autogen/website/docs/Getting-Started.md

Getting Started

FLAML is a lightweight Python library that finds accurate machine learning models automatically, efficiently and economically. It frees users from selecting learners and hyperparameters for each learner.

Main Features

1. For common machine learning tasks like classification and regression, it quickly finds quality models for user-provided data with low computational resources. It supports both classical machine learning models and deep neural networks.

2. It is easy to customize or extend. Users can find their desired customizability from a smooth range: minimal customization (computational resource budget), medium customization (e.g., scikit-style learner, search space and metric), or full customization (arbitrary training and evaluation code). Users can customize only when and what they need to, and leave the rest to the library.

3. It supports fast and economical automatic tuning, capable of handling large search space with heterogeneous evaluation cost and complex constraints/guidance/early stopping. FLAML is powered by a new, cost-effective hyperparameter optimization and learner selection method invented by Microsoft Research.

Quickstart

Install FLAML from pip: pip install flaml. Find more options in Installation.

There are several ways of using flaml:

Task-oriented AutoML

For example, with three lines of code, you can start using this economical and fast AutoML engine as a scikit-learn style estimator.

from flaml import AutoML
automl = AutoML()
automl.fit(X_train, y_train, task="classification", time_budget=60)

It automatically tunes the hyperparameters and selects the best model from default learners such as LightGBM, XGBoost, random forest etc. for the specified time budget 60 seconds. Customizing the optimization metrics, learners and search spaces etc. is very easy. For example,

automl.add_learner("mylgbm", MyLGBMEstimator)
automl.fit(X_train, y_train, task="classification", metric=custom_metric, estimator_list=["mylgbm"], time_budget=60)

Tune user-defined function

You can run generic hyperparameter tuning for a custom function (machine learning or beyond). For example,

from flaml import tune
from flaml.automl.model import LGBMEstimator


def train_lgbm(config: dict) -> dict:
    # convert config dict to lgbm params
    params = LGBMEstimator(**config).params
    # train the model
    train_set = lightgbm.Dataset(csv_file_name)
    model = lightgbm.train(params, train_set)
    # evaluate the model
    pred = model.predict(X_test)
    mse = mean_squared_error(y_test, pred)
    # return eval results as a dictionary
    return {"mse": mse}


# load a built-in search space from flaml
flaml_lgbm_search_space = LGBMEstimator.search_space(X_train.shape)
# specify the search space as a dict from hp name to domain; you can define your own search space same way
config_search_space = {hp: space["domain"] for hp, space in flaml_lgbm_search_space.items()}
# give guidance about hp values corresponding to low training cost, i.e., {"n_estimators": 4, "num_leaves": 4}
low_cost_partial_config = {
    hp: space["low_cost_init_value"]
    for hp, space in flaml_lgbm_search_space.items()
    if "low_cost_init_value" in space
}
# run the tuning, minimizing mse, with total time budget 3 seconds
analysis = tune.run(
    train_lgbm, metric="mse", mode="min", config=config_search_space,
    low_cost_partial_config=low_cost_partial_config, time_budget_s=3, num_samples=-1,
)

Please see this script for the complete version of the above example.

Zero-shot AutoML

FLAML offers a unique, seamless and effortless way to leverage AutoML for the commonly used classifiers and regressors such as LightGBM and XGBoost. For example, if you are using lightgbm.LGBMClassifier as your current learner, all you need to do is to replace from lightgbm import LGBMClassifier by:

from flaml.default import LGBMClassifier

Then, you can use it just like you use the original LGMBClassifier. Your other code can remain unchanged. When you call the fit() function from flaml.default.LGBMClassifier, it will automatically instantiate a good data-dependent hyperparameter configuration for your dataset, which is expected to work better than the default configuration.

Where to Go Next?

• Understand the use cases for Task-oriented AutoML, Tune user-defined function and Zero-shot AutoML.
• Find code examples under "Examples": from AutoML - Classification to Tune - PyTorch.
• Find talks and tutorials about FLAML.
• Learn about research around FLAML.
• Refer to SDK and FAQ.

If you like our project, please give it a star on GitHub. If you are interested in contributing, please read Contributor's Guide.