Merge branch '2.0.x' into 2.1.x

README.rst

@@ -49,7 +49,7 @@ FACET is composed of the following key components:
 |                 | tried and tested pipelining paradigm with new capabilities for model  |
 |                 | selection, inspection, and simulation.                                |
 |                 | FACET also introduces                                                 |
-|                 | `sklearndf <https://github.com/BCG-Gamma/sklearndf>`__                |
+|                 | `sklearndf <https://github.com/BCG-X-Official/sklearndf>`__                |
 |                 | [`documentation <https://bcg-gamma.github.io/sklearndf/index.html>`__]|
 |                 | an augmented version of *scikit-learn* with enhanced support for      |
 |                 | *pandas* data frames that ensures end-to-end traceability of features.|
@@ -412,7 +412,7 @@ FACET is stable and is being supported long-term.
 
 Contributions to FACET are welcome and appreciated.
 For any bug reports or feature requests/enhancements please use the appropriate
-`GitHub form <https://github.com/BCG-Gamma/facet/issues>`_, and if you wish to do so,
+`GitHub form <https://github.com/BCG-X-Official/facet/issues>`_, and if you wish to do so,
 please open a PR addressing the issue.
 
 We do ask that for any major changes please discuss these with us first via an issue or
@@ -426,7 +426,7 @@ License
 -------
 
 FACET is licensed under Apache 2.0 as described in the
-`LICENSE <https://github.com/BCG-Gamma/facet/blob/develop/LICENSE>`_ file.
+`LICENSE <https://github.com/BCG-X-Official/facet/blob/develop/LICENSE>`_ file.
 
 
 Acknowledgements
@@ -476,7 +476,7 @@ or have a look at
 .. |pypi| image:: https://badge.fury.io/py/gamma-facet.svg
     :target: https://pypi.org/project/gamma-facet/
 
-.. |azure_build| image:: https://dev.azure.com/gamma-facet/facet/_apis/build/status/BCG-Gamma.facet?repoName=BCG-Gamma%2Ffacet&branchName=develop
+.. |azure_build| image:: https://dev.azure.com/gamma-facet/facet/_apis/build/status/BCG-X-Official.facet?repoName=BCG-Gamma%2Ffacet&branchName=develop
    :target: https://dev.azure.com/gamma-facet/facet/_build?definitionId=7&_a=summary
 
 .. |azure_code_cov| image:: https://img.shields.io/azure-devops/coverage/gamma-facet/facet/7/2.0.x

condabuild/meta.yaml

@@ -58,13 +58,13 @@ test:
     - pytest -vs test
 
 about:
-  home: https://github.com/BCG-Gamma/facet
+  home: https://github.com/BCG-X-Official/facet
   license: Apache Software License v2.0
   license_file: LICENSE
   description: |
     FACET is an open source library for human-explainable AI. It combines sophisticated
     model inspection and model-based simulation to enable better explanations of
     your supervised machine learning models.
-  dev_url: https://github.com/BCG-Gamma/facet
+  dev_url: https://github.com/BCG-X-Official/facet
   doc_url: https://bcg-gamma.github.io/facet/
-  doc_source_url: https://github.com/BCG-Gamma/facet/blob/develop/README.rst
+  doc_source_url: https://github.com/BCG-X-Official/facet/blob/develop/README.rst

pypi_description.rst

@@ -36,7 +36,7 @@ workflow, enhancing
 tried and tested pipelining paradigm with new capabilities for model
 selection, inspection, and simulation.
 FACET also introduces
-`sklearndf <https://github.com/BCG-Gamma/sklearndf>`__, an augmented
+`sklearndf <https://github.com/BCG-X-Official/sklearndf>`__, an augmented
 version of *scikit-learn* with enhanced support for *pandas* data
 frames that ensures end-to-end traceability of features.
 
@@ -51,7 +51,7 @@ License
 ---------------------------
 
 FACET is licensed under Apache 2.0 as described in the
-`LICENSE <https://github.com/BCG-Gamma/facet/blob/develop/LICENSE>`_ file.
+`LICENSE <https://github.com/BCG-X-Official/facet/blob/develop/LICENSE>`_ file.
 
