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# comp_ds.py
import os
import glob
import pandas as pd
import torch
from torch.utils.data import TensorDataset, DataLoader
import torch.optim as optim
import numpy as np
from scipy.stats import ttest_ind
from KDEpy import FFTKDE
from models.vae import VAE, train as train_vae, k_fold_validation_vae
from models.iwae import IWAE, train as train_iwae, k_fold_validation_iwae
from models.kde_mcmc import mcmc_sampling
from mks_test import mkstest
from utils import plot_pca, plot_tsne, svd_reduce, svd_reconstruct, shannon
import pdb
# --------------------
# Configuration
# --------------------
latent_dim = 8
hidden_dim = 64
num_epochs = 800
batch_size = 128
learning_rate = 1e-3
K = 20
random_seed = 42
torch.manual_seed(random_seed)
np.random.seed(random_seed)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def process_file(filepath: str):
dataset_name = os.path.splitext(os.path.basename(filepath))[0]
print(f"\n=== Processing {dataset_name} ===")
# Load data (first column as index, first row as header)
df = pd.read_csv(filepath, index_col=0)
# data = df.values.T # shape: (n_samples, n_features)
data = np.log1p(df.values)
n_samples, input_dim = data.shape
Z, V_red = svd_reduce(data, max_fraction=0.1)
input_dim_red = Z.shape[1]
print(f"Data: {n_samples} samples, {input_dim} features")
loader = DataLoader(
TensorDataset(torch.from_numpy(Z).float()), batch_size=batch_size, shuffle=True
)
print(f"Loaded reduced data: {n_samples} samples, {input_dim_red} features")
# ----- Vanilla VAE -----
vae = VAE(input_dim_red, hidden_dim, latent_dim).to(device)
opt_vae = optim.Adam(vae.parameters(), lr=learning_rate)
train_vae(vae, loader, opt_vae, num_epochs=num_epochs, device=device)
vae.eval()
with torch.no_grad():
z = torch.randn(n_samples, latent_dim, device=device)
gen_vae_red = vae.decode(z).cpu().numpy()
gen_vae = svd_reconstruct(gen_vae_red, V_red)
plot_pca(data.T, gen_vae.T, "VAE", dataset_name)
# plot_tsne(data, gen_vae, "VAE", dataset_name)
# ----- IWAE -----
iwae = IWAE(input_dim_red, latent_dim, hidden_dim, K).to(device)
opt_iwae = optim.Adam(iwae.parameters(), lr=learning_rate)
# train and print avg loss per epoch
train_iwae(iwae, loader, opt_iwae, num_epochs=num_epochs, device=device)
iwae.eval()
with torch.no_grad():
z = torch.randn(n_samples, latent_dim, device=device)
gen_iwae_red = iwae.sample(n_samples)
gen_iwae = svd_reconstruct(gen_iwae_red, V_red)
plot_pca(data.T, gen_iwae.T, "IWAE", dataset_name)
