Add SuperSimpleNorm and update synthetic env
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D-X-Y
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@ -1,9 +1,9 @@
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.02 #
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#####################################################
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# python exps/synthetic/baseline.py #
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#####################################################
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import os, sys, copy
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############################################################################
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# CUDA_VISIBLE_DEVICES=0 python exps/synthetic/baseline.py #
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############################################################################
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import os, sys, copy, random
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import torch
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import numpy as np
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import argparse
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@ -28,6 +28,8 @@ 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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def draw_fig(save_dir, timestamp, scatter_list):
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save_path = save_dir / "{:04d}".format(timestamp)
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@ -67,28 +69,55 @@ def draw_fig(save_dir, timestamp, scatter_list):
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def main(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=500)
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dynamic_env, function = create_example_v1(100, num_per_task=1000)
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additional_xaxis = np.arange(-6, 6, 0.1)
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for timestamp, dataset in tqdm(dynamic_env, ncols=50):
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num = dataset.shape[0]
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xaxis = dataset[:, 0].numpy()
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additional_xaxis = np.arange(-6, 6, 0.2)
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models = dict()
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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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function.set_timestamp(timestamp)
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yaxis = function(xaxis)
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# xaxis = np.concatenate((additional_xaxis, xaxis))
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yaxis_all = function.noise_call(xaxis_all)
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# split the dataset
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indexes = list(range(xaxis_all.shape[0]))
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random.shuffle(indexes)
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train_indexes = indexes[:len(indexes)//2]
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valid_indexes = indexes[len(indexes)//2:]
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train_xs, train_ys = xaxis_all[train_indexes], yaxis_all[train_indexes]
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valid_xs, valid_ys = xaxis_all[valid_indexes], yaxis_all[valid_indexes]
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model, loss_fn, train_loss = optimize_fn(train_xs, train_ys)
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# model, loss_fn, train_loss = optimize_fn(xaxis_all, yaxis_all)
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pred_valid_ys, valid_loss = evaluate_fn(model, valid_xs, valid_ys, loss_fn)
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print("[{:03d}] T-{:03d}, train-loss={:.5f}, valid-loss={:.5f}".format(idx, timestamp, train_loss, valid_loss))
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# the first plot
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scatter_list = []
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scatter_list.append(
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{
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"xaxis": xaxis,
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"yaxis": yaxis,
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"xaxis": valid_xs,
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"yaxis": valid_ys,
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"color": "k",
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"s": 10,
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"alpha": 0.99,
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"label": "Timestamp={:02d}".format(timestamp),
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}
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)
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scatter_list.append(
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{
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"xaxis": valid_xs,
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"yaxis": pred_valid_ys,
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"color": "r",
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"s": 10,
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"alpha": 0.5,
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"label": "MLP at now"
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}
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)
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draw_fig(save_dir, timestamp, scatter_list)
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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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@ -33,6 +33,14 @@ class FitFunc(abc.ABC):
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def __call__(self, x):
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raise NotImplementedError
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def noise_call(self, x, std=0.1):
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clean_y = self.__call__(x)
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if isinstance(clean_y, np.ndarray):
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noise_y = clean_y + np.random.normal(scale=std, size=clean_y.shape)
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else:
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raise ValueError("Unkonwn type: {:}".format(type(clean_y)))
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return noise_y
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@abc.abstractmethod
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def _getitem(self, x):
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raise NotImplementedError
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63
lib/utils/temp_sync.py
Normal file
63
lib/utils/temp_sync.py
Normal file
@ -0,0 +1,63 @@
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# To be deleted.
