import torch
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import sys
from two_dimensional import two_d_gpsa
# sys.path.append("../..")
# from models.gpsa_vi_lmc import VariationalWarpGP
from gpsa import VariationalGPSA, matern12_kernel, rbf_kernel
from gpsa.plotting import callback_twod
sys.path.append("../../data")
from simulated.generate_twod_data import generate_twod_data
from plotting.callbacks import callback_twod
from util import ConvergenceChecker
## For PASTE
import scanpy as sc
sys.path.append("../../../paste")
from src.paste import PASTE, visualization
device = "cuda" if torch.cuda.is_available() else "cpu"
LATEX_FONTSIZE = 50
n_spatial_dims = 2
n_views = 2
n_outputs = 10
m_G = 25
m_X_per_view = 25
MAX_EPOCHS = 2000
PRINT_EVERY = -1
N_LATENT_GPS = {"expression": 3}
if __name__ == "__main__":
kernel_variance_list = [0.01, 0.1, 0.5]
n_repeats = 3
error_mat = np.zeros((n_repeats, len(kernel_variance_list)))
error_mat_paste = np.zeros((n_repeats, len(kernel_variance_list)))
for ii in range(n_repeats):
for jj, kernel_variance in enumerate(kernel_variance_list):
X, Y, G_means, model, err_paste = two_d_gpsa(
warp_kernel_variance=kernel_variance,
n_outputs=n_outputs,
n_epochs=MAX_EPOCHS,
plot_intermediate=False,
n_latent_gps=N_LATENT_GPS,
)
aligned_coords = G_means["expression"].detach().numpy().squeeze()
n_samples_per_view = n_samples_per_view = X.shape[0] // n_views
view1_aligned_coords = aligned_coords[:n_samples_per_view]
view2_aligned_coords = aligned_coords[n_samples_per_view:]
err = np.mean(
np.sum((view1_aligned_coords - view2_aligned_coords) ** 2, axis=1)
)
error_mat[ii, jj] = err
error_mat_paste[ii, jj] = err_paste
if ii == 0:
import matplotlib
font = {"size": LATEX_FONTSIZE}
matplotlib.rc("font", **font)
matplotlib.rcParams["text.usetex"] = True
fig = plt.figure(figsize=(10, 10))
data_expression_ax = fig.add_subplot(211, frameon=False)
latent_expression_ax = fig.add_subplot(212, frameon=False)
callback_twod(
model,
X,
Y,
data_expression_ax=data_expression_ax,
latent_expression_ax=latent_expression_ax,
X_aligned=G_means,
)
plt.tight_layout()
plt.savefig(
"../../plots/two_d_experiments/two_d_simulation_kernel_variance={}.png".format(
kernel_variance
)
)
# plt.show()
plt.close()
# import ipdb; ipdb.set_trace()
import matplotlib
font = {"size": 30}
matplotlib.rc("font", **font)
matplotlib.rcParams["text.usetex"] = True
plt.figure(figsize=(7, 5))
error_df_gpsa = pd.melt(
pd.DataFrame(error_mat[: ii + 1, :], columns=kernel_variance_list)
)
error_df_gpsa["method"] = ["GPSA"] * error_df_gpsa.shape[0]
error_df_paste = pd.melt(
pd.DataFrame(error_mat_paste[: ii + 1, :], columns=kernel_variance_list)
)
error_df_paste["method"] = ["PASTE"] * error_df_paste.shape[0]
error_df = pd.concat([error_df_gpsa, error_df_paste], axis=0)
error_df.to_csv("./out/error_vary_warp_magnitude.csv")
sns.lineplot(
data=error_df, x="variable", y="value", hue="method", err_style="bars"
)
plt.xlabel("Warp magnitude")
plt.ylabel("Alignent error")
plt.tight_layout()
plt.savefig("../../plots/two_d_experiments/error_plot_warp_magnitude.png")
plt.close()
import ipdb
ipdb.set_trace()
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