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format code and add example

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lzh222333
2021-03-12 08:24:21 +00:00
parent 6d8aa215d6
commit 9d84d389ab
8 changed files with 310 additions and 216 deletions
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176
examples/taskmanager/task_manager_rolling.ipynb
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@@ -1,176 +0,0 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import qlib\n",
"from qlib.config import REG_CN\n",
"from qlib.workflow.task.gen import RollingGen, task_generator\n",
"from qlib.workflow.task.manage import TaskManager\n",
"from qlib.config import C\n",
"\n",
"data_handler_template = {\n",
" \"start_time\": \"2008-01-01\",\n",
" \"end_time\": \"2020-08-01\",\n",
" \"fit_start_time\": \"2008-01-01\",\n",
" \"fit_end_time\": \"2014-12-31\",\n",
" \"instruments\": 'csi100',\n",
"}\n",
"\n",
"dataset_template = {\n",
" \"class\": \"DatasetH\",\n",
" \"module_path\": \"qlib.data.dataset\",\n",
" \"kwargs\": {\n",
" \"handler\": {\n",
" \"class\": \"Alpha158\",\n",
" \"module_path\": \"qlib.contrib.data.handler\",\n",
" \"kwargs\": data_handler_template,\n",
" },\n",
" \"segments\": {\n",
" \"train\": (\"2008-01-01\", \"2014-12-31\"),\n",
" \"valid\": (\"2015-01-01\", \"2016-12-31\"),\n",
" \"test\": (\"2017-01-01\", \"2020-08-01\"),\n",
" },\n",
" },\n",
" }\n",
"\n",
"record_template = [\n",
" {\n",
" \"class\": \"SignalRecord\",\n",
" \"module_path\": \"qlib.workflow.record_temp\",\n",
" },\n",
" {\n",
" \"class\": \"SigAnaRecord\",\n",
" \"module_path\": \"qlib.workflow.record_temp\",\n",
" }\n",
"]\n",
"\n",
"# use lgb\n",
"lgb_task_template = {\n",
" \"model\": {\n",
" \"class\": \"LGBModel\",\n",
" \"module_path\": \"qlib.contrib.model.gbdt\",\n",
" },\n",
" \"dataset\": dataset_template,\n",
" \"record\": record_template,\n",
"}\n",
"\n",
"# use xgboost\n",
"xgboost_task_template = {\n",
" \"model\": {\n",
" \"class\": \"XGBModel\",\n",
" \"module_path\": \"qlib.contrib.model.xgboost\",\n",
" },\n",
" \"dataset\": dataset_template,\n",
" \"record\": record_template,\n",
"}\n",
"\n",
"provider_uri = \"~/.qlib/qlib_data/cn_data\" # target_dir\n",
"qlib.init(provider_uri=provider_uri, region=REG_CN)\n",
"\n",
"C[\"mongo\"] = {\n",
" \"task_url\" : \"mongodb://localhost:27017/\", # maybe you need to change it to your url\n",
" \"task_db_name\" : \"rolling_db\"\n",
"}\n",
"\n",
"exp_name = 'rolling_exp' # experiment name, will be used as the experiment in MLflow\n",
"task_pool = 'rolling_task' # task pool name, will be used as the document in MongoDB"
]
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"tasks = task_generator(\n",
" xgboost_task_template, # default task name\n",
" RollingGen(step=550,rtype=RollingGen.ROLL_SD), # generate different date segment\n",
" task_lgb=lgb_task_template # use \"task_lgb\" as the task name\n",
")\n",
"# Uncomment next two lines to see the generated tasks\n",
"# from pprint import pprint\n",
"# pprint(tasks)\n",
"tm = TaskManager(task_pool=task_pool)\n",
"tm.create_task(tasks) # all tasks will be saved to MongoDB"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"from qlib.workflow.task.manage import run_task\n",
"from qlib.workflow.task.collect import TaskCollector\n",
"from qlib.model.trainer import task_train\n",
"\n",
"run_task(task_train, task_pool, experiment_name=exp_name) # all tasks will be trained using \"task_train\" method"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"def get_task_key(task):\n",
" task_key = task[\"task_key\"]\n",
" rolling_end_timestamp = task[\"dataset\"][\"kwargs\"][\"segments\"][\"test\"][1]\n",
" return task_key, rolling_end_timestamp.strftime('%Y-%m-%d')\n",
"\n",
"def my_filter(task):\n",
" # only choose the results of \"task_lgb\" and test segment end in 2019 from all tasks\n",
" task_key, rolling_end = get_task_key(task)\n",
" if task_key==\"task_lgb\" and rolling_end.startswith('2019'):\n",
" return True\n",
" return False\n",
"\n",
"# name tasks by \"get_task_key\" and filter tasks by \"my_filter\"\n",
"pred_rolling = TaskCollector.collect_predictions(exp_name, get_task_key, my_filter) \n",
"pred_rolling"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "3.6.5-final"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

