Dump version v0.0.5

.github/workflows/pythonpackage.yml

@@ -3,13 +3,45 @@ name: Test package
 on: [pull_request]
 
 jobs:
+ build-ubuntu-latest-full:
+ runs-on: ubuntu-latest
+ strategy:
+ max-parallel: 6
+ matrix:
+ python-version: [3.8]
+ test-tool: [pylint, flake8]
+
+ steps:
+ - uses: actions/checkout@v1
+ - name: Set up Python ${{ matrix.python-version }}
+ uses: actions/setup-python@v1
+ with:
+ python-version: ${{ matrix.python-version }}
+ - name: Install dependencies
+ run: |
+ python -m pip install --upgrade pip
+ pip install --upgrade setuptools
+ pip install -r requirements.txt
+ - name: Test with ${{ matrix.test-tool }}
+ run: |
+ pip install ${{ matrix.test-tool }}
+ tests/run_tests.sh ${{ matrix.test-tool }}
+ - name: Lint with flake8
+ run: |
+ pip install flake8
+ tests/run_tests.sh flake8
+ - name: Test with pytest
+ run: |
+ pip install pytest
+ tests/run_tests.sh pytest
+
  build-ubuntu-latest:
  runs-on: ubuntu-latest
  strategy:
  max-parallel: 6
  matrix:
- python-version: [3.7, 3.8, 3.9]
- test-tool: [pylint, flake8, pytest]
+ python-version: [3.8, 3.9, 3.10.x]
+ test-tool: [pytest]
 
  steps:
  - uses: actions/checkout@v1
@@ -40,7 +72,7 @@ jobs:
  strategy:
  max-parallel: 4
  matrix:
- python-version: [3.7, 3.8, 3.9]
+ python-version: [3.8, 3.9, 3.10.x]
  test-tool: [pytest]
 
  steps:

hipe4ml_converter/h4ml_converter.py

@@ -46,15 +46,18 @@ def convert_model_onnx(self, input_shape, target_opset=13):
 
  training_columns = self.model_handler.get_training_columns()
  n_features = len(training_columns)
- feature_names = [f"f{i_feat}" for i_feat in range(n_features)]
  model = self.model_handler.get_original_model()
+ feature_names = [f"f{i_feat}" for i_feat in range(n_features)]
  model.get_booster().feature_names = feature_names
 
  self.model_onnx = onnxmltools.convert.convert_xgboost(
  model, target_opset=target_opset,
  initial_types=[("input", FloatTensorType(shape=[input_shape, n_features]))]
  )
 
+ # restore original names
+ model.get_booster().feature_names = list(training_columns)
+
  return self.model_onnx
 
  def convert_model_hummingbird(self, backend, input_shape=None):
@@ -97,6 +100,9 @@ def convert_model_hummingbird(self, backend, input_shape=None):
  else:
  self.model_hummingbird = ml.convert(model, backend, extra_config={"n_features":n_features})
 
+ # restore original names
+ model.get_booster().feature_names = list(training_columns)
+
  return self.model_hummingbird
 
  def dump_model_onnx(self, filename):

requirements.txt

@@ -1,12 +1,11 @@
 psutil
 dutil
-prophet==1.0.1
-hipe4ml>=0.0.14
-torch>=1.6.0
+hipe4ml>=0.0.15
+torch>=1.7.0
 scikit-learn>=0.21.3
-onnxconverter-common>=1.9.0
+onnxconverter-common>=1.13.0
 skl2onnx>=1.12.0
-onnxmltools>=1.11.1
+onnxmltools>=1.6.0,<=1.11.0
 onnxruntime>=1.12.0
-lightgbm>=2.2
-hummingbird_ml[extra]==0.4.2
+lightgbm>=2.2,<=3.3.5
+hummingbird_ml[extra]>=0.4.9

setup.py

@@ -55,7 +55,7 @@ def __call__(self):
  name="hipe4ml_converter",
 
  # LAST-TAG is a placeholder. Automatically replaced at deploy time with the right tag
- version="0.0.4",
+ version="0.0.5",
  description="Minimal heavy ion physics environment for Machine Learning",
  url="https://github.com/hipe4ml/hipe4ml_converter",
  author="hipe4ml-developers",
@@ -68,9 +68,9 @@ def __call__(self):
  "Intended Audience :: Developers",
  "Topic :: Scientific/Engineering :: Physics",
  "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
- "Programming Language :: Python :: 3.7",
  "Programming Language :: Python :: 3.8",
- "Programming Language :: Python :: 3.9"
+ "Programming Language :: Python :: 3.9",
+ "Programming Language :: Python :: 3.10"
  ],
 
  # What does your project relate to?
@@ -84,13 +84,13 @@ def __call__(self):
  # installed. For an analysis of "install_requires" vs pip's requirements files see:
  # https://packaging.python.org/en/latest/requirements.html
  install_requires=[
- "psutil", "dutil", "prophet==1.0.1", "pyarrow>=5.0.0", "hipe4ml>=0.0.14",
- "ipython>=7.16.1", "jedi==0.17.2", "torch>=1.6.0",
- "scikit-learn>=0.21.3", "onnxconverter-common>=1.9.0",
- "skl2onnx>=1.12.0", "onnxmltools>=1.11.1", "onnxruntime>=1.12.0",
- "lightgbm>=2.2", "hummingbird_ml[extra]==0.4.2"
+ "psutil", "dutil", "pyarrow>=5.0.0", "hipe4ml>=0.0.15",
+ "ipython>=7.16.1", "jedi==0.17.2", "torch>=1.7.0",
+ "scikit-learn>=0.21.3", "onnxconverter-common>=1.13.0",
+ "skl2onnx>=1.12.0", "onnxmltools>=1.6.0,<=1.11.0", "onnxruntime>=1.12.0",
+ "lightgbm>=2.2,<=3.3.5", "hummingbird_ml[extra]>=0.4.9"
  ],
- python_requires=">=3.7",
+ python_requires=">=3.8",
 
  # List additional groups of dependencies here (e.g. development dependencies). You can install
  # these using the following syntax, for example:

tests/test_conversion.py

@@ -35,7 +35,7 @@ def test_convert_model_onnx():
  Test the conversion to onnx
  """
  assert isinstance(model_converter.convert_model_onnx(1),
- onnx_ml_pb2.ModelProto)
+ onnx_ml_pb2.ModelProto) # pylint: disable=no-member
 
 
 def test_dump_model_onnx():