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finished document and example
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lzh222333
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docs/advanced/task_managment.rst
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docs/advanced/task_managment.rst
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.. _task_managment:
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=================================
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Task Management
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=================================
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.. currentmodule:: qlib
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Introduction
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=============
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The `Workflow <../component/introduction.html>`_ part introduce how to run research workflow in a loosely-coupled way. But it can only execute one ``task`` when you use ``qrun``. To automatically generate and execute different tasks, Task Management module provide a whole process including `Task Generating`_, `Task Storing`_, `Task Running`_ and `Task Collecting`_.
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With this module, users can run their ``task`` automatically at different periods, in different losses or even by different models.
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An example of the entire process is shown `here <>`_.
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Task Generating
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===============
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A ``task`` consists of `Model`, `Dataset`, `Record` or anything added by users.
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The specific task template can be viewed in
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`Task Section <../component/workflow.html#task-section>`_.
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Even though the task template is fixed, Users can use ``TaskGen`` to generate different ``task`` by task template.
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Here is the base class of TaskGen:
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.. autoclass:: qlib.workflow.task.gen.TaskGen
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:members:
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``Qlib`` provider a class `RollingGen<https://github.com/microsoft/qlib/tree/main/qlib/workflow/task/gen.py>`_ to generate a list of ``task`` of dataset in different date segments.
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This allows users to verify the effect of data from different periods on the model in one experiment.
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Task Storing
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===============
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In order to achieve higher efficiency and the possibility of cluster operation, ``Task Manager`` will store all tasks in `MongoDB <https://www.mongodb.com/>`_.
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Users **MUST** finished the configuration of `MongoDB <https://www.mongodb.com/>`_ when using this module.
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Users need to provide the url and database of ``task`` storing like this.
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.. code-block:: python
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from qlib.config import C
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C["mongo"] = {
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"task_url" : "mongodb://localhost:27017/", # maybe you need to change it to your url
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"task_db_name" : "rolling_db" # you can custom database name
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}
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The CRUD methods of ``task`` can be found in TaskManager. More methods can be seen in the `Github<https://github.com/microsoft/qlib/tree/main/qlib/workflow/task/manage.py>`_.
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.. autoclass:: qlib.workflow.task.manage.TaskManager
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:members:
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Task Running
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===============
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After generating and storing those ``task``, it's time to run the ``task`` in the *WAITING* status.
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``qlib`` provide a method to run those ``task`` in task pool, however users can also customize how tasks are executed.
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An easy way to get the ``task_func`` is using ``qlib.model.trainer.task_train`` directly.
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It will run the whole workflow defined by ``task``, which includes *Model*, *Dataset*, *Record*.
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.. autofunction:: qlib.workflow.task.manage.run_task
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Task Collecting
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===============
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To see the results of ``task`` after running, ``Qlib`` provide a task collector to collect the tasks by filter condition (optional).
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The collector will return a dict of filtered key (users defined by task config) and value (predict scores from ``pred.pkl``).
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.. autoclass:: qlib.workflow.task.collect.TaskCollector
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:members:
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445
examples/taskmanager/task_manager_rolling.ipynb
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445
examples/taskmanager/task_manager_rolling.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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"import mlflow\n",
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"mlflow.end_run()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {
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"collapsed": true
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stderr",
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"text": [
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"[8348:MainThread](2021-03-09 14:55:48,543) INFO - qlib.Initialization - [config.py:279] - default_conf: client.\n",
