diff --git a/exps/LFNA/vis-synthetic.py b/exps/LFNA/vis-synthetic.py
index 60b5abe..395eb0f 100644
--- a/exps/LFNA/vis-synthetic.py
+++ b/exps/LFNA/vis-synthetic.py
@@ -1,7 +1,7 @@
#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.02 #
############################################################################
-# CUDA_VISIBLE_DEVICES=0 python exps/LFNA/vis-synthetic.py #
+# python exps/LFNA/vis-synthetic.py #
############################################################################
import os, sys, copy, random
import torch
@@ -83,7 +83,7 @@ def find_max(cur, others):
def compare_cl(save_dir):
save_dir = Path(str(save_dir))
save_dir.mkdir(parents=True, exist_ok=True)
- dynamic_env, function = create_example_v1(
+ dynamic_env, cl_function = create_example_v1(
# timestamp_config=dict(num=200, min_timestamp=-1, max_timestamp=1.0),
timestamp_config=dict(num=200),
num_per_task=1000,
@@ -91,7 +91,6 @@ def compare_cl(save_dir):
models = dict()
- cl_function = copy.deepcopy(function)
cl_function.set_timestamp(0)
cl_xaxis_min = None
cl_xaxis_max = None
@@ -99,23 +98,15 @@ def compare_cl(save_dir):
all_data = OrderedDict()
for idx, (timestamp, dataset) in enumerate(tqdm(dynamic_env, ncols=50)):
- xaxis_all = dataset[:, 0].numpy()
+ xaxis_all = dataset[0][:, 0].numpy()
+ yaxis_all = dataset[1][:, 0].numpy()
current_data = dict()
-
- function.set_timestamp(timestamp)
- yaxis_all = function.noise_call(xaxis_all)
current_data["lfna_xaxis_all"] = xaxis_all
current_data["lfna_yaxis_all"] = yaxis_all
# compute cl-min
cl_xaxis_min = find_min(cl_xaxis_min, xaxis_all.mean() - xaxis_all.std())
cl_xaxis_max = find_max(cl_xaxis_max, xaxis_all.mean() + xaxis_all.std())
- """
- cl_xaxis_all = np.arange(cl_xaxis_min, cl_xaxis_max, step=0.05)
- cl_yaxis_all = cl_function.noise_call(cl_xaxis_all)
- current_data["cl_xaxis_all"] = cl_xaxis_all
- current_data["cl_yaxis_all"] = cl_yaxis_all
- """
all_data[timestamp] = current_data
global_cl_xaxis_all = np.arange(cl_xaxis_min, cl_xaxis_max, step=0.1)
@@ -170,10 +161,12 @@ def compare_cl(save_dir):
xdir=save_dir
)
)
- video_cmd = "{:} -pix_fmt yuv420p {xdir}/compare-cl.mp4".format(base_cmd, xdir=save_dir)
+ video_cmd = "{:} -pix_fmt yuv420p {xdir}/compare-cl.mp4".format(
+ base_cmd, xdir=save_dir
+ )
print(video_cmd + "\n")
os.system(video_cmd)
- # os.system("{:} {xdir}/vis.webm".format(base_cmd, xdir=save_dir))
+ os.system("{:} -pix_fmt yuv420p {xdir}/vis.webm".format(base_cmd, xdir=save_dir))
if __name__ == "__main__":
diff --git a/lib/datasets/__init__.py b/lib/datasets/__init__.py
index 62e79cc..6ed35dd 100644
--- a/lib/datasets/__init__.py
+++ b/lib/datasets/__init__.py
@@ -5,7 +5,8 @@ from .get_dataset_with_transform import get_datasets, get_nas_search_loaders
from .SearchDatasetWrap import SearchDataset
from .math_base_funcs import QuadraticFunc, CubicFunc, QuarticFunc
-from .math_adv_funcs import DynamicQuadraticFunc, ConstantFunc
+from .math_dynamic_funcs import DynamicQuadraticFunc
+from .math_adv_funcs import ConstantFunc
from .math_adv_funcs import ComposedSinFunc
from .synthetic_utils import TimeStamp
diff --git a/lib/datasets/math_adv_funcs.py b/lib/datasets/math_adv_funcs.py
index 4315258..d84a5e0 100644
--- a/lib/datasets/math_adv_funcs.py
+++ b/lib/datasets/math_adv_funcs.py
@@ -14,41 +14,6 @@ from .math_base_funcs import QuadraticFunc
from .math_base_funcs import QuarticFunc
-class DynamicQuadraticFunc(FitFunc):
- """The dynamic quadratic function that outputs f(x) = a * x^2 + b * x + c.
