Use Furo Theme (#971)

Signed-off-by: xadupre <xadupre@microsoft.com>
onnx · Mar 7, 2023 · 58f7d84 · 58f7d84
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diff --git a/docs/api_summary.rst b/docs/api_summary.rst
@@ -8,10 +8,6 @@ API Summary
 Summary of public functions and classes exposed
 in *scikit-onnx*.
 
-.. contents::
-    :local:
-
-
 Version
 =======
 

diff --git a/docs/conf.py b/docs/conf.py
@@ -1,6 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-
-
 # -*- coding: utf-8 -*-
 #
 # Configuration file for the Sphinx documentation builder.
@@ -58,10 +56,8 @@
 
 # -- Options for HTML output -------------------------------------------------
 
-html_theme = "sphinx_mo"
 html_static_path = ['_static']
-html_theme = "pydata_sphinx_theme"
-html_theme_path = ['_static']
+html_theme = "furo"
 html_logo = "logo_main.png"
 
 # -- Options for graphviz ----------------------------------------------------

diff --git a/docs/examples/plot_benchmark_cdist.py b/docs/examples/plot_benchmark_cdist.py
@@ -11,9 +11,6 @@
 CDist and compares its execution time between
 *onnxruntime* and *scipy*.
 
-.. contents::
-    :local:
-
 ONNX Graph with CDist
 +++++++++++++++++++++
 

diff --git a/docs/examples/plot_benchmark_pipeline.py b/docs/examples/plot_benchmark_pipeline.py
@@ -8,9 +8,6 @@
 The following example checks up on every step in a pipeline,
 compares and benchmarks the predictions.
 
-.. contents::
-    :local:
-
 Create a pipeline
 +++++++++++++++++
 

diff --git a/docs/examples/plot_black_op.py b/docs/examples/plot_black_op.py
@@ -13,9 +13,6 @@
 Some converters may convert a model in different ways
 if the users wants to blacklist some operators.
 
-.. contents::
-    :local:
-
 GaussianMixture
 +++++++++++++++
 

diff --git a/docs/examples/plot_cast_transformer.py b/docs/examples/plot_cast_transformer.py
@@ -21,9 +21,6 @@
 follows another path in the tree. Let's see how to solve
 that issue.
 
-.. contents::
-    :local:
-
 An example with fails
 +++++++++++++++++++++
 

diff --git a/docs/examples/plot_complex_pipeline.py b/docs/examples/plot_complex_pipeline.py
@@ -16,9 +16,6 @@
 :ref:`l-complex-pipeline`.
 
 
-.. contents::
-    :local:
-
 Create and train a complex pipeline
 +++++++++++++++++++++++++++++++++++
 

diff --git a/docs/examples/plot_convert_decision_function.py b/docs/examples/plot_convert_decision_function.py
@@ -15,9 +15,6 @@
 is used to change the default behaviour. Let's see
 that on a simple example.
 
-.. contents::
-    :local:
-
 Train a model and convert it
 ++++++++++++++++++++++++++++
 

diff --git a/docs/examples/plot_convert_model.py b/docs/examples/plot_convert_model.py
@@ -14,9 +14,6 @@
 * convert it into *ONNX* with *sklearn-onnx*,
 * predict with *onnxruntime*.
 
-.. contents::
-    :local:
-
 Train a model
 +++++++++++++
 

diff --git a/docs/examples/plot_convert_syntax.py b/docs/examples/plot_convert_syntax.py
@@ -10,9 +10,6 @@
 This example leverages some code added to implement custom converters
 in an easy way.
 
-.. contents::
-    :local:
-
 Predict with onnxruntime
 ++++++++++++++++++++++++
 

diff --git a/docs/examples/plot_convert_zipmap.py b/docs/examples/plot_convert_zipmap.py
@@ -15,9 +15,6 @@
 always needed. Let's see how to deactivate this behaviour
 on the Iris example.
 
-.. contents::
-    :local:
-
 Train a model and convert it
 ++++++++++++++++++++++++++++
 

diff --git a/docs/examples/plot_custom_model.py b/docs/examples/plot_custom_model.py
@@ -23,10 +23,6 @@
 This example proposes a way to train a machine learned model
 which approximates the outputs of a *t-SNE* transformer.
 