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"[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",
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"[8348:MainThread](2021-03-09 14:55:50,597) INFO - qlib.Initialization - [__init__.py:48] - qlib successfully initialized based on client settings.\n",
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"[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"
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]
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}
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],
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"source": [
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"import qlib\n",
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"from qlib.config import REG_CN\n",
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"from qlib.workflow.task.gen import RollingGen, task_generator\n",
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"from qlib.workflow.task.manage import TaskManager\n",
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"from qlib.config import C\n",
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"\n",
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"data_handler_template = {\n",
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" \"start_time\": \"2008-01-01\",\n",
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" \"end_time\": \"2020-08-01\",\n",
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" \"fit_start_time\": \"2008-01-01\",\n",
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" \"fit_end_time\": \"2014-12-31\",\n",
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" \"instruments\": 'csi100',\n",
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"}\n",
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"\n",
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"dataset_template = {\n",
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" \"class\": \"DatasetH\",\n",
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" \"module_path\": \"qlib.data.dataset\",\n",
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" \"kwargs\": {\n",
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" \"handler\": {\n",
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" \"class\": \"Alpha158\",\n",
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" \"module_path\": \"qlib.contrib.data.handler\",\n",
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" \"kwargs\": data_handler_template,\n",
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" },\n",
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" \"segments\": {\n",
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" \"train\": (\"2008-01-01\", \"2014-12-31\"),\n",
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" \"valid\": (\"2015-01-01\", \"2016-12-31\"),\n",
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" \"test\": (\"2017-01-01\", \"2020-08-01\"),\n",
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" },\n",
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" },\n",
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" }\n",
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"\n",
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"record_template = [\n",
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" {\n",
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" \"class\": \"SignalRecord\",\n",
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" \"module_path\": \"qlib.workflow.record_temp\",\n",
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" },\n",
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" {\n",
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" \"class\": \"SigAnaRecord\",\n",
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" \"module_path\": \"qlib.workflow.record_temp\",\n",
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" }\n",
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"]\n",
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"\n",
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"# use lgb\n",
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"lgb_task_template = {\n",
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" \"model\": {\n",
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" \"class\": \"LGBModel\",\n",
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" \"module_path\": \"qlib.contrib.model.gbdt\",\n",
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" },\n",
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" \"dataset\": dataset_template,\n",
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" \"record\": record_template,\n",
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"}\n",
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"\n",
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"# use xgboost\n",
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"xgboost_task_template = {\n",
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" \"model\": {\n",
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" \"class\": \"XGBModel\",\n",
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" \"module_path\": \"qlib.contrib.model.xgboost\",\n",
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" },\n",
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" \"dataset\": dataset_template,\n",
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" \"record\": record_template,\n",
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"}\n",
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"\n",
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"provider_uri = \"~/.qlib/qlib_data/cn_data\" # target_dir\n",
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"qlib.init(provider_uri=provider_uri, region=REG_CN)\n",
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"\n",
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"C[\"mongo\"] = {\n",
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" \"task_url\" : \"mongodb://localhost:27017/\", # maybe you need to change it to your url\n",
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" \"task_db_name\" : \"rolling_db3\"\n",
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"}\n",
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"\n",
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"exp_name = 'rolling_exp3' # experiment name, will be used as the experiment in MLflow\n",
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"task_pool = 'rolling_task3' # task pool name, will be used as the document in MongoDB"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"[{'dataset': {'class': 'DatasetH',\n",
