Added Perceptron classifier converter

skl2onnx/_supported_operators.py

@@ -12,7 +12,7 @@
 
 # Linear classifiers
 from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
-from sklearn.linear_model import SGDClassifier
+from sklearn.linear_model import Perceptron, SGDClassifier
 from sklearn.svm import LinearSVC
 
 # Linear regressors
@@ -113,7 +113,7 @@
 # included in the following list and one output for everything not in
 # the list.
 sklearn_classifier_list = [
-    LogisticRegression, LogisticRegressionCV, SGDClassifier,
+    LogisticRegression, LogisticRegressionCV, Perceptron, SGDClassifier,
     LinearSVC, SVC, NuSVC,
     GradientBoostingClassifier, RandomForestClassifier,
     DecisionTreeClassifier,
@@ -171,6 +171,7 @@ def build_sklearn_operator_name_map():
         LogisticRegressionCV: 'SklearnLinearClassifier',
         NuSVC: 'SklearnSVC',
         NuSVR: 'SklearnSVR',
+        Perceptron: 'SklearnSGDClassifier',
         Ridge: 'SklearnLinearRegressor',
         RidgeCV: 'SklearnLinearRegressor',
         SGDRegressor: 'SklearnLinearRegressor',

tests/test_sklearn_perceptron_converter.py

@@ -0,0 +1,104 @@
+"""Tests scikit-learn's Perceptron converter."""
+
+import unittest
+import numpy as np
+from sklearn.linear_model import Perceptron
+from skl2onnx import convert_sklearn
+from skl2onnx.common.data_types import FloatTensorType, Int64TensorType
+from skl2onnx.common.data_types import onnx_built_with_ml
+from test_utils import dump_data_and_model, fit_classification_model
+
+
+class TestPerceptronClassifierConverter(unittest.TestCase):
+
+    @unittest.skipIf(not onnx_built_with_ml(),
+                     reason="Requires ONNX-ML extension.")
+    def test_model_perceptron_binary_class(self):
+        model, X = fit_classification_model(
+            Perceptron(random_state=42), 2)
+        model_onnx = convert_sklearn(
+            model,
+            "scikit-learn Perceptron binary classifier",
+            [("input", FloatTensorType([None, X.shape[1]]))],
+        )
+        self.assertIsNotNone(model_onnx)
+        dump_data_and_model(
+            X.astype(np.float32),
+            model,
+            model_onnx,
+            basename="SklearnPerceptronClassifierBinary-Out0",
+            allow_failure="StrictVersion(onnx.__version__)"
+                          " < StrictVersion('1.2') or "
+                          "StrictVersion(onnxruntime.__version__)"
+                          " <= StrictVersion('0.2.1')",
+        )
+
+    @unittest.skipIf(not onnx_built_with_ml(),
+                     reason="Requires ONNX-ML extension.")
+    def test_model_perceptron_multi_class(self):
+        model, X = fit_classification_model(
+            Perceptron(random_state=42), 5)
+        model_onnx = convert_sklearn(
+            model,
+            "scikit-learn Perceptron multi-class classifier",
+            [("input", FloatTensorType([None, X.shape[1]]))],
+        )
+        self.assertIsNotNone(model_onnx)
+        dump_data_and_model(
+            X.astype(np.float32),
+            model,
+            model_onnx,
+            basename="SklearnPerceptronClassifierMulti-Out0",
+            allow_failure="StrictVersion(onnx.__version__)"
+                          " < StrictVersion('1.2') or "
+                          "StrictVersion(onnxruntime.__version__)"
+                          " <= StrictVersion('0.2.1')",
+        )
+
+    @unittest.skipIf(not onnx_built_with_ml(),
+                     reason="Requires ONNX-ML extension.")
+    def test_model_perceptron_binary_class_int(self):
+        model, X = fit_classification_model(
+            Perceptron(random_state=42), 2, is_int=True)
+        model_onnx = convert_sklearn(
+            model,
+            "scikit-learn Perceptron binary classifier",
+            [("input", Int64TensorType([None, X.shape[1]]))],
+        )
+        self.assertIsNotNone(model_onnx)
+        dump_data_and_model(
+            X.astype(np.int64),
+            model,
+            model_onnx,
+            basename="SklearnPerceptronClassifierBinaryInt-Out0",
+            allow_failure="StrictVersion(onnx.__version__)"
+                          " < StrictVersion('1.2') or "
+                          "StrictVersion(onnxruntime.__version__)"
+                          " <= StrictVersion('0.2.1')",
+        )
+
+    @unittest.skipIf(not onnx_built_with_ml(),
+                     reason="Requires ONNX-ML extension.")
+    def test_model_perceptron_multi_class_int(self):
+        model, X = fit_classification_model(
+            Perceptron(random_state=42), 5, is_int=True)
+        model_onnx = convert_sklearn(
+            model,
+            "scikit-learn Perceptron multi-class classifier",
+            [("input", Int64TensorType([None, X.shape[1]]))],
+        )
+        self.assertIsNotNone(model_onnx)
+        dump_data_and_model(
+            X.astype(np.int64),
+            model,
+            model_onnx,
+            basename="SklearnPerceptronClassifierMultiInt-Out0",
+            allow_failure="StrictVersion(onnx.__version__)"
+                          " < StrictVersion('1.2') or "
+                          "StrictVersion(onnxruntime.__version__)"
+                          " <= StrictVersion('0.2.1')",
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()