Classes

class DecisionTreeCCPClassifier (estimator_: sklearn.base.BaseEstimator, desired_complexity: int = 1, complexity_measure='max_rules', *args, **kwargs)

A decision tree classifier.

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

Parameters

criterion : {"gini", "entropy", "log_loss"}, default="gini"

The function to measure the quality of a split. Supported criteria are "gini" for the Gini impurity and "log_loss" and "entropy" both for the Shannon information gain, see :ref:tree_mathematical_formulation.

splitter : {"best", "random"}, default="best"

The strategy used to choose the split at each node. Supported strategies are "best" to choose the best split and "random" to choose the best random split.

max_depth : int, default=None

The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.

min_samples_split : int or float, default=2

The minimum number of samples required to split an internal node:

• If int, then consider min_samples_split as the minimum number.
• If float, then min_samples_split is a fraction and ceil(min_samples_split * n_samples) are the minimum number of samples for each split.

Changed in version: 0.18

Added float values for fractions.

min_samples_leaf : int or float, default=1

The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least min_samples_leaf training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression.

• If int, then consider min_samples_leaf as the minimum number.
• If float, then min_samples_leaf is a fraction and ceil(min_samples_leaf * n_samples) are the minimum number of samples for each node.

Changed in version: 0.18

Added float values for fractions.

min_weight_fraction_leaf : float, default=0.0

The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided.

max_features : int, float or {"sqrt", "log2"}, default=None

The number of features to consider when looking for the best split:

- If int, then consider <code>max\_features</code> features at each split.
- If float, then <code>max\_features</code> is a fraction and
  `max(1, int(max_features * n_features_in_))` features are considered at
  each split.
- If "sqrt", then `max_features=sqrt(n_features)`.
- If "log2", then `max_features=log2(n_features)`.
- If None, then `max_features=n_features`.

Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than max_features features.

random_state : int, RandomState instance or None, default=None

Controls the randomness of the estimator. The features are always randomly permuted at each split, even if splitter is set to "best". When max_features < n_features, the algorithm will select max_features at random at each split before finding the best split among them. But the best found split may vary across different runs, even if max_features=n_features. That is the case, if the improvement of the criterion is identical for several splits and one split has to be selected at random. To obtain a deterministic behaviour during fitting, random_state has to be fixed to an integer. See :term:Glossary <random_state> for details.

max_leaf_nodes : int, default=None

Grow a tree with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes.

min_impurity_decrease : float, default=0.0

A node will be split if this split induces a decrease of the impurity greater than or equal to this value.

The weighted impurity decrease equation is the following::

N_t / N * (impurity - N_t_R / N_t * right_impurity
                    - N_t_L / N_t * left_impurity)

where N is the total number of samples, N_t is the number of samples at the current node, N_t_L is the number of samples in the left child, and N_t_R is the number of samples in the right child.

N, N_t, N_t_R and N_t_L all refer to the weighted sum, if sample_weight is passed.

Added in version: 0.19

class_weight : dict, list of dict or "balanced", default=None

Weights associated with classes in the form {class_label: weight}. If None, all classes are supposed to have weight one. For multi-output problems, a list of dicts can be provided in the same order as the columns of y.

Note that for multioutput (including multilabel) weights should be defined for each class of every column in its own dict. For example, for four-class multilabel classification weights should be [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of [{1:1}, {2:5}, {3:1}, {4:1}].

The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y))

For multi-output, the weights of each column of y will be multiplied.

Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified.

ccp_alpha : non-negative float, default=0.0

Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ccp_alpha will be chosen. By default, no pruning is performed. See :ref:minimal_cost_complexity_pruning for details.

Added in version: 0.22

monotonic_cst : array-like of int of shape (n_features), default=None

Indicates the monotonicity constraint to enforce on each feature. - 1: monotonic increase - 0: no constraint - -1: monotonic decrease

If monotonic_cst is None, no constraints are applied.

Monotonicity constraints are not supported for: - multiclass classifications (i.e. when n_classes > 2), - multioutput classifications (i.e. when n_outputs_ > 1), - classifications trained on data with missing values.

