Manually copied naming changes over from master.

CONTRIBUTING.md

@@ -6,9 +6,9 @@ If you are here, it means you are interested in helping us out. A hearty welcome
 * Give us feedback and bug reports regarding the software or the documentation.
 * Improve our examples, tutorials, and documentation.
 
-### mlnet and ML.NET
+### NimbusML and ML.NET
 
-`mlnet` provides Python bindings for the [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) library of machine learning algorithms. If you would like to contribute to the underlying library of algorithms, please check out [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet).  If you would like to contribute to the `mlnet` python bindings project, please read on.
+NimbusML provides Python bindings for the [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) library of machine learning algorithms. If you would like to contribute to the underlying library of algorithms, please check out [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet).  If you would like to contribute to the NimbusML python bindings project, please read on.
 
 ## New Contributers
 

README.md

@@ -75,7 +75,7 @@ results = pipeline.predict(test_data["Text"])
 
 
 
-Many additional examples and tutorials can be found in the [documentation](https://docs.microsoft.com/en-us/nimbusml/overview).
+Many additional examples and tutorials can be found in the [documentation](https://docs.microsoft.com/en-us/NimbusML/overview).
 
 
 ## Building

build/ci/phase-template.yml

@@ -54,5 +54,5 @@ phases:
       displayName: Publish wheel file to VSTS artifacts
       inputs:
         pathToPublish: $(Build.SourcesDirectory)/target
-        artifactName: Mlnet Wheels
+        artifactName: NimbusML Wheels
         artifactType: container

build/signed_build_phase.yml

@@ -55,13 +55,13 @@ phases:
     displayName: Copy wheel file to Staging Directory in preparation for publishing 
     inputs:
       SourceFolder: $(Build.SourcesDirectory)/target
-      Contents: mlnet-*.whl
+      Contents: nimbusml-*.whl
       TargetFolder: $(Build.StagingDirectory)/artifacts
 
   - task: PublishBuildArtifacts@1
     condition: and(always(), ne(variables['Build.Reason'], 'PullRequest'))
     displayName: Publish wheel file to VSTS artifacts
     inputs:
       pathToPublish: $(Build.StagingDirectory)/artifacts
-      artifactName: Mlnet Wheels
+      artifactName: NimbusML Wheels
       artifactType: container

docs/README.md

@@ -1,15 +1,15 @@
 Documents Index
 ===============
 
-Intro to mlnet
+Intro to NimbusML
 ===============
 
-`mlnet` provides state-of-the-art ML algorithms, transforms and components, aiming to make them useful for all developers, data scientists, and information workers and helpful in all products, services and devices.
+NimbusML provides state-of-the-art ML algorithms, transforms and components, aiming to make them useful for all developers, data scientists, and information workers and helpful in all products, services and devices.
 
 Project Docs
 ============
 
-- [API](https://docs.microsoft.com/en-us/mlnet/overview)
-- [Tutorials](https://docs.microsoft.com/en-us/mlnet/tutorials)
+- [API](https://docs.microsoft.com/en-us/nimbusml/overview)
+- [Tutorials](https://docs.microsoft.com/en-us/nimbusml/tutorials)
 - [Developer Guide](developers/developer-guide.md)
 - [Contributing to ML.NET](CONTRIBUTING.md)

docs/developers/developer-guide.md

@@ -6,8 +6,8 @@ NimbusML runs on Windows, Linux, and macOS and supports Python 3.7, 3.6, 3.5, an
 Building the repository
 =======================
 
-The `mlnet` repo can be built directly from a terminal or cmd prompt. See the platform-specific build instructions for your dev environment:
+The NimbusML repo can be built directly from a terminal or cmd prompt. See the platform-specific build instructions for your dev environment:
 
 | [Windows](windows-build.md) | [Linux](linux-build.md) | [Mac](mac-build.md) |
 
-`mlnet` official builds are produced in Azure Dev Ops, as specified by the file `.vsts-ci.yml`.
+Nimbus official builds are produced in Azure Dev Ops, as specified by the file `.vsts-ci.yml`.

docs/developers/linux-build.md

@@ -1,4 +1,4 @@
-Building `mlnet` from source on Linux
+Building NimbusML from source on Linux
 ==========================================
 ## Prerequisites
 1. gcc >= 5.4
@@ -17,4 +17,4 @@ This downloads dependencies (.NET SDK, specific versions of Python and Boost), b
 For additional options including running tests and building components independently, see `./build.sh -h`.
 
