2DHetero_code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c9eb85a7",
   "metadata": {},
   "source": [
    "# Table of Contents \n",
    "[Libraries Used](#libraries) \n",
    "\n",
    "Part I: [Read Data and EDA](#prelim)\n",
    "\n",
    "Part II: [Feature selection and importance](#featureselection)\n",
    "\n",
    "Part III: [Model selection and data training](#modelselection)\n",
    "\n",
    "Part IV: [Hyperparameter optimization of best model](#hyperparameter)\n",
    "\n",
    "Part V: [Employing XGBoost Regression - Best model](#bestmodelp)\n",
    "\n",
    "Part VI: [Predictions with designed ML model - Out of sample](#makeprediction1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2d7e7875",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Libraries\n",
    "\n",
    "#General libs\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy import sort\n",
    "\n",
    "# SKlearn libraries for learning.\n",
    "from sklearn.model_selection import cross_val_score, cross_val_predict, KFold, RepeatedKFold\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.feature_selection import SelectKBest, f_regression\n",
    "from sklearn.model_selection import ShuffleSplit, cross_validate, train_test_split\n",
    "from sklearn import linear_model \n",
    "from xgboost import XGBRegressor\n",
    "import xgboost as xgb\n",
    "import lightgbm as lgbm\n",
    "\n",
    "# SKlearn libraries for evaluation.\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import r2_score, mean_squared_error,mean_absolute_error\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# Control the hyperparameter optimization for the best model. It can be expensive\n",
    "hyperparam_opt=False\n",
    "\n",
    "#Set to true to download figures\n",
    "savefigs=False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8d465a6",
   "metadata": {},
   "source": [
    "<a id='prelim'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fb7669a",
   "metadata": {},
   "source": [
    "## Part I: Read Data and EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e9c2cfa",
   "metadata": {},
   "source": [
    "## Loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3cc7bd59",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "oneT_0K = pd.read_csv('2DH-data/1T0K-Mechanicalppts.txt', index_col = 0, delimiter = ',')\n",
    "oneT_300K = pd.read_csv('2DH-data/1T300K-Mechanicalppts.txt', index_col = 0, delimiter = ',')\n",
    "oneT_600K = pd.read_csv('2DH-data/1T600K-Mechanicalppts.txt', index_col = 0, delimiter = ',')\n",
    "\n",
    "twoH_0K = pd.read_csv('2DH-data/2H0K-Mechanicalppts.txt', index_col = 0, delimiter = ',')\n",
    "twoH_300K = pd.read_csv('2DH-data/2H300K-Mechanicalppts.txt', index_col = 0, delimiter = ',')\n",
    "twoH_600K = pd.read_csv('2DH-data/2H600K-Mechanicalppts.txt', index_col = 0, delimiter =',')\n",
    "\n",
    "twoone_0K = pd.read_csv('2DH-data/2H1T0K-Mechanicalppts.txt', index_col = 0, delimiter = ',')\n",
    "twoone_300K = pd.read_csv('2DH-data/2H1T300K-Mechanicalppts.txt', index_col = 0, delimiter = ',')\n",
    "\n",
    "P_0K = pd.read_csv('2DH-data/P0K-Mechanicalppts.txt', index_col = 0, delimiter = ',')\n",
    "P_300K = pd.read_csv('2DH-data/P300K-Mechanicalppts.txt', index_col = 0, delimiter = ',')\n",
    "P_600K = pd.read_csv('2DH-data/P600K-Mechanicalppts.txt', index_col = 0, delimiter =',')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "48f7ec10",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>SG</th>\n",
       "      <th>C11</th>\n",
       "      <th>C12</th>\n",
       "      <th>Y_2D_a</th>\n",
       "      <th>Y_2D_b</th>\n",
       "      <th>G_a</th>\n",
       "      <th>G_b</th>\n",
       "      <th>B_a</th>\n",
       "      <th>B_b</th>\n",
       "      <th>V_a</th>\n",
       "      <th>V_b</th>\n",
       "      <th>aa</th>\n",
       "      <th>bb</th>\n",
       "      <th>cc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6654.000000</td>\n",
       "      <td>6654.000000</td>\n",
       "      <td>6654.000000</td>\n",
       "      <td>9171.000000</td>\n",
       "      <td>9166.000000</td>\n",
       "      <td>9166.000000</td>\n",
       "      <td>9166.000000</td>\n",
       "      <td>9166.000000</td>\n",
       "      <td>9166.000000</td>\n",
       "      <td>9166.000000</td>\n",
       "      <td>9166.000000</td>\n",
       "      <td>9166.000000</td>\n",
       "      <td>9166.000000</td>\n",
       "      <td>2512.000000</td>\n",
       "      <td>2517.000000</td>\n",
       "      <td>2517.000000</td>\n",
       "      <td>2517.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.086940</td>\n",
       "      <td>5.091577</td>\n",
       "      <td>18.849509</td>\n",
       "      <td>38.251772</td>\n",
       "      <td>100.786024</td>\n",
       "      <td>4.731174</td>\n",
       "      <td>167.845443</td>\n",
       "      <td>168.216552</td>\n",
       "      <td>47.871787</td>\n",
       "      <td>48.096763</td>\n",
       "      <td>53.056998</td>\n",
       "      <td>52.792533</td>\n",
       "      <td>0.459607</td>\n",
       "      <td>0.208149</td>\n",
       "      <td>6.845177</td>\n",
       "      <td>6.845177</td>\n",
       "      <td>18.582420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.795289</td>\n",
       "      <td>1.799328</td>\n",
       "      <td>3.084972</td>\n",
       "      <td>66.004020</td>\n",
       "      <td>960.949524</td>\n",
       "      <td>3172.990189</td>\n",
       "      <td>9691.662391</td>\n",
       "      <td>9691.563816</td>\n",
       "      <td>1109.733424</td>\n",
       "      <td>1109.713999</td>\n",
       "      <td>2065.263317</td>\n",
       "      <td>2065.124869</td>\n",
       "      <td>8.483219</td>\n",
       "      <td>8.350377</td>\n",
       "      <td>0.823542</td>\n",
       "      <td>0.823542</td>\n",
       "      <td>3.154251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.733000</td>\n",
       "      <td>2.790000</td>\n",
       "      <td>6.578000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-91270.750000</td>\n",
       "      <td>-303657.290000</td>\n",
       "      <td>-84520.030000</td>\n",
       "      <td>-84520.030000</td>\n",
       "      <td>-1043.580000</td>\n",
       "      <td>-468.250000</td>\n",
       "      <td>-197464.020000</td>\n",
       "      <td>-197464.020000</td>\n",
       "      <td>-182.660000</td>\n",
       "      <td>-182.660000</td>\n",
       "      <td>5.092000</td>\n",
       "      <td>5.092000</td>\n",
       "      <td>5.212000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.328000</td>\n",
       "      <td>3.325000</td>\n",
       "      <td>16.853000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>43.887500</td>\n",
       "      <td>13.842500</td>\n",
       "      <td>29.532500</td>\n",
       "      <td>29.330000</td>\n",
       "      <td>10.930000</td>\n",
       "      <td>10.882500</td>\n",
       "      <td>32.012500</td>\n",
       "      <td>32.087500</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>6.308000</td>\n",
       "      <td>6.308000</td>\n",
       "      <td>16.515000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.901000</td>\n",
       "      <td>4.901000</td>\n",
       "      <td>18.920500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>118.050000</td>\n",
       "      <td>42.505000</td>\n",
       "      <td>95.825000</td>\n",
       "      <td>95.935000</td>\n",
       "      <td>35.355000</td>\n",
       "      <td>35.460000</td>\n",
       "      <td>81.930000</td>\n",
       "      <td>82.140000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>6.674000</td>\n",
       "      <td>6.674000</td>\n",
       "      <td>18.675000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.646000</td>\n",
       "      <td>6.649000</td>\n",
       "      <td>21.035500</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>181.747500</td>\n",
       "      <td>71.317500</td>\n",
       "      <td>162.755000</td>\n",
       "      <td>162.767500</td>\n",
       "      <td>62.387500</td>\n",
       "      <td>62.580000</td>\n",
       "      <td>125.820000</td>\n",
       "      <td>125.830000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>7.153000</td>\n",
       "      <td>7.153000</td>\n",
       "      <td>20.828000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.766000</td>\n",
       "      <td>10.766000</td>\n",
       "      <td>27.118000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>1214.090000</td>\n",
       "      <td>1235.740000</td>\n",
       "      <td>918995.400000</td>\n",
       "      <td>918995.400000</td>\n",
       "      <td>106193.270000</td>\n",
       "      <td>106193.270000</td>\n",
       "      <td>1732.310000</td>\n",
       "      <td>1224.910000</td>\n",
       "      <td>486.450000</td>\n",
       "      <td>207.500000</td>\n",
       "      <td>10.769000</td>\n",
       "      <td>10.769000</td>\n",
       "      <td>26.875000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 a            b            c           SG           C11  \\\n",
       "count  6654.000000  6654.000000  6654.000000  9171.000000   9166.000000   \n",
       "mean      5.086940     5.091577    18.849509    38.251772    100.786024   \n",
       "std       1.795289     1.799328     3.084972    66.004020    960.949524   \n",
       "min       2.733000     2.790000     6.578000     1.000000 -91270.750000   \n",
       "25%       3.328000     3.325000    16.853000     1.000000     43.887500   \n",
       "50%       4.901000     4.901000    18.920500     1.000000    118.050000   \n",
       "75%       6.646000     6.649000    21.035500     8.000000    181.747500   \n",
       "max      10.766000    10.766000    27.118000   156.000000   1214.090000   \n",
       "\n",
       "                 C12         Y_2D_a         Y_2D_b            G_a  \\\n",
       "count    9166.000000    9166.000000    9166.000000    9166.000000   \n",
       "mean        4.731174     167.845443     168.216552      47.871787   \n",
       "std      3172.990189    9691.662391    9691.563816    1109.733424   \n",
       "min   -303657.290000  -84520.030000  -84520.030000   -1043.580000   \n",
       "25%        13.842500      29.532500      29.330000      10.930000   \n",
       "50%        42.505000      95.825000      95.935000      35.355000   \n",
       "75%        71.317500     162.755000     162.767500      62.387500   \n",
       "max      1235.740000  918995.400000  918995.400000  106193.270000   \n",
       "\n",
       "                 G_b            B_a            B_b          V_a          V_b  \\\n",
       "count    9166.000000    9166.000000    9166.000000  9166.000000  2512.000000   \n",
       "mean       48.096763      53.056998      52.792533     0.459607     0.208149   \n",
       "std      1109.713999    2065.263317    2065.124869     8.483219     8.350377   \n",
       "min      -468.250000 -197464.020000 -197464.020000  -182.660000  -182.660000   \n",
       "25%        10.882500      32.012500      32.087500     0.200000     0.150000   \n",
       "50%        35.460000      81.930000      82.140000     0.360000     0.360000   \n",
       "75%        62.580000     125.820000     125.830000     0.620000     0.680000   \n",
       "max    106193.270000    1732.310000    1224.910000   486.450000   207.500000   \n",
       "\n",
       "                aa           bb           cc  \n",
       "count  2517.000000  2517.000000  2517.000000  \n",
       "mean      6.845177     6.845177    18.582420  \n",
       "std       0.823542     0.823542     3.154251  \n",
       "min       5.092000     5.092000     5.212000  \n",
       "25%       6.308000     6.308000    16.515000  \n",
       "50%       6.674000     6.674000    18.675000  \n",
