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Add SuperSimpleNorm and update synthetic env

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This commit is contained in:
D-X-Y
2021-04-23 02:12:11 -07:00
parent a5b7d986b3
commit 9b895bdf2e
13 changed files with 238 additions and 519 deletions
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55
exps/synthetic/baseline.py
View File

@@ -1,9 +1,9 @@
#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.02 #
#####################################################
# python exps/synthetic/baseline.py #
#####################################################
import os, sys, copy
############################################################################
# CUDA_VISIBLE_DEVICES=0 python exps/synthetic/baseline.py #
############################################################################
import os, sys, copy, random
import torch
import numpy as np
import argparse
@@ -28,6 +28,8 @@ from datasets import ConstantGenerator, SinGenerator, SyntheticDEnv
from datasets import DynamicQuadraticFunc
from datasets.synthetic_example import create_example_v1
from utils.temp_sync import optimize_fn, evaluate_fn
def draw_fig(save_dir, timestamp, scatter_list):
save_path = save_dir / "{:04d}".format(timestamp)
@@ -67,28 +69,55 @@ def draw_fig(save_dir, timestamp, scatter_list):
def main(save_dir):
save_dir = Path(str(save_dir))
save_dir.mkdir(parents=True, exist_ok=True)
dynamic_env, function = create_example_v1(100, num_per_task=500)
dynamic_env, function = create_example_v1(100, num_per_task=1000)
additional_xaxis = np.arange(-6, 6, 0.1)
for timestamp, dataset in tqdm(dynamic_env, ncols=50):
num = dataset.shape[0]
xaxis = dataset[:, 0].numpy()
additional_xaxis = np.arange(-6, 6, 0.2)
models = dict()
for idx, (timestamp, dataset) in enumerate(tqdm(dynamic_env, ncols=50)):
xaxis_all = dataset[:, 0].numpy()
# xaxis_all = np.concatenate((additional_xaxis, xaxis_all))
# compute the ground truth
function.set_timestamp(timestamp)
yaxis = function(xaxis)
# xaxis = np.concatenate((additional_xaxis, xaxis))
yaxis_all = function.noise_call(xaxis_all)
# split the dataset
indexes = list(range(xaxis_all.shape[0]))
random.shuffle(indexes)
train_indexes = indexes[:len(indexes)//2]
valid_indexes = indexes[len(indexes)//2:]
train_xs, train_ys = xaxis_all[train_indexes], yaxis_all[train_indexes]
valid_xs, valid_ys = xaxis_all[valid_indexes], yaxis_all[valid_indexes]
model, loss_fn, train_loss = optimize_fn(train_xs, train_ys)
# model, loss_fn, train_loss = optimize_fn(xaxis_all, yaxis_all)
pred_valid_ys, valid_loss = evaluate_fn(model, valid_xs, valid_ys, loss_fn)
print("[{:03d}] T-{:03d}, train-loss={:.5f}, valid-loss={:.5f}".format(idx, timestamp, train_loss, valid_loss))
# the first plot
scatter_list = []
scatter_list.append(
{
"xaxis": xaxis,
"yaxis": yaxis,
"xaxis": valid_xs,
"yaxis": valid_ys,
"color": "k",
"s": 10,
"alpha": 0.99,
"label": "Timestamp={:02d}".format(timestamp),
}
)
scatter_list.append(
{
"xaxis": valid_xs,
"yaxis": pred_valid_ys,
"color": "r",
"s": 10,
"alpha": 0.5,
"label": "MLP at now"
}
)
draw_fig(save_dir, timestamp, scatter_list)
print("Save all figures into {:}".format(save_dir))
save_dir = save_dir.resolve()

8
lib/datasets/math_base_funcs.py
View File

@@ -33,6 +33,14 @@ class FitFunc(abc.ABC):
def __call__(self, x):
raise NotImplementedError
def noise_call(self, x, std=0.1):
clean_y = self.__call__(x)
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
@abc.abstractmethod
def _getitem(self, x):
raise NotImplementedError

