target.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.impute import SimpleImputer\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "import matplotlib.pyplot as plt\n",
    "from math import sqrt\n",
    "import numpy as np\n",
    "from thermo import simple_formula_parser\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        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>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>number_of_elements</th>\n",
       "      <th>mean_atomic_mass</th>\n",
       "      <th>wtd_mean_atomic_mass</th>\n",
       "      <th>gmean_atomic_mass</th>\n",
       "      <th>wtd_gmean_atomic_mass</th>\n",
       "      <th>entropy_atomic_mass</th>\n",
       "      <th>wtd_entropy_atomic_mass</th>\n",
       "      <th>range_atomic_mass</th>\n",
       "      <th>...</th>\n",
       "      <th>Au</th>\n",
       "      <th>Hg</th>\n",
       "      <th>Tl</th>\n",
       "      <th>Pb</th>\n",
       "      <th>Bi</th>\n",
       "      <th>Po</th>\n",
       "      <th>At</th>\n",
       "      <th>Rn</th>\n",
       "      <th>critical_temp</th>\n",
       "      <th>material</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>92.729214</td>\n",
       "      <td>61.051113</td>\n",
       "      <td>73.132787</td>\n",
       "      <td>37.513930</td>\n",
       "      <td>1.449309</td>\n",
       "      <td>1.146919</td>\n",
       "      <td>122.90607</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Y1Ba1.5Ca0.5Cu3O7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>64.632218</td>\n",
       "      <td>55.790060</td>\n",
       "      <td>48.775792</td>\n",
       "      <td>35.178830</td>\n",
       "      <td>1.139875</td>\n",
       "      <td>0.931491</td>\n",
       "      <td>122.90607</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>76.9</td>\n",
       "      <td>Yb16Ba1Cu2O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "      <td>97.481600</td>\n",
       "      <td>152.464120</td>\n",
       "      <td>70.110388</td>\n",
       "      <td>137.387467</td>\n",
       "      <td>1.154762</td>\n",
       "      <td>0.387216</td>\n",
       "      <td>157.05460</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65.3</td>\n",
       "      <td>Yb1.8Ba0.2Cu1O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>97.481600</td>\n",
       "      <td>104.627000</td>\n",
       "      <td>70.110388</td>\n",
       "      <td>73.428983</td>\n",
       "      <td>1.154762</td>\n",
       "      <td>0.809544</td>\n",
       "      <td>157.05460</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78.6</td>\n",
       "      <td>Yb6Ba1Cu1O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>97.481600</td>\n",
       "      <td>139.466267</td>\n",
       "      <td>70.110388</td>\n",
       "      <td>115.819575</td>\n",
       "      <td>1.154762</td>\n",
       "      <td>0.605632</td>\n",
       "      <td>157.05460</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>79.3</td>\n",
       "      <td>Yb1.62La0.18Ba0.18Sr0.02Cu1O</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 171 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1  number_of_elements  mean_atomic_mass  \\\n",
       "0           0            16                   5         92.729214   \n",
       "1           1            28                   4         64.632218   \n",
       "2           2            29                   4         97.481600   \n",
       "3           3            30                   4         97.481600   \n",
       "4           4            31                   4         97.481600   \n",
       "\n",
       "   wtd_mean_atomic_mass  gmean_atomic_mass  wtd_gmean_atomic_mass  \\\n",
       "0             61.051113          73.132787              37.513930   \n",
       "1             55.790060          48.775792              35.178830   \n",
       "2            152.464120          70.110388             137.387467   \n",
       "3            104.627000          70.110388              73.428983   \n",
       "4            139.466267          70.110388             115.819575   \n",
       "\n",
       "   entropy_atomic_mass  wtd_entropy_atomic_mass  range_atomic_mass  ...   Au  \\\n",
       "0             1.449309                 1.146919          122.90607  ...  0.0   \n",
       "1             1.139875                 0.931491          122.90607  ...  0.0   \n",
       "2             1.154762                 0.387216          157.05460  ...  0.0   \n",
       "3             1.154762                 0.809544          157.05460  ...  0.0   \n",
       "4             1.154762                 0.605632          157.05460  ...  0.0   \n",
       "\n",
       "    Hg   Tl   Pb   Bi  Po  At  Rn  critical_temp                      material  \n",
       "0  0.0  0.0  0.0  0.0   0   0   0           82.0             Y1Ba1.5Ca0.5Cu3O7  \n",
