Metadata-Version: 2.1
Name: primrose
Version: 2.0.0
Summary: Primrose: a framework for simple, quick modeling deployments
Author-email: WW Data Science <datascience@ww.com>
Maintainer-email: Brian Graham <brian.graham@ww.com>, Calvin Woo <calvin.woo@ww.com>, Pierre Winter <pierre.winter@ww.com>, Rakesh Ramesh <rakesh.ramesh@ww.com>
License: Apache License
                                   Version 2.0, January 2004
                                http://www.apache.org/licenses/
        
           TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
        
           1. Definitions.
        
              "License" shall mean the terms and conditions for use, reproduction,
              and distribution as defined by Sections 1 through 9 of this document.
        
              "Licensor" shall mean the copyright owner or entity authorized by
              the copyright owner that is granting the License.
        
              "Legal Entity" shall mean the union of the acting entity and all
              other entities that control, are controlled by, or are under common
              control with that entity. For the purposes of this definition,
              "control" means (i) the power, direct or indirect, to cause the
              direction or management of such entity, whether by contract or
              otherwise, or (ii) ownership of fifty percent (50%) or more of the
              outstanding shares, or (iii) beneficial ownership of such entity.
        
              "You" (or "Your") shall mean an individual or Legal Entity
              exercising permissions granted by this License.
        
              "Source" form shall mean the preferred form for making modifications,
              including but not limited to software source code, documentation
              source, and configuration files.
        
              "Object" form shall mean any form resulting from mechanical
              transformation or translation of a Source form, including but
              not limited to compiled object code, generated documentation,
              and conversions to other media types.
        
              "Work" shall mean the work of authorship, whether in Source or
              Object form, made available under the License, as indicated by a
              copyright notice that is included in or attached to the work
              (an example is provided in the Appendix below).
        
              "Derivative Works" shall mean any work, whether in Source or Object
              form, that is based on (or derived from) the Work and for which the
              editorial revisions, annotations, elaborations, or other modifications
              represent, as a whole, an original work of authorship. For the purposes
              of this License, Derivative Works shall not include works that remain
              separable from, or merely link (or bind by name) to the interfaces of,
              the Work and Derivative Works thereof.
        
              "Contribution" shall mean any work of authorship, including
              the original version of the Work and any modifications or additions
              to that Work or Derivative Works thereof, that is intentionally
              submitted to Licensor for inclusion in the Work by the copyright owner
              or by an individual or Legal Entity authorized to submit on behalf of
              the copyright owner. For the purposes of this definition, "submitted"
              means any form of electronic, verbal, or written communication sent
              to the Licensor or its representatives, including but not limited to
              communication on electronic mailing lists, source code control systems,
              and issue tracking systems that are managed by, or on behalf of, the
              Licensor for the purpose of discussing and improving the Work, but
              excluding communication that is conspicuously marked or otherwise
              designated in writing by the copyright owner as "Not a Contribution."
        
              "Contributor" shall mean Licensor and any individual or Legal Entity
              on behalf of whom a Contribution has been received by Licensor and
              subsequently incorporated within the Work.
        
           2. Grant of Copyright License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              copyright license to reproduce, prepare Derivative Works of,
              publicly display, publicly perform, sublicense, and distribute the
              Work and such Derivative Works in Source or Object form.
        
           3. Grant of Patent License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              (except as stated in this section) patent license to make, have made,
              use, offer to sell, sell, import, and otherwise transfer the Work,
              where such license applies only to those patent claims licensable
              by such Contributor that are necessarily infringed by their
              Contribution(s) alone or by combination of their Contribution(s)
              with the Work to which such Contribution(s) was submitted. If You
              institute patent litigation against any entity (including a
              cross-claim or counterclaim in a lawsuit) alleging that the Work
              or a Contribution incorporated within the Work constitutes direct
              or contributory patent infringement, then any patent licenses
              granted to You under this License for that Work shall terminate
              as of the date such litigation is filed.
        
           4. Redistribution. You may reproduce and distribute copies of the
              Work or Derivative Works thereof in any medium, with or without
              modifications, and in Source or Object form, provided that You
              meet the following conditions:
        
              (a) You must give any other recipients of the Work or
                  Derivative Works a copy of this License; and
        
              (b) You must cause any modified files to carry prominent notices
                  stating that You changed the files; and
        
              (c) You must retain, in the Source form of any Derivative Works
                  that You distribute, all copyright, patent, trademark, and
                  attribution notices from the Source form of the Work,
                  excluding those notices that do not pertain to any part of
                  the Derivative Works; and
        
              (d) If the Work includes a "NOTICE" text file as part of its
                  distribution, then any Derivative Works that You distribute must
                  include a readable copy of the attribution notices contained
                  within such NOTICE file, excluding those notices that do not
                  pertain to any part of the Derivative Works, in at least one
                  of the following places: within a NOTICE text file distributed
                  as part of the Derivative Works; within the Source form or
                  documentation, if provided along with the Derivative Works; or,
                  within a display generated by the Derivative Works, if and
                  wherever such third-party notices normally appear. The contents
                  of the NOTICE file are for informational purposes only and
                  do not modify the License. You may add Your own attribution
                  notices within Derivative Works that You distribute, alongside
                  or as an addendum to the NOTICE text from the Work, provided
                  that such additional attribution notices cannot be construed
                  as modifying the License.
        
              You may add Your own copyright statement to Your modifications and
              may provide additional or different license terms and conditions
              for use, reproduction, or distribution of Your modifications, or
              for any such Derivative Works as a whole, provided Your use,
              reproduction, and distribution of the Work otherwise complies with
              the conditions stated in this License.
        
           5. Submission of Contributions. Unless You explicitly state otherwise,
              any Contribution intentionally submitted for inclusion in the Work
              by You to the Licensor shall be under the terms and conditions of
              this License, without any additional terms or conditions.
              Notwithstanding the above, nothing herein shall supersede or modify
              the terms of any separate license agreement you may have executed
              with Licensor regarding such Contributions.
        
           6. Trademarks. This License does not grant permission to use the trade
              names, trademarks, service marks, or product names of the Licensor,
              except as required for reasonable and customary use in describing the
              origin of the Work and reproducing the content of the NOTICE file.
        
           7. Disclaimer of Warranty. Unless required by applicable law or
              agreed to in writing, Licensor provides the Work (and each
              Contributor provides its Contributions) on an "AS IS" BASIS,
              WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
              implied, including, without limitation, any warranties or conditions
              of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
              PARTICULAR PURPOSE. You are solely responsible for determining the
              appropriateness of using or redistributing the Work and assume any
              risks associated with Your exercise of permissions under this License.
        
           8. Limitation of Liability. In no event and under no legal theory,
              whether in tort (including negligence), contract, or otherwise,
              unless required by applicable law (such as deliberate and grossly
              negligent acts) or agreed to in writing, shall any Contributor be
              liable to You for damages, including any direct, indirect, special,
              incidental, or consequential damages of any character arising as a
              result of this License or out of the use or inability to use the
              Work (including but not limited to damages for loss of goodwill,
              work stoppage, computer failure or malfunction, or any and all
              other commercial damages or losses), even if such Contributor
              has been advised of the possibility of such damages.
        
           9. Accepting Warranty or Additional Liability. While redistributing
              the Work or Derivative Works thereof, You may choose to offer,
              and charge a fee for, acceptance of support, warranty, indemnity,
              or other liability obligations and/or rights consistent with this
              License. However, in accepting such obligations, You may act only
              on Your own behalf and on Your sole responsibility, not on behalf
              of any other Contributor, and only if You agree to indemnify,
              defend, and hold each Contributor harmless for any liability
              incurred by, or claims asserted against, such Contributor by reason
              of your accepting any such warranty or additional liability.
        
           END OF TERMS AND CONDITIONS
        
           APPENDIX: How to apply the Apache License to your work.
        
              To apply the Apache License to your work, attach the following
              boilerplate notice, with the fields enclosed by brackets "[]"
              replaced with your own identifying information. (Don't include
              the brackets!)  The text should be enclosed in the appropriate
              comment syntax for the file format. We also recommend that a
              file or class name and description of purpose be included on the
              same "printed page" as the copyright notice for easier
              identification within third-party archives.
        
