abacus cosmos using machine learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import random forest regressor and multioutput regressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.multioutput import MultiOutputRegressor\n",
    "from sklearn.metrics import confusion_matrix,classification_report,accuracy_score,mean_squared_error\n",
    "from sklearn.preprocessing import MinMaxScaler,StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "file1 = 'AbacusCosmos_1100box.txt'\n",
    "file2 = 'AbacusCosmos_1100box_planck.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read file using pandas\n",
    "data1 = pd.read_csv(file1, sep=\" \", header=None,index_col = 0)\n",
    "data2 = pd.read_csv(file2, sep=\" \", header=None,index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>40</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>68.950040</td>\n",
       "      <td>62.993740</td>\n",
       "      <td>72.285620</td>\n",
       "      <td>66.219550</td>\n",
       "      <td>74.208300</td>\n",
       "      <td>71.523580</td>\n",
       "      <td>68.604320</td>\n",
       "      <td>66.668850</td>\n",
       "      <td>65.845990</td>\n",
       "      <td>72.669590</td>\n",
       "      <td>...</td>\n",
       "      <td>64.619420</td>\n",
       "      <td>69.790060</td>\n",
       "      <td>63.337290</td>\n",
       "      <td>66.115610</td>\n",
       "      <td>70.124050</td>\n",
       "      <td>73.231890</td>\n",
       "      <td>63.771890</td>\n",
       "      <td>70.546610</td>\n",
       "      <td>74.518710</td>\n",
       "      <td>71.869200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.698315</td>\n",
       "      <td>0.669024</td>\n",
       "      <td>0.739153</td>\n",
       "      <td>0.670652</td>\n",
       "      <td>0.733331</td>\n",
       "      <td>0.706278</td>\n",
       "      <td>0.717132</td>\n",
       "      <td>0.708528</td>\n",
       "      <td>0.694629</td>\n",
       "      <td>0.718771</td>\n",
       "      <td>...</td>\n",
       "      <td>0.674386</td>\n",
       "      <td>0.702410</td>\n",
       "      <td>0.681278</td>\n",
       "      <td>0.685586</td>\n",
       "      <td>0.692022</td>\n",
       "      <td>0.713713</td>\n",
       "      <td>0.659099</td>\n",
       "      <td>0.727426</td>\n",
       "      <td>0.747050</td>\n",
       "      <td>0.725756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.301685</td>\n",
       "      <td>0.330976</td>\n",
       "      <td>0.260847</td>\n",
       "      <td>0.329348</td>\n",
       "      <td>0.266669</td>\n",
       "      <td>0.293722</td>\n",
       "      <td>0.282868</td>\n",
       "      <td>0.291472</td>\n",
       "      <td>0.305371</td>\n",
       "      <td>0.281229</td>\n",
       "      <td>...</td>\n",
       "      <td>0.325614</td>\n",
       "      <td>0.297590</td>\n",
       "      <td>0.318722</td>\n",
       "      <td>0.314414</td>\n",
       "      <td>0.307978</td>\n",
       "      <td>0.286287</td>\n",
       "      <td>0.340901</td>\n",
       "      <td>0.272574</td>\n",
       "      <td>0.252950</td>\n",
       "      <td>0.274244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.930033</td>\n",
       "      <td>0.982304</td>\n",
       "      <td>0.970472</td>\n",
       "      <td>0.975418</td>\n",
       "      <td>0.954402</td>\n",
       "      <td>0.954106</td>\n",
       "      <td>0.960314</td>\n",
       "      <td>0.987369</td>\n",
       "      <td>0.957165</td>\n",
       "      <td>0.931160</td>\n",
       "      <td>...</td>\n",
       "      <td>0.956390</td>\n",
       "      <td>0.976934</td>\n",
       "      <td>0.983535</td>\n",
       "      <td>0.982377</td>\n",
       "      <td>0.941344</td>\n",
       "      <td>0.962267</td>\n",
       "      <td>0.962861</td>\n",
       "      <td>0.973076</td>\n",
       "      <td>0.950712</td>\n",
       "      <td>0.953249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.853824</td>\n",
       "      <td>0.719284</td>\n",
       "      <td>0.851027</td>\n",
       "      <td>0.858119</td>\n",
       "      <td>0.888737</td>\n",
       "      <td>0.912931</td>\n",
       "      <td>0.768274</td>\n",
       "      <td>0.733582</td>\n",
       "      <td>0.691564</td>\n",
       "      <td>0.890242</td>\n",
       "      <td>...</td>\n",
       "      <td>0.735058</td>\n",
       "      <td>0.890489</td>\n",
       "      <td>0.646633</td>\n",
       "      <td>0.743564</td>\n",
       "      <td>0.905562</td>\n",
       "      <td>0.978598</td>\n",
       "      <td>0.737968</td>\n",
       "      <td>0.825002</td>\n",
       "      <td>0.885869</td>\n",
       "      <td>0.881437</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            1          2          3          4          5          6   \\\n",
       "0                                                                       \n",
       "0.0  68.950040  62.993740  72.285620  66.219550  74.208300  71.523580   \n",
       "0.0   0.698315   0.669024   0.739153   0.670652   0.733331   0.706278   \n",
       "0.0   0.301685   0.330976   0.260847   0.329348   0.266669   0.293722   \n",
       "0.0   0.930033   0.982304   0.970472   0.975418   0.954402   0.954106   \n",
       "0.0   0.853824   0.719284   0.851027   0.858119   0.888737   0.912931   \n",
       "\n",
       "            7          8          9          10  ...         31         32  \\\n",
       "0                                                ...                         \n",
       "0.0  68.604320  66.668850  65.845990  72.669590  ...  64.619420  69.790060   \n",
       "0.0   0.717132   0.708528   0.694629   0.718771  ...   0.674386   0.702410   \n",
       "0.0   0.282868   0.291472   0.305371   0.281229  ...   0.325614   0.297590   \n",
