Improve exception handling

calibration.py

@@ -1,3 +1,4 @@
+import logging
 import numpy as np
 
 
@@ -20,7 +21,18 @@ def calibration_curve(y_true, y_prob, *, n_bins=5, strategy="uniform"):
             "must be either 'quantile' or 'uniform'."
         )
 
+    try:
         binids = np.digitize(y_prob, bins) - 1
+    except Exception as e:
+        np.set_printoptions(threshold=sys.maxsize)
+        logging.info("=" * 40)
+        logging.info(f"n_bins={n_bins}, strategy={strategy}")
+        logging.info(f"y_true={repr(y_prob)}")
+        logging.info(f"bins={repr(bins)}")
+        logging.info("=" * 40)
+
+        raise e
+
 
     bin_sums = np.bincount(binids, weights=y_prob, minlength=len(bins))
     bin_true = np.bincount(binids, weights=y_true, minlength=len(bins))

strmlt.py

@@ -165,24 +165,14 @@ if __name__ == "__main__":
 
     # ---
 
-    for strategy in ['uniform', 'quantile']:
-        for n_bins in range(1, 300):
     try:
         fig = plotly_calibration(y_true, y_pred, n_bins=n_bins, strategy=strategy)
-                # st.plotly_chart(fig, use_container_width=True)
+        st.plotly_chart(fig, use_container_width=True)
 
         fig = plotly_calibration_odds(y_true, y_pred, n_bins=n_bins, strategy=strategy)
-                # st.plotly_chart(fig, use_container_width=True)
-                
-            except:
-                st.warning("Hey! Unfortunately, there occured a mysterious error, which I haven't been able to reproduce locally. Try refreshing the page or changing the number of bins a bit. <3")
-
-                np.set_printoptions(threshold=sys.maxsize)
-                logging.info("=" * 40)
-                logging.info(f"n_bins={n_bins}, strategy={strategy}")
-                logging.info(f"true={repr(y_true)}")
-                logging.info(f"y_pred={repr(y_pred)}")
-                logging.info("=" * 40)
+        st.plotly_chart(fig, use_container_width=True)
+    except Exception as e:
+        st.warning("Hey! Unfortunately, a very mysterious error occured. Try refreshing the page or changing the number of bins a bit.")
 
     # overconf = overconfidence(y_true, y_pred)
     # st.write(f"Your over/under- confidence score is {overconf:.2f}.")