reco utils to recommenders

MANIFEST.in

@@ -1 +1 @@
-include reco_utils/README.md
+include recommenders/README.md

README.md

@@ -22,7 +22,7 @@ This repository contains examples and best practices for building recommendation
 - [Model Select and Optimize](examples/04_model_select_and_optimize): Tuning and optimizing hyperparameters for recommender models
 - [Operationalize](examples/05_operationalize): Operationalizing models in a production environment on Azure
 
-Several utilities are provided in [reco_utils](reco_utils) to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting training/test data. Implementations of several state-of-the-art algorithms are included for self-study and customization in your own applications. See the [reco_utils documentation](https://readthedocs.org/projects/microsoft-recommenders/).
+Several utilities are provided in [recommenders](recommenders) to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting training/test data. Implementations of several state-of-the-art algorithms are included for self-study and customization in your own applications. See the [recommenders documentation](https://readthedocs.org/projects/microsoft-recommenders/).
 
 For a more detailed overview of the repository, please see the documents on the [wiki page](https://github.com/microsoft/recommenders/wiki/Documents-and-Presentations).
 
@@ -65,7 +65,7 @@ jupyter notebook
 
 6. Run the [SAR Python CPU MovieLens](examples/00_quick_start/sar_movielens.ipynb) notebook under the `00_quick_start` folder. Make sure to change the kernel to "Python (reco)".
 
-For additional options to install the package (support for GPU, Spark etc.) see [this guide](reco_utils/README.md).
+For additional options to install the package (support for GPU, Spark etc.) see [this guide](recommenders/README.md).
 
 **NOTE** - The [Alternating Least Squares (ALS)](examples/00_quick_start/als_movielens.ipynb) notebooks require a PySpark environment to run. Please follow the steps in the [setup guide](SETUP.md#dependencies-setup) to run these notebooks in a PySpark environment. For the deep learning algorithms, it is recommended to use a GPU machine and to follow the steps in the [setup guide](SETUP.md#dependencies-setup) to set up Nvidia libraries.
 
@@ -143,7 +143,7 @@ This project welcomes contributions and suggestions. Before contributing, please
 
 ## Build Status
 
-These tests are the nightly builds, which compute the smoke and integration tests. `main` is our principal branch and `staging` is our development branch. We use `pytest` for testing python utilities in [reco_utils](reco_utils) and `papermill` for the [notebooks](examples). For more information about the testing pipelines, please see the [test documentation](tests/README.md).
+These tests are the nightly builds, which compute the smoke and integration tests. `main` is our principal branch and `staging` is our development branch. We use `pytest` for testing python utilities in [recommenders](recommenders) and `papermill` for the [notebooks](examples). For more information about the testing pipelines, please see the [test documentation](tests/README.md).
 
 ### DSVM Build Status
 

SETUP.md

@@ -52,7 +52,7 @@ conda update conda -n root
 conda update anaconda        # use 'conda install anaconda' if the package is not installed
 ```
 
-There are different ways one may use the recommenders utilities. The most convenient one is probably by installing the `recommenders` package from [PyPI](https://pypi.org). For instructions on how to do these, see [this guide](reco_utils/README.md).
+There are different ways one may use the recommenders utilities. The most convenient one is probably by installing the `recommenders` package from [PyPI](https://pypi.org). For instructions on how to do these, see [this guide](recommenders/README.md).
 
 An alternative is to run all the recommender utilities directly from a local copy of the source code. This requires installing all the necessary dependencies from Anaconda and PyPI. For instructions on how to do this, see [this guide](conda.md)
 
@@ -307,12 +307,12 @@ To install the repo manually onto Databricks, follow the steps:
 After installation, you can now create a new notebook and import the utilities from Databricks in order to confirm that the import worked.
 
