feat: add support to sklearn TargetEncoder

skl2onnx/_supported_operators.py

@@ -239,6 +239,12 @@
 except ImportError:
     # not available in 0.19
     KBinsDiscretizer = None
+try:
+    from sklearn.preprocessing import TargetEncoder
+except ImportError:
+    # Not available in scikit-learn < 1.3
+    TargetEncoder = None
+
 from sklearn.preprocessing import (
     LabelBinarizer,
     LabelEncoder,
@@ -511,6 +517,7 @@ def build_sklearn_operator_name_map():
             RidgeClassifierCV: "SklearnLinearClassifier",
             SGDRegressor: "SklearnLinearRegressor",
             StandardScaler: "SklearnScaler",
+            TargetEncoder: "SklearnTargetEncoder",
             TheilSenRegressor: "SklearnLinearRegressor",
         }
     )

skl2onnx/operator_converters/__init__.py

@@ -61,6 +61,7 @@
 from . import sgd_oneclass_svm
 from . import stacking
 from . import support_vector_machines
+from . import target_encoder
 from . import text_vectoriser
 from . import tfidf_transformer
 from . import tfidf_vectoriser
@@ -128,6 +129,7 @@
     sgd_oneclass_svm,
     stacking,
     support_vector_machines,
+    target_encoder,
     text_vectoriser,
     tfidf_transformer,
     tfidf_vectoriser,

skl2onnx/operator_converters/target_encoder.py

@@ -0,0 +1,105 @@
+# SPDX-License-Identifier: Apache-2.0
+import numpy as np
+
+from ..common._apply_operation import apply_cast, apply_concat, apply_reshape
+from ..common._container import ModelComponentContainer
+from ..common.data_types import (
+    FloatTensorType,
+    Int64TensorType,
+)
+from ..common._registration import register_converter
+from ..common._topology import Scope, Operator
+from ..proto import onnx_proto
+
+
+def convert_sklearn_target_encoder(
+    scope: Scope, operator: Operator, container: ModelComponentContainer
+):
+    op = operator.raw_operator
+    result = []
+    input_idx = 0
+    dimension_idx = 0
+
+    # NotImplementedError(   # TODO: assert that we have binary output
+    if (op.target_type_ == "multiclass") or (
+        isinstance(op.classes_.dtype, np.int64) and (len(op.classes_) > 2)
+    ):
+        raise NotImplementedError("multiclass TargetEncoder is not supported")
+    for categories, encodings in zip(op.categories_, op.encodings_):
+        if len(categories) == 0:
+            continue
+
+        current_input = operator.inputs[input_idx]
+        if current_input.get_second_dimension() == 1:
+            feature_column = current_input
+            input_idx += 1
+        else:
+            index_name = scope.get_unique_variable_name("index")
+            container.add_initializer(
+                index_name, onnx_proto.TensorProto.INT64, [], [dimension_idx]
+            )
+
+            feature_column = scope.declare_local_variable(
+                "feature_column",
+                current_input.type.__class__([current_input.get_first_dimension(), 1]),
+            )
+
+            container.add_node(
+                "ArrayFeatureExtractor",
+                [current_input.onnx_name, index_name],
+                feature_column.onnx_name,
+                op_domain="ai.onnx.ml",
+                name=scope.get_unique_operator_name("ArrayFeatureExtractor"),
+            )
+
+            dimension_idx += 1
+            if dimension_idx == current_input.get_second_dimension():
+                dimension_idx = 0
+                input_idx += 1
+
+        attrs = {"name": scope.get_unique_operator_name("LabelEncoder")}
+        if isinstance(feature_column.type, FloatTensorType):
+            attrs["keys_floats"] = np.array(
+                [float(s) for s in categories], dtype=np.float32
+            )
+        elif isinstance(feature_column.type, Int64TensorType):
+            attrs["keys_int64s"] = np.array(
+                [int(s) for s in categories], dtype=np.int64
+            )
+        else:
+            attrs["keys_strings"] = np.array(
+                [str(s).encode("utf-8") for s in categories]
+            )
+        attrs["values_floats"] = encodings
+        attrs["default_float"] = op.target_mean_
+
+        result.append(scope.get_unique_variable_name("ordinal_output"))
+        label_encoder_output = scope.get_unique_variable_name("label_encoder")
+
+        container.add_node(
+            "LabelEncoder",
+            feature_column.onnx_name,
+            label_encoder_output,
+            op_domain="ai.onnx.ml",
+            **attrs,
+        )
+        apply_reshape(
+            scope,
+            label_encoder_output,
+            result[-1],
+            container,
+            desired_shape=(-1, 1),
+        )
+
+    concat_result_name = scope.get_unique_variable_name("concat_result")
+    apply_concat(scope, result, concat_result_name, container, axis=1)
+    apply_cast(
+        scope,
+        concat_result_name,
+        operator.output_full_names,
+        container,
+        to=onnx_proto.TensorProto.FLOAT,
+    )
+
+
+register_converter("SklearnTargetEncoder", convert_sklearn_target_encoder)

skl2onnx/shape_calculators/__init__.py

@@ -47,6 +47,7 @@
 from . import sgd_oneclass_svm
 from . import svd
 from . import support_vector_machines
+from . import target_encoder
 from . import text_vectorizer
 from . import tuned_threshold_classifier
 from . import tfidf_transformer
@@ -99,6 +100,7 @@
     sgd_oneclass_svm,
     svd,
     support_vector_machines,
+    target_encoder,
     text_vectorizer,
     tfidf_transformer,
     tuned_threshold_classifier,

skl2onnx/shape_calculators/target_encoder.py

@@ -0,0 +1,29 @@
+# SPDX-License-Identifier: Apache-2.0
+
+
+from ..common._registration import register_shape_calculator
+from ..common.data_types import FloatTensorType
+from ..common.data_types import Int64TensorType, StringTensorType
+from ..common.utils import check_input_and_output_numbers
+from ..common.utils import check_input_and_output_types
+
+
+def calculate_sklearn_target_encoder_output_shapes(operator):
+    """
+    This function just copy the input shape to the output because target
+    encoder only alters input features' values, not their shape.
+    """
+    check_input_and_output_numbers(operator, output_count_range=1)
+    check_input_and_output_types(
+        operator, good_input_types=[FloatTensorType, Int64TensorType, StringTensorType]
+    )
+
+    N = operator.inputs[0].get_first_dimension()
+    shape = [N, len(operator.raw_operator.categories_)]
+
+    operator.outputs[0].type = FloatTensorType(shape=shape)
+
+
+register_shape_calculator(
+    "SklearnTargetEncoder", calculate_sklearn_target_encoder_output_shapes
+)