Add curated schema for Birch

lale/lib/sklearn/__init__.py

@@ -26,6 +26,7 @@
 * lale.lib.sklearn. `BaggingClassifier`_
 * lale.lib.sklearn. `BernoulliNB`_
 * lale.lib.sklearn. `BernoulliRBM`_
+* lale.lib.sklearn. `Birch`_
 * lale.lib.sklearn. `DecisionTreeClassifier`_
 * lale.lib.sklearn. `DummyClassifier`_
 * lale.lib.sklearn. `ExtraTreesClassifier`_
@@ -96,6 +97,7 @@
 .. _`BernoulliNB`: lale.lib.sklearn.bernoulli_nb.html
 .. _`BernoulliRBM`: lale.lib.sklearn.bernoulli_rbm.html
 .. _`Binarizer`: lale.lib.sklearn.binarizer.html
+.. _`Birch`: lale.lib.sklearn.birch.html
 .. _`ColumnTransformer`: lale.lib.sklearn.column_transformer.html
 .. _`DecisionTreeClassifier`: lale.lib.sklearn.decision_tree_classifier.html
 .. _`DecisionTreeRegressor`: lale.lib.sklearn.decision_tree_regressor.html
@@ -156,6 +158,7 @@
 from .bernoulli_nb import BernoulliNB
 from .bernoulli_rbm import BernoulliRBM
 from .binarizer import Binarizer
+from .birch import Birch
 from .column_transformer import ColumnTransformer
 from .decision_tree_classifier import DecisionTreeClassifier
 from .decision_tree_regressor import DecisionTreeRegressor

lale/lib/sklearn/birch.py

@@ -0,0 +1,148 @@
+from sklearn.cluster import Birch 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": "Implements the Birch clustering algorithm.",
+    "allOf": [
+        {
+            "type": "object",
+            "required": [
+                "threshold",
+                "branching_factor",
+                "n_clusters",
+                "compute_labels",
+                "copy",
+            ],
+            "relevantToOptimizer": [
+                "branching_factor",
+                "n_clusters",
+                "compute_labels",
+            ],
+            "additionalProperties": False,
+            "properties": {
+                "threshold": {
+                    "type": "number",
+                    "default": 0.5,
+                    "description": "The radius of the subcluster obtained by merging a new sample and the closest subcluster should be lesser than the threshold",
+                },
+                "branching_factor": {
+                    "type": "integer",
+                    "minimumForOptimizer": 50,
+                    "maximumForOptimizer": 51,
+                    "distribution": "uniform",
+                    "default": 50,
+                    "description": "Maximum number of CF subclusters in each node",
+                },
+                "n_clusters": {
+                    "description": "Number of clusters after the final clustering step, which treats the subclusters from the leaves as new samples",
+                    "default": 3,
+                    "anyOf": [
+                        {
+                            "description": "The model fit is AgglomerativeClustering with n_clusters set to be equal to the int.",
+                            "type": "integer",
+                            "minimumForOptimizer": 2,
+                            "maximumForOptimizer": 8,
+                            "distribution": "uniform",
+                        },
+                        {
+                            "forOptimizer": False,
+                            "description": "sklearn.cluster Estimator: If a model is provided, the model is fit treating the subclusters as new samples and the initial data is mapped to the label of the closest subcluster.",
+                            "laleType": "operator",
+                        },
+                        {
+                            "enum": [None],
+                            "description": "The final clustering step is not performed and the subclusters are returned as they are.",
+                        },
+                    ],
+                },
+                "compute_labels": {
+                    "type": "boolean",
+                    "default": True,
+                    "description": "Whether or not to compute labels for each fit.",
+                },
+                "copy": {
+                    "type": "boolean",
+                    "default": True,
+                    "description": "Whether or not to make a copy of the given data",
+                },
+            },
+        }
+    ],
+}
+_input_fit_schema = {
+    "$schema": "http://json-schema.org/draft-04/schema#",
+    "description": "Build a CF Tree for the input data.",
+    "type": "object",
+    "required": ["X", "y"],
+    "properties": {
+        "X": {
+            "type": "array",
+            "items": {"type": "array", "items": {"type": "number"}},
+            "description": "Input data.",
+        },
+        "y": {},
+    },
+}
+_input_transform_schema = {
+    "$schema": "http://json-schema.org/draft-04/schema#",
+    "description": "Transform X into subcluster centroids dimension.",
+    "type": "object",
+    "required": ["X"],
+    "properties": {
+        "X": {
+            "type": "array",
+            "items": {"type": "array", "items": {"type": "number"}},
+            "description": "Input data.",
+        }
+    },
+}
+_output_transform_schema = {
+    "$schema": "http://json-schema.org/draft-04/schema#",
+    "description": "Transformed data.",
+    "type": "array",
+    "items": {"type": "array", "items": {"type": "number"}},
+}
+_input_predict_schema = {
+    "$schema": "http://json-schema.org/draft-04/schema#",
+    "description": "Predict data using the ``centroids_`` of subclusters.",
+    "type": "object",
+    "required": ["X"],
+    "properties": {
+        "X": {
+            "type": "array",
+            "items": {"type": "array", "items": {"type": "number"}},
+            "description": "Input data.",
+        }
+    },
+}
+_output_predict_schema = {
+    "$schema": "http://json-schema.org/draft-04/schema#",
+    "description": "Labelled data.",
+    "laleType": "Any",
+    "XXX TODO XXX": "ndarray, shape(n_samples)",
+}
+_combined_schemas = {
+    "$schema": "http://json-schema.org/draft-04/schema#",
+    "description": """`Birch`_ clustering algorithm.
+
+    .. _`Birch`: https://scikit-learn.org/stable/modules/generated/sklearn.cluster.Birch
+    """,
+    "documentation_url": "https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.birch.html",
+    "import_from": "sklearn.cluster",
+    "type": "object",
+    "tags": {"pre": [], "op": ["transformer", "estimator"], "post": []},
+    "properties": {
+        "hyperparams": _hyperparams_schema,
+        "input_fit": _input_fit_schema,
+        "input_transform": _input_transform_schema,
+        "output_transform": _output_transform_schema,
+        "input_predict": _input_predict_schema,
+        "output_predict": _output_predict_schema,
+    },
+}
+Birch = make_operator(Op, _combined_schemas)
+
+set_docstrings(Birch)

test/test_core_classifiers.py

@@ -130,6 +130,7 @@ def test_classifier(self):
 classifiers = [
     "lale.lib.sklearn.BernoulliNB",
     "lale.lib.sklearn.BernoulliRBM",
+    "lale.lib.sklearn.Birch",
     "lale.lib.sklearn.DummyClassifier",
     "lale.lib.sklearn.RandomForestClassifier",
     "lale.lib.sklearn.DecisionTreeClassifier",