xautodl/notebooks/spaces/test.py
2021-05-26 01:53:44 -07:00

77 lines
2.1 KiB
Python

import os
import sys
import qlib
import pprint
import numpy as np
import pandas as pd
from pathlib import Path
import torch
__file__ = os.path.dirname(os.path.realpath("__file__"))
lib_dir = (Path(__file__).parent / ".." / "lib").resolve()
print("library path: {:}".format(lib_dir))
assert lib_dir.exists(), "{:} does not exist".format(lib_dir)
if str(lib_dir) not in sys.path:
sys.path.insert(0, str(lib_dir))
from trade_models import get_transformer
from qlib import config as qconfig
from qlib.utils import init_instance_by_config
from qlib.model.base import Model
from qlib.data.dataset import DatasetH
from qlib.data.dataset.handler import DataHandlerLP
qlib.init(provider_uri="~/.qlib/qlib_data/cn_data", region=qconfig.REG_CN)
dataset_config = {
"class": "DatasetH",
"module_path": "qlib.data.dataset",
"kwargs": {
"handler": {
"class": "Alpha360",
"module_path": "qlib.contrib.data.handler",
"kwargs": {
"start_time": "2008-01-01",
"end_time": "2020-08-01",
"fit_start_time": "2008-01-01",
"fit_end_time": "2014-12-31",
"instruments": "csi100",
},
},
"segments": {
"train": ("2008-01-01", "2014-12-31"),
"valid": ("2015-01-01", "2016-12-31"),
"test": ("2017-01-01", "2020-08-01"),
},
},
}
pprint.pprint(dataset_config)
dataset = init_instance_by_config(dataset_config)
df_train, df_valid, df_test = dataset.prepare(
["train", "valid", "test"],
col_set=["feature", "label"],
data_key=DataHandlerLP.DK_L,
)
model = get_transformer(None)
print(model)
features = torch.from_numpy(df_train["feature"].values).float()
labels = torch.from_numpy(df_train["label"].values).squeeze().float()
batch = list(range(2000))
predicts = model(features[batch])
mask = ~torch.isnan(labels[batch])
pred = predicts[mask]
label = labels[batch][mask]
loss = torch.nn.functional.mse_loss(pred, label)
from sklearn.metrics import mean_squared_error
mse_loss = mean_squared_error(pred.numpy(), label.numpy())