26
examples/taskmanager/task_manager_rolling.py
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@@ -16,7 +16,11 @@ dataset_config = {
"class": "DatasetH",
"module_path": "qlib.data.dataset",
"kwargs": {
"handler": {"class": "Alpha158", "module_path": "qlib.contrib.data.handler", "kwargs": data_handler_config,},
"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"),
@@ -26,20 +30,32 @@ dataset_config = {
}
record_config = [
{"class": "SignalRecord", "module_path": "qlib.workflow.record_temp",},
{"class": "SigAnaRecord", "module_path": "qlib.workflow.record_temp",},
{
"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",},
"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",},
"model": {
"class": "XGBModel",
"module_path": "qlib.contrib.model.xgboost",
},
"dataset": dataset_config,
"record": record_config,
}

244
examples/taskmanager/task_manager_rolling_with_updating.py Normal file
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@@ -0,0 +1,244 @@
import qlib
import fire
import mlflow
from qlib.config import C
from qlib.workflow import R
from qlib.config import REG_CN
from qlib.model.trainer import task_train
from qlib.workflow.task.manage import run_task
from qlib.workflow.task.manage import TaskManager
from qlib.workflow.task.utils import TimeAdjuster
from qlib.workflow.task.update import ModelUpdater
from qlib.workflow.task.collect import TaskCollector
from qlib.workflow.task.gen import RollingGen, task_generator
data_handler_config = {
"start_time": "2013-01-01",
"end_time": "2020-09-25",
"fit_start_time": "2013-01-01",
"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": ("2013-01-01", "2014-12-31"),
"valid": ("2015-01-01", "2015-12-31"),
"test": ("2016-01-01", "2017-01-01"),
},
},
}
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,
}
# This part corresponds to "Task Generating" in the document
def task_generating(**kwargs):
print("========================================= task_generating =========================================")
rolling_generator = RollingGen(step=rolling_step, rtype=RollingGen.ROLL_EX)
tasks = task_generator(rolling_generator, **kwargs)
# See the generated tasks in a easy way
from pprint import pprint
pprint(tasks)
return tasks
# This part corresponds to "Task Storing" in the document
def task_storing(tasks):
print("========================================= task_storing =========================================")
tm = TaskManager(task_pool=task_pool)
tm.create_task(tasks) # all tasks will be saved to MongoDB
# This part corresponds to "Task Running" in the document
def task_running():
print("========================================= task_running =========================================")
run_task(task_train, task_pool, experiment_name=exp_name) # all tasks will be trained using "task_train" method
# This part corresponds to "Task Collecting" in the document
def task_collecting():
print("========================================= task_collecting =========================================")
def get_task_key(task_config):
task_key = task_config["task_key"]
rolling_end_timestamp = task_config["dataset"]["kwargs"]["segments"]["test"][1]
if rolling_end_timestamp == None:
rolling_end_timestamp = TimeAdjuster().last_date()
return task_key, rolling_end_timestamp.strftime("%Y-%m-%d")
def lgb_filter(task_config):
# only choose the results of "task_lgb"
task_key, rolling_end = get_task_key(task_config)
if task_key == "task_lgb":
return True
return False
task_collector = TaskCollector(exp_name)
pred_rolling = task_collector.collect_predictions(
get_task_key, lgb_filter
) # name tasks by "get_task_key" and filter tasks by "my_filter"
print(pred_rolling)
# Reset all things to the first status, be careful to save important data
def reset(force_end=False):
print("========================================= reset =========================================")
TaskManager(task_pool=task_pool).remove()
exp = R.get_exp(experiment_name=exp_name)