63
lib/utils/temp_sync.py Normal file
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@@ -0,0 +1,63 @@
# To be deleted.
import copy
import torch
from xlayers.super_core import SuperSequential, SuperMLPv1
from xlayers.super_core import SuperSimpleNorm
from xlayers.super_core import SuperLinear
def optimize_fn(xs, ys, device="cpu", max_iter=2000, max_lr=0.1):
xs = torch.FloatTensor(xs).view(-1, 1).to(device)
ys = torch.FloatTensor(ys).view(-1, 1).to(device)
model = SuperSequential(
SuperSimpleNorm(xs.mean().item(), xs.std().item()),
SuperLinear(1, 200),
torch.nn.LeakyReLU(),
SuperLinear(200, 100),
torch.nn.LeakyReLU(),
SuperLinear(100, 1),
).to(device)
model.train()
optimizer = torch.optim.Adam(
model.parameters(), lr=max_lr, amsgrad=True
)
loss_func = torch.nn.MSELoss()
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=0.3,
)
best_loss, best_param = None, None
for _iter in range(max_iter):
preds = model(xs)
optimizer.zero_grad()
loss = loss_func(preds, ys)
loss.backward()
optimizer.step()
lr_scheduler.step()
if best_loss is None or best_loss > loss.item():
best_loss = loss.item()
best_param = copy.deepcopy(model.state_dict())
# print('loss={:}, best-loss={:}'.format(loss.item(), best_loss))
model.load_state_dict(best_param)
return model, loss_func, best_loss
def evaluate_fn(model, xs, ys, loss_fn, device="cpu"):
with torch.no_grad():
inputs = torch.FloatTensor(xs).view(-1, 1).to(device)
ys = torch.FloatTensor(ys).view(-1, 1).to(device)
preds = model(inputs)
loss = loss_fn(preds, ys)
preds = preds.view(-1).cpu().numpy()
return preds, loss.item()

6
lib/xlayers/super_container.py
View File

@@ -91,6 +91,8 @@ class SuperSequential(SuperModule):
def abstract_search_space(self):
root_node = spaces.VirtualNode(id(self))
for index, module in enumerate(self):
if not isinstance(module, SuperModule):
continue
space = module.abstract_search_space
if not spaces.is_determined(space):
root_node.append(str(index), space)
@@ -98,9 +100,9 @@ class SuperSequential(SuperModule):
def apply_candidate(self, abstract_child: spaces.VirtualNode):
super(SuperSequential, self).apply_candidate(abstract_child)
for index in range(len(self)):
for index, module in enumerate(self):
if str(index) in abstract_child:
self.__getitem__(index).apply_candidate(abstract_child[str(index)])
module.apply_candidate(abstract_child[str(index)])
def forward_candidate(self, input):
return self.forward_raw(input)

1
lib/xlayers/super_core.py
View File

@@ -9,6 +9,7 @@ from .super_module import SuperModule
from .super_container import SuperSequential
from .super_linear import SuperLinear
from .super_linear import SuperMLPv1, SuperMLPv2
from .super_norm import SuperSimpleNorm
from .super_norm import SuperLayerNorm1D
from .super_attention import SuperAttention
from .super_transformer import SuperTransformerEncoderLayer

12
lib/xlayers/super_module.py
View File

@@ -3,6 +3,7 @@
#####################################################
import abc
import warnings
from typing import Optional, Union, Callable
import torch
import torch.nn as nn
@@ -45,6 +46,17 @@ class SuperModule(abc.ABC, nn.Module):
self.apply(_reset_super_run)
def add_module(self, name: str, module: Optional[torch.nn.Module]) -> None:
if not isinstance(module, SuperModule):
warnings.warn(
"Add {:} module, which is not SuperModule, into {:}".format(
name, self.__class__.__name__
)
+ "\n"
+ "It may cause some functions invalid."
)
super(SuperModule, self).add_module(name, module)
def apply_verbose(self, verbose):
def _reset_verbose(m):
if isinstance(m, SuperModule):

