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stringlengths 0
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⌀ | embed_func_code
sequencelengths 768
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17,038 | imodels.tree.figs | _predict_tree | Predict for a single tree | def _predict_tree(self, root: Node, X):
"""Predict for a single tree"""
def _predict_tree_single_point(root: Node, x):
if root.left is None and root.right is None:
return root.value[0, 0]
left = x[root.feature] <= root.threshold
if left:
if root.left is None: # we don't actually have to worry about this case
return root.value
else:
return _predict_tree_single_point(root.left, x)
else:
if (
root.right is None
): # we don't actually have to worry about this case
return root.value
else:
return _predict_tree_single_point(root.right, x)
preds = np.zeros(X.shape[0])
for i in range(X.shape[0]):
preds[i] = _predict_tree_single_point(root, X[i])
return preds
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17,041 | imodels.tree.figs | _tree_to_str | null | def _tree_to_str(self, root: Node, prefix=""):
if root is None:
return ""
elif root.threshold is None:
return ""
pprefix = prefix + "\t"
return (
prefix
+ str(root)
+ "\n"
+ self._tree_to_str(root.left, pprefix)
+ self._tree_to_str(root.right, pprefix)
)
| (self, root: imodels.tree.figs.Node, prefix='') | [
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17,042 | imodels.tree.figs | _tree_to_str_with_data | null | def _tree_to_str_with_data(self, X, y, root: Node, prefix=""):
if root is None:
return ""
elif root.threshold is None:
return ""
pprefix = prefix + "\t"
left = X[:, root.feature] <= root.threshold
return (
prefix
+ root.print_root(y)
+ "\n"
+ self._tree_to_str_with_data(X[left], y[left], root.left, pprefix)
+ self._tree_to_str_with_data(X[~left], y[~left], root.right, pprefix)
)
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] |
17,045 | imodels.tree.figs | fit |
Params
------
_sample_weight: array-like of shape (n_samples,), default=None
Sample weights. If None, 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.
| def fit(
self,
X,
y=None,
feature_names=None,
verbose=False,
sample_weight=None,
categorical_features=None,
):
"""
Params
------
_sample_weight: array-like of shape (n_samples,), default=None
Sample weights. If None, 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.
"""
if categorical_features is not None:
X, self._encoder = self._encode_categories(X, categorical_features)
X, y, feature_names = check_fit_arguments(self, X, y, feature_names)
if sample_weight is not None:
sample_weight = _check_sample_weight(sample_weight, X)
self.trees_ = [] # list of the root nodes of added trees
self.complexity_ = 0 # tracks the number of rules in the model
y_predictions_per_tree = {} # predictions for each tree
y_residuals_per_tree = {} # based on predictions above
# set up initial potential_splits
# everything in potential_splits either is_root (so it can be added directly to self.trees_)
# or it is a child of a root node that has already been added
idxs = np.ones(X.shape[0], dtype=bool)
node_init = self._construct_node_with_stump(
X=X,
y=y,
idxs=idxs,
tree_num=-1,
sample_weight=sample_weight,
max_features=self.max_features,
)
potential_splits = [node_init]
for node in potential_splits:
node.setattrs(is_root=True)
potential_splits = sorted(potential_splits, key=lambda x: x.impurity_reduction)
# start the greedy fitting algorithm
finished = False
while len(potential_splits) > 0 and not finished:
# print('potential_splits', [str(s) for s in potential_splits])
# get node with max impurity_reduction (since it's sorted)
split_node = potential_splits.pop()
# don't split on node
if split_node.impurity_reduction < self.min_impurity_decrease:
finished = True
break
elif (
split_node.is_root
and self.max_trees is not None
and len(self.trees_) >= self.max_trees
):
# If the node is the root of a new tree and we have reached self.max_trees,
# don't split on it, but allow later splits to continue growing existing trees
continue
# split on node
if verbose:
print("\nadding " + str(split_node))
self.complexity_ += 1
# if added a tree root
if split_node.is_root:
# start a new tree
self.trees_.append(split_node)
# update tree_num
for node_ in [split_node, split_node.left_temp, split_node.right_temp]:
if node_ is not None:
node_.tree_num = len(self.trees_) - 1
# add new root potential node
node_new_root = Node(
is_root=True, idxs=np.ones(X.shape[0], dtype=bool), tree_num=-1
)
potential_splits.append(node_new_root)
# add children to potential splits
# assign left_temp, right_temp to be proper children
# (basically adds them to tree in predict method)
split_node.setattrs(left=split_node.left_temp, right=split_node.right_temp)
# add children to potential_splits
potential_splits.append(split_node.left)
potential_splits.append(split_node.right)
# update predictions for altered tree
for tree_num_ in range(len(self.trees_)):
y_predictions_per_tree[tree_num_] = self._predict_tree(
self.trees_[tree_num_], X
)
# dummy 0 preds for possible new trees
y_predictions_per_tree[-1] = np.zeros(X.shape[0])
# update residuals for each tree
# -1 is key for potential new tree
for tree_num_ in list(range(len(self.trees_))) + [-1]:
y_residuals_per_tree[tree_num_] = deepcopy(y)
# subtract predictions of all other trees
# Since the current tree makes a constant prediction over the node being split,
# one may ignore its contributions to the residuals without affecting the impurity decrease.
for tree_num_other_ in range(len(self.trees_)):
if not tree_num_other_ == tree_num_:
y_residuals_per_tree[tree_num_] -= y_predictions_per_tree[
tree_num_other_
]
# recompute all impurities + update potential_split children
potential_splits_new = []
for potential_split in potential_splits:
y_target = y_residuals_per_tree[potential_split.tree_num]
# re-calculate the best split
potential_split_updated = self._construct_node_with_stump(
X=X,
y=y_target,
idxs=potential_split.idxs,
tree_num=potential_split.tree_num,
sample_weight=sample_weight,
max_features=self.max_features,
)
# need to preserve certain attributes from before (value at this split + is_root)
# value may change because residuals may have changed, but we want it to store the value from before
potential_split.setattrs(
feature=potential_split_updated.feature,
threshold=potential_split_updated.threshold,
impurity_reduction=potential_split_updated.impurity_reduction,
impurity=potential_split_updated.impurity,
left_temp=potential_split_updated.left_temp,
right_temp=potential_split_updated.right_temp,
)
# this is a valid split
if potential_split.impurity_reduction is not None:
potential_splits_new.append(potential_split)
# sort so largest impurity reduction comes last (should probs make this a heap later)
potential_splits = sorted(
potential_splits_new, key=lambda x: x.impurity_reduction
)
if verbose:
print(self)
if self.max_rules is not None and self.complexity_ >= self.max_rules:
finished = True
break
# annotate final tree with node_id and value_sklearn, and prepare importance_data_
importance_data = []
for tree_ in self.trees_:
node_counter = iter(range(0, int(1e06)))
def _annotate_node(node: Node, X, y):
if node is None:
return
# TODO does not incorporate sample weights
value_counts = pd.Series(y).value_counts()
try:
neg_count = value_counts[0.0]
except KeyError:
neg_count = 0
try:
pos_count = value_counts[1.0]
except KeyError:
pos_count = 0
value_sklearn = np.array([neg_count, pos_count], dtype=float)
node.setattrs(node_id=next(node_counter), value_sklearn=value_sklearn)
idxs_left = X[:, node.feature] <= node.threshold
_annotate_node(node.left, X[idxs_left], y[idxs_left])
_annotate_node(node.right, X[~idxs_left], y[~idxs_left])
_annotate_node(tree_, X, y)
# now that the samples per node are known, we can start to compute the importances
importance_data_tree = np.zeros(len(self.feature_names_))
def _importances(node: Node):
if node is None or node.left is None:
return 0.0
# TODO does not incorporate sample weights, but will if added to value_sklearn
importance_data_tree[node.feature] += (
np.sum(node.value_sklearn) * node.impurity
- np.sum(node.left.value_sklearn) * node.left.impurity
- np.sum(node.right.value_sklearn) * node.right.impurity
)
return (
np.sum(node.value_sklearn)
+ _importances(node.left)
+ _importances(node.right)
)
# require the tree to have more than 1 node, otherwise just leave | (self, X, y=None, feature_names=None, verbose=False, sample_weight=None, categorical_features=None) | [
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] |
17,048 | imodels.tree.figs | plot | null | def plot(
self,
cols=2,
feature_names=None,
filename=None,
label="all",
impurity=False,
tree_number=None,
dpi=150,
fig_size=None,
):
is_single_tree = len(self.trees_) < 2 or tree_number is not None
n_cols = int(cols)
n_rows = int(np.ceil(len(self.trees_) / n_cols))
if feature_names is None:
if hasattr(self, "feature_names_") and self.feature_names_ is not None:
feature_names = self.feature_names_
n_plots = int(len(self.trees_)) if tree_number is None else 1
fig, axs = plt.subplots(n_plots, dpi=dpi)
if fig_size is not None:
fig.set_size_inches(fig_size, fig_size)
n_classes = 1 if isinstance(self, RegressorMixin) else 2
ax_size = int(len(self.trees_))
for i in range(n_plots):
r = i // n_cols
c = i % n_cols
if not is_single_tree:
ax = axs[i]
else:
ax = axs
try:
dt = extract_sklearn_tree_from_figs(
self, i if tree_number is None else tree_number, n_classes
)
plot_tree(
dt,
ax=ax,
feature_names=feature_names,
label=label,
impurity=impurity,
)
except IndexError:
ax.axis("off")
continue
ttl = f"Tree {i}" if n_plots > 1 else f"Tree {tree_number}"
ax.set_title(ttl)
if filename is not None:
plt.savefig(filename)
return
plt.show()
| (self, cols=2, feature_names=None, filename=None, label='all', impurity=False, tree_number=None, dpi=150, fig_size=None) | [
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17,049 | imodels.tree.figs | predict | null | def predict(self, X, categorical_features=None):
if hasattr(self, "_encoder"):
X = self._encode_categories(X, categorical_features=categorical_features)
X = check_array(X)
preds = np.zeros(X.shape[0])
for tree in self.trees_:
preds += self._predict_tree(tree, X)
if isinstance(self, RegressorMixin):
return preds
elif isinstance(self, ClassifierMixin):
return (preds > 0.5).astype(int)
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17,050 | imodels.tree.figs | predict_proba | Predict probability for classifiers:
Default behavior is to constrain the outputs to the range of probabilities, i.e. 0 to 1, with a sigmoid function.
Set use_clipped_prediction=True to use prior behavior of clipping between 0 and 1 instead.
| def predict_proba(self, X, categorical_features=None, use_clipped_prediction=False):
"""Predict probability for classifiers:
Default behavior is to constrain the outputs to the range of probabilities, i.e. 0 to 1, with a sigmoid function.
Set use_clipped_prediction=True to use prior behavior of clipping between 0 and 1 instead.
"""
if hasattr(self, "_encoder"):
X = self._encode_categories(X, categorical_features=categorical_features)
X = check_array(X)
if isinstance(self, RegressorMixin):
return NotImplemented
preds = np.zeros(X.shape[0])
for tree in self.trees_:
preds += self._predict_tree(tree, X)
if use_clipped_prediction:
# old behavior, pre v1.3.9
# constrain to range of probabilities by clipping
preds = np.clip(preds, a_min=0.0, a_max=1.0)
else:
# constrain to range of probabilities with a sigmoid function
preds = expit(preds)
return np.vstack((1 - preds, preds)).transpose()
| (self, X, categorical_features=None, use_clipped_prediction=False) | [
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17,051 | imodels.tree.figs | print_tree | null | def print_tree(self, X, y, feature_names=None):
s = "------------\n" + "\n\t+\n".join(
[self._tree_to_str_with_data(X, y, t) for t in self.trees_]
)
if feature_names is None:
if hasattr(self, "feature_names_") and self.feature_names_ is not None:
feature_names = self.feature_names_
if feature_names is not None:
for i in range(len(feature_names))[::-1]:
s = s.replace(f"X_{i}", feature_names[i])
return s
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17,053 | sklearn.utils._metadata_requests | set_fit_request | Request metadata passed to the ``fit`` method.
Note that this method is only relevant if
``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).
Please see :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
The options for each parameter are:
- ``True``: metadata is requested, and passed to ``fit`` if provided. The request is ignored if metadata is not provided.
- ``False``: metadata is not requested and the meta-estimator will not pass it to ``fit``.
- ``None``: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
- ``str``: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (``sklearn.utils.metadata_routing.UNCHANGED``) retains the
existing request. This allows you to change the request for some
parameters and not others.
.. versionadded:: 1.3
.. note::
This method is only relevant if this estimator is used as a
sub-estimator of a meta-estimator, e.g. used inside a
:class:`~sklearn.pipeline.Pipeline`. Otherwise it has no effect.
Parameters
----------
categorical_features : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``categorical_features`` parameter in ``fit``.
feature_names : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``feature_names`` parameter in ``fit``.
sample_weight : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``sample_weight`` parameter in ``fit``.
verbose : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``verbose`` parameter in ``fit``.
Returns
-------
self : object
The updated object.
| def __get__(self, instance, owner):
# we would want to have a method which accepts only the expected args
def func(**kw):
"""Updates the request for provided parameters
This docstring is overwritten below.
See REQUESTER_DOC for expected functionality
"""
if not _routing_enabled():
raise RuntimeError(
"This method is only available when metadata routing is enabled."
" You can enable it using"
" sklearn.set_config(enable_metadata_routing=True)."
)
if self.validate_keys and (set(kw) - set(self.keys)):
raise TypeError(
f"Unexpected args: {set(kw) - set(self.keys)}. Accepted arguments"
f" are: {set(self.keys)}"
)
requests = instance._get_metadata_request()
method_metadata_request = getattr(requests, self.name)
for prop, alias in kw.items():
if alias is not UNCHANGED:
method_metadata_request.add_request(param=prop, alias=alias)
instance._metadata_request = requests
return instance
# Now we set the relevant attributes of the function so that it seems
# like a normal method to the end user, with known expected arguments.
func.__name__ = f"set_{self.name}_request"
params = [
inspect.Parameter(
name="self",
kind=inspect.Parameter.POSITIONAL_OR_KEYWORD,
annotation=owner,
)
]
params.extend(
[
inspect.Parameter(
k,
inspect.Parameter.KEYWORD_ONLY,
default=UNCHANGED,
annotation=Optional[Union[bool, None, str]],
)
for k in self.keys
]
)
func.__signature__ = inspect.Signature(
params,
return_annotation=owner,
)
doc = REQUESTER_DOC.format(method=self.name)
for metadata in self.keys:
doc += REQUESTER_DOC_PARAM.format(metadata=metadata, method=self.name)
doc += REQUESTER_DOC_RETURN
func.__doc__ = doc
return func
| (self: imodels.tree.figs.FIGSClassifier, *, categorical_features: Union[bool, NoneType, str] = '$UNCHANGED$', feature_names: Union[bool, NoneType, str] = '$UNCHANGED$', sample_weight: Union[bool, NoneType, str] = '$UNCHANGED$', verbose: Union[bool, NoneType, str] = '$UNCHANGED$') -> imodels.tree.figs.FIGSClassifier | [
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17,055 | sklearn.utils._metadata_requests | set_predict_proba_request | Request metadata passed to the ``predict_proba`` method.