recs = TaskCollector(exp_name).list_recorders(only_finished=True)
for rid in recs:
exp.delete_recorder(rid)
try:
if force_end:
mlflow.end_run()
except Exception:
pass
def set_online_model_to_latest():
print(
"========================================= set_online_model_to_latest ========================================="
)
model_updater = ModelUpdater(experiment_name=exp_name)
latest_records, latest_test = model_updater.collect_latest_records()
model_updater.reset_online_model(latest_records.values())
# Run this firstly to see the workflow in Task Management
def first_run():
print("========================================= first_run =========================================")
reset(force_end=True)
# use "task_lgb" and "task_xgboost" as the task name
tasks = task_generating(**{"task_xgboost": task_xgboost_config, "task_lgb": task_lgb_config})
task_storing(tasks)
task_running()
task_collecting()
set_online_model_to_latest()
# Update the predictions of online model
def update_predictions():
print("========================================= update_predictions =========================================")
model_updater = ModelUpdater(experiment_name=exp_name)
model_updater.update_online_pred()
# Update the models using the latest date and set them to online model
def update_model():
print("========================================= update_model =========================================")
# get the latest recorders
model_updater = ModelUpdater(experiment_name=exp_name)
latest_records, latest_test = model_updater.collect_latest_records()
# date adjustment based on trade day of Calendar in Qlib
time_adjuster = TimeAdjuster()
calendar_latest = time_adjuster.last_date()
print("The latest date is ", calendar_latest)
if time_adjuster.cal_interval(calendar_latest, latest_test[0]) > rolling_step:
print("Need update models!")
tasks = {}
for rid, rec in latest_records.items():
old_task = rec.task
test_begin = old_task["dataset"]["kwargs"]["segments"]["test"][0]
# modify the test segment to generate new tasks
old_task["dataset"]["kwargs"]["segments"]["test"] = (test_begin, calendar_latest)
tasks[old_task["task_key"]] = old_task
# retrain the latest model
new_tasks = task_generating(**tasks)
task_storing(new_tasks)
task_running()
task_collecting()
latest_records, _ = model_updater.collect_latest_records()
# set the latest model to online model
model_updater.reset_online_model(latest_records.values())
# Run whole workflow completely
def whole_workflow():
print("========================================= whole_workflow =========================================")
# run this at the first time
first_run()
# run this every day
update_predictions()
# run this every "rolling_steps" day
update_model()
if __name__ == "__main__":
####### to train the first version's models, use the command below
# python task_manager_rolling_with_updating.py first_run
####### to update the models using the latest date and set them to online model, use the command below
# python task_manager_rolling_with_updating.py update_model
####### to update the predictions to the latest date, use the command below
# python task_manager_rolling_with_updating.py update_predictions
####### to run whole workflow completely, use the command below
# python task_manager_rolling_with_updating.py whole_workflow
#################### you need to finish the configurations below #########################
provider_uri = "~/.qlib/qlib_data/cn_data" # data_dir
qlib.init(provider_uri=provider_uri, region=REG_CN)
C["mongo"] = {
"task_url": "mongodb://localhost:27017/", # your MongoDB url
"task_db_name": "rolling_db", # database name
}
exp_name = "rolling_exp" # experiment name, will be used as the experiment in MLflow
task_pool = "rolling_task" # task pool name, will be used as the document in MongoDB
rolling_step = 550
##########################################################################################
fire.Fire()

