I've found a bug where GAMChanger sometimes doesn't populate the 'metrics' / 'feature' / 'history' panel. It seems that when this happens, the GAMChanger interface has failed to load the validation samples, because the status bar says "0/0 validation samples selected".

This seems to occur sometimes based on the data that is provided, and might have something to do with missing data points, but I'm struggling to figure out exactly what the cause is.

~Below is the smallest reproducing example I can come up with.~

See following comment for a better MWE.

import pandas as pd
import gamchanger as gc
from interpret.glassbox import ExplainableBoostingRegressor

# Works
X = pd.read_csv('demo-X-succeed.csv')
y = pd.read_csv('demo-y-succeed.csv')['OrderedFractionOfEstate']

# Doesn't work
#X = pd.read_csv('demo-X-fail.csv')
#y = pd.read_csv('demo-y-fail.csv')['OrderedFractionOfEstate']

ebm = ExplainableBoostingRegressor(interactions=False)
ebm.fit(X, y)

gc.visualize(ebm, X, y)

~I've attached the CSV files, which differ in that the 'succeed' files have a single extra data point. That is, when loading 'demo-[X|y]-fail.csv' the GamChanger interface loads, but the side panel doesn't populate (unexpected behaviour). When loading 'demo-[X|y]-succeed.csv', the GamChanger interface loads and the side panel populates the metrics as expected.~

demo-X-fail.csv demo-X-succeed.csv demo-y-fail.csv demo-y-succeed.csv

In several domains, Contribution Scores/Shape Functions have real world meaning. It would be useful to visualise these learned shape functions on their correct scale.

For example, when using an EBM to predict the effects of a individual Air Handler Unit's (AHU) heating valve on the energy used by an attached boiler system, these shape fucntions approximate the efficiency of the AHU. E.g. for each 1% increase in valve openness, 0.2kWh increase in energy usage. However, as you can see, the predicted values are in kWh (energy). In this domain, negative energy doesn't make sense, and then using 0% valve openess, we would expect 0 extra heat usage. Therefore, the ability to rescale the shape function so that it is non-negative (in this case) would be really useful for stateholder understanding and model interpretation.

When using GAM Changer on the outer/end bins, you cannot split these bins into extra bins. Also, as you cannot split this bin, it seems you cannot make a steped interpolation (either monotonic increasing or decreasing).

Finally, it would be really useful to be able to increase the domain of the EBM past the upper (lower) most value in the training domain. These are likely to be the times expert knowledge is going to be most useful, when the model cannot "see" the data.

P.s. Amazing work. This is soooo useful ⭐⭐⭐⭐⭐

Hello,

There is a check for nans in 0.1.4 (line 268 of gamchanger.py) that breaks on string values. If I use version 0.1.3, the notebook still works. I think columns of string type should be encoded before this check.

Thanks, Jessica

[/usr/local/lib/python3.7/dist-packages/gamchanger/gamchanger.py](https://localhost:8080/#) in get_sample_data(ebm, x_test, y_test, resort_categorical)
    266 
    267     # Drop all rows with any NA values
--> 268     if np.isnan(x_test_copy).any():
    269         na_row_indexes = np.isnan(x_test_copy).any(axis=1)
    270         x_test_copy = x_test_copy[~na_row_indexes]

TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''

I don't know if this is a bug or if the zoom is just limited, but when I try to modify the function for some features that are long-tailed distributed, the points end up overlapping, and I cannot continue using the interface. 🤔

When displaying the explanations from the interpret package, this doesn't happen because plotly has an 'infinite' zoom-in.

I've got a model that has no continuous features. When I use the GAM changer, it fails to initialize the GAM view in the middle of the screen. This PR fixes the behavior so it chooses a categorical feature if no continuous features are available in the dropdown.