def jellyfish():
#import jellyfish
comments, dates, labels = load_data()
y_train, y_test, comments_train, comments_test = \
train_test_split(labels, comments)
#ft = TextFeatureTransformer(word_range=(1, 1),
#tokenizer_func=jellyfish.porter_stem).fit(comments_train)
#ft = TextFeatureTransformer(word_range=(1, 1),
#tokenizer_func=None).fit(comments_train)
tracer()
countvect_char = TfidfVectorizer(ngram_range=(1, 5),
analyzer="char",
binary=False)
countvect_word = TfidfVectorizer(ngram_range=(1, 3),
analyzer="word",
binary=False)
badwords = BadWordCounter()
ft = FeatureStacker([("badwords", badwords), ("chars", countvect_char),
("words", countvect_word)])
clf = LogisticRegression(C=1, tol=1e-8)
X_train = ft.transform(comments_train)
clf.fit(X_train, y_train)
X_test = ft.transform(comments_test)
probs = clf.predict_proba(X_test)
print("auc: %f" % auc_score(y_test, probs[:, 1]))
def bagging():
from sklearn.feature_selection import SelectPercentile, chi2
comments, dates, labels = load_data()
select = SelectPercentile(score_func=chi2, percentile=4)
clf = LogisticRegression(tol=1e-8, penalty='l2', C=7)
#clf = BaggingClassifier(logr, n_estimators=50)
countvect_char = TfidfVectorizer(ngram_range=(1, 5),
analyzer="char",
binary=False)
countvect_word = TfidfVectorizer(ngram_range=(1, 3),
analyzer="word",
binary=False)
badwords = BadWordCounter()
ft = FeatureStacker([("badwords", badwords), ("chars", countvect_char),
("words", countvect_word)])
#ft = TextFeatureTransformer()
pipeline = Pipeline([('vect', ft), ('select', select), ('logr', clf)])
cv = ShuffleSplit(len(comments),
n_iterations=20,
test_size=0.2,
indices=True)
scores = []
for train, test in cv:
X_train, y_train = comments[train], labels[train]
X_test, y_test = comments[test], labels[test]
pipeline.fit(X_train, y_train)
probs = pipeline.predict_proba(X_test)
scores.append(auc_score(y_test, probs[:, 1]))
print("score: %f" % scores[-1])
print(np.mean(scores), np.std(scores))
def test_stacker():
comments, dates, labels = load_data()
clf = LogisticRegression(tol=1e-8, C=0.01, penalty='l2')
countvect_char = TfidfVectorizer(ngram_range=(1, 5),
analyzer="char",
binary=False)
countvect_word = TfidfVectorizer(ngram_range=(1, 3),
analyzer="word",
binary=False)
badwords = BadWordCounter()
select = SelectPercentile(score_func=chi2)
char_select = Pipeline([('char_count', countvect_char),
('select', select)])
words_select = Pipeline([('word_count', countvect_word),
('select', select)])
badwords_select = Pipeline([('badwords', badwords), ('select', select)])
stack = FeatureStacker([("badwords", badwords_select),
("chars", char_select), ("words", words_select)])
#stack.fit(comments)
#features = stack.transform(comments)
#print("training and transforming for linear model")
print("training grid search")
pipeline = Pipeline([("features", stack), ("clf", clf)])
param_grid = dict(clf__C=[0.31, 0.42, 0.54],
features__words__select__percentile=[5, 7])
grid = GridSearchCV(pipeline,
cv=5,
param_grid=param_grid,
verbose=4,
n_jobs=1,
score_func=auc_score)
grid.fit(comments, labels)
tracer()
def build_base_model():
select = SelectPercentile(score_func=chi2, percentile=18)
clf = LogisticRegression(tol=1e-8, penalty='l2', C=7)
countvect_char = TfidfVectorizer(ngram_range=(1, 5),
analyzer="char",
binary=False)
badwords = BadWordCounter()
ft = FeatureStacker([
("badwords", badwords),
("chars", countvect_char),
])
pipeline = Pipeline([('vect', ft), ('select', select), ('logr', clf)])
return pipeline
def jellyfish():
#import jellyfish
comments, dates, labels = load_data()
y_train, y_test, comments_train, comments_test = \
train_test_split(labels, comments)
#ft = TextFeatureTransformer(word_range=(1, 1),
#tokenizer_func=jellyfish.porter_stem).fit(comments_train)
#ft = TextFeatureTransformer(word_range=(1, 1),
#tokenizer_func=None).fit(comments_train)
tracer()
countvect_char = TfidfVectorizer(ngram_range=(1, 5),
analyzer="char", binary=False)
countvect_word = TfidfVectorizer(ngram_range=(1, 3),
analyzer="word", binary=False)
badwords = BadWordCounter()
ft = FeatureStacker([("badwords", badwords), ("chars", countvect_char),
("words", countvect_word)])
clf = LogisticRegression(C=1, tol=1e-8)
X_train = ft.transform(comments_train)
clf.fit(X_train, y_train)
X_test = ft.transform(comments_test)
probs = clf.predict_proba(X_test)
print("auc: %f" % auc_score(y_test, probs[:, 1]))
def build_elasticnet_model():
select = SelectPercentile(score_func=chi2, percentile=16)
clf = SGDClassifier(loss='log',
penalty="elasticnet",
shuffle=True,
alpha=0.0001,
rho=0.95,
n_iter=20)
countvect_char = TfidfVectorizer(ngram_range=(1, 5),
analyzer="char",
binary=False)
countvect_word = TfidfVectorizer(ngram_range=(1, 3),
analyzer="word",
binary=False,
min_df=3)
badwords = BadWordCounter()
scaler = MinMaxScaler()
badwords_pipe = Pipeline([('bad', badwords), ('scaler', scaler)])
ft = FeatureStacker([("badwords", badwords_pipe),
("chars", countvect_char), ("words", countvect_word)])
pipeline = Pipeline([('vect', ft), ('select', select), ('logr', clf)])
return pipeline