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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Training on more temporal data is better. \n",
"If it is not better with default split, try a fixed split. If it is not better with that try gradually sampling older data less. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from utils.tools import dotdict\n",
"from exp.exp_informer import Exp_Informer\n",
"import torch\n",
"from utils.ipynb_helpers import setting_from_args, handle_gpu"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"args = dotdict()\n",
"\n",
"args.model = \"informer\" # model of experiment, options: [informer, informerstack, informerlight(TBD)]\n",
"\n",
"args.data = \"custom\" # data\n",
"args.root_path = \"./data/stock/\" # root path of data file\n",
"\n",
"\n",
"args.data_path = \"close.csv\" # data file\n",
"args.features = \"MS\" # forecasting task, options:[M, S, MS]; M:multivariate predict multivariate, S:univariate predict univariate, MS:multivariate predict univariate\n",
"args.target = \"XOM_close\" # target feature in S or MS task\n",
"args.freq = \"t\" # freq for time features encoding, options:[s:secondly, t:minutely, h:hourly, d:daily, b:business days, w:weekly, m:monthly], you can also use more detailed freq like 15min or 3h\n",
"args.checkpoints = \"./checkpoints\" # location of model checkpoints\n",
"\n",
"# Informer decoder input: concat[start token series(label_len), zero padding series(pred_len)]\n",
"\n",
"args.c_out = 1 # output size\n",
"args.factor = 5 # probsparse attn factor\n",
"args.d_model = 512 # dimension of model\n",
"args.n_heads = 8 # num of heads\n",
"args.e_layers = 2 # num of encoder layers\n",
"args.d_layers = 1 # num of decoder layers\n",
"args.d_ff = 2048 # dimension of fcn in model\n",
"args.dropout = 0.05 # dropout\n",
"args.attn = \"prob\" # attention used in encoder, options:[prob, full]\n",
"args.t_embed = \"timeF\" # time features encoding, options:[timeF, fixed, learned]\n",
"args.activation = \"gelu\" # activation\n",
"args.distil = True # whether to use distilling in encoder\n",
"args.output_attention = False # whether to output attention in ecoder\n",
"args.mix = True\n",
"args.padding = 0\n",
"\n",
"args.batch_size = 32\n",
"args.learning_rate = 0.00001\n",
"args.loss = \"mse\"\n",
"args.lradj = \"type1\"\n",
"args.use_amp = False # whether to use automatic mixed precision training\n",
"\n",
"args.num_workers = 0\n",
"args.itr = 1 # number of runs\n",
"args.max_epochs = 10\n",
"args.patience = 4\n",
"args.des = \"assumption_time\"\n",
"\n",
"args.inverse = True # Defaultly False but @Zac thinks it should be True\n",
"\n",
"handle_gpu(args, None)\n",
"\n",
"# idk what this is for\n",
"args.detail_freq = args.freq\n",
"args.freq = args.freq[-1:]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"args.seq_len = 128 # input sequence length of Informer encoder\n",
"args.label_len = 64 # start token length of Informer decoder\n",
"args.pred_len = 16 # prediction sequence length\n",
"\n",
"args.cols = [args.target]\n",
"args.enc_in = 1 # encoder input size\n",
"args.dec_in = 1 # decoder input size\n",
"\n",
"\n",
"# Test data is always just the data after this date\n",
"args.date_test = \"2022-04-01\"\n",
"\n",
"exp = None\n",
"setting = None\n",
"\n",
"# \"2022-01-01\",\n",
"date_starts = [\"2021-01-01\", \"2020-01-01\", \"2018-01-01\", None]\n",
"ii = 0\n",
"for date_start in date_starts:\n",
"\n",
" args.date_start = date_start\n",
"\n",
" print(\"Args in experiment:\")\n",
" print(args)\n",
"\n",
" Exp = Exp_Informer\n",
" for _ in range(args.itr):\n",
" # setting record of experiments\n",
" setting = setting_from_args(args, ii)\n",
"\n",
" # set experiments\n",
" exp = Exp(args)\n",
"\n",
" # train\n",
" print(f\"=======date_start : {args.date_start}=====================\")\n",
" print(f\">>>>>>>start training : {setting}>>>>>>>>>>>>>>>>>>>>>>>>>>\")\n",
" exp.train(setting)\n",
"\n",
" # test\n",
" print(f\">>>>>>>testing : {setting}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\")\n",
" exp.test(setting)\n",
"\n",
" torch.cuda.empty_cache()\n",
" ii += 1"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "former",
"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": "3.10.6 (main, Oct 24 2022, 16:07:47) [GCC 11.2.0]"
},
"vscode": {
"interpreter": {
"hash": "44e5710a47a66ec240c2a0834fd7c20e15c61536e70be6891d892a39679ad994"
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}
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"nbformat": 4,
"nbformat_minor": 2
}
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