based_on_init.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def R_score(y_pred, y):\n",
    "    u = np.mean(y)\n",
    "    R2 = 1 - np.sum(np.square(y - y_pred)) / np.sum(np.square(y - u))\n",
    "    R = np.sign(R2) * np.sqrt(np.abs(R2))\n",
    "    return R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "excl = ['id', 'y', 'timestamp']\n",
    "train = pd.read_hdf('train.h5')\n",
    "feature_cols = [c for c in train.columns if c not in excl]\n",
    "y = train.y\n",
    "X_train = train[feature_cols]\n",
    "d_mean = X_train.median(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>fundamental_2</th>\n",
       "      <th>fundamental_3</th>\n",
       "      <th>fundamental_5</th>\n",
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       "      <th>technical_39</th>\n",
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       "      <th>technical_41</th>\n",
       "      <th>technical_42</th>\n",
       "      <th>technical_43</th>\n",
       "      <th>technical_44</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>-0.050577</td>\n",
       "      <td>3.379575</td>\n",
       "      <td>-0.157525</td>\n",
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       "      <td>-0.155937</td>\n",
       "      <td>1.219439</td>\n",
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       "      <td>0.151881</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.175710</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.178495</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.007262</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.751021</td>\n",
       "      <td>1.035936</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.015100</td>\n",
       "      <td>0.180894</td>\n",
       "      <td>0.139445</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
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       "<p>5 rows × 108 columns</p>\n",
       "</div>"
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      "text/plain": [
       "   derived_0  derived_1  derived_2  derived_3  derived_4  fundamental_0  \\\n",
       "0   0.370326  -0.006316   0.222831  -0.213030   0.729277      -0.335633   \n",
       "1   0.014765  -0.038064  -0.017425   0.320652  -0.034134       0.004413   \n",
       "2  -0.010622  -0.050577   3.379575  -0.157525  -0.068550      -0.155937   \n",
       "3        NaN        NaN        NaN        NaN        NaN       0.178495   \n",
       "4   0.176693  -0.025284  -0.057680   0.015100   0.180894       0.139445   \n",
       "\n",
       "   fundamental_1  fundamental_2  fundamental_3  fundamental_5      ...       \\\n",
       "0       0.113292       1.621238      -0.179404            NaN      ...        \n",
       "1       0.114285      -0.210185       0.216281        0.09675      ...        \n",
       "2       1.219439      -0.764516            NaN            NaN      ...        \n",
       "3            NaN      -0.007262      -0.097903            NaN      ...        \n",
       "4      -0.125687      -0.018707       0.196391            NaN      ...        \n",
       "\n",
       "   technical_35  technical_36  technical_37  technical_38  technical_39  \\\n",
       "0      0.937880      0.775208           NaN           NaN           NaN   \n",
       "1      0.232154      0.025590           NaN           NaN           NaN   \n",
       "2      0.372688      0.151881           NaN           NaN           NaN   \n",
       "3      0.751021      1.035936           NaN           NaN           NaN   \n",
       "4      0.595206      0.630232           NaN           NaN           NaN   \n",
       "\n",
       "   technical_40  technical_41  technical_42  technical_43  technical_44  \n",
       "0     -0.414776           NaN           NaN          -2.0           NaN  \n",
       "1     -0.273607           NaN           NaN          -2.0           NaN  \n",
       "2     -0.175710           NaN           NaN          -2.0           NaN  \n",
       "3     -0.211506           NaN           NaN          -2.0           NaN  \n",
       "4     -0.001957           NaN           NaN           0.0           NaN  \n",
       "\n",
       "[5 rows x 108 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fill NaN "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "n = X_train.isnull().sum(axis=1)\n",
    "for c in X_train.columns:\n",
    "    X_train[c + '_nan_'] = X_train[c].isnull()\n",
    "    X_train[c + '_nan_'] = X_train[c + '_nan_'].map(lambda x : 1 if x else 0)\n",
    "    d_mean[c + '_nan_'] = 0\n",
    "X_train = X_train.fillna(d_mean)\n",
    "X_train['znull'] = n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <td>-0.017425</td>\n",
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       "      <td>-0.210185</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>0.033375</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>-0.057680</td>\n",
       "      <td>0.015100</td>\n",
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       "      <td>0.139445</td>\n",
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      ],
      "text/plain": [
       "   derived_0  derived_1  derived_2  derived_3  derived_4  fundamental_0  \\\n",
       "0   0.370326  -0.006316   0.222831  -0.213030   0.729277      -0.335633   \n",
       "1   0.014765  -0.038064  -0.017425   0.320652  -0.034134       0.004413   \n",
       "2  -0.010622  -0.050577   3.379575  -0.157525  -0.068550      -0.155937   \n",
       "3  -0.000837   0.005523   0.021095   0.002476   0.011752       0.178495   \n",
       "4   0.176693  -0.025284  -0.057680   0.015100   0.180894       0.139445   \n",
       "\n",
       "   fundamental_1  fundamental_2  fundamental_3  fundamental_5     ...      \\\n",
       "0       0.113292       1.621238      -0.179404       0.033375     ...       \n",
       "1       0.114285      -0.210185       0.216281       0.096750     ...       \n",
       "2       1.219439      -0.764516      -0.040183       0.033375     ...       \n",
       "3      -0.007395      -0.007262      -0.097903       0.033375     ...       \n",
       "4      -0.125687      -0.018707       0.196391       0.033375     ...       \n",
       "\n",
       "   technical_36_nan__nan_  technical_37_nan__nan_  technical_38_nan__nan_  \\\n",
       "0                       0                       0                       0   \n",
       "1                       0                       0                       0   \n",
       "2                       0                       0                       0   \n",
       "3                       0                       0                       0   \n",
       "4                       0                       0                       0   \n",
       "\n",
       "   technical_39_nan__nan_  technical_40_nan__nan_  technical_41_nan__nan_  \\\n",
       "0                       0                       0                       0   \n",
       "1                       0                       0                       0   \n",
       "2                       0                       0                       0   \n",
       "3                       0                       0                       0   \n",
       "4                       0                       0                       0   \n",
       "\n",
       "   technical_42_nan__nan_  technical_43_nan__nan_  technical_44_nan__nan_  \\\n",
       "0                       0                       0                       0   \n",
       "1                       0                       0                       0   \n",
       "2                       0                       0                       0   \n",
       "3                       0                       0                       0   \n",
       "4                       0                       0                       0   \n",
       "\n",
       "   znull_nan_  \n",
       "0           0  \n",
       "1           0  \n",
       "2           0  \n",
       "3           0  \n",
       "4           0  \n",
       "\n",
       "[5 rows x 326 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cut y "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "low_y_cut = -0.075\n",
    "high_y_cut = 0.075\n",
    "y_is_above_cut = (y > high_y_cut)\n",
    "y_is_below_cut = (y < low_y_cut)\n",
    "y_is_within_cut = (~y_is_above_cut & ~y_is_below_cut)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extra Trees model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rfr = ExtraTreesRegressor(n_estimators=100, max_depth=4, n_jobs=-1, random_state=17, verbose=0)\n",
    "clf_tree = rfr.fit(X_train, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# linear model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf_linear = LinearRegression(n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_linear = np.array(train[feature_cols].fillna(d_mean).loc[y_is_within_cut,'technical_20'].values).reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_linear = train.loc[y_is_within_cut, 'y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=-1, normalize=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_linear.fit(X_linear, y_linear)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ymean_dict = dict(train.groupby([\"id\"])[\"y\"].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       " 1452: -0.0015850185,\n",
       " 1454: 0.00029593694,\n",
       " 1457: 0.00044864183,\n",
       " 1459: -0.00029310846,\n",
       " 1460: -0.00047775061,\n",
       " 1462: 0.00042100484,\n",
       " 1464: 1.1219278e-06,\n",
       " 1465: -0.00056863687,\n",
       " 1466: -0.00025951819,\n",
       " 1467: -0.0003297489,\n",
       " 1473: -0.00022412825,\n",
       " 1474: -0.0021775072,\n",
       " 1476: 0.0004724625,\n",
