def classify_lyrics_pos(genre_lyrics_map):
vectorizer = DictVectorizer()
all_lyrics_pos_tags = []
all_lyrics_genres = []
for genre in genre_lyrics_map.keys():
genre_lyrics = genre_lyrics_map[genre]
for song_lyrics in genre_lyrics:
pos_tags_map = song_lyrics["features"]["pos_tags_map"]
all_lyrics_pos_tags.append(pos_tags_map)
all_lyrics_genres.append(genre)
pos_train, pos_test, genres_train, genres_test = train_test_split(all_lyrics_pos_tags, all_lyrics_genres, test_size=0.33)
vectorizer.fit(all_lyrics_pos_tags)
vect = vectorizer.transform((all_lyrics_pos_tags))
print("vect = " + str(vect))
classifiers_to_use = get_classifiers()
partial_fit_classifiers = classifiers_to_use["partial"]
full_fit_classifiers = classifiers_to_use["full"]
teach_classifiers(partial_fit_classifiers, full_fit_classifiers, vectorizer, pos_train, genres_train, app_data.LYRICS_GENRES_METAL)
test_classifiers(partial_fit_classifiers, full_fit_classifiers, vectorizer, pos_test, genres_test)
print_top_features(partial_fit_classifiers + full_fit_classifiers, vectorizer, app_data.LYRICS_GENRES_METAL)
def cat_vectorize(train_data, test_data, num_cols):
# categorical attributes
cat_train_data = train_data.drop(num_cols, axis=1)
cat_test_data = test_data.drop(num_cols, axis=1)
cat_train_data.fillna('NA', inplace=True)
cat_test_data.fillna('NA', inplace=True)
cat_train_data_values = cat_train_data.T.to_dict().values()
cat_test_data_values = cat_test_data.T.to_dict().values()
# vectorize (encode as one hot)
vectorizer = DictVectorizer(sparse=False)
vec_train_data = vectorizer.fit_transform(cat_train_data_values)
vec_test_data = vectorizer.transform(cat_test_data_values)
return vec_train_data, vec_test_data
class EntityDetectionPU(ClassifierMixin):
def __init__(self, prior=.5, sigma=.1, lam=1, basis='gauss', n_basis=200):
self.clf = SparsePU_SL(prior=prior,
sigma=sigma,
lam=lam,
basis=basis,
n_basis=n_basis)
# self.clf = PU_SL(prior=prior, sigma=sigma, lam=lam, basis=basis, n_basis=n_basis)
# self.clf = SVC()
self.featureizer = DictVectorizer(sparse=True)
def fit(self, X, y):
x_feat = self.featureizer.fit_transform(X)
self.clf.fit(x_feat, y)
return self
def predict(self, X):
x_feat = self.featureizer.transform(X)
return self.clf.predict(x_feat)
def predict_sent(self, X_sent):
for xi in X_sent:
yield (self.predict([xi]))
# categorical attributes
cat_train = X_train.drop(numeric_cols, axis=1)
cat_test = X_test.drop(numeric_cols, axis=1)
cat_train.fillna('NA', inplace=True)
cat_test.fillna('NA', inplace=True)
x_cat_train = cat_train.T.to_dict().values()
x_cat_test = cat_test.T.to_dict().values()
# vectorize (encode as one hot)
vectorizer = DictVectorizer(sparse=False)
vec_x_cat_train = vectorizer.fit_transform(x_cat_train)
vec_x_cat_test = vectorizer.transform(x_cat_test)
# build the feature vector
x_train = np.hstack((x_num_train, vec_x_cat_train))
x_test = np.hstack((x_num_test, vec_x_cat_test))
#clfLR = LogisticRegression().fit(x_train, y_train.values)
#pred = clfLR.predict(x_test)
#print classification_report(y_test.values, pred, digits=4)
#print accuracy_score(y_test.values, pred)
clfTree = tree.DecisionTreeClassifier().fit(x_train, y_train)
predict = clfTree.predict(x_test)
#print classification_report(y_test.values, pred, digits=4)
class Model(object):
"""
Class for abstracting the different classification models.
