8. RFR.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A Two-Step Approach to Solar Power Generation Prediction Based on Weather Data Using Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Basic Data Science Packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "#ML scikit learn RFR and HyperParameter Tuner GridSearchCV\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import r2_score\n",
    "  \n",
    "#Plotting graph\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#Utility Module for computing and displaying metrics\n",
    "from utility_functions import metrics\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Data\n",
    "- Along with extracting observed weather condition columns and forecast weather condition columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "thesis_dataset = pd.read_csv('data/final_thesis_dataset.csv',parse_dates=[0], index_col=0)\n",
    "\n",
    "observed_values = ['SWTDN', 'SWGDN', 'T', 'v1', 'v2', 'v_50m', 'z0']\n",
    "forecast_values = ['skyType', 'winddir', 'windspeed', 'humidity', 'temp', 'rho', 'p', 'dew', 'visibility', 'cloudcover']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Collating a RandomForest Regressor for each observed attribute so that the extracted observation values be replaced with RFR predicted values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1min 59s\n"
     ]
    },
    {
     "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>Observed Weather Condition</th>\n",
       "      <th>R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SWTDN</td>\n",
       "      <td>0.160638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SWGDN</td>\n",
       "      <td>0.126945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>T</td>\n",
       "      <td>0.950292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>v1</td>\n",
       "      <td>0.612917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>v2</td>\n",
       "      <td>0.594589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>v_50m</td>\n",
       "      <td>0.519659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>z0</td>\n",
       "      <td>-1.890561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Observed Weather Condition        R2\n",
       "0                      SWTDN  0.160638\n",
       "1                      SWGDN  0.126945\n",
       "2                          T  0.950292\n",
       "3                         v1  0.612917\n",
       "4                         v2  0.594589\n",
       "5                      v_50m  0.519659\n",
       "6                         z0 -1.890561"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "thesis_dataset = thesis_dataset.fillna(thesis_dataset.mean())\n",
    "train = thesis_dataset[:6098] \n",
    "test = thesis_dataset[6098:] \n",
    "\n",
    "df = pd.DataFrame(columns=['Observed Weather Condition', 'R2'])\n",
    "\n",
    "#Iterating through each observed variable and finding the best fit for applying RFR to \n",
    "#get best r2 score for synthetically derived observed value\n",
    "for index, observed_value in enumerate(observed_values):\n",
    "    param_grid = {  \n",
    "        'bootstrap': [True], \n",
    "        'max_depth': [4, 5, 10, None], \n",
    "        'max_features': ['auto', 'log2'], \n",
    "        'n_estimators': [6, 7, 8, 9, 10, 11, 12, 13, 15]\n",
    "    }\n",
    "\n",
    "    rfr = RandomForestRegressor(random_state = 1)\n",
    "\n",
    "    grid = GridSearchCV(estimator = rfr, param_grid = param_grid, \n",
    "                              cv = 3, n_jobs = 1, verbose = 0)\n",
    "\n",
    "    grid.fit(train[forecast_values], train[observed_value])\n",
    "    \n",
    "    test_pred = grid.predict(test[forecast_values])\n",
    "    pred_observed = grid.predict(thesis_dataset[forecast_values])\n",
    "    \n",
    "    score = r2_score(test[observed_value], test_pred)\n",
    "    df.loc[index] = [observed_value, score]\n",
    "    \n",
    "    #Replace the dataset with the synthetic values only if r2 > 70%\n",
    "    if score > 0.7:\n",
    "        thesis_dataset[observed_value] = pred_observed\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Fetch the first 70% records as train and rest as test\n",
    "    - 8711 * 70% = 6098 records from the start (Train)\n",
    "    - 8711 - 6098 = 2613 remaining records as (Test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_solar_train = train[['SWTDN', 'SWGDN', 'T','skyType', 'winddir', 'windspeed', 'humidity', 'temp', 'rho', 'p', 'dew', 'visibility', 'cloudcover']]\n",
    "y_solar_train = train['DE_solar_generation_actual']\n",
    "X_solar_test = test[['SWTDN', 'SWGDN', 'T','skyType', 'winddir', 'windspeed', 'humidity', 'temp', 'rho', 'p', 'dew', 'visibility', 'cloudcover']]\n",
