Upgrade max supported opset version (#1098)

* upgrade version

Signed-off-by: Xavier Dupre <xadupre@microsoft.com>

* requirements

Signed-off-by: Xavier Dupre <xadupre@microsoft.com>

* fix ohe

Signed-off-by: Xavier Dupre <xadupre@microsoft.com>

* better message

Signed-off-by: Xavier Dupre <xadupre@microsoft.com>

* documentation

Signed-off-by: Xavier Dupre <xadupre@microsoft.com>

* fix missing dependency

Signed-off-by: Xavier Dupre <xadupre@microsoft.com>

---------

Signed-off-by: Xavier Dupre <xadupre@microsoft.com>

CHANGELOGS.md

@@ -2,6 +2,12 @@
 
 ## 1.17.0 (development)
 
+* Upgrade the maximum supported opset to 21,
+ update requirements to scikit-learn>=1.1,
+ older versions are not tested anymore,
+ [#1098](https://github.com/onnx/sklearn-onnx/pull/1098)
+* Support infrequent categories for OneHotEncoder
+ [#1029](https://github.com/onnx/sklearn-onnx/pull/1029)
 * Support kernel Matern in Gaussian Process
  [#978](https://github.com/onnx/sklearn-onnx/pull/978)
 * Fix for multidimensional gaussian process

README.md

@@ -18,7 +18,7 @@ including models or transformers coming from external libraries.
 ## Documentation
 Full documentation including tutorials is available at [https://onnx.ai/sklearn-onnx/](https://onnx.ai/sklearn-onnx/).
 [Supported scikit-learn Models](https://onnx.ai/sklearn-onnx/supported.html)
-Last supported opset is 19.
+Last supported opset is 21.
 
 You may also find answers in [existing issues](https://github.com/onnx/sklearn-onnx/issues?utf8=%E2%9C%93&q=is%3Aissue)
 or submit a new one.

docs/api_summary.rst

@@ -124,6 +124,6 @@ Topology
 --------
 
 .. autoclass:: skl2onnx.common._topology.Topology
- :members: compile, topological_operator_iterator
+ :members:
 
 .. autofunction:: skl2onnx.common._topology.convert_topology

docs/exts/sphinx_skl2onnx_extension.py

@@ -174,6 +174,19 @@ def make_ref(name):
 
  rows.append("")
  for name in sorted_keys:
+ if name in {
+ "OnnxDecorrelateTransformer",
+ "OnnxGrowthCalculator",
+ "OnnxPredictableTSNE",
+ "OnnxSklearnLGBMClassifier",
+ "OnnxSklearnLGBMRegressor",
+ "OnnxSklearnXGBClassifier",
+ "OnnxSklearnXGBRegressor",
+ "OnnxSklearnPipeline",
+ "OnnxSklearnColumnTransformer",
+ "OnnxSklearnFeatureUnion",
+ }:
+ continue
  rows = []
  cl = cls[name]
  rows.append(".. _l-sklops-{}:".format(cl.__name__))

requirements-dev.txt

@@ -1,5 +1,6 @@
 # tests
 black
+jinja2
 onnxruntime-extensions
 onnxscript
 pandas

requirements.txt

@@ -1,3 +1,3 @@
 onnx>=1.2.1
-scikit-learn>=0.19
+scikit-learn>=1.1
 onnxconverter-common>=1.7.0

skl2onnx/__init__.py

@@ -9,7 +9,7 @@
 __producer_version__ = __version__
 __domain__ = "ai.onnx"
 __model_version__ = 0
-__max_supported_opset__ = 19 # Converters are tested up to this version.
+__max_supported_opset__ = 21 # Converters are tested up to this version.
 
 
 from .convert import convert_sklearn, to_onnx, wrap_as_onnx_mixin # noqa

skl2onnx/algebra/automation.py

@@ -3,7 +3,6 @@
 import textwrap
 import onnx
 import onnx.defs # noqa
-from onnx.defs import OpSchema
 
 
 def _get_doc_template():
@@ -115,154 +114,6 @@ def get_domain_list():
  )
 
