liuyu/qlib
1
0
Fork 0
mirror of https://github.com/microsoft/qlib.git synced 2026-07-02 10:31:00 +08:00
Code Activity

update task manager

Browse Source
This commit is contained in:
lzh222333
2021-03-10 10:58:49 +00:00
parent 83dbdfb45e
commit e2f58274ba
5 changed files with 34 additions and 299 deletions
Download Patch File Download Diff File Expand all files Collapse all files

297
examples/taskmanager/task_manager_rolling.ipynb
View File

@@ -2,32 +2,11 @@
"cells": [
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"import mlflow\n",
"mlflow.end_run()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"[8348:MainThread](2021-03-09 14:55:48,543) INFO - qlib.Initialization - [config.py:279] - default_conf: client.\n",
"[8348:MainThread](2021-03-09 14:55:50,592) WARNING - qlib.Initialization - [config.py:295] - redis connection failed(host=127.0.0.1 port=6379), cache will not be used!\n",
"[8348:MainThread](2021-03-09 14:55:50,597) INFO - qlib.Initialization - [__init__.py:48] - qlib successfully initialized based on client settings.\n",
"[8348:MainThread](2021-03-09 14:55:50,601) INFO - qlib.Initialization - [__init__.py:49] - data_path=C:\\Users\\lzh222333\\.qlib\\qlib_data\\cn_data\n"
]
}
],
"outputs": [],
"source": [
"import qlib\n",
"from qlib.config import REG_CN\n",
@@ -96,109 +75,17 @@
"\n",
"C[\"mongo\"] = {\n",
" \"task_url\" : \"mongodb://localhost:27017/\", # maybe you need to change it to your url\n",
" \"task_db_name\" : \"rolling_db3\"\n",
" \"task_db_name\" : \"rolling_db\"\n",
"}\n",
"\n",
"exp_name = 'rolling_exp3' # experiment name, will be used as the experiment in MLflow\n",
"task_pool = 'rolling_task3' # task pool name, will be used as the document in MongoDB"
"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": 25,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[{'dataset': {'class': 'DatasetH',\n",
" 'kwargs': {'handler': {'class': 'Alpha158',\n",
" 'kwargs': {'end_time': '2020-08-01',\n",
" 'fit_end_time': '2014-12-31',\n",
" 'fit_start_time': '2008-01-01',\n",
" 'instruments': 'csi100',\n",
" 'start_time': '2008-01-01'},\n",
" 'module_path': 'qlib.contrib.data.handler'},\n",
" 'segments': {'test': (Timestamp('2017-01-03 00:00:00'),\n",
" Timestamp('2019-04-08 00:00:00')),\n",
" 'train': (Timestamp('2008-01-02 00:00:00'),\n",
" Timestamp('2014-12-31 00:00:00')),\n",
" 'valid': (Timestamp('2015-01-05 00:00:00'),\n",
" Timestamp('2016-12-30 00:00:00'))}},\n",
" 'module_path': 'qlib.data.dataset'},\n",
" 'model': {'class': 'XGBModel', 'module_path': 'qlib.contrib.model.xgboost'},\n",
" 'record': [{'class': 'SignalRecord',\n",
" 'module_path': 'qlib.workflow.record_temp'},\n",
" {'class': 'SigAnaRecord',\n",
" 'module_path': 'qlib.workflow.record_temp'}],\n",
" 'task_key': 1},\n",
" {'dataset': {'class': 'DatasetH',\n",
" 'kwargs': {'handler': {'class': 'Alpha158',\n",
" 'kwargs': {'end_time': '2020-08-01',\n",
" 'fit_end_time': '2014-12-31',\n",
" 'fit_start_time': '2008-01-01',\n",
" 'instruments': 'csi100',\n",
" 'start_time': '2008-01-01'},\n",
" 'module_path': 'qlib.contrib.data.handler'},\n",
" 'segments': {'test': (Timestamp('2019-04-09 00:00:00'),\n",
" Timestamp('2021-07-12 00:00:00')),\n",
" 'train': (Timestamp('2010-04-23 00:00:00'),\n",
