`piml.models`.FIGSClassifier¶

class piml.models.FIGSClassifier(feature_names=None, feature_types=None, max_iter=20, max_depth=None, splitter='best', min_samples_leaf=1, min_impurity_decrease=0, learning_rate=1, random_state=None)¶

Fast interpretable greedy-tree sums classifier.

FIGS is an algorithm for fitting concise rule-based models. This is a re-implementation of the FIGS algorithm of imodels (https://github.com/csinva/imodels).

Parameters:

feature_nameslist or None, default=None: The list of feature names.
feature_typeslist or None, default=None: The list of feature types. Available types include “numerical” and “categorical”.
max_iterint, default=20: The max number of iterations, each iteration is a splitting step.
max_depthint, default=None: The max tree depth, which means no constraint on max_depth.
split{‘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.
min_samples_leafint, 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.
min_impurity_decreasefloat, default=0.0: A node will be split if this split induces a decrease of the impurity greater than or equal to this value.
learning_ratefloat, default=1.0: The learning rate of each tree.
random_stateint, default=0: The random seed.

Attributes:

feature_names_list of str: The feature name list of all input features.
feature_types_list of str: The feature type list of all input features.
n_features_in_int: The number of input features.
intercept_float: The intercept term in addition to trees.
trees_dict: The dictionary of Tree objects
n_tree_int: The number of trees included in the model
n_iter_int: The number of iteration.
early_stop_boolean: Whether early stopping is triggered.

Methods

`decision_function`(X)	Returns numpy array of raw predictions.
`fit`(X, y[, sample_weight])	Fit FIGS model.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`parse_model`()	Interpret the model.
`predict`(X)	Returns numpy array of predicted class.
`predict_proba`(X)	Returns numpy array of predicted probabilities of each class.
`score`(X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
`set_params`(**params)	Set the parameters of this estimator.
`set_score_request`(*[, sample_weight])	Request metadata passed to the `score` method.

decision_function(X)¶

Returns numpy array of raw predictions.

Parameters:

Xnp.ndarray of shape (n_samples, n_features): Data features.

Returns:

predictionsnp.ndarray of shape (n_samples, ): numpy array of raw predictions.

fit(X, y, sample_weight=None)¶

Fit FIGS model.

Parameters:

Xnp.ndarray of shape (n_samples, n_features): Data features.
ynp.ndarray of shape (n_samples, ): Target response.
sample_weightnp.ndarray of shape (n_samples, ): Sample weight, by default None.

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.

parse_model()¶

Interpret the model.

Returns:

An instance of FIGSInterpreter: The interpretation results.

predict(X)¶

Returns numpy array of predicted class.

Parameters:

Xnp.ndarray of shape (n_samples, n_features): Data features

Returns:

prednp.ndarray of shape (n_samples, ): numpy array of predicted class values.

predict_proba(X)¶

Returns numpy array of predicted probabilities of each class.

Parameters:

Xnp.ndarray of shape (n_samples, n_features): Data features.

Returns:

pred_probanp.ndarray of shape (n_samples, 2): numpy array of predicted proba values.

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_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: Union[bool, None, str] = '$UNCHANGED$') → FIGSClassifier¶

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.

New 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.

Examples using `piml.models.FIGSClassifier`¶

FIGS Classification (Taiwan Credit)

Model Comparison: Classification

piml.models.FIGSClassifier¶

Examples using piml.models.FIGSClassifier¶

`piml.models`.FIGSClassifier¶

Examples using `piml.models.FIGSClassifier`¶