def test_end_to_end_noise_without_oneplus_budget(self):
max_freq = 3
this_multi_set = generate_multi_set([(1, 2), (2, 3), (3, 1), (10, 1)])
that_multi_set = generate_multi_set([(1, 1), (3, 1), (4, 5), (5, 1)])
this_noised_sketch = stratified_sketch.StratifiedSketch.init_from_exact_multi_set(
max_freq,
this_multi_set,
epsilon=0.8,
epsilon_split=0,
noiser_class=PlusNoiser,
union=stratified_sketch.ExactSetOperator.union,
cardinality_sketch_factory=ExactMultiSet.get_sketch_factory(),
random_seed=1)
that_noised_sketch = stratified_sketch.StratifiedSketch.init_from_exact_multi_set(
max_freq,
that_multi_set,
epsilon=0.8,
epsilon_split=0,
noiser_class=PlusNoiser,
union=stratified_sketch.ExactSetOperator.union,
cardinality_sketch_factory=ExactMultiSet.get_sketch_factory(),
random_seed=1)
estimator = stratified_sketch.PairwiseEstimator(
denoiser_class=MinusDenoiser,
sketch_operator=stratified_sketch.ExactSetOperator,
cardinality_estimator=LosslessEstimator())
estimated = estimator(this_noised_sketch, that_noised_sketch)
expected = [6, 4, 3]
self.assertEqual(estimated, expected)
def test_noiser_for_exact_multi_set(self):
s = ExactMultiSet()
s.add_ids([1, 2])
n = AddRandomElementsNoiser(num_random_elements=3,
random_state=np.random.RandomState(1))
s_copy = n(s)
self.assertLen(s, 2)
self.assertLen(s_copy, 5)
def run_one(self):
"""Run one iteration.
Returns:
A pd.DataFrame that has 2f+1 columns, where f is the maximum
frequency. The column names are num_sets, estimated_cardinality_i
and true_cardinality_i, for i = 1, ..., f.
"""
set_generator = self.set_generator_factory(self.set_random_state)
sketch_random_seed = self.sketch_random_state.randint(2**32 - 1)
# Build the sketches and keep track of actual ids for
# later comparison.
sketches = []
actual_ids = []
for campaign_ids in set_generator:
actual_ids.append(campaign_ids)
sketch = self.sketch_estimator_config.sketch_factory(
sketch_random_seed)
sketch.add_ids(campaign_ids)
sketches.append(sketch)
# Optionally noise the sketches.
if hasattr(self.sketch_estimator_config, 'sketch_noiser'
) and self.sketch_estimator_config.sketch_noiser:
sketch_noiser = self.sketch_estimator_config.sketch_noiser
sketches = [sketch_noiser(s) for s in sketches]
# Estimate cardinality for 1, 2, ..., n pubs.
estimator = self.sketch_estimator_config.estimator
# A set that keeps the running union.
true_union = ExactMultiSet()
metrics = []
max_freq = self.sketch_estimator_config.max_frequency
for i in range(len(sketches)):
estimated_cardinality = self._extend_histogram(
estimator(sketches[:i + 1]), max_freq)
if hasattr(self.sketch_estimator_config, 'estimate_noiser'
) and self.sketch_estimator_config.estimate_noiser:
estimated_cardinality = [
self.sketch_estimator_config.estimate_noiser(e)
for e in estimated_cardinality
]
true_union.add_ids(actual_ids[i])
true_cardinality = self._extend_histogram(
LosslessEstimator()([true_union]), max_freq)
shuffle_distance = self._shuffle_distance(estimated_cardinality,
true_cardinality)
metrics.append([i + 1] + estimated_cardinality + true_cardinality +
[shuffle_distance])
df_columns = ([NUM_SETS] + [
ESTIMATED_CARDINALITY_BASENAME + str(i + 1)
for i in range(max_freq)
] + [TRUE_CARDINALITY_BASENAME + str(i + 1)
for i in range(max_freq)] + [SHUFFLE_DISTANCE])
df = pd.DataFrame(metrics, columns=df_columns)
return df
def test_heterogeneous_multi_set_generator_test_impression_count(self):
g = HeterogeneousMultiSetGenerator(1000, [10], [(1, 1)],
np.random.RandomState(1))