" Timestamp('2017-05-24 00:00:00')),\n",
" 'valid': (Timestamp('2017-05-25 00:00:00'),\n",
" Timestamp('2019-04-08 00:00:00'))}},\n",
" 'module_path': 'qlib.data.dataset'},\n",
" 'model': {'class': 'XGBModel', 'module_path': 'qlib.contrib.model.xgboost'},\n",
" 'record': [{'class': 'SignalRecord',\n",
" 'module_path': 'qlib.workflow.record_temp'},\n",
" {'class': 'SigAnaRecord',\n",
" 'module_path': 'qlib.workflow.record_temp'}],\n",
" 'task_key': 1},\n",
" {'dataset': {'class': 'DatasetH',\n",
" 'kwargs': {'handler': {'class': 'Alpha158',\n",
" 'kwargs': {'end_time': '2020-08-01',\n",
" 'fit_end_time': '2014-12-31',\n",
" 'fit_start_time': '2008-01-01',\n",
" 'instruments': 'csi100',\n",
" 'start_time': '2008-01-01'},\n",
" 'module_path': 'qlib.contrib.data.handler'},\n",
" 'segments': {'test': (Timestamp('2017-01-03 00:00:00'),\n",
" Timestamp('2019-04-08 00:00:00')),\n",
" 'train': (Timestamp('2008-01-02 00:00:00'),\n",
" Timestamp('2014-12-31 00:00:00')),\n",
" 'valid': (Timestamp('2015-01-05 00:00:00'),\n",
" Timestamp('2016-12-30 00:00:00'))}},\n",
" 'module_path': 'qlib.data.dataset'},\n",
" 'model': {'class': 'LGBModel', 'module_path': 'qlib.contrib.model.gbdt'},\n",
" 'record': [{'class': 'SignalRecord',\n",
" 'module_path': 'qlib.workflow.record_temp'},\n",
" {'class': 'SigAnaRecord',\n",
" 'module_path': 'qlib.workflow.record_temp'}],\n",
" 'task_key': 'task_lgb'},\n",
" {'dataset': {'class': 'DatasetH',\n",
" 'kwargs': {'handler': {'class': 'Alpha158',\n",
" 'kwargs': {'end_time': '2020-08-01',\n",
" 'fit_end_time': '2014-12-31',\n",
" 'fit_start_time': '2008-01-01',\n",
" 'instruments': 'csi100',\n",
" 'start_time': '2008-01-01'},\n",
" 'module_path': 'qlib.contrib.data.handler'},\n",
" 'segments': {'test': (Timestamp('2019-04-09 00:00:00'),\n",
" Timestamp('2021-07-12 00:00:00')),\n",
" 'train': (Timestamp('2010-04-23 00:00:00'),\n",
" Timestamp('2017-05-24 00:00:00')),\n",
" 'valid': (Timestamp('2017-05-25 00:00:00'),\n",
" Timestamp('2019-04-08 00:00:00'))}},\n",
" 'module_path': 'qlib.data.dataset'},\n",
" 'model': {'class': 'LGBModel', 'module_path': 'qlib.contrib.model.gbdt'},\n",
" 'record': [{'class': 'SignalRecord',\n",
" 'module_path': 'qlib.workflow.record_temp'},\n",
" {'class': 'SigAnaRecord',\n",
" 'module_path': 'qlib.workflow.record_temp'}],\n",
" 'task_key': 'task_lgb'}]\n",
"Total Tasks, New Tasks: 4 0\n"
]
}
],
"execution_count": null,
"outputs": [],
"source": [
"tasks = task_generator(\n",
" xgboost_task_template, # default task name\n",
@@ -206,8 +93,8 @@
" 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",
"# 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"
],
@@ -220,133 +107,8 @@
},
{
"cell_type": "code",
"execution_count": 26,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"2021-03-09 14:55:51.600 | INFO | qlib.workflow.task.manage:run_task:355 - {'model': {'class': 'XGBModel', 'module_path': 'qlib.contrib.model.xgboost'}, 'dataset': {'class': 'DatasetH', 'module_path': 'qlib.data.dataset', 'kwargs': {'handler': {'class': 'Alpha158', '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': (Timestamp('2008-01-02 00:00:00'), Timestamp('2014-12-31 00:00:00')), 'valid': (Timestamp('2015-01-05 00:00:00'), Timestamp('2016-12-30 00:00:00')), 'test': (Timestamp('2017-01-03 00:00:00'), Timestamp('2019-04-08 00:00:00'))}}}, 'record': [{'class': 'SignalRecord', 'module_path': 'qlib.workflow.record_temp'}, {'class': 'SigAnaRecord', 'module_path': 'qlib.workflow.record_temp'}], 'task_key': 1}\n",
