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Update synthetic environment

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This commit is contained in:
D-X-Y
2021-04-22 20:31:20 +08:00
parent 275831b375
commit 78ca90459c
8 changed files with 526 additions and 271 deletions
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2
lib/datasets/__init__.py
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@@ -4,5 +4,5 @@
from .get_dataset_with_transform import get_datasets, get_nas_search_loaders
from .SearchDatasetWrap import SearchDataset
from .synthetic_adaptive_environment import QuadraticFunction
from .synthetic_adaptive_environment import QuadraticFunc, CubicFunc, QuarticFunc
from .synthetic_adaptive_environment import SynAdaptiveEnv

171
lib/datasets/synthetic_adaptive_environment.py
View File

@@ -2,38 +2,43 @@
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
#####################################################
import math
import abc
import numpy as np
from typing import Optional
import torch
import torch.utils.data as data
class QuadraticFunction:
"""The quadratic function that outputs f(x) = a * x^2 + b * x + c."""
class FitFunc(abc.ABC):
"""The fit function that outputs f(x) = a * x^2 + b * x + c."""
def __init__(self, list_of_points=None):
self._params = dict(a=None, b=None, c=None)
def __init__(self, freedom: int, list_of_points=None):
self._params = dict()
for i in range(freedom):
self._params[i] = None
self._freedom = freedom
if list_of_points is not None:
self.fit(list_of_points)
def set(self, a, b, c):
self._params["a"] = a
self._params["b"] = b
self._params["c"] = c
def set(self, _params):
self._params = copy.deepcopy(_params)
def check_valid(self):
for key, value in self._params.items():
if value is None:
raise ValueError("The {:} is None".format(key))
@abc.abstractmethod
def __getitem__(self, x):
self.check_valid()
return self._params["a"] * x * x + self._params["b"] * x + self._params["c"]
raise NotImplementedError
@abc.abstractmethod
def _getitem(self, x):
raise NotImplementedError
def fit(
self,
list_of_points,
transf=lambda x: x,
max_iter=900,
lr_max=1.0,
verbose=False,
@@ -44,16 +49,24 @@ class QuadraticFunction:
data.shape
)
x, y = data[:, 0], data[:, 1]
weights = torch.nn.Parameter(torch.Tensor(3))
weights = torch.nn.Parameter(torch.Tensor(self._freedom))
torch.nn.init.normal_(weights, mean=0.0, std=1.0)
optimizer = torch.optim.Adam([weights], lr=lr_max, amsgrad=True)
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[int(max_iter*0.25), int(max_iter*0.5), int(max_iter*0.75)], gamma=0.1)
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer,
milestones=[
int(max_iter * 0.25),
int(max_iter * 0.5),
int(max_iter * 0.75),
],
gamma=0.1,
)
if verbose:
print("The optimizer: {:}".format(optimizer))
best_loss = None
for _iter in range(max_iter):
y_hat = transf(weights[0] * x * x + weights[1] * x + weights[2])
y_hat = self._getitem(x, weights)
loss = torch.mean(torch.abs(y - y_hat))
optimizer.zero_grad()
loss.backward()
@@ -61,23 +74,105 @@ class QuadraticFunction:
lr_scheduler.step()
if verbose:
print(
"In QuadraticFunction's fit, loss at the {:02d}/{:02d}-th iter is {:}".format(
"In the fit, loss at the {:02d}/{:02d}-th iter is {:}".format(
_iter, max_iter, loss.item()
)
)
# Update the params
if best_loss is None or best_loss > loss.item():
best_loss = loss.item()
self._params["a"] = weights[0].item()
self._params["b"] = weights[1].item()
self._params["c"] = weights[2].item()
for i in range(self._freedom):
self._params[i] = weights[i].item()
def __repr__(self):
return "{name}(freedom={freedom})".format(
name=self.__class__.__name__, freedom=freedom
)
class QuadraticFunc(FitFunc):
"""The quadratic function that outputs f(x) = a * x^2 + b * x + c."""
def __init__(self, list_of_points=None):
super(QuadraticFunc, self).__init__(3, list_of_points)
def __getitem__(self, x):
self.check_valid()
return self._params[0] * x * x + self._params[1] * x + self._params[2]
def _getitem(self, x, weights):
return weights[0] * x * x + weights[1] * x + weights[2]
def __repr__(self):
return "{name}(y = {a} * x^2 + {b} * x + {c})".format(
name=self.__class__.__name__,
a=self._params["a"],
b=self._params["b"],
c=self._params["c"],
a=self._params[0],
b=self._params[1],
c=self._params[2],
)
class CubicFunc(FitFunc):
"""The cubic function that outputs f(x) = a * x^3 + b * x^2 + c * x + d."""
def __init__(self, list_of_points=None):
super(CubicFunc, self).__init__(4, list_of_points)
def __getitem__(self, x):
self.check_valid()
return (
self._params[0] * x ** 3
+ self._params[1] * x ** 2
+ self._params[2] * x
+ self._params[3]
)
def _getitem(self, x, weights):
return weights[0] * x ** 3 + weights[1] * x ** 2 + weights[2] * x + weights[3]
def __repr__(self):
return "{name}(y = {a} * x^3 + {b} * x^2 + {c} * x + {d})".format(
name=self.__class__.__name__,
a=self._params[0],
b=self._params[1],
c=self._params[2],
d=self._params[3],
)
class QuarticFunc(FitFunc):
"""The quartic function that outputs f(x) = a * x^4 + b * x^3 + c * x^2 + d * x + e."""
def __init__(self, list_of_points=None):
super(QuarticFunc, self).__init__(5, list_of_points)
def __getitem__(self, x):
self.check_valid()
return (
self._params[0] * x ** 4
+ self._params[1] * x ** 3
+ self._params[2] * x ** 2
+ self._params[3] * x
+ self._params[4]
)
def _getitem(self, x, weights):
return (
weights[0] * x ** 4
+ weights[1] * x ** 3
+ weights[2] * x ** 2
+ weights[3] * x
+ weights[4]
)
def __repr__(self):
return "{name}(y = {a} * x^4 + {b} * x^3 + {c} * x^2 + {d} * x + {e})".format(
name=self.__class__.__name__,
a=self._params[0],
b=self._params[1],
c=self._params[2],
d=self._params[3],
e=self._params[3],
)
@@ -95,28 +190,29 @@ class SynAdaptiveEnv(data.Dataset):
def __init__(
self,
num: int = 100,
num_sin_phase: int = 4,
num_sin_phase: int = 7,
min_amplitude: float = 1,
max_amplitude: float = 4,
phase_shift: float = 0,
mode: Optional[str] = None,
):
self._amplitude_scale = QuadraticFunction(
[(0, min_amplitude), (0.5, max_amplitude), (0, min_amplitude)]
self._amplitude_scale = QuadraticFunc(
[(0, min_amplitude), (0.5, max_amplitude), (1, min_amplitude)]
)
self._num_sin_phase = num_sin_phase
self._interval = 1.0 / (float(num) - 1)
self._total_num = num
self._period_phase_shift = QuadraticFunction()
fitting_data = []
temp_max_scalar = 2 ** num_sin_phase
temp_max_scalar = 2 ** (num_sin_phase - 1)
for i in range(num_sin_phase):
value = (2 ** i) / temp_max_scalar
fitting_data.append((value, math.sin(value)))
self._period_phase_shift.fit(fitting_data, transf=lambda x: torch.sin(x))
next_value = (2 ** (i + 1)) / temp_max_scalar
for _phase in (0, 0.25, 0.5, 0.75):
inter_value = value + (next_value - value) * _phase
fitting_data.append((inter_value, math.pi * (2 * i + _phase)))
self._period_phase_shift = QuarticFunc(fitting_data)
# Training Set 60%
num_of_train = int(self._total_num * 0.6)
@@ -135,11 +231,6 @@ class SynAdaptiveEnv(data.Dataset):
self._indexes = all_indexes[num_of_train + num_of_valid :]
else:
raise ValueError("Unkonwn mode of {:}".format(mode))
# transformation function
self._transform = None
def set_transform(self, fn):
self._transform = fn
def __iter__(self):
self._iter_num = 0
@@ -164,6 +255,14 @@ class SynAdaptiveEnv(data.Dataset):
return len(self._indexes)
def __repr__(self):
return "{name}({cur_num:}/{total} elements)".format(
name=self.__class__.__name__, cur_num=self._total_num, total=len(self)
return (
"{name}({cur_num:}/{total} elements,\n"
"amplitude={amplitude},\n"
"period_phase_shift={period_phase_shift})".format(
name=self.__class__.__name__,
cur_num=self._total_num,
total=len(self),
amplitude=self._amplitude_scale,
period_phase_shift=self._period_phase_shift,
)
)