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import os
import torch
import numpy as np
from tqdm.auto import tqdm
from torch.utils.data import Dataset
from models.interpretable_diffusion.model_utils import (
normalize_to_neg_one_to_one,
unnormalize_to_zero_to_one,
)
from utils.masking_utils import noise_mask
import torch
import random
model = torch.nn.Linear(2, 1)
class SineDataset(Dataset):
def __init__(
self,
window=128,
num=30000,
dim=12,
save2npy=True,
neg_one_to_one=True,
seed=123,
period='train',
output_dir='./OUTPUT',
predict_length=None,
missing_ratio=None,
style='separate',
distribution='geometric',
mean_mask_length=3
):
super(SineDataset, self).__init__()
assert period in ['train', 'test'], 'period must be train or test.'
if period == 'train':
assert ~(predict_length is not None or missing_ratio is not None), ''
self.pred_len, self.missing_ratio = predict_length, missing_ratio
self.style, self.distribution, self.mean_mask_length = style, distribution, mean_mask_length
self.dir = os.path.join(output_dir, 'samples')
os.makedirs(self.dir, exist_ok=True)
self.rawdata = self.sine_data_generation(no=num, seq_len=window, dim=dim, save2npy=save2npy,
seed=seed, dir=self.dir, period=period)
self.auto_norm = neg_one_to_one
self.samples = self.normalize(self.rawdata)
self.var_num = dim
self.sample_num = self.samples.shape[0]
self.window = window
self.period, self.save2npy = period, save2npy
if period == 'test':
if missing_ratio is not None:
self.masking = self.mask_data(seed)
elif predict_length is not None:
masks = np.ones(self.samples.shape)
masks[:, -predict_length:, :] = 0
self.masking = masks.astype(bool)
else:
raise NotImplementedError()
def normalize(self, rawdata):
if self.auto_norm:
data = normalize_to_neg_one_to_one(rawdata)
return data
def unnormalize(self, data):
if self.auto_norm:
data = unnormalize_to_zero_to_one(data)
return data
@staticmethod
def sine_data_generation(no, seq_len, dim, save2npy=True, seed=123, dir="./", period='train'):
"""Sine data generation.
Args:
- no: the number of samples
- seq_len: sequence length of the time-series
- dim: feature dimensions
Returns:
- data: generated data
"""
# Store the state of the RNG to restore later.
st0 = np.random.get_state()
np.random.seed(seed)
# Initialize the output
data = list()
# Generate sine data
for i in tqdm(range(0, no), total=no, desc="Sampling sine-dataset"):
# Initialize each time-series
temp = list()
# For each feature
for k in range(dim):
# Randomly drawn frequency and phase
freq = np.random.uniform(0, 0.1)
phase = np.random.uniform(0, 0.1)
# Generate sine signal based on the drawn frequency and phase
temp_data = [np.sin(freq * j + phase) for j in range(seq_len)]
temp.append(temp_data)
# Align row/column
temp = np.transpose(np.asarray(temp))
# Normalize to [0,1]
temp = (temp + 1)*0.5
# Stack the generated data
data.append(temp)
# Restore RNG.
np.random.set_state(st0)
data = np.array(data)
if save2npy:
np.save(os.path.join(dir, f"sine_ground_truth_{seq_len}_{period}.npy"), data)
return data
def mask_data(self, seed=2023):
masks = np.ones_like(self.samples)
# Store the state of the RNG to restore later.
st0 = np.random.get_state()
np.random.seed(seed)
for idx in range(self.samples.shape[0]):
x = self.samples[idx, :, :] # (seq_length, feat_dim) array
mask = noise_mask(x, self.missing_ratio, self.mean_mask_length, self.style,
self.distribution) # (seq_length, feat_dim) boolean array
masks[idx, :, :] = mask
if self.save2npy:
np.save(os.path.join(self.dir, f"sine_masking_{self.window}.npy"), masks)
# Restore RNG.
np.random.set_state(st0)
return masks.astype(bool)
def __getitem__(self, ind):
if self.period == 'test':
x = self.samples[ind, :, :] # (seq_length, feat_dim) array
m = self.masking[ind, :, :] # (seq_length, feat_dim) boolean array
return torch.from_numpy(x).float(), torch.from_numpy(m)
x = self.samples[ind, :, :] # (seq_length, feat_dim) array
return torch.from_numpy(x).float()
def __len__(self):
