from sklearn.naive_bayes import BernoulliNB
from sklearn.svm import LinearSVC, SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.grid_search import GridSearchCV
from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier
from sklearn import metrics
from old_hamshahri_reader import OldHamshahriReader
import config
tuned_params = [{'C': [1, 10, 100, 1000]}]
svc_tuned_params = [{'kernel': ['rbf'], 'C': [1, 10, 100, 1000], 'gamma': [0.001, 0.0001]},]
if __name__ == '__main__':
rd = OldHamshahriReader(root=config.CORPORA_ROOT)
docs, labels = rd.sklearn_docs(config.TOT_DOCS)
#vectorizer = CountVectorizer(docs)
vectorizer = TfidfVectorizer(lowercase=False, max_df=0.8)
fs = vectorizer.fit_transform(docs)
#vectorizer.build_preprocessor()
selector = SelectPercentile(chi2, percentile=10)
selector.fit(fs, labels)
fs = selector.transform(fs)
fs_train, fs_test, labels_train, labels_test = train_test_split(
fs, labels, test_size=0.4, random_state=0
)
clf = None
pred = None
from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier
from sklearn import metrics
from old_hamshahri_reader import OldHamshahriReader
import config
tuned_params = [{'C': [1, 10, 100, 1000]}]
svc_tuned_params = [
{
'kernel': ['rbf'],
'C': [1, 10, 100, 1000],
'gamma': [0.001, 0.0001]
},
]
if __name__ == '__main__':
rd = OldHamshahriReader(root=config.CORPORA_ROOT)
docs, labels = rd.sklearn_docs(config.TOT_DOCS)
#vectorizer = CountVectorizer(docs)
vectorizer = TfidfVectorizer(lowercase=False, max_df=0.8)
fs = vectorizer.fit_transform(docs)
#vectorizer.build_preprocessor()
selector = SelectPercentile(chi2, percentile=10)
selector.fit(fs, labels)
fs = selector.transform(fs)
fs_train, fs_test, labels_train, labels_test = train_test_split(
fs, labels, test_size=0.4, random_state=0)
clf = None
pred = None
grid_search = False