 
-def get_rst_doc(op_name=None):
- """
- Returns a documentation in RST format
- for all :class:`OnnxOperator`.
-
- :param op_name: operator name of None for all
- :return: string
-
- The function relies on module *jinja2* or replaces it
- with a simple rendering if not present.
- """
- if op_name is None:
- schemas = onnx.defs.get_all_schemas_with_history()
- elif isinstance(op_name, str):
- schemas = [
- schema
- for schema in onnx.defs.get_all_schemas_with_history()
- if schema.name == op_name
- ]
- if len(schemas) > 1:
- raise RuntimeError(
- "Multiple operators have the same name '{}'.".format(op_name)
- )
- elif not isinstance(op_name, list):
- schemas = [op_name]
- if len(schemas) == 0:
- raise ValueError("Unable to find any operator with name '{}'.".format(op_name))
-
- # from onnx.backend.sample.ops import collect_sample_implementations
- # from onnx.backend.test.case import collect_snippets
- # SNIPPETS = collect_snippets()
- # SAMPLE_IMPLEMENTATIONS = collect_sample_implementations()
- def format_name_with_domain(sch):
- if sch.domain:
- return "{} ({})".format(sch.name, sch.domain)
- return sch.name
-
- def get_is_homogeneous(obj):
- try:
- return obj.is_homogeneous
- except AttributeError:
- try:
- return obj.isHomogeneous
- except AttributeError:
- return False
-
- def format_option(obj):
- opts = []
- if OpSchema.FormalParameterOption.Optional == obj.option:
- opts.append("optional")
- elif OpSchema.FormalParameterOption.Variadic == obj.option:
- opts.append("variadic")
- if get_is_homogeneous(obj):
- opts.append("heterogeneous")
- if opts:
- return " (%s)" % ", ".join(opts)
- return ""
-
- def getconstraint(const, ii):
- if const.type_param_str:
- name = const.type_param_str
- else:
- name = str(ii)
- if const.allowed_type_strs:
- name += " " + ", ".join(const.allowed_type_strs)
- return name
-
- def getname(obj, i):
- name = obj.name
- if len(name) == 0:
- return str(i)
- return name
-
- def process_documentation(doc):
- if doc is None:
- doc = ""
- doc = textwrap.dedent(doc)
- main_docs_url = "https://github.com/onnx/onnx/blob/main/"
- rep = {
- "[the doc](IR.md)": "`ONNX <{0}docs/IR.md>`_",
- "[the doc](Broadcasting.md)": (
- "`Broadcasting in ONNX <{0}docs/Broadcasting.md>`_"
- ),
- "<dl>": "",
- "</dl>": "",
- "<dt>": "* ",
- "<dd>": " ",
- "</dt>": "",
- "</dd>": "",
- "<tt>": "``",
- "</tt>": "``",
- "<br>": "\n",
- }
- for k, v in rep.items():
- doc = doc.replace(k, v.format(main_docs_url))
- move = 0
- lines = []
- for line in doc.split("\n"):
- if line.startswith("```"):
- if move > 0:
- move -= 4
- lines.append("\n")
- else:
- lines.append("::\n")
- move += 4
- elif move > 0:
- lines.append(" " * move + line)
- else:
- lines.append(line)
- return "\n".join(lines)
-
- def build_doc_url(sch):
- doc_url = "https://github.com/onnx/onnx/blob/main/docs/Operators"
- if "ml" in sch.domain:
- doc_url += "-ml"
- doc_url += ".md"
- doc_url += "#"
- if sch.domain not in (None, "", "ai.onnx"):
- doc_url += sch.domain + "."
- return doc_url
-
- def get_type_str(inou):
- try:
- return inou.type_str
- except AttributeError:
- return inou.typeStr
-
- fnwd = format_name_with_domain
- tmpl = _template_operator
- docs = tmpl.render(
- schemas=schemas,
- OpSchema=OpSchema,
- len=len,
- getattr=getattr,
- sorted=sorted,
- format_option=format_option,
- getconstraint=getconstraint,
- getname=getname,
- enumerate=enumerate,
- format_name_with_domain=fnwd,
- process_documentation=process_documentation,
- build_doc_url=build_doc_url,
- str=str,
- get_type_str=get_type_str,
- )
- return docs
-
-
 def _get_doc_template_sklearn():
  try:
  from jinja2 import Template

skl2onnx/algebra/onnx_ops.py

@@ -4,7 +4,6 @@
 Place holder for all ONNX operators.
 """
 import sys
-import os
 import numpy as np
 
 try:
@@ -14,8 +13,6 @@
 import onnx
 from ..common.data_types import DataType
 from ..common._topology import Variable
-from .automation import get_rst_doc
-from ._cache import cache_folder
 
 
 def ClassFactory(
@@ -169,7 +166,6 @@ def dynamic_class_creation(cache=False):
  <https://github.com/onnx/onnx/blob/main/docs/
  Operators-ml.md>`_.
  """
- cache_dir = cache_folder()
  res = {}
  for schema in onnx.defs.get_all_schemas_with_history():
  if schema.support_level == schema.SupportType.EXPERIMENTAL:
@@ -199,22 +195,8 @@ def _c(obj, label, i):
  outputs = [_c(o, "O", i) for i, o in enumerate(schema.outputs)]
  args = [p for p in schema.attributes]
 
