Apply black and ruff.

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

.github/workflows/black-ruff.yml

@@ -0,0 +1,16 @@
+name: Black Format Checker
+on: [push, pull_request]
+jobs:
+  black-format-check:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: psf/black@stable
+        with:
+          options: "--diff --check"
+          src: "."
+  ruff-format-check:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - uses: chartboost/ruff-action@v1

docs/conf.py

@@ -4,49 +4,42 @@
 
 # Configuration file for the Sphinx documentation builder.
 
-import os
-import sys
-import shutil
 import onnxmltools
 import sphinx_readable_theme
-import tabulate
-import sphinx_gallery.gen_gallery
-
-
 
 
 # -- Project information -----------------------------------------------------
 
-project = 'onnxmltools'
-copyright = '2018-2020, Microsoft'
-author = 'Microsoft'
+project = "onnxmltools"
+copyright = "2018-2020, Microsoft"
+author = "Microsoft"
 version = onnxmltools.__version__
 release = version
 
 # -- General configuration ---------------------------------------------------
 
 extensions = [
-    'sphinx.ext.intersphinx',
-    'sphinx.ext.imgmath',
-    'sphinx.ext.ifconfig',
-    'sphinx.ext.viewcode',
+    "sphinx.ext.intersphinx",
+    "sphinx.ext.imgmath",
+    "sphinx.ext.ifconfig",
+    "sphinx.ext.viewcode",
     "sphinx.ext.autodoc",
-    'sphinx.ext.githubpages',
+    "sphinx.ext.githubpages",
     "sphinx_gallery.gen_gallery",
-    'sphinx.ext.autodoc',
+    "sphinx.ext.autodoc",
 ]
 
-templates_path = ['_templates']
-source_suffix = ['.rst']
+templates_path = ["_templates"]
+source_suffix = [".rst"]
 
-master_doc = 'index'
+master_doc = "index"
 language = "en"
 exclude_patterns = []
-pygments_style = 'default'
+pygments_style = "default"
 
 # -- Options for HTML output -------------------------------------------------
 
-html_static_path = ['_static']
+html_static_path = ["_static"]
 html_theme = "readable"
 html_theme_path = [sphinx_readable_theme.get_html_theme_path()]
 html_logo = "ONNXMLTools_logo_main.png"
@@ -58,19 +51,19 @@
 # -- Options for intersphinx extension ---------------------------------------
 
 # Example configuration for intersphinx: refer to the Python standard library.
-intersphinx_mapping = {'https://docs.python.org/': None}
+intersphinx_mapping = {"https://docs.python.org/": None}
 
 # -- Options for Sphinx Gallery ----------------------------------------------
 
 sphinx_gallery_conf = {
-     'examples_dirs': 'examples',
-     'gallery_dirs': 'auto_examples',
+    "examples_dirs": "examples",
+    "gallery_dirs": "auto_examples",
 }
 
 # -- Setup actions -----------------------------------------------------------
 
+
 def setup(app):
     # Placeholder to initialize the folder before
     # generating the documentation.
     return app
-

docs/examples/plot_convert_h2o.py

@@ -21,18 +21,19 @@
 import os
 import numpy
 import onnx
+from onnx.tools.net_drawer import GetPydotGraph, GetOpNodeProducer
+import matplotlib.pyplot as plt
 import sklearn
-from sklearn.linear_model import LogisticRegression
 from sklearn.datasets import load_iris
 from sklearn.model_selection import train_test_split
 import onnxruntime as rt
 import h2o
 from h2o.estimators.gbm import H2OGradientBoostingEstimator
-import skl2onnx
-import onnxmltools
 from onnxconverter_common.data_types import FloatTensorType
+import onnxmltools
 from onnxmltools.convert import convert_h2o
 
+
 iris = load_iris()
 X, y = iris.data, iris.target
 X_train, X_test, y_train, y_test = train_test_split(X, y)
@@ -56,7 +57,7 @@
 # Convert a model into ONNX
 # +++++++++++++++++++++++++
 
