def feature_reduce_f_class_if(X, Y, num_features_to_keep):
test = SelectKBest(score_func=f_classif, k=num_features_to_keep)
fit = test.fit(X, Y)
#return the data with reduced features
return fit.transform(X)
def test_select_kbest_classif():
"""
Test whether the relative univariate feature selection
gets the correct items in a simple classification problem
with the k best heuristic
"""
X, Y = make_classification(n_samples=200,
n_features=20,
n_informative=3,
n_redundant=2,
n_repeated=0,
n_classes=8,
n_clusters_per_class=1,
flip_y=0.0,
class_sep=10,
shuffle=False,
random_state=0)
univariate_filter = SelectKBest(f_classif, k=5)
X_r = univariate_filter.fit(X, Y).transform(X)
X_r2 = GenericUnivariateSelect(f_classif, mode='k_best',
param=5).fit(X, Y).transform(X)
assert_array_equal(X_r, X_r2)
support = univariate_filter.get_support()
gtruth = np.zeros(20)
gtruth[:5] = 1
assert_array_equal(support, gtruth)
def feature_selection(feat_select, X, y):
"""" Implements various kinds of feature selection """
# K-best
if re.match('.*-best', feat_select) is not None:
n = int(feat_select.split('-')[0])
selector = SelectKBest(k=n)
import warnings
with warnings.catch_warnings():
warnings.simplefilter('ignore', category=UserWarning)
features_selected = np.where(
selector.fit(X, y).get_support() == True)[0]
elif re.match('.*-randombest', feat_select) is not None:
n = int(feat_select.split('-')[0])
from random import shuffle
features = range(0, X.shape[1])
shuffle(features)
features_selected = features[:n]
return features_selected
def feature_selection(feat_select, X, y):
"""" Implements various kinds of feature selection """
# K-best
if re.match('.*-best', feat_select) is not None:
n = int(feat_select.split('-')[0])
selector = SelectKBest(k=n)
import warnings
with warnings.catch_warnings():
warnings.simplefilter('ignore', category=UserWarning)
features_selected = np.where(
selector.fit(X, y).get_support() == True)[0]
elif re.match('.*-randombest', feat_select) is not None:
n = int(feat_select.split('-')[0])
from random import shuffle
features = range(0, X.shape[1])
shuffle(features)
features_selected = features[:n]
return features_selected
def extract(max_gram, feat_dims, save_model=False):
print "extract feature"
vectorizer = TfidfVectorizer( min_df=2, max_df=0.95, max_features=None,
ngram_range=(1, max_gram), sublinear_tf = True )
vectorizer = vectorizer.fit(reviews_train + reviews_unsup)
feats_train_ori = vectorizer.transform(reviews_train)
feats_test_ori = vectorizer.transform(reviews_test)
print "size of orginal train features", feats_train_ori.shape
for feat_dim in feat_dims:
print "perform feature selection"
fselect = SelectKBest(chi2 , k=feat_dim)
feats_train = fselect.fit_transform(feats_train_ori, labels_train)
feats_test = fselect.transform(feats_test_ori)
print "save features"
np.savez("feats/%d_%d.npz" % (max_gram, feat_dim),
feats_train=feats_train, feats_test=feats_test,
labels_train=labels_train, labels_test=labels_test)
if save_model:
print "save models"
with open("models/vectorizer_%d.pkl" % max_gram, "wb") as fout:
pickle.dump(vectorizer, fout, -1)
with open("models/fselect_%d_%d.pkl" % (max_gram, feat_dim), "wb") as fout:
pickle.dump(fselect, fout, -1)
def feature_reduce(X, Y, num_features_to_keep):
#use the chi-squared method to reduce features and reshape data
test = SelectKBest(score_func=chi2, k=num_features_to_keep)
fit = test.fit(X, Y)
#return the data with reduced features
return fit.transform(X)
def test_select_kbest_classif():
"""
Test whether the relative univariate feature selection
gets the correct items in a simple classification problem
with the k best heuristic
"""
X, Y = make_classification(
n_samples=200,
n_features=20,
n_informative=3,
n_redundant=2,
n_repeated=0,
n_classes=8,
n_clusters_per_class=1,
flip_y=0.0,
class_sep=10,
shuffle=False,
random_state=0,
)
univariate_filter = SelectKBest(f_classif, k=5)
X_r = univariate_filter.fit(X, Y).transform(X)
X_r2 = GenericUnivariateSelect(f_classif, mode="k_best", param=5).fit(X, Y).transform(X)
assert_array_equal(X_r, X_r2)
support = univariate_filter.get_support()
gtruth = np.zeros(20)
gtruth[:5] = 1
assert_array_equal(support, gtruth)
def corr_matrix_of_important_words(term_doc_mat, word_list, scores,
n_features_to_keep):
selector = SelectKBest(k=n_features_to_keep).fit(term_doc_mat, scores)
informative_words_index = selector.get_support(indices=True)
labels = [word_list[i] for i in informative_words_index]
data = pd.DataFrame(term_doc_mat[:, informative_words_index].todense(),
columns=labels)
data['Score'] = df_one_company.Rating
return (data.corr())
def feature_selection(feat_select, X, y):
"""" Implements various kinds of feature selection """
# K-best
if re.match('.*-best', feat_select) is not None:
n = feat_select.split('-')[0]
selector = SelectKBest(k=int(n))
features_selected = np.where(
selector.fit(X, y).get_support() == True)[0]
return features_selected
def feature_selection(feat_select, X, y):
"""" Implements various kinds of feature selection """
# K-best
if re.match('.*-best', feat_select) is not None:
n = feat_select.split('-')[0]
selector = SelectKBest(k=int(n))
features_selected = np.where(
selector.fit(X, y).get_support() == True)[0]
return features_selected
def build_dict_feature_imdb(double_features):
sentences_train = []
for ff in tqdm.tqdm(glob.glob(os.path.join(path_train_pos, '*.txt')),
desc="train pos"):
with io.open(ff, 'r', encoding='utf-8') as f:
sentences_train.append(f.readline().strip())
for ff in tqdm.tqdm(glob.glob(os.path.join(path_train_neg, '*.txt')),
desc="train neg"):
with io.open(ff, 'r', encoding='utf-8') as f:
sentences_train.append(f.readline().strip())
sentences_test = []
for ff in tqdm.tqdm(glob.glob(os.path.join(path_test_pos, '*.txt')),
desc="test pos"):
with io.open(ff, 'r', encoding='utf-8') as f:
sentences_test.append(f.readline().strip())
for ff in tqdm.tqdm(glob.glob(os.path.join(path_test_neg, '*.txt')),
desc="test neg"):
with io.open(ff, 'r', encoding='utf-8') as f:
sentences_test.append(f.readline().strip())
if model == "svm":
X_train, vectorizer_fitted = build_dic_svm(sentences_train,
double_features)
X_test, _ = build_dic_svm(sentences_test, double_features,
vectorizer_fitted)
n = X_train.shape[0] / 2
y_train = [1] * n + [0] * n
y_test = [1] * n + [0] * n
elif model == "cnn" or model == "lstm":
X_train, tokenizer = build_dic_nn(sentences=sentences_train,
double_features=double_features)
X_test, _ = build_dic_nn(sentences=sentences_test,
double_features=double_features,
tokenizer=tokenizer)
n = len(X_train) / 2
y_train = [1] * n + [0] * n
y_test = [1] * n + [0] * n
if feature_selection:
print("Doing feature selection")
if hashing_trick:
fselect = SelectKBest(chi2, k=200000)
else:
if negation:
fselect = SelectKBest(chi2, k=200000)
else:
fselect = SelectKBest(chi2, k=200000)
X_train = fselect.fit_transform(X_train, y_train)
X_test = fselect.transform(X_test)
return X_train, X_test, y_train, y_test
def fit(self, k=100, percent=None):
selector = SelectKBest(k=k)
selector.fit(self.doc_vecs.todense(), np.asarray(self.labels))
scores = selector.scores_
indices = np.argsort(scores)
if k is not None:
select = k
elif percent is not None:
select = int(len(scores) * percent)
else:
raise ValueError('One of `k` or `percent` parameter must be not None.')
