class AdaBoostRegressorImpl():
def __init__(self,
base_estimator=None,
n_estimators=50,
learning_rate=1.0,
loss='linear',
random_state=None):
self._hyperparams = {
'base_estimator': base_estimator,
'n_estimators': n_estimators,
'learning_rate': learning_rate,
'loss': loss,
'random_state': random_state
}
self._wrapped_model = Op(**self._hyperparams)
def fit(self, X, y=None):
if (y is not None):
self._wrapped_model.fit(X, y)
else:
self._wrapped_model.fit(X)
return self
def predict(self, X):
return self._wrapped_model.predict(X)
class AdaBoostRegressorImpl():
def __init__(self,
base_estimator=None,
n_estimators=50,
learning_rate=1.0,
loss='linear',
random_state=None):
if isinstance(base_estimator, lale.operators.Operator):
if isinstance(base_estimator, lale.operators.IndividualOp):
base_estimator = base_estimator._impl_instance()._wrapped_model
else:
raise ValueError(
"If base_estimator is a Lale operator, it needs to be an individual operator. "
)
self._hyperparams = {
'base_estimator': base_estimator,
'n_estimators': n_estimators,
'learning_rate': learning_rate,
'loss': loss,
'random_state': random_state
}
self._wrapped_model = SKLModel(**self._hyperparams)
def fit(self, X, y=None):
if (y is not None):
self._wrapped_model.fit(X, y)
else:
self._wrapped_model.fit(X)
return self
def predict(self, X):
return self._wrapped_model.predict(X)
from sklearn.ensemble.weight_boosting import AdaBoostRegressor
from sklearn.model_selection._split import train_test_split
tl = TrendLine(data_type='train')
data_df = tl.get()
train_set, test_set = train_test_split(data_df,
test_size=0.2,
random_state=np.random.randint(1, 1000))
y_train = train_set['time_to_failure']
x_train_seg = train_set['segment_id']
x_train = train_set.drop(['time_to_failure', 'segment_id'], axis=1)
y_test = test_set['time_to_failure']
x_test_seg = test_set['segment_id']
x_test = test_set.drop(['time_to_failure', 'segment_id'], axis=1)
adbReg = AdaBoostRegressor(n_estimators=50,
learning_rate=1.0,
loss='linear',
random_state=42)
adbReg.fit(x_train, y_train)
y_pred = adbReg.predict(x_test)
# y_pred = x_train.mean(axis=1)
print('MAE Score for acerage ', mean_absolute_error(y_test, y_pred))