fix a couple of issue in runtime

_unittests/ut_onnx_conv/test_onnx_conv_knn.py

@@ -8,6 +8,7 @@
 from pandas import DataFrame
 from scipy.spatial.distance import cdist as scipy_cdist
 from pyquickhelper.pycode import ExtTestCase, unittest_require_at_least
+from sklearn.calibration import CalibratedClassifierCV
 from sklearn.datasets import load_iris
 from sklearn.model_selection import train_test_split
 from sklearn.neighbors import (
@@ -23,7 +24,9 @@
     from onnxruntime.capi.onnxruntime_pybind11_state import InvalidArgument as OrtInvalidArgument
 except ImportError:
     OrtInvalidArgument = RuntimeError
-from mlprodict.onnx_conv import register_converters, to_onnx, get_onnx_opset
+from mlprodict.onnx_conv import (
+    register_converters, to_onnx, get_onnx_opset
+)
 from mlprodict.onnx_conv.sklconv.knn import onnx_cdist
 from mlprodict.onnxrt import OnnxInference
 from mlprodict.onnxrt.ops_cpu.op_topk import topk_sorted_implementation
@@ -279,6 +282,10 @@ def test_onnx_test_knn_single_reg64(self):
     def test_onnx_test_knn_single_reg32_target2(self):
         self.onnx_test_knn_single_classreg(numpy.float32, n_targets=2)
 
+    def test_onnx_test_knn_single_reg32_target2_onnxruntime(self):
+        self.onnx_test_knn_single_classreg(
+            numpy.float32, n_targets=2, runtime="onnxruntime1")
+
     def test_onnx_test_knn_single_reg32_k1(self):
         self.onnx_test_knn_single_classreg(numpy.float32, n_neighbors=1)
 
@@ -327,6 +334,10 @@ def test_onnx_test_knn_single_reg_equal(self):
     def test_onnx_test_knn_single_bin32(self):
         self.onnx_test_knn_single_classreg(numpy.float32, kind='bin')
 
+    def test_onnx_test_knn_single_bin32_onnxruntime(self):
+        self.onnx_test_knn_single_classreg(
+            numpy.float32, kind='bin', runtime="onnxruntime1")
+
     def test_onnx_test_knn_single_bin32_cdist(self):
         self.onnx_test_knn_single_classreg(
             numpy.float32, kind='bin', optim='cdist')
@@ -384,6 +395,26 @@ def test_onnx_test_knn_transform(self):
             decimal=5)
         self.assertEqualArray(ind, y['index'])
 
+    # calibrated
+
+    def test_model_calibrated_classifier_cv_isotonic_binary_knn(self):
+        data = load_iris()
+        X, y = data.data, data.target
+        y[y > 1] = 1
+        clf = KNeighborsClassifier().fit(X, y)
+        model = CalibratedClassifierCV(clf, cv=2, method="isotonic").fit(X, y)
+        model_onnx = skl2onnx.convert_sklearn(
+            model,
+            "scikit-learn CalibratedClassifierCV",
+            [("input", FloatTensorType([None, X.shape[1]]))],
+        )
+        oinf = OnnxInference(model_onnx, runtime='python')
+        y = oinf.run({'input': X.astype(numpy.float32)})
+        pred = clf.predict(X)
+        probs = clf.predict_proba(X)
+        self.assertEqual(pred, y['output_label'])
+        self.assertEqual(probs, DataFrame(y['output_probability']).values)
+
 
 if __name__ == "__main__":
     unittest.main()

mlprodict/onnx_conv/sklconv/knn.py

@@ -242,7 +242,7 @@ def _convert_nearest_neighbors(scope, operator, container):
             # shape = (ntargets, ) + shape
             training_labels = training_labels.T
             shape = OnnxConcat(numpy.array([ndim], dtype=numpy.int64),
-                               shape, op_version=opv)
+                               shape, op_version=opv, axis=0)
             axis = 2
         else:
             training_labels = training_labels.ravel()

mlprodict/onnxrt/ops_cpu/_op.py

@@ -454,11 +454,17 @@ def _run(self, a, b):  # pylint: disable=W0221
         if self.inplaces.get(0, False) and a.size >= b.size:
             if len(a.shape) == 1 and b.shape == (1, 1):
                 a = a.reshape(1, a.shape[0])
-            self.numpy_fct(a, b, out=a)
-            return (a, )
+            try:
+                self.numpy_fct(a, b, out=a)
+                return (a, )
+            except ValueError:
+                return (self.numpy_fct(a, b), )
         if self.inplaces.get(1, False) and a.size <= b.size:
             if len(b.shape) == 1 and a.shape == (1, 1):
                 b = b.reshape(b.shape[0], 1)
-            self.numpy_fct(a, b, out=b)
-            return (b, )
+            try:
+                self.numpy_fct(a, b, out=b)
+                return (b, )
+            except ValueError:
+                return (self.numpy_fct(a, b), )
         return (self.numpy_fct(a, b), )