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import copy
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import torch
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from xlayers.super_core import SuperSequential, SuperMLPv1
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from xlayers.super_core import SuperSimpleNorm
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from xlayers.super_core import SuperLinear
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def optimize_fn(xs, ys, device="cpu", max_iter=2000, max_lr=0.1):
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xs = torch.FloatTensor(xs).view(-1, 1).to(device)
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ys = torch.FloatTensor(ys).view(-1, 1).to(device)
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model = SuperSequential(
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SuperSimpleNorm(xs.mean().item(), xs.std().item()),
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SuperLinear(1, 200),
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torch.nn.LeakyReLU(),
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SuperLinear(200, 100),
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torch.nn.LeakyReLU(),
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SuperLinear(100, 1),
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).to(device)
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model.train()
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optimizer = torch.optim.Adam(
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model.parameters(), lr=max_lr, amsgrad=True
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)
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loss_func = torch.nn.MSELoss()
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lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
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optimizer,
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milestones=[
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int(max_iter * 0.25),
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int(max_iter * 0.5),
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int(max_iter * 0.75),
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],
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gamma=0.3,
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)
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best_loss, best_param = None, None
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for _iter in range(max_iter):
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preds = model(xs)
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optimizer.zero_grad()
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loss = loss_func(preds, ys)
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loss.backward()
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optimizer.step()
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lr_scheduler.step()
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if best_loss is None or best_loss > loss.item():
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best_loss = loss.item()
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best_param = copy.deepcopy(model.state_dict())
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# print('loss={:}, best-loss={:}'.format(loss.item(), best_loss))
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model.load_state_dict(best_param)
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return model, loss_func, best_loss
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def evaluate_fn(model, xs, ys, loss_fn, device="cpu"):
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with torch.no_grad():
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inputs = torch.FloatTensor(xs).view(-1, 1).to(device)
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ys = torch.FloatTensor(ys).view(-1, 1).to(device)
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preds = model(inputs)
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loss = loss_fn(preds, ys)
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preds = preds.view(-1).cpu().numpy()
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return preds, loss.item()
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@ -91,6 +91,8 @@ class SuperSequential(SuperModule):
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def abstract_search_space(self):
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root_node = spaces.VirtualNode(id(self))
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for index, module in enumerate(self):
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if not isinstance(module, SuperModule):
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continue
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space = module.abstract_search_space
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if not spaces.is_determined(space):
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root_node.append(str(index), space)
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@ -98,9 +100,9 @@ class SuperSequential(SuperModule):
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def apply_candidate(self, abstract_child: spaces.VirtualNode):
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super(SuperSequential, self).apply_candidate(abstract_child)
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for index in range(len(self)):
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for index, module in enumerate(self):
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if str(index) in abstract_child:
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self.__getitem__(index).apply_candidate(abstract_child[str(index)])
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module.apply_candidate(abstract_child[str(index)])
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def forward_candidate(self, input):
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return self.forward_raw(input)
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@ -9,6 +9,7 @@ from .super_module import SuperModule
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from .super_container import SuperSequential
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from .super_linear import SuperLinear
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from .super_linear import SuperMLPv1, SuperMLPv2
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from .super_norm import SuperSimpleNorm
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from .super_norm import SuperLayerNorm1D
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from .super_attention import SuperAttention
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from .super_transformer import SuperTransformerEncoderLayer
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@ -3,6 +3,7 @@
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#####################################################
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import abc
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import warnings
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from typing import Optional, Union, Callable
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import torch
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import torch.nn as nn
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@ -45,6 +46,17 @@ class SuperModule(abc.ABC, nn.Module):
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self.apply(_reset_super_run)
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def add_module(self, name: str, module: Optional[torch.nn.Module]) -> None:
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if not isinstance(module, SuperModule):
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warnings.warn(
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"Add {:} module, which is not SuperModule, into {:}".format(
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name, self.__class__.__name__
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)
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+ "\n"
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+ "It may cause some functions invalid."
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)
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super(SuperModule, self).add_module(name, module)
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def apply_verbose(self, verbose):
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def _reset_verbose(m):
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if isinstance(m, SuperModule):
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@ -82,3 +82,43 @@ class SuperLayerNorm1D(SuperModule):
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elementwise_affine=self._elementwise_affine,
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)
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)
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class SuperSimpleNorm(SuperModule):
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"""Super simple normalization."""