 .. Begin-Badges
 

pyproject.toml

@@ -8,7 +8,7 @@ exclude = [".idea", "tmp", "dist", ".tox", ".pytest_cache"]
 [tool.flit.metadata]
 module = "facet"
 author = "Boston Consulting Group (BCG)"
-home-page = "https://github.com/BCG-Gamma/facet"
+home-page = "https://github.com/BCG-X-Official/facet"
 description-file = "pypi_description.rst"
 dist-name = "gamma-facet"
 license = "Apache Software License v2.0"
@@ -65,7 +65,7 @@ docs = [
 
 [tool.flit.metadata.urls]
 Documentation = "https://bcg-gamma.github.io/facet/"
-Repository = "https://github.com/BCG-Gamma/facet"
+Repository = "https://github.com/BCG-X-Official/facet"
 
 [build]
 # comma-separated list of packages to be built from source in pip min builds

sphinx/auxiliary/Diabetes_getting_started_example.ipynb

@@ -25,7 +25,7 @@
     "    \n",
     "- **Enhanced Machine Learning Workflow**  \n",
     "\n",
-    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-Gamma/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.       "
+    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-X-Official/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.       "
    ]
   },
   {

sphinx/auxiliary/Facet_sphinx_tutorial_template.ipynb

@@ -43,7 +43,7 @@
     "    \n",
     "- **Enhanced Machine Learning Workflow**  \n",
     "\n",
-    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-Gamma/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.     \n",
+    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-X-Official/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.     \n",
     "\n",
     "***\n",
     "\n",
@@ -86,7 +86,7 @@
     "\n",
     "1. Common packages (pandas, matplotlib, etc.)\n",
     "2. Required FACET classes (inspection, selection, validation, simulation, etc.)\n",
-    "3. Other BCG Gamma packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-Gamma/pytools)) when using FACET"
+    "3. Other BCG Gamma packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-X-Official/pytools)) when using FACET"
    ]
   },
   {
@@ -128,7 +128,7 @@
    "source": [
     "**sklearndf imports**\n",
     "\n",
-    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
+    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
     "\n",
     "`from sklearn.pipeline import Pipeline`\n",
     "\n",
@@ -152,7 +152,7 @@
    "source": [
     "**pytools imports**\n",
     "\n",
-    "pytools (see on [GitHub](https://github.com/BCG-Gamma/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
+    "pytools (see on [GitHub](https://github.com/BCG-X-Official/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
    ]
   },
   {

sphinx/make.py

@@ -9,14 +9,13 @@
 
 
 if __name__ == "__main__":
-
     # noinspection PyUnusedLocal
     def run_make(branch: str, working_directory: str) -> None:
         """Stub, overwritten by bootstrap.py"""
 
     # run the common make file available in the pytools repo
     with request.urlopen(
-        f"https://raw.githubusercontent.com/BCG-Gamma/pytools/{BRANCH}"
+        f"https://raw.githubusercontent.com/BCG-X-Official/pytools/{BRANCH}"
         f"/sphinx/base/bootstrap.py"
     ) as response:
         exec(response.read().decode("utf-8"), globals())

sphinx/source/contribution_guide.rst

@@ -296,7 +296,7 @@ The ``sphinx/source`` folder contains:
 
 
 The two key scripts are ``make.py`` and ``conf.py``. The base configuration for the
-these scripts can be found in `pytools/sphinx <https://github.com/BCG-Gamma/pytools/tree/develop/sphinx/base>`_.
+these scripts can be found in `pytools/sphinx <https://github.com/BCG-X-Official/pytools/tree/develop/sphinx/base>`_.
 The reason for this is to minimise code given the standardization of the documentation
 build across multiple packages.
 