       "1  0.0  0.0  0.0  0.0   0   0   0           76.9                   Yb16Ba1Cu2O  \n",
       "2  0.0  0.0  0.0  0.0   0   0   0           65.3                Yb1.8Ba0.2Cu1O  \n",
       "3  0.0  0.0  0.0  0.0   0   0   0           78.6                    Yb6Ba1Cu1O  \n",
       "4  0.0  0.0  0.0  0.0   0   0   0           79.3  Yb1.62La0.18Ba0.18Sr0.02Cu1O  \n",
       "\n",
       "[5 rows x 171 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('ybaco_materials_all.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['critical_temp']\n",
    "x = df.drop(['critical_temp'], axis=1).drop(['Unnamed: 0'], axis = 1).drop(['Unnamed: 0.1'], axis = 1).select_dtypes(exclude=['object'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X, test_X, train_y, test_y = train_test_split(x.values, y.values, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_imputer = SimpleImputer()\n",
    "train_X = df_imputer.fit_transform(train_X)\n",
    "test_X = df_imputer.transform(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(alpha=1, base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=0.5, gamma=0,\n",
       "             importance_type='gain', learning_rate=0.02, max_delta_step=0,\n",
       "             max_depth=16, min_child_weight=1, missing=None, n_estimators=374,\n",
       "             n_jobs=1, nthread=None, objective='reg:squarederror',\n",
       "             random_state=0, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
       "             seed=None, silent=None, subsample=1, verbosity=1)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = XGBRegressor(objective ='reg:squarederror', colsample_bytree = 0.50,learning_rate = 0.02, max_depth = 16, alpha = 1, n_estimators = 374)\n",
    "model.fit(train_X, train_y, verbose=False) # treinando o modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg = 0\n",
    "predictions = 0\n",
    "for i in range(1, 26):\n",
    "    predictions = model.predict(test_X) # testando a predição\n",
    "    avg += sqrt(mean_absolute_error(predictions, test_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rmse value: 3.0633237336677563\n"
     ]
    }
   ],
   "source": [
    "print(\"rmse value: \" + str(avg/25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Predict Tc versus Observed Tc using XGBoost Regressor\")\n",
    "plt.plot(test_y, predictions, \"o\", color=\"black\")\n",
    "plt.plot(range(-10, 125), range(-10, 125), color = 'gray')\n",
    "plt.xlabel(\"Observed Tc(K)\")\n",
    "plt.ylabel(\"Predicted Tc(K)\")\n",
    "#plt.show -> plt.savefig\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost Regressor data: \n",
      "Predicted value for Y1Ba2Cu3O7:  90.67667\n",
      "Observed value for Y1Ba2Cu3O7:  91.85\n"
     ]
    }
   ],
   "source": [
    "ybaco7 = df.loc[df['Unnamed: 0'] == 29]\n",
    "ybaco7 = ybaco7.drop(['material'], axis=1)\n",
    "observedValue = np.array(ybaco7['critical_temp'])\n",
    "ybaco7 = ybaco7.drop(['critical_temp'], axis=1)\n",
    "\n",
    "ybaco7 = ybaco7.drop(['Unnamed: 0'], axis = 1).drop(['Unnamed: 0.1'], axis = 1).select_dtypes(exclude=['object'])\n",
    "\n",
    "ybaco7 = np.array(ybaco7)\n",
    "print(\"XGBoost Regressor data: \")\n",
    "print(\"Predicted value for Y1Ba2Cu3O7: \", model.predict(ybaco7)[0])\n",
    "print(\"Observed value for Y1Ba2Cu3O7: \", observedValue[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "element_data = pd.read_csv('element_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Unnamed: 0</th>\n",
       "      <th>Element</th>\n",
       "      <th>AtomicMass</th>\n",
       "      <th>AtomicNumber</th>\n",
       "      <th>FirstIonizationEnergy</th>\n",
       "      <th>AtomicRadius</th>\n",
       "      <th>Block</th>\n",
       "      <th>BoilingPoint</th>\n",
       "      <th>BrinellHardness</th>\n",
       "      <th>CovalentRadius</th>\n",
       "      <th>...</th>\n",
       "      <th>Series</th>\n",
       "      <th>SoundSpeed</th>\n",
       "      <th>SpaceGroupNumber</th>\n",
       "      <th>SpecificHeat</th>\n",
       "      <th>ThermalConductivity</th>\n",
       "      <th>ThermalExpansion</th>\n",
       "      <th>Valence</th>\n",
       "      <th>VaporizationHeat</th>\n",
       "      <th>VolumeMagneticSusceptibility</th>\n",
       "      <th>SuperconductingPoint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>astatine</td>\n",
       "      <td>At</td>\n",
       "      <td>210.0</td>\n",
       "      <td>85</td>\n",
       "      <td>916.3</td>\n",
       "      <td>127.0</td>\n",
       "      <td>p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150</td>\n",
       "      <td>...</td>\n",
       "      <td>Halogen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>radon</td>\n",
       "      <td>Rn</td>\n",
       "      <td>222.0</td>\n",