-initial_type = [('float_input', FloatTensorType([None, 4]))]
+initial_type = [("float_input", FloatTensorType([None, 4]))]
 onx = convert_h2o(pth, initial_types=initial_type)
 
 h2o.cluster().shutdown()
@@ -68,31 +69,31 @@
 sess = rt.InferenceSession(onx.SerializeToString())
 input_name = sess.get_inputs()[0].name
 label_name = sess.get_outputs()[0].name
-pred_onx = sess.run(
-    [label_name], {input_name: X_test.astype(numpy.float32)})[0]
+pred_onx = sess.run([label_name], {input_name: X_test.astype(numpy.float32)})[0]
 print(pred_onx)
 
 ##################################
 # Display the ONNX graph
 # ++++++++++++++++++++++
 #
 # Finally, let's see the graph converted with *onnxmltools*.
-import os
-import matplotlib.pyplot as plt
-from onnx.tools.net_drawer import GetPydotGraph, GetOpNodeProducer
 
 pydot_graph = GetPydotGraph(
-    onx.graph, name=onx.graph.name, rankdir="TB",
+    onx.graph,
+    name=onx.graph.name,
+    rankdir="TB",
     node_producer=GetOpNodeProducer(
-        "docstring", color="yellow", fillcolor="yellow", style="filled"))
+        "docstring", color="yellow", fillcolor="yellow", style="filled"
+    ),
+)
 pydot_graph.write_dot("model.dot")
 
-os.system('dot -O -Gdpi=300 -Tpng model.dot')
+os.system("dot -O -Gdpi=300 -Tpng model.dot")
 
 image = plt.imread("model.dot.png")
 fig, ax = plt.subplots(figsize=(40, 20))
 ax.imshow(image)
-ax.axis('off')
+ax.axis("off")
 
 
 #################################

docs/examples/plot_convert_keras.py

@@ -19,34 +19,34 @@
 
 """
 
+import os
+import matplotlib.pyplot as plt
+from onnx.tools.net_drawer import GetPydotGraph, GetOpNodeProducer
 import numpy
 import onnx
 import sklearn
-from sklearn.linear_model import LogisticRegression
 from sklearn.datasets import load_iris
 from sklearn.model_selection import train_test_split
 import keras
 from keras.models import Sequential
 from keras.layers import Dense
 import onnxruntime as rt
 
-import skl2onnx
 import onnxmltools
 from onnxconverter_common.data_types import FloatTensorType
 from onnxmltools.convert import convert_keras
 
+
 iris = load_iris()
 X, y = iris.data, iris.target
 y_multi = numpy.zeros((y.shape[0], 3), dtype=numpy.int64)
 y_multi[:, y] = 1
 X_train, X_test, y_train, y_test = train_test_split(X, y_multi)
 
 model = Sequential()
-model.add(Dense(units=10, activation='relu', input_dim=4))
-model.add(Dense(units=3, activation='softmax'))
-model.compile(loss='categorical_crossentropy',
-              optimizer='sgd',
-              metrics=['accuracy'])
+model.add(Dense(units=10, activation="relu", input_dim=4))
+model.add(Dense(units=3, activation="softmax"))
+model.compile(loss="categorical_crossentropy", optimizer="sgd", metrics=["accuracy"])
 model.fit(X_train, y_train, epochs=5, batch_size=16)
 print("keras prediction")
 print(model.predict(X_test.astype(numpy.float32)))
@@ -55,7 +55,7 @@
 # Convert a model into ONNX
 # +++++++++++++++++++++++++
 
-initial_type = [('float_input', FloatTensorType([None, 4]))]
+initial_type = [("float_input", FloatTensorType([None, 4]))]
 onx = convert_keras(model, initial_types=initial_type)
 
