Classes

class HSTree (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)

Base class for all estimators in scikit-learn.

Inheriting from this class provides default implementations of:

• setting and getting parameters used by GridSearchCV and friends;
• textual and HTML representation displayed in terminals and IDEs;
• estimator serialization;
• parameters validation;
• data validation;
• feature names validation.

Read more in the :ref:User Guide <rolling_your_own_estimator>.

Notes

All estimators should specify all the parameters that can be set at the class level in their __init__ as explicit keyword arguments (no *args or **kwargs).

Examples

>>> import numpy as np
>>> from sklearn.base import BaseEstimator
>>> class MyEstimator(BaseEstimator):
...     def __init__(self, *, param=1):
...         self.param = param
...     def fit(self, X, y=None):
...         self.is_fitted_ = True
...         return self
...     def predict(self, X):
...         return np.full(shape=X.shape[0], fill_value=self.param)
>>> estimator = MyEstimator(param=2)
>>> estimator.get_params()
{'param': 2}
>>> X = np.array([[1, 2], [2, 3], [3, 4]])
>>> y = np.array([1, 0, 1])
>>> estimator.fit(X, y).predict(X)
array([2, 2, 2])
>>> estimator.set_params(param=3).fit(X, y).predict(X)
array([3, 3, 3])

HSTree (Tree with hierarchical shrinkage applied). Hierarchical shinkage is an extremely fast post-hoc regularization method which works on any decision tree (or tree-based ensemble, such as Random Forest). It does not modify the tree structure, and instead regularizes the tree by shrinking the prediction over each node towards the sample means of its ancestors (using a single regularization parameter). Experiments over a wide variety of datasets show that hierarchical shrinkage substantially increases the predictive performance of individual decision trees and decision-tree ensembles. https://arxiv.org/abs/2202.00858

Params

estimator_: sklearn tree or tree ensemble model (e.g. RandomForest or GradientBoosting) Defaults to CART Classification Tree with 20 max leaf nodes Note: this estimator will be directly modified

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

max_leaf_nodes: int If estimator is None, then max_leaf_nodes is passed to the default decision tree

Expand source code

class HSTree(BaseEstimator):
    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,
    ):
        """HSTree (Tree with hierarchical shrinkage applied).
        Hierarchical shinkage is an extremely fast post-hoc regularization method which works on any decision tree (or tree-based ensemble, such as Random Forest).
        It does not modify the tree structure, and instead regularizes the tree by shrinking the prediction over each node towards the sample means of its ancestors (using a single regularization parameter).
        Experiments over a wide variety of datasets show that hierarchical shrinkage substantially increases the predictive performance of individual decision trees and decision-tree ensembles.
        https://arxiv.org/abs/2202.00858

        Params
        ------
        estimator_: sklearn tree or tree ensemble model (e.g. RandomForest or GradientBoosting)
            Defaults to CART Classification Tree with 20 max leaf nodes
            Note: this estimator will be directly modified

        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

        max_leaf_nodes: int
            If estimator is None, then max_leaf_nodes is passed to the default decision tree
        """
        super().__init__()
        self.reg_param = reg_param
        self.estimator_ = estimator_
        self.shrinkage_scheme_ = shrinkage_scheme_
        self.random_state = random_state
        if checks.check_is_fitted(self.estimator_):
            self._shrink()
        if max_leaf_nodes is not None:
            self.estimator_.max_leaf_nodes = max_leaf_nodes
            self.estimator_.random_state = random_state

    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

    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

    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

    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)

    def predict(self, X, *args, **kwargs):
        return self.estimator_.predict(X, *args, **kwargs)

    def predict_proba(self, X, *args, **kwargs):
        if hasattr(self.estimator_, "predict_proba"):
            return self.estimator_.predict_proba(X, *args, **kwargs)
        else:
            return NotImplemented

    def score(self, X, y, *args, **kwargs):
        if hasattr(self.estimator_, "score"):
            return self.estimator_.score(X, y, *args, **kwargs)
        else:
            return NotImplemented

    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)

    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

Ancestors

• sklearn.base.BaseEstimator
• sklearn.utils._estimator_html_repr._HTMLDocumentationLinkMixin
• sklearn.utils._metadata_requests._MetadataRequester

Subclasses

• HSTreeClassifier
• HSTreeRegressor

Methods

def fit(self, X, y, sample_weight=None, *args, **kwargs)

Expand source code

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

def get_params(self, deep=True)

Get parameters for this estimator.

Parameters

deep : bool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params : dict

Parameter names mapped to their values.

Expand source code

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

def predict(self, X, *args, **kwargs)

Expand source code

def predict(self, X, *args, **kwargs):
    return self.estimator_.predict(X, *args, **kwargs)

def predict_proba(self, X, *args, **kwargs)

Expand source code

def predict_proba(self, X, *args, **kwargs):
    if hasattr(self.estimator_, "predict_proba"):
        return self.estimator_.predict_proba(X, *args, **kwargs)
    else:
        return NotImplemented

def score(self, X, y, *args, **kwargs)

Expand source code

def score(self, X, y, *args, **kwargs):
    if hasattr(self.estimator_, "score"):
        return self.estimator_.score(X, y, *args, **kwargs)
    else:
        return NotImplemented

def set_fit_request(self: HSTree, *, sample_weight: Union[bool, ForwardRef(None), str] = '$UNCHANGED$') ‑> HSTree

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.

