update

stock-forcast-models/mainInformer.py

@@ -98,9 +98,7 @@ Exp = Exp_Informer
 for ii in range(args.itr):
     # setting record of experiments
     setting = '{}_{}_ft{}_sl{}_ll{}_pl{}_dm{}_nh{}_el{}_dl{}_df{}_at{}_fc{}_eb{}_dt{}_mx{}_{}_{}'.format(args.model, args.data, args.features,
-                                                                                                       args.seq_len, args.label_len, args.pred_len,
-                                                                                                       args.d_model, args.n_heads, args.e_layers, args.d_layers, args.d_ff, args.attn, args.factor,
-                                                                                                       args.embed, args.distil, args.mix, args.des, ii)
+                                                                                     args.embed, args.distil, args.mix, args.des, ii)
 
     exp = Exp(args)  # set experiments
     print('>>>>>>>start training : {}>>>>>>>>>>>>>>>>>>>>>>>>>>'.format(setting))

stock-forcast-models/mainInformer.py.bak

@@ -0,0 +1,114 @@
+import argparse
+import os
+import mindspore
+import mindspore.context as context
+from mindspore.train.serialization import load_checkpoint, save_checkpoint
+
+from exp.exp_informer import Exp_Informer
+
+context.set_auto_parallel_context(enable_auto_memory=True)
+
+parser = argparse.ArgumentParser(description='[Informer] Long Sequences Forecasting')
+
+parser.add_argument('--model', type=str, required=True, default='informer', help='model of experiment, options: [informer, informerstack, informerlight(TBD)]')
+
+parser.add_argument('--data', type=str, required=True, default='ETTh1', help='data')
+parser.add_argument('--root_path', type=str, default='./data/ETT/', help='root path of the data file')
+parser.add_argument('--data_path', type=str, default='ETTh1.csv', help='data file')
+parser.add_argument('--features', type=str, default='M', help='forecasting task, options:[M, S, MS]; M:multivariate predict multivariate, S:univariate predict univariate, MS:multivariate predict univariate')
+parser.add_argument('--target', type=str, default='OT', help='target feature in S or MS task')
+parser.add_argument('--freq', type=str, default='h', help='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')
+parser.add_argument('--checkpoints', type=str, default='./checkpoints/', help='location of model checkpoints')
+
+parser.add_argument('--seq_len', type=int, default=96, help='input sequence length of Informer encoder')
+parser.add_argument('--label_len', type=int, default=48, help='start token length of Informer decoder')
+parser.add_argument('--pred_len', type=int, default=24, help='prediction sequence length')
+# Informer decoder input: concat[start token series(label_len), zero padding series(pred_len)]
+
+parser.add_argument('--enc_in', type=int, default=7, help='encoder input size')
+parser.add_argument('--dec_in', type=int, default=7, help='decoder input size')
+parser.add_argument('--c_out', type=int, default=7, help='output size')
+parser.add_argument('--d_model', type=int, default=512, help='dimension of model')
+parser.add_argument('--n_heads', type=int, default=8, help='num of heads')
+parser.add_argument('--e_layers', type=int, default=2, help='num of encoder layers')
+parser.add_argument('--d_layers', type=int, default=1, help='num of decoder layers')
+parser.add_argument('--s_layers', type=str, default='3,2,1', help='num of stack encoder layers')
+parser.add_argument('--d_ff', type=int, default=2048, help='dimension of fcn')
+parser.add_argument('--factor', type=int, default=5, help='probsparse attn factor')
+parser.add_argument('--padding', type=int, default=0, help='padding type')
+parser.add_argument('--distil', action='store_false', help='whether to use distilling in encoder, using this argument means not using distilling', default=True)
+parser.add_argument('--dropout', type=float, default=0.05, help='dropout')
+parser.add_argument('--attn', type=str, default='prob', help='attention used in encoder, options:[prob, full]')
+parser.add_argument('--embed', type=str, default='timeF', help='time features encoding, options:[timeF, fixed, learned]')
+parser.add_argument('--activation', type=str, default='gelu', help='activation')
+parser.add_argument('--output_attention', action='store_true', help='whether to output attention in ecoder')
+parser.add_argument('--do_predict', action='store_true', help='whether to predict unseen future data')
+parser.add_argument('--mix', action='store_false', help='use mix attention in generative decoder', default=True)
+parser.add_argument('--cols', type=str, nargs='+', help='certain cols from the data files as the input features')
+parser.add_argument('--num_workers', type=int, default=0, help='data loader num workers')
+parser.add_argument('--itr', type=int, default=2, help='experiments times')
+parser.add_argument('--train_epochs', type=int, default=6, help='train epochs')
+parser.add_argument('--batch_size', type=int, default=32, help='batch size of train input data')
+parser.add_argument('--patience', type=int, default=3, help='early stopping patience')
+parser.add_argument('--learning_rate', type=float, default=0.0001, help='optimizer learning rate')
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--des', type=str, default='test', help='exp description')
+parser.add_argument('--loss', type=str, default='mse', help='loss function')
+parser.add_argument('--lradj', type=str, default='type1', help='adjust learning rate')
+parser.add_argument('--use_amp', action='store_true', help='use automatic mixed precision training', default=False)
+parser.add_argument('--inverse', action='store_true', help='inverse output data', default=False)
+
+parser.add_argument('--use_gpu', type=bool, default=True, help='use gpu')
+parser.add_argument('--device_target', type=str, default='GPU', choices=['GPU', 'CPU'], help='device target')
+parser.add_argument('--device_id', type=int, default=0, help='device id')
+
+args = parser.parse_args()
+
+device_target = args.device_target
+device_id = args.device_id
+
+context.set_context(mode=context.GRAPH_MODE, device_target=device_target, device_id=device_id)
+
+data_parser = {
+    'ETTh1': {'data': 'ETTh1.csv', 'T': 'OT', 'M': [7, 7, 7], 'S': [1, 1, 1], 'MS': [7, 7, 1]},
+    'ETTh2': {'data': 'ETTh2.csv', 'T': 'OT', 'M': [7, 7, 7], 'S': [1, 1, 1], 'MS': [7, 7, 1]},
+    'ETTm1': {'data': 'ETTm1.csv', 'T': 'OT', 'M': [7, 7, 7], 'S': [1, 1, 1], 'MS': [7, 7, 1]},
+    'ETTm2': {'data': 'ETTm2.csv', 'T': 'OT', 'M': [7, 7, 7], 'S': [1, 1, 1], 'MS': [7, 7, 1]},
+    'WTH': {'data': 'WTH.csv', 'T': 'WetBulbCelsius', 'M': [12, 12, 12], 'S': [1, 1, 1], 'MS': [12, 12, 1]},
+    'ECL': {'data': 'ECL.csv', 'T': 'MT_320', 'M': [321, 321, 321], 'S': [1, 1, 1], 'MS': [321, 321, 1]},
+    'Solar': {'data': 'solar_AL.csv', 'T': 'POWER_136', 'M': [137, 137, 137], 'S': [1, 1, 1], 'MS': [137, 137, 1]},
+}
+
+if args.data in data_parser.keys():
+    data_info = data_parser[args.data]
+    args.data_path = data_info['data']
+    args.target = data_info['T']
+    args.enc_in, args.dec_in, args.c_out = data_info[args.features]
+
+args.s_layers = [int(s_l) for s_l in args.s_layers.replace(' ', '').split(',')]
+args.detail_freq = args.freq
+args.freq = args.freq[-1:]
+
+print('Args in experiment:')
+print(args)
+
+Exp = Exp_Informer
+
+for ii in range(args.itr):
+    # setting record of experiments
+    setting = '{}_{}_ft{}_sl{}_ll{}_pl{}_dm{}_nh{}_el{}_dl{}_df{}_at{}_fc{}_eb{}_dt{}_mx{}_{}_{}'.format(args.model, args.data, args.features,
+                                                                                                       args.seq_len, args.label_len, args.pred_len,
+                                                                                                       args.d_model, args.n_heads, args.e_layers, args.d_layers, args.d_ff, args.attn, args.factor,
+                                                                                                       args.embed, args.distil, args.mix, args.des, ii)
+
+    exp = Exp(args)  # set experiments
+    print('>>>>>>>start training : {}>>>>>>>>>>>>>>>>>>>>>>>>>>'.format(setting))
+    exp.train(setting)
+
+    print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))
+    exp.test(setting)
+
+    if args.do_predict:
+        print('>>>>>>>predicting : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))
+        exp.predict(setting, True)

stock-forcast-web/vue.config.js

@@ -1,3 +1,4 @@
+
 // vue.config.js
 module.exports = {
     chainWebpack: config => {