def main():
feature_vector = FeatureVector(SMS_COLLECTION)
feature_vector.data_process(sep='\t')
messages = feature_vector.messages
feature_vector.transformer()
bow_transformer = feature_vector.bow_transformer
messages_bow = feature_vector.messages_bow
print "Describing the messages ..."
print messages.groupby('label').describe()
print 'sparse matrix shape:', messages_bow.shape
print 'number of non-zeros:', messages_bow.nnz
print 'sparsity: %.2f%%' % (100.0 * messages_bow.nnz / (messages_bow.shape[0] * messages_bow.shape[1]))
print "TF_IDF normalization ... "
tfidf_transformer = TfidfTransformer().fit(messages_bow)
messages_tfidf = tfidf_transformer.transform(messages_bow)
print "transform all ham/spam ... ", messages_tfidf.shape
spam_detector = MultinomialNB().fit(messages_tfidf, messages['label'])
all_predictions = spam_detector.predict(messages_tfidf)
print 'accuracy', accuracy_score(messages['label'], all_predictions)
print 'confusion matrix\n', confusion_matrix(messages['label'], all_predictions)
print '(row=expected, col=predicted)'
print classification_report(messages['label'], all_predictions)
test_bow_transformer(messages, bow_transformer, tfidf_transformer, spam_detector)
def main():
print ("SVM Approach")
print ("Generating messages ...")
feature_vector = FeatureVector(SMS_COLLECTION)
feature_vector.data_process(sep='\t')
messages = feature_vector.messages
print "Splitting into train and cross-validation sets ..."
msg_train, msg_test, label_train, label_test = train_test_split(messages['message'], messages['label'], test_size=0.2)
print len(msg_train), len(msg_test), len(msg_train) + len(msg_test)
print msg_train.shape, msg_test.shape
print "Creating Pipeline for the analyzing and training ..."
pipeline = Pipeline([
('bow', CountVectorizer(analyzer=split_into_lemmas)), # strings to token integer counts
('tfidf', TfidfTransformer()), # integer counts to weighted TF-IDF scores
('classifier', SVC()), # train on TF-IDF vectors w/ Naive Bayes classifier
])
# 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']},
]
print("pipeline:", [name for name, _ in pipeline.steps])
for name, v in pipeline.steps:
print name
print v
grid_svm = GridSearchCV(
pipeline, # 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(label_train, n_folds=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 "\nScores for various cases ..."
for i in xrange(len(svm_detector.grid_scores_)):
print svm_detector.grid_scores_[i]
curve = plot_learning_curve(pipeline, "accuracy vs. training set size", msg_train, label_train, cv=5)
curve.savefig("./plots/acc-vs-trainSize_SVM.png")
pipeline.fit(msg_train, label_train) #trained here
print "Score in 20% of test dataset"
test_predictions = svm_detector.predict(msg_test)
print 'accuracy', accuracy_score(label_test, test_predictions)
print 'confusion matrix\n', confusion_matrix(label_test, test_predictions)
print '(row=expected, col=predicted)'
print classification_report(label_test, test_predictions)
def main():
print("DecisionTree Approach")
print("Generating messages ...")
feature_vector = FeatureVector(SMS_COLLECTION)
feature_vector.data_process(sep='\t')
messages = feature_vector.messages
print "Splitting into train and cross-validation sets ..."
msg_train, msg_test, label_train, label_test = train_test_split(
messages['message'], messages['label'], test_size=0.2)
print len(msg_train), len(msg_test), len(msg_train) + len(msg_test)
print msg_train.shape, msg_test.shape
print "\nCreating Pipeline for the analyzing and training ..."
dt_old = Pipeline([
('bow', CountVectorizer(
analyzer=split_into_lemmas)), # strings to token integer counts
('tfidf',
TfidfTransformer()), # integer counts to weighted TF-IDF scores
('classifier',
DecisionTreeClassifier(min_samples_split=20, random_state=99)
), # train on TF-IDF vectors w/ DecisionTree classifier
])
print("pipeline:", [name for name, _ in dt_old.steps])
print("-- 10-fold cross-validation , without any grid search")
dt_old.fit(msg_train, label_train)
scores = cross_val_score(dt_old, msg_train, label_train, cv=10)
print "mean: {:.3f} (std: {:.3f})".format(scores.mean(), scores.std())
from sklearn.externals.six import StringIO
import pydot
dot_data = StringIO()
classes = ["ham", "spam"]
vocab = dt_old.named_steps['bow'].get_feature_names()
vocab1 = [v.encode('ascii', 'ignore') for v in vocab]
# print "vocab: ", vocab1
with open("./plots/heme.dot", "w") as f:
export_graphviz(dt_old.named_steps['classifier'],
out_file=f,
max_depth=13,
feature_names=vocab1)
print("Creating a visualization of decision tree")
# graph = pydot.graph_from_dot_data(dot_data.getvalue())
# graph.write_pdf("./plots/heme.pdf")
print "\nScore in 20% of test dataset"
test_predictions = dt_old.predict(msg_test)
print 'accuracy', accuracy_score(label_test, test_predictions)
print 'confusion matrix\n', confusion_matrix(label_test, test_predictions)
print '(row=expected, col=predicted)'
print classification_report(label_test, test_predictions)
def main():
print("Naive-Bayes Approach")
print "Generating messages ..."
feature_vector = FeatureVector(SMS_COLLECTION)
feature_vector.data_process(sep='\t')
messages = feature_vector.messages
print "Splitting into train and cross-validation sets ..."
msg_train, msg_test, label_train, label_test = train_test_split(
messages['message'], messages['label'], test_size=0.2)
print len(msg_train), len(msg_test), len(msg_train) + len(msg_test)
print msg_train.shape, msg_test.shape
print "Creating Pipeline for the analyzing and training ..."
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
])
print(pipeline)
curve = plot_learning_curve(pipeline,
"accuracy vs. training set size",
msg_train,
label_train,
cv=5)
curve.savefig("./plots/acc-vs-trainSize_naive.png")
pipeline.fit(msg_train, label_train) #trained here
print "Score in 20% of test dataset"
test_predictions = pipeline.predict(msg_test)
print 'accuracy', accuracy_score(label_test, test_predictions)
print 'confusion matrix\n', confusion_matrix(label_test, test_predictions)
print '(row=expected, col=predicted)'
print classification_report(label_test, test_predictions)