40
lib/xlayers/super_norm.py
View File

@@ -82,3 +82,43 @@ class SuperLayerNorm1D(SuperModule):
elementwise_affine=self._elementwise_affine,
)
)
class SuperSimpleNorm(SuperModule):
"""Super simple normalization."""
def __init__(self, mean, std, inplace=False) -> None:
super(SuperSimpleNorm, self).__init__()
self._mean = mean
self._std = std
self._inplace = inplace
@property
def abstract_search_space(self):
return spaces.VirtualNode(id(self))
def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
# check inputs ->
return self.forward_raw(input)
def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
if not self._inplace:
tensor = input.clone()
else:
tensor = input
mean = torch.as_tensor(self._mean, dtype=tensor.dtype, device=tensor.device)
std = torch.as_tensor(self._std, dtype=tensor.dtype, device=tensor.device)
if (std == 0).any():
raise ValueError(
"std evaluated to zero after conversion to {}, leading to division by zero.".format(
dtype
)
)
while mean.ndim < tensor.ndim:
mean, std = torch.unsqueeze(mean, dim=0), torch.unsqueeze(std, dim=0)
return tensor.sub_(mean).div_(std)
def extra_repr(self) -> str:
return "mean={mean}, std={mean}, inplace={inplace}".format(
mean=self._mean, std=self._std, inplace=self._inplace
)

107
notebooks/TOT/synthetic-env.ipynb
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@@ -107,113 +107,6 @@
"visualize_env()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "supreme-basis",
"metadata": {},
"outputs": [],
"source": [
"# def optimize_fn(xs, ys, test_sets):\n",
"# xs = torch.FloatTensor(xs).view(-1, 1)\n",
"# ys = torch.FloatTensor(ys).view(-1, 1)\n",
" \n",
"# model = SuperSequential(\n",
"# SuperMLPv1(1, 10, 20, torch.nn.ReLU),\n",
"# SuperMLPv1(20, 10, 1, torch.nn.ReLU)\n",
"# )\n",
"# optimizer = torch.optim.Adam(\n",
"# model.parameters(),\n",
"# lr=0.01, weight_decay=1e-4, amsgrad=True\n",
"# )\n",
"# for _iter in range(100):\n",
"# preds = model(ys)\n",
"\n",
"# optimizer.zero_grad()\n",
"# loss = torch.nn.functional.mse_loss(preds, ys)\n",
"# loss.backward()\n",
"# optimizer.step()\n",
" \n",
"# with torch.no_grad():\n",
"# answers = []\n",
"# for test_set in test_sets:\n",
"# test_set = torch.FloatTensor(test_set).view(-1, 1)\n",
"# preds = model(test_set).view(-1).numpy()\n",
"# answers.append(preds.tolist())\n",
"# return answers\n",
"\n",
"# def f(x):\n",
"# return np.cos( 0.5 * x + x * x)\n",
"\n",
"# def get_data(mode):\n",
"# dataset = SynAdaptiveEnv(mode=mode)\n",
"# times, xs, ys = [], [], []\n",
"# for i, (_, t, x) in enumerate(dataset):\n",
"# times.append(t)\n",
"# xs.append(x)\n",
"# dataset.set_transform(f)\n",
"# for i, (_, _, y) in enumerate(dataset):\n",
"# ys.append(y)\n",
"# return times, xs, ys\n",