"""
def __init__(self, train_tweets, train_targets, vect_options, tfidf_options, extra_params):
self.grid_params = {
# 'vect__ngram_range': [(1,1),(1,2),(2,2)],
# 'tfidf__use_idf': (True,False),
# 'tfidf__smooth_idf': (True, False),
# 'tfidf__sublinear_tf': (True, False),
}
self.grid_params = dict(self.grid_params.items()+extra_params.items())
self.vect_options = vect_options
self.tfidf_options = tfidf_options
self.feature_set = {}
self.train_tweets = train_tweets
self.train_targets = train_targets
self.only_text_features = False
def train_on_feature_set(self, cross_validate=True, use_tfidf=True):
"""
Performs training with the given model using the given feature set
"""
#Establish document text feature vectors
print "Vectorizing"
# self.tokenizer = CountVectorizer().build_tokenizer()
self.vect = CountVectorizer(**self.vect_options)
self.tfidf_transformer = TfidfTransformer(**self.tfidf_options)
self.dict_transformer = TfidfTransformer(**self.tfidf_options)
# train_counts_tf = tfidf_transformer.fit_transform(train_counts)
count_vector = self.vect.fit_transform([t.text for t in self.train_tweets])
tfidf_count = self.tfidf_transformer.fit_transform(count_vector)
if self.only_text_features:
combined_vector = tfidf_count
else:
self.dict_vectorizer = DictVectorizer()
dict_vector = self.dict_vectorizer.fit_transform(self.feature_set)
f=codecs.open("feature_set.txt", "w", "utf8")
for d in dict_vector:
f.write(d.__str__())
f.close()
tfidf_dict = self.dict_transformer.fit_transform(dict_vector)
f=codecs.open("feature_set_tdidf.txt", "w", "utf8")
for d in tfidf_dict:
f.write(d.__str__())
f.close()
combined_vector = sp.hstack([tfidf_count, tfidf_dict])
# combined_features = FeatureUnion()
#Crossvalidation
cross_validation = StratifiedKFold(self.train_targets, n_folds=10)
#Build a Pipeline with TFidfVectorizer and classifier
pipeline_classifier = Pipeline([
# ('vect', self.vect),
# ('tfidf', self.tfidf_transformer),
('clf', self.classifier)
])
#Perform grid search
print "Performing grid search with classifier of instance ",str(self.classifier.__class__.__name__)
self.grid = GridSearchCV(pipeline_classifier, self.grid_params, cv=cross_validation, refit=True, n_jobs=-1,verbose=1)
self.grid.fit(combined_vector, self.train_targets)
self.best_estimator = self.grid.best_estimator_
self.best_parameters = self.grid.best_params_
self.best_score = self.grid.best_score_
print "Results for ",self.classifier.__class__.__name__
print "Best params: ", self.best_parameters
print "Best score: ", self.best_score
print "Storing estimator... "
utils.store_model(self.classifier.__class__.__name__, self.best_parameters, self.best_score)
return self.grid
def grid_search_on_text_features(self, cross_validate=True, file_postfix=""):
"""
Performs a grid search using text features on the given dataset. Stores the parameters for the optimal classifier.
"""
self.grid_params = {
'vect__ngram_range': [(1,1),(1,2),(2,2),(1,3),(2,3),(3,3),(1,4)],
'vect__use_idf': (True,False),
'vect__smooth_idf': (True, False),
'vect__sublinear_tf': (True, False),
'vect__max_df': (0.5,),
}
self.vect = TfidfVectorizer()
cross_validation = StratifiedKFold(self.train_targets, n_folds=10)
#Build a Pipeline with TFidfVectorizer and classifier
pipeline_classifier = Pipeline([
('vect', self.vect),
('clf', self.classifier)]
)
#Perform grid search
print "Performing grid search with classifier of instance ",str(self.classifier.__class__.__name__)
self.grid = GridSearchCV(pipeline_classifier, self.grid_params, cv=cross_validation, refit=True, n_jobs=-1,verbose=1)
self.grid.fit([t.text for t in self.train_tweets], self.train_targets)
self.best_estimator = self.grid.best_estimator_
self.best_parameters = self.grid.best_params_
self.best_score = self.grid.best_score_
print "Results for ",self.classifier.__class__.__name__
print "Best params: ", self.best_parameters
print "Best score: ", self.best_score
print "Storing estimator... "
utils.store_model(self.classifier.__class__.__name__, self.best_parameters, self.best_score, file_postfix=file_postfix)
return self.grid
def classify(self, tweets, sentimentvalues=None):
"""
Performs the classification process on list of tweets.
"""
if sentimentvalues!=None:
self.test_words_and_values = sentimentvalues
count_vector = self.vect.transform([t.text for t in tweets])
tfidf_count = self.tfidf_transformer.transform(count_vector)
if self.only_text_features:
combined_vector = tfidf_count
else:
dict_vector = self.dict_vectorizer.transform([features.get_feature_set(t, self.featureset, v) for t,v in zip(tweets, self.test_words_and_values)])
tfidf_dict = self.dict_transformer.transform(dict_vector)
combined_vector = sp.hstack([tfidf_count, tfidf_dict])
predictions = self.best_estimator.predict(combined_vector)
return predictions
def classify_text(self, texts):
"""
Performs classification with only text features.
"""
count_vector = self.vect.transform([t for t in texts])
text_vector = self.tfidf_transformer.transform(count_vector)
predictions = self.best_estimator.predict(text_vector)
return predictions
def test_and_return_results(self, test_tweets, test_targets, sentimentvalues):
"""
Tests the classifier on a given test set, and returns the accuracy, precision, recall, and f1 score.