e = ExactMultiSet()
for ids in g:
e.add_ids(ids)
h = LosslessEstimator()([e])
self.assertEqual(h[0], 10)
self.assertGreater(len(h), 1)
def test_heterogeneous_multi_set_generator_with_frequency_cap(self):
g = HeterogeneousMultiSetGenerator(1000, [100], [(1, 1)],
np.random.RandomState(1),
freq_cap=1)
e = ExactMultiSet()
for ids in g:
e.add_ids(ids)
h = LosslessEstimator()([e])
self.assertEqual(h, [100])
def test_homogeneous_pmf_multiset_generator_single_set(self):
pmfgen = HomogeneousPmfMultiSetGenerator(100, [2], [[1]],
np.random.RandomState(1))
hists = []
for s in pmfgen:
e = ExactMultiSet()
e.add_ids(s)
hists.append(LosslessEstimator()([e]))
self.assertLen(hists, 1)
self.assertEqual(hists[0], [2])
def test_publisher_constant_frequency_set_generator(self):
gen = PublisherConstantFrequencySetGenerator(100, [1, 2, 3], 3,
np.random.RandomState(1))
hists = []
for s in gen:
e = ExactMultiSet()
e.add_ids(s)
hists.append(LosslessEstimator()([e]))
self.assertLen(hists, 3)
self.assertEqual(hists[0], [1, 1, 1])
self.assertEqual(hists[1], [2, 2, 2])
self.assertEqual(hists[2], [3, 3, 3])
def test_less_one_estimator_multiple_for_exact_multi_set(self):
s1 = ExactMultiSet()
s1.add_ids([1, 2])
s2 = ExactMultiSet()
s2.add_ids([1, 3, 4])
e = LessOneEstimator()
self.assertEqual(e([s1, s2]), [3, 0])
def test_independent_set_estimator_two_sketches_single_frequency(self): sketch1 = ExactMultiSet() sketch1.add_ids(range(50)) sketch2 = ExactMultiSet() sketch2.add_ids(range(50)) estimator = IndependentSetEstimator(LosslessEstimator(), 100) result = estimator([sketch1, sketch2]) self.assertEqual(result, [75, 25])
def test_independent_set_estimator_two_sketches_multiple_frequencies(self): sketch1 = ExactMultiSet() sketch1.add_ids(list(range(50)) + list(range(20))) sketch2 = ExactMultiSet() sketch2.add_ids(list(range(30)) + list(range(10))) estimator = IndependentSetEstimator(LosslessEstimator(), 100) result = estimator([sketch1, sketch2]) self.assertEqual(result, [65, 34, 9, 2])
def test_multi_frequency_sketch_for_exact_multi_set(self):
s = ExactMultiSet()
s.add_ids([1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 1, 2, 3, 1, 2, 1, 1])
self.assertEqual(s.frequency(1), 6)
self.assertEqual(s.frequency(2), 4)
self.assertEqual(s.frequency(3), 3)
self.assertEqual(s.frequency(4), 2)
self.assertEqual(s.frequency(5), 1)
self.assertEqual(s.frequency(6), 1)
self.assertEqual(LosslessEstimator()([s]), [6, 4, 3, 2, 1, 1])
def setUp(self):
super(PairwiseEstimatorTest, self).setUp()
max_freq = 3
this_multi_set = generate_multi_set([(1, 2), (2, 3), (3, 1), (10, 1)])
that_multi_set = generate_multi_set([(1, 1), (3, 1), (4, 5), (5, 1)])
self.this_sketch = stratified_sketch.StratifiedSketch.init_from_exact_multi_set(
max_freq,
this_multi_set,
cardinality_sketch_factory=ExactMultiSet.get_sketch_factory(),
random_seed=1)
self.that_sketch = stratified_sketch.StratifiedSketch.init_from_exact_multi_set(
max_freq,
that_multi_set,
cardinality_sketch_factory=ExactMultiSet.get_sketch_factory(),
random_seed=1)
self.estimator = stratified_sketch.PairwiseEstimator(
sketch_operator=stratified_sketch.ExactSetOperator,
cardinality_estimator=LosslessEstimator())
self.merge_expected = {
ONE_PLUS: {
1: 1,
2: 1,
3: 1,
4: 1,
5: 1,
10: 1
},
1: {
5: 1,
10: 1
},
2: {
3: 1
},
'3+': {
1: 1,
2: 1,
4: 1
},
}
def test_sketch_building_from_exact_multi_set(self):
max_freq = 3
vids = [1, 2, 2, 3, 3, 3, 4, 4, 4, 4]
input_set = ExactMultiSet()
for vid in vids:
input_set.add(vid)
