Base learner for NGBClassifier

Am I right there is no option of a Base learner for NGBClassifier?

As soon as I pass a classifier Base learner i got the following error. At the same time if i pass learner = RandomForestRegressor(n_estimators=500, max_depth=7, min_samples_leaf=50, n_jobs=-1, max_features="sqrt", max_samples=.66, random_state=42) as a learner everything works just fine The example was taken from

from ngboost import NGBClassifier
from ngboost.distns import k_categorical, Bernoulli
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

_X, _y = load_breast_cancer(True)
_y[0:15] = 2 # artificially make this a 3-class problem instead of a 2-class problem
X_cls_train, X_cls_test, Y_cls_train, Y_cls_test  = train_test_split(_X, _y, test_size=0.2)
learner = RandomForestClassifier(n_estimators=500, max_depth=7, min_samples_leaf=50, n_jobs=-1,
    max_features="sqrt", max_samples=.66, random_state=42, class_weight='balanced')

ngb_cat = NGBClassifier(Base=learner, Dist=k_categorical(3), verbose=False) # tell ngboost that there are 3 possible outcomes
_ = ngb_cat.fit(X_cls_train, Y_cls_train) # Y should have only 3 values: {0,1,2}

ValueError                                Traceback (most recent call last)
<ipython-input-21-8eb58a515847> in <module>
     10 
     11 ngb_cat = NGBClassifier(Base=learner, Dist=k_categorical(3), verbose=False) # tell ngboost that there are 3 possible outcomes
---> 12 _ = ngb_cat.fit(X_cls_train, Y_cls_train) # Y should have only 3 values: {0,1,2}

~/.local/lib/python3.7/site-packages/ngboost/ngboost.py in fit(self, X, Y, X_val, Y_val, sample_weight, val_sample_weight, train_loss_monitor, val_loss_monitor, early_stopping_rounds)
    255             grads = D.grad(Y_batch, natural=self.natural_gradient)
    256 
--> 257             proj_grad = self.fit_base(X_batch, grads, weight_batch)
    258             scale = self.line_search(proj_grad, P_batch, Y_batch, weight_batch)
    259 

~/.local/lib/python3.7/site-packages/ngboost/ngboost.py in fit_base(self, X, grads, sample_weight)
    139     def fit_base(self, X, grads, sample_weight=None):
    140         models = [
--> 141             clone(self.Base).fit(X, g, sample_weight=sample_weight) for g in grads.T
    142         ]
    143         fitted = np.array([m.predict(X) for m in models]).T

~/.local/lib/python3.7/site-packages/ngboost/ngboost.py in <listcomp>(.0)
    139     def fit_base(self, X, grads, sample_weight=None):
    140         models = [
--> 141             clone(self.Base).fit(X, g, sample_weight=sample_weight) for g in grads.T
    142         ]
    143         fitted = np.array([m.predict(X) for m in models]).T

~/.local/lib/python3.7/site-packages/sklearn/ensemble/_forest.py in fit(self, X, y, sample_weight)
    328         self.n_outputs_ = y.shape[1]
    329 
--> 330         y, expanded_class_weight = self._validate_y_class_weight(y)
    331 
    332         if getattr(y, "dtype", None) != DOUBLE or not y.flags.contiguous:

~/.local/lib/python3.7/site-packages/sklearn/ensemble/_forest.py in _validate_y_class_weight(self, y)
    556 
    557     def _validate_y_class_weight(self, y):
--> 558         check_classification_targets(y)
    559 
    560         y = np.copy(y)

~/.local/lib/python3.7/site-packages/sklearn/utils/multiclass.py in check_classification_targets(y)
    170     if y_type not in ['binary', 'multiclass', 'multiclass-multioutput',
    171                       'multilabel-indicator', 'multilabel-sequences']:
--> 172         raise ValueError("Unknown label type: %r" % y_type)
    173 
    174 

ValueError: Unknown label type: 'continuous'

stanfordmlgroup / ngboost

Base learner for NGBClassifier #225