 ```{python}
-import reco_utils
+import recommenders
 ```
 
 ### Troubleshooting Installation on Azure Databricks
 
-* For the [reco_utils](reco_utils) import to work on Databricks, it is important to zip the content correctly. The zip has to be performed inside the Recommenders folder, if you zip directly above the Recommenders folder, it won't work.
+* For the [recommenders](recommenders) import to work on Databricks, it is important to zip the content correctly. The zip has to be performed inside the Recommenders folder, if you zip directly above the Recommenders folder, it won't work.
 
 ### Prepare Azure Databricks for Operationalization
 
@@ -378,7 +378,7 @@ You can then open the Jupyter notebook server at http://localhost:8888
 
 The process of making a new release and publishing it to pypi is as follows:
 
-First make sure that the tag that you want to add, e.g. `0.6.0`, is added in [reco_utils.py/__init__.py](reco_utils.py/__init__.py). Follow the [contribution guideline](CONTRIBUTING.md) to add the change.
+First make sure that the tag that you want to add, e.g. `0.6.0`, is added in [recommenders.py/__init__.py](recommenders.py/__init__.py). Follow the [contribution guideline](CONTRIBUTING.md) to add the change.
 
 1. Make sure that the code in main passes all the tests (unit and nightly tests).
 1. Create a tag with the version number: e.g. `git tag -a 0.6.0 -m "Recommenders 0.6.0"`.

contrib/azureml_designer_modules/entries/map_entry.py

@@ -8,7 +8,7 @@
     load_data_frame_from_directory,
     save_data_frame_to_directory,
 )
-from reco_utils.evaluation.python_evaluation import map_at_k
+from recommenders.evaluation.python_evaluation import map_at_k
 
 
 if __name__ == "__main__":

contrib/azureml_designer_modules/entries/ndcg_entry.py

@@ -8,7 +8,7 @@
     load_data_frame_from_directory,
     save_data_frame_to_directory,
 )
-from reco_utils.evaluation.python_evaluation import ndcg_at_k
+from recommenders.evaluation.python_evaluation import ndcg_at_k
 
 
 if __name__ == "__main__":

contrib/azureml_designer_modules/entries/precision_at_k_entry.py

@@ -8,7 +8,7 @@
     load_data_frame_from_directory,
     save_data_frame_to_directory,
 )
-from reco_utils.evaluation.python_evaluation import precision_at_k
+from recommenders.evaluation.python_evaluation import precision_at_k
 
 
 if __name__ == "__main__":

contrib/azureml_designer_modules/entries/recall_at_k_entry.py

@@ -8,7 +8,7 @@
     load_data_frame_from_directory,
     save_data_frame_to_directory,
 )
-from reco_utils.evaluation.python_evaluation import recall_at_k
+from recommenders.evaluation.python_evaluation import recall_at_k
 
 
 if __name__ == "__main__":

contrib/azureml_designer_modules/entries/stratified_splitter_entry.py

@@ -1,7 +1,7 @@
 import argparse
 
 from azureml.studio.core.logger import module_logger as logger
-from reco_utils.datasets.python_splitters import python_stratified_split
+from recommenders.datasets.python_splitters import python_stratified_split
 from azureml.studio.core.data_frame_schema import DataFrameSchema
 from azureml.studio.core.io.data_frame_directory import (
     load_data_frame_from_directory,

contrib/azureml_designer_modules/entries/train_sar_entry.py

@@ -4,7 +4,7 @@
 import joblib
 from pathlib import Path
 
-from reco_utils.models.sar import SAR
+from recommenders.models.sar import SAR
 
 from azureml.studio.core.logger import module_logger as logger
 from azureml.studio.core.utils.fileutils import ensure_folder

docs/source/conf.py

@@ -21,7 +21,7 @@
 sys.path.insert(0, os.path.abspath(os.path.join("..", "..")))
 sys.setrecursionlimit(1500)
 
-from reco_utils import TITLE, VERSION, COPYRIGHT, AUTHOR
+from recommenders import TITLE, VERSION, COPYRIGHT, AUTHOR
 
 # -- Project information -----------------------------------------------------
 

docs/source/datasets.rst

