# !/usr/bin/env python
# Created by "Thieu" at 09:29, 23/09/2020 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
from permetrics.evaluator import Evaluator
import numpy as np
[docs]class ClassificationMetric(Evaluator):
"""
This is class contains all classification metrics (for both binary and multiple classification problem)
Notes
~~~~~
+ Extension of: https://scikit-learn.org/stable/modules/model_evaluation.html#classification-metrics
"""
def __init__(self, y_true=None, y_pred=None, decimal=5, **kwargs):
"""
Args:
y_true (tuple, list, np.ndarray): The ground truth values
y_pred (tuple, list, np.ndarray): The prediction values
decimal (int): The number of fractional parts after the decimal point
**kwargs ():
"""
super().__init__(y_true, y_pred, decimal, **kwargs)
if kwargs is None: kwargs = {}
self.set_keyword_arguments(kwargs)
[docs] def mean_log_likelihood(self, y_true=None, y_pred=None, multi_output="raw_values", decimal=None, non_zero=True, positive=True):
"""
Mean Log Likelihood (MLL): Best possible score is ..., the higher value is better. Range = (-inf, +inf)
Link: https://github.com/benhamner/Metrics/blob/master/Python/ml_metrics/elementwise.py#L235
Args:
y_true (tuple, list, np.ndarray): The ground truth values
y_pred (tuple, list, np.ndarray): The prediction values
multi_output: Can be "raw_values" or list weights of variables such as [0.5, 0.2, 0.3] for 3 columns, (Optional, default = "raw_values")
decimal (int): The number of fractional parts after the decimal point (Optional, default = 5)
non_zero (bool): Remove all rows contain 0 value in y_pred (some methods have denominator is y_pred) (Optional, default = True)
positive (bool): Calculate metric based on positive values only or not (Optional, default = True)
Returns:
result (float, int, np.ndarray): MLL metric
"""
y_true, y_pred, one_dim, decimal = self.get_processed_data(y_true, y_pred, decimal)
if non_zero:
y_true, y_pred = self.get_non_zero_data(y_true, y_pred, one_dim, 1)
else:
y_pred[y_pred == 0] = self.EPSILON
y_true, y_pred = self.get_positive_data(y_true, y_pred, one_dim, 2)
if one_dim:
return np.round(np.mean(-(y_true * np.log(y_pred) + (1 - y_true) * np.log(1 - y_pred))), decimal)
else:
result = np.mean(-(y_true * np.log(y_pred) + (1 - y_true) * np.log(1 - y_pred)), axis=0)
return self.get_multi_output_result(result, multi_output, decimal)
[docs] def single_log_likelihood(self, y_true=None, y_pred=None, decimal=None, non_zero=True, positive=True):
"""
Log Likelihood (LL): Best possible score is ..., the higher value is better. Range = (-inf, +inf)
Note: Computes the log likelihood between two numbers, or for element between a pair of list, tuple or numpy arrays.
Args:
y_true (tuple, list, np.ndarray): The ground truth values
y_pred (tuple, list, np.ndarray): The prediction values
multi_output: Can be "raw_values" or list weights of variables such as [0.5, 0.2, 0.3] for 3 columns, (Optional, default = "raw_values")
decimal (int): The number of fractional parts after the decimal point (Optional, default = 5)
non_zero (bool): Remove all rows contain 0 value in y_pred (some methods have denominator is y_pred) (Optional, default = True)
positive (bool): Calculate metric based on positive values only or not (Optional, default = True)
Returns:
result (float, int, np.ndarray): LL metric
"""
y_true, y_pred, one_dim, decimal = self.get_processed_data(y_true, y_pred, decimal)
if non_zero:
y_true, y_pred = self.get_non_zero_data(y_true, y_pred, one_dim, 1)
else:
y_pred[y_pred == 0] = self.EPSILON
y_true, y_pred = self.get_positive_data(y_true, y_pred, one_dim, 2)
return np.round(-(y_true * np.log(y_pred) + (1 - y_true) * np.log(1 - y_pred)), decimal)
MLL = mll = mean_log_likelihood
LL = ll = single_log_likelihood