Deep learning model calibration using Platt scaling approach via sklearn

platt-model-calibration.py

from sklearn.linear_model import LogisticRegression as LR

y_train = train_results[class_labels].values
pred_train = train_results[pred_labels].values
pred_calibrated = np.zeros_like(pred)

for i in range(len(class_labels)):
lr = LR(solver='liblinear', max_iter=10000)
lr.fit(pred_train[:, i].reshape(-1, 1), y_train[:, i])
pred_calibrated[:, i] = lr.predict_proba(pred[:, i].reshape(-1, 1))[:,1]