The constraints hold over the probability of the positive class.

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

Added in version: 1.4

Attributes

classes_ : ndarray of shape (n_classes,) or list of ndarray

The classes labels (single output problem), or a list of arrays of class labels (multi-output problem).

feature_importances_ : ndarray of shape (n_features,)

The impurity-based feature importances. The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance [4]_.

Warning: impurity-based feature importances can be misleading for high cardinality features (many unique values). See :func:sklearn.inspection.permutation_importance as an alternative.

max_features_ : int

The inferred value of max_features.

n_classes_ : int or list of int

The number of classes (for single output problems), or a list containing the number of classes for each output (for multi-output problems).

n_features_in_ : int

Number of features seen during :term:fit.

Added in version: 0.24

feature_names_in_ : ndarray of shape (n_features_in_,)

Names of features seen during :term:fit. Defined only when X has feature names that are all strings.

Added in version: 1.0

n_outputs_ : int

The number of outputs when fit is performed.

tree_ : Tree instance

The underlying Tree object. Please refer to help(sklearn.tree._tree.Tree) for attributes of Tree object and :ref:sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py for basic usage of these attributes.

See Also

DecisionTreeRegressor

A decision tree regressor.

Notes

The default values for the parameters controlling the size of the trees (e.g. max_depth, min_samples_leaf, etc.) lead to fully grown and unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values.

The :meth:predict method operates using the :func:numpy.argmax function on the outputs of :meth:predict_proba. This means that in case the highest predicted probabilities are tied, the classifier will predict the tied class with the lowest index in :term:classes_.

References

.. [1] https://en.wikipedia.org/wiki/Decision_tree_learning

.. [2] L. Breiman, J. Friedman, R. Olshen, and C. Stone, "Classification and Regression Trees", Wadsworth, Belmont, CA, 1984.

.. [3] T. Hastie, R. Tibshirani and J. Friedman. "Elements of Statistical Learning", Springer, 2009.

.. [4] L. Breiman, and A. Cutler, "Random Forests", https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm

Examples

>>> from sklearn.datasets import load_iris
>>> from sklearn.model_selection import cross_val_score
>>> from sklearn.tree import DecisionTreeClassifier
>>> clf = DecisionTreeClassifier(random_state=0)
>>> iris = load_iris()
>>> cross_val_score(clf, iris.data, iris.target, cv=10)
...                             # doctest: +SKIP
...
array([ 1.     ,  0.93...,  0.86...,  0.93...,  0.93...,
        0.93...,  0.93...,  1.     ,  0.93...,  1.      ])

Expand source code

class DecisionTreeCCPClassifier(DecisionTreeClassifier):
    def __init__(self, estimator_: BaseEstimator, desired_complexity: int = 1, complexity_measure='max_rules', *args,
                 **kwargs):
        self.desired_complexity = desired_complexity
        # print('est', estimator_)
        self.estimator_ = estimator_
        self.complexity_measure = complexity_measure

    def _get_alpha(self, X, y, sample_weight=None, *args, **kwargs):
        path = self.estimator_.cost_complexity_pruning_path(X, y)
        ccp_alphas, impurities = path.ccp_alphas, path.impurities
        complexities = {}
        low = 0
        high = len(ccp_alphas) - 1
        cur = 0
        while low <= high:
            cur = (high + low) // 2
            est_params = self.estimator_.get_params()
            est_params['ccp_alpha'] = ccp_alphas[cur]
            copied_estimator = deepcopy(self.estimator_).set_params(**est_params)
            copied_estimator.fit(X, y)
            if self._get_complexity(copied_estimator, self.complexity_measure) < self.desired_complexity:
                high = cur - 1
            elif self._get_complexity(copied_estimator, self.complexity_measure) > self.desired_complexity:
                low = cur + 1
            else:
                break
        self.alpha = ccp_alphas[cur]