return self.sample_num
# class SineDataset(Dataset):
# def __init__(
# self,
# window=128,
# num=223,
# dim=3,
# save2npy=True,
# neg_one_to_one=True,
# seed=123,
# period="train",
# output_dir="./OUTPUT",
# predict_length=None,
# missing_ratio=None,
# style="separate",
# distribution="geometric",
# mean_mask_length=3,
# **kargs,
# ):
# super(SineDataset, self).__init__()
# assert period in ["train", "test"], "period must be train or test."
# if period == "train":
# assert ~(predict_length is not None or missing_ratio is not None), ""
# self.pred_len, self.missing_ratio = predict_length, missing_ratio
# self.style, self.distribution, self.mean_mask_length = (
# style,
# distribution,
# mean_mask_length,
# )
# self.dir = os.path.join(output_dir, "samples")
# os.makedirs(self.dir, exist_ok=True)
# self.rawdata = self.sine_data_generation(
# no=num,
# seq_len=window,
# dim=dim,
# save2npy=save2npy,
# seed=seed,
# dir=self.dir,
# period=period,
# )
# self.auto_norm = neg_one_to_one
# self.samples = self.normalize(self.rawdata)
# self.var_num = dim
# self.sample_num = self.samples.shape[0]
# self.window = window
# self.period, self.save2npy = period, save2npy
# if period == "test":
# if missing_ratio is not None:
# self.masking = self.mask_data(seed)
# elif predict_length is not None:
# masks = np.ones(self.samples.shape)
# masks[:, -predict_length:, :] = 0
# self.masking = masks.astype(bool)
# else:
# raise NotImplementedError()
# def normalize(self, rawdata):
# if self.auto_norm:
# data = normalize_to_neg_one_to_one(rawdata)
# return data
# def unnormalize(self, data):
# if self.auto_norm:
# data = unnormalize_to_zero_to_one(data)
# return data
# @staticmethod
# def sine_data_generation(
# no, seq_len, dim, save2npy=True, seed=123, dir="./", period="train"
# ):
# """Sine data generation.
# Args:
# - no: the number of samples
# - seq_len: sequence length of the time-series
# - dim: feature dimensions
# Returns:
# - data: generated data
# """
# # Store the state of the RNG to restore later.
# st0 = np.random.get_state()
# np.random.seed(seed)
# # Initialize the output
# data = list()
# # Generate sine data
# for i in tqdm(range(0, no), total=no, desc="Sampling sine-dataset"):
# # Initialize each time-series
# # temp = list()
# # # For each feature
# # for k in range(dim):
# # # Randomly drawn frequency and phase
# # freq = np.random.uniform(0, 0.1)
# # phase = np.random.uniform(0, 0.1)
# # # Generate sine signal based on the drawn frequency and phase
# # temp_data = [np.sin(freq * j + phase) for j in range(seq_len)]
# # temp.append(temp_data)
# # # Align row/column
# # temp = np.transpose(np.asarray(temp))
# # # Normalize to [0,1]
# # temp = (temp + 1) * 0.5
# # Stack the generated data
# # data.append(temp)
# # lrs = []
# # for i in range(60):
# # lr_sched.step()
# # lrs.append(
# # optimizer.param_groups[0]["lr"]
# # )
# temp = []
# for k in range(dim):
# lrs = []
# optimizer = torch.optim.SGD(model.parameters(), lr=0.3)
# lr_sched = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=7, T_mult=1, eta_min=0.005, last_epoch=-1)
# for _ in range(random.randint(1, 14)):
# lr_sched.step()
# for _ in range(seq_len):
# lr_sched.step()
# lrs.append(
# optimizer.param_groups[0]["lr"]
# )
# temp.append(lrs)
# # lrs.append(
# # optimizer.param_groups[0]["lr"]
# # )
# temp = np.transpose(np.asarray(temp))
# data.append(temp)
# # plt.plot(lrs)
# # Restore RNG.
# np.random.set_state(st0)
# data = np.array(data)
# if save2npy:
# np.save(
# os.path.join(dir, f"sine_ground_truth_{seq_len}_{period}.npy"), data
# )
# return data
# def mask_data(self, seed=2023):
# masks = np.ones_like(self.samples)
# # Store the state of the RNG to restore later.
# st0 = np.random.get_state()
# np.random.seed(seed)
# for idx in range(self.samples.shape[0]):
# x = self.samples[idx, :, :] # (seq_length, feat_dim) array
# mask = noise_mask(
# x,
# self.missing_ratio,
# self.mean_mask_length,
# self.style,
# self.distribution,
# ) # (seq_length, feat_dim) boolean array
# masks[idx, :, :] = mask
# if self.save2npy:
# np.save(os.path.join(self.dir, f"sine_masking_{self.window}.npy"), masks)
# # Restore RNG.
# np.random.set_state(st0)
# return masks.astype(bool)
# def __getitem__(self, ind):
# if self.period == "test":
# x = self.samples[ind, :, :] # (seq_length, feat_dim) array
# m = self.masking[ind, :, :] # (seq_length, feat_dim) boolean array
# return torch.from_numpy(x).float(), torch.from_numpy(m)
# x = self.samples[ind, :, :] # (seq_length, feat_dim) array
# return torch.from_numpy(x).float()
# def __len__(self):
# return self.sample_num
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