@@ -20,7 +20,7 @@ information, ratings, and a plaintext review.
     J. McAuley and J. Leskovec, "Hidden factors and hidden topics: understanding rating dimensions with review text", 
     RecSys, 2013.
 
-.. automodule:: reco_utils.datasets.amazon_reviews
+.. automodule:: recommenders.datasets.amazon_reviews
     :members:
 
 CORD-19
@@ -44,7 +44,7 @@ to generate new insights in support of the fight against this infectious disease
     Funk, K., Kinney, R., Liu, Z., Merrill, W. and Mooney, P. "Cord-19: The COVID-19 Open Research Dataset.", 2020.
 
 
-.. automodule:: reco_utils.datasets.covid_utils
+.. automodule:: recommenders.datasets.covid_utils
     :members:
 
 Criteo
@@ -55,7 +55,7 @@ for millions of display Ads. Every Ad has has 40 attributes, the first attribute
 that the Ad has been clicked on and a 0 represents it wasn't clicked on. The rest consist of 13 integer columns and 
 26 categorical columns.
 
-.. automodule:: reco_utils.datasets.criteo
+.. automodule:: recommenders.datasets.criteo
     :members:
 
 MIND
@@ -76,7 +76,7 @@ this impression. To protect user privacy, each user was de-linked from the produ
 
 
 
-.. automodule:: reco_utils.datasets.mind
+.. automodule:: recommenders.datasets.mind
     :members:  
 
 MovieLens
@@ -101,52 +101,52 @@ It comes with several sizes:
     ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4, Article 19, 
     DOI=http://dx.doi.org/10.1145/2827872, 2015.
 
-.. automodule:: reco_utils.datasets.movielens
+.. automodule:: recommenders.datasets.movielens
     :members:
 
 Download utilities 
 ******************
 
-.. automodule:: reco_utils.datasets.download_utils
+.. automodule:: recommenders.datasets.download_utils
     :members:
 
 
 Cosmos CLI utilities
 *********************
 
-.. automodule:: reco_utils.datasets.cosmos_cli
+.. automodule:: recommenders.datasets.cosmos_cli
     :members:
 
 
 Pandas dataframe utilities
 ***************************
 
-.. automodule:: reco_utils.datasets.pandas_df_utils
+.. automodule:: recommenders.datasets.pandas_df_utils
     :members:
 
 
 Splitter utilities
 ******************
 
-.. automodule:: reco_utils.datasets.python_splitters
+.. automodule:: recommenders.datasets.python_splitters
     :members:
 
-.. automodule:: reco_utils.datasets.spark_splitters
+.. automodule:: recommenders.datasets.spark_splitters
     :members:
 
-.. automodule:: reco_utils.datasets.split_utils
+.. automodule:: recommenders.datasets.split_utils
     :members:
 
 
 Sparse utilities
 ****************
 
-.. automodule:: reco_utils.datasets.sparse
+.. automodule:: recommenders.datasets.sparse
     :members:
 
 
 Knowledge graph utilities
 *************************
 
-.. automodule:: reco_utils.datasets.wikidata
+.. automodule:: recommenders.datasets.wikidata
     :members:

docs/source/evaluation.rst

@@ -6,11 +6,11 @@ Evaluation module
 Python evaluation
 ===============================
 
-.. automodule:: reco_utils.evaluation.python_evaluation
+.. automodule:: recommenders.evaluation.python_evaluation
     :members:
 
 PySpark evaluation
 ===============================
 
-.. automodule:: reco_utils.evaluation.spark_evaluation
+.. automodule:: recommenders.evaluation.spark_evaluation
     :members:

docs/source/index.rst

@@ -4,7 +4,7 @@ Recommender Utilities
 
 The `Recommenders repository <https://github.com/Microsoft/Recommenders>`_ provides examples and best practices for building recommendation systems, provided as Jupyter notebooks. 
 
-The module `reco_utils <https://github.com/microsoft/recommenders/tree/main/reco_utils>`_ contains functions to simplify common tasks used when developing and 
+The module `recommenders <https://github.com/microsoft/recommenders/tree/main/recommenders>`_ contains functions to simplify common tasks used when developing and 
 evaluating recommender systems. 
 
 .. toctree::