def kNN(train_data, train_labels, test):
log_state('Use kNN classifier')
clf = KNeighborsClassifier(n_neighbors=5)
clf.fit(train_data, train_labels)
predict_labels = clf.predict(test)
dump_picle(predict_labels, './data/predict_labels/predict_labels.p')
logger.info('kNN classifier training complete, saved predict labels to pickle')
return
def logit(train_data, train_labels, test):
log_state('Use logistic regression classifier')
clf = linear_model.LogisticRegression(C=1e5)
clf.fit(train_data, train_labels)
predict_labels = clf.predict(test)
dump_picle(predict_labels, './data/predict_labels/predict_labels.p')
logger.info('MaxEnt classifier training complete, saved predict labels to pickle')
return
def svm_classify(train_data, train_labels, test):
log_state('Use SVM classifier')
clf = svm.SVC(C=5.0, kernel='linear')
clf.fit(train_data, train_labels)
predict_labels = clf.predict(test)
dump_picle(predict_labels, './data/predict_labels/predict_labels.p')
logger.info('SVM classifier training complete, saved predict labels to pickle')
return
def kNN(train_data, train_labels, test):
log_state('Use kNN classifier')
clf = KNeighborsClassifier(n_neighbors=5)
clf.fit(train_data, train_labels)
predict_labels = clf.predict(test)
dump_picle(predict_labels, './data/predict_labels/predict_labels.p')
logger.info(
'kNN classifier training complete, saved predict labels to pickle')
return
def logit(train_data, train_labels, test):
log_state('Use logistic regression classifier')
clf = linear_model.LogisticRegression(C=1e5)
clf.fit(train_data, train_labels)
predict_labels = clf.predict(test)
dump_picle(predict_labels, './data/predict_labels/predict_labels.p')
logger.info(
'MaxEnt classifier training complete, saved predict labels to pickle')
return
def svm_classify(train_data, train_labels, test):
log_state('Use SVM classifier')
clf = svm.SVC(C=5.0, kernel='linear')
clf.fit(train_data, train_labels)
predict_labels = clf.predict(test)
dump_picle(predict_labels, './data/predict_labels/predict_labels.p')
logger.info(
'SVM classifier training complete, saved predict labels to pickle')
return
def gNB(train_data, train_labels, test, save_result=False):
log_state('Use Gaussian Naive Bayes classifier')
clf = GaussianNB()
clf.fit(train_data, train_labels)
predict_labels = clf.predict(test)
predict_proba = clf.predict_proba(test)
if save_result == True:
dump_picle(predict_labels, './data/predict_labels/predict_labels.p')
dump_picle(predict_proba, './data/predict_labels/predict_proba.p')
logger.info('Classifier training complete, saved predict labels to pickle')
return predict_labels
def gNB(train_data, train_labels, test, save_result=False):
log_state('Use Gaussian Naive Bayes classifier')
clf = GaussianNB()
clf.fit(train_data, train_labels)
predict_labels = clf.predict(test)
predict_proba = clf.predict_proba(test)
if save_result == True:
dump_picle(predict_labels, './data/predict_labels/predict_labels.p')
dump_picle(predict_proba, './data/predict_labels/predict_proba.p')
logger.info('Classifier training complete, saved predict labels to pickle')
return predict_labels
def TFIDF_estimator():
log_state('Start generating features')
class StemmedTfidfVectorizer(TfidfVectorizer):
def build_analyzer(self):
# 利用NLTK进行词干化处理
english_stemmer = nltk.stem.SnowballStemmer('english')
analyzer = super(TfidfVectorizer, self).build_analyzer()
return lambda doc: (english_stemmer.stem(w) for w in analyzer(doc))
vectorizer_param = {'preprocessor': preprocessor, 'ngram_range': parameters['ngram_range'], 'analyzer': 'word',
'min_df': parameters['min_df'], 'max_df': parameters['max_df'],
'binary': parameters['TF_binary'], 'norm': parameters['norm'],
'sublinear_tf': parameters['sublinear_tf'], 'max_features': parameters['max_features']}
log_state((sorted(list(vectorizer_param.items()))))
log_state('Training data size: ' + str(parameters['test_data_size']))
return StemmedTfidfVectorizer(**vectorizer_param)
def TFIDF_estimator():
log_state('Start generating features')
class StemmedTfidfVectorizer(TfidfVectorizer):
def build_analyzer(self):
# 利用NLTK进行词干化处理
english_stemmer = nltk.stem.SnowballStemmer('english')
analyzer = super(TfidfVectorizer, self).build_analyzer()
return lambda doc: (english_stemmer.stem(w) for w in analyzer(doc))
vectorizer_param = {
'preprocessor': preprocessor,
'ngram_range': parameters['ngram_range'],
'analyzer': 'word',
'min_df': parameters['min_df'],
'max_df': parameters['max_df'],
'binary': parameters['TF_binary'],
'norm': parameters['norm'],
'sublinear_tf': parameters['sublinear_tf'],
'max_features': parameters['max_features']
}
log_state((sorted(list(vectorizer_param.items()))))
log_state('Training data size: ' + str(parameters['test_data_size']))
return StemmedTfidfVectorizer(**vectorizer_param)
from vectorizers import punctuation_estimator
from logger_manager import log_state
from anew_vectorizer import strength_vectorizer, avg_affective_vectorizer
vectorizer_param = {'preprocessor': preprocessor, 'ngram_range': parameters['ngram_range'], 'analyzer': 'word',
'min_df': parameters['min_df'], 'max_df': parameters['max_df'],
'binary': parameters['TF_binary'], 'norm': parameters['norm'],
'sublinear_tf': parameters['sublinear_tf'], 'max_features': parameters['max_features']}
if __name__ == "__main__":
unigram = StemmedTfidfVectorizer(**vectorizer_param)
anew = anew_vectorizer()
pct = punctuation_estimator()
strength = strength_vectorizer()
avg_strength = avg_affective_vectorizer()
log_state('combine unigram and avg strength features')
combined_features = FeatureUnion([('unigram', unigram), ('avg_strength', avg_strength)])
# log_state('combine unigram and strength features')
# combined_features =FeatureUnion([('unigram',unigram),('strength',strength)])
# log_state('combine unigram and anew features')
# combined_features =FeatureUnion([('unigram',unigram),('anew',anew)])
# log_state('combine unigram and punctuation features')
# combined_features =FeatureUnion([('unigram',unigram),('pct',pct)])
texts, _ = load_train_data('Sentiment140')
transformed_train = combined_features.fit_transform(texts)
testdata, _ = load_test_data()
transformed_test = combined_features.transform(testdata)
dump_picle(combined_features.get_feature_names(), './data/features/feature_names.p')