support_vector_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing libraries and the preprocessed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "import math\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "X_train=pd.read_csv('X_train').drop(labels=['Site Time'], axis=1) #the timestamp data which was the index is dropped\n",
    "X_test=pd.read_csv('X_test').drop(labels=['Site Time'], axis=1)\n",
    "y_train=pd.read_csv('y_train').drop(labels=['Site Time'], axis=1)\n",
    "y_test=pd.read_csv('y_test').drop(labels=['Site Time'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first run of grid search and cross validation, fitting a support vector machine to the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 4 folds for each of 30 candidates, totalling 120 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done  86 tasks      | elapsed:    9.5s\n",
      "[Parallel(n_jobs=-1)]: Done 105 out of 120 | elapsed:   14.3s remaining:    2.0s\n",
      "[Parallel(n_jobs=-1)]: Done 120 out of 120 | elapsed:   41.5s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=TimeSeriesSplit(max_train_size=None, n_splits=4),\n",
       "             estimator=SVR(epsilon=10), n_jobs=-1,\n",
       "             param_grid=[{'C': array([1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04]),\n",
       "                          'gamma': array([1.00000000e-05, 5.62341325e-04, 3.16227766e-02, 1.77827941e+00,\n",
       "       1.00000000e+02]),\n",
       "                          'kernel': ['rbf']},\n",
       "                         {'C': array([1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04]),\n",
       "                          'kernel': ['linear']}],\n",
       "             scoring=make_scorer(MSE, greater_is_better=False), verbose=5)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.svm import SVR #support vector regression\n",
    "\n",
    "def MSE(y_true,y_pred): #creating a root mean squared error scorer from the mean squared error\n",
    "    mse =  math.sqrt(mean_squared_error(y_true, y_pred))\n",
    "    return mse\n",
    "\n",
    "tuned_parameters = [{'kernel': ['rbf'], 'gamma': np.logspace(-5,2, num=5),\n",
    "                     'C': np.logspace(0,4,num=5)},\n",
    "                    {'kernel': ['linear'], 'C': np.logspace(0,4,num=5)}] #the hyperparameter grid to search\n",
    "reg = GridSearchCV( #10 for epsilon means not to penalize any predictions with a difference of less 10 from the target\n",
    "    SVR(epsilon=10), tuned_parameters, n_jobs=-1, refit=True, verbose=5, cv=TimeSeriesSplit(n_splits=4), scoring=make_scorer(MSE, greater_is_better=False)#this search will be run parallel, the final best model refitted using the entire training data, with updates through output, cross validation folds generated through a splitter specific for time series data, and the MSE function will be used for scoring \n",
    ")\n",
    "reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluating the accuracy after the first run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 10000.0, 'gamma': 0.03162277660168379, 'kernel': 'rbf'}\n",
      "r2 score: 0.7344351604680259\n",
      "Root mean squared error: 139.81742996329479\n"
     ]
    }
   ],
   "source": [
    "print(reg.best_params_) #the set of best parameters found\n",
    "\n",
    "import joblib\n",
    "\n",
    "predict=pd.DataFrame(reg.predict(X_test)) #generating the prediction\n",
    "\n",
    "time_transformer=joblib.load('time_transformer') #retrieving the scaler so that only the daylight values are evaluated\n",
    "\n",
    "t=predict.join(X_test).join(y_test) #putting all three DataFrames together\n",
    "t=t.rename(columns={0:'predict'})\n",
    "t['time']=time_transformer.inverse_transform(pd.DataFrame(t['time'])) #reversing the scaling done to the cyclical time column\n",
    "t=t[t['time']>0] #keeping only values that are during the day\n",
    "\n",
    "mod_predict=t['predict'].copy() #extracting the daylight-only predictions\n",
    "mod_y_test=t['one ahead power Kilowatts'].copy() #extracting the daylight-only target values\n",
    "\n",
    "print(\"r2 score: \"+str(r2_score(mod_predict, mod_y_test))) #r2\n",
    "print(\"Root mean squared error: \"+str(math.sqrt(mean_squared_error(mod_predict, mod_y_test)))) #rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second grid search run, a refinement done by focusing on the area near the first search's results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 4 folds for each of 25 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    3.0s\n",
      "[Parallel(n_jobs=-1)]: Done  56 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 19.0min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=TimeSeriesSplit(max_train_size=None, n_splits=4),\n",
       "             estimator=SVR(epsilon=10), n_jobs=-1,\n",
