Grid searching is a method to find the best possible combination of hyper-parameters at which the model achieves the highest accuracy. Before applying Grid Searching on any algorithm, Data is used to divided into training and validation set, a validation set is used to validate the models. A model with all possible combinations of hyperparameters is tested on the validation set to choose the best combination.
Implementation:
Grid Searching can be applied to any hyperparameters algorithm whose performance can be improved by tuning hyperparameter. For example, we can apply grid searching on K-Nearest Neighbors by validating its performance on a set of values of K in it. Same thing we can do with Logistic Regression by using a set of values of learning rate to find the best learning rate at which Logistic Regression achieves the best accuracy.
Diabetes Dataset used in this implementation can be downloaded from link .
It has 8 features columns like i.e “Age”, “Glucose” e.t.c, and the target variable “Outcome” for 108 patients. So in this, we will train a Logistic Regression Classifier model to predict the presence of diabetes or not for patients with such information.
Code: Implementation of Grid Searching on Logistic Regression from Scratch
Python3
# Importing libraries import numpy as np import pandas as pd from sklearn.model_selection import train_test_split # Grid Searching in Logistic Regression class LogitRegression() : def __init__( self, learning_rate, iterations ) : self.learning_rate = learning_rate self.iterations = iterations # Function for model training def fit( self, X, Y ) : # no_of_training_examples, no_of_features self.m, self.n = X.shape # weight initialization self.W = np.zeros( self.n ) self.b = 0 self.X = X self.Y = Y # gradient descent learning for i in range( self.iterations ) : self.update_weights() return self # Helper function to update weights in gradient descent def update_weights( self ) : A = 1 / ( 1 + np.exp( - ( self.X.dot( self.W ) + self.b ) ) ) # calculate gradients tmp = ( A - self.Y.T ) tmp = np.reshape( tmp, self.m ) dW = np.dot( self.X.T, tmp ) / self.m db = np.sum( tmp ) / self.m # update weights self.W = self.W - self.learning_rate * dW self.b = self.b - self.learning_rate * db return self # Hypothetical function h( x ) def predict( self, X ) : Z = 1 / ( 1 + np.exp( - ( X.dot( self.W ) + self.b ) ) ) Y = np.where( Z > 0.5, 1, 0 ) return Y # Driver code def main() : # Importing dataset df = pd.read_csv( "diabetes.csv" ) X = df.iloc[:,:-1].values Y = df.iloc[:,-1:].values # Splitting dataset into train and validation set X_train, X_valid, Y_train, Y_valid = train_test_split( X, Y, test_size = 1/3, random_state = 0 ) # Model training max_accuracy = 0 # learning_rate choices learning_rates = [ 0.1, 0.2, 0.3, 0.4, 0.5, 0.01, 0.02, 0.03, 0.04, 0.05 ] # iterations choices iterations = [ 100, 200, 300, 400, 500 ] # available combination of learning_rate and iterations parameters = [] for i in learning_rates : for j in iterations : parameters.append( ( i, j ) ) print("Available combinations : ", parameters ) # Applying linear searching in list of available combination # to achieved maximum accuracy on CV set for k in range( len( parameters ) ) : model = LogitRegression( learning_rate = parameters[k][0], iterations = parameters[k][1] ) model.fit( X_train, Y_train ) # Prediction on validation set Y_pred = model.predict( X_valid ) # measure performance on validation set correctly_classified = 0 # counter count = 0 for count in range( np.size( Y_pred ) ) : if Y_valid[count] == Y_pred[count] : correctly_classified = correctly_classified + 1 curr_accuracy = ( correctly_classified / count ) * 100 if max_accuracy < curr_accuracy : max_accuracy = curr_accuracy print( "Maximum accuracy achieved by our model through grid searching : ", max_accuracy ) if __name__ == "__main__" : main() |
Output:
Available combinations : [(0.1, 100), (0.1, 200), (0.1, 300), (0.1, 400), (0.1, 500), (0.2, 100), (0.2, 200), (0.2, 300), (0.2, 400), (0.2, 500), (0.3, 100), (0.3, 200), (0.3, 300), (0.3, 400), (0.3, 500), (0.4, 100), (0.4, 200), (0.4, 300), (0.4, 400), (0.4, 500), (0.5, 100), (0.5, 200), (0.5, 300), (0.5, 400), (0.5, 500), (0.01, 100), (0.01, 200), (0.01, 300), (0.01, 400), (0.01, 500), (0.02, 100), (0.02, 200), (0.02, 300), (0.02, 400), (0.02, 500), (0.03, 100), (0.03, 200), (0.03, 300), (0.03, 400), (0.03, 500), (0.04, 100), (0.04, 200), (0.04, 300), (0.04, 400), (0.04, 500), (0.05, 100), (0.05, 200), (0.05, 300), (0.05, 400), (0.05, 500)] Maximum accuracy achieved by our model through grid searching : 60.0
In the above, we applied grid searching on all possible combinations of learning rates and the number of iterations to find the peak of the model at which it achieves the highest accuracy.
Code: Implementation of Grid Searching on Logistic Regression of sklearn
Python3
# Importing Libraries import pandas as pd import numpy as np from sklearn.linear_model import LogisticRegression from sklearn.model_selection import GridSearchCV # Driver Code def main() : # Importing dataset df = pd.read_csv( "diabetes.csv" ) X = df.iloc[:,:-1].values Y = df.iloc[:,-1:].values # Splitting dataset into train and test set X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size = 1/3, random_state = 0 ) # Model training max_accuracy = 0 # grid searching for learning rate parameters = { 'C' : [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] } model = LogisticRegression() grid = GridSearchCV( model, parameters ) grid.fit( X_train, Y_train ) # Prediction on test set Y_pred = grid.predict( X_test ) # measure performance correctly_classified = 0 # counter count = 0 for count in range( np.size( Y_pred ) ) : if Y_test[count] == Y_pred[count] : correctly_classified = correctly_classified + 1 accuracy = ( correctly_classified / count ) * 100 print( "Maximum accuracy achieved by sklearn model through grid searching : ", np.round( accuracy, 2 ) ) if __name__ == "__main__" : main() |
Output:
Maximum accuracy achieved by sklearn model through grid searching : 62.86
Note: Grid Searching plays a vital role in tuning hyperparameters for the mathematically complex models.
