make UniformEncoder the default for cat and boolea

sdv-dev · Aug 10, 2023 · 83f4112 · 83f4112
1 parent 1a0e24b
commit 83f4112
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Showing 5 changed files with 30 additions and 30 deletions.
diff --git a/rdt/transformers/__init__.py b/rdt/transformers/__init__.py
@@ -91,8 +91,8 @@ def get_transformer_name(transformer):
 
 DEFAULT_TRANSFORMERS = {
     'numerical': FloatFormatter(),
-    'categorical': LabelEncoder(add_noise=True),
-    'boolean': LabelEncoder(add_noise=True),
+    'categorical': UniformEncoder(),
+    'boolean': UniformEncoder(),
     'datetime': UnixTimestampEncoder(),
     'text': RegexGenerator(),
     'pii': AnonymizedFaker(),

diff --git a/rdt/transformers/categorical.py b/rdt/transformers/categorical.py
@@ -163,7 +163,7 @@ def _transform(self, data):
         def map_labels(label):
             return np.random.uniform(self.intervals[label][0], self.intervals[label][1])
 
-        return data_with_none.map(map_labels)
+        return data_with_none.map(map_labels).astype(float)
 
     def _reverse_transform(self, data):
         """Convert float values back to the original categorical values.

diff --git a/tests/integration/test_hyper_transformer.py b/tests/integration/test_hyper_transformer.py
@@ -10,8 +10,8 @@
 from rdt.errors import ConfigNotSetError, InvalidConfigError, InvalidDataError, NotFittedError
 from rdt.transformers import (
     AnonymizedFaker, BaseTransformer, BinaryEncoder, ClusterBasedNormalizer, FloatFormatter,
-    FrequencyEncoder, LabelEncoder, OneHotEncoder, RegexGenerator, UnixTimestampEncoder,
-    get_default_transformer, get_default_transformers)
+    FrequencyEncoder, LabelEncoder, OneHotEncoder, RegexGenerator, UniformEncoder,
+    UnixTimestampEncoder, get_default_transformer, get_default_transformers)
 from rdt.transformers.datetime import OptimizedTimestampEncoder
 from rdt.transformers.numerical import GaussianNormalizer
 from rdt.transformers.pii.anonymizer import PseudoAnonymizedFaker
@@ -93,17 +93,17 @@ def get_transformed_data():
         'integer': [1., 2., 1., 3., 1., 4., 2., 3.],
         'float': [0.1, 0.2, 0.1, 0.2, 0.1, 0.4, 0.2, 0.3],
         'categorical': [
-            0.9690758764963199, 0.8816575994729887, 1.1326495454234662, 1.7988488918189502,
-            0.9265972159030215, 1.885454600378942, 0.9280858691537548, 0.5093227924068265
+            0.6056724228102, 0.551035999670618, 0.6747435795337998, 0.9245683344321064,
+            0.5791232599393884, 0.9570454751421033, 0.5800536682210967, 0.3183267452542666
         ],
         'bool': [
-            0.26161253184788935, 0.5735484647493089, 0.026673806296574787, 1.197229599974477,
-            0.8860641570557322, 0.33432787358513416, 1.1089412122841389, 0.6182653878449814
+            0.196209398885917, 0.4301613485619816, 0.02000535472243109, 0.7993073999936193,
+            0.6645481177917991, 0.25074590518885065, 0.7772353030710347, 0.46369904088373604
         ],
         'datetime': datetimes,
         'names': [
-            0.24180193241041126, 1.9297787196579723, 1.5617500744772101, 0.6811042561384157,
-            0.48017218468846856, 2.2867787591284823, 0.25476586891248476, 0.620052082101593
+            0.15112620775650704, 0.857444679914493, 0.7654375186193025, 0.42569016008650984,
+            0.30010761543029285, 0.9108473448910603, 0.15922866807030298, 0.3875325513134956
         ]
     }, index=TEST_DATA_INDEX)
 
