Merge branch 'hongooi/dev' of https://github.com/microsoft/forecasting into hongooi/dev

.gitignore

@@ -1,6 +1,6 @@
-**/__pycache__
-**/.ipynb_checkpoints
-*.egg-info/
-.vscode/
-
-data/*
+**/__pycache__
+**/.ipynb_checkpoints
+*.egg-info/
+.vscode/
+
+ojdata/*

CONTRIBUTING.md

@@ -3,7 +3,6 @@
 Contribution are welcome! Here's a few things to know:
 
 * [Microsoft Contributor License Agreement](#microsoft-contributor-license-agreement)
-* [Setting up Environment](#Setting-up-environment)
 * [Steps to Contributing](#steps-to-contributing)
 * [Coding Guidelines](#forecasting-team-contribution-guidelines)
 * [Code of Conduct](#code-of-conduct)
@@ -14,58 +13,6 @@ Most contributions require you to agree to a Contributor License Agreement (CLA)
 
 When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
 
-## Setting up Environment
-
-Follow the instruction bellow to read about compute requirements and install required libraries.
-
-
-#### Compute environment
-
-We recommend using a virtual machine to run the example notebooks and scripts. The easiest way to get started is to use the [Azure Data Science Virtual Machine (DSVM) for Linux (Ubuntu)](https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-ubuntu-intro). This VM will come installed with all the system requirements that are needed to create the conda environment described below and then run the notebooks in this repository. 
-
-#### Conda environment
-
-To install the package contained in this repository, navigate to the directory where you pulled the Forecasting repo to run:
-```bash
-conda update conda
-conda env create -f tools/environment.yaml
-```
-This will create the appropriate conda environment to run experiments. Next activate the installed environment:
-```bash
-conda activate forecast
-```
-
-During development, in case you need to update the environment due to a conda env file change, you can run
-```
-conda env update --file tools/environment.yaml
-```
-from the root of Forecasting repo.
-
-#### Package installation
-
-Next you will need to install the common package for forecasting:
-```bash
-pip install -e forecasting_lib
-```
-
-The library is installed in developer mode with the `-e` flag. This means that all changes made to the library locally, are immediately available.
-
-## Getting Started with Examples
-
-### Dataset
-
-This repository provides examples on how to run various forecasting algorithms on a publicly available `orangeJuice` dataset. This dataset containes weekly sales of refrigerated orange juice at 83 stores, as well as, demographic information on those stores. The data is provided via [`bayesm`](https://CRAN.R-project.org/package=bayesm) package in `R`, and further described in [Chapter 5, Bayesian Statistics and Marketing by Rossi, Allenby, and McCulloch](http://www.perossi.org/home/bsm-1). `Forecasting_lib` package provides utilies for downloading and processing this data. 
-
-### Run Examples
-
-#### Notebooks
-We provide example notebooks under `examples/` to demonstrate how to train and evaluate forecasting algorithms.
-
-Make sure to run the notebooks in the conda environment we previously set up (`forecast`). To register the conda environment in Jupyter, please run:
-
-```
-python -m ipykernel install --user --name forecast
-```
 
 ## Steps to Contributing
 

README.md

@@ -4,7 +4,10 @@ This repository contains examples and best practices for building Forecasting so
 
 ## Getting Started
 
-To get started, navigate to the [Contributing Guide](./CONTRIBUTING.md), which lists instructions on how to set up your environment and dependencies, download the data and run examples provided in the repository.
+To get started, navigate to the [Setup Guide](./SETUP.md), which lists instructions on how to set up your environment and dependencies, download the data and run examples provided in the repository.
+
+## Contributing
+We hope that the open source community would contribute to the content and bring in the latest SOTA algorithm. This project welcomes contributions and suggestions. Before contributing, please see our [Contributing Guide](./CONTRIBUTING.md).
 
 ## Build Status
 | Build | Branch | Status |

SETUP.md

@@ -0,0 +1,52 @@
+## Setting up Environment
+
+Follow the instruction bellow to read about compute requirements and install required libraries.
+
+
+#### Compute environment
+
+We recommend using a virtual machine to run the example notebooks and scripts. The easiest way to get started is to use the [Azure Data Science Virtual Machine (DSVM) for Linux (Ubuntu)](https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-ubuntu-intro). This VM will come installed with all the system requirements that are needed to create the conda environment described below and then run the notebooks in this repository. 
+
+#### Conda environment
+
+To install the package contained in this repository, navigate to the directory where you pulled the Forecasting repo to run:
+```bash
+conda update conda
+conda env create -f tools/environment.yaml
+```
+This will create the appropriate conda environment to run experiments. Next activate the installed environment:
+```bash
+conda activate forecast
+```
+
+During development, in case you need to update the environment due to a conda env file change, you can run
+```
+conda env update --file tools/environment.yaml
+```
+from the root of Forecasting repo.
+
+#### Package installation
+
+Next you will need to install the common package for forecasting:
+```bash
+pip install -e forecasting_lib
+```
+
+The library is installed in developer mode with the `-e` flag. This means that all changes made to the library locally, are immediately available.
+
+## Getting Started with Examples
+
+### Dataset
+
+This repository provides examples on how to run various forecasting algorithms on a publicly available `orangeJuice` dataset. This dataset containes weekly sales of refrigerated orange juice at 83 stores, as well as, demographic information on those stores. The data is provided via [`bayesm`](https://CRAN.R-project.org/package=bayesm) package in `R`, and further described in [Chapter 5, Bayesian Statistics and Marketing by Rossi, Allenby, and McCulloch](http://www.perossi.org/home/bsm-1). `Forecasting_lib` package provides utilies for downloading and processing this data. 
+
+### Run Examples
+
+#### Notebooks
+We provide example notebooks under `examples/` to demonstrate how to train and evaluate forecasting algorithms.
+
+Make sure to run the notebooks in the conda environment we previously set up (`forecast`). To register the conda environment in Jupyter, please run:
+
+```
+python -m ipykernel install --user --name forecast
+```

chglog.txt

@@ -1,4 +0,0 @@
-x.y or date or however we mark a release
---------------------------------------------
-Highlights
-

contrib/README.md

@@ -1,13 +1,3 @@
 # Contrib
 
 Independent or incubating algorithms and utilities are candidates for the `contrib` folder. This folder will house contributions which may not easily fit into the core repository or need time to refactor the code and add necessary tests.
-
-| Name | Description |
-|------|-------------|
-| [ES-RNN](es_rnn/README.md) | Hybrid ES-RNN model using GPU |
-
-## Contrib Build status
-
-| Name | Status |
-|------|--------|
-| **ES-RNN** | [![Build Status](https://dev.azure.com/best-practices/forecasting/_apis/build/status/contrib%20es_rnn?branchName=master)](https://dev.azure.com/best-practices/forecasting/_build/latest?definitionId=107&branchName=master) |

examples/02_model/placeholder.txt

@@ -0,0 +1 @@
+tbd

examples/03_evaluate/placeholder.txt

@@ -0,0 +1 @@
+tbd

examples/04_model_select/placeholder.txt

@@ -0,0 +1 @@
+tbd

examples/05_operationalize/placeholder.txt

@@ -0,0 +1 @@
+tbd