From f6542045fec11fcc08a83a5cf2b807f2e4c9c288 Mon Sep 17 00:00:00 2001
From: Vadym Doroshenko <53558779+dvadym@users.noreply.github.com>
Date: Thu, 17 Oct 2024 11:44:03 +0200
Subject: [PATCH] Add min to FrequencyBin (#529)

---
 analysis/tests/parameter_tuning_test.py       |   1 +
 .../computing_histograms.py                   |   3 +-
 pipeline_dp/dataset_histograms/histograms.py  |   7 +-
 .../sum_histogram_computation.py              |   3 +-
 .../computing_histograms_test.py              | 243 ++++++++++++------
 tests/dataset_histograms/histograms_test.py   | 102 ++++++--
 .../sum_histogram_computation_test.py         | 166 ++++++++----
 tests/private_contribution_bounds_test.py     |  75 +-----
 8 files changed, 387 insertions(+), 213 deletions(-)

diff --git a/analysis/tests/parameter_tuning_test.py b/analysis/tests/parameter_tuning_test.py
index d63fc2f1..e0855f63 100644
--- a/analysis/tests/parameter_tuning_test.py
+++ b/analysis/tests/parameter_tuning_test.py
@@ -56,6 +56,7 @@ def _frequency_bin(max_value: float = 0.0,
                    lower: float = 0.0) -> histograms.FrequencyBin:
     return histograms.FrequencyBin(max=max_value,
                                    lower=lower,
+                                   min=None,
                                    upper=None,
                                    count=None,
                                    sum=None)
diff --git a/pipeline_dp/dataset_histograms/computing_histograms.py b/pipeline_dp/dataset_histograms/computing_histograms.py
index 5b26e056..c751a7aa 100644
--- a/pipeline_dp/dataset_histograms/computing_histograms.py
+++ b/pipeline_dp/dataset_histograms/computing_histograms.py
@@ -112,7 +112,8 @@ def _map_to_frequency_bin(value: int,
                                             upper=bin_upper,
                                             count=frequency,
                                             sum=frequency * value,
-                                            max=value)
+                                            max=value,
+                                            min=value)
 
     col = backend.map_tuple(col, _map_to_frequency_bin, "To FrequencyBin")
     # (lower_bin_value, hist.FrequencyBin)
diff --git a/pipeline_dp/dataset_histograms/histograms.py b/pipeline_dp/dataset_histograms/histograms.py
index 8b897a7f..3f8455b6 100644
--- a/pipeline_dp/dataset_histograms/histograms.py
+++ b/pipeline_dp/dataset_histograms/histograms.py
@@ -42,15 +42,18 @@ class FrequencyBin:
     count: int
     sum: Union[int, float]
     max: Union[int, float]
+    min: Union[int, float]
 
     def __add__(self, other: 'FrequencyBin') -> 'FrequencyBin':
         self._check_same_bin(other)
         return FrequencyBin(self.lower, self.upper, self.count + other.count,
-                            self.sum + other.sum, max(self.max, other.max))
+                            self.sum + other.sum, max(self.max, other.max),
+                            min(self.min, other.min))
 
     def __eq__(self, other):
         return (self.lower == other.lower and self.count == other.count and
-                self.sum == other.sum and self.max == other.max)
+                self.sum == other.sum and self.max == other.max and
+                self.min == self.min)
 
     def _check_same_bin(self, other: 'FrequencyBin'):
         assert self.lower == other.lower
diff --git a/pipeline_dp/dataset_histograms/sum_histogram_computation.py b/pipeline_dp/dataset_histograms/sum_histogram_computation.py
index baa43136..997f02df 100644
--- a/pipeline_dp/dataset_histograms/sum_histogram_computation.py
+++ b/pipeline_dp/dataset_histograms/sum_histogram_computation.py
@@ -122,7 +122,8 @@ def _map_to_frequency_bin(
                                    upper=bin_upper,
                                    count=1,
                                    sum=value,
-                                   max=value)
+                                   max=value,
+                                   min=value)
         return (index, bin_lower), bucket
 
     col = backend.map_with_side_inputs(col, _map_to_frequency_bin,
diff --git a/tests/dataset_histograms/computing_histograms_test.py b/tests/dataset_histograms/computing_histograms_test.py
index 0a6b28c7..05f46f39 100644
--- a/tests/dataset_histograms/computing_histograms_test.py
+++ b/tests/dataset_histograms/computing_histograms_test.py
@@ -41,24 +41,51 @@ def test_to_bin_lower_upper_logarithmic(self):
         dict(testcase_name='small_histogram',
              input=[3, 3, 1, 1, 2, 10],
              expected=[
-                 hist.FrequencyBin(lower=1, upper=2, count=2, sum=2, max=1),
-                 hist.FrequencyBin(lower=2, upper=3, count=1, sum=2, max=2),
-                 hist.FrequencyBin(lower=3, upper=4, count=2, sum=6, max=3),
-                 hist.FrequencyBin(lower=10, upper=11, count=1, sum=10, max=10)
+                 hist.FrequencyBin(lower=1,
+                                   upper=2,
+                                   count=2,
+                                   sum=2,
+                                   min=1,
+                                   max=1),
+                 hist.FrequencyBin(lower=2,
+                                   upper=3,
+                                   count=1,
+                                   sum=2,
+                                   min=1,
+                                   max=2),
+                 hist.FrequencyBin(lower=3,
+                                   upper=4,
+                                   count=2,
+                                   sum=6,
+                                   min=1,
+                                   max=3),
+                 hist.FrequencyBin(lower=10,
+                                   upper=11,
+                                   count=1,
+                                   sum=10,
+                                   min=1,
+                                   max=10)
              ]),
         dict(testcase_name='histogram_with_bins_wider_1',
              input=[1005, 3, 12345, 12346],
              expected=[
-                 hist.FrequencyBin(lower=3, upper=4, count=1, sum=3, max=3),
+                 hist.FrequencyBin(lower=3,
+                                   upper=4,
+                                   count=1,
+                                   sum=3,
+                                   min=1,
+                                   max=3),
                  hist.FrequencyBin(lower=1000,
                                    upper=1010,
                                    count=1,
                                    sum=1005,
+                                   min=1,
                                    max=1005),
                  hist.FrequencyBin(lower=12300,
                                    upper=12400,
                                    count=2,
                                    sum=24691,
+                                   min=1,
                                    max=12346)
              ]),
     )
@@ -101,29 +128,36 @@ def test_list_to_contribution_histograms(self):
                 testcase_name='small_histogram',
                 input=[(1, 1), (1, 2), (2, 1)],  # (privacy_id, partition)
                 expected=[
-                    hist.FrequencyBin(lower=1, upper=2, count=1, sum=1, max=1),
-                    hist.FrequencyBin(lower=2, upper=3, count=1, sum=2, max=2)
+                    hist.FrequencyBin(
+                        lower=1, upper=2, count=1, sum=1, min=1, max=1),
+                    hist.FrequencyBin(
+                        lower=2, upper=3, count=1, sum=2, min=1, max=2)