 ### Known Issues
-The LightGBM estimator fails on Linux when building from source. The official `mlnet` Linux wheel package on Pypi.org has a working version of LightGBM.
+The LightGBM estimator fails on Linux when building from source. The official NimbusML Linux wheel package on Pypi.org has a working version of LightGBM.

docs/developers/mac-build.md

@@ -1,4 +1,4 @@
-Building `mlnet` from source on Mac
+Building NimbusML from source on Mac
 ==========================================
 ## Prerequisites
 1. Xcode Command Line Tools (for Clang compiler)

docs/developers/windows-build.md

@@ -1,4 +1,4 @@
-Building `mlnet` from source on Windows
+Building NimbusML from source on Windows
 ==========================================
 ## Prerequisites
 1. Visual Studio 2015 or higher

docs/project-docs/style-guide.md

@@ -1,12 +1,12 @@
 Contributing to NimbusML
 ======================
 
-This document describes contribution guidelines that are specific to `mlnet`. Please read [Python Style Guide](https://www.python.org/dev/peps/pep-0008/) for more general Python style guidelines.
+This document describes contribution guidelines that are specific to NimbusML. Please read [Python Style Guide](https://www.python.org/dev/peps/pep-0008/) for more general Python style guidelines.
 
 Coding Style Changes
 --------------------
 
-We intend to bring `mlnet` into full conformance with the style guidelines described in [Python Style Guide](https://www.python.org/dev/peps/pep-0008/). We plan to do that with tooling, in a holistic way. In the meantime, please:
+We intend to bring NimbusML into full conformance with the style guidelines described in [Python Style Guide](https://www.python.org/dev/peps/pep-0008/). We plan to do that with tooling, in a holistic way. In the meantime, please:
 
 * **DO NOT** send PRs for style changes. For example, do not send PRs that are focused on changing usage of ```Int32``` to ```int```.
 * **DO NOT** send PRs for upgrading code to use newer language features, though it's ok to use newer language features as part of new code that's written.  For example, it's ok to use expression-bodied members as part of new code you write, but do not send a PR focused on changing existing properties or methods to use the feature.

src/python/docs/sphinx/concepts/columns.rst

@@ -17,7 +17,7 @@ How To Select Columns to Transform
 ``transform()`` and ``fit_transform()`` methods of trainers and transforms. By default, all
 columns are transformed equally.
 
-``nimbusml`` additionally provides a syntax to transform only a subset of columns. This is a useful
+NimbusML additionally provides a syntax to transform only a subset of columns. This is a useful
 feature for many transforms, especially when the dataset containts columns of mixed types. For
 example, a dataset with both numeric features and free text features. Similarly for trainers, the
 concept of :ref:`roles` provides a mechanism to select which columns to use as labels and features.
@@ -55,7 +55,7 @@ What if we only want to encode one of the columns? We simply use the ``<<`` oper
 transform to restrict operations to the columns of interest. The ``<<`` operatator is syntactic
 sugar for setting the ``columns`` argument of the transform.
 