    "y_solar_test = test['DE_solar_generation_actual']\n",
    "\n",
    "X_wind_train = train[['v1', 'v2', 'v_50m', 'z0','skyType', 'winddir', 'windspeed', 'humidity', 'temp', 'rho', 'p', 'dew', 'visibility', 'cloudcover']]\n",
    "y_wind_train = train['DE_wind_generation_actual']\n",
    "X_wind_test = test[['v1', 'v2', 'v_50m', 'z0','skyType', 'winddir', 'windspeed', 'humidity', 'temp', 'rho', 'p', 'dew', 'visibility', 'cloudcover']]\n",
    "y_wind_test = test['DE_wind_generation_actual']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collating a dictionary of the suitable parameter value for the GridSearch to find optimal settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 30 candidates, totalling 90 fits\n",
      "[CV] bootstrap=True, max_depth=5, max_features=auto, n_estimators=5 ..\n",
      "[CV]  bootstrap=True, max_depth=5, max_features=auto, n_estimators=5, score=(train=0.974, test=0.888), total=   0.1s\n",
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      "[CV] bootstrap=True, max_depth=5, max_features=auto, n_estimators=6 ..\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, max_depth=10, max_features=log2, n_estimators=10, score=(train=0.988, test=0.851), total=   0.1s\n",
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      "[CV]  bootstrap=True, max_depth=10, max_features=log2, n_estimators=11, score=(train=0.988, test=0.847), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=10, max_features=log2, n_estimators=11 \n",
      "[CV]  bootstrap=True, max_depth=10, max_features=log2, n_estimators=11, score=(train=0.990, test=0.935), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=10, max_features=log2, n_estimators=11 \n",
      "[CV]  bootstrap=True, max_depth=10, max_features=log2, n_estimators=11, score=(train=0.987, test=0.944), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=10, max_features=log2, n_estimators=15 \n",
      "[CV]  bootstrap=True, max_depth=10, max_features=log2, n_estimators=15, score=(train=0.989, test=0.858), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=10, max_features=log2, n_estimators=15 \n",
      "[CV]  bootstrap=True, max_depth=10, max_features=log2, n_estimators=15, score=(train=0.991, test=0.931), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=10, max_features=log2, n_estimators=15 \n",
      "[CV]  bootstrap=True, max_depth=10, max_features=log2, n_estimators=15, score=(train=0.988, test=0.948), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=auto, n_estimators=5 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=auto, n_estimators=5, score=(train=0.994, test=0.872), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=auto, n_estimators=5 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=auto, n_estimators=5, score=(train=0.994, test=0.953), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=auto, n_estimators=5 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=auto, n_estimators=5, score=(train=0.993, test=0.962), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=auto, n_estimators=6 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=auto, n_estimators=6, score=(train=0.995, test=0.875), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=auto, n_estimators=6 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=auto, n_estimators=6, score=(train=0.994, test=0.953), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=auto, n_estimators=6 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=auto, n_estimators=6, score=(train=0.993, test=0.963), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=auto, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=auto, n_estimators=10, score=(train=0.996, test=0.882), total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=auto, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=auto, n_estimators=10, score=(train=0.995, test=0.957), total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=auto, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=auto, n_estimators=10, score=(train=0.994, test=0.964), total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=auto, n_estimators=11 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=auto, n_estimators=11, score=(train=0.996, test=0.882), total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=auto, n_estimators=11 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=auto, n_estimators=11, score=(train=0.996, test=0.956), total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=auto, n_estimators=11 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=auto, n_estimators=11, score=(train=0.994, test=0.964), total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=auto, n_estimators=15 