{ "cells": [ { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "import mlflow\n", "mlflow.end_run()" ] }, { "cell_type": "code", "execution_count": 24, "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" ] } ], "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_db3\"\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" ] }, { "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" ] } ], "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": 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 } ], "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": 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 } ], "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", " # 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(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 }