           Copyright [yyyy] [name of copyright owner]
        
           Licensed under the Apache License, Version 2.0 (the "License");
           you may not use this file except in compliance with the License.
           You may obtain a copy of the License at
        
               http://www.apache.org/licenses/LICENSE-2.0
        
           Unless required by applicable law or agreed to in writing, software
           distributed under the License is distributed on an "AS IS" BASIS,
           WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
           See the License for the specific language governing permissions and
           limitations under the License.
        
Project-URL: Homepage, https://github.com/ww-tech/primrose
Project-URL: Documentation, https://ww-tech.github.io/primrose/
Classifier: Programming Language :: Python :: 3.8
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas (>=1.5.3)
Requires-Dist: click (>=8.1.3)
Requires-Dist: scikit-learn (>=1.2.2)
Requires-Dist: jstyleson (>=0.0.2)
Requires-Dist: networkx (>=3.0)
Requires-Dist: dill (>=0.3.6)
Requires-Dist: pyyaml (>=6.0)
Requires-Dist: google-cloud-storage (>=2.7.0)
Requires-Dist: matplotlib (>=3.7.1)
Requires-Dist: jinja2 (>=3.1.2)
Requires-Dist: boto3 (>=1.26.93)
Requires-Dist: mysql-connector-python (>=8.0.32)
Requires-Dist: slackclient (>=2.9.4)
Requires-Dist: testfixtures (>=7.1.0)
Requires-Dist: moto (>=4.1.4)
Requires-Dist: nltk (>=3.8.1)
Requires-Dist: pydot (>=1.4.2)
Provides-Extra: r
Requires-Dist: rpy2 (>=3.5.10) ; extra == 'r'
Provides-Extra: plotting
Requires-Dist: pygraphviz (>=1.10) ; extra == 'plotting'
Provides-Extra: postgres
Requires-Dist: psycopg2-binary (>=2.9.5) ; extra == 'postgres'
Provides-Extra: test
Requires-Dist: pytest (>=7.2.2) ; extra == 'test'

# Overview
[![CI/CD](https://github.com/ww-tech/primrose/actions/workflows/ci.yml/badge.svg)](https://github.com/ww-tech/primrose/actions/workflows/ci.yml)
[![PyPI pyversions](https://img.shields.io/pypi/pyversions/primrose.svg)](https://pypi.python.org/pypi/primrose/)
[![PyPI version](https://badge.fury.io/py/primrose.svg)](https://badge.fury.io/py/primrose)
[![PyPI license](https://img.shields.io/pypi/l/primrose.svg)](https://pypi.python.org/pypi/primrose/)
[![Docs status](https://img.shields.io/website/https/ww-tech.github.io/primrose?down_color=red&down_message=docs&label=docs&up_color=success&up_message=up)](https://ww-tech.github.io/primrose/)


<p align="center">
   <img src="img/primrose_logo.png" width="100">
</p>

## Primrose at a glance

`Primrose` is a simple **Python** framework for executing **in-memory** workflows defined by directed acyclic graphs (**DAGs**) via configuration files. Data in `primrose` flows from one node to another while **avoiding serialization**, except for when explicitly specified by the user. `Primrose` nodes are designed for **simple batch-based machine learning workflows**, which have datasets small enough to fit into a single machine's memory.

## Table of Contents
We suggest reading the documentation in the following order:

 - Overview and motivation for `primrose`&mdash;this file.
 - [Getting Started](README_GETTING_STARTED.md): run your first `primrose` jobs.
 - [DAG Configurations](README_DAG_CONFIG.md): `primrose` adopts a configuration-as-code paradigm. This section introduces `primrose` configuration files.
 - [Metadata](README_METADATA.md): this covers more advanced options of the configuration files.
 - [Command Line Interface (CLI)](README_CLI.md): run commands using the CLI.
 - [Developer Notes](README_DEVELOPER_NOTES.md): how to create your own new Node classes.
 - [DataObject](README_DATAOBJECT.md): a deep dive into `DataObject`, the core data handling and book-keeping object.