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=auto, n_estimators=15, score=(train=0.996, test=0.883), total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=auto, n_estimators=15 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=auto, n_estimators=15, score=(train=0.996, test=0.957), total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=auto, n_estimators=15 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=auto, n_estimators=15, score=(train=0.995, test=0.964), total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=log2, n_estimators=5 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=log2, n_estimators=5, score=(train=0.992, test=0.853), total=   0.0s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=log2, n_estimators=5 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=log2, n_estimators=5, score=(train=0.992, test=0.930), total=   0.0s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=log2, n_estimators=5 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=log2, n_estimators=5, score=(train=0.990, test=0.952), total=   0.0s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=log2, n_estimators=6 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=log2, n_estimators=6, score=(train=0.993, test=0.863), total=   0.0s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=log2, n_estimators=6 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=log2, n_estimators=6, score=(train=0.993, test=0.932), total=   0.0s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=log2, n_estimators=6 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=log2, n_estimators=6, score=(train=0.991, test=0.949), total=   0.0s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=log2, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=log2, n_estimators=10, score=(train=0.994, test=0.870), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=log2, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=log2, n_estimators=10, score=(train=0.994, test=0.932), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=log2, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=log2, n_estimators=10, score=(train=0.993, test=0.935), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=log2, n_estimators=11 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=log2, n_estimators=11, score=(train=0.994, test=0.869), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=log2, n_estimators=11 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=log2, n_estimators=11, score=(train=0.994, test=0.936), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=log2, n_estimators=11 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=log2, n_estimators=11, score=(train=0.993, test=0.936), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=log2, n_estimators=15 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=log2, n_estimators=15, score=(train=0.995, test=0.867), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=log2, n_estimators=15 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=log2, n_estimators=15, score=(train=0.995, test=0.939), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=log2, n_estimators=15 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=log2, n_estimators=15, score=(train=0.994, test=0.944), total=   0.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  90 out of  90 | elapsed:    8.9s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, estimator=RandomForestRegressor(random_state=1), n_jobs=1,\n",
       "             param_grid={'bootstrap': [True], 'max_depth': [5, 10, None],\n",
       "                         'max_features': ['auto', 'log2'],\n",
       "                         'n_estimators': [5, 6, 10, 11, 15]},\n",
       "             return_train_score=True, verbose=3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {  \n",
    "    'bootstrap': [True], \n",
    "    'max_depth': [5, 10, None], \n",
    "    'max_features': ['auto', 'log2'], \n",
    "    'n_estimators': [5, 6, 10, 11, 15]\n",
    "}\n",
    "\n",
    "rfr = RandomForestRegressor(random_state = 1)\n",
    "\n",
    "solar_grid = GridSearchCV(estimator = rfr, param_grid = param_grid, \n",
    "                          cv = 3, n_jobs = 1, verbose = 3, return_train_score=True)\n",
    "\n",
    "solar_grid.fit(X_solar_train, y_solar_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'bootstrap': True, 'max_depth': 5, 'max_features': 'auto', 'n_estimators': 15}\n",
      "RandomForestRegressor(max_depth=5, n_estimators=15, random_state=1)\n"
     ]
    }
   ],
   "source": [
    "# print best parameter after tuning\n",
    "print(solar_grid.best_params_)\n",
    "  \n",
    "# print how our model looks after hyper-parameter tuning\n",