                 ]),
             dict(
                 testcase_name='Each privacy id, 1 contribution',
                 input=[(i, i) for i in range(100)],  # (privacy_id, partition)
                 expected=[
                     hist.FrequencyBin(
-                        lower=1, upper=2, count=100, sum=100, max=1),
+                        lower=1, upper=2, count=100, sum=100, min=1, max=1),
                 ]),
             dict(
                 testcase_name='1 privacy id many contributions to 1 partition',
                 input=[(0, 0)],  # (privacy_id, partition)
                 expected=[
-                    hist.FrequencyBin(lower=1, upper=2, count=1, sum=1, max=1),
+                    hist.FrequencyBin(
+                        lower=1, upper=2, count=1, sum=1, min=1, max=1),
                 ]),
             dict(
                 testcase_name=
                 '1 privacy id many contributions to many partition',
                 input=[(0, i) for i in range(1234)],  # (privacy_id, partition)
                 expected=[
-                    hist.FrequencyBin(
-                        lower=1230, upper=1240, count=1, sum=1234, max=1234),
+                    hist.FrequencyBin(lower=1230,
+                                      upper=1240,
+                                      count=1,
+                                      sum=1234,
+                                      min=1,
+                                      max=1234),
                 ]),
             dict(
                 testcase_name='2 privacy ids, same partitions contributed',
@@ -131,7 +165,7 @@ def test_list_to_contribution_histograms(self):
                 [(1, i) for i in range(10, 25)],  # (privacy_id, partition)
                 expected=[
                     hist.FrequencyBin(
-                        lower=15, upper=16, count=2, sum=30, max=15),
+                        lower=15, upper=16, count=2, sum=30, min=1, max=15),
                 ]),
         ),
         pre_aggregated=(False, True))
@@ -160,22 +194,24 @@ def test_compute_l0_contributions_histogram(self, testcase_name, input,
                 testcase_name='small_histogram',
                 input=[(1, 1), (1, 2), (2, 1)],  # (privacy_id, partition)
                 expected=[
-                    hist.FrequencyBin(lower=1, upper=2, count=1, sum=1, max=1),
-                    hist.FrequencyBin(lower=2, upper=3, count=1, sum=2, max=2)
+                    hist.FrequencyBin(
+                        lower=1, upper=2, count=1, sum=1, min=1, max=1),
+                    hist.FrequencyBin(
+                        lower=2, upper=3, count=1, sum=2, min=1, max=2)
                 ]),
             dict(
                 testcase_name='Each privacy id, 1 contribution',
                 input=[(i, i) for i in range(100)],  # (privacy_id, partition)
                 expected=[
                     hist.FrequencyBin(
-                        lower=1, upper=2, count=100, sum=100, max=1),
+                        lower=1, upper=2, count=100, sum=100, min=1, max=1),
                 ]),
             dict(
                 testcase_name='1 privacy id many contributions to 1 partition',
                 input=[(0, 0)] * 100,  # (privacy_id, partition)
                 expected=[
                     hist.FrequencyBin(
-                        lower=100, upper=101, count=1, sum=100, max=100),
+                        lower=100, upper=101, count=1, sum=100, min=1, max=100),
                 ]),
             dict(
                 testcase_name=
@@ -183,8 +219,12 @@ def test_compute_l0_contributions_histogram(self, testcase_name, input,
                 input=[(0, i // 2) for i in range(1235)
                       ],  # (privacy_id, partition)
                 expected=[
-                    hist.FrequencyBin(
-                        lower=1230, upper=1240, count=1, sum=1235, max=1235),
+                    hist.FrequencyBin(lower=1230,
+                                      upper=1240,
+                                      count=1,
+                                      sum=1235,
+                                      min=1,
+                                      max=1235),
                 ]),
             dict(
                 testcase_name='2 privacy ids, same partitions contributed',
@@ -192,7 +232,7 @@ def test_compute_l0_contributions_histogram(self, testcase_name, input,
                 [(1, i) for i in range(10, 25)],  # (privacy_id, partition)
                 expected=[
                     hist.FrequencyBin(
-                        lower=15, upper=16, count=2, sum=30, max=15),
+                        lower=15, upper=16, count=2, sum=30, min=1, max=15),
                 ]),
             dict(
                 testcase_name='3 privacy ids',
@@ -201,9 +241,9 @@ def test_compute_l0_contributions_histogram(self, testcase_name, input,
                 [(2, i) for i in range(11)],  # (privacy_id, partition)
                 expected=[
                     hist.FrequencyBin(
-                        lower=11, upper=12, count=1, sum=11, max=11),
+                        lower=11, upper=12, count=1, sum=11, min=1, max=11),
                     hist.FrequencyBin(
-                        lower=15, upper=16, count=2, sum=30, max=15),
+                        lower=15, upper=16, count=2, sum=30, min=1, max=15),
                 ]),
         ),
         pre_aggregated=(False, True))
@@ -233,22 +273,24 @@ def test_compute_l1_contributions_histogram(self, testcase_name, input,
                 input=[(1, 1), (1, 2), (2, 1),
                        (1, 1)],  # (privacy_id, partition)
                 expected=[
-                    hist.FrequencyBin(lower=1, upper=2, count=2, sum=2, max=1),
-                    hist.FrequencyBin(lower=2, upper=3, count=1, sum=2, max=2)
+                    hist.FrequencyBin(
+                        lower=1, upper=2, count=2, sum=2, min=1, max=1),
+                    hist.FrequencyBin(
+                        lower=2, upper=3, count=1, sum=2, min=1, max=2)
                 ]),
             dict(
                 testcase_name='Each privacy id, 1 contribution',
                 input=[(i, i) for i in range(100)],  # (privacy_id, partition)
                 expected=[
                     hist.FrequencyBin(
-                        lower=1, upper=2, count=100, sum=100, max=1),
+                        lower=1, upper=2, count=100, sum=100, min=1, max=1),
                 ]),
             dict(
                 testcase_name='1 privacy id many contributions to 1 partition',
                 input=[(0, 0)] * 100,  # (privacy_id, partition)
                 expected=[
                     hist.FrequencyBin(
-                        lower=100, upper=101, count=1, sum=100, max=100),
+                        lower=100, upper=101, count=1, sum=100, min=1, max=100),
                 ]),
             dict(
                 testcase_name=
@@ -256,24 +298,27 @@ def test_compute_l1_contributions_histogram(self, testcase_name, input,
                 input=[(0, i) for i in range(1234)],  # (privacy_id, partition)
                 expected=[
                     hist.FrequencyBin(
-                        lower=1, upper=2, count=1234, sum=1234, max=1),
+                        lower=1, upper=2, count=1234, sum=1234, min=1, max=1),
                 ]),
             dict(
                 testcase_name='2 privacy ids, same partitions contributed',
                 input=[(0, i) for i in range(15)] +
                 [(1, i) for i in range(10, 25)],  # (privacy_id, partition)
                 expected=[
