Brush::Eval Namespace Reference

Classes
class	Evaluation
	Class for evaluating the fitness of individuals in a population. More...
class	Scorer
class	Scorer< P >

Functions
float	mse (const VectorXf &y, const VectorXf &yhat, VectorXf &loss, const vector< float > &class_weights)
	mean squared error
VectorXf	log_loss (const VectorXf &y, const VectorXf &predict_proba, const vector< float > &class_weights=vector< float >())
	Calculates the log loss between the predicted probabilities and the true labels.
float	mean_log_loss (const VectorXf &y, const VectorXf &predict_proba, VectorXf &loss, const vector< float > &class_weights)
	log loss
float	average_precision_score (const VectorXf &y, const VectorXf &predict_proba, VectorXf &loss, const vector< float > &class_weights=vector< float >())
	Calculates the average precision score between the predicted probabilities and the true labels.
VectorXf	multi_log_loss (const VectorXf &y, const ArrayXXf &predict_proba, const vector< float > &class_weights=vector< float >())
	Calculates the multinomial log loss between the predicted probabilities and the true labels.
float	mean_multi_log_loss (const VectorXf &y, const ArrayXXf &predict_proba, VectorXf &loss, const vector< float > &class_weights=vector< float >())
	Calculates the mean multinomial log loss between the predicted probabilities and the true labels.

Function Documentation

average_precision_score()

float Brush::Eval::average_precision_score	(	const VectorXf &	y,
		const VectorXf &	predict_proba,
		VectorXf &	loss,
		const vector< float > &	class_weights = vector< float >() )

Calculates the average precision score between the predicted probabilities and the true labels.

Parameters

y	The true labels.
predict_proba	The predicted probabilities.
loss	Reference to store the calculated losses for each sample.
class_weights	The optional class weights.

Returns

The average precision score.

Definition at line 59 of file metrics.cpp.

Here is the call graph for this function:

Here is the caller graph for this function:

log_loss()

VectorXf Brush::Eval::log_loss	(	const VectorXf &	y,
		const VectorXf &	predict_proba,
		const vector< float > &	class_weights = vector< float >() )

Calculates the log loss between the predicted probabilities and the true labels.

Parameters

y	The true labels.
predict_proba	The predicted probabilities.
class_weights	The optional class weights.

Returns

The log loss.

Definition at line 16 of file metrics.cpp.

Here is the call graph for this function:

Here is the caller graph for this function:

mean_log_loss()

float Brush::Eval::mean_log_loss	(	const VectorXf &	y,
		const VectorXf &	predict_proba,
		VectorXf &	loss,
		const vector< float > &	class_weights = vector< float >() )

log loss

Calculates the mean log loss between the predicted probabilities and the true labels.

Parameters

y	The true labels.
predict_proba	The predicted probabilities.
loss	Reference to store the calculated losses for each sample.
class_weights	The optional class weights.

Returns

The mean log loss.

Definition at line 51 of file metrics.cpp.

Here is the call graph for this function:

Here is the caller graph for this function:

mean_multi_log_loss()

float Brush::Eval::mean_multi_log_loss	(	const VectorXf &	y,
		const ArrayXXf &	predict_proba,
		VectorXf &	loss,
		const vector< float > &	class_weights = vector< float >() )

Calculates the mean multinomial log loss between the predicted probabilities and the true labels.

Parameters

y	The true labels.
predict_proba	The predicted probabilities.
loss	Reference to store the calculated losses for each sample.
class_weights	The optional class weights.

Returns

The mean multinomial log loss.

Definition at line 169 of file metrics.cpp.

Here is the call graph for this function:

Here is the caller graph for this function:

mse()

float Brush::Eval::mse	(	const VectorXf &	y,
		const VectorXf &	yhat,
		VectorXf &	loss,
		const vector< float > &	class_weights = vector< float >() )

mean squared error

Calculates the mean squared error between the predicted values and the true values.

Parameters

y	The true values.
yhat	The predicted values.
loss	Reference to store the calculated losses for each sample.
class_weights	The optional class weights (not used for MSE).

Returns

The mean squared error.

Definition at line 9 of file metrics.cpp.

Here is the call graph for this function:

Here is the caller graph for this function:

multi_log_loss()

VectorXf Brush::Eval::multi_log_loss	(	const VectorXf &	y,
		const ArrayXXf &	predict_proba,
		const vector< float > &	class_weights = vector< float >() )

Calculates the multinomial log loss between the predicted probabilities and the true labels.

Parameters

y	The true labels.
predict_proba	The predicted probabilities.
class_weights	The optional class weights.

Returns

The multinomial log loss.

Definition at line 118 of file metrics.cpp.

Here is the caller graph for this function: