def test_fit(self):
print("test_fit")
nb = NaiveBayesClassifier()
Xis = np.array([[3, 4], [2, 3]])
yis = [0, 1]
nb.prior_probs = np.zeros(2, dtype=np.float64)
# Testing value updation with known calculation.
self.assertEqual(nb.prior_probs[0], 0)
self.assertEqual(nb.prior_probs[1], 0)
self.assertEqual(nb.fit(Xis, yis), None)
self.assertEqual(nb.prior_probs[0], 0.5)
self.assertEqual(nb.prior_probs[1], 0.5)
from NaiveBayes import NaiveBayesClassifier
#importing the class
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn import datasets
import matplotlib.pyplot as plt
#Calculating accuracy --(actual-prediction)/(total no of samples)
def accuracy(y_actual, yhat):
accuracy = np.sum(y_actual == yhat) / len(y_actual)
return accuracy
X, y = datasets.make_classification(n_samples=1000,
n_features=10,
n_classes=2,
random_state=123)
#Dividing the dataset into train and test -80-20 division
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=123)
nb = NaiveBayesClassifier()
nb.fit(X_train, y_train) #fitting the model
predictions = nb.predict(X_test) #predicting on the test set
print("Accuracy", accuracy(y_test, predictions)) #calculating the accuracy
