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Filtering mobile spam messages with Naive Bayes (includes text mining transformations)
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# Download data set via: | |
# http://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection | |
import numpy as np | |
import pandas as pd | |
import string | |
from nltk import word_tokenize | |
from nltk.stem.porter import PorterStemmer | |
from sklearn import preprocessing | |
from sklearn.feature_extraction.text import CountVectorizer | |
from sklearn.naive_bayes import MultinomialNB | |
from sklearn.metrics import accuracy_score | |
from sklearn.metrics import confusion_matrix | |
# dataset | |
df = pd.read_csv("SMSSpamCollection", sep="\t", names=["type", "text'"], header=0) | |
# define training and test set | |
classes = df.type | |
corpus = df.text | |
df_train = corpus.ix[0:4169] | |
df_test = corpus.ix[4170:] | |
target_train = classes.ix[0:4169] | |
target_test = classes.ix[4170:] | |
# some preprocessing via nltk | |
stemmer = PorterStemmer() | |
class SimpleTokenizer(object): | |
def __init__(self): | |
self.stemmer = PorterStemmer() | |
def __call__(self, doc): | |
return [self.stemmer.stem(tokens.lower()) for tokens in word_tokenize(doc)] | |
# compute vector model | |
vectorizer = CountVectorizer(tokenizer=SimpleTokenizer(), stop_words="english") | |
X_train = vectorizer.fit_transform(df_train) | |
X_test = vectorizer.transform(df_test) | |
y_train = np.asarray(target_train.values, dtype="|S6") | |
y_test = np.asarray(target_test.values, dtype="|S6") | |
lb = preprocessing.LabelBinarizer() | |
lb.fit(y_train) | |
lb.fit(y_test) | |
# classification | |
clf = MultinomialNB() | |
clf.fit(X_train, y_train) | |
pred = clf.predict(X_test) | |
# metrics | |
confusion_matrix(y_test, pred) | |
accuracy_score(y_test, pred) |
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