expected = {
'1+': {
1: 1,
2: 1,
3: 1,
4: 1
},
1: {
1: 1
},
2: {
2: 1
},
'3+': {
3: 1,
4: 1
}
}
s = stratified_sketch.StratifiedSketch.init_from_exact_multi_set(
max_freq,
input_set,
cardinality_sketch_factory=ExactMultiSet.get_sketch_factory(),
union=stratified_sketch.ExactSetOperator.union,
random_seed=1)
self.assertLen(s.sketches.keys(), len(expected.keys()))
for freq, sketch in s.sketches.items():
self.assertEqual(sketch.ids(), expected[freq])
def generate_sketches_from_sets(self,
multi_sets,
max_freq,
epsilon=0,
epsilon_split=0):
sketches = []
for multi_set in multi_sets:
s = stratified_sketch.StratifiedSketch.init_from_exact_multi_set(
max_freq,
multi_set,
union=stratified_sketch.ExactSetOperator.union,
epsilon=epsilon,
epsilon_split=epsilon_split,
noiser_class=PlusNoiser,
cardinality_sketch_factory=ExactMultiSet.get_sketch_factory(),
random_seed=1)
sketches.append(s)
return sketches
def test_noise_without_oneplus_budget(self):
max_freq = 3
s = stratified_sketch.StratifiedSketch(
max_freq=max_freq,
epsilon=0.8,
epsilon_split=0,
noiser_class=PlusNoiser,
union=stratified_sketch.ExactSetOperator.union,
cardinality_sketch_factory=ExactMultiSet.get_sketch_factory(),
random_seed=1)
vids = [1, 2, 2, 3, 3, 3, 4, 4, 4, 4]
for vid in vids:
s.add(vid)
s.create_sketches()
expected = {
'1+': {
9.0: 1,
10.0: 1,
11.0: 1,
12.0: 1
},
1: {
9.0: 1
},
2: {
10.0: 1
},
'3+': {
11.0: 1,
12.0: 1
}
}
self.assertLen(s.sketches.keys(), len(expected.keys()))
for freq, sketch in s.sketches.items():
self.assertEqual(sketch.ids(), expected[freq])
s._destroy_sketches()
self.assertEqual(s.sketches, {})
def test_sketch_create_and_destroy(self):
max_freq = 3
s = stratified_sketch.StratifiedSketch(
max_freq=max_freq,
union=stratified_sketch.ExactSetOperator.union,
cardinality_sketch_factory=ExactMultiSet.get_sketch_factory(),
random_seed=1)
for k in range(max_freq + 2):
for i in range(k):
s.add(k)
s.create_sketches()
expected = {
'1+': {
1: 1,
2: 1,
3: 1,
4: 1
},
1: {
1: 1
},
2: {
2: 1
},
'3+': {
3: 1,
4: 1
}
}
self.assertLen(s.sketches.keys(), len(expected.keys()))
for freq, sketch in s.sketches.items():
self.assertEqual(sketch.ids(), expected[freq])
s._destroy_sketches()
self.assertEqual(s.sketches, {})
def test_sketch_building_from_set_generator(self):
universe_size = 1000
set_sizes = [100] * 5
max_freq = 3
expected_sets = [[1, 1, 1, 2, 2, 3], [1, 1, 1, 3, 3, 4]]
set_gen = FakeSetGenerator(expected_sets)
s = stratified_sketch.StratifiedSketch.init_from_set_generator(
max_freq,
set_generator=set_gen,
union=stratified_sketch.ExactSetOperator.union,
cardinality_sketch_factory=ExactMultiSet.get_sketch_factory(),
random_seed=1)
expected = {
'1+': {
4: 1,
1: 1,
2: 1,
3: 1
},
1: {
4: 1
},
2: {
2: 1
},
'3+': {
1: 1,
3: 1
}
}
self.assertLen(s.sketches.keys(), len(expected.keys()))
for freq, sketch in s.sketches.items():
self.assertEqual(sketch.ids(), expected[freq])
def __call__(self, sketch):
denoised_sketch = ExactMultiSet()
for x in sketch.ids():
denoised_sketch.add_ids([x - self.constant] * sketch.frequency(x))
return denoised_sketch
def setUp(self):
super(InteroperabilityTest, self).setUp()
self.number_of_trials = 2
self.universe_size = 2000
self.set_size_list = [5, 7, 9]
self.large_set_size = 6
self.small_set_size = 3
self.sketch_size = 128
self.number_of_sets = 3
self.set_size = 50
self.num_large_sets = 1
self.num_small_sets = 3
self.order = set_generator.ORDER_RANDOM
self.user_activity_association = (
