0.1.0rc0 release plus minor fixes

.github/workflows/release.yml

@@ -32,4 +32,5 @@ jobs:
         with:
           username: ${{ secrets.DOCKER_USERNAME }}
           password: ${{ secrets.DOCKER_PASSWORD }}
+          repository: capeprivacy/cape-python
           tag_with_ref: true

cape_privacy/pandas/__init__.py

@@ -1,5 +1,6 @@
 from cape_privacy.pandas import dtypes
+from cape_privacy.pandas import registry
 from cape_privacy.pandas import transformations
 from cape_privacy.pandas.transformer import transformer
 
-__all__ = ["dtypes", "transformations", "transformer"]
+__all__ = ["dtypes", "transformations", "transformer", "registry"]

cape_privacy/spark/__init__.py

@@ -14,6 +14,7 @@ def is_available():
     from cape_privacy.spark import dtypes
     from cape_privacy.spark import transformations
     from cape_privacy.spark.transformer import transformer
+    from cape_privacy.spark import registry
 
     def is_available():
         return True
@@ -24,6 +25,7 @@ def is_available():
         "is_available",
         "transformations",
         "transformer",
+        "registry",
     ]
 
 del importlib

examples/simple_transformation.py

@@ -1,11 +1,11 @@
 import pandas as pd
 import numpy as np
 
-import cape_privacy.pandas as cape
+import cape_privacy as cape
 
 df = pd.DataFrame(np.ones(5,), columns=["value"])
 
 policy = cape.parse_policy("perturb_value_field.yaml")
-df = cape.apply_policy([policy], df)
+df = cape.apply_policy(policy, df)
 
 print(df.head())

examples/spark_example.py

@@ -20,6 +20,6 @@
 rounding_spec = policy.transformations[0]
 type_arg = getattr(dtypes, rounding_spec.args['dtype'])
 rounding_spec.args['dtype'] = type_arg
-rounder = tfms.Rounding(**rounding_spec.args)
+rounder = tfms.NumericRounding(**rounding_spec.args)
 df = df.select(rounder(functions.col('value')))
 df.show()

examples/spark_round.yaml

@@ -1,19 +1,13 @@
 label: spark-round-float
+version: 1
 transformations:
       - name: roundToZero
         type: roundNumeric
-        args:
-          dtype:
-            value: Float
-          precision:
-            value: 0
-spec:
-  label: spark-round-float
-  rules:
-  - target: records::creditcards.transactions
-    action: read
-    effect: allow
-    transformations:
-      - field: value
-        named: roundFloat
-  version: 1
+        dtype: Float
+        precision: 0
+rules:
+  - match:
+      name: value
+    actions:
+      - transform:
+          name: roundFloat

examples/tutorials/apply_policy_on_pandas_df.py

@@ -11,7 +11,7 @@
 policy = cape_privacy.parse_policy("mask_personal_information.yaml")
 # Apply the policy to the DataFrame
 # [NOTE] will be updated to `cape_privacy.apply_policy` #49 is merged
-df = cape_privacy.pandas.policy.apply_policies([policy], df)
+df = cape_privacy.apply_policy(policy, df)
 # Output the masked dataset
 print("Masked Dataset:")
 print(df.head())

examples/tutorials/apply_policy_on_spark_df.py

@@ -10,7 +10,7 @@
 policy = cape_privacy.parse_policy("mask_personal_information.yaml")
 # Apply the policy to the DataFrame
 # [NOTE] will be updated to `cape_privacy.apply_policy` #49 is merged
-df = cape_privacy.spark.policy.apply_policies([policy], df)
+df = cape_privacy.apply_policy(policy, df)
 # Output the masked dataset
 print("Masked Dataset:")
 print(df.show())

setup.py

@@ -2,8 +2,8 @@
 import setuptools
 
 setuptools.setup(
-    name="cape_privacy",
-    version="0.0.1",
+    name="cape-privacy",
+    version="0.1.0rc0",
     packages=setuptools.find_packages(),
     python_requires=">=3.6",
     license="Apache License 2.0",