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thank you for releasing the code for your paper! It makes things a lot easier for other researchers in the same area.
I have a question about reproducing your paper's experimental results on the adult dataset. I have run the experimental_script.sh script and obtained a couple of new folders:
out_params containing tf checkpoints and models
out_evals containing a number of .z files which, to the best of my understanding, contain the learned codes and associated labels corresponding to the sensible parameter c. Each file has a name such as [...]/inv-rep/out_evals/adult/l0.0001_b0.01_d30/test/grid_navib_epoch[num_epochs]_z_and_c.z.
Now, would my script below be a reasonable way to reproduce your results as far as the "adversarial loss" (i.e. a supervised classifier's accuracy on the codes when trying to predict the sensible attribute) is concerned?
importjoblibimportsklearnfromsklearn.linear_modelimportLogisticRegressionfromsklearn.metricsimportaccuracy_scorez_test, s_test=joblib.load('../out_evals/adult/l0.0001_b0.01_d30/test/grid_navib_epoch500_z_and_c.z')
z_val, s_val=joblib.load('../out_evals/adult/l0.0001_b0.01_d30/val/grid_navib_epoch500_z_and_c.z')
z_train, s_train=joblib.load('../out_evals/adult/l0.0001_b0.01_d30/train/grid_navib_epoch500_z_and_c.z')
model=LogisticRegression() # or any other model for that mattermodel.fit(z_train, s_train)
s_pred=model.predict(z_test)
print(accuracy_score(s_test, s_pred))
Thank you kindly!
The text was updated successfully, but these errors were encountered:
Hi,
thank you for releasing the code for your paper! It makes things a lot easier for other researchers in the same area.
I have a question about reproducing your paper's experimental results on the adult dataset. I have run the
experimental_script.sh
script and obtained a couple of new folders:out_params
containing tf checkpoints and modelsout_evals
containing a number of.z
files which, to the best of my understanding, contain the learned codes and associated labels corresponding to the sensible parameterc
. Each file has a name such as[...]/inv-rep/out_evals/adult/l0.0001_b0.01_d30/test/grid_navib_epoch[num_epochs]_z_and_c.z
.Now, would my script below be a reasonable way to reproduce your results as far as the "adversarial loss" (i.e. a supervised classifier's accuracy on the codes when trying to predict the sensible attribute) is concerned?
Thank you kindly!
The text was updated successfully, but these errors were encountered: