v1.2.0

v1.2.0 by @elsacho: Added new proper loss functions:

• ProperLpLoss(p=p): Metrics to evaluate $E[ \Vert f(X) - E[Y|f(X)] \Vert_p ]$ where $f(X)$ are the
  predictions of the classifier, $p >= 1$, including p=float("inf")
• TopClassLoss: A wrapper to variationally evaluate top-class errors.
• OverConfidenceLoss & UnderConfidenceLoss: Wrappers to variationally evaluate
  over/under-confidence in binary predictors.
• MetricsWithCalibration can now handle arbitrary classifiers and Lp-type losses.
• New classifiers: Added WS_CatboostClassifier and WS_LGBMClassifier for
  evaluating calibration errors.
• removed sklearn < 1.7 constraint.