       "             param_grid=[{'C': array([  100.        ,   316.22776602,  1000.        ,  3162.27766017,\n",
       "       10000.        ]),\n",
       "                          'gamma': array([ 0.01      ,  0.05623413,  0.31622777,  1.77827941, 10.        ]),\n",
       "                          'kernel': ['rbf']}],\n",
       "             scoring=make_scorer(MSE, greater_is_better=False), verbose=5)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuned_parameters = [{'kernel': ['rbf'], 'gamma': np.logspace(-2, 1, num=5),\n",
    "                     'C': np.logspace(2,4,num=5)}] #values will change depending on the parameters found by the first run\n",
    "reg = GridSearchCV(\n",
    "    SVR(epsilon=10), tuned_parameters, n_jobs=-1, refit=True, verbose=5, cv=TimeSeriesSplit(n_splits=4), scoring=make_scorer(MSE, greater_is_better=False)\n",
    ")\n",
    "reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluating the accuracy after the second run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 3162.2776601683795, 'gamma': 0.05623413251903491, 'kernel': 'rbf'}\n",
      "r2 score: 0.9317719449266925\n",
      "Root mean squared error: 71.99487963876774\n"
     ]
    }
   ],
   "source": [
    "print(reg.best_params_)\n",
    "\n",
    "predict=pd.DataFrame(reg.predict(X_test))\n",
    "\n",
    "time_transformer=joblib.load('time_transformer')\n",
    "\n",
    "t=predict.join(X_test).join(y_test)\n",
    "t=t.rename(columns={0:'predict'})\n",
    "t['time']=time_transformer.inverse_transform(pd.DataFrame(t['time']))\n",
    "t=t[t['time']>0]\n",
    "\n",
    "mod_predict=t['predict'].copy()\n",
    "mod_y_test=t['one ahead power Kilowatts'].copy()\n",
    "\n",
    "print(\"r2 score: \"+str(r2_score(mod_predict, mod_y_test)))\n",
    "print(\"Root mean squared error: \"+str(math.sqrt(mean_squared_error(mod_predict, mod_y_test))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the prediction against the target\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "predict=predict.rename(columns={0:'Prediction'})\n",
    "y_test=y_test.rename(columns={'one ahead power Kilowatts': 'Target'})\n",
    "merged=predict.join(y_test) #joining the target and prediction for plotting\n",
    "\n",
    "fig = px.line(merged)\n",
    "fig.update_layout(\n",
    "    xaxis_title=\"Time\",\n",
    "    yaxis_title=\"Power (kw)\",\n",
    ")\n",
    "fig.show()"
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  {
   "cell_type": "markdown",
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   "source": [
    "Getting the accuracy score for each combination of gamma and C, two hyperparameters"
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   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "results=pd.DataFrame(reg.cv_results_)[['param_gamma', 'param_C', 'mean_test_score']].dropna() #only keeping the columns for gamma, C, and the score\n",
    "results['mean_test_score']=results['mean_test_score']*-1 #the scores are negative, so making them positive\n",
    "\n",
    "d=np.empty(shape=(len(list(results['param_C'].unique())), len(list(results['param_gamma'].unique())))) #an array to hold the scores, the columns are the gammas and the rows the Cs\n",
    "for i in range(len(list(results['param_C'].unique()))): #going through each C value\n",
    "    for j in range(len(list(results['param_C'].unique()))): #going through each gamma value\n",
    "        d[i,j]=results.loc[(results['param_C'] == list(results['param_C'].unique())[i]) & (results['param_gamma'] == list(results['param_gamma'].unique())[j])]['mean_test_score'] #getting the score that corresponds to a specific combination of C and gamma. Accessing is done by merging two boolean masks of the results array of which cells have the selected C and gamma"
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   "source": [
    "Plotting the contour graph"
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   "execution_count": 7,
   "metadata": {},
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   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "fig = go.Figure(data =\n",
    "    go.Contour(\n",
    "        z=[list(i) for i in d], #the colors of the plot will be determined by each row\n",
    "        x=np.log10(list(results['param_gamma'].unique())), \n",
    "        y=np.log10(list(results['param_C'].unique())), \n",
    "        contours=dict(\n",
    "            coloring ='heatmap',\n",
    "            showlabels = True, \n",
    "            labelfont = dict( \n",
    "                size = 12,\n",
    "                color = 'white',\n",
    "            )\n",
    "    ), \n",
    "    colorscale='mint',\n",
    "    )\n",
    ")\n",
    "\n",
    "fig.update_layout(\n",
    "    xaxis_title=\"log10 of gamma\",\n",
    "    yaxis_title=\"log10 of C\",\n",
    ")\n",
    "fig.show()"
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  },
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