       "75%       7.153000     7.153000    20.828000  \n",
       "max      10.769000    10.769000    26.875000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datas = [oneT_0K, twoH_0K, twoone_0K, P_0K, oneT_300K, twoH_300K, twoone_300K, P_300K, oneT_600K, twoH_600K, P_600K]\n",
    "Fullset= pd.concat(datas)\n",
    "Fullset.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "907a9bd3",
   "metadata": {},
   "source": [
    "### Cleaning data\n",
    "* SG is assumed to remain constant with temperature\n",
    "* Only stable materials are included (C11>0 and C11>C12)\n",
    "* Column of temperature is added\n",
    "* Only materials where a = b are included"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a54d1695",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sriharik/miniconda3/envs/nano281/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "/Users/sriharik/miniconda3/envs/nano281/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "oneT_0K = oneT_0K[['a', 'b', 'c', 'SG', 'C11', 'C12']]\n",
    "oneT_0K['Phase'] = '1T' \n",
    "oneT_0K['Temp'] = '0K'\n",
    "\n",
    "## Considering only physical solutions (C11>0 and C11>C12) and systems where a = b.\n",
    "oneT_0K = oneT_0K[(oneT_0K['C11']> 0) & (oneT_0K['C11']>oneT_0K['C12']) & (oneT_0K['a']==oneT_0K['b']) ]   \n",
    "\n",
    "#display(oneT_0K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4316aa7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "oneT_300K = oneT_300K[['a', 'b', 'c', 'SG', 'C11', 'C12']]\n",
    "oneT_300K = oneT_300K[(oneT_300K['C11']> 0) & (oneT_300K['C11']>oneT_300K['C12']) & (oneT_300K['a']==oneT_300K['b']) ]\n",
    "oneT_300K['a'] = oneT_300K['a']/2\n",
    "oneT_300K['Phase'] = '1T' \n",
    "oneT_300K['Temp'] = '300K'\n",
    "\n",
    "## SG in temperature dependent calculations are assumed to be the same as that for 0K. \n",
    "oneT_300K['SG'].replace(1.0, 156, True)\n",
    "\n",
    "#display(oneT_300K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "34311533",
   "metadata": {},
   "outputs": [],
   "source": [
    "#oneT_600K.drop(labels=['Y_2D_a', 'Y_2D_b', 'G_a', 'G_b', 'B_a', 'B_b', 'V_a', 'V_b'], axis = 'columns', inplace = True)\n",
    "oneT_600K.rename(columns={\"aa\" : \"a\", \"bb\" : \"b\", \"cc\" : \"c\"}, inplace = True)\n",
    "oneT_600K = oneT_600K[['a', 'b', 'c', 'SG', 'C11', 'C12']]\n",
    "oneT_600K = oneT_600K[(oneT_600K['C11']> 0) & (oneT_600K['C11']>oneT_600K['C12']) & (oneT_600K['a']==oneT_600K['b']) ]\n",
    "oneT_600K['a'] = oneT_600K['a']/2\n",
    "oneT_600K['Phase'] = '1T' \n",
    "oneT_600K['Temp'] = '600K'\n",
    "oneT_600K['SG'].replace(1.0, 156, True)\n",
    "\n",
    "#display(oneT_600K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "456b2e66",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sriharik/miniconda3/envs/nano281/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "/Users/sriharik/miniconda3/envs/nano281/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "twoH_0K = twoH_0K[['a', 'b', 'c', 'SG', 'C11', 'C12']]\n",
    "twoH_0K['Phase'] = '2H'\n",
    "twoH_0K['Temp'] = '0K'\n",
    "\n",
    "#display(twoH_0K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "aa3a8406",
   "metadata": {},
   "outputs": [],
   "source": [
    "twoH_300K = twoH_300K[['a', 'b', 'c', 'SG', 'C11', 'C12']]\n",
    "twoH_300K = twoH_300K[(twoH_300K['C11']> 0) & (twoH_300K['C11']>twoH_300K['C12']) & (twoH_300K['a']==twoH_300K['b']) ]\n",
    "twoH_300K['a'] = twoH_300K['a']/2\n",
    "twoH_300K['Phase'] = '2H'\n",
    "twoH_300K['Temp'] = '300K'\n",
    "\n",
    "## Replacing the SG in twoH_300K with the correct ones according to twoH_0K.\n",
    "matSG_8 = twoH_0K.index[twoH_0K['SG'] == 8].tolist()\n",
    "notPresent = ['Hf-Ti-Te-Se', 'Pb-Sn-Te-Se', 'Ti-Hf-Se-Te', 'Si-Hf-Se-Te', 'Si-Hf-S-Se', 'Sn-Ge-Te-Se', 'Mo-Sn-S-Se', 'Hf-Si-Te-Se', 'Pb-Si-Se-Te', 'Sn-Si-S-Te', 'Hf-Sn-Se-S', 'Hf-Ge-Se-Te', 'Cr-Ti-Se-Te', 'Sn-Ge-Te-S', 'Sn-Hf-S-Se', 'Pb-Sn-Se-Te', 'Ti-Cr-Te-Se', 'Pb-Sn-S-Te', 'Pb-Mo-S-Se', 'Pb-Sn-Te-S', 'Sn-Mn-S-Se', 'Ge-Ti-Te-Se', 'Ge-Hf-Se-Te', 'Ti-Pb-S-Se','Ti-Mn-Te-Se', 'Mn-Ti-Se-Te', 'Pb-Ge-Te-O', 'Ge-Pb-Se-Te']\n",
    "\n",
    "toRemove = [i for i in matSG_8]\n",
    "for i in matSG_8:\n",
    "    if i in notPresent:\n",
    "        toRemove.remove(i)\n",
    "\n",
    "twoH_300K.loc[toRemove, 'SG'] = 8\n",
    "\n",
    "indexSG156 = twoH_300K.index[twoH_300K['SG'] == 1].tolist()\n",
    "twoH_300K.loc[indexSG156, 'SG'] = 156\n",
    "\n",
    "#display(twoH_300K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "03afdc33",
   "metadata": {},
   "outputs": [],
   "source": [
    "twoH_600K.rename(columns={\"aa\" : \"a\", \"bb\" : \"b\", \"cc\" : \"c\"}, inplace = True)\n",
    "twoH_600K = twoH_600K[['a', 'b', 'c', 'SG', 'C11', 'C12']]\n",
    "twoH_600K = twoH_600K[(twoH_600K['C11']> 0) & (twoH_600K['C11']>twoH_600K['C12']) & (twoH_600K['a']==twoH_600K['b']) ]\n",
    "twoH_600K['a'] = twoH_600K['a']/2\n",
    "twoH_600K['Phase'] = '2H' \n",
    "twoH_600K['Temp'] = '600K'\n",
    "\n",
    "twoH_600K['SG'].replace(1.0, 156, True)\n",
    "\n",
    "#display(twoH_600K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3d8cd35b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sriharik/miniconda3/envs/nano281/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "/Users/sriharik/miniconda3/envs/nano281/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "twoone_0K = twoone_0K[['a', 'b', 'c', 'SG', 'C11', 'C12']]\n",
    "twoone_0K['Phase'] = '2H-1T'\n",
    "twoone_0K['Temp'] = '0K'\n",
    "twoone_0K = twoone_0K[(twoone_0K['C11']> 0) & (twoone_0K['C11']>twoone_0K['C12']) & (twoone_0K['a']==twoone_0K['b']) ]\n",
    "\n",
    "#display(twoone_0K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2258cd3a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "twoone_300K = twoone_300K[['a', 'b', 'c', 'SG', 'C11', 'C12']]\n",
    "twoone_300K = twoone_300K[(twoone_300K['C11']> 0) & (twoone_300K['C11']>twoone_300K['C12']) & (twoone_300K['a']==twoone_300K['b']) ]\n",
    "twoone_300K['a'] = twoone_300K['a']/2\n",
    "twoone_300K['Phase'] = '2H-1T'\n",
    "twoone_300K['Temp'] = '300K'\n",
    "\n",
    "#display(twoone_300K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3059122f",
   "metadata": {},
   "outputs": [],
   "source": [
    "P_0K = P_0K[['a', 'b', 'c', 'SG', 'C11', 'C12']]\n",
    "P_0K = P_0K[(P_0K['C11']> 0) & (P_0K['C11']>P_0K['C12']) & (P_0K['a']==P_0K['b']) ]\n",
    "P_0K['Phase'] = 'Planar'\n",
    "P_0K['Temp'] = '0K'\n",
    "\n",
    "#display(P_0K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "13fe53e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "P_300K = P_300K[['a', 'b', 'c', 'SG', 'C11', 'C12']]\n",
    "P_300K = P_300K[(P_300K['C11']> 0) & (P_300K['C11']>P_300K['C12']) & (P_300K['a']==P_300K['b']) ]\n",
    "P_300K['a'] = P_300K['a']/2\n",
    "P_300K['Phase'] = 'Planar'\n",
    "P_300K['Temp'] = '300K'\n",
    "P_300K['SG'].replace(1.0, 156, True)\n",
    "#display(P_300K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "195501f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "P_600K.rename(columns={\"aa\" : \"a\", \"bb\" : \"b\", \"cc\" : \"c\"}, inplace = True)\n",
    "P_600K = P_600K[['a', 'b', 'c', 'SG', 'C11', 'C12']]\n",
    "P_600K = P_600K[(P_600K['C11']> 0) & (P_600K['C11']>P_600K['C12']) & (P_600K['a']==P_600K['b']) ]\n",
    "P_600K['a'] = P_600K['a']/2\n",
    "P_600K['Phase'] = 'Planar' \n",
    "P_600K['Temp'] = '600K'\n",
    "P_600K['SG'].replace(1.0, 156, True)\n",
    "\n",
    "#display(P_600K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6f1868f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "datas = [oneT_0K, twoH_0K, twoone_0K, P_0K, oneT_300K, twoH_300K, twoone_300K, P_300K, oneT_600K, twoH_600K, P_600K]\n",
    "dataset = pd.concat(datas)\n",
    "del dataset[\"b\"] #Note, we have trained the data for a=b, so, we remove the b column\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6b60d848",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 6436 entries, Cr-Ge-O-Se to Zn-Te-Sr-Se\n",
      "Data columns (total 7 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   a       6436 non-null   float64\n",
      " 1   c       6436 non-null   float64\n",
      " 2   SG      6436 non-null   float64\n",
      " 3   C11     6436 non-null   float64\n",
      " 4   C12     6436 non-null   float64\n",
      " 5   Phase   6436 non-null   object \n",
      " 6   Temp    6436 non-null   object \n",
      "dtypes: float64(5), object(2)\n",
      "memory usage: 402.2+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3823bdb5",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>c</th>\n",
       "      <th>SG</th>\n",
       "      <th>C11</th>\n",
       "      <th>C12</th>\n",
       "      <th>Phase</th>\n",
       "      <th>Temp</th>\n",
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       "      <th>Material</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cr-Ge-O-Se</th>\n",
       "      <td>3.2190</td>\n",
       "      <td>20.358</td>\n",
       "      <td>156.0</td>\n",
       "      <td>83.84</td>\n",
       "      <td>38.68</td>\n",
       "      <td>1T</td>\n",
       "      <td>0K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cr-Ge-O-S</th>\n",
       "      <td>3.1640</td>\n",
       "      <td>21.068</td>\n",
       "      <td>156.0</td>\n",
       "      <td>213.20</td>\n",
       "      <td>84.54</td>\n",
       "      <td>1T</td>\n",
       "      <td>0K</td>\n",
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       "    <tr>\n",
       "      <th>Cr-Ge-Se-O</th>\n",
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       "      <td>156.0</td>\n",
       "      <td>235.61</td>\n",
       "      <td>49.62</td>\n",
       "      <td>1T</td>\n",
       "      <td>0K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cr-Ge-Se-S</th>\n",
       "      <td>3.3320</td>\n",
       "      <td>18.996</td>\n",
       "      <td>156.0</td>\n",
       "      <td>140.26</td>\n",
       "      <td>3.52</td>\n",
       "      <td>1T</td>\n",
       "      <td>0K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cr-Ge-Se-Te</th>\n",
       "      <td>3.2660</td>\n",
       "      <td>19.770</td>\n",
       "      <td>156.0</td>\n",
       "      <td>109.27</td>\n",
       "      <td>56.37</td>\n",
       "      <td>1T</td>\n",
       "      <td>0K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zn-Te-Mg-O</th>\n",
       "      <td>3.6080</td>\n",
       "      <td>21.629</td>\n",
       "      <td>156.0</td>\n",
       "      <td>19.46</td>\n",
       "      <td>-16.31</td>\n",
       "      <td>Planar</td>\n",
       "      <td>600K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zn-Te-Mg-S</th>\n",
       "      <td>4.1945</td>\n",
       "      <td>16.004</td>\n",
       "      <td>156.0</td>\n",
       "      <td>30.02</td>\n",
       "      <td>8.83</td>\n",
       "      <td>Planar</td>\n",
       "      <td>600K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zn-Te-Sr-O</th>\n",
       "      <td>4.1610</td>\n",
       "      <td>16.265</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0.62</td>\n",
       "      <td>-9.17</td>\n",
       "      <td>Planar</td>\n",
       "      <td>600K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zn-Te-Sr-S</th>\n",
       "      <td>4.5665</td>\n",
       "      <td>13.502</td>\n",
       "      <td>156.0</td>\n",
       "      <td>23.74</td>\n",
       "      <td>11.36</td>\n",
       "      <td>Planar</td>\n",
       "      <td>600K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zn-Te-Sr-Se</th>\n",
       "      <td>4.5855</td>\n",
       "      <td>13.390</td>\n",
       "      <td>156.0</td>\n",