Added in version: 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

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.

Expand source code

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

class HSTreeClassifier (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)

Base class for all estimators in scikit-learn.

Inheriting from this class provides default implementations of:

• setting and getting parameters used by GridSearchCV and friends;
• textual and HTML representation displayed in terminals and IDEs;
• estimator serialization;
• parameters validation;
• data validation;
• feature names validation.

Read more in the :ref:User Guide <rolling_your_own_estimator>.

Notes

All estimators should specify all the parameters that can be set at the class level in their __init__ as explicit keyword arguments (no *args or **kwargs).

Examples

>>> import numpy as np
>>> from sklearn.base import BaseEstimator
>>> class MyEstimator(BaseEstimator):
...     def __init__(self, *, param=1):
...         self.param = param
...     def fit(self, X, y=None):
...         self.is_fitted_ = True
...         return self
...     def predict(self, X):
...         return np.full(shape=X.shape[0], fill_value=self.param)
>>> estimator = MyEstimator(param=2)
>>> estimator.get_params()
{'param': 2}
>>> X = np.array([[1, 2], [2, 3], [3, 4]])
>>> y = np.array([1, 0, 1])
>>> estimator.fit(X, y).predict(X)
array([2, 2, 2])
>>> estimator.set_params(param=3).fit(X, y).predict(X)
array([3, 3, 3])

HSTree (Tree with hierarchical shrinkage applied). Hierarchical shinkage is an extremely fast post-hoc regularization method which works on any decision tree (or tree-based ensemble, such as Random Forest). It does not modify the tree structure, and instead regularizes the tree by shrinking the prediction over each node towards the sample means of its ancestors (using a single regularization parameter). Experiments over a wide variety of datasets show that hierarchical shrinkage substantially increases the predictive performance of individual decision trees and decision-tree ensembles. https://arxiv.org/abs/2202.00858

Params

estimator_: sklearn tree or tree ensemble model (e.g. RandomForest or GradientBoosting) Defaults to CART Classification Tree with 20 max leaf nodes Note: this estimator will be directly modified

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

max_leaf_nodes: int If estimator is None, then max_leaf_nodes is passed to the default decision tree

Expand source code

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,
        )

Ancestors

• HSTree
• sklearn.base.BaseEstimator
• sklearn.utils._estimator_html_repr._HTMLDocumentationLinkMixin
• sklearn.utils._metadata_requests._MetadataRequester
• sklearn.base.ClassifierMixin

Subclasses

• HSDecisionTreeCCPClassifierCV
• HSTreeClassifierCV

Inherited members

• HSTree:
  • get_params
  • set_fit_request

class HSTreeClassifierCV (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)

Base class for all estimators in scikit-learn.

Inheriting from this class provides default implementations of:

• setting and getting parameters used by GridSearchCV and friends;
• textual and HTML representation displayed in terminals and IDEs;
• estimator serialization;
• parameters validation;
• data validation;
• feature names validation.

Read more in the :ref:User Guide <rolling_your_own_estimator>.

Notes

All estimators should specify all the parameters that can be set at the class level in their __init__ as explicit keyword arguments (no *args or **kwargs).

Examples

>>> import numpy as np
>>> from sklearn.base import BaseEstimator
>>> class MyEstimator(BaseEstimator):
...     def __init__(self, *, param=1):
...         self.param = param
...     def fit(self, X, y=None):
...         self.is_fitted_ = True
...         return self
...     def predict(self, X):
...         return np.full(shape=X.shape[0], fill_value=self.param)
>>> estimator = MyEstimator(param=2)
>>> estimator.get_params()
{'param': 2}
>>> X = np.array([[1, 2], [2, 3], [3, 4]])
>>> y = np.array([1, 0, 1])
>>> estimator.fit(X, y).predict(X)
array([2, 2, 2])
>>> estimator.set_params(param=3).fit(X, y).predict(X)
array([3, 3, 3])

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.

Expand source code

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

Ancestors

• HSTreeClassifier
• HSTree
• sklearn.base.BaseEstimator
• sklearn.utils._estimator_html_repr._HTMLDocumentationLinkMixin
• sklearn.utils._metadata_requests._MetadataRequester
• sklearn.base.ClassifierMixin

Methods

def fit(self, X, y, *args, **kwargs)

Expand source code

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)

Inherited members

• HSTreeClassifier:
  • get_params
  • set_fit_request

class HSTreeRegressor (estimator_: sklearn.base.BaseEstimator = DecisionTreeRegressor(max_leaf_nodes=20), reg_param: float = 1, shrinkage_scheme_: str = 'node_based', max_leaf_nodes: int = None, random_state: int = None)

Base class for all estimators in scikit-learn.

