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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
# %matplotlib inline
import matplotlib.pyplot as plt
import plotly.graph_objects as go
from datasets import *
from models import MLP, SNMLP
from torch.quasirandom import SobolEngine
import time
import sys
def LV_embedding(X, iters):
X = X.clone()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
X = X.to(device)
print(device)
ambient_dim = X.size(-1)
width = ambient_dim * 16
# Note in particular the lack of the bottleneck choice below
encoder = MLP(ambient_dim, ambient_dim, [width] * 4).to(device)
# Note also the change in the decoder to have spectral normalization
decoder = SNMLP(ambient_dim, ambient_dim, [width] * 4).to(device)
opt = torch.optim.Adam(list(encoder.parameters()) + list(decoder.parameters()), lr=1e-4)
畏, 位 = 0.01, 0.03
START_TIME = time.time()
for i in range(iters):
opt.zero_grad()
z = encoder(X)
rec_loss = F.mse_loss(decoder(z), X)
# Note below the least volume loss
vol_loss = torch.exp(torch.log(z.std(0) + 畏).mean())
loss = rec_loss + 位 * vol_loss
loss.backward()
opt.step()
if (i+1) % 1000 == 0:
print('Epoch {}: rec = {}, vol = {}'.format(i, rec_loss, vol_loss))
END_TIME = time.time()
INTERVAL = END_TIME-START_TIME
f = open("/home/gridsan/ryu/Bank_High_DIM/LVAE_Test_July9/LV_embedding_Adam_July9.txt", "a")
f.write('Epoch {}: rec = {}, vol = {}, Time: {}'.format(i, rec_loss, vol_loss, INTERVAL))
f.write('\n')
f.close()
encoder.eval()
decoder.eval()
with torch.no_grad():
z = encoder(X)
idx = z.std(0).argsort(descending=True)
return z.to('cpu'), z.std(0).to('cpu'), idx.to('cpu'), encoder.to('cpu'), decoder.to('cpu')
def LV_embedding_AdamW(X, iters):
X = X.clone()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
X = X.to(device)
print(device)
ambient_dim = X.size(-1)
width = ambient_dim * 16
# Note in particular the lack of the bottleneck choice below
encoder = MLP(ambient_dim, ambient_dim, [width] * 4).to(device)
# Note also the change in the decoder to have spectral normalization
decoder = SNMLP(ambient_dim, ambient_dim, [width] * 4).to(device)
opt = torch.optim.AdamW(list(encoder.parameters()) + list(decoder.parameters()), lr=1e-4)
畏, 位 = 0.01, 0.03
START_TIME = time.time()
for i in range(iters):
opt.zero_grad()
z = encoder(X)
rec_loss = F.mse_loss(decoder(z), X)
# Note below the least volume loss
vol_loss = torch.exp(torch.log(z.std(0) + 畏).mean())
loss = rec_loss + 位 * vol_loss
loss.backward()
opt.step()
if (i+1) % 1000 == 0:
print('Epoch {}: rec = {}, vol = {}'.format(i, rec_loss, vol_loss))
END_TIME = time.time()
INTERVAL = END_TIME-START_TIME
f = open("/home/gridsan/ryu/Bank_High_DIM/LVAE_Test_July9/LV_embedding_AdamW_July9.txt", "a")
f.write('Epoch {}: rec = {}, vol = {}, Time: {}'.format(i, rec_loss, vol_loss, INTERVAL))
f.write('\n')
f.close()
encoder.eval()
decoder.eval()
with torch.no_grad():
z = encoder(X)
idx = z.std(0).argsort(descending=True)
return z.to('cpu'), z.std(0).to('cpu'), idx.to('cpu'), encoder.to('cpu'), decoder.to('cpu')
def LV_embedding_5e4(X, iters):
X = X.clone()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
X = X.to(device)
print(device)
ambient_dim = X.size(-1)
width = ambient_dim * 16
# Note in particular the lack of the bottleneck choice below
encoder = MLP(ambient_dim, ambient_dim, [width] * 4).to(device)
# Note also the change in the decoder to have spectral normalization
decoder = SNMLP(ambient_dim, ambient_dim, [width] * 4).to(device)
opt = torch.optim.Adam(list(encoder.parameters()) + list(decoder.parameters()), lr=5e-4)
畏, 位 = 0.01, 0.03
START_TIME = time.time()
for i in range(iters):
opt.zero_grad()
z = encoder(X)
rec_loss = F.mse_loss(decoder(z), X)
# Note below the least volume loss
vol_loss = torch.exp(torch.log(z.std(0) + 畏).mean())
loss = rec_loss + 位 * vol_loss
loss.backward()
opt.step()
if (i+1) % 1000 == 0:
print('Epoch {}: rec = {}, vol = {}'.format(i, rec_loss, vol_loss))
END_TIME = time.time()
INTERVAL = END_TIME-START_TIME
f = open("/home/gridsan/ryu/Bank_High_DIM/LVAE_Test_July9/LV_embedding_5e4_July9.txt", "a")
f.write('Epoch {}: rec = {}, vol = {}, Time: {}'.format(i, rec_loss, vol_loss, INTERVAL))
f.write('\n')
f.close()
encoder.eval()
decoder.eval()
with torch.no_grad():
z = encoder(X)
idx = z.std(0).argsort(descending=True)
return z.to('cpu'), z.std(0).to('cpu'), idx.to('cpu'), encoder.to('cpu'), decoder.to('cpu')
def sampling_z(z):
z_dim = z.shape[1]
sobol = SobolEngine(z_dim, scramble=True)
Z_samples = sobol.draw(2000)
for ii in range(z_dim):
Z_samples[:,ii] = Z_samples[:,ii] * (z[:,ii].max() - z[:,ii].min()) + z[:,ii].min()
return Z_samples
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