-
-.. contents::
-    :local:
-
 Implementation of the new transform
 +++++++++++++++++++++++++++++++++++
 

diff --git a/docs/examples/plot_custom_parser.py b/docs/examples/plot_custom_parser.py
@@ -14,9 +14,6 @@
 above a given threshold. That's implemented in method
 *validate*.
 
-.. contents::
-    :local:
-
 Iris and scoring
 ++++++++++++++++
 

diff --git a/docs/examples/plot_custom_parser_alternative.py b/docs/examples/plot_custom_parser_alternative.py
@@ -19,9 +19,6 @@
 above a given threshold. That's implemented in method
 *validate*.
 
-.. contents::
-    :local:
-
 Iris and scoring
 ++++++++++++++++
 

diff --git a/docs/examples/plot_gpr.py b/docs/examples/plot_gpr.py
@@ -15,9 +15,6 @@
 but for this particular model, it is better to use double.
 Let's see how to create an ONNX file using doubles.
 
-.. contents::
-    :local:
-
 Train a model
 +++++++++++++
 

diff --git a/docs/examples/plot_intermediate_outputs.py b/docs/examples/plot_intermediate_outputs.py
@@ -12,9 +12,6 @@
 One option is to look into the output of every node of the
 ONNX graph.
 
-.. contents::
-    :local:
-
 Create and train a complex pipeline
 +++++++++++++++++++++++++++++++++++
 

diff --git a/docs/examples/plot_investigate_pipeline.py b/docs/examples/plot_investigate_pipeline.py
@@ -8,9 +8,6 @@
 The following example shows how to look into a converted
 models and easily find errors at every step of the pipeline.
 
-.. contents::
-    :local:
-
 Create a pipeline
 +++++++++++++++++
 

diff --git a/docs/examples/plot_logging.py b/docs/examples/plot_logging.py
@@ -12,10 +12,6 @@
 by a custom converter. If the error message is not explicit enough,
 it is possible to enable logging.
 
-
-.. contents::
-    :local:
-
 Train a model
 +++++++++++++
 

diff --git a/docs/examples/plot_nmf.py b/docs/examples/plot_nmf.py
@@ -18,9 +18,6 @@
 requires the estimation of a new matrix *W* with a
 gradient descent.
 
-.. contents::
-    :local:
-
 Building a simple model
 +++++++++++++++++++++++
 

diff --git a/docs/examples/plot_onnx_operators.py b/docs/examples/plot_onnx_operators.py
@@ -24,10 +24,6 @@
 But it is quite verbose and makes it difficult to describe big graphs.
 *sklearn-onnx* implements a nicer way to test *ONNX* operators.
 
-
-.. contents::
-    :local:
-
 ONNX Python API
 +++++++++++++++
 

diff --git a/docs/examples/plot_pipeline.py b/docs/examples/plot_pipeline.py
@@ -11,9 +11,6 @@
 how to draw a model and to retrieve it in *json*
 format.
 
-.. contents::
-    :local:
-
 Retrieve a model in JSON format
 +++++++++++++++++++++++++++++++
 

diff --git a/docs/examples/plot_pipeline_lightgbm.py b/docs/examples/plot_pipeline_lightgbm.py
@@ -16,9 +16,6 @@
 the whole pipeline as long as it knows the converter associated to
 a *LGBMClassifier*. Let's see how to do it.
 
-.. contents::
-    :local:
-
 Train a LightGBM classifier
 +++++++++++++++++++++++++++
 """

diff --git a/docs/examples/plot_pipeline_xgboost.py b/docs/examples/plot_pipeline_xgboost.py
@@ -16,9 +16,6 @@
 the whole pipeline as long as it knows the converter associated to
 a *XGBClassifier*. Let's see how to do it.
 
-.. contents::
-    :local:
-
 Train a XGBoost classifier
 ++++++++++++++++++++++++++
 """

diff --git a/docs/examples/plot_tfidfvectorizer.py b/docs/examples/plot_tfidfvectorizer.py
@@ -13,9 +13,6 @@
 compose/plot_column_transformer.html>`_
 which builds a pipeline to classify text.
 