indices = indices[:select]
self._filtered_words = [self.words[i] for i in indices]
def test_select_kbest_regression():
"""
Test whether the relative univariate feature selection
gets the correct items in a simple regression problem
with the k best heuristic
"""
X, Y = make_regression(n_samples=200, n_features=20, n_informative=5, shuffle=False, random_state=0)
univariate_filter = SelectKBest(f_regression, k=5)
X_r = univariate_filter.fit(X, Y).transform(X)
X_r2 = GenericUnivariateSelect(f_regression, mode="k_best", param=5).fit(X, Y).transform(X)
assert_array_equal(X_r, X_r2)
support = univariate_filter.get_support()
gtruth = np.zeros(20)
gtruth[:5] = 1
assert_array_equal(support, gtruth)
def test_select_kbest_regression():
"""
Test whether the relative univariate feature selection
gets the correct items in a simple regression problem
with the k best heuristic
"""
X, Y = make_regression(n_samples=200, n_features=20,
n_informative=5, shuffle=False, random_state=0)
univariate_filter = SelectKBest(f_regression, k=5)
X_r = univariate_filter.fit(X, Y).transform(X)
X_r2 = GenericUnivariateSelect(f_regression, mode='k_best',
param=5).fit(X, Y).transform(X)
assert_array_equal(X_r, X_r2)
support = univariate_filter.get_support()
gtruth = np.zeros(20)
gtruth[:5] = 1
assert_array_equal(support, gtruth)
def get_best_features(self, data, labels, k=3):
'''
Using the scikit-learn library, narrow down feature set.
'''
num_feat = len(data.columns)
while num_feat > k:
num_feat = max(k, num_feat // 2)
selector = SelectKBest(f_classif, k=num_feat)
selector.fit(data, labels)
chosen = selector.get_support()
if sum(selector._pvalues[chosen]) > 0:
data = data[data.columns[chosen]]
else:
# Many of our p-vals are zero. Accept all.
data = data[data.columns[selector._pvalues == 0]]
num_feat = k
return data.columns
def reduce_dim(vec, num_dim, method, label=None):
"""
Dimension reduction. Two approaches are provided.
SVD: Truncated SVD maps feature vectors into different subspaces.
chi2: Chi-square independence test examine the pairwise dependence of features and labels
"""
print "Performing dimension reduction"
# Reduce the dimensions using truncated SVD or Chi-Square independence test
if method == "SVD":
svd = TruncatedSVD(n_components=num_dim)
vec = svd.fit_transform(vec)
# test = svd.transform(vec)
elif method == "chi2" or method == "f_classif":
fselect = SelectKBest((chi2 if method == "chi2" else f_classif), k=num_dim)
vec = fselect.fit_transform(vec, label)
# test = fselect.transform(vec)
return vec
def apply_feature_selection(X_train, y_train, X_test, features):
if CONFIG['preprocessing']['use_feature_selection'] == 'random_forest':
clf = RandomForestClassifier()
clf = clf.fit(X_train.toarray(), y_train)
features_scores = [(feature, score) for (score, feature) in sorted(
zip(clf.feature_importances_, features), reverse=True)]
selected_features = features_scores[:CONFIG['preprocessing']
['top_features_to_select']]
selected_indeces = np.searchsorted(features,
[f[0] for f in selected_features])
X_train = X_train[:, selected_indeces]
X_test = X_test[:, selected_indeces]
return X_train, y_train, X_test, selected_features
if CONFIG['preprocessing']['use_feature_selection'] == 'chi2':
algorithm = chi2
elif CONFIG['preprocessing']['use_feature_selection'] == 'ANOVA':
algorithm = f_classif
else:
raise ValueError("No implementation for " +
str(CONFIG['preprocessing']['use_feature_selection']))
feature_selector = SelectKBest(
algorithm, k=CONFIG['preprocessing']['top_features_to_select'])
feature_selector.fit(X_train, y_train)
X_train = feature_selector.fit_transform(X_train, y_train)
X_test = feature_selector.transform(X_test)
features = [
(feature, score)
for (score, feature
) in sorted(zip(feature_selector.scores_, features), reverse=True)
]
selected_features = features[:CONFIG['preprocessing']
['top_features_to_select']]
return X_train, y_train, X_test, selected_features
def build_dict_feature_spd(double_features):
sentences_pos = []
ff = os.path.join(dataset_path_spd, 'rt-polarity_utf8.pos')
with io.open(ff, 'r', encoding='UTF-8') as f:
for line in tqdm.tqdm(f, desc="sentences pos"):
# time.sleep(0.001)
sentences_pos.append(line)
sentences_neg = []
ff = os.path.join(dataset_path_spd, 'rt-polarity_utf8.neg')
with io.open(ff, 'r', encoding='UTF-8') as f:
for line in tqdm.tqdm(f, desc="sentences neg"):
# time.sleep(0.001)
sentences_neg.append(line)
sentences = sentences_pos + sentences_neg
y = [1] * (len(sentences_pos)) + [0] * (len(sentences_neg))
sentences_train, sentences_test, y_train, y_test = train_test_split(
