def predict(model, test_files, test_ids, CoreNLP_train_data, CoreNLP_test_data,
F, *args, **kwargs):
(M, ) = model
X = preprocessing.scale(
np.hstack([
# features.featurize('binary_bag_of_words', F['bag_of_words'], test_ids)
features.featurize('CoreNLP_sentence_info', None, test_files,
CoreNLP_test_data),
features.featurize('liwc', F['LIWC'], test_ids),
features.featurize(
'MRC_bag_of_words',
F['MRC_bag_of_words'],
test_ids,
project.CoreNLP.tokens_with_key(CoreNLP_test_data),
binary=True),
features.featurize('dependency_relations',
F['dependency_relations'],
test_ids,
CoreNLP_test_data,
binary=True),
features.featurize('production_rules',
F['production_rules'],
test_ids,
CoreNLP_test_data,
binary=True)
]))
return M.predict(X)
def train(train_files, train_ids, Y, CoreNLP_train_data, CoreNLP_test_data, F, *args, **kwargs):
X = np.hstack([
# features.featurize('binary_bag_of_words', F['bag_of_words'], train_ids)
features.featurize('CoreNLP_sentence_info', None, train_files, CoreNLP_train_data)
,features.featurize('liwc', F['LIWC'], train_ids)
,features.featurize('MRC_bag_of_words', F['MRC_bag_of_words'], train_ids, project.CoreNLP.tokens_with_key(CoreNLP_train_data), binary=True)
,features.featurize('production_rules', F['production_rules'], train_ids, CoreNLP_train_data, binary=True)
])
pca = PCA()
M = LogisticRegression()
info("> Running PCA...")
pca.fit(X)
pipe = Pipeline(steps=[('pca', pca), ('logistic', M)])
n_components = [25, 50, 100, 250, 500]
Cs = np.logspace(-4, 4, 3)
info("Running gridsearch...")
estimator = GridSearchCV(pipe, {'pca__n_components': n_components, 'logistic__C': Cs})
info("Fitting...")
estimator.fit(X, Y)
return (estimator,)
def train(train_files, train_ids, Y, CoreNLP_train_data, CoreNLP_test_data, F,
*args, **kwargs):
X = preprocessing.scale(
np.hstack([
# features.featurize('binary_bag_of_words', F['bag_of_words'], train_ids)
features.featurize('CoreNLP_sentence_info', None, train_files,
CoreNLP_train_data),
features.featurize('liwc', F['LIWC'], train_ids),
features.featurize(
'MRC_bag_of_words',
F['MRC_bag_of_words'],
train_ids,
project.CoreNLP.tokens_with_key(CoreNLP_train_data),
binary=True),
features.featurize('dependency_relations',
F['dependency_relations'],
train_ids,
CoreNLP_train_data,
binary=True),
features.featurize('production_rules',
F['production_rules'],
train_ids,
CoreNLP_train_data,
binary=True)
]))
M = svm.LinearSVC()
M.fit(X, Y)
return (M, )
def predict(model, test_files, test_ids, CoreNLP_train_data, CoreNLP_test_data, F, *args, **kwargs):
(M1, M2, M3) = model
X = np.hstack([
# features.featurize('binary_bag_of_words', F['bag_of_words'], test_ids)
features.featurize('CoreNLP_sentence_info', None, test_files, CoreNLP_test_data)
,features.featurize('liwc', F['LIWC'], test_ids)
,features.featurize('MRC_bag_of_words', F['MRC_bag_of_words'], test_ids, project.CoreNLP.tokens_with_key(CoreNLP_test_data), binary=True)
,features.featurize('dependency_relations', F['dependency_relations'], test_ids, CoreNLP_test_data, binary=True)
,features.featurize('production_rules', F['production_rules'], test_ids, CoreNLP_test_data, binary=True)
])
Y1 = M1.predict(X)
Y2 = M2.predict(preprocessing.scale(X))
Y3 = M3.predict(X)
Y = majority_vote(Y1, Y2, Y3)
y1n = len(Y1)
y2n = len(Y2)
y3n = len(Y3)
yn = len(Y)
assert(y1n == y2n and y2n == y3n and y3n == yn)
return Y
def predict(model, test_files, test_ids, CoreNLP_train_data, CoreNLP_test_data, F, *args, **kwargs):
(M,) = model
X = preprocessing.scale(np.hstack([
# features.featurize('binary_bag_of_words', F['bag_of_words'], test_ids)
features.featurize('CoreNLP_sentence_info', None, test_files, CoreNLP_test_data)
,features.featurize('liwc', F['LIWC'], test_ids)
,features.featurize('MRC_bag_of_words', F['MRC_bag_of_words'], test_ids, project.CoreNLP.tokens_with_key(CoreNLP_test_data), binary=True)
,features.featurize('dependency_relations', F['dependency_relations'], test_ids, CoreNLP_test_data, binary=True)
,features.featurize('production_rules', F['production_rules'], test_ids, CoreNLP_test_data, binary=True)
]))
return M.predict(X)
def train(train_files, train_ids, Y, CoreNLP_train_data, CoreNLP_test_data, F, *args, **kwargs):
X = preprocessing.scale(np.hstack([
# features.featurize('binary_bag_of_words', F['bag_of_words'], train_ids)
features.featurize('CoreNLP_sentence_info', None, train_files, CoreNLP_train_data)
,features.featurize('liwc', F['LIWC'], train_ids)
,features.featurize('MRC_bag_of_words', F['MRC_bag_of_words'], train_ids, project.CoreNLP.tokens_with_key(CoreNLP_train_data), binary=True)
,features.featurize('dependency_relations', F['dependency_relations'], train_ids, CoreNLP_train_data, binary=True)
,features.featurize('production_rules', F['production_rules'], train_ids, CoreNLP_train_data, binary=True)
]))
M = svm.LinearSVC()
M.fit(X, Y)
return (M,)
def predict(model, test_files, test_ids, CoreNLP_train_data, CoreNLP_test_data, F, *args, **kwargs):
(M,) = model
X = np.hstack([
# features.featurize('binary_bag_of_words', F['bag_of_words'], test_ids)
features.featurize('CoreNLP_sentence_info', None, test_files, CoreNLP_test_data)
,features.featurize('liwc', F['LIWC'], test_ids)
,features.featurize('MRC_bag_of_words', F['MRC_bag_of_words'], test_ids, project.CoreNLP.tokens_with_key(CoreNLP_test_data), binary=True)
,features.featurize('production_rules', F['production_rules'], test_ids, CoreNLP_test_data, binary=True)
])
info("Predicting...")
return estimator.predict(X)
def train(train_files, train_ids, Y, CoreNLP_train_data, CoreNLP_test_data, F, *args, **kwargs):
X = np.hstack([
# features.featurize('binary_bag_of_words', F['bag_of_words'], train_ids)
features.featurize('CoreNLP_sentence_info', None, train_files, CoreNLP_train_data)
,features.featurize('liwc', F['LIWC'], train_ids)
,features.featurize('MRC_bag_of_words', F['MRC_bag_of_words'], train_ids, project.CoreNLP.tokens_with_key(CoreNLP_train_data), binary=True)
,features.featurize('dependency_relations', F['dependency_relations'], train_ids, CoreNLP_train_data, binary=True)
,features.featurize('production_rules', F['production_rules'], train_ids, CoreNLP_train_data, binary=True)
])
M1 = LogisticRegression()
M2 = LinearSVC()
M3 = BernoulliNB()
M1.fit(X, Y)
M2.fit(preprocessing.scale(X), Y)
M3.fit(X, Y)
return (M1, M2, M3)