       "0.0   0.960314   0.987369   0.957165   0.931160  ...   0.956390   0.976934   \n",
       "0.0   0.768274   0.733582   0.691564   0.890242  ...   0.735058   0.890489   \n",
       "\n",
       "            33         34         35         36         37         38  \\\n",
       "0                                                                       \n",
       "0.0  63.337290  66.115610  70.124050  73.231890  63.771890  70.546610   \n",
       "0.0   0.681278   0.685586   0.692022   0.713713   0.659099   0.727426   \n",
       "0.0   0.318722   0.314414   0.307978   0.286287   0.340901   0.272574   \n",
       "0.0   0.983535   0.982377   0.941344   0.962267   0.962861   0.973076   \n",
       "0.0   0.646633   0.743564   0.905562   0.978598   0.737968   0.825002   \n",
       "\n",
       "            39         40  \n",
       "0                          \n",
       "0.0  74.518710  71.869200  \n",
       "0.0   0.747050   0.725756  \n",
       "0.0   0.252950   0.274244  \n",
       "0.0   0.950712   0.953249  \n",
       "0.0   0.885869   0.881437  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = pd.concat([data1,data2], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.000000</th>\n",
       "      <td>68.950040</td>\n",
       "      <td>62.993740</td>\n",
       "      <td>72.285620</td>\n",
       "      <td>66.219550</td>\n",
       "      <td>74.208300</td>\n",
       "      <td>71.523580</td>\n",
       "      <td>68.604320</td>\n",
       "      <td>66.668850</td>\n",
       "      <td>65.845990</td>\n",
       "      <td>72.669590</td>\n",
       "      <td>...</td>\n",
       "      <td>67.260000</td>\n",
       "      <td>67.260000</td>\n",
       "      <td>67.260000</td>\n",
       "      <td>67.260000</td>\n",
       "      <td>67.260000</td>\n",
       "      <td>67.260000</td>\n",
       "      <td>67.260000</td>\n",
       "      <td>67.260000</td>\n",
       "      <td>67.260000</td>\n",
       "      <td>67.260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.000000</th>\n",
       "      <td>0.698315</td>\n",
       "      <td>0.669024</td>\n",
       "      <td>0.739153</td>\n",
       "      <td>0.670652</td>\n",
       "      <td>0.733331</td>\n",
       "      <td>0.706278</td>\n",
       "      <td>0.717132</td>\n",
       "      <td>0.708528</td>\n",
       "      <td>0.694629</td>\n",
       "      <td>0.718771</td>\n",
       "      <td>...</td>\n",
       "      <td>0.685847</td>\n",
       "      <td>0.685847</td>\n",
       "      <td>0.685847</td>\n",
       "      <td>0.685847</td>\n",
       "      <td>0.685847</td>\n",
       "      <td>0.685847</td>\n",
       "      <td>0.685847</td>\n",
       "      <td>0.685847</td>\n",
       "      <td>0.685847</td>\n",
       "      <td>0.685847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.000000</th>\n",
       "      <td>0.301685</td>\n",
       "      <td>0.330976</td>\n",
       "      <td>0.260847</td>\n",
       "      <td>0.329348</td>\n",
       "      <td>0.266669</td>\n",
       "      <td>0.293722</td>\n",
       "      <td>0.282868</td>\n",
       "      <td>0.291472</td>\n",
       "      <td>0.305371</td>\n",
       "      <td>0.281229</td>\n",
       "      <td>...</td>\n",
       "      <td>0.314153</td>\n",
       "      <td>0.314153</td>\n",
       "      <td>0.314153</td>\n",
       "      <td>0.314153</td>\n",
       "      <td>0.314153</td>\n",
       "      <td>0.314153</td>\n",
       "      <td>0.314153</td>\n",
       "      <td>0.314153</td>\n",
       "      <td>0.314153</td>\n",
       "      <td>0.314153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.000000</th>\n",
       "      <td>0.930033</td>\n",
       "      <td>0.982304</td>\n",
       "      <td>0.970472</td>\n",
       "      <td>0.975418</td>\n",
       "      <td>0.954402</td>\n",
       "      <td>0.954106</td>\n",
       "      <td>0.960314</td>\n",
       "      <td>0.987369</td>\n",
       "      <td>0.957165</td>\n",
       "      <td>0.931160</td>\n",
       "      <td>...</td>\n",
       "      <td>0.965200</td>\n",
       "      <td>0.965200</td>\n",
       "      <td>0.965200</td>\n",
       "      <td>0.965200</td>\n",
       "      <td>0.965200</td>\n",
       "      <td>0.965200</td>\n",
       "      <td>0.965200</td>\n",
       "      <td>0.965200</td>\n",
       "      <td>0.965200</td>\n",
       "      <td>0.965200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.000000</th>\n",
       "      <td>0.853824</td>\n",
       "      <td>0.719284</td>\n",
       "      <td>0.851027</td>\n",
       "      <td>0.858119</td>\n",
       "      <td>0.888737</td>\n",
       "      <td>0.912931</td>\n",
       "      <td>0.768274</td>\n",
       "      <td>0.733582</td>\n",
       "      <td>0.691564</td>\n",
       "      <td>0.890242</td>\n",
       "      <td>...</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>0.830000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.000000</th>\n",
       "      <td>-1.139198</td>\n",
       "      <td>-0.764783</td>\n",
       "      <td>-1.083356</td>\n",
       "      <td>-0.982489</td>\n",
       "      <td>-1.221581</td>\n",
       "      <td>-1.289901</td>\n",
       "      <td>-0.969291</td>\n",
       "      <td>-0.787883</td>\n",
       "      <td>-0.795959</td>\n",
       "      <td>-1.235559</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.011920</th>\n",
       "      <td>15809.341914</td>\n",
       "      <td>10568.146304</td>\n",
       "      <td>18681.724985</td>\n",
       "      <td>13378.118729</td>\n",
       "      <td>18656.861800</td>\n",
       "      <td>16579.401340</td>\n",
       "      <td>14648.890305</td>\n",
       "      <td>13362.988898</td>\n",
       "      <td>11458.562191</td>\n",
       "      <td>18085.618533</td>\n",
