Merge branch 'main' into ch

Author: xadupre

docs/pipeline.rst

@@ -5,9 +5,9 @@
 Convert a pipeline
 ==================
 
-*skl2onnx* converts any machine learning pipeline into
-*ONNX* pipelines. Every transformer or predictors is converted
-into one or multiple nodes into the *ONNX* graph.
+*skl2onnx* converts any machine learning pipeline into an
+*ONNX* pipeline. Every transformer or predictor is converted
+into one or multiple nodes in the *ONNX* graph.
 Any `ONNX backend <https://github.com/onnx/onnx/blob/main/docs/ImplementingAnOnnxBackend.md>`_
 can then use this graph to compute equivalent outputs for the same inputs.
 
@@ -17,8 +17,8 @@ Convert complex pipelines
 =========================
 
 *scikit-learn* introduced
-`ColumnTransformer <https://scikit-learn.org/stable/modules/generated/sklearn.compose.ColumnTransformer.html>`_
-useful to build complex pipelines such as the following one:
+`ColumnTransformer <https://scikit-learn.org/stable/modules/generated/sklearn.compose.ColumnTransformer.html>`_,
+useful for building complex pipelines such as the following one:
 
 ::
 
@@ -56,7 +56,7 @@ useful to build complex pipelines such as the following one:
         ('classifier', classifier)
     ])
 
-Which we can represents as:
+Which we can represent as:
 
 .. blockdiag::
 
@@ -112,15 +112,15 @@ Parser, shape calculator, converter
 
 .. index:: parser, shape calculator, converter
 
-Three kinds of functions are involved into the conversion
+Three kinds of functions are involved in the conversion
 of a *scikit-pipeline*. Each of them is called in the following
 order:
 
 * **parser(scope, model, inputs, custom_parser)**:
-  the parser builds the expected outputs of a model,
-  as the resulting graph must contain unique names,
-  *scope* contains all names already given,
-  *model* is the model to convert,
+  The parser builds the expected outputs of a model.
+  As the resulting graph must contain unique names,
+  *scope* contains all names already given.
+  *model* is the model to convert.
   *inputs* are the *inputs* the model receives
   in the *ONNX* graph. It is a list of
   :class:`Variable <skl2onnx.common._topology.Variable>`.
@@ -130,32 +130,32 @@ order:
   machine learned problems. The shape calculator
   changes the shapes and types for each of them
   depending on the model and is called after all
-  outputs were defined (topology). This steps defines
+  outputs are defined (topology). This step defines
   the number of outputs and their types for every node
   and sets them to a default shape ``[None, None]``
   which the output node has one row and no known
   columns yet.
-* **shape_calculator(model):**
+* **shape_calculator(model)**:
   The shape calculator changes the shape
   of the outputs created by the parser. Once this function
   returned its results, the graph structure is fully defined
   and cannot be changed. The shape calculator should
   not change types. Many runtimes are implemented in C++
   and do not support implicit casts. A change of type
   might make the runtime fail due to a type mismatch
-  between two consecutive nodes produces by two different
+  between two consecutive nodes produced by two different
   converters.
-* **converter(scope, operator, container):**
+* **converter(scope, operator, container)**:
   The converter converts the transformers or predictors into
-  *ONNX* nodes. Each node can an *ONNX*
+  *ONNX* nodes. Each node can be an *ONNX*
   `operator <https://github.com/onnx/onnx/blob/main/docs/Operators.md>`_ or
   `ML operator <https://github.com/onnx/onnx/blob/main/docs/Operators.md>`_ or
   custom *ONNX* operators.
 
 As *sklearn-onnx* may convert pipelines with model coming from other libraries,
 the library must handle parsers, shape calculators or converters coming
-from other packages. This can be done is two ways. The first one
-consists in calling function :func:`convert_sklearn <skl2onnx.convert_sklearn>`
+from other packages. This can be done in two ways. The first one
+consists of calling function :func:`convert_sklearn <skl2onnx.convert_sklearn>`
 by mapping the model type to a specific parser, a specific shape calculator
 or a specific converter. It is possible to avoid these specifications
 by registering the new parser or shape calculator or converter
@@ -169,13 +169,13 @@ One example follows.
 New converters in a pipeline
 ============================
 
-Many libraries implement *scikit-learn* API and their models can
+Many libraries implement the *scikit-learn* API and their models can
 be included in *scikit-learn* pipelines. However, *sklearn-onnx* cannot
-a pipeline which include a model such as *XGBoost* or *LightGbm*
+convert a pipeline which includes a model such as *XGBoost* or *LightGBM*
 if it does not know the corresponding converters: it needs to be registered.
-That's the purpose of function :func:`skl2onnx.update_registered_converter`.
+That's the purpose of the function :func:`skl2onnx.update_registered_converter`.
 The following example shows how to register a new converter or
-or update an existing one. Four elements are registered:
+update an existing one. Four elements are registered:
 
 * the model class
 * an alias, usually the class name prefixed by the library name
@@ -193,23 +193,22 @@ The following lines shows what these four elements are for a random forest:
                                 calculate_linear_classifier_output_shapes,
                                 convert_sklearn_random_forest_classifier)
 
-See example :ref:`example-lightgbm` to see a complete example
-with a *LightGbm* model.
+See :ref:`example-lightgbm` for a complete example with a *LightGBM* model.
 
 Titanic example
 ===============
 
 The first example was a simplified pipeline coming from *scikit-learn*'s documentation:
 `Column Transformer with Mixed Types <https://scikit-learn.org/stable/auto_examples/compose/plot_column_transformer_mixed_types.html#sphx-glr-auto-examples-compose-plot-column-transformer-mixed-types-py>`_.
 The full story is available in a runnable example: :ref:`example-complex-pipeline`
-which also shows up some mistakes that a user could come accross
+which also shows some mistakes that a user could come across
 when trying to convert a pipeline.
 
 Parameterize the conversion
 ===========================
 
-Most of the converter do not require specific options
-to convert a *scikit-learn* model. It always produces the same
+Most of the converters do not require specific options
+to convert a *scikit-learn* model and produce the same
 results. However, in some cases, the conversion cannot produce
 a model which returns the exact same results. The user may want
 to optimize the conversion by giving the converter additional
@@ -220,16 +219,16 @@ pipeline. That why the option mechanism was implemented:
 Investigate discrepencies
 =========================
 
-A wrong converter may introduce introduce discrepencies
-in a converter pipeline but it is not alway easy to
+A wrong converter may introduce discrepancies
+in a converted pipeline but it is not always easy to
 isolate the source of the differences. The function
 :func:`collect_intermediate_steps
 <skl2onnx.helpers.collect_intermediate_steps>`
-may then be used to investigate each component independently.
-The following piece of code is extracted from unit test
+may be used to investigate each component independently.
+The following piece of code is taken from unit test
 `test_investigate.py <https://github.com/onnx/sklearn-onnx/
 blob/main/tests/test_investigate.py>`_ and converts
-a pipeline and each of its components independently.
+a pipeline and each of its components independently:
 
 ::