Support opset 15 (onnx>=1.10)

_doc/examples/plot_time_tree_ensemble.py

@@ -34,7 +34,6 @@
 from xgboost import XGBClassifier
 from lightgbm import LGBMClassifier
 from onnxruntime import InferenceSession
-from sklearn.experimental import enable_hist_gradient_boosting  # pylint: disable=W0611
 from sklearn.ensemble import HistGradientBoostingClassifier
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.datasets import make_classification

_doc/sphinxdoc/source/_exts/generate_automated_pages.py

@@ -7,7 +7,6 @@
 from pandas import DataFrame, read_excel, read_csv, concat, Series
 from sklearn.exceptions import ConvergenceWarning
 from sklearn.utils._testing import ignore_warnings
-from sklearn.experimental import enable_hist_gradient_boosting  # pylint: disable=W0611
 from sklearn.ensemble import AdaBoostRegressor, HistGradientBoostingRegressor
 from sklearn.gaussian_process import GaussianProcessClassifier
 import sphinx

_doc/sphinxdoc/source/conf.py

@@ -3,7 +3,6 @@
 import os
 import alabaster
 from pyquickhelper.helpgen.default_conf import set_sphinx_variables
-from sklearn.experimental import enable_hist_gradient_boosting  # pylint: disable=W0611
 try:
     from mlprodict.onnx_conv import register_converters, register_rewritten_operators
 except ImportError as e:

_unittests/ut__skl2onnx/test_sklearn_label_encoder_converter.py

@@ -51,7 +51,7 @@ def test_model_label_encoder_int(self):
         data = numpy.array([10, 3, 5, -34, 0], dtype=numpy.int64)
         model.fit(data)
         # opset=13, 14, ...
-        for op in sorted(set([9, 10, 11, 12, 13, 14, TARGET_OPSET])):
+        for op in sorted(set([9, 10, 11, 12, 13, 14, 15, TARGET_OPSET])):
             if op > TARGET_OPSET:
                 continue
             with self.subTest(opset=op):

_unittests/ut_npy/test_wrappers.py

@@ -28,7 +28,7 @@ def __init__(self, operator, inputs, outputs):
 
 class container_dummy:
     def __init__(self):
-        self.target_opset = 14
+        self.target_opset = 15
 
 
 class TestWrappers(ExtTestCase):

_unittests/ut_onnx_conv/test_onnx_conv_knn.py

@@ -244,7 +244,7 @@ def onnx_test_knn_single_classreg(self, dtype, n_targets=1, debug=False,
 
         if target_opset is None:
             opsets = list(sorted(set([
-                9, 10, 11, 12, 13, 14, get_opset_number_from_onnx()])))  # opset=13, 14, ...
+                9, 10, 11, 12, 13, 14, 15, get_opset_number_from_onnx()])))  # opset=13, 14, ...
         else:
             opsets = [target_opset]
         for ops in opsets:
@@ -475,7 +475,7 @@ def test_onnx_test_knn_transform(self):
         clr = NearestNeighbors(n_neighbors=3)
         clr.fit(X_train)
 
-        for to in (10, 11, 12, 13, 14):  # opset=13, 14, ...
+        for to in (10, 11, 12, 13, 14, 15):  # opset=13, 14, ...
             if to > get_opset_number_from_onnx():
                 break
             try:

_unittests/ut_onnx_conv/test_skl2onnx_ensemble.py

@@ -12,7 +12,6 @@
     ExtraTreesClassifier, ExtraTreesRegressor
 )
 try:
-    from sklearn.experimental import enable_hist_gradient_boosting  # pylint: disable=W0611
     from sklearn.ensemble import (
         HistGradientBoostingClassifier,
         HistGradientBoostingRegressor

_unittests/ut_onnxrt/test_benchmark_replay.py

@@ -19,8 +19,8 @@ def test_benchmark_replay(self):
             FileNotFoundError)
         res = list(enumerate_validated_operator_opsets(
             # opset=13, 14, ...
-            0, fLOG=None, models={"LogisticRegression"}, opset_min=14,
-            opset_max=14, benchmark=False, store_models=True, dump_all=True,
+            0, fLOG=None, models={"LogisticRegression"}, opset_min=15,
+            opset_max=15, benchmark=False, store_models=True, dump_all=True,
             dump_folder=temp, filter_exp=lambda m, p: (
                 "64" not in p and "b-cl" in p and "dec" not in p)))
         self.assertNotEmpty(res)