       "      <td>...</td>\n",
       "      <td>13561.852544</td>\n",
       "      <td>16092.150464</td>\n",
       "      <td>12308.224025</td>\n",
       "      <td>16759.067535</td>\n",
       "      <td>16499.169633</td>\n",
       "      <td>17125.589538</td>\n",
       "      <td>13490.302842</td>\n",
       "      <td>13458.653743</td>\n",
       "      <td>15166.950567</td>\n",
       "      <td>17335.242986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.022847</th>\n",
       "      <td>13864.460361</td>\n",
       "      <td>9595.560755</td>\n",
       "      <td>15747.265127</td>\n",
       "      <td>12324.836666</td>\n",
       "      <td>15965.767133</td>\n",
       "      <td>14746.989689</td>\n",
       "      <td>12558.978571</td>\n",
       "      <td>11599.994643</td>\n",
       "      <td>10007.632561</td>\n",
       "      <td>15605.108012</td>\n",
       "      <td>...</td>\n",
       "      <td>13617.143844</td>\n",
       "      <td>14323.512675</td>\n",
       "      <td>15483.399389</td>\n",
       "      <td>14063.010819</td>\n",
       "      <td>12918.976422</td>\n",
       "      <td>14117.637671</td>\n",
       "      <td>14907.025190</td>\n",
       "      <td>14029.632798</td>\n",
       "      <td>14707.902784</td>\n",
       "      <td>13558.657842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.034121</th>\n",
       "      <td>11863.536864</td>\n",
       "      <td>8351.804524</td>\n",
       "      <td>12835.591483</td>\n",
       "      <td>10928.107265</td>\n",
       "      <td>13285.724380</td>\n",
       "      <td>12760.679041</td>\n",
       "      <td>10399.724921</td>\n",
       "      <td>9580.805533</td>\n",
       "      <td>8407.381286</td>\n",
       "      <td>13143.663242</td>\n",
       "      <td>...</td>\n",
       "      <td>10778.172323</td>\n",
       "      <td>10621.403355</td>\n",
       "      <td>11724.809144</td>\n",
       "      <td>10786.540608</td>\n",
       "      <td>10829.474340</td>\n",
       "      <td>11119.621037</td>\n",
       "      <td>10620.810089</td>\n",
       "      <td>10578.477661</td>\n",
       "      <td>9798.770873</td>\n",
       "      <td>11250.083830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.045586</th>\n",
       "      <td>9027.403739</td>\n",
       "      <td>6241.961321</td>\n",
       "      <td>9689.804443</td>\n",
       "      <td>8359.970418</td>\n",
       "      <td>10157.472809</td>\n",
       "      <td>9848.382400</td>\n",
       "      <td>7770.613293</td>\n",
       "      <td>7043.343046</td>\n",
       "      <td>6218.525057</td>\n",
       "      <td>10037.164301</td>\n",
       "      <td>...</td>\n",
       "      <td>8236.195748</td>\n",
       "      <td>8150.044753</td>\n",
       "      <td>8165.752807</td>\n",
       "      <td>8133.842598</td>\n",
       "      <td>7767.590179</td>\n",
       "      <td>7608.473595</td>\n",
       "      <td>8022.438941</td>\n",
       "      <td>8034.760052</td>\n",
       "      <td>8429.564677</td>\n",
       "      <td>8053.361676</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    1             2             3             4   \\\n",
       "0                                                                  \n",
       "0.000000     68.950040     62.993740     72.285620     66.219550   \n",
       "0.000000      0.698315      0.669024      0.739153      0.670652   \n",
       "0.000000      0.301685      0.330976      0.260847      0.329348   \n",
       "0.000000      0.930033      0.982304      0.970472      0.975418   \n",
       "0.000000      0.853824      0.719284      0.851027      0.858119   \n",
       "0.000000     -1.139198     -0.764783     -1.083356     -0.982489   \n",
       "0.011920  15809.341914  10568.146304  18681.724985  13378.118729   \n",
       "0.022847  13864.460361   9595.560755  15747.265127  12324.836666   \n",
       "0.034121  11863.536864   8351.804524  12835.591483  10928.107265   \n",
       "0.045586   9027.403739   6241.961321   9689.804443   8359.970418   \n",
       "\n",
       "                    5             6             7             8   \\\n",
       "0                                                                  \n",
       "0.000000     74.208300     71.523580     68.604320     66.668850   \n",
       "0.000000      0.733331      0.706278      0.717132      0.708528   \n",
       "0.000000      0.266669      0.293722      0.282868      0.291472   \n",
       "0.000000      0.954402      0.954106      0.960314      0.987369   \n",
       "0.000000      0.888737      0.912931      0.768274      0.733582   \n",
       "0.000000     -1.221581     -1.289901     -0.969291     -0.787883   \n",
       "0.011920  18656.861800  16579.401340  14648.890305  13362.988898   \n",
       "0.022847  15965.767133  14746.989689  12558.978571  11599.994643   \n",
       "0.034121  13285.724380  12760.679041  10399.724921   9580.805533   \n",
       "0.045586  10157.472809   9848.382400   7770.613293   7043.343046   \n",
       "\n",
       "                    9             10  ...            11            12  \\\n",
       "0                                     ...                               \n",
       "0.000000     65.845990     72.669590  ...     67.260000     67.260000   \n",
       "0.000000      0.694629      0.718771  ...      0.685847      0.685847   \n",
       "0.000000      0.305371      0.281229  ...      0.314153      0.314153   \n",
       "0.000000      0.957165      0.931160  ...      0.965200      0.965200   \n",
       "0.000000      0.691564      0.890242  ...      0.830000      0.830000   \n",
       "0.000000     -0.795959     -1.235559  ...     -1.000000     -1.000000   \n",
       "0.011920  11458.562191  18085.618533  ...  13561.852544  16092.150464   \n",
