Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Changed model_classes logger warning and made sure tabular only warns once #2604

Merged
merged 4 commits into from
Jun 18, 2023
Merged

Changed model_classes logger warning and made sure tabular only warns once #2604

Show file tree
Hide file tree
Changes from 2 commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
3e493f8
Changed model_classes logger warning, and made sure that tabular only…
nirhutnik Jun 16, 2023
5c46d9c
Added fix for BoostingOverfit error message
nirhutnik Jun 18, 2023
26fc1ff
removed comment
nirhutnik Jun 18, 2023
e7a141b
pylint
nirhutnik Jun 18, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Jump to
Jump to file
Failed to load files.
Diff view
Diff view
4 changes: 3 additions & 1 deletion deepchecks/nlp/context.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -343,7 +343,9 @@ def model_classes(self) -> t.List:
# If in infer_observed_and_model_labels we didn't find classes on model, or user didn't pass any,
# then using the observed
self._model_classes = self._observed_classes
get_logger().warning('Could not find model\'s classes, using the observed classes')
get_logger().warning('Could not find model\'s classes, using the observed classes. '
'In order to make sure the classes used by the model are inferred correctly, '
'please use the model_classes argument')
return self._model_classes

@property
Expand Down
39 changes: 23 additions & 16 deletions deepchecks/tabular/checks/model_evaluation/boosting_overfit.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -32,10 +32,12 @@
class PartialBoostingModel:
"""Wrapper for boosting models which limits the number of estimators being used in the prediction."""

_UNSUPPORTED_MODEL_ERROR = (
'Check is relevant for Boosting models of type '
'{supported_models}, but received model of type {model_type}'
)
_UNSUPPORTED_MODEL_ERROR = \
'Check is relevant for Boosting models of type {supported_models}, but received model of type {model_type}'

_NO_MODEL_ERROR = \
'Check is relevant only when receiving the model, but predictions/probabilities were received instead. ' \
'In order to use this check, please pass the model to the run() method.'

_SUPPORTED_CLASSIFICATION_MODELS = (
'AdaBoostClassifier',
Expand Down Expand Up @@ -78,6 +80,16 @@ def __init__(self, model, step):
else:
self.model = model

@classmethod
def _raise_not_suppored_model_error(cls, model_class):
if model_class != '_DummyModel':
raise ModelValidationError(cls._UNSUPPORTED_MODEL_ERROR.format(
supported_models=cls._SUPPORTED_MODELS,
model_type=model_class
))
else:
raise ModelValidationError(cls._NO_MODEL_ERROR)

def predict_proba(self, x):
if self.model_class in ['AdaBoostClassifier', 'GradientBoostingClassifier']:
return self.model.predict_proba(x)
Expand All @@ -88,10 +100,7 @@ def predict_proba(self, x):
elif self.model_class == 'CatBoostClassifier':
return self.model.predict_proba(x, ntree_end=self.step)
else:
raise ModelValidationError(self._UNSUPPORTED_MODEL_ERROR.format(
supported_models=self._SUPPORTED_CLASSIFICATION_MODELS,
model_type=self.model_class
))
self._raise_not_suppored_model_error(self.model_class)

def predict(self, x):
if self.model_class in ['AdaBoostClassifier', 'GradientBoostingClassifier', 'AdaBoostRegressor',
Expand All @@ -104,10 +113,7 @@ def predict(self, x):
elif self.model_class in ['CatBoostClassifier', 'CatBoostRegressor']:
return self.model.predict(x, ntree_end=self.step)
else:
raise ModelValidationError(self._UNSUPPORTED_MODEL_ERROR.format(
supported_models=self._SUPPORTED_MODELS,
model_type=self.model_class
))
self._raise_not_suppored_model_error(self.model_class)

@classmethod
def n_estimators(cls, model):
Expand All @@ -123,10 +129,11 @@ def n_estimators(cls, model):
elif model_class in ['CatBoostClassifier', 'CatBoostRegressor']:
return model.tree_count_
else:
raise ModelValidationError(cls._UNSUPPORTED_MODEL_ERROR.format(
supported_models=cls._SUPPORTED_MODELS,
model_type=model_class
))
cls._raise_not_suppored_model_error(model_class=model_class)
# raise ModelValidationError(cls._UNSUPPORTED_MODEL_ERROR.format(
nirhutnik marked this conversation as resolved.
Show resolved Hide resolved
# supported_models=cls._SUPPORTED_MODELS,
# model_type=model_class
# ))


class BoostingOverfit(TrainTestCheck):
Expand Down
7 changes: 5 additions & 2 deletions deepchecks/tabular/context.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -291,8 +291,11 @@ def model_classes(self) -> t.List:
"""Return ordered list of possible label classes for classification tasks or None for regression."""
if self._model_classes is None and self.task_type in (TaskType.BINARY, TaskType.MULTICLASS):
# If in infer_task_type we didn't find classes on model, or user didn't pass any, then using the observed
get_logger().warning('Could not find model\'s classes, using the observed classes')
return self.observed_classes
get_logger().warning('Could not find model\'s classes, using the observed classes. '
'In order to make sure the classes used by the model are inferred correctly, '
'please use the model_classes argument')
self._model_classes = self.observed_classes

return self._model_classes

@property
Expand Down