"[8348:MainThread](2021-03-09 14:56:46,051) INFO - qlib.timer - [log.py:81] - Time cost: 54.448s | Loading data Done\n",
"[8348:MainThread](2021-03-09 14:56:46,440) INFO - qlib.timer - [log.py:81] - Time cost: 0.322s | DropnaLabel Done\n",
"[8348:MainThread](2021-03-09 14:56:52,461) INFO - qlib.timer - [log.py:81] - Time cost: 6.019s | CSZScoreNorm Done\n",
"[8348:MainThread](2021-03-09 14:56:52,464) INFO - qlib.timer - [log.py:81] - Time cost: 6.411s | fit & process data Done\n",
"[8348:MainThread](2021-03-09 14:56:52,468) INFO - qlib.timer - [log.py:81] - Time cost: 60.865s | Init data Done\n",
"[8348:MainThread](2021-03-09 14:56:52,471) INFO - qlib.workflow - [expm.py:245] - No tracking URI is provided. Use the default tracking URI.\n",
"[8348:MainThread](2021-03-09 14:56:52,500) INFO - qlib.workflow - [exp.py:181] - Experiment 2 starts running ...\n",
"[8348:MainThread](2021-03-09 14:56:52,567) INFO - qlib.workflow - [recorder.py:233] - Recorder dd6bceb6d319493686ab6565633c0b5a starts running under Experiment 2 ...\n",
"[0]\ttrain-rmse:1.05165\tvalid-rmse:1.05565\n",
"[20]\ttrain-rmse:0.97071\tvalid-rmse:1.00077\n",
"[40]\ttrain-rmse:0.95124\tvalid-rmse:1.00609\n",
"[59]\ttrain-rmse:0.93833\tvalid-rmse:1.00945\n",
"[8348:MainThread](2021-03-09 14:59:37,266) INFO - qlib.workflow - [record_temp.py:126] - Signal record 'pred.pkl' has been saved as the artifact of the Experiment 2\n",
"'The following are prediction results of the XGBModel model.'\n",
" score\n",
"datetime instrument \n",
"2017-01-03 SH600000 -0.103259\n",
" SH600010 -0.084365\n",
" SH600015 -0.107433\n",
" SH600016 -0.064723\n",
" SH600018 -0.038639\n",
"{'IC': 0.05347474869798698,\n",
" 'ICIR': 0.29781294430945265,\n",
" 'Rank IC': 0.0484064337863249,\n",
" 'Rank ICIR': 0.36035393716962033}\n",
"2021-03-09 14:59:38.633 | INFO | qlib.workflow.task.manage:run_task:355 - {'model': {'class': 'XGBModel', 'module_path': 'qlib.contrib.model.xgboost'}, 'dataset': {'class': 'DatasetH', 'module_path': 'qlib.data.dataset', 'kwargs': {'handler': {'class': 'Alpha158', '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': (Timestamp('2010-04-23 00:00:00'), Timestamp('2017-05-24 00:00:00')), 'valid': (Timestamp('2017-05-25 00:00:00'), Timestamp('2019-04-08 00:00:00')), 'test': (Timestamp('2019-04-09 00:00:00'), Timestamp('2021-07-12 00:00:00'))}}}, 'record': [{'class': 'SignalRecord', 'module_path': 'qlib.workflow.record_temp'}, {'class': 'SigAnaRecord', 'module_path': 'qlib.workflow.record_temp'}], 'task_key': 1}\n",
"[8348:MainThread](2021-03-09 15:00:36,591) INFO - qlib.timer - [log.py:81] - Time cost: 57.954s | Loading data Done\n",
"[8348:MainThread](2021-03-09 15:00:36,997) INFO - qlib.timer - [log.py:81] - Time cost: 0.338s | DropnaLabel Done\n",
"[8348:MainThread](2021-03-09 15:00:43,728) INFO - qlib.timer - [log.py:81] - Time cost: 6.728s | CSZScoreNorm Done\n",
"[8348:MainThread](2021-03-09 15:00:43,731) INFO - qlib.timer - [log.py:81] - Time cost: 7.137s | fit & process data Done\n",