27
examples/taskmanager/update_online_pred.py
View File

@@ -5,12 +5,12 @@ from qlib.config import REG_CN
import fire
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",
}
"start_time": "2008-01-01",
"end_time": "2020-08-01",
"fit_start_time": "2008-01-01",
"fit_end_time": "2014-12-31",
"instruments": "csi100",
}
task = {
"model": {
@@ -44,22 +44,26 @@ task = {
},
},
},
"record": {"class": "SignalRecord", "module_path": "qlib.workflow.record_temp",},
"record": {
"class": "SignalRecord",
"module_path": "qlib.workflow.record_temp",
},
}
provider_uri = "~/.qlib/qlib_data/cn_data" # target_dir
def first_train(experiment_name="online_svr"):
qlib.init(provider_uri=provider_uri, region=REG_CN)
model_updater = ModelUpdater(experiment_name)
rid = task_train(task_config=task, experiment_name=experiment_name)
model_updater.reset_online_model(rid)
def update_online_pred(experiment_name="online_svr"):
qlib.init(provider_uri=provider_uri, region=REG_CN)
model_updater = ModelUpdater(experiment_name)
@@ -68,8 +72,9 @@ def update_online_pred(experiment_name="online_svr"):
print(rid)
model_updater.update_online_pred()
if __name__ == '__main__':
if __name__ == "__main__":
fire.Fire()
# to train a model and set it to online model, use the command below
# python update_online_pred.py first_train

19
qlib/workflow/task/collect.py
View File

@@ -18,7 +18,7 @@ class TaskCollector:
def list_recorders(self, rec_filter_func=None, task_filter_func=None, only_finished=True, only_have_task=False):
"""
Return a dict of {rid:Recorder} by recorder filter and task filter. It is not necessary to use those filter.
Return a dict of {rid:Recorder} by recorder filter and task filter. It is not necessary to use those filter.
If you don't train with "task_train", then there is no "task" which includes the task config.
If there is a "task", then it will become rec.task which can be get simply.
@@ -48,7 +48,7 @@ class TaskCollector:
if task_filter_func is not None:
only_have_task = True
for rid, rec in recs.items():
if (only_finished and rec.status == rec.STATUS_FI) or only_finished==False:
if (only_finished and rec.status == rec.STATUS_FI) or only_finished == False:
if rec_filter_func is None or rec_filter_func(rec):
task = None
try:
@@ -60,7 +60,7 @@ class TaskCollector:
if task_filter_func is None or task_filter_func(task):
rec.task = task
recs_flt[rid] = rec
return recs_flt
def collect_predictions(
@@ -83,7 +83,7 @@ class TaskCollector:
dict
the dict of predictions
"""
recs_flt = self.list_recorders(task_filter_func=task_filter_func,only_have_task=True)
recs_flt = self.list_recorders(task_filter_func=task_filter_func, only_have_task=True)
# group
recs_group = {}
@@ -108,18 +108,17 @@ class TaskCollector:
self,
task_filter_func=None,
):
recs_flt = self.list_recorders(task_filter_func=task_filter_func,only_have_task=True)
recs_flt = self.list_recorders(task_filter_func=task_filter_func, only_have_task=True)
if len(recs_flt) == 0:
self.logger.warning("Can not collect any recorders...")
return None, None
max_test = max(rec.task['dataset']['kwargs']['segments']['test'] for rec in recs_flt.values())
max_test = max(rec.task["dataset"]["kwargs"]["segments"]["test"] for rec in recs_flt.values())
latest_record = {}
for rid, rec in recs_flt.items():
if rec.task['dataset']['kwargs']['segments']['test'] == max_test:
if rec.task["dataset"]["kwargs"]["segments"]["test"] == max_test:
latest_record[rid] = rec
self.logger.info(f"Collect {len(latest_record)} latest records in {self.exp_name}")
return latest_record, max_test

2
qlib/workflow/task/manage.py
View File

@@ -21,6 +21,7 @@ from .utils import get_mongodb
from qlib import auto_init
from qlib import get_module_logger
class TaskManager:
"""TaskManager
here is the what will a task looks like
@@ -361,4 +362,3 @@ def run_task(task_func, task_pool, force_release=False, *args, **kwargs):
ever_run = True
return ever_run

