【记录】pytorch_tabular

183****0229

地址：https://github.com/manujosephv/pytorch_tabular
介绍：为表格数据建模深度学习模型的标准框架。[PyTorch和PyTorch Lightning框架]
PyTorch Tabular 旨在让表格形式数据的深度学习变得容易，并且可供现实案例和研究使用。
安装： pip install pytorch_tabular[all]
文档：https://pytorch-tabular.readthedocs.io/en/latest/
可用模型：

FeedForward Network with Category Embedding is a simple FF network, but with an Embedding layers for the categorical columns.
Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data is a model presented in ICLR 2020 and according to the authors have beaten well-tuned Gradient Boosting models on many datasets.
TabNet: Attentive Interpretable Tabular Learning is another model coming out of Google Research which uses Sparse Attention in multiple steps of decision making to model the output.
Mixture Density Networks is a regression model which uses gaussian components to approximate the target function and provide a probabilistic prediction out of the box.
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks is a model which tries to learn interactions between the features in an automated way and create a better representation and then use this representation in downstream task
TabTransformer is an adaptation of the Transformer model for Tabular Data which creates contextual representations for categorical features.
FT Transformer from Revisiting Deep Learning Models for Tabular Data

使用：

from pytorch_tabular import TabularModel
from pytorch_tabular.models import CategoryEmbeddingModelConfig
from pytorch_tabular.config import DataConfig, OptimizerConfig, TrainerConfig, ExperimentConfig

data_config = DataConfig(
    target=['target'], #target should always be a list. Multi-targets are only supported for regression. Multi-Task Classification is not implemented
    continuous_cols=num_col_names,
    categorical_cols=cat_col_names,
)
trainer_config = TrainerConfig(
    auto_lr_find=True, # Runs the LRFinder to automatically derive a learning rate
    batch_size=1024,
    max_epochs=100,
    gpus=1, #index of the GPU to use. 0, means CPU
)
optimizer_config = OptimizerConfig()

model_config = CategoryEmbeddingModelConfig(
    task="classification",
    layers="1024-512-512",  # Number of nodes in each layer
    activation="LeakyReLU", # Activation between each layers
    learning_rate = 1e-3
)

tabular_model = TabularModel(
    data_config=data_config,
    model_config=model_config,
    optimizer_config=optimizer_config,
    trainer_config=trainer_config,
)
tabular_model.fit(train=train, validation=val)
result = tabular_model.evaluate(test)
pred_df = tabular_model.predict(test)
tabular_model.save_model("examples/basic")
loaded_model = TabularModel.load_from_checkpoint("examples/basic")