make release-tag: Merge branch 'master' into stable

Author: npatki

HISTORY.md

@@ -1,5 +1,14 @@
 # History
 
+## v0.3.1 - 2021-07-12
+
+This release fixes a bug to make the privacy metrics available in the API docs.
+It also updates dependencies to ensure compatibility with the rest of the SDV ecosystem.
+
+### Issues closed
+
+* `CategoricalSVM` not being imported - Issue [#65](https://github.com/sdv-dev/SDMetrics/issues/65) by @csala
+
 ## v0.3.0 - 2021-03-30
 
 This release includes privacy metrics to evaluate if the real data could be obtained or

conda/meta.yaml

@@ -1,5 +1,5 @@
 {% set name = 'sdmetrics' %}
-{% set version = '0.3.0' %}
+{% set version = '0.3.1.dev2' %}
 
 package:
   name: "{{ name|lower }}"
@@ -26,7 +26,7 @@ requirements:
     - pytorch >=1.4,<2
     - sktime >=0.4,<0.6
     - copulas>=0.5.0,<0.6
-    - rdt >=0.4.1,<0.5
+    - rdt >=0.5.0,<0.6
   run:
     - python >=3.6,<3.9
     - scikit-learn >=0.23,<1
@@ -37,7 +37,7 @@ requirements:
     - pytorch >=1.4,<2
     - sktime >=0.4,<0.6
     - copulas>=0.5.0,<0.6
-    - rdt >=0.4.1,<0.5
+    - rdt >=0.5.0,<0.6
 
 about:
   home: "https://github.com/sdv-dev/SDMetrics"

sdmetrics/__init__.py

@@ -4,7 +4,7 @@
 
 __author__ = 'MIT Data To AI Lab'
 __email__ = 'dailabmit@gmail.com'
-__version__ = '0.3.0'
+__version__ = '0.3.1.dev2'
 
 import pandas as pd
 

sdmetrics/single_table/README.md

@@ -37,7 +37,7 @@ Implemented metrics:
       on a DecisionTreeClassifier from scikit-learn.
     * `MulticlassMLPClassifier`: ML Efficacy metric for multiclass classifications problems, based
       on an MLPClassifier from scikit-learn.
-    * `LinearRegressionClassifier`: ML Efficacy metric for regression problems, based
+    * `LinearRegression`: ML Efficacy metric for regression problems, based
       on a LinearRegression from scikit-learn.
     * `MLPRegressor`: ML Efficacy metric for regression problems, based
       on an MLPRegressor from scikit-learn.

sdmetrics/single_table/__init__.py

@@ -20,10 +20,15 @@
     ContinuousKLDivergence, DiscreteKLDivergence, MultiColumnPairsMetric)
 from sdmetrics.single_table.multi_single_column import (
     CSTest, KSTest, KSTestExtended, MultiSingleColumnMetric)
-from sdmetrics.single_table.privacy import (
-    CategoricalCAP, CategoricalEnsemble, CategoricalGeneralizedCAP, CategoricalKNN, CategoricalNB,
-    CategoricalPrivacyMetric, CategoricalRF, CategoricalZeroCAP, NumericalLR, NumericalMLP,
-    NumericalPrivacyMetric, NumericalRadiusNearestNeighbor, NumericalSVR)
+from sdmetrics.single_table.privacy.base import CategoricalPrivacyMetric, NumericalPrivacyMetric
+from sdmetrics.single_table.privacy.cap import (
+    CategoricalCAP, CategoricalGeneralizedCAP, CategoricalZeroCAP)
+from sdmetrics.single_table.privacy.categorical_sklearn import (
+    CategoricalKNN, CategoricalNB, CategoricalRF, CategoricalSVM)
+from sdmetrics.single_table.privacy.ensemble import CategoricalEnsemble
+from sdmetrics.single_table.privacy.numerical_sklearn import (
+    NumericalLR, NumericalMLP, NumericalSVR)
+from sdmetrics.single_table.privacy.radius_nearest_neighbor import NumericalRadiusNearestNeighbor
 
 __all__ = [
     'bayesian_network',
@@ -69,6 +74,7 @@
     'CategoricalKNN',
     'CategoricalNB',
     'CategoricalRF',
+    'CategoricalSVM',
     'CategoricalPrivacyMetric',
     'NumericalPrivacyMetric',
     'CategoricalEnsemble',

sdmetrics/single_table/privacy/__init__.py

@@ -2,24 +2,25 @@
 from sdmetrics.single_table.privacy.cap import (
     CategoricalCAP, CategoricalGeneralizedCAP, CategoricalZeroCAP)
 from sdmetrics.single_table.privacy.categorical_sklearn import (
-    CategoricalKNN, CategoricalNB, CategoricalRF)
+    CategoricalKNN, CategoricalNB, CategoricalRF, CategoricalSVM)
 from sdmetrics.single_table.privacy.ensemble import CategoricalEnsemble
 from sdmetrics.single_table.privacy.numerical_sklearn import (
     NumericalLR, NumericalMLP, NumericalSVR)
 from sdmetrics.single_table.privacy.radius_nearest_neighbor import NumericalRadiusNearestNeighbor
 
 __all__ = [
     'CategoricalCAP',
-    'CategoricalZeroCAP',
+    'CategoricalEnsemble',
     'CategoricalGeneralizedCAP',
-    'NumericalMLP',
-    'NumericalLR',
-    'NumericalSVR',
     'CategoricalKNN',
     'CategoricalNB',
-    'CategoricalRF',
     'CategoricalPrivacyMetric',
+    'CategoricalRF',
+    'CategoricalSVM',
+    'CategoricalZeroCAP',
+    'NumericalLR',
+    'NumericalMLP',
     'NumericalPrivacyMetric',
-    'CategoricalEnsemble',
-    'NumericalRadiusNearestNeighbor'
+    'NumericalRadiusNearestNeighbor',
+    'NumericalSVR',
 ]

setup.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.3.0
+current_version = 0.3.1.dev2
 commit = True
 tag = True
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)(\.(?P<release>[a-z]+)(?P<candidate>\d+))?

setup.py

@@ -20,7 +20,7 @@
     'sktime>=0.4,<0.6',
     'torch>=1.4,<2',
     'copulas>=0.5.0,<0.6',
-    'rdt>=0.4.1,<0.5',
+    'rdt>=0.5.0,<0.6',
 ]
 
 setup_requires = [
@@ -99,6 +99,6 @@
     test_suite='tests',
     tests_require=tests_require,
     url='https://github.com/sdv-dev/SDMetrics',
-    version='0.3.0',
+    version='0.3.1.dev2',
     zip_safe=False,
 )