-                    hist.FrequencyBin(lower=1, upper=2, count=30, sum=30,
-                                      max=1),
+                    hist.FrequencyBin(
+                        lower=1, upper=2, count=30, sum=30, min=1, max=1),
                 ]),
             dict(
                 testcase_name='2 privacy ids',
                 input=[(0, 0), (0, 0), (0, 1), (1, 0), (1, 0), (1, 0),
                        (1, 2)],  # (privacy_id, partition)
                 expected=[
-                    hist.FrequencyBin(lower=1, upper=2, count=2, sum=2, max=1),
-                    hist.FrequencyBin(lower=2, upper=3, count=1, sum=2, max=2),
-                    hist.FrequencyBin(lower=3, upper=4, count=1, sum=3, max=3),
+                    hist.FrequencyBin(
+                        lower=1, upper=2, count=2, sum=2, min=1, max=1),
+                    hist.FrequencyBin(
+                        lower=2, upper=3, count=1, sum=2, min=1, max=2),
+                    hist.FrequencyBin(
+                        lower=3, upper=4, count=1, sum=3, min=1, max=3),
                 ]),
         ),
         pre_aggregated=(False, True))
@@ -305,22 +350,24 @@ def test_compute_linf_contributions_histogram(self, testcase_name, input,
                 input=[(1, 1), (1, 2), (2, 1),
                        (1, 1)],  # (privacy_id, partition)
                 expected=[
-                    hist.FrequencyBin(lower=1, upper=2, count=1, sum=1, max=1),
-                    hist.FrequencyBin(lower=3, upper=3, count=1, sum=3, max=3)
+                    hist.FrequencyBin(
+                        lower=1, upper=2, count=1, sum=1, min=1, max=1),
+                    hist.FrequencyBin(
+                        lower=3, upper=3, count=1, sum=3, min=1, max=3)
                 ]),
             dict(
                 testcase_name='Each privacy id, 1 contribution',
                 input=[(i, i) for i in range(100)],  # (privacy_id, partition)
                 expected=[
                     hist.FrequencyBin(
-                        lower=1, upper=2, count=100, sum=100, max=1),
+                        lower=1, upper=2, count=100, sum=100, min=1, max=1),
                 ]),
             dict(
                 testcase_name='1 privacy id many contributions to 1 partition',
                 input=[(0, 0)] * 100,  # (privacy_id, partition)
                 expected=[
                     hist.FrequencyBin(
-                        lower=100, upper=101, count=1, sum=100, max=100),
+                        lower=100, upper=101, count=1, sum=100, min=1, max=100),
                 ]),
             dict(
                 testcase_name=
@@ -328,16 +375,17 @@ def test_compute_linf_contributions_histogram(self, testcase_name, input,
                 input=[(0, i) for i in range(1234)],  # (privacy_id, partition)
                 expected=[
                     hist.FrequencyBin(
-                        lower=1, upper=2, count=1234, sum=1234, max=1),
+                        lower=1, upper=2, count=1234, sum=1234, min=1, max=1),
                 ]),
             dict(
                 testcase_name='2 privacy ids, same partitions contributed',
                 input=[(0, i) for i in range(15)] +
                 [(1, i) for i in range(10, 25)],  # (privacy_id, partition)
                 expected=[
-                    hist.FrequencyBin(lower=1, upper=2, count=20, sum=20,
-                                      max=1),
-                    hist.FrequencyBin(lower=2, upper=3, count=5, sum=10, max=2),
+                    hist.FrequencyBin(
+                        lower=1, upper=2, count=20, sum=20, min=1, max=1),
+                    hist.FrequencyBin(
+                        lower=2, upper=3, count=5, sum=10, min=1, max=2),
                 ]),
         ),
         pre_aggregated=(False, True))
@@ -366,21 +414,24 @@ def test_compute_partitions_count_histogram(self, testcase_name, input,
                 testcase_name='small_histogram',
                 input=[(1, 1), (1, 2), (2, 1)],  # (privacy_id, partition)
                 expected=[
-                    hist.FrequencyBin(lower=1, upper=2, count=1, sum=1, max=1),
-                    hist.FrequencyBin(lower=2, upper=3, count=1, sum=2, max=2)
+                    hist.FrequencyBin(
+                        lower=1, upper=2, count=1, sum=1, min=1, max=1),
+                    hist.FrequencyBin(
+                        lower=2, upper=3, count=1, sum=2, min=1, max=2)
                 ]),
             dict(
                 testcase_name='Each privacy id, 1 contribution',
                 input=[(i, i) for i in range(100)],  # (privacy_id, partition)
                 expected=[
                     hist.FrequencyBin(
-                        lower=1, upper=2, count=100, sum=100, max=1),
+                        lower=1, upper=2, count=100, sum=100, min=1, max=1),
                 ]),
             dict(
                 testcase_name='1 privacy id many contributions to 1 partition',
                 input=[(0, 0)],  # (privacy_id, partition)
                 expected=[
-                    hist.FrequencyBin(lower=1, upper=2, count=1, sum=1, max=1),
+                    hist.FrequencyBin(
+                        lower=1, upper=2, count=1, sum=1, min=1, max=1),
                 ]),
             dict(
                 testcase_name=
@@ -388,16 +439,17 @@ def test_compute_partitions_count_histogram(self, testcase_name, input,
                 input=[(0, i) for i in range(1234)],  # (privacy_id, partition)
                 expected=[
                     hist.FrequencyBin(
-                        lower=1, upper=2, count=1234, sum=1234, max=1),
+                        lower=1, upper=2, count=1234, sum=1234, min=1, max=1),
                 ]),
             dict(
                 testcase_name='2 privacy ids, same partitions contributed',
                 input=[(0, i) for i in range(15)] +
                 [(1, i) for i in range(10, 25)],  # (privacy_id, partition)
                 expected=[
-                    hist.FrequencyBin(lower=1, upper=2, count=20, sum=20,
-                                      max=1),
-                    hist.FrequencyBin(lower=2, upper=3, count=5, sum=10, max=2),
+                    hist.FrequencyBin(
+                        lower=1, upper=2, count=20, sum=20, min=1, max=1),
+                    hist.FrequencyBin(
+                        lower=2, upper=3, count=5, sum=10, min=1, max=2),
                 ]),
         ),
         pre_aggregated=(False, True))
@@ -435,23 +487,38 @@ def test_compute_partitions_privacy_id_count_histogram(
                 input=lambda: [(1, 1, 0.5), (1, 2, 1.5), (2, 1, -2.5),
                                (1, 1, 0.5)],  # (privacy_id, partition, value)
                 expected_cross_partition=lambda: [
-                    hist.FrequencyBin(lower=1, upper=2, count=1, sum=1, max=1),
-                    hist.FrequencyBin(lower=2, upper=3, count=1, sum=2, max=2)
+                    hist.FrequencyBin(
+                        lower=1, upper=2, count=1, sum=1, min=1, max=1),
+                    hist.FrequencyBin(
+                        lower=2, upper=3, count=1, sum=2, min=1, max=2)
                 ],
                 expected_per_partition=lambda: [
-                    hist.FrequencyBin(lower=1, upper=2, count=2, sum=2, max=1),
-                    hist.FrequencyBin(lower=2, upper=3, count=1, sum=2, max=2)
+                    hist.FrequencyBin(
+                        lower=1, upper=2, count=2, sum=2, min=1, max=1),
+                    hist.FrequencyBin(
+                        lower=2, upper=3, count=1, sum=2, min=1, max=2)
