6cda6f735d04adb96866c64f18c28d57d79ee535,rankeval/analysis/statistical.py,,_multi_kfold_scoring,#Any#Any#Any#Any#,222

Before Change

    score : numpy.ndarray
        A matrix num_instances x L.
    
    progress_bar = IntProgress(min=0, max=L, description="Computing L scores")
    display(progress_bar)    

    scores = np.zeros( (dataset.n_instances, L), dtype=np.float32)

    for l in range(L):
        progress_bar.value += 1

        scores[:,l] = _kfold_scoring(dataset, k, algo)

    progress_bar.bar_style = "success"
    progress_bar.close()
    
    return scores

After Change

    score : numpy.ndarray
        A matrix num_instances x L.
    
    if ipywidgets:
        progress_bar = ipywidgets.IntProgress(
            min=0, max=L, description="Computing L scores")
        display(progress_bar)

    scores = np.zeros( (dataset.n_instances, L), dtype=np.float32)

    for l in range(L):
        if ipywidgets:
            progress_bar.value += 1

        scores[:,l] = _kfold_scoring(dataset, k, algo)

    if ipywidgets:
        progress_bar.bar_style = "success"
        progress_bar.close()
    
    return scores


def bias_variance(datasets=[], algos=[], metrics=[], L=10, k=2):

Instances