"""
self.test_words_and_values = sentimentvalues
predictions = self.classify(test_tweets)
binary_predictions = utils.reduce_targets(predictions)
binary_test_targets = utils.reduce_targets(test_targets)
accuracy = metrics.accuracy_score(binary_test_targets, binary_predictions)
precision = metrics.precision_score(binary_test_targets, binary_predictions)
recall = metrics.recall_score(binary_test_targets, binary_predictions)
f1_score = metrics.f1_score(binary_test_targets, binary_predictions)
print "Scores: ", accuracy, precision, recall, f1_score
return accuracy, precision, recall, f1_score
def get_correctly_classified_tweets(self, tweets_and_sentiment):
"""
Classifies the given set of tweets and returns the ones that were correctly classified.
"""
tweets, sentimentvalues = zip(*tweets_and_sentiment)
if sentimentvalues!=None:
self.test_words_and_values = sentimentvalues
count_vector = self.vect.transform([t.text for t in tweets])
tfidf_count = self.tfidf_transformer.transform(count_vector)
if self.only_text_features:
combined_vector = tfidf_count
else:
dict_vector = self.dict_vectorizer.transform([features.get_feature_set(t, self.featureset, v) for t,v in zip(tweets, self.test_words_and_values)])
tfidf_dict = self.dict_transformer.transform(dict_vector)
combined_vector = sp.hstack([tfidf_count, tfidf_dict])
predictions = self.best_estimator.predict(combined_vector)
tweets, targets = utils.make_subjectivity_targets(tweets)
#return the tweets where the target match prediction
correct_tweets = []
correct_sentimentvalues = []
for i in xrange(len(tweets)):
if predictions[i]==targets[i]:
correct_tweets.append(tweets[i])
correct_sentimentvalues.append(sentimentvalues[i])
return correct_tweets, correct_sentimentvalues
def set_feature_set(self, featureset, sentimentvalues):
"""
Extracts and stores the given feature set for classification.
"""
self.featureset = featureset
if featureset=='SA' or featureset=='PA':
self.only_text_features=True
self.feature_set = {}
else:
words_and_values = sentimentvalues
self.feature_set = [features.get_feature_set(t, self.featureset, v) for t,v in zip(self.train_tweets,words_and_values)]
test_features = [id_features[id] for id in test_ids]
labels = {'0': 0, '1': 1, '2': 2}
train_labels = [labels[id_severity_train[id]] for id in train_ids]
test_fake_labels = [train_labels[0]] * len(test_ids)
vectorizer = DictVectorizer()
X_train = vectorizer.fit_transform(train_features)
features = vectorizer.get_feature_names()
save_train_features = False
if save_train_features:
np.savetxt('x_train.txt',
X_train.toarray(),
delimiter=',',
header=','.join(features))
X_test = vectorizer.transform(test_features)
#scaler = prep.MinMaxScaler(feature_range=(0, 1), copy=True)
scaler = prep.StandardScaler(copy=True, with_mean=True, with_std=True)
X_train = scaler.fit_transform(X_train.toarray())
X_test = scaler.transform(X_test.toarray())
do_feature_elimination = False
if do_feature_elimination:
estimator = RandomForestClassifier(n_estimators=2000,
criterion='entropy',
max_depth=None,
min_samples_split=16,
min_samples_leaf=1,
min_weight_fraction_leaf=0.0,
max_features='auto',
train_ids = sorted(id_location_train.keys())
test_ids = sorted(id_location_test.keys())
train_features = [id_features[id] for id in train_ids]
test_features = [id_features[id] for id in test_ids]
labels = {'0':0, '1':1, '2':2}
train_labels = [labels[id_severity_train[id]] for id in train_ids]
test_fake_labels = [train_labels[0]] * len(test_ids)
vectorizer = DictVectorizer()
X_train = vectorizer.fit_transform(train_features)
features = vectorizer.get_feature_names()
save_train_features = False
if save_train_features:
np.savetxt('x_train.txt', X_train.toarray(), delimiter=',', header=','.join(features))
X_test = vectorizer.transform(test_features)
#scaler = prep.MinMaxScaler(feature_range=(0, 1), copy=True)
scaler = prep.StandardScaler(copy=True, with_mean=True, with_std=True)
X_train = scaler.fit_transform(X_train.toarray())
X_test = scaler.transform(X_test.toarray())
do_feature_elimination = False
if do_feature_elimination:
estimator = RandomForestClassifier(n_estimators=2000, criterion='entropy', max_depth=None,
min_samples_split=16, min_samples_leaf=1, min_weight_fraction_leaf=0.0,
max_features='auto', max_leaf_nodes=None, bootstrap=False, oob_score=False,
n_jobs=10, random_state=None, verbose=0, warm_start=False, class_weight=None)
selector = RFECV(estimator, step=1, cv=5, scoring='log_loss')
X_train = selector.fit_transform(X_train, train_labels)
print 'after feature elimination', X_train.shape