-.. contents::
-    :local:
-
 Train a pipeline with TfidfVectorizer
 +++++++++++++++++++++++++++++++++++++
 

diff --git a/docs/introduction.rst b/docs/introduction.rst
@@ -5,9 +5,6 @@
 Introduction
 ============
 
-.. contents::
-    :local:
-
 Quick start
 ===========
 

diff --git a/docs/parameterized.rst b/docs/parameterized.rst
@@ -16,9 +16,6 @@ or :func:`to_onnx <skl2onnx.to_onnx>`.
 Every option ends up creating a different ONNX graph.
 Below is the list of models which enable this mechanism.
 
-.. contents::
-    :local:
-
 GaussianProcessRegressor, NearestNeighbors
 ==========================================
 

diff --git a/docs/pipeline.rst b/docs/pipeline.rst
@@ -5,9 +5,6 @@
 Convert a pipeline
 ==================
 
-.. contents::
-    :local:
-
 *skl2onnx* converts any machine learning pipeline into
 *ONNX* pipelines. Every transformer or predictors is converted
 into one or multiple nodes into the *ONNX* graph.

diff --git a/docs/requirements.txt b/docs/requirements.txt
@@ -4,6 +4,7 @@ category_encoders
 codecov
 coverage
 flake8
+furo
 joblib
 lightgbm
 loky
@@ -16,7 +17,6 @@ onnxruntime
 pillow
 py-spy
 pandas
-pydata_sphinx_theme
 pydot
 pyinstrument
 pyod

diff --git a/docs/supported.rst b/docs/supported.rst
@@ -14,9 +14,6 @@ such as :class:`OnnxSklearnPipeline` does. They wrap existing
 which inherits from :class:`OnnxOperatorMixin` which
 implements *to_onnx* methods.
 
-.. contents::
-    :local:
-
 .. _l-converter-list:
 
 Covered Converters

diff --git a/docs/tutorial/plot_abegin_convert_pipeline.py b/docs/tutorial/plot_abegin_convert_pipeline.py
@@ -14,10 +14,6 @@
 converts it into ONNX and finally computes the predictions
 a different runtime.
 
-.. contents::
-    :local:
-
-
 Training a pipeline
 +++++++++++++++++++
 """

diff --git a/docs/tutorial/plot_bbegin_measure_time.py b/docs/tutorial/plot_bbegin_measure_time.py
@@ -11,10 +11,6 @@
 and compares the processing time required by each option
 to compute predictions.
 
-.. contents::
-    :local:
-
-
 Training a pipeline
 +++++++++++++++++++
 """

diff --git a/docs/tutorial/plot_catwoe_transformer.py b/docs/tutorial/plot_catwoe_transformer.py
@@ -17,9 +17,6 @@
 
 .. index:: WOE, WOEEncoder
 
-.. contents::
-    :local:
-
 A simple example
 ++++++++++++++++
 

diff --git a/docs/tutorial/plot_cbegin_opset.py b/docs/tutorial/plot_cbegin_opset.py
@@ -28,9 +28,6 @@
 <https://scikit-learn.org/stable/modules/generated/
 sklearn.ensemble.IsolationForest.html>`_ and digs into opsets.
 
-.. contents::
-    :local:
-
 Data
 ++++
 

diff --git a/docs/tutorial/plot_dbegin_options.py b/docs/tutorial/plot_dbegin_options.py
@@ -14,10 +14,6 @@
 users needs two different conversion for the same model?
 Let's see how this may be done.
 
-.. contents::
-    :local:
-
-
 Option *zipmap*
 +++++++++++++++
 

diff --git a/docs/tutorial/plot_dbegin_options_list.py b/docs/tutorial/plot_dbegin_options_list.py
@@ -13,9 +13,6 @@
 if they use a black listed operator, a couple of them
 produces a different ONNX graph.
 
-.. contents::
-    :local:
-
 GaussianMixture
 +++++++++++++++
 

diff --git a/docs/tutorial/plot_dbegin_options_zipmap.py b/docs/tutorial/plot_dbegin_options_zipmap.py
@@ -13,9 +13,6 @@
 to its class id or name. That mechanism retains the class names
 but is slower. Let's see what other options are available.
 
-.. contents::
-    :local:
-
 Train a model and convert it
 ++++++++++++++++++++++++++++
 

diff --git a/docs/tutorial/plot_ebegin_float_double.py b/docs/tutorial/plot_ebegin_float_double.py
@@ -30,9 +30,6 @@
 an example which always produces discrepencies and some ways
 to overcome this situation.
 
-.. contents::
-    :local:
-
 More into the issue
 +++++++++++++++++++