Added a title argument to the two result plots.

coba/experiments/results.py

@@ -645,7 +645,7 @@ def plot(self,
 
             ax.autoscale(axis='both')
 
-            ax.set_title(title, loc='left', pad=15)
+            ax.set_title(title, loc='left')
             ax.set_ylabel(ylabel)
             ax.set_xlabel(xlabel)
 
@@ -1002,6 +1002,7 @@ def plot_learners(self,
         errevery: int = None,
         labels  : Sequence[str] = None,
         colors  : Union[int,Sequence[Union[str,int]]] = None,
+        title   : str = None,
         xlabel  : str = None,
         ylabel  : str = None,
         xlim    : Tuple[Optional[Number],Optional[Number]] = None,
@@ -1028,6 +1029,7 @@ def plot_learners(self,
             errevery: This determines the frequency of errorbars. If `None` they appear 5% of the time.
             labels: The legend labels to use in the plot. These should be in order of the actual legend labels.
             colors: The colors used to plot the learners plot.
+            title : The title give the plot.
             xlabel: The label on the x-axis.
             ylabel: The label on the y-axis.
             xlim: Define the x-axis limits to plot. If `None` the x-axis limits will be inferred.
@@ -1073,7 +1075,7 @@ def plot_learners(self,
             y_location = "Total" if x != 'index' else ""
             y_avg_type = ("Instant" if span == 1 else f"Span {span}" if span else "Progressive")
             y_samples  = f"({len(Y)} Environments)"
-            title      = ' '.join(filter(None,[y_location, y_avg_type, ylabel, y_samples]))
+            title      = title if title is not None else (' '.join(filter(None,[y_location, y_avg_type, ylabel, y_samples])))
 
             xrotation = 90 if x != 'index' and len(lines[0].X)>5 else 0
             yrotation = 0
@@ -1101,6 +1103,7 @@ def plot_contrast(self,
         errevery: int = None,
         labels  : Sequence[str] = None,
         colors  : Sequence[str] = None,
+        title   : str = None,
         xlabel  : str = None,
         ylabel  : str = None,
         xlim    : Tuple[Optional[Number],Optional[Number]] = None,
@@ -1129,6 +1132,7 @@ def plot_contrast(self,
                 the standard error is shown, and if 'sd' the standard deviation is shown.
             errevery: This determines the frequency of errorbars. If `None` they appear 5% of the time.
             labels: The legend labels to use in the plot. These should be in order of the actual legend labels.
+            title : The title give the plot.
             colors: The colors used to plot the learners plot.
             xlabel: The label on the x-axis.
             ylabel: The label on the y-axis.
@@ -1274,7 +1278,7 @@ def plot_contrast(self,
 
             xlabel = xlabel or ("Interaction" if x=='index' else x[0] if len(x) == 1 else x)
             ylabel = ylabel or (f"$\Delta$ {y}" if mode=="diff" else f"P($\Delta$ {y} > 0)")
-            title  = f"{ylabel} ({len(_Y)} Environments)"
+            title  = title if title is not None else (f"{ylabel} ({len(_Y)} Environments)")
 
             self._plotter.plot(ax, lines, title, xlabel, ylabel, xlim, ylim, xticks, yticks, xrotation, yrotation, out)
 

coba/tests/test_experiments_results.py

@@ -1379,6 +1379,27 @@ def test_plot_learners_xlabel_ylabel(self):
         self.assertEqual(1, len(plotter.plot_calls))
         self.assertEqual(expected_lines, plotter.plot_calls[0][1])
 
+    def test_plot_learners_title(self):
+        envs = [['environment_id'],[0]]
+        lrns = [['learner_id', 'family'],[1,'learner_1'],[2,'learner_2']]
+        vals = [['evaluator_id'],[0]]
+        ints = [['environment_id','learner_id','evaluator_id','index','reward'],[0,1,0,1,1],[0,1,0,2,2],[0,2,0,1,1],[0,2,0,2,2]]
+
+        plotter = TestPlotter()
+        result = Result(envs, lrns, vals, ints)
+
+        result.set_plotter(plotter)
+        result.plot_learners(title='abc')
+
+        expected_lines = [
+            Points([1,2],[1,1.5],[],[0,0],0,1,'1. learner_1','-', 1),
+            Points([1,2],[1,1.5],[],[0,0],1,1,'2. learner_2','-', 1)
+        ]
+
+        self.assertEqual("abc", plotter.plot_calls[0][2])
+        self.assertEqual(1, len(plotter.plot_calls))
+        self.assertEqual(expected_lines, plotter.plot_calls[0][1])
+
     def test_plot_learners_all_str_to_none(self):
         envs = [['environment_id'],[0]]
         lrns = [['learner_id', 'family'],[1,'learner_1'],[2,None]]
@@ -2043,6 +2064,37 @@ def test_plot_contrast_xlabel_ylabel(self):
         self.assertEqual('x', plotter.plot_calls[0][3])
         self.assertEqual('y', plotter.plot_calls[0][4])
 
+    def test_plot_contrast_title(self):
+        envs = [['environment_id'],[0],[1],[2],[3]]
+        lrns = [['learner_id', 'family'],[1,'learner_1'],[2,'learner_2']]
+        vals = [['evaluator_id'],[0]]
+        ints = [['environment_id','learner_id','evaluator_id','index','reward'],
+            [0,1,0,1,0],[0,1,0,2,3],[0,1,0,3,9],
+            [0,2,0,1,1],[0,2,0,2,2],[0,2,0,3,6],
+            [1,1,0,1,1],[1,1,0,2,2],[1,1,0,3,6],
+            [1,2,0,1,0],[1,2,0,2,3],[1,2,0,3,9],
+            [2,1,0,1,0],[2,1,0,2,0],[2,1,0,3,6],
+            [2,2,0,1,0],[2,2,0,2,3],[2,2,0,3,9],
+            [3,1,0,1,0],[3,1,0,2,3],[3,1,0,3,9],
+            [3,2,0,1,0],[3,2,0,2,0],[3,2,0,3,6],
+        ]
+
+        plotter = TestPlotter()
+        result = Result(envs, lrns, vals, ints)
+
+        result.set_plotter(plotter)
+        result.plot_contrast(2,1,title='abc')
+
+        expected_lines = [
+            Points(('2','1'), (-2,-1), None, (0,0), 0     , 1, 'l1 (2)', '.', 1.),
+            Points(('0','3'), ( 1, 2), None, (0,0), 2     , 1, 'l2 (2)', '.', 1.),
+            Points(('2','3'), ( 0, 0), None,  None, "#888", 1, None    , '-', .5),
+        ]
+
+        self.assertEqual(1, len(plotter.plot_calls))
+        self.assertEqual(expected_lines, plotter.plot_calls[0][1])
+        self.assertEqual('abc', plotter.plot_calls[0][2])
+
     def test_plot_contrast_one_environment_env_not_index(self):
         envs = [['environment_id','a'],[0,1],[1,2],[2,3]]
         lrns = [['learner_id', 'family'],[1,'learner_1'],[2,'learner_2']]