https://github.com/rapidsai/legate-boost
GBM implementation on Legate
https://github.com/rapidsai/legate-boost
Last synced: 3 months ago
JSON representation
GBM implementation on Legate
- Host: GitHub
- URL: https://github.com/rapidsai/legate-boost
- Owner: rapidsai
- License: apache-2.0
- Created: 2023-06-21T14:23:26.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-10-02T21:54:52.000Z (3 months ago)
- Last Synced: 2025-10-02T23:36:12.301Z (3 months ago)
- Language: Python
- Homepage: https://rapidsai.github.io/legate-boost/
- Size: 1.98 MB
- Stars: 13
- Watchers: 6
- Forks: 11
- Open Issues: 22
-
Metadata Files:
- Readme: README.md
- Contributing: contributing.md
- License: LICENSE
Awesome Lists containing this project
README
# legate-boost
GBM implementation on Legate. The primary goals of `legate-boost` is to provide a state-of-the-art distributed GBM implementation on Legate, capable of running on CPUs or GPUs at supercomputer scale.
[API Documentation](https://rapidsai.github.io/legate-boost)
For developers - see [contributing](contributing.md)
## Installation
Install using `conda`.
```shell
# stable release
conda install -c legate -c conda-forge -c nvidia legate-boost
# nightly release
conda install -c legate/label/experimental -c legate -c conda-forge -c nvidia legate-boost
```
On systems without a GPU, the CPU-only package should automatically be installed.
On systems with a GPU and compatible CUDA version, the GPU package should automatically be installed.
To force `conda` to prefer one, pass the build strings `*_cpu*` or `*_gpu*`, for example:
```shell
# nightly release (CPU-only)
conda install --dry-run -c legate/label/experimental -c legate -c conda-forge -c nvidia \
'legate-boost=*=*_cpu*'
```
For more details on building from source and setting up a development environment, see [`contributing.md`](./contributing.md).
## Simple example
Run with the legate launcher
```bash
legate example_script.py
```
```python
>>> import cupynumeric as cn
>>> import legateboost as lb
>>> X = cn.random.random((1000, 10))
>>> y = cn.random.random(X.shape[0])
>>> model = lb.LBRegressor().fit(X, y)
```
## Features
### Model ensembling
`legate-boost` can create models from linear combinations of other models. Ensembling is as easy as:
```python
>>> import cupynumeric as cn
>>> import legateboost as lb
>>> X = cn.random.random((1000, 10))
>>> X_train_a = X[:500]
>>> X_train_b = X[500:]
>>> y = cn.random.random(X.shape[0])
>>> y_train_a = y[:500]
>>> y_train_b = y[500:]
>>> model_a = lb.LBRegressor().fit(X_train_a, y_train_a)
>>> len(model_a)
100
>>> model_b = lb.LBRegressor().fit(X_train_b, y_train_b)
>>> len(model_b)
100
>>> model_c = (model_a + model_b) * 0.5
>>> len(model_c)
200
```
### Probabilistic regression
`legate-boost` can learn distributions for continuous data. This is useful in cases where simply predicting the mean does not carry enough information about the training data:
The above example can be found here: [examples/probabilistic_regression](https://github.com/rapidsai/legate-boost/tree/main/examples/probabalistic_regression).
### Batch training
`legate-boost` can train on datasets that do not fit into memory by splitting the dataset into batches and training the model with `partial_fit`.
```python
>>> import cupynumeric as cn
>>> import legateboost as lb
>>> from sklearn.utils import gen_even_slices
>>> X = cn.random.random((1000, 10))
>>> y = cn.random.random(X.shape[0])
>>> total_estimators = 100
>>> estimators_per_batch = 10
>>> n_batches = total_estimators // estimators_per_batch
>>> train_batches = [(X[i], y[i]) for i in gen_even_slices(X.shape[0], n_batches)]
>>> model = lb.LBRegressor(n_estimators=estimators_per_batch)
>>> for i in range(total_estimators // estimators_per_batch):
... X_batch, y_batch = train_batches[i % n_batches]
... model = model.partial_fit(X_batch, y_batch)
```
The above example can be found here: [examples/batch_training](https://github.com/rapidsai/legate-boost/tree/main/examples/batch_training).
### Different model types
`legate-boost` supports tree models, linear models, kernel ridge regression models, custom user models and any combinations of these models.
The following example shows a model combining linear and decision tree base learners on a synthetic dataset.
```python
model = lb.LBRegressor(base_models=(lb.models.Linear(), lb.models.Tree(max_depth=1),), **params).fit(X, y)
```
The second example shows a model combining kernel ridge regression and decision tree base learners on the wine quality dataset.
```python
model = lb.LBRegressor(base_models=(lb.models.KRR(sigma=0.5), lb.models.Tree(max_depth=5),), **params).fit(X, y)
```
