`dpdt`.DPDTreeClassifier#

class dpdt.DPDTreeClassifier(max_depth=3, max_nb_trees=1000, cart_nodes_list=(3,), random_state=42)[source]#

Bases: ClassifierMixin, BaseEstimator

Dynamic Programming Decision Tree (DPDTree) classifier.

Parameters:

max_depthint: The maximum depth of the tree.
max_nb_treesint, default=1000: The maximum number of trees.
random_stateint, default=42: Fixes randomness of the classifier. Randomness happens in the calls to cart.
cart_nodes_listlist of int, default=(3,): List containing the number of leaf nodes for the CART trees at each depth.

Attributes:

X_ndarray, shape (n_samples, n_features): The input passed during fit().
y_ndarray, shape (n_samples,): The labels passed during fit().
classes_ndarray, shape (n_classes,): The classes seen at fit().
n_features_in_int: Number of features seen during fit.
feature_names_in_ndarray of shape (n_features_in_,): Names of features seen during fit. Defined only when X has feature names that are all strings.
mdplist of list of State: The Markov Decision Process represented as a list of lists of states, where each inner list contains the states at a specific depth.
zetasarray-like: Array of zeta values to be used in the computation.
treesdict: A dictionary representing the tree policies. The keys are tuples representing the state observation and depth, and the values are the optimal tree for each zeta value.
init_oarray-like: The initial observation of the MDP.

Methods

`expand_node_`(node[, depth])	Node Expansion:
`fit`(X, y[, feature_costs, infinite_memory])	Fit the DPDTree classifier.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`get_pareto_front`(X, y)	Compute the decision path lengths / test accuracy pareto front of DPDTrees.
`predict`(X)	Predict class for X.
`score`(X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
`set_fit_request`(*[, feature_costs, ...])	Request metadata passed to the `fit` method.
`set_params`(**params)	Set the parameters of this estimator.
`set_score_request`(*[, sample_weight])	Request metadata passed to the `score` method.

Examples

>>> from dpdt import DPDTreeClassifier
>>> from sklearn import datasets
>>>
>>> X, y = datasets.load_breast_cancer(return_X_y=True)
>>>
>>> clf = DPDTreeClassifier(max_depth=3, random_state=42)
>>> clf.fit(X, y)
>>> print(clf.score(X, y))

expand_node_(node, depth=0)[source]#

Node Expansion:

For each node at the current depth, calculates the unique classes and their counts for the samples in the node (node.nz).
Computes the best possible reward (rstar) and the action (astar) leading to the next state.
If further expansion is possible (i.e., depth budget allows and there are at least two classes), initializes a DecisionTreeClassifier to determine the splits.
Fits the classifier on the samples in the node, and identifies potential splits (features and thresholds).

Action Creation and Transition:

For each split, creates an Action and determines the left and right child nodes based on the split.
Creates the left and right nodes as new states, and adds transitions to the action.
If an action has valid transitions, adds it to the current node.

fit(X, y, feature_costs=None, infinite_memory=False)[source]#

Fit the DPDTree classifier.

Creates a root state with the concatenated minimum and maximum values of self.X_ with slight offsets.
Initializes a terminal_state as an array of zeros with a length of twice the number of features in self.X_.
Initializes the root state with all samples (nz as an array of True values).

Parameters:

Xarray-like of shape (n_samples, n_features): The training input samples.
yarray-like of shape (n_samples,): The target values.
feature_costs (optional): list of float, default=None: List containing the features costs.
infinite_memory (optional): bool, default=None: If true force runs garbage collection. This allows to run CART with an infinite number of leave nodes.. It will multiply runtime by 500.

Returns:

selfobject: Fitted estimator.

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)#

Get parameters for this estimator.

Parameters:

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

Returns:

paramsdict: Parameter names mapped to their values.

get_pareto_front(X, y)[source]#

Compute the decision path lengths / test accuracy pareto front of DPDTrees.

Parameters:

Xarray-like of shape (n_samples, n_features): The input samples.
yarray-like of shape (n_samples,): The target values.

Returns:

scoresarray-like of shape (n_samples): The test accuracies of the trees.
decision_path_lengtharray-like of shape (n_samples): The average number of decision nodes traversal in each tree.

predict(X)[source]#

Predict class for X.

Parameters:

Xarray-like of shape (n_samples, n_features): The input samples.

Returns:

y_predarray of shape (n_samples,): The predicted classes.

score(X, y, sample_weight=None)#

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:

Xarray-like of shape (n_samples, n_features): Test samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs): True labels for X.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.

Returns:

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

set_fit_request(*, feature_costs: bool | None | str = '$UNCHANGED$', infinite_memory: bool | None | str = '$UNCHANGED$') → DPDTreeClassifier#

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide 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 Pipeline. Otherwise it has no effect.

Parameters:

feature_costsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for feature_costs parameter in fit.
infinite_memorystr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for infinite_memory parameter in fit.

Returns:

selfobject: The updated object.

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → DPDTreeClassifier#

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide 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.

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 Pipeline. Otherwise it has no effect.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in score.

Returns:

selfobject: The updated object.

dpdt.DPDTreeClassifier#

`dpdt`.DPDTreeClassifier#