def exp_mi(xmiss, w, y, regularize, m=10, nuisance=False):
res_tau_dr = []
res_tau_ols = []
res_tau_ols_ps = []
res_tau_resid = []
res_ps = np.empty([len(w), 1])
res_y0 = np.empty([len(y), 1])
res_y1 = np.empty([len(y), 1])
for i in range(m):
imp = IterativeImputer(sample_posterior=True, random_state=i)
x_imp_mice = imp.fit_transform(xmiss)
if nuisance:
tau_tmp, nu_tmp = compute_estimates(x_imp_mice, w, y, regularize,
nuisance)
res_ps = np.concatenate(
(res_ps, nu_tmp['ps_hat'].reshape([len(w), 1])), axis=1)
res_y0 = np.concatenate(
(res_y0, nu_tmp['y0_hat'].reshape([len(y), 1])), axis=1)
res_y1 = np.concatenate(
(res_y1, nu_tmp['y1_hat'].reshape([len(y), 1])), axis=1)
else:
tau_tmp = compute_estimates(x_imp_mice, w, y, regularize)
res_tau_dr.append(tau_tmp['tau_dr'])
res_tau_ols.append(tau_tmp['tau_ols'])
res_tau_ols_ps.append(tau_tmp['tau_ols_ps'])
res_tau_resid.append(tau_tmp['tau_resid'])
if nuisance:
return {
'tau_dr': np.mean(res_tau_dr),
'tau_ols': np.mean(res_tau_ols),
'tau_ols_ps': np.mean(res_tau_ols_ps),
'tau_resid': np.mean(res_tau_resid),
}, {
'ps_hat': np.mean(res_ps[:, 1:], axis=1),
'y0_hat': np.mean(res_y0[:, 1:], axis=1),
'y1_hat': np.mean(res_y1[:, 1:], axis=1),
}
return {
'tau_dr': np.mean(res_tau_dr),
'tau_ols': np.mean(res_tau_ols),
'tau_ols_ps': np.mean(res_tau_ols_ps),
'tau_resid': np.mean(res_tau_resid),
}
def exp_mf(xmiss,
w,
y,
regularize,
nuisance=False,
return_zhat=False,
grid_len=15):
err, grid = cv_softimpute(xmiss, grid_len=grid_len)
zhat, _ = softimpute(xmiss, lamb=grid[np.argmin(err)])
if nuisance:
tau, nu = compute_estimates(zhat, w, y, regularize, nuisance)
if return_zhat:
return tau, nu, zhat.shape[1], zhat
return tau, nu, zhat.shape[1]
if return_zhat:
return compute_estimates(zhat, w, y, regularize), zhat.shape[1], zhat
return compute_estimates(zhat, w, y, regularize), zhat.shape[1]
def exp_complete(z, x, w, y, regularize, nuisance=False):
algo_ = [z, x]
algo_name = ['Z', 'X']
tau = dict()
nu = dict()
for name, zhat in zip(algo_name, algo_):
if nuisance:
tau_tmp, nu_tmp = compute_estimates(zhat, w, y, regularize,
nuisance)
nu[name] = nu_tmp
else:
tau_tmp = compute_estimates(zhat, w, y, regularize)
tau[name] = tau_tmp
if nuisance:
return tau, nu
return tau
def exp_complete(z, x, w, y, regularize):
algo_ = [z, x]
algo_name = ['Z', 'X']
tau = dict()
for name, zhat in zip(algo_name, algo_):
tau_tmp = compute_estimates(zhat, w, y, regularize)
tau[name] = tau_tmp
return tau
def exp_mdc(xmiss, w, y,
d_miwae,
sig_prior,
num_samples_zmul,
learning_rate,
n_epochs,
regularize):
tau = dict()
xhat, zhat, zhat_mul, elbo = miwae_es(xmiss, d=d_miwae, sig_prior=sig_prior,
num_samples_zmul=num_samples_zmul,
l_rate=learning_rate, n_epochs=n_epochs)
# Tau estimated on Zhat=E[Z|X]
tau['MDC.process'] = compute_estimates(zhat, w, y, regularize)
# Tau estimated on Zhat^(b), l=1,...,B sampled from posterior
res_mul_tau_dr = []
res_mul_tau_ols = []
res_mul_tau_ols_ps = []
res_mul_tau_resid = []
for zhat_b in zhat_mul:
tau_tmp = compute_estimates(zhat_b, w, y)
res_mul_tau_dr.append(tau_tmp['tau_dr'])
res_mul_tau_ols.append(tau_tmp['tau_ols'])
res_mul_tau_ols_ps.append(tau_tmp['tau_ols_ps'])
res_mul_tau_resid.append(tau_tmp['tau_resid'])
res_mul_tau_dr = np.mean(res_mul_tau_dr)
res_mul_tau_ols = np.mean(res_mul_tau_ols)
res_mul_tau_ols_ps = np.mean(res_mul_tau_ols_ps)
res_mul_tau_resid = np.mean(res_mul_tau_resid)
tau['MDC.mi'] = {
'tau_dr': np.mean(res_mul_tau_dr),
'tau_ols': np.mean(res_mul_tau_ols),
'tau_ols_ps': np.mean(res_mul_tau_ols_ps),
'tau_resid': np.mean(res_mul_tau_resid)
}
return tau, elbo
def exp_mi(xmiss, w, y, regularize, m=10):
res_tau_dr = []
res_tau_ols = []
res_tau_ols_ps = []
res_tau_resid = []
for i in range(m):
imp = IterativeImputer(sample_posterior = True, random_state = i)
x_imp_mice = imp.fit_transform(xmiss)
