seldonian.parse_tree.zhat_funcs

Module containing the measure functions (zhats) for computing unbiased estimates of base variables

measure_function_vector_mapper: dict

Maps the measure function name that appears in behavioral constraint strings to the zhat function for that measure function.

Functions

batcher(func, N, batch_size, num_batches)	Calls func num_batches times, batching up the inputs.
evaluate_statistic(model, statistic_name, ...)	Evaluate the mean of a statistical function over the whole sample provided.
sample_from_statistic(model, statistic_name, ...)	Calculate a statistical function for each observation in the sample.
vector_Accuracy(model, theta, X, Y, **kwargs)	Calculate probabilistic accuracy for each observation.
vector_Error(model, theta, X, Y, **kwargs)	Calculate error (Y_hat - Y) for each observation in the dataset
vector_Error_Rate(model, theta, X, Y, **kwargs)	Calculate error rate for each observation.
vector_False_Negative_Rate(model, theta, X, ...)	Calculate false negative rate for each observation.
vector_False_Positive_Rate(model, theta, X, ...)	Calculate false positive rate for each observation.
vector_IS_estimate(model, theta, episodes, ...)	Calculate the unweighted importance sampling estimate on each episodes in the dataframe
vector_Negative_Rate(model, theta, X, Y, ...)	Calculate negative rate for each observation.
vector_PDIS_estimate(model, theta, episodes, ...)	Calculate per decision importance sampling estimate on each episodes in the dataframe
vector_Positive_Rate(model, theta, X, Y, ...)	Calculate positive rate for each observation.
vector_Squared_Error(model, theta, X, Y, ...)	Calculate squared error for each observation in the dataset
vector_True_Negative_Rate(model, theta, X, ...)	Calculate true negative rate for each observation.
vector_True_Positive_Rate(model, theta, X, ...)	Calculate true positive rate for each observation.
vector_WIS_estimate(model, theta, episodes, ...)	Calculate weighted importance sampling estimate on each episodes in the dataframe
vector_auxiliary_return_US_estimate(model, ...)	Get the auxiliary reward returns for episodes whose actions fall within the theta bounding box.
vector_confusion_matrix(model, theta, X, Y, ...)	Get the 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.