       " 1477: 0.0016976006,\n",
       " 1478: 0.00089347281,\n",
       " 1479: 6.9069298e-05,\n",
       " 1481: 0.00012014867,\n",
       " 1482: -0.00060052506,\n",
       " 1483: 0.00061698805,\n",
       " 1485: 0.0001526663,\n",
       " 1486: 0.00052955473,\n",
       " 1489: 7.4326803e-05,\n",
       " 1491: -0.00043393642,\n",
       " 1492: -0.00054151873,\n",
       " 1493: -0.00024891764,\n",
       " 1494: -0.00034441214,\n",
       " 1495: -0.0010653523,\n",
       " 1497: -0.0008329174,\n",
       " 1498: 0.0011646512,\n",
       " 1499: -0.00015240972,\n",
       " ...}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ymean_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_tree = clf_tree.predict(X_train).clip(low_y_cut, high_y_cut) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_linear = clf_linear.predict(np.array(X_train['technical_20']).reshape(-1,1)).clip(low_y_cut, high_y_cut) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def combine_models(y_tree, y_linear, model_weights, dict_weights):\n",
    "    pred = y_tree * model_weights + (1 - model_weights) * y_linear\n",
    "    train['y_pred'] = pred\n",
    "    pred = train.apply(lambda r: 0.95 * r['y_pred'] + 0.05 * ymean_dict[r['id']]\n",
    "                           if r['id'] in ymean_dict else r['y_pred'], axis=1)\n",
    "    pred = [float(format(x, '.6f')) for x in train['y_pred']]\n",
    "    return pred\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y0 = combine_models(y_tree, y_linear, 0.65, 0.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       " 0.000272,\n",
       " 0.000201,\n",
       " 0.000206,\n",
       " 0.000388,\n",
       " 0.001136,\n",
       " -9.3e-05,\n",
       " -0.000123,\n",
       " 0.000643,\n",
       " 0.000391,\n",
       " 0.00027,\n",
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       " -0.000658,\n",
       " -0.000134,\n",
       " -0.000208,\n",
       " ...]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.029620990131762143"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R_score(y0, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def grid_search(model_weights, dict_weights):\n",
    "    best_est = {'model_weight' : 0, 'dict_weight': 0, 'score': -9999}\n",
    "    for model_weight in model_weights:\n",
    "        for dict_weight in dict_weights:\n",
    "            y_pred = combine_models(y_tree, y_linear, model_weight, dict_weight)\n",
    "            score = R_score(y_pred, y)\n",
    "            if score > best_est['score']:\n",
    "                best_est['score'] = score\n",
    "                best_est['model_weight'] = model_weight\n",
    "                best_est['dict_weight'] = dict_weight\n",
    "    return best_est\n",
    "            \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'dict_weight': 0.1, 'model_weight': 0.8, 'score': 0.031515694425364386}\n"
     ]
    }
   ],
   "source": [
    "model_weights = [0.5, 0.6, 0,7, 0.8, 0,9]\n",
    "dict_weights = [0.1, 0.05, 0.02]\n",
    "print(grid_search(model_weights, dict_weights))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'dict_weight': 0.1, 'model_weight': 0.85, 'score': 0.032058865104607086}\n"
     ]
    }
   ],
   "source": [
    "model_weights = [0.75, 0.8, 0.85]\n",
    "dict_weights = [0.1, 0.08, 0.12]\n",
    "print(grid_search(model_weights, dict_weights))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.012728074988517333"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R_score(y_linear, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.033450507507379816"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R_score(y_tree, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0296209901318\n"
     ]
    }
   ],
   "source": [
    "print(R_score(combine_models(y_tree, y_linear, 0.65, 1), y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>derived_0</th>\n",
       "      <th>derived_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
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       "      <td>0.002478</td>\n",
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       "      <th>688</th>\n",
       "      <td>0.428701</td>\n",
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       "    <tr>\n",
       "      <th>778</th>\n",
       "      <td>0.045536</td>\n",
       "      <td>0.053580</td>\n",
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       "    <tr>\n",
       "      <th>895</th>\n",
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       "    <tr>\n",
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       "      <td>0.106547</td>\n",
       "      <td>0.031361</td>\n",
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       "    <tr>\n",
       "      <th>1027</th>\n",
       "      <td>0.542401</td>\n",
       "      <td>0.984190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1131</th>\n",
