def test_transforms_featurecombiner(self):
# import pdb; pdb.set_trace()
# args
data = "$input_data"
features = ["feature1", "feature2"]
output_data = "$training_data"
model = "$transform_model"
# call
node = transforms_featurecombiner(
data=data, features=features, output_data=output_data, model=model
)
# check
assert isinstance(node, EntryPoint)
assert node.inputs["Data"] == data
assert node.inputs["Features"] == features
assert node.outputs["OutputData"] == output_data
assert node.outputs["Model"] == model
assert node.input_variables == {data}
assert node.output_variables == {output_data, model}
def test_logistic_regression_graph(self):
# import pdb; pdb.set_trace()
# args
data = "$input_data"
features = ["xint1"]
output_data = "$training_data"
model = "$transform_model"
# call
feature_node = transforms_featurecombiner(data=data,
features=features,
output_data=output_data,
model=model)
# args
training_data = "$training_data"
quiet = False
label_column = "ylogical"
predictor_model = "$predictor_model"
# call
lr_node = trainers_logisticregressionbinaryclassifier(
# , FeatureColumn = "Features"
training_data=training_data,
quiet=quiet,
label_column=label_column,
predictor_model=predictor_model)
# args
transform_model = "$transform_model"
predictor_model = "$predictor_model"
model = "$output_model"
# call
combine_node = transforms_twoheterogeneousmodelcombiner(
transform_model=transform_model,
predictor_model=predictor_model,
model=model)
# compose graph
# graph_sub = Graph(feature_node, lr_node, combine_node)
# print(graph_sub)
all_nodes = [feature_node, lr_node, combine_node]
graph = Graph(dict(input_data=""), dict(output_model=""), False,
*all_nodes)
# print(graph)
graph.run(X=None, dryrun=True)