"[8348:MainThread](2021-03-09 15:00:43,734) INFO - qlib.timer - [log.py:81] - Time cost: 65.097s | Init data Done\n",
"[8348:MainThread](2021-03-09 15:00:43,740) INFO - qlib.workflow - [expm.py:245] - No tracking URI is provided. Use the default tracking URI.\n",
"[8348:MainThread](2021-03-09 15:00:43,768) INFO - qlib.workflow - [exp.py:181] - Experiment 2 starts running ...\n",
"[8348:MainThread](2021-03-09 15:00:43,851) INFO - qlib.workflow - [recorder.py:233] - Recorder de2f892b569c436ba642a23e99f4f2b0 starts running under Experiment 2 ...\n",
"[0]\ttrain-rmse:1.05178\tvalid-rmse:1.05345\n",
"[20]\ttrain-rmse:0.96764\tvalid-rmse:0.99546\n",
"[40]\ttrain-rmse:0.94957\tvalid-rmse:0.99798\n",
"[57]\ttrain-rmse:0.93592\tvalid-rmse:1.00030\n",
"[8348:MainThread](2021-03-09 15:03:12,764) INFO - qlib.workflow - [record_temp.py:126] - Signal record 'pred.pkl' has been saved as the artifact of the Experiment 2\n",
"'The following are prediction results of the XGBModel model.'\n",
" score\n",
"datetime instrument \n",
"2019-04-09 SH600000 0.006996\n",
" SH600009 -0.102482\n",
" SH600010 0.016398\n",
" SH600011 0.004459\n",
" SH600015 -0.128315\n",
"{'IC': 0.013224093132176661,\n",
" 'ICIR': 0.08254897170570956,\n",
" 'Rank IC': 0.02472594591723197,\n",
" 'Rank ICIR': 0.16330982475433398}\n",
"2021-03-09 15:03:13.593 | INFO | qlib.workflow.task.manage:run_task:355 - {'model': {'class': 'LGBModel', 'module_path': 'qlib.contrib.model.gbdt'}, 'dataset': {'class': 'DatasetH', 'module_path': 'qlib.data.dataset', 'kwargs': {'handler': {'class': 'Alpha158', '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': (Timestamp('2008-01-02 00:00:00'), Timestamp('2014-12-31 00:00:00')), 'valid': (Timestamp('2015-01-05 00:00:00'), Timestamp('2016-12-30 00:00:00')), 'test': (Timestamp('2017-01-03 00:00:00'), Timestamp('2019-04-08 00:00:00'))}}}, 'record': [{'class': 'SignalRecord', 'module_path': 'qlib.workflow.record_temp'}, {'class': 'SigAnaRecord', 'module_path': 'qlib.workflow.record_temp'}], 'task_key': 'task_lgb'}\n",
"[8348:MainThread](2021-03-09 15:04:06,545) INFO - qlib.timer - [log.py:81] - Time cost: 52.814s | Loading data Done\n",
"[8348:MainThread](2021-03-09 15:04:06,919) INFO - qlib.timer - [log.py:81] - Time cost: 0.312s | DropnaLabel Done\n",
"[8348:MainThread](2021-03-09 15:04:12,850) INFO - qlib.timer - [log.py:81] - Time cost: 5.928s | CSZScoreNorm Done\n",
"[8348:MainThread](2021-03-09 15:04:12,853) INFO - qlib.timer - [log.py:81] - Time cost: 6.305s | fit & process data Done\n",
"[8348:MainThread](2021-03-09 15:04:12,856) INFO - qlib.timer - [log.py:81] - Time cost: 59.125s | Init data Done\n",
"[8348:MainThread](2021-03-09 15:04:12,859) INFO - qlib.workflow - [expm.py:245] - No tracking URI is provided. Use the default tracking URI.\n",
"[8348:MainThread](2021-03-09 15:04:12,888) INFO - qlib.workflow - [exp.py:181] - Experiment 2 starts running ...\n",
"[8348:MainThread](2021-03-09 15:04:12,958) INFO - qlib.workflow - [recorder.py:233] - Recorder 15df799127a74656829978c1b9352e60 starts running under Experiment 2 ...\n",
"Training until validation scores don't improve for 50 rounds\n",
"[20]\ttrain's l2: 0.970491\tvalid's l2: 0.987723\n",
"[40]\ttrain's l2: 0.957984\tvalid's l2: 0.990056\n",