       "      <td>-0.464875</td>\n",
       "      <td>0.110491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>0.446576</td>\n",
       "      <td>0.839643</td>\n",
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       "    <tr>\n",
       "      <th>1265</th>\n",
       "      <td>0.246445</td>\n",
       "      <td>2.486589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1505</th>\n",
       "      <td>0.346856</td>\n",
       "      <td>0.163746</td>\n",
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       "    <tr>\n",
       "      <th>1720</th>\n",
       "      <td>0.329198</td>\n",
       "      <td>1.195000</td>\n",
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       "    <tr>\n",
       "      <th>1786</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1799</th>\n",
       "      <td>0.046887</td>\n",
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       "      <th>3028</th>\n",
       "      <td>0.045536</td>\n",
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       "    <tr>\n",
       "      <th>3064</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3090</th>\n",
       "      <td>0.106855</td>\n",
       "      <td>0.317498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3102</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3132</th>\n",
       "      <td>-0.006813</td>\n",
       "      <td>-0.005120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3145</th>\n",
       "      <td>-0.028834</td>\n",
       "      <td>0.364610</td>\n",
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       "    <tr>\n",
       "      <th>3168</th>\n",
       "      <td>0.019854</td>\n",
       "      <td>-0.059028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3177</th>\n",
       "      <td>0.168662</td>\n",
       "      <td>0.139686</td>\n",
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       "    <tr>\n",
       "      <th>3185</th>\n",
       "      <td>0.001860</td>\n",
       "      <td>0.288232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3228</th>\n",
       "      <td>-0.022911</td>\n",
       "      <td>-0.007495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3261</th>\n",
       "      <td>0.021824</td>\n",
       "      <td>0.177892</td>\n",
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       "    <tr>\n",
       "      <th>3271</th>\n",
       "      <td>0.002867</td>\n",
       "      <td>-0.033901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3293</th>\n",
       "      <td>-0.048774</td>\n",
       "      <td>-0.005995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3327</th>\n",
       "      <td>0.098978</td>\n",
       "      <td>0.334103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3328</th>\n",
       "      <td>0.222413</td>\n",
       "      <td>0.154229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1707450</th>\n",
       "      <td>-0.002128</td>\n",
       "      <td>0.009835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1707526</th>\n",
       "      <td>0.077781</td>\n",
       "      <td>-0.012360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1707782</th>\n",
       "      <td>-0.171759</td>\n",
       "      <td>-0.065963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1707805</th>\n",
       "      <td>-0.956431</td>\n",
       "      <td>0.051088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1707877</th>\n",
       "      <td>-0.572477</td>\n",
       "      <td>0.107897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1707888</th>\n",
       "      <td>-0.346736</td>\n",
       "      <td>0.012804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1707954</th>\n",
       "      <td>-0.204101</td>\n",
       "      <td>-0.021183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1708071</th>\n",
       "      <td>-1.602946</td>\n",
       "      <td>2.323400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1708127</th>\n",
       "      <td>0.079914</td>\n",
       "      <td>0.027798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1708134</th>\n",
       "      <td>-1.229504</td>\n",
       "      <td>0.820526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1708184</th>\n",
       "      <td>-0.431535</td>\n",
       "      <td>0.399916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1708200</th>\n",
       "      <td>-0.246534</td>\n",
       "      <td>-0.034703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1708246</th>\n",
       "      <td>0.337245</td>\n",
       "      <td>16.612499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1708288</th>\n",
       "      <td>0.024198</td>\n",
       "      <td>-0.039157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1708319</th>\n",
       "      <td>-1.177217</td>\n",