       "      <td>54.77</td>\n",
       "      <td>-12.71</td>\n",
       "      <td>Planar</td>\n",
       "      <td>600K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6436 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  a       c     SG     C11    C12   Phase  Temp\n",
       "Material                                                       \n",
       "Cr-Ge-O-Se   3.2190  20.358  156.0   83.84  38.68      1T    0K\n",
       "Cr-Ge-O-S    3.1640  21.068  156.0  213.20  84.54      1T    0K\n",
       "Cr-Ge-Se-O   3.0220  23.092  156.0  235.61  49.62      1T    0K\n",
       "Cr-Ge-Se-S   3.3320  18.996  156.0  140.26   3.52      1T    0K\n",
       "Cr-Ge-Se-Te  3.2660  19.770  156.0  109.27  56.37      1T    0K\n",
       "...             ...     ...    ...     ...    ...     ...   ...\n",
       "Zn-Te-Mg-O   3.6080  21.629  156.0   19.46 -16.31  Planar  600K\n",
       "Zn-Te-Mg-S   4.1945  16.004  156.0   30.02   8.83  Planar  600K\n",
       "Zn-Te-Sr-O   4.1610  16.265  156.0    0.62  -9.17  Planar  600K\n",
       "Zn-Te-Sr-S   4.5665  13.502  156.0   23.74  11.36  Planar  600K\n",
       "Zn-Te-Sr-Se  4.5855  13.390  156.0   54.77 -12.71  Planar  600K\n",
       "\n",
       "[6436 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88c0f8a0",
   "metadata": {},
   "source": [
    "#### Including rarely occuring SG values\n",
    "\n",
    "Any space group number occuring less than 4 times is categorized arbitrarily as 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c8597433",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0K</td>\n",
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       "    <tr>\n",
       "      <th>Cr-Ge-Se-Te</th>\n",
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       "      <td>19.46</td>\n",
       "      <td>-16.31</td>\n",
       "      <td>Planar</td>\n",
       "      <td>600K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zn-Te-Mg-S</th>\n",
       "      <td>4.1945</td>\n",
       "      <td>16.004</td>\n",
       "      <td>156.0</td>\n",
       "      <td>30.02</td>\n",
       "      <td>8.83</td>\n",
       "      <td>Planar</td>\n",
       "      <td>600K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zn-Te-Sr-O</th>\n",
       "      <td>4.1610</td>\n",
       "      <td>16.265</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0.62</td>\n",
       "      <td>-9.17</td>\n",
       "      <td>Planar</td>\n",
       "      <td>600K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zn-Te-Sr-S</th>\n",
       "      <td>4.5665</td>\n",
       "      <td>13.502</td>\n",
       "      <td>156.0</td>\n",
       "      <td>23.74</td>\n",
       "      <td>11.36</td>\n",
       "      <td>Planar</td>\n",
       "      <td>600K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zn-Te-Sr-Se</th>\n",
       "      <td>4.5855</td>\n",
       "      <td>13.390</td>\n",
       "      <td>156.0</td>\n",
       "      <td>54.77</td>\n",
       "      <td>-12.71</td>\n",
       "      <td>Planar</td>\n",
       "      <td>600K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6436 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  a       c     SG     C11    C12   Phase  Temp\n",
       "Material                                                       \n",
       "Cr-Ge-O-Se   3.2190  20.358  156.0   83.84  38.68      1T    0K\n",
       "Cr-Ge-O-S    3.1640  21.068  156.0  213.20  84.54      1T    0K\n",
       "Cr-Ge-Se-O   3.0220  23.092  156.0  235.61  49.62      1T    0K\n",
       "Cr-Ge-Se-S   3.3320  18.996  156.0  140.26   3.52      1T    0K\n",
       "Cr-Ge-Se-Te  3.2660  19.770  156.0  109.27  56.37      1T    0K\n",
       "...             ...     ...    ...     ...    ...     ...   ...\n",
       "Zn-Te-Mg-O   3.6080  21.629  156.0   19.46 -16.31  Planar  600K\n",
       "Zn-Te-Mg-S   4.1945  16.004  156.0   30.02   8.83  Planar  600K\n",
       "Zn-Te-Sr-O   4.1610  16.265  156.0    0.62  -9.17  Planar  600K\n",
       "Zn-Te-Sr-S   4.5665  13.502  156.0   23.74  11.36  Planar  600K\n",
       "Zn-Te-Sr-Se  4.5855  13.390  156.0   54.77 -12.71  Planar  600K\n",
       "\n",
       "[6436 rows x 7 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SG_in_model = dataset.SG.value_counts().reset_index(name=\"count\").query(\"count > 4\")[\"index\"]\n",
    "\n",
    "def replace_rare_SG_occurances(dataset):\n",
    "    dataset.loc[~dataset[\"SG\"].isin(SG_in_model), \"SG\"] = 0\n",
    "    return dataset\n",
    "    \n",
    "replace_rare_SG_occurances(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "522f2dec",
   "metadata": {},
   "source": [
    "## Encoding of categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "114918c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label Encoding: SG - 3\n",
      "Label Encoding: Temp - 3\n"
     ]
    }
   ],
   "source": [
    "encSG = LabelEncoder()\n",
    "encT = LabelEncoder()\n",
    "enc = [encSG, encT]\n",
    "categorical_variables = ['SG','Temp']\n",
    "for c, encoder in zip(categorical_variables, enc):\n",
    "    print(f\"Label Encoding: {c} - {dataset[c].nunique()}\")\n",
    "    encoder.fit(dataset[c])\n",
    "    dataset[c] = encoder.transform(dataset[c])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "80e9f0eb",
   "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>a</th>\n",
       "      <th>c</th>\n",
       "      <th>SG</th>\n",
       "      <th>C11</th>\n",
       "      <th>C12</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Material</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>Cr-Ge-O-Se</th>\n",
       "      <td>3.2190</td>\n",
       "      <td>20.358</td>\n",
       "      <td>2</td>\n",
       "      <td>83.84</td>\n",
       "      <td>38.68</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cr-Ge-O-S</th>\n",
       "      <td>3.1640</td>\n",
       "      <td>21.068</td>\n",
       "      <td>2</td>\n",
       "      <td>213.20</td>\n",
       "      <td>84.54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cr-Ge-Se-O</th>\n",
       "      <td>3.0220</td>\n",
       "      <td>23.092</td>\n",
       "      <td>2</td>\n",
       "      <td>235.61</td>\n",
       "      <td>49.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cr-Ge-Se-S</th>\n",
       "      <td>3.3320</td>\n",
       "      <td>18.996</td>\n",
       "      <td>2</td>\n",
       "      <td>140.26</td>\n",
       "      <td>3.52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cr-Ge-Se-Te</th>\n",
       "      <td>3.2660</td>\n",
       "      <td>19.770</td>\n",
       "      <td>2</td>\n",
       "      <td>109.27</td>\n",
       "      <td>56.37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zn-Te-Mg-O</th>\n",
       "      <td>3.6080</td>\n",
       "      <td>21.629</td>\n",
       "      <td>2</td>\n",
       "      <td>19.46</td>\n",
       "      <td>-16.31</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zn-Te-Mg-S</th>\n",
       "      <td>4.1945</td>\n",
       "      <td>16.004</td>\n",
       "      <td>2</td>\n",
       "      <td>30.02</td>\n",
       "      <td>8.83</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zn-Te-Sr-O</th>\n",
       "      <td>4.1610</td>\n",
       "      <td>16.265</td>\n",
       "      <td>2</td>\n",
       "      <td>0.62</td>\n",
       "      <td>-9.17</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zn-Te-Sr-S</th>\n",
       "      <td>4.5665</td>\n",
       "      <td>13.502</td>\n",
       "      <td>2</td>\n",
       "      <td>23.74</td>\n",
       "      <td>11.36</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zn-Te-Sr-Se</th>\n",
       "      <td>4.5855</td>\n",
       "      <td>13.390</td>\n",
       "      <td>2</td>\n",
       "      <td>54.77</td>\n",
       "      <td>-12.71</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6436 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  a       c  SG     C11    C12  Temp\n",
       "Material                                            \n",
       "Cr-Ge-O-Se   3.2190  20.358   2   83.84  38.68     0\n",
       "Cr-Ge-O-S    3.1640  21.068   2  213.20  84.54     0\n",
       "Cr-Ge-Se-O   3.0220  23.092   2  235.61  49.62     0\n",
       "Cr-Ge-Se-S   3.3320  18.996   2  140.26   3.52     0\n",
       "Cr-Ge-Se-Te  3.2660  19.770   2  109.27  56.37     0\n",
       "...             ...     ...  ..     ...    ...   ...\n",
       "Zn-Te-Mg-O   3.6080  21.629   2   19.46 -16.31     2\n",
       "Zn-Te-Mg-S   4.1945  16.004   2   30.02   8.83     2\n",
       "Zn-Te-Sr-O   4.1610  16.265   2    0.62  -9.17     2\n",
       "Zn-Te-Sr-S   4.5665  13.502   2   23.74  11.36     2\n",
       "Zn-Te-Sr-Se  4.5855  13.390   2   54.77 -12.71     2\n",
       "\n",
       "[6436 rows x 6 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merged = pd.concat([dataset, PhaseDummies], axis = 'columns')\n",
    "finaldata = dataset.drop(labels='Phase', axis='columns')\n",
    "finaldata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "834e5d62",
   "metadata": {},
   "source": [
    "<a id='featureselection'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a907e2b",
   "metadata": {},
   "source": [
    "## Part II: Feature selection and feature importance\n",
    "\n",
    "A simple correlation map is used to assess the correlation of different features with the target (a). In a typical ML problen we drop features based on this map to reduce the computation cost. We could have dropped some of these features on the basis of their weak correlation with 'a', but since this problem does not have too large a feature set we go ahead with all of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5c1d62e6",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(10, 10))\n",
    "sns.heatmap(finaldata.corr(method = 'spearman').round(2), annot = True, cmap=\"PiYG\", vmin=-1, vmax=1, ax=ax);\n",
    "\n",
    "if savefigs==True: \n",
    "    f.savefig(\"Figures/CorrMap_H\", dpi = 1200, transparent = False, facecolor = 'white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2ae71d02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest, f_regression\n",
    "\n",
    "fea = [c for c in finaldata.columns if c!= \"a\"]\n",
    "Ximp = finaldata[fea]\n",
    "Yimp = finaldata[\"a\"]\n",
    "sel = SelectKBest(f_regression, k=\"all\")\n",
    "sel.fit_transform(Ximp, Yimp)\n",
    "importance = sel.scores_\n",
    "impGraph = pd.Series(np.cbrt(importance), Ximp.columns)\n",
    "impGraph.plot.bar(color = \"Pink\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c457e4f7",
   "metadata": {},
   "source": [
    "For regression, the vacuum size 'c' of a system has the maximum importance followed by its C11 value and whether the structure is planar or not. It seems that temperature is not important for regression. This is also seen in the datasets where the lattice constant column has almost identical values for all three temepratures. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "840785d3",
   "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>a</th>\n",
       "      <th>c</th>\n",
       "      <th>SG</th>\n",
       "      <th>C11</th>\n",
       "      <th>C12</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Material</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>Cr-Ge-O-Se</th>\n",
       "      <td>3.2190</td>\n",
       "      <td>20.358</td>\n",
       "      <td>2</td>\n",
       "      <td>83.84</td>\n",
       "      <td>38.68</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cr-Ge-O-S</th>\n",
       "      <td>3.1640</td>\n",
       "      <td>21.068</td>\n",
       "      <td>2</td>\n",
       "      <td>213.20</td>\n",
       "      <td>84.54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cr-Ge-Se-O</th>\n",
       "      <td>3.0220</td>\n",