set_generator.USER_ACTIVITY_ASSOCIATION_INDEPENDENT)
self.shared_prop = 0.2
self.num_bloom_filter_hashes = 2
self.exponential_bloom_filter_decay_rate = 10
self.geometic_bloom_filter_probability = 0.08
self.noiser_epsilon = np.log(3)
self.noiser_flip_probability = .25
self.set_random_state = np.random.RandomState(42)
self.sketch_random_state = np.random.RandomState(137)
self.noise_random_state = np.random.RandomState(3)
# non-noised estimators
estimator_config_cascading_legions = SketchEstimatorConfig(
name='cascading_legions',
sketch_factory=CascadingLegions.get_sketch_factory(
self.sketch_size, self.sketch_size),
estimator=Estimator())
estimator_config_bloom_filter = SketchEstimatorConfig(
name='bloom_filter-union_estimator',
sketch_factory=BloomFilter.get_sketch_factory(
self.sketch_size, self.num_bloom_filter_hashes),
estimator=UnionEstimator())
estimator_config_geometric_bloom_filter = SketchEstimatorConfig(
name='geo_bloom_filter-first_moment_geo',
sketch_factory=GeometricBloomFilter.get_sketch_factory(
self.sketch_size, self.geometic_bloom_filter_probability),
estimator=FirstMomentEstimator(method='geo'))
estimator_config_logarithmic_bloom_filter = SketchEstimatorConfig(
name='log_bloom_filter-first_moment_log',
sketch_factory=LogarithmicBloomFilter.get_sketch_factory(
self.sketch_size),
estimator=FirstMomentEstimator(method='log'))
estimator_config_exponential_bloom_filter = SketchEstimatorConfig(
name='exp_bloom_filter-first_moment_exp',
sketch_factory=ExponentialBloomFilter.get_sketch_factory(
self.sketch_size, self.exponential_bloom_filter_decay_rate),
estimator=FirstMomentEstimator(method='exp'))
estimator_config_voc = SketchEstimatorConfig(
name='vector_of_counts-sequential',
sketch_factory=VectorOfCounts.get_sketch_factory(self.sketch_size),
estimator=SequentialEstimator())
estimator_config_exact = SketchEstimatorConfig(
name='exact_set-lossless',
sketch_factory=ExactMultiSet.get_sketch_factory(),
estimator=LosslessEstimator())
estimator_config_hll = SketchEstimatorConfig(
name='hyper_log_log',
sketch_factory=HyperLogLogPlusPlus.get_sketch_factory(
self.sketch_size),
estimator=HllCardinality())
estimator_config_expadbf_first_moment_global_dp = SketchEstimatorConfig(
name='estimator_config_expadbf_first_moment_global_d',
sketch_factory=ExponentialBloomFilter.get_sketch_factory(
length=10**5, decay_rate=10),
estimator=FirstMomentEstimator(
method=FirstMomentEstimator.METHOD_EXP,
noiser=GeometricEstimateNoiser(epsilon=math.log(3))))
config_list = [
estimator_config_exact,
estimator_config_cascading_legions,
estimator_config_bloom_filter,
estimator_config_logarithmic_bloom_filter,
estimator_config_exponential_bloom_filter,
estimator_config_geometric_bloom_filter,
estimator_config_voc,
estimator_config_hll,
estimator_config_expadbf_first_moment_global_dp,
]
self.name_to_non_noised_estimator_config = {
config.name: config
for config in config_list
}
# noised estimators
noised_estimator_config_cascading_legions = SketchEstimatorConfig(
name='cascading_legions',
sketch_factory=CascadingLegions.get_sketch_factory(
self.sketch_size, self.sketch_size),
estimator=Estimator(),
sketch_noiser=Noiser(self.noiser_flip_probability))
noised_estimator_config_bloom_filter = SketchEstimatorConfig(
name='bloom_filter-union_estimator',
sketch_factory=BloomFilter.get_sketch_factory(
self.sketch_size, self.num_bloom_filter_hashes),
estimator=UnionEstimator(),
sketch_noiser=BlipNoiser(self.noiser_epsilon,
self.noise_random_state))