                 ],
                 expected_sum_per_partition=lambda: [
                     # see for explanation why these values
                     # test_compute_linf_sum_contributions_histogram.
-                    hist.FrequencyBin(
-                        lower=-2.5, upper=-2.5004, count=1, sum=-2.5, max=-2.5),
-                    hist.FrequencyBin(
-                        lower=1.0, upper=-1.0004, count=1, sum=1.0, max=1.0),
-                    hist.FrequencyBin(
-                        lower=1.4996,
-                        upper=1.5, count=1, sum=1.5, max=1.5),
+                    hist.FrequencyBin(lower=-2.5,
+                                      upper=-2.5004,
+                                      count=1,
+                                      sum=-2.5,
+                                      min=1,
+                                      max=-2.5),
+                    hist.FrequencyBin(lower=1.0,
+                                      upper=-1.0004,
+                                      count=1,
+                                      sum=1.0,
+                                      min=1,
+                                      max=1.0),
+                    hist.FrequencyBin(lower=1.4996,
+                                      upper=1.5,
+                                      count=1,
+                                      sum=1.5,
+                                      min=1,
+                                      max=1.5),
                 ]),
             dict(
                 testcase_name='Each privacy id, 1 contribution',
@@ -459,15 +526,15 @@ def test_compute_partitions_privacy_id_count_histogram(
                               ],  # (privacy_id, partition, value)
                 expected_cross_partition=lambda: [
                     hist.FrequencyBin(
-                        lower=1, upper=2, count=100, sum=100, max=1),
+                        lower=1, upper=2, count=100, sum=100, min=1, max=1),
                 ],
                 expected_per_partition=lambda: [
                     hist.FrequencyBin(
-                        lower=1, upper=2, count=100, sum=100, max=1),
+                        lower=1, upper=2, count=100, sum=100, min=1, max=1),
                 ],
                 expected_sum_per_partition=lambda: [
                     hist.FrequencyBin(
-                        lower=1, upper=1, count=100, sum=100, max=1),
+                        lower=1, upper=1, count=100, sum=100, min=1, max=1),
                 ],
             ),
             dict(
@@ -475,16 +542,20 @@ def test_compute_partitions_privacy_id_count_histogram(
                 input=lambda: [(0, 0, 1.0)] *
                 100,  # (privacy_id, partition, value)
                 expected_cross_partition=lambda: [
-                    hist.FrequencyBin(lower=1, upper=2, count=1, sum=1, max=1),
+                    hist.FrequencyBin(
+                        lower=1, upper=2, count=1, sum=1, min=1, max=1),
                 ],
                 expected_per_partition=lambda: [
                     hist.FrequencyBin(
-                        lower=100, upper=101, count=1, sum=100, max=100),
+                        lower=100, upper=101, count=1, sum=100, min=1, max=100),
                 ],
                 expected_sum_per_partition=lambda: [
-                    hist.FrequencyBin(
-                        lower=100.0, upper=100.0, count=1, sum=100.0, max=100.0
-                    ),
+                    hist.FrequencyBin(lower=100.0,
+                                      upper=100.0,
+                                      count=1,
+                                      sum=100.0,
+                                      min=1,
+                                      max=100.0),
                 ],
             ),
             dict(
@@ -493,16 +564,24 @@ def test_compute_partitions_privacy_id_count_histogram(
                 input=lambda: [(0, i, 1.0) for i in range(1234)
                               ],  # (privacy_id, partition, value)
                 expected_cross_partition=lambda: [
-                    hist.FrequencyBin(
-                        lower=1230, upper=1240, count=1, sum=1234, max=1234),
+                    hist.FrequencyBin(lower=1230,
+                                      upper=1240,
+                                      count=1,
+                                      sum=1234,
+                                      min=1,
+                                      max=1234),
                 ],
                 expected_per_partition=lambda: [
                     hist.FrequencyBin(
-                        lower=1, upper=2, count=1234, sum=1234, max=1),
+                        lower=1, upper=2, count=1234, sum=1234, min=1, max=1),
                 ],
                 expected_sum_per_partition=lambda: [
-                    hist.FrequencyBin(
-                        lower=1.0, upper=1.0, count=1234, sum=1234.0, max=1),
+                    hist.FrequencyBin(lower=1.0,
+                                      upper=1.0,
+                                      count=1234,
+                                      sum=1234.0,
+                                      min=1,
+                                      max=1),
                 ],
             ),
             dict(
@@ -512,15 +591,15 @@ def test_compute_partitions_privacy_id_count_histogram(
                 ],  # (privacy_id, partition, value)
                 expected_cross_partition=lambda: [
                     hist.FrequencyBin(
-                        lower=15, upper=16, count=2, sum=30, max=15),
+                        lower=15, upper=16, count=2, sum=30, min=1, max=15),
                 ],
                 expected_per_partition=lambda: [
-                    hist.FrequencyBin(lower=1, upper=2, count=30, sum=30, max=1
-                                     ),
+                    hist.FrequencyBin(
+                        lower=1, upper=2, count=30, sum=30, min=1, max=1),
                 ],
                 expected_sum_per_partition=lambda: [
                     hist.FrequencyBin(
-                        lower=1.0, upper=1.0, count=30, sum=30, max=1),
+                        lower=1.0, upper=1.0, count=30, sum=30, min=1, max=1),
                 ],
             ),
             dict(
@@ -529,16 +608,20 @@ def test_compute_partitions_privacy_id_count_histogram(
                                (1, 0, 0.0), (1, 0, 1.3), (1, 0, 0.7),
                                (1, 2, 2.0)],  # (privacy_id, partition, value)
                 expected_cross_partition=lambda: [
-                    hist.FrequencyBin(lower=2, upper=3, count=2, sum=4, max=2),
+                    hist.FrequencyBin(
+                        lower=2, upper=3, count=2, sum=4, min=1, max=2),
                 ],
                 expected_per_partition=lambda: [
-                    hist.FrequencyBin(lower=1, upper=2, count=2, sum=2, max=1),
-                    hist.FrequencyBin(lower=2, upper=3, count=1, sum=2, max=2),
-                    hist.FrequencyBin(lower=3, upper=4, count=1, sum=3, max=3),
+                    hist.FrequencyBin(
+                        lower=1, upper=2, count=2, sum=2, min=1, max=1),
+                    hist.FrequencyBin(
+                        lower=2, upper=3, count=1, sum=2, min=1, max=2),
+                    hist.FrequencyBin(
+                        lower=3, upper=4, count=1, sum=3, min=1, max=3),
                 ],
                 expected_sum_per_partition=lambda: [