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" 'kwargs': {'handler': {'class': 'Alpha158',\n",
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" 'kwargs': {'end_time': '2020-08-01',\n",
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" 'fit_end_time': '2014-12-31',\n",
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" 'fit_start_time': '2008-01-01',\n",
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" 'instruments': 'csi100',\n",
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" 'start_time': '2008-01-01'},\n",
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" 'module_path': 'qlib.contrib.data.handler'},\n",
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" 'segments': {'test': (Timestamp('2017-01-03 00:00:00'),\n",
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" Timestamp('2019-04-08 00:00:00')),\n",
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" 'train': (Timestamp('2008-01-02 00:00:00'),\n",
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" Timestamp('2014-12-31 00:00:00')),\n",
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" 'valid': (Timestamp('2015-01-05 00:00:00'),\n",
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" Timestamp('2016-12-30 00:00:00'))}},\n",
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" 'module_path': 'qlib.data.dataset'},\n",
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" 'model': {'class': 'XGBModel', 'module_path': 'qlib.contrib.model.xgboost'},\n",
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" 'record': [{'class': 'SignalRecord',\n",
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" 'module_path': 'qlib.workflow.record_temp'},\n",
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" {'class': 'SigAnaRecord',\n",
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" 'module_path': 'qlib.workflow.record_temp'}],\n",
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" 'task_key': 1},\n",
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" {'dataset': {'class': 'DatasetH',\n",
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" 'kwargs': {'handler': {'class': 'Alpha158',\n",
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" 'kwargs': {'end_time': '2020-08-01',\n",
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" 'fit_end_time': '2014-12-31',\n",
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" 'fit_start_time': '2008-01-01',\n",
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" 'instruments': 'csi100',\n",
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" 'start_time': '2008-01-01'},\n",
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" 'module_path': 'qlib.contrib.data.handler'},\n",
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" 'segments': {'test': (Timestamp('2019-04-09 00:00:00'),\n",
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" Timestamp('2021-07-12 00:00:00')),\n",
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" 'train': (Timestamp('2010-04-23 00:00:00'),\n",
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" Timestamp('2017-05-24 00:00:00')),\n",
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" 'valid': (Timestamp('2017-05-25 00:00:00'),\n",
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" Timestamp('2019-04-08 00:00:00'))}},\n",
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" 'module_path': 'qlib.data.dataset'},\n",
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" 'model': {'class': 'XGBModel', 'module_path': 'qlib.contrib.model.xgboost'},\n",
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" 'record': [{'class': 'SignalRecord',\n",
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" 'module_path': 'qlib.workflow.record_temp'},\n",
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" {'class': 'SigAnaRecord',\n",
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" 'module_path': 'qlib.workflow.record_temp'}],\n",
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" 'task_key': 1},\n",
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" {'dataset': {'class': 'DatasetH',\n",
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" 'kwargs': {'handler': {'class': 'Alpha158',\n",
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" 'kwargs': {'end_time': '2020-08-01',\n",
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" 'fit_end_time': '2014-12-31',\n",
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" 'fit_start_time': '2008-01-01',\n",
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" 'instruments': 'csi100',\n",
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" 'start_time': '2008-01-01'},\n",
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" 'module_path': 'qlib.contrib.data.handler'},\n",
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" 'segments': {'test': (Timestamp('2017-01-03 00:00:00'),\n",
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" Timestamp('2019-04-08 00:00:00')),\n",
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" 'train': (Timestamp('2008-01-02 00:00:00'),\n",
|
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" Timestamp('2014-12-31 00:00:00')),\n",
|
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" 'valid': (Timestamp('2015-01-05 00:00:00'),\n",
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" Timestamp('2016-12-30 00:00:00'))}},\n",
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" 'module_path': 'qlib.data.dataset'},\n",
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" 'model': {'class': 'LGBModel', 'module_path': 'qlib.contrib.model.gbdt'},\n",
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" 'record': [{'class': 'SignalRecord',\n",
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" 'module_path': 'qlib.workflow.record_temp'},\n",
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" {'class': 'SigAnaRecord',\n",
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" 'module_path': 'qlib.workflow.record_temp'}],\n",
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" 'task_key': 'task_lgb'},\n",
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" {'dataset': {'class': 'DatasetH',\n",
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" 'kwargs': {'handler': {'class': 'Alpha158',\n",
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" 'kwargs': {'end_time': '2020-08-01',\n",
|
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" 'fit_end_time': '2014-12-31',\n",
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|
" 'fit_start_time': '2008-01-01',\n",
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" 'instruments': 'csi100',\n",
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" 'start_time': '2008-01-01'},\n",
|
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|