Note that this method is only relevant if
``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).
Please see :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
The options for each parameter are:
- ``True``: metadata is requested, and passed to ``predict_proba`` if provided. The request is ignored if metadata is not provided.
- ``False``: metadata is not requested and the meta-estimator will not pass it to ``predict_proba``.
- ``None``: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
- ``str``: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (``sklearn.utils.metadata_routing.UNCHANGED``) retains the
existing request. This allows you to change the request for some
parameters and not others.
.. versionadded:: 1.3
.. note::
This method is only relevant if this estimator is used as a
sub-estimator of a meta-estimator, e.g. used inside a
:class:`~sklearn.pipeline.Pipeline`. Otherwise it has no effect.
Parameters
----------
categorical_features : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``categorical_features`` parameter in ``predict_proba``.
use_clipped_prediction : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``use_clipped_prediction`` parameter in ``predict_proba``.
Returns
-------
self : object
The updated object.
| def __get__(self, instance, owner):
# we would want to have a method which accepts only the expected args
def func(**kw):
"""Updates the request for provided parameters
This docstring is overwritten below.
See REQUESTER_DOC for expected functionality
"""
if not _routing_enabled():
raise RuntimeError(
"This method is only available when metadata routing is enabled."
" You can enable it using"
" sklearn.set_config(enable_metadata_routing=True)."
)
if self.validate_keys and (set(kw) - set(self.keys)):
raise TypeError(
f"Unexpected args: {set(kw) - set(self.keys)}. Accepted arguments"
f" are: {set(self.keys)}"
)
requests = instance._get_metadata_request()
method_metadata_request = getattr(requests, self.name)
for prop, alias in kw.items():
if alias is not UNCHANGED:
method_metadata_request.add_request(param=prop, alias=alias)
instance._metadata_request = requests
return instance
# Now we set the relevant attributes of the function so that it seems
# like a normal method to the end user, with known expected arguments.
func.__name__ = f"set_{self.name}_request"
params = [
inspect.Parameter(
name="self",
kind=inspect.Parameter.POSITIONAL_OR_KEYWORD,
annotation=owner,
)
]
params.extend(
[
inspect.Parameter(
k,
inspect.Parameter.KEYWORD_ONLY,
default=UNCHANGED,
annotation=Optional[Union[bool, None, str]],
)
for k in self.keys
]
)
func.__signature__ = inspect.Signature(
params,
return_annotation=owner,
)
doc = REQUESTER_DOC.format(method=self.name)
for metadata in self.keys:
doc += REQUESTER_DOC_PARAM.format(metadata=metadata, method=self.name)
doc += REQUESTER_DOC_RETURN
func.__doc__ = doc
return func
| (self: imodels.tree.figs.FIGSClassifier, *, categorical_features: Union[bool, NoneType, str] = '$UNCHANGED$', use_clipped_prediction: Union[bool, NoneType, str] = '$UNCHANGED$') -> imodels.tree.figs.FIGSClassifier | [
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] |
17,056 | sklearn.utils._metadata_requests | set_predict_request | Request metadata passed to the ``predict`` method.
Note that this method is only relevant if
``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).
Please see :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
The options for each parameter are:
- ``True``: metadata is requested, and passed to ``predict`` if provided. The request is ignored if metadata is not provided.
- ``False``: metadata is not requested and the meta-estimator will not pass it to ``predict``.
- ``None``: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
- ``str``: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (``sklearn.utils.metadata_routing.UNCHANGED``) retains the
existing request. This allows you to change the request for some
parameters and not others.
.. versionadded:: 1.3
.. note::
This method is only relevant if this estimator is used as a
sub-estimator of a meta-estimator, e.g. used inside a
:class:`~sklearn.pipeline.Pipeline`. Otherwise it has no effect.
Parameters
----------
categorical_features : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``categorical_features`` parameter in ``predict``.
Returns
-------
self : object
The updated object.
| def __get__(self, instance, owner):
# we would want to have a method which accepts only the expected args
def func(**kw):
"""Updates the request for provided parameters
This docstring is overwritten below.
See REQUESTER_DOC for expected functionality
"""
if not _routing_enabled():
raise RuntimeError(
"This method is only available when metadata routing is enabled."
" You can enable it using"
" sklearn.set_config(enable_metadata_routing=True)."
)
if self.validate_keys and (set(kw) - set(self.keys)):
raise TypeError(
f"Unexpected args: {set(kw) - set(self.keys)}. Accepted arguments"
f" are: {set(self.keys)}"
)
requests = instance._get_metadata_request()
method_metadata_request = getattr(requests, self.name)
for prop, alias in kw.items():
if alias is not UNCHANGED:
method_metadata_request.add_request(param=prop, alias=alias)
instance._metadata_request = requests
return instance
# Now we set the relevant attributes of the function so that it seems
# like a normal method to the end user, with known expected arguments.
func.__name__ = f"set_{self.name}_request"
params = [
inspect.Parameter(
name="self",
kind=inspect.Parameter.POSITIONAL_OR_KEYWORD,
annotation=owner,
)
]
params.extend(
[
inspect.Parameter(
k,
inspect.Parameter.KEYWORD_ONLY,
default=UNCHANGED,
annotation=Optional[Union[bool, None, str]],
)
for k in self.keys
]
)
func.__signature__ = inspect.Signature(
params,
return_annotation=owner,
)
doc = REQUESTER_DOC.format(method=self.name)
for metadata in self.keys:
doc += REQUESTER_DOC_PARAM.format(metadata=metadata, method=self.name)
doc += REQUESTER_DOC_RETURN
func.__doc__ = doc
return func
| (self: imodels.tree.figs.FIGSClassifier, *, categorical_features: Union[bool, NoneType, str] = '$UNCHANGED$') -> imodels.tree.figs.FIGSClassifier | [
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17,058 | imodels.tree.figs | FIGSClassifierCV | null | class FIGSClassifierCV(FIGSCV):
def __init__(
self,
n_rules_list: List[int] = [6, 12, 24, 30, 50],
n_trees_list: List[int] = [5, 5, 5, 5, 5],
cv: int = 3,
scoring="accuracy",
*args,
**kwargs,
):
super(FIGSClassifierCV, self).__init__(
figs=FIGSClassifier,
n_rules_list=n_rules_list,
n_trees_list=n_trees_list,
cv=cv,
scoring=scoring,
*args,
**kwargs,
)
| (n_rules_list: List[int] = [6, 12, 24, 30, 50], n_trees_list: List[int] = [5, 5, 5, 5, 5], cv: int = 3, scoring='accuracy', *args, **kwargs) | [
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17,059 | imodels.tree.figs | __init__ | null | def __init__(
self,
n_rules_list: List[int] = [6, 12, 24, 30, 50],
n_trees_list: List[int] = [5, 5, 5, 5, 5],
cv: int = 3,
scoring="accuracy",
*args,
**kwargs,
):
super(FIGSClassifierCV, self).__init__(
figs=FIGSClassifier,
n_rules_list=n_rules_list,
n_trees_list=n_trees_list,
cv=cv,
scoring=scoring,
*args,
**kwargs,
)
| (self, n_rules_list: List[int] = [6, 12, 24, 30, 50], n_trees_list: List[int] = [5, 5, 5, 5, 5], cv: int = 3, scoring='accuracy', *args, **kwargs) | [
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17,060 | imodels.tree.figs | fit | null | def fit(self, X, y):
self.scores_ = []
for _i, n_rules in enumerate(self.n_rules_list):
est = self._figs_class(max_rules=n_rules, max_trees=self.n_trees_list[_i])
cv_scores = cross_val_score(est, X, y, cv=self.cv, scoring=self.scoring)
mean_score = np.mean(cv_scores)
if len(self.scores_) == 0:
self.figs = est
elif mean_score > np.max(self.scores_):
self.figs = est
self.scores_.append(mean_score)
self.figs.fit(X=X, y=y)
| (self, X, y) | [
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17,061 | imodels.tree.figs | predict | null | def predict(self, X):
return self.figs.predict(X)
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17,062 | imodels.tree.figs | predict_proba | null | def predict_proba(self, X):
return self.figs.predict_proba(X)
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17,063 | imodels.tree.figs | FIGSRegressor | null | class FIGSRegressor(FIGS, RegressorMixin):
...
| (max_rules: int = 12, max_trees: int = None, min_impurity_decrease: float = 0.0, random_state=None, max_features: str = None) | [
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17,099 | imodels.tree.figs | FIGSRegressorCV | null | class FIGSRegressorCV(FIGSCV):
def __init__(
self,
n_rules_list: List[int] = [6, 12, 24, 30, 50],
n_trees_list: List[int] = [5, 5, 5, 5, 5],
cv: int = 3,
scoring="r2",
*args,
**kwargs,
):
super(FIGSRegressorCV, self).__init__(
figs=FIGSRegressor,
n_rules_list=n_rules_list,
n_trees_list=n_trees_list,
cv=cv,
scoring=scoring,
*args,
**kwargs,
)
| (n_rules_list: List[int] = [6, 12, 24, 30, 50], n_trees_list: List[int] = [5, 5, 5, 5, 5], cv: int = 3, scoring='r2', *args, **kwargs) | [
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17,100 | imodels.tree.figs | __init__ | null | def __init__(
self,
n_rules_list: List[int] = [6, 12, 24, 30, 50],
n_trees_list: List[int] = [5, 5, 5, 5, 5],
cv: int = 3,
scoring="r2",
*args,
**kwargs,
):
super(FIGSRegressorCV, self).__init__(
figs=FIGSRegressor,
n_rules_list=n_rules_list,
n_trees_list=n_trees_list,
cv=cv,
scoring=scoring,
*args,
**kwargs,
)
| (self, n_rules_list: List[int] = [6, 12, 24, 30, 50], n_trees_list: List[int] = [5, 5, 5, 5, 5], cv: int = 3, scoring='r2', *args, **kwargs) | [
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17,104 | imodels.rule_set.fplasso | FPLassoClassifier | null | class FPLassoClassifier(FPLasso, ClassifierMixin):
def _init_prediction_task(self):
self.prediction_task = 'classification'
| (minsupport=0.1, maxcardinality=2, verbose=False, n_estimators=100, tree_size=4, sample_fract='default', max_rules=2000, memory_par=0.01, tree_generator=None, lin_trim_quantile=0.025, lin_standardise=True, exp_rand_tree_size=True, include_linear=True, alpha=None, random_state=None) | [
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17,106 | imodels.rule_set.fplasso | __init__ | null | def __init__(self,
minsupport=0.1,
maxcardinality=2,
verbose=False,
n_estimators=100,
tree_size=4,
sample_fract='default',
max_rules=2000,
memory_par=0.01,
tree_generator=None,
lin_trim_quantile=0.025,
lin_standardise=True,
exp_rand_tree_size=True,
include_linear=True,
alpha=None,
random_state=None):
super().__init__(n_estimators,
tree_size,
sample_fract,
max_rules,
memory_par,
tree_generator,
lin_trim_quantile,
lin_standardise,
exp_rand_tree_size,
include_linear,
alpha,
random_state)
self.minsupport = minsupport
self.maxcardinality = maxcardinality
self.verbose = verbose
| (self, minsupport=0.1, maxcardinality=2, verbose=False, n_estimators=100, tree_size=4, sample_fract='default', max_rules=2000, memory_par=0.01, tree_generator=None, lin_trim_quantile=0.025, lin_standardise=True, exp_rand_tree_size=True, include_linear=True, alpha=None, random_state=None) | [
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17,110 | imodels.rule_set.rule_fit | __str__ | null | def __str__(self):
if not hasattr(self, 'coef'):
s = self.__class__.__name__
s += "("
s += "max_rules="
s += repr(self.max_rules)
s += ")"
return s
else:
s = '> ------------------------------\n'
s += '> RuleFit:\n'
s += '> \tPredictions are made by summing the coefficients of each rule\n'
s += '> ------------------------------\n'
return s + self.visualize().to_string(index=False) + '\n'
| (self) | [
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17,114 | imodels.rule_set.fplasso | _extract_rules | null | def _extract_rules(self, X, y) -> List[str]:
X = pd.DataFrame(X, columns=self.feature_placeholders)
itemsets = extract_fpgrowth(X, minsupport=self.minsupport,
maxcardinality=self.maxcardinality,
verbose=self.verbose)
return itemsets_to_rules(itemsets)
| (self, X, y) -> List[str] | [
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17,118 | imodels.rule_set.rule_fit | _get_rules | Return the estimated rules
Parameters
----------
exclude_zero_coef: If True (default), returns only the rules with an estimated
coefficient not equalt to zero.
subregion: If None (default) returns global importances (FP 2004 eq. 28/29), else returns importance over
subregion of inputs (FP 2004 eq. 30/31/32).
Returns
-------
rules: pandas.DataFrame with the rules. Column 'rule' describes the rule, 'coef' holds
the coefficients and 'support' the support of the rule in the training
data set (X)
| def _get_rules(self, exclude_zero_coef=False, subregion=None):
"""Return the estimated rules
Parameters
----------
exclude_zero_coef: If True (default), returns only the rules with an estimated
coefficient not equalt to zero.
subregion: If None (default) returns global importances (FP 2004 eq. 28/29), else returns importance over
subregion of inputs (FP 2004 eq. 30/31/32).
