def test_categorical(k: int, learner, breast_cancer_data: Tuple4Array):
X_train, X_test, y_train, _ = breast_cancer_data
dist = k_categorical(k)
y_train = np.random.randint(0, k, (len(y_train)))
# test early stopping features
ngb = NGBClassifier(Dist=dist, Score=LogScore, Base=learner, verbose=False)
ngb.fit(X_train, y_train)
y_pred = ngb.predict(X_test)
y_prob = ngb.predict_proba(X_test)
y_dist = ngb.pred_dist(X_test)
def test_categorical(self, learners, cls_data): X_cls_train, X_cls_test, Y_cls_train, Y_cls_test = cls_data for K in [2,4,7]: Dist = k_categorical(K) Y_cls_train = np.random.randint(0,K,(len(Y_cls_train))) for Learner in learners: # test early stopping features ngb = NGBClassifier(Dist=Dist, Score=LogScore, Base=Learner, verbose=False) ngb.fit(X_cls_train, Y_cls_train) y_pred = ngb.predict(X_cls_test) y_prob = ngb.predict_proba(X_cls_test) y_dist = ngb.pred_dist(X_cls_test)
def __setstate__(self, state_dict):
if "K" in state_dict.keys():
state_dict["Dist"] = k_categorical(state_dict["K"])
state_dict["Manifold"] = manifold(state_dict["Score"], state_dict["Dist"])
self.__dict__ = state_dict
from ngboost.distns import k_categorical
from ngboost import NGBClassifier
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.tree import DecisionTreeRegressor
from sklearn.linear_model import Ridge
if __name__ == "__main__":
# An example where the base learner is also searched over (this is how you would vary tree depth):
X, Y = load_breast_cancer(True)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)
b1 = DecisionTreeRegressor(criterion='friedman_mse', max_depth=2)
b2 = DecisionTreeRegressor(criterion='friedman_mse', max_depth=4)
b3 = Ridge(alpha=0.0)
param_grid = {
'n_estimators': [20, 50],
'minibatch_frac': [1.0, 0.5],
'Base': [b1, b2]
}
ngb = NGBClassifier(natural_gradient=True,
verbose=False,
Dist=k_categorical(2))
grid_search = GridSearchCV(ngb, param_grid=param_grid, cv=5)
grid_search.fit(X_train, Y_train)
print(grid_search.best_params_)
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from ngboost import NGBClassifier
from ngboost.distns import k_categorical
if __name__ == "__main__":
X, y = load_breast_cancer(True)
y[0:
15] = 2 # artificially make this a 3-class problem instead of a 2-class problem
X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=0.2)
ngb = NGBClassifier(Dist=k_categorical(
3)) # tell ngboost that there are 3 possible outcomes
ngb.fit(X_train, Y_train) # Y should have only 3 values: {0,1,2}
# predicted probabilities of class 0, 1, and 2 (columns) for each observation (row)
preds = ngb.predict_proba(X_test)
def ng_model(self):
ngb_cat = NGBClassifier(Dist=k_categorical(2), verbose=True)
ng_clf = ngb_cat.fit(self.X_t, self.y_t)
print(ng_clf.feature_importances_)
return ng_clf
from sklearn.datasets import load_breast_cancer
from sklearn.linear_model import Ridge
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.tree import DecisionTreeRegressor
from ngboost import NGBClassifier
from ngboost.distns import k_categorical
if __name__ == "__main__":
# An example where the base learner is also searched over (this is how you would vary tree depth):
X, Y = load_breast_cancer(True)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)
b1 = DecisionTreeRegressor(criterion="friedman_mse", max_depth=2)
b2 = DecisionTreeRegressor(criterion="friedman_mse", max_depth=4)
b3 = Ridge(alpha=0.0)
param_grid = {
"n_estimators": [20, 50],
"minibatch_frac": [1.0, 0.5],
"Base": [b1, b2],
}
ngb = NGBClassifier(natural_gradient=True, verbose=False, Dist=k_categorical(2))
grid_search = GridSearchCV(ngb, param_grid=param_grid, cv=5)
grid_search.fit(X_train, Y_train)
print(grid_search.best_params_)
from ngboost import NGBClassifier
from ngboost.distns import k_categorical
from ngboost.learners import default_tree_learner
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
if __name__ == "__main__":
X, y = load_breast_cancer(True)
y[0:15] = 2 # artificially make this a 3-class problem instead of a 2-class problem
X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=0.2)
ngb = NGBClassifier(
Dist=k_categorical(3)
) # tell ngboost that there are 3 possible outcomes
ngb.fit(X_train, Y_train) # Y should have only 3 values: {0,1,2}
# predicted probabilities of class 0, 1, and 2 (columns) for each observation (row)
preds = ngb.predict_proba(X_test)
