build_classifier

transtab.build_classifier(categorical_columns=None, numerical_columns=None, binary_columns=None, feature_extractor=None, num_class=2, hidden_dim=128, num_layer=2, num_attention_head=8, hidden_dropout_prob=0, ffn_dim=256, activation='relu', device='cuda:0', checkpoint=None, **kwargs) → transtab.modeling_transtab.TransTabClassifier[source]

Build a transtab.modeling_transtab.TransTabClassifier.

Parameters

  • categorical_columns (list) – a list of categorical feature names.

  • numerical_columns (list) – a list of numerical feature names.

  • binary_columns (list) – a list of binary feature names, accept binary indicators like (yes,no); (true,false); (0,1).

  • feature_extractor (TransTabFeatureExtractor) – a feature extractor to tokenize the input tables. if not passed the model will build itself.

  • num_class (int) – number of output classes to be predicted.

  • hidden_dim (int) – the dimension of hidden embeddings.

  • num_layer (int) – the number of transformer layers used in the encoder.

  • num_attention_head (int) – the numebr of heads of multihead self-attention layer in the transformers.

  • hidden_dropout_prob (float) – the dropout ratio in the transformer encoder.

  • ffn_dim (int) – the dimension of feed-forward layer in the transformer layer.

  • activation (str) – the name of used activation functions, support "relu", "gelu", "selu", "leakyrelu".

  • device (str) – the device, "cpu" or "cuda:0".

  • checkpoint (str) – the directory to load the pretrained TransTab model.

Returns

Return type

A TransTabClassifier model.

Warning

If categorical_columns, numerical_columns, and binary_columns are ALL not specified, the model takes ALL as categorical columns, which may undermine the performance significantly.