Returns
-------
rules: pandas.DataFrame with the rules. Column 'rule' describes the rule, 'coef' holds
the coefficients and 'support' the support of the rule in the training
data set (X)
"""
n_features = len(self.coef) - len(self.rules_)
rule_ensemble = list(self.rules_without_feature_names_)
output_rules = []
# Add coefficients for linear effects
for i in range(0, n_features):
if self.lin_standardise:
coef = self.coef[i] * self.friedscale.scale_multipliers[i]
else:
coef = self.coef[i]
if subregion is None:
importance = abs(coef) * self.stddev[i]
else:
subregion = np.array(subregion)
importance = sum(abs(coef) * abs([x[i] for x in self.winsorizer.trim(subregion)] - self.mean[i])) / len(
subregion)
output_rules += [(self.feature_names[i],
'linear', coef, 1, importance)]
# Add rules
for i in range(0, len(self.rules_)):
rule = rule_ensemble[i]
coef = self.coef[i + n_features]
if subregion is None:
importance = abs(coef) * (rule.support *
(1 - rule.support)) ** (1 / 2)
else:
rkx = self.transform(subregion, [rule])[:, -1]
importance = sum(
abs(coef) * abs(rkx - rule.support)) / len(subregion)
output_rules += [(self.rules_[i].rule, 'rule',
coef, rule.support, importance)]
rules = pd.DataFrame(output_rules, columns=[
"rule", "type", "coef", "support", "importance"])
if exclude_zero_coef:
rules = rules.ix[rules.coef != 0]
return rules
| (self, exclude_zero_coef=False, subregion=None) | [
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17,120 | imodels.rule_set.fplasso | _init_prediction_task | null | def _init_prediction_task(self):
self.prediction_task = 'classification'
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17,122 | imodels.rule_set.rule_fit | _predict_continuous_output | Predict outcome of linear model for X
| def _predict_continuous_output(self, X):
"""Predict outcome of linear model for X
"""
if type(X) == pd.DataFrame:
X = X.values.astype(np.float32)
y_pred = np.zeros(X.shape[0])
y_pred += self._eval_weighted_rule_sum(X)
if self.include_linear:
if self.lin_standardise:
X = self.friedscale.scale(X)
y_pred += X @ self.coef[:X.shape[1]]
return y_pred + self.intercept
| (self, X) | [
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17,126 | imodels.rule_set.rule_fit | _score_rules | null | def _score_rules(self, X, y, rules) -> Tuple[List[Rule], List[float], float]:
X_concat = np.zeros([X.shape[0], 0])
# standardise linear variables if requested (for regression model only)
if self.include_linear:
# standard deviation and mean of winsorized features
self.winsorizer.train(X)
winsorized_X = self.winsorizer.trim(X)
self.stddev = np.std(winsorized_X, axis=0)
self.mean = np.mean(winsorized_X, axis=0)
if self.lin_standardise:
self.friedscale.train(X)
X_regn = self.friedscale.scale(X)
else:
X_regn = X.copy()
X_concat = np.concatenate((X_concat, X_regn), axis=1)
X_rules = self.transform(X, rules)
if X_rules.shape[0] > 0:
X_concat = np.concatenate((X_concat, X_rules), axis=1)
# no rules fit and self.include_linear == False
if X_concat.shape[1] == 0:
return [], [], 0
prediction_task = 'regression' if isinstance(
self, RegressorMixin) else 'classification'
return score_linear(X_concat, y, rules,
prediction_task=prediction_task,
max_rules=self.max_rules,
alpha=self.alpha,
cv=self.cv,
random_state=self.random_state)
| (self, X, y, rules) -> Tuple[List[imodels.util.rule.Rule], List[float], float] | [
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17,129 | imodels.rule_set.fplasso | fit | null | def fit(self, X, y=None, feature_names=None, undiscretized_features=[]):
self.undiscretized_features = undiscretized_features
super().fit(X, y, feature_names=feature_names)
return self
| (self, X, y=None, feature_names=None, undiscretized_features=[]) | [
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] |
17,133 | imodels.rule_set.rule_fit | predict | Predict. For regression returns continuous output.
For classification, returns discrete output.
| def predict(self, X):
'''Predict. For regression returns continuous output.
For classification, returns discrete output.
'''
check_is_fitted(self)
if scipy.sparse.issparse(X):
X = X.toarray()
X = check_array(X)
if isinstance(self, RegressorMixin):
return self._predict_continuous_output(X)
else:
return np.argmax(self.predict_proba(X), axis=1)
| (self, X) | [
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17,134 | imodels.rule_set.rule_fit | predict_proba | null | def predict_proba(self, X):
check_is_fitted(self)
if scipy.sparse.issparse(X):
X = X.toarray()
X = check_array(X)
continuous_output = self._predict_continuous_output(X)
logits = np.vstack(
(1 - continuous_output, continuous_output)).transpose()
return softmax(logits, axis=1)
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] |
17,136 | sklearn.utils._metadata_requests | set_fit_request | Request metadata passed to the ``fit`` method.
Note that this method is only relevant if
``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).
Please see :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
The options for each parameter are:
- ``True``: metadata is requested, and passed to ``fit`` if provided. The request is ignored if metadata is not provided.
- ``False``: metadata is not requested and the meta-estimator will not pass it to ``fit``.
- ``None``: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
- ``str``: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (``sklearn.utils.metadata_routing.UNCHANGED``) retains the
existing request. This allows you to change the request for some
parameters and not others.
.. versionadded:: 1.3
.. note::
This method is only relevant if this estimator is used as a
sub-estimator of a meta-estimator, e.g. used inside a
:class:`~sklearn.pipeline.Pipeline`. Otherwise it has no effect.
Parameters
----------
feature_names : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``feature_names`` parameter in ``fit``.
undiscretized_features : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``undiscretized_features`` parameter in ``fit``.
Returns
-------
self : object
The updated object.
| def __get__(self, instance, owner):
# we would want to have a method which accepts only the expected args
def func(**kw):
"""Updates the request for provided parameters
This docstring is overwritten below.
See REQUESTER_DOC for expected functionality
"""
if not _routing_enabled():
raise RuntimeError(
"This method is only available when metadata routing is enabled."
" You can enable it using"
" sklearn.set_config(enable_metadata_routing=True)."
)
if self.validate_keys and (set(kw) - set(self.keys)):
raise TypeError(
f"Unexpected args: {set(kw) - set(self.keys)}. Accepted arguments"
f" are: {set(self.keys)}"
)
requests = instance._get_metadata_request()
method_metadata_request = getattr(requests, self.name)
for prop, alias in kw.items():
if alias is not UNCHANGED:
method_metadata_request.add_request(param=prop, alias=alias)
instance._metadata_request = requests
return instance
# Now we set the relevant attributes of the function so that it seems
# like a normal method to the end user, with known expected arguments.
func.__name__ = f"set_{self.name}_request"
params = [
inspect.Parameter(
name="self",
kind=inspect.Parameter.POSITIONAL_OR_KEYWORD,
annotation=owner,
)
]
params.extend(
[
inspect.Parameter(
k,
inspect.Parameter.KEYWORD_ONLY,
default=UNCHANGED,
annotation=Optional[Union[bool, None, str]],
)
for k in self.keys
]
)
func.__signature__ = inspect.Signature(
params,
return_annotation=owner,
)
doc = REQUESTER_DOC.format(method=self.name)
for metadata in self.keys:
doc += REQUESTER_DOC_PARAM.format(metadata=metadata, method=self.name)
doc += REQUESTER_DOC_RETURN
func.__doc__ = doc
return func
| (self: imodels.rule_set.fplasso.FPLassoClassifier, *, feature_names: Union[bool, NoneType, str] = '$UNCHANGED$', undiscretized_features: Union[bool, NoneType, str] = '$UNCHANGED$') -> imodels.rule_set.fplasso.FPLassoClassifier | [
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17,140 | sklearn.utils._metadata_requests | set_transform_request | Request metadata passed to the ``transform`` method.
Note that this method is only relevant if
``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).
Please see :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
The options for each parameter are:
- ``True``: metadata is requested, and passed to ``transform`` if provided. The request is ignored if metadata is not provided.
- ``False``: metadata is not requested and the meta-estimator will not pass it to ``transform``.
- ``None``: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
- ``str``: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (``sklearn.utils.metadata_routing.UNCHANGED``) retains the
existing request. This allows you to change the request for some
parameters and not others.
.. versionadded:: 1.3
.. note::
This method is only relevant if this estimator is used as a
sub-estimator of a meta-estimator, e.g. used inside a
:class:`~sklearn.pipeline.Pipeline`. Otherwise it has no effect.
Parameters
----------
rules : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``rules`` parameter in ``transform``.
Returns
-------
self : object
The updated object.
| def __get__(self, instance, owner):
# we would want to have a method which accepts only the expected args
def func(**kw):
"""Updates the request for provided parameters
This docstring is overwritten below.
See REQUESTER_DOC for expected functionality
"""
if not _routing_enabled():
raise RuntimeError(
"This method is only available when metadata routing is enabled."
" You can enable it using"
" sklearn.set_config(enable_metadata_routing=True)."
)
if self.validate_keys and (set(kw) - set(self.keys)):
raise TypeError(
f"Unexpected args: {set(kw) - set(self.keys)}. Accepted arguments"
f" are: {set(self.keys)}"
)
requests = instance._get_metadata_request()
method_metadata_request = getattr(requests, self.name)
for prop, alias in kw.items():
if alias is not UNCHANGED:
method_metadata_request.add_request(param=prop, alias=alias)
instance._metadata_request = requests
return instance
# Now we set the relevant attributes of the function so that it seems
# like a normal method to the end user, with known expected arguments.
func.__name__ = f"set_{self.name}_request"
params = [
inspect.Parameter(
name="self",
kind=inspect.Parameter.POSITIONAL_OR_KEYWORD,
annotation=owner,
)
]
params.extend(
[
inspect.Parameter(
k,
inspect.Parameter.KEYWORD_ONLY,
default=UNCHANGED,
annotation=Optional[Union[bool, None, str]],
)
for k in self.keys
]
)
func.__signature__ = inspect.Signature(
params,
return_annotation=owner,
)
doc = REQUESTER_DOC.format(method=self.name)
for metadata in self.keys:
doc += REQUESTER_DOC_PARAM.format(metadata=metadata, method=self.name)
doc += REQUESTER_DOC_RETURN
func.__doc__ = doc
return func
| (self: imodels.rule_set.fplasso.FPLassoClassifier, *, rules: Union[bool, NoneType, str] = '$UNCHANGED$') -> imodels.rule_set.fplasso.FPLassoClassifier | [
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] |
17,141 | imodels.rule_set.rule_fit | transform | Transform dataset.
Parameters
----------
X : array-like matrix, shape=(n_samples, n_features)
Input data to be transformed. Use ``dtype=np.float32`` for maximum
efficiency.
Returns
-------
X_transformed: matrix, shape=(n_samples, n_out)
Transformed data set
| def _score_rules(self, X, y, rules) -> Tuple[List[Rule], List[float], float]:
X_concat = np.zeros([X.shape[0], 0])
# standardise linear variables if requested (for regression model only)
if self.include_linear:
# standard deviation and mean of winsorized features
self.winsorizer.train(X)
winsorized_X = self.winsorizer.trim(X)
self.stddev = np.std(winsorized_X, axis=0)
self.mean = np.mean(winsorized_X, axis=0)
if self.lin_standardise:
self.friedscale.train(X)
X_regn = self.friedscale.scale(X)
else:
X_regn = X.copy()
X_concat = np.concatenate((X_concat, X_regn), axis=1)
X_rules = self.transform(X, rules)
if X_rules.shape[0] > 0:
X_concat = np.concatenate((X_concat, X_rules), axis=1)
# no rules fit and self.include_linear == False
if X_concat.shape[1] == 0:
return [], [], 0
prediction_task = 'regression' if isinstance(
self, RegressorMixin) else 'classification'
return score_linear(X_concat, y, rules,
prediction_task=prediction_task,
max_rules=self.max_rules,
alpha=self.alpha,
cv=self.cv,
random_state=self.random_state)
| (self, X=None, rules=None) | [
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17,142 | imodels.rule_set.rule_fit | visualize | null | def visualize(self, decimals=2):
rules = self._get_rules()
rules = rules[rules.coef != 0].sort_values("support", ascending=False)
pd.set_option('display.max_colwidth', None)
return rules[['rule', 'coef']].round(decimals)
| (self, decimals=2) | [
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17,143 | imodels.rule_set.fplasso | FPLassoRegressor | null | class FPLassoRegressor(FPLasso, RegressorMixin):
def _init_prediction_task(self):
self.prediction_task = 'regression'
| (minsupport=0.1, maxcardinality=2, verbose=False, n_estimators=100, tree_size=4, sample_fract='default', max_rules=2000, memory_par=0.01, tree_generator=None, lin_trim_quantile=0.025, lin_standardise=True, exp_rand_tree_size=True, include_linear=True, alpha=None, random_state=None) | [
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17,159 | imodels.rule_set.fplasso | _init_prediction_task | null | def _init_prediction_task(self):
self.prediction_task = 'regression'
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17,182 | imodels.rule_set.fpskope | FPSkopeClassifier | null | class FPSkopeClassifier(SkopeRulesClassifier):
def __init__(self,
minsupport=0.1,
maxcardinality=2,
verbose=False,
precision_min=0.5,
recall_min=0.01,
n_estimators=10,
max_samples=.8,
max_samples_features=1.,
bootstrap=False,
bootstrap_features=False,
max_depth=3,
max_depth_duplication=None,
max_features=1.,
min_samples_split=2,
n_jobs=1,
random_state=None):
super().__init__(precision_min,
recall_min,
n_estimators,
max_samples,
max_samples_features,
bootstrap,
bootstrap_features,
max_depth,
max_depth_duplication,
max_features,
min_samples_split,
n_jobs,
random_state,
verbose)
self.minsupport = minsupport
self.maxcardinality = maxcardinality
self.verbose = verbose
def fit(self, X, y=None, feature_names=None, undiscretized_features=[], sample_weight=None):
self.undiscretized_features = undiscretized_features
super().fit(X, y, feature_names=feature_names, sample_weight=sample_weight)
return self
def _extract_rules(self, X, y) -> List[str]:
X = pd.DataFrame(X, columns=self.feature_placeholders)
itemsets = extract_fpgrowth(X, minsupport=self.minsupport,
maxcardinality=self.maxcardinality,
verbose=self.verbose)
return [itemsets_to_rules(itemsets)], [np.arange(X.shape[0])], [np.arange(len(self.feature_names))]
def _score_rules(self, X, y, rules) -> List[Rule]:
return score_precision_recall(X, y,
rules,
self.estimators_samples_,
self.estimators_features_,
self.feature_placeholders,
oob=False)
| (minsupport=0.1, maxcardinality=2, verbose=False, precision_min=0.5, recall_min=0.01, n_estimators=10, max_samples=0.8, max_samples_features=1.0, bootstrap=False, bootstrap_features=False, max_depth=3, max_depth_duplication=None, max_features=1.0, min_samples_split=2, n_jobs=1, random_state=None) | [
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17,184 | imodels.rule_set.fpskope | __init__ | null | def __init__(self,
minsupport=0.1,
maxcardinality=2,
verbose=False,
precision_min=0.5,
recall_min=0.01,
n_estimators=10,
max_samples=.8,
max_samples_features=1.,
bootstrap=False,
bootstrap_features=False,
max_depth=3,
max_depth_duplication=None,
max_features=1.,
min_samples_split=2,
n_jobs=1,
random_state=None):
super().__init__(precision_min,
recall_min,
n_estimators,
max_samples,
max_samples_features,
bootstrap,
bootstrap_features,
max_depth,
max_depth_duplication,
max_features,
min_samples_split,
n_jobs,
random_state,
verbose)
self.minsupport = minsupport
self.maxcardinality = maxcardinality
self.verbose = verbose
| (self, minsupport=0.1, maxcardinality=2, verbose=False, precision_min=0.5, recall_min=0.01, n_estimators=10, max_samples=0.8, max_samples_features=1.0, bootstrap=False, bootstrap_features=False, max_depth=3, max_depth_duplication=None, max_features=1.0, min_samples_split=2, n_jobs=1, random_state=None) | [
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17,191 | imodels.rule_set.fpskope | _extract_rules | null | def _extract_rules(self, X, y) -> List[str]:
X = pd.DataFrame(X, columns=self.feature_placeholders)
itemsets = extract_fpgrowth(X, minsupport=self.minsupport,
maxcardinality=self.maxcardinality,
verbose=self.verbose)
return [itemsets_to_rules(itemsets)], [np.arange(X.shape[0])], [np.arange(len(self.feature_names))]
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17,197 | imodels.rule_set.skope_rules | _predict_top_rules | Predict if a particular sample is an outlier or not,
using the n_rules most performing rules.