_unittests/ut_onnxrt/test_onnx_profiling.py

@@ -41,7 +41,7 @@ def test_profile_onnxruntime1(self):
         del model_def.opset_import[:]  # pylint: disable=E1101
         op_set = model_def.opset_import.add()  # pylint: disable=E1101
         op_set.domain = ''
-        op_set.version = 14  # opset=13, 14, ...
+        op_set.version = 15  # opset=13, 14, ...
 
         X = (numpy.random.randn(4, 2) * 100000).astype(  # pylint: disable=E1101
             numpy.float32)

_unittests/ut_onnxrt/test_onnxrt_python_runtime_.py

@@ -3204,7 +3204,7 @@ def test_onnxt_runtime_reduce_sum(self):
         X = numpy.array([[2, 1], [0, 1]], dtype=float)
 
         # opset=13, 14, ...
-        for opset in (10, 11, 12, 13, 14, get_opset_number_from_onnx()):
+        for opset in (10, 11, 12, 13, 14, 15, get_opset_number_from_onnx()):
             if onnx_opset_version() < opset:
                 continue
             if opset < 13:
@@ -3314,7 +3314,7 @@ def test_onnxt_runtime_reduce_sum_noop(self):
         X = numpy.array([], dtype=float).reshape((2, 0))
 
         # opset=13, 14, ...
-        for opset in (13, 14, get_opset_number_from_onnx()):
+        for opset in (13, 14, 15, get_opset_number_from_onnx()):
             if onnx_opset_version() < opset:
                 continue
 
@@ -3572,7 +3572,7 @@ def test_onnxt_runtime_slice(self):
     @wraplog()
     def test_onnxt_runtime_slice_step_none(self):
         # opset=13, 14, ...
-        for opset in [13, 14, get_opset_number_from_onnx()]:
+        for opset in [13, 14, 15, get_opset_number_from_onnx()]:
             if opset > get_opset_number_from_onnx():
                 continue
             with self.subTest(opset=opset):
@@ -3593,7 +3593,7 @@ def test_onnxt_runtime_slice_step_none(self):
     @wraplog()
     def test_onnxt_runtime_split(self):
         # opset=13, 14, ...
-        for opset in [10, 11, 12, 13, 14, get_opset_number_from_onnx()]:
+        for opset in [10, 11, 12, 13, 14, 15, get_opset_number_from_onnx()]:
             if opset > get_opset_number_from_onnx():
                 continue
             with self.subTest(opset=opset):
@@ -3645,7 +3645,7 @@ def test_onnxt_runtime_sqrt(self):
     @wraplog()
     def test_onnxt_runtime_squeeze(self):
         # opset=13, 14, ...
-        for opset in [10, 11, 12, 13, 14, get_opset_number_from_onnx()]:
+        for opset in [10, 11, 12, 13, 14, 15, get_opset_number_from_onnx()]:
             if opset > get_opset_number_from_onnx():
                 continue
             with self.subTest(opset=opset):
@@ -3838,7 +3838,7 @@ def test_onnxt_runtime_transpose(self):
     @wraplog()
     def test_onnxt_runtime_unsqueeze(self):
         # opset=13, 14, ...
-        for opset in [10, 11, 12, 13, 14, get_opset_number_from_onnx()]:
+        for opset in [10, 11, 12, 13, 14, 15, get_opset_number_from_onnx()]:
             if opset > get_opset_number_from_onnx():
                 continue
             with self.subTest(opset=opset):
@@ -4187,7 +4187,8 @@ def test_make_constant(self):
             (11, OnnxConstant_11),
             (9, OnnxConstant_9)]
 