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def __init__(self, mean, std, inplace=False) -> None:
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super(SuperSimpleNorm, self).__init__()
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self._mean = mean
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self._std = std
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self._inplace = inplace
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@property
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def abstract_search_space(self):
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return spaces.VirtualNode(id(self))
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def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
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# check inputs ->
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return self.forward_raw(input)
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def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
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if not self._inplace:
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tensor = input.clone()
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else:
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tensor = input
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mean = torch.as_tensor(self._mean, dtype=tensor.dtype, device=tensor.device)
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std = torch.as_tensor(self._std, dtype=tensor.dtype, device=tensor.device)
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if (std == 0).any():
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raise ValueError(
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"std evaluated to zero after conversion to {}, leading to division by zero.".format(
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dtype
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)
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)
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while mean.ndim < tensor.ndim:
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mean, std = torch.unsqueeze(mean, dim=0), torch.unsqueeze(std, dim=0)
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return tensor.sub_(mean).div_(std)
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def extra_repr(self) -> str:
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return "mean={mean}, std={mean}, inplace={inplace}".format(
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mean=self._mean, std=self._std, inplace=self._inplace
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)
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@ -107,113 +107,6 @@
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"visualize_env()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "supreme-basis",
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"metadata": {},
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"outputs": [],
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"source": [
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"# def optimize_fn(xs, ys, test_sets):\n",
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"# xs = torch.FloatTensor(xs).view(-1, 1)\n",
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"# ys = torch.FloatTensor(ys).view(-1, 1)\n",
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" \n",
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"# model = SuperSequential(\n",
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"# SuperMLPv1(1, 10, 20, torch.nn.ReLU),\n",
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"# SuperMLPv1(20, 10, 1, torch.nn.ReLU)\n",
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"# )\n",
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"# optimizer = torch.optim.Adam(\n",
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"# model.parameters(),\n",
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"# lr=0.01, weight_decay=1e-4, amsgrad=True\n",
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"# )\n",
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"# for _iter in range(100):\n",
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"# preds = model(ys)\n",
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"\n",
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"# optimizer.zero_grad()\n",
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"# loss = torch.nn.functional.mse_loss(preds, ys)\n",
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"# loss.backward()\n",
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"# optimizer.step()\n",
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" \n",
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"# with torch.no_grad():\n",
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"# answers = []\n",
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"# for test_set in test_sets:\n",
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"# test_set = torch.FloatTensor(test_set).view(-1, 1)\n",
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"# preds = model(test_set).view(-1).numpy()\n",
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"# answers.append(preds.tolist())\n",
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"# return answers\n",
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"\n",
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"# def f(x):\n",
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"# return np.cos( 0.5 * x + x * x)\n",
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"\n",
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"# def get_data(mode):\n",
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"# dataset = SynAdaptiveEnv(mode=mode)\n",
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"# times, xs, ys = [], [], []\n",
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"# for i, (_, t, x) in enumerate(dataset):\n",
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"# times.append(t)\n",
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"# xs.append(x)\n",
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"# dataset.set_transform(f)\n",
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"# for i, (_, _, y) in enumerate(dataset):\n",
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"# ys.append(y)\n",
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"# return times, xs, ys\n",
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"\n",
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"# def visualize_syn(save_path):\n",
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"# save_dir = (save_path / '..').resolve()\n",
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"# save_dir.mkdir(parents=True, exist_ok=True)\n",
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" \n",
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"# dpi, width, height = 40, 2000, 900\n",