noised_estimator_config_geometric_bloom_filter = SketchEstimatorConfig(
name='geo_bloom_filter-first_moment_geo',
sketch_factory=GeometricBloomFilter.get_sketch_factory(
self.sketch_size, self.geometic_bloom_filter_probability),
estimator=FirstMomentEstimator(
method='geo', denoiser=SurrealDenoiser(epsilon=math.log(3))),
sketch_noiser=BlipNoiser(self.noiser_epsilon,
self.noise_random_state))
noised_estimator_config_logarithmic_bloom_filter = SketchEstimatorConfig(
name='log_bloom_filter-first_moment_log',
sketch_factory=LogarithmicBloomFilter.get_sketch_factory(
self.sketch_size),
estimator=FirstMomentEstimator(
method='log', denoiser=SurrealDenoiser(epsilon=math.log(3))),
sketch_noiser=BlipNoiser(self.noiser_epsilon,
self.noise_random_state))
noised_estimator_config_exponential_bloom_filter = SketchEstimatorConfig(
name='exp_bloom_filter-first_moment_exp',
sketch_factory=ExponentialBloomFilter.get_sketch_factory(
self.sketch_size, self.exponential_bloom_filter_decay_rate),
estimator=FirstMomentEstimator(
method='exp', denoiser=SurrealDenoiser(epsilon=math.log(3))),
sketch_noiser=BlipNoiser(self.noiser_epsilon,
self.noise_random_state))
noised_estimator_config_voc = SketchEstimatorConfig(
name='vector_of_counts-sequential',
sketch_factory=VectorOfCounts.get_sketch_factory(self.sketch_size),
estimator=SequentialEstimator(),
sketch_noiser=LaplaceNoiser())
noised_estimator_config_exact = SketchEstimatorConfig(
name='exact_set-lossless',
sketch_factory=ExactMultiSet.get_sketch_factory(),
estimator=LosslessEstimator(),
sketch_noiser=AddRandomElementsNoiser(1, self.noise_random_state))
noised_config_list = [
noised_estimator_config_exact,
noised_estimator_config_cascading_legions,
noised_estimator_config_bloom_filter,
noised_estimator_config_logarithmic_bloom_filter,
noised_estimator_config_exponential_bloom_filter,
noised_estimator_config_geometric_bloom_filter,
noised_estimator_config_voc,
]
self.name_to_noised_estimator_config = {
config.name: config
for config in noised_config_list
}
def test_less_one_estimator_for_empty_set(self):
s = ExactMultiSet()
e = LessOneEstimator()
self.assertRaises(ValueError, lambda: e([s]))
def test_less_one_estimator_no_freq1or2(self):
s = ExactMultiSet()
s.add_ids([1, 1, 1, 2, 2, 2, 3, 3, 3])
e = LessOneEstimator()
self.assertEqual(e([s]), [2, 2, 2])
def test_lossless_estimator_for_empty_set(self):
s = ExactMultiSet()
e = LosslessEstimator()
self.assertEqual(e([s]), [0])
def test_lossless_estimator_for_exact_multi_set(self):
s = ExactMultiSet()
s.add_ids([1, 2, 3, 1, 2, 1])
e = LosslessEstimator()
self.assertEqual(e([s]), [3, 2, 1])
def test_sketch_for_exact_multi_set(self):
s = ExactMultiSet()
s.add_ids([1, 2])
self.assertLen(s, 2, "ID set has wrong length.")
self.assertIn(2, s, "ID set is missing an ID.")
self.assertNotIn(3, s, "ID set contains an unexpected ID.")
def test_independent_set_estimator_universe_size_exceeded(self):
sketch = ExactMultiSet()
sketch.add_ids(range(11))
estimator = IndependentSetEstimator(LosslessEstimator(), 10)
with self.assertRaises(AssertionError):
result = estimator([sketch])
def test_independent_set_estimator_single_sketch(self): sketch = ExactMultiSet() sketch.add_ids([1, 2, 2, 3, 3, 3, 4, 5]) estimator = IndependentSetEstimator(LosslessEstimator(), 100) result = estimator([sketch]) self.assertEqual(result, [5, 2, 1])
def generate_multi_set(tuple_list):
multi_set = ExactMultiSet()
for tuple in tuple_list:
multi_set.add_ids([tuple[0]] * tuple[1])
return multi_set