                     hist.FrequencyBin(
-                        lower=2.0, upper=2.0, count=4, sum=8, max=2),
+                        lower=2.0, upper=2.0, count=4, sum=8, min=1, max=2),
                 ],
             )),
         pre_aggregated=(False, True))
diff --git a/tests/dataset_histograms/histograms_test.py b/tests/dataset_histograms/histograms_test.py
index d0e2f257..959d1678 100644
--- a/tests/dataset_histograms/histograms_test.py
+++ b/tests/dataset_histograms/histograms_test.py
@@ -23,7 +23,7 @@
 
 def frequency_bin(lower: Union[int, float],
                   upper: Union[int, float]) -> FrequencyBin:
-    return FrequencyBin(lower, upper, count=0, sum=0, max=0)
+    return FrequencyBin(lower, upper, count=0, sum=0, min=lower, max=lower)
 
 
 class HistogramTest(parameterized.TestCase):
@@ -35,19 +35,55 @@ class HistogramTest(parameterized.TestCase):
                                    upper=1010,
                                    count=10,
                                    sum=10100,
+                                   min=1000,
                                    max=1009),
              ],
              q=[0.05, 0.1, 0.5, 0.8, 0.9],
              expected_quantiles=[1000, 1000, 1000, 1000, 1000]),
         dict(testcase_name='6 bins histogram',
              bins=[
-                 hist.FrequencyBin(lower=1, upper=2, count=2, sum=2, max=1),
-                 hist.FrequencyBin(lower=2, upper=3, count=1, sum=2, max=2),
-                 hist.FrequencyBin(lower=3, upper=4, count=1, sum=3, max=3),
-                 hist.FrequencyBin(lower=4, upper=5, count=2, sum=8, max=4),
-                 hist.FrequencyBin(lower=5, upper=6, count=2, sum=10, max=5),
-                 hist.FrequencyBin(lower=6, upper=7, count=1, sum=6, max=6),
-                 hist.FrequencyBin(lower=10, upper=12, count=1, sum=11, max=11)
+                 hist.FrequencyBin(lower=1,
+                                   upper=2,
+                                   count=2,
+                                   sum=2,
+                                   min=1,
+                                   max=1),
+                 hist.FrequencyBin(lower=2,
+                                   upper=3,
+                                   count=1,
+                                   sum=2,
+                                   min=2,
+                                   max=2),
+                 hist.FrequencyBin(lower=3,
+                                   upper=4,
+                                   count=1,
+                                   sum=3,
+                                   min=3,
+                                   max=3),
+                 hist.FrequencyBin(lower=4,
+                                   upper=5,
+                                   count=2,
+                                   sum=8,
+                                   min=4,
+                                   max=4),
+                 hist.FrequencyBin(lower=5,
+                                   upper=6,
+                                   count=2,
+                                   sum=10,
+                                   min=5,
+                                   max=5),
+                 hist.FrequencyBin(lower=6,
+                                   upper=7,
+                                   count=1,
+                                   sum=6,
+                                   min=6,
+                                   max=6),
+                 hist.FrequencyBin(lower=10,
+                                   upper=12,
+                                   count=1,
+                                   sum=11,
+                                   min=10,
+                                   max=11)
              ],
              q=[0.001, 0.05, 0.1, 0.5, 0.8, 0.9],
              expected_quantiles=[1, 1, 1, 4, 6, 10]))
@@ -64,18 +100,54 @@ def test_quantile_contributions(self, bins, q, expected_quantiles):
                                    upper=1021,
                                    count=10,
                                    sum=10100,
+                                   min=1,
                                    max=1020),
              ],
              expected_ratios=[(0, 1), (1000, 100 / 10100), (1020, 0.0)]),
         dict(testcase_name='7 bins histogram',
              bins=[
-                 hist.FrequencyBin(lower=1, upper=2, count=8, sum=8, max=1),
-                 hist.FrequencyBin(lower=2, upper=3, count=2, sum=4, max=2),
-                 hist.FrequencyBin(lower=3, upper=4, count=1, sum=3, max=3),
-                 hist.FrequencyBin(lower=4, upper=5, count=2, sum=8, max=4),
-                 hist.FrequencyBin(lower=5, upper=6, count=2, sum=10, max=5),
-                 hist.FrequencyBin(lower=6, upper=7, count=1, sum=6, max=6),
-                 hist.FrequencyBin(lower=11, upper=12, count=1, sum=11, max=11),
+                 hist.FrequencyBin(lower=1,
+                                   upper=2,
+                                   count=8,
+                                   sum=8,
+                                   min=1,
+                                   max=1),
+                 hist.FrequencyBin(lower=2,
+                                   upper=3,
+                                   count=2,
+                                   sum=4,
+                                   min=2,
+                                   max=2),
+                 hist.FrequencyBin(lower=3,
+                                   upper=4,
+                                   count=1,
+                                   sum=3,
+                                   min=3,
+                                   max=3),
+                 hist.FrequencyBin(lower=4,
+                                   upper=5,
+                                   count=2,
+                                   sum=8,
+                                   min=4,
+                                   max=4),
+                 hist.FrequencyBin(lower=5,
+                                   upper=6,
+                                   count=2,
+                                   sum=10,
+                                   min=5,
+                                   max=5),
+                 hist.FrequencyBin(lower=6,
+                                   upper=7,
+                                   count=1,
+                                   sum=6,
+                                   min=6,
+                                   max=6),
+                 hist.FrequencyBin(lower=11,
+                                   upper=12,
+                                   count=1,
+                                   sum=11,
+                                   min=11,
+                                   max=11),
              ],
              expected_ratios=[(0, 1), (1, 0.66), (2, 0.48), (3, 0.34),
                               (4, 0.22), (5, 0.14), (6, 0.1), (11, 0.0)]))
diff --git a/tests/dataset_histograms/sum_histogram_computation_test.py b/tests/dataset_histograms/sum_histogram_computation_test.py
index 8a013c8b..43b4ce2a 100644
--- a/tests/dataset_histograms/sum_histogram_computation_test.py
+++ b/tests/dataset_histograms/sum_histogram_computation_test.py
@@ -48,17 +48,28 @@ class SumHistogramComputationTest(parameterized.TestCase):
                 expected=lambda: [
                     # step is (1.5 - (-2.5)) / 1e4 = 0.0004,
                     # ((2, 1), -2.5)
-                    hist.FrequencyBin(
-                        lower=-2.5, upper=-2.5004, count=1, sum=-2.5, max=-2.5),
