4_Random Forest Regressor Attempt.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "applicable-jordan",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import math\n",
    "import patsy\n",
    "import scipy.stats as stats\n",
    "import statsmodels.api as sm\n",
    "from sklearn import linear_model, model_selection, metrics\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, KFold\n",
    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
    "from sklearn.linear_model import LinearRegression ,Ridge, RidgeCV, Lasso, LassoCV, ElasticNet, ElasticNetCV\n",
    "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "starting-italian",
   "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>TITLE</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>EBERT_RATING</th>\n",
       "      <th>MPAA</th>\n",
       "      <th>RUNTIME</th>\n",
       "      <th>GENRE</th>\n",
       "      <th>SUB_GENRE</th>\n",
       "      <th>MOVIELENS_RATING</th>\n",
       "      <th>IMDB_RATING</th>\n",
       "      <th>BUDGET</th>\n",
       "      <th>OPENING_GROSS</th>\n",
       "      <th>DOMESTIC_GROSS</th>\n",
       "      <th>WORLDWIDE_GROSS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>Two Brothers</td>\n",
       "      <td>2004</td>\n",
       "      <td>2.5</td>\n",
       "      <td>PG</td>\n",
       "      <td>109</td>\n",
       "      <td>Foreign</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>3.354167</td>\n",
       "      <td>7.1</td>\n",
       "      <td>59660000</td>\n",
       "      <td>6144160</td>\n",
       "      <td>19176754</td>\n",
       "      <td>62174008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>Club Paradise</td>\n",
       "      <td>1986</td>\n",
       "      <td>2.0</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>95</td>\n",
       "      <td>Romance</td>\n",
       "      <td>Drama</td>\n",
       "      <td>2.486111</td>\n",
       "      <td>5.2</td>\n",
       "      <td>15000000</td>\n",
       "      <td>4152296</td>\n",
       "      <td>12308521</td>\n",
       "      <td>12308521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>16 Blocks</td>\n",
       "      <td>2006</td>\n",
       "      <td>3.0</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>105</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>Crime</td>\n",
       "      <td>3.307337</td>\n",
       "      <td>6.6</td>\n",
       "      <td>52000000</td>\n",
       "      <td>11855260</td>\n",
       "      <td>36895141</td>\n",
       "      <td>65664721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975</th>\n",
       "      <td>Adventures in Babysitting</td>\n",
       "      <td>1987</td>\n",
       "      <td>2.5</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>102</td>\n",
       "      <td>Family</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>3.227057</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7000000</td>\n",
       "      <td>2901297</td>\n",
       "      <td>34368475</td>\n",
       "      <td>34368475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>Spaceballs</td>\n",
       "      <td>1987</td>\n",
       "      <td>2.5</td>\n",
       "      <td>PG</td>\n",
       "      <td>96</td>\n",
       "      <td>Science Fiction</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>3.401829</td>\n",
       "      <td>7.1</td>\n",
       "      <td>22700000</td>\n",
       "      <td>6613837</td>\n",
       "      <td>38119483</td>\n",
       "      <td>38119483</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          TITLE  YEAR  EBERT_RATING   MPAA  RUNTIME  \\\n",
       "739                Two Brothers  2004           2.5     PG      109   \n",
       "2018              Club Paradise  1986           2.0  PG-13       95   \n",
       "562                   16 Blocks  2006           3.0  PG-13      105   \n",
       "1975  Adventures in Babysitting  1987           2.5  PG-13      102   \n",
       "1978                 Spaceballs  1987           2.5     PG       96   \n",
       "\n",
       "                GENRE SUB_GENRE  MOVIELENS_RATING  IMDB_RATING    BUDGET  \\\n",
       "739           Foreign   Fantasy          3.354167          7.1  59660000   \n",
       "2018          Romance     Drama          2.486111          5.2  15000000   \n",
