Kaggler v0.9.4 Release with Stacked DAE

Today, Kaggler v0.9.4 is released with additional features for DAE as follows:

• In addition to the swap noise (swap_prob), the Gaussian noise (noise_std) and zero masking (mask_prob) have been added to DAE to overcome overfitting.
• Stacked DAE is available through the n_layer input argument (see Figure 3. in Vincent et al. (2010), “Stacked Denoising Autoencoders” for reference).

For example, to build a stacking DAE with 3 pairs of encoder/decoder and all three types of noises, you can do:

from kaggler.preprocessing import DAE

dae = DAE(cat_cols=cat_cols, num_cols=num_cols, n_layer=3, noise_std=.05, swap_prob=.2, masking_prob=.1)
X = dae.fit_transform(pd.concat([trn, tst], axis=0))

If you’re using previous versions, please upgrade Kaggler using

pip install -U kaggler.

You can find Kaggle notebooks featured with Kaggler’s DAE as follows:

• Kaggler DAE + AutoLGB Baseline: shows how to train a LightGBM model using Kaggler’s DAE features and AutoLGB model at the current TPS May competition
• DAE with 2 Lines of Code with Kaggler: shows how to use Kaggler’s DAE with the previous TPS April competition.

Any feedbacks, suggestions, questions for the package are welcome.

Hope it helps! :)