Reformulate the synthetic codes
This commit is contained in:
D-X-Y
@@ -4,5 +4,7 @@
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from .get_dataset_with_transform import get_datasets, get_nas_search_loaders
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from .SearchDatasetWrap import SearchDataset
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from .synthetic_adaptive_environment import QuadraticFunc, CubicFunc, QuarticFunc
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from .synthetic_adaptive_environment import SynAdaptiveEnv
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from .math_base_funcs import QuadraticFunc, CubicFunc, QuarticFunc
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from .math_base_funcs import DynamicQuadraticFunc
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from .synthetic_utils import SinGenerator, ConstantGenerator
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from .synthetic_env import SyntheticDEnv
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@@ -176,93 +176,31 @@ class QuarticFunc(FitFunc):
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)
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class SynAdaptiveEnv(data.Dataset):
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"""The synethtic dataset for adaptive environment.
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class DynamicQuadraticFunc(FitFunc):
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"""The dynamic quadratic function that outputs f(x) = a * x^2 + b * x + c."""
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- x in [0, 1]
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- y = amplitude-scale-of(x) * sin( period-phase-shift-of(x) )
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- where
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- the amplitude scale is a quadratic function of x
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- the period-phase-shift is another quadratic function of x
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def __init__(self, list_of_points=None):
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super(DynamicQuadraticFunc, self).__init__(3, list_of_points)
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self._timestamp = None
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"""
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def __init__(
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self,
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num: int = 100,
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num_sin_phase: int = 7,
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min_amplitude: float = 1,
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max_amplitude: float = 4,
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phase_shift: float = 0,
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mode: Optional[str] = None,
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):
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self._amplitude_scale = QuadraticFunc(
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[(0, min_amplitude), (0.5, max_amplitude), (1, min_amplitude)]
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def __getitem__(self, x):
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self.check_valid()
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return (
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self._params[0][self._timestamp] * x * x
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+ self._params[1][self._timestamp] * x
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+ self._params[2][self._timestamp]
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)
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self._num_sin_phase = num_sin_phase
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self._interval = 1.0 / (float(num) - 1)
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self._total_num = num
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def _getitem(self, x, weights):
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raise NotImplementedError
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fitting_data = []
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temp_max_scalar = 2 ** (num_sin_phase - 1)
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for i in range(num_sin_phase):
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value = (2 ** i) / temp_max_scalar
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next_value = (2 ** (i + 1)) / temp_max_scalar
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for _phase in (0, 0.25, 0.5, 0.75):
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inter_value = value + (next_value - value) * _phase
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fitting_data.append((inter_value, math.pi * (2 * i + _phase)))
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self._period_phase_shift = QuarticFunc(fitting_data)
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# Training Set 60%
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num_of_train = int(self._total_num * 0.6)
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# Validation Set 20%
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num_of_valid = int(self._total_num * 0.2)
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# Test Set 20%
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num_of_set = self._total_num - num_of_train - num_of_valid
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all_indexes = list(range(self._total_num))
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if mode is None:
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self._indexes = all_indexes
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elif mode.lower() in ("train", "training"):
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self._indexes = all_indexes[:num_of_train]
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elif mode.lower() in ("valid", "validation"):
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self._indexes = all_indexes[num_of_train : num_of_train + num_of_valid]
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elif mode.lower() in ("test", "testing"):
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self._indexes = all_indexes[num_of_train + num_of_valid :]
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else:
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raise ValueError("Unkonwn mode of {:}".format(mode))
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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 = self._indexes[index]
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position = self._interval * index
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value = self._amplitude_scale[position] * math.sin(
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self._period_phase_shift[position]
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)
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return index, position, value
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def __len__(self):
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return len(self._indexes)
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def set_timestamp(self, timestamp):
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self._timestamp = timestamp
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def __repr__(self):
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return (
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"{name}({cur_num:}/{total} elements,\n"
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"amplitude={amplitude},\n"
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"period_phase_shift={period_phase_shift})".format(
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name=self.__class__.__name__,
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cur_num=self._total_num,
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total=len(self),
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amplitude=self._amplitude_scale,
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period_phase_shift=self._period_phase_shift,
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)
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return "{name}(y = {a} * x^2 + {b} * x + {c})".format(
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name=self.__class__.__name__,
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a=self._params[0],
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b=self._params[1],
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c=self._params[2],
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)
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81
lib/datasets/synthetic_env.py
Normal file
81
lib/datasets/synthetic_env.py
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@@ -0,0 +1,81 @@
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#####################################################
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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 abc
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import numpy as np
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from typing import List, Optional
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import torch
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import torch.utils.data as data
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from .synthetic_utils import UnifiedSplit
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class SyntheticDEnv(UnifiedSplit, 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_generators: List[data.Dataset],
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cov_generators: List[List[data.Dataset]],
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num_per_task: int = 5000,
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mode: Optional[str] = None,
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):
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self._ndim = len(mean_generators)
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assert self._ndim == len(
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cov_generators
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), "length does not match {:} vs. {:}".format(self._ndim, len(cov_generators))
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for cov_generator in cov_generators:
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assert self._ndim == len(
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cov_generator
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), "length does not match {:} vs. {:}".format(
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self._ndim, len(cov_generator)
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)
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self._num_per_task = num_per_task
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self._total_num = len(mean_generators[0])
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for mean_generator in mean_generators:
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assert self._total_num == len(mean_generator)
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for cov_generator in cov_generators:
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for cov_g in cov_generator:
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assert self._total_num == len(cov_g)
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self._mean_generators = mean_generators
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self._cov_generators = cov_generators
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UnifiedSplit.__init__(self, self._total_num, mode)
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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 = self._indexes[index]
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mean_list = [generator[index][-1] for generator in self._mean_generators]
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cov_matrix = [
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[cov_gen[index][-1] for cov_gen in cov_generator]
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for cov_generator in self._cov_generators
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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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return index, torch.Tensor(dataset)
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def __len__(self):
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return len(self._indexes)
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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})".format(
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name=self.__class__.__name__,
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cur_num=len(self),
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total=self._total_num,
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ndim=self._ndim,
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num_per_task=self._num_per_task,
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)
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157
lib/datasets/synthetic_utils.py
Normal file
157
lib/datasets/synthetic_utils.py
Normal file
@@ -0,0 +1,157 @@
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
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#####################################################
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import math
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import abc
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import numpy as np
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from typing import Optional
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import torch
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import torch.utils.data as data
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from .math_base_funcs import QuadraticFunc, QuarticFunc
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class UnifiedSplit:
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"""A class to unify the split strategy."""