       "562          Thriller     Crime          3.307337          6.6  52000000   \n",
       "1975           Family    Comedy          3.227057          7.0   7000000   \n",
       "1978  Science Fiction    Comedy          3.401829          7.1  22700000   \n",
       "\n",
       "      OPENING_GROSS  DOMESTIC_GROSS  WORLDWIDE_GROSS  \n",
       "739         6144160        19176754         62174008  \n",
       "2018        4152296        12308521         12308521  \n",
       "562        11855260        36895141         65664721  \n",
       "1975        2901297        34368475         34368475  \n",
       "1978        6613837        38119483         38119483  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accessing cleaned df\n",
    "df = pd.read_pickle('./data/cleaned_df.pickle')\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "mature-merchant",
   "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>YEAR</th>\n",
       "      <th>EBERT_RATING</th>\n",
       "      <th>RUNTIME</th>\n",
       "      <th>MOVIELENS_RATING</th>\n",
       "      <th>IMDB_RATING</th>\n",
       "      <th>BUDGET</th>\n",
       "      <th>OPENING_GROSS</th>\n",
       "      <th>DOMESTIC_GROSS</th>\n",
       "      <th>WORLDWIDE_GROSS</th>\n",
       "      <th>GENRE_Action</th>\n",
       "      <th>...</th>\n",
       "      <th>SUB_GENRE_Mystery</th>\n",
       "      <th>SUB_GENRE_N</th>\n",
       "      <th>SUB_GENRE_Neo-noir</th>\n",
       "      <th>SUB_GENRE_Road Movie</th>\n",
       "      <th>SUB_GENRE_Romance</th>\n",
       "      <th>SUB_GENRE_Science Fiction</th>\n",
       "      <th>SUB_GENRE_Sport</th>\n",
       "      <th>SUB_GENRE_Sports Film</th>\n",
       "      <th>SUB_GENRE_Suspense</th>\n",
       "      <th>SUB_GENRE_Thriller</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>3.5</td>\n",
       "      <td>106</td>\n",
       "      <td>3.642730</td>\n",
       "      <td>7.1</td>\n",
       "      <td>30000000</td>\n",
       "      <td>9303145</td>\n",
       "      <td>32172757</td>\n",
       "      <td>63414135</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>2.5</td>\n",
       "      <td>98</td>\n",
       "      <td>3.451807</td>\n",
       "      <td>6.8</td>\n",
       "      <td>11000000</td>\n",
       "      <td>47122</td>\n",
       "      <td>18390117</td>\n",
       "      <td>59520298</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>2.0</td>\n",
       "      <td>106</td>\n",
       "      <td>2.939024</td>\n",
       "      <td>5.7</td>\n",
       "      <td>35000000</td>\n",
       "      <td>5750288</td>\n",
       "      <td>13103272</td>\n",
       "      <td>30962335</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1982</td>\n",
       "      <td>4.0</td>\n",
       "      <td>105</td>\n",
       "      <td>3.825658</td>\n",
       "      <td>7.8</td>\n",
       "      <td>2600000</td>\n",
       "      <td>11623</td>\n",
       "      <td>8144</td>\n",
       "      <td>8144</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>2.0</td>\n",
       "      <td>104</td>\n",
       "      <td>3.225352</td>\n",
       "      <td>6.2</td>\n",
       "      <td>15000000</td>\n",
       "      <td>6812900</td>\n",
       "      <td>15026056</td>\n",
       "      <td>37930465</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   YEAR  EBERT_RATING  RUNTIME  MOVIELENS_RATING  IMDB_RATING    BUDGET  \\\n",
       "0  2013           3.5      106          3.642730          7.1  30000000   \n",
       "1  2012           2.5       98          3.451807          6.8  11000000   \n",
       "2  2012           2.0      106          2.939024          5.7  35000000   \n",
       "3  1982           4.0      105          3.825658          7.8   2600000   \n",
       "4  2012           2.0      104          3.225352          6.2  15000000   \n",
       "\n",
       "   OPENING_GROSS  DOMESTIC_GROSS  WORLDWIDE_GROSS  GENRE_Action  ...  \\\n",
       "0        9303145        32172757         63414135             0  ...   \n",
       "1          47122        18390117         59520298             0  ...   \n",
       "2        5750288        13103272         30962335             0  ...   \n",
       "3          11623            8144             8144             0  ...   \n",
