# -*- coding: utf-8 -*-

"""

Created on Sun Sep 11 23:06:06 2016

@author: DIP

"""

from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer

def build_feature_matrix(documents, feature_type='frequency',

ngram_range=(1, 1), min_df=0.0, max_df=1.0):

feature_type = feature_type.lower().strip()

if feature_type == 'binary':

vectorizer = CountVectorizer(binary=True, min_df=min_df,

max_df=max_df, ngram_range=ngram_range)

elif feature_type == 'frequency':

vectorizer = CountVectorizer(binary=False, min_df=min_df,

max_df=max_df, ngram_range=ngram_range)

elif feature_type == 'tfidf':

vectorizer = TfidfVectorizer(min_df=min_df, max_df=max_df,

ngram_range=ngram_range)

else:

raise Exception("Wrong feature type entered. Possible values: 'binary', 'frequency', 'tfidf'")

feature_matrix = vectorizer.fit_transform(documents).astype(float)

return vectorizer, feature_matrix

from sklearn import metrics

import numpy as np

import pandas as pd

def display_evaluation_metrics(true_labels, predicted_labels, positive_class=1):

print 'Accuracy:', np.round(

metrics.accuracy_score(true_labels,

predicted_labels),

2)

print 'Precision:', np.round(

metrics.precision_score(true_labels,

predicted_labels,

pos_label=positive_class,

average='binary'),

2)

print 'Recall:', np.round(

metrics.recall_score(true_labels,

predicted_labels,

pos_label=positive_class,

average='binary'),

2)

print 'F1 Score:', np.round(

metrics.f1_score(true_labels,

predicted_labels,

pos_label=positive_class,

average='binary'),

2)

def display_confusion_matrix(true_labels, predicted_labels, classes=[1,0]):

cm = metrics.confusion_matrix(y_true=true_labels,

y_pred=predicted_labels,

labels=classes)

cm_frame = pd.DataFrame(data=cm,

columns=pd.MultiIndex(levels=[['Predicted:'], classes],

labels=[[0,0],[0,1]]),

index=pd.MultiIndex(levels=[['Actual:'], classes],

labels=[[0,0],[0,1]]))

print cm_frame

def display_classification_report(true_labels, predicted_labels, classes=[1,0]):

report = metrics.classification_report(y_true=true_labels,

y_pred=predicted_labels,

labels=classes)

print report