from app.dame_utils import DameUtils import argparse parser = argparse.ArgumentParser() parser.add_argument('ml', choices=[ 'nltk', 'svc', 'sgd', 'gaussianNB', 'multinomialNB', 'bernoulliNB', 'forest', 'tree', 'mlp' ]) parser.add_argument('--noshow', dest='noshow', action='store_true') parser.add_argument('--verbose', default=False, action="store_true") args = parser.parse_args() ds = DameSexmachine() X = np.array(ds.features_list(path="files/names/allnoundefined.csv")) y = ds.gender_list(path="files/names/allnoundefined.csv") X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42) if (args.verbose): print(X) print(y) if (args.ml == "svc"): svc = SVC(random_state=42) svc.fit(X_train, y_train) svc_disp = plot_roc_curve(svc, X_test, y_test) elif (args.ml == "forest"): rfc = RandomForestClassifier(n_estimators=10, random_state=42) rfc.fit(X_train, y_train)
def test_sexmachine_features_list_all(self): s = DameSexmachine() fl = s.features_list(path="files/names/all.csv") self.assertTrue(len(fl) > 1000)
def test_sexmachine_features_list_all_method_returns_correct_result(self): s = DameSexmachine() fl = s.features_list(path="files/names/all.csv") self.assertTrue(len(fl) > 1000)
def test_sexmachine_features_list(self): s = DameSexmachine() fl = s.features_list() self.assertTrue(len(fl) > 20)
def test_sexmachine_features_list_method_returns_correct_result(self): s = DameSexmachine() fl = s.features_list() self.assertTrue(len(fl) > 20)