 ###################################
@@ -65,8 +65,7 @@
 sess = rt.InferenceSession(onx.SerializeToString())
 input_name = sess.get_inputs()[0].name
 output_name = sess.get_outputs()[0].name
-pred_onx = sess.run(
-    [output_name], {input_name: X_test.astype(numpy.float32)})[0]
+pred_onx = sess.run([output_name], {input_name: X_test.astype(numpy.float32)})[0]
 print("ONNX prediction")
 print(pred_onx)
 
@@ -75,22 +74,23 @@
 # ++++++++++++++++++++++
 #
 # Finally, let's see the graph converted with *onnxmltools*.
-import os
-import matplotlib.pyplot as plt
-from onnx.tools.net_drawer import GetPydotGraph, GetOpNodeProducer
 
 pydot_graph = GetPydotGraph(
-    onx.graph, name=onx.graph.name, rankdir="TB",
+    onx.graph,
+    name=onx.graph.name,
+    rankdir="TB",
     node_producer=GetOpNodeProducer(
-        "docstring", color="yellow", fillcolor="yellow", style="filled"))
+        "docstring", color="yellow", fillcolor="yellow", style="filled"
+    ),
+)
 pydot_graph.write_dot("model.dot")
 
-os.system('dot -O -Gdpi=300 -Tpng model.dot')
+os.system("dot -O -Gdpi=300 -Tpng model.dot")
 
 image = plt.imread("model.dot.png")
 fig, ax = plt.subplots(figsize=(40, 20))
 ax.imshow(image)
-ax.axis('off')
+ax.axis("off")
 
 
 #################################

docs/examples/plot_convert_libsvm.py

@@ -18,18 +18,18 @@
 +++++++++++++
 
 """
-
+import os
+import matplotlib.pyplot as plt
+from onnx.tools.net_drawer import GetPydotGraph, GetOpNodeProducer
 import numpy
 import onnx
 import sklearn
-from sklearn.linear_model import LogisticRegression
 from sklearn.datasets import load_iris
 from sklearn.model_selection import train_test_split
 from svm import C_SVC as SVC
 import svmutil
 import onnxruntime as rt
 
-import skl2onnx
 import onnxmltools
 from onnxconverter_common.data_types import FloatTensorType
 from onnxmltools.convert import convert_libsvm
@@ -54,7 +54,7 @@
 # Convert a model into ONNX
 # +++++++++++++++++++++++++
 
-initial_type = [('float_input', FloatTensorType([None, 4]))]
+initial_type = [("float_input", FloatTensorType([None, 4]))]
 onx = convert_libsvm(clr, initial_types=initial_type)
 
 ###################################
@@ -64,31 +64,31 @@
 sess = rt.InferenceSession(onx.SerializeToString())
 input_name = sess.get_inputs()[0].name
 label_name = sess.get_outputs()[0].name
-pred_onx = sess.run(
-    [label_name], {input_name: X_test.astype(numpy.float32)})[0]
+pred_onx = sess.run([label_name], {input_name: X_test.astype(numpy.float32)})[0]
 print(pred_onx)
 
 ##################################
 # Display the ONNX graph
 # ++++++++++++++++++++++
 #
 # Finally, let's see the graph converted with *onnxmltools*.
-import os
-import matplotlib.pyplot as plt
-from onnx.tools.net_drawer import GetPydotGraph, GetOpNodeProducer
 
 pydot_graph = GetPydotGraph(
-    onx.graph, name=onx.graph.name, rankdir="TB",
+    onx.graph,
+    name=onx.graph.name,
+    rankdir="TB",
     node_producer=GetOpNodeProducer(
-        "docstring", color="yellow", fillcolor="yellow", style="filled"))
+        "docstring", color="yellow", fillcolor="yellow", style="filled"
+    ),
+)
 pydot_graph.write_dot("model.dot")
 
-os.system('dot -O -Gdpi=300 -Tpng model.dot')
+os.system("dot -O -Gdpi=300 -Tpng model.dot")
 
 image = plt.imread("model.dot.png")
 fig, ax = plt.subplots(figsize=(40, 20))
 ax.imshow(image)
-ax.axis('off')
+ax.axis("off")
 
 
 #################################