Naive Bayes Classifier for text classification

This repository comprises of Naive Bayes classifier with text parser for generating(fitting and vectorizing) feature vectors from text

ParseText

Vectorize textfile and returns a sparse matrix.

Methods:

ParseText.ParseText.featureidx(self)  
	return mapping from feature/token/word to index
	  
ParseText.ParseText.featureidx(self)  
	return the list of words used as features  
  
ParseText.ParseText.getfilenames(self)  
	return the names of the labels used

ParseText.ParseText.getlabels(self)  
	return the labels of the fit data  

ParseText.ParseText.fit_transform(self, filename, delimiter=',')  
	Read csv of the format <samplename> <label> <attribute> <frequency> ...)  
	and return the feature vectors and the labels

ParseText.ParseText.vectorize(self, filename, delimiter=',')  
	Vectorize a file given the features created using fit  

NaiveBayes

Class for running Naive Bayes on input vectors  

Methods:  
__init__(self, smoothing=1.0, tfidf=False):  
	set the smoothing/alpha param for smoothing the likelihood, default laplacian smoothing with smoothing = 1.0  
	Set tfidf to True to use tfidf weights instead of count, idf = tf*(log((n+1)/(wordindocs+1))+1)

NaiveBayes.NaiveBayes.fit(self, X, y)  
	return fitted likelihood and prior probabilities.  
	X should be a list, ndarray or Sparse matrix
	y shoudl be a list or ndarray 
	Note: Both Likelihood and prior are log values, inorder to get the actual need to apply exp()
  
NaiveBayes.NaiveBayes.predict(self, X)
	return the predictions for the input X. X should be a list, np array or Sparse matrix