    "print(solar_grid.best_estimator_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running predictions on the best model decided and Plotting them side by side"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": [
    "train_solar_pred = solar_grid.predict(X_solar_train)\n",
    "test_solar_pred = solar_grid.predict(X_solar_test)\n",
    "\n",
    "df = thesis_dataset[['DE_solar_generation_actual']]\n",
    "\n",
    "train_df = train[['DE_solar_generation_actual']]\n",
    "train_df.loc[:, 'DE_solar_generation_actual'] = train_solar_pred\n",
    "\n",
    "test_df = test[['DE_solar_generation_actual']]\n",
    "test_df.loc[:, 'DE_solar_generation_actual'] = test_solar_pred\n",
    "\n",
    "# Plot all predictions\n",
    "inversetransform, =plt.plot(df, label = 'Actual')\n",
    "trainpredx, =plt.plot(train_df, color='orange', label = 'Train')\n",
    "testpredx, =plt.plot(test_df, color='green', label = 'Test')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Solar Output')\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.title(\"Predicted vs. Actual Solar Generation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression Metrics for Solar Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>R2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</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>rfr</th>\n",
       "      <td>518.724614</td>\n",
       "      <td>1201.170098</td>\n",
       "      <td>34.657901</td>\n",
       "      <td>3.545526</td>\n",
       "      <td>0.913412</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              MAE          MSE       RMSE     RMSLE        R2\n",
       "model                                                        \n",
       "rfr    518.724614  1201.170098  34.657901  3.545526  0.913412"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics('rfr', test[['DE_solar_generation_actual']], test_solar_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performing the same tuning for Wind Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 36 candidates, totalling 108 fits\n",
      "[CV] bootstrap=True, max_depth=5, max_features=auto, n_estimators=5 ..\n",
      "[CV]  bootstrap=True, max_depth=5, max_features=auto, n_estimators=5, score=(train=0.931, test=0.884), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=5, max_features=auto, n_estimators=5 ..\n",
      "[CV]  bootstrap=True, max_depth=5, max_features=auto, n_estimators=5, score=(train=0.955, test=0.898), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=5, max_features=auto, n_estimators=5 ..\n",
      "[CV]  bootstrap=True, max_depth=5, max_features=auto, n_estimators=5, score=(train=0.951, test=0.886), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=5, max_features=auto, n_estimators=6 ..\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, max_depth=5, max_features=auto, n_estimators=6, score=(train=0.932, test=0.885), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=5, max_features=auto, n_estimators=6 ..\n",
      "[CV]  bootstrap=True, max_depth=5, max_features=auto, n_estimators=6, score=(train=0.955, test=0.899), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=5, max_features=auto, n_estimators=6 ..\n",
      "[CV]  bootstrap=True, max_depth=5, max_features=auto, n_estimators=6, score=(train=0.951, test=0.889), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=5, max_features=auto, n_estimators=7 ..\n",
      "[CV]  bootstrap=True, max_depth=5, max_features=auto, n_estimators=7, score=(train=0.932, test=0.883), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=5, max_features=auto, n_estimators=7 ..\n",
      "[CV]  bootstrap=True, max_depth=5, max_features=auto, n_estimators=7, score=(train=0.956, test=0.901), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=5, max_features=auto, n_estimators=7 ..\n",
      "[CV]  bootstrap=True, max_depth=5, max_features=auto, n_estimators=7, score=(train=0.952, test=0.890), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=5, max_features=auto, n_estimators=8 ..\n",
      "[CV]  bootstrap=True, max_depth=5, max_features=auto, n_estimators=8, score=(train=0.932, test=0.885), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=5, max_features=auto, n_estimators=8 ..\n",
      "[CV]  bootstrap=True, max_depth=5, max_features=auto, n_estimators=8, score=(train=0.956, test=0.900), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=5, max_features=auto, n_estimators=8 ..\n",
      "[CV]  bootstrap=True, max_depth=5, max_features=auto, n_estimators=8, score=(train=0.952, test=0.891), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=5, max_features=auto, n_estimators=9 ..\n",
      "[CV]  bootstrap=True, max_depth=5, max_features=auto, n_estimators=9, score=(train=0.932, test=0.887), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=5, max_features=auto, n_estimators=9 ..\n",