"[60]\ttrain's l2: 0.947201\tvalid's l2: 0.991459\n",
"Early stopping, best iteration is:\n",
"[18]\ttrain's l2: 0.971834\tvalid's l2: 0.987481\n",
"[8348:MainThread](2021-03-09 15:04:19,847) INFO - qlib.workflow - [record_temp.py:126] - Signal record 'pred.pkl' has been saved as the artifact of the Experiment 2\n",
"'The following are prediction results of the LGBModel model.'\n",
" score\n",
"datetime instrument \n",
"2017-01-03 SH600000 -0.013089\n",
" SH600010 -0.006642\n",
" SH600015 -0.035137\n",
" SH600016 -0.034634\n",
" SH600018 -0.029493\n",
"{'IC': 0.05704431372255674,\n",
" 'ICIR': 0.28879437007622133,\n",
" 'Rank IC': 0.05181220321608411,\n",
" 'Rank ICIR': 0.3233833799543165}\n",
"2021-03-09 15:04:21.111 | INFO | qlib.workflow.task.manage:run_task:355 - {'model': {'class': 'LGBModel', 'module_path': 'qlib.contrib.model.gbdt'}, 'dataset': {'class': 'DatasetH', 'module_path': 'qlib.data.dataset', 'kwargs': {'handler': {'class': 'Alpha158', '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': (Timestamp('2010-04-23 00:00:00'), Timestamp('2017-05-24 00:00:00')), 'valid': (Timestamp('2017-05-25 00:00:00'), Timestamp('2019-04-08 00:00:00')), 'test': (Timestamp('2019-04-09 00:00:00'), Timestamp('2021-07-12 00:00:00'))}}}, 'record': [{'class': 'SignalRecord', 'module_path': 'qlib.workflow.record_temp'}, {'class': 'SigAnaRecord', 'module_path': 'qlib.workflow.record_temp'}], 'task_key': 'task_lgb'}\n",
"[8348:MainThread](2021-03-09 15:05:16,072) INFO - qlib.timer - [log.py:81] - Time cost: 54.958s | Loading data Done\n",
"[8348:MainThread](2021-03-09 15:05:16,466) INFO - qlib.timer - [log.py:81] - Time cost: 0.334s | DropnaLabel Done\n",
"[8348:MainThread](2021-03-09 15:05:22,281) INFO - qlib.timer - [log.py:81] - Time cost: 5.812s | CSZScoreNorm Done\n",
"[8348:MainThread](2021-03-09 15:05:22,283) INFO - qlib.timer - [log.py:81] - Time cost: 6.209s | fit & process data Done\n",
"[8348:MainThread](2021-03-09 15:05:22,286) INFO - qlib.timer - [log.py:81] - Time cost: 61.172s | Init data Done\n",
"[8348:MainThread](2021-03-09 15:05:22,291) INFO - qlib.workflow - [expm.py:245] - No tracking URI is provided. Use the default tracking URI.\n",
"[8348:MainThread](2021-03-09 15:05:22,317) INFO - qlib.workflow - [exp.py:181] - Experiment 2 starts running ...\n",
"[8348:MainThread](2021-03-09 15:05:22,386) INFO - qlib.workflow - [recorder.py:233] - Recorder 0c814539f55842b9b6310843fc5ec708 starts running under Experiment 2 ...\n",
"Training until validation scores don't improve for 50 rounds\n",
"[20]\ttrain's l2: 0.969033\tvalid's l2: 0.98571\n",
"[40]\ttrain's l2: 0.955399\tvalid's l2: 0.986164\n",
"[60]\ttrain's l2: 0.943514\tvalid's l2: 0.986301\n",
"Early stopping, best iteration is:\n",
"[26]\ttrain's l2: 0.964587\tvalid's l2: 0.985356\n",
"[8348:MainThread](2021-03-09 15:05:29,546) INFO - qlib.workflow - [record_temp.py:126] - Signal record 'pred.pkl' has been saved as the artifact of the Experiment 2\n",
"'The following are prediction results of the LGBModel model.'\n",
" score\n",
"datetime instrument \n",
"2019-04-09 SH600000 0.029586\n",
" SH600009 0.004306\n",
" SH600010 -0.004411\n",
" SH600011 0.002707\n",
" SH600015 -0.029124\n",
"{'IC': 0.020784811232504984,\n",
