The file naivebayes.py contanins the implementation Guassian and Multinomail Naive Bayes Classifier
To make it much more simple I have restricted the input domain only to numpy arrays.
The formula is used compute the posterior probability, in Guassian Naive Bayes for calculating the Likelihood we use the normal uni/multivariate distribution(depending on the features)
self.n_features_ # Number of features
self.n_classes_ # Number of classes
self.class_mean_ # Contains the class mean's
self.class_std_ # Contains the class standard deviation's
self.prior_proba_ # Prior Probabilities of each class
self.class_encoding_ # Contains Class encodingsIn [1]: import pandas as pd
In [2]: from naivebayes import GaussianNB
In [3]: from sklearn.model_selection import train_test_split
In [4]: data = pd.read_csv('Iris.csv', index_col='Id')
In [5]: train_X, test_X, train_y, test_y = train_test_split(data.loc[:, data.columns != 'Species'], data['Species'], test_size=0.2)
In [6]: clf = GaussianNB()
In [7]: clf.fit(train_X.values, train_y.values)
In [8]: clf.predict(test_X.values)
Out[8]: array([2, 2, 0, 1, 1, 2, 0, 0, 0, 2, 0, 2, 1, 1, 2, 1, 1, 0, 1, 1, 2, 0, 0, 2, 1, 0, 2, 0, 0, 0])
In [9]: clf.evaluate(train_X.values, train_y.values) # R^2 score on training data
Out[9]: 0.9248747913188647
In [10]: clf.evaluate(test_X.values, test_y.values) # R^2 score on testing data
Out[10]: 0.9486301369863014
Change the data reading according to your dataset file.
The above figure refers to the Multinomial Naive Bayes formula.
self.n_features_ # Number of features
self.n_classes_ # Number of classes
self.prior_proba_ # Prior Probabilities of each class
self.class_encoding_ # Contains Class encodingsIn [1]: import pandas as pd
In [2]: from naivebayes import MultinomialNB
In [3]: from sklearn.model_selection import train_test_split
In [4]: data = pd.read_csv('imdb.zip', compression='zip')
In [5]: train_X, test_X, train_y, test_y = train_test_split(data.loc[:, data.columns != 'sentiment'], data['sentiment'], test_size=0.2)
In [6]: clf = MultinomialNB()
In [7]: clf.fit(train_X.values, train_y.values)
In [8]: clf.predict(test_X.values)
Out[8]: array([0 0 0 ... 1 0 0])
In [9]: clf.evaluate(train_X.values, train_y.values) # MSE on training data
Out[9]: 0.4501
In [10]: clf.evaluate(test_X.values, test_y.values) # MSE on testing data
Out[10]: 0.4517