- if "_" in name:
- class_name = "Onnx" + name
- else:
- class_name = "Onnx" + schema.name
-
- filename = os.path.join(
- cache_dir, schema.name + "_" + str(schema.since_version) + ".rst"
- )
- if not cache and os.path.exists(filename):
- with open(filename, "r", encoding="utf-8") as f:
- doc = f.read()
- else:
- doc = get_rst_doc(schema)
- if cache:
- with open(filename, "w", encoding="utf-8") as f:
- f.write(doc)
+ class_name = "Onnx" + (name if "_" in name else schema.name)
+ doc = f"See `{name} <https://onnx.ai/onnx/operators/onnx__{name}.html>`_."
 
  cl = ClassFactory(
  class_name,

skl2onnx/common/_topology.py

@@ -63,6 +63,7 @@ def _default_OPSET_TO_IR_VERSION():
  18: 8,
  19: 9,
  20: 9,
+ 21: 10,
  }
 
 

skl2onnx/operator_converters/one_hot_encoder.py

@@ -45,13 +45,6 @@ def convert_sklearn_one_hot_encoder(
 
  enum_cats = []
  index_inputs = 0
- to_drop = ohe_op._drop_idx_after_grouping
- if to_drop is not None:
- # raise NotImplementedError(
- # f"The converter is not implemented when "
- # f"_drop_idx_after_grouping is not None: {to_drop}."
- # )
- pass
 
  for index, cats in enumerate(ohe_op.categories_):
  filtered_cats = ohe_op._compute_transformed_categories(index)

tests/test_algebra_onnx_doc.py

@@ -6,7 +6,7 @@
 from numpy.testing import assert_almost_equal
 import onnx
 from skl2onnx.algebra.onnx_ops import dynamic_class_creation
-from skl2onnx.algebra.automation import get_rst_doc_sklearn, get_rst_doc
+from skl2onnx.algebra.automation import get_rst_doc_sklearn
 from test_utils import TARGET_OPSET
 
 
@@ -43,27 +43,16 @@ def test_transpose2(self):
  res = self.predict_with_onnxruntime(model_def, X)
  assert_almost_equal(res["Y"], X)
 
- @unittest.skipIf(
- sys.platform.startswith("win"), reason="onnx schema are incorrect on Windows"
- )
- @unittest.skipIf(TARGET_OPSET <= 20, reason="not available")
- def test_doc_onnx(self):
- rst = get_rst_doc()
- assert "**Summary**" in rst
-
  @unittest.skipIf(
  sys.platform.startswith("win"), reason="onnx schema are incorrect on Windows"
  )
  @unittest.skipIf(TARGET_OPSET <= 20, reason="not available")
  def test_doc_sklearn(self):
- try:
- rst = get_rst_doc_sklearn()
- assert ".. _l-sklops-OnnxSklearnBernoulliNB:" in rst
- except KeyError as e:
- assert "SklearnGaussianProcessRegressor" in str(
- e
- ) or "SklearnGaussianProcessClassifier" in str(e)
+ rst = get_rst_doc_sklearn()
+ assert (
+ ".. _l-sklops-OnnxSklearnBernoulliNB:" in rst
+ ), f"Unable to find a substring in {rst}"
 
 
 if __name__ == "__main__":
- unittest.main()
+ unittest.main(verbosity=2)

tests/test_sklearn_one_hot_encoder_converter.py

@@ -472,6 +472,7 @@ def test_shape_inference_onnx(self):
  def test_shape_inference_onnxruntime(self):
  self._shape_inference("onnxruntime")
 
+ @unittest.skipIf(not skl12(), reason="sparse output not available")
  def test_min_frequency(self):
  data = pandas.DataFrame(
  [

tests/test_utils/__init__.py

@@ -60,6 +60,8 @@ def create_tensor(N, C, H=None, W=None):
 
 
 def _get_ir_version(opv):
+ if opv >= 21:
+ return 10
  if opv >= 19:
  return 9
  if opv >= 15:
@@ -83,7 +85,11 @@ def max_onnxruntime_opset():
  <https://github.com/microsoft/onnxruntime/blob/main/docs/Versioning.md>`_.
  """
  vi = pv.Version(ort_version.split("+")[0])
- if vi >= pv.Version("1.16.0"):
+ if vi >= pv.Version("1.18.0"):
+ return 21
+ if vi >= pv.Version("1.17.0"):
+ return 20
+ if vi >= pv.Version("1.15.0"):
  return 19
  if vi >= pv.Version("1.14.0"):
  return 18
@@ -120,6 +126,7 @@ def max_onnxruntime_opset():
  )
 )
 
+# opset-ml == 4 still not implemented in onnxruntime
 value_ml = 3
 if TARGET_OPSET <= 16:
  # TreeEnsemble* for opset-ml == 3 is implemented in onnxruntime==1.12.0