       "      <td>23.092</td>\n",
       "      <td>2</td>\n",
       "      <td>235.61</td>\n",
       "      <td>49.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cr-Ge-Se-S</th>\n",
       "      <td>3.3320</td>\n",
       "      <td>18.996</td>\n",
       "      <td>2</td>\n",
       "      <td>140.26</td>\n",
       "      <td>3.52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cr-Ge-Se-Te</th>\n",
       "      <td>3.2660</td>\n",
       "      <td>19.770</td>\n",
       "      <td>2</td>\n",
       "      <td>109.27</td>\n",
       "      <td>56.37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zn-Te-Mg-O</th>\n",
       "      <td>3.6080</td>\n",
       "      <td>21.629</td>\n",
       "      <td>2</td>\n",
       "      <td>19.46</td>\n",
       "      <td>-16.31</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zn-Te-Mg-S</th>\n",
       "      <td>4.1945</td>\n",
       "      <td>16.004</td>\n",
       "      <td>2</td>\n",
       "      <td>30.02</td>\n",
       "      <td>8.83</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zn-Te-Sr-O</th>\n",
       "      <td>4.1610</td>\n",
       "      <td>16.265</td>\n",
       "      <td>2</td>\n",
       "      <td>0.62</td>\n",
       "      <td>-9.17</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zn-Te-Sr-S</th>\n",
       "      <td>4.5665</td>\n",
       "      <td>13.502</td>\n",
       "      <td>2</td>\n",
       "      <td>23.74</td>\n",
       "      <td>11.36</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zn-Te-Sr-Se</th>\n",
       "      <td>4.5855</td>\n",
       "      <td>13.390</td>\n",
       "      <td>2</td>\n",
       "      <td>54.77</td>\n",
       "      <td>-12.71</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6436 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  a       c  SG     C11    C12  Temp\n",
       "Material                                            \n",
       "Cr-Ge-O-Se   3.2190  20.358   2   83.84  38.68     0\n",
       "Cr-Ge-O-S    3.1640  21.068   2  213.20  84.54     0\n",
       "Cr-Ge-Se-O   3.0220  23.092   2  235.61  49.62     0\n",
       "Cr-Ge-Se-S   3.3320  18.996   2  140.26   3.52     0\n",
       "Cr-Ge-Se-Te  3.2660  19.770   2  109.27  56.37     0\n",
       "...             ...     ...  ..     ...    ...   ...\n",
       "Zn-Te-Mg-O   3.6080  21.629   2   19.46 -16.31     2\n",
       "Zn-Te-Mg-S   4.1945  16.004   2   30.02   8.83     2\n",
       "Zn-Te-Sr-O   4.1610  16.265   2    0.62  -9.17     2\n",
       "Zn-Te-Sr-S   4.5665  13.502   2   23.74  11.36     2\n",
       "Zn-Te-Sr-Se  4.5855  13.390   2   54.77 -12.71     2\n",
       "\n",
       "[6436 rows x 6 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Drop one of the phase columns to avoid multi-collinearity. We drop 1T column.\n",
    "\n",
    "#finaldata.drop('1T', axis = 'columns', inplace = True)\n",
    "display(finaldata)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ac07c53",
   "metadata": {},
   "source": [
    "<a id='modelselection'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64c4f6de",
   "metadata": {},
   "source": [
    "## Part III: Model selection and data training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71ce6e77",
   "metadata": {},
   "source": [
    "## Remove 20% of the dataset as unseen data for out-of-sample test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c6e1aa2d",
   "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>a</th>\n",
       "      <th>c</th>\n",
       "      <th>SG</th>\n",
       "      <th>C11</th>\n",
       "      <th>C12</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Material</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>W-Cr-S-O</th>\n",
       "      <td>3.0150</td>\n",
       "      <td>23.210</td>\n",
       "      <td>2</td>\n",
       "      <td>254.74</td>\n",
       "      <td>43.39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pb-W-Te-Se</th>\n",
       "      <td>3.2925</td>\n",
       "      <td>17.853</td>\n",
       "      <td>2</td>\n",
       "      <td>154.00</td>\n",
       "      <td>65.92</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ge-O-Mn-Te</th>\n",
       "      <td>3.1270</td>\n",
       "      <td>21.566</td>\n",
       "      <td>0</td>\n",
       "      <td>185.22</td>\n",
       "      <td>69.79</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Be-Se-Zn-O</th>\n",
       "      <td>3.5340</td>\n",
       "      <td>22.542</td>\n",
       "      <td>2</td>\n",
       "      <td>116.61</td>\n",
       "      <td>36.02</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pb-Cr-Se-Te</th>\n",
       "      <td>3.7690</td>\n",
       "      <td>14.845</td>\n",
       "      <td>2</td>\n",
       "      <td>49.46</td>\n",
       "      <td>25.29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mn-Si-Se-S</th>\n",
       "      <td>3.2280</td>\n",
       "      <td>18.576</td>\n",
       "      <td>2</td>\n",
       "      <td>172.83</td>\n",
       "      <td>70.06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sn-Pb-Se-O</th>\n",
       "      <td>3.5870</td>\n",
       "      <td>16.391</td>\n",
       "      <td>2</td>\n",
       "      <td>58.60</td>\n",
       "      <td>11.56</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sn-W-Te-S</th>\n",
       "      <td>3.1900</td>\n",
       "      <td>20.725</td>\n",
       "      <td>2</td>\n",
       "      <td>180.50</td>\n",
       "      <td>97.23</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cr-Ge-S-Se</th>\n",
       "      <td>3.1505</td>\n",
       "      <td>19.499</td>\n",
       "      <td>2</td>\n",
       "      <td>80.36</td>\n",
       "      <td>7.16</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ti-Mo-Te-Se</th>\n",
       "      <td>3.4710</td>\n",
       "      <td>17.502</td>\n",
       "      <td>2</td>\n",
       "      <td>105.53</td>\n",
       "      <td>32.55</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5148 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  a       c  SG     C11    C12  Temp\n",
       "Material                                            \n",
       "W-Cr-S-O     3.0150  23.210   2  254.74  43.39     0\n",
       "Pb-W-Te-Se   3.2925  17.853   2  154.00  65.92     2\n",
       "Ge-O-Mn-Te   3.1270  21.566   0  185.22  69.79     0\n",
       "Be-Se-Zn-O   3.5340  22.542   2  116.61  36.02     0\n",
       "Pb-Cr-Se-Te  3.7690  14.845   2   49.46  25.29     0\n",
       "...             ...     ...  ..     ...    ...   ...\n",
       "Mn-Si-Se-S   3.2280  18.576   2  172.83  70.06     1\n",
       "Sn-Pb-Se-O   3.5870  16.391   2   58.60  11.56     2\n",
       "Sn-W-Te-S    3.1900  20.725   2  180.50  97.23     2\n",
       "Cr-Ge-S-Se   3.1505  19.499   2   80.36   7.16     2\n",
       "Ti-Mo-Te-Se  3.4710  17.502   2  105.53  32.55     0\n",
       "\n",
       "[5148 rows x 6 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>a</th>\n",
       "      <th>c</th>\n",
       "      <th>SG</th>\n",
       "      <th>C11</th>\n",
       "      <th>C12</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Material</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>Mn-Ti-Te-Se</th>\n",
       "      <td>3.4805</td>\n",
       "      <td>15.975</td>\n",
       "      <td>2</td>\n",
       "      <td>69.13</td>\n",
       "      <td>4.33</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Si-Mn-S-O</th>\n",
       "      <td>3.0865</td>\n",
       "      <td>22.141</td>\n",
       "      <td>2</td>\n",
       "      <td>244.71</td>\n",
       "      <td>84.34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hf-Mo-O-Te</th>\n",
       "      <td>3.3070</td>\n",
       "      <td>19.284</td>\n",
       "      <td>2</td>\n",
       "      <td>267.12</td>\n",
       "      <td>52.37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hf-Ge-S-Se</th>\n",
       "      <td>3.6835</td>\n",
       "      <td>14.265</td>\n",
       "      <td>2</td>\n",
       "      <td>68.31</td>\n",
       "      <td>12.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pb-Mn-O-Se</th>\n",
       "      <td>3.3550</td>\n",
       "      <td>18.737</td>\n",
       "      <td>2</td>\n",
       "      <td>126.53</td>\n",
       "      <td>50.09</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pb-Ge-S-Te</th>\n",
       "      <td>4.0395</td>\n",
       "      <td>11.861</td>\n",
       "      <td>2</td>\n",
       "      <td>36.87</td>\n",
       "      <td>-16.38</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ba-Te-Zn-S</th>\n",
       "      <td>5.2415</td>\n",
       "      <td>10.249</td>\n",
       "      <td>2</td>\n",
       "      <td>10.28</td>\n",
       "      <td>6.94</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ge-Pb-Te-Se</th>\n",
       "      <td>4.4720</td>\n",
       "      <td>10.545</td>\n",
       "      <td>2</td>\n",
       "      <td>37.61</td>\n",
       "      <td>16.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ti-Se-W-Te</th>\n",
       "      <td>3.4990</td>\n",
       "      <td>17.714</td>\n",
       "      <td>0</td>\n",
       "      <td>154.49</td>\n",
       "      <td>29.91</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cr-Hf-S-Te</th>\n",
       "      <td>3.2180</td>\n",
       "      <td>18.693</td>\n",
       "      <td>1</td>\n",
       "      <td>161.13</td>\n",
       "      <td>74.88</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1288 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  a       c  SG     C11    C12  Temp\n",
       "Material                                            \n",
       "Mn-Ti-Te-Se  3.4805  15.975   2   69.13   4.33     2\n",
       "Si-Mn-S-O    3.0865  22.141   2  244.71  84.34     1\n",
       "Hf-Mo-O-Te   3.3070  19.284   2  267.12  52.37     0\n",
       "Hf-Ge-S-Se   3.6835  14.265   2   68.31  12.40     1\n",
       "Pb-Mn-O-Se   3.3550  18.737   2  126.53  50.09     2\n",
       "...             ...     ...  ..     ...    ...   ...\n",
       "Pb-Ge-S-Te   4.0395  11.861   2   36.87 -16.38     2\n",
       "Ba-Te-Zn-S   5.2415  10.249   2   10.28   6.94     2\n",
       "Ge-Pb-Te-Se  4.4720  10.545   2   37.61  16.58     0\n",
       "Ti-Se-W-Te   3.4990  17.714   0  154.49  29.91     0\n",
       "Cr-Hf-S-Te   3.2180  18.693   1  161.13  74.88     0\n",
       "\n",
       "[1288 rows x 6 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data, data_test = train_test_split(finaldata, test_size = 0.2, random_state = 42)\n",
    "data = pd.DataFrame(data)\n",
    "data_test = pd.DataFrame(data_test)\n",
    "display(data)\n",
    "display(data_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "351bd038",
   "metadata": {},
   "source": [
    "### Define features and target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1d49e971",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [c for c in data.columns if c!= \"a\"]\n",
    "X = data[features]\n",
    "y = data[\"a\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f376b385",
   "metadata": {},
   "source": [
    "## Linear Regression\n",
    "\n",
    "Here we first do a simple train test split (7:3) followed by a cross validation test to assess the accuracy of a default Linear Regression model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5bfe9ac8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7389 accuracy with a standard deviation of 0.0091\n",
      "[0.74088064 0.74896523 0.73241124 0.73518354 0.73903437 0.75477513\n",
      " 0.7354315  0.73742042 0.73004429 0.73658397 0.740986   0.74344288\n",
      " 0.71941025 0.73319384 0.7557975 ]\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42)\n",
    "lr = linear_model.LinearRegression()\n",
    "\n",
