seldonian.models.objectives

Objective functions

Functions

Error_Rate(model, theta, X, Y, **kwargs)	Calculate mean error rate over the whole sample.
False_Negative_Rate(model, theta, X, Y, **kwargs)	Calculate probabilistic mean false negative rate over the whole sample.
False_Positive_Rate(model, theta, X, Y, **kwargs)	Calculate probabilistic mean false positive rate over the whole sample.
IS_estimate(model, theta, episodes, **kwargs)	Calculate the vanilla importance sampling estimate using all episodes.
Mean_Error(model, theta, X, Y, **kwargs)	Calculate mean error (y_hat-y) over the whole sample
Mean_Squared_Error(model, theta, X, Y, **kwargs)	Calculate mean squared error over the whole sample
Negative_Rate(model, theta, X, Y, **kwargs)	Calculate mean negative rate for the whole sample.
PDIS_estimate(model, theta, episodes, **kwargs)	Calculate per-decision importance sampling (PDIS) estimate using all episodes.
Positive_Rate(model, theta, X, Y, **kwargs)	Calculate mean positive rate for the whole sample.
True_Negative_Rate(model, theta, X, Y, **kwargs)	Calculate mean true negative rate for the whole sample.
True_Positive_Rate(model, theta, X, Y, **kwargs)	Calculate mean true positive rate for the whole sample.
US_estimate(model, theta, episodes, **kwargs)	Get the expected return of the PRIMARY reward for behavior episodes whose actions (cr,cf) fall within the theta bounding box.
WIS_estimate(model, theta, episodes, **kwargs)	Calculate the weighted importance sampling (WIS) estimate using all episodes.
binary_logistic_loss(model, theta, X, Y, ...)	Calculate mean logistic loss over all data points for binary classification
confusion_matrix(model, theta, X, Y, l_i, ...)	Get the mean probability of predicting class label l_k if the true class label was l_i. This is the C[l_i,l_k] element of the confusion matrix, C. Let: i = number of datapoints j = number of features (including bias term, if provied) k = number of classes.
gradient_Bounded_Squared_Error(model, theta, ...)	Analytical gradient of the bounded squared error (BSE)
gradient_Mean_Squared_Error(model, theta, X, ...)	Gradient of the mean squared error w.r.t.
gradient_binary_logistic_loss(model, theta, ...)	Gradient of binary logistic loss w.r.t.
multiclass_logistic_loss(model, theta, X, Y, ...)	Calculate mean logistic loss over all data points for multi-class classification