Parameters
----------
X : array-like, shape (n_samples, n_features)
The input samples. Internally, it will be converted to
``dtype=np.float32``
n_rules : int
The number of rules used for the prediction. If one of the
n_rules most performing rules is activated, the prediction
is equal to 1.
Returns
-------
is_outlier : array, shape (n_samples,)
For each observations, tells whether or not (1 or 0) it should
be considered as an outlier according to the selected rules.
| def _predict_top_rules(self, X, n_rules) -> np.ndarray:
"""Predict if a particular sample is an outlier or not,
using the n_rules most performing rules.
Parameters
----------
X : array-like, shape (n_samples, n_features)
The input samples. Internally, it will be converted to
``dtype=np.float32``
n_rules : int
The number of rules used for the prediction. If one of the
n_rules most performing rules is activated, the prediction
is equal to 1.
Returns
-------
is_outlier : array, shape (n_samples,)
For each observations, tells whether or not (1 or 0) it should
be considered as an outlier according to the selected rules.
"""
return np.array((self._score_top_rules(X) > len(self.rules_) - n_rules),
dtype=int)
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17,198 | imodels.rule_set.skope_rules | _prune_rules | null | def _prune_rules(self, rules) -> List[Rule]:
return deduplicate(
prune_mins(rules, self.precision_min, self.recall_min),
self.max_depth_duplication
)
| (self, rules) -> List[imodels.util.rule.Rule] | [
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17,201 | imodels.rule_set.skope_rules | _rules_vote | Score representing a vote of the base classifiers (rules).
The score of an input sample is computed as the sum of the binary
rules outputs: a score of k means than k rules have voted positively.
Parameters
----------
X : array-like, shape (n_samples, n_features)
The training input samples.
Returns
-------
scores : array, shape (n_samples,)
The score of the input samples.
The higher, the more abnormal. Positive scores represent outliers,
null scores represent inliers.
| def _rules_vote(self, X) -> np.ndarray:
"""Score representing a vote of the base classifiers (rules).
The score of an input sample is computed as the sum of the binary
rules outputs: a score of k means than k rules have voted positively.
Parameters
----------
X : array-like, shape (n_samples, n_features)
The training input samples.
Returns
-------
scores : array, shape (n_samples,)
The score of the input samples.
The higher, the more abnormal. Positive scores represent outliers,
null scores represent inliers.
"""
# Check if fit had been called
check_is_fitted(self, ['rules_', 'estimators_samples_', 'max_samples_'])
# Input validation
X = check_array(X)
if X.shape[1] != self.n_features_:
raise ValueError("X.shape[1] = %d should be equal to %d, "
"the number of features at training time."
" Please reshape your data."
% (X.shape[1], self.n_features_))
df = pandas.DataFrame(X, columns=self.feature_placeholders)
selected_rules = self.rules_without_feature_names_
scores = np.zeros(X.shape[0])
for (r, _) in selected_rules:
scores[list(df.query(r).index)] += 1
return scores
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17,202 | imodels.rule_set.fpskope | _score_rules | null | def _score_rules(self, X, y, rules) -> List[Rule]:
return score_precision_recall(X, y,
rules,
self.estimators_samples_,
self.estimators_features_,
self.feature_placeholders,
oob=False)
| (self, X, y, rules) -> List[imodels.util.rule.Rule] | [
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17,203 | imodels.rule_set.skope_rules | _score_top_rules | Score representing an ordering between the base classifiers (rules).
The score is high when the instance is detected by a performing rule.
If there are n rules, ordered by increasing OOB precision, a score of k
means than the kth rule has voted positively, but not the (k-1) first
rules.
Parameters
----------
X : array-like, shape (n_samples, n_features)
The training input samples.
Returns
-------
scores : array, shape (n_samples,)
The score of the input samples.
Positive scores represent outliers, null scores represent inliers.
| def _score_top_rules(self, X) -> np.ndarray:
"""Score representing an ordering between the base classifiers (rules).
The score is high when the instance is detected by a performing rule.
If there are n rules, ordered by increasing OOB precision, a score of k
means than the kth rule has voted positively, but not the (k-1) first
rules.
Parameters
----------
X : array-like, shape (n_samples, n_features)
The training input samples.
Returns
-------
scores : array, shape (n_samples,)
The score of the input samples.
Positive scores represent outliers, null scores represent inliers.
"""
# Check if fit had been called
check_is_fitted(self, ['rules_', 'estimators_samples_', 'max_samples_'])
# Input validation
X = check_array(X)
if X.shape[1] != self.n_features_:
raise ValueError("X.shape[1] = %d should be equal to %d, "
"the number of features at training time."
" Please reshape your data."
% (X.shape[1], self.n_features_))
df = pandas.DataFrame(X, columns=self.feature_placeholders)
selected_rules = self.rules_without_feature_names_
scores = np.zeros(X.shape[0])
for (k, r) in enumerate(list((selected_rules))):
scores[list(df.query(r.rule).index)] = np.maximum(
len(selected_rules) - k,
scores[list(df.query(r.rule).index)])
return scores
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17,206 | imodels.rule_set.fpskope | fit | null | def fit(self, X, y=None, feature_names=None, undiscretized_features=[], sample_weight=None):
self.undiscretized_features = undiscretized_features
super().fit(X, y, feature_names=feature_names, sample_weight=sample_weight)
return self
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17,209 | imodels.rule_set.skope_rules | predict | Predict if a particular sample is an outlier or not.
Parameters
----------
X : array-like, shape (n_samples, n_features)
The input samples. Internally, it will be converted to
``dtype=np.float32``
Returns
-------
is_outlier : array, shape (n_samples,)
For each observations, tells whether or not (1 or 0) it should
be considered as an outlier according to the selected rules.
| def predict(self, X) -> np.ndarray:
"""Predict if a particular sample is an outlier or not.
Parameters
----------
X : array-like, shape (n_samples, n_features)
The input samples. Internally, it will be converted to
``dtype=np.float32``
Returns
-------
is_outlier : array, shape (n_samples,)
For each observations, tells whether or not (1 or 0) it should
be considered as an outlier according to the selected rules.
"""
X = check_array(X)
return np.argmax(self.predict_proba(X), axis=1)
| (self, X) -> numpy.ndarray | [
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17,210 | imodels.rule_set.skope_rules | predict_proba | Predict probability of a particular sample being an outlier or not
| def predict_proba(self, X) -> np.ndarray:
'''Predict probability of a particular sample being an outlier or not
'''
X = check_array(X)
weight_sum = np.sum([w[0] for (r, w) in self.rules_without_feature_names_])
if weight_sum == 0:
return np.vstack((np.ones(X.shape[0]), np.zeros(X.shape[0]))).transpose()
y = self._eval_weighted_rule_sum(X) / weight_sum
return np.vstack((1 - y, y)).transpose()
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17,212 | sklearn.utils._metadata_requests | set_fit_request | Request metadata passed to the ``fit`` method.
Note that this method is only relevant if
``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).
Please see :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
The options for each parameter are:
- ``True``: metadata is requested, and passed to ``fit`` if provided. The request is ignored if metadata is not provided.
- ``False``: metadata is not requested and the meta-estimator will not pass it to ``fit``.
- ``None``: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
- ``str``: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (``sklearn.utils.metadata_routing.UNCHANGED``) retains the
existing request. This allows you to change the request for some
parameters and not others.
.. versionadded:: 1.3
.. note::
This method is only relevant if this estimator is used as a
sub-estimator of a meta-estimator, e.g. used inside a
:class:`~sklearn.pipeline.Pipeline`. Otherwise it has no effect.
Parameters
----------
feature_names : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``feature_names`` parameter in ``fit``.
sample_weight : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``sample_weight`` parameter in ``fit``.
undiscretized_features : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``undiscretized_features`` parameter in ``fit``.
Returns
-------
self : object
The updated object.
| def __get__(self, instance, owner):
# we would want to have a method which accepts only the expected args
def func(**kw):
"""Updates the request for provided parameters
This docstring is overwritten below.
See REQUESTER_DOC for expected functionality
"""
if not _routing_enabled():
raise RuntimeError(
"This method is only available when metadata routing is enabled."
" You can enable it using"
" sklearn.set_config(enable_metadata_routing=True)."
)
if self.validate_keys and (set(kw) - set(self.keys)):
raise TypeError(
f"Unexpected args: {set(kw) - set(self.keys)}. Accepted arguments"
f" are: {set(self.keys)}"
)
requests = instance._get_metadata_request()
method_metadata_request = getattr(requests, self.name)
for prop, alias in kw.items():
if alias is not UNCHANGED:
method_metadata_request.add_request(param=prop, alias=alias)
instance._metadata_request = requests
return instance
# Now we set the relevant attributes of the function so that it seems
# like a normal method to the end user, with known expected arguments.
func.__name__ = f"set_{self.name}_request"
params = [
inspect.Parameter(
name="self",
kind=inspect.Parameter.POSITIONAL_OR_KEYWORD,
annotation=owner,
)
]
params.extend(
[
inspect.Parameter(
k,
inspect.Parameter.KEYWORD_ONLY,
default=UNCHANGED,
annotation=Optional[Union[bool, None, str]],
)
for k in self.keys
]
)
func.__signature__ = inspect.Signature(
params,
return_annotation=owner,
)
doc = REQUESTER_DOC.format(method=self.name)
for metadata in self.keys:
doc += REQUESTER_DOC_PARAM.format(metadata=metadata, method=self.name)
doc += REQUESTER_DOC_RETURN
func.__doc__ = doc
return func
| (self: imodels.rule_set.fpskope.FPSkopeClassifier, *, feature_names: Union[bool, NoneType, str] = '$UNCHANGED$', sample_weight: Union[bool, NoneType, str] = '$UNCHANGED$', undiscretized_features: Union[bool, NoneType, str] = '$UNCHANGED$') -> imodels.rule_set.fpskope.FPSkopeClassifier | [
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17,215 | imodels.rule_list.greedy_rule_list | GreedyRuleListClassifier | null | class GreedyRuleListClassifier(BaseEstimator, RuleList, ClassifierMixin):
def __init__(self, max_depth: int = 5, class_weight=None,
criterion: str = 'gini'):
'''
Params
------
max_depth
Maximum depth the list can achieve
criterion: str
Criterion used to split
'gini', 'entropy', or 'log_loss'
'''
self.max_depth = max_depth
self.class_weight = class_weight
self.criterion = criterion
self.depth = 0 # tracks the fitted depth
def fit(self, X, y, depth: int = 0, feature_names=None, verbose=False):
"""
Params
------
X: array_like
Feature set
y: array_like
target variable
depth
the depth of the current layer (used to recurse)
"""
X, y, feature_names = check_fit_arguments(self, X, y, feature_names)
return self.fit_node_recursive(X, y, depth=0, verbose=verbose)
def fit_node_recursive(self, X, y, depth: int, verbose):
# base case 1: no data in this group
if y.size == 0:
return []
# base case 2: all y is the same in this group
elif np.all(y == y[0]):
return [{'val': y[0], 'num_pts': y.size}]
# base case 3: max depth reached
elif depth == self.max_depth:
return [{'val': np.mean(y), 'num_pts': y.size}]
# recursively generate rule list
else:
# find a split with the best value for the criterion
m = DecisionTreeClassifier(max_depth=1, criterion=self.criterion)
m.fit(X, y)
col = m.tree_.feature[0]
cutoff = m.tree_.threshold[0]
# col, cutoff, criterion_val = self._find_best_split(X, y)
if col == -2:
return []
y_left = y[X[:, col] < cutoff] # left-hand side data
y_right = y[X[:, col] >= cutoff] # right-hand side data
# put higher probability of class 1 on the right-hand side
if len(y_left) > 0 and np.mean(y_left) > np.mean(y_right):
flip = True
tmp = deepcopy(y_left)
y_left = deepcopy(y_right)
y_right = tmp
x_left = X[X[:, col] >= cutoff]
else:
flip = False
x_left = X[X[:, col] < cutoff]
# print
if verbose:
print(
f'{np.mean(100 * y):.2f} -> {self.feature_names_[col]} -> {np.mean(100 * y_left):.2f} ({y_left.size}) {np.mean(100 * y_right):.2f} ({y_right.size})')
# save info
par_node = [{
'col': self.feature_names_[col],
'index_col': col,
'cutoff': cutoff,
'val': np.mean(y_left), # will be the values before splitting in the next lower level
'flip': flip,
'val_right': np.mean(y_right),
'num_pts': y.size,
'num_pts_right': y_right.size
}]
# generate tree for the non-leaf data
par_node = par_node + \
self.fit_node_recursive(x_left, y_left, depth + 1, verbose=verbose)
self.depth += 1 # increase the depth since we call fit once
self.rules_ = par_node
self.complexity_ = len(self.rules_)
self.classes_ = unique_labels(y)
return par_node
def predict_proba(self, X):
check_is_fitted(self)
X = check_array(X)
n = X.shape[0]
probs = np.zeros(n)
for i in range(n):
x = X[i]
for j, rule in enumerate(self.rules_):
if j == len(self.rules_) - 1:
probs[i] = rule['val']
continue
regular_condition = x[rule["index_col"]] >= rule["cutoff"]
flipped_condition = x[rule["index_col"]] < rule["cutoff"]
condition = flipped_condition if rule["flip"] else regular_condition
if condition:
probs[i] = rule['val_right']
break
return np.vstack((1 - probs, probs)).transpose() # probs (n, 2)
def predict(self, X):
check_is_fitted(self)
X = check_array(X)
return np.argmax(self.predict_proba(X), axis=1)
"""
def __str__(self):
# s = ''
# for rule in self.rules_:
# s += f"mean {rule['val'].round(3)} ({rule['num_pts']} pts)\n"
# if 'col' in rule:
# s += f"if {rule['col']} >= {rule['cutoff']} then {rule['val_right'].round(3)} ({rule['num_pts_right']} pts)\n"
# return s
"""
def __str__(self):
'''Print out the list in a nice way
'''
s = '> ------------------------------\n> Greedy Rule List\n> ------------------------------\n'
def red(s):
# return f"\033[91m{s}\033[00m"
return s
def cyan(s):
# return f"\033[96m{s}\033[00m"
return s
def rule_name(rule):
if rule['flip']:
return '~' + rule['col']
return rule['col']