sentences, y, test_size=0.2, random_state=58)
if model == "svm":
X_train, vectorizer = build_dic_svm(sentences_train, double_features)
X_test, _ = build_dic_svm(sentences_test, double_features, vectorizer)
elif model == "cnn" or model == "lstm":
X_train, tokenizer = build_dic_nn(sentences=sentences_train,
double_features=double_features)
X_test, _ = build_dic_nn(sentences=sentences_test,
double_features=double_features,
tokenizer=tokenizer)
if feature_selection:
print("Doing feature selection")
if hashing_trick:
fselect = SelectKBest(chi2, k=9500)
else:
if negation:
fselect = SelectKBest(chi2, k=9500)
else:
fselect = SelectKBest(chi2, k=8500)
X_train = fselect.fit_transform(X_train, y_train)
X_test = fselect.transform(X_test)
return X_train, X_test, y_train, y_test
print_step('Importing Data 3/13')
tfidf_train2, tfidf_test2 = load_cache('text_tfidf')
print_step('Importing Data 4/13')
tfidf_train3, tfidf_test3 = load_cache('text_char_tfidf')
print_step('Importing Data 5/13')
train = hstack((tfidf_train2, tfidf_train3)).tocsr()
print_step('Importing Data 6/13')
test = hstack((tfidf_test2, tfidf_test3)).tocsr()
print(train.shape)
print(test.shape)
print_step('SelectKBest 1/2')
fselect = SelectKBest(f_regression, k=100000)
train = fselect.fit_transform(train, target)
print_step('SelectKBest 2/2')
test = fselect.transform(test)
print(train.shape)
print(test.shape)
print_step('Importing Data 7/13')
train = hstack((tfidf_train, train)).tocsr()
print_step('Importing Data 8/13')
test = hstack((tfidf_test, test)).tocsr()
print(train.shape)
print(test.shape)
print_step('GC')
del tfidf_test
'loader__loader':
'bids-meg',
'loader__bids_win':
'700',
'loader__task':
'reftep',
'loader__load_fx':
'reftep-iplv',
'fetch__subject_names': ['sub-1'],
'fetch__prepro': [Transformer()],
'prepro': ['sample_slicer', 'target_transformer'],
'target_transformer__fx':
lambda x: np.log(x),
'balancer__attr':
'all',
'estimator': [('fsel', SelectKBest(k=50, score_func=f_regression)),
('clf', SVR(C=1, kernel='linear'))],
'cv':
ShuffleSplit,
'cv__n_splits':
10,
#'cv__test_size': 0.25,
'analysis__scoring': ['r2', 'explained_variance'],
'analysis':
RoiRegression,
'analysis__n_jobs':
-1,
'analysis__permutation':
0,
'analysis__verbose':
0,
for review in test['Reviews']:
clean_test_reviews.append(" ".join(review_to_wordlist(review)))
# In[ ]:
vectorizer = TfidfVectorizer(min_df=2,
max_df=0.95,
max_features=200000,
ngram_range=(1, 4),
sublinear_tf=True)
vectorizer = vectorizer.fit(clean_train_reviews)
train_features = vectorizer.transform(clean_train_reviews)
test_features = vectorizer.transform(clean_test_reviews)
fselect = SelectKBest(chi2, k=10000)
train_features = fselect.fit_transform(train_features, train["Rating"])
test_features = fselect.transform(test_features)
# # Machine learning
# In[ ]:
classifiers = [
('RandomForestClassifierG',
RandomForestClassifier(n_jobs=-1, criterion='gini')),
('RandomForestClassifierE',
RandomForestClassifier(n_jobs=-1, criterion='entropy')),
('AdaBoostClassifier', AdaBoostClassifier()),
('ExtraTreesClassifier', ExtraTreesClassifier(n_jobs=-1)),
('DecisionTreeClassifier', DecisionTreeClassifier()),
def validate(params):
transf_type = params['transf_type']
if transf_type == 'drop':
transf = FunctionTransformer(drop_transform, validate=False)
elif transf_type == 'dr+inp+sc+pca':
transf = make_pipeline(
drop_transform,
SimpleImputer(),
StandardScaler(),
PCA(n_components=params['n_pca_components']),
)
elif transf_type == 'dr+inp':
transf = make_pipeline(
drop_transform,
SimpleImputer(),
)
elif transf_type == 'dr+inp+sc':
transf = make_pipeline(drop_transform, SimpleImputer(),
StandardScaler())
elif transf_type == 'union':
transf = create_union_transf(params)
elif transf_type == 'poly_kbest':
transf = make_pipeline(
drop_transform,
SimpleImputer(),
StandardScaler(),
PolynomialFeatures(degree=2, interaction_only=True),
SelectKBest(f_regression, params['best_features']),
)
else:
raise AttributeError(f'unknown transformer type: {transf_type}')
est_type = params['est_type']
if est_type == 'xgboost':
est = create_xgb_est(params)
elif est_type == 'gblinear':
est = create_gblinear_est(params)
elif est_type == 'exttree':
est = ExtraTreesRegressor(n_estimators=params['n_estimators'],
n_jobs=-1)
elif est_type == 'gp':
est = GaussianProcessRegressor()
elif est_type == 'ridge':
est = Ridge(alpha=params['alpha'])
else:
raise AttributeError(f'unknown estimator type: {est_type}')
if params['bagging']:
BaggingRegressor(est,
n_estimators=params['n_bag_estimators'],
max_features=1.,
max_samples=1.)
