#!/usr/bin/env python
# Created by "Thieu" at 06:52, 10/08/2023 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import cross_val_predict
from sklearn.model_selection import cross_validate
from sklearn.model_selection import learning_curve
from sklearn.model_selection import permutation_test_score
from sklearn.model_selection import validation_curve
from permetrics.regression import RegressionMetric
from permetrics.classification import ClassificationMetric
[docs]def get_metrics(problem, y_true, y_pred, metrics=None, testcase="test"):
if problem == "regression":
evaluator = RegressionMetric(y_true, y_pred)
paras = [{}, ] * len(metrics)
else:
evaluator = ClassificationMetric(y_true, y_pred)
paras = [{"average": "weighted"}, ] * len(metrics)
if type(metrics) is dict:
result = evaluator.get_metrics_by_dict(metrics)
elif type(metrics) in (tuple, list):
result = evaluator.get_metrics_by_list_names(metrics, paras)
else:
raise ValueError("metrics parameter should be a list or dict")
final_result = {}
for key, value in result.items():
if testcase is None or testcase == "":
final_result[f"{key}"] = value
else:
final_result[f"{key}_{testcase}"] = value
return final_result
[docs]def get_all_regression_metrics():
UNUSED_METRICS = ("RE", "RB", "AE", "SE", "SLE")
dict_results = {}
for key, value in RegressionMetric.SUPPORT.items():
if (key not in UNUSED_METRICS) and (value["type"] in ("min", "max")):
dict_results[key] = value["type"]
return dict_results
[docs]def get_all_classification_metrics():
dict_results = {}
for key, value in ClassificationMetric.SUPPORT.items():
if value["type"] in ("min", "max"):
dict_results[key] = value["type"]
return dict_results