def test_rfr(self):
from lale.lib.sklearn import RandomForestRegressor
reg = RandomForestRegressor(bootstrap=True, criterion='friedman_mse', max_depth=4,
max_features=0.9832410473940374, max_leaf_nodes=None, min_impurity_decrease=0.0,
min_impurity_split=None, min_samples_leaf=3, min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=29, n_jobs=4, oob_score=False, random_state=33, verbose=0, warm_start=False)
reg.fit(self.X_train, self.y_train)
def test_pipeline_AWTTR_1(self):
trainable = AutoaiTSPipeline(steps=[(
"AutoaiWindowTransformedTargetRegressor",
AutoaiWindowTransformedTargetRegressor(
regressor=SmallDataWindowTransformer() >> SimpleImputer() >>
RandomForestRegressor()),
)])
self.doTestPipeline(trainable, self.y, self.y, self.y, self.y)
def test_pipeline_AWWR(self):
trainable = AutoaiTSPipeline(steps=[(
"AutoaiWindowTransformedTargetRegressor",
AutoaiWindowedWrappedRegressor(
regressor=SmallDataWindowTransformer() >> SimpleImputer() >>
RandomForestRegressor()),
)])
self.doTestPipeline(trainable,
self.y,
self.y,
self.y,
self.y,
optimization=True)
def test_pipeline_AWTTR_2(self):
trainable = AutoaiTSPipeline(steps=[(
"AutoaiWindowTransformedTargetRegressor",
AutoaiWindowTransformedTargetRegressor(
regressor=SmallDataWindowTransformer() >> SimpleImputer() >>
RandomForestRegressor(),
estimator_prediction_type="rowwise",
),
)])
self.doTestPipeline(trainable,
self.y,
self.y,
self.y,
self.y,
optimization=True)
'input_predict': _input_predict_schema,
'output': _output_predict_schema
}
}
HyperoptRegressor = lale.operators.make_operator(HyperoptRegressorImpl,
_combined_schemas)
if __name__ == '__main__':
from lale.lib.lale import ConcatFeatures
from lale.lib.sklearn import Nystroem, PCA, RandomForestRegressor
from sklearn.metrics import r2_score
pca = PCA(n_components=3)
nys = Nystroem(n_components=3)
concat = ConcatFeatures()
rf = RandomForestRegressor()
trainable = (pca & nys) >> concat >> rf
#trainable = nys >>rf
import sklearn.datasets
from lale.helpers import cross_val_score
diabetes = sklearn.datasets.load_diabetes()
X, y = sklearn.utils.shuffle(diabetes.data,
diabetes.target,
random_state=42)
hp_n = HyperoptRegressor(estimator=trainable, max_evals=20)
hp_n_trained = hp_n.fit(X, y)
predictions = hp_n_trained.predict(X)
mse = r2_score(y, [round(pred) for pred in predictions])
def test_max_samples(self):
with self.assertRaisesRegex(jsonschema.ValidationError,
"argument 'max_samples' was unexpected"):
_ = RandomForestRegressor(max_samples=0.01)
def test_ccp_alpha(self):
with self.assertRaisesRegex(jsonschema.ValidationError,
"argument 'ccp_alpha' was unexpected"):
_ = RandomForestRegressor(ccp_alpha=0.01)
def test_n_estimators(self):
default = RandomForestRegressor.hyperparam_defaults()["n_estimators"]
self.assertEqual(default, 10)
def test_with_defaults(self):
trainable = RandomForestRegressor()
trained = trainable.fit(self.train_X, self.train_y)
_ = trained.predict(self.test_X)