"\n",
"# def visualize_syn(save_path):\n",
"# save_dir = (save_path / '..').resolve()\n",
"# save_dir.mkdir(parents=True, exist_ok=True)\n",
" \n",
"# dpi, width, height = 40, 2000, 900\n",
"# figsize = width / float(dpi), height / float(dpi)\n",
"# LabelSize, LegendFontsize, font_gap = 40, 40, 5\n",
" \n",
"# fig = plt.figure(figsize=figsize)\n",
" \n",
"# times, xs, ys = get_data(None)\n",
" \n",
"# def draw_ax(cur_ax, xaxis, yaxis, xlabel, ylabel,\n",
"# alpha=0.1, color='k', linestyle='-', legend=None, plot_only=False):\n",
"# if legend is not None:\n",
"# cur_ax.plot(xaxis[:1], yaxis[:1], color=color, label=legend)\n",
"# cur_ax.plot(xaxis, yaxis, color=color, linestyle=linestyle, alpha=alpha, label=None)\n",
"# if not plot_only:\n",
"# cur_ax.set_xlabel(xlabel, fontsize=LabelSize)\n",
"# cur_ax.set_ylabel(ylabel, rotation=0, fontsize=LabelSize)\n",
"# for tick in cur_ax.xaxis.get_major_ticks():\n",
"# tick.label.set_fontsize(LabelSize - font_gap)\n",
"# tick.label.set_rotation(10)\n",
"# for tick in cur_ax.yaxis.get_major_ticks():\n",
"# tick.label.set_fontsize(LabelSize - font_gap)\n",
" \n",
"# cur_ax = fig.add_subplot(2, 1, 1)\n",
"# draw_ax(cur_ax, times, xs, \"time\", \"x\", alpha=1.0, legend=None)\n",
"\n",
"# cur_ax = fig.add_subplot(2, 1, 2)\n",
"# draw_ax(cur_ax, times, ys, \"time\", \"y\", alpha=0.1, legend=\"ground truth\")\n",
" \n",
"# train_times, train_xs, train_ys = get_data(\"train\")\n",
"# draw_ax(cur_ax, train_times, train_ys, None, None, alpha=1.0, color='r', legend=None, plot_only=True)\n",
" \n",
"# valid_times, valid_xs, valid_ys = get_data(\"valid\")\n",
"# draw_ax(cur_ax, valid_times, valid_ys, None, None, alpha=1.0, color='g', legend=None, plot_only=True)\n",
" \n",
"# test_times, test_xs, test_ys = get_data(\"test\")\n",
"# draw_ax(cur_ax, test_times, test_ys, None, None, alpha=1.0, color='b', legend=None, plot_only=True)\n",
" \n",
"# # optimize MLP models\n",
"# # [train_preds, valid_preds, test_preds] = optimize_fn(train_xs, train_ys, [train_xs, valid_xs, test_xs])\n",
"# # draw_ax(cur_ax, train_times, train_preds, None, None,\n",
"# # alpha=1.0, linestyle='--', color='r', legend=\"MLP\", plot_only=True)\n",
"# # import pdb; pdb.set_trace()\n",
"# # draw_ax(cur_ax, valid_times, valid_preds, None, None,\n",
"# # alpha=1.0, linestyle='--', color='g', legend=None, plot_only=True)\n",
"# # draw_ax(cur_ax, test_times, test_preds, None, None,\n",
"# # alpha=1.0, linestyle='--', color='b', legend=None, plot_only=True)\n",
"\n",
"# plt.legend(loc=1, fontsize=LegendFontsize)\n",
"\n",
"# fig.savefig(save_path, dpi=dpi, bbox_inches=\"tight\", format=\"pdf\")\n",
"# plt.close(\"all\")\n",
"# # plt.show()"
]
}
],
"metadata": {
"kernelspec": {

