black

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

docs/conf.py

@@ -13,40 +13,38 @@
 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 +56,19 @@
 # -- 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

@@ -56,7 +56,7 @@
 # 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()
@@ -68,8 +68,7 @@
 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)
 
 ##################################
@@ -82,17 +81,21 @@
 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

@@ -42,11 +42,9 @@
 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 +53,7 @@
 # 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 +63,7 @@
 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)
 
@@ -80,17 +77,21 @@
 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

@@ -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)
 
 ###################################
@@ -64,8 +64,7 @@
 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)
 
 ##################################
@@ -78,17 +77,21 @@
 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_lightgbm.py

@@ -44,7 +44,7 @@
 # Convert a model into ONNX
 # +++++++++++++++++++++++++
 
-initial_type = [('float_input', FloatTensorType([None, 4]))]
+initial_type = [("float_input", FloatTensorType([None, 4]))]
 onx = convert_lightgbm(clr, initial_types=initial_type)
 
 ###################################
@@ -54,8 +54,7 @@
 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)
 
 ###############################################
@@ -68,17 +67,16 @@
 
 dtrain = Dataset(X_train, label=y_train)
 
-param = {'objective': 'multiclass', 'num_class': 3}
+param = {"objective": "multiclass", "num_class": 3}
 bst = train_lgbm(param, dtrain, 10)
 
-initial_type = [('float_input', FloatTensorType([None, 4]))]
+initial_type = [("float_input", FloatTensorType([None, 4]))]
 onx = convert_lightgbm(bst, initial_types=initial_type)
 
 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)
 
 
@@ -92,17 +90,21 @@
 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_sklearn.py

@@ -56,7 +56,7 @@
 # Convert a model into ONNX
 # +++++++++++++++++++++++++
 
-initial_type = [('float_input', FloatTensorType([None, 4]))]
+initial_type = [("float_input", FloatTensorType([None, 4]))]
 onx = convert_sklearn(clr, initial_types=initial_type)
 
 with open("rf_iris.onnx", "wb") as f:
@@ -68,25 +68,23 @@
 sess = rt.InferenceSession("rf_iris.onnx")
 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)
 
 #######################################
 # Full example with a logistic regression
 
 clr = LogisticRegression()
 clr.fit(X_train, y_train)
-initial_type = [('float_input', FloatTensorType([None, X_train.shape[1]]))]
+initial_type = [("float_input", FloatTensorType([None, X_train.shape[1]]))]
 onx = convert_sklearn(clr, initial_types=initial_type)
 with open("logreg_iris.onnx", "wb") as f:
     f.write(onx.SerializeToString())
 
 sess = rt.InferenceSession("logreg_iris.onnx")
 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)
 
 
@@ -100,17 +98,21 @@
 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")
 
 
 #################################