        # for alpha in ccp_alphas:
        #    est_params = self.estimator_.get_params()
        #    est_params['ccp_alpha'] = alpha
        #    copied_estimator =  deepcopy(self.estimator_).set_params(**est_params)
        #    copied_estimator.fit(X, y)
        #    complexities[alpha] = self._get_complexity(copied_estimator,self.complexity_measure)
        # closest_alpha, closest_leaves = min(complexities.items(), key=lambda x: abs(self.desired_complexity - x[1]))
        # self.alpha = closest_alpha

    def fit(self, X, y, sample_weight=None, *args, **kwargs):
        params_for_fitting = self.estimator_.get_params()
        self._get_alpha(X, y, sample_weight, *args, **kwargs)
        params_for_fitting['ccp_alpha'] = self.alpha
        self.estimator_.set_params(**params_for_fitting)
        self.estimator_.fit(X, y, *args, **kwargs)

    def _get_complexity(self, BaseEstimator, complexity_measure):
        return compute_tree_complexity(BaseEstimator.tree_, complexity_measure)

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

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

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

Ancestors

• sklearn.tree._classes.DecisionTreeClassifier
• sklearn.base.ClassifierMixin
• sklearn.tree._classes.BaseDecisionTree
• sklearn.base.MultiOutputMixin
• sklearn.base.BaseEstimator
• sklearn.utils._estimator_html_repr._HTMLDocumentationLinkMixin
• sklearn.utils._metadata_requests._MetadataRequester

Methods

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

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.

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're doing.

Returns

self : DecisionTreeClassifier

Fitted estimator.

Expand source code

def fit(self, X, y, sample_weight=None, *args, **kwargs):
    params_for_fitting = self.estimator_.get_params()
    self._get_alpha(X, y, sample_weight, *args, **kwargs)
    params_for_fitting['ccp_alpha'] = self.alpha
    self.estimator_.set_params(**params_for_fitting)
    self.estimator_.fit(X, y, *args, **kwargs)

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

Predict class or regression value for X.

For a classification model, the predicted class for each sample in X is returned. For a regression model, the predicted value based on X is returned.

Parameters

X : {array-like, sparse matrix} of shape (n_samples, n_features)

The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.

check_input : bool, default=True

Allow to bypass several input checking. Don't use this parameter unless you know what you're doing.

Returns

y : array-like of shape (n_samples,) or (n_samples, n_outputs)

The predicted classes, or the predict values.

Expand source code

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

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

Predict class probabilities of the input samples X.

The predicted class probability is the fraction of samples of the same class in a leaf.

Parameters

X : {array-like, sparse matrix} of shape (n_samples, n_features)

The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.

check_input : bool, default=True

Allow to bypass several input checking. Don't use this parameter unless you know what you're doing.

Returns

proba : ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1

The class probabilities of the input samples. The order of the classes corresponds to that in the attribute :term:classes_.

Expand source code

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

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

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters

X : array-like of shape (n_samples, n_features)

Test samples.

y : array-like of shape (n_samples,) or (n_samples, n_outputs)

True labels for X.

sample_weight : array-like of shape (n_samples,), default=None

Sample weights.

Returns

score : float

Mean accuracy of self.predict(X) w.r.t. y.

Expand source code

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

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

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(*args, **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)} in {self.name}. "
            f"Accepted arguments are: {set(self.keys)}"
        )

    # This makes it possible to use the decorated method as an unbound method,
    # for instance when monkeypatching.
    # https://github.com/scikit-learn/scikit-learn/issues/28632
    if instance is None:
        _instance = args[0]
        args = args[1:]
    else:
        _instance = instance

    # Replicating python's behavior when positional args are given other than
    # `self`, and `self` is only allowed if this method is unbound.
    if args:
        raise TypeError(
            f"set_{self.name}_request() takes 0 positional argument but"
            f" {len(args)} were given"
        )

    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 DecisionTreeCCPRegressor (estimator_: sklearn.base.BaseEstimator, desired_complexity: int = 1, complexity_measure='max_rules', *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])

Expand source code

class DecisionTreeCCPRegressor(BaseEstimator):

    def __init__(self, estimator_: BaseEstimator, desired_complexity: int = 1, complexity_measure='max_rules', *args,
                 **kwargs):
        self.desired_complexity = desired_complexity
        # print('est', estimator_)
        self.estimator_ = estimator_
        self.alpha = 0.0
        self.complexity_measure = complexity_measure

    def _get_alpha(self, X, y, sample_weight=None):
        path = self.estimator_.cost_complexity_pruning_path(X, y)
        ccp_alphas, impurities = path.ccp_alphas, path.impurities
        complexities = {}
        low = 0
        high = len(ccp_alphas) - 1
        cur = 0
        while low <= high:
            cur = (high + low) // 2
            est_params = self.estimator_.get_params()
            est_params['ccp_alpha'] = ccp_alphas[cur]
            copied_estimator = deepcopy(self.estimator_).set_params(**est_params)
            copied_estimator.fit(X, y)
            if self._get_complexity(copied_estimator, self.complexity_measure) < self.desired_complexity:
                high = cur - 1
            elif self._get_complexity(copied_estimator, self.complexity_measure) > self.desired_complexity:
                low = cur + 1
            else:
                break
        self.alpha = ccp_alphas[cur]

    #  path = self.estimator_.cost_complexity_pruning_path(X,y)
    #  ccp_alphas, impurities = path.ccp_alphas, path.impurities
    #  complexities = {}
    #  for alpha in ccp_alphas:
    #      est_params = self.estimator_.get_params()
    #      est_params['ccp_alpha'] = alpha
    #      copied_estimator =  deepcopy(self.estimator_).set_params(**est_params)
    #      copied_estimator.fit(X, y)
    #      complexities[alpha] = self._get_complexity(copied_estimator,self.complexity_measure)
    #  closest_alpha, closest_leaves = min(complexities.items(), key=lambda x: abs(self.desired_complexity - x[1]))
    #  self.alpha = closest_alpha

    def fit(self, X, y, sample_weight=None):
        params_for_fitting = self.estimator_.get_params()
        self._get_alpha(X, y, sample_weight)
        params_for_fitting['ccp_alpha'] = self.alpha
        self.estimator_.set_params(**params_for_fitting)
        self.estimator_.fit(X, y)

    def _get_complexity(self, BaseEstimator, complexity_measure):
        return compute_tree_complexity(BaseEstimator.tree_, self.complexity_measure)

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

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

Ancestors

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

Methods

def fit(self, X, y, sample_weight=None)

Expand source code

def fit(self, X, y, sample_weight=None):
    params_for_fitting = self.estimator_.get_params()
    self._get_alpha(X, y, sample_weight)
    params_for_fitting['ccp_alpha'] = self.alpha
    self.estimator_.set_params(**params_for_fitting)
    self.estimator_.fit(X, y)

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

Expand source code

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

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

Expand source code

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

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

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(*args, **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)} in {self.name}. "
            f"Accepted arguments are: {set(self.keys)}"
        )

    # This makes it possible to use the decorated method as an unbound method,
    # for instance when monkeypatching.
    # https://github.com/scikit-learn/scikit-learn/issues/28632
    if instance is None:
        _instance = args[0]
        args = args[1:]
    else:
        _instance = instance

    # Replicating python's behavior when positional args are given other than
    # `self`, and `self` is only allowed if this method is unbound.
    if args:
        raise TypeError(
            f"set_{self.name}_request() takes 0 positional argument but"
            f" {len(args)} were given"
        )

    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 HSDecisionTreeCCPClassifierCV (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)

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 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)

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, sample_weight=None, *args, **kwargs)

Expand source code

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)

Inherited members

• HSTreeClassifier:
  • get_params
  • set_fit_request
  • set_score_request

class HSDecisionTreeCCPRegressorCV (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)

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 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)

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, sample_weight=None, *args, **kwargs)

Expand source code

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)

Inherited members

• HSTreeRegressor:
  • get_params
  • set_fit_request
  • set_score_request