コード例 #1
0
def compute_theil_on_bins(y_pred, mask, bin_indices, target_efficiencies,
                          sample_weight):
    y_pred = column_or_1d(y_pred)
    sample_weight = check_sample_weight(y_pred, sample_weight=sample_weight)

    # ignoring events from other classes
    y_pred = y_pred[mask]
    bin_indices = bin_indices[mask]
    sample_weight = sample_weight[mask]

    bin_weights = compute_bin_weights(bin_indices=bin_indices,
                                      sample_weight=sample_weight)
    cuts = compute_cut_for_efficiency(target_efficiencies,
                                      mask=numpy.ones(len(y_pred), dtype=bool),
                                      y_pred=y_pred,
                                      sample_weight=sample_weight)
    result = 0.
    for cut in cuts:
        bin_efficiencies = compute_bin_efficiencies(
            y_pred,
            bin_indices=bin_indices,
            cut=cut,
            sample_weight=sample_weight)
        result += theil(bin_efficiencies, weights=bin_weights)
    return result / len(cuts)
コード例 #2
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def compute_sde_on_bins(y_pred,
                        mask,
                        bin_indices,
                        target_efficiencies,
                        power=2.,
                        sample_weight=None):
    # ignoring events from other classes
    sample_weight = check_sample_weight(y_pred, sample_weight=sample_weight)
    y_pred = y_pred[mask]
    bin_indices = bin_indices[mask]
    sample_weight = sample_weight[mask]

    bin_weights = compute_bin_weights(bin_indices=bin_indices,
                                      sample_weight=sample_weight)
    cuts = compute_cut_for_efficiency(target_efficiencies,
                                      mask=numpy.ones(len(y_pred), dtype=bool),
                                      y_pred=y_pred,
                                      sample_weight=sample_weight)

    result = 0.
    for cut in cuts:
        bin_efficiencies = compute_bin_efficiencies(
            y_pred,
            bin_indices=bin_indices,
            cut=cut,
            sample_weight=sample_weight)
        result += weighted_deviation(bin_efficiencies,
                                     weights=bin_weights,
                                     power=power)

    return (result / len(cuts))**(1. / power)
コード例 #3
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def compute_theil_on_bins(y_pred, mask, bin_indices, target_efficiencies, sample_weight):
    y_pred = column_or_1d(y_pred)
    sample_weight = check_sample_weight(y_pred, sample_weight=sample_weight)

    # ignoring events from other classes
    y_pred = y_pred[mask]
    bin_indices = bin_indices[mask]
    sample_weight = sample_weight[mask]

    bin_weights = compute_bin_weights(bin_indices=bin_indices, sample_weight=sample_weight)
    cuts = compute_cut_for_efficiency(target_efficiencies, mask=numpy.ones(len(y_pred), dtype=bool),
                                      y_pred=y_pred, sample_weight=sample_weight)
    result = 0.
    for cut in cuts:
        bin_efficiencies = compute_bin_efficiencies(y_pred, bin_indices=bin_indices,
                                                    cut=cut, sample_weight=sample_weight)
        result += theil(bin_efficiencies, weights=bin_weights)
    return result / len(cuts)
コード例 #4
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def compute_sde_on_bins(y_pred, mask, bin_indices, target_efficiencies, power=2., sample_weight=None):
    # ignoring events from other classes
    sample_weight = check_sample_weight(y_pred, sample_weight=sample_weight)
    y_pred = y_pred[mask]
    bin_indices = bin_indices[mask]
    sample_weight = sample_weight[mask]

    bin_weights = compute_bin_weights(bin_indices=bin_indices, sample_weight=sample_weight)
    cuts = compute_cut_for_efficiency(target_efficiencies, mask=numpy.ones(len(y_pred), dtype=bool),
                                      y_pred=y_pred, sample_weight=sample_weight)

    result = 0.
    for cut in cuts:
        bin_efficiencies = compute_bin_efficiencies(y_pred, bin_indices=bin_indices,
                                                    cut=cut, sample_weight=sample_weight)
        result += weighted_deviation(bin_efficiencies, weights=bin_weights, power=power)

    return (result / len(cuts)) ** (1. / power)