Brush C++ API
A flexible interpretable machine learning framework
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metrics.h
Go to the documentation of this file.
1#ifndef METRICS_H
2#define METRICS_H
3
4#include "../data/data.h"
5
6namespace Brush {
11namespace Eval {
12
13/* Scoring functions */
14
15// regression ------------------------------------------------------------------
16
25float mse(const VectorXf& y, const VectorXf& yhat, VectorXf& loss,
26 const vector<float>& class_weights=vector<float>() );
27
28// binary classification -------------------------------------------------------
29
37VectorXf log_loss(const VectorXf& y, const VectorXf& predict_proba,
38 const vector<float>& class_weights=vector<float>());
39
48float mean_log_loss(const VectorXf& y, const VectorXf& predict_proba, VectorXf& loss,
49 const vector<float>& class_weights = vector<float>());
50
59float average_precision_score(const VectorXf& y, const VectorXf& predict_proba,
60 VectorXf& loss,
61 const vector<float>& class_weights=vector<float>());
62
71float zero_one_loss(const VectorXf& y, const VectorXf& predict_proba,
72 VectorXf& loss,
73 const vector<float>& class_weights=vector<float>() );
74
83float bal_zero_one_loss(const VectorXf& y, const VectorXf& predict_proba,
84 VectorXf& loss,
85 const vector<float>& class_weights=vector<float>() );
86
87// multiclass classification ---------------------------------------------------
88
96VectorXf multi_log_loss(const VectorXf& y, const ArrayXXf& predict_proba,
97 const vector<float>& class_weights=vector<float>());
98
107float mean_multi_log_loss(const VectorXf& y, const ArrayXXf& predict_proba,
108 VectorXf& loss,
109 const vector<float>& class_weights=vector<float>());
110
119float multi_zero_one_loss(const VectorXf& y, const ArrayXXf& predict_proba,
120 VectorXf& loss,
121 const vector<float>& class_weights=vector<float>() );
122
123
124} // metrics
125} // Brush
126
127#endif
Brush::Eval::multi_zero_one_loss
float multi_zero_one_loss(const VectorXf &y, const ArrayXXf &predict_proba, VectorXf &loss, const vector< float > &class_weights)
Accuracy for multi-classification.
Definition metrics.cpp:264
Brush::Eval::zero_one_loss
float zero_one_loss(const VectorXf &y, const VectorXf &predict_proba, VectorXf &loss, const vector< float > &class_weights)
Accuracy for binary classification.
Definition metrics.cpp:71
Brush::Eval::mean_log_loss
float mean_log_loss(const VectorXf &y, const VectorXf &predict_proba, VectorXf &loss, const vector< float > &class_weights)
log loss
Definition metrics.cpp:43
Brush::Eval::mean_multi_log_loss
float mean_multi_log_loss(const VectorXf &y, const ArrayXXf &predict_proba, VectorXf &loss, const vector< float > &class_weights)
Calculates the mean multinomial log loss between the predicted probabilities and the true labels.
Definition metrics.cpp:253
Brush::Eval::average_precision_score
float average_precision_score(const VectorXf &y, const VectorXf &predict_proba, VectorXf &loss, const vector< float > &class_weights)
Calculates the average precision score between the predicted probabilities and the true labels.
Definition metrics.cpp:124
Brush::Eval::mse
float mse(const VectorXf &y, const VectorXf &yhat, VectorXf &loss, const vector< float > &class_weights)
mean squared error
Definition metrics.cpp:9
Brush::Eval::multi_log_loss
VectorXf multi_log_loss(const VectorXf &y, const ArrayXXf &predict_proba, const vector< float > &class_weights)
Calculates the multinomial log loss between the predicted probabilities and the true labels.
Definition metrics.cpp:202
Brush::Eval::log_loss
VectorXf log_loss(const VectorXf &y, const VectorXf &predict_proba, const vector< float > &class_weights)
Calculates the log loss between the predicted probabilities and the true labels.
Definition metrics.cpp:17
Brush::Eval::bal_zero_one_loss
float bal_zero_one_loss(const VectorXf &y, const VectorXf &predict_proba, VectorXf &loss, const vector< float > &class_weights)
Balanced accuracy for binary classification.
Definition metrics.cpp:92
Brush
< nsga2 selection operator for getting the front
Definition bandit.cpp:4
Eval
Namespace containing scoring functions for evaluation metrics.