# plot_tsne(data, gen_iwae, "IWAE", dataset_name)
# ----- KDE-MCMC -----
# kde = FFTKDE(kernel="gaussian").fit(Z)
# bw = kde.bw
# init = Z[np.random.choice(n_samples)]
# gen_mcmc_red = mcmc_sampling(
# initial_point=init,
# data=Z,
# bandwidth=bw,
# num_samples=n_samples,
# step_size=0.5,
# leapfrog_steps=10,
# burn_in=100,
# )
# gen_mcmc = svd_reconstruct(gen_mcmc_red, V_red)
# plot_pca(data, gen_mcmc, "KDE-MCMC", dataset_name)
# plot_tsne(data, gen_mcmc, "KDE-MCMC", dataset_name)
# NOTE mkstest takes a long time to run, and the library itself is a bit buggy
# print("VAE mkstest:")
# mkstest(gen_vae, data, alpha=0.05, verbose=True)
# print("IWAE mkstest:")
# mkstest(gen_iwae, data, alpha=0.05, verbose=True)
# print("KDE-MCMC mkstest:")
# mkstest(gen_mcmc, data, alpha=0.05, verbose=True)
# NOTE shannon diversity can't compute negative values so it's not included in the analysis
# H_orig = shannon(data)
# H_vae = shannon(gen_vae)
# H_iwae = shannon(gen_iwae)
# H_mcmc = shannon(gen_mcmc)
# t_vae, p_vae = ttest_ind(H_orig, H_vae)
# t_iwae, p_iwae = ttest_ind(H_orig, H_iwae)
# t_mcmc, p_mcmc = ttest_ind(H_orig, H_mcmc)
# print("Shannon diversity t-test results:")
# print(f" VAE: t={t_vae:.3f}, p={p_vae:.3e}")
# print(f" IWAE: t={t_iwae:.3f}, p={p_iwae:.3e}")
# print(f" MCMC: t={t_mcmc:.3f}, p={p_mcmc:.3e}")
# means (axis=1 mean for each gene)
# mean_orig = np.mean(data, axis=0)
# mean_vae = np.mean(gen_vae, axis=0)
# mean_iwae = np.mean(gen_iwae, axis=0)
# mean_mcmc = np.mean(gen_mcmc, axis=0)
# t-tests on means
# t_mean_vae, p_mean_vae = ttest_ind(mean_orig, mean_vae)
# t_mean_iwae, p_mean_iwae = ttest_ind(mean_orig, mean_iwae)
# t_mean_mcmc, p_mean_mcmc = ttest_ind(mean_orig, mean_mcmc)
# print("Mean t-test results:")
# print(f" VAE: t={t_mean_vae:.3f}, p={p_mean_vae:.3e}")
# print(f" IWAE: t={t_mean_iwae:.3f}, p={p_mean_iwae:.3e}")
# print(f" MCMC: t={t_mean_mcmc:.3f}, p={p_mean_mcmc:.3e}")
# medians
# med_orig = np.median(data, axis=0)
# med_vae = np.median(gen_vae, axis=0)
# med_iwae = np.median(gen_iwae, axis=0)
# med_mcmc = np.median(gen_mcmc, axis=0)
# from scipy.stats import wilcoxon
# paired Wilcoxon signed-rank tests on medians
# w_med_vae, p_w_med_vae = wilcoxon(med_orig, med_vae)
# w_med_iwae, p_w_med_iwae = wilcoxon(med_orig, med_iwae)
# if you had paired KDE-MCMC medians too:
# w_med_mcmc, p_w_med_mcmc = wilcoxon(med_orig, med_mcmc)
# print("Wilcoxon signed-rank test on medians:")
# print(f" VAE: W={w_med_vae:.3f}, p={p_w_med_vae:.3e}")
# print(f" IWAE: W={w_med_iwae:.3f}, p={p_w_med_iwae:.3e}")
# print(f" MCMC: W={w_med_mcmc:.3f}, p={p_w_med_mcmc:.3e}")
# from scipy.stats import mannwhitneyu
# u_med_vae, p_u_med_vae = mannwhitneyu(med_orig, med_vae)
# u_med_iwae, p_u_med_iwae = mannwhitneyu(med_orig, med_iwae)
# print("Mann–Whitney U test on medians:")
# print(f" VAE: W={u_med_vae:.3f}, p={p_u_med_vae:.3e}")
# print(f" IWAE: W={u_med_iwae:.3f}, p={p_u_med_iwae:.3e}")
# print(f" MCMC: W={w_med_mcmc:.3f}, p={p_w_med_mcmc:.3e}")
# NOTE TCGA will *not* run on MCMC because of the high dimensionality
if __name__ == "__main__":
os.makedirs("./output", exist_ok=True)
# process_file("./input/momspi16s.csv")
# process_file("./input/t2d16s.csv")
process_file("./input/ibd.csv")
process_file("./input/vaginal.csv")
# process_file("./input/TCGA_HNSC_rawcount_data_t.csv")