2
scripts/trade/baseline.sh
View File

@@ -17,7 +17,7 @@ gpu=$1
market=$2
# algorithms="NAIVE-V1 NAIVE-V2 MLP GRU LSTM ALSTM XGBoost LightGBM SFM TabNet DoubleE"
algorithms="MLP GRU LSTM ALSTM XGBoost LightGBM SFM TabNet DoubleE"
algorithms="XGBoost LightGBM SFM TabNet DoubleE"
for alg in ${algorithms}
do

251
synthetic-env.ipynb
View File

@@ -1,251 +0,0 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "filled-multiple",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The root path: /Users/xuanyidong\n",
"The library path: /Users/xuanyidong/lib\n"
]
},
{
"ename": "AssertionError",
"evalue": "/Users/xuanyidong/lib does not exist",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m~/Desktop/AutoDL-Projects\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"The root path: {:}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroot_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"The library path: {:}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlib_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mlib_dir\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"{:} does not exist\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlib_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 18\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlib_dir\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlib_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAssertionError\u001b[0m: /Users/xuanyidong/lib does not exist"
]
}
],
"source": [
"import os, sys\n",
"import torch\n",
"from pathlib import Path\n",
"import numpy as np\n",
"import matplotlib\n",
"from matplotlib import cm\n",
"# matplotlib.use(\"agg\")\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.ticker as ticker\n",
"\n",
"\n",
"__file__ = os.path.dirname(os.path.realpath(\"__file__\"))\n",
"root_dir = (Path(__file__).parent / \"..\").resolve()\n",
"lib_dir = (root_dir / \"lib\").resolve()\n",
"print(\"The root path: {:}\".format(root_dir))\n",
"print(\"The library path: {:}\".format(lib_dir))\n",
"assert lib_dir.exists(), \"{:} does not exist\".format(lib_dir)\n",
"if str(lib_dir) not in sys.path:\n",
" sys.path.insert(0, str(lib_dir))\n",
"\n",
"from datasets import ConstantGenerator, SinGenerator, SyntheticDEnv\n",
"from datasets import DynamicQuadraticFunc\n",
"from datasets.synthetic_example import create_example_v1"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "detected-second",
"metadata": {},
"outputs": [],
"source": [
"def visualize_env():\n",
" \n",
" dpi, width, height = 10, 800, 400\n",
" figsize = width / float(dpi), height / float(dpi)\n",
" LabelSize, LegendFontsize, font_gap = 40, 40, 5\n",
"\n",
" fig = plt.figure(figsize=figsize)\n",
"\n",
" dynamic_env, function = create_example_v1(100, num_per_task=250)\n",
" \n",
" timeaxis, xaxis, yaxis = [], [], []\n",
" for timestamp, dataset in dynamic_env:\n",
" num = dataset.shape[0]\n",
" timeaxis.append(torch.zeros(num) + timestamp)\n",
" xaxis.append(dataset[:,0])\n",
" # compute the ground truth\n",
" function.set_timestamp(timestamp)\n",
" yaxis.append(function(dataset[:,0]))\n",
" timeaxis = torch.cat(timeaxis).numpy()\n",
" xaxis = torch.cat(xaxis).numpy()\n",
" yaxis = torch.cat(yaxis).numpy()\n",
"\n",
" cur_ax = fig.add_subplot(2, 1, 1)\n",
" cur_ax.scatter(timeaxis, xaxis, color=\"k\", linestyle=\"-\", alpha=0.9, label=None)\n",
" cur_ax.set_xlabel(\"Time\", fontsize=LabelSize)\n",
" cur_ax.set_ylabel(\"X\", rotation=0, fontsize=LabelSize)\n",
" for tick in cur_ax.xaxis.get_major_ticks():\n",
" tick.label.set_fontsize(LabelSize - font_gap)\n",
" tick.label.set_rotation(10)\n",
" for tick in cur_ax.yaxis.get_major_ticks():\n",
" tick.label.set_fontsize(LabelSize - font_gap)\n",
" \n",
" cur_ax = fig.add_subplot(2, 1, 2)\n",
" cur_ax.scatter(timeaxis, yaxis, color=\"k\", linestyle=\"-\", alpha=0.9, label=None)\n",
" cur_ax.set_xlabel(\"Time\", fontsize=LabelSize)\n",
" cur_ax.set_ylabel(\"Y\", rotation=0, fontsize=LabelSize)\n",
" for tick in cur_ax.xaxis.get_major_ticks():\n",
" tick.label.set_fontsize(LabelSize - font_gap)\n",
" tick.label.set_rotation(10)\n",
" for tick in cur_ax.yaxis.get_major_ticks():\n",
" tick.label.set_fontsize(LabelSize - font_gap)\n",
" plt.show()\n",
"\n",
"visualize_env()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "supreme-basis",
