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custom_data_loader.py
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custom_data_loader.py
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import numpy as np
import pyro.distributions as dist
import torch
from Utils import Utils
def custom_data_loader(N):
zx = dist.Bernoulli(0.5).sample([N])
zt = dist.Bernoulli(0.5).sample([N])
zyf = dist.Bernoulli(0.5).sample([N])
zycf = dist.Bernoulli(0.5).sample([N])
z_t_1 = torch.cat((zx, zt, zyf, zycf), -1)
print(z_t_1.size())
print(z_t_1[0])
zx = dist.Bernoulli(0.6).sample([N])
zt = dist.Bernoulli(0.6).sample([N])
zyf = dist.Bernoulli(0.6).sample([N])
zycf = dist.Bernoulli(0.6).sample([N])
z_t_0 = torch.cat((zx, zt, zyf, zycf), -1)
print(z_t_0.size())
print(z_t_0[0])
sigma_t_1 = 5 * z_t_1 + 3 * (1 - z_t_1)
sigma_t_0 = 5 * z_t_0 + 3 * (1 - z_t_0)
x_t_1 = dist.Normal(z_t_1, sigma_t_1).sample([10]).t()
x_t_0 = dist.Normal(z_t_0, sigma_t_0).sample([10]).t()
print("KL: ", Utils.kl_divergence(z_t_1.numpy(), z_t_0.numpy(),
sigma_t_1.numpy(), sigma_t_0.numpy()))
x = torch.cat((x_t_1, x_t_0), dim=0)
print(x.size())
t_t = torch.ones(4 * N)
t_c = torch.zeros(4 * N)
t = torch.cat((t_t, t_c), dim=0)
print(t.shape)
n_Y = dist.Normal(0, 0.1).sample([1])
print(n_Y.size())
w_y = dist.Uniform(-1, 1).sample([10, 1])
print((torch.mm(x, w_y).add(n_Y)).size())
y_t0 = (torch.mm(x, w_y) + n_Y).squeeze(-1)
n_Y = dist.Normal(0, 0.1).sample([1])
print(n_Y.size())
w_y = dist.Uniform(-1, 1).sample([10, 1])
print((torch.mm(x, w_y).add(n_Y)).size())
y_t1 = (torch.mm(x, w_y) + n_Y).squeeze(-1)
# print(y.size())
# y_t0 = y[:, 0]
# y_t1 = y[:, 1]
y_f = t * y_t1 + (1 - t) * y_t0
y_cf = (1 - t) * y_t1 + t * y_t0
np_x = x.numpy()
np_t = Utils.convert_to_col_vector(t.numpy())
np_y_f = Utils.convert_to_col_vector(y_f.numpy())
np_y_cf = Utils.convert_to_col_vector(y_cf.numpy())
np_mu0 = Utils.convert_to_col_vector(y_t0.numpy())
np_mu1 = Utils.convert_to_col_vector(y_t1.numpy())
print(np_x.shape)
print(np_t.shape)
# print(t[t==1].shape)
# print(t[t==0].shape)
print(np_mu0.shape)
print(np_mu1.shape)
np_X = np.concatenate((np_x, np_t, np_y_f, np_y_cf, np_mu0, np_mu1), axis=1)
print(np_X.shape)
# np.random.shuffle(X)
np.save("Dataset/Custom_GANITE_8.npy", np_X)
def generate_data_DR_VIDAL(N, alpha=0.25, beta=1, gamma=1):
"""
This implements the generative process of [1], but using larger feature and
latent spaces ([1] assumes ``feature_dim=1`` and ``latent_dim=5``).
"""
zx = dist.Bernoulli(0.5).sample([N])
zt = dist.Bernoulli(0.5).sample([N])
zyf = dist.Bernoulli(0.5).sample([N])
zycf = dist.Bernoulli(0.5).sample([N])
x_x = dist.Normal(zx, 5 * zx + 3 * (1 - zx)).sample([7]).t()
x_t = dist.Normal(zt, 2 * zt + 0.5 * (1 - zt)).sample([1]).t()
x_yf = dist.Normal(zyf, 10 * zyf + 6 * (1 - zyf)).sample([1]).t()
x_ycf = dist.Normal(zycf, 10 * zycf + 6 * (1 - zycf)).sample([1]).t()
x = torch.cat([x_x, x_t, x_yf, x_ycf], -1)
print("X: ", x.size())
w = dist.Uniform(-0.1, 0.1).sample([10, 1])
n_t = dist.Normal(0, 0.1).sample([1])
print("W: ", w.size())
print("n_t: ", n_t.size())
print((torch.mm(x, w) + n_t).size())
t = dist.Bernoulli(torch.sigmoid(torch.mm(x, w) + n_t)).sample().squeeze(-1)
print("t: ", t.size())
# print(t)
# n_Y = dist.Normal(0, 0.1).sample([2])
# print(n_Y.size())
# w_y = dist.Uniform(-1, 1).sample([10, 2])
# print((torch.mm(x, w_y).add(n_Y)).size())
# y = (torch.mm(x, w_y) + n_Y).squeeze(-1)
n_Y = dist.Normal(0, 0.1).sample([1])
print(n_Y.size())
w_y = dist.Uniform(-1, 1).sample([10, 1])
print((torch.mm(x, w_y).add(n_Y)).size())
y_t0 = (torch.mm(x, w_y) + n_Y).squeeze(-1)
n_Y = dist.Normal(0, 0.1).sample([1])
print(n_Y.size())
w_y = dist.Uniform(-1, 1).sample([10, 1])
print((torch.mm(x, w_y).add(n_Y)).size())
y_t1 = (torch.mm(x, w_y) + n_Y).squeeze(-1)
# print(y.size())
# y_t0 = y[:, 0]
# y_t1 = y[:, 1]
y_f = t * y_t1 + (1 - t) * y_t0
y_cf = (1 - t) * y_t1 + t * y_t0
np_x = x.numpy()
np_t = Utils.convert_to_col_vector(t.numpy())
np_y_f = Utils.convert_to_col_vector(y_f.numpy())
np_y_cf = Utils.convert_to_col_vector(y_cf.numpy())
np_mu0 = Utils.convert_to_col_vector(y_t0.numpy())
np_mu1 = Utils.convert_to_col_vector(y_t1.numpy())
print(np_x.shape)
print(np_t.shape)
# print(t[t==1].shape)
# print(t[t==0].shape)
print(np_mu0.shape)
print(np_mu1.shape)
np_X = np.concatenate((np_x, np_t, np_y_f, np_y_cf, np_mu0, np_mu1), axis=1)
print(np_X.shape)
# np.random.shuffle(X)
np.save("Dataset/Custom_GANITE_8.npy", np_X)
def generate_data_cevae(N, alpha=0.75, beta=3, gamma=2):
"""
This implements the generative process of [1], but using larger feature and
latent spaces ([1] assumes ``feature_dim=1`` and ``latent_dim=5``).