-        expected_type = {14: Constant_12, 12: Constant_12, 13: Constant_12,
+        expected_type = {15: Constant_12, 14: Constant_12,
+                         12: Constant_12, 13: Constant_12,
                          11: Constant_11, 9: Constant_9}
 
         if (not sys.platform.startswith('win') or
@@ -4227,7 +4228,7 @@ def test_make_constant(self):
                     self.assertEqualArray(exp, got['Ad_C0'])
 
     def test_op_constant(self):
-        for opv in [9, 10, 11, 12, 13, 14]:  # opset=13, 14, ...
+        for opv in [9, 10, 11, 12, 13, 14, 15]:  # opset=13, 14, ...
             for dtype in [numpy.float32, numpy.float64,
                           numpy.int32, numpy.int64]:
                 with self.subTest(opv=opv, dtype=dtype):

_unittests/ut_onnxrt/test_onnxrt_python_runtime_control_loop.py

@@ -62,8 +62,8 @@ def expect(node, inputs, outputs, name):
             del model_def.opset_import[:]  # pylint: disable=E1101
             op_set = model_def.opset_import.add()  # pylint: disable=E1101
             op_set.domain = ''
-            op_set.version = 14
-            model_def.ir_version = 7
+            op_set.version = 15
+            model_def.ir_version = 8
 
         test_cases = {
             'at_back': [numpy.array([10, 11, 12]).astype(numpy.int64)],
@@ -191,8 +191,8 @@ def test_loop(self):
         del model_def.opset_import[:]  # pylint: disable=E1101
         op_set = model_def.opset_import.add()  # pylint: disable=E1101
         op_set.domain = ''
-        op_set.version = 14
-        model_def.ir_version = 7
+        op_set.version = 15
+        model_def.ir_version = 8
 
         expected = numpy.array([
             1., 1., 2., 1., 2., 3., 1., 2.,
@@ -307,8 +307,8 @@ def test_loop_additional_input(self):
         del model_def.opset_import[:]  # pylint: disable=E1101
         op_set = model_def.opset_import.add()  # pylint: disable=E1101
         op_set.domain = ''
-        op_set.version = 14
-        model_def.ir_version = 7
+        op_set.version = 15
+        model_def.ir_version = 8
 
         expected = numpy.array([
             1., 1., 2., 1., 2., 3., 1., 2.,

_unittests/ut_onnxrt/test_onnxrt_python_runtime_ml_tree_rf.py

@@ -10,7 +10,6 @@
     from sklearn.utils._testing import ignore_warnings
 except ImportError:
     from sklearn.utils.testing import ignore_warnings
-from sklearn.experimental import enable_hist_gradient_boosting  # pylint: disable=W0611
 from sklearn.ensemble import RandomForestRegressor, HistGradientBoostingRegressor
 from pyquickhelper.loghelper import fLOG
 from pyquickhelper.pycode import (

_unittests/ut_onnxrt/test_onnxrt_switch_types.py

@@ -107,7 +107,7 @@ def test_onnxt_iris_gaussian_process_exp_sine_squared_13(self):
         model_def = to_onnx(
             clr, X_train.astype(numpy.float32),
             options={GaussianProcessRegressor: {'return_std': True}},
-            target_opset=14)  # opset=13, 14, ...
+            target_opset=15)  # opset=13, 14, ...
         oinf = OnnxInference(model_def, runtime='python')
 
         res = oinf.run({'X': X_test.astype(numpy.float32)})

_unittests/ut_testing/test_skl2onnx_testing_check.py

@@ -8,7 +8,6 @@
 from sklearn.datasets import make_classification
 from sklearn.model_selection import train_test_split
 try:
-    from sklearn.experimental import enable_hist_gradient_boosting  # pylint: disable=W0611
     from sklearn.ensemble import HistGradientBoostingClassifier
 except ImportError:
     HistGradientBoostingClassifier = None

_unittests/ut_tools/test_model_info.py

@@ -5,7 +5,6 @@
 import inspect
 import unittest
 import numpy
-from sklearn.experimental import enable_hist_gradient_boosting  # pylint: disable=W0611
 from sklearn.datasets import load_iris
 from sklearn.linear_model import LogisticRegression, LinearRegression
 from sklearn.neighbors import KNeighborsClassifier

_unittests/ut_tools/test_onnx_micro_runtime.py

@@ -12,8 +12,10 @@
 
 class TestOnnxMicroRuntime(ExtTestCase):
 