tau_tmp = compute_estimates(x_imp_mice, w, y, regularize)
res_tau_dr.append(tau_tmp['tau_dr'])
res_tau_ols.append(tau_tmp['tau_ols'])
res_tau_ols_ps.append(tau_tmp['tau_ols_ps'])
res_tau_resid.append(tau_tmp['tau_resid'])
return {
'tau_dr': np.mean(res_tau_dr),
'tau_ols': np.mean(res_tau_ols),
'tau_ols_ps': np.mean(res_tau_ols_ps),
'tau_resid': np.mean(res_tau_resid),
}
def exp_mf(xmiss, w, y, regularize, grid_len=15):
err, grid = cv_softimpute(xmiss, grid_len = grid_len)
zhat, _ = softimpute(xmiss, lamb = grid[np.argmin(err)])
return compute_estimates(zhat, w, y, regularize), zhat.shape[1]
def exp_mean(xmiss, w, y, regularize):
x_imp_mean = SimpleImputer(strategy = 'mean').fit_transform(xmiss)
return compute_estimates(x_imp_mean, w, y, regularize)
def exp_mdc(xmiss,
w,
y,
d_miwae,
mu_prior,
sig_prior,
num_samples_zmul,
learning_rate,
n_epochs,
regularize,
nuisance=False,
return_zhat=False,
save_session=False,
session_file=None,
session_file_complete=None):
tau = dict()
nu = dict()
epochs = -1
xhat = []
zhat = []
zhat_mul = []
elbo = None
epochs = None
if session_file_complete is not None:
tmp = glob.glob(session_file_complete + '.*')
sess = tf.Session(graph=tf.reset_default_graph())
if len(tmp) > 0:
new_saver = tf.train.import_meta_graph(session_file_complete +
'.meta')
new_saver.restore(sess, session_file_complete)
with open(session_file_complete + '.pkl', 'rb') as f:
xhat, zhat, zhat_mul, elbo, epochs = pickle.load(f)
if session_file_complete is None or len(tmp) == 0:
xhat, zhat, zhat_mul, elbo, epochs = miwae_es(
xmiss,
d_miwae=d_miwae,
mu_prior=mu_prior,
sig_prior=sig_prior,
num_samples_zmul=num_samples_zmul,
l_rate=learning_rate,
n_epochs=n_epochs,
save_session=save_session,
session_file=session_file)
if session_file_complete is not None:
new_saver = tf.train.import_meta_graph(session_file_complete +
'.meta')
new_saver.restore(
sess,
session_file_complete) #tf.train.latest_checkpoint('./'))
with open(session_file_complete + '.pkl',
'wb') as file_data: # Python 3: open(..., 'wb')
pickle.dump([xhat, zhat, zhat_mul, elbo, epochs], file_data)
# Tau estimated on Zhat=E[Z|X]
if nuisance:
tau['MDC.process'], nu['MDC.process'] = compute_estimates(
zhat, w, y, regularize, nuisance)
else:
tau['MDC.process'] = compute_estimates(zhat, w, y, regularize)
# Tau estimated on Zhat^(b), l=1,...,B sampled from posterior
res_mul_tau_dr = []
res_mul_tau_ols = []
res_mul_tau_ols_ps = []
res_mul_tau_resid = []
res_mul_ps = np.empty([len(w), 1])
res_mul_y0 = np.empty([len(y), 1])
res_mul_y1 = np.empty([len(y), 1])
for zhat_b in zhat_mul:
if nuisance:
tau_tmp, nu_tmp = compute_estimates(zhat_b, w, y, regularize,
nuisance)
res_mul_ps = np.concatenate(
(res_mul_ps, nu_tmp['ps_hat'].reshape([len(w), 1])), axis=1)
res_mul_y0 = np.concatenate(
(res_mul_y0, nu_tmp['y0_hat'].reshape([len(y), 1])), axis=1)
res_mul_y1 = np.concatenate(
(res_mul_y1, nu_tmp['y1_hat'].reshape([len(y), 1])), axis=1)
else:
tau_tmp = compute_estimates(zhat_b, w, y, regularize)
res_mul_tau_dr.append(tau_tmp['tau_dr'])
res_mul_tau_ols.append(tau_tmp['tau_ols'])
res_mul_tau_ols_ps.append(tau_tmp['tau_ols_ps'])
res_mul_tau_resid.append(tau_tmp['tau_resid'])
res_mul_tau_dr = np.mean(res_mul_tau_dr)
res_mul_tau_ols = np.mean(res_mul_tau_ols)
res_mul_tau_ols_ps = np.mean(res_mul_tau_ols_ps)
res_mul_tau_resid = np.mean(res_mul_tau_resid)
if nuisance:
nu['MDC.mi'] = {
'ps_hat': np.mean(res_mul_ps[:, 1:], axis=1),
'y0_hat': np.mean(res_mul_y0[:, 1:], axis=1),
'y1_hat': np.mean(res_mul_y1[:, 1:], axis=1)
}
tau['MDC.mi'] = {
'tau_dr': np.mean(res_mul_tau_dr),
'tau_ols': np.mean(res_mul_tau_ols),
'tau_ols_ps': np.mean(res_mul_tau_ols_ps),
'tau_resid': np.mean(res_mul_tau_resid)
}
if nuisance:
if return_zhat:
return tau, nu, elbo, zhat, zhat_mul
return tau, nu, elbo
if return_zhat:
return tau, elbo, zhat, zhat_mul
return tau, elbo
