torch_concepts.data.preprocessing.autoencoder.extract_embs_from_autoencoder¶

extract_embs_from_autoencoder(df, autoencoder_kwargs={})[source]¶

Extract embeddings from a pandas DataFrame using an autoencoder.

Convenience function that trains an autoencoder on tabular data and returns the learned latent representations.

Parameters:

df – Input pandas DataFrame.
autoencoder_kwargs – Dictionary of keyword arguments for AutoencoderTrainer. Can include ‘device’ to specify training device (default: ‘cpu’).

Returns:

Latent representations of shape (n_samples, latent_dim).

Return type:

torch.Tensor

Example

>>> import pandas as pd
>>> import torch
>>> from torch_concepts.data.preprocessing.autoencoder import extract_embs_from_autoencoder
>>>
>>> # Create sample DataFrame
>>> df = pd.DataFrame(torch.randn(100, 50).numpy())
>>>
>>> # Extract embeddings
>>> embeddings = extract_embs_from_autoencoder(
...     df,
...     autoencoder_kwargs={
...         'latent_dim': 10,
...         'epochs': 50,
...         'batch_size': 32,
...         'noise': 0.1,
...         'device': 'cpu'  # or 'cuda' if desired
...     }
... )
>>> print(embeddings.shape)
torch.Size([100, 10])