+                    hist.FrequencyBin(lower=-2.5,
+                                      upper=-2.5004,
+                                      count=1,
+                                      sum=-2.5,
+                                      min=-2.5,
+                                      max=-2.5),
                     # 2 times ((1, 1), 0.5), they are summed up and put into a
                     # bin as one.
-                    hist.FrequencyBin(
-                        lower=1.0, upper=-1.0004, count=1, sum=1.0, max=1.0),
+                    hist.FrequencyBin(lower=1.0,
+                                      upper=-1.0004,
+                                      count=1,
+                                      sum=1.0,
+                                      min=1.0,
+                                      max=1.0),
                     # ((1, 1, 1.5), 1.5 is max and not included,
                     # therefore 1.5 - 0.0004 = 1.4996.
-                    hist.FrequencyBin(
-                        lower=1.4996,
-                        upper=1.5, count=1, sum=1.5, max=1.5),
+                    hist.FrequencyBin(lower=1.4996,
+                                      upper=1.5,
+                                      count=1,
+                                      sum=1.5,
+                                      min=1.5,
+                                      max=1.5),
                 ]),
             dict(
                 testcase_name='Different privacy ids, 1 equal contribution',
@@ -66,7 +77,7 @@ class SumHistogramComputationTest(parameterized.TestCase):
                 # ((privacy_id, partition), value)
                 expected=lambda: [
                     hist.FrequencyBin(
-                        lower=1, upper=1, count=100, sum=100, max=1),
+                        lower=1, upper=1, count=100, sum=100, min=1, max=1),
                 ]),
             dict(
                 testcase_name='Different privacy ids, 1 different contribution',
@@ -78,10 +89,15 @@ class SumHistogramComputationTest(parameterized.TestCase):
                                       upper=float(i + 1),
                                       count=1,
                                       sum=i,
+                                      min=i,
                                       max=i) for i in range(9999)
                 ] + [
-                    hist.FrequencyBin(
-                        lower=9999, upper=1000, count=2, sum=19999, max=10000)
+                    hist.FrequencyBin(lower=9999,
+                                      upper=1000,
+                                      count=2,
+                                      sum=19999,
+                                      min=9999,
+                                      max=10000)
                 ]),
             dict(
                 testcase_name='1 privacy id many contributions to 1 '
@@ -89,9 +105,12 @@ class SumHistogramComputationTest(parameterized.TestCase):
                 input=lambda: [(
                     (0, 0), 1.0)] * 100,  # ((privacy_id, partition), value)
                 expected=lambda: [
-                    hist.FrequencyBin(
-                        lower=100.0, upper=100.0, count=1, sum=100.0, max=100.0
-                    ),
+                    hist.FrequencyBin(lower=100.0,
+                                      upper=100.0,
+                                      count=1,
+                                      sum=100.0,
+                                      min=100.0,
+                                      max=100.0),
                 ]),
             dict(
                 testcase_name=
@@ -99,8 +118,12 @@ class SumHistogramComputationTest(parameterized.TestCase):
                 input=lambda: [((0, i), 1.0) for i in range(1234)],
                 # ((privacy_id, partition), value)
                 expected=lambda: [
-                    hist.FrequencyBin(
-                        lower=1.0, upper=1.0, count=1234, sum=1234.0, max=1),
+                    hist.FrequencyBin(lower=1.0,
+                                      upper=1.0,
+                                      count=1234,
+                                      sum=1234.0,
+                                      min=1.0,
+                                      max=1),
                 ]),
             dict(
                 testcase_name=
@@ -113,10 +136,15 @@ class SumHistogramComputationTest(parameterized.TestCase):
                                       upper=float(i + 1),
                                       count=1,
                                       sum=i,
+                                      min=i,
                                       max=i) for i in range(9999)
                 ] + [
-                    hist.FrequencyBin(
-                        lower=9999, upper=1000, count=2, sum=19999, max=10000)
+                    hist.FrequencyBin(lower=9999,
+                                      upper=1000,
+                                      count=2,
+                                      sum=19999,
+                                      min=9999,
+                                      max=10000)
                 ]),
             dict(
                 testcase_name=
@@ -126,7 +154,7 @@ class SumHistogramComputationTest(parameterized.TestCase):
                 # ((privacy_id, partition), value)
                 expected=lambda: [
                     hist.FrequencyBin(
-                        lower=1.0, upper=1.0, count=30, sum=30, max=1),
+                        lower=1.0, upper=1.0, count=30, sum=30, min=1, max=1),
                 ]),
             dict(
                 testcase_name='2 privacy ids, same partitions differently '
@@ -137,11 +165,15 @@ class SumHistogramComputationTest(parameterized.TestCase):
                 # step = (1 - (-1)) / 1e4 = 0.0002,
                 # therefore last lower is 1 - 0.0002 = 0.9998.
                 expected=lambda: [
+                    hist.FrequencyBin(lower=-1.0,
+                                      upper=-1.0002,
+                                      count=15,
+                                      sum=-15,
+                                      min=-1,
+                                      max=-1),
                     hist.FrequencyBin(
-                        lower=-1.0, upper=-1.0002, count=15, sum=-15, max=-1),
-                    hist.
