'kNN': KNN(),
'Naive Bayes': Naive_Bayes(),
'Random Forest': RandomForest(),
'SVM': SVM(),
'Gradient Boosting': GradientBoost()
}
for model in models:
for aug in [True, False]:
if aug:
models[model].fit(X_aug_t, y_aug_t)
name = model + " (aug)"
else:
models[model].fit(X_train_t, y_train_t)
name = model
y_pred = models[model].predict(X_test_t)
acc = accuracy_score(y_test_t, y_pred)
roc = roc_auc_score(y_test_t, y_pred)
f1 = f1_score(y_test_t, y_pred)
print('%s accuracy: %s' % (name, round(acc, 4)))
print('%s AUC: %s' % (name, round(roc, 4)))
print('%s F1: %s' % (name, round(f1, 4)))
if not args.no_export:
export_results(name=name, acc=acc, roc=roc, f1=f1)
# data_glove = Dataset('glove')
# X_train_g, X_test_g, y_train_g, y_test_g = train_test_split(data.X, data.y)
from sklearn.metrics import accuracy_score, roc_auc_score, f1_score
from sklearn.model_selection import train_test_split
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("-x",
"--export-results",
dest="export",
action='store_true',
help="Exports results to results.csv")
args = parser.parse_args()
data = Dataset('twitter')
X_train, X_test, y_train, y_test = train_test_split(data.X, data.y)
grad_boost = GradientBoost()
grad_boost.fit(X_train, y_train)
y_pred = grad_boost.predict(X_test)
print(y_pred)
acc = accuracy_score(y_test, y_pred)
roc = roc_auc_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)
print('TF-IDF + xgb accuracy:', round(acc, 4))
print('TF-IDF + xgb AUC:', round(roc, 4))
print('TF-IDF + xgb F1:', round(f1, 4))
if args.export:
export_results(acc=acc, roc=roc, f1=f1)
from sklearn.metrics import accuracy_score, roc_auc_score, f1_score
from sklearn.model_selection import train_test_split
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("-x",
"--export-results",
dest="export",
action='store_true',
help="Exports results to results.csv")
args = parser.parse_args()
data = Dataset('twitter')
X_train, X_test, y_train, y_test = train_test_split(data.X, data.y)
dummy = Dummy()
dummy.fit(X_train, y_train)
y_pred = dummy.predict(X_test)
acc = accuracy_score(y_test, y_pred)
roc = roc_auc_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)
print('Dummy accuracy:', round(acc, 4))
print('Dummy AUC:', round(roc, 4))
print('Dummy F1:', round(f1, 4))
if args.export:
export_results(name='Dummy', acc=acc, roc=roc, f1=f1)