf=1, 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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "etr = ExtraTreesRegressor(n_jobs=-1, n_estimators=128, random_state=seed, max_depth=5, max_features=0.6)\n",
    "etr.fit(X_train.loc[X_train.r > high_r, etr_features], y_train[X_train.r > high_r])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_bad = Ridge()\n",
    "ridge_bad.fit(X_train.loc[X_train.r > high_r, ['tec20-30', 'technical_20_diff']], y_train[X_train.r > high_r])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pred = etr.predict(X_test[etr_features]).clip(low_y_cut, high_y_cut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = ridge.predict(X_test[['tec20-30', 'technical_20_diff']]).clip(low_y_cut, high_y_cut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01265528041427667"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twosigmafunc.R_score(pred, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "min_stamp = X_test.timestamp.min()\n",
    "max_stamp = X_test.timestamp.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 ridge: -0.00552716746113 bad: -0.0203759937889 combine: 0.0118511580754\n",
      "1100 ridge: 0.0117636443537 bad: -0.0350548933463 combine: -0.00229231794399\n",
      "1200 ridge: 0.0195745805043 bad: -0.0333909619403 combine: 0.0144482667558\n",
      "1300 ridge: 0.0155557697507 bad: -0.0325028548145 combine: 0.0120162202315\n",
      "1400 ridge: -0.0124457922406 bad: -0.0367569119258 combine: -0.016174904885\n",
      "1500 ridge: 0.00875705333811 bad: -0.0267987545045 combine: 0.00814992434783\n",
      "1600 ridge: 0.0286685088215 bad: 0.0106438169174 combine: 0.0279543850855\n",
      "1700 ridge: 0.0293695188969 bad: 0.0138399542834 combine: 0.0286995444938\n",
      "1800 ridge: 0.0270153585809 bad: 0.0116096589755 combine: 0.026754169743\n"
     ]
    }
   ],
   "source": [
    "X_test['y_pred'] = y_pred\n",
    "X_test['y_bad'] = pred\n",
    "stamp = min_stamp\n",
    "y_true_acc = []\n",
    "y_pred_acc = []\n",
    "reward_ri = []\n",
    "reward_bad = []\n",
    "reward_com = []\n",
    "while stamp <= max_stamp:        \n",
    "    y_true = y_test.loc[X_test.timestamp <= stamp]\n",
    "    y_ri = X_test.y_pred.loc[X_test.timestamp <= stamp]\n",
    "    y_bad = X_test.y_bad.loc[X_test.timestamp <= stamp]\n",
    "    y_com = 0.7 * y_ri + 0.3 * y_bad\n",
    "#         print(y_true.shape, y_pred.shape)\n",
    "    reward_ri.append(twosigmafunc.R_score(np.array(y_ri), np.array(y_true)))\n",
    "    reward_bad.append(twosigmafunc.R_score(np.array(y_bad), np.array(y_true)))\n",
    "    reward_com.append(twosigmafunc.R_score(np.array(y_com), np.array(y_true)))\n",
    "    stamp += 1\n",
    "    if stamp % 100 == 0:\n",
    "        print(stamp, 'ridge:', reward_ri[-1], 'bad:', reward_bad[-1], 'combine:', reward_com[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.15, 0.07)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ce+0P0HWdn/zkh9xww4+/sexzB3IktrfExKhBu+ik50kIIYQQQoijhMlkwul0ct11V2Iy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2isq2FKS75ww2339C7Ueeb003n4qXtZtu7Bgx6eOno6yZChrt+crDnNs5jY0MKTW54pvZcVm1bS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110216940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('High Residual')\n",
    "plt.xlabel('timestamp')\n",
    "plt.ylabel('R score')\n",
    "plt.plot([min_stamp, max_stamp], [0, 0], 'k--')\n",
    "plt.plot(range(min_stamp, max_stamp + 1), np.array(reward_ri), label='ridge')\n",
    "plt.plot(range(min_stamp, max_stamp + 1), np.array(reward_bad), label='ETR on high residual data')\n",
    "# plt.plot(range(min_stamp, max_stamp + 1), np.array(reward_com), label='combined')\n",
    "plt.legend(loc='best')\n",
    "plt.ylim(-0.15, 0.07)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = (14.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}