add simple readme

Author: diegomarvid

README.md

@@ -1,104 +1,70 @@
-# Repo Template
-
-Kick off a project with the right foot.
-
-A repository template for easily setting up a well behaved development environment for a smooth
-collaboration experience.
-
-This template takes care of setting up and configuring:
-
-- A **virtual environment**
-- **Formatting and linting** tools
-- Some shared default **VSCode settings**
-- A **Pull Request template**
-- A **GitHub Action** that runs formatting and linting checks
-
-Any of these configurations and features can be disabled/modified freely after set up if the team
-chooses to.
-
-Note: [pyenv](https://github.com/pyenv/pyenv#installation) and
-[poetry](https://python-poetry.org/docs/#installation) are used for setting up a virtual environment
-with the correct python version. Make sure both of those are installed correctly in your machine.
-
-# Usage
-
-1. Click the `Use this template` button at the top of this repo's home page to spawn a new repo
-   from this template.
-
-2. Clone the new repo to your local environment.
-
-3. Run `sh init.sh <your_project_name> <python version>`.
-
-   Note that:
-
-   - the project's accepted python versions will be set to `^<python version>` - feel free
-     to change this manually in the `pyproject.toml` file after running the script.
-   - your project's source code should be placed in the newly-created folder with your project's
-     name, so that absolute imports (`from my_project.my_module import func`) work everywhere.
-
-4. Nuke this readme and the `init.sh` file.
-
-5. Add to git the changes made by the init script, such as the newly created `poetry.toml`,
-   `poetry.lock` and `.python-version` files.
-
-6. Commit and push your changes - your project is all set up.
-
-7. [Recommended] Set up the following in your GitHub project's `Settings` tab:
-   - Enable branch protection for the `main` branch in the `Branches` menu to prevent non-reviewed
-     pushes/merges to it.
-   - Enable `Automatically delete head branches` in the `General` tab for feature branches to be
-     cleaned up when merged.
-
-# For ongoing projects
-
-If you want to improve the current configs of an existing project, these files are the ones you'll
-probably want to steal some content from:
-
-- [VSCode settings](.vscode/settings.json)
-- [Flake8 config](.flake8)
-- [Black and iSort configs](pyproject.toml)
-- [Style check GitHub Action](.github/workflows/style-checks.yaml)
-
-Additionally, you might want to check the
-[project's source code is correctly installed via Poetry](https://stackoverflow.com/questions/66586856/how-can-i-make-my-project-available-in-the-poetry-environment)
-for intra-project imports to work as expected across the board.
-
-# For developers of this template
-
-To test new changes made to this template:
-
-1. Run the template in test mode with `test=true sh init.sh <your_project_name> <python version>`,
-   which will not delete the [project_base/test.py](project_base/test.py) file from the source
-   directory.
-
-2. Use that file to check everything works as expected (see details in its docstring).
-
-3. Make sure not to version any of the files created by the script. `git reset --hard` + manually
-   deleting the created files not yet added to versioning works, for example.
-
-# Issues and suggestions
-
-Feel free to report issues or propose improvements to this template via GitHub issues or through the
-`#team-tech-meta` channel in Slack.
-
-# Can I use it without Poetry?
-
-This template currently sets up your virtual environment via poetry only.
-
-If you want to use a different dependency manager, you'll have to manually do the following:
-
-1. Remove the `.venv` environment and the `pyproject.toml` and `poetry.lock` files.
-2. Create a new environment with your dependency manager of choice.
-3. Install flake, black and isort as dev dependencies.
-4. Install the current project's source.
-5. Set the path to your new environment's python in the `python.pythonPath` and
-   `python.defaultInterpreterPath` in [vscode settings](.vscode/settings.json).
-
-Disclaimer: this has not been tested, additional steps may be needed.
-
-# Troubleshooting
-
-### pyenv not picking up correct python version from .python-version
-
-Make sure the `PYENV_VERSION` env var isn't set in your current shell
-(and if it is, run `unset PYENV_VERSION`).
+# Pipeline Library
+
+The purpose of this library is to create pipelines for ML as simple as possible. At the moment we support XGBoost models, but we are working to support more models.
+
+This is an example of how to use the library to run an XGBoost pipeline:
+
+We create a `train.json` file with the following content:
+
+```json
+{
+    "custom_steps_path": "examples/ocf/",
+    "save_path": "runs/xgboost_train.pkl",
+    "pipeline": {
+        "name": "XGBoostTrainingPipeline",
+        "description": "Training pipeline for XGBoost models.",
+        "steps": [
+            {
+                "step_type": "OCFGenerateStep",
+                "parameters": {
+                    "path": "examples/ocf/data/trainset_new.parquet"
+                }
+            },
+            {
+                "step_type": "OCFCleanStep",
+                "parameters": {}
+            },
+            {
+                "step_type": "TabularSplitStep",
+                "parameters": {
+                    "id_column": "ss_id",
+                    "train_percentage": 0.95
+                }
+            },
+            {
+                "step_type": "XGBoostFitModelStep",
+                "parameters": {
+                    "target": "average_power_kw",
+                    "drop_columns": [
+                        "ss_id"
+                    ],
+                    "xgb_params": {
+                        "max_depth": 12,
+                        "eta": 0.12410097733370863,
+                        "objective": "reg:squarederror",
+                        "eval_metric": "mae",
+                        "n_jobs": -1,
+                        "n_estimators": 2,
+                        "min_child_weight": 7,
+                        "subsample": 0.8057743223537057,
+                        "colsample_bytree": 0.6316852278944352
+                    },
+                    "save_model": true
+                }
+            }
+        ]
+    }
+}
+```
+
+The user can define custom steps to generate and clean their own data and use them in the pipeline. Then we can run the pipeline with the following code:
+
+```python
+import logging
+
+from pipeline_lib.core import Pipeline
+
+logging.basicConfig(level=logging.INFO)
+
+Pipeline.from_json("train.json").run()
+```