# rule = self.rules_[0]
# s += f"{red((100 * rule['val']).round(3))}% IwI ({rule['num_pts']} pts)\n"
for rule in self.rules_:
s += u'\u2193\n' + f"{cyan((100 * rule['val']).round(2))}% risk ({rule['num_pts']} pts)\n"
# s += f"\t{'Else':>45} => {cyan((100 * rule['val']).round(2)):>6}% IwI ({rule['val'] * rule['num_pts']:.0f}/{rule['num_pts']} pts)\n"
if 'col' in rule:
# prefix = f"if {rule['col']} >= {rule['cutoff']}"
prefix = f"if {rule_name(rule)}"
val = f"{100 * rule['val_right'].round(3)}"
s += f"\t{prefix} ==> {red(val)}% risk ({rule['num_pts_right']} pts)\n"
# rule = self.rules_[-1]
# s += f"{red((100 * rule['val']).round(3))}% IwI ({rule['num_pts']} pts)\n"
return s
######## HERE ONWARDS CUSTOM SPLITTING (DEPRECATED IN FAVOR OF SKLEARN STUMP) ########
######################################################################################
def _find_best_split(self, x, y):
"""
Find the best split from all features
returns: the column to split on, the cutoff value, and the actual criterion_value
"""
col = None
min_criterion_val = 1e10
cutoff = None
# iterating through each feature
for i, c in enumerate(x.T):
# find the best split of that feature
criterion_val, cur_cutoff = self._split_on_feature(c, y)
# found perfect cutoff
if criterion_val == 0:
return i, cur_cutoff, criterion_val
# check if it's best so far
elif criterion_val <= min_criterion_val:
min_criterion_val = criterion_val
col = i
cutoff = cur_cutoff
return col, cutoff, min_criterion_val
def _split_on_feature(self, col, y):
"""
col: the column we split on
y: target var
"""
min_criterion_val = 1e10
cutoff = 0.5
# iterate through each value in the column
for value in np.unique(col):
# separate y into 2 groups
y_predict = col < value
# get criterion val of this split
criterion_val = self._weighted_criterion(y_predict, y)
# check if it's the smallest one so far
if criterion_val <= min_criterion_val:
min_criterion_val = criterion_val
cutoff = value
return min_criterion_val, cutoff
def _weighted_criterion(self, split_decision, y_real):
"""Returns criterion calculated over a split
split decision, True/False, and y_true can be multi class
"""
if split_decision.shape[0] != y_real.shape[0]:
print('They have to be the same length')
return None
# choose th | (max_depth: int = 5, class_weight=None, criterion: str = 'gini') | [
0.03446049988269806,
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0.001359042595140636,
0.014307916164398193,
-0.025201205164194107,
-0.020728671923279762,
-0.04420161619782448,
0.014255544170737267,
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0.004234242253005505,
0.013909892179071903,
-0.014423133805394173,
0.01143795344978571,
0.02576681785285473,
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0.03211425244808197,
0.009866805747151375,
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0.00967303104698658,
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-0.034083425998687744,
-0.03758184611797333,
-0.008588938973844051,
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-0.053628500550985336,
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0.06209174543619156,
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0.0527067594230175,
-0.03297314792871475,
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0.036450620740652084,
-0.015083015896379948,
0.009783011861145496,
0.005624707788228989,
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0.01738736592233181,
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0.030731644481420517,
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0.018487168475985527,
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0.022792112082242966,
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0.014527876861393452,
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0.06267830729484558,
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0.0035219888668507338,
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0.009070757776498795,
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0.03077354095876217,
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0.0036267319228500128,
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0.01920989528298378,
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0.02761029824614525,
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17,217 | imodels.rule_list.greedy_rule_list | __init__ |
Params
------
max_depth
Maximum depth the list can achieve
criterion: str
Criterion used to split
'gini', 'entropy', or 'log_loss'
| def __init__(self, max_depth: int = 5, class_weight=None,
criterion: str = 'gini'):
'''
Params
------
max_depth
Maximum depth the list can achieve
criterion: str
Criterion used to split
'gini', 'entropy', or 'log_loss'
'''
self.max_depth = max_depth
self.class_weight = class_weight
self.criterion = criterion
self.depth = 0 # tracks the fitted depth
| (self, max_depth: int = 5, class_weight=None, criterion: str = 'gini') | [
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] |
17,221 | imodels.rule_list.greedy_rule_list | __str__ | Print out the list in a nice way
| def __str__(self):
'''Print out the list in a nice way
'''
s = '> ------------------------------\n> Greedy Rule List\n> ------------------------------\n'
def red(s):
# return f"\033[91m{s}\033[00m"
return s
def cyan(s):
# return f"\033[96m{s}\033[00m"
return s
def rule_name(rule):
if rule['flip']:
return '~' + rule['col']
return rule['col']
# rule = self.rules_[0]
# s += f"{red((100 * rule['val']).round(3))}% IwI ({rule['num_pts']} pts)\n"
for rule in self.rules_:
s += u'\u2193\n' + f"{cyan((100 * rule['val']).round(2))}% risk ({rule['num_pts']} pts)\n"
# s += f"\t{'Else':>45} => {cyan((100 * rule['val']).round(2)):>6}% IwI ({rule['val'] * rule['num_pts']:.0f}/{rule['num_pts']} pts)\n"
if 'col' in rule:
# prefix = f"if {rule['col']} >= {rule['cutoff']}"
prefix = f"if {rule_name(rule)}"
val = f"{100 * rule['val_right'].round(3)}"
s += f"\t{prefix} ==> {red(val)}% risk ({rule['num_pts_right']} pts)\n"
# rule = self.rules_[-1]
# s += f"{red((100 * rule['val']).round(3))}% IwI ({rule['num_pts']} pts)\n"
return s
| (self) | [
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17,224 | imodels.rule_list.greedy_rule_list | _entropy_criterion | Returns entropy of a divided group of data
Data may have multiple classes
| def _entropy_criterion(self, y):
"""Returns entropy of a divided group of data
Data may have multiple classes
"""
s = 0
n = len(y)
classes = set(y)
# for each class, get entropy
for c in classes:
# weights for each class
weight = sum(y == c) / n
def _entropy_from_counts(c1, c2):
"""Returns entropy of a group of data
c1: count of one class
c2: count of another class
"""
if c1 == 0 or c2 == 0: # when there is only one class in the group, entropy is 0
return 0
def _entropy_func(p): return -p * math.log(p, 2)
p1 = c1 * 1.0 / (c1 + c2)
p2 = c2 * 1.0 / (c1 + c2)
return _entropy_func(p1) + _entropy_func(p2)
# weighted avg
s += weight * _entropy_from_counts(sum(y == c), sum(y != c))
return s
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] |
17,225 | imodels.rule_list.greedy_rule_list | _find_best_split |
Find the best split from all features
returns: the column to split on, the cutoff value, and the actual criterion_value
| def _find_best_split(self, x, y):
"""
Find the best split from all features
returns: the column to split on, the cutoff value, and the actual criterion_value
"""
col = None
min_criterion_val = 1e10
cutoff = None
# iterating through each feature
for i, c in enumerate(x.T):
# find the best split of that feature
criterion_val, cur_cutoff = self._split_on_feature(c, y)
# found perfect cutoff
if criterion_val == 0:
return i, cur_cutoff, criterion_val
# check if it's best so far
elif criterion_val <= min_criterion_val:
min_criterion_val = criterion_val
col = i
cutoff = cur_cutoff
return col, cutoff, min_criterion_val
| (self, x, y) | [
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17,230 | imodels.rule_list.greedy_rule_list | _gini_criterion | Returns gini index for one node
= sum(pc * (1 – pc))
| def _gini_criterion(self, y):
'''Returns gini index for one node
= sum(pc * (1 – pc))
'''
s = 0
n = y.shape[0]
classes = np.unique(y)
# for each class, get entropy
for c in classes:
# weights for each class
n_c = np.sum(y == c)
p_c = n_c / n
# weighted avg
s += p_c * (1 - p_c)
return s
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17,232 | imodels.rule_list.greedy_rule_list | _neg_corr_criterion | Returns negative correlation between y
and the binary splitting variable split_decision
y must be binary
| def _neg_corr_criterion(self, split_decision, y):
'''Returns negative correlation between y
and the binary splitting variable split_decision
y must be binary
'''
if np.unique(y).size < 2:
return 0
elif np.unique(y).size != 2:
print('y must be binary output for corr criterion')
# y should be 1 more often on the "right side" of the split
if y.sum() < y.size / 2:
y = 1 - y
return -1 * np.corrcoef(split_decision.astype(np.int), y)[0, 1]
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17,235 | imodels.rule_list.greedy_rule_list | _split_on_feature |
col: the column we split on
y: target var
| def _split_on_feature(self, col, y):
"""
col: the column we split on
y: target var
"""
min_criterion_val = 1e10
cutoff = 0.5
# iterate through each value in the column
for value in np.unique(col):
# separate y into 2 groups
y_predict = col < value
# get criterion val of this split
criterion_val = self._weighted_criterion(y_predict, y)
# check if it's the smallest one so far
if criterion_val <= min_criterion_val:
min_criterion_val = criterion_val
cutoff = value
return min_criterion_val, cutoff
| (self, col, y) | [
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17,238 | imodels.rule_list.greedy_rule_list | _weighted_criterion | Returns criterion calculated over a split
split decision, True/False, and y_true can be multi class
| def _weighted_criterion(self, split_decision, y_real):
"""Returns criterion calculated over a split
split decision, True/False, and y_true can be multi class
"""
if split_decision.shape[0] != y_real.shape[0]:
print('They have to be the same length')
return None
# choose the splitting criterion
if self.criterion == 'entropy':
criterion_func = self._entropy_criterion
elif self.criterion == 'gini':
criterion_func = self._gini_criterion
elif self.criterion == 'neg_corr':
return self._neg_corr_criterion(split_decision, y_real)
# left-hand side criterion
s_left = criterion_func(y_real[split_decision])
# right-hand side criterion
s_right = criterion_func(y_real[~split_decision])
# overall criterion, again weighted average
n = y_real.shape[0]
if self.class_weight is not None:
sample_weights = np.ones(n)
for c in self.class_weight.keys():
idxs_c = y_real == c
sample_weights[idxs_c] = self.class_weight[c]
total_weight = np.sum(sample_weights)
weight_left = np.sum(sample_weights[split_decision]) / total_weight
# weight_right = np.sum(sample_weights[~split_decision]) / total_weight
else:
tot_left_samples = np.sum(split_decision == 1)
weight_left = tot_left_samples / n
s = weight_left * s_left + (1 - weight_left) * s_right
return s
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17,239 | imodels.rule_list.greedy_rule_list | fit |
Params
------
X: array_like
Feature set
y: array_like
target variable
depth
the depth of the current layer (used to recurse)
| def fit(self, X, y, depth: int = 0, feature_names=None, verbose=False):
"""
Params
------
X: array_like
Feature set
y: array_like
target variable
depth
the depth of the current layer (used to recurse)
"""
X, y, feature_names = check_fit_arguments(self, X, y, feature_names)
return self.fit_node_recursive(X, y, depth=0, verbose=verbose)
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17,240 | imodels.rule_list.greedy_rule_list | fit_node_recursive | null | def fit_node_recursive(self, X, y, depth: int, verbose):
# base case 1: no data in this group
if y.size == 0:
return []
# base case 2: all y is the same in this group
elif np.all(y == y[0]):
return [{'val': y[0], 'num_pts': y.size}]
# base case 3: max depth reached
elif depth == self.max_depth:
return [{'val': np.mean(y), 'num_pts': y.size}]
# recursively generate rule list
else:
# find a split with the best value for the criterion
m = DecisionTreeClassifier(max_depth=1, criterion=self.criterion)
m.fit(X, y)
col = m.tree_.feature[0]
cutoff = m.tree_.threshold[0]
# col, cutoff, criterion_val = self._find_best_split(X, y)
if col == -2:
return []
y_left = y[X[:, col] < cutoff] # left-hand side data
y_right = y[X[:, col] >= cutoff] # right-hand side data
# put higher probability of class 1 on the right-hand side
if len(y_left) > 0 and np.mean(y_left) > np.mean(y_right):
flip = True
tmp = deepcopy(y_left)
y_left = deepcopy(y_right)
y_right = tmp
x_left = X[X[:, col] >= cutoff]
else:
flip = False
x_left = X[X[:, col] < cutoff]
# print
if verbose:
print(
f'{np.mean(100 * y):.2f} -> {self.feature_names_[col]} -> {np.mean(100 * y_left):.2f} ({y_left.size}) {np.mean(100 * y_right):.2f} ({y_right.size})')
# save info
par_node = [{
'col': self.feature_names_[col],
'index_col': col,
'cutoff': cutoff,
'val': np.mean(y_left), # will be the values before splitting in the next lower level
'flip': flip,
'val_right': np.mean(y_right),
'num_pts': y.size,
'num_pts_right': y_right.size
}]
# generate tree for the non-leaf data
par_node = par_node + \
self.fit_node_recursive(x_left, y_left, depth + 1, verbose=verbose)
self.depth += 1 # increase the depth since we call fit once
self.rules_ = par_node
self.complexity_ = len(self.rules_)
self.classes_ = unique_labels(y)
return par_node
| (self, X, y, depth: int, verbose) | [
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17,243 | imodels.rule_list.greedy_rule_list | predict | null | def predict(self, X):
check_is_fitted(self)
X = check_array(X)
return np.argmax(self.predict_proba(X), axis=1)
| (self, X) | [
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17,244 | imodels.rule_list.greedy_rule_list | predict_proba | null | def predict_proba(self, X):
check_is_fitted(self)
X = check_array(X)
n = X.shape[0]
probs = np.zeros(n)
for i in range(n):
x = X[i]
for j, rule in enumerate(self.rules_):
if j == len(self.rules_) - 1:
probs[i] = rule['val']
continue
regular_condition = x[rule["index_col"]] >= rule["cutoff"]
flipped_condition = x[rule["index_col"]] < rule["cutoff"]
condition = flipped_condition if rule["flip"] else regular_condition
if condition:
probs[i] = rule['val_right']
break
return np.vstack((1 - probs, probs)).transpose() # probs (n, 2)
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17,246 | sklearn.utils._metadata_requests | set_fit_request | Request metadata passed to the ``fit`` method.