+    opset = 15  # opset=13, 14, ...
+
     def test_onnx_micro_runtime(self):
-        opset = 14  # opset=13, 14, ...
+        opset = TestOnnxMicroRuntime.opset
         dtype = numpy.float32
         x = numpy.array([1, 2, 4, 5, 5, 4]).astype(
             numpy.float32).reshape((3, 2))
@@ -32,7 +34,7 @@ def test_onnx_micro_runtime_exc1(self):
         self.assertRaise(lambda: OnnxMicroRuntime(None), TypeError)
 
     def test_onnx_micro_runtime_exc2(self):
-        opset = 14  # opset=13, 14, ...
+        opset = TestOnnxMicroRuntime.opset
         dtype = numpy.float32
         x = numpy.array([1, 2, 4, 5, 5, 4]).astype(
             numpy.float32).reshape((3, 2))
@@ -45,7 +47,7 @@ def test_onnx_micro_runtime_exc2(self):
         self.assertRaise(lambda: rt.run(x), TypeError)
 
     def test_onnx_micro_runtime_shape(self):
-        opset = 14  # opset=13, 14, ...
+        opset = TestOnnxMicroRuntime.opset
         x = numpy.array([1, 2, 4, 5, 5, 4]).astype(
             numpy.float32).reshape((3, 2))
         cop = OnnxShape('X', op_version=opset, output_names=['Y'])
@@ -55,7 +57,7 @@ def test_onnx_micro_runtime_shape(self):
         self.assertEqual(numpy.array(x.shape, dtype=numpy.int64), out['Y'])
 
     def test_onnx_micro_runtime_transpose(self):
-        opset = 14  # opset=13, 14, ...
+        opset = TestOnnxMicroRuntime.opset
         x = numpy.array([1, 2, 4, 5, 5, 4]).astype(
             numpy.float32).reshape((3, 2))
         cop = OnnxTranspose('X', perm=[1, 0], op_version=opset,
@@ -66,7 +68,7 @@ def test_onnx_micro_runtime_transpose(self):
         self.assertEqual(x.T, out['Y'])
 
     def test_onnx_micro_runtime_matmul(self):
-        opset = 14  # opset=13, 14, ...
+        opset = TestOnnxMicroRuntime.opset
         x = numpy.array([1, 2, 4, 5]).astype(
             numpy.float32).reshape((2, 2))
         cop = OnnxMatMul('X', 'X', op_version=opset,
@@ -77,7 +79,7 @@ def test_onnx_micro_runtime_matmul(self):
         self.assertEqual(numpy.matmul(x, x), out['Y'])
 
     def test_onnx_micro_runtime_squeeze(self):
-        opset = 14  # opset=13, 14, ...
+        opset = TestOnnxMicroRuntime.opset
         x = numpy.array([1, 2, 4, 5]).astype(
             numpy.float32).reshape((2, 2, 1))
         cop = OnnxSqueeze('X', numpy.array([2], dtype=numpy.int64),
@@ -88,7 +90,7 @@ def test_onnx_micro_runtime_squeeze(self):
         self.assertEqual(numpy.squeeze(x), out['Y'])
 
     def test_onnx_micro_runtime_unsqueeze(self):
-        opset = 14  # opset=13, 14, ...
+        opset = TestOnnxMicroRuntime.opset
         x = numpy.array([1, 2, 4, 5]).astype(
             numpy.float32).reshape((2, 2))
         cop = OnnxUnsqueeze('X', numpy.array([2], dtype=numpy.int64),
@@ -99,7 +101,7 @@ def test_onnx_micro_runtime_unsqueeze(self):
         self.assertEqual(x.reshape((2, 2, 1)), out['Y'])
 
     def test_onnx_micro_runtime_gemm(self):
-        opset = 14  # opset=13, 14, ...
+        opset = TestOnnxMicroRuntime.opset
         x = numpy.array([1, 2, 4, 5]).astype(
             numpy.float32).reshape((2, 2))
         for ta in [0, 1]:

_unittests/ut_tools/test_ort.py

@@ -12,8 +12,10 @@
 
 class TestOrt(ExtTestCase):
 
+    opset = 15  # opset = 13, 14, ...
+
     def test_prepare_c_profiling(self):
-        opset = 14  # opset=13, 14, ...
+        opset = TestOrt.opset
         dtype = numpy.float32
         x = numpy.array([1, 2, 4, 5, 5, 4]).astype(
             numpy.float32).reshape((3, 2))

