Update change logs history (#1049)

* Investigate issue 1047 about LabelEncoder Signed-off-by: Xavier Dupre <[email protected]> * add another unit test Signed-off-by: Xavier Dupre <[email protected]> * update change logs Signed-off-by: Xavier Dupre <[email protected]> * disable a test for old version of scikit-learn Signed-off-by: Xavier Dupre <[email protected]> --------- Signed-off-by: Xavier Dupre <[email protected]>
onnx · Dec 11, 2023 · fdb52ce · fdb52ce
1 parent ae29a33
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diff --git a/CHANGELOGS.md b/CHANGELOGS.md
@@ -4,10 +4,13 @@
 
 * Supports cosine distance (LocalOutlierFactor, ...)
   [#1050](https://github.com/onnx/sklearn-onnx/pull/1050),
+* Supports multiple columns for OrdinalEncoder
+  [#1044](https://github.com/onnx/sklearn-onnx/pull/1044) (by @max-509)
 * Add an example on how to handle FunctionTransformer
   [#1042](https://github.com/onnx/sklearn-onnx/pull/1042),
   Versions of `scikit-learn < 1.0` are not tested any more.
-* FeatureHasher, raise an error when the delimiter length is > 1,
+* Supports lists of strings as inputs for FeatureHasher
+  [#1025](https://github.com/onnx/sklearn-onnx/pull/1036),
   [#1036](https://github.com/onnx/sklearn-onnx/pull/1036)
 * skl2onnx works with onnx==1.15.0,
   [#1034](https://github.com/onnx/sklearn-onnx/pull/1034)

diff --git a/tests/test_sklearn_ordinal_encoder.py b/tests/test_sklearn_ordinal_encoder.py
@@ -2,17 +2,21 @@
 
 """Tests scikit-learn's OrdinalEncoder converter."""
 import unittest
+from numpy.testing import assert_almost_equal
 import packaging.version as pv
 import numpy as np
 import pandas as pd
 import onnxruntime
 from sklearn import __version__ as sklearn_version
+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import make_pipeline
+from sklearn.ensemble import RandomForestRegressor
 
 try:
     from sklearn.preprocessing import OrdinalEncoder
 except ImportError:
     pass
-from skl2onnx import convert_sklearn
+from skl2onnx import convert_sklearn, to_onnx
 from skl2onnx.common.data_types import (
     Int64TensorType,
     StringTensorType,
@@ -30,6 +34,11 @@ def ordinal_encoder_support():
     return pv.Version(vers) >= pv.Version("0.20.0")
 
 
+def set_output_support():
+    vers = ".".join(sklearn_version.split(".")[:2])
+    return pv.Version(vers) >= pv.Version("1.2")
+
+
 class TestSklearnOrdinalEncoderConverter(unittest.TestCase):
     @unittest.skipIf(
         not ordinal_encoder_support(),
@@ -172,6 +181,89 @@ def test_model_ordinal_encoder_cat_list(self):
             data, model, model_onnx, basename="SklearnOrdinalEncoderCatList"
         )
 
+    @unittest.skipIf(
+        not set_output_support(),
+        reason="'ColumnTransformer' object has no attribute 'set_output'",
+    )
+    @unittest.skipIf(
+        not ordinal_encoder_support(),
+        reason="OrdinalEncoder was not available before 0.20",
+    )
+    def test_ordinal_encoder_pipeline_int64(self):
+        from onnxruntime import InferenceSession
+
+        data = pd.DataFrame({"cat": ["cat2", "cat1"], "num": [0, 1]})
+        data["num"] = data["num"].astype(np.float32)
+        y = np.array([0, 1], dtype=np.float32)
+        preprocessor = ColumnTransformer(
+            transformers=[
+                ("cat", OrdinalEncoder(dtype=np.int64), ["cat"]),
+                ("num", "passthrough", ["num"]),
+            ],
+            sparse_threshold=1,
+            verbose_feature_names_out=False,
+        ).set_output(transform="pandas")
+        model = make_pipeline(
+            preprocessor, RandomForestRegressor(n_estimators=3, max_depth=2)
+        )
+        model.fit(data, y)
+        expected = model.predict(data)
+        model_onnx = to_onnx(model, data[:1], target_opset=TARGET_OPSET)
+        sess = InferenceSession(
+            model_onnx.SerializeToString(), providers=["CPUExecutionProvider"]
+        )
+        got = sess.run(
+            None,
+            {
+                "cat": data["cat"].values.reshape((-1, 1)),
+                "num": data["num"].values.reshape((-1, 1)),
+            },
+        )
+        assert_almost_equal(expected, got[0].ravel())
+
+    @unittest.skipIf(
+        not set_output_support(),
+        reason="'ColumnTransformer' object has no attribute 'set_output'",
+    )
+    @unittest.skipIf(
+        not ordinal_encoder_support(),
+        reason="OrdinalEncoder was not available before 0.20",
+    )
+    def test_ordinal_encoder_pipeline_string_int64(self):
+        from onnxruntime import InferenceSession
+
+        data = pd.DataFrame(
+            {"C1": ["cat2", "cat1", "cat3"], "C2": [1, 0, 1], "num": [0, 1, 1]}
+        )
+        data["num"] = data["num"].astype(np.float32)
+        y = np.array([0, 1, 2], dtype=np.float32)
+        preprocessor = ColumnTransformer(
+            transformers=[
+                ("cat", OrdinalEncoder(dtype=np.int64), ["C1", "C2"]),
+                ("num", "passthrough", ["num"]),
+            ],
+            sparse_threshold=1,
+            verbose_feature_names_out=False,
+        ).set_output(transform="pandas")
+        model = make_pipeline(
+            preprocessor, RandomForestRegressor(n_estimators=3, max_depth=2)
+        )
+        model.fit(data, y)
+        expected = model.predict(data)
+        model_onnx = to_onnx(model, data[:1], target_opset=TARGET_OPSET)
+        sess = InferenceSession(
+            model_onnx.SerializeToString(), providers=["CPUExecutionProvider"]
+        )
+        got = sess.run(
+            None,
+            {
+                "C1": data["C1"].values.reshape((-1, 1)),
+                "C2": data["C2"].values.reshape((-1, 1)),
+                "num": data["num"].values.reshape((-1, 1)),
+            },
+        )
+        assert_almost_equal(expected, got[0].ravel())
+
 
 if __name__ == "__main__":
-    unittest.main()
+    unittest.main(verbosity=2)