Wrapper for autoai_libs.nsfa transformer added

lale/lib/autoai_libs/__init__.py

@@ -62,6 +62,7 @@
 * lale.lib.autoai_libs. `TGen`_
 * lale.lib.autoai_libs. `FS1`_
 * lale.lib.autoai_libs. `FS2`_
+* lale.lib.autoai_libs. `NSFA`_
 
 .. _`ColumnSelector`: lale.lib.autoai_libs.column_selector.html
 .. _`NumpyColumnSelector`: lale.lib.autoai_libs.numpy_column_selector.html
@@ -88,6 +89,7 @@
 .. _`TGen`: lale.lib.autoai_libs.tgen.html
 .. _`FS1`: lale.lib.autoai_libs.fs1.html
 .. _`FS2`: lale.lib.autoai_libs.fs2.html
+.. _`NSFA`: lale.lib.autoai_libs.nsfa.html
 """
 
 from lale import register_lale_wrapper_modules
@@ -102,6 +104,7 @@
 from .float_str2_float import FloatStr2Float as FloatStr2Float
 from .fs1 import FS1 as FS1
 from .fs2 import FS2 as FS2
+from .nsfa import NSFA as NSFA
 from .num_imputer import NumImputer as NumImputer
 from .numpy_column_selector import NumpyColumnSelector as NumpyColumnSelector
 from .numpy_permute_array import NumpyPermuteArray as NumpyPermuteArray

lale/lib/autoai_libs/nsfa.py

@@ -0,0 +1,127 @@
+# Copyright 2020 IBM Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import autoai_libs.cognito.transforms.transform_utils
+
+import lale.docstrings
+import lale.operators
+
+
+class _NSFAImpl:
+    def __init__(self, **hyperparams):
+        self._wrapped_model = autoai_libs.cognito.transforms.transform_utils.NSFA(
+            **hyperparams
+        )
+
+    def fit(self, X, **fit_params):
+        self._wrapped_model.fit(X, **fit_params)
+        return self
+
+    def transform(self, X):
+        result = self._wrapped_model.transform(X)
+        return result
+
+
+_hyperparams_schema = {
+    "allOf": [
+        {
+            "description": "This first object lists all constructor arguments with their types, but omits constraints for conditional hyperparameters.",
+            "type": "object",
+            "additionalProperties": False,
+            "required": [
+                "significance"
+            ],
+            "relevantToOptimizer": [],
+            "properties": {
+                "significance": {
+                    "description": "Array with a feature significance values for each column.",
+                    "anyOf": [
+                        {"type": "array", "items": {"type": "number", "minimum": 0.0}},
+                        {"type": "array", "items": {"type": "integer", "minimum": 0}},
+                        {
+                            "enum": [None],
+                            "description": "Passing None will result in some failure to eliminate insignificant data.",
+                        }
+                    ],
+                    "default": None
+                },
+                "protected_cols": {
+                    "description": "Array with indices of features that are protected by fairness definition.",
+                    "anyOf": [
+                        {"type": "array", "items": {"type": "integer", "minimum": 0}},
+                        {"enum": [None]}
+                    ],
+                    "default": None
+                },
+                "analyzer": {
+                    "description": "A tool used to analyse insignificant columns.",
+                    "laleType": "Any",
+                    "default": None
+                }
+            },
+        }
+    ]
+}
+
+_input_fit_schema = {
+    "type": "object",
+    "required": ["X"],
+    "additionalProperties": False,
+    "properties": {
+        "X": {
+            "type": "array",
+            "items": {"type": "array", "items": {"laleType": "Any"}}
+        }
+    }
+}
+
+_input_transform_schema = {
+    "type": "object",
+    "required": ["X"],
+    "additionalProperties": False,
+    "properties": {
+        "X": {
+            "type": "array",
+            "items": {"type": "array", "items": {"laleType": "Any"}}
+        }
+    }
+}
+
+_output_transform_schema = {
+    "description": "Features; the outer array is over samples.",
+    "type": "array",
+    "items": {"type": "array", "items": {"laleType": "Any"}}
+}
+
+_combined_schemas = {
+    "$schema": "http://json-schema.org/draft-04/schema#",
+    "description": """Operator from `autoai_libs`_. Feature transformation for dimension reduction by significance analysis.
+
+.. _`autoai_libs`: https://pypi.org/project/autoai-libs""",
+    "documentation_url": "https://lale.readthedocs.io/en/latest/modules/lale.lib.autoai_libs.nsfa.html",
+    "import_from": "autoai_libs.cognito.transforms.transform_utils",
+    "type": "object",
+    "tags": {"pre": [], "op": ["transformer"], "post": []},
+    "properties": {
+        "hyperparams": _hyperparams_schema,
+        "input_fit": _input_fit_schema,
+        "input_transform": _input_transform_schema,
+        "output_transform": _output_transform_schema,
+    }
+}
+
+
+NSFA = lale.operators.make_operator(_NSFAImpl, _combined_schemas)
+
+lale.docstrings.set_docstrings(NSFA)