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def __init__(self, total_num, mode):
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# Training Set 60%
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num_of_train = int(total_num * 0.6)
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# Validation Set 20%
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num_of_valid = int(total_num * 0.2)
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# Test Set 20%
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num_of_set = total_num - num_of_train - num_of_valid
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all_indexes = list(range(total_num))
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if mode is None:
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self._indexes = all_indexes
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elif mode.lower() in ("train", "training"):
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self._indexes = all_indexes[:num_of_train]
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elif mode.lower() in ("valid", "validation"):
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self._indexes = all_indexes[num_of_train : num_of_train + num_of_valid]
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elif mode.lower() in ("test", "testing"):
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self._indexes = all_indexes[num_of_train + num_of_valid :]
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else:
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raise ValueError("Unkonwn mode of {:}".format(mode))
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self._mode = mode
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@property
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def mode(self):
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return self._mode
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class SinGenerator(UnifiedSplit, data.Dataset):
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"""The synethtic generator for the dynamically changing environment.
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- x in [0, 1]
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- y = amplitude-scale-of(x) * sin( period-phase-shift-of(x) )
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- where
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- the amplitude scale is a quadratic function of x
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- the period-phase-shift is another quadratic function of x
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"""
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def __init__(
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self,
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num: int = 100,
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num_sin_phase: int = 7,
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min_amplitude: float = 1,
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max_amplitude: float = 4,
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phase_shift: float = 0,
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mode: Optional[str] = None,
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):
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self._amplitude_scale = QuadraticFunc(
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[(0, min_amplitude), (0.5, max_amplitude), (1, min_amplitude)]
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)
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self._num_sin_phase = num_sin_phase
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self._interval = 1.0 / (float(num) - 1)
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self._total_num = num
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fitting_data = []
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temp_max_scalar = 2 ** (num_sin_phase - 1)
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for i in range(num_sin_phase):
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value = (2 ** i) / temp_max_scalar
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next_value = (2 ** (i + 1)) / temp_max_scalar
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for _phase in (0, 0.25, 0.5, 0.75):
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inter_value = value + (next_value - value) * _phase
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fitting_data.append((inter_value, math.pi * (2 * i + _phase)))
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self._period_phase_shift = QuarticFunc(fitting_data)
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UnifiedSplit.__init__(self, self._total_num, mode)
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self._transform = lambda x: x
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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 set_transform(self, transform):
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self._transform = transform
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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 = self._indexes[index]
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position = self._interval * index
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value = self._amplitude_scale[position] * math.sin(
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self._period_phase_shift[position]
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)
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return index, position, self._transform(value)
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def __len__(self):
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return len(self._indexes)
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def __repr__(self):
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return (
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"{name}({cur_num:}/{total} elements,\n"
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"amplitude={amplitude},\n"
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"period_phase_shift={period_phase_shift})".format(
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name=self.__class__.__name__,
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cur_num=len(self),
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total=self._total_num,
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amplitude=self._amplitude_scale,
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period_phase_shift=self._period_phase_shift,
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)
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)
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class ConstantGenerator(UnifiedSplit, data.Dataset):
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"""The constant generator."""
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def __init__(
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self,
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num: int = 100,
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constant: float = 0.1,
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mode: Optional[str] = None,
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):
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self._total_num = num
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self._constant = constant
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UnifiedSplit.__init__(self, self._total_num, mode)
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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 = self._indexes[index]
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return index, index, self._constant
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def __len__(self):
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return len(self._indexes)
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def __repr__(self):
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return "{name}({cur_num:}/{total} elements)".format(
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name=self.__class__.__name__,
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cur_num=len(self),
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total=self._total_num,
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)
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