@@ -452,7 +452,7 @@ The build process for the PyPI and conda distributions uses the following key
 files:
 
 - ``make.py``: generic Python script for package builds. Most configuration is imported
-  from pytools `make.py <https://github.com/BCG-Gamma/pytools/blob/develop/make.py>`__
+  from pytools `make.py <https://github.com/BCG-X-Official/pytools/blob/develop/make.py>`__
   which is a build script that wraps the package build, as well as exposing the matrix
   dependency definitions specified in the ``pyproject.toml`` as environment variables
 - ``pyproject.toml``: metadata for PyPI, build settings and package dependencies

sphinx/source/faqs.rst

@@ -14,7 +14,7 @@ on `stackoverflow <https://stackoverflow.com/>`_.
 1. **What if I find a bug or have an idea for a new feature?**
 
     For bug reports or feature requests please use our
-    `GitHub issue tracker <https://github.com/BCG-Gamma/facet/issues>`_.
+    `GitHub issue tracker <https://github.com/BCG-X-Official/facet/issues>`_.
     For any other enquiries please feel free to contact us at FacetTeam@bcg.com.
 
 2. **How does FACET's novel algorithm calculate pairwise feature redundancy and synergy?**

sphinx/source/tutorial/Classification_with_Facet.ipynb

@@ -31,7 +31,7 @@
     "    \n",
     "- **Enhanced Machine Learning Workflow**  \n",
     "\n",
-    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-Gamma/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.    \n",
+    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-X-Official/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.    \n",
     "\n",
     "***\n",
     "\n",
@@ -108,7 +108,7 @@
     "\n",
     "1. Common packages (pandas, matplotlib, etc.)\n",
     "2. Required FACET classes (inspection, selection, validation, simulation, etc.)\n",
-    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-Gamma/pytools)) when using FACET"
+    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-X-Official/pytools)) when using FACET"
    ]
   },
   {
@@ -162,7 +162,7 @@
    "source": [
     "**sklearndf imports**\n",
     "\n",
-    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
+    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
     "\n",
     "`from sklearn.pipeline import Pipeline`\n",
     "\n",
@@ -194,7 +194,7 @@
    "source": [
     "**pytools imports**\n",
     "\n",
-    "pytools (see on [GitHub](https://github.com/BCG-Gamma/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
+    "pytools (see on [GitHub](https://github.com/BCG-X-Official/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
    ]
   },
   {
@@ -1557,7 +1557,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For convenience when working in a non-notebook environment, all of the `Drawer`'s provided by the [pytools](https://github.com/BCG-Gamma/pytools) package also support a `style='text'` flag."
+    "For convenience when working in a non-notebook environment, all of the `Drawer`'s provided by the [pytools](https://github.com/BCG-X-Official/pytools) package also support a `style='text'` flag."
    ]
   },
   {

sphinx/source/tutorial/Model_simulation_deep_dive.ipynb

@@ -40,7 +40,7 @@
     "    \n",
     "- **Enhanced Machine Learning Workflow**  \n",
     "\n",
-    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-Gamma/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.\n",
+    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-X-Official/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.\n",
     "\n",
     "***\n",
     "\n",
@@ -117,7 +117,7 @@
     "\n",
     "1. Common packages (pandas, matplotlib, etc.)\n",
     "2. Required FACET classes (inspection, selection, validation, simulation, etc.)\n",
-    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/BCG-Gamma/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-Gamma/pytools)) when using FACET"
+    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/BCG-X-Official/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-X-Official/pytools)) when using FACET"
    ]
   },
   {
@@ -168,7 +168,7 @@
    "source": [
     "**sklearndf imports**\n",
     "\n",
-    "Instead of using the \"regular\" *scikit-learn* package, we are going to use *sklearndf* (see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)). *sklearndf* is an open source library designed to address a common issue with *scikit-learn*: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. *sklearndf* retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard *scikit-learn* import:\n",
+    "Instead of using the \"regular\" *scikit-learn* package, we are going to use *sklearndf* (see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)). *sklearndf* is an open source library designed to address a common issue with *scikit-learn*: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. *sklearndf* retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard *scikit-learn* import:\n",
     "\n",
     "`from sklearn.pipeline import Pipeline`\n",
     "\n",
@@ -193,7 +193,7 @@
    "source": [
     "**pytools imports**\n",
     "\n",
-    "pytools (see on [GitHub](https://github.com/BCG-Gamma/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
+    "pytools (see on [GitHub](https://github.com/BCG-X-Official/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
    ]
   },
   {