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