Note that this method is only relevant if
``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).
Please see :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
The options for each parameter are:
- ``True``: metadata is requested, and passed to ``fit`` if provided. The request is ignored if metadata is not provided.
- ``False``: metadata is not requested and the meta-estimator will not pass it to ``fit``.
- ``None``: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
- ``str``: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (``sklearn.utils.metadata_routing.UNCHANGED``) retains the
existing request. This allows you to change the request for some
parameters and not others.
.. versionadded:: 1.3
.. note::
This method is only relevant if this estimator is used as a
sub-estimator of a meta-estimator, e.g. used inside a
:class:`~sklearn.pipeline.Pipeline`. Otherwise it has no effect.
Parameters
----------
depth : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``depth`` parameter in ``fit``.
feature_names : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``feature_names`` parameter in ``fit``.
verbose : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``verbose`` parameter in ``fit``.
Returns
-------
self : object
The updated object.
| def __get__(self, instance, owner):
# we would want to have a method which accepts only the expected args
def func(**kw):
"""Updates the request for provided parameters
This docstring is overwritten below.
See REQUESTER_DOC for expected functionality
"""
if not _routing_enabled():
raise RuntimeError(
"This method is only available when metadata routing is enabled."
" You can enable it using"
" sklearn.set_config(enable_metadata_routing=True)."
)
if self.validate_keys and (set(kw) - set(self.keys)):
raise TypeError(
f"Unexpected args: {set(kw) - set(self.keys)}. Accepted arguments"
f" are: {set(self.keys)}"
)
requests = instance._get_metadata_request()
method_metadata_request = getattr(requests, self.name)
for prop, alias in kw.items():
if alias is not UNCHANGED:
method_metadata_request.add_request(param=prop, alias=alias)
instance._metadata_request = requests
return instance
# Now we set the relevant attributes of the function so that it seems
# like a normal method to the end user, with known expected arguments.
func.__name__ = f"set_{self.name}_request"
params = [
inspect.Parameter(
name="self",
kind=inspect.Parameter.POSITIONAL_OR_KEYWORD,
annotation=owner,
)
]
params.extend(
[
inspect.Parameter(
k,
inspect.Parameter.KEYWORD_ONLY,
default=UNCHANGED,
annotation=Optional[Union[bool, None, str]],
)
for k in self.keys
]
)
func.__signature__ = inspect.Signature(
params,
return_annotation=owner,
)
doc = REQUESTER_DOC.format(method=self.name)
for metadata in self.keys:
doc += REQUESTER_DOC_PARAM.format(metadata=metadata, method=self.name)
doc += REQUESTER_DOC_RETURN
func.__doc__ = doc
return func
| (self: imodels.rule_list.greedy_rule_list.GreedyRuleListClassifier, *, depth: Union[bool, NoneType, str] = '$UNCHANGED$', feature_names: Union[bool, NoneType, str] = '$UNCHANGED$', verbose: Union[bool, NoneType, str] = '$UNCHANGED$') -> imodels.rule_list.greedy_rule_list.GreedyRuleListClassifier | [
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17,249 | imodels.tree.cart_wrapper | GreedyTreeClassifier | Wrapper around sklearn greedy tree classifier
| class GreedyTreeClassifier(DecisionTreeClassifier):
"""Wrapper around sklearn greedy tree classifier
"""
def fit(self, X, y, feature_names=None, sample_weight=None, check_input=True):
"""Build a decision tree classifier from the training set (X, y).
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csc_matrix``.
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
The target values (class labels) as integers or strings.
feature_names : array-like of shape (n_features)
The names of the features
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If None, 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. Splits are also
ignored if they would result in any single class carrying a
negative weight in either child node.
check_input : bool, default=True
Allow to bypass several input checking.
Don't use this parameter unless you know what you do.
Returns
-------
self : DecisionTreeClassifier
Fitted estimator.
"""
X, y, feature_names = check_fit_arguments(self, X, y, feature_names)
super().fit(X, y, sample_weight=sample_weight, check_input=check_input)
self._set_complexity()
def _set_complexity(self):
"""Set complexity as number of non-leaf nodes
"""
self.complexity_ = compute_tree_complexity(self.tree_)
def __str__(self):
s = '> ------------------------------\n'
s += '> Greedy CART Tree:\n'
s += '> \tPrediction is made by looking at the value in the appropriate leaf of the tree\n'
s += '> ------------------------------' + '\n'
if hasattr(self, 'feature_names') and self.feature_names is not None:
return s + export_text(self, feature_names=self.feature_names, show_weights=True)
else:
return s + export_text(self, show_weights=True)
| (*, criterion='gini', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, class_weight=None, ccp_alpha=0.0, monotonic_cst=None) | [
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17,255 | imodels.tree.cart_wrapper | __str__ | null | def __str__(self):
s = '> ------------------------------\n'
s += '> Greedy CART Tree:\n'
s += '> \tPrediction is made by looking at the value in the appropriate leaf of the tree\n'
s += '> ------------------------------' + '\n'
if hasattr(self, 'feature_names') and self.feature_names is not None:
return s + export_text(self, feature_names=self.feature_names, show_weights=True)
else:
return s + export_text(self, show_weights=True)
| (self) | [
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17,267 | imodels.tree.cart_wrapper | _set_complexity | Set complexity as number of non-leaf nodes
| def _set_complexity(self):
"""Set complexity as number of non-leaf nodes
"""
self.complexity_ = compute_tree_complexity(self.tree_)
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17,275 | imodels.tree.cart_wrapper | fit | Build a decision tree classifier from the training set (X, y).
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csc_matrix``.
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
The target values (class labels) as integers or strings.
feature_names : array-like of shape (n_features)
The names of the features
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If None, 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. Splits are also
ignored if they would result in any single class carrying a
negative weight in either child node.
check_input : bool, default=True
Allow to bypass several input checking.
Don't use this parameter unless you know what you do.
Returns
-------
self : DecisionTreeClassifier
Fitted estimator.
| def fit(self, X, y, feature_names=None, sample_weight=None, check_input=True):
"""Build a decision tree classifier from the training set (X, y).
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csc_matrix``.
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
The target values (class labels) as integers or strings.
feature_names : array-like of shape (n_features)
The names of the features
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If None, 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. Splits are also
ignored if they would result in any single class carrying a
negative weight in either child node.
check_input : bool, default=True
Allow to bypass several input checking.
Don't use this parameter unless you know what you do.
Returns
-------
self : DecisionTreeClassifier
Fitted estimator.
"""
X, y, feature_names = check_fit_arguments(self, X, y, feature_names)
super().fit(X, y, sample_weight=sample_weight, check_input=check_input)
self._set_complexity()
| (self, X, y, feature_names=None, sample_weight=None, check_input=True) | [
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17,284 | sklearn.utils._metadata_requests | set_fit_request | Request metadata passed to the ``fit`` method.
Note that this method is only relevant if
``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).
Please see :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
The options for each parameter are:
- ``True``: metadata is requested, and passed to ``fit`` if provided. The request is ignored if metadata is not provided.
- ``False``: metadata is not requested and the meta-estimator will not pass it to ``fit``.
- ``None``: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
- ``str``: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (``sklearn.utils.metadata_routing.UNCHANGED``) retains the
existing request. This allows you to change the request for some
parameters and not others.
.. versionadded:: 1.3
.. note::
This method is only relevant if this estimator is used as a
sub-estimator of a meta-estimator, e.g. used inside a
:class:`~sklearn.pipeline.Pipeline`. Otherwise it has no effect.
Parameters
----------
check_input : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``check_input`` parameter in ``fit``.
feature_names : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``feature_names`` parameter in ``fit``.
sample_weight : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``sample_weight`` parameter in ``fit``.
Returns
-------
self : object
The updated object.
| def __get__(self, instance, owner):
# we would want to have a method which accepts only the expected args
def func(**kw):
"""Updates the request for provided parameters
This docstring is overwritten below.
See REQUESTER_DOC for expected functionality
"""
if not _routing_enabled():
raise RuntimeError(
"This method is only available when metadata routing is enabled."
" You can enable it using"
" sklearn.set_config(enable_metadata_routing=True)."
)
if self.validate_keys and (set(kw) - set(self.keys)):
raise TypeError(
f"Unexpected args: {set(kw) - set(self.keys)}. Accepted arguments"
f" are: {set(self.keys)}"
)
requests = instance._get_metadata_request()
method_metadata_request = getattr(requests, self.name)
for prop, alias in kw.items():
if alias is not UNCHANGED:
method_metadata_request.add_request(param=prop, alias=alias)
instance._metadata_request = requests
return instance
# Now we set the relevant attributes of the function so that it seems
# like a normal method to the end user, with known expected arguments.
func.__name__ = f"set_{self.name}_request"
params = [
inspect.Parameter(
name="self",
kind=inspect.Parameter.POSITIONAL_OR_KEYWORD,
annotation=owner,
)
]
params.extend(
[
inspect.Parameter(
k,
inspect.Parameter.KEYWORD_ONLY,
default=UNCHANGED,
annotation=Optional[Union[bool, None, str]],
)
for k in self.keys
]
)
func.__signature__ = inspect.Signature(
params,
return_annotation=owner,
)
doc = REQUESTER_DOC.format(method=self.name)
for metadata in self.keys:
doc += REQUESTER_DOC_PARAM.format(metadata=metadata, method=self.name)
doc += REQUESTER_DOC_RETURN
func.__doc__ = doc
return func
| (self: imodels.tree.cart_wrapper.GreedyTreeClassifier, *, check_input: Union[bool, NoneType, str] = '$UNCHANGED$', feature_names: Union[bool, NoneType, str] = '$UNCHANGED$', sample_weight: Union[bool, NoneType, str] = '$UNCHANGED$') -> imodels.tree.cart_wrapper.GreedyTreeClassifier | [
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17,289 | imodels.tree.cart_wrapper | GreedyTreeRegressor | Wrapper around sklearn greedy tree regressor
| class GreedyTreeRegressor(DecisionTreeRegressor):
"""Wrapper around sklearn greedy tree regressor
"""
def fit(self, X, y, feature_names=None, sample_weight=None, check_input=True):
"""Build a decision tree regressor from the training set (X, y).
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csc_matrix``.
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
The target values (real numbers). Use ``dtype=np.float64`` and
``order='C'`` for maximum efficiency.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If None, 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 do.
Returns
-------
self : DecisionTreeRegressor
Fitted estimator.
"""
if feature_names is not None:
self.feature_names = feature_names
else:
self.feature_names = ["X" + str(i + 1) for i in range(X.shape[1])]
super().fit(X, y, sample_weight=sample_weight, check_input=check_input)
self._set_complexity()
def _set_complexity(self):
"""Set complexity as number of non-leaf nodes
"""
self.complexity_ = compute_tree_complexity(self.tree_)
def __str__(self):
if hasattr(self, 'feature_names') and self.feature_names is not None:
return 'GreedyTree:\n' + export_text(self, feature_names=self.feature_names, show_weights=True)
else:
return 'GreedyTree:\n' + export_text(self, show_weights=True)
| (*, criterion='squared_error', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, ccp_alpha=0.0, monotonic_cst=None) | [
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17,295 | imodels.tree.cart_wrapper | __str__ | null | def __str__(self):
if hasattr(self, 'feature_names') and self.feature_names is not None:
return 'GreedyTree:\n' + export_text(self, feature_names=self.feature_names, show_weights=True)
else:
return 'GreedyTree:\n' + export_text(self, show_weights=True)
| (self) | [
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17,316 | imodels.tree.cart_wrapper | fit | Build a decision tree regressor from the training set (X, y).
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csc_matrix``.
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
The target values (real numbers). Use ``dtype=np.float64`` and
``order='C'`` for maximum efficiency.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If None, 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 do.
Returns
-------
self : DecisionTreeRegressor
Fitted estimator.
| def fit(self, X, y, feature_names=None, sample_weight=None, check_input=True):
"""Build a decision tree regressor from the training set (X, y).
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csc_matrix``.
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
The target values (real numbers). Use ``dtype=np.float64`` and
``order='C'`` for maximum efficiency.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If None, 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 do.
Returns
-------
self : DecisionTreeRegressor
Fitted estimator.
"""
if feature_names is not None:
self.feature_names = feature_names
else:
self.feature_names = ["X" + str(i + 1) for i in range(X.shape[1])]
super().fit(X, y, sample_weight=sample_weight, check_input=check_input)
self._set_complexity()
| (self, X, y, feature_names=None, sample_weight=None, check_input=True) | [
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17,327 | imodels.tree.cart_ccp | HSDecisionTreeCCPClassifierCV | null | class HSDecisionTreeCCPClassifierCV(HSTreeClassifier):
def __init__(self, estimator_: BaseEstimator, reg_param_list: List[float] = [0.1, 1, 10, 50, 100, 500],
desired_complexity: int = 1, cv: int = 3, scoring=None, *args, **kwargs):
super().__init__(estimator_=estimator_, reg_param=None)
self.reg_param_list = np.array(reg_param_list)
self.cv = cv
self.scoring = scoring
self.desired_complexity = desired_complexity
def fit(self, X, y, sample_weight=None, *args, **kwargs):
m = DecisionTreeCCPClassifier(self.estimator_, desired_complexity=self.desired_complexity)
m.fit(X, y, sample_weight, *args, **kwargs)
self.scores_ = []
for reg_param in self.reg_param_list:
est = HSTreeClassifier(deepcopy(m.estimator_), reg_param)
cv_scores = cross_val_score(est, X, y, cv=self.cv, scoring=self.scoring)
self.scores_.append(np.mean(cv_scores))
self.reg_param = self.reg_param_list[np.argmax(self.scores_)]
super().fit(X=X, y=y)
| (estimator_: sklearn.base.BaseEstimator, reg_param_list: List[float] = [0.1, 1, 10, 50, 100, 500], desired_complexity: int = 1, cv: int = 3, scoring=None, *args, **kwargs) | [
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17,329 | imodels.tree.cart_ccp | __init__ | null | def __init__(self, estimator_: BaseEstimator, reg_param_list: List[float] = [0.1, 1, 10, 50, 100, 500],
desired_complexity: int = 1, cv: int = 3, scoring=None, *args, **kwargs):
super().__init__(estimator_=estimator_, reg_param=None)
self.reg_param_list = np.array(reg_param_list)
self.cv = cv
self.scoring = scoring
self.desired_complexity = desired_complexity
| (self, estimator_: sklearn.base.BaseEstimator, reg_param_list: List[float] = [0.1, 1, 10, 50, 100, 500], desired_complexity: int = 1, cv: int = 3, scoring=None, *args, **kwargs) | [
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17,330 | imodels.tree.hierarchical_shrinkage | __repr__ | null | def __repr__(self):
# s = self.__class__.__name__
# s += "("
# s += "estimator_="
# s += repr(self.estimator_)
# s += ", "
# s += "reg_param="
# s += str(self.reg_param)
# s += ", "
# s += "shrinkage_scheme_="
# s += self.shrinkage_scheme_
# s += ")"
# return s
attr_list = ["estimator_", "reg_param", "shrinkage_scheme_"]
s = self.__class__.__name__
s += "("
for attr in attr_list:
s += attr + "=" + repr(getattr(self, attr)) + ", "
s = s[:-2] + ")"
return s
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17,333 | imodels.tree.hierarchical_shrinkage | __str__ | null | def __str__(self):
# check if fitted
if not checks.check_is_fitted(self.estimator_):
s = self.__class__.__name__
s += "("
s += "est="
s += repr(self.estimator_)
s += ", "
s += "reg_param="
s += str(self.reg_param)
s += ")"
return s
else:
s = "> ------------------------------\n"
s += "> Decision Tree with Hierarchical Shrinkage\n"
s += "> \tPrediction is made by looking at the value in the appropriate leaf of the tree\n"
s += "> ------------------------------" + "\n"
if hasattr(self, "feature_names") and self.feature_names is not None:
return s + export_text(
self.estimator_, feature_names=self.feature_names, show_weights=True
)
else:
return s + export_text(self.estimator_, show_weights=True)
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17,342 | imodels.tree.hierarchical_shrinkage | _shrink | null | def _shrink(self):
if hasattr(self.estimator_, "tree_"):
self._shrink_tree(self.estimator_.tree_, self.reg_param)
elif hasattr(self.estimator_, "estimators_"):
for t in self.estimator_.estimators_:
if isinstance(t, np.ndarray):
assert t.size == 1, "multiple trees stored under tree_?"