       "4        6812900        15026056         37930465             0  ...   \n",
       "\n",
       "   SUB_GENRE_Mystery  SUB_GENRE_N  SUB_GENRE_Neo-noir  SUB_GENRE_Road Movie  \\\n",
       "0                  0            0                   0                     0   \n",
       "1                  0            0                   0                     0   \n",
       "2                  0            0                   0                     0   \n",
       "3                  0            0                   0                     0   \n",
       "4                  0            0                   0                     0   \n",
       "\n",
       "   SUB_GENRE_Romance  SUB_GENRE_Science Fiction  SUB_GENRE_Sport  \\\n",
       "0                  0                          0                0   \n",
       "1                  0                          0                0   \n",
       "2                  0                          0                0   \n",
       "3                  0                          0                0   \n",
       "4                  0                          0                0   \n",
       "\n",
       "   SUB_GENRE_Sports Film  SUB_GENRE_Suspense  SUB_GENRE_Thriller  \n",
       "0                      0                   0                   0  \n",
       "1                      0                   0                   0  \n",
       "2                      0                   0                   0  \n",
       "3                      0                   0                   0  \n",
       "4                      0                   0                   0  \n",
       "\n",
       "[5 rows x 72 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genre_dummies = pd.get_dummies(df['GENRE'], prefix='GENRE', drop_first=False)\n",
    "mpaa_dummies = pd.get_dummies(df['MPAA'], prefix='MPAA', drop_first=False)\n",
    "subgenre_dummies = pd.get_dummies(df['SUB_GENRE'], prefix='SUB_GENRE', drop_first=False)\n",
    "\n",
    "df = df.join(genre_dummies).join(mpaa_dummies).join(subgenre_dummies)\n",
    "df.drop(['TITLE', 'GENRE', 'SUB_GENRE', 'MPAA'], inplace=True, axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "victorian-benefit",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso validation scores:  [0.3856128676663836, 0.3378177988794032, 0.31655080966985694, 0.3705325110998453, 0.3660368038241626]\n",
      "Lasso mean cv r^2: 0.355 +- 0.025\n",
      "Lasso RMSE:  0.7212055407460362\n",
      "Lasso MAE:  0.5682185787745628 \n",
      "\n",
      "Lasso training scores:  [0.41724399939010914, 0.4239831073046503, 0.4299196164242318, 0.41786188840723415, 0.4246078363544331]\n",
      "Lasso mean cv r^2: 0.423 +- 0.005\n",
      "Lasso RMSE:  0.6690423195800024\n",
      "Lasso MAE:  0.5362326009676057 \n",
      "\n",
      "Random Forest Regressor\n",
      "Validation set scores - \n",
      "R2:  [0.3518106956168966, 0.3707955970362963, 0.32662280343490235, 0.35647971054919037, 0.36187704180473257]\n",
      "mean R2:  0.354 +- 0.015\n",
      "RMSE:  0.7235677743134193\n",
      "MAE:  0.5751880887587383\n",
      "Training set scores - \n",
      "R2:  [0.4404658065894864, 0.4413485209835012, 0.4527874143134166, 0.4330103279946892, 0.43232546832985064]\n",
      "mean R2:  0.440 +- 0.007\n",
      "RMSE:  0.664540300054735\n",
      "MAE:  0.5403181972498129\n"
     ]
    }
   ],
   "source": [
    "X, y = df.drop('EBERT_RATING', axis=1), df.EBERT_RATING\n",
    "X, X_test, y, y_test = train_test_split(X, y, test_size=0.3, random_state=10)\n",
    "\n",
    "kf = KFold(n_splits=5, shuffle=True, random_state=0)\n",
    "X, y = np.array(X), np.array(y)\n",
    "\n",
    "cv_rf_r2s, cv_rf_r2s_tr = [], []\n",
    "cv_lasso_r2s, cv_lasso_r2s_tr = [], []\n",
    "\n",
    "for train_ind, val_ind in kf.split(X,y):\n",
    "    \n",
    "    X_train, y_train = X[train_ind], y[train_ind]\n",
    "    X_val, y_val = X[val_ind], y[val_ind] \n",
    "    \n",
    "    scaler = StandardScaler()\n",
    "    X_train_scaled = scaler.fit_transform(X_train)\n",
    "    X_val_scaled = scaler.transform(X_val)\n",
    "    \n",
    "   # Lasso model\n",
    "    lasso = Lasso(alpha=0.006)\n",
    "    lasso.fit(X_train_scaled, y_train)\n",
    "    cv_lasso_r2s.append(lasso.score(X_val_scaled, y_val))\n",
