193 lines
6.1 KiB
Python
193 lines
6.1 KiB
Python
#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
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#####################################################
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import math
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import random
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import numpy as np
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from typing import List, Optional, Dict
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import torch
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import torch.utils.data as data
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from .synthetic_utils import TimeStamp
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def is_list_tuple(x):
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return isinstance(x, (tuple, list))
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def zip_sequence(sequence):
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def _combine(*alist):
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if is_list_tuple(alist[0]):
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return [_combine(*xlist) for xlist in zip(*alist)]
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else:
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return torch.cat(alist, dim=0)
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def unsqueeze(a):
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if is_list_tuple(a):
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return [unsqueeze(x) for x in a]
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else:
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return a.unsqueeze(dim=0)
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with torch.no_grad():
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sequence = [unsqueeze(a) for a in sequence]
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return _combine(*sequence)
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class SyntheticDEnv(data.Dataset):
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"""The synethtic dynamic environment."""
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def __init__(
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self,
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mean_functors: List[data.Dataset],
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cov_functors: List[List[data.Dataset]],
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num_per_task: int = 5000,
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timestamp_config: Optional[Dict] = None,
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mode: Optional[str] = None,
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timestamp_noise_scale: float = 0.3,
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):
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self._ndim = len(mean_functors)
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assert self._ndim == len(
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cov_functors
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), "length does not match {:} vs. {:}".format(self._ndim, len(cov_functors))
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for cov_functor in cov_functors:
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assert self._ndim == len(
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cov_functor
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), "length does not match {:} vs. {:}".format(self._ndim, len(cov_functor))
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self._num_per_task = num_per_task
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if timestamp_config is None:
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timestamp_config = dict(mode=mode)
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elif "mode" not in timestamp_config:
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timestamp_config["mode"] = mode
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self._timestamp_generator = TimeStamp(**timestamp_config)
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self._timestamp_noise_scale = timestamp_noise_scale
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self._mean_functors = mean_functors
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self._cov_functors = cov_functors
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self._oracle_map = None
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self._seq_length = None
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@property
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def seq_length(self):
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return self._seq_length
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@property
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def min_timestamp(self):
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return self._timestamp_generator.min_timestamp
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@property
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def max_timestamp(self):
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return self._timestamp_generator.max_timestamp
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@property
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def timestamp_interval(self):
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return self._timestamp_generator.interval
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def random_timestamp(self):
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return (
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random.random() * (self.max_timestamp - self.min_timestamp)
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+ self.min_timestamp
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)
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def reset_max_seq_length(self, seq_length):
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self._seq_length = seq_length
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def get_timestamp(self, index):
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if index is None:
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timestamps = []
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for index in range(len(self._timestamp_generator)):
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timestamps.append(self._timestamp_generator[index][1])
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return tuple(timestamps)
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else:
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index, timestamp = self._timestamp_generator[index]
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return timestamp
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def set_oracle_map(self, functor):
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self._oracle_map = functor
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def __iter__(self):
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self._iter_num = 0
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return self
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def __next__(self):
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if self._iter_num >= len(self):
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raise StopIteration
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self._iter_num += 1
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return self.__getitem__(self._iter_num - 1)
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def __getitem__(self, index):
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assert 0 <= index < len(self), "{:} is not in [0, {:})".format(index, len(self))
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index, timestamp = self._timestamp_generator[index]
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if self._seq_length is None:
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return self.__call__(timestamp)
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else:
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noise = (
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random.random() * self.timestamp_interval * self._timestamp_noise_scale
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)
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timestamps = [
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timestamp + i * self.timestamp_interval + noise
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for i in range(self._seq_length)
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]
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# xdata = [self.__call__(timestamp) for timestamp in timestamps]
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# return zip_sequence(xdata)
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return self.seq_call(timestamps)
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def seq_call(self, timestamps):
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with torch.no_grad():
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if isinstance(timestamps, torch.Tensor):
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timestamps = timestamps.cpu().tolist()
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xdata = [self.__call__(timestamp) for timestamp in timestamps]
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return zip_sequence(xdata)
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def __call__(self, timestamp):
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mean_list = [functor(timestamp) for functor in self._mean_functors]
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cov_matrix = [
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[abs(cov_gen(timestamp)) for cov_gen in cov_functor]
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for cov_functor in self._cov_functors
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]
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dataset = np.random.multivariate_normal(
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mean_list, cov_matrix, size=self._num_per_task
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)
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if self._oracle_map is None:
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return torch.Tensor([timestamp]), torch.Tensor(dataset)
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else:
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targets = self._oracle_map.noise_call(dataset, timestamp)
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return torch.Tensor([timestamp]), (
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torch.Tensor(dataset),
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torch.Tensor(targets),
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)
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def __len__(self):
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return len(self._timestamp_generator)
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def __repr__(self):
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return "{name}({cur_num:}/{total} elements, ndim={ndim}, num_per_task={num_per_task}, range=[{xrange_min:.5f}~{xrange_max:.5f}], mode={mode})".format(
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name=self.__class__.__name__,
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cur_num=len(self),
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total=len(self._timestamp_generator),
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ndim=self._ndim,
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num_per_task=self._num_per_task,
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xrange_min=self.min_timestamp,
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xrange_max=self.max_timestamp,
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mode=self._timestamp_generator.mode,
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)
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class EnvSampler:
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def __init__(self, env, batch, enlarge):
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indexes = list(range(len(env)))
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self._indexes = indexes * enlarge
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self._batch = batch
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self._iterations = len(self._indexes) // self._batch
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def __iter__(self):
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random.shuffle(self._indexes)
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for it in range(self._iterations):
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indexes = self._indexes[it * self._batch : (it + 1) * self._batch]
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yield indexes
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def __len__(self):
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return self._iterations
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