      "[CV]  bootstrap=True, max_depth=5, max_features=auto, n_estimators=9, score=(train=0.956, test=0.899), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=5, max_features=auto, n_estimators=9 ..\n",
      "[CV]  bootstrap=True, max_depth=5, max_features=auto, n_estimators=9, score=(train=0.952, test=0.892), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=5, max_features=auto, n_estimators=10 .\n",
      "[CV]  bootstrap=True, max_depth=5, max_features=auto, n_estimators=10, score=(train=0.932, test=0.888), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=5, max_features=auto, n_estimators=10 .\n",
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      "[CV]  bootstrap=True, max_depth=None, max_features=log2, n_estimators=10, score=(train=0.991, test=0.857), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=log2, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=log2, n_estimators=10, score=(train=0.995, test=0.876), total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=None, max_features=log2, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=None, max_features=log2, n_estimators=10, score=(train=0.995, test=0.825), total=   0.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 108 out of 108 | elapsed:   10.1s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, estimator=RandomForestRegressor(random_state=1), n_jobs=1,\n",
       "             param_grid={'bootstrap': [True], 'max_depth': [5, 10, None],\n",
       "                         'max_features': ['auto', 'log2'],\n",
       "                         'n_estimators': [5, 6, 7, 8, 9, 10]},\n",
       "             return_train_score=True, verbose=3)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {  \n",
    "    'bootstrap': [True], \n",
    "    'max_depth': [5, 10, None], \n",
    "    'max_features': ['auto', 'log2'], \n",
    "    'n_estimators': [5, 6, 7, 8, 9, 10]\n",
    "}\n",
    "\n",
    "rfr = RandomForestRegressor(random_state = 1)\n",
    "\n",
    "wind_grid = GridSearchCV(estimator = rfr, param_grid = param_grid, \n",
    "                          cv = 3, n_jobs = 1, verbose = 3, return_train_score=True)\n",
    "\n",
    "wind_grid.fit(X_wind_train, y_wind_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'bootstrap': True, 'max_depth': 5, 'max_features': 'auto', 'n_estimators': 9}\n",
      "RandomForestRegressor(max_depth=5, n_estimators=9, random_state=1)\n"
     ]
    }
   ],
   "source": [
    "# print best parameter after tuning\n",
    "print(wind_grid.best_params_)\n",
    "  \n",
    "# print how our model looks after hyper-parameter tuning\n",
    "print(wind_grid.best_estimator_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running predictions on the best model decided and Plotting them side by side"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": [
    "train_wind_pred = wind_grid.predict(X_wind_train)\n",
    "test_wind_pred = wind_grid.predict(X_wind_test)\n",
    "\n",
    "df = thesis_dataset[['DE_wind_generation_actual']]\n",
    "\n",
    "train_df = train[['DE_wind_generation_actual']]\n",
    "train_df.loc[:, 'DE_wind_generation_actual'] = train_wind_pred\n",
    "\n",
    "test_df = test[['DE_wind_generation_actual']]\n",
    "test_df.loc[:, 'DE_wind_generation_actual'] = test_wind_pred\n",
    "\n",
    "# Plot all predictions\n",
    "inversetransform, =plt.plot(df, label = 'Actual')\n",
    "trainpredx, =plt.plot(train_df, color='orange', label = 'Train')\n",
    "testpredx, =plt.plot(test_df, color='green', label = 'Test')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('wind Output')\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.title(\"Predicted vs. Actual wind Generation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression Metrics for Wind Data"
   ]
  },
  {
   "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>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>R2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</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>rfr</th>\n",
       "      <td>1748.421168</td>\n",
       "      <td>2433.214386</td>\n",
       "      <td>49.327623</td>\n",
       "      <td>3.898484</td>\n",
       "      <td>0.895874</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               MAE          MSE       RMSE     RMSLE        R2\n",
       "model                                                         \n",
       "rfr    1748.421168  2433.214386  49.327623  3.898484  0.895874"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics('rfr', test[['DE_wind_generation_actual']], test_wind_pred)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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