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Cleanml (#185)

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* reorg ml

* return y_pred in eval_estimator

* add train loss into metric_for_logging dict
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Qingyun Wu 2021-09-02 16:07:30 -04:00 committed by GitHub
parent 63bba92fd0
commit 5fdfa2559b
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1 changed files with 30 additions and 21 deletions
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flaml/ml.py
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@ -157,6 +157,32 @@ def get_y_pred(estimator, X, eval_metric, obj):
return y_pred return y_pred
def _eval_estimator(config, estimator, X_train, y_train, X_test, y_test, weight_test,
groups_test, eval_metric, obj, labels=None,
log_training_metric=False, fit_kwargs={}):
if isinstance(eval_metric, str):
pred_start = time.time()
test_pred_y = get_y_pred(estimator, X_test, eval_metric, obj)
pred_time = (time.time() - pred_start) / X_test.shape[0]
test_loss = sklearn_metric_loss_score(eval_metric, test_pred_y, y_test,
labels, weight_test, groups_test)
metric_for_logging = {}
if log_training_metric:
train_pred_y = get_y_pred(estimator, X_train, eval_metric, obj)
metric_for_logging['train_loss'] = sklearn_metric_loss_score(
eval_metric, train_pred_y, y_train, labels,
fit_kwargs.get('sample_weight'), fit_kwargs.get('groups'))
else: # customized metric function
test_loss, metric_for_logging = eval_metric(
X_test, y_test, estimator, labels, X_train, y_train, weight_test,
fit_kwargs.get('sample_weight'), config, groups_test,
fit_kwargs.get('groups'))
if isinstance(metric_for_logging, dict):
pred_time = metric_for_logging.get('pred_time', 0)
test_pred_y = None # eval_metric may return test_pred_y but not necessarily. Setting None for now.
return test_loss, metric_for_logging, pred_time, test_pred_y
def get_test_loss(config, estimator, X_train, y_train, X_test, y_test, weight_test, def get_test_loss(config, estimator, X_train, y_train, X_test, y_test, weight_test,
groups_test, eval_metric, obj, labels=None, budget=None, groups_test, eval_metric, obj, labels=None, budget=None,
log_training_metric=False, fit_kwargs={}): log_training_metric=False, fit_kwargs={}):
@ -167,27 +193,10 @@ def get_test_loss(config, estimator, X_train, y_train, X_test, y_test, weight_te
# fit_kwargs['X_val'] = X_test # fit_kwargs['X_val'] = X_test
# fit_kwargs['y_val'] = y_test # fit_kwargs['y_val'] = y_test
estimator.fit(X_train, y_train, budget, **fit_kwargs) estimator.fit(X_train, y_train, budget, **fit_kwargs)
if isinstance(eval_metric, str): test_loss, metric_for_logging, pred_time, _ = _eval_estimator(config, estimator,
pred_start = time.time() X_train, y_train, X_test, y_test,
test_pred_y = get_y_pred(estimator, X_test, eval_metric, obj) weight_test, groups_test, eval_metric, obj,
pred_time = (time.time() - pred_start) / X_test.shape[0] labels, log_training_metric, fit_kwargs)
test_loss = sklearn_metric_loss_score(eval_metric, test_pred_y, y_test,
labels, weight_test, groups_test)
if log_training_metric:
test_pred_y = get_y_pred(estimator, X_train, eval_metric, obj)
metric_for_logging = sklearn_metric_loss_score(
eval_metric, test_pred_y, y_train, labels,
fit_kwargs.get('sample_weight'), fit_kwargs.get('groups'))
else:
metric_for_logging = None
else: # customized metric function
test_loss, metrics = eval_metric(
X_test, y_test, estimator, labels, X_train, y_train, weight_test,
fit_kwargs.get('sample_weight'), config, groups_test,
fit_kwargs.get('groups'))
if isinstance(metrics, dict):
pred_time = metrics.get('pred_time', 0)
metric_for_logging = metrics
train_time = time.time() - start train_time = time.time() - start
return test_loss, metric_for_logging, train_time, pred_time return test_loss, metric_for_logging, train_time, pred_time