_unittests/ut_tools/test_sklearn_helper.py

@@ -70,11 +70,16 @@ def test_statistics_rf(self):
         res = inspect_sklearn_model(clr)
         self.assertEqual(res['max_depth'], 4)
         self.assertEqual(res['ntrees'], 10)
-        onx = to_onnx(clr, X_train[:1].astype(numpy.float32))
-        ostats = onnx_statistics(onx)
-        for k, v in {'nnodes': 11, 'doc_string': '', 'domain': 'ai.onnx', 'model_version': 0,
-                     'producer_name': 'skl2onnx', 'ai.onnx.ml': 1}.items():
-            self.assertEqual(ostats[k], v)
+        for dtype in [numpy.float32, numpy.float64]:
+            with self.subTest(dtype=dtype):
+                onx = to_onnx(clr, X_train[:1].astype(dtype))
+                ostats = onnx_statistics(onx)
+                for k, v in {'nnodes': 1, 'doc_string': '',
+                             'domain': 'ai.onnx', 'model_version': 0,
+                             'producer_name': 'skl2onnx', 'ai.onnx.ml': 1}.items():
+                    if k == 'ai.onnx.ml' and k not in ostats:
+                        continue
+                    self.assertEqual(ostats[k], v)
 
     @ignore_warnings(category=(UserWarning, RuntimeWarning, DeprecationWarning))
     def test_statistics_adaboost(self):
@@ -99,11 +104,17 @@ def test_statistics_pipeline_rf(self):
         self.assertEqual(res['max_depth'], 4)
         self.assertEqual(res['ntrees'], 10)
         self.assertEqual(res['nop'], 11)
-        onx = to_onnx(clr, X_train[:1].astype(numpy.float32))
-        ostats = onnx_statistics(onx)
-        for k, v in {'nnodes': 12, 'doc_string': '', 'domain': 'ai.onnx', 'model_version': 0,
-                     'producer_name': 'skl2onnx', 'ai.onnx.ml': 1}.items():
-            self.assertEqual(ostats[k], v)
+        expected = {numpy.float32: 2, numpy.float64: 3}
+        for dtype in [numpy.float32, numpy.float64]:
+            with self.subTest(dtype=dtype):
+                onx = to_onnx(clr, X_train[:1].astype(dtype))
+                ostats = onnx_statistics(onx)
+                for k, v in {'nnodes': expected[dtype], 'doc_string': '',
+                             'domain': 'ai.onnx', 'model_version': 0,
+                             'producer_name': 'skl2onnx', 'ai.onnx.ml': 1}.items():
+                    if k == 'ai.onnx.ml' and k not in ostats:
+                        continue
+                    self.assertEqual(ostats[k], v)
 
     @ignore_warnings(category=(UserWarning, RuntimeWarning, DeprecationWarning))
     def test_statistics_lin(self):

azure-pipelines.yml

@@ -167,7 +167,7 @@ jobs:
          # export MACOSX_DEPLOYMENT_TARGET=10.13
          python setup.py build_ext --inplace
     displayName: 'Build package inplace'
-  - script: python -u setup.py unittests -g ".*((LONG)|(SKIP)|(notebooks)).*" -d 50
+  - script: python -u setup.py unittests -g ".*((LONG)|(SKIP)|(notebooks)|(asv_benchmark)|(test_onnx_helper)|(test_onnx_inference)|(test_onnxrt_python_runtime_ml)|(test_custom_embedded_any_models)|(test_custom_)|(test_onnx_pipeline)|(test_onnx_speedup_cluster)).*" -d 7
     displayName: 'Runs Unit Tests'
   - script: |
       python -m pip install cibuildwheel

mlprodict/asv_benchmark/_create_asv_helper.py

@@ -378,8 +378,6 @@ def _additional_imports(model_name):
     """
     if model_name == 'IterativeImputer':
         return ["from sklearn.experimental import enable_iterative_imputer  # pylint: disable=W0611"]
-    if model_name in ('HistGradientBoostingClassifier', 'HistGradientBoostingClassifier'):
-        return ["from sklearn.experimental import enable_hist_gradient_boosting  # pylint: disable=W0611"]
     return None