Which model support "multiclass" task

[astrung]

Please make sure that this is a feature request.

System information

• DeepTables version: 0.2.3.1
• Are you willing to contribute it: No

Describe the feature and the current behavior/state.
When i test with various models in ModelConfig by change nets=['fibi_nets'], I see some model only support for binary classification, and when I tried to use with multi classification, I always get this error: ValueError: Unexpected logit output.{}

So how can I know which model support for multi classification, and which model is only for binary ?
Please help

[oaksharks]

Hi @astrung,
Is there any categorical features in your dataset?
If not, you can set auto_discrete=True to discretize continuous features into categorical.
I think the network needs categorical features rather than does not support multi-classification.

References:

DeepTables/deeptables/models/deepnets.py

Lines 338 to 350 in a95f842

	def fibi_nets(embeddings, flatten_emb_layer, dense_layer, concat_emb_dense, config, model_desc):
	"""
	The SENET layer can convert an embedding layer into the SENET-Like embedding fea�tures, which helps
	to boost feature discriminability. The following Bilinear-Interaction layer models second order
	feature interactions on the original embedding and the SENET-Like embedding respec�tively. Subsequently,
	these cross features are concatenated by a combination layer which merges the outputs of
	Bilinear-Interaction layer.
	"""
	senet_index = counter.next_num('senet_layer')
	senet_emb_concat = _concat_embeddings(embeddings, f'concat_senet_embedding_{senet_index}')
	if senet_emb_concat is None:
	model_desc.add_net('fibi', (None), (None))
	return None

[astrung]

@oaksharks
thank you for your fast response.
Yes after setting auto_discrete=True, my code works, but I get too many below warnings:

/opt/conda/lib/python3.7/site-packages/sklearn/preprocessing/_discretization.py:233: UserWarning: Bins whose width are 
too small (i.e., <= 1e-8) in feature 0 are removed. Consider decreasing the number of bins. 
"decreasing the number of bins." % jj

How can I fix it ? Do I have problem in my features ?

[oaksharks]

Maybe the value of your features is too small, here is how DT calculates the number of the bins:
https://github.com/DataCanvasIO/Hypernets/blob/7b486d0a9206181d4f08f502160f8d6ba3a4994b/hypernets/tabular/sklearn_ex.py#L727-L737