Inheriting from this class provides default implementations of:

• setting and getting parameters used by GridSearchCV and friends;
• textual and HTML representation displayed in terminals and IDEs;
• estimator serialization;
• parameters validation;
• data validation;
• feature names validation.

Read more in the :ref:User Guide <rolling_your_own_estimator>.

Notes

All estimators should specify all the parameters that can be set at the class level in their __init__ as explicit keyword arguments (no *args or **kwargs).

Examples

>>> import numpy as np
>>> from sklearn.base import BaseEstimator
>>> class MyEstimator(BaseEstimator):
...     def __init__(self, *, param=1):
...         self.param = param
...     def fit(self, X, y=None):
...         self.is_fitted_ = True
...         return self
...     def predict(self, X):
...         return np.full(shape=X.shape[0], fill_value=self.param)
>>> estimator = MyEstimator(param=2)
>>> estimator.get_params()
{'param': 2}
>>> X = np.array([[1, 2], [2, 3], [3, 4]])
>>> y = np.array([1, 0, 1])
>>> estimator.fit(X, y).predict(X)
array([2, 2, 2])
>>> estimator.set_params(param=3).fit(X, y).predict(X)
array([3, 3, 3])

HSTree (Tree with hierarchical shrinkage applied). Hierarchical shinkage is an extremely fast post-hoc regularization method which works on any decision tree (or tree-based ensemble, such as Random Forest). It does not modify the tree structure, and instead regularizes the tree by shrinking the prediction over each node towards the sample means of its ancestors (using a single regularization parameter). Experiments over a wide variety of datasets show that hierarchical shrinkage substantially increases the predictive performance of individual decision trees and decision-tree ensembles. https://arxiv.org/abs/2202.00858

Params

estimator_: sklearn tree or tree ensemble model (e.g. RandomForest or GradientBoosting) Defaults to CART Classification Tree with 20 max leaf nodes Note: this estimator will be directly modified

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

max_leaf_nodes: int If estimator is None, then max_leaf_nodes is passed to the default decision tree

Expand source code

class HSTreeRegressor(HSTree, RegressorMixin):
    def __init__(
        self,
        estimator_: BaseEstimator = DecisionTreeRegressor(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,
        )

Ancestors

• HSTree
• sklearn.base.BaseEstimator
• sklearn.utils._estimator_html_repr._HTMLDocumentationLinkMixin
• sklearn.utils._metadata_requests._MetadataRequester
• sklearn.base.RegressorMixin

Subclasses

• HSDecisionTreeCCPRegressorCV
• HSTreeRegressorCV

Inherited members

• HSTree:
  • get_params
  • set_fit_request

class HSTreeRegressorCV (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)

Base class for all estimators in scikit-learn.

Inheriting from this class provides default implementations of:

• setting and getting parameters used by GridSearchCV and friends;
• textual and HTML representation displayed in terminals and IDEs;
• estimator serialization;
• parameters validation;
• data validation;
• feature names validation.

Read more in the :ref:User Guide <rolling_your_own_estimator>.

Notes

All estimators should specify all the parameters that can be set at the class level in their __init__ as explicit keyword arguments (no *args or **kwargs).

Examples

>>> import numpy as np
>>> from sklearn.base import BaseEstimator
>>> class MyEstimator(BaseEstimator):
...     def __init__(self, *, param=1):
...         self.param = param
...     def fit(self, X, y=None):
...         self.is_fitted_ = True
...         return self
...     def predict(self, X):
...         return np.full(shape=X.shape[0], fill_value=self.param)
>>> estimator = MyEstimator(param=2)
>>> estimator.get_params()
{'param': 2}
>>> X = np.array([[1, 2], [2, 3], [3, 4]])
>>> y = np.array([1, 0, 1])
>>> estimator.fit(X, y).predict(X)
array([2, 2, 2])
>>> estimator.set_params(param=3).fit(X, y).predict(X)
array([3, 3, 3])

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.

Expand source code

class HSTreeRegressorCV(HSTreeRegressor):
    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_ = DecisionTreeRegressor(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]
        kf = KFold(n_splits=self.cv)
        scorer = kwargs.get("scoring", mean_squared_error)
        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 = HSTreeRegressor(base_est, reg_param)
                est_hs.fit(X_in, y_in)
                self.scores_[i].append(scorer(est_hs.predict(X_out), y_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

Ancestors

• HSTreeRegressor
• HSTree
• sklearn.base.BaseEstimator
• sklearn.utils._estimator_html_repr._HTMLDocumentationLinkMixin
• sklearn.utils._metadata_requests._MetadataRequester
• sklearn.base.RegressorMixin

Methods

def fit(self, X, y, *args, **kwargs)

Expand source code

def fit(self, X, y, *args, **kwargs):
    self.scores_ = [[] for _ in self.reg_param_list]
    kf = KFold(n_splits=self.cv)
    scorer = kwargs.get("scoring", mean_squared_error)
    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 = HSTreeRegressor(base_est, reg_param)
            est_hs.fit(X_in, y_in)
            self.scores_[i].append(scorer(est_hs.predict(X_out), y_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)

Inherited members

• HSTreeRegressor:
  • get_params
  • set_fit_request