inferpy.criticism package¶
Submodules¶
inferpy.criticism.evaluate module¶
Module with the functionality for evaluating the InferPy models
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inferpy.criticism.evaluate.
ALLOWED_METRICS
= ['binary_accuracy', 'categorical_accuracy', 'sparse_categorical_accuracy', 'log_loss', 'binary_crossentropy', 'categorical_crossentropy', 'sparse_categorical_crossentropy', 'hinge', 'squared_hinge', 'mse', 'MSE', 'mean_squared_error', 'mae', 'MAE', 'mean_absolute_error', 'mape', 'MAPE', 'mean_absolute_percentage_error', 'msle', 'MSLE', 'mean_squared_logarithmic_error', 'poisson', 'cosine', 'cosine_proximity', 'log_lik', 'log_likelihood']¶ List with all the allowed metrics for evaluation
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inferpy.criticism.evaluate.
evaluate
(metrics, data, n_samples=500, output_key=None, seed=None)[source]¶ Evaluate a fitted inferpy model using a set of metrics. This function encapsulates the equivalent Edward one.
Parameters: metrics – list of str indicating the metrics or sccore functions to be used. An example of use:
# evaluate the predicted data y=y_pred given that x=x_test mse = inf.evaluate('mean_squared_error', data={x: x_test, y: y_pred}, output_key=y)
Returns: A list of evaluations or a single evaluation. Return type: list of float or float Raises: NotImplementedError
– If an input metric does not match an implemented metric.