CSForestClassifier#

class empulse.models.CSForestClassifier(n_estimators=100, *, tp_cost=0.0, tn_cost=0.0, fn_cost=0.0, fp_cost=0.0, combination='majority_voting', max_features='auto', max_samples=1.0, max_depth=None, min_samples_split=2, min_samples_leaf=1, bootstrap=True, bootstrap_features=False, n_jobs=1, verbose=False, pruned=False, random_state=None)[source]#

Random Forest classifier to optimize instance-dependent cost loss.

Parameters:

n_estimatorsint, default=100

The number of trees in the forest.

tp_costfloat or array-like, shape=(n_samples,), default=0.0

Cost of true positives. If float, then all true positives have the same cost. If array-like, then it is the cost of each true positive classification. Is overwritten if another tp_cost is passed to the fit method.

Note

It is not recommended to pass instance-dependent costs to the __init__ method. Instead, pass them to the fit method.

fp_costfloat or array-like, shape=(n_samples,), default=0.0

Cost of false positives. If float, then all false positives have the same cost. If array-like, then it is the cost of each false positive classification. Is overwritten if another fp_cost is passed to the fit method.

Note

It is not recommended to pass instance-dependent costs to the __init__ method. Instead, pass them to the fit method.

tn_costfloat or array-like, shape=(n_samples,), default=0.0

Cost of true negatives. If float, then all true negatives have the same cost. If array-like, then it is the cost of each true negative classification. Is overwritten if another tn_cost is passed to the fit method.

Note

It is not recommended to pass instance-dependent costs to the __init__ method. Instead, pass them to the fit method.

fn_costfloat or array-like, shape=(n_samples,), default=0.0

Cost of false negatives. If float, then all false negatives have the same cost. If array-like, then it is the cost of each false negative classification. Is overwritten if another fn_cost is passed to the fit method.

Note

It is not recommended to pass instance-dependent costs to the __init__ method. Instead, pass them to the fit method.

combinationstring, optional default=”majority_voting”

Which combination method to use:

If "majority_voting" then combine by majority voting
If "weighted_voting" then combine by weighted voting using the out of bag savings as the weight for each estimator.

max_depthint, 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.

max_features{‘auto’, ‘sqrt’, ‘log2’, None}, int or float, default=’auto’

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

If int, then consider max_features features at each split.
If float, then max_features is a percentage and int(max_features * n_features) features are considered at each split.
If "auto", then max_features=sqrt(n_features).
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.

max_samplesint or float, default=1.0

The number of samples to draw from X to train each base estimator.

If None (default), then draw X.shape[0] samples.
If int, then draw max_samples samples.
If float, then draw max(round(n_samples * max_samples), 1) samples. Thus, max_samples should be in the interval (0.0, 1.0].

min_samples_splitint 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.

min_samples_leafint 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.

prunedbool, optional (default=True)

Whenever or not to prune the decision tree using cost-based pruning

bootstrap: bool, default=True

Whether samples are drawn with replacement. If False, sampling without replacement is performed.

bootstrap_features: bool, default=False

Whether features are drawn with replacement.

n_jobsint, optional (default=1)

The number of jobs to run in parallel for both fit and predict. If -1, then the number of jobs is set to the number of cores.

verboseint, optional (default=0)

Controls the verbosity of the building process.

random_stateint, RandomState instance or None, default=None

If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used by np.random.

Attributes:

estimator_: :class:`~empulse.models.CSTreeClassifier`: The base estimator from which the ensemble is grown.
estimators_: list of estimators: The collection of fitted base estimators.
estimators_samples_: list of arrays: The subset of drawn samples (i.e., the in-bag samples) for each base estimator.
estimators_features_: list of arrays: The subset of drawn features for each base estimator.

References

[1]

Correa Bahnsen, A., Aouada, D., & Ottersten, B. “Ensemble of Example-Dependent Cost-Sensitive Decision Trees”, 2015, http://arxiv.org/abs/1505.04637.

fit(X, y, *, tp_cost=Parameter.UNCHANGED, tn_cost=Parameter.UNCHANGED, fn_cost=Parameter.UNCHANGED, fp_cost=Parameter.UNCHANGED)#

Build a Bagging ensemble of estimators from the training set (X, y).

Parameters:

X{array-like, sparse matrix} of shape = [n_samples, n_features]: The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.
yarray-like, shape = [n_samples]: The target values.
tp_costfloat or array-like, shape=(n_samples,), default=$UNCHANGED$: Cost of true positives. If float, then all true positives have the same cost. If array-like, then it is the cost of each true positive classification.
fp_costfloat or array-like, shape=(n_samples,), default=$UNCHANGED$: Cost of false positives. If float, then all false positives have the same cost. If array-like, then it is the cost of each false positive classification.
tn_costfloat or array-like, shape=(n_samples,), default=$UNCHANGED$: Cost of true negatives. If float, then all true negatives have the same cost. If array-like, then it is the cost of each true negative classification.
fn_costfloat or array-like, shape=(n_samples,), default=$UNCHANGED$: Cost of false negatives. If float, then all false negatives have the same cost. If array-like, then it is the cost of each false negative classification.

Returns:

selfobject: Returns self.

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.

predict(X)#

Predict class for X.

The predicted class of an input sample is computed as the class with the highest mean predicted probability. If base estimators do not implement a predict_proba method, then it resorts to voting.

Parameters:

X{array-like, sparse matrix} of shape = [n_samples, n_features]: The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.

Returns:

predarray of shape = [n_samples]: The predicted classes.

predict_proba(X)#

Predict class probabilities for X.

The predicted class probabilities of an input sample is computed as the mean predicted class probabilities of the base estimators in the ensemble. If base estimators do not implement a predict_proba method, then it resorts to voting and the predicted class probabilities of an input sample represents the proportion of estimators predicting each class.

Parameters:

X{array-like, sparse matrix} of shape = [n_samples, n_features]: The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.

Returns:

parray of shape = [n_samples, n_classes]: The class probabilities of the input samples. The order of the classes corresponds to that in the attribute 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(*, fn_cost='$UNCHANGED$', fp_cost='$UNCHANGED$', tn_cost='$UNCHANGED$', tp_cost='$UNCHANGED$')#

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:

fn_coststr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for fn_cost parameter in fit.
fp_coststr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for fp_cost parameter in fit.
tn_coststr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for tn_cost parameter in fit.
tp_coststr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for tp_cost 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='$UNCHANGED$')#

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.