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Merge pull request microsoft#444 from zhupr/feature_importance
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add get_feature_importance to model interpret
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@you-n-g
you-n-g committed May 28, 2021
2 parents 2c1d408 + 922aa4b commit 8cd34d6
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Showing 26 changed files with 592 additions and 822 deletions.
19 changes: 3 additions & 16 deletions examples/highfreq/workflow.py
View file
@@ -1,24 +1,13 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.

import sys
import fire
from pathlib import Path

import qlib
import pickle
import numpy as np
import pandas as pd
from qlib.config import REG_CN, HIGH_FREQ_CONFIG
from qlib.contrib.model.gbdt import LGBModel
from qlib.contrib.data.handler import Alpha158
from qlib.contrib.strategy.strategy import TopkDropoutStrategy
from qlib.contrib.evaluate import (
backtest as normal_backtest,
risk_analysis,
)

from qlib.utils import init_instance_by_config, exists_qlib_data

from qlib.utils import init_instance_by_config
from qlib.data.dataset.handler import DataHandlerLP
from qlib.data.ops import Operators
from qlib.data.data import Cal
Expand Down Expand Up @@ -96,9 +85,7 @@ def _init_qlib(self):
# use yahoo_cn_1min data
QLIB_INIT_CONFIG = {**HIGH_FREQ_CONFIG, **self.SPEC_CONF}
provider_uri = QLIB_INIT_CONFIG.get("provider_uri")
if not exists_qlib_data(provider_uri):
print(f"Qlib data is not found in {provider_uri}")
GetData().qlib_data(target_dir=provider_uri, interval="1min", region=REG_CN)
GetData().qlib_data(target_dir=provider_uri, interval="1min", region=REG_CN, exists_skip=True)
qlib.init(**QLIB_INIT_CONFIG)

def _prepare_calender_cache(self):
Expand Down
58 changes: 14 additions & 44 deletions examples/hyperparameter/LightGBM/hyperparameter_158.py
View file
@@ -1,46 +1,9 @@
import qlib
from qlib.config import REG_CN
from qlib.utils import exists_qlib_data, init_instance_by_config
import optuna

provider_uri = "~/.qlib/qlib_data/cn_data"
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="cn")
qlib.init(provider_uri=provider_uri, region="cn")

market = "csi300"
benchmark = "SH000300"

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,
}
dataset_task = {
"dataset": {
"class": "DatasetH",
"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"),
},
},
},
}
dataset = init_instance_by_config(dataset_task["dataset"])
from qlib.config import REG_CN
from qlib.utils import init_instance_by_config
from qlib.tests.config import CSI300_DATASET_CONFIG
from qlib.tests.data import GetData


def objective(trial):
Expand All @@ -65,12 +28,19 @@ def objective(trial):
},
},
}

evals_result = dict()
model = init_instance_by_config(task["model"])
model.fit(dataset, evals_result=evals_result)
return min(evals_result["valid"])


study = optuna.Study(study_name="LGBM_158", storage="sqlite:///db.sqlite3")
study.optimize(objective, n_jobs=6)
if __name__ == "__main__":

provider_uri = "~/.qlib/qlib_data/cn_data"
GetData().qlib_data(target_dir=provider_uri, region=REG_CN, exists_skip=True)
qlib.init(provider_uri=provider_uri, region="cn")

dataset = init_instance_by_config(CSI300_DATASET_CONFIG)

study = optuna.Study(study_name="LGBM_158", storage="sqlite:///db.sqlite3")
study.optimize(objective, n_jobs=6)
57 changes: 15 additions & 42 deletions examples/hyperparameter/LightGBM/hyperparameter_360.py
View file
@@ -1,46 +1,11 @@
import qlib
from qlib.config import REG_CN
from qlib.utils import exists_qlib_data, init_instance_by_config
import optuna
from qlib.config import REG_CN
from qlib.utils import init_instance_by_config
from qlib.tests.data import GetData
from qlib.tests.config import get_dataset_config, CSI300_MARKET, DATASET_ALPHA360_CLASS

provider_uri = "~/.qlib/qlib_data/cn_data"
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="cn")
qlib.init(provider_uri=provider_uri, region="cn")

market = "csi300"
benchmark = "SH000300"

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,
}
dataset_task = {
"dataset": {
"class": "DatasetH",
"module_path": "qlib.data.dataset",
"kwargs": {
"handler": {
"class": "Alpha360",
"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"),
},
},
},
}
dataset = init_instance_by_config(dataset_task["dataset"])
DATASET_CONFIG = get_dataset_config(market=CSI300_MARKET, dataset_class=DATASET_ALPHA360_CLASS)


def objective(trial):
Expand Down Expand Up @@ -72,5 +37,13 @@ def objective(trial):
return min(evals_result["valid"])


study = optuna.Study(study_name="LGBM_360", storage="sqlite:///db.sqlite3")
study.optimize(objective, n_jobs=6)
if __name__ == "__main__":

provider_uri = "~/.qlib/qlib_data/cn_data"
GetData().qlib_data(target_dir=provider_uri, region=REG_CN, exists_skip=True)
qlib.init(provider_uri=provider_uri, region=REG_CN)

dataset = init_instance_by_config(DATASET_CONFIG)

study = optuna.Study(study_name="LGBM_360", storage="sqlite:///db.sqlite3")
study.optimize(objective, n_jobs=6)
32 changes: 32 additions & 0 deletions examples/model_interpreter/feature.py
View file
@@ -0,0 +1,32 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.


