nussl/evaluation/report_card.py
def associate_metrics(separation_model, df, test_dataset):
    """
    For a given pandas dataframe (the output of ``aggregate_score_files``), this
    function will associate the high level summary statistics with a model.

    Args:
        separation_model (SeparationModel): A separation object that will have the metrics
            associated with it.
        df (pandas.DataFrame): DataFrame containing the metrics computed during
            evaluation.
        test_dataset (BaseDataset): A dataset object used for the evaluation of the
            metrics.

    Returns:
        (SeparationBase)
    """
    excluded_columns = ['source', 'file']
    metrics = [x for x in list(df.columns) if x not in excluded_columns]
    results = {
        m: {
            'mean': np.mean(df[m]),
            'median': np.median(df[m]),
            'std': np.std(df[m])
        }
        for m in metrics
    }
    separation_model.metadata['evaluation'] = results
    separation_model.metadata['test_dataset'] = copy.deepcopy(test_dataset.metadata)
    return separation_model

    For a given pandas dataframe (the output of ``aggregate_score_files``), this
    function will associate the high level summary statistics with a model.

    Args:
        separation_model (SeparationModel): A separation object that will have the metrics
            associated with it.
        df (pandas.DataFrame): DataFrame containing the metrics computed during
            evaluation.
        test_dataset (BaseDataset): A dataset object used for the evaluation of the
            metrics.

    Returns:
        (SeparationBase)
    
For a given pandas dataframe (the output of ``aggregate_score_files``), this
function will associate the high level summary statistics with a model.
For a given pandas dataframe ( the output of ` ` aggregate_score_files ` ` ) this function will associate the high level summary statistics with a model .
def associate_metrics(separation_model, df, test_dataset):
    excluded_columns = ['source', 'file']
    metrics = [x for x in list(df.columns) if x not in excluded_columns]
    results = {
        m: {
            'mean': np.mean(df[m]),
            'median': np.median(df[m]),
            'std': np.std(df[m])
        }
        for m in metrics
    }
    separation_model.metadata['evaluation'] = results
    separation_model.metadata['test_dataset'] = copy.deepcopy(test_dataset.metadata)
    return separation_model
def associate_metrics ( separation_model , df , test_dataset ) : excluded_columns = [ 'source' , 'file' ] metrics = [ x for x in list ( df . columns ) if x not in excluded_columns ] results = { m : { 'mean' : np . mean ( df [ m ] ) , 'median' : np . median ( df [ m ] ) , 'std' : np . std ( df [ m ] ) } for m in metrics } separation_model . metadata [ 'evaluation' ] = results separation_model . metadata [ 'test_dataset' ] = copy . deepcopy ( test_dataset . metadata ) return separation_model
For a given pandas dataframe (the output of ``aggregate_score_files``), this
function will associate the high level summary statistics with a model.
For a given pandas dataframe ( the output of ` ` aggregate_score_files ` ` ) this function will associate the high level summary statistics with a model .
"""
    For a given pandas dataframe (the output of ``aggregate_score_files``), this
    function will associate the high level summary statistics with a model.

    Args:
        separation_model (SeparationModel): A separation object that will have the metrics
            associated with it.
        df (pandas.DataFrame): DataFrame containing the metrics computed during
            evaluation.
        test_dataset (BaseDataset): A dataset object used for the evaluation of the
            metrics.

    Returns:
        (SeparationBase)
    """
None
separation_model
A separation object that will have the metrics
associated with it.
A separation object that will have the metrics associated with it .
df
DataFrame containing the metrics computed during
evaluation.
DataFrame containing the metrics computed during evaluation .
test_dataset
A dataset object used for the evaluation of the
metrics.
A dataset object used for the evaluation of the metrics .