MLGB means Machine Learning of the Great Boss, and is called 「妙计包」.
MLGB is a library that includes many models of CTR Prediction & Recommender System by TensorFlow & PyTorch.
- Easy! Use
mlgb.get_model(model_name, **kwargs)to get a complex model. - Fast! Better performance through better code.
- Enjoyable! 50+ ranking & matching models to use, 2 languages(TensorFlow & PyTorch) to deploy.
# PYPI
pip install --upgrade mlgb
# Conda
conda install conda-forge::mlgbimport mlgb
# parameters of get_model:
help(mlgb.get_model)
"""
get_model(feature_names, model_name='LR', task='binary', aim='ranking', lang='TensorFlow', device=None, seed=None, **kwargs)
:param feature_names: tuple(tuple(dict)), must. Embedding need vocabulary size and custom embed_dim of features.
:param model_name: str, default 'LR'. Union[`mlgb.ranking_models`, `mlgb.matching_models`, `mlgb.mtl_models`]
:param task: str, default 'binary'. Union['binary', 'regression', 'multiclass:{int}']
:param aim: str, default 'ranking'. Union['ranking', 'matching', 'mtl']
:param lang: str, default 'TensorFlow'. Union['TensorFlow', 'PyTorch', 'tf', 'torch']
:param device: Optional[str, int], default None. Only for PyTorch.
:param seed: Optional[int], default None.
:param **kwargs: more model parameters by `mlgb.get_model_help(model_name)`.
"""
# parameters of model:
mlgb.get_model_help(model_name='LR', lang='tf')
"""
class LR(tf.keras.src.models.model.Model)
| LR(feature_names, task='binary', seed=None, inputs_if_multivalued=False, inputs_if_sequential=False, inputs_if_embed_dense=False, embed_dim=32, embed_2d_dim=None, embed_l2=0.0, embed_initializer=None, pool_mv_mode='Pooling:average', pool_mv_axis=2, pool_mv_l2=0.0, pool_mv_initializer=None, pool_seq_mode='Pooling:average', pool_seq_axis=1, pool_seq_l2=0.0, pool_seq_initializer=None, linear_if_bias=True, linear_l1=0.0, linear_l2=0.0, linear_initializer=None)
|
| Methods defined here:
|
| __init__(self, feature_names, task='binary', seed=None, inputs_if_multivalued=False, inputs_if_sequential=False, inputs_if_embed_dense=False, embed_dim=32, embed_2d_dim=None, embed_l2=0.0, embed_initializer=None, pool_mv_mode='Pooling:average', pool_mv_axis=2, pool_mv_l2=0.0, pool_mv_initializer=None, pool_seq_mode='Pooling:average', pool_seq_axis=1, pool_seq_l2=0.0, pool_seq_initializer=None, linear_if_bias=True, linear_l1=0.0, linear_l2=0.0, linear_initializer=None)
| Model Name: LR(LinearOrLogisticRegression)
| Paper Team: Microsoft
| Paper Year: 2007
| Paper Name: <Predicting Clicks: Estimating the Click-Through Rate for New Ads>
| Paper Link: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/predictingclicks.pdf
|
| Task Inputs Parameters:
| :param feature_names: tuple(tuple(dict)), must. Embedding need vocabulary size and custom embed_dim of features.
| :param task: str, default 'binary'. Union['binary', 'regression']
| :param seed: Optional[int], default None.
| :param inputs_if_multivalued: bool, default False.
| :param inputs_if_sequential: bool, default False.
| :param inputs_if_embed_dense: bool, default False.
| :param embed_dim: int, default 32.
| :param embed_2d_dim: Optional[int], default None. When None, each field has own embed_dim by feature_names.
| :param embed_l2: float, default 0.0.
| :param embed_initializer: Optional[str], default None. When None, activation judge first, xavier_normal end.
| :param pool_mv_mode: str, default 'Pooling:average'. Pooling mode of multivalued inputs. Union[
| 'Attention', 'Weighted', 'Pooling:max', 'Pooling:average', 'Pooling:sum']
| :param pool_mv_axis: int, default 2. Pooling axis of multivalued inputs.
| :param pool_mv_l2: float, default 0.0. When pool_mv_mode is in ('Weighted', 'Attention'), it works.
| :param pool_mv_initializer: Optional[str], default None. When None, activation judge first,
| xavier_normal end. When pool_mv_mode is in ('Weighted', 'Attention'), it works.
| :param pool_seq_mode: str, default 'Pooling:average'. Pooling mode of sequential inputs. Union[
| 'Attention', 'Weighted', 'Pooling:max', 'Pooling:average', 'Pooling:sum']
| :param pool_seq_axis: int, default 1. Pooling axis of sequential inputs.
| :param pool_seq_l2: float, default 0.0. When pool_seq_mode is in ('Weighted', 'Attention'), it works.
| :param pool_seq_initializer: Optional[str], default None. When None, activation judge first,
| xavier_normal end. When pool_seq_mode is in ('Weighted', 'Attention'), it works.
|
| Task Model Parameters:
| :param linear_if_bias: bool, default True.
| :param linear_l1: float, default 0.0.
| :param linear_l2: float, default 0.0.
| :param linear_initializer: Optional[str], default None. When None, activation judge first, xavier_normal end.
"""| Code Examples |
|---|
| TensorFlow |
| PyTorch |
If you use this for research, please cite it using the following BibTeX entry. Thanks.
@misc{uliontse2020mlgb,
author = {UlionTse},
title = {MLGB is a library that includes many models of CTR Prediction & Recommender System by TensorFlow & PyTorch},
year = {2020},
publisher = {GitHub},
journal = {GitHub Repository},
howpublished = {\url{https://github.com/UlionTse/mlgb}},
}