"metadata": {},
"outputs": [],
"source": [
"# def optimize_fn(xs, ys, test_sets):\n",
"# xs = torch.FloatTensor(xs).view(-1, 1)\n",
"# ys = torch.FloatTensor(ys).view(-1, 1)\n",
" \n",
"# model = SuperSequential(\n",
"# SuperMLPv1(1, 10, 20, torch.nn.ReLU),\n",
"# SuperMLPv1(20, 10, 1, torch.nn.ReLU)\n",
"# )\n",
"# optimizer = torch.optim.Adam(\n",
"# model.parameters(),\n",
"# lr=0.01, weight_decay=1e-4, amsgrad=True\n",
"# )\n",
"# for _iter in range(100):\n",
"# preds = model(ys)\n",
"\n",
"# optimizer.zero_grad()\n",
"# loss = torch.nn.functional.mse_loss(preds, ys)\n",
"# loss.backward()\n",
"# optimizer.step()\n",
" \n",
"# with torch.no_grad():\n",
"# answers = []\n",
"# for test_set in test_sets:\n",
"# test_set = torch.FloatTensor(test_set).view(-1, 1)\n",
"# preds = model(test_set).view(-1).numpy()\n",
"# answers.append(preds.tolist())\n",
"# return answers\n",
"\n",
"# def f(x):\n",
"# return np.cos( 0.5 * x + x * x)\n",
"\n",
"# def get_data(mode):\n",
"# dataset = SynAdaptiveEnv(mode=mode)\n",
"# times, xs, ys = [], [], []\n",
"# for i, (_, t, x) in enumerate(dataset):\n",
"# times.append(t)\n",
"# xs.append(x)\n",
"# dataset.set_transform(f)\n",
"# for i, (_, _, y) in enumerate(dataset):\n",
"# ys.append(y)\n",
"# return times, xs, ys\n",
"\n",
"# def visualize_syn(save_path):\n",
"# save_dir = (save_path / '..').resolve()\n",
"# save_dir.mkdir(parents=True, exist_ok=True)\n",
" \n",
"# dpi, width, height = 40, 2000, 900\n",
"# figsize = width / float(dpi), height / float(dpi)\n",
"# LabelSize, LegendFontsize, font_gap = 40, 40, 5\n",
" \n",
"# fig = plt.figure(figsize=figsize)\n",
" \n",
"# times, xs, ys = get_data(None)\n",
" \n",
"# def draw_ax(cur_ax, xaxis, yaxis, xlabel, ylabel,\n",
"# alpha=0.1, color='k', linestyle='-', legend=None, plot_only=False):\n",
"# if legend is not None:\n",
"# cur_ax.plot(xaxis[:1], yaxis[:1], color=color, label=legend)\n",
"# cur_ax.plot(xaxis, yaxis, color=color, linestyle=linestyle, alpha=alpha, label=None)\n",
"# if not plot_only:\n",
"# cur_ax.set_xlabel(xlabel, fontsize=LabelSize)\n",
"# cur_ax.set_ylabel(ylabel, rotation=0, fontsize=LabelSize)\n",
"# for tick in cur_ax.xaxis.get_major_ticks():\n",
"# tick.label.set_fontsize(LabelSize - font_gap)\n",
"# tick.label.set_rotation(10)\n",
"# for tick in cur_ax.yaxis.get_major_ticks():\n",
"# tick.label.set_fontsize(LabelSize - font_gap)\n",
" \n",
"# cur_ax = fig.add_subplot(2, 1, 1)\n",
"# draw_ax(cur_ax, times, xs, \"time\", \"x\", alpha=1.0, legend=None)\n",
"\n",
"# cur_ax = fig.add_subplot(2, 1, 2)\n",
"# draw_ax(cur_ax, times, ys, \"time\", \"y\", alpha=0.1, legend=\"ground truth\")\n",
" \n",
"# train_times, train_xs, train_ys = get_data(\"train\")\n",
"# draw_ax(cur_ax, train_times, train_ys, None, None, alpha=1.0, color='r', legend=None, plot_only=True)\n",
" \n",
"# valid_times, valid_xs, valid_ys = get_data(\"valid\")\n",
"# draw_ax(cur_ax, valid_times, valid_ys, None, None, alpha=1.0, color='g', legend=None, plot_only=True)\n",
" \n",
"# test_times, test_xs, test_ys = get_data(\"test\")\n",
"# draw_ax(cur_ax, test_times, test_ys, None, None, alpha=1.0, color='b', legend=None, plot_only=True)\n",
" \n",
"# # optimize MLP models\n",
"# # [train_preds, valid_preds, test_preds] = optimize_fn(train_xs, train_ys, [train_xs, valid_xs, test_xs])\n",
"# # draw_ax(cur_ax, train_times, train_preds, None, None,\n",
"# # alpha=1.0, linestyle='--', color='r', legend=\"MLP\", plot_only=True)\n",
"# # import pdb; pdb.set_trace()\n",
"# # draw_ax(cur_ax, valid_times, valid_preds, None, None,\n",
"# # alpha=1.0, linestyle='--', color='g', legend=None, plot_only=True)\n",
"# # draw_ax(cur_ax, test_times, test_preds, None, None,\n",
"# # alpha=1.0, linestyle='--', color='b', legend=None, plot_only=True)\n",
"\n",
"# plt.legend(loc=1, fontsize=LegendFontsize)\n",
"\n",
"# fig.savefig(save_path, dpi=dpi, bbox_inches=\"tight\", format=\"pdf\")\n",
"# plt.close(\"all\")\n",
"# # plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "shared-envelope",
"metadata": {},
"outputs": [],
"source": [
"# Visualization\n",
"# home_dir = Path.home()\n",
"# desktop_dir = home_dir / 'Desktop'\n",
"# print('The Desktop is at: {:}'.format(desktop_dir))\n",
"# visualize_syn(desktop_dir / 'tot-synthetic-v0.pdf')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