- The a, b, and c is a function of timestamp.
- """
-
- def __init__(self, list_of_points=None):
- super(DynamicQuadraticFunc, self).__init__(3, list_of_points)
- self._timestamp = None
-
- def __call__(self, x, timestamp=None):
- self.check_valid()
- if timestamp is None:
- timestamp = self._timestamp
- a = self._params[0](timestamp)
- b = self._params[1](timestamp)
- c = self._params[2](timestamp)
- convert_fn = lambda x: x[-1] if isinstance(x, (tuple, list)) else x
- a, b, c = convert_fn(a), convert_fn(b), convert_fn(c)
- return a * x * x + b * x + c
-
- def _getitem(self, x, weights):
- raise NotImplementedError
-
- def set_timestamp(self, timestamp):
- self._timestamp = timestamp
-
- def __repr__(self):
- return "{name}({a} * x^2 + {b} * x + {c})".format(
- name=self.__class__.__name__,
- a=self._params[0],
- b=self._params[1],
- c=self._params[2],
- )
-
-
class ConstantFunc(FitFunc):
"""The constant function: f(x) = c."""
diff --git a/lib/datasets/math_base_funcs.py b/lib/datasets/math_base_funcs.py
index cab66a2..42a4bd4 100644
--- a/lib/datasets/math_base_funcs.py
+++ b/lib/datasets/math_base_funcs.py
@@ -13,20 +13,20 @@ import torch.utils.data as data
class FitFunc(abc.ABC):
"""The fit function that outputs f(x) = a * x^2 + b * x + c."""
- def __init__(self, freedom: int, list_of_points=None, _params=None):
+ def __init__(self, freedom: int, list_of_points=None, params=None):
self._params = dict()
for i in range(freedom):
self._params[i] = None
self._freedom = freedom
- if list_of_points is not None and _params is not None:
- raise ValueError("list_of_points and _params can not be set simultaneously")
+ if list_of_points is not None and params is not None:
+ raise ValueError("list_of_points and params can not be set simultaneously")
if list_of_points is not None:
self.fit(list_of_points=list_of_points)
- if _params is not None:
- self.set(_params)
+ if params is not None:
+ self.set(params)
- def set(self, _params):
- self._params = copy.deepcopy(_params)
+ def set(self, params):
+ self._params = copy.deepcopy(params)
def check_valid(self):
for key, value in self._params.items():
diff --git a/lib/datasets/math_dynamic_funcs.py b/lib/datasets/math_dynamic_funcs.py
new file mode 100644
index 0000000..0a86716
--- /dev/null
+++ b/lib/datasets/math_dynamic_funcs.py
@@ -0,0 +1,66 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
+#####################################################
+import math
+import abc
+import copy
+import numpy as np
+from typing import Optional
+import torch
+import torch.utils.data as data
+
+from .math_base_funcs import FitFunc
+
+
+class DynamicFunc(FitFunc):
+ """The dynamic quadratic function, where each param is a function."""
+
+ def __init__(self, freedom: int, params=None):
+ super(DynamicFunc, self).__init__(freedom, None, params)
+ self._timestamp = None
+
+ def __call__(self, x, timestamp=None):
+ raise NotImplementedError
+
+ def _getitem(self, x, weights):
+ raise NotImplementedError
+
+ def set_timestamp(self, timestamp):
+ self._timestamp = timestamp
+
+ def noise_call(self, x, timestamp=None, std=0.1):
+ clean_y = self.__call__(x, timestamp)
+ if isinstance(clean_y, np.ndarray):
+ noise_y = clean_y + np.random.normal(scale=std, size=clean_y.shape)
+ else:
+ raise ValueError("Unkonwn type: {:}".format(type(clean_y)))
+ return noise_y
+
+
+class DynamicQuadraticFunc(DynamicFunc):
+ """The dynamic quadratic function that outputs f(x) = a * x^2 + b * x + c.
