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2020-11-30 18:54:31 +08:00
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42
docs/component/data.rst
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@@ -29,7 +29,18 @@ Qlib Format Data
------------------
We've specially designed a data structure to manage financial data, please refer to the `File storage design section in Qlib paper <https://arxiv.org/abs/2009.11189>`_ for detailed information.
Such data will be stored with filename suffix `.bin` (We'll call them `.bin` file, `.bin` format, or qlib format). `.bin` file is designed for scientific computing on finance data
Such data will be stored with filename suffix `.bin` (We'll call them `.bin` file, `.bin` format, or qlib format). `.bin` file is designed for scientific computing on finance data.
``Qlib`` provides two different off-the-shelf dataset, which can be accessed through this `link <https://github.com/microsoft/qlib/blob/main/qlib/contrib/data/handler.py>`_:
======================== ================= ================
Dataset US Market China Market
======================== ================= ================
Alpha360 √ √
Alpha158 √ √
======================== ================= ================
Qlib Format Dataset
--------------------
@@ -45,7 +56,7 @@ In addition to China-Stock data, ``Qlib`` also includes a US-Stock dataset, whic
python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/us_data --region us
After running the above command, users can find china-stock and us-stock data in Qlib format in the ``~/.qlib/csv_data/cn_data`` directory and ``~/.qlib/csv_data/us_data`` directory respectively.
After running the above command, users can find china-stock and us-stock data in ``Qlib`` format in the ``~/.qlib/csv_data/cn_data`` directory and ``~/.qlib/csv_data/us_data`` directory respectively.
``Qlib`` also provides the scripts in ``scripts/data_collector`` to help users crawl the latest data on the Internet and convert it to qlib format.
@@ -54,8 +65,7 @@ When ``Qlib`` is initialized with this dataset, users could build and evaluate t
Converting CSV Format into Qlib Format
-------------------------------------------
``Qlib`` has provided the script ``scripts/dump_bin.py`` to convert data in CSV format into `.bin` files (Qlib format).
``Qlib`` has provided the script ``scripts/dump_bin.py`` to convert **any** data in CSV format into `.bin` files (``Qlib`` format) as long as they are in the correct format.
Users can download the demo china-stock data in CSV format as follows for reference to the CSV format.
@@ -130,9 +140,21 @@ After conversion, users can find their Qlib format data in the directory `~/.qli
In the convention of `Qlib` data processing, `open, close, high, low, volume, money and factor` will be set to NaN if the stock is suspended.
China-Stock Mode & US-Stock Mode
Multiple Stock Modes
--------------------------------
``Qlib`` now provides two different stock modes for users: China-Stock Mode & US-Stock Mode. Here are some different settings of these two modes:
============== ================= ================
Region Trade Unit Limit Threshold
============== ================= ================
China 100 0.099
US 1 None
============== ================= ================
The `trade unit` defines the unit number of stocks can be used in a trade, and the `limit threshold` defines the bound set to the percentage of ups and downs of a stock.
- If users use ``Qlib`` in china-stock mode, china-stock data is required. Users can use ``Qlib`` in china-stock mode according to the following steps:
- Download china-stock in qlib format, please refer to section `Qlib Format Dataset <#qlib-format-dataset>`_.
- Initialize ``Qlib`` in china-stock mode
@@ -208,13 +230,19 @@ QlibDataLoader
The ``QlibDataLoader`` class in ``Qlib`` is such an interface that allows users to load raw data from the ``Qlib`` data source.
StaticDataLoader
---------------
The ``StaticDataLoader`` class in ``Qlib`` is such an interface that allows users to load raw data from file or as provided.
Interface
------------
Here are some interfaces of the ``QlibDataLoader`` class:
.. autoclass:: qlib.data.dataset.loader.QlibDataLoader
:members: load, load_group_df
.. autoclass:: qlib.data.dataset.loader.DataLoader
:members:
API
-----------