@@ -171,17 +171,17 @@ def test_hypertransformer_default_inputs():
         'integer': [1., 2., 1., 3., 1., 4., 2., 3.],
         'float': [0.1, 0.2, 0.1, 0.2, 0.1, 0.4, 0.2, 0.3],
         'categorical': [
-            0.9690758764963199, 0.8816575994729887, 1.1326495454234662, 2.7988488918189502,
-            0.9265972159030215, 2.8854546003789423, 0.9280858691537548, 0.5093227924068265
+            0.6056724228102, 0.551035999670618, 0.6415811931779333, 0.9497122229547376,
+            0.5791232599393884, 0.9713636500947356, 0.5800536682210967, 0.3183267452542666
         ],
         'bool': [
-            0.26161253184788935, 1.573548464749309, 0.026673806296574787, 2.1972295999744773,
-            0.8860641570557322, 1.334327873585134, 2.108941212284139, 0.6182653878449814
+            0.13080626592394468, 0.6433871161873272, 0.013336903148287393, 0.7993073999936193,
+            0.4430320785278661, 0.5835819683962835, 0.7772353030710347, 0.3091326939224907
         ],
         'datetime': expected_datetimes,
         'names': [
-            0.24180193241041126, 1.9297787196579723, 1.5617500744772101, 0.6811042561384157,
-            0.48017218468846856, 2.2867787591284823, 0.25476586891248476, 0.620052082101593
+            0.15112620775650704, 0.857444679914493, 0.7654375186193025, 0.42569016008650984,
+            0.30010761543029285, 0.9108473448910603, 0.15922866807030298, 0.3875325513134956
         ]
     }, index=TEST_DATA_INDEX)
     pd.testing.assert_frame_equal(transformed, expected_transformed)
@@ -212,10 +212,10 @@ def test_hypertransformer_default_inputs():
 
     assert isinstance(ht.field_transformers['integer'], FloatFormatter)
     assert isinstance(ht.field_transformers['float'], FloatFormatter)
-    assert isinstance(ht.field_transformers['categorical'], LabelEncoder)
-    assert isinstance(ht.field_transformers['bool'], LabelEncoder)
+    assert isinstance(ht.field_transformers['categorical'], UniformEncoder)
+    assert isinstance(ht.field_transformers['bool'], UniformEncoder)
     assert isinstance(ht.field_transformers['datetime'], UnixTimestampEncoder)
-    assert isinstance(ht.field_transformers['names'], LabelEncoder)
+    assert isinstance(ht.field_transformers['names'], UniformEncoder)
 
     get_default_transformers.cache_clear()
     get_default_transformer.cache_clear()
@@ -254,10 +254,10 @@ def test_hypertransformer_field_transformers():
         'transformers': {
             'integer': FloatFormatter(missing_value_replacement='mean'),
             'float': FloatFormatter(missing_value_replacement='mean'),
-            'categorical': LabelEncoder(add_noise=True),
-            'bool': LabelEncoder(add_noise=True),
+            'categorical': UniformEncoder(),
+            'bool': UniformEncoder(),
             'datetime': DummyTransformerNotMLReady(),
-            'names': LabelEncoder(add_noise=True)
+            'names': UniformEncoder()
         }
     }
 

diff --git a/tests/unit/test_hyper_transformer.py b/tests/unit/test_hyper_transformer.py
@@ -296,8 +296,8 @@ def test_detect_initial_config(self, logger_mock):
         field_transformers = {k: repr(v) for (k, v) in ht.field_transformers.items()}
         assert field_transformers == {
             'col1': 'FloatFormatter()',
-            'col2': 'LabelEncoder(add_noise=True)',
-            'col3': 'LabelEncoder(add_noise=True)',
+            'col2': 'UniformEncoder()',
+            'col3': 'UniformEncoder()',
             'col4': 'UnixTimestampEncoder()',
             'col5': 'FloatFormatter()'
         }
@@ -313,8 +313,8 @@ def test_detect_initial_config(self, logger_mock):
             '    },',
             '    "transformers": {',
             '        "col1": FloatFormatter(),',
-            '        "col2": LabelEncoder(add_noise=True),',
-            '        "col3": LabelEncoder(add_noise=True),',
+            '        "col2": UniformEncoder(),',
+            '        "col3": UniformEncoder(),',
             '        "col4": UnixTimestampEncoder(),',
             '        "col5": FloatFormatter()',
             '    }',

diff --git a/tests/unit/transformers/test___init__.py b/tests/unit/transformers/test___init__.py
@@ -1,7 +1,7 @@
 import pytest
 
 from rdt.transformers import (
-    AnonymizedFaker, BinaryEncoder, FloatFormatter, LabelEncoder, RegexGenerator,
+    AnonymizedFaker, BinaryEncoder, FloatFormatter, RegexGenerator, UniformEncoder,
     UnixTimestampEncoder, get_default_transformers, get_transformer_class, get_transformer_name)
 from rdt.transformers.addons.identity.identity import IdentityTransformer
 
@@ -109,8 +109,8 @@ def test_get_default_transformers():
     # Assert
     expected_dict = {
         'numerical': FloatFormatter,
-        'categorical': LabelEncoder,
-        'boolean': LabelEncoder,
+        'categorical': UniformEncoder,
+        'boolean': UniformEncoder,
         'datetime': UnixTimestampEncoder,
         'text': RegexGenerator,
         'pii': AnonymizedFaker,