Grid Searching From Scratch using Python (original) (raw)
Last Updated : 21 Mar, 2024
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.
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.