updates for equal mass binning

ece.py

@@ -103,8 +103,8 @@ def create_bins(n_bins=10, scheme="equal-range", bin_range=None, P=None):
 
 def discretize_into_bins(P, bins):
 
-    contains_rightmost = bool(bins[-1] > 1) #outlier bins
-    contains_leftmost = bool(bins[0] == 0) #beyond [before] bin_range[0]
+    contains_rightmost = bool(bins[-1] > 1)  # outlier bins
+    contains_leftmost = bool(bins[0] == 0)  # beyond [before] bin_range[0]
     # bins_with_left_edge = np.insert(bins, 0, 0, axis=0)
 
     oneDbins = np.digitize(
@@ -143,7 +143,7 @@ def discretize_into_bins(P, bins):
 def manual_binned_statistic(P, y_correct, bins, statistic="mean"):
     bin_assignments = discretize_into_bins(np.expand_dims(P, 0), bins)[0]
 
-    # indexed as in julia! 
+    # indexed as in julia!
     result = np.empty([len(bins)], float)
     result.fill(np.nan)  # cannot assume each bin will have observations
 
@@ -160,8 +160,10 @@ def manual_binned_statistic(P, y_correct, bins, statistic="mean"):
 def bin_calibrated_accuracy(bins, proxy="upper-edge"):
     assert proxy in ["center", "upper-edge"], f"Unsupported proxy{proxy}"
 
+    contains_leftmost = bool(bins[0] == 0)  # beyond [before] bin_range[0]
+
     if proxy == "upper-edge":
-        return bins[1:]
+        return bins[1:] if contains_leftmost else bins
 
     if proxy == "center":
         return bins[:-1] + np.diff(bins) / 2
@@ -175,7 +177,7 @@ def CE_estimate(y_correct, P, bins=None, p=1, proxy="upper-edge", detail=False):
     Summary: weighted average over the accuracy/confidence difference of discrete bins of prediction probability
     """
 
-    n_bins = len(bins) - 1 #true number of bins
+    n_bins = len(bins) - 1  # true number of bins
     bin_range = [min(bins), max(bins)]
 
     # average bin probability #55 for bin 50-60, mean per bin; or right/upper bin edges
@@ -185,9 +187,6 @@ def CE_estimate(y_correct, P, bins=None, p=1, proxy="upper-edge", detail=False):
     bin_numbers, weights_ece = np.unique(bin_assignment, return_counts=True)
     anindices = bin_numbers - 1  # reduce bin counts; left edge; indexes right by default
 
-    import pdb; pdb.set_trace()  # breakpoint 83c9148b //
-
-
     # Expected calibration error
     if p < np.inf:  # L^p-CE
         CE = np.average(