    "kfold = KFold(n_splits = 5, shuffle = True, random_state = 42)\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "#lr = linear_model.LinearRegression()\n",
    "cv = RepeatedKFold(n_splits = 5, n_repeats = 3, random_state = 42)\n",
    "scores = cross_val_score(lr, X, y, cv = cv)\n",
    "r2_mean = scores.mean()\n",
    "print(\"%0.4f accuracy with a standard deviation of %0.4f\" % (r2_mean, scores.std()))\n",
    "print(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "51b6ba6c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yhat_lr = cross_val_predict(lr, X, y, cv = kfold)\n",
    "\n",
    "f, ax = plt.subplots(figsize=(8, 8))\n",
    "label_mlr = \"$R^2$ = %.2f\" % r2_mean\n",
    "plt.plot(y, yhat_lr, 'o', label=label_mlr, color = \"magenta\")\n",
    "plt.ylabel(r\"$a_{predicted}$ ($\\AA$)\")\n",
    "plt.xlabel(r\"$a$ ($\\AA$)\")\n",
    "plt.legend()\n",
    "plt.xlim([2.5, 6])\n",
    "plt.ylim([2.5, 6])\n",
    "plt.plot([2.5, 6], [2.5, 6], 'k--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eae28d24",
   "metadata": {},
   "source": [
    "### Explore and search for the best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e5ea258d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model - LinearReg || Model Score: 0.738, Adj_R^2: 0.737, Test Score: 0.744, Cross Val Score: 0.741\n",
      "Model - LinearReg || MSE: 0.041, MAE: 0.136, STD: 0.003, MAPE: 3.810%\n",
      "Model - LinearReg  || Cross Validation Progress: \n",
      "\n",
      "   fit_time  score_time  test_score\n",
      "0  0.003035    0.002197    0.742113\n",
      "1  0.003259    0.001881    0.744727\n",
      "2  0.002755    0.001712    0.740301\n",
      "3  0.002916    0.001967    0.734974\n",
      "4  0.003078    0.001866    0.743620\n",
      "----------------------------------------------------------------------------------------------------------\n",
      "Model - BayeRidge || Model Score: 0.738, Adj_R^2: 0.737, Test Score: 0.744, Cross Val Score: 0.741\n",
      "Model - BayeRidge || MSE: 0.041, MAE: 0.136, STD: 0.003, MAPE: 3.809%\n",
      "Model - BayeRidge  || Cross Validation Progress: \n",
      "\n",
      "   fit_time  score_time  test_score\n",
      "0  0.003836    0.002066    0.742076\n",
      "1  0.003558    0.002061    0.744759\n",
      "2  0.003510    0.002081    0.740279\n",
      "3  0.003237    0.001884    0.734929\n",
      "4  0.003155    0.001976    0.743574\n",
      "----------------------------------------------------------------------------------------------------------\n",
      "Model - LassoLars || Model Score: 0.000, Adj_R^2: -0.001, Test Score: -0.000, Cross Val Score: -0.001\n",
      "Model - LassoLars || MSE: 0.161, MAE: 0.284, STD: 0.000, MAPE: 8.111%\n",
      "Model - LassoLars  || Cross Validation Progress: \n",
      "\n",
      "   fit_time  score_time  test_score\n",
      "0  0.003080    0.001967   -0.001104\n",
      "1  0.002914    0.001966   -0.000018\n",
      "2  0.002896    0.001899   -0.000969\n",
      "3  0.002857    0.001868   -0.000032\n",
      "4  0.002974    0.001830   -0.000869\n",
      "----------------------------------------------------------------------------------------------------------\n",
      "Model - LeastAngLars || Model Score: 0.738, Adj_R^2: 0.737, Test Score: 0.744, Cross Val Score: 0.741\n",
      "Model - LeastAngLars || MSE: 0.041, MAE: 0.136, STD: 0.003, MAPE: 3.810%\n",
      "Model - LeastAngLars  || Cross Validation Progress: \n",
      "\n",
      "   fit_time  score_time  test_score\n",
      "0  0.003452    0.001870    0.742113\n",
      "1  0.003394    0.001864    0.744727\n",
      "2  0.003410    0.001845    0.740301\n",
      "3  0.003417    0.001818    0.734974\n",
      "4  0.003366    0.001808    0.743620\n",
      "----------------------------------------------------------------------------------------------------------\n",
      "[0]\tvalidation_0-rmse:2.05098\n",
      "[1]\tvalidation_0-rmse:1.44699\n",
      "[2]\tvalidation_0-rmse:1.02436\n",
      "[3]\tvalidation_0-rmse:0.73179\n",
      "[4]\tvalidation_0-rmse:0.52897\n",
      "[5]\tvalidation_0-rmse:0.39022\n",
      "[6]\tvalidation_0-rmse:0.29654\n",
      "[7]\tvalidation_0-rmse:0.23582\n",
      "[8]\tvalidation_0-rmse:0.19797\n",
      "[9]\tvalidation_0-rmse:0.17589\n",
      "[10]\tvalidation_0-rmse:0.16225\n",
      "[11]\tvalidation_0-rmse:0.15521\n",
      "[12]\tvalidation_0-rmse:0.15095\n",
      "[13]\tvalidation_0-rmse:0.14916\n",
      "[14]\tvalidation_0-rmse:0.14733\n",
      "[15]\tvalidation_0-rmse:0.14719\n",
      "[16]\tvalidation_0-rmse:0.14627\n",
      "[17]\tvalidation_0-rmse:0.14582\n",
      "[18]\tvalidation_0-rmse:0.14581\n",
      "[19]\tvalidation_0-rmse:0.14595\n",
      "[20]\tvalidation_0-rmse:0.14514\n",
      "[21]\tvalidation_0-rmse:0.14459\n",
      "[22]\tvalidation_0-rmse:0.14483\n",
      "[23]\tvalidation_0-rmse:0.14472\n",
      "[24]\tvalidation_0-rmse:0.14450\n",
      "[25]\tvalidation_0-rmse:0.14373\n",
      "[26]\tvalidation_0-rmse:0.14352\n",
      "[27]\tvalidation_0-rmse:0.14326\n",
      "[28]\tvalidation_0-rmse:0.14320\n",
      "[29]\tvalidation_0-rmse:0.14295\n",
      "[30]\tvalidation_0-rmse:0.14297\n",
      "[31]\tvalidation_0-rmse:0.14328\n",
      "[32]\tvalidation_0-rmse:0.14330\n",
      "[33]\tvalidation_0-rmse:0.14338\n",
      "[34]\tvalidation_0-rmse:0.14348\n",
      "[35]\tvalidation_0-rmse:0.14306\n",
      "[36]\tvalidation_0-rmse:0.14279\n",
      "[37]\tvalidation_0-rmse:0.14273\n",
      "[38]\tvalidation_0-rmse:0.14274\n",
      "[39]\tvalidation_0-rmse:0.14315\n",
      "[40]\tvalidation_0-rmse:0.14295\n",
      "[41]\tvalidation_0-rmse:0.14278\n",
      "[42]\tvalidation_0-rmse:0.14257\n",
      "[43]\tvalidation_0-rmse:0.14261\n",
      "[44]\tvalidation_0-rmse:0.14303\n",
      "[45]\tvalidation_0-rmse:0.14295\n",
      "[46]\tvalidation_0-rmse:0.14287\n",
      "[47]\tvalidation_0-rmse:0.14247\n",
      "[48]\tvalidation_0-rmse:0.14244\n",
      "[49]\tvalidation_0-rmse:0.14235\n",
      "[50]\tvalidation_0-rmse:0.14225\n",
      "[51]\tvalidation_0-rmse:0.14219\n",
      "[52]\tvalidation_0-rmse:0.14220\n",
      "[53]\tvalidation_0-rmse:0.14203\n",
      "[54]\tvalidation_0-rmse:0.14208\n",
      "[55]\tvalidation_0-rmse:0.14226\n",
      "[56]\tvalidation_0-rmse:0.14203\n",
      "[57]\tvalidation_0-rmse:0.14164\n",
      "[58]\tvalidation_0-rmse:0.14158\n",
      "[59]\tvalidation_0-rmse:0.14165\n",
      "[60]\tvalidation_0-rmse:0.14161\n",
      "[61]\tvalidation_0-rmse:0.14147\n",
      "[62]\tvalidation_0-rmse:0.14141\n",
      "[63]\tvalidation_0-rmse:0.14148\n",
      "[64]\tvalidation_0-rmse:0.14142\n",
      "[65]\tvalidation_0-rmse:0.14161\n",
      "[66]\tvalidation_0-rmse:0.14144\n",
      "[67]\tvalidation_0-rmse:0.14144\n",
      "[68]\tvalidation_0-rmse:0.14160\n",
      "[69]\tvalidation_0-rmse:0.14127\n",
      "[70]\tvalidation_0-rmse:0.14104\n",
      "[71]\tvalidation_0-rmse:0.14104\n",
      "[72]\tvalidation_0-rmse:0.14089\n",
      "[73]\tvalidation_0-rmse:0.14085\n",
      "[74]\tvalidation_0-rmse:0.14098\n",
      "[75]\tvalidation_0-rmse:0.14101\n",
      "[76]\tvalidation_0-rmse:0.14098\n",
      "[77]\tvalidation_0-rmse:0.14114\n",
      "[78]\tvalidation_0-rmse:0.14142\n",
      "[79]\tvalidation_0-rmse:0.14148\n",
      "[80]\tvalidation_0-rmse:0.14177\n",
      "[81]\tvalidation_0-rmse:0.14185\n",
      "[82]\tvalidation_0-rmse:0.14203\n",
      "[83]\tvalidation_0-rmse:0.14204\n",
      "[84]\tvalidation_0-rmse:0.14224\n",
      "[85]\tvalidation_0-rmse:0.14222\n",
      "[86]\tvalidation_0-rmse:0.14236\n",
      "[87]\tvalidation_0-rmse:0.14213\n",
      "[88]\tvalidation_0-rmse:0.14219\n",
      "[89]\tvalidation_0-rmse:0.14205\n",
      "[90]\tvalidation_0-rmse:0.14199\n",
      "[91]\tvalidation_0-rmse:0.14186\n",
      "[92]\tvalidation_0-rmse:0.14184\n",
      "[93]\tvalidation_0-rmse:0.14212\n",
      "[94]\tvalidation_0-rmse:0.14215\n",
      "[95]\tvalidation_0-rmse:0.14214\n",
      "[96]\tvalidation_0-rmse:0.14212\n",
      "[97]\tvalidation_0-rmse:0.14208\n",
      "[98]\tvalidation_0-rmse:0.14214\n",
      "[99]\tvalidation_0-rmse:0.14221\n",
      "[100]\tvalidation_0-rmse:0.14216\n",
      "[101]\tvalidation_0-rmse:0.14217\n",
      "[102]\tvalidation_0-rmse:0.14213\n",
      "[103]\tvalidation_0-rmse:0.14203\n",
      "[104]\tvalidation_0-rmse:0.14201\n",
      "[105]\tvalidation_0-rmse:0.14199\n",
      "[106]\tvalidation_0-rmse:0.14187\n",
      "[107]\tvalidation_0-rmse:0.14191\n",
      "[108]\tvalidation_0-rmse:0.14182\n",
      "[109]\tvalidation_0-rmse:0.14178\n",
      "[110]\tvalidation_0-rmse:0.14187\n",
      "[111]\tvalidation_0-rmse:0.14178\n",
      "[112]\tvalidation_0-rmse:0.14176\n",
      "[113]\tvalidation_0-rmse:0.14173\n",
      "[114]\tvalidation_0-rmse:0.14182\n",
      "[115]\tvalidation_0-rmse:0.14190\n",
      "[116]\tvalidation_0-rmse:0.14183\n",
      "[117]\tvalidation_0-rmse:0.14189\n",
      "[118]\tvalidation_0-rmse:0.14183\n",
      "[119]\tvalidation_0-rmse:0.14183\n",
      "[120]\tvalidation_0-rmse:0.14186\n",
      "[121]\tvalidation_0-rmse:0.14189\n",
      "[122]\tvalidation_0-rmse:0.14185\n",
      "[123]\tvalidation_0-rmse:0.14178\n",
      "[124]\tvalidation_0-rmse:0.14186\n",
      "[125]\tvalidation_0-rmse:0.14192\n",
      "[126]\tvalidation_0-rmse:0.14190\n",
      "[127]\tvalidation_0-rmse:0.14185\n",
      "[128]\tvalidation_0-rmse:0.14173\n",
      "[129]\tvalidation_0-rmse:0.14178\n",
      "[130]\tvalidation_0-rmse:0.14171\n",
      "[131]\tvalidation_0-rmse:0.14168\n",
      "[132]\tvalidation_0-rmse:0.14168\n",
      "[133]\tvalidation_0-rmse:0.14167\n",
      "[134]\tvalidation_0-rmse:0.14170\n",
      "[135]\tvalidation_0-rmse:0.14174\n",
      "[136]\tvalidation_0-rmse:0.14174\n",
      "[137]\tvalidation_0-rmse:0.14172\n",
      "[138]\tvalidation_0-rmse:0.14181\n",
      "[139]\tvalidation_0-rmse:0.14186\n",
      "[140]\tvalidation_0-rmse:0.14188\n",
      "[141]\tvalidation_0-rmse:0.14194\n",
      "[142]\tvalidation_0-rmse:0.14194\n",
      "[143]\tvalidation_0-rmse:0.14194\n",
      "[144]\tvalidation_0-rmse:0.14193\n",
      "[145]\tvalidation_0-rmse:0.14195\n",
      "[146]\tvalidation_0-rmse:0.14187\n",
      "[147]\tvalidation_0-rmse:0.14190\n",
      "[148]\tvalidation_0-rmse:0.14188\n",
      "[149]\tvalidation_0-rmse:0.14188\n",
      "[150]\tvalidation_0-rmse:0.14176\n",
      "[151]\tvalidation_0-rmse:0.14176\n",
      "[152]\tvalidation_0-rmse:0.14182\n",
      "[153]\tvalidation_0-rmse:0.14178\n",
      "[154]\tvalidation_0-rmse:0.14177\n",
      "[155]\tvalidation_0-rmse:0.14173\n",
      "[156]\tvalidation_0-rmse:0.14164\n",
      "[157]\tvalidation_0-rmse:0.14170\n",
      "[158]\tvalidation_0-rmse:0.14170\n",
      "[159]\tvalidation_0-rmse:0.14176\n",
      "[160]\tvalidation_0-rmse:0.14174\n",
      "[161]\tvalidation_0-rmse:0.14176\n",
      "[162]\tvalidation_0-rmse:0.14171\n",
      "[163]\tvalidation_0-rmse:0.14169\n",
      "[164]\tvalidation_0-rmse:0.14168\n",
      "[165]\tvalidation_0-rmse:0.14168\n",
      "[166]\tvalidation_0-rmse:0.14167\n",
      "[167]\tvalidation_0-rmse:0.14171\n",
      "[168]\tvalidation_0-rmse:0.14174\n",
      "[169]\tvalidation_0-rmse:0.14179\n",
      "[170]\tvalidation_0-rmse:0.14194\n",
      "[171]\tvalidation_0-rmse:0.14190\n",
      "[172]\tvalidation_0-rmse:0.14188\n",
      "[173]\tvalidation_0-rmse:0.14194\n",
      "Model - XgbostReg || Model Score: 0.974, Adj_R^2: 0.974, Test Score: 0.877, Cross Val Score: 0.855\n",
      "Model - XgbostReg || MSE: 0.020, MAE: 0.084, STD: 0.006, MAPE: 2.349%\n",
      "Model - XgbostReg  || Cross Validation Progress: \n",
      "\n",
      "   fit_time  score_time  test_score\n",
      "0  0.653100    0.003842    0.862017\n",
      "1  0.660344    0.003738    0.850691\n",
      "2  0.655642    0.004408    0.845224\n",