## Introduction

 `Primrose` is a Python framework for quick, simple machine learning and recommender deployments developed by the data science team at [WW](https://www.weightwatchers.com/us/). It is essentially a workflow management tool which is specialized for the needs of machine learning tasks with small to medium sized datasets (&le; 100GB). Like many orchestration tools, `Primrose` *nodes* are defined in a [directed-acyclic-graph](https://en.wikipedia.org/wiki/Directed_acyclic_graph) which defines dependencies and control flow.

 Here's an example DAG showing data cleaning, model training, and model serialization:

 <p align="center">
   <img src="img/hello_world_tennis.png" width="500">
</p>

 It exists within an ecosystem of other great open source workflow management tools (like [Airflow](https://airflow.apache.org/), [Luigi](https://luigi.readthedocs.io/en/stable/), [Kubeflow](https://www.kubeflow.org/docs/about/kubeflow/) or [Prefect](https://docs.prefect.io/guide/)) while carving it's own niche based on the following design goals:

1. **Avoid unnecessary serialization:** `Primrose` keeps data in-memory between task steps, and only performs (de)serialization operations when explicitly requested by the user. Data is transported between nodes through use of a `DataObject` abstraction, which contextually delivers the correct data to each `Primrose` node at runtime. As a consequence of this design choice, `Primrose` runs on a single machine and can be deployed as a job within a single container, like any other Python script or cron job. In addition to operating on persistent data passed between nodes, `Primrose` can also be used to call external services in a manner similar to a [Luigi](https://luigi.readthedocs.io/en/stable/) job. In this way, Spark jobs or Hadoop scripts can be called and the framework simply dictates dependencies.

    * *As a comparison...* many solutions in this space are focused on long-running jobs which may be distributed across several computing nodes. Furthermore, to facilitate parallelization, save states for redundancy, and process datasets which are too large for memory, orchestrators often require that data is serialized between each workflow task. For smaller datasets, the IO time associated with these steps can be much longer than the time spent in computation.

    * *Primrose is not...* a solution which scales across clusters or a complex dependency management solution with dynamic DAGs (yet).

2. **Batch processesing for ML:** `Primrose` was built to facilitate frequent batches of model training or predictions that must read and write from/to multiple sources. Rather than requiring users to define their DAG structure in Python code, `Primrose` adopts a `configuration-as-code` approach. `Primrose` users create implementations of node objects once, then any DAG structural modifications or parameterization changes are processed through configuration json files. This way, deployment changes to DAG operations (such as modifying a DAG to serve model predictions instead of training) can be handled purely through configuration files. This avoids the need to build new Python scripts for production modifications. Furthermore, `Primrose` nodes are based on common machine learning tasks to make data scientist's lives easier. This cuts down on development time for building new models and maximizes code re-use among projects and teams. See the modeling examples in the source and documentation for more info!
    * *As a comparison...* in `Primrose`, users simply need to specify in their configuration file that they want common ML operations to act on the `DataObject`. These ML operations can certainly be implemented by users in Luigi or Airflow, but we found operations such as test-train splits or classifier cross-validation to be so common that they warranted nodes pre-dedicated to these operations.  [Prefect](https://docs.prefect.io/guide/) has made some great strides in this area, and we encourage users to check their solution out.

    * *Primrose is not...* an auto-ml tool or machine-learning toolkit which implements its own algorithms. Any Python machine learning library can be used with `Primrose`, simply by building model or pipeline nodes that implement the user's choice of library.

3. **Simplicity:**

    **Standardization of deployments:** `Primrose` is meant to help make deployment and model building as simple as possible. From a developer operations perspective, it requires no external scheduler or cluster to run deployments. `Primrose` code can simply be containerized with a `primrose` Python entrypoint, and deployed as a job on a k8s or any other container management service.

    **Standardization of development:** From a software engineering perspective, another advantage of `Primrose` stems form the standardization of model and recommender code. Modifying feature engineering pipelines or adding recommender features is simplified by writing additions to self-contained `Primrose` nodes and making additions to a configuration file.

    * *As a comparison...* `Primrose` can be leveraged as a piece of a larger ETL job (a `Primrose` job could be a job within an Airflow DAG), or run on it's own as a self-contained, single node ETL job. Some orchestration solutions (Airflow, for example) require running persistent clusters and services for managing jobs.

