Evaluation

This module provides evaluation methods for classification, regression and clustering. Available metrics include:

1. AUC: Compute AUC for binary classification.
2. KS: Compute Kolmogorov-Smirnov for binary classification.
3. LIFT: Compute lift of binary classification.
4. PRECISION: Compute the precision for binary and multi-classification
5. RECALL: Compute the recall for binary and multi-classification
6. ACCURACY: Compute the accuracy for binary and multi-classification
7. EXPLAINED_VARIANCE: Compute explain variance for regression tasks
8. MEAN_ABSOLUTE_ERROR: Compute mean absolute error for regression tasks
9. MEAN_SQUARED_ERROR: Compute mean square error for regression tasks
10. MEAN_SQUARED_LOG_ERROR: Compute mean squared logarithmic error for regression tasks
11. MEDIAN_ABSOLUTE_ERROR: Compute median absolute error for regression tasks
12. R2_SCORE: Compute R^2 (coefficient of determination) score for regression tasks
13. ROOT_MEAN_SQUARED_ERROR: Compute the root of mean square error for regression tasks
14. JACCARD_SIMILARITY_SCORE：Compute Jaccard similarity score for clustering tasks (labels are needed)
15. ADJUSTED_RAND_SCORE：Compute adjusted rand score for clustering tasks (labels are needed)
16. FOWLKES_MALLOWS_SCORE：Compute Fowlkes Mallows score for clustering tasks (labels are needed)
17. DAVIES_BOULDIN_INDEX：Compute Davies Bouldin index for clustering tasks
18. DISTANCE_MEASURE:Compute cluster information in clustering algorithms
19. CONTINGENCY_MATRIX:Compute contingency matrix for clustering tasks (labels are needed)
20. PSI: Compute Population Stability Index.
21. F1-Score: Compute F1-Score for binary tasks.

Param

evaluation_param

Attributes

Classes

EvaluateParam(eval_type='binary', pos_label=1, need_run=True, metrics=None, run_clustering_arbiter_metric=False, unfold_multi_result=False)

Bases: BaseParam

Define the evaluation method of binary/multiple classification and regression

Parameters:

Name	Type	Description	Default
eval_type		support 'binary' for HomoLR, HeteroLR and Secureboosting, support 'regression' for Secureboosting, 'multi' is not support these version	'binary'
unfold_multi_result	bool	unfold multi result and get several one-vs-rest binary classification results	False
pos_label	int or float or str	specify positive label type, depend on the data's label. this parameter effective only for 'binary'	1
need_run		Indicate if this module needed to be run	True

Source code in python/federatedml/param/evaluation_param.py

def __init__(self, eval_type="binary", pos_label=1, need_run=True, metrics=None,
             run_clustering_arbiter_metric=False, unfold_multi_result=False):
    super().__init__()
    self.eval_type = eval_type
    self.pos_label = pos_label
    self.need_run = need_run
    self.metrics = metrics
    self.unfold_multi_result = unfold_multi_result
    self.run_clustering_arbiter_metric = run_clustering_arbiter_metric

    self.default_metrics = {
        consts.BINARY: consts.ALL_BINARY_METRICS,
        consts.MULTY: consts.ALL_MULTI_METRICS,
        consts.REGRESSION: consts.ALL_REGRESSION_METRICS,
        consts.CLUSTERING: consts.ALL_CLUSTER_METRICS
    }

    self.allowed_metrics = {
        consts.BINARY: consts.ALL_BINARY_METRICS,
        consts.MULTY: consts.ALL_MULTI_METRICS,
        consts.REGRESSION: consts.ALL_REGRESSION_METRICS,
        consts.CLUSTERING: consts.ALL_CLUSTER_METRICS
    }

Attributes

eval_type = eval_type instance-attribute

pos_label = pos_label instance-attribute

need_run = need_run instance-attribute

metrics = metrics instance-attribute

unfold_multi_result = unfold_multi_result instance-attribute

run_clustering_arbiter_metric = run_clustering_arbiter_metric instance-attribute

default_metrics = {consts.BINARY: consts.ALL_BINARY_METRICS, consts.MULTY: consts.ALL_MULTI_METRICS, consts.REGRESSION: consts.ALL_REGRESSION_METRICS, consts.CLUSTERING: consts.ALL_CLUSTER_METRICS} instance-attribute

allowed_metrics = {consts.BINARY: consts.ALL_BINARY_METRICS, consts.MULTY: consts.ALL_MULTI_METRICS, consts.REGRESSION: consts.ALL_REGRESSION_METRICS, consts.CLUSTERING: consts.ALL_CLUSTER_METRICS} instance-attribute

Functions

check()

Source code in python/federatedml/param/evaluation_param.py

def check(self):

    descr = "evaluate param's "
    self.eval_type = self.check_and_change_lower(self.eval_type,
                                                 [consts.BINARY, consts.MULTY, consts.REGRESSION,
                                                  consts.CLUSTERING],
                                                 descr)

    if type(self.pos_label).__name__ not in ["str", "float", "int"]:
        raise ValueError(
            "evaluate param's pos_label {} not supported, should be str or float or int type".format(
                self.pos_label))

    if type(self.need_run).__name__ != "bool":
        raise ValueError(
            "evaluate param's need_run {} not supported, should be bool".format(
                self.need_run))

    if self.metrics is None or len(self.metrics) == 0:
        self.metrics = self.default_metrics[self.eval_type]
        LOGGER.warning('use default metric {} for eval type {}'.format(self.metrics, self.eval_type))

    self.check_boolean(self.unfold_multi_result, 'multi_result_unfold')

    self.metrics = self._check_valid_metric(self.metrics)

    return True

check_single_value_default_metric()

Source code in python/federatedml/param/evaluation_param.py

def check_single_value_default_metric(self):
    self._use_single_value_default_metrics()

    # in validation strategy, psi f1-score and confusion-mat pr-quantile are not supported in cur version
    if self.metrics is None or len(self.metrics) == 0:
        self.metrics = self.default_metrics[self.eval_type]
        LOGGER.warning('use default metric {} for eval type {}'.format(self.metrics, self.eval_type))

    ban_metric = [consts.PSI, consts.F1_SCORE, consts.CONFUSION_MAT, consts.QUANTILE_PR]
    for metric in self.metrics:
        if metric in ban_metric:
            self.metrics.remove(metric)
    self.check()

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最后更新: 2021-11-15