"""
z = dist.Bernoulli(0.5).sample([N])
x = dist.Normal(z, (5 ** 2) * z + (3 ** 2) * (1 - z)).sample([5]).t()
print(x.size())
t = dist.Bernoulli(alpha * z + (1 - alpha) * (1 - z)).sample()
print("T: ", t.size())
y_f = dist.Bernoulli(logits=beta * (z + gamma * (2 * t - 1))).sample()
y_cf = dist.Bernoulli(logits=beta * (z + gamma * (2 * t - 1))).sample()
# print("Y", y.size())
# Compute true ite for evaluation (via Monte Carlo approximation).
t0_t1 = torch.tensor([[0.], [1.]])
y_t0, y_t1 = dist.Bernoulli(logits=beta * (z + gamma * (2 * t0_t1 - 1))).mean
print("Y, ", y_f.size())
print("y_t0, ", y_t0.size())
true_ite = y_t1 - y_t0
print(true_ite)
np_x = x.numpy()
np_t = Utils.convert_to_col_vector(t.numpy())
np_mu0 = Utils.convert_to_col_vector(y_t0.numpy())
np_mu1 = Utils.convert_to_col_vector(y_t1.numpy())
np_y_f = Utils.convert_to_col_vector(y_f.numpy())
np_y_cf = Utils.convert_to_col_vector(y_cf.numpy())
print(np_x.shape)
print(np_t.shape)
# print(t[t==1].shape)
# print(t[t==0].shape)
print(np_mu0.shape)
print(np_mu1.shape)
np_X = np.concatenate((np_x, np_t, np_y_f, np_y_cf, np_mu0, np_mu1), axis=1)
print(np_X.shape)
# np.random.shuffle(X)
np.save("Dataset/Custom_GANITE_8.npy", np_X)
@staticmethod
def load_custom(iter_id):
X = np.load("Dataset/Custom_GANITE_8.npy")
covariates_x = X[:, 0:10]
print(covariates_x.shape)
t = Utils.convert_to_col_vector(X[:, -5])
y_f = Utils.convert_to_col_vector(X[:, -4])
y_cf = Utils.convert_to_col_vector(X[:, -3])
np_mu0 = Utils.convert_to_col_vector(X[:, -2])
np_mu1 = Utils.convert_to_col_vector(X[:, -1])
np_train_X, np_test_X, np_train_T, np_test_T, np_train_yf, np_test_yf, np_train_ycf, \
np_test_ycf, np_train_mu0, np_test_mu0, np_train_mu1, np_test_mu1 = \
Utils.test_train_split_custom(covariates_x, t, y_f,
y_cf, iter_id,
np_mu0, np_mu1, split_size=0.8)
print("Numpy Train Statistics:")
print(np_train_X.shape)
print(np_train_T.shape)
n_treated = np_train_T[np_train_T == 1]
# print(n_treated.shape[0])
# print(np_train_T.shape[0])
n_treated = n_treated.shape[0]
n_total = np_train_T.shape[0]
# print("Numpy Val Statistics:")
# print(np_val_X.shape)
# print(np_val_T.shape)
# print(np_val_yf.shape)
# print(np_val_ycf.shape)
print("Numpy Temp Statistics:")
print(np_test_X.shape)
print(np_test_T.shape)
# print("KL: ", Utils.kl_divergence(mean_T, mean_C, cov, cov))
return np_train_X, np_train_T, np_train_yf, np_train_ycf, \
np_test_X, np_test_T, np_test_yf, np_test_ycf, \
np_train_mu0, np_test_mu0, np_train_mu1, np_test_mu1, \
n_treated, n_total
# DataLoader.generate_data_cevae(10000)
# DataLoader.generate_data_DR_VIDAL(1000)
# DataLoader.custom_data_loader(1000)