Merge pull request #108 from PythonPredictions/issue-#32-improve-PIGs

issue-#32-improve-PIGs

Author: Sam Borms

cobra/evaluation/pigs_tables.py

@@ -147,9 +147,9 @@ def plot_incidence(pig_tables: pd.DataFrame,
     with plt.style.context("seaborn-ticks"):
         fig, ax = plt.subplots(figsize=dim)
 
-        # -----------------
+        # --------------------------
         # Left axis - average target
-        # -----------------
+        # --------------------------
         ax.plot(df_plot['label'], df_plot['avg_target'],
                 color="#00ccff", marker=".",
                 markersize=20, linewidth=3,
@@ -168,7 +168,9 @@ def plot_incidence(pig_tables: pd.DataFrame,
         ax.set_ylabel('incidence' if model_type == "classification" else "mean target value",
                       fontsize=16)
         ax.set_xlabel('{} bins' ''.format(variable), fontsize=16)
-        ax.xaxis.set_tick_params(rotation=45, labelsize=14)
+        ax.xaxis.set_tick_params(labelsize=14)
+        plt.setp(ax.get_xticklabels(),
+                 rotation=45, ha="right", rotation_mode="anchor")
         ax.yaxis.set_tick_params(labelsize=14)
 
         if model_type == "classification":
@@ -177,6 +179,24 @@ def plot_incidence(pig_tables: pd.DataFrame,
             ax.set_yticks(np.arange(0, max(df_plot['avg_target'])+0.05, 0.05))
             ax.yaxis.set_major_formatter(
                 FuncFormatter(lambda y, _: '{:.1%}'.format(y)))
+        elif model_type == "regression":
+            # If both the difference between the highest avg target of all bins
+            # versus the global avg target AND the difference between the
+            # lowest avg target versus the global avg target are both smaller
+            # than 25% of the global avg target itself, we increase the y
+            # axis range, to avoid that the minor avg target differences are
+            # spread out over the configure figure height, suggesting
+            # incorrectly that there are big differences in avg target across
+            # the bins and versus the global avg target.
+            # (Motivation for the AND above: if on one end there IS enough
+            # difference, the effect that we discuss here does not occur.)
+            global_avg_target = max(df_plot['global_avg_target']) # series of same number, for every bin.
+            if (np.abs((max(df_plot['avg_target']) - global_avg_target))
+                    / global_avg_target < 0.25) \
+                and (np.abs((min(df_plot['avg_target']) - global_avg_target))
+                    / global_avg_target < 0.25):
+                ax.set_ylim(global_avg_target * 0.75,
+                            global_avg_target * 1.25)
 
         # Remove ticks but keep the labels
         ax.tick_params(axis='both', which='both', length=0)
@@ -192,13 +212,13 @@ def plot_incidence(pig_tables: pd.DataFrame,
                 align='center', color="#939598", zorder=1)
 
         # Set labels & ticks
-        ax2.set_ylabel('population size', fontsize=16)
         ax2.set_xlabel('{} bins' ''.format(variable), fontsize=16)
         ax2.xaxis.set_tick_params(rotation=45, labelsize=14)
+
         ax2.yaxis.set_tick_params(labelsize=14)
         ax2.yaxis.set_major_formatter(
             FuncFormatter(lambda y, _: '{:.1%}'.format(y)))
-
+        ax2.set_ylabel('population size', fontsize=16)
         ax2.tick_params(axis='y', colors="#939598")
         ax2.yaxis.label.set_color('#939598')