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"# figsize = width / float(dpi), height / float(dpi)\n",
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"# LabelSize, LegendFontsize, font_gap = 40, 40, 5\n",
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" \n",
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"# fig = plt.figure(figsize=figsize)\n",
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" \n",
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"# times, xs, ys = get_data(None)\n",
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" \n",
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"# def draw_ax(cur_ax, xaxis, yaxis, xlabel, ylabel,\n",
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"# alpha=0.1, color='k', linestyle='-', legend=None, plot_only=False):\n",
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"# if legend is not None:\n",
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"# cur_ax.plot(xaxis[:1], yaxis[:1], color=color, label=legend)\n",
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"# cur_ax.plot(xaxis, yaxis, color=color, linestyle=linestyle, alpha=alpha, label=None)\n",
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"# if not plot_only:\n",
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"# cur_ax.set_xlabel(xlabel, fontsize=LabelSize)\n",
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"# cur_ax.set_ylabel(ylabel, rotation=0, fontsize=LabelSize)\n",
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"# for tick in cur_ax.xaxis.get_major_ticks():\n",
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"# tick.label.set_fontsize(LabelSize - font_gap)\n",
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"# tick.label.set_rotation(10)\n",
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"# for tick in cur_ax.yaxis.get_major_ticks():\n",
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"# tick.label.set_fontsize(LabelSize - font_gap)\n",
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" \n",
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"# cur_ax = fig.add_subplot(2, 1, 1)\n",
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"# draw_ax(cur_ax, times, xs, \"time\", \"x\", alpha=1.0, legend=None)\n",
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"\n",
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"# cur_ax = fig.add_subplot(2, 1, 2)\n",
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"# draw_ax(cur_ax, times, ys, \"time\", \"y\", alpha=0.1, legend=\"ground truth\")\n",
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" \n",
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"# train_times, train_xs, train_ys = get_data(\"train\")\n",
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"# draw_ax(cur_ax, train_times, train_ys, None, None, alpha=1.0, color='r', legend=None, plot_only=True)\n",
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" \n",
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"# valid_times, valid_xs, valid_ys = get_data(\"valid\")\n",
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"# draw_ax(cur_ax, valid_times, valid_ys, None, None, alpha=1.0, color='g', legend=None, plot_only=True)\n",
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" \n",
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"# test_times, test_xs, test_ys = get_data(\"test\")\n",
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"# draw_ax(cur_ax, test_times, test_ys, None, None, alpha=1.0, color='b', legend=None, plot_only=True)\n",
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" \n",
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"# # optimize MLP models\n",
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"# # [train_preds, valid_preds, test_preds] = optimize_fn(train_xs, train_ys, [train_xs, valid_xs, test_xs])\n",
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"# # draw_ax(cur_ax, train_times, train_preds, None, None,\n",
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"# # alpha=1.0, linestyle='--', color='r', legend=\"MLP\", plot_only=True)\n",
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"# # import pdb; pdb.set_trace()\n",
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"# # draw_ax(cur_ax, valid_times, valid_preds, None, None,\n",
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"# # alpha=1.0, linestyle='--', color='g', legend=None, plot_only=True)\n",
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"# # draw_ax(cur_ax, test_times, test_preds, None, None,\n",
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"# # alpha=1.0, linestyle='--', color='b', legend=None, plot_only=True)\n",
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"\n",
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"# plt.legend(loc=1, fontsize=LegendFontsize)\n",
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"\n",
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"# fig.savefig(save_path, dpi=dpi, bbox_inches=\"tight\", format=\"pdf\")\n",
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"# plt.close(\"all\")\n",
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"# # plt.show()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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@ -17,7 +17,7 @@ gpu=$1
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market=$2
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# algorithms="NAIVE-V1 NAIVE-V2 MLP GRU LSTM ALSTM XGBoost LightGBM SFM TabNet DoubleE"
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algorithms="MLP GRU LSTM ALSTM XGBoost LightGBM SFM TabNet DoubleE"
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algorithms="XGBoost LightGBM SFM TabNet DoubleE"
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for alg in ${algorithms}
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do
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@ -1,251 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "filled-multiple",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The root path: /Users/xuanyidong\n",
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"The library path: /Users/xuanyidong/lib\n"
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]
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},
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{
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"ename": "AssertionError",
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"evalue": "/Users/xuanyidong/lib does not exist",
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"output_type": "error",
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"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
|
||||
}
|
||||
@ -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
|
||||
}
|
||||
@ -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
53
tests/test_super_norm.py
Normal 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)
|
||||
Loading…
Reference in New Issue
Block a user