-All transforms in ``nimbusml`` have an implicit ``columns`` parameter to tell which columns to process,
+All transforms in NimbusML have an implicit ``columns`` parameter to tell which columns to process,
 and optionally how to name the output columns, if any. Refer to the reference sections for each
 transform to see what format is allowed for the ``columns`` argument.
 

src/python/docs/sphinx/concepts/experimentvspipeline.rst

@@ -64,7 +64,7 @@ operations.
 Optimized Chaining of Trainers/Transforms
 """""""""""""""""""""""""""""""""""""""""
 
-Using ``nimbusml``, trainers and transforms within a :py:class:`nimbusml.Pipeline` will
+Using NimbusML, trainers and transforms within a :py:class:`nimbusml.Pipeline` will
 generally result in better performance compared to using them in a
 `sklearn.Pipeline <https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html>`_.
 Data copying is minimized when processing is limited to within the C# libraries, and if all

src/python/docs/sphinx/concepts/metrics.rst

@@ -55,7 +55,7 @@ This corresponds to evaltype='binary'.
     The computed AUC is defined as the probability that the score
     for a positive example is higher than the score for a negative one
     (see `AucAggregator.cs <https://github.com/dotnet/machinelearning/blob/master/src/Microsoft.ML.Data/Evaluators/AucAggregator.cs#L135>`_
-    in `ML.net <https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet>`_).
+    in `ML.NET <https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet>`_).
     This expression is asymptotically equivalent to the area under the curve
     which is what
     `scikit-learn <https://scikit-learn.org/stable/modules/generated/sklearn.metrics.auc.html>`_ computation.

src/python/docs/sphinx/concepts/roles.rst

@@ -14,7 +14,7 @@ Column Roles for Trainers
 Roles and Learners
 ------------------
 
-Columns play different roles in the context of trainers. ``nimbusml`` supports the following roles, as defined in :py:class:`nimbusml.Role`
+Columns play different roles in the context of trainers. NimbusML supports the following roles, as defined in :py:class:`nimbusml.Role`
 
 * Role.Label - the column representing the dependent variable.
 * Role.Feature - the column(s) representing the independent variable(s).
@@ -126,7 +126,7 @@ Example of GroupId Role
 
 Same goes for the group. Rankers needs the GroupId to link rows to rank. A ranker for search engine needs a
 dataset with a row per displayed result. The GroupId is ued to tell the learner which results belong to the
-same query, to group together the candidate set of documents for a single query. ``nimbusml`` needs features,
+same query, to group together the candidate set of documents for a single query. NimbusML needs features,
 a target (relevance label of the result) and a GroupId.
 
 Below is an example of using GroupId at the trainer.

src/python/docs/sphinx/concepts/schema.rst

@@ -16,13 +16,13 @@ Schema
 Introduction to Schema
 ----------------------
 
-The ``nimbusml`` data framework relies on a schema to understand the column names and mix of column
+The NimbusML data framework relies on a schema to understand the column names and mix of column
 types in the dataset, which may originate from any of the supported :ref:`datasources`. It is 
 automatically inferred when a :py:class:`nimbusml.FileDataStream` or :py:class:`nimbusml.DataSchema` is created.
 
 Transforms have the ability to operate on subsets of columns in the dataset, as well as alter the
 resulting output schema, which effects other transforms downstream. For users, it would be very useful to 
-understand how ``nimbusml`` processes the data in a pipeline for debugging purposes or training the model with :py:class:`nimbusml.FileDataStream`.
+understand how NimbusML processes the data in a pipeline for debugging purposes or training the model with :py:class:`nimbusml.FileDataStream`.
 
 The schema comes with two formats for its representation, (1) object representation and (2) string format. After generating a :py:class:`nimbusml.FileDataStream`, users can view the 
 object representation of the schema by using ``repr()`` function:
@@ -168,7 +168,7 @@ all of types R8, I8 and TX, with column names *X1*, *X2* and *X3*.
 Example of Schema for a File
 """"""""""""""""""""""""""""""""""""""
 
-The transforms and trainers in ``nimbusml`` support various :ref:`datasources` as inputs.
+The transforms and trainers in NimbusML support various :ref:`datasources` as inputs.
 When the data is in a ``pandas.DataFrame``, the schema is inferred automatically from the
 ``dtype`` of the columns.
 

