Multiple small changes to the TF Privacy Report:

     - Fix the legend to the bottom right
     - Manually set the size of the plot figure.
     - Fix a typo in the subplot title.

PiperOrigin-RevId: 337064528

tensorflow_privacy/privacy/membership_inference_attack/README.md

@@ -20,7 +20,7 @@ the model are used (e.g., losses, logits, predictions). Neither model internals
 
 ### Codelab
 
-The easiest way to get started is to go through [the introductory codelab](https://github.com/tensorflow/privacy/blob/master/tensorflow_privacy/privacy/membership_inference_attack/codelab.ipynb).
+The easiest way to get started is to go through [the introductory codelab](https://github.com/tensorflow/privacy/blob/master/tensorflow_privacy/privacy/membership_inference_attack/codelabs/codelab.ipynb).
 This trains a simple image classification model and tests it against a series
 of membership inference attacks.
 

tensorflow_privacy/privacy/membership_inference_attack/data_structures.py

@@ -609,8 +609,8 @@ def _get_attack_results_filename(attack_results: AttackResults, index: int):
   """Creates a filename for a specific set of AttackResults."""
   metadata = attack_results.privacy_report_metadata
   if metadata is not None:
-    return '%s_%s_%s.pickle' % (metadata.model_variant_label,
-                                metadata.epoch_num, index)
+    return '%s_%s_epoch_%s.pickle' % (metadata.model_variant_label, index,
+                                      metadata.epoch_num)
   return '%s.pickle' % index
 
 

tensorflow_privacy/privacy/membership_inference_attack/example.py

@@ -169,7 +169,7 @@ def main(unused_argv):
   epoch_figure = privacy_report.plot_by_epochs(
       epoch_results, [PrivacyMetric.ATTACKER_ADVANTAGE, PrivacyMetric.AUC])
   epoch_figure.show()
-  privacy_utility_figure = privacy_report.plot_privacy_vs_accuracy_single_model(
+  privacy_utility_figure = privacy_report.plot_privacy_vs_accuracy(
       epoch_results, [PrivacyMetric.ATTACKER_ADVANTAGE, PrivacyMetric.AUC])
   privacy_utility_figure.show()
 

tensorflow_privacy/privacy/membership_inference_attack/privacy_report.py

@@ -32,10 +32,11 @@
 
 def plot_by_epochs(results: AttackResultsCollection,
                    privacy_metrics: Iterable[PrivacyMetric]) -> plt.Figure:
-  """Plots privacy vulnerabilities vs epoch numbers for a single model variant.
+  """Plots privacy vulnerabilities vs epoch numbers.
 
   In case multiple privacy metrics are specified, the plot will feature
-  multiple subplots (one subplot per metrics).
+  multiple subplots (one subplot per metrics). Multiple model variants
+  are supported.
   Args:
     results: AttackResults for the plot
     privacy_metrics: List of enumerated privacy metrics that should be plotted.
@@ -54,12 +55,13 @@ def plot_by_epochs(results: AttackResultsCollection,
       privacy_metrics=privacy_metrics)
 
 
-def plot_privacy_vs_accuracy_single_model(
-    results: AttackResultsCollection, privacy_metrics: Iterable[PrivacyMetric]):
-  """Plots privacy vulnerabilities vs accuracy plots for a single model variant.
+def plot_privacy_vs_accuracy(results: AttackResultsCollection,
+                             privacy_metrics: Iterable[PrivacyMetric]):
+  """Plots privacy vulnerabilities vs accuracy plots.
 
   In case multiple privacy metrics are specified, the plot will feature
-  multiple subplots (one subplot per metrics).
+  multiple subplots (one subplot per metrics). Multiple model variants
+  are supported.
   Args:
     results: AttackResults for the plot
     privacy_metrics: List of enumerated privacy metrics that should be plotted.
@@ -106,7 +108,8 @@ def _generate_subplots(all_results_df: pd.DataFrame, x_axis_metric: str,
                        figure_title: str,
                        privacy_metrics: Iterable[PrivacyMetric]):
   """Create one subplot per privacy metric for a specified x_axis_metric."""
-  fig, axes = plt.subplots(1, len(privacy_metrics))
+  fig, axes = plt.subplots(
+      1, len(privacy_metrics), figsize=(5 * len(privacy_metrics), 5))
   # Set a title for the entire group of subplots.
   fig.suptitle(figure_title)
   if len(privacy_metrics) == 1:
@@ -116,11 +119,12 @@ def _generate_subplots(all_results_df: pd.DataFrame, x_axis_metric: str,
     for legend_label in legend_labels:
       single_label_results = all_results_df.loc[all_results_df[LEGEND_LABEL_STR]
                                                 == legend_label]
-      axes[i].plot(single_label_results[x_axis_metric],
-                   single_label_results[str(privacy_metric)])
-    axes[i].legend(legend_labels)
+      sorted_label_results = single_label_results.sort_values(x_axis_metric)
+      axes[i].plot(sorted_label_results[x_axis_metric],
+                   sorted_label_results[str(privacy_metric)])
+    axes[i].legend(legend_labels, loc='lower right')
     axes[i].set_xlabel(x_axis_metric)
-    axes[i].set_title('%s for Entire dataset' % ENTIRE_DATASET_SLICE_STR)
+    axes[i].set_title('%s for %s' % (privacy_metric, ENTIRE_DATASET_SLICE_STR))
 
   return fig
 

tensorflow_privacy/privacy/membership_inference_attack/privacy_report_test.py

@@ -141,11 +141,11 @@ def test_multiple_metrics_plot_by_epochs_multiple_models(self):
   def test_plot_privacy_vs_accuracy_single_model_no_metadata(self):
     # Raise error if metadata is missing
     self.assertRaises(
-        ValueError, privacy_report.plot_privacy_vs_accuracy_single_model,
+        ValueError, privacy_report.plot_privacy_vs_accuracy,
         AttackResultsCollection((self.attack_results_no_metadata,)), ['AUC'])
 
   def test_single_metric_plot_privacy_vs_accuracy_single_model(self):
-    fig = privacy_report.plot_privacy_vs_accuracy_single_model(
+    fig = privacy_report.plot_privacy_vs_accuracy(
         AttackResultsCollection((self.results_epoch_10, self.results_epoch_15)),
         ['AUC'])
     # extract data from figure.
@@ -158,7 +158,7 @@ def test_single_metric_plot_privacy_vs_accuracy_single_model(self):
     self.assertEqual(fig._suptitle.get_text(), 'Privacy vs Utility Analysis')
 
   def test_multiple_metrics_plot_privacy_vs_accuracy_single_model(self):
-    fig = privacy_report.plot_privacy_vs_accuracy_single_model(
+    fig = privacy_report.plot_privacy_vs_accuracy(
         AttackResultsCollection((self.results_epoch_10, self.results_epoch_15)),
         ['AUC', 'Attacker advantage'])
     # extract data from figure.
@@ -174,7 +174,7 @@ def test_multiple_metrics_plot_privacy_vs_accuracy_single_model(self):
     self.assertEqual(fig._suptitle.get_text(), 'Privacy vs Utility Analysis')
 
   def test_multiple_metrics_plot_privacy_vs_accuracy_multiple_model(self):
-    fig = privacy_report.plot_privacy_vs_accuracy_single_model(
+    fig = privacy_report.plot_privacy_vs_accuracy(
         AttackResultsCollection((self.results_epoch_10, self.results_epoch_15,
                                  self.results_epoch_15_model_2)),
         ['AUC', 'Attacker advantage'])