GammaRegressor model failed to execute

[nosacapital]

I finally got Lazypredict to work on the M1 Mac but have encountered another problem. Without too much emphasis on the whole file, here is the last bit of code I am running:

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=123)

from lazypredict.Supervised import LazyRegressor
reg = LazyRegressor(verbose=0, ignore_warnings=False, custom_metric=None)
models, predictions = reg.fit(X_train, X_test, y_train.values.ravel(), y_test)

All seems well until I get to 24% when I get the following message: GammaRegressor model failed to execute Some value(s) of y are out of the valid range of the loss 'HalfGammaLoss'.

Before using the ravel function, I got suggestions to use the function, hence, the code above. Any suggestions would be quite appreciated.

Thanks

[shpatrickguo]

Have you tried removing the GammaRegressor model from the list of regressors.

In lazypredict/Supervised.py, by default regressor = "all"

    def __init__(
        self,
        verbose=0,
        ignore_warnings=True,
        custom_metric=None,
        predictions=False,
        random_state=42,
        regressors="all",
    )

Instead you could put in an list of models:

            if self.regressors == "all":
            self.regressors = REGRESSORS
        else:
            try:
                temp_list = []
                for regressor in self.regressors:
                    full_name = (regressor.__name__, regressor)
                    temp_list.append(full_name)
                self.regressors = temp_list
            except Exception as exception:
                print(exception)
                print("Invalid Regressor(s)")

So your code could be like this:

from sklearn.linear_model import LinearRegression, Ridge, Lasso
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn.svm import SVR
from lazypredict.Supervised import LazyRegressor

REGRESSORS = [LinearRegression, Ridge, Lasso, DecisionTreeRegressor, RandomForestRegressor, GradientBoostingRegressor, KNeighborsRegressor, SVR]
reg = LazyRegressor(verbose=0, ignore_warnings=False, custom_metric=None, regressors=REGRESSORS)
models, predictions = reg.fit(X_train, X_test, y_train.values.ravel(), y_test)