How to support multiple output y

[veya2ztn]

The sklearn support for muliple output, i.e. the shape of y can be (B,L) rather than (B,) For example,

from sklearn.ensemble import RandomForestRegressor
x_train = np.random.randn(100,100)
y_train = np.random.randn(100,50)
rfr = RandomForestRegressor(n_estimators=20,n_jobs=1)
rfr.fit(x_train, y_train) 

But here, for thundergbm, if we use multiple y, we get

rfr = TGBMRegressor(256)
rfr.fit(x_train, y_train) 

--> 797     raise ValueError("bad input shape {0}".format(shape))
    798 
    799 

ValueError: bad input shape (2000, 200)

I notice the source code using sklearn.utils.check_X_y and turn on the option 'multi_output=True' The there goes wrong for your fit function at set(y) part.

How do we use multiple output y

[zeyiwen]

ThunderGBM currently doesn't support multiple output, but we would definitely consider supporting this feature in the future upgrade.