-                    FrequencyBin(lower=0.9998, upper=1, count=15, sum=15, max=1
-                                ),
+                        lower=0.9998,
+                        upper=1, count=15, sum=15, min=1, max=1),
                 ]),
         ),
         pre_aggregated=(False, True))
@@ -186,15 +218,15 @@ def test_compute_linf_sum_contributions_histogram_2_columns(
         expected = [
             hist.Histogram(hist.HistogramType.LINF_SUM_CONTRIBUTIONS, [
                 hist.FrequencyBin(
-                    lower=1.0, upper=1.0004, count=1, sum=1, max=1),
+                    lower=1.0, upper=1.0004, count=1, sum=1, min=1, max=1),
                 hist.FrequencyBin(
-                    lower=4.9996, upper=5.0, count=2, sum=10, max=5)
+                    lower=4.9996, upper=5.0, count=2, sum=10, min=5, max=5)
             ]),
             hist.Histogram(hist.HistogramType.LINF_SUM_CONTRIBUTIONS, [
                 hist.FrequencyBin(
-                    lower=10.0, upper=10.004, count=1, sum=10, max=10),
+                    lower=10.0, upper=10.004, count=1, sum=10, min=10, max=10),
                 hist.FrequencyBin(
-                    lower=49.996, upper=50.0, count=2, sum=100, max=50)
+                    lower=49.996, upper=50.0, count=2, sum=100, min=50, max=50)
             ])
         ]
         if pre_aggregated:
@@ -227,12 +259,17 @@ def test_compute_linf_sum_contributions_histogram_2_columns(
                         (1, 1), 0.5)],  # ((privacy_id, partition), value)
                 expected=lambda: [
                     # Bucket step = 3/10**4 = 0.0003
-                    hist.FrequencyBin(
-                        lower=-1.5, upper=-1.4997, count=1, sum=-1.5, max=-1.5),
+                    hist.FrequencyBin(lower=-1.5,
+                                      upper=-1.4997,
+                                      count=1,
+                                      sum=-1.5,
+                                      min=-1.5,
+                                      max=-1.5),
                     hist.FrequencyBin(lower=1.4996999999999998,
                                       upper=1.5,
                                       count=1,
                                       sum=1.5,
+                                      min=1.5,
                                       max=1.5)
                 ]),
             dict(
@@ -242,7 +279,7 @@ def test_compute_linf_sum_contributions_histogram_2_columns(
                 # ((privacy_id, partition), value)
                 expected=lambda: [
                     hist.FrequencyBin(
-                        lower=1, upper=1, count=100, sum=100, max=1),
+                        lower=1, upper=1, count=100, sum=100, min=1, max=1),
                 ]),
             dict(
                 testcase_name='Different privacy ids, 1 different contribution',
@@ -254,10 +291,15 @@ def test_compute_linf_sum_contributions_histogram_2_columns(
                                       upper=float(i + 1),
                                       count=1,
                                       sum=i,
+                                      min=i,
                                       max=i) for i in range(9999)
                 ] + [
-                    hist.FrequencyBin(
-                        lower=9999, upper=1000, count=2, sum=19999, max=10000)
+                    hist.FrequencyBin(lower=9999,
+                                      upper=1000,
+                                      count=2,
+                                      sum=19999,
+                                      min=9999,
+                                      max=10000)
                 ]),
             dict(
                 testcase_name='1 privacy id many contributions to 1 '
@@ -265,9 +307,12 @@ def test_compute_linf_sum_contributions_histogram_2_columns(
                 input=lambda: [(
                     (0, 0), 1.0)] * 100,  # ((privacy_id, partition), value)
                 expected=lambda: [
-                    hist.FrequencyBin(
-                        lower=100.0, upper=100.0, count=1, sum=100.0, max=100.0
-                    ),
+                    hist.FrequencyBin(lower=100.0,
+                                      upper=100.0,
+                                      count=1,
+                                      sum=100.0,
+                                      min=100.0,
+                                      max=100.0),
                 ]),
             dict(
                 testcase_name=
@@ -275,8 +320,12 @@ def test_compute_linf_sum_contributions_histogram_2_columns(
                 input=lambda: [((0, i), 1.0) for i in range(1234)],
                 # ((privacy_id, partition), value)
                 expected=lambda: [
-                    hist.FrequencyBin(
-                        lower=1.0, upper=1.0, count=1234, sum=1234.0, max=1),
+                    hist.FrequencyBin(lower=1.0,
+                                      upper=1.0,
+                                      count=1234,
+                                      sum=1234.0,
+                                      min=1,
+                                      max=1),
                 ]),
             dict(
                 testcase_name=
@@ -289,10 +338,15 @@ def test_compute_linf_sum_contributions_histogram_2_columns(
                                       upper=float(i + 1),
                                       count=1,
                                       sum=i,
+                                      min=i,
                                       max=i) for i in range(9999)
                 ] + [
-                    hist.FrequencyBin(
-                        lower=9999, upper=1000, count=2, sum=19999, max=10000)
+                    hist.FrequencyBin(lower=9999,
+                                      upper=1000,
+                                      count=2,
+                                      sum=19999,
+                                      min=9999,
+                                      max=10000)
                 ]),
             dict(
                 testcase_name=
@@ -301,11 +355,18 @@ def test_compute_linf_sum_contributions_histogram_2_columns(
                     (1, i), 1.0) for i in range(10, 25)],
                 # ((privacy_id, partition), value)
                 expected=lambda: [
-                    hist.FrequencyBin(
-                        lower=1.0, upper=1.0001, count=20, sum=20.0, max=1.0),
-                    hist.FrequencyBin(
-                        lower=1.9999,
-                        upper=2.0, count=5, sum=10.0, max=2.0)
+                    hist.FrequencyBin(lower=1.0,
+                                      upper=1.0001,
+                                      count=20,
+                                      sum=20.0,
+                                      min=1.0,
+                                      max=1.0),
+                    hist.FrequencyBin(lower=1.9999,
+                                      upper=2.0,
+                                      count=5,
+                                      sum=10.0,
+                                      min=2.0,
+                                      max=2.0)
                 ]),
             dict(
                 testcase_name='2 privacy ids, same partitions differently '
@@ -320,15 +381,20 @@ def test_compute_linf_sum_contributions_histogram_2_columns(
                                       upper=-0.9998,
                                       count=10,
                                       sum=-10.0,
+                                      min=-1.0,
                                       max=-1.0),
                     hist.FrequencyBin(lower=0.0,
                                       upper=0.00019999999999997797,
                                       count=5,
                                       sum=0.0,
+                                      min=0,
                                       max=0.0),
-                    hist.FrequencyBin(
-                        lower=0.9998,
-                        upper=1.0, count=10, sum=10.0, max=1.0)
+                    hist.FrequencyBin(lower=0.9998,
+                                      upper=1.0,
+                                      count=10,
+                                      sum=10.0,
+                                      min=1.0,
+                                      max=1.0)
                 ]),
         ),
         pre_aggregated=(False, True))
@@ -369,15 +435,15 @@ def test_compute_partition_sum_histogram_2_columns(self,
         expected = [
             hist.Histogram(hist.HistogramType.SUM_PER_PARTITION, [
                 hist.FrequencyBin(
-                    lower=5.0, upper=5.0001, count=1, sum=5, max=5),
+                    lower=5.0, upper=5.0001, count=1, sum=5, min=5, max=5),
                 hist.FrequencyBin(
-                    lower=5.9999, upper=6.0, count=1, sum=6, max=6)
+                    lower=5.9999, upper=6.0, count=1, sum=6, min=6, max=6)
             ]),
             hist.Histogram(hist.HistogramType.SUM_PER_PARTITION, [
                 hist.FrequencyBin(
-                    lower=50.0, upper=50.001, count=1, sum=50, max=50),
+                    lower=50.0, upper=50.001, count=1, sum=50, min=50, max=50),
                 hist.FrequencyBin(
-                    lower=59.999, upper=60.0, count=1, sum=60, max=60)
+                    lower=59.999, upper=60.0, count=1, sum=60, min=60, max=60)
             ])
         ]
         if pre_aggregated:
diff --git a/tests/private_contribution_bounds_test.py b/tests/private_contribution_bounds_test.py
index 768c1f85..6aeab1ac 100644
--- a/tests/private_contribution_bounds_test.py
+++ b/tests/private_contribution_bounds_test.py
@@ -54,16 +54,19 @@ def test_score_laplace_noise_valid_values_calculates_score_correctly(self):
                                                             upper=2,
                                                             count=100,
                                                             sum=100,
+                                                            min=1,
                                                             max=1),
                                           hist.FrequencyBin(lower=2,
                                                             upper=6,
                                                             count=10,
                                                             sum=10,
+                                                            min=2,
                                                             max=5),
                                           hist.FrequencyBin(lower=6,
                                                             upper=100,
                                                             count=20,
                                                             sum=20,
+                                                            min=6,
                                                             max=60)
                                       ])
         l0_scoring_function = private_contribution_bounds.L0ScoringFunction(
@@ -95,39 +98,6 @@ def test_score_laplace_noise_valid_values_calculates_score_correctly(self):
         # -0.5 * 200 * 200 / 0.9 * sqrt(2) - 0.5 * 0
         self.assertAlmostEqual(-31427.0, score_200, places=1)
 
-    def test_score_laplace_noise_invalid_values_throws_exception(self):
-        params = construct_params(
-            aggregation_noise_kind=pipeline_dp.NoiseKind.LAPLACE,
-            aggregation_eps=0.9,
-            max_partitions_contributed_upper_bound=100)
-        number_of_partitions = 200
-        l0_histogram = hist.Histogram(name=hist.HistogramType.L0_CONTRIBUTIONS,
-                                      bins=[
-                                          hist.FrequencyBin(lower=1,
-                                                            upper=2,
-                                                            count=100,
-                                                            sum=100,
-                                                            max=1),
-                                          hist.FrequencyBin(lower=2,
-                                                            upper=6,
-                                                            count=10,
-                                                            sum=10,
-                                                            max=5),
-                                          hist.FrequencyBin(lower=6,
-                                                            upper=100,
-                                                            count=20,
-                                                            sum=20,
-                                                            max=60)
-                                      ])
-        l0_scoring_function = private_contribution_bounds.L0ScoringFunction(
-            params, number_of_partitions, l0_histogram)
-
-        with self.assertRaises(RuntimeError):
-            l0_scoring_function.score(-1)
-
-        with self.assertRaises(RuntimeError):
-            l0_scoring_function.score(0)
-
     def test_score_gaussian_noise_valid_values_calculates_score_correctly(self):
         params = construct_params(
             aggregation_noise_kind=pipeline_dp.NoiseKind.GAUSSIAN,
@@ -141,16 +111,19 @@ def test_score_gaussian_noise_valid_values_calculates_score_correctly(self):
                                                             upper=2,
                                                             count=100,
                                                             sum=100,
+                                                            min=1,
                                                             max=1),
                                           hist.FrequencyBin(lower=2,
                                                             upper=6,
                                                             count=10,
                                                             sum=10,
+                                                            min=2,
                                                             max=5),
                                           hist.FrequencyBin(lower=6,
                                                             upper=100,
                                                             count=20,
                                                             sum=20,
+                                                            min=6,
                                                             max=60)
                                       ])
         l0_scoring_function = private_contribution_bounds.L0ScoringFunction(
@@ -183,37 +156,6 @@ def test_score_gaussian_noise_valid_values_calculates_score_correctly(self):
         # -0.5 * 200 * sqrt(200) * sigma - 0.5 * 0
         self.assertAlmostEqual(-3977, score_200, delta=10)
 
-    def test_score_gaussian_noise_invalid_values_throws_exception(self):
-        params = construct_params(
-            aggregation_noise_kind=pipeline_dp.NoiseKind.GAUSSIAN,
-            aggregation_eps=0.9,
-            aggregation_delta=0.001,
-            max_partitions_contributed_upper_bound=100)
-        number_of_partitions = 200
-        l0_histogram = hist.Histogram(name=hist.HistogramType.L0_CONTRIBUTIONS,
-                                      bins=[
-                                          hist.FrequencyBin(lower=1,
-                                                            upper=2,
-                                                            count=100,
-                                                            sum=100,
-                                                            max=1),
-                                          hist.FrequencyBin(lower=2,
-                                                            upper=6,
-                                                            count=10,
-                                                            sum=10,
-                                                            max=5),
-                                          hist.FrequencyBin(lower=6,
-                                                            upper=100,
-                                                            count=20,
-                                                            sum=20,
-                                                            max=60)
-                                      ])
-        l0_scoring_function = private_contribution_bounds.L0ScoringFunction(
-            params, number_of_partitions, l0_histogram)
-
-        with self.assertRaises(RuntimeError):
-            l0_scoring_function.score(0)
-
 
 class PrivateL0CalculatorTest(unittest.TestCase):
 
@@ -232,16 +174,19 @@ def test_calculate_returns_one_of_the_lower_bounds(self):
                                                             upper=2,
                                                             count=100,
                                                             sum=100,
+                                                            min=1,
                                                             max=1),
                                           hist.FrequencyBin(lower=2,
                                                             upper=6,
                                                             count=10,
                                                             sum=10,
+                                                            min=2,
                                                             max=5),
                                           hist.FrequencyBin(lower=6,
                                                             upper=100,
                                                             count=20,
                                                             sum=20,
+                                                            min=6,
                                                             max=60)
                                       ])
         histograms = [
@@ -278,11 +223,13 @@ def test_calculate_one_bound_has_much_higher_score_returns_it(self):
                                                             upper=2,
                                                             count=1,
                                                             sum=1,
+                                                            min=1,
                                                             max=1),
                                           hist.FrequencyBin(lower=2,
                                                             upper=3,
                                                             count=10000,
                                                             sum=10000,
+                                                            min=2,
                                                             max=2)
                                       ])
         histograms = [