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

decision tree pass optional kwargs #537

Merged
merged 1 commit into from
May 2, 2024
Merged

decision tree pass optional kwargs #537

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
28 changes: 22 additions & 6 deletions python/interpret-core/interpret/glassbox/_decisiontree.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -260,12 +260,14 @@ def _model(self):
# This method should be overriden
return None

def fit(self, X, y):
def fit(self, X, y, sample_weight=None, check_input=True):
"""Fits model to provided instances.

Args:
X: Numpy array for training instances.
y: Numpy array as training labels.
sample_weight (optional[np.ndarray]): (n_samples,) Sample weights. If None (default), then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node.
check_input (bool): default=True. Allow to bypass several input checking. Don't use this parameter unless you know what you're doing.

Returns:
Itself.
Expand All @@ -289,7 +291,7 @@ def fit(self, X, y):
)

model = self._model()
model.fit(X, y)
model.fit(X, y, sample_weight=sample_weight, check_input=check_input)

unique_val_counts = np.zeros(len(self.feature_names_in_), dtype=np.int64)
for col_idx in range(len(self.feature_names_in_)):
Expand Down Expand Up @@ -571,18 +573,25 @@ def __init__(self, feature_names=None, feature_types=None, max_depth=3, **kwargs
def _model(self):
return self.sk_model_

def fit(self, X, y):
def fit(self, X, y, sample_weight=None, check_input=True):
"""Fits model to provided instances.

Args:
X: Numpy array for training instances.
y: Numpy array as training labels.
sample_weight (optional[np.ndarray]): (n_samples,) Sample weights. If None (default), then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node.
check_input (bool): default=True. Allow to bypass several input checking. Don't use this parameter unless you know what you're doing.

Returns:
Itself.
"""
self.sk_model_ = SKRT(max_depth=self.max_depth, **self.kwargs)
return super().fit(X, y)
return super().fit(
X,
y,
sample_weight=sample_weight,
check_input=check_input,
)


class ClassificationTree(BaseShallowDecisionTree, ClassifierMixin, ExplainerMixin):
Expand All @@ -607,18 +616,25 @@ def __init__(self, feature_names=None, feature_types=None, max_depth=3, **kwargs
def _model(self):
return self.sk_model_

def fit(self, X, y):
def fit(self, X, y, sample_weight=None, check_input=True):
"""Fits model to provided instances.

Args:
X: Numpy array for training instances.
y: Numpy array as training labels.
sample_weight (optional[np.ndarray]): (n_samples,) Sample weights. If None (default), then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node.
check_input (bool): default=True. Allow to bypass several input checking. Don't use this parameter unless you know what you're doing.

Returns:
Itself.
"""
self.sk_model_ = SKDT(max_depth=self.max_depth, **self.kwargs)
return super().fit(X, y)
return super().fit(
X,
y,
sample_weight=sample_weight,
check_input=check_input,
)

def predict_proba(self, X):
"""Probability estimates on provided instances.
Expand Down