pl = make_pipeline(transf, est)
if params['per_group_regr']:
pl = PerGroupRegressor(estimator=pl,
split_condition=['os', 'cpuFreq', 'memSize_MB'],
n_jobs=1,
verbose=1)
return cv_test(pl, n_folds=params['n_folds'])
'_train.npz')
# filepaths.append(feature_set_path + 'bigramOnlyTfidfWordData' + tag + '_train.npz')
# filepaths.append(feature_set_path + 'trigramOnlyBinaryWordData' + tag + '_train.npz')
# filepaths.append(feature_set_path + 'trigramOnlyTfidfWordData' + tag + '_train.npz')
for file in filepaths:
print file
print tag
Xn = csr_matrix(np.array((0, 0)))
yn = load_numpy_matrix(feature_set_path + r'valueVector' + tag +
'_train.npy')
print Counter(yn)
Xn = load_sparse_csr(file)
Xn = SelectKBest(score_func=chi2,
k=min(200000,
int(Xn.shape[1] *
(perc / 100.0)))).fit_transform(Xn, yn)
if split:
sss = StratifiedShuffleSplit(yn, 1, test_size=0.75)
for train, test in sss:
Xn, yn = Xn[train], yn[train]
parameter_tuning(Xn, yn, scale=-1)
if sparse_2_tests:
filepaths = list()
# filepaths.append(feature_set_path+ 'binaryCharacterData' + tag + '_train.npz')
# filepaths.append(feature_set_path+ 'tfidfCharacterData' + tag + '_train.npz')
#
# filepaths.append(feature_set_path+ 'binaryCharacterSkipgramData' + tag + '_train.npz')
print('[{}] Train FM completed'.format(time.time() - start_time))
predsFM = model.predict(sparse_merge_test)
print('[{}] Predict FM completed'.format(time.time() - start_time))
else:
for i in range(rounds):
model.fit(sparse_merge_train, y_train)
predsFM = model.predict(sparse_merge_test)
print('[{}] Iteration {}/{} -- RMSLE: {}'.format(time.time() - start_time, i + 1, rounds, rmse(predsFM, y_test)))
del model
gc.collect()
if not SUBMIT_MODE:
print("FM_FTRL dev RMSLE:", rmse(predsFM, y_test))
fselect = SelectKBest(f_regression, k=48000)
train_features = fselect.fit_transform(sparse_merge_train, y_train)
test_features = fselect.transform(sparse_merge_test)
print('[{}] Select best completed'.format(time.time() - start_time))
del sparse_merge_train
del sparse_merge_test
gc.collect()
print('[{}] Garbage collection'.format(time.time() - start_time))
tv = TfidfVectorizer(max_features=250000,
ngram_range=(1, 3),
stop_words=None)
X_name_train = tv.fit_transform(df_train['name'])
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
X_test = selector.transform(X_test)
do_feature_selection = False
if do_feature_selection:
ch2 = SelectKBest(chi2, k=4000)
X_train = ch2.fit_transform(X_train, train_labels)
X_test = ch2.transform(X_test)
do_pca = False
if do_pca:
k = 100
add_pca_to_original = True
X_train = X_train.toarray()
X_test = X_test.toarray()
pca = PCA(n_components=k, copy=True, whiten=False)
X_train_pca = pca.fit_transform(X_train)
X_test_pca = pca.transform(X_test)
if add_pca_to_original:
X_train = np.hstack((X_train, X_train_pca))
def run(n_jobs):
path = "/media/robbis/Seagate_Pt1/data/working_memory/"
conf_file = "%s/data/working_memory.conf" % (path)
### Load datasets ###
iterator_kwargs = {
"loader__img_pattern": [
#'power_parcel.mat',
'power_normalized.mat',
#'connectivity_matrix.mat'
'mpsi_normalized.mat'
],
"fetch__prepro": [['none'], ['none']],
"loader__task": ["POWER", "CONN"]
}
config_kwargs = {
'loader': DataLoader,
'loader__configuration_file': conf_file,
'loader__loader': 'mat',
'loader__task': 'POWER',
#'fetch__n_subjects': 57,
"loader__data_path": "%s/data/" % (path),
"loader__subjects": "%s/data/participants.csv" % (path),
}
iterator = AnalysisIterator(iterator_kwargs,
AnalysisConfigurator,
config_kwargs=config_kwargs,
kind='list')
ds_list = [generate(configurator) for configurator in iterator]
for i, ds in enumerate(ds_list):
ds_ = ds.copy()
if i == 0:
k = np.arange(1, 88, 10)
ds_ = DatasetFxNormalizer(ds_fx=np.mean).transform(ds_)
else:
k = np.arange(1, 400, 50)
#ds_ = DatasetFxNormalizer(ds_fx=np.mean).transform(ds_)
_default_options = {
#'sample_slicer__targets' : [['0back', '2back'], ['0back', 'rest'], ['rest', '2back']],
#'kwargs__ds': ds_list,
'sample_slicer__targets': [['0back'], ['2back']],
'target_transformer__attr': [
'accuracy_0back_both', 'accuracy_2back_both', 'rt_0back_both',
'rt_2back_both'
],
'sample_slicer__band': [['alpha'], ['beta'], ['theta'], ['gamma']],
'estimator__fsel__k':
k,
'clf__C': [1, 10, 100],
'clf__kernel': ['linear', 'rbf']
}
_default_config = {
'prepro': ['sample_slicer', 'target_transformer'],
'sample_slicer__band': ['gamma'],
'sample_slicer__targets': ['0back', '2back'],
'estimator': [('fsel', SelectKBest(score_func=f_regression, k=5)),
('clf', SVR(C=10, kernel='linear'))],
'estimator__clf__C':
1,
'estimator__clf__kernel':
'linear',
'cv':
GroupShuffleSplit,
'cv__n_splits':
75,
'cv__test_size':
0.25,
'analysis_scoring': ['r2', 'neg_mean_squared_error'],
'analysis':
RoiRegression,
'analysis__n_jobs':
n_jobs,
'analysis__permutation':
0,
'analysis__verbose':
0,
'kwargs__roi': ['matrix_values'],
'kwargs__cv_attr':
'subjects',
}
iterator = AnalysisIterator(_default_options,
AnalysisConfigurator,
config_kwargs=_default_config)
for conf in iterator:
kwargs = conf._get_kwargs()
a = AnalysisPipeline(conf,
name="triton+behavioural").fit(ds_, **kwargs)
a.save()
del a
])
ds = loader.fetch(prepro=prepro)
_default_options = {
'sample_slicer__targets': [['0back', '2back']],
'sample_slicer__band': [[c] for c in np.unique(ds.sa.band)],
'estimator__fsel__k': np.arange(1, 1200, 50),
}
_default_config = {
'prepro': ['sample_slicer'],
#'ds_normalizer__ds_fx': np.std,
'sample_slicer__band': ['gamma'],
'sample_slicer__targets': ['0back', '2back'],
'estimator': [('fsel', SelectKBest(k=150)),
