analysis: Allow optional annotation of plot_tradeoff graphs

EwoutH · EwoutH · commit aed87b5e671a · 2022-05-12T16:05:02.000+02:00
Allow to annotate the points of plot_tradeoff graphs (which use peeling_trajectory DataFrames) with id labels.

This makes it easy to quickly identify points on the tradeoff graphs which look interesting. Works best with large graphs (using "fig.set_size_inches").
diff --git a/ema_workbench/analysis/logistic_regression.py b/ema_workbench/analysis/logistic_regression.py
@@ -254,20 +254,21 @@ def update(self, model, selected):
             new_row, ignore_index=True, sort=True
         )
 
-    def show_tradeoff(self, cmap=mpl.cm.viridis):  # @UndefinedVariable
+    def show_tradeoff(self, cmap=mpl.cm.viridis, annotated=False):  # @UndefinedVariable
         """Visualize the trade off between coverage and density. Color
         is used to denote the number of restricted dimensions.
 
         Parameters
         ----------
         cmap : valid matplotlib colormap
+        annotated : bool, optional. Shows point labels if True.
 
         Returns
         -------
         a Figure instance
 
         """
-        return sdutil.plot_tradeoff(self.peeling_trajectory, cmap=cmap)
+        return sdutil.plot_tradeoff(self.peeling_trajectory, cmap=cmap, annotated=annotated)
 
     # @UndefinedVariable
     def show_threshold_tradeoff(self, i, cmap=mpl.cm.viridis_r, step=0.1):
diff --git a/ema_workbench/analysis/prim.py b/ema_workbench/analysis/prim.py
@@ -805,20 +805,21 @@ def show_ppt(self):
         """show the peeling and pasting trajectory in a figure"""
         return sdutil.plot_ppt(self.peeling_trajectory)
 
-    def show_tradeoff(self, cmap=mpl.cm.viridis):  # @UndefinedVariable
+    def show_tradeoff(self, cmap=mpl.cm.viridis, annotated=False):  # @UndefinedVariable
         """Visualize the trade off between coverage and density. Color
         is used to denote the number of restricted dimensions.
 
         Parameters
         ----------
         cmap : valid matplotlib colormap
+        annotated : bool, optional. Shows point labels if True.
 
         Returns
         -------
         a Figure instance
 
         """
-        return sdutil.plot_tradeoff(self.peeling_trajectory, cmap=cmap)
+        return sdutil.plot_tradeoff(self.peeling_trajectory, cmap=cmap, annotated=annotated)
 
     def show_pairs_scatter(self, i=None, dims=None, cdf=False):
         """Make a pair wise scatter plot of all the restricted
diff --git a/ema_workbench/analysis/scenario_discovery_util.py b/ema_workbench/analysis/scenario_discovery_util.py
@@ -648,13 +648,14 @@ def plot_ppt(peeling_trajectory):
     return fig
 
 
-def plot_tradeoff(peeling_trajectory, cmap=mpl.cm.viridis):  # @UndefinedVariable
+def plot_tradeoff(peeling_trajectory, cmap=mpl.cm.viridis, annotated=False):  # @UndefinedVariable
     """Visualize the trade off between coverage and density. Color
     is used to denote the number of restricted dimensions.
 
     Parameters
     ----------
     cmap : valid matplotlib colormap
+    annotated : bool, optional. Shows point labels if True.
 
     Returns
     -------
@@ -681,6 +682,10 @@ def plot_tradeoff(peeling_trajectory, cmap=mpl.cm.viridis):  # @UndefinedVariabl
     ax.set_ylim(bottom=0, top=1.2)
     ax.set_xlim(left=0, right=1.2)
 
+    if annotated:
+        for idx, row in peeling_trajectory.iterrows():
+            ax.annotate(row['id'], (row['coverage'], row['density']))
+
     ticklocs = np.arange(0, max(peeling_trajectory["res_dim"]) + 1, step=1)
     cb = fig.colorbar(p, spacing="uniform", ticks=ticklocs, drawedges=True)
     cb.set_label("nr. of restricted dimensions")
