First Commit-JeetMehta&SaiShruthiSistla

website/docusaurus.config.js

@@ -43,6 +43,13 @@ module.exports = {
           label: 'GitHub',
           position: 'right',
         },
+        {
+          type: 'docsVersionDropdown',
+          position: 'right',
+
+        },
+
+
       ],
     },
     footer: {

website/versioned_docs/version-1.0.4/Contribute.md

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+# Contributing
+
+This project welcomes and encourages all forms of contributions, including but not limited to:
+
+-  Pushing patches.
+-  Code review of pull requests.
+-  Documentation, examples and test cases.
+-  Readability improvement, e.g., improvement on docstr and comments.
+-  Community participation in [issues](https://github.com/microsoft/FLAML/issues), [discussions](https://github.com/microsoft/FLAML/discussions), and [discord](https://discord.gg/7ZVfhbTQZ5).
+-  Tutorials, blog posts, talks that promote the project.
+-  Sharing application scenarios and/or related research.
+
+You can take a look at the [Roadmap for Upcoming Features](https://github.com/microsoft/FLAML/wiki/Roadmap-for-Upcoming-Features) to identify potential things to work on.
+
+Most contributions require you to agree to a
+Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
+the rights to use your contribution. For details, visit <https://cla.opensource.microsoft.com>.
+
+If you are new to GitHub [here](https://help.github.com/categories/collaborating-with-issues-and-pull-requests/) is a detailed help source on getting involved with development on GitHub.
+
+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., status check, comment). Simply follow the instructions
+provided by the bot. You will only need to do this once across all repos using our CLA.
+
+This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
+For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
+contact [[email protected]](mailto:[email protected]) with any additional questions or comments.
+
+## How to make a good bug report
+
+When you submit an issue to [GitHub](https://github.com/microsoft/FLAML/issues), please do your best to
+follow these guidelines! This will make it a lot easier to provide you with good
+feedback:
+
+- The ideal bug report contains a short reproducible code snippet. This way
+  anyone can try to reproduce the bug easily (see [this](https://stackoverflow.com/help/mcve) for more details). If your snippet is
+  longer than around 50 lines, please link to a [gist](https://gist.github.com) or a GitHub repo.
+
+- If an exception is raised, please **provide the full traceback**.
+
+- Please include your **operating system type and version number**, as well as
+  your **Python, flaml, scikit-learn versions**. The version of flaml
+  can be found by running the following code snippet:
+```python
+import flaml
+print(flaml.__version__)
+```
+
+- Please ensure all **code snippets and error messages are formatted in
+  appropriate code blocks**.  See [Creating and highlighting code blocks](https://help.github.com/articles/creating-and-highlighting-code-blocks)
+  for more details.
+
+
+## Becoming a Reviewer
+
+There is currently no formal reviewer solicitation process. Current reviewers identify reviewers from active contributors. If you are willing to become a reviewer, you are welcome to let us know on gitter.
+
+## Developing
+
+### Setup
+
+```bash
+git clone https://github.com/microsoft/FLAML.git
+pip install -e FLAML[test,notebook]
+```
+
+In case the `pip install` command fails, try escaping the brackets such as `pip install -e FLAML\[test,notebook\]`
+
+### Docker
+
+We provide a simple [Dockerfile](https://github.com/microsoft/FLAML/blob/main/Dockerfile).
+
+```bash
+docker build https://github.com/microsoft/FLAML.git#main -t flaml-dev
+docker run -it flaml-dev
+```
+
+### Develop in Remote Container
+
+If you use vscode, you can open the FLAML folder in a [Container](https://code.visualstudio.com/docs/remote/containers).
+We have provided the configuration in [devcontainer](https://github.com/microsoft/FLAML/blob/main/.devcontainer).
+
+### Pre-commit
+
+Run `pre-commit install` to install pre-commit into your git hooks. Before you commit, run
+`pre-commit run` to check if you meet the pre-commit requirements. If you use Windows (without WSL) and can't commit after installing pre-commit, you can run `pre-commit uninstall` to uninstall the hook. In WSL or Linux this is supposed to work.
+
+### Coverage
+
+Any code you commit should not decrease coverage. To run all unit tests:
+
+```bash
+coverage run -m pytest test
+```
+
+Then you can see the coverage report by
+`coverage report -m` or `coverage html`.
+
+### Documentation
+
+To build and test documentation locally, install [Node.js](https://nodejs.org/en/download/). For example,
+
+```bash
+nvm install --lts
+```
+
+Then:
+
+```console
+npm install --global yarn  # skip if you use the dev container we provided
+pip install pydoc-markdown==4.5.0  # skip if you use the dev container we provided
+cd website
+yarn install --frozen-lockfile --ignore-engines
+pydoc-markdown
+yarn start
+```
+
+The last command starts a local development server and opens up a browser window.
+Most changes are reflected live without having to restart the server.
+
+Note:
+some tips in this guide are based off the contributor guide from [ray](https://docs.ray.io/en/latest/ray-contribute/getting-involved.html), [scikit-learn](https://scikit-learn.org/stable/developers/contributing.html), or [hummingbird](https://github.com/microsoft/hummingbird/blob/main/CONTRIBUTING.md).

