Update codes
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@ -24,10 +24,7 @@ if str(lib_dir) not in sys.path:
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sys.path.insert(0, str(lib_dir))
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from datasets import ConstantGenerator, SinGenerator, SyntheticDEnv
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from datasets import DynamicQuadraticFunc
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from datasets.synthetic_example import create_example_v1
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from utils.temp_sync import optimize_fn, evaluate_fn
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@ -61,43 +58,72 @@ def draw_multi_fig(save_dir, timestamp, scatter_list, wh, fig_title=None):
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tick.label.set_rotation(10)
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for tick in cur_ax.yaxis.get_major_ticks():
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tick.label.set_fontsize(LabelSize - font_gap)
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plt.legend(loc=1, fontsize=LegendFontsize)
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cur_ax.legend(loc=1, fontsize=LegendFontsize)
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fig.savefig(str(save_path) + ".pdf", dpi=dpi, bbox_inches="tight", format="pdf")
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fig.savefig(str(save_path) + ".png", dpi=dpi, bbox_inches="tight", format="png")
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plt.close("all")
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def find_min(cur, others):
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if cur is None:
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return float(others.min())
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else:
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return float(min(cur, others.min()))
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def find_max(cur, others):
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if cur is None:
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return float(others.max())
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else:
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return float(max(cur, others.max()))
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def compare_cl(save_dir):
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save_dir = Path(str(save_dir))
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save_dir.mkdir(parents=True, exist_ok=True)
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dynamic_env, function = create_example_v1(100, num_per_task=1000)
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dynamic_env, function = create_example_v1(
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timestamp_config=dict(num=200, min_timestamp=-1, max_timestamp=1.0),
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num_per_task=1000,
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)
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additional_xaxis = np.arange(-6, 6, 0.2)
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models = dict()
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cl_function = copy.deepcopy(function)
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cl_function.set_timestamp(0)
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cl_xaxis_all = None
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cl_xaxis_min = None
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cl_xaxis_max = None
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all_data = OrderedDict()
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for idx, (timestamp, dataset) in enumerate(tqdm(dynamic_env, ncols=50)):
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xaxis_all = dataset[:, 0].numpy()
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# xaxis_all = np.concatenate((additional_xaxis, xaxis_all))
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# compute the ground truth
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current_data = dict()
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function.set_timestamp(timestamp)
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yaxis_all = function.noise_call(xaxis_all)
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current_data["lfna_xaxis_all"] = xaxis_all
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current_data["lfna_yaxis_all"] = yaxis_all
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# create CL data
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if cl_xaxis_all is None:
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cl_xaxis_all = xaxis_all
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else:
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cl_xaxis_all = np.concatenate((cl_xaxis_all, xaxis_all + timestamp * 0.2))
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cl_yaxis_all = cl_function(cl_xaxis_all)
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import pdb
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pdb.set_trace()
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# compute cl-min
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cl_xaxis_min = find_min(cl_xaxis_min, xaxis_all)
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cl_xaxis_max = find_max(cl_xaxis_max, xaxis_all) + idx * 0.1
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cl_xaxis_all = np.arange(cl_xaxis_min, cl_xaxis_max, step=0.05)
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cl_yaxis_all = cl_function.noise_call(cl_xaxis_all)
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current_data["cl_xaxis_all"] = cl_xaxis_all
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current_data["cl_yaxis_all"] = cl_yaxis_all
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all_data[timestamp] = current_data
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for idx, (timestamp, xdata) in enumerate(tqdm(all_data.items(), ncols=50)):
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scatter_list = []
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scatter_list.append(
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{
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"xaxis": xaxis_all,
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"yaxis": yaxis_all,
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"xaxis": xdata["lfna_xaxis_all"],
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"yaxis": xdata["lfna_yaxis_all"],
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"color": "k",
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"s": 10,
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"alpha": 0.99,
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@ -107,6 +133,9 @@ def compare_cl(save_dir):
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}
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)
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cl_xaxis_all = current_data["cl_xaxis_all"]
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cl_yaxis_all = current_data["cl_yaxis_all"]
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scatter_list.append(
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{
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"xaxis": cl_xaxis_all,
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@ -121,15 +150,21 @@ def compare_cl(save_dir):
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)
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draw_multi_fig(
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save_dir, timestamp, scatter_list,
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wh=(2000, 1300), fig_title="Timestamp={:03d}".format(timestamp)
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save_dir,
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timestamp,
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scatter_list,
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wh=(2000, 1300),
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fig_title="Timestamp={:03d}".format(timestamp),
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)
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print("Save all figures into {:}".format(save_dir))
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save_dir = save_dir.resolve()
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cmd = "ffmpeg -y -i {xdir}/%04d.png -pix_fmt yuv420p -vf fps=2 -vf scale=2000:1300 -vb 5000k {xdir}/vis.mp4".format(
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xdir=save_dir
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base_cmd = (
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"ffmpeg -y -i {xdir}/%04d.png -vf fps=2 -vf scale=2000:1300 -vb 5000k".format(
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xdir=save_dir
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)
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)
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os.system(cmd)
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os.system("{:} -pix_fmt yuv420p {xdir}/vis.mp4".format(base_cmd, xdir=save_dir))
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os.system("{:} -c:a libvorbis {xdir}/vis.webm".format(base_cmd, xdir=save_dir))
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if __name__ == "__main__":
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@ -19,7 +19,7 @@ class SyntheticDEnv(data.Dataset):
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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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time_stamp_config: Optional[Dict] = None,
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timestamp_config: Optional[Dict] = None,
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mode: Optional[str] = None,
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):
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self._ndim = len(mean_functors)
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@ -31,12 +31,12 @@ class SyntheticDEnv(data.Dataset):
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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 time_stamp_config is None:
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time_stamp_config = dict(mode=mode)
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if timestamp_config is None:
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timestamp_config = dict(mode=mode)
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else:
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time_stamp_config["mode"] = mode
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timestamp_config["mode"] = mode
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self._timestamp_generator = TimeStamp(**time_stamp_config)
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self._timestamp_generator = TimeStamp(**timestamp_config)
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self._mean_functors = mean_functors
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self._cov_functors = cov_functors
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@ -2,21 +2,23 @@
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
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#####################################################
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from .math_base_funcs import DynamicQuadraticFunc
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from .math_adv_funcs import DynamicQuadraticFunc
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from .math_adv_funcs import ConstantFunc, ComposedSinFunc
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from .synthetic_env import SyntheticDEnv
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def create_example_v1(timestamps=50, num_per_task=5000):
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def create_example_v1(
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timestamp_config=dict(num=100, min_timestamp=0.0, max_timestamp=1.0),
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num_per_task=5000,
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):
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mean_generator = ComposedSinFunc()
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std_generator = ComposedSinFunc(min_amplitude=0.5, max_amplitude=0.5)
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std_generator.set_transform(lambda x: x + 1)
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dynamic_env = SyntheticDEnv(
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[mean_generator],
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[[std_generator]],
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num_per_task=num_per_task,
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time_stamp_config=dict(num=timestamps),
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timestamp_config=timestamp_config,
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)
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function = DynamicQuadraticFunc()
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8
scripts/black.sh
Normal file
8
scripts/black.sh
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@ -0,0 +1,8 @@
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#!/bin/bash
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# bash ./scripts/black.sh
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black ./tests/
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black ./lib/datasets
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black ./lib/xlayers
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black ./exps/LFNA
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black ./exps/trading
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