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  191. [ 0. , 0.56161845, -0.36004869, 1. , 1.59019353,
  192. 1. , 0. , 0. , 0. , 1. ],
  193. [ 0. , 0.93871378, -0.10856731, 1. , -0.97622401,
  194. 0. , 0. , 1. , 0. , 1. ],
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  200. 0. , 1. , 0. , 0. , 1. ]])
  201.  
  202. array([ 3, 11, 3, 4, 6, 11, 9, 2, 13, 4, 1, 10, 13, 8, 5, 8, 1,
  203. 11, 1, 10, 6, 3, 8, 3, 8, 2, 20, 9, 13, 9, 3, 4, 8, 2,
  204. 1, 3, 5, 6, 3, 1, 3, 9, 2, 14, 4, 9, 4, 13, 10, 2, 15,
  205. 1, 2, 8, 4, 12, 14, 3, 13, 3, 11, 7, 10, 10, 6, 13, 5, 5,
  206. 10, 9, 2, 4, 5, 3, 19, 5, 4, 2, 7, 5, 6, 1, 9, 1, 1,
  207. 1, 12, 5, 11, 3, 5, 12, 2, 3, 3, 4, 3, 5, 2, 4])
  208.  
  209. class TotalReservations(object):
  210. """ Class to carry out machine learning tasks """
  211.  
  212.  
  213.  
  214. def __init__(self,train_X_std,train_Y,test_X_std,test_Y,estimator,param_grid):
  215. """ Constructor to initialze the data
  216.  
  217. """
  218.  
  219. self.train_X=train_X_std
  220. self.train_Y=train_Y
  221. self.test_X=test_X_std
  222. self.test_Y=test_Y
  223. # The hyper parameter grid for this estimator
  224. self.param_grid=param_grid
  225. # The actual ML estimator from sklearn
  226. self.estimator=estimator
  227.  
  228.  
  229.  
  230. def grid_search(self):
  231.  
  232. """This function does Cross Validation using Grid Search
  233.  
  234. """
  235.  
  236. from sklearn.model_selection import GridSearchCV
  237. self.g_cv = GridSearchCV(estimator=self.estimator,param_grid=self.param_grid,cv=5)
  238. self.g_cv.fit(self.train_X,self.train_Y)
  239.  
  240.  
  241.  
  242. def train_performance(self):
  243. """ Method to get the training performance of the estimator across all parameters
  244.  
  245. """
  246.  
  247. self.mean_train_score=self.g_cv.cv_results_['mean_train_score']
  248. self.mean_val_score=self.g_cv.cv_results_['mean_test_score']
  249.  
  250. # Printing the Dummy Regressor score
  251. self.dummy_regressor(self.train_X,self.train_Y,self.test_X,self.test_Y)
  252.  
  253. print("nThe mean train scores are {}n".format(self.mean_train_score))
  254.  
  255. print("The mean validation scores are {}n".format(self.mean_val_score))
  256.  
  257. print("The score on held out data is: {score}n Hyper-Parameters for Best Score : {param}n"
  258. .format(score=self.g_cv.best_score_,param=self.g_cv.best_params_))
  259.  
  260. # For Ridge Regression
  261. from sklearn.linear_model import Ridge
  262. ridge_lr=Ridge(random_state=42)
  263.  
  264. #***************************************************************************************
  265.  
  266.  
  267. # Creating Hyperparameter values Grid for Random Forests
  268. # Actual to be used
  269.  
  270.  
  271. # Creating Hyperparameters values Grid for Ridge Regression
  272. grid_ridge={'alpha':[0.0001,0.001,0.01,0.1,1.0]}
  273.  
  274. td_ridge.grid_search()
  275.  
  276. # Training Performance of SGD Regressor
  277. td_ridge.train_performance()
  278.  
  279.  
  280. The mean train scores are [0.21945918 0.21945918 0.21945917 0.2194588 0.21942437]
  281.  
  282. The mean validation scores are [0.18215611 0.18215637 0.18215894 0.18218431 0.18240627]
  283.  
  284. The score on held out data is: 0.18240626691460574
  285. Hyper-Parameters for Best Score : {'alpha': 1.0}
  286.  
  287. The R2 Score of ridge_regressor on test data is: 0.17520249693363288
  288.  
  289. The mse of ridge_regressor on test data is: 17.791933297637534
  290. The mean absolute error of ridge_regressor on test data is: 3.381908224949849
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