plot update

TODO.txt

@@ -6,3 +6,4 @@
 - [TODO] add Friedman's method and DeBias
 - [TODO] check ignore warning stuff
     check https://docs.python.org/3/library/warnings.html#temporarily-suppressing-warnings
+- [TODO] nmd and md are not selectable from qp.evaluation.evaluate as a string

quapy/plot.py

@@ -568,3 +568,39 @@ def _join_data_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, x_error
             method_order.append(method)
 
     return data
+
+
+def calibration_plot(prob_classifier, X, y, nbins=10, savepath=None):
+    posteriors = prob_classifier.predict_proba(X)
+    assert posteriors.ndim==2, 'calibration plot only works for binary problems'
+    posteriors = posteriors[:,1]
+    pred_y = posteriors>=0.5
+    bins = np.linspace(0, 1, nbins + 1)
+    binned_values = np.digitize(posteriors, bins, right=False)
+    print(np.unique(binned_values))
+    correct = pred_y == y
+    bin_centers = (bins[:-1] + bins[1:]) / 2
+    bins_names = np.arange(nbins)
+    y_axis = [correct[binned_values==bin].mean() for bin in bins_names]
+    y_axis = [v if not np.isnan(v) else 0 for v in y_axis]
+    # Crear el gráfico de barras
+    plt.bar(bin_centers, y_axis, width=bins[1]-bins[0], edgecolor='black', alpha=0.7)
+
+    # Etiquetas y título
+    plt.xlabel("Bin")
+    plt.ylabel("Value")
+    plt.title("Bar plot of calculated values per bin")
+    plt.xticks(bin_centers, [f"{b:.2f}" for b in bin_centers], rotation=45)
+
+    # Mostrar el gráfico
+    plt.tight_layout()
+    plt.show()
+
+if __name__ == '__main__':
+    import quapy as qp
+    from sklearn.linear_model import LogisticRegression
+    data = qp.datasets.fetch_UCIBinaryDataset(qp.datasets.UCI_BINARY_DATASETS[6])
+    train, test = data.train_test
+    classifier = LogisticRegression()
+    classifier.fit(*train.Xy)
+    calibration_plot(classifier, *test.Xy)