niftynet.evaluation.regression_evaluations module
This module defines built-in evaluation functions for regression applications
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class
BaseRegressionEvaluation
(reader, app_param, eval_param)[source]
Bases: niftynet.evaluation.base_evaluations.BaseEvaluation
Interface for scalar regression metrics
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layer_op
(subject_id, data)[source]
Perform one evaluation calculation for one subject
:param subject_id: subject identifier string
:param data: a data dictionary as built by ImageReader
:return: a list of pandas.DataFrame objects
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metric
(reg, ref)[source]
Computes a scalar value for the metric
:param reg: np.array with inferred regression
:param ref: np array with the reference output
:return: scalar metric value
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class
mse
(reader, app_param, eval_param)[source]
Bases: niftynet.evaluation.regression_evaluations.BaseRegressionEvaluation
Computes mean squared error
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metric
(reg, ref)[source]
Computes a scalar value for the metric
:param reg: np.array with inferred regression
:param ref: np array with the reference output
:return: scalar metric value
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class
rmse
(reader, app_param, eval_param)[source]
Bases: niftynet.evaluation.regression_evaluations.BaseRegressionEvaluation
Computes root mean squared error
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metric
(reg, ref)[source]
Computes a scalar value for the metric
:param reg: np.array with inferred regression
:param ref: np array with the reference output
:return: scalar metric value
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class
mae
(reader, app_param, eval_param)[source]
Bases: niftynet.evaluation.regression_evaluations.BaseRegressionEvaluation
Computes mean absolute error
-
metric
(reg, ref)[source]
Computes a scalar value for the metric
:param reg: np.array with inferred regression
:param ref: np array with the reference output
:return: scalar metric value