def train_and_predict_catboost():
(train_X, train_y), (test_X, test_y) = get_data(scaler='none',
one_hot=False,
convt_cat=False)
train_y = train_y.flatten()
test_y = test_y.flatten()
cat_features = [1, 2, 4, 5, 6]
train_pool = catboost.Pool(data=train_X,
label=train_y,
cat_features=cat_features)
test_pool = catboost.Pool(data=test_X,
label=test_y,
cat_features=cat_features)
model = catboost.CatBoostClassifier(loss_function='MultiClass',
depth=None,
random_seed=42,
cat_features=[1, 2, 4, 5, 6],
silent=False)
model.fit(train_pool)
y_pred = model.predict(test_pool)
y_true = test_y
print(y_pred.shape)
print(np.unique(y_pred))
helpers.evaluate_clf(y_true, y_pred)
pred_for_train = model.predict(train_X)
pred_for_test = model.predict(test_X)
return pred_for_train, pred_for_test
def stacked_ensemble(use_saved):
if not use_saved:
(train_X, train_y), (test_X, test_y) = get_data(scaler='minmax',
one_hot=False,
convt_cat=False)
train_y = train_y.reshape(-1, 1)
test_y = test_y.reshape(-1, 1)
train = train_y
test = test_y
train_y, test_y = train_and_predict_lgbm()
train = np.hstack([train, train_y.reshape(-1, 1)])
test = np.hstack([test, test_y.reshape(-1, 1)])
train_y, test_y = train_and_predict_catboost()
train = np.hstack([train, train_y.reshape(-1, 1)])
test = np.hstack([test, test_y.reshape(-1, 1)])
train_y, test_y = train_and_predict_mlp()
train = np.hstack([train, train_y.reshape(-1, 1)])
test = np.hstack([test, test_y.reshape(-1, 1)])
train_y, test_y = train_and_predict_naive_bayes()
train = np.hstack([train, train_y.reshape(-1, 1)])
test = np.hstack([test, test_y.reshape(-1, 1)])
print(train.shape)
print(test.shape)
np.save('tmp/ens_train.npy', train)
np.save('tmp/ens_test.npy', test)
train = np.load('tmp/ens_train.npy')
test = np.load('tmp/ens_test.npy')
train_X = train[:, 1:]
train_y = train[:, 0]
test_X = test[:, 1:]
test_y = test[:, 0]
model = GradientBoostingClassifier(n_estimators=1000,
max_depth=None,
random_state=42)
model.fit(train_X, train_y)
y_pred = model.predict(test_X)
y_true = test_y
helpers.evaluate_clf(y_true, y_pred)
pred_for_train = model.predict(train_X)
return y_pred, y_true
def train_and_predict_naive_bayes():
(train_X, train_y), (test_X, test_y) = get_data(scaler='minmax',
one_hot=False,
convt_cat=True)
model = MultinomialNB(alpha=1.0, fit_prior=True, class_prior=None)
model.fit(train_X, train_y)
y_pred = model.predict(test_X)
y_true = test_y
helpers.evaluate_clf(y_true, y_pred)
pred_for_train = model.predict(train_X)
pred_for_test = model.predict(test_X)
return pred_for_train, pred_for_test
def train_and_predict_lgbm():
(train_X, train_y), (test_X, test_y) = get_data()
model = lgbm.LGBMClassifier(boosting_type='goss',
max_depth=-1,
n_estimators=1000,
random_state=42)
model.fit(train_X, train_y)
y_pred = model.predict(test_X)
y_true = test_y
helpers.evaluate_clf(y_true, y_pred)
pred_for_train = model.predict(train_X)
pred_for_test = model.predict(test_X)
return pred_for_train, pred_for_test
def train_and_predict_mlp():
(train_X, train_y), (test_X, test_y) = get_data(scaler='standard',
one_hot=False,
convt_cat=True)
model = MLPClassifier(hidden_layer_sizes=(100, 100),
activation='relu',
solver='adam',
alpha=1e-4,
batch_size=100,
learning_rate='adaptive',
max_iter=500,
random_state=42)
model.fit(train_X, train_y)
y_pred = model.predict(test_X)
y_true = test_y
helpers.evaluate_clf(y_true, y_pred)
pred_for_train = model.predict(train_X)
pred_for_test = model.predict(test_X)
return pred_for_train, pred_for_test