" 'ICIR': 0.11590182186569555,\n",
" 'Rank IC': 0.028925697036767055,\n",
" 'Rank ICIR': 0.16388058980901396}\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"True"
]
},
"metadata": {},
"execution_count": 26
}
],
"execution_count": null,
"outputs": [],
"source": [
"from qlib.workflow.task.manage import run_task\n",
"from qlib.workflow.task.collect import TaskCollector\n",
@@ -363,43 +125,12 @@
},
{
"cell_type": "code",
"execution_count": 27,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Loading data: 100%|██████████| 4/4 [00:00<00:00, 37.38it/s]\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{('task_lgb', '2019-04-08'): datetime instrument\n",
" 2017-01-03 SH600000 -0.013089\n",
" SH600010 -0.006642\n",
" SH600015 -0.035137\n",
" SH600016 -0.034634\n",
" SH600018 -0.029493\n",
" ... \n",
" 2019-04-08 SZ002415 0.049199\n",
" SZ002450 -0.013450\n",
" SZ002594 0.022395\n",
" SZ002736 0.091433\n",
" SZ300059 -0.016237\n",
" Name: score, Length: 55000, dtype: float64}"
]
},
"metadata": {},
"execution_count": 27
}
],
"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",
" #rolling_end_datatime = rolling_end_timestamp.to_pydatetime()\n",
" return task_key, rolling_end_timestamp.strftime('%Y-%m-%d')\n",
"\n",
"def my_filter(task):\n",
@@ -410,7 +141,7 @@
" return False\n",
"\n",
"# name tasks by \"get_task_key\" and filter tasks by \"my_filter\"\n",
"pred_rolling = TaskCollector.collect(exp_name, get_task_key, my_filter) \n",
"pred_rolling = TaskCollector.collect_predictions(exp_name, get_task_key, my_filter) \n",
"pred_rolling"
],
"metadata": {

9
examples/taskmanager/task_manager_rolling.py
View File

@@ -85,7 +85,7 @@ tm = TaskManager(task_pool=task_pool)
tm.create_task(tasks) # all tasks will be saved to MongoDB
from qlib.workflow.task.manage import run_task
from qlib.workflow.task.collect import RollingCollector
from qlib.workflow.task.collect import TaskCollector
from qlib.model.trainer import task_train
run_task(task_train, task_pool, experiment_name=exp_name) # all tasks will be trained using "task_train" method
@@ -93,7 +93,6 @@ run_task(task_train, task_pool, experiment_name=exp_name) # all tasks will be tr
def get_task_key(task_config):
task_key = task_config["task_key"]
rolling_end_timestamp = task_config["dataset"]["kwargs"]["segments"]["test"][1]
#rolling_end_datatime = rolling_end_timestamp.to_pydatetime()
return task_key, rolling_end_timestamp.strftime('%Y-%m-%d')
def my_filter(task_config):
@@ -103,6 +102,6 @@ def my_filter(task_config):
return True
return False
collector = RollingCollector(get_task_key, my_filter)
pred_rolling = collector(exp_name) # name tasks by "get_task_key" and filter tasks by "my_filter"
print(pred_rolling)
# name tasks by "get_task_key" and filter tasks by "my_filter"
pred_rolling = TaskCollector.collect_predictions(exp_name, get_task_key, my_filter)
pred_rolling

10
qlib/model/trainer.py
View File

@@ -6,7 +6,7 @@ from qlib.workflow import R
from qlib.workflow.record_temp import SignalRecord
def task_train(task_config: dict, experiment_name: str):
def task_train(task_config: dict, experiment_name: str) -> str:
"""
task based training
@@ -16,6 +16,11 @@ def task_train(task_config: dict, experiment_name: str):
A dict describes a task setting.