31
qlib/workflow/task/update.py
View File

@@ -1,4 +1,4 @@
from typing import Union,List
from typing import Union, List
from qlib.workflow import R
from tqdm.auto import tqdm
from qlib.data import D
@@ -10,6 +10,7 @@ from qlib.model.trainer import task_train
from qlib.workflow.recorder import Recorder
from qlib.workflow.task.collect import TaskCollector
class ModelUpdater(TaskCollector):
"""
The model updater to re-train model or update predictions
@@ -31,7 +32,7 @@ class ModelUpdater(TaskCollector):
self.exp = R.get_exp(experiment_name=experiment_name)
self.logger = get_module_logger("ModelUpdater")
def set_online_model(self, recorder: Union[str,Recorder]):
def set_online_model(self, recorder: Union[str, Recorder]):
"""online model will be identified at the tags of the record
Parameters
@@ -39,12 +40,12 @@ class ModelUpdater(TaskCollector):
recorder: Union[str,Recorder]
the id of a Recorder or the Recorder instance
"""
if isinstance(recorder,str):
if isinstance(recorder, str):
recorder = self.exp.get_recorder(recorder_id=recorder)
recorder.set_tags(**{ModelUpdater.ONLINE_TAG: ModelUpdater.ONLINE_TAG_TRUE})
def cancel_online_model(self, recorder: Union[str,Recorder]):
if isinstance(recorder,str):
def cancel_online_model(self, recorder: Union[str, Recorder]):
if isinstance(recorder, str):
recorder = self.exp.get_recorder(recorder_id=recorder)
recorder.set_tags(**{ModelUpdater.ONLINE_TAG: ModelUpdater.ONLINE_TAG_FALSE})
@@ -53,7 +54,7 @@ class ModelUpdater(TaskCollector):
for rid, rec in recs.items():
self.cancel_online_model(rec)
def reset_online_model(self, recorders: List[Union[str,Recorder]]):
def reset_online_model(self, recorders: List[Union[str, Recorder]]):
"""cancel all online model and reset the given model to online model
Parameters
@@ -65,7 +66,7 @@ class ModelUpdater(TaskCollector):
for rec_or_rid in recorders:
self.set_online_model(rec_or_rid)
def update_pred(self, recorder: Union[str,Recorder]):
def update_pred(self, recorder: Union[str, Recorder]):
"""update predictions to the latest day in Calendar based on rid
Parameters
@@ -73,17 +74,19 @@ class ModelUpdater(TaskCollector):
recorder: Union[str,Recorder]
the id of a Recorder or the Recorder instance
"""
if isinstance(recorder,str):
if isinstance(recorder, str):
recorder = self.exp.get_recorder(recorder_id=recorder)
old_pred = recorder.load_object("pred.pkl")
last_end = old_pred.index.get_level_values("datetime").max()
task_config = recorder.load_object("task") # recorder.task
task_config = recorder.load_object("task") # recorder.task
# updated to the latest trading day
cal = D.calendar(start_time=last_end + pd.Timedelta(days=1), end_time=None)
if len(cal) == 0:
self.logger.info(f"The prediction in {recorder.info['id']} of {self.exp_name} are latest. No need to update.")
self.logger.info(
f"The prediction in {recorder.info['id']} of {self.exp_name} are latest. No need to update."
)
return
start_time, end_time = cal[0], cal[-1]
@@ -100,7 +103,9 @@ class ModelUpdater(TaskCollector):
recorder.save_objects(**{"pred.pkl": cb_pred})
self.logger.info(f"Finish updating new {new_pred.shape[0]} predictions in {recorder.info['id']} of {self.exp_name}.")
self.logger.info(
f"Finish updating new {new_pred.shape[0]} predictions in {recorder.info['id']} of {self.exp_name}."
)
def update_all_pred(self, rec_filter_func=None):
"""update all predictions in this experiment after filter.
@@ -126,7 +131,7 @@ class ModelUpdater(TaskCollector):
the count of updated record
"""
recs = self.list_recorders(rec_filter_func=rec_filter_func,only_have_task=True)
recs = self.list_recorders(rec_filter_func=rec_filter_func, only_have_task=True)
for rid, rec in recs.items():
self.update_pred(rec)
return len(recs)
@@ -150,5 +155,5 @@ class ModelUpdater(TaskCollector):
dict
{rid : recorder of the online model}
"""
return self.list_recorders(rec_filter_func=self.online_filter)

1
qlib/workflow/task/utils.py
View File

@@ -50,6 +50,7 @@ class TimeAdjuster:
if idx >= len(self.cals):
return None
return self.cals[idx]
def max(self):
"""
(Deprecated)