Naive Bayes & SVM for SMS SPAM FILTERING (Python)

sms-spam-filtering.py

import matplotlib.pyplot as plt
import csv
from textblob import TextBlob
import pandas
import sklearn
import pickle
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import SVC, LinearSVC
from sklearn.metrics import classification_report, f1_score, accuracy_score, confusion_matrix
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV, StratifiedKFold, cross_val_score, train_test_split, learning_curve 
from sklearn.tree import DecisionTreeClassifier 

def split_into_tokens(message):
    return TextBlob(message).words

def split_into_lemmas(message):
    message = message.lower()
    words = TextBlob(message).words
    # for each word, take its "base form" = lemma 
    return [word.lemma for word in words]
    
def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
                        n_jobs=-1, train_sizes=np.linspace(.1, 1.0, 5)):
    """
    Generate a simple plot of the test and traning learning curve.

    Parameters
    ----------
    estimator : object type that implements the "fit" and "predict" methods
        An object of that type which is cloned for each validation.

    title : string
        Title for the chart.

    X : array-like, shape (n_samples, n_features)
        Training vector, where n_samples is the number of samples and
        n_features is the number of features.

    y : array-like, shape (n_samples) or (n_samples, n_features), optional
        Target relative to X for classification or regression;
        None for unsupervised learning.

    ylim : tuple, shape (ymin, ymax), optional
        Defines minimum and maximum yvalues plotted.

    cv : integer, cross-validation generator, optional
        If an integer is passed, it is the number of folds (defaults to 3).
        Specific cross-validation objects can be passed, see
        sklearn.cross_validation module for the list of possible objects

    n_jobs : integer, optional
        Number of jobs to run in parallel (default 1).
    """
    plt.figure()
    plt.title(title)
    if ylim is not None:
        plt.ylim(*ylim)
    plt.xlabel("Training examples")
    plt.ylabel("Score")
    train_sizes, train_scores, test_scores = learning_curve(
        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)
    train_scores_mean = np.mean(train_scores, axis=1)
    train_scores_std = np.std(train_scores, axis=1)
    test_scores_mean = np.mean(test_scores, axis=1)
    test_scores_std = np.std(test_scores, axis=1)
    plt.grid()

    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
                     train_scores_mean + train_scores_std, alpha=0.1,
                     color="r")
    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,
                     test_scores_mean + test_scores_std, alpha=0.1, color="g")
    plt.plot(train_sizes, train_scores_mean, 'o-', color="r",
             label="Training score")
    plt.plot(train_sizes, test_scores_mean, 'o-', color="g",
             label="Cross-validation score")

    plt.legend(loc="best")
    return plt

if __name__ == '__main__':
	print('# Read Data Set')
	# read message dataset
	messages = pandas.read_csv('SMSSpamCollection', sep='\t', quoting=csv.QUOTE_NONE,
							   names=["label", "message"])

	# Split data set
	print('# Data set:')

	msg_train, msg_test, label_train, label_test = \
		train_test_split(messages['message'], messages['label'], test_size=0.2)

	print('Training data: {}, Test data: {}, Total: {}'.format(len(msg_train), len(msg_test), len(msg_train) + len(msg_test)))
	
	# Bayes
	print('# Using Naive Bayes : \n')

	pipeline = Pipeline([
		('bow', CountVectorizer(analyzer=split_into_lemmas)),  # strings to token integer counts
		('tfidf', TfidfTransformer()),  # integer counts to weighted TF-IDF scores
		('classifier', MultinomialNB()),  # train on TF-IDF vectors w/ Naive Bayes classifier
	])

	params = {
    'tfidf__use_idf': (True, False),
    'bow__analyzer': (split_into_lemmas, split_into_tokens),
	}

	grid = GridSearchCV(
		pipeline,  # pipeline from above
		params,  # parameters to tune via cross validation
		refit=True,  # fit using all available data at the end, on the best found param combination
		n_jobs=-1,  # number of cores to use for parallelization; -1 for "all cores"
		scoring='accuracy',  # what score are we optimizing?
		cv=StratifiedKFold(n_splits=5),  # what type of cross validation to use
	)
	
	nb_detector = grid.fit(msg_train, label_train)
	predictions = nb_detector.predict(msg_test)
	print('Confusion Matrix: \n')
	print(confusion_matrix(label_test, predictions))
	print(classification_report(label_test, predictions))
	
	# SVM
	print('# Using SVM : \n')
	pipeline_svm = Pipeline([
    ('bow', CountVectorizer(analyzer=split_into_lemmas)),
    ('tfidf', TfidfTransformer()),
    ('classifier', SVC()),  # <== change here
	])

	# pipeline parameters to automatically explore and tune
	param_svm = [
	  {'classifier__C': [1, 10, 100, 1000], 'classifier__kernel': ['linear']},
	  {'classifier__C': [1, 10, 100, 1000], 'classifier__gamma': [0.001, 0.0001], 'classifier__kernel': ['rbf']},
	]
	grid_svm = GridSearchCV(
		pipeline_svm,  # pipeline from above
		param_grid=param_svm,  # parameters to tune via cross validation
		refit=True,  # fit using all data, on the best detected classifier
		n_jobs=-1,  # number of cores to use for parallelization; -1 for "all cores"
		scoring='accuracy',  # what score are we optimizing?
		cv=StratifiedKFold(n_splits=5),  # what type of cross validation to use
	)
	svm_detector = grid_svm.fit(msg_train, label_train) # find the best combination from param_svm
	print('Confusion Matrix : \n')
	print(confusion_matrix(label_test, svm_detector.predict(msg_test)))
	print(classification_report(label_test, svm_detector.predict(msg_test)))
	#plotGraf = plot_learning_curve(pipeline, "accuracy vs. training set size", msg_train, label_train, cv=5)
	#plotGraf.show()