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import torch
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from torch import nn
def get_quantile(samples, q, dim=1):
return torch.quantile(samples, q, dim=dim).cpu().numpy()
def plot_sample(ori_data, gen_data, masks, sample_idx=0):
plt.rcParams["font.size"] = 12
fig, axes = plt.subplots(nrows=7, ncols=4, figsize=(12, 15))
sample_num, seq_len, feat_dim = ori_data.shape
observed = ori_data * masks
quantiles = []
quantiles.append(
get_quantile(torch.from_numpy(gen_data), 0.5, dim=0) * (1 - masks) + observed
)
quantiles.append(
get_quantile(torch.from_numpy(gen_data), 0.05, dim=0) * (1 - masks) + observed
)
quantiles.append(
get_quantile(torch.from_numpy(gen_data), 0.95, dim=0) * (1 - masks) + observed
)
for feat_idx in range(feat_dim):
row = feat_idx // 4
col = feat_idx % 4
df_x = pd.DataFrame(
{
"x": np.arange(0, seq_len),
"val": ori_data[sample_idx, :, feat_idx],
"y": masks[sample_idx, :, feat_idx],
}
)
df_x = df_x[df_x.y != 0]
df_o = pd.DataFrame(
{
"x": np.arange(0, seq_len),
"val": ori_data[sample_idx, :, feat_idx],
"y": (1 - masks)[sample_idx, :, feat_idx],
}
)
df_o = df_o[df_o.y != 0]
axes[row][col].plot(
range(0, seq_len),
quantiles[0][sample_idx, :, feat_idx],
color="g",
linestyle="solid",
label="Diffusion-TS",
)
axes[row][col].fill_between(
range(0, seq_len),
quantiles[1][sample_idx, :, feat_idx],
quantiles[2][sample_idx, :, feat_idx],
color="g",
alpha=0.3,
)
axes[row][col].plot(df_o.x, df_o.val, color="b", marker="o", linestyle="None")
axes[row][col].plot(df_x.x, df_x.val, color="r", marker="x", linestyle="None")
if col == 0:
plt.setp(axes[row, 0], ylabel="value")
if row == -1:
plt.setp(axes[-1, col], xlabel="time")
plt.tight_layout()
plt.show()
class MaskedLoss(nn.Module):
"""Masked MSE Loss"""
def __init__(self, reduction: str = "mean", mode="mse"):
super().__init__()
self.reduction = reduction
if mode == "mse":
self.loss = nn.MSELoss(reduction=self.reduction)
else:
self.loss = nn.L1Loss(reduction=self.reduction)
def forward(
self, y_pred: torch.Tensor, y_true: torch.Tensor, mask: torch.BoolTensor
) -> torch.Tensor:
"""Compute the loss between a target value and a prediction.
Args:
y_pred: Estimated values
y_true: Target values
mask: boolean tensor with 0s at places where values should be ignored and 1s where they should be considered
Returns
-------
if reduction == 'none':
(num_active,) Loss for each active batch element as a tensor with gradient attached.
if reduction == 'mean':
scalar mean loss over batch as a tensor with gradient attached.
"""
# for this particular loss, one may also elementwise multiply y_pred and y_true with the inverted mask
masked_pred = torch.masked_select(y_pred, mask)
masked_true = torch.masked_select(y_true, mask)
return self.loss(masked_pred, masked_true)
def random_mask(observed_values, missing_ratio=0.1, seed=1984):
observed_masks = ~np.isnan(observed_values)
# randomly set some percentage as ground-truth
masks = observed_masks.reshape(-1).copy()
obs_indices = np.where(masks)[0].tolist()
# Store the state of the RNG to restore later.
st0 = np.random.get_state()
np.random.seed(seed)
miss_indices = np.random.choice(
obs_indices, (int)(len(obs_indices) * missing_ratio), replace=False
)
# Restore RNG.
np.random.set_state(st0)
masks[miss_indices] = False
gt_masks = masks.reshape(observed_masks.shape)
observed_values = np.nan_to_num(observed_values)
return (
torch.from_numpy(observed_values).float(),
torch.from_numpy(observed_masks).float(),
torch.from_numpy(gt_masks).float(),
)
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