def predict_cumulative_hazard(self, X, times=None, ancillary_X=None):
"""
Return the cumulative hazard rate of subjects in X at time points.
Parameters
----------
X: numpy array or DataFrame
a (n,d) covariate numpy array or DataFrame. If a DataFrame, columns
can be in any order. If a numpy array, columns must be in the
same order as the training data.
times: iterable, optional
an iterable of increasing times to predict the cumulative hazard at. Default
is the set of all durations (observed and unobserved). Uses a linear interpolation if
points in time are not in the index.
ancillary_X: numpy array or DataFrame, optional
a (n,d) covariate numpy array or DataFrame. If a DataFrame, columns
can be in any order. If a numpy array, columns must be in the
same order as the training data.
Returns
-------
cumulative_hazard_ : DataFrame
the cumulative hazard of individuals over the timeline
"""
import numpy as np
times = coalesce(times, self.timeline, np.unique(self.durations))
exp_mu_, sigma_ = self._prep_inputs_for_prediction_and_return_scores(
X, ancillary_X)
mu_ = np.log(exp_mu_)
Z = np.subtract.outer(np.log(times), mu_) / sigma_
return pd.DataFrame(-logsf(Z), columns=_get_index(X), index=times)
def predict_cumulative_hazard(self, X, times=None, ancillary_X=None):
"""
Return the cumulative hazard rate of subjects in X at time points.
Parameters
----------
X: numpy array or DataFrame
a (n,d) covariate numpy array or DataFrame. If a DataFrame, columns
can be in any order. If a numpy array, columns must be in the
same order as the training data.
times: iterable, optional
an iterable of increasing times to predict the cumulative hazard at. Default
is the set of all durations (observed and unobserved). Uses a linear interpolation if
points in time are not in the index.
ancillary_X: numpy array or DataFrame, optional
a (n,d) covariate numpy array or DataFrame. If a DataFrame, columns
can be in any order. If a numpy array, columns must be in the
same order as the training data.
Returns
-------
cumulative_hazard_ : DataFrame
the cumulative hazard of individuals over the timeline
"""
import numpy as np
times = coalesce(times, self.timeline, np.unique(self.durations))
exp_mu_, sigma_ = self._prep_inputs_for_prediction_and_return_scores(X, ancillary_X)
mu_ = np.log(exp_mu_)
Z = np.subtract.outer(np.log(times), mu_) / sigma_
return pd.DataFrame(-logsf(Z), columns=_get_index(X), index=times)
def _cumulative_hazard(self, params, T, Xs):
mu_params = params["mu_"]
mu_ = np.dot(Xs["mu_"], mu_params)
sigma_params = params["sigma_"]
sigma_ = np.exp(np.dot(Xs["sigma_"], sigma_params))
Z = (np.log(T) - mu_) / sigma_
return -logsf(Z)
def _cumulative_hazard(self, params, T, *Xs):
mu_params = params[self._LOOKUP_SLICE["mu_"]]
mu_ = np.dot(Xs[0], mu_params)
sigma_params = params[self._LOOKUP_SLICE["sigma_"]]
sigma_ = np.exp(np.dot(Xs[1], sigma_params))
Z = (np.log(T) - mu_) / sigma_
return -logsf(Z)
def _cumulative_hazard(self, params, T, *Xs):
mu_params = params[self._LOOKUP_SLICE["mu_"]]
mu_ = np.dot(Xs[0], mu_params)
sigma_params = params[self._LOOKUP_SLICE["sigma_"]]
sigma_ = np.exp(np.dot(Xs[1], sigma_params))
Z = (np.log(T) - mu_) / sigma_
return -logsf(Z)
def _log_hazard(self, params, T, Xs):
mu_params = params["mu_"]
mu_ = np.dot(Xs["mu_"], mu_params)
sigma_params = params["sigma_"]
log_sigma_ = np.dot(Xs["sigma_"], sigma_params)
sigma_ = np.exp(log_sigma_)
Z = (np.log(T) - mu_) / sigma_
return norm.logpdf(Z) - log_sigma_ - np.log(T) - logsf(Z)
def _log_hazard(self, params, T, *Xs):
mu_params = params[self._LOOKUP_SLICE["mu_"]]
mu_ = np.dot(Xs[0], mu_params)
sigma_params = params[self._LOOKUP_SLICE["sigma_"]]
log_sigma_ = np.dot(Xs[1], sigma_params)
sigma_ = np.exp(log_sigma_)
Z = (np.log(T) - mu_) / sigma_
return logpdf(Z) - log_sigma_ - np.log(T) - logsf(Z)
def _log_hazard(self, params, T, *Xs):
mu_params = params[self._LOOKUP_SLICE["mu_"]]
mu_ = np.dot(Xs[0], mu_params)
sigma_params = params[self._LOOKUP_SLICE["sigma_"]]
log_sigma_ = np.dot(Xs[1], sigma_params)
sigma_ = np.exp(log_sigma_)
Z = (np.log(T) - mu_) / sigma_
return norm.logpdf(Z) - log_sigma_ - np.log(T) - logsf(Z)
def _log_hazard(self, params, times):
mu_, sigma_ = params
Z = (np.log(times) - mu_) / sigma_
return norm.logpdf(Z, loc=0,
scale=1) - np.log(sigma_) - np.log(times) - logsf(Z)
def _cumulative_hazard(self, params, times):
mu_, sigma_ = params
Z = (np.log(times) - mu_) / sigma_
return -logsf(Z)