+ The a, b, and c is a function of timestamp.
+ """
+
+ def __init__(self, params=None):
+ super(DynamicQuadraticFunc, self).__init__(3, params)
+
+ def __call__(self, x, timestamp=None):
+ self.check_valid()
+ if timestamp is None:
+ timestamp = self._timestamp
+ a = self._params[0](timestamp)
+ b = self._params[1](timestamp)
+ c = self._params[2](timestamp)
+ convert_fn = lambda x: x[-1] if isinstance(x, (tuple, list)) else x
+ a, b, c = convert_fn(a), convert_fn(b), convert_fn(c)
+ return a * x * x + b * x + c
+
+ def __repr__(self):
+ return "{name}({a} * x^2 + {b} * x + {c}, timestamp={timestamp})".format(
+ name=self.__class__.__name__,
+ a=self._params[0],
+ b=self._params[1],
+ c=self._params[2],
+ timestamp=self._timestamp,
+ )
diff --git a/lib/datasets/synthetic_env.py b/lib/datasets/synthetic_env.py
index fa52dc3..ad73d6b 100644
--- a/lib/datasets/synthetic_env.py
+++ b/lib/datasets/synthetic_env.py
@@ -41,6 +41,11 @@ class SyntheticDEnv(data.Dataset):
self._mean_functors = mean_functors
self._cov_functors = cov_functors
+ self._oracle_map = None
+
+ def set_oracle_map(self, functor):
+ self._oracle_map = functor
+
def __iter__(self):
self._iter_num = 0
return self
@@ -63,7 +68,11 @@ class SyntheticDEnv(data.Dataset):
dataset = np.random.multivariate_normal(
mean_list, cov_matrix, size=self._num_per_task
)
- return timestamp, torch.Tensor(dataset)
+ if self._oracle_map is None:
+ return timestamp, torch.Tensor(dataset)
+ else:
+ targets = self._oracle_map.noise_call(dataset, timestamp)
+ return timestamp, (torch.Tensor(dataset), torch.Tensor(targets))
def __len__(self):
return len(self._timestamp_generator)
diff --git a/lib/datasets/synthetic_example.py b/lib/datasets/synthetic_example.py
index 40e917f..f72f15c 100644
--- a/lib/datasets/synthetic_example.py
+++ b/lib/datasets/synthetic_example.py
@@ -1,8 +1,9 @@
#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
#####################################################
+import copy
-from .math_adv_funcs import DynamicQuadraticFunc
+from .math_dynamic_funcs import DynamicQuadraticFunc
from .math_adv_funcs import ConstantFunc, ComposedSinFunc
from .synthetic_env import SyntheticDEnv
@@ -11,7 +12,6 @@ def create_example_v1(
timestamp_config=None,
num_per_task=5000,
):
- # timestamp_config=dict(num=100, min_timestamp=0.0, max_timestamp=1.0),
mean_generator = ComposedSinFunc()
std_generator = ComposedSinFunc(min_amplitude=0.5, max_amplitude=0.5)
@@ -32,4 +32,6 @@ def create_example_v1(
num_sin_phase=5, phase_shift=0.4, max_amplitude=0.9
)
function.set(function_param)
+
+ dynamic_env.set_oracle_map(copy.deepcopy(function))
return dynamic_env, function
diff --git a/scripts/black.sh b/scripts/black.sh
index 10c55fc..ba1a10e 100644
--- a/scripts/black.sh
+++ b/scripts/black.sh
@@ -6,3 +6,4 @@ black ./lib/datasets
black ./lib/xlayers
black ./exps/LFNA
black ./exps/trading
+black ./lib/procedures