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#include "../util/utils.h"
6
7namespace Brush {
12namespace Eval {
13
14/* Scoring functions */
15
16// regression ------------------------------------------------------------------
17
26float mse(const VectorXf& y, const VectorXf& yhat, VectorXf& loss,
27 const vector<float>& class_weights=vector<float>() );
28
29// binary classification -------------------------------------------------------
30
38VectorXf log_loss(const VectorXf& y, const VectorXf& predict_proba,
39 const vector<float>& class_weights=vector<float>());
40
49float mean_log_loss(const VectorXf& y, const VectorXf& predict_proba, VectorXf& loss,
50 const vector<float>& class_weights = vector<float>());
51
60float average_precision_score(const VectorXf& y, const VectorXf& predict_proba,
61 VectorXf& loss,
62 const vector<float>& class_weights=vector<float>());
63
72float zero_one_loss(const VectorXf& y, const VectorXf& predict_proba,
73 VectorXf& loss,
74 const vector<float>& class_weights=vector<float>() );
75
84float bal_zero_one_loss(const VectorXf& y, const VectorXf& predict_proba,
85 VectorXf& loss,
86 const vector<float>& class_weights=vector<float>() );
87
88// multiclass classification ---------------------------------------------------
89
97VectorXf multi_log_loss(const VectorXf& y, const ArrayXXf& predict_proba,
98 const vector<float>& class_weights=vector<float>());
99
108float mean_multi_log_loss(const VectorXf& y, const ArrayXXf& predict_proba,
109 VectorXf& loss,
110 const vector<float>& class_weights=vector<float>());
111
120float multi_zero_one_loss(const VectorXf& y, const ArrayXXf& predict_proba,
121 VectorXf& loss,
122 const vector<float>& class_weights=vector<float>() );
123
124
125} // metrics
126} // Brush
127
128#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:282
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:73
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:45
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:271
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:132
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:220
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:100
Brush
< nsga2 selection operator for getting the front
Definition bandit.cpp:4
Eval
Namespace containing scoring functions for evaluation metrics.