src/python/docs/sphinx/concepts/types.rst

@@ -15,7 +15,7 @@ Types
 Column Types
 ------------
 
-``nimbusml`` wraps a library written in C#, which is a strongly typed language. Columns of the input data sources are ascribed a type, which is used by
+NimbusML wraps a library written in C#, which is a strongly typed language. Columns of the input data sources are ascribed a type, which is used by
 transforms and trainers to decide if they can operate on that column. Some transforms may only allow
 text data types, while others only numeric. Trainers almost exclusively require the features and
 labels to be of a numeric type.
@@ -41,7 +41,7 @@ VectorDataViewType Columns
 A VectorDataViewType column contains a vector of values of a homogenous type, and is associated with a
 ``column_name``.
 
-The following table shows how ``nimbusml`` processes a dataset:
+The following table shows how NimbusML processes a dataset:
 
 .. image:: ../_static/images/table_car.png
 The third column is a VectorDataViewType column named *Features* with 10 ``slots``. A VectorDataViewType column can

src/python/docs/sphinx/index.rst

@@ -9,7 +9,7 @@ ML.NET for Python
 Getting Started
 ===============
 
-``nimbusml`` is a Python module that provides experimental Python bindings for [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet). 
+NimbusML is a Python module that provides experimental Python bindings for [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet). 
 It provides battle-tested state-of-the-art ML algorithms, transforms and components,
 aiming to make them useful for all developers, data scientists, and information
 workers and helpful in all products, services and devices. The components are
@@ -18,21 +18,21 @@ Bing and other teams at Microsoft.
 
 ``nimbusml`` is interoperable with ``scikit-learn`` estimators and transforms, while adding 
 a suite of highly optimized algorithms written in C++ and C# for speed and performance. 
-``nimbusml`` trainers and transforms support the following data structures for the ``fit()`` and ``transform()`` methods:
+NimbusML trainers and transforms support the following data structures for the ``fit()`` and ``transform()`` methods:
 
 * ``numpy.ndarray`` 
 * ``scipy.sparse_cst``
 * ``pandas.DataFrame``.
 
-In addition, ``nimbusml`` also supports streaming from files without loading the dataset
+In addition, NimbusML also supports streaming from files without loading the dataset
 into memory, which allows training on data significantly 
 exceeding memory using [``FileDataStream``](docs-ref-autogen/nimbusml.FileDataStream.yml).
 
-With [``FileDataStream``](docs-ref-autogen/nimbusml.FileDataStream.yml), ``nimbusml`` is able to handle
+With [``FileDataStream``](docs-ref-autogen/nimbusml.FileDataStream.yml), NimbusML is able to handle
 up to **billion** features and **billions** of training examples for select algorithms.
 
 
-``nimbusml`` can be easily used for the following problems:
+NimbusML can be easily used for the following problems:
 
 .. image:: _static/images/examples1.png
       :target: tutorials/1-3.md

src/python/docs/sphinx/installationguide.rst

@@ -26,7 +26,7 @@ The library requires the following dependencies, which will be installed automat
 Installation 
 -------------
 
-``nimbusml`` can be installed using ``pip``:
+NimbusML can be installed using ``pip``:
 
 .. code-block:: console
 
@@ -41,10 +41,10 @@ For a quick test, please run:
 Building
 --------------------
 
-The ``nimbusml`` package can also be built from the `source repo <https://github.com/Microsoft/ML.NET-for-Python>`_
-on Github. For more details about building and testing, please refer to our `GitHub repo <https://github.com/Microsoft/ML.NET-for-Python>`_
+The NimbusML package can also be built from the `source repo <https://github.com/Microsoft/NimbusML>`_
+on Github. For more details about building and testing, please refer to our `GitHub repo <https://github.com/Microsoft/NimbusML>`_
 
 Contributing
 ------------
 
-This is an open source package and we welcome contributions. The source code for the  ``nimbusml`` package is `available in GitHub <https://github.com/Microsoft/ML.NET-for-Python>`_.
+This is an open source package and we welcome contributions. The source code for the  NimbusML package is `available in GitHub <https://github.com/Microsoft/NimbusML>`_.