       "0.022847  10007.632561  15605.108012  ...  13617.143844  14323.512675   \n",
       "0.034121   8407.381286  13143.663242  ...  10778.172323  10621.403355   \n",
       "0.045586   6218.525057  10037.164301  ...   8236.195748   8150.044753   \n",
       "\n",
       "                    13            14            15            16  \\\n",
       "0                                                                  \n",
       "0.000000     67.260000     67.260000     67.260000     67.260000   \n",
       "0.000000      0.685847      0.685847      0.685847      0.685847   \n",
       "0.000000      0.314153      0.314153      0.314153      0.314153   \n",
       "0.000000      0.965200      0.965200      0.965200      0.965200   \n",
       "0.000000      0.830000      0.830000      0.830000      0.830000   \n",
       "0.000000     -1.000000     -1.000000     -1.000000     -1.000000   \n",
       "0.011920  12308.224025  16759.067535  16499.169633  17125.589538   \n",
       "0.022847  15483.399389  14063.010819  12918.976422  14117.637671   \n",
       "0.034121  11724.809144  10786.540608  10829.474340  11119.621037   \n",
       "0.045586   8165.752807   8133.842598   7767.590179   7608.473595   \n",
       "\n",
       "                    17            18            19            20  \n",
       "0                                                                 \n",
       "0.000000     67.260000     67.260000     67.260000     67.260000  \n",
       "0.000000      0.685847      0.685847      0.685847      0.685847  \n",
       "0.000000      0.314153      0.314153      0.314153      0.314153  \n",
       "0.000000      0.965200      0.965200      0.965200      0.965200  \n",
       "0.000000      0.830000      0.830000      0.830000      0.830000  \n",
       "0.000000     -1.000000     -1.000000     -1.000000     -1.000000  \n",
       "0.011920  13490.302842  13458.653743  15166.950567  17335.242986  \n",
       "0.022847  14907.025190  14029.632798  14707.902784  13558.657842  \n",
       "0.034121  10620.810089  10578.477661   9798.770873  11250.083830  \n",
       "0.045586   8022.438941   8034.760052   8429.564677   8053.361676  \n",
       "\n",
       "[10 rows x 60 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = new_data.transpose().copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0.000000</th>\n",
       "      <th>0.000000</th>\n",
       "      <th>0.000000</th>\n",
       "      <th>0.000000</th>\n",
       "      <th>0.000000</th>\n",
       "      <th>0.000000</th>\n",
       "      <th>0.011920</th>\n",
       "      <th>0.022847</th>\n",
       "      <th>0.034121</th>\n",
       "      <th>0.045586</th>\n",
       "      <th>...</th>\n",
       "      <th>5.740654</th>\n",
       "      <th>5.752068</th>\n",
       "      <th>5.763479</th>\n",
       "      <th>5.774891</th>\n",
       "      <th>5.786302</th>\n",
       "      <th>5.797716</th>\n",
       "      <th>5.809130</th>\n",
       "      <th>5.820543</th>\n",
       "      <th>5.831955</th>\n",
       "      <th>5.843366</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68.95004</td>\n",
       "      <td>0.698315</td>\n",
       "      <td>0.301685</td>\n",
       "      <td>0.930033</td>\n",
       "      <td>0.853824</td>\n",
       "      <td>-1.139198</td>\n",
       "      <td>15809.341914</td>\n",
       "      <td>13864.460361</td>\n",
       "      <td>11863.536864</td>\n",
       "      <td>9027.403739</td>\n",
       "      <td>...</td>\n",
       "      <td>12.818603</td>\n",
       "      <td>12.773812</td>\n",
       "      <td>12.727524</td>\n",
       "      <td>12.665790</td>\n",
       "      <td>12.621257</td>\n",
       "      <td>12.573108</td>\n",
       "      <td>12.525960</td>\n",
       "      <td>12.473956</td>\n",
       "      <td>12.423609</td>\n",
       "      <td>12.379444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62.99374</td>\n",
       "      <td>0.669024</td>\n",
       "      <td>0.330976</td>\n",
       "      <td>0.982304</td>\n",
       "      <td>0.719284</td>\n",
       "      <td>-0.764783</td>\n",
       "      <td>10568.146304</td>\n",
       "      <td>9595.560755</td>\n",
       "      <td>8351.804524</td>\n",
       "      <td>6241.961321</td>\n",
       "      <td>...</td>\n",
       "      <td>10.117571</td>\n",
       "      <td>10.077865</td>\n",
       "      <td>10.039500</td>\n",
       "      <td>10.005285</td>\n",
       "      <td>9.971843</td>\n",
       "      <td>9.929925</td>\n",
       "      <td>9.892739</td>\n",
       "      <td>9.858199</td>\n",
       "      <td>9.825866</td>\n",
       "      <td>9.789331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>72.28562</td>\n",
       "      <td>0.739153</td>\n",
       "      <td>0.260847</td>\n",
       "      <td>0.970472</td>\n",
       "      <td>0.851027</td>\n",
       "      <td>-1.083356</td>\n",
       "      <td>18681.724985</td>\n",
       "      <td>15747.265127</td>\n",
       "      <td>12835.591483</td>\n",
       "      <td>9689.804443</td>\n",
       "      <td>...</td>\n",
       "      <td>13.557494</td>\n",
       "      <td>13.505047</td>\n",
       "      <td>13.447689</td>\n",
       "      <td>13.395983</td>\n",
       "      <td>13.342144</td>\n",
       "      <td>13.293052</td>\n",
       "      <td>13.241906</td>\n",
       "      <td>13.195606</td>\n",
       "      <td>13.137567</td>\n",
       "      <td>13.092850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>66.21955</td>\n",
       "      <td>0.670652</td>\n",
       "      <td>0.329348</td>\n",
       "      <td>0.975418</td>\n",
       "      <td>0.858119</td>\n",
       "      <td>-0.982489</td>\n",
       "      <td>13378.118729</td>\n",
       "      <td>12324.836666</td>\n",