import qlib
from qlib.config import REG_CN

from qlib.utils import init_instance_by_config
from qlib.tests.data import GetData
from qlib.tests.config import CSI300_GBDT_TASK


if __name__ == "__main__":

# use default data
provider_uri = "~/.qlib/qlib_data/cn_data" # target_dir
GetData().qlib_data(target_dir=provider_uri, region=REG_CN, exists_skip=True)

qlib.init(provider_uri=provider_uri, region=REG_CN)

###################################
# train model
###################################
# model initialization
model = init_instance_by_config(CSI300_GBDT_TASK["model"])
dataset = init_instance_by_config(CSI300_GBDT_TASK["dataset"])
model.fit(dataset)

# get model feature importance
feature_importance = model.get_feature_importance()
print("feature importance:")
print(feature_importance)
62 changes: 4 additions & 58 deletions examples/model_rolling/task_manager_rolling.py
View file
Expand Up @@ -17,63 +17,7 @@
from qlib.workflow.task.collect import RecorderCollector
from qlib.model.ens.group import RollingGroup
from qlib.model.trainer import TrainerRM


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": "csi100",
}

dataset_config = {
"class": "DatasetH",
"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"),
},
},
}

record_config = [
{
"class": "SignalRecord",
"module_path": "qlib.workflow.record_temp",
},
{
"class": "SigAnaRecord",
"module_path": "qlib.workflow.record_temp",
},
]

# use lgb
task_lgb_config = {
"model": {
"class": "LGBModel",
"module_path": "qlib.contrib.model.gbdt",
},
"dataset": dataset_config,
"record": record_config,
}

# use xgboost
task_xgboost_config = {
"model": {
"class": "XGBModel",
"module_path": "qlib.contrib.model.xgboost",
},
"dataset": dataset_config,
"record": record_config,
}
from qlib.tests.config import CSI100_RECORD_LGB_TASK_CONFIG, CSI100_RECORD_XGBOOST_TASK_CONFIG


class RollingTaskExample:
Expand All @@ -85,11 +29,13 @@ def __init__(
task_db_name="rolling_db",
experiment_name="rolling_exp",
task_pool="rolling_task",
task_config=[task_xgboost_config, task_lgb_config],
task_config=None,
rolling_step=550,
rolling_type=RollingGen.ROLL_SD,
):
# TaskManager config
if task_config is None:
task_config = [CSI100_RECORD_XGBOOST_TASK_CONFIG, CSI100_RECORD_LGB_TASK_CONFIG]
mongo_conf = {
"task_url": task_url,
"task_db_name": task_db_name,
Expand Down
62 changes: 4 additions & 58 deletions examples/online_srv/online_management_simulate.py
View file
Expand Up @@ -13,63 +13,7 @@
from qlib.workflow.online.strategy import RollingStrategy
from qlib.workflow.task.gen import RollingGen
from qlib.workflow.task.manage import TaskManager


data_handler_config = {
"start_time": "2018-01-01",
"end_time": "2018-10-31",
"fit_start_time": "2018-01-01",
"fit_end_time": "2018-03-31",
"instruments": "csi100",
}

dataset_config = {
"class": "DatasetH",
"module_path": "qlib.data.dataset",
"kwargs": {
"handler": {
"class": "Alpha158",
"module_path": "qlib.contrib.data.handler",
"kwargs": data_handler_config,
},
"segments": {
"train": ("2018-01-01", "2018-03-31"),
"valid": ("2018-04-01", "2018-05-31"),
"test": ("2018-06-01", "2018-09-10"),
},
},
}

record_config = [
{
"class": "SignalRecord",
"module_path": "qlib.workflow.record_temp",
},
{
"class": "SigAnaRecord",
"module_path": "qlib.workflow.record_temp",
},
]

# use lgb model
task_lgb_config = {
"model": {
"class": "LGBModel",
"module_path": "qlib.contrib.model.gbdt",
},
"dataset": dataset_config,
"record": record_config,
}

# use xgboost model
task_xgboost_config = {
"model": {
"class": "XGBModel",
"module_path": "qlib.contrib.model.xgboost",
},
"dataset": dataset_config,
"record": record_config,
}
from qlib.tests.config import CSI100_RECORD_LGB_TASK_CONFIG, CSI100_RECORD_XGBOOST_TASK_CONFIG


class OnlineSimulationExample:
Expand All @@ -84,7 +28,7 @@ def __init__(
rolling_step=80,
start_time="2018-09-10",
end_time="2018-10-31",
tasks=[task_xgboost_config, task_lgb_config],
tasks=None,
):
"""
Init OnlineManagerExample.
Expand All @@ -101,6 +45,8 @@ def __init__(
end_time (str, optional): the end time of simulating. Defaults to "2018-10-31".
tasks (dict or list[dict]): a set of the task config waiting for rolling and training
"""
if tasks is None:
tasks = [CSI100_RECORD_XGBOOST_TASK_CONFIG, CSI100_RECORD_LGB_TASK_CONFIG]
self.exp_name = exp_name
self.task_pool = task_pool
self.start_time = start_time
Expand Down

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