src/python/docs/sphinx/overview.rst

@@ -2,7 +2,7 @@
 Overview 
 ========
 
-``nimbusml`` provides state-of-the-art ML algorithms, transforms and components,
+NimbusML provides state-of-the-art ML algorithms, transforms and components,
 aiming to make them useful for all developers, data scientists, and information
 workers and helpful in all products, services and devices. The components are
 authored by the team members, as well as numerous contributors from MSR, CISL,

src/python/docs/sphinx/toc.yml

@@ -8,31 +8,31 @@
   - expanded: false
     href: tutorials.md#quick-start
     items:
-    - href: tutorials/quickstart-nimbusml-python.md
+    - href: tutorials/A_A-Classification-with-Synthetic-Data.md
       name: Classification
-    - href: tutorials/quickstart-nimbusml-python-regression.md
+    - href: tutorials/A_C-Regression-with-Synthetic-Data.md
       name: Regression
-    - href: tutorials/sentimental-analysis-twitter.md
+    - href: tutorials/A_B-Twitter-Sentiment-1.md
       name: Sentiment Analysis (Using Pandas)   
     name: Quick Start
   - expanded: false
     href: tutorials.md#important-concept
     items:
-    - href: tutorials/sentimental-analysis-twitter-loading.md
+    - href: tutorials/B_A-Fast-Data-Loading-with-Schema-Twitter-Sentiment-2.md
       name: Streaming Data Loading
-    - href: tutorials/nimbusml-python-column-selection.md
+    - href: tutorials/B_B-Syntax-for-Column-Selection-Classification-Using-Flight-Schedule-Data.md
       name: Column Selection
-    - href: tutorials/image-clustering.md
+    - href: tutorials/B_C-Image-Processing-Clustering.md
       name: Image Processing
-    - href: tutorials/wikipedia-detox-analysis.md
+    - href: tutorials/B_D-Working-with-Scikit-Learn-Toolkit-Classification-Using-Wikipedia-Detox-Data.md
       name: Sentiment Analysis
-    - href: tutorials/defining-column-roles.md
+    - href: tutorials/B_E-Learning-to-Rank-with-Microsoft-Bing-Data.md
       name: Subset Ranking Estimation
     name: Important Concepts
   - expanded: false
     href: tutorials.md#more-examples
     items:
-    - href: tutorials/pipeline-visualization.md
+    - href: tutorials/C_A-Visualize-a-pipeline.md
       name: Pipeline Visualization
     - href: loadsavemodels.md
       name: Loading and Saving Models

src/python/nimbusml/examples/Sentiment.py

@@ -19,7 +19,7 @@
 # No need to fit any real data, just a dummy call to fit() to ensure the
 # column name 'review' is present when transform() is invoked
 
-# Skip until ML.Net resolve the resouce issue with Sentiment transform
+# Skip until ML.NET resolve the resouce issue with Sentiment transform
 # y = analyze.fit_transform(customer_reviews)
 
 # View the sentiment scores!!

src/python/nimbusml/examples/examples_from_dataframe/WordEmbedding_df.py

@@ -3,7 +3,7 @@
 import pandas
 from nimbusml import Pipeline
 from nimbusml.feature_extraction.text import WordEmbedding
-from nimbusml.feature_extraction.text import NGramFeaturizer
+from nimbusml.feature_extraction.text.ngramfeaturizer import NGramFeaturizer
 from nimbusml.feature_extraction.text.extractor import Ngram
 
 # create the data

src/python/nimbusml/tests/feature_extraction/text/test_sentiment.py

@@ -19,7 +19,7 @@ class TestSentiment(unittest.TestCase):
 
     @unittest.skip(
         "BUG: Error: *** System.InvalidOperationException: 'resourcePath', "
-        "issue with ML.Net")
+        "issue with ML.NET")
     def test_sentiment(self):
         # Bug 142794
         data = pd.DataFrame({"Sentiment": [0,