      "3  1.125494    0.003897    0.855925\n",
      "4  1.025391    0.003927    0.861020\n",
      "----------------------------------------------------------------------------------------------------------\n",
      "[1]\tvalid_0's l2: 0.137335\n",
      "[2]\tvalid_0's l2: 0.117999\n",
      "[3]\tvalid_0's l2: 0.102243\n",
      "[4]\tvalid_0's l2: 0.0894206\n",
      "[5]\tvalid_0's l2: 0.0787119\n",
      "[6]\tvalid_0's l2: 0.0700753\n",
      "[7]\tvalid_0's l2: 0.0628325\n",
      "[8]\tvalid_0's l2: 0.0569091\n",
      "[9]\tvalid_0's l2: 0.0519529\n",
      "[10]\tvalid_0's l2: 0.0479629\n",
      "[11]\tvalid_0's l2: 0.0444707\n",
      "[12]\tvalid_0's l2: 0.0413937\n",
      "[13]\tvalid_0's l2: 0.0387602\n",
      "[14]\tvalid_0's l2: 0.0365786\n",
      "[15]\tvalid_0's l2: 0.0347153\n",
      "[16]\tvalid_0's l2: 0.0331481\n",
      "[17]\tvalid_0's l2: 0.0319509\n",
      "[18]\tvalid_0's l2: 0.0308216\n",
      "[19]\tvalid_0's l2: 0.0299034\n",
      "[20]\tvalid_0's l2: 0.0291618\n",
      "[21]\tvalid_0's l2: 0.0284369\n",
      "[22]\tvalid_0's l2: 0.0278474\n",
      "[23]\tvalid_0's l2: 0.0273211\n",
      "[24]\tvalid_0's l2: 0.026814\n",
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      "[27]\tvalid_0's l2: 0.025824\n",
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      "[31]\tvalid_0's l2: 0.0249238\n",
      "[32]\tvalid_0's l2: 0.0247659\n",
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      "[35]\tvalid_0's l2: 0.0244471\n",
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      "[38]\tvalid_0's l2: 0.0242566\n",
      "[39]\tvalid_0's l2: 0.0242391\n",
      "[40]\tvalid_0's l2: 0.0241245\n",
      "[41]\tvalid_0's l2: 0.0240191\n",
      "[42]\tvalid_0's l2: 0.0239542\n",
      "[43]\tvalid_0's l2: 0.0238869\n",
      "[44]\tvalid_0's l2: 0.0237985\n",
      "[45]\tvalid_0's l2: 0.0236958\n",
      "[46]\tvalid_0's l2: 0.0236626\n",
      "[47]\tvalid_0's l2: 0.0236191\n",
      "[48]\tvalid_0's l2: 0.0236335\n",
      "[49]\tvalid_0's l2: 0.023621\n",
      "[50]\tvalid_0's l2: 0.0235978\n",
      "[51]\tvalid_0's l2: 0.0236274\n",
      "[52]\tvalid_0's l2: 0.0236242\n",
      "[53]\tvalid_0's l2: 0.0236257\n",
      "[54]\tvalid_0's l2: 0.0236312\n",
      "[55]\tvalid_0's l2: 0.0236434\n",
      "[56]\tvalid_0's l2: 0.0236468\n",
      "[57]\tvalid_0's l2: 0.0236352\n",
      "[58]\tvalid_0's l2: 0.0236853\n",
      "[59]\tvalid_0's l2: 0.0237001\n",
      "[60]\tvalid_0's l2: 0.023728\n",
      "[61]\tvalid_0's l2: 0.0236948\n",
      "[62]\tvalid_0's l2: 0.0237293\n",
      "[63]\tvalid_0's l2: 0.0237943\n",
      "[64]\tvalid_0's l2: 0.0238277\n",
      "[65]\tvalid_0's l2: 0.023826\n",
      "[66]\tvalid_0's l2: 0.0238388\n",
      "[67]\tvalid_0's l2: 0.0238036\n",
      "[68]\tvalid_0's l2: 0.0237832\n",
      "[69]\tvalid_0's l2: 0.0238032\n",
      "[70]\tvalid_0's l2: 0.0237758\n",
      "[71]\tvalid_0's l2: 0.0237851\n",
      "[72]\tvalid_0's l2: 0.0238203\n",
      "[73]\tvalid_0's l2: 0.0238176\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sriharik/miniconda3/envs/nano281/lib/python3.7/site-packages/lightgbm/sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[74]\tvalid_0's l2: 0.0237095\n",
      "[75]\tvalid_0's l2: 0.0237374\n",
      "[76]\tvalid_0's l2: 0.0237694\n",
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      "[83]\tvalid_0's l2: 0.0237879\n",
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      "[85]\tvalid_0's l2: 0.0237962\n",
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      "[91]\tvalid_0's l2: 0.023911\n",
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      "[94]\tvalid_0's l2: 0.023876\n",
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      "[96]\tvalid_0's l2: 0.0239229\n",
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      "[99]\tvalid_0's l2: 0.0240588\n",
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      "[101]\tvalid_0's l2: 0.0240437\n",
      "[102]\tvalid_0's l2: 0.0240158\n",
      "[103]\tvalid_0's l2: 0.0240523\n",
      "[104]\tvalid_0's l2: 0.0240451\n",
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      "[142]\tvalid_0's l2: 0.0243145\n",
      "[143]\tvalid_0's l2: 0.0243326\n",
      "[144]\tvalid_0's l2: 0.0243708\n",
      "[145]\tvalid_0's l2: 0.0243843\n",
      "[146]\tvalid_0's l2: 0.0243759\n",
      "[147]\tvalid_0's l2: 0.0243968\n",
      "[148]\tvalid_0's l2: 0.0243835\n",
      "[149]\tvalid_0's l2: 0.0244074\n",
      "[150]\tvalid_0's l2: 0.0243758\n",
      "Model - LGBMReg || Model Score: 0.919, Adj_R^2: 0.918, Test Score: 0.854, Cross Val Score: 0.850\n",
      "Model - LGBMReg || MSE: 0.024, MAE: 0.091, STD: 0.007, MAPE: 2.530%\n",
      "Model - LGBMReg  || Cross Validation Progress: \n",
      "\n",
      "   fit_time  score_time  test_score\n",
      "0  0.923071    0.015735    0.846601\n",
      "1  1.294995    0.015201    0.839337\n",
      "2  0.853785    0.015446    0.855341\n",
      "3  0.741448    0.010127    0.859435\n",
      "4  0.590681    0.008352    0.848179\n",
      "----------------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#Lets use train/split ratio of 70/30 - Use ShuffleSplit\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42)\n",
    "#kfold = KFold(n_splits = 5, shuffle = True, random_state = 42 )\n",
    "\n",
    "# Use some linear reg model and some with boosting capabilities\n",
    "\n",
    "hyper_params_lgbm = {\n",
    "    \"objective\" : \"multiclass\",\n",
    "    'task': 'train',\n",
    "    'boosting_type': 'gbdt',\n",
    "    'objective': 'regression',\n",
    "    'n_estimators': 500,\n",
    "    \"verbosity\" : -1 \n",
    "}\n",
    "\n",
    "hyper_params_xgb = { \n",
    "    'objective': 'reg:squarederror',  # error evaluation for multiclass tasks\n",
    "    'n_estimators': 500,\n",
    "    'eval_metric': mean_absolute_error\n",
    "    }\n",
    "#Use ShuffleSplit for cross validation of dataset. Kfold also gives similar result\n",
    "cv_split = ShuffleSplit(n_splits = 5, test_size = 0.30, random_state = 42)\n",
    "\n",
    "# Initializing the Algorithms # I have tried a couple of them. Just showing a handful here\n",
    "algorithm_list = {'LinearReg': linear_model.LinearRegression(), \n",
    "                  'BayeRidge':linear_model.BayesianRidge(),\n",
    "                  'LassoLars': linear_model.LassoLars(alpha=0.01),\n",
    "                  'LeastAngLars':linear_model.Lars(),\n",
    "                  'XgbostReg': xgb.XGBRegressor(**hyper_params_xgb),\n",
    "                  'LGBMReg': lgbm.LGBMRegressor(**hyper_params_lgbm)\n",
    "                 }\n",
    "# Fit the various model\n",
    "for key,model in algorithm_list.items():\n",
    "    try:\n",
    "        if key == 'XgbostReg' or key == 'LGBMReg':\n",
    "            model.fit(X_train, y_train,early_stopping_rounds=100,eval_set=[(X_test, y_test)])\n",
    "        else:\n",
    "            model.fit(X_train, y_train)\n",
    "        # Cross-validate the result to avoid over fitting\n",
    "        cv_results = cross_validate(model, X_train, y_train, cv  = cv_split)\n",
    "#        cv_results = RepeatedKFold(n_splits = 5, n_repeats = 3, random_state = 42)\n",
    "        adj_R2 = 1 - (1-model.score(X_train, y_train))*(len(y_train)-1)/(len(y_train)-X_train.shape[1]-1)\n",
    "        y_pred = model.predict(X_test) \n",
    "        # Print model and test metrics\n",
    "        print(f'Model - {key: <5} || Model Score: %.3f, Adj_R^2: %.3f, Test Score: %.3f, Cross Val Score: %.3f' \n",
    "              %(model.score(X_train, y_train),adj_R2, model.score(X_test, y_test), cv_results.get('test_score').mean()))\n",
    "        \n",
    "        print(f'Model - {key: <5} || MSE: %.3f, MAE: %.3f, STD: %.3f, MAPE: %.3f%%' \n",
    "              %(metrics.mean_squared_error(y_test, y_pred), metrics.mean_absolute_error(y_test, y_pred), \n",
    "                cv_results.get('test_score').std(), metrics.mean_absolute_percentage_error(y_test, y_pred)*100))\n",
    "        print(f'Model - {key: <5}  || Cross Validation Progress: \\n')\n",
    "        print(pd.DataFrame(cv_results))\n",
    "        print('----------------------------------------------------------------------------------------------------------')\n",
    "    except Exception as e:\n",
    "        print(f\"Exception occurred in {str(e)}\")\n",
    "        pass "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce8cd624",
   "metadata": {},
   "source": [
    "### Comment on the ML models performance \n",
    "***The boosting models performed the best. We will adopt the Xgboost model***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e47e88da",
   "metadata": {},
   "source": [
    "<a id='hyperparameter'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5beb779",
   "metadata": {},
   "source": [
    "## Part IV: Hyperparameter Optimization of best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "aa526a8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "if (hyperparam_opt==True):\n",
    "    # Doing hyperparameter optimization\n",
    "\n",
    "    from sklearn.model_selection import StratifiedShuffleSplit\n",
    "    from sklearn import model_selection\n",
    "\n",
    "    n_estimators = [int(x) for x in np.linspace(start =50, stop = 500, num = 5)]\n",
    "    max_depth = [int(x) for x in np.linspace(start =5, stop = 20, num = 5)]\n",
    "    #bootstrap = [True, False]\n",
    "\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42)\n",
    "\n",
    "    kfold = ShuffleSplit(n_splits = 5, test_size = 0.3, random_state = 42) \n",
    "    #KFold(n_splits = 10, shuffle = True, random_state = rng )\n",
    "\n",
    "    param_grid = {\n",
    "        'n_estimators': n_estimators,\n",
    "        'max_depth': max_depth,\n",
    "        'objective':['reg:squarederror'],\n",
    "        'learning_rate': [0.02, 0.04, 0.06, 0.08, 0.10],\n",
    "        'colsample_bytree': [0.5, 0.6, 0.7, 0.8]\n",
    "    }\n",
    "\n",
    "    bestmodel = XGBRegressor()\n",
    "\n",
    "    xgb_bestmodel = model_selection.GridSearchCV( \n",
    "        estimator=bestmodel,\n",
    "        param_grid=param_grid,\n",
    "        scoring=\"r2\",\n",
    "        n_jobs = 4,\n",
    "        cv=kfold,\n",
    "        verbose = True\n",
    "\n",
    "    )\n",
    "\n",
    "    xgb_bestmodel.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "    #bestparams = model.best_estimator_.get_params()\n",
    "    bestparams = xgb_bestmodel.best_params_\n",
    "    bestscore = xgb_bestmodel.best_score_\n",
    "\n",
    "    print(\"Best paramters for the chosen model: %s \" % bestparams)\n",
    "    print(\"Best r2 score: %.6f \" % bestscore)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "38ed9543",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nBest hyperparameter\\nBest paramters for the chosen model: {'colsample_bytree': 0.8, 'learning_rate': 0.02, 'max_depth': 8, 'n_estimators': 500, 'objective': 'reg:squarederror'} \\nBest r2 score: 0.884865 \\n\""
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "Best hyperparameter\n",
    "Best paramters for the chosen model: {'colsample_bytree': 0.8, 'learning_rate': 0.02, 'max_depth': 8, 'n_estimators': 500, 'objective': 'reg:squarederror'} \n",
    "Best r2 score: 0.884865 \n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "befbedc8",
   "metadata": {},
   "source": [
    "<a id='bestmodelp'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d39815ce",
   "metadata": {},
   "source": [
    "## Part V: Employing XGBoost Regression - Best model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "58cfbaf1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-rmse:2.62734\n",
      "[1]\tvalidation_0-rmse:2.36797\n",