       "      <td>10928.107265</td>\n",
       "      <td>8359.970418</td>\n",
       "      <td>...</td>\n",
       "      <td>14.112195</td>\n",
       "      <td>14.054219</td>\n",
       "      <td>14.001385</td>\n",
       "      <td>13.945112</td>\n",
       "      <td>13.898409</td>\n",
       "      <td>13.835032</td>\n",
       "      <td>13.790999</td>\n",
       "      <td>13.738860</td>\n",
       "      <td>13.677889</td>\n",
       "      <td>13.627670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>74.20830</td>\n",
       "      <td>0.733331</td>\n",
       "      <td>0.266669</td>\n",
       "      <td>0.954402</td>\n",
       "      <td>0.888737</td>\n",
       "      <td>-1.221581</td>\n",
       "      <td>18656.861800</td>\n",
       "      <td>15965.767133</td>\n",
       "      <td>13285.724380</td>\n",
       "      <td>10157.472809</td>\n",
       "      <td>...</td>\n",
       "      <td>14.243636</td>\n",
       "      <td>14.178737</td>\n",
       "      <td>14.122188</td>\n",
       "      <td>14.072933</td>\n",
       "      <td>14.015208</td>\n",
       "      <td>13.956736</td>\n",
       "      <td>13.905112</td>\n",
       "      <td>13.856379</td>\n",
       "      <td>13.798439</td>\n",
       "      <td>13.743013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 518 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000      0.011920  \\\n",
       "1  68.95004  0.698315  0.301685  0.930033  0.853824 -1.139198  15809.341914   \n",
       "2  62.99374  0.669024  0.330976  0.982304  0.719284 -0.764783  10568.146304   \n",
       "3  72.28562  0.739153  0.260847  0.970472  0.851027 -1.083356  18681.724985   \n",
       "4  66.21955  0.670652  0.329348  0.975418  0.858119 -0.982489  13378.118729   \n",
       "5  74.20830  0.733331  0.266669  0.954402  0.888737 -1.221581  18656.861800   \n",
       "\n",
       "0      0.022847      0.034121      0.045586  ...   5.740654   5.752068  \\\n",
       "1  13864.460361  11863.536864   9027.403739  ...  12.818603  12.773812   \n",
       "2   9595.560755   8351.804524   6241.961321  ...  10.117571  10.077865   \n",
       "3  15747.265127  12835.591483   9689.804443  ...  13.557494  13.505047   \n",
       "4  12324.836666  10928.107265   8359.970418  ...  14.112195  14.054219   \n",
       "5  15965.767133  13285.724380  10157.472809  ...  14.243636  14.178737   \n",
       "\n",
       "0   5.763479   5.774891   5.786302   5.797716   5.809130   5.820543  \\\n",
       "1  12.727524  12.665790  12.621257  12.573108  12.525960  12.473956   \n",
       "2  10.039500  10.005285   9.971843   9.929925   9.892739   9.858199   \n",
       "3  13.447689  13.395983  13.342144  13.293052  13.241906  13.195606   \n",
       "4  14.001385  13.945112  13.898409  13.835032  13.790999  13.738860   \n",
       "5  14.122188  14.072933  14.015208  13.956736  13.905112  13.856379   \n",
       "\n",
       "0   5.831955   5.843366  \n",
       "1  12.423609  12.379444  \n",
       "2   9.825866   9.789331  \n",
       "3  13.137567  13.092850  \n",
       "4  13.677889  13.627670  \n",
       "5  13.798439  13.743013  \n",
       "\n",
       "[5 rows x 518 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop(data.iloc[:,0:5],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data.iloc[:,0:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "scalar = MinMaxScaler()\n",
    "#X_t = scalar.fit_transform(X)\n",
    "#X = pd.DataFrame(X_t,columns=X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_t = scalar.fit_transform(y)\n",
    "y = pd.DataFrame(y_t,columns=y.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0.0</th>\n",
       "      <th>0.0</th>\n",
       "      <th>0.0</th>\n",
       "      <th>0.0</th>\n",
       "      <th>0.0</th>\n",
       "      <th>0.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.558223</td>\n",
       "      <td>0.572098</td>\n",
       "      <td>0.427902</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.588521</td>\n",
       "      <td>0.323056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.107846</td>\n",
       "      <td>0.314917</td>\n",
       "      <td>0.685083</td>\n",
       "      <td>0.874823</td>\n",
       "      <td>0.206366</td>\n",
       "      <td>0.846334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.810438</td>\n",
       "      <td>0.930669</td>\n",
       "      <td>0.069331</td>\n",
       "      <td>0.676812</td>\n",
       "      <td>0.580577</td>\n",
       "      <td>0.401100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.351761</td>\n",
       "      <td>0.329213</td>\n",
       "      <td>0.670787</td>\n",
       "      <td>0.759586</td>\n",
       "      <td>0.600720</td>\n",
       "      <td>0.542071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.955818</td>\n",
       "      <td>0.879549</td>\n",
       "      <td>0.120451</td>\n",
       "      <td>0.407850</td>\n",
       "      <td>0.687689</td>\n",
       "      <td>0.207918</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0       0.0       0.0       0.0       0.0       0.0       0.0\n",
       "0  0.558223  0.572098  0.427902  0.000000  0.588521  0.323056\n",
       "1  0.107846  0.314917  0.685083  0.874823  0.206366  0.846334\n",
       "2  0.810438  0.930669  0.069331  0.676812  0.580577  0.401100\n",
       "3  0.351761  0.329213  0.670787  0.759586  0.600720  0.542071\n",
       "4  0.955818  0.879549  0.120451  0.407850  0.687689  0.207918"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_depth = 40\n",
    "n_estimators = 250"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "def get_score(n_estimators):\n",
    "    \"\"\"Return the average MAE over 5 CV folds of random forest model.\n",
    "    \n",
    "    Keyword argument:\n",
