Need to figure out how to test the output of matplotlib.

There is a problem in the visualization part. When I am trying to plot the graph in the example, I see the following mistake:

TypeError Traceback (most recent call last)
in
12 ice_plot(ice_df, frac_to_plot=0.1,
13 color_by='x3', cmap=PuOr,
---> 14 ax=ice_ax);
15
16 ice_ax.set_xlabel('$X_2$');

C:\ProgramData\Anaconda3\lib\site-packages\pycebox\ice.py in ice_plot(ice_data, frac_to_plot, plot_points, point_kwargs, x_quantile, plot_pdp, centered, centered_quantile, color_by, cmap, ax, pdp_kwargs, **kwargs)
128 if frac_to_plot < 1.:
129 n_cols = ice_data.shape[1]
--> 130 icols = np.random.choice(n_cols, size=frac_to_plot * n_cols, replace=False)
131 plot_ice_data = ice_data.iloc[:, icols]
132 else:

mtrand.pyx in mtrand.RandomState.choice()

TypeError: 'float' object cannot be interpreted as an integer

Is there any way to give some form of predict_proba function to the ice() function in order to see the probability as opposed to the prediction?

Thanks!
Nema

Hi Austin,

I was wondering if it is possible to average across multiple runs/folds of the same model.

I am trying at the moment, but the resulting ICE plots do not make sense. The per run plots make sense but when I average them across both runs and folds the data gets screwed.

Cheers,

Dan

Does PyCEbox support PySpark models as well or any plan of releasing PySpark support in a future release?

There is a typo in the ice_plot() function when calling the _get_quantiles() function. In lines 124 and 137, the ice_plot() calls __get_quantiles() (which is undefined) instead of _get_quantiles(), which results in an error if trying to use quantiles or center the ICE curves.

See request in #18. I don't have a ton of time at the moment, so pull requests are welcome.

Hey Austin,

This package rocks, thanks for publishing it!

I have a question and a potential small bug in the ice_plot method, specifically on the "frac_to_plot" parameter.

It is my understanding that you simply take the fraction and multiply by the number of columns, and then pass this to the "size" parameter of np.random.choice(). I think we should make sure that the number being passed is an integer, not a float. Otherwise np.random.choice() will not accept a float as a parameter for "size".

Current:
icols = np.random.choice(n_cols, size=frac_to_plot * n_cols, replace=False)

Fix:
icols = np.random.choice(n_cols, size=int(frac_to_plot * n_cols), replace=False)

Best,
Andrew

key interaction is between $X_2$ and $X_2$.

should be

key interaction is between $X_2$ and $X_3$.