def run(dataset: Dataset, config: TaskConfig):
from frameworks.shared.caller import run_in_venv
X_train, X_test = impute_array(dataset.train.X_enc, dataset.test.X_enc)
y_train, y_test = dataset.train.y_enc, dataset.test.y_enc
data = dict(train=dict(X=X_train, y=y_train),
test=dict(X=X_test, y=y_test))
def process_results(results):
if results.probabilities is not None and not results.probabilities.shape: # numpy load always return an array
prob_format = results.probabilities.item()
if prob_format == "predictions":
target_values_enc = dataset.target.label_encoder.transform(
dataset.target.values)
results.probabilities = Encoder(
'one-hot', target=False,
encoded_type=float).fit(target_values_enc).transform(
results.predictions)
else:
raise ValueError(
f"Unknown probabilities format: {prob_format}")
return results
return run_in_venv(__file__,
"exec.py",
input_data=data,
dataset=dataset,
config=config,
process_results=process_results)
def run(dataset: Dataset, config: TaskConfig):
log.info(f"\n**** Decision Tree [sklearn v{sklearn.__version__}] ****\n")
is_classification = config.type == 'classification'
X_train, X_test = impute_array(*unsparsify(dataset.train.X_enc, dataset.test.X_enc, fmt='array'))
y_train, y_test = unsparsify(dataset.train.y_enc, dataset.test.y_enc, fmt='array')
estimator = DecisionTreeClassifier if is_classification else DecisionTreeRegressor
predictor = estimator(random_state=config.seed, **config.framework_params)
with Timer() as training:
predictor.fit(X_train, y_train)
with Timer() as predict:
predictions = predictor.predict(X_test)
probabilities = predictor.predict_proba(X_test) if is_classification else None
save_predictions(dataset=dataset,
output_file=config.output_predictions_file,
probabilities=probabilities,
predictions=predictions,
truth=y_test,
target_is_encoded=is_classification)
return dict(
models_count=1,
training_duration=training.duration,
predict_duration=predict.duration
)
def run(dataset: Dataset, config: TaskConfig):
log.info(
f"\n**** Gradient Boosting [sklearn v{sklearn.__version__}] ****\n")
is_classification = config.type == 'classification'
X_train, X_test = impute_array(dataset.train.X_enc, dataset.test.X_enc)
y_train, y_test = dataset.train.y, dataset.test.y
estimator = GradientBoostingClassifier if is_classification else GradientBoostingRegressor
predictor = estimator(random_state=config.seed, **config.framework_params)
with Timer() as training:
predictor.fit(X_train, y_train)
predictions = predictor.predict(X_test)
probabilities = predictor.predict_proba(
X_test) if is_classification else None
save_predictions(dataset=dataset,
output_file=config.output_predictions_file,
probabilities=probabilities,
predictions=predictions,
truth=y_test)
return dict(models_count=1, training_duration=training.duration)
def run(dataset: Dataset, config: TaskConfig):
from amlb.datautils import impute_array
from frameworks.shared.caller import run_in_venv
X_train_enc, X_test_enc = impute_array(dataset.train.X_enc,
dataset.test.X_enc)
data = dict(train=dict(X_enc=X_train_enc, y_enc=dataset.train.y_enc),
test=dict(X_enc=X_test_enc, y_enc=dataset.test.y_enc))
return run_in_venv(__file__,
"exec.py",
input_data=data,
dataset=dataset,
config=config)
def run(dataset: Dataset, config: TaskConfig):
from amlb.datautils import impute_array
from frameworks.shared.caller import run_in_venv
encode = config.framework_params.get('_encode', True)
X_train, X_test = impute_array(
dataset.train.X_enc,
dataset.test.X_enc) if encode else (dataset.train.X, dataset.test.X)
y_train, y_test = (dataset.train.y_enc,
dataset.test.y_enc) if encode else (dataset.train.y,
dataset.test.y)
data = dict(train=dict(X=X_train, y=y_train),
test=dict(X=X_test, y=y_test))
return run_in_venv(__file__,
"exec.py",
input_data=data,
dataset=dataset,
config=config)