Finalize example vis codes

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__":

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

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."""
 

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():

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,
+        )

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)

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

scripts/black.sh

@@ -6,3 +6,4 @@ black ./lib/datasets
 black ./lib/xlayers
 black ./exps/LFNA
 black ./exps/trading
+black ./lib/procedures