    "    n_estimators -- the number of trees in the forest\n",
    "    \"\"\"\n",
    "  #fit Random Forest Regressor\n",
    "    reg_rf = RandomForestRegressor(\n",
    "        n_estimators=n_estimators,\n",
    "        criterion='mse',\n",
    "        max_depth=None,\n",
    "        min_samples_split=0.5,\n",
    "        min_samples_leaf=1,\n",
    "        min_weight_fraction_leaf=0.0,\n",
    "        max_features='auto',\n",
    "        max_leaf_nodes=None,\n",
    "        min_impurity_decrease=0.0,\n",
    "        min_impurity_split=None,\n",
    "        bootstrap=True,\n",
    "        oob_score=False,\n",
    "        n_jobs=None,\n",
    "        random_state=None,\n",
    "        verbose=0,\n",
    "        warm_start=False,\n",
    "    )\n",
    "    scores = -1 * cross_val_score(reg_rf, X, y,\n",
    "                                  cv=4,\n",
    "                                  scoring='neg_mean_absolute_error')\n",
    "    return scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = {}\n",
    "for i in range(1,10):\n",
    "    results[50*i] = get_score(50*i) # Your code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "150\n"
     ]
    }
   ],
   "source": [
    "n_estimators_best = min(results, key=results.get)\n",
    "print (n_estimators_best)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "#plt.plot(results.keys(), results.values())\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#fit Random Forest Regressor\n",
    "#depth = [10,20,30,40,50,60]\n",
    "#estimators = [10,20,30,100,200,300,400]\n",
    "error = {}\n",
    "\n",
    "for n_estimators in range(10,500,10):\n",
    "    reg_rf = RandomForestRegressor(\n",
    "        n_estimators=n_estimators,\n",
    "        criterion='mse',\n",
    "        max_depth=500,\n",
    "        min_samples_split=0.5,\n",
    "        min_samples_leaf=1,\n",
    "        min_weight_fraction_leaf=0.0,\n",
    "        max_features='auto',\n",
    "        max_leaf_nodes=None,\n",
    "        min_impurity_decrease=0.0,\n",
    "        min_impurity_split=None,\n",
    "        bootstrap=False,\n",
    "        oob_score=False,\n",
    "        n_jobs=-1,\n",
    "        random_state=None,\n",
    "        verbose=0,\n",
    "        warm_start=False,\n",
    "    )\n",
    "    reg_rf.fit(X_train,y_train)\n",
    "    y_rf = reg_rf.predict(X_test)\n",
    "    error[n_estimators] = mean_squared_error(y_test,y_rf)\n",
    "best = min(error, key=error.get)\n",
    "print (best)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plt.plot(error.keys(),error.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fit Random Forest Regressor\n",
    "\n",
    "reg_rf = RandomForestRegressor(\n",
    "    n_estimators=35,\n",
    "    criterion='mse',\n",
    "    max_depth=None,\n",
    "    min_samples_split=2,\n",
    "    min_samples_leaf=1,\n",
    "    min_weight_fraction_leaf=0.0,\n",
    "    max_features='auto',\n",
    "    max_leaf_nodes=None,\n",
    "    min_impurity_decrease=0.0,\n",
    "    min_impurity_split=None,\n",
    "    bootstrap=False,\n",
    "    oob_score=False,\n",
    "    n_jobs=-1,\n",
    "    random_state=None,\n",
    "    verbose=0,\n",
    "    warm_start=True,\n",
    ")\n",
    "\n",
    "reg_rf.fit(X_train,y_train)\n",
    "y_rf = reg_rf.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ajana/anaconda3/envs/tensorflow_gpu/lib/python3.7/site-packages/sklearn/base.py:420: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').\n",
      "  \"multioutput='uniform_average').\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-0.4843589051416581"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_rf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0.0    0.048737\n",
      "0.0    0.042237\n",
      "0.0    0.042237\n",
      "0.0    0.046987\n",
      "0.0    0.031120\n",
      "0.0    0.042459\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print (np.mean((y_rf - y_test)**2, axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>68.95004</td>\n",
       "      <td>0.698315</td>\n",
       "      <td>0.301685</td>\n",
       "      <td>0.930033</td>\n",
       "      <td>0.853824</td>\n",
       "      <td>-1.139198</td>\n",
       "      <td>65.385050</td>\n",
       "      <td>0.675494</td>\n",
       "      <td>0.324506</td>\n",
       "      <td>0.961427</td>\n",
       "      <td>0.768344</td>\n",
       "      <td>-0.869400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>71.52358</td>\n",
       "      <td>0.706278</td>\n",
       "      <td>0.293722</td>\n",
       "      <td>0.954106</td>\n",
       "      <td>0.912931</td>\n",
       "      <td>-1.289901</td>\n",
       "      <td>67.387180</td>\n",
       "      <td>0.684912</td>\n",
       "      <td>0.315088</td>\n",
       "      <td>0.968036</td>\n",
       "      <td>0.812685</td>\n",
       "      <td>-0.998085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>63.77189</td>\n",
       "      <td>0.659099</td>\n",
       "      <td>0.340901</td>\n",
       "      <td>0.962860</td>\n",
       "      <td>0.737968</td>\n",
       "      <td>-0.761961</td>\n",
       "      <td>70.117755</td>\n",
       "      <td>0.715965</td>\n",
       "      <td>0.284035</td>\n",
       "      <td>0.966161</td>\n",
       "      <td>0.832709</td>\n",
       "      <td>-1.030547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>67.26000</td>\n",
       "      <td>0.685847</td>\n",
       "      <td>0.314153</td>\n",
       "      <td>0.965200</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>67.260000</td>\n",
       "      <td>0.685847</td>\n",
       "      <td>0.314153</td>\n",
       "      <td>0.965200</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64.07216</td>\n",
       "      <td>0.670416</td>\n",
       "      <td>0.329584</td>\n",
