nussl/evaluation/evaluation_base.py
def evaluate_helper(self, references, estimates, **kwargs):
        """
        This function should be implemented by each class that inherits this
        class. The function should take in a numpy array containing the references and 
        one for the estimates and compute evaluation measures between the two arrays. 
        The results should be stored in a list of dictionaries. For example, a 
        BSSEval evaluator may return a dictionary as follows, for a single estimate:

        .. code-block:: none

                           #or windows or both
            [   {          #ch0  ch1   # results for first estimate
                    "SDR": [5.6, 5.2], # metric
                    "SIR": [9.2, 8.9], # metric
                    "SAR": [4.1, 4.3]  # metric
                }, 
                ...                    # more results for other estimates
            ]

        Each metric should be a key in the dictionary pointing to a value which is a
        list. The list will contain the metrics for however the algorithm was implemented 
        (e.g. there might be two value, one for each channel in a stereo mix, or there
        might be a sequence, one for each window that was evaluated.)
        
        Args:
            references (np.ndarray): References kept in a numpy array. Should have shape
                (..., n_channels, n_sources).
            estimates (np.ndarray): Estimates kept in whatever format you want. Should have
                shape (..., n_channels, n_sources).
            kwargs (dict): Keyword arguments with any additional arguments to be used in 
                the function (e.g. window_size, hop_length).
        
        Returns:
            A list of dictionary containing the measures corresponding to each estimate
            and reference.
        """

        raise NotImplementedError('Must implement evaluate_helper in a subclass!')

        This function should be implemented by each class that inherits this
        class. The function should take in a numpy array containing the references and 
        one for the estimates and compute evaluation measures between the two arrays. 
        The results should be stored in a list of dictionaries. For example, a 
        BSSEval evaluator may return a dictionary as follows, for a single estimate:

        .. code-block:: none

                           #or windows or both
            [   {          #ch0  ch1   # results for first estimate
                    "SDR": [5.6, 5.2], # metric
                    "SIR": [9.2, 8.9], # metric
                    "SAR": [4.1, 4.3]  # metric
                }, 
                ...                    # more results for other estimates
            ]

        Each metric should be a key in the dictionary pointing to a value which is a
        list. The list will contain the metrics for however the algorithm was implemented 
        (e.g. there might be two value, one for each channel in a stereo mix, or there
        might be a sequence, one for each window that was evaluated.)
        
        Args:
            references (np.ndarray): References kept in a numpy array. Should have shape
                (..., n_channels, n_sources).
            estimates (np.ndarray): Estimates kept in whatever format you want. Should have
                shape (..., n_channels, n_sources).
            kwargs (dict): Keyword arguments with any additional arguments to be used in 
                the function (e.g. window_size, hop_length).
        
        Returns:
            A list of dictionary containing the measures corresponding to each estimate
            and reference.
        
This function should be implemented by each class that inherits this
class. The function should take in a numpy array containing the references and
one for the estimates and compute evaluation measures between the two arrays.
The results should be stored in a list of dictionaries. For example, a
BSSEval evaluator may return a dictionary as follows, for a single estimate.

code-block:: none



Each metric should be a key in the dictionary pointing to a value which is a
list. The list will contain the metrics for however the algorithm was implemented
This function should be implemented by each class that inherits this class . The function should take in a numpy array containing the references and one for the estimates and compute evaluation measures between the two arrays . The results should be stored in a list of dictionaries . For example a BSSEval evaluator may return a dictionary as follows for a single estimate . code - block :: none Each metric should be a key in the dictionary pointing to a value which is a list . The list will contain the metrics for however the algorithm was implemented
def evaluate_helper(self, references, estimates, **kwargs):
        raise NotImplementedError('Must implement evaluate_helper in a subclass!')
def evaluate_helper ( self , references , estimates , ** kwargs ) : raise NotImplementedError ( 'Must implement evaluate_helper in a subclass!' )
This function should be implemented by each class that inherits this
class.
This function should be implemented by each class that inherits this class .
"""
        This function should be implemented by each class that inherits this
        class. The function should take in a numpy array containing the references and 
        one for the estimates and compute evaluation measures between the two arrays. 
        The results should be stored in a list of dictionaries. For example, a 
        BSSEval evaluator may return a dictionary as follows, for a single estimate:

        .. code-block:: none

                           #or windows or both
            [   {          #ch0  ch1   # results for first estimate
                    "SDR": [5.6, 5.2], # metric
                    "SIR": [9.2, 8.9], # metric
                    "SAR": [4.1, 4.3]  # metric
                }, 
                ...                    # more results for other estimates
            ]

        Each metric should be a key in the dictionary pointing to a value which is a
        list. The list will contain the metrics for however the algorithm was implemented 
        (e.g. there might be two value, one for each channel in a stereo mix, or there
        might be a sequence, one for each window that was evaluated.)
        
        Args:
            references (np.ndarray): References kept in a numpy array. Should have shape
                (..., n_channels, n_sources).
            estimates (np.ndarray): Estimates kept in whatever format you want. Should have
                shape (..., n_channels, n_sources).
            kwargs (dict): Keyword arguments with any additional arguments to be used in 
                the function (e.g. window_size, hop_length).
        
        Returns:
            A list of dictionary containing the measures corresponding to each estimate
            and reference.
        """
A list of dictionary containing the measures corresponding to each estimate
and reference.
A list of dictionary containing the measures corresponding to each estimate and reference .
self
references
References kept in a numpy array. Should have shape
(..., n_channels, n_sources).
References kept in a numpy array . Should have shape ( ... n_channels n_sources ) .
estimates
Estimates kept in whatever format you want. Should have
shape (..., n_channels, n_sources).
Estimates kept in whatever format you want . Should have shape ( ... n_channels n_sources ) .
kwargs
Keyword arguments with any additional arguments to be used in
the function .
Keyword arguments with any additional arguments to be used in the function .