Add curated schema for complement_nb

Author: shinnar

lale/lib/sklearn/__init__.py

@@ -27,6 +27,7 @@
 * lale.lib.sklearn. `BernoulliNB`_
 * lale.lib.sklearn. `CalibratedClassifierCV`_
 * lale.lib.sklearn. `CCA`_
+* lale.lib.sklearn. `ComplementNB`_
 * lale.lib.sklearn. `DecisionTreeClassifier`_
 * lale.lib.sklearn. `DummyClassifier`_
 * lale.lib.sklearn. `ExtraTreesClassifier`_
@@ -102,6 +103,7 @@
 .. _`Birch`: lale.lib.sklearn.birch.html
 .. _`CalibratedClassifierCV`: lale.lib.sklearn.calibrated_classifier_cv.html
 .. _`ColumnTransformer`: lale.lib.sklearn.column_transformer.html
+.. _`ComplementNB`: lale.lib.sklearn.complement_nb.html
 .. _`DecisionTreeClassifier`: lale.lib.sklearn.decision_tree_classifier.html
 .. _`DecisionTreeRegressor`: lale.lib.sklearn.decision_tree_regressor.html
 .. _`DummyClassifier`: lale.lib.sklearn.dummy_classifier.html
@@ -165,6 +167,7 @@
 from .calibrated_classifier_cv import CalibratedClassifierCV
 from .cca import CCA
 from .column_transformer import ColumnTransformer
+from .complement_nb import ComplementNB
 from .decision_tree_classifier import DecisionTreeClassifier
 from .decision_tree_regressor import DecisionTreeRegressor
 from .dummy_classifier import DummyClassifier

lale/lib/sklearn/complement_nb.py

@@ -0,0 +1,117 @@
+from sklearn.naive_bayes import ComplementNB as Op
+
+from lale.docstrings import set_docstrings
+from lale.operators import make_operator
+
+_hyperparams_schema = {
+    "$schema": "http://json-schema.org/draft-04/schema#",
+    "description": "The Complement Naive Bayes classifier described in Rennie et al. (2003).",
+    "allOf": [
+        {
+            "type": "object",
+            "required": ["alpha", "fit_prior", "class_prior", "norm"],
+            "relevantToOptimizer": [],
+            "additionalProperties": False,
+            "properties": {
+                "alpha": {
+                    "type": "number",
+                    "default": 1.0,
+                    "description": "Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).",
+                },
+                "fit_prior": {
+                    "type": "boolean",
+                    "default": True,
+                    "description": "Only used in edge case with a single class in the training set.",
+                },
+                "class_prior": {
+                    "anyOf": [
+                        {"type": "array", "items": {"type": "number"}},
+                        {"enum": [None]},
+                    ],
+                    "default": None,
+                    "description": "Prior probabilities of the classes.  Not used.",
+                },
+                "norm": {
+                    "type": "boolean",
+                    "default": False,
+                    "description": "Whether or not a second normalization of the weights is performed",
+                },
+            },
+        },
+    ],
+}
+_input_fit_schema = {
+    "$schema": "http://json-schema.org/draft-04/schema#",
+    "description": "Fit Naive Bayes classifier according to X, y",
+    "type": "object",
+    "required": ["X", "y"],
+    "properties": {
+        "X": {
+            "type": "array",
+            "items": {"type": "array", "items": {"type": "number"}},
+            "description": "Training vectors, where n_samples is the number of samples and n_features is the number of features.",
+        },
+        "y": {
+            "type": "array",
+            "items": {"type": "number"},
+            "description": "Target values.",
+        },
+        "sample_weight": {
+            "anyOf": [{"type": "array", "items": {"type": "number"}}, {"enum": [None]}],
+            "default": None,
+            "description": "Weights applied to individual samples (1",
+        },
+    },
+}
+_input_predict_schema = {
+    "$schema": "http://json-schema.org/draft-04/schema#",
+    "description": "Perform classification on an array of test vectors X.",
+    "type": "object",
+    "required": ["X"],
+    "properties": {
+        "X": {"type": "array", "items": {"type": "array", "items": {"type": "number"}}}
+    },
+}
+_output_predict_schema = {
+    "$schema": "http://json-schema.org/draft-04/schema#",
+    "description": "Predicted target values for X",
+    "type": "array",
+    "items": {"type": "number"},
+}
+_input_predict_proba_schema = {
+    "$schema": "http://json-schema.org/draft-04/schema#",
+    "description": "Return probability estimates for the test vector X.",
+    "type": "object",
+    "required": ["X"],
+    "properties": {
+        "X": {"type": "array", "items": {"type": "array", "items": {"type": "number"}}}
+    },
+}
+_output_predict_proba_schema = {
+    "$schema": "http://json-schema.org/draft-04/schema#",
+    "description": "Returns the probability of the samples for each class in the model",
+    "type": "array",
+    "items": {"type": "array", "items": {"type": "number"}},
+}
+_combined_schemas = {
+    "$schema": "http://json-schema.org/draft-04/schema#",
+    "description": """`Complement Naive Bayes`_ classifier described in Rennie et al. (2003).
+
+.. _`Complement Naive Bayes`: https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.ComplementNB
+    """,
+    "documentation_url": "https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.complement_nb.html",
+    "import_from": "sklearn.naive_bayes",
+    "type": "object",
+    "tags": {"pre": [], "op": ["estimator"], "post": []},
+    "properties": {
+        "hyperparams": _hyperparams_schema,
+        "input_fit": _input_fit_schema,
+        "input_predict": _input_predict_schema,
+        "output_predict": _output_predict_schema,
+        "input_predict_proba": _input_predict_proba_schema,
+        "output_predict_proba": _output_predict_proba_schema,
+    },
+}
+ComplementNB = make_operator(Op, _combined_schemas)
+
+set_docstrings(ComplementNB)

test/test_core_classifiers.py

@@ -131,6 +131,7 @@ def test_classifier(self):
     "lale.lib.sklearn.BernoulliNB",
     "lale.lib.sklearn.CalibratedClassifierCV",
     "lale.lib.sklearn.CCA",
+    "lale.lib.sklearn.ComplementNB",
     "lale.lib.sklearn.DummyClassifier",
     "lale.lib.sklearn.RandomForestClassifier",
     "lale.lib.sklearn.DecisionTreeClassifier",