('clf', SVC(C=1, kernel='linear'))],
'estimator__clf__C': 1,
'estimator__clf__kernel': 'linear',
'cv': GroupShuffleSplit,
'cv__n_splits': 75,
'cv__test_size': 0.25,
'scores': ['accuracy'],
'analysis': RoiDecoding,
'analysis__n_jobs': -1,
'analysis__permutation': 0,
'analysis__verbose': 0,
'kwargs__roi': ['matrix_values'],
'kwargs__cv_attr': 'subjects',
}
def dimensionality_reduction(train_vec, test_vec, y_train_data):
print("Performing feature selection based on chi2 independence test")
fselect = SelectKBest(chi2, k=4500)
train_vec = fselect.fit_transform(train_vec, y_train_data)
test_vec = fselect.transform(test_vec)
return train_vec, test_vec
print "Vectorizing input texts"
train_vec = count_vec.fit_transform(train_list)
test_vec = count_vec.transform(test_list)
# Dimemsion Reduction
if dim_reduce == "SVD":
print "Performing dimension reduction"
svd = TruncatedSVD(n_components = num_dim)
train_vec = svd.fit_transform(train_vec)
test_vec = svd.transform(test_vec)
print "Explained variance ratio =", svd.explained_variance_ratio_.sum()
elif dim_reduce == "chi2":
print "Performing feature selection based on chi2 independence test"
fselect = SelectKBest(chi2, k=num_dim)
train_vec = fselect.fit_transform(train_vec, train_data.sentiment)
test_vec = fselect.transform(test_vec)
# Transform into numpy arrays
if "numpy.ndarray" not in str(type(train_vec)):
train_vec = train_vec.toarray()
test_vec = test_vec.toarray()
# Feature Scaling
if scaling != "no":
if scaling == "standard":
scaler = preprocessing.StandardScaler()
else:
# print('Shape of Y:', Y.shape)
# print('first row: ', Y[0])
# SCORER
scorer = make_scorer(score_func=singleLabelScore, greater_is_better=False)
# PREPROCESSING
# SCALING
minMaxScaler = MinMaxScaler(feature_range=(0.0, 1.0))
#normalizer = skprep.Normalizer()
columnDeleter = fs.FeatureDeleter()
# FEATURE SELECTION
varianceThresholdSelector = VarianceThreshold(threshold=(0))
percentileSelector = SelectPercentile(score_func=f_classif, percentile=20)
kBestSelector = SelectKBest(f_classif, 1000)
# FEATURE EXTRACTION
#rbmPipe = skpipe.Pipeline(steps=[('scaling', minMaxScaler), ('rbm', rbm)])
nmf = NMF(n_components=150)
pca = PCA(n_components=80)
sparse_pca = SparsePCA(n_components=700, max_iter=3, verbose=2)
kernel_pca = KernelPCA(n_components=150) # Costs huge amounts of ram
randomized_pca = RandomizedPCA(n_components=500)
# REGRESSORS
random_forest_regressor = RandomForestRegressor(n_estimators=256)
gradient_boosting_regressor = GradientBoostingRegressor(n_estimators=60)
support_vector_regressor = svm.SVR()
# CLASSIFIERS
sss = StratifiedShuffleSplit(y, 1, test_size=0.40, random_state=42)
y_train = []
y_test = []
for train, test in sss:
print train
np.save('train_vect', train)
np.save('test_vect', test)
y_train = y[train]
y_test = y[test]
processed_comment_list = extract_global_bag_of_words_processed(commentList)
train_v, test_v = np.load('train_vect.npy'), np.load('test_vect.npy')
train_list = []
test_list = []
for v in train_v:
train_list.append(processed_comment_list[v])
for v in test_v:
test_list.append(processed_comment_list[v])
#train, test, terms = extract_words(CountVectorizer(analyzer=UnigramAnalyzer(), dtype=float), train_list, test_list)
train, test, terms = extract_words(CountVectorizer(analyzer=BigramAnalyzer(), dtype=float), train_list, test_list)
selector2 = SelectKBest(score_func=chi2, k=min(50, train.shape[1])).fit(train,y_train)
ind = [zero_based_index for zero_based_index in list(selector2.get_support(indices=True))]
print np.asarray(terms)[selector2.get_support()]
'motor_resp': ["P", "S"],
'evidence': [5]
},
#'balancer__balancer': RandomUnderSampler(sampling_strategy={"P": 20, "S": 20}, return_indices=True),
'kwargs__roi_values': [('decision', [1]), ('decision', [2]),
('decision', [3]), ('decision', [4]),
('decision', [5]), ('motor+resp', [1]),
('motor+resp', [2]), ('motor+resp', [3]),
('motor+resp', [4]), ('motor+resp', [5])],
}
]
_default_config = {
'prepro': ['target_transformer', 'sample_slicer', 'balancer'],
"balancer__attr": 'subject',
'estimator': [('fsel', SelectKBest(k=50)),
('clf', SVC(C=1, kernel='linear'))],
'estimator__clf__C': 1,
'estimator__clf__kernel': 'linear',
'cv': LeaveOneGroupOut,
'scores': ['accuracy'],
'analysis': RoiDecoding,
'analysis__n_jobs': -1,
'analysis__permutation': 0,
'analysis__verbose': 0,
#'kwargs__roi': labels,
#'kwargs__roi_values': [('image+type', [2])],
#'kwargs__prepro': ['feature_normalizer', 'sample_normalizer'],
'kwargs__cv_attr': 'subject'
}
def main():
os.chdir("/Users/[email protected]/Desktop/workspace/sentiment.analysis")
##################### Initialization #####################
write_to_csv = False
tune_parameter = False
Mix = True
# term_vector_type = {"TFIDF", "Binary", "Int", "Word2vec", "Word2vec_pretrained"}
# {"TFIDF", "Int", "Binary"}: Bag-of-words model with {tf-idf, word counts, presence/absence} representation
# {"Word2vec", "Word2vec_pretrained"}: Google word2vec representation {without, with} pre-trained models
# Specify model_name if there's a pre-trained model to be loaded
#vector_type = "TFIDF"
vector_type = 'Word2vec_pretrained'
#model_name = "selftrainBad.bin"
model_name = "wiki.fr.vec"
# model_type = {"bin", "reg"}
# Specify whether pre-trained word2vec model is binary
#model_type = "bin"
# Parameters for word2vec
# num_features need to be identical with the pre-trained model
num_features = 300 # Word vector dimensionality
min_word_count = 5 # Minimum word count to be included for training
