How can I improve calibration curves?

I am training a binary xgboost classifer with an imbalance of : 85% = 0 class and 14 % = class 1.

This was achieved after i took a random sample fromaround 11m to 1M.

When i calibrate i get the following:

It seems that using isotonic or sigmoid doesn't really improve the calbration much. Any idea how i can improve it?

    sig_clf = CalibratedClassifierCV(model, method=sigmoid, cv=prefit)
    iso_clf = CalibratedClassifierCV(model, method=isotonic, cv=prefit)

    sig_clf.fit(x_valid, y_valid)
    iso_clf.fit(x_valid, y_valid)
    prob_pos_sigmoid = sig_clf.predict_proba(x_test)[:, 1]
    prob_pos_iso = iso_clf.predict_proba(x_test)[:, 1]
    y_test_uncalibrated = model.predict_proba(x_test)[:, 1]

Above is what i use to plot the above graph. I have used 'prefit' since i calibrated after i have trained and fitted the model.