       "      <td>0.976326</td>\n",
       "      <td>0.775957</td>\n",
       "      <td>-0.792247</td>\n",
       "      <td>62.748180</td>\n",
       "      <td>0.645115</td>\n",
       "      <td>0.354885</td>\n",
       "      <td>0.964982</td>\n",
       "      <td>0.769882</td>\n",
       "      <td>-0.796463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>67.26000</td>\n",
       "      <td>0.685847</td>\n",
       "      <td>0.314153</td>\n",
       "      <td>0.965200</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>67.260000</td>\n",
       "      <td>0.685847</td>\n",
       "      <td>0.314153</td>\n",
       "      <td>0.965200</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>66.11561</td>\n",
       "      <td>0.685586</td>\n",
       "      <td>0.314414</td>\n",
       "      <td>0.982377</td>\n",
       "      <td>0.743564</td>\n",
       "      <td>-0.865620</td>\n",
       "      <td>62.748180</td>\n",
       "      <td>0.645115</td>\n",
       "      <td>0.354885</td>\n",
       "      <td>0.964982</td>\n",
       "      <td>0.769882</td>\n",
       "      <td>-0.796463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>67.26000</td>\n",
       "      <td>0.685847</td>\n",
       "      <td>0.314153</td>\n",
       "      <td>0.965200</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>67.260000</td>\n",
       "      <td>0.685847</td>\n",
       "      <td>0.314153</td>\n",
       "      <td>0.965200</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>65.19518</td>\n",
       "      <td>0.686572</td>\n",
       "      <td>0.313428</td>\n",
       "      <td>0.989783</td>\n",
       "      <td>0.777796</td>\n",
       "      <td>-0.827385</td>\n",
       "      <td>69.631410</td>\n",
       "      <td>0.714564</td>\n",
       "      <td>0.285436</td>\n",
       "      <td>0.963645</td>\n",
       "      <td>0.806957</td>\n",
       "      <td>-1.014790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>67.26000</td>\n",
       "      <td>0.685847</td>\n",
       "      <td>0.314153</td>\n",
       "      <td>0.965200</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>67.260000</td>\n",
       "      <td>0.685847</td>\n",
       "      <td>0.314153</td>\n",
       "      <td>0.965200</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>67.26000</td>\n",
       "      <td>0.685847</td>\n",
       "      <td>0.314153</td>\n",
       "      <td>0.965200</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>67.260000</td>\n",
       "      <td>0.685847</td>\n",
       "      <td>0.314153</td>\n",
       "      <td>0.965200</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>67.26000</td>\n",
       "      <td>0.685847</td>\n",
       "      <td>0.314153</td>\n",
       "      <td>0.965200</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>67.260000</td>\n",
       "      <td>0.685847</td>\n",
       "      <td>0.314153</td>\n",
       "      <td>0.965200</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>69.79006</td>\n",
       "      <td>0.702410</td>\n",
       "      <td>0.297590</td>\n",
       "      <td>0.976934</td>\n",
       "      <td>0.890489</td>\n",
       "      <td>-1.138622</td>\n",
       "      <td>67.807697</td>\n",
       "      <td>0.696545</td>\n",
       "      <td>0.303455</td>\n",
       "      <td>0.973524</td>\n",
       "      <td>0.805780</td>\n",
       "      <td>-0.983410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>66.21955</td>\n",
       "      <td>0.670652</td>\n",
       "      <td>0.329348</td>\n",
       "      <td>0.975418</td>\n",
       "      <td>0.858119</td>\n",
       "      <td>-0.982489</td>\n",
       "      <td>62.748180</td>\n",
       "      <td>0.645115</td>\n",
       "      <td>0.354885</td>\n",
       "      <td>0.964982</td>\n",
       "      <td>0.769882</td>\n",
       "      <td>-0.796463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>67.26000</td>\n",
       "      <td>0.685847</td>\n",
       "      <td>0.314153</td>\n",
       "      <td>0.965200</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>67.869791</td>\n",
       "      <td>0.693231</td>\n",
       "      <td>0.306769</td>\n",
       "      <td>0.964800</td>\n",
       "      <td>0.824075</td>\n",
       "      <td>-1.003803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>62.08498</td>\n",
       "      <td>0.657980</td>\n",
       "      <td>0.342020</td>\n",
       "      <td>0.950037</td>\n",
       "      <td>0.714087</td>\n",
       "      <td>-0.744883</td>\n",
       "      <td>66.563103</td>\n",
       "      <td>0.699555</td>\n",
       "      <td>0.300445</td>\n",
       "      <td>0.973227</td>\n",
       "      <td>0.748390</td>\n",
       "      <td>-0.828423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>65.84599</td>\n",
       "      <td>0.694629</td>\n",
       "      <td>0.305371</td>\n",
       "      <td>0.957165</td>\n",
       "      <td>0.691564</td>\n",
       "      <td>-0.795959</td>\n",
       "      <td>69.292663</td>\n",
       "      <td>0.705499</td>\n",
       "      <td>0.294501</td>\n",
       "      <td>0.952493</td>\n",
       "      <td>0.837133</td>\n",
       "      <td>-1.023607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>68.60432</td>\n",
       "      <td>0.717132</td>\n",
       "      <td>0.282868</td>\n",
       "      <td>0.960314</td>\n",
       "      <td>0.768274</td>\n",
       "      <td>-0.969291</td>\n",
       "      <td>70.117755</td>\n",
       "      <td>0.715965</td>\n",
       "      <td>0.284035</td>\n",
       "      <td>0.966161</td>\n",
       "      <td>0.832709</td>\n",
       "      <td>-1.030547</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4         5          0  \\\n",
