def fit(self, X, T, E, X_val=None, T_val=None, E_val=None, **kwargs):
"""Fits an NGBoost survival model to the data.
For additional parameters see ngboost.NGboost.fit
Parameters:
X : DataFrame object or List or
numpy array of predictors (n x p) in Numeric format
T : DataFrame object or List or
numpy array of times to event or censoring (n) (floats).
E : DataFrame object or List or
numpy array of event indicators (n).
E[i] = 1 <=> T[i] is the time of an event, else censoring time
T_val : DataFrame object or List or
validation-set times, in numeric format if any
E_val : DataFrame object or List or
validation-set event idicators, in numeric format if any
"""
X = check_array(X)
if X_val is not None:
X_val = check_array(X_val)
return super().fit(
X,
Y_from_censored(T, E),
X_val=X_val,
Y_val=Y_from_censored(T_val, E_val),
**kwargs,
)
def fit(self, X, T, E, X_val=None, T_val=None, E_val=None, **kwargs):
'''
Fits an NGBoost survival model to the data. For additional parameters see ngboost.NGboost.fit
Parameters:
X : numpy array of predictors (n x p)
T : numpy array of times to event or censoring (n). Should be floats
E : numpy array of event indicators (n). E[i] = 1 <=> T[i] is the time of an event, else censoring time
T_val : validation-set times, if any
E_val : validation-set event idicators, if any
'''
return super().fit(X,
Y_from_censored(T, E),
X_val=X_val,
Y_val=Y_from_censored(T_val, E_val),
**kwargs)
def d_score(self, Y):
return super().d_score(Y_from_censored(Y))