Show interactive iteration vs. score plot when using fit()

evalml/models/auto_base.py

@@ -150,7 +150,8 @@ def fit(self, X, y, feature_types=None, raise_errors=False, show_iteration_plot=
             pbar._instances.clear()
             start = time.time()
             while time.time() - start <= self.max_time:
-                self._do_iteration(X, y, pbar, raise_errors, plot)
+                self._do_iteration(X, y, pbar, raise_errors)
+                plot.update()
             pbar.close()
         else:
             pbar = tqdm(range(self.max_pipelines), disable=not self.verbose, file=stdout, bar_format='{desc}   {percentage:3.0f}%|{bar}| Elapsed:{elapsed}')
@@ -162,7 +163,8 @@ def fit(self, X, y, feature_types=None, raise_errors=False, show_iteration_plot=
                     pbar.close()
                     self.logger.log("\n\nMax time elapsed. Stopping search early.")
                     break
-                self._do_iteration(X, y, pbar, raise_errors, plot)
+                self._do_iteration(X, y, pbar, raise_errors)
+                plot.update()
             pbar.close()
 
         self.logger.log("\n✔ Optimization finished")
@@ -176,7 +178,7 @@ def _check_multiclass(self, y):
             if ProblemTypes.MULTICLASS not in obj.problem_types:
                 raise ValueError("Additional objective {} is not compatible with a multiclass problem.".format(obj.name))
 
-    def _do_iteration(self, X, y, pbar, raise_errors, plot):
+    def _do_iteration(self, X, y, pbar, raise_errors):
         # determine which pipeline to build
         pipeline_class = self._select_pipeline()
 
@@ -237,9 +239,6 @@ def _do_iteration(self, X, y, pbar, raise_errors, plot):
                          training_time=training_time,
                          cv_data=cv_data)
 
-        # Update plot with new score
-        plot.update()
-
         desc = "✔" + desc[1:]
         pbar.set_description_str(desc=desc, refresh=True)
         if self.verbose:  # To force new line between progress bar iterations

evalml/models/pipeline_search_plots.py

@@ -23,7 +23,6 @@ def __init__(self, data, show_plot=True):
             'title': title,
             'xaxis': {
                 'title': 'Iteration',
-                'tickformat': ',d',
                 'rangemode': 'tozero'
             },
             'yaxis': {
@@ -34,8 +33,9 @@ def __init__(self, data, show_plot=True):
         self.best_score_by_iter_fig.update_layout(showlegend=False)
 
     def update(self):
-        iter_idx = self.data.rankings['id'].idxmax()
-        self.curr_iteration_scores.append(self.data.rankings['score'].iloc[iter_idx])
+        iter_idx = self.data.results['search_order']
+        pipeline_res = self.data.results['pipeline_results']
+        iter_scores = [pipeline_res[i]['score'] for i in range(len(pipeline_res))]
 
         if self.data.objective.greater_is_better:
             iter_max_score = self.data.rankings['score'].max()
@@ -45,8 +45,8 @@ def update(self):
 
         # Update entire line plot
         curr_score_trace = self.best_score_by_iter_fig.data[1]
-        curr_score_trace.x = list(range(len(self.curr_iteration_scores)))
-        curr_score_trace.y = self.curr_iteration_scores
+        curr_score_trace.x = iter_idx
+        curr_score_trace.y = iter_scores
 
         best_score_trace = self.best_score_by_iter_fig.data[0]
         best_score_trace.x = list(range(len(self.best_iteration_scores)))