    "    cv_lasso_r2s_tr.append(lasso.score(X_train_scaled, y_train))\n",
    "    \n",
    "    lasso_train_preds = lasso.predict(X_train_scaled)\n",
    "    lasso_train_rmse = mean_squared_error(y_train, lasso_train_preds, squared=False)\n",
    "    lasso_train_mae = mean_absolute_error(y_train, lasso_train_preds)\n",
    "    \n",
    "    lasso_val_preds = lasso.predict(X_val_scaled)\n",
    "    lasso_val_rmse = mean_squared_error(y_val, lasso_val_preds, squared=False)\n",
    "    lasso_val_mae = mean_absolute_error(y_val, lasso_val_preds)\n",
    " #-------------------------------------------------------------------------------------#    \n",
    "    \n",
    "    # Create a Random Forest regressor object from Random Forest REgressor class\n",
    "    rf = RandomForestRegressor(n_estimators=3000, max_features = 10, max_depth = 5, random_state=0)\n",
    "    \n",
    "    # Fit the random forest regressor with tarining data represented by X_train and y_train\n",
    "    rf.fit(X_train, y_train)\n",
    "    cv_rf_r2s.append(rf.score(X_val, y_val))\n",
    "    cv_rf_r2s_tr.append(rf.score(X_train, y_train))\n",
    "\n",
    "    rf_train_pred = rf.predict(X_train)\n",
    "    rf_train_rmse = mean_squared_error(y_train, rf_train_pred, squared=False)\n",
    "    rf_train_mae = mean_absolute_error(y_train, rf_train_pred)\n",
    "    \n",
    "    rf_val_pred = rf.predict(X_val)\n",
    "    rf_val_rmse = mean_squared_error(y_val, rf_val_pred, squared=False)\n",
    "    rf_val_mae = mean_absolute_error(y_val, rf_val_pred)\n",
    "\n",
    "print('Lasso validation scores: ', cv_lasso_r2s)\n",
    "print(f'Lasso mean cv r^2: {np.mean(cv_lasso_r2s):.3f} +- {np.std(cv_lasso_r2s):.3f}')\n",
    "print(\"Lasso RMSE: \", lasso_val_rmse)\n",
    "print(\"Lasso MAE: \", lasso_val_mae, \"\\n\")\n",
    "    \n",
    "print('Lasso training scores: ', cv_lasso_r2s_tr)\n",
    "print(f'Lasso mean cv r^2: {np.mean(cv_lasso_r2s_tr):.3f} +- {np.std(cv_lasso_r2s_tr):.3f}')\n",
    "print(\"Lasso RMSE: \", lasso_train_rmse)\n",
    "print(\"Lasso MAE: \", lasso_train_mae, \"\\n\")    \n",
    "    \n",
    "print('Random Forest Regressor')\n",
    "print(\"Validation set scores - \")\n",
    "print(\"R2: \", cv_rf_r2s)\n",
    "print(f\"mean R2:  {np.mean(cv_rf_r2s):.3f} +- {np.std(cv_rf_r2s):.3f}\")\n",
    "print(\"RMSE: \", rf_val_rmse)\n",
    "print(\"MAE: \", rf_val_mae)\n",
    "\n",
    "print(\"Training set scores - \")\n",
    "print(\"R2: \", cv_rf_r2s_tr)\n",
    "print(f\"mean R2:  {np.mean(cv_rf_r2s_tr):.3f} +- {np.std(cv_rf_r2s_tr):.3f}\")\n",
    "print(\"RMSE: \", rf_train_rmse)\n",
    "print(\"MAE: \", rf_train_mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "christian-plate",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-Square Error associated with Lasso Regression is:  0.3270133434501441\n",
      "R-Square Error associated with Random Forest Regression is:  0.3394055278861985\n"
     ]
    }
   ],
   "source": [
    "X_scaled = scaler.fit_transform(X)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "    \n",
    "# Predict rating from test dataset with Lasso Regression\n",
    "lasso.fit(X_scaled, y)\n",
    "y_predict_lasso = lasso.predict((X_test_scaled))\n",
    "\n",
    "r_square_lasso = metrics.r2_score(y_test, y_predict_lasso)\n",
    "print('R-Square Error associated with Lasso Regression is: ', r_square_lasso)\n",
    "\n",
    "# Predict rating from test dataset with Random Forest Regression\n",
    "rf.fit(X, y)\n",
    "y_predict_rf = rf.predict((X_test))\n",
    "\n",
    "r_square = metrics.r2_score(y_test, y_predict_rf)\n",
    "print('R-Square Error associated with Random Forest Regression is: ', r_square)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "thrown-wagner",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5838267972621387 0.5857998382116659\n"
     ]
    }
   ],
   "source": [
    "lasso_mae = mean_absolute_error(y_test, y_predict_lasso)\n",
    "ridge_mae = mean_absolute_error(y_test, y_predict_rf)\n",
    "print(lasso_mae, ridge_mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "revolutionary-bachelor",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:metis] *",
   "language": "python",
   "name": "conda-env-metis-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}