Merge branch 'main' into update_parameter_tuning

analysis/parameter_tuning.py

@@ -202,7 +202,7 @@ def _find_candidates_quantiles(histogram: histograms.Histogram,
     """Implementation of QUANTILES strategy."""
     quantiles_to_use = [0.9, 0.95, 0.98, 0.99, 0.995]
     candidates = histogram.quantiles(quantiles_to_use)
-    candidates.append(histogram.max_value)
+    candidates.append(histogram.max_value())
     candidates = list(set(candidates))  # remove duplicates
     candidates.sort()
     return candidates[:max_candidates]
@@ -211,7 +211,7 @@ def _find_candidates_quantiles(histogram: histograms.Histogram,
 def _find_candidates_constant_relative_step(histogram: histograms.Histogram,
                                             max_candidates: int) -> List[int]:
     """Implementation of CONSTANT_RELATIVE_STEP strategy."""
-    max_value = histogram.max_value
+    max_value = histogram.max_value()
     assert max_value >= 1, "max_value has to be >= 1."
     max_candidates = min(max_candidates, max_value)
     assert max_candidates > 0, "max_candidates have to be positive"

analysis/tests/parameter_tuning_test.py

@@ -64,9 +64,10 @@ def test_find_candidate_parameters_quantiles_strategy(
     ):
         mock_l0_histogram = histograms.Histogram(None, None)
         mock_l0_histogram.quantiles = mock.Mock(return_value=[1, 1, 2])
-        setattr(mock_l0_histogram.__class__, 'max_value', 6)
+        mock_l0_histogram.max_value = mock.Mock(return_value=6)
         mock_linf_histogram = histograms.Histogram(None, None)
         mock_linf_histogram.quantiles = mock.Mock(return_value=[3, 6, 6])
+        mock_linf_histogram.max_value = mock.Mock(return_value=6)
 
         mock_histograms = histograms.DatasetHistograms(mock_l0_histogram, None,
                                                        mock_linf_histogram,
@@ -91,9 +92,10 @@ def test_find_candidate_parameters_maximum_number_of_candidates_is_respected_whe
             self):
         mock_l0_histogram = histograms.Histogram(None, None)
         mock_l0_histogram.quantiles = mock.Mock(return_value=[1, 2, 3])
-        setattr(mock_l0_histogram.__class__, 'max_value', 6)
+        mock_l0_histogram.max_value = mock.Mock(return_value=6)
         mock_linf_histogram = histograms.Histogram(None, None)
         mock_linf_histogram.quantiles = mock.Mock(return_value=[4, 5, 6])
+        mock_linf_histogram.max_value = mock.Mock(return_value=6)
 
         mock_histograms = histograms.DatasetHistograms(mock_l0_histogram, None,
                                                        mock_linf_histogram,
@@ -116,9 +118,10 @@ def test_find_candidate_parameters_more_candidates_for_l_0_when_not_so_many_l_in
             self):
         mock_l0_histogram = histograms.Histogram(None, None)
         mock_l0_histogram.quantiles = mock.Mock(return_value=[1, 2, 3, 4, 5])
-        setattr(mock_l0_histogram.__class__, 'max_value', 6)
+        mock_l0_histogram.max_value = mock.Mock(return_value=6)
         mock_linf_histogram = histograms.Histogram(None, None)
         mock_linf_histogram.quantiles = mock.Mock(return_value=[6, 7])
+        mock_linf_histogram.max_value = mock.Mock(return_value=6)
 
         mock_histograms = histograms.DatasetHistograms(mock_l0_histogram, None,
                                                        mock_linf_histogram,
@@ -146,9 +149,10 @@ def test_find_candidate_parameters_more_candidates_for_l_inf_when_not_so_many_l_
             self):
         mock_l0_histogram = histograms.Histogram(None, None)
         mock_l0_histogram.quantiles = mock.Mock(return_value=[1])
-        setattr(mock_l0_histogram.__class__, 'max_value', 8)
+        mock_l0_histogram.max_value = mock.Mock(return_value=8)
         mock_linf_histogram = histograms.Histogram(None, None)
         mock_linf_histogram.quantiles = mock.Mock(return_value=[3, 4, 5, 6, 7])
+        mock_linf_histogram.max_value = mock.Mock(return_value=8)
 
         mock_histograms = histograms.DatasetHistograms(mock_l0_histogram, None,
                                                        mock_linf_histogram,
@@ -205,7 +209,7 @@ def test_find_candidate_parameters_more_candidates_for_l_inf_when_not_so_many_l_
     def test_find_candidate_parameters_constant_relative_ste_strategy(
             self, max_value, max_candidates, expected_candidates):
         mock_l0_histogram = histograms.Histogram(None, None)
-        setattr(histograms.Histogram, 'max_value', max_value)
+        mock_l0_histogram.max_value = mock.Mock(return_value=max_value)
 
         mock_histograms = histograms.DatasetHistograms(mock_l0_histogram, None,
                                                        None, None, None)
@@ -246,15 +250,15 @@ def test_tune_count(self):
         # Assert.
         tune_result, per_partition_utility_analysis = result
         per_partition_utility_analysis = list(per_partition_utility_analysis)
-        self.assertLen(per_partition_utility_analysis, 40)
+        self.assertLen(per_partition_utility_analysis, 10)
 
         tune_result = list(tune_result)[0]
 
         self.assertEqual(tune_options, tune_result.options)
         self.assertEqual(contribution_histograms,
                          tune_result.contribution_histograms)
         utility_reports = tune_result.utility_reports
-        self.assertLen(utility_reports, 4)
+        self.assertLen(utility_reports, 1)
         self.assertIsInstance(utility_reports[0], metrics.UtilityReport)
         self.assertLen(utility_reports[0].metric_errors, 1)
         self.assertEqual(utility_reports[0].metric_errors[0].metric,

pipeline_dp/dataset_histograms/histograms.py

@@ -75,7 +75,6 @@ def total_count(self):
     def total_sum(self):
         return sum([bin.sum for bin in self.bins])
 
-    @property
     def max_value(self):
         return self.bins[-1].max
 
@@ -141,9 +140,9 @@ def compute_ratio_dropped(
     ratio_dropped = []
     bins = contribution_histogram.bins
     previous_value = bins[-1].lower  # lower of the largest bin.
-    if contribution_histogram.max_value != previous_value:
+    if contribution_histogram.max_value() != previous_value:
         # Add ratio for max_value when max_value is not lower in bins.
-        ratio_dropped.append((contribution_histogram.max_value, 0.0))
+        ratio_dropped.append((contribution_histogram.max_value(), 0.0))
 
     for bin in bins[::-1]:
         current_value = bin.lower