       "0   68.95004  0.698315  0.301685  0.930033  0.853824 -1.139198  65.385050   \n",
       "1   71.52358  0.706278  0.293722  0.954106  0.912931 -1.289901  67.387180   \n",
       "2   63.77189  0.659099  0.340901  0.962860  0.737968 -0.761961  70.117755   \n",
       "3   67.26000  0.685847  0.314153  0.965200  0.830000 -1.000000  67.260000   \n",
       "4   64.07216  0.670416  0.329584  0.976326  0.775957 -0.792247  62.748180   \n",
       "5   67.26000  0.685847  0.314153  0.965200  0.830000 -1.000000  67.260000   \n",
       "6   66.11561  0.685586  0.314414  0.982377  0.743564 -0.865620  62.748180   \n",
       "7   67.26000  0.685847  0.314153  0.965200  0.830000 -1.000000  67.260000   \n",
       "8   65.19518  0.686572  0.313428  0.989783  0.777796 -0.827385  69.631410   \n",
       "9   67.26000  0.685847  0.314153  0.965200  0.830000 -1.000000  67.260000   \n",
       "10  67.26000  0.685847  0.314153  0.965200  0.830000 -1.000000  67.260000   \n",
       "11  67.26000  0.685847  0.314153  0.965200  0.830000 -1.000000  67.260000   \n",
       "12  69.79006  0.702410  0.297590  0.976934  0.890489 -1.138622  67.807697   \n",
       "13  66.21955  0.670652  0.329348  0.975418  0.858119 -0.982489  62.748180   \n",
       "14  67.26000  0.685847  0.314153  0.965200  0.830000 -1.000000  67.869791   \n",
       "15  62.08498  0.657980  0.342020  0.950037  0.714087 -0.744883  66.563103   \n",
       "16  65.84599  0.694629  0.305371  0.957165  0.691564 -0.795959  69.292663   \n",
       "17  68.60432  0.717132  0.282868  0.960314  0.768274 -0.969291  70.117755   \n",
       "\n",
       "           1         2         3         4         5  \n",
       "0   0.675494  0.324506  0.961427  0.768344 -0.869400  \n",
       "1   0.684912  0.315088  0.968036  0.812685 -0.998085  \n",
       "2   0.715965  0.284035  0.966161  0.832709 -1.030547  \n",
       "3   0.685847  0.314153  0.965200  0.830000 -1.000000  \n",
       "4   0.645115  0.354885  0.964982  0.769882 -0.796463  \n",
       "5   0.685847  0.314153  0.965200  0.830000 -1.000000  \n",
       "6   0.645115  0.354885  0.964982  0.769882 -0.796463  \n",
       "7   0.685847  0.314153  0.965200  0.830000 -1.000000  \n",
       "8   0.714564  0.285436  0.963645  0.806957 -1.014790  \n",
       "9   0.685847  0.314153  0.965200  0.830000 -1.000000  \n",
       "10  0.685847  0.314153  0.965200  0.830000 -1.000000  \n",
       "11  0.685847  0.314153  0.965200  0.830000 -1.000000  \n",
       "12  0.696545  0.303455  0.973524  0.805780 -0.983410  \n",
       "13  0.645115  0.354885  0.964982  0.769882 -0.796463  \n",
       "14  0.693231  0.306769  0.964800  0.824075 -1.003803  \n",
       "15  0.699555  0.300445  0.973227  0.748390 -0.828423  \n",
       "16  0.705499  0.294501  0.952493  0.837133 -1.023607  \n",
       "17  0.715965  0.284035  0.966161  0.832709 -1.030547  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([pd.DataFrame(scalar.inverse_transform(y_test.reset_index(drop=True))),\n",
    "                        pd.DataFrame(scalar.inverse_transform(y_rf))], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.jointplot(y_rf[:,0],y_test.iloc[:,0],kind = 'kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.jointplot(y_test.iloc[:,2],y_test.iloc[:,1],kind = \"kde\")\n",
    "sns.jointplot(y_rf[:,2],y_rf[:,1],kind = 'kde',color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBRegressor\n",
    "\n",
    "#fit multioutput regressor\n",
    "multioutputregressor = MultiOutputRegressor(XGBRegressor(max_depth=10,n_estimators=200, \n",
    "                                                         learning_rate=0.5,\n",
    "                                                         objective = 'reg:squarederror',\n",
    "                                                         booster='gbtree',\n",
    "                                                         n_jobs=-1))\n",
    "multioutputregressor.fit(X_train, y_train)\n",
    "# predicting\n",
    "y_multirf = multioutputregressor.predict(X_test)\n",
    "print (np.mean((y_multirf - y_test)**2, axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "multioutputregressor.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.concat([pd.DataFrame(scalar.inverse_transform(y_test.reset_index(drop=True))),\n",
    "                        pd.DataFrame(scalar.inverse_transform(y_multirf))], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.jointplot(y_test.iloc[:,0],y_test.iloc[:,1],kind = \"kde\")\n",
    "sns.jointplot(y_rf[:,0],y_rf[:,1],kind = 'kde',color='red')\n",
    "sns.jointplot(y_multirf[:,0],y_multirf[:,1],kind = 'kde',color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.pairplot(pd.DataFrame(y_rf),diag_kind=\"kde\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.distplot(X.iloc[1],hist=False,rug=True,kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {\"n_estimators\":[10,20,30,40,50,60,70,80,90,100],\n",
    "    \"max_depth\":[None,5,10,15,20,25,30],\n",
    "    \"min_samples_split\":[0.5],\n",
    "    \"max_features\":('auto','sqrt','log2'),\n",
    "    \"bootstrap\":[True],\n",
    "    \"oob_score\":[True]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = GridSearchCV(reg_rf, parameters, cv=5)\n",
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted(clf.cv_results_.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print (np.mean((grid - y_test)**2, axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.concat([pd.DataFrame(scalar.inverse_transform(y_test.reset_index(drop=True))),\n",
    "                        pd.DataFrame(scalar.inverse_transform(grid))], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.jointplot(y_test.iloc[:,2],y_test.iloc[:,1],kind = \"kde\")\n",
    "sns.jointplot(grid[:,2],grid[:,1],kind = 'kde',color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}