num_workers = 4 # Number of threads to run in parallel
context = 4 # Context window size
downsampling = 1e-3 # Downsample setting for frequent words
# training_model = {"RF", "NB", "SVM", "BT", "no"}
training_model = "SVM"
# feature scaling = {"standard", "signed", "unsigned", "no"}
# Note: Scaling is needed for SVM
scaling = "no"
# dimension reduction = {"SVD", "chi2", "no"}
# Note: For NB models, we cannot perform truncated SVD as it will make input negative
# chi2 is the feature selectioin based on chi2 independence test
dim_reduce = "no"
num_dim = 200
##################### End of Initialization #####################
print('parameter settings: ')
print('vector_type:' + vector_type)
print('training_model: ' + training_model)
print('scaling: ' + scaling)
print('dim_reduce: ' + dim_reduce )
########################### Main Program ###########################
train_list = []
test_list_t = []
test_list_h = []
test_list_c = []
word2vec_input = []
train_list2 = []
pred = []
language = 'french'
train_language = 'german'
test_language = 'french'
trainFile = train_language + 'TrainData_100k.csv'
trainFile2 = test_language + 'TrainData_100k.csv' ##
testFile_t = test_language + 'TestData_cftwt.csv'
testFile_h = test_language + 'TestData_cfdata.csv'
testFile_c = test_language + 'TestData_deft.csv'
#unlabFile = 'frenchUnlab.csv'
train_data = pd.read_csv("data/" + trainFile, header=0, delimiter=",", quoting=0 )#, encoding='utf-8')
if Mix == True:
train_data2 = pd.read_csv("data/" + trainFile2, header=0, delimiter=",", quoting=0 )
test_data_t = pd.read_csv("data/" + testFile_t, header=0, delimiter=",", quoting=0)# , encoding='utf-8')
test_data_h = pd.read_csv("data/" + testFile_h, header=0, delimiter=",", quoting=0)# , encoding='utf-8')
test_data_c = pd.read_csv("data/" + testFile_c, header=0, delimiter=",", quoting=0)# , encoding='utf-8')
# unlab_train_data = pd.read_csv("data/" + unlabFile, header=0, delimiter=",", quoting=0)# , encoding='utf-8')
if vector_type == "Word2vec":
unlab_train_data = pd.read_csv("data/frenchUnlabeledTrainData.csv", header=0, delimiter=",", quoting=0)
tokenizer = nltk.data.load('tokenizers/punkt/'+ language+'.pickle')
logging.basicConfig(format='%(asctime)s: %(message)s', level=logging.INFO)
ground_truth_t = test_data_t.sentiment
ground_truth_h = test_data_h.sentiment
ground_truth_c = test_data_c.sentiment
# Extract words from reviews
# xrange is faster when iterating
if vector_type == "Word2vec" or vector_type == "Word2vec_pretrained":
for i in xrange(0, len(train_data.review)):
if vector_type == "Word2vec":
# Decode utf-8 coding first
word2vec_input.extend(review_to_doublelist(train_data.review[i].decode("utf-8"), language, tokenizer ))
# print train_data.id[i]
train_list.append(clean_review(train_data.review[i], language, output_format="list" ))
#if i%1000 == 0:
#print "Cleaning training review", i
if Mix == True:
for i in xrange(0, len(train_data2.review)):
# print train_data.id[i]
train_list2.append(clean_review(train_data2.review[i], language, output_format="list" ))
#if i%1000 == 0:
#print "Cleaning training review", i
if vector_type == "Word2vec":
for i in xrange(0, len(unlab_train_data.review)):
#print unlab_train_data.review[i]
word2vec_input.extend(review_to_doublelist(unlab_train_data.review[i].decode("utf-8"), language, tokenizer))
#if i%1000 == 0:
#print "Cleaning unlabeled training review", i
for i in xrange(0, len(test_data_t.review)):
test_list_t.append(clean_review(test_data_t.review[i], language, output_format="list"))
#if i%1000 == 0:
#print "Cleaning test review", i
for i in xrange(0, len(test_data_h.review)):
test_list_h.append(clean_review(test_data_h.review[i], language, output_format="list"))
#if i%1000 == 0:
#print "Cleaning test review", i
for i in xrange(0, len(test_data_c.review)):
test_list_c.append(clean_review(test_data_c.review[i], language, output_format="list"))
#if i%1000 == 0:
#print "Cleaning test review", i
elif vector_type != "no":
for i in xrange(0, len(train_data.review)):
# Append raw texts rather than lists as Count/TFIDF vectorizers take raw texts as inputs
train_list.append(clean_review(train_data.review[i], language) )
#if i%1000 == 0:
# print "Cleaning training review", i
for i in xrange(0, len(test_data.review)):
# Append raw texts rather than lists as Count/TFIDF vectorizers take raw texts as inputs
test_list.append(clean_review(test_data.review[i], language))
#if i%1000 == 0:
# print "Cleaning test review", i
# Generate vectors from words
if vector_type == "Word2vec_pretrained" or vector_type == "Word2vec":
if vector_type == "Word2vec_pretrained":
print "Loading the pre-trained model"
if model_name.endswith == ".bin":
#model = word2vec.Word2Vec.load_word2vec_format(model_name, binary=True)
model = gensim.models.KeyedVectors.load_word2vec_format(model_name, binary=True , unicode_errors='ignore')
else:
#model = gensim.models.KeyedVectors.load_word2vec_format(model_name, binary=False , unicode_errors='ignore')
train_model = gensim.models.KeyedVectors.load_word2vec_format('wiki.multi.'+ train_language +'.vec', binary=False , unicode_errors='ignore')
test_model = gensim.models.KeyedVectors.load_word2vec_format('wiki.multi.'+ test_language +'.vec', binary=False , unicode_errors='ignore')
if vector_type == "Word2vec":
print "Training word2vec word vectors"
model = word2vec.Word2Vec(word2vec_input, workers=num_workers, \
size=num_features, min_count = min_word_count, \
window = context, sample = downsampling)