t = t[0]
self._shrink_tree(t.tree_, self.reg_param)
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17,343 | imodels.tree.hierarchical_shrinkage | _shrink_tree | Shrink the tree | def _shrink_tree(
self, tree, reg_param, i=0, parent_val=None, parent_num=None, cum_sum=0
):
"""Shrink the tree"""
if reg_param is None:
reg_param = 1.0
left = tree.children_left[i]
right = tree.children_right[i]
is_leaf = left == right
n_samples = tree.weighted_n_node_samples[i]
if isinstance(self, RegressorMixin) or isinstance(
self.estimator_, GradientBoostingClassifier
):
val = deepcopy(tree.value[i, :, :])
else: # If classification, normalize to probability vector
val = tree.value[i, :, :] / n_samples
# Step 1: Update cum_sum
# if root
if parent_val is None and parent_num is None:
cum_sum = val
# if has parent
else:
if self.shrinkage_scheme_ == "node_based":
val_new = (val - parent_val) / (1 + reg_param / parent_num)
elif self.shrinkage_scheme_ == "constant":
val_new = (val - parent_val) / (1 + reg_param)
else: # leaf_based
val_new = 0
cum_sum += val_new
# Step 2: Update node values
if (
self.shrinkage_scheme_ == "node_based"
or self.shrinkage_scheme_ == "constant"
):
tree.value[i, :, :] = cum_sum
else: # leaf_based
if is_leaf: # update node values if leaf_based
root_val = tree.value[0, :, :]
tree.value[i, :, :] = root_val + (val - root_val) / (
1 + reg_param / n_samples
)
else:
tree.value[i, :, :] = val
# Step 3: Recurse if not leaf
if not is_leaf:
self._shrink_tree(
tree,
reg_param,
left,
parent_val=val,
parent_num=n_samples,
cum_sum=deepcopy(cum_sum),
)
self._shrink_tree(
tree,
reg_param,
right,
parent_val=val,
parent_num=n_samples,
cum_sum=deepcopy(cum_sum),
)
# edit the non-leaf nodes for later visualization (doesn't effect predictions)
return tree
| (self, tree, reg_param, i=0, parent_val=None, parent_num=None, cum_sum=0) | [
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17,346 | imodels.tree.cart_ccp | fit | null | def fit(self, X, y, sample_weight=None, *args, **kwargs):
m = DecisionTreeCCPClassifier(self.estimator_, desired_complexity=self.desired_complexity)
m.fit(X, y, sample_weight, *args, **kwargs)
self.scores_ = []
for reg_param in self.reg_param_list:
est = HSTreeClassifier(deepcopy(m.estimator_), reg_param)
cv_scores = cross_val_score(est, X, y, cv=self.cv, scoring=self.scoring)
self.scores_.append(np.mean(cv_scores))
self.reg_param = self.reg_param_list[np.argmax(self.scores_)]
super().fit(X=X, y=y)
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17,348 | imodels.tree.hierarchical_shrinkage | get_params | null | def get_params(self, deep=True):
d = {
"reg_param": self.reg_param,
"estimator_": self.estimator_,
"shrinkage_scheme_": self.shrinkage_scheme_,
"max_leaf_nodes": self.estimator_.max_leaf_nodes,
}
if deep:
return deepcopy(d)
return d
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17,350 | imodels.tree.hierarchical_shrinkage | predict_proba | null | def predict_proba(self, X, *args, **kwargs):
if hasattr(self.estimator_, "predict_proba"):
return self.estimator_.predict_proba(X, *args, **kwargs)
else:
return NotImplemented
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17,351 | imodels.tree.hierarchical_shrinkage | score | null | def score(self, X, y, *args, **kwargs):
if hasattr(self.estimator_, "score"):
return self.estimator_.score(X, y, *args, **kwargs)
else:
return NotImplemented
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17,354 | imodels.tree.cart_ccp | HSDecisionTreeCCPRegressorCV | null | class HSDecisionTreeCCPRegressorCV(HSTreeRegressor):
def __init__(self, estimator_: BaseEstimator, reg_param_list: List[float] = [0.1, 1, 10, 50, 100, 500],
desired_complexity: int = 1, cv: int = 3, scoring=None, *args, **kwargs):
super().__init__(estimator_=estimator_, reg_param=None)
self.reg_param_list = np.array(reg_param_list)
self.cv = cv
self.scoring = scoring
self.desired_complexity = desired_complexity
def fit(self, X, y, sample_weight=None, *args, **kwargs):
m = DecisionTreeCCPRegressor(self.estimator_, desired_complexity=self.desired_complexity)
m.fit(X, y, sample_weight, *args, **kwargs)
self.scores_ = []
for reg_param in self.reg_param_list:
est = HSTreeRegressor(deepcopy(m.estimator_), reg_param)
cv_scores = cross_val_score(est, X, y, cv=self.cv, scoring=self.scoring)
self.scores_.append(np.mean(cv_scores))
self.reg_param = self.reg_param_list[np.argmax(self.scores_)]
super().fit(X=X, y=y)
| (estimator_: sklearn.base.BaseEstimator, reg_param_list: List[float] = [0.1, 1, 10, 50, 100, 500], desired_complexity: int = 1, cv: int = 3, scoring=None, *args, **kwargs) | [
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17,373 | imodels.tree.cart_ccp | fit | null | def fit(self, X, y, sample_weight=None, *args, **kwargs):
m = DecisionTreeCCPRegressor(self.estimator_, desired_complexity=self.desired_complexity)
m.fit(X, y, sample_weight, *args, **kwargs)
self.scores_ = []
for reg_param in self.reg_param_list:
est = HSTreeRegressor(deepcopy(m.estimator_), reg_param)
cv_scores = cross_val_score(est, X, y, cv=self.cv, scoring=self.scoring)
self.scores_.append(np.mean(cv_scores))
self.reg_param = self.reg_param_list[np.argmax(self.scores_)]
super().fit(X=X, y=y)
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17,381 | imodels.tree.gosdt.pygosdt_shrinkage | HSOptimalTreeClassifier | null | class HSOptimalTreeClassifier(BaseEstimator):
def __init__(self, estimator_: OptimalTreeClassifier, reg_param: float = 1, shrinkage_scheme_: str = 'node_based'):
"""
Params
------
reg_param: float
Higher is more regularization (can be arbitrarily large, should not be < 0)
shrinkage_scheme: str
Experimental: Used to experiment with different forms of shrinkage. options are:
(i) node_based shrinks based on number of samples in parent node
(ii) leaf_based only shrinks leaf nodes based on number of leaf samples
(iii) constant shrinks every node by a constant lambda
"""
super().__init__()
self.reg_param = reg_param
# print('est', estimator_)
self.estimator_ = estimator_
# self.tree_ = estimator_.tree_
self.shrinkage_scheme_ = shrinkage_scheme_
def _calc_probs(self, node):
lbls = np.array([float(l) for l in node["labels"]]) if "labels" in node else np.array(
[float(node['prediction'])])
node['probs'] = np.mean(lbls == 1)
node['n_obs'] = len(node.get('labels', []))
if "prediction" in node:
node['prediction'] = np.round(node['probs'])
return
self._calc_probs(node['true'])
self._calc_probs(node['false'])
def impute_nodes(self, X, y):
"""
Returns
---
the leaf by which this sample would be classified
"""
source_node = self.estimator_.tree_.source
for i in range(len(y)):
sample, label = X[i, ...], y[i]
_add_label(source_node, label)
nodes = [source_node]
while len(nodes) > 0:
node = nodes.pop()
if "prediction" in node:
continue
else:
value = sample[node["feature"]]
reference = node["reference"]
relation = node["relation"]
if relation == "==":
is_true = value == reference
elif relation == ">=":
is_true = value >= reference
elif relation == "<=":
is_true = value <= reference
elif relation == "<":
is_true = value < reference
elif relation == ">":
is_true = value > reference
else:
raise "Unsupported relational operator {}".format(node["relation"])
next_node = node['true'] if is_true else node['false']
_add_label(next_node, label)
nodes.append(next_node)
self._calc_probs(source_node)
self.estimator_.tree_.source = source_node
# def fit(self, *args, **kwargs):
# X = kwargs['X'] if "X" in kwargs else args[0]
# y = kwargs['y'] if "y" in kwargs else args[1]
def shrink_tree(self):
root = self.estimator_.tree_.source
shrink_node(root, self.reg_param, None, None, 0, self.shrinkage_scheme_, 0)
def predict_proba(self, X):
probs = []
for i in range(X.shape[0]):
sample = X[i, ...]
node = self.estimator_.tree_.__find_leaf__(sample)
probs.append([1 - node["probs"], node["probs"]])
return np.array(probs)
def fit(self, *args, **kwargs):
X = kwargs['X'] if "X" in kwargs else args[0]
y = kwargs['y'] if "y" in kwargs else args[1]
if not hasattr(self.estimator_, "tree_"):
self.estimator_.fit(X, y)
self.impute_nodes(X, y)
self.shrink_tree()
def predict(self, X):
return self.estimator_.predict(X)
def score(self, X, y, weight=None):
self.estimator_.score(X, y, weight)
@property
def complexity_(self):
return self.estimator_.complexity_
| (estimator_: imodels.tree.gosdt.pygosdt.OptimalTreeClassifier, reg_param: float = 1, shrinkage_scheme_: str = 'node_based') | [
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17,383 | imodels.tree.gosdt.pygosdt_shrinkage | __init__ |
Params
------
reg_param: float
Higher is more regularization (can be arbitrarily large, should not be < 0)
shrinkage_scheme: str
Experimental: Used to experiment with different forms of shrinkage. options are:
(i) node_based shrinks based on number of samples in parent node
(ii) leaf_based only shrinks leaf nodes based on number of leaf samples
(iii) constant shrinks every node by a constant lambda
| def __init__(self, estimator_: OptimalTreeClassifier, reg_param: float = 1, shrinkage_scheme_: str = 'node_based'):
"""
Params
------
reg_param: float
Higher is more regularization (can be arbitrarily large, should not be < 0)
shrinkage_scheme: str
Experimental: Used to experiment with different forms of shrinkage. options are:
(i) node_based shrinks based on number of samples in parent node
(ii) leaf_based only shrinks leaf nodes based on number of leaf samples
(iii) constant shrinks every node by a constant lambda
"""
super().__init__()
self.reg_param = reg_param
# print('est', estimator_)
self.estimator_ = estimator_
# self.tree_ = estimator_.tree_
self.shrinkage_scheme_ = shrinkage_scheme_
| (self, estimator_: imodels.tree.gosdt.pygosdt.OptimalTreeClassifier, reg_param: float = 1, shrinkage_scheme_: str = 'node_based') | [
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17,387 | imodels.tree.gosdt.pygosdt_shrinkage | _calc_probs | null | def _calc_probs(self, node):
lbls = np.array([float(l) for l in node["labels"]]) if "labels" in node else np.array(
[float(node['prediction'])])
node['probs'] = np.mean(lbls == 1)
node['n_obs'] = len(node.get('labels', []))
if "prediction" in node:
node['prediction'] = np.round(node['probs'])
return
self._calc_probs(node['true'])
self._calc_probs(node['false'])
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17,398 | imodels.tree.gosdt.pygosdt_shrinkage | fit | null | def fit(self, *args, **kwargs):
X = kwargs['X'] if "X" in kwargs else args[0]
y = kwargs['y'] if "y" in kwargs else args[1]
if not hasattr(self.estimator_, "tree_"):
self.estimator_.fit(X, y)
self.impute_nodes(X, y)
self.shrink_tree()
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] |
17,401 | imodels.tree.gosdt.pygosdt_shrinkage | impute_nodes |
Returns
---
the leaf by which this sample would be classified
| def impute_nodes(self, X, y):
"""
Returns
---
the leaf by which this sample would be classified
"""
source_node = self.estimator_.tree_.source
for i in range(len(y)):
sample, label = X[i, ...], y[i]
_add_label(source_node, label)
nodes = [source_node]
while len(nodes) > 0:
node = nodes.pop()
if "prediction" in node:
continue
else:
value = sample[node["feature"]]
reference = node["reference"]
relation = node["relation"]
if relation == "==":
is_true = value == reference
elif relation == ">=":
is_true = value >= reference
elif relation == "<=":
is_true = value <= reference
elif relation == "<":
is_true = value < reference
elif relation == ">":
is_true = value > reference
else:
raise "Unsupported relational operator {}".format(node["relation"])
next_node = node['true'] if is_true else node['false']
_add_label(next_node, label)
nodes.append(next_node)
self._calc_probs(source_node)
self.estimator_.tree_.source = source_node
| (self, X, y) | [
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17,402 | imodels.tree.gosdt.pygosdt_shrinkage | predict | null | def predict(self, X):
return self.estimator_.predict(X)
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17,403 | imodels.tree.gosdt.pygosdt_shrinkage | predict_proba | null | def predict_proba(self, X):
probs = []
for i in range(X.shape[0]):
sample = X[i, ...]
node = self.estimator_.tree_.__find_leaf__(sample)
probs.append([1 - node["probs"], node["probs"]])
return np.array(probs)
| (self, X) | [
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17,404 | imodels.tree.gosdt.pygosdt_shrinkage | score | null | def score(self, X, y, weight=None):
self.estimator_.score(X, y, weight)
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17,406 | sklearn.utils._metadata_requests | set_score_request | Request metadata passed to the ``score`` method.
