182 lines
5.6 KiB
Python
182 lines
5.6 KiB
Python
"""
|
|
The :mod:`sklearn.metrics` module includes score functions, performance metrics
|
|
and pairwise metrics and distance computations.
|
|
"""
|
|
|
|
|
|
from ._ranking import auc
|
|
from ._ranking import average_precision_score
|
|
from ._ranking import coverage_error
|
|
from ._ranking import det_curve
|
|
from ._ranking import dcg_score
|
|
from ._ranking import label_ranking_average_precision_score
|
|
from ._ranking import label_ranking_loss
|
|
from ._ranking import ndcg_score
|
|
from ._ranking import precision_recall_curve
|
|
from ._ranking import roc_auc_score
|
|
from ._ranking import roc_curve
|
|
from ._ranking import top_k_accuracy_score
|
|
|
|
from ._classification import accuracy_score
|
|
from ._classification import balanced_accuracy_score
|
|
from ._classification import class_likelihood_ratios
|
|
from ._classification import classification_report
|
|
from ._classification import cohen_kappa_score
|
|
from ._classification import confusion_matrix
|
|
from ._classification import f1_score
|
|
from ._classification import fbeta_score
|
|
from ._classification import hamming_loss
|
|
from ._classification import hinge_loss
|
|
from ._classification import jaccard_score
|
|
from ._classification import log_loss
|
|
from ._classification import matthews_corrcoef
|
|
from ._classification import precision_recall_fscore_support
|
|
from ._classification import precision_score
|
|
from ._classification import recall_score
|
|
from ._classification import zero_one_loss
|
|
from ._classification import brier_score_loss
|
|
from ._classification import multilabel_confusion_matrix
|
|
|
|
from ._dist_metrics import DistanceMetric
|
|
|
|
from . import cluster
|
|
from .cluster import adjusted_mutual_info_score
|
|
from .cluster import adjusted_rand_score
|
|
from .cluster import rand_score
|
|
from .cluster import pair_confusion_matrix
|
|
from .cluster import completeness_score
|
|
from .cluster import consensus_score
|
|
from .cluster import homogeneity_completeness_v_measure
|
|
from .cluster import homogeneity_score
|
|
from .cluster import mutual_info_score
|
|
from .cluster import normalized_mutual_info_score
|
|
from .cluster import fowlkes_mallows_score
|
|
from .cluster import silhouette_samples
|
|
from .cluster import silhouette_score
|
|
from .cluster import calinski_harabasz_score
|
|
from .cluster import v_measure_score
|
|
from .cluster import davies_bouldin_score
|
|
|
|
from .pairwise import euclidean_distances
|
|
from .pairwise import nan_euclidean_distances
|
|
from .pairwise import pairwise_distances
|
|
from .pairwise import pairwise_distances_argmin
|
|
from .pairwise import pairwise_distances_argmin_min
|
|
from .pairwise import pairwise_kernels
|
|
from .pairwise import pairwise_distances_chunked
|
|
|
|
from ._regression import explained_variance_score
|
|
from ._regression import max_error
|
|
from ._regression import mean_absolute_error
|
|
from ._regression import mean_squared_error
|
|
from ._regression import mean_squared_log_error
|
|
from ._regression import median_absolute_error
|
|
from ._regression import mean_absolute_percentage_error
|
|
from ._regression import mean_pinball_loss
|
|
from ._regression import r2_score
|
|
from ._regression import mean_tweedie_deviance
|
|
from ._regression import mean_poisson_deviance
|
|
from ._regression import mean_gamma_deviance
|
|
from ._regression import d2_tweedie_score
|
|
from ._regression import d2_pinball_score
|
|
from ._regression import d2_absolute_error_score
|
|
|
|
|
|
from ._scorer import check_scoring
|
|
from ._scorer import make_scorer
|
|
from ._scorer import SCORERS
|
|
from ._scorer import get_scorer
|
|
from ._scorer import get_scorer_names
|
|
|
|
|
|
from ._plot.det_curve import DetCurveDisplay
|
|
from ._plot.roc_curve import RocCurveDisplay
|
|
from ._plot.precision_recall_curve import PrecisionRecallDisplay
|
|
from ._plot.confusion_matrix import ConfusionMatrixDisplay
|
|
from ._plot.regression import PredictionErrorDisplay
|
|
|
|
|
|
__all__ = [
|
|
"accuracy_score",
|
|
"adjusted_mutual_info_score",
|
|
"adjusted_rand_score",
|
|
"auc",
|
|
"average_precision_score",
|
|
"balanced_accuracy_score",
|
|
"calinski_harabasz_score",
|
|
"check_scoring",
|
|
"class_likelihood_ratios",
|
|
"classification_report",
|
|
"cluster",
|
|
"cohen_kappa_score",
|
|
"completeness_score",
|
|
"ConfusionMatrixDisplay",
|
|
"confusion_matrix",
|
|
"consensus_score",
|
|
"coverage_error",
|
|
"d2_tweedie_score",
|
|
"d2_absolute_error_score",
|
|
"d2_pinball_score",
|
|
"dcg_score",
|
|
"davies_bouldin_score",
|
|
"DetCurveDisplay",
|
|
"det_curve",
|
|
"DistanceMetric",
|
|
"euclidean_distances",
|
|
"explained_variance_score",
|
|
"f1_score",
|
|
"fbeta_score",
|
|
"fowlkes_mallows_score",
|
|
"get_scorer",
|
|
"hamming_loss",
|
|
"hinge_loss",
|
|
"homogeneity_completeness_v_measure",
|
|
"homogeneity_score",
|
|
"jaccard_score",
|
|
"label_ranking_average_precision_score",
|
|
"label_ranking_loss",
|
|
"log_loss",
|
|
"make_scorer",
|
|
"nan_euclidean_distances",
|
|
"matthews_corrcoef",
|
|
"max_error",
|
|
"mean_absolute_error",
|
|
"mean_squared_error",
|
|
"mean_squared_log_error",
|
|
"mean_pinball_loss",
|
|
"mean_poisson_deviance",
|
|
"mean_gamma_deviance",
|
|
"mean_tweedie_deviance",
|
|
"median_absolute_error",
|
|
"mean_absolute_percentage_error",
|
|
"multilabel_confusion_matrix",
|
|
"mutual_info_score",
|
|
"ndcg_score",
|
|
"normalized_mutual_info_score",
|
|
"pair_confusion_matrix",
|
|
"pairwise_distances",
|
|
"pairwise_distances_argmin",
|
|
"pairwise_distances_argmin_min",
|
|
"pairwise_distances_chunked",
|
|
"pairwise_kernels",
|
|
"PrecisionRecallDisplay",
|
|
"precision_recall_curve",
|
|
"precision_recall_fscore_support",
|
|
"precision_score",
|
|
"PredictionErrorDisplay",
|
|
"r2_score",
|
|
"rand_score",
|
|
"recall_score",
|
|
"RocCurveDisplay",
|
|
"roc_auc_score",
|
|
"roc_curve",
|
|
"SCORERS",
|
|
"get_scorer_names",
|
|
"silhouette_samples",
|
|
"silhouette_score",
|
|
"top_k_accuracy_score",
|
|
"v_measure_score",
|
|
"zero_one_loss",
|
|
"brier_score_loss",
|
|
]
|