CSTreeClassifier#

class empulse.models.CSTreeClassifier(*, tp_cost=0.0, tn_cost=0.0, fn_cost=0.0, fp_cost=0.0, criterion='direct_cost', criterion_weight=False, num_pct=100, max_features=None, max_depth=None, min_samples_split=2, min_samples_leaf=1, min_gain=0.001, pruned=True, random_state=None)[source]#

Decision tree classifier to optimize instance-dependent cost loss.

Parameters:
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.

criterion{“direct_cost”, “pi_cost”, “gini_cost” or “entropy_cost”}, default=”direct_cost”

The function to measure the quality of a split. Supported criteria are “direct_cost” for the Direct Cost impurity measure, “pi_cost”, “gini_cost”, and “entropy_cost”.

criterion_weightbool, default=False

Whenever or not to weight the gain according to the population distribution.

num_pctint, default=100

Number of percentiles to evaluate the splits for each feature.

max_features{“auto”, “sqrt”, “log2” or None }, int or float, default=None

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

  • 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_depthint or None, 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_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.

min_gainfloat, default=0.001

The minimum gain that a split must produce in order to be taken into account.

prunedbool, default=True

Whenever to prune the decision tree using cost-based pruning.

random_stateint, RandomState instance or None, default=None

Controls the randomness of the estimator. The features are always randomly permuted at each split. When random_state is None, the random number generator is the numpy.random.RandomState instance used by numpy:numpy.random.

Attributes:
tree_Tree object

The underlying Tree object.

References

[1]

Correa Bahnsen, A., Aouada, D., & Ottersten, B. “Example-Dependent Cost-Sensitive Decision Trees. Expert Systems with Applications”, Expert Systems with Applications, 42(19), 6609–6619, 2015, http://doi.org/10.1016/j.eswa.2015.04.042

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

Build an example-dependent cost-sensitive decision tree from the training set.

Parameters:
Xarray-like of shape (n_samples, n_features)

The input samples.

yarray-like of shape (n_samples,)

Ground truth (correct) labels.

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)[source]#

Predict class of X.

The predicted class for each sample in X is returned.

Parameters:
Xarray-like of shape = [n_samples, n_features]

The input samples.

Returns:
yarray of shape = [n_samples]

The predicted classes,

predict_proba(X)[source]#

Predict class probabilities of the input samples X.

Parameters:
Xarray-like of shape = [n_samples, n_features]

The input samples.

Returns:
probarray of shape = [n_samples, 2]

The class probabilities of the input samples.

pruning(X, y, cost_mat)[source]#

Prune the decision tree.

Parameters:
Xarray of shape = [n_samples, n_features]

The input samples.

y_truearray indicator matrix

Ground truth (correct) labels.

cost_matarray of shape = [n_samples, 4]

Cost matrix of the classification problem Where the columns represent the costs of: false positives, false negatives, true positives and true negatives, for each example.

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.