def test_04_execute_pipeline(self):
try:
with open("pp_pipeline.py", "w") as pipeline_f:
pipeline_f.write(TestAutoAIOutputConsumption.pipeline_content)
import importlib.util
import lale.operators
spec = importlib.util.spec_from_file_location(
"pp_pipeline", "pp_pipeline.py")
pipeline_module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(pipeline_module)
TestAutoAIOutputConsumption.pp_pipeline = pipeline_module.pipeline
assert isinstance(
TestAutoAIOutputConsumption.pp_pipeline,
lale.operators.TrainablePipeline,
)
except Exception as e:
assert False, f"{e}"
finally:
try:
os.remove("pp_pipeline.py")
except OSError:
println_pos("Couldn't remove pp_pipeline.py file")
def test_08_refine_model_with_lale(self):
from lale import wrap_imported_operators
from lale.lib.lale import Hyperopt
wrap_imported_operators()
try:
println_pos(
f"type(prefix_model) {type(TestAutoAIOutputConsumption.prefix_model)}"
)
println_pos(f"type(LR) {type(LR)}")
# This is for classifiers, regressors needs to have different operators & different scoring metrics (e.g 'r2')
new_model = TestAutoAIOutputConsumption.prefix_model >> (LR | Tree
| KNN)
train_X = TestAutoAIOutputConsumption.training_df.drop(
["Risk"], axis=1).values
train_y = TestAutoAIOutputConsumption.training_df["Risk"].values
hyperopt = Hyperopt(estimator=new_model,
cv=2,
max_evals=3,
scoring="roc_auc")
hyperopt_pipelines = hyperopt.fit(train_X, train_y)
TestAutoAIOutputConsumption.refined_model = (
hyperopt_pipelines.get_pipeline())
except Exception as e:
assert False, f"Exception was thrown during model refinery: {e}"
def test_01_load_pickled_model(self):
try:
TestAutoAIOutputConsumption.model = joblib.load(
TestAutoAIOutputConsumption.pickled_model_path)
println_pos(
f"type(model) {type(TestAutoAIOutputConsumption.model)}")
println_pos(f"model {str(TestAutoAIOutputConsumption.model)}")
except Exception as e:
assert False, f"Exception was thrown during model pickle: {e}"
def test_03_print_pipeline(self):
lale_pipeline = TestAutoAIOutputConsumption.model
wrapped_pipeline = wrap_pipeline_segments(lale_pipeline)
TestAutoAIOutputConsumption.pipeline_content = wrapped_pipeline.pretty_print(
)
assert type(TestAutoAIOutputConsumption.pipeline_content) == str
assert len(TestAutoAIOutputConsumption.pipeline_content) > 0
println_pos(
f'pretty-printed """{TestAutoAIOutputConsumption.pipeline_content}"""'
)
assert ("lale.wrap_imported_operators()"
in TestAutoAIOutputConsumption.pipeline_content)