145
synthetic-visualize-env.ipynb
View File

@@ -1,145 +0,0 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "filled-multiple",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The root path: /Users/xuanyidong\n",
"The library path: /Users/xuanyidong/lib\n"
]
},
{
"ename": "AssertionError",
"evalue": "/Users/xuanyidong/lib does not exist",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m~/Desktop/AutoDL-Projects\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"The root path: {:}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroot_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"The library path: {:}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlib_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mlib_dir\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"{:} does not exist\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlib_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 18\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlib_dir\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlib_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAssertionError\u001b[0m: /Users/xuanyidong/lib does not exist"
]
}
],
"source": [
"import os, sys\n",
"import torch\n",
"from pathlib import Path\n",
"import numpy as np\n",
"import matplotlib\n",
"from matplotlib import cm\n",
"matplotlib.use(\"agg\")\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.ticker as ticker\n",
"\n",
"\n",
"__file__ = os.path.dirname(os.path.realpath(\"__file__\"))\n",
"root_dir = (Path(__file__).parent / \"..\").resolve()\n",
"lib_dir = (root_dir / \"lib\").resolve()\n",
"print(\"The root path: {:}\".format(root_dir))\n",
"print(\"The library path: {:}\".format(lib_dir))\n",
"assert lib_dir.exists(), \"{:} does not exist\".format(lib_dir)\n",
"if str(lib_dir) not in sys.path:\n",
" sys.path.insert(0, str(lib_dir))\n",
"\n",
"from datasets import ConstantGenerator, SinGenerator, SyntheticDEnv\n",
"from datasets import DynamicQuadraticFunc\n",
"from datasets.synthetic_example import create_example_v1"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "detected-second",
"metadata": {},
"outputs": [],
"source": [
"def draw_fig(save_dir, timestamp, xaxis, yaxis):\n",
" save_path = save_dir / '{:04d}'.format(timestamp)\n",
" # print('Plot the figure at timestamp-{:} into {:}'.format(timestamp, save_path))\n",
" dpi, width, height = 40, 1500, 1500\n",
" figsize = width / float(dpi), height / float(dpi)\n",
" LabelSize, LegendFontsize, font_gap = 80, 80, 5\n",
"\n",
" fig = plt.figure(figsize=figsize)\n",
" \n",
" cur_ax = fig.add_subplot(1, 1, 1)\n",
" cur_ax.scatter(xaxis, yaxis, color=\"k\", s=10, alpha=0.9, label=\"Timestamp={:02d}\".format(timestamp))\n",
" cur_ax.set_xlabel(\"X\", fontsize=LabelSize)\n",
" cur_ax.set_ylabel(\"f(X)\", rotation=0, fontsize=LabelSize)\n",
" cur_ax.set_xlim(-6, 6)\n",
" cur_ax.set_ylim(-40, 40)\n",
" for tick in cur_ax.xaxis.get_major_ticks():\n",
" tick.label.set_fontsize(LabelSize - font_gap)\n",
" tick.label.set_rotation(10)\n",
" for tick in cur_ax.yaxis.get_major_ticks():\n",
" tick.label.set_fontsize(LabelSize - font_gap)\n",
" \n",
" plt.legend(loc=1, fontsize=LegendFontsize)\n",
" fig.savefig(str(save_path) + '.pdf', dpi=dpi, bbox_inches=\"tight\", format=\"pdf\")\n",
" fig.savefig(str(save_path) + '.png', dpi=dpi, bbox_inches=\"tight\", format=\"png\")\n",
" plt.close(\"all\")\n",
"\n",
"\n",
"def visualize_env(save_dir):\n",
" save_dir.mkdir(parents=True, exist_ok=True)\n",
" dynamic_env, function = create_example_v1(100, num_per_task=500)\n",
" \n",
" additional_xaxis = np.arange(-6, 6, 0.1)\n",
" for timestamp, dataset in dynamic_env:\n",
" num = dataset.shape[0]\n",
" # timeaxis = (torch.zeros(num) + timestamp).numpy()\n",
" xaxis = dataset[:,0].numpy()\n",
" xaxis = np.concatenate((additional_xaxis, xaxis))\n",
" # compute the ground truth\n",
" function.set_timestamp(timestamp)\n",
" yaxis = function(xaxis)\n",
" draw_fig(save_dir, timestamp, xaxis, yaxis)\n",
"\n",
"home_dir = Path.home()\n",
"desktop_dir = home_dir / 'Desktop'\n",
"vis_save_dir = desktop_dir / 'vis-synthetic'\n",
"visualize_env(vis_save_dir)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "greatest-pepper",
"metadata": {},
"outputs": [],
"source": [
"# Plot the data\n",
"cmd = 'ffmpeg -y -i {:}/%04d.png -pix_fmt yuv420p -vf fps=2 -vf scale=1000:1000 -vb 5000k {:}/vis.mp4'.format(vis_save_dir, vis_save_dir)\n",
"print(cmd)\n",
"os.system(cmd)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