experiment_name: str
The name of experiment
Returns
----------
rid : str
The id of the recorder of this task
"""
# model initiaiton
@@ -29,7 +34,7 @@ def task_train(task_config: dict, experiment_name: str):
model.fit(dataset)
recorder = R.get_recorder()
R.save_objects(**{"params.pkl": model})
R.save_objects(param=task_config) # keep the original format and datatype
R.save_objects(**{"task.pkl": task_config}) # keep the original format and datatype
# generate records: prediction, backtest, and analysis
records = task_config.get("record", [])
@@ -48,3 +53,4 @@ def task_train(task_config: dict, experiment_name: str):
record["kwargs"].update(rconf)
ar = init_instance_by_config(record)
ar.generate()
return record.info["id"]

15
qlib/workflow/task/collect.py
View File

@@ -1,7 +1,7 @@
from qlib.workflow import R
import pandas as pd
from typing import Union
from tqdm.auto import tqdm
from qlib import get_module_logger
class TaskCollector:
@@ -10,10 +10,8 @@ class TaskCollector:
"""
@staticmethod
def collect(
experiment_name: str,
get_key_func,
filter_func=None,
def collect_predictions(
experiment_name: str, get_key_func, filter_func=None,
):
"""
@@ -34,8 +32,8 @@ class TaskCollector:
recs = exp.list_recorders()
recs_flt = {}
for rid, rec in tqdm(recs.items(), desc="Loading data"):
params = rec.load_object("param")
for rid, rec in recs.items():
params = rec.load_object("task.pkl")
if rec.status == rec.STATUS_FI:
if filter_func is None or filter_func(params):
rec.params = params
@@ -57,6 +55,7 @@ class TaskCollector:
pred = pd.concat(pred_l).sort_index()
reduce_group[k] = pred
get_module_logger("TaskCollector").info(f"Collect {len(reduce_group)} predictions in {experiment_name}")
return reduce_group
@@ -82,7 +81,7 @@ class RollingCollector:
recs_flt = {}
for rid, rec in tqdm(recs.items(), desc="Loading data"):
params = rec.load_object("param")
params = rec.load_object("task.pkl")
if rec.status == rec.STATUS_FI:
if self.flt_func is None or self.flt_func(params):
rec.params = params

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

@@ -168,7 +168,7 @@ class RollingGen(TaskGen):
# 1) prepare the end point
segments = copy.deepcopy(self.ta.align_seg(t["dataset"]["kwargs"]["segments"]))
test_end = self.ta.last_date() if segments[self.test_key][1] is None else segments[self.test_key][1]
# 2) and the init test segments
# 2) and init test segments
test_start_idx = self.ta.align_idx(segments[self.test_key][0])
segments[self.test_key] = (self.ta.get(test_start_idx), self.ta.get(test_start_idx + self.step - 1))
else: