Follow workflow_by_code.py to train LSTM model

[JessieJie1998]

I replaced the LightGBM model with LSTM in workflow_by_code.py but had trouble when running the code. The error is "Index Error: too many indices for tensor of dimension 0". Can someone help me with workflow_by_code-LSTM.py
I can run 'qrun benchmarks/LSTM/workflow_confid_lstm_Alpha158.yml' successfully.

import sys, site
from pathlib import Path


try:
    import qlib
except ImportError:
    # install qlib
    get_ipython().system(' pip install pyqlib')
    # reload
    site.main()

scripts_dir = Path.cwd().parent.joinpath("scripts")
if not scripts_dir.joinpath("get_data.py").exists():
    # download get_data.py script
    scripts_dir = Path("~/tmp/qlib_code/scripts").expanduser().resolve()
    scripts_dir.mkdir(parents=True, exist_ok=True)
    import requests
    with requests.get("https://raw.githubusercontent.com/microsoft/qlib/main/scripts/get_data.py") as resp:
        with open(scripts_dir.joinpath("get_data.py"), "wb") as fp:
            fp.write(resp.content)



import qlib
import sys
print(qlib.__file__)
import pandas as pd
from qlib.config import REG_CN
from qlib.contrib.model.pytorch_lstm_ts import LSTM
from qlib.contrib.data.handler import Alpha158, AlphaJessie
from qlib.contrib.strategy.strategy import TopkDropoutStrategy
from qlib.contrib.evaluate import (
    backtest as normal_backtest,
    risk_analysis,
)
from qlib.utils import exists_qlib_data, init_instance_by_config
from qlib.workflow import R
from qlib.workflow.record_temp import SignalRecord, PortAnaRecord
from qlib.utils import flatten_dict


# use default data
# NOTE: need to download data from remote: python scripts/get_data.py qlib_data_cn --target_dir ~/.qlib/qlib_data/cn_data
provider_uri = "~/.qlib/qlib_data/cn_data"  # target_dir
if not exists_qlib_data(provider_uri):
    print(f"Qlib data is not found in {provider_uri}")
    sys.path.append(str(scripts_dir))
    from get_data import GetData
    GetData().qlib_data(target_dir=provider_uri, region=REG_CN)
qlib.init(provider_uri=provider_uri, region=REG_CN, dataset_cache=None)




market = "csi100"
benchmark = "SH000300"


# # train model


###################################
# train model
###################################
data_handler_config = {
    "start_time": "2008-01-01",
    "end_time": "2020-08-01",
    "fit_start_time": "2008-01-01",
    "fit_end_time": "2014-12-31",
    "instruments": market,
    "infer_processors": [
        {
            "class" : "FilterCol", 
            "kwargs": { "fields_group": "feature", 
                           "col_list": ["RESI5", "WVMA5", "RSQR5", "KLEN", "RSQR10", "CORR5", "CORD5", "CORR10", 
                                        "ROC60", "RESI10", "VSTD5", "RSQR60", "CORR60", "WVMA60", "STD5", 
                                        "RSQR20", "CORD60", "CORD10", "CORR20", "KLOW"],
                          },
        },
        {
             "class" : "RobustZScoreNorm", 
             "kwargs":{"fields_group": "feature",
                          "clip_outlier": True,
                      },
        },
        {
            "class": "Fillna",
            "kwargs": {
                "fields_group": "feature",
            }
        }
    ],
    "learn_processors": [
        {
            "class" : "DropnaLabel", 
        },
        {
            "class" : "CSRankNorm", 
            "kwargs":{"fields_group": "label",},
        },
    ],
    "label": ["Ref($close, -2) / Ref($close, -1) - 1"] ,
}

task = {
    "model": {
        "class": "LSTM",
        "module_path": "qlib.contrib.model.pytorch_lstm_ts",
        "kwargs": {
            "d_feat": 20,
            "hidden_size": 64,
            "num_layers": 2,
            "dropout": 0.0,
            "n_epochs": 200,
            "lr": 1e-3,
            "early_stop": 10,
            "batch_size": 800,
            "metric": "loss",
            "loss": "mse",
            "n_jobs": 20,
            "GPU": 0,
            "rnn_type": "GRU",
        },
    },
    "dataset": {
        "class": "TSDatasetH",
        "module_path": "qlib.data.dataset",
        "kwargs": {
            "handler": {
                "class": "Alpha158",
                "module_path": "qlib.contrib.data.handler",
                "kwargs": data_handler_config,
            },
            "segments": {
                "train": ("2008-01-01", "2014-12-31"),
                "valid": ("2015-01-01", "2016-12-31"),
                "test": ("2017-01-01", "2020-08-01"),
            },
            "step_len": 20,
        },
    },
}

# model initiaiton
model = init_instance_by_config(task["model"])
dataset = init_instance_by_config(task["dataset"])

# start exp to train modelg
with R.start(experiment_name="train_model"):
    R.log_params(**flatten_dict(task))
    model.fit(dataset)
    R.save_objects(trained_model=model)
    rid = R.get_recorder().id

[Derek-Wds]

Hi @JessieJie1998 , this error is caused by the fact that pytorch loader gives the model a batch (the last batch) with only one data sample, which results in error when calculating the loss. You could either fix the bug by adding drop_last=True argument to the train_loader and valid_loader in the file pytorch_lstm_ts.py, or you could wait for the merge of the related PR.

Hope it helps!

[JessieJie1998]

Thanks!