mirror of
https://github.com/wassname/ETSformer.git
synced 2026-06-27 16:43:49 +08:00
wassname
461 lines
11 KiB
Plaintext
461 lines
11 KiB
Plaintext
{
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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": "0c996f31",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-11-28T07:35:38.923189Z",
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"start_time": "2022-11-28T07:35:38.921272Z"
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}
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},
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"outputs": [],
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"source": [
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"import os\n",
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"os.sys.path.append('..')"
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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": 2,
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"id": "23a27691",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-11-28T07:35:38.952520Z",
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"start_time": "2022-11-28T07:35:38.923991Z"
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}
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},
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"outputs": [],
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"source": [
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"import warnings\n",
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"warnings.simplefilter(\"ignore\")\n",
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"\n",
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"# autoreload import your package\n",
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"%load_ext autoreload\n",
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"%autoreload 2"
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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": "e60bfdb0",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-11-28T07:35:38.970942Z",
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"start_time": "2022-11-28T07:35:38.953975Z"
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}
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},
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"outputs": [],
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"source": [
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"from loguru import logger\n",
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"logger.remove()\n",
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"logger.add(os.sys.stdout, level=\"ERROR\", colorize=True, format=\"<level>{time} | {message}</level>\")\n",
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"# import_dir(ta_dir, verbose=False)\n",
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"warnings.simplefilter(\"ignore\")"
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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": 4,
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"id": "d52338d2",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-11-28T07:35:39.765300Z",
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"start_time": "2022-11-28T07:35:38.972353Z"
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}
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},
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"outputs": [],
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"source": [
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"import torch\n",
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"import pandas as pd\n",
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"from matplotlib import pyplot as plt\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "markdown",
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"id": "20d78524",
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"metadata": {},
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"source": [
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"# Args"
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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": 5,
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"id": "750cdcc3",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-11-28T07:35:40.047980Z",
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"start_time": "2022-11-28T07:35:39.766356Z"
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}
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},
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"outputs": [],
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"source": [
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"from run import set_seed, get_args, Exp_Main"
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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": null,
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"id": "b3a40300",
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"metadata": {
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"ExecuteTime": {
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"start_time": "2022-11-28T07:36:20.551Z"
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}
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},
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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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"Args in experiment:\n",
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"Namespace(K=0, activation='sigmoid', batch_size=32, c_out=1, checkpoints='./checkpoints/', d_ff=2048, d_layers=2, d_model=512, damping_learning_rate=0, data='custom', data_path='OXY_2019.csv.gz', dec_in=7, des=\"'Exp'\", devices='0,1,2,3', dropout=0.2, e_layers=2, embed='timeF', enc_in=1, features='S', freq='h', gpu=0, itr=1, label_len=0, learning_rate=0.001, lradj='exponential_with_warmup', min_lr=1e-30, model='ETSformer', model_id='Exchange', n_heads=8, num_workers=0, optim='adam', output_attention=False, patience=5, pred_len=48, root_path='../dataset/stocks/', seq_len=128, smoothing_learning_rate=0, std=0.2, target='RSMKs_18_144_72_2ref_2ref', train_epochs=15, use_gpu=True, use_multi_gpu=False, warmup_epochs=3)\n",
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"Use GPU: cuda:0\n",
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">>>>>>>start training : Exchange_ETSformer_custom_ftS_sl128_pl48_dm512_nh8_el2_dl2_df2048_K0_lr0.001_'Exp'_0>>>>>>>>>>>>>>>>>>>>>>>>>>\n",
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"train 37203\n",
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"val 5294\n",
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"test 10632\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "60de03af10a54c008b02a13e686d7a8e",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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" 0%| | 0/15 [00:00<?, ?epoch/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"train: 0%| | 0/1162 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# mimic cli args to avoid code duplication\n",
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"argv = \"\"\"python -u run.py \\\n",
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" --root_path ../dataset/stocks/ \\\n",
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" --data_path OXY_2019.csv.gz \\\n",
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" --checkpoints ./checkpoints/ \\\n",
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" --model_id Exchange \\\n",
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" --model ETSformer \\\n",
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" --data custom \\\n",
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" --features S \\\n",
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" --seq_len 128 \\\n",
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" --pred_len 48 \\\n",
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" --e_layers 2 \\\n",
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" --d_layers 2 \\\n",
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" --enc_in 1 \\\n",
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" --c_out 1 \\\n",
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" --num_workers 0 \\\n",
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" --des 'Exp' \\\n",
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" --K 0 \\\n",
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" --learning_rate 1e-3 \\\n",
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" --target RSMKs_18_144_72_2ref_2ref \\\n",
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" --itr 1\n",
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"\"\"\"\n",
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"argv = argv.replace(\"\\\\n\", \"\").split()[3:]\n",
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"args = get_args(argv)\n",
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"args\n",
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"\n",
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"\n",
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"Exp = Exp_Main\n",
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"ii=0\n",
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"set_seed(ii)\n",
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"# setting record of experiments\n",
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"setting = '{}_{}_{}_ft{}_sl{}_pl{}_dm{}_nh{}_el{}_dl{}_df{}_K{}_lr{}_{}_{}'.format(\n",
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" args.model_id,\n",
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" args.model,\n",
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" args.data,\n",
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" args.features,\n",
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" args.seq_len,\n",
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" args.pred_len,\n",
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" args.d_model,\n",
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" args.n_heads,\n",
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" args.e_layers,\n",
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" args.d_layers,\n",
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" args.d_ff,\n",
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" args.K,\n",
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" args.learning_rate,\n",
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" args.des, ii)\n",
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"\n",
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"# if os.path.exists(os.path.join(args.checkpoints, setting)):\n",
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"# print('skipping exists')\n",
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"# continue\n",
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"\n",
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"exp = Exp(args) # set experiments\n",
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"print('>>>>>>>start training : {}>>>>>>>>>>>>>>>>>>>>>>>>>>'.format(setting))\n",
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"exp.train(setting)\n",
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"\n",
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"print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))\n",
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"exp.test(setting, data='val')\n",
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"exp.test(setting, data='test')\n",