       "      <td>86</td>\n",
       "      <td>1036.5</td>\n",
       "      <td>120.0</td>\n",
       "      <td>p</td>\n",
       "      <td>-61.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150</td>\n",
       "      <td>...</td>\n",
       "      <td>NobleGas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.65</td>\n",
       "      <td>0.00361</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 Element  AtomicMass  AtomicNumber  FirstIonizationEnergy  \\\n",
       "84   astatine      At       210.0            85                  916.3   \n",
       "85      radon      Rn       222.0            86                 1036.5   \n",
       "\n",
       "    AtomicRadius Block  BoilingPoint  BrinellHardness  CovalentRadius  ...  \\\n",
       "84         127.0     p           NaN              NaN             150  ...   \n",
       "85         120.0     p         -61.7              NaN             150  ...   \n",
       "\n",
       "      Series  SoundSpeed  SpaceGroupNumber  SpecificHeat  ThermalConductivity  \\\n",
       "84   Halogen         NaN               NaN           NaN              2.00000   \n",
       "85  NobleGas         NaN               NaN         93.65              0.00361   \n",
       "\n",
       "    ThermalExpansion  Valence  VaporizationHeat VolumeMagneticSusceptibility  \\\n",
       "84               NaN        7              40.0                          NaN   \n",
       "85               NaN        6              17.0                          NaN   \n",
       "\n",
       "    SuperconductingPoint  \n",
       "84                   NaN  \n",
       "85                   NaN  \n",
       "\n",
       "[2 rows x 37 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "element_data.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ybaco7 = df.loc[df['Unnamed: 0'] == 29]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ybaco7 = ybaco7['material']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "ybaco7 = np.array(ybaco7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "ybaco7 = ybaco7[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "ybaco7 = simple_formula_parser(str(ybaco7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Y': 1, 'Ba': 2, 'Cu': 3, 'O': 7}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ybaco7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "elementList = []\n",
    "for chem in ybaco7:\n",
    "    elementList.append(chem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Y', 'Ba', 'Cu', 'O']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elementList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "elementList = np.array(elementList)\n",
    "elementList = elementList[np.newaxis, :]\n",
    "elementList = np.resize(elementList, (2, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Y', 'Ba', 'Cu', 'O'],\n",
       "       ['Y', 'Ba', 'Cu', 'O']], dtype='<U2')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elementList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "shadowList = []\n",
    "for chem in ybaco7.values():\n",
    "    shadowList.append(chem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "shadowList = np.array(shadowList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['Y' 'Ba' 'Cu' 'O']\n",
      " ['1' '2' '3' '7']]\n"
     ]
    }
   ],
   "source": [
    "elementList[1] = shadowList\n",
    "print(elementList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['yttrium' 'Y' 88.90585 39 615.4 212.0 'd' 3345.0 588.0 190\n",
      "  'simple_hexagonal' 4472.0 1800000.0 29.6 1.22 11.4 3.0 4240.0\n",
      "  'paramagnetic' 6.66e-08 1526.0 5.920000000000001e-09 1.988e-05 5\n",
      "  'solid' 0.24 5.7e-07 'TransitionMetal' 3300.0 194.0 298.0 17.0 1.06e-05\n",
      "  3 380.0 0.00029780000000000003 1.3]]\n",
      "[['barium' 'Ba' 137.327 56 502.5 253.0 's' 1870.0 nan 215\n",
      "  'body_centered_cubic' 3510.0 2900000.0 13.95 0.89 8.0 2.0 3338.0\n",
      "  'paramagnetic' 1.1299999999999999e-08 727.0 1.55e-09 3.912e-05 6\n",
      "  'solid' nan 3.5e-07 'AlkalineEarthMetal' 1620.0 229.0 205.0 18.0\n",
      "  2.0600000000000003e-05 2 140.0 3.966e-05 nan]]\n",
      "[['copper' 'Cu' 63.54600000000001 29 745.0 145.0 'd' 2562.0 874.0 132\n",
      "  'face_centered_cubic' 8960.0 59000000.0 118.4 1.9 13.1 11.0 8020.0\n",
      "  'diamagnetic' -1.0800000000000002e-09 1084.62 -6.86e-11 7.09e-06 4\n",
      "  'solid' 0.34 1.7e-08 'TransitionMetal' 3570.0 225.0 384.4 400.0\n",
      "  1.65e-05 2 300.0 -9.63e-06 nan]]\n",
      "[['oxygen' 'O' 15.9994 8 1313.1 48.0 'p' -182.9 nan 66\n",
      "  'base_centered_monoclinic' 1.429 nan 141.0 3.44 0.222 16.0 nan\n",
      "  'paramagnetic' 1.34e-06 -218.3 4.27e-08 0.0112 2 'gaseous' nan nan\n",
      "  'Chalcogen' 317.5 12.0 919.0 0.02658 nan 2 3.41 1.91e-06 nan]]\n"
     ]
    }
   ],
   "source": [
