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Add Tabnet.
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lwwang1995
142
examples/workflow_by_code_tabnet.py
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142
examples/workflow_by_code_tabnet.py
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# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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import sys
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from pathlib import Path
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import qlib
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import pandas as pd
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from qlib.config import REG_CN
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from qlib.contrib.model.tabnet import TabNetModel
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from qlib.contrib.data.handler import ALPHA360_Denoise
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from qlib.contrib.strategy.strategy import TopkDropoutStrategy
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from qlib.contrib.evaluate import (
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backtest as normal_backtest,
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risk_analysis,
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)
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from qlib.utils import exists_qlib_data
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# from qlib.model.learner import train_model
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from qlib.utils import init_instance_by_config
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import pickle
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if __name__ == "__main__":
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# use default data
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provider_uri = "~/.qlib/qlib_data/cn_data" # target_dir
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if not exists_qlib_data(provider_uri):
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print(f"Qlib data is not found in {provider_uri}")
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sys.path.append(str(Path(__file__).resolve().parent.parent.joinpath("scripts")))
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from get_data import GetData
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GetData().qlib_data(target_dir=provider_uri, region=REG_CN)
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qlib.init(provider_uri=provider_uri, region=REG_CN)
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MARKET = "csi300"
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BENCHMARK = "SH000300"
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###################################
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# train model
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###################################
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DATA_HANDLER_CONFIG = {
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"start_time": "2008-01-01",
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"end_time": "2020-08-01",
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"fit_start_time": "2008-01-01",
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"fit_end_time": "2014-12-31",
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"instruments": MARKET,
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}
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TRAINER_CONFIG = {
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"train_start_time": "2008-01-01",
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"train_end_time": "2014-12-31",
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"validate_start_time": "2015-01-01",
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"validate_end_time": "2016-12-31",
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"test_start_time": "2017-01-01",
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"test_end_time": "2020-08-01",
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}
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task = {
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"model": {
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"class": "TabNetModel",
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"module_path": "qlib.contrib.model.tabnet",
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"kwargs": {
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"n_d": 8,
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"n_a": 8,
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"n_steps": 3,
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"gamma": 1.3,
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"n_independent": 2,
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"n_shared": 2,
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"seed": 0,
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"momentum": 0.02,
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"lambda_sparse": 1e-3,
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"optimizer_params": {'lr':2e-3}
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},
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},
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"dataset": {
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"class": "DatasetH",
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"module_path": "qlib.data.dataset",
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"kwargs": {
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"handler": {
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"class": "ALPHA360_Denoise",
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"module_path": "qlib.contrib.data.handler",
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"kwargs": DATA_HANDLER_CONFIG,
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},
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"segments": {
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"train": ("2008-01-01", "2014-12-31"),
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"valid": ("2015-01-01", "2016-12-31"),
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"test": ("2017-01-01", "2020-08-01"),
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},
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},
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}
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# You shoud record the data in specific sequence
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# "record": ['SignalRecord', 'SigAnaRecord', 'PortAnaRecord'],
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}
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# model = train_model(task)
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model = init_instance_by_config(task["model"])
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dataset = init_instance_by_config(task["dataset"])
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model.fit(dataset)
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pred_score = model.predict(dataset)
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# save pred_score to file
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pred_score_path = Path("~/tmp/qlib/pred_score.pkl").expanduser()
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pred_score_path.parent.mkdir(exist_ok=True, parents=True)
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pred_score.to_pickle(pred_score_path)
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###################################
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# backtest
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###################################
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STRATEGY_CONFIG = {
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"topk": 50,
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"n_drop": 5,
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}
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BACKTEST_CONFIG = {
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"verbose": False,
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"limit_threshold": 0.095,
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"account": 100000000,
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"benchmark": BENCHMARK,
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"deal_price": "close",
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"open_cost": 0.0005,
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"close_cost": 0.0015,
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"min_cost": 5,
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}
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# use default strategy
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# custom Strategy, refer to: TODO: Strategy API url
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strategy = TopkDropoutStrategy(**STRATEGY_CONFIG)
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report_normal, positions_normal = normal_backtest(pred_score, strategy=strategy, **BACKTEST_CONFIG)
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###################################
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# analyze
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# If need a more detailed analysis, refer to: examples/train_and_bakctest.ipynb
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###################################
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analysis = dict()
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analysis["excess_return_without_cost"] = risk_analysis(report_normal["return"] - report_normal["bench"])
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analysis["excess_return_with_cost"] = risk_analysis(
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report_normal["return"] - report_normal["bench"] - report_normal["cost"]
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)
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analysis_df = pd.concat(analysis) # type: pd.DataFrame
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print(analysis_df)
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80
qlib/contrib/model/tabnet.py
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80
qlib/contrib/model/tabnet.py
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# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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import numpy as np
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import pandas as pd
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from pytorch_tabnet.tab_model import TabNetRegressor
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from ...model.base import Model
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from ...data.dataset import DatasetH
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from ...data.dataset.handler import DataHandlerLP
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class TabNetModel(Model):
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"""TabNetModel Model"""
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def __init__(self, n_d, n_a,
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n_steps,
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gamma,
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n_independent,
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n_shared,
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seed,
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momentum,
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lambda_sparse,
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optimizer_params,
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**kwargs):
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self.model = None
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self.n_d = n_d
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self.n_a = n_a
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self.n_steps = n_steps
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self.gamma = gamma
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self.n_independent = n_independent
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self.n_shared = n_shared
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self.seed = seed
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self.momentum = momentum
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self.lambda_sparse = lambda_sparse
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self.optimizer_params = optimizer_params
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def fit(
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self,
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dataset: DatasetH,
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n_d=8,
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n_a=8,
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n_steps=3,
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gamma=1.3,
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n_independent=2,
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n_shared=2,
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seed=0,
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momentum=0.02,
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lambda_sparse=1e-3,
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optimizer_params={'lr':2e-3},
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**kwargs
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):
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df_train, df_valid = dataset.prepare(
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["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
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)
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x_train, y_train = df_train["feature"].values, df_train["label"].values*100
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x_valid, y_valid = df_valid["feature"].values, df_valid["label"].values*100
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self.model = TabNetRegressor(
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n_d=self.n_d,
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n_a=self.n_a,
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n_steps=self.n_steps,
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gamma=self.gamma,
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n_independent=self.n_independent,
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n_shared=self.n_shared,
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seed=self.seed,
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momentum=self.momentum,
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lambda_sparse=self.lambda_sparse,
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optimizer_params=self.optimizer_params,
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**kwargs
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)
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self.model.fit(x_train, y_train, eval_set=[(x_valid, y_valid)])
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def predict(self, dataset):
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if self.model is None:
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raise ValueError("model is not fitted yet!")
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x_test = dataset.prepare("test", col_set="feature")
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test_pred = self.model.predict(x_test.values)
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return pd.Series(test_pred.reshape([-1]), index=x_test.index)
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