# If no further training and only query is needed, this trims unnecessary memory
model.init_sims(replace=True)
# Save the model for later use
word_vectors = model.wv
model.save(model_name)
print "Vectorizing training review"
train_vec = gen_review_vecs(train_list, train_model, num_features)
if Mix == True:
train_vec2 = gen_review_vecs(train_list2, test_model, num_features)
train_vec = np.append(train_vec , train_vec2 , axis = 0)
#train_vec = np.concatenate((train_vec, train_vec2) , axis = 0)
print "Vectorizing test review"
test_vec_c = gen_review_vecs(test_list_c,test_model, num_features)
test_vec_h = gen_review_vecs(test_list_h,test_model, num_features)
test_vec_t = gen_review_vecs(test_list_t,test_model, num_features)
elif vector_type != "no":
if vector_type == "TFIDF":
# Unit of gram is "word", only top 5000/10000 words are extracted
count_vec = TfidfVectorizer(analyzer="word", max_features=10000, ngram_range=(1,2), sublinear_tf=True)
elif vector_type == "Binary" or vector_type == "Int":
count_vec = CountVectorizer(analyzer="word", max_features=10000, \
binary = (vector_type == "Binary"), \
ngram_range=(1,2))
# Return a scipy sparse term-document matrix
print "Vectorizing input texts"
train_vec = count_vec.fit_transform(train_list)
test_vec_h = count_vec.transform(test_list_h)
test_vec_t = count_vec.transform(test_list_t)
test_vec_c = count_vec.transform(test_list_c)
# Dimemsion Reduction
if dim_reduce == "SVD":
print "Performing dimension reduction"
svd = TruncatedSVD(n_components = num_dim)
train_vec = svd.fit_transform(train_vec)
test_vec_h = svd.transform(test_vec_h)
test_vec_t = svd.transform(test_vec_t)
test_vec_c = svd.transform(test_vec_c)
print "Explained variance ratio =", svd.explained_variance_ratio_.sum()
elif dim_reduce == "chi2":
print "Performing feature selection based on chi2 independence test"
fselect = SelectKBest(chi2, k=num_dim)
train_vec = fselect.fit_transform(train_vec, train_data.sentiment)
test_vec = fselect.transform(test_vec)
# Transform into numpy arrays
if "numpy.ndarray" not in str(type(train_vec)):
train_vec = train_vec.toarray()
test_vec_h = test_vec_h.toarray()
test_vec_t = test_vec_t.toarray()
test_vec_c = test_vec_c.toarray()
# Feature Scaling
if scaling != "no":
if scaling == "standard":
scaler = preprocessing.StandardScaler()
else:
if scaling == "unsigned":
scaler = preprocessing.MinMaxScaler(feature_range=(0,1))
elif scaling == "signed":
scaler = preprocessing.MinMaxScaler(feature_range=(-1,1))
print "Scaling vectors"
train_vec = scaler.fit_transform(train_vec)
test_vec = scaler.transform(test_vec)
# Model training
if training_model == "RF" or training_model == "BT":
# Initialize the Random Forest or bagged tree based the model chosen
rfc = RFC(n_estimators = 100, oob_score = True, \
max_features = (None if training_model=="BT" else "auto"))
print "Training %s" % ("Random Forest" if training_model=="RF" else "bagged tree")
rfc = rfc.fit(train_vec, train_data.sentiment)
print "OOB Score =", rfc.oob_score_
pred = rfc.predict(test_vec)
elif training_model == "NB":
nb = naive_bayes.MultinomialNB()
cv_score = cross_val_score(nb, train_vec, train_data.sentiment, cv=10)
print "Training Naive Bayes"
print "CV Score = ", cv_score.mean()
nb = nb.fit(train_vec, train_data.sentiment)
pred = nb.predict(test_vec)
elif training_model == "SVM":
svc = svm.LinearSVC()
#svc = svm.SVC(kernel = 'linear', probability = True) #seems it takes so long time to train??
print 'complete 0'
param = {'C': [1e15,1e13,1e11,1e9,1e7,1e5,1e3,1e1,1e-1,1e-3,1e-5]}
print "Training SVM"
if tune_parameter == True:
svc = GridSearchCV(estimator=svc, param_grid = param, cv=10)
#next 2 Lines are for enable probability
svc = CalibratedClassifierCV(svc)
#print 'complete 1'
sentiment_array = []
for sent in train_data.sentiment:
sentiment_array.append(sent)
if Mix == True:
for sent in train_data2.sentiment:
sentiment_array.append(sent)
svc = svc.fit(train_vec, sentiment_array)
#svc = svc.fit(train_vec, train_data.sentiment)
print 'complete 2'
#pred_t = svc.predict(test_vec_t)
#pred_h = svc.predict(test_vec_h)
#pred_c = svc.predict(test_vec_c)
#pred_proba_t = svc.predict_proba(test_vec_t)
#pred1 = svc.predict_proba(test_vec)
#print(pred1)
#print(pred_proba_t)
print('Accuracy on "cftwt.csv" dataset:')
evaluate_on_testdata(test_vec_t, svc , ground_truth_t)
print('Accuracy on "cfdata.csv" dataset:')
evaluate_on_testdata(test_vec_h, svc , ground_truth_h)
print('Accuracy on "deft.csv" dataset:')
evaluate_on_testdata(test_vec_c, svc , ground_truth_c)
print('training dataset is : ')
if Mix:
print "used Mixed datasets"
print trainFile
if tune_parameter == True:
print "Optimized parameters:", svc.best_estimator_ #print the best parameter when using GridSearchCV
print "Best CV score:", svc.best_score_
#filename =vector_type+ 'finalized_model.pkl'
#s = pickle.dump(svc, open(filename, 'wb'))
# Output the results
if write_to_csv:
output = pd.DataFrame(data = {"id": test_data.id, "sentiment": pred})
output.to_csv("data/" + vector_type +"submission.csv", index=False)
print "Vectorizing..." vectorizer = TfidfVectorizer(min_df=2, max_df=0.95, max_features=200000, ngram_range=(1, 3), sublinear_tf=True) vectorizer = vectorizer.fit(opinions) features = vectorizer.transform(opinions) features_test = vectorizer.transform(opinions_test) # In[13]: print "Reducing dimension..." from sklearn.feature_selection.univariate_selection import SelectKBest, chi2, f_classif fselect = SelectKBest(chi2, k=10000) # In[14]: train_data_features = fselect.fit_transform(features, article["trend"]) test_data_features = fselect.transform(features_test) # # Train the model # In[128]: print "Training..." model1 = MultinomialNB(alpha=0.0005)
("svc_3",svm.SVC(gamma=.1,
degree=3,
kernel="rbf",
C=10)),]),
Pipeline([("rfe_Lsvc",
RFE(estimator=svm.LinearSVC(),
n_features_to_select=240,step=1)),
("svc_5",svm.SVC(C=1000,
gamma=.1,
degree=5,
kernel="rbf")),]),
Pipeline([("rfe_Lsvc",
RFE(estimator=svm.LinearSVC(),
n_features_to_select=282,step=1)),
("svc",svm.SVC()),]),
Pipeline([("85_best",SelectKBest(k=100)),
("svc",svm.SVC(C=.01)),]),
Pipeline([("normalize", StandardScaler()),
("grid_search_svm", GridSearchCV(
svm.SVC(), {
'C': 10**np.arange(5),
'gamma': [0, 1e-5, 1e-3, 1e-1,],
'kernel': ['linear','rbf'],
"degree":range(1,10),
},
cv=5,
scoring="roc_auc",
n_jobs=-1))]),
]
if __name__ == "__main__":
idx_end = idx_start + N_test y_test[idx_start:idx_end] = cat idx_start += N_test print X_train.shape, y_train.shape print X_test.shape, y_test.shape print "start classification" # vectorization vectorizer = TfidfVectorizer(strip_accents="unicode", ngram_range=(1,1)) X_train = vectorizer.fit_transform(X_train) X_test = vectorizer.transform(X_test) # feature reduction ch2 = SelectKBest(chi2, k="all") ch2.fit(X_train, y_train) X_train = ch2.fit_transform(X_train, y_train) X_test = ch2.transform(X_test) # training clf = LinearSVC() clf.fit(X_train, y_train) if validation_mode == "train": X_test = X_train y_test = y_train # predict categories predicted = clf.predict(X_test)
'RandomizedLasso':RandomizedLasso(), 'RandomizedLogisticRegression':RandomizedLogisticRegression(), 'RandomizedPCA':RandomizedPCA(), 'Ridge':Ridge(), 'RidgeCV':RidgeCV(), 'RidgeClassifier':RidgeClassifier(), 'RidgeClassifierCV':RidgeClassifierCV(), 'RobustScaler':RobustScaler(), 'SGDClassifier':SGDClassifier(), 'SGDRegressor':SGDRegressor(), 'SVC':SVC(), 'SVR':SVR(), 'SelectFdr':SelectFdr(), 'SelectFpr':SelectFpr(), 'SelectFwe':SelectFwe(), 'SelectKBest':SelectKBest(), 'SelectPercentile':SelectPercentile(), 'ShrunkCovariance':ShrunkCovariance(), 'SkewedChi2Sampler':SkewedChi2Sampler(), 'SparsePCA':SparsePCA(), 'SparseRandomProjection':SparseRandomProjection(), 'SpectralBiclustering':SpectralBiclustering(), 'SpectralClustering':SpectralClustering(), 'SpectralCoclustering':SpectralCoclustering(), 'SpectralEmbedding':SpectralEmbedding(), 'StandardScaler':StandardScaler(), 'TSNE':TSNE(), 'TheilSenRegressor':TheilSenRegressor(), 'VBGMM':VBGMM(), 'VarianceThreshold':VarianceThreshold(),}
vectorizer = TfidfVectorizer(min_df=2,
max_df=0.95,
max_features=200000,
ngram_range=(1, 4),
sublinear_tf=True)
vectorizer = vectorizer.fit(clean_train_reviews +
unlabeled_clean_train_reviews)
train_data_features = vectorizer.transform(clean_train_reviews)
test_data_features = vectorizer.transform(clean_test_reviews)
print "Reducing dimension..."
from sklearn.feature_selection.univariate_selection import SelectKBest, chi2, f_classif
fselect = SelectKBest(chi2, k=70000)
train_data_features = fselect.fit_transform(train_data_features,
train["sentiment"])
test_data_features = fselect.transform(test_data_features)
print "Training..."
model1 = MultinomialNB(alpha=0.0005)
model1.fit(train_data_features, train["sentiment"])
model2 = SGDClassifier(loss='modified_huber',
n_iter=5,
random_state=0,
shuffle=True)
model2.fit(train_data_features, train["sentiment"])
# return a scipy sparse term-document matrix
print("Vectorizing input texts")
train_vec = count_vec.fit_transform(train_list)
test_vec = count_vec.transform(test_list)
# Dimension Reduction
if dim_reduce == "SVD":
print("performing dimension reduction")
svd = TruncatedSVD(n_components=num_dim)
train_vec = svd.fit_transform(train_vec)
test_vec = svd.transform(test_vec)
print("Explained variance ratio =", svd.explained_variance_ratio_.sum())
elif dim_reduce == "chi2":
print("performing feature selection based on chi2 independce test")
fselect = SelectKBest(chi2, k=num_dim)
train_vec = fselect.fit_transform(train_vec, train_data.sentiment)
test_vec = fselect.transform(test_vec)
# Transform into numpy arrays
if "numpy.ndarray" not in str(type(train_vec)):
train_vec = train_vec.toarray()
test_vec = test_vec.toarray()
# Feature Scaling
if scaling != "no":
if scaler == "standard":
scaler = preprocessing.StandardScaler()
else:
if scaling == "unsigned":
scaler = preprocessing.MinMaxScaler(feature_range=(0, 1))
for review in test['review']:
clean_test_reviews.append( " ".join( KaggleWord2VecUtility.review_to_wordlist( review )))
print "Vectorizing..."
vectorizer = TfidfVectorizer( min_df=2, max_df=0.95, max_features = 200000, ngram_range = ( 1, 4 ),
sublinear_tf = True )
vectorizer = vectorizer.fit(clean_train_reviews + unlabeled_clean_train_reviews)
train_data_features = vectorizer.transform( clean_train_reviews )
test_data_features = vectorizer.transform( clean_test_reviews )
print "Reducing dimension..."
from sklearn.feature_selection.univariate_selection import SelectKBest, chi2, f_classif
fselect = SelectKBest(chi2 , k=70000)
train_data_features = fselect.fit_transform(train_data_features, train["sentiment"])
test_data_features = fselect.transform(test_data_features)
print "Training..."
model1 = MultinomialNB(alpha=0.0005)
model1.fit( train_data_features, train["sentiment"] )
model2 = SGDClassifier(loss='modified_huber', n_iter=5, random_state=0, shuffle=True)
model2.fit( train_data_features, train["sentiment"] )
p1 = model1.predict_proba( test_data_features )[:,1]
p2 = model2.predict_proba( test_data_features )[:,1]
print "Writing results..."
