fix unit test on mac osx, update clip operator, update history

HISTORY.rst

@@ -5,18 +5,19 @@
 History
 =======
 
-current - 2019-12-08 - 0.00Mb
+current - 2019-12-13 - 0.00Mb
 =============================
 
+* `87`: Add converter for HistGradientBoostRegressor (2019-12-09)
 * `85`: Implements a precompiled run method in OnnxInference (runtime='python_compiled') (2019-12-07)
 * `84`: Automatically creates files to profile time_predict function in the benchmark with py-spy (2019-12-04)
-* `82`: Function translate_fct2onnx: use of opset_version (2019-12-04)
 * `83`: ONNX: includes experimental operators in the benchmark (2019-12-04)
+* `82`: Function translate_fct2onnx: use of opset_version (2019-12-04)
 * `81`: ONNX benchmark: track_score returns scores equal to 0 or 1 (unexpected) (2019-12-04)
-* `77`: Improves ONNX benchmark to measure zipmap impact. (2019-12-03)
 * `80`: ONNX: extend benchmark to decision_function for some models (2019-12-03)
-* `75`: ONNX: fix random_state whevever it is available when running benchmark (2019-11-27)
+* `77`: Improves ONNX benchmark to measure zipmap impact. (2019-12-03)
 * `76`: Implements ArgMax 12, ArgMax 12 (python onnx runtime) (2019-11-27)
+* `75`: ONNX: fix random_state whevever it is available when running benchmark (2019-11-27)
 
 0.3.765 - 2019-11-21 - 0.22Mb
 =============================

_unittests/ut_onnxrt/test_onnxrt_python_runtime_ml_svm.py

@@ -3,7 +3,6 @@
 """
 import unittest
 from logging import getLogger
-import platform
 import warnings
 import numpy
 import sklearn
@@ -36,25 +35,17 @@ def test_openmp_compilation_float(self):
         from mlprodict.onnxrt.ops_cpu.op_svm_regressor_ import RuntimeSVMRegressorFloat  # pylint: disable=E0611
         ru = RuntimeSVMRegressorFloat()
         r = ru.runtime_options()
-        if platform.system().lower() == 'darwin':
-            # openmp disabled
-            self.assertEqual('', r)
-        else:
-            self.assertEqual('OPENMP', r)
-            nb = ru.omp_get_max_threads()
-            self.assertGreater(nb, 0)
+        self.assertEqual('OPENMP', r)
+        nb = ru.omp_get_max_threads()
+        self.assertGreater(nb, 0)
 
     def test_openmp_compilation_double(self):
         from mlprodict.onnxrt.ops_cpu.op_svm_regressor_ import RuntimeSVMRegressorDouble  # pylint: disable=E0611
         ru = RuntimeSVMRegressorDouble()
         r = ru.runtime_options()
-        if platform.system().lower() == 'darwin':
-            # openmp disabled
-            self.assertEqual('', r)
-        else:
-            self.assertEqual('OPENMP', r)
-            nb = ru.omp_get_max_threads()
-            self.assertGreater(nb, 0)
+        self.assertEqual('OPENMP', r)
+        nb = ru.omp_get_max_threads()
+        self.assertGreater(nb, 0)
 
     @ignore_warnings(category=(UserWarning, ConvergenceWarning, RuntimeWarning))
     def test_onnxrt_python_SVR(self):

_unittests/ut_onnxrt/test_onnxrt_python_runtime_ml_tree_rf.py

@@ -81,8 +81,9 @@ def onnxrt_python_RandomForestRegressor_dtype(
     def test_onnxrt_python_RandomForestRegressor32(self):
         self.onnxrt_python_RandomForestRegressor_dtype(numpy.float32)
 
-    @ignore_warnings(category=(UserWarning, RuntimeWarning, DeprecationWarning))
+    @skipif_circleci('too long')
     @skipif_appveyor("issue with opset 11")
+    @ignore_warnings(category=(UserWarning, RuntimeWarning, DeprecationWarning))
     def test_onnxrt_python_RandomForestRegressor64(self):
         self.onnxrt_python_RandomForestRegressor_dtype(numpy.float64)
 
@@ -91,15 +92,16 @@ def test_onnxrt_python_HistGradientBoostingRegressor32_hist(self):
         self.onnxrt_python_RandomForestRegressor_dtype(
             numpy.float32, use_hist=True)
 
-    @ignore_warnings(category=(UserWarning, RuntimeWarning, DeprecationWarning))
+    @skipif_circleci('too long')
     @skipif_appveyor("issue with opset 11")
+    @ignore_warnings(category=(UserWarning, RuntimeWarning, DeprecationWarning))
     def test_onnxrt_python_HistGradientBoostingRegressor64_hist(self):
         self.onnxrt_python_RandomForestRegressor_dtype(
             numpy.float64, use_hist=True)
 
-    @ignore_warnings(category=(UserWarning, RuntimeWarning, DeprecationWarning))
     @skipif_appveyor("issue with opset 11")
     @skipif_circleci('too long')
+    @ignore_warnings(category=(UserWarning, RuntimeWarning, DeprecationWarning))
     def test_onnxrt_python_HistGradientBoostingRegressor64_hist_compiled(self):
         self.onnxrt_python_RandomForestRegressor_dtype(
             numpy.float64, use_hist=True, runtime="python_compiled")

mlprodict/onnxrt/ops_cpu/op_clip.py

@@ -60,14 +60,21 @@ def run(self, x, *minmax):  # pylint: disable=E0202,W0221
         return res
 
     def _run(self, data, *minmax):  # pylint: disable=W0221
-        amin = minmax[0] if len(minmax) > 0 else -3.4028234663852886e+38
-        amax = minmax[1] if len(minmax) > 1 else 3.4028234663852886e+38
         if self.inplaces.get(0, False):
-            res = numpy.clip(data, amin, amax, out=data)
-        else:
-            res = numpy.clip(data, amin, amax)
+            return self._run_inplace(data, *minmax)
+        le = len(minmax)
+        amin = minmax[0] if le > 0 else None  # -3.4028234663852886e+38
+        amax = minmax[1] if le > 1 else None  # 3.4028234663852886e+38
+        res = numpy.clip(data, amin, amax)
         return (res, ) if res.dtype == data.dtype else (res.astype(data.dtype), )
 
+    def _run_inplace(self, data, *minmax):  # pylint: disable=W0221
+        le = len(minmax)
+        amin = minmax[0] if le > 0 else None  # -3.4028234663852886e+38
+        amax = minmax[1] if le > 1 else None  # 3.4028234663852886e+38
+        res = numpy.clip(data, amin, amax, out=data)
+        return (res, )
+
     def infer_shapes(self, x, *minmax):  # pylint: disable=E0202,W0221
         try:
             return self._infer_shapes(x)