website/versioned_docs/version-1.0.4/Examples/AutoML-Classification.md

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+# AutoML - Classification
+
+### A basic classification example
+
+```python
+from flaml import AutoML
+from sklearn.datasets import load_iris
+
+# Initialize an AutoML instance
+automl = AutoML()
+# Specify automl goal and constraint
+automl_settings = {
+    "time_budget": 1,  # in seconds
+    "metric": 'accuracy',
+    "task": 'classification',
+    "log_file_name": "iris.log",
+}
+X_train, y_train = load_iris(return_X_y=True)
+# Train with labeled input data
+automl.fit(X_train=X_train, y_train=y_train,
+           **automl_settings)
+# Predict
+print(automl.predict_proba(X_train))
+# Print the best model
+print(automl.model.estimator)
+```
+
+#### Sample of output
+```
+[flaml.automl: 11-12 18:21:44] {1485} INFO - Data split method: stratified
+[flaml.automl: 11-12 18:21:44] {1489} INFO - Evaluation method: cv
+[flaml.automl: 11-12 18:21:44] {1540} INFO - Minimizing error metric: 1-accuracy
+[flaml.automl: 11-12 18:21:44] {1577} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'catboost', 'xgboost', 'extra_tree', 'lrl1']
+[flaml.automl: 11-12 18:21:44] {1826} INFO - iteration 0, current learner lgbm
+[flaml.automl: 11-12 18:21:44] {1944} INFO - Estimated sufficient time budget=1285s. Estimated necessary time budget=23s.
+[flaml.automl: 11-12 18:21:44] {2029} INFO -  at 0.2s,	estimator lgbm's best error=0.0733,	best estimator lgbm's best error=0.0733
+[flaml.automl: 11-12 18:21:44] {1826} INFO - iteration 1, current learner lgbm
+[flaml.automl: 11-12 18:21:44] {2029} INFO -  at 0.3s,	estimator lgbm's best error=0.0733,	best estimator lgbm's best error=0.0733
+[flaml.automl: 11-12 18:21:44] {1826} INFO - iteration 2, current learner lgbm
+[flaml.automl: 11-12 18:21:44] {2029} INFO -  at 0.4s,	estimator lgbm's best error=0.0533,	best estimator lgbm's best error=0.0533
+[flaml.automl: 11-12 18:21:44] {1826} INFO - iteration 3, current learner lgbm
+[flaml.automl: 11-12 18:21:44] {2029} INFO -  at 0.6s,	estimator lgbm's best error=0.0533,	best estimator lgbm's best error=0.0533
+[flaml.automl: 11-12 18:21:44] {1826} INFO - iteration 4, current learner lgbm
+[flaml.automl: 11-12 18:21:44] {2029} INFO -  at 0.6s,	estimator lgbm's best error=0.0533,	best estimator lgbm's best error=0.0533
+[flaml.automl: 11-12 18:21:44] {1826} INFO - iteration 5, current learner xgboost
+[flaml.automl: 11-12 18:21:45] {2029} INFO -  at 0.9s,	estimator xgboost's best error=0.0600,	best estimator lgbm's best error=0.0533
+[flaml.automl: 11-12 18:21:45] {1826} INFO - iteration 6, current learner lgbm
+[flaml.automl: 11-12 18:21:45] {2029} INFO -  at 1.0s,	estimator lgbm's best error=0.0533,	best estimator lgbm's best error=0.0533
+[flaml.automl: 11-12 18:21:45] {1826} INFO - iteration 7, current learner extra_tree
+[flaml.automl: 11-12 18:21:45] {2029} INFO -  at 1.1s,	estimator extra_tree's best error=0.0667,	best estimator lgbm's best error=0.0533
+[flaml.automl: 11-12 18:21:45] {2242} INFO - retrain lgbm for 0.0s
+[flaml.automl: 11-12 18:21:45] {2247} INFO - retrained model: LGBMClassifier(learning_rate=0.2677050123105203, max_bin=127,
+               min_child_samples=12, n_estimators=4, num_leaves=4,
+               reg_alpha=0.001348364934537134, reg_lambda=1.4442580148221913,
+               verbose=-1)
+[flaml.automl: 11-12 18:21:45] {1608} INFO - fit succeeded
+[flaml.automl: 11-12 18:21:45] {1610} INFO - Time taken to find the best model: 0.3756711483001709
+```
+
+### A more advanced example including custom learner and metric
+
+[Link to notebook](https://github.com/microsoft/FLAML/blob/main/notebook/automl_classification.ipynb) | [Open in colab](https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/automl_classification.ipynb)