    * *Primrose is not...* able to manage its own job scheduling or timing. This is left to user using k8s job scheduling or manual cron job assignments on a virtual machine.


There are many solutions in this space, and we encourage users to explore other options that may be most appropriate for their workflows. We view `Primrose` as a simple solution for managing production ML jobs.


## Getting Started

`Primrose` has a couple of optional tools:
* a PostgreSQL database reader
* a plotting tool
* an R-dataset reader. E.g., to read in R's iris dataset see config/example_read_r.json

These require a few external dependencies, prior to its installation. If interested in their functionality, follow the appropriate instructions for your OS below. Otherwise, you can proceed with the basic package installation.

### Installation

You can install the latest `Primrose` release via pypi
```
pip install primrose
```
or you can clone the repository and install via `setup.py`.
```
git clone https://github.com/ww-tech/primrose.git
cd primrose
python setup.py install
```

To install the complete `Primrose` package (after dependencies have been installed):
```
    pip install primrose[postgres, plotting]
```

To install `Primrose` with just the PostgreSQL option:

```
pip install primrose[postgres]
```

To install `primrose` with just the plotting option:

```
pip install primrose[plotting]
```

To install `primrose` with just the R dataset reading option:

```
pip install primrose[R]
```
### External dependenices

**PostgreSQL**

#### MacOSX

We recommend using homebrew to manage OS level external packages. If you do not already have homebrew installed, please visit [their website](https://brew.sh/).

Instructions:
1. Use homebrew to install `postgresql` library.
   ```
   brew install postgresql
   ```

2. Use `pip` to install `psycopg2`
   ```
    pip install psycopg2
   ```

#### Debian/Ubuntu

Instructions:
1. Install the `libpq-dev` library
   ```
   apt-get install libpq-dev
   ```

**Plotting**

#### MacOSX

We recommend using homebrew to manage OS level external packages. If you do not already have homebrew installed, please visit [their website](https://brew.sh/).

Instructions:
1. Use homebrew to install `graphviz` library.
   ```
   brew install graphviz
   ```
2. If you are using a virtual environment such as `Anaconda` or `virtualenv`, you may need to specify a backend for `matplotlib`.
   ```
   mkdir -p ~/.matplotlib && touch ~/.matplotlib/matplotlibrc
   echo backend: TkAgg >> ~/.matplotlib/matplotlibrc
    ```

#### Debian/Ubuntu

Instructions:
1. Install `graphviz` library.
   ```
   apt-get install graphviz
   ```
2. If you are using a virtual environment such as `Anaconda` or `virtualenv`, you may need to specify a backend for `matplotlib`.
   ```
   mkdir -p ~/.config/matplotlib && touch ~/.config/matplotlib/matplotlibrc
   echo backend: Agg >> ~/.config/matplotlib/matplotlibrc
   ```

## Next
You are now ready to run your first `primrose` jobs: [Getting Started](README_GETTING_STARTED.md).

## License
Copyright 2019 WW International, Inc.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

## Contributing
To contribute, start a feature branch and install
[black](https://github.com/psf/black) in your dev environment.

```
pip install black
```


Black is our python formatter of choice. We have also set up
[pre-commit](https://pre-commit.com/) hooks to enforce this formatting.
This means you will get a commit error when you try to commit without
adhering to our black formatting. Check out these packages for further
instructions on their usage.

Once you have made your commits, make a PR to master! We have dedicated
time to review open PRs at least once per week, so we shouldn't miss any
new PRs.

Please adhere to coding best practices and make sure to do everything in
the PR template checklist.
* Write small, modular, reusable, and testable code where possible.
* Write tests ([pytest](https://docs.pytest.org/en/latest/) or
  [unittest](https://docs.python.org/3/library/unittest.html)) for that
  code and make sure it passes (pytest).
* [Squash your commits](https://github.com/wprig/wprig/wiki/How-to-squash-commits) so that commits are [fewer but more meaningful](https://blog.carbonfive.com/2017/08/28/always-squash-and-rebase-your-git-commits/).
* Update our [documentation](https://ww-tech.github.io/primrose/).
That means adhering to our [docstring format](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html) and adding [markdown](https://www.markdownguide.org/) when necessary.