Note that this method is only relevant if
``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).
Please see :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
The options for each parameter are:
- ``True``: metadata is requested, and passed to ``score`` if provided. The request is ignored if metadata is not provided.
- ``False``: metadata is not requested and the meta-estimator will not pass it to ``score``.
- ``None``: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
- ``str``: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (``sklearn.utils.metadata_routing.UNCHANGED``) retains the
existing request. This allows you to change the request for some
parameters and not others.
.. versionadded:: 1.3
.. note::
This method is only relevant if this estimator is used as a
sub-estimator of a meta-estimator, e.g. used inside a
:class:`~sklearn.pipeline.Pipeline`. Otherwise it has no effect.
Parameters
----------
weight : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``weight`` parameter in ``score``.
Returns
-------
self : object
The updated object.
| def __get__(self, instance, owner):
# we would want to have a method which accepts only the expected args
def func(**kw):
"""Updates the request for provided parameters
This docstring is overwritten below.
See REQUESTER_DOC for expected functionality
"""
if not _routing_enabled():
raise RuntimeError(
"This method is only available when metadata routing is enabled."
" You can enable it using"
" sklearn.set_config(enable_metadata_routing=True)."
)
if self.validate_keys and (set(kw) - set(self.keys)):
raise TypeError(
f"Unexpected args: {set(kw) - set(self.keys)}. Accepted arguments"
f" are: {set(self.keys)}"
)
requests = instance._get_metadata_request()
method_metadata_request = getattr(requests, self.name)
for prop, alias in kw.items():
if alias is not UNCHANGED:
method_metadata_request.add_request(param=prop, alias=alias)
instance._metadata_request = requests
return instance
# Now we set the relevant attributes of the function so that it seems
# like a normal method to the end user, with known expected arguments.
func.__name__ = f"set_{self.name}_request"
params = [
inspect.Parameter(
name="self",
kind=inspect.Parameter.POSITIONAL_OR_KEYWORD,
annotation=owner,
)
]
params.extend(
[
inspect.Parameter(
k,
inspect.Parameter.KEYWORD_ONLY,
default=UNCHANGED,
annotation=Optional[Union[bool, None, str]],
)
for k in self.keys
]
)
func.__signature__ = inspect.Signature(
params,
return_annotation=owner,
)
doc = REQUESTER_DOC.format(method=self.name)
for metadata in self.keys:
doc += REQUESTER_DOC_PARAM.format(metadata=metadata, method=self.name)
doc += REQUESTER_DOC_RETURN
func.__doc__ = doc
return func
| (self: imodels.tree.gosdt.pygosdt_shrinkage.HSOptimalTreeClassifier, *, weight: Union[bool, NoneType, str] = '$UNCHANGED$') -> imodels.tree.gosdt.pygosdt_shrinkage.HSOptimalTreeClassifier | [
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17,407 | imodels.tree.gosdt.pygosdt_shrinkage | shrink_tree | null | def shrink_tree(self):
root = self.estimator_.tree_.source
shrink_node(root, self.reg_param, None, None, 0, self.shrinkage_scheme_, 0)
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17,408 | imodels.tree.gosdt.pygosdt_shrinkage | HSOptimalTreeClassifierCV | null | class HSOptimalTreeClassifierCV(HSOptimalTreeClassifier):
def __init__(self, estimator_: OptimalTreeClassifier,
reg_param_list: List[float] = [0.1, 1, 10, 50, 100, 500], shrinkage_scheme_: str = 'node_based',
cv: int = 3, scoring="accuracy", *args, **kwargs):
"""Note: args, kwargs are not used but left so that imodels-experiments can still pass redundant args
"""
super().__init__(estimator_, reg_param=None)
self.reg_param_list = np.array(reg_param_list)
self.cv = cv
self.scoring = scoring
self.shrinkage_scheme_ = shrinkage_scheme_
# print('estimator', self.estimator_,
# 'checks.check_is_fitted(estimator)', checks.check_is_fitted(self.estimator_))
# if checks.check_is_fitted(self.estimator_):
# raise Warning('Passed an already fitted estimator,'
# 'but shrinking not applied until fit method is called.')
def fit(self, X, y, *args, **kwargs):
self.scores_ = []
opt = copy.deepcopy(self.estimator_)
for reg_param in self.reg_param_list:
est = HSOptimalTreeClassifier(opt, reg_param)
cv_scores = cross_val_score(est, X, y, cv=self.cv, scoring=self.scoring)
self.scores_.append(np.mean(cv_scores))
self.reg_param = self.reg_param_list[np.argmax(self.scores_)]
super().fit(X=X, y=y)
| (estimator_: imodels.tree.gosdt.pygosdt.OptimalTreeClassifier, reg_param_list: List[float] = [0.1, 1, 10, 50, 100, 500], shrinkage_scheme_: str = 'node_based', cv: int = 3, scoring='accuracy', *args, **kwargs) | [
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17,410 | imodels.tree.gosdt.pygosdt_shrinkage | __init__ | Note: args, kwargs are not used but left so that imodels-experiments can still pass redundant args
| def __init__(self, estimator_: OptimalTreeClassifier,
reg_param_list: List[float] = [0.1, 1, 10, 50, 100, 500], shrinkage_scheme_: str = 'node_based',
cv: int = 3, scoring="accuracy", *args, **kwargs):
"""Note: args, kwargs are not used but left so that imodels-experiments can still pass redundant args
"""
super().__init__(estimator_, reg_param=None)
self.reg_param_list = np.array(reg_param_list)
self.cv = cv
self.scoring = scoring
self.shrinkage_scheme_ = shrinkage_scheme_
# print('estimator', self.estimator_,
# 'checks.check_is_fitted(estimator)', checks.check_is_fitted(self.estimator_))
# if checks.check_is_fitted(self.estimator_):
# raise Warning('Passed an already fitted estimator,'
# 'but shrinking not applied until fit method is called.')
| (self, estimator_: imodels.tree.gosdt.pygosdt.OptimalTreeClassifier, reg_param_list: List[float] = [0.1, 1, 10, 50, 100, 500], shrinkage_scheme_: str = 'node_based', cv: int = 3, scoring='accuracy', *args, **kwargs) | [
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17,425 | imodels.tree.gosdt.pygosdt_shrinkage | fit | null | def fit(self, X, y, *args, **kwargs):
self.scores_ = []
opt = copy.deepcopy(self.estimator_)
for reg_param in self.reg_param_list:
est = HSOptimalTreeClassifier(opt, reg_param)
cv_scores = cross_val_score(est, X, y, cv=self.cv, scoring=self.scoring)
self.scores_.append(np.mean(cv_scores))
self.reg_param = self.reg_param_list[np.argmax(self.scores_)]
super().fit(X=X, y=y)
| (self, X, y, *args, **kwargs) | [
0.0073976824060082436,
0.0057462118566036224,
0.02763611264526844,
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17,435 | imodels.tree.hierarchical_shrinkage | HSTreeClassifier | null | class HSTreeClassifier(HSTree, ClassifierMixin):
def __init__(
self,
estimator_: BaseEstimator = DecisionTreeClassifier(max_leaf_nodes=20),
reg_param: float = 1,
shrinkage_scheme_: str = "node_based",
max_leaf_nodes: int = None,
random_state: int = None,
):
super().__init__(
estimator_=estimator_,
reg_param=reg_param,
shrinkage_scheme_=shrinkage_scheme_,
max_leaf_nodes=max_leaf_nodes,
random_state=random_state,
)
| (estimator_: sklearn.base.BaseEstimator = DecisionTreeClassifier(max_leaf_nodes=20), reg_param: float = 1, shrinkage_scheme_: str = 'node_based', max_leaf_nodes: int = None, random_state: int = None) | [
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17,437 | imodels.tree.hierarchical_shrinkage | __init__ | null | def __init__(
self,
estimator_: BaseEstimator = DecisionTreeClassifier(max_leaf_nodes=20),
reg_param: float = 1,
shrinkage_scheme_: str = "node_based",
max_leaf_nodes: int = None,
random_state: int = None,
):
super().__init__(
estimator_=estimator_,
reg_param=reg_param,
shrinkage_scheme_=shrinkage_scheme_,
max_leaf_nodes=max_leaf_nodes,
random_state=random_state,
)
| (self, estimator_: sklearn.base.BaseEstimator = DecisionTreeClassifier(max_leaf_nodes=20), reg_param: float = 1, shrinkage_scheme_: str = 'node_based', max_leaf_nodes: Optional[int] = None, random_state: Optional[int] = None) | [
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17,454 | imodels.tree.hierarchical_shrinkage | fit | null | def fit(self, X, y, sample_weight=None, *args, **kwargs):
# remove feature_names if it exists (note: only works as keyword-arg)
# None returned if not passed
feature_names = kwargs.pop("feature_names", None)
X, y, feature_names = check_fit_arguments(self, X, y, feature_names)
if feature_names is not None:
self.feature_names = feature_names
self.estimator_ = self.estimator_.fit(
X, y, *args, sample_weight=sample_weight, **kwargs
)
self._shrink()
# compute complexity
if hasattr(self.estimator_, "tree_"):
self.complexity_ = compute_tree_complexity(self.estimator_.tree_)
elif hasattr(self.estimator_, "estimators_"):
self.complexity_ = 0
for i in range(len(self.estimator_.estimators_)):
t = deepcopy(self.estimator_.estimators_[i])
if isinstance(t, np.ndarray):
assert t.size == 1, "multiple trees stored under tree_?"
t = t[0]
self.complexity_ += compute_tree_complexity(t.tree_)
return self
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] |
17,462 | imodels.tree.hierarchical_shrinkage | HSTreeClassifierCV | null | class HSTreeClassifierCV(HSTreeClassifier):
def __init__(
self,
estimator_: BaseEstimator = None,
reg_param_list: List[float] = [0, 0.1, 1, 10, 50, 100, 500],
shrinkage_scheme_: str = "node_based",
max_leaf_nodes: int = 20,
cv: int = 3,
scoring=None,
*args,
**kwargs
):
"""Cross-validation is used to select the best regularization parameter for hierarchical shrinkage.
Params
------
estimator_
Sklearn estimator (already initialized).
If no estimator_ is passed, sklearn decision tree is used
max_rules
If estimator is None, then max_leaf_nodes is passed to the default decision tree
args, kwargs
Note: args, kwargs are not used but left so that imodels-experiments can still pass redundant args.
"""
if estimator_ is None:
estimator_ = DecisionTreeClassifier(max_leaf_nodes=max_leaf_nodes)
super().__init__(estimator_, reg_param=None)
self.reg_param_list = np.array(reg_param_list)
self.cv = cv
self.scoring = scoring
self.shrinkage_scheme_ = shrinkage_scheme_
# print('estimator', self.estimator_,
# 'checks.check_is_fitted(estimator)', checks.check_is_fitted(self.estimator_))
# if checks.check_is_fitted(self.estimator_):
# raise Warning('Passed an already fitted estimator,'
# 'but shrinking not applied until fit method is called.')
def fit(self, X, y, *args, **kwargs):
self.scores_ = [[] for _ in self.reg_param_list]
scorer = kwargs.get("scoring", log_loss)
kf = KFold(n_splits=self.cv)
for train_index, test_index in kf.split(X):
X_out, y_out = X[test_index, :], y[test_index]
X_in, y_in = X[train_index, :], y[train_index]
base_est = deepcopy(self.estimator_)
base_est.fit(X_in, y_in)
for i, reg_param in enumerate(self.reg_param_list):
est_hs = HSTreeClassifier(base_est, reg_param)
est_hs.fit(X_in, y_in, *args, **kwargs)
self.scores_[i].append(
scorer(y_out, est_hs.predict_proba(X_out)))
self.scores_ = [np.mean(s) for s in self.scores_]
cv_criterion = _get_cv_criterion(scorer)
self.reg_param = self.reg_param_list[cv_criterion(self.scores_)]
super().fit(X=X, y=y, *args, **kwargs)
def __repr__(self):
attr_list = [
"estimator_",
"reg_param_list",
"shrinkage_scheme_",
"cv",
"scoring",
]
s = self.__class__.__name__
s += "("
for attr in attr_list:
s += attr + "=" + repr(getattr(self, attr)) + ", "
s = s[:-2] + ")"
return s
| (estimator_: sklearn.base.BaseEstimator = None, reg_param_list: List[float] = [0, 0.1, 1, 10, 50, 100, 500], shrinkage_scheme_: str = 'node_based', max_leaf_nodes: int = 20, cv: int = 3, scoring=None, *args, **kwargs) | [
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17,464 | imodels.tree.hierarchical_shrinkage | __init__ | Cross-validation is used to select the best regularization parameter for hierarchical shrinkage.
Params
------
estimator_
Sklearn estimator (already initialized).
If no estimator_ is passed, sklearn decision tree is used
max_rules
If estimator is None, then max_leaf_nodes is passed to the default decision tree
args, kwargs
Note: args, kwargs are not used but left so that imodels-experiments can still pass redundant args.
| def __init__(
self,
estimator_: BaseEstimator = None,
reg_param_list: List[float] = [0, 0.1, 1, 10, 50, 100, 500],
shrinkage_scheme_: str = "node_based",
max_leaf_nodes: int = 20,
cv: int = 3,
scoring=None,
*args,
**kwargs
):
"""Cross-validation is used to select the best regularization parameter for hierarchical shrinkage.
Params
------
estimator_
Sklearn estimator (already initialized).
If no estimator_ is passed, sklearn decision tree is used
max_rules
If estimator is None, then max_leaf_nodes is passed to the default decision tree
args, kwargs
Note: args, kwargs are not used but left so that imodels-experiments can still pass redundant args.
"""
if estimator_ is None:
estimator_ = DecisionTreeClassifier(max_leaf_nodes=max_leaf_nodes)
super().__init__(estimator_, reg_param=None)
self.reg_param_list = np.array(reg_param_list)
self.cv = cv
self.scoring = scoring
self.shrinkage_scheme_ = shrinkage_scheme_
# print('estimator', self.estimator_,
# 'checks.check_is_fitted(estimator)', checks.check_is_fitted(self.estimator_))
# if checks.check_is_fitted(self.estimator_):
# raise Warning('Passed an already fitted estimator,'
# 'but shrinking not applied until fit method is called.')
| (self, estimator_: Optional[sklearn.base.BaseEstimator] = None, reg_param_list: List[float] = [0, 0.1, 1, 10, 50, 100, 500], shrinkage_scheme_: str = 'node_based', max_leaf_nodes: int = 20, cv: int = 3, scoring=None, *args, **kwargs) | [
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