    "for element in elementList[0]:\n",
    "    temp = element_data.loc[element_data['Element'] == element]\n",
    "    temp = np.array(temp)\n",
    "    print(temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code above is for test the data we extracted from Kam Hamidieh's model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "forestModel = RandomForestRegressor(n_estimators=60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=60, n_jobs=None, oob_score=False,\n",
       "                      random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forestModel.fit(train_X, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_forest = 0\n",
    "predictions_forest = 0\n",
    "for i in range(1, 26):\n",
    "    predictions_forest = forestModel.predict(test_X) # testando a predição da random forest\n",
    "    avg_forest += sqrt(mean_absolute_error(predictions_forest, test_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForests Regressor RMSE(for comparison):  3.067717820862451\n"
     ]
    }
   ],
   "source": [
    "print('RandomForests Regressor RMSE(for comparison): ', str(avg_forest/25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Predict Tc versus Observed Tc using RandomForests Regressor\")\n",
    "plt.plot(test_y, predictions_forest, \"o\", color=\"black\")\n",
    "plt.plot(range(-10, 125), range(-10, 125), color = 'gray')\n",
    "plt.xlabel(\"Observed Tc(K)\")\n",
    "plt.ylabel(\"Predicted Tc(K)\")\n",
    "#plt.show -> plt.savefig\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForests Regressor data: \n",
      "Predicted value for Y1Ba2Cu3O7:  90.3446666666667\n",
      "Observed value for Y1Ba2Cu3O7:  91.85\n"
     ]
    }
   ],
   "source": [
    "ybaco7 = df.loc[df['Unnamed: 0'] == 29]\n",
    "ybaco7 = ybaco7.drop(['material'], axis=1)\n",
    "observedValue = np.array(ybaco7['critical_temp'])\n",
    "ybaco7 = ybaco7.drop(['critical_temp'], axis=1)\n",
    "\n",
    "ybaco7 = ybaco7.drop(['Unnamed: 0'], axis = 1).drop(['Unnamed: 0.1'], axis = 1).select_dtypes(exclude=['object'])\n",
    "\n",
    "ybaco7 = np.array(ybaco7)\n",
    "print(\"RandomForests Regressor data: \")\n",
    "print(\"Predicted value for Y1Ba2Cu3O7: \", forestModel.predict(ybaco7)[0])\n",
    "print(\"Observed value for Y1Ba2Cu3O7: \", observedValue[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6559524724542691"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(test_X, test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6662998415911972"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forestModel.score(test_X, test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "knnModel = KNeighborsRegressor(n_jobs=-1, weights='distance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                    metric_params=None, n_jobs=-1, n_neighbors=5, p=2,\n",
       "                    weights='distance')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knnModel.fit(train_X, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.270464214906569"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knnModel.score(test_X, test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_knn = 0\n",
    "predictions_knn = 0\n",
    "for i in range(1, 26):\n",
    "    predictions_knn = knnModel.predict(test_X) # testando a predição com knn\n",
    "    avg_knn += sqrt(mean_absolute_error(predictions_knn, test_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN Regressor RMSE(for comparison):  3.733798165170613\n"
     ]
    }
   ],
   "source": [
    "print('KNN Regressor RMSE(for comparison): ', str(avg_knn/25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Predict Tc versus Observed Tc using KNN Regressor\")\n",
    "plt.plot(test_y, predictions_knn, \"o\", color=\"black\")\n",
    "plt.plot(range(-10, 125), range(-10, 125), color = 'gray')\n",
    "plt.xlabel(\"Observed Tc(K)\")\n",
    "plt.ylabel(\"Predicted Tc(K)\")\n",
    "#plt.show -> plt.savefig\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN Regressor data: \n",
      "Predicted value for Y1Ba2Cu3O7:  88.26498168975674\n",
      "Observed value for Y1Ba2Cu3O7:  91.85\n"
     ]
    }
   ],
   "source": [
    "ybaco7 = df.loc[df['Unnamed: 0'] == 29]\n",
    "ybaco7 = ybaco7.drop(['material'], axis=1)\n",
    "observedValue = np.array(ybaco7['critical_temp'])\n",
    "ybaco7 = ybaco7.drop(['critical_temp'], axis=1)\n",
    "\n",
    "ybaco7 = ybaco7.drop(['Unnamed: 0'], axis = 1).drop(['Unnamed: 0.1'], axis = 1).select_dtypes(exclude=['object'])\n",
    "\n",
    "ybaco7 = np.array(ybaco7)\n",
    "print(\"KNN Regressor data: \")\n",
    "print(\"Predicted value for Y1Ba2Cu3O7: \", knnModel.predict(ybaco7)[0])\n",
    "print(\"Observed value for Y1Ba2Cu3O7: \", observedValue[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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