" 'module_path': 'qlib.contrib.data.handler'},\n",
|
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|
" '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": [
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||||||
|
"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",
|
||||||
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"from pprint import pprint\n",
|
||||||
|
"pprint(tasks)\n",
|
||||||
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"tm = TaskManager(task_pool=task_pool)\n",
|
||||||
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"tm.create_task(tasks) # all tasks will be saved to MongoDB"
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||||||
|
],
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||||||
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"metadata": {
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||||||
|
"collapsed": false,
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||||||
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"pycharm": {
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||||||
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"name": "#%%\n"
|
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|
}
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||||||
|
}
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||||||
|
},
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|
{
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||||||
|
"cell_type": "code",
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|
"execution_count": 26,
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|
"outputs": [
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{
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"output_type": "stream",
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"name": "stderr",
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||||||
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"text": [
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||||||
|
"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",
|
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"[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",
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||||||
|
"[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
|
||||||
|
}
|
||||||
108
examples/taskmanager/task_manager_rolling.py
Normal file
108
examples/taskmanager/task_manager_rolling.py
Normal file
@@ -0,0 +1,108 @@
|
|||||||
|
import qlib
|
||||||
|
from qlib.config import REG_CN
|
||||||
|
from qlib.workflow.task.gen import RollingGen, task_generator
|
||||||
|
from qlib.workflow.task.manage import TaskManager
|
||||||
|
from qlib.config import C
|
||||||
|
|
||||||
|
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,
|
||||||
|
}
|
||||||
|
|
||||||
|
provider_uri = "~/.qlib/qlib_data/cn_data" # target_dir
|
||||||
|
qlib.init(provider_uri=provider_uri, region=REG_CN)
|
||||||
|
|
||||||
|
C["mongo"] = {
|
||||||
|
"task_url" : "mongodb://localhost:27017/", # maybe you need to change it to your url
|
||||||
|
"task_db_name" : "rolling_db"
|
||||||
|
}
|
||||||
|
|
||||||
|
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
|
||||||
|
|
||||||
|
tasks = task_generator(
|
||||||
|
task_xgboost_config, # default task name
|
||||||
|
RollingGen(step=550,rtype=RollingGen.ROLL_SD), # generate different date segment
|
||||||
|
task_lgb=task_lgb_config # use "task_lgb" as the task name
|
||||||
|
)
|
||||||
|
|
||||||
|
# Uncomment next two lines to see the generated tasks
|
||||||
|
# from pprint import pprint
|
||||||
|
# pprint(tasks)
|
||||||
|
|
||||||
|
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.model.trainer import task_train
|
||||||
|
|
||||||
|
run_task(task_train, task_pool, experiment_name=exp_name) # all tasks will be trained using "task_train" method
|
||||||
|
|
||||||
|
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):
|
||||||
|
# only choose the results of "task_lgb" and test in 2019 from all tasks
|
||||||
|
task_key, rolling_end = get_task_key(task_config)
|
||||||
|
if task_key=="task_lgb" and rolling_end.startswith('2019'):
|
||||||
|
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)
|
||||||
@@ -1,177 +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_config = {\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_config = {\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_config,\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_config = [\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",
|
|
||||||
"task_lgb_config = {\n",
|
|
||||||
" \"model\": {\n",
|
|
||||||
" \"class\": \"LGBModel\",\n",
|
|
||||||
" \"module_path\": \"qlib.contrib.model.gbdt\",\n",
|
|
||||||
" },\n",
|
|
||||||
" \"dataset\": dataset_config,\n",
|
|
||||||
" \"record\": record_config,\n",
|
|
||||||
"}\n",
|
|
||||||
"\n",
|
|
||||||
"# use xgboost\n",
|
|
||||||
"task_xgboost_config = {\n",
|
|
||||||
" \"model\": {\n",
|
|
||||||
" \"class\": \"XGBModel\",\n",
|
|
||||||
" \"module_path\": \"qlib.contrib.model.xgboost\",\n",
|
|
||||||
" },\n",
|
|
||||||
" \"dataset\": dataset_config,\n",
|
|
||||||
" \"record\": record_config,\n",
|
|
||||||
"}\n",
|
|
||||||
"provider_uri = r\"../qlib-main/qlib_data/cn_data\"\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",
|
|
||||||
" task_xgboost_config, # default task name\n",
|
|
||||||
" RollingGen(step=550,rtype=RollingGen.ROLL_SD), # generate different date segment\n",
|
|
||||||
" task_lgb=task_lgb_config # 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 RollingCollector\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_config):\n",
|
|
||||||
" task_key = task_config[\"task_key\"]\n",
|
|
||||||
" rolling_end_timestamp = task_config[\"dataset\"][\"kwargs\"][\"segments\"][\"test\"][1]\n",
|
|
||||||
" rolling_end_datatime = rolling_end_timestamp.to_pydatetime()\n",
|
|
||||||
" return task_key, rolling_end_datatime.strftime('%Y-%m-%d')\n",
|
|
||||||
"\n",
|
|
||||||
"def my_filter(task_config):\n",
|
|
||||||
" # only choose the results of \"task_lgb\" and test in 2019 from all tasks\n",
|
|
||||||
" task_key, rolling_end = get_task_key(task_config)\n",
|
|
||||||
" if task_key==\"task_lgb\" and rolling_end.startswith('2019'):\n",
|
|
||||||
" return True\n",
|
|
||||||
" return False\n",
|
|
||||||
"\n",
|
|
||||||
"collector = RollingCollector(get_task_key, my_filter)\n",
|
|
||||||
"pred_rolling = collector(exp_name) # name tasks by \"get_task_key\" and filter tasks by \"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": "2.7.6"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"nbformat": 4,
|
|
||||||
"nbformat_minor": 0
|
|
||||||
}
|
|
||||||
Reference in New Issue
Block a user