fixed reliability plot

local_app.py

@@ -58,7 +58,7 @@ metric = ECE()
 # module = evaluate.load("jordyvl/ece")
 # launch_gradio_widget(module)
 
-"""
+"""l
 Switch inputs and compute_fn
 """
 
@@ -105,7 +105,7 @@ def over_under_confidence(results):
 
 
 def reliability_plot(results):
-    #DEV: might still need to write tests in case of equal mass binning
+    # DEV: might still need to write tests in case of equal mass binning
     fig = plt.figure()
     ax1 = plt.subplot2grid((3, 1), (0, 0), rowspan=2)
     ax2 = plt.subplot2grid((3, 1), (2, 0))
@@ -118,8 +118,8 @@ def reliability_plot(results):
     # if upper edge then minus binsize; same for center [but half]
     # rwidth is dependent on the binning
     B, bins, patches = ax1.hist(
-        results["y_bar"], weights=results["p_bar"][:-1] #rwidth=len(results["p_bar"]/len(results["p_bar"]-1 )) #, range=(0,1),
-    )  # , rwidth=1, align="right") #    
+        results["y_bar"], weights=p.nan_to_num(results["p_bar"][:-1], copy=True, nan=0)
+    )
     colors = over_under_confidence(results)
     for b in range(len(B)):
         patches[b].set_facecolor(colors[b])  # color based on over/underconfidence
@@ -141,7 +141,7 @@ def reliability_plot(results):
     anindices = np.where(~np.isnan(results["p_bar"][:-1]))[0]
     bin_freqs = np.zeros(n_bins)
     bin_freqs[anindices] = results["bin_freq"]
-    ax2.hist(results["y_bar"], weights=bin_freqs, color="midnightblue") #bins=results["y_bar"], 
+    ax2.hist(results["y_bar"], bins=results["y_bar"], weights=bin_freqs, color="midnightblue")
 
     # DEV: nicer would be to plot like a polygon
     # see: https://github.com/markus93/fit-on-the-test/blob/main/Experiments_Synthetic/binnings.py
@@ -191,13 +191,13 @@ def compute_and_plot(data, n_bins, bin_range, scheme, proxy, p):
         p=p,
         detail=True,
     )
-
+    print(results)
     plot = reliability_plot(results)
     return results["ECE"], plot
 
 
 outputs = [gr.outputs.Textbox(label="ECE"), gr.Plot(label="Reliability diagram")]
-# outputs[1].value = default_plot().__dict__
+# outputs[1].value = default_plot().__dict__ #Does not work; yet needs to be JSON encoded
 
 iface = gr.Interface(
     fn=compute_and_plot,