      "[2]\tvalidation_0-rmse:2.13541\n",
      "[3]\tvalidation_0-rmse:1.92513\n",
      "[4]\tvalidation_0-rmse:1.73571\n",
      "[5]\tvalidation_0-rmse:1.56516\n",
      "[6]\tvalidation_0-rmse:1.41292\n",
      "[7]\tvalidation_0-rmse:1.27494\n",
      "[8]\tvalidation_0-rmse:1.15144\n",
      "[9]\tvalidation_0-rmse:1.03996\n",
      "[10]\tvalidation_0-rmse:0.94050\n",
      "[11]\tvalidation_0-rmse:0.85036\n",
      "[12]\tvalidation_0-rmse:0.77037\n",
      "[13]\tvalidation_0-rmse:0.69780\n",
      "[14]\tvalidation_0-rmse:0.63304\n",
      "[15]\tvalidation_0-rmse:0.57548\n",
      "[16]\tvalidation_0-rmse:0.52372\n",
      "[17]\tvalidation_0-rmse:0.47716\n",
      "[18]\tvalidation_0-rmse:0.43579\n",
      "[19]\tvalidation_0-rmse:0.39890\n",
      "[20]\tvalidation_0-rmse:0.36618\n",
      "[21]\tvalidation_0-rmse:0.33726\n",
      "[22]\tvalidation_0-rmse:0.31189\n",
      "[23]\tvalidation_0-rmse:0.28961\n",
      "[24]\tvalidation_0-rmse:0.26966\n",
      "[25]\tvalidation_0-rmse:0.25202\n",
      "[26]\tvalidation_0-rmse:0.23704\n",
      "[27]\tvalidation_0-rmse:0.22317\n",
      "[28]\tvalidation_0-rmse:0.21109\n",
      "[29]\tvalidation_0-rmse:0.20092\n",
      "[30]\tvalidation_0-rmse:0.19192\n",
      "[31]\tvalidation_0-rmse:0.18516\n",
      "[32]\tvalidation_0-rmse:0.17902\n",
      "[33]\tvalidation_0-rmse:0.17343\n",
      "[34]\tvalidation_0-rmse:0.16884\n",
      "[35]\tvalidation_0-rmse:0.16506\n",
      "[36]\tvalidation_0-rmse:0.16132\n",
      "[37]\tvalidation_0-rmse:0.15831\n",
      "[38]\tvalidation_0-rmse:0.15577\n",
      "[39]\tvalidation_0-rmse:0.15380\n",
      "[40]\tvalidation_0-rmse:0.15197\n",
      "[41]\tvalidation_0-rmse:0.15054\n",
      "[42]\tvalidation_0-rmse:0.14920\n",
      "[43]\tvalidation_0-rmse:0.14790\n",
      "[44]\tvalidation_0-rmse:0.14674\n",
      "[45]\tvalidation_0-rmse:0.14579\n",
      "[46]\tvalidation_0-rmse:0.14508\n",
      "[47]\tvalidation_0-rmse:0.14461\n",
      "[48]\tvalidation_0-rmse:0.14405\n",
      "[49]\tvalidation_0-rmse:0.14346\n",
      "[50]\tvalidation_0-rmse:0.14300\n",
      "[51]\tvalidation_0-rmse:0.14283\n",
      "[52]\tvalidation_0-rmse:0.14230\n",
      "[53]\tvalidation_0-rmse:0.14199\n",
      "[54]\tvalidation_0-rmse:0.14170\n",
      "[55]\tvalidation_0-rmse:0.14140\n",
      "[56]\tvalidation_0-rmse:0.14114\n",
      "[57]\tvalidation_0-rmse:0.14103\n",
      "[58]\tvalidation_0-rmse:0.14091\n",
      "[59]\tvalidation_0-rmse:0.14082\n",
      "[60]\tvalidation_0-rmse:0.14084\n",
      "[61]\tvalidation_0-rmse:0.14080\n",
      "[62]\tvalidation_0-rmse:0.14081\n",
      "[63]\tvalidation_0-rmse:0.14065\n",
      "[64]\tvalidation_0-rmse:0.14051\n",
      "[65]\tvalidation_0-rmse:0.14035\n",
      "[66]\tvalidation_0-rmse:0.14042\n",
      "[67]\tvalidation_0-rmse:0.14043\n",
      "[68]\tvalidation_0-rmse:0.14033\n",
      "[69]\tvalidation_0-rmse:0.14024\n",
      "[70]\tvalidation_0-rmse:0.14024\n",
      "[71]\tvalidation_0-rmse:0.14032\n",
      "[72]\tvalidation_0-rmse:0.14028\n",
      "[73]\tvalidation_0-rmse:0.14016\n",
      "[74]\tvalidation_0-rmse:0.14017\n",
      "[75]\tvalidation_0-rmse:0.14015\n",
      "[76]\tvalidation_0-rmse:0.14006\n",
      "[77]\tvalidation_0-rmse:0.14001\n",
      "[78]\tvalidation_0-rmse:0.14002\n",
      "[79]\tvalidation_0-rmse:0.13988\n",
      "[80]\tvalidation_0-rmse:0.13987\n",
      "[81]\tvalidation_0-rmse:0.13991\n",
      "[82]\tvalidation_0-rmse:0.13992\n",
      "[83]\tvalidation_0-rmse:0.13994\n",
      "[84]\tvalidation_0-rmse:0.13990\n",
      "[85]\tvalidation_0-rmse:0.13976\n",
      "[86]\tvalidation_0-rmse:0.13971\n",
      "[87]\tvalidation_0-rmse:0.13968\n",
      "[88]\tvalidation_0-rmse:0.13973\n",
      "[89]\tvalidation_0-rmse:0.13976\n",
      "[90]\tvalidation_0-rmse:0.13972\n",
      "[91]\tvalidation_0-rmse:0.13958\n",
      "[92]\tvalidation_0-rmse:0.13946\n",
      "[93]\tvalidation_0-rmse:0.13941\n",
      "[94]\tvalidation_0-rmse:0.13934\n",
      "[95]\tvalidation_0-rmse:0.13935\n",
      "[96]\tvalidation_0-rmse:0.13926\n",
      "[97]\tvalidation_0-rmse:0.13927\n",
      "[98]\tvalidation_0-rmse:0.13931\n",
      "[99]\tvalidation_0-rmse:0.13935\n",
      "[100]\tvalidation_0-rmse:0.13948\n",
      "[101]\tvalidation_0-rmse:0.13945\n",
      "[102]\tvalidation_0-rmse:0.13963\n",
      "[103]\tvalidation_0-rmse:0.13961\n",
      "[104]\tvalidation_0-rmse:0.13956\n",
      "[105]\tvalidation_0-rmse:0.13950\n",
      "[106]\tvalidation_0-rmse:0.13948\n",
      "[107]\tvalidation_0-rmse:0.13938\n",
      "[108]\tvalidation_0-rmse:0.13920\n",
      "[109]\tvalidation_0-rmse:0.13928\n",
      "[110]\tvalidation_0-rmse:0.13924\n",
      "[111]\tvalidation_0-rmse:0.13929\n",
      "[112]\tvalidation_0-rmse:0.13919\n",
      "[113]\tvalidation_0-rmse:0.13924\n",
      "[114]\tvalidation_0-rmse:0.13916\n",
      "[115]\tvalidation_0-rmse:0.13914\n",
      "[116]\tvalidation_0-rmse:0.13910\n",
      "[117]\tvalidation_0-rmse:0.13908\n",
      "[118]\tvalidation_0-rmse:0.13901\n",
      "[119]\tvalidation_0-rmse:0.13903\n",
      "[120]\tvalidation_0-rmse:0.13905\n",
      "[121]\tvalidation_0-rmse:0.13915\n",
      "[122]\tvalidation_0-rmse:0.13905\n",
      "[123]\tvalidation_0-rmse:0.13906\n",
      "[124]\tvalidation_0-rmse:0.13896\n",
      "[125]\tvalidation_0-rmse:0.13885\n",
      "[126]\tvalidation_0-rmse:0.13873\n",
      "[127]\tvalidation_0-rmse:0.13880\n",
      "[128]\tvalidation_0-rmse:0.13880\n",
      "[129]\tvalidation_0-rmse:0.13875\n",
      "[130]\tvalidation_0-rmse:0.13876\n",
      "[131]\tvalidation_0-rmse:0.13876\n",
      "[132]\tvalidation_0-rmse:0.13870\n",
      "[133]\tvalidation_0-rmse:0.13861\n",
      "[134]\tvalidation_0-rmse:0.13860\n",
      "[135]\tvalidation_0-rmse:0.13858\n",
      "[136]\tvalidation_0-rmse:0.13857\n",
      "[137]\tvalidation_0-rmse:0.13855\n",
      "[138]\tvalidation_0-rmse:0.13851\n",
      "[139]\tvalidation_0-rmse:0.13852\n",
      "[140]\tvalidation_0-rmse:0.13847\n",
      "[141]\tvalidation_0-rmse:0.13839\n",
      "[142]\tvalidation_0-rmse:0.13838\n",
      "[143]\tvalidation_0-rmse:0.13837\n",
      "[144]\tvalidation_0-rmse:0.13837\n",
      "[145]\tvalidation_0-rmse:0.13829\n",
      "[146]\tvalidation_0-rmse:0.13826\n",
      "[147]\tvalidation_0-rmse:0.13828\n",
      "[148]\tvalidation_0-rmse:0.13822\n",
      "[149]\tvalidation_0-rmse:0.13821\n",
      "[150]\tvalidation_0-rmse:0.13820\n",
      "[151]\tvalidation_0-rmse:0.13827\n",
      "[152]\tvalidation_0-rmse:0.13822\n",
      "[153]\tvalidation_0-rmse:0.13820\n",
      "[154]\tvalidation_0-rmse:0.13827\n",
      "[155]\tvalidation_0-rmse:0.13826\n",
      "[156]\tvalidation_0-rmse:0.13823\n",
      "[157]\tvalidation_0-rmse:0.13826\n",
      "[158]\tvalidation_0-rmse:0.13832\n",
      "[159]\tvalidation_0-rmse:0.13830\n",
      "[160]\tvalidation_0-rmse:0.13834\n",
      "[161]\tvalidation_0-rmse:0.13839\n",
      "[162]\tvalidation_0-rmse:0.13837\n",
      "[163]\tvalidation_0-rmse:0.13838\n",
      "[164]\tvalidation_0-rmse:0.13837\n",
      "[165]\tvalidation_0-rmse:0.13841\n",
      "[166]\tvalidation_0-rmse:0.13840\n",
      "[167]\tvalidation_0-rmse:0.13843\n",
      "[168]\tvalidation_0-rmse:0.13841\n",
      "[169]\tvalidation_0-rmse:0.13841\n",
      "[170]\tvalidation_0-rmse:0.13832\n",
      "[171]\tvalidation_0-rmse:0.13833\n",
      "[172]\tvalidation_0-rmse:0.13833\n",
      "[173]\tvalidation_0-rmse:0.13831\n",
      "[174]\tvalidation_0-rmse:0.13822\n",
      "[175]\tvalidation_0-rmse:0.13825\n",
      "[176]\tvalidation_0-rmse:0.13826\n",
      "[177]\tvalidation_0-rmse:0.13825\n",
      "[178]\tvalidation_0-rmse:0.13835\n",
      "[179]\tvalidation_0-rmse:0.13829\n",
      "[180]\tvalidation_0-rmse:0.13829\n",
      "[181]\tvalidation_0-rmse:0.13827\n",
      "[182]\tvalidation_0-rmse:0.13823\n",
      "[183]\tvalidation_0-rmse:0.13826\n",
      "[184]\tvalidation_0-rmse:0.13828\n",
      "[185]\tvalidation_0-rmse:0.13828\n",
      "[186]\tvalidation_0-rmse:0.13819\n",
      "[187]\tvalidation_0-rmse:0.13825\n",
      "[188]\tvalidation_0-rmse:0.13831\n",
      "[189]\tvalidation_0-rmse:0.13831\n",
      "[190]\tvalidation_0-rmse:0.13834\n",
      "[191]\tvalidation_0-rmse:0.13832\n",
      "[192]\tvalidation_0-rmse:0.13830\n",
      "[193]\tvalidation_0-rmse:0.13840\n",
      "[194]\tvalidation_0-rmse:0.13833\n",
      "[195]\tvalidation_0-rmse:0.13832\n",
      "[196]\tvalidation_0-rmse:0.13833\n",
      "[197]\tvalidation_0-rmse:0.13838\n",
      "[198]\tvalidation_0-rmse:0.13836\n",
      "[199]\tvalidation_0-rmse:0.13837\n",
      "[200]\tvalidation_0-rmse:0.13837\n",
      "[201]\tvalidation_0-rmse:0.13836\n",
      "[202]\tvalidation_0-rmse:0.13842\n",
      "[203]\tvalidation_0-rmse:0.13843\n",
      "[204]\tvalidation_0-rmse:0.13842\n",
      "[205]\tvalidation_0-rmse:0.13838\n",
      "[206]\tvalidation_0-rmse:0.13837\n",
      "[207]\tvalidation_0-rmse:0.13834\n",
      "[208]\tvalidation_0-rmse:0.13838\n",
      "[209]\tvalidation_0-rmse:0.13831\n",
      "[210]\tvalidation_0-rmse:0.13825\n",
      "[211]\tvalidation_0-rmse:0.13830\n",
      "[212]\tvalidation_0-rmse:0.13831\n",
      "[213]\tvalidation_0-rmse:0.13835\n",
      "[214]\tvalidation_0-rmse:0.13835\n",
      "[215]\tvalidation_0-rmse:0.13835\n",
      "[216]\tvalidation_0-rmse:0.13836\n",
      "[217]\tvalidation_0-rmse:0.13835\n",
      "[218]\tvalidation_0-rmse:0.13832\n",
      "[219]\tvalidation_0-rmse:0.13832\n",
      "[220]\tvalidation_0-rmse:0.13831\n",
      "[221]\tvalidation_0-rmse:0.13837\n",
      "[222]\tvalidation_0-rmse:0.13831\n",
      "[223]\tvalidation_0-rmse:0.13832\n",
      "[224]\tvalidation_0-rmse:0.13832\n",
      "[225]\tvalidation_0-rmse:0.13833\n",
      "[226]\tvalidation_0-rmse:0.13837\n",
      "[227]\tvalidation_0-rmse:0.13830\n",
      "[228]\tvalidation_0-rmse:0.13830\n",
      "[229]\tvalidation_0-rmse:0.13829\n",
      "[230]\tvalidation_0-rmse:0.13825\n",
      "[231]\tvalidation_0-rmse:0.13827\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[260]\tvalidation_0-rmse:0.13812\n",
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      "[396]\tvalidation_0-rmse:0.13828\n",
      "[397]\tvalidation_0-rmse:0.13827\n",
      "[398]\tvalidation_0-rmse:0.13827\n",
      "[399]\tvalidation_0-rmse:0.13827\n",
      "[400]\tvalidation_0-rmse:0.13826\n",
      "[401]\tvalidation_0-rmse:0.13827\n",
      "[402]\tvalidation_0-rmse:0.13826\n",
      "[403]\tvalidation_0-rmse:0.13826\n",
      "[404]\tvalidation_0-rmse:0.13825\n",
      "[405]\tvalidation_0-rmse:0.13825\n",
      "[406]\tvalidation_0-rmse:0.13828\n",
      "[407]\tvalidation_0-rmse:0.13828\n",
      "Model Score: 0.995, Adj_R^2: 0.918, Test Score: 0.882, Cross Val Score: 0.863\n",
      "MSE: 0.019, MAE: 0.079, STD: 0.005, MAPE: 2.206%\n",
      "Cross Validation Progress: \n",
      "\n",
      "   fit_time  score_time  test_score\n",
      "0  1.048720    0.003972    0.868843\n",
      "1  1.271520    0.006954    0.854016\n",
      "2  1.079215    0.007692    0.862102\n",
      "3  0.963398    0.004011    0.865790\n",
      "4  1.131310    0.005570    0.865946\n"
     ]
    }
   ],
   "source": [
    "# Use boosting model, which was the best of all the models tested\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42)\n",
    "\n",
    "param_best = {'colsample_bytree': 0.8, \n",
    "              'learning_rate': 0.1, \n",
    "              'max_depth': 8, \n",
    "              'n_estimators': 500, \n",
    "              'objective': 'reg:squarederror'}\n",
    "\n",
    "xgbr = XGBRegressor(**param_best)\n",
    "xgbr.fit(X_train, y_train,early_stopping_rounds=100,eval_set=[(X_test, y_test)])\n",
    "cv_results = cross_validate(xgbr, X_train, y_train, cv  = cv_split)\n",
    "\n",
