def _get_gradients(self, X, events, start, stop, weights, beta): # pylint: disable=too-many-locals
"""
Calculates the first and second order vector differentials, with respect to beta.
Returns
-------
hessian: (d, d) numpy array,
gradient: (d,) numpy array
log_likelihood: float
"""
_, d = X.shape
hessian = np.zeros((d, d))
gradient = np.zeros(d)
log_lik = 0
# weights = weights[:, None]
unique_death_times = np.unique(stop[events])
for t in unique_death_times:
# I feel like this can be made into some tree-like structure
ix = (start < t) & (t <= stop)
X_at_t = X[ix]
weights_at_t = weights[ix]
stops_events_at_t = stop[ix]
events_at_t = events[ix]
phi_i = weights_at_t * np.exp(np.dot(X_at_t, beta))
phi_x_i = phi_i[:, None] * X_at_t
phi_x_x_i = np.dot(X_at_t.T, phi_x_i)
# Calculate sums of Risk set
risk_phi = array_sum_to_scalar(phi_i)
risk_phi_x = matrix_axis_0_sum_to_array(phi_x_i)
risk_phi_x_x = phi_x_x_i
# Calculate the sums of Tie set
deaths = events_at_t & (stops_events_at_t == t)
tied_death_counts = array_sum_to_scalar(deaths.astype(int)) # should always at least 1
xi_deaths = X_at_t[deaths]
x_death_sum = matrix_axis_0_sum_to_array(weights_at_t[deaths, None] * xi_deaths)
weight_count = array_sum_to_scalar(weights_at_t[deaths])
weighted_average = weight_count / tied_death_counts
#
# This code is near identical to the _batch algorithm in CoxPHFitter. In fact, see _batch for comments.
#
if tied_death_counts > 1:
# A good explaination for how Efron handles ties. Consider three of five subjects who fail at the time.
# As it is not known a priori that who is the first to fail, so one-third of
# (φ1 + φ2 + φ3) is adjusted from sum_j^{5} φj after one fails. Similarly two-third
# of (φ1 + φ2 + φ3) is adjusted after first two individuals fail, etc.
# a lot of this is now in einstien notation for performance, but see original "expanded" code here
# https://github.com/CamDavidsonPilon/lifelines/blob/e7056e7817272eb5dff5983556954f56c33301b1/lifelines/fitters/cox_time_varying_fitter.py#L458-L490
tie_phi = array_sum_to_scalar(phi_i[deaths])
tie_phi_x = matrix_axis_0_sum_to_array(phi_x_i[deaths])
tie_phi_x_x = np.dot(xi_deaths.T, phi_i[deaths, None] * xi_deaths)
increasing_proportion = np.arange(tied_death_counts) / tied_death_counts
denom = 1.0 / (risk_phi - increasing_proportion * tie_phi)
numer = risk_phi_x - np.outer(increasing_proportion, tie_phi_x)
a1 = np.einsum("ab, i->ab", risk_phi_x_x, denom) - np.einsum(
"ab, i->ab", tie_phi_x_x, increasing_proportion * denom
)
else:
# no tensors here, but do some casting to make it easier in the converging step next.
denom = 1.0 / np.array([risk_phi])
numer = risk_phi_x
a1 = risk_phi_x_x * denom
summand = numer * denom[:, None]
a2 = summand.T.dot(summand)
gradient = gradient + x_death_sum - weighted_average * summand.sum(0)
log_lik = log_lik + np.dot(x_death_sum, beta) + weighted_average * np.log(denom).sum()
hessian = hessian + weighted_average * (a2 - a1)
return hessian, gradient, log_lik
def _get_gradients(self, X, events, start, stop, weights, beta): # pylint: disable=too-many-locals
"""
Calculates the first and second order vector differentials, with respect to beta.
Returns
-------
hessian: (d, d) numpy array,
gradient: (d,) numpy array
log_likelihood: float
"""
_, d = X.shape
hessian = np.zeros((d, d))
gradient = np.zeros(d)
log_lik = 0
# weights = weights[:, None]
unique_death_times = np.unique(stop[events])
for t in unique_death_times:
# I feel like this can be made into some tree-like structure
ix = (start < t) & (t <= stop)
X_at_t = X[ix]
weights_at_t = weights[ix]
stops_events_at_t = stop[ix]
events_at_t = events[ix]
phi_i = weights_at_t * np.exp(np.dot(X_at_t, beta))
phi_x_i = phi_i[:, None] * X_at_t
phi_x_x_i = np.dot(X_at_t.T, phi_x_i)
# Calculate sums of Risk set
risk_phi = array_sum_to_scalar(phi_i)
risk_phi_x = matrix_axis_0_sum_to_array(phi_x_i)
risk_phi_x_x = phi_x_x_i
# Calculate the sums of Tie set
deaths = events_at_t & (stops_events_at_t == t)
tied_death_counts = array_sum_to_scalar(
deaths.astype(int)) # should always at least 1
xi_deaths = X_at_t[deaths]
x_death_sum = matrix_axis_0_sum_to_array(
weights_at_t[deaths, None] * xi_deaths)
weight_count = array_sum_to_scalar(weights_at_t[deaths])
weighted_average = weight_count / tied_death_counts
#
# This code is near identical to the _batch algorithm in CoxPHFitter. In fact, see _batch for comments.
#
if tied_death_counts > 1:
# A good explaination for how Efron handles ties. Consider three of five subjects who fail at the time.
# As it is not known a priori that who is the first to fail, so one-third of
# (φ1 + φ2 + φ3) is adjusted from sum_j^{5} φj after one fails. Similarly two-third
# of (φ1 + φ2 + φ3) is adjusted after first two individuals fail, etc.
# a lot of this is now in einstien notation for performance, but see original "expanded" code here
# https://github.com/CamDavidsonPilon/lifelines/blob/e7056e7817272eb5dff5983556954f56c33301b1/lifelines/fitters/cox_time_varying_fitter.py#L458-L490
tie_phi = array_sum_to_scalar(phi_i[deaths])
tie_phi_x = matrix_axis_0_sum_to_array(phi_x_i[deaths])
tie_phi_x_x = np.dot(xi_deaths.T,
phi_i[deaths, None] * xi_deaths)
increasing_proportion = np.arange(
tied_death_counts) / tied_death_counts
denom = 1.0 / (risk_phi - increasing_proportion * tie_phi)
numer = risk_phi_x - np.outer(increasing_proportion, tie_phi_x)
a1 = np.einsum("ab, i->ab", risk_phi_x_x, denom) - np.einsum(
"ab, i->ab", tie_phi_x_x, increasing_proportion * denom)
else:
# no tensors here, but do some casting to make it easier in the converging step next.
denom = 1.0 / np.array([risk_phi])
numer = risk_phi_x
a1 = risk_phi_x_x * denom
summand = numer * denom[:, None]
a2 = summand.T.dot(summand)
gradient = gradient + x_death_sum - weighted_average * summand.sum(
0)
log_lik = log_lik + np.dot(
x_death_sum, beta) + weighted_average * np.log(denom).sum()
hessian = hessian + weighted_average * (a2 - a1)
return hessian, gradient, log_lik