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expose strategy param for calibration curve #118

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expose strategy param for calibration curve #118

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2 changes: 1 addition & 1 deletion requirements.txt
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
matplotlib>=1.4.0
scikit-learn>=0.18
scikit-learn>=0.21
scipy>=0.9
joblib>=0.10
10 changes: 8 additions & 2 deletions scikitplot/metrics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -908,7 +908,7 @@ def plot_silhouette(X, cluster_labels, title='Silhouette Analysis',
return ax


def plot_calibration_curve(y_true, probas_list, clf_names=None, n_bins=10,
def plot_calibration_curve(y_true, probas_list, clf_names=None, n_bins=10, strategy="uniform",
title='Calibration plots (Reliability Curves)',
ax=None, figsize=None, cmap='nipy_spectral',
title_fontsize="large", text_fontsize="medium"):
Expand Down Expand Up @@ -938,6 +938,12 @@ def plot_calibration_curve(y_true, probas_list, clf_names=None, n_bins=10,
n_bins (int, optional): Number of bins. A bigger number requires more
data.

strategy (str, optional): Strategy used to define the widths of the bins.
uniform
The bins have identical widths.
quantile
The bins have the same number of samples and depend on `y_prob`.

title (string, optional): Title of the generated plot. Defaults to
"Calibration plots (Reliabilirt Curves)"

Expand Down Expand Up @@ -1024,7 +1030,7 @@ def plot_calibration_curve(y_true, probas_list, clf_names=None, n_bins=10,
probas = (probas - probas.min()) / (probas.max() - probas.min())

fraction_of_positives, mean_predicted_value = \
calibration_curve(y_true, probas, n_bins=n_bins)
calibration_curve(y_true, probas, n_bins=n_bins, strategy=strategy)

color = plt.cm.get_cmap(cmap)(float(i) / len(probas_list))

Expand Down