    "adj_R2_train = 1 - (1-model.score(X_train, y_train))*(len(y_train)-1)/(len(y_train)-X_train.shape[1]-1)\n",
    "adj_R2_test = 1 - (1-model.score(X_test, y_test))*(len(y_test)-1)/(len(y_test)-X_test.shape[1]-1)\n",
    "y_pred = xgbr.predict(X_test) \n",
    "R2_value = xgbr.score(X_test, y_test)\n",
    "MSE = metrics.mean_squared_error(y_test, y_pred)\n",
    "MAE = metrics.mean_absolute_error(y_test, y_pred)\n",
    "MAPE = metrics.mean_absolute_percentage_error(y_test, y_pred)\n",
    "        \n",
    "print(f'Model Score: %.3f, Adj_R^2: %.3f, Test Score: %.3f, Cross Val Score: %.3f' \n",
    "      %(xgbr.score(X_train, y_train),adj_R2_train, xgbr.score(X_test, y_test), cv_results.get('test_score').mean()))\n",
    "        \n",
    "print(f'MSE: %.3f, MAE: %.3f, STD: %.3f, MAPE: %.3f%%' \n",
    "      %(MSE, MAE,cv_results.get('test_score').std(), MAPE*100))\n",
    "        \n",
    "print(f'Cross Validation Progress: \\n')\n",
    "print(pd.DataFrame(cv_results))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7106a269",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(8, 8))\n",
    "#label_xgbr = f'$R^2$ = %.3f, Adj_$R^2$ = %.3f, \\n MSE =  %.3f, MAE = %.3f' %(R2_value,adj_R2_test, MSE, MAE)    \n",
    "plt.plot(y_test, y_pred, 'o', color = \"green\")\n",
    "plt.ylabel(r\"$a_{predicted}$ ($\\AA$)\")\n",
    "plt.xlabel(r\"$a$ ($\\AA$)\")\n",
    "#plt.legend()\n",
    "plt.xlim([2, 6])\n",
    "plt.ylim([2, 6])\n",
    "plt.plot([2, 6], [2, 6], 'k--')\n",
    "plt.annotate(r\"$R^2$ = %.2f, MSE = %.3f\" % (R2_value, MSE), (2.1, 5.8), fontsize=15);\n",
    "#plt.show()\n",
    "\n",
    "if savefigs==True:\n",
    "    f.savefig(\"Figures/BestModel_H\", dpi = 1200, transparent = False, facecolor = 'white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7cd10f42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = X_train.columns\n",
    "y = xgbr.feature_importances_\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,8))    \n",
    "width = 0.75 # the width of the bars \n",
    "ind = np.arange(len(y))  # the x locations for the groups\n",
    "ax.barh(ind, y, width, color=\"blue\")\n",
    "ax.set_yticks(ind+width/2)\n",
    "ax.set_yticklabels(x, minor=False)\n",
    "plt.title('Distribution of Feature Importance')\n",
    "plt.xlabel('Importance')\n",
    "plt.ylabel('Features')\n",
    "\n",
    "# Print the values on the barchart\n",
    "ax.bar_label(ax.containers[0],label_type='edge',fmt='%.2f')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "if savefigs==True:\n",
    "    fig.savefig(\"Figures/FeatImp_H\", dpi = 1200, transparent = False, facecolor = 'white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "241df459",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thresh=0.052, n=5, Accuracy: 87.45%\n",
      "Thresh=0.063, n=4, Accuracy: 86.98%\n",
      "Thresh=0.079, n=3, Accuracy: 84.99%\n",
      "Thresh=0.221, n=2, Accuracy: 83.16%\n",
      "Thresh=0.585, n=1, Accuracy: 79.49%\n"
     ]
    }
   ],
   "source": [
    "# Check the evolution of features\n",
    "thresholds = sort(y)\n",
    "for thresh in thresholds:\n",
    "    # select features using threshold\n",
    "    selection = SelectFromModel(xgbr, threshold=thresh, prefit=True)\n",
    "    select_X_train = selection.transform(X_train)\n",
    "    # train model\n",
    "    selection_model = XGBRegressor()\n",
    "    selection_model.fit(select_X_train, y_train)\n",
    "    # evaluate model\n",
    "    select_X_test = selection.transform(X_test)\n",
    "    y_pred = selection_model.predict(select_X_test)\n",
    "    predictions = [round(value) for value in y_pred]\n",
    "    accuracy = metrics.r2_score(y_test, y_pred)   \n",
    "    print(\"Thresh=%.3f, n=%d, Accuracy: %.2f%%\" % (thresh, select_X_train.shape[1], accuracy*100.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f341ba9",
   "metadata": {},
   "source": [
    "<a id='makeprediction1'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0463ff06",
   "metadata": {},
   "source": [
    "## Part VI: Predictions with designed ML model - Out of sample "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0920cb6e",
   "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>c</th>\n",
       "      <th>SG</th>\n",
       "      <th>C11</th>\n",
       "      <th>C12</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Material</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>Mn-Ti-Te-Se</th>\n",
       "      <td>15.975</td>\n",
       "      <td>2</td>\n",
       "      <td>69.13</td>\n",
       "      <td>4.33</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Si-Mn-S-O</th>\n",
       "      <td>22.141</td>\n",
       "      <td>2</td>\n",
       "      <td>244.71</td>\n",
       "      <td>84.34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hf-Mo-O-Te</th>\n",
       "      <td>19.284</td>\n",
       "      <td>2</td>\n",
       "      <td>267.12</td>\n",
       "      <td>52.37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hf-Ge-S-Se</th>\n",
       "      <td>14.265</td>\n",
       "      <td>2</td>\n",
       "      <td>68.31</td>\n",
       "      <td>12.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pb-Mn-O-Se</th>\n",
       "      <td>18.737</td>\n",
       "      <td>2</td>\n",
       "      <td>126.53</td>\n",
       "      <td>50.09</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  c  SG     C11    C12  Temp\n",
       "Material                                    \n",
       "Mn-Ti-Te-Se  15.975   2   69.13   4.33     2\n",
       "Si-Mn-S-O    22.141   2  244.71  84.34     1\n",
       "Hf-Mo-O-Te   19.284   2  267.12  52.37     0\n",
       "Hf-Ge-S-Se   14.265   2   68.31  12.40     1\n",
       "Pb-Mn-O-Se   18.737   2  126.53  50.09     2"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_final = [c for c in data_test.columns if c!= \"a\"]\n",
    "X_predict_data_1 = data_test[x_final]\n",
    "y_predict_data_1 = data_test[\"a\"]\n",
    "X_predict_data_1.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3e163451",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#xgbr.fit(x,y)\n",
    "prediction_1 = xgbr.predict(X_predict_data_1)\n",
    "\n",
    "r2_score = xgbr.score(X_predict_data_1, y_predict_data_1)\n",
    "mse = mean_squared_error(y_predict_data_1, prediction_1)\n",
    "\n",
    "f, ax = plt.subplots(figsize=(8, 8))\n",
    "#sns.scatterplot(x=y_predict_data_1, y=prediction_1)\n",
    "plt.plot(y_predict_data_1, prediction_1, 'o', color = \"magenta\")\n",
    "#r2_score =  r2_score(y_predict_data_1, prediction_1)\n",
    "plt.ylabel(r\"$a_{predicted}$ ($\\AA$)\")\n",
    "plt.xlabel(r\"$a$ ($\\AA$)\")\n",
    "plt.xlim([2, 6])\n",
    "plt.ylim([2, 6])\n",
    "plt.plot([2, 6], [2, 6], 'k--')\n",
    "plt.annotate(r\"$R^2$ = %.2f, MSE = %.3f\" % (r2_score, mse), (2.1, 5.8), fontsize=15);\n",
    "\n",
    "if savefigs==True:\n",
    "    f.savefig(\"Figures/Prediction1_H\", dpi = 1200, transparent = False, facecolor = 'white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "001393fc",
   "metadata": {
    "scrolled": false
   },
   "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>c</th>\n",
       "      <th>C11</th>\n",
       "      <th>C12</th>\n",
       "      <th>a</th>\n",
       "      <th>a_predicted</th>\n",
       "      <th>% error of a</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Material</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>Mn-Ti-Te-Se</th>\n",
       "      <td>15.975</td>\n",
       "      <td>69.13</td>\n",
       "      <td>4.33</td>\n",
       "      <td>3.4805</td>\n",
       "      <td>3.538563</td>\n",
       "      <td>1.668231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Si-Mn-S-O</th>\n",
       "      <td>22.141</td>\n",
       "      <td>244.71</td>\n",
       "      <td>84.34</td>\n",
       "      <td>3.0865</td>\n",
       "      <td>3.000497</td>\n",
       "      <td>2.786429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hf-Mo-O-Te</th>\n",
       "      <td>19.284</td>\n",
       "      <td>267.12</td>\n",
       "      <td>52.37</td>\n",
       "      <td>3.3070</td>\n",
       "      <td>3.250557</td>\n",
       "      <td>1.706761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hf-Ge-S-Se</th>\n",
       "      <td>14.265</td>\n",
       "      <td>68.31</td>\n",
       "      <td>12.40</td>\n",
       "      <td>3.6835</td>\n",
       "      <td>3.640032</td>\n",
       "      <td>1.180078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pb-Mn-O-Se</th>\n",
       "      <td>18.737</td>\n",
       "      <td>126.53</td>\n",
       "      <td>50.09</td>\n",
       "      <td>3.3550</td>\n",
       "      <td>3.291253</td>\n",
       "      <td>1.900057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pb-Ge-S-Te</th>\n",
       "      <td>11.861</td>\n",
       "      <td>36.87</td>\n",
       "      <td>-16.38</td>\n",
       "      <td>4.0395</td>\n",
       "      <td>4.171201</td>\n",
       "      <td>3.260335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ba-Te-Zn-S</th>\n",
       "      <td>10.249</td>\n",
       "      <td>10.28</td>\n",
       "      <td>6.94</td>\n",
       "      <td>5.2415</td>\n",
       "      <td>5.256816</td>\n",
       "      <td>0.292205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ge-Pb-Te-Se</th>\n",
       "      <td>10.545</td>\n",
       "      <td>37.61</td>\n",
       "      <td>16.58</td>\n",
       "      <td>4.4720</td>\n",
       "      <td>4.499371</td>\n",
       "      <td>0.612054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ti-Se-W-Te</th>\n",
       "      <td>17.714</td>\n",
       "      <td>154.49</td>\n",
       "      <td>29.91</td>\n",
       "      <td>3.4990</td>\n",
       "      <td>3.475130</td>\n",
       "      <td>0.682199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cr-Hf-S-Te</th>\n",
       "      <td>18.693</td>\n",
       "      <td>161.13</td>\n",
       "      <td>74.88</td>\n",
       "      <td>3.2180</td>\n",
       "      <td>3.255345</td>\n",
       "      <td>1.160514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1288 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  c     C11    C12       a  a_predicted  % error of a\n",
       "Material                                                             \n",
       "Mn-Ti-Te-Se  15.975   69.13   4.33  3.4805     3.538563      1.668231\n",
       "Si-Mn-S-O    22.141  244.71  84.34  3.0865     3.000497      2.786429\n",
       "Hf-Mo-O-Te   19.284  267.12  52.37  3.3070     3.250557      1.706761\n",
       "Hf-Ge-S-Se   14.265   68.31  12.40  3.6835     3.640032      1.180078\n",
       "Pb-Mn-O-Se   18.737  126.53  50.09  3.3550     3.291253      1.900057\n",
       "...             ...     ...    ...     ...          ...           ...\n",
       "Pb-Ge-S-Te   11.861   36.87 -16.38  4.0395     4.171201      3.260335\n",
       "Ba-Te-Zn-S   10.249   10.28   6.94  5.2415     5.256816      0.292205\n",
       "Ge-Pb-Te-Se  10.545   37.61  16.58  4.4720     4.499371      0.612054\n",
       "Ti-Se-W-Te   17.714  154.49  29.91  3.4990     3.475130      0.682199\n",
       "Cr-Hf-S-Te   18.693  161.13  74.88  3.2180     3.255345      1.160514\n",
       "\n",
       "[1288 rows x 6 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = X_predict_data_1.copy()\n",
    "result['a'] = y_predict_data_1\n",
    "result['a_predicted'] = prediction_1\n",
    "result.drop(['SG', 'Temp'], axis = 1, inplace = True)\n",
    "#result.drop(['SG-156', 'SG-8', '0K', '1T', 'Phase', 'Temp'], axis = 1, inplace = True)\n",
    "result['% error of a'] = (abs(prediction_1 - y_predict_data_1 ))*100/y_predict_data_1\n",
    "display(result)"
   ]
  },
  {
   "cell_type": "markdown",
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