14
tests/test_super_container.py
View File

@@ -72,3 +72,17 @@ def test_super_sequential(batch, seq_dim, input_dim, order):
out3_dim.abstract(reuse_last=True).random(reuse_last=True).value,
)
assert tuple(outputs.shape) == output_shape
def test_super_sequential_v1():
model = super_core.SuperSequential(
super_core.SuperSimpleNorm(1, 1),
torch.nn.ReLU(),
super_core.SuperLinear(10, 10),
)
inputs = torch.rand(10, 10)
print(model)
outputs = model(inputs)
abstract_search_space = model.abstract_search_space
print(abstract_search_space)

53
tests/test_super_norm.py Normal file
View File

@@ -0,0 +1,53 @@
#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
#####################################################
# pytest ./tests/test_super_norm.py -s #
#####################################################
import sys, random
import unittest
import pytest
from pathlib import Path
lib_dir = (Path(__file__).parent / ".." / "lib").resolve()
print("library path: {:}".format(lib_dir))
if str(lib_dir) not in sys.path:
sys.path.insert(0, str(lib_dir))
import torch
from xlayers import super_core
import spaces
class TestSuperSimpleNorm(unittest.TestCase):
"""Test the super simple norm."""
def test_super_simple_norm(self):
out_features = spaces.Categorical(12, 24, 36)
bias = spaces.Categorical(True, False)
model = super_core.SuperSequential(
super_core.SuperSimpleNorm(5, 0.5),
super_core.SuperLinear(10, out_features, bias=bias),
)
print("The simple super module is:\n{:}".format(model))
model.apply_verbose(True)
print(model.super_run_type)
self.assertTrue(model[1].bias)
inputs = torch.rand(20, 10)
print("Input shape: {:}".format(inputs.shape))
outputs = model(inputs)
self.assertEqual(tuple(outputs.shape), (20, 36))
abstract_space = model.abstract_search_space
abstract_space.clean_last()
abstract_child = abstract_space.random()
print("The abstract searc space:\n{:}".format(abstract_space))
print("The abstract child program:\n{:}".format(abstract_child))
model.set_super_run_type(super_core.SuperRunMode.Candidate)
model.apply_candidate(abstract_child)
output_shape = (20, abstract_child["1"]["_out_features"].value)
outputs = model(inputs)
self.assertEqual(tuple(outputs.shape), output_shape)