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"\n",
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"torch.cuda.empty_cache()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "72811788",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-11-28T05:28:20.356399Z",
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"start_time": "2022-11-28T05:28:20.339961Z"
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}
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},
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"source": [
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"# Plot"
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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": null,
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"id": "92b67ed7",
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"metadata": {
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"ExecuteTime": {
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"start_time": "2022-11-28T07:36:25.268Z"
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}
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},
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"outputs": [],
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"source": [
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"setting"
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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": null,
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"id": "ebea79f7",
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"metadata": {
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"ExecuteTime": {
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"start_time": "2022-11-28T07:36:25.268Z"
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}
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},
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"outputs": [],
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"source": [
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"ds, dl = exp._get_data('test')"
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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": null,
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"id": "9833f218",
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"metadata": {
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"ExecuteTime": {
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"start_time": "2022-11-28T07:36:25.268Z"
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}
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},
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"outputs": [],
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"source": [
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"preds, trues = exp.test(setting, data='test')"
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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": null,
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"id": "5c88dbf7",
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"metadata": {
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"ExecuteTime": {
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"start_time": "2022-11-28T07:36:25.268Z"
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}
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},
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"outputs": [],
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"source": [
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"i=990\n",
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"(batch_x, batch_y, batch_x_mark, batch_y_mark) = ds[i]\n",
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"l1 = batch_x.shape[0]\n",
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"l2 = batch_y.shape[0]\n",
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"plt.plot(range(l1), batch_x[:, -1], label='past')\n",
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"plt.plot(range(l1, l1+l2), batch_y[:, -1], color='blue', ls='--', label='true')\n",
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"plt.plot(range(l1, l1+l2), preds[i], label='pred');\n",
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"plt.legend(loc='lower left')"
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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": null,
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"id": "0a25923b",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-11-28T07:33:57.703528Z",
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"start_time": "2022-11-28T07:33:57.689258Z"
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}
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"id": "665b98c3",
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"metadata": {},
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"source": [
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"# TODO check index"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fc6ff0cd",
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"metadata": {},
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"source": [
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"- s_end = index + self.seq_len\n",
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"- r_begin = index + self.seq_len - self.label_len\n",
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"- r_end = index + self.seq_len + self.pred_len"
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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": null,
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"id": "f482f601",
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"metadata": {
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"ExecuteTime": {
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"start_time": "2022-11-28T07:36:25.268Z"
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},
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"f = ds.root_path + '/' + ds.data_path\n",
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"df = pd.read_csv(f).set_index('date', drop=False)\n",
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"df = df[ds.cols[1:]]\n",
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"# df[:] = ds.scaler.transform(df.values)\n",
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"df\n",
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"# df"
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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": null,
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"id": "1893beae",
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"metadata": {
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"ExecuteTime": {
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"start_time": "2022-11-28T07:36:25.268Z"
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}
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},
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"outputs": [],
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"source": [
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"from data_provider.data_loader import Dataset_Custom\n",
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"ds2 = Dataset_Custom(\n",
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" ds.root_path,\n",
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" flag=\"test\",\n",
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"# size=ds.size,\n",
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" size=[ds.seq_len, ds.label_len, ds.pred_len],\n",
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" features=ds.features,\n",
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" data_path=ds.data_path,\n",
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" target=ds.target,\n",
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" scale=False,\n",
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" timeenc=ds.timeenc,\n",
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" freq=ds.freq\n",
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")\n",
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"\n",
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"i=99\n",
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"(batch_x, batch_y, batch_x_mark, batch_y_mark) = ds2[i]\n",
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"batch_x[-1]"
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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": null,
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"id": "f0a63562",
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"metadata": {
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"ExecuteTime": {
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"start_time": "2022-11-28T07:36:25.268Z"
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}
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},
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"outputs": [],
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"source": [
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"dt = ds2.index.iloc[i-1]\n",
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"df.loc[dt]"
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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": null,
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"id": "65c56ec5",
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"metadata": {
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"ExecuteTime": {
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"start_time": "2022-11-28T07:36:25.268Z"
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}
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},
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"outputs": [],
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"source": [
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"assert df.loc[dt].close == batch_x[-1, 0], 'index should be right'\n",
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"'OK'"
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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": null,
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"id": "692aadfa",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2dd11010",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e4413025",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "deeptime",
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"language": "python",
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"name": "deeptime"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.13"
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},
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"toc": {
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"base_numbering": 1,
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"nav_menu": {},
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"number_sections": true,
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"sideBar": true,
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"skip_h1_title": false,
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"title_cell": "Table of Contents",
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"title_sidebar": "Contents",
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"toc_cell": false,
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"toc_position": {},
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"toc_section_display": true,
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"toc_window_display": false
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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