       "      <td>27.167053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1708355</th>\n",
       "      <td>-0.112709</td>\n",
       "      <td>-0.034418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1708493</th>\n",
       "      <td>-0.119416</td>\n",
       "      <td>0.003827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1708533</th>\n",
       "      <td>-0.856950</td>\n",
       "      <td>0.355388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1708671</th>\n",
       "      <td>1.875825</td>\n",
       "      <td>0.172751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1708929</th>\n",
       "      <td>-1.612512</td>\n",
       "      <td>0.801362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1709046</th>\n",
       "      <td>-0.016873</td>\n",
       "      <td>0.004130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1709332</th>\n",
       "      <td>0.337245</td>\n",
       "      <td>16.612499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1709740</th>\n",
       "      <td>0.000576</td>\n",
       "      <td>-0.050634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710048</th>\n",
       "      <td>-0.572501</td>\n",
       "      <td>0.107918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710160</th>\n",
       "      <td>-0.019453</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710442</th>\n",
       "      <td>0.042375</td>\n",
       "      <td>-0.040367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710445</th>\n",
       "      <td>-4.431458</td>\n",
       "      <td>0.701565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710663</th>\n",
       "      <td>-0.742302</td>\n",
       "      <td>0.177442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710687</th>\n",
       "      <td>-0.365683</td>\n",
       "      <td>0.577198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710691</th>\n",
       "      <td>-2.552512</td>\n",
       "      <td>1.164953</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16150 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         derived_0  derived_1\n",
       "185       0.004057   0.311185\n",
       "378       0.059387   0.002478\n",
       "688       0.428701   0.074175\n",
       "778       0.045536   0.053580\n",
       "895      -0.028834   0.352537\n",
       "962            NaN        NaN\n",
       "974       0.106547   0.031361\n",
       "1027      0.542401   0.984190\n",
       "1131     -0.464875   0.110491\n",
       "1255      0.446576   0.839643\n",
       "1265      0.246445   2.486589\n",
       "1505      0.346856   0.163746\n",
       "1720      0.329198   1.195000\n",
       "1786           NaN        NaN\n",
       "1799      0.046887   0.070782\n",
       "3028      0.045536   0.052529\n",
       "3064           NaN        NaN\n",
       "3090      0.106855   0.317498\n",
       "3102           NaN        NaN\n",
       "3132     -0.006813  -0.005120\n",
       "3145     -0.028834   0.364610\n",
       "3168      0.019854  -0.059028\n",
       "3177      0.168662   0.139686\n",
       "3185      0.001860   0.288232\n",
       "3228     -0.022911  -0.007495\n",
       "3261      0.021824   0.177892\n",
       "3271      0.002867  -0.033901\n",
       "3293     -0.048774  -0.005995\n",
       "3327      0.098978   0.334103\n",
       "3328      0.222413   0.154229\n",
       "...            ...        ...\n",
       "1707450  -0.002128   0.009835\n",
       "1707526   0.077781  -0.012360\n",
       "1707782  -0.171759  -0.065963\n",
       "1707805  -0.956431   0.051088\n",
       "1707877  -0.572477   0.107897\n",
       "1707888  -0.346736   0.012804\n",
       "1707954  -0.204101  -0.021183\n",
       "1708071  -1.602946   2.323400\n",
       "1708127   0.079914   0.027798\n",
       "1708134  -1.229504   0.820526\n",
       "1708184  -0.431535   0.399916\n",
       "1708200  -0.246534  -0.034703\n",
       "1708246   0.337245  16.612499\n",
       "1708288   0.024198  -0.039157\n",
       "1708319  -1.177217  27.167053\n",
       "1708355  -0.112709  -0.034418\n",
       "1708493  -0.119416   0.003827\n",
       "1708533  -0.856950   0.355388\n",
       "1708671   1.875825   0.172751\n",
       "1708929  -1.612512   0.801362\n",
       "1709046  -0.016873   0.004130\n",
       "1709332   0.337245  16.612499\n",
       "1709740   0.000576  -0.050634\n",
       "1710048  -0.572501   0.107918\n",
       "1710160  -0.019453        NaN\n",
       "1710442   0.042375  -0.040367\n",
       "1710445  -4.431458   0.701565\n",
       "1710663  -0.742302   0.177442\n",
       "1710687  -0.365683   0.577198\n",
       "1710691  -2.552512   1.164953\n",
       "\n",
       "[16150 rows x 2 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.loc[train['y']>0.08][['derived_0', 'derived_1']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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