Kaggle competition: Toxic Comment Classification Challenge
<SOS> metric: MCAUC Kaggle data source: toxic-comment-classification-challenge<categorify>
import numpy as np
import pandas as pd
ae_cells_train = cells_autoencoder.encoder(cells_train ).numpy()
ae_cells_test = cells_autoencoder.encoder(cells_test ).numpy()
autoencoder_error = mean_squared_error(cells_test,cells_autoencoder.decoder(ae_cells_test ).numpy())
 print("Cells reconstruction error is " + str(autoencoder_error))<compute_test_metric>
ftgru = pd.read_csv('.. /input/leakyfasttext/leaky_submission.csv')
 ftlstm = pd.read_csv('.. /input/fasttextglove/fin.csv')
 ggru = pd.read_csv('.. /input/fasttextglove/submission_GloveGRU.csv')
 glstm = pd.read_csv('.. /input/fasttextglove/submission_GloveLSTM.csv')
<categorify><EOS> 
submission = pd.DataFrame()
submission['id'] = ggru['id']
for label in labels:
submission[label] = ftgru[label].rank(pct=True)* 0.25 + ftlstm[label].rank(pct=True)* 0.25 + ggru[label].rank(pct=True)* 0.25 + glstm[label].rank(pct=True)* 0.25
submission.to_csv('submission.csv', index=False )