41
docs/component/model.rst
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@@ -18,45 +18,10 @@ Base Class & Interface
The base class provides the following interfaces:
- `__init__(**kwargs)`
- Initialization.
- `fit(self, dataset, **kwargs)`
- Train model.
- Parameter:
- `dataset`, ``Qlib``'s ``DatasetH`` type. For more information about ``DatasetH``, users can refer to the related document: `Qlib Dataset <../component/data.html#dataset>`_.
The `dataset` is passed into the `model`'s method because there are some unique data preprocessing procedures for each, we want to give each model maximum flexibility to handle the data that is suitable for their own.
The following code example shows how to retrieve `x_train`, `y_train` and `w_train` from the `dataset`:
.. code-block:: Python
# get features and labels
df_train, df_valid = dataset.prepare(
["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
)
x_train, y_train = df_train["feature"], df_train["label"]
x_valid, y_valid = df_valid["feature"], df_valid["label"]
# get weights
try:
wdf_train, wdf_valid = dataset.prepare(["train", "valid"], col_set=["weight"], data_key=DataHandlerLP.DK_L)
w_train, w_valid = wdf_train["weight"], wdf_valid["weight"]
except KeyError as e:
w_train = pd.DataFrame(np.ones_like(y_train.values), index=y_train.index)
w_valid = pd.DataFrame(np.ones_like(y_valid.values), index=y_valid.index)
- `predict(self, dataset, **kwargs)`
- Predict test data.
- Parameter:
- `dataset`, ``Qlib``'s ``DatasetH`` type. The usage is similar to the example above.
- Returns:
- Predic results with type: `pandas.Series`.
- `finetune(self, dataset, **kwargs)`
- Finetune the model.
- Parameter:
- `dataset`, ``Qlib``'s ``DatasetH`` type. The usage is similar to the example above.
.. autoclass:: qlib.model.base.Model
:members:
``Qlib`` also provides a base class `qlib.model.base.ModelFT <../reference/api.html#qlib.model.base.ModelFT>`_, which includes the method for finetuning the model.
For other interfaces such as `finetune`, please refer to `Model API <../reference/api.html#module-qlib.model.base>`_.

2
docs/component/recorder.rst
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@@ -72,6 +72,8 @@ The ``Experiment`` class is solely responsible for a single experiment, and it w
For other interfaces such as `search_records`, `delete_recorder`, please refer to `Experiment API <../reference/api.html#experiment>`_.
``Qlib`` also provides a default ``Experiment``, which will be created and used under certain situations when users use the APIs such as `log_metrics` or `get_exp`. If the default ``Experiment`` is used, there will be related logged information when running ``Qlib``. Users are able to change the name of the default ``Experiment`` in the config file of ``Qlib`` or during ``Qlib``'s `initialization <../start/initialization.html#parameters>`_, which is set to be '`Experiment`'.
Recorder
===================

57
docs/component/workflow.rst
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@@ -11,8 +11,8 @@ Introduction
The components in `Qlib Framework <../introduction/introduction.html#framework>`_ are designed in a loosely-coupled way. Users could build their own Quant research workflow with these components like `Example <https://github.com/microsoft/qlib/blob/main/examples/workflow_by_code.py>`_.
Besides, ``Qlib`` provides more user-friendly interfaces named ``qrun`` to automatically run the whole workflow defined by configuration. A concrete execution of the whole workflow is called an `experiment`.
With ``qrun``, user can easily run an `experiment`, which includes the following steps:
Besides, ``Qlib`` provides more user-friendly interfaces named ``qrun`` to automatically run the whole workflow defined by configuration. Running the whole workflow is called an `execution`.
With ``qrun``, user can easily start an `execution`, which includes the following steps:
- Data
- Loading
@@ -25,7 +25,7 @@ With ``qrun``, user can easily run an `experiment`, which includes the following
- Forecast signal analysis
- Backtest
For each `experiment`, ``Qlib`` has a complete system to tracking all the information as well as artifacts generated during training, inference and evaluation phase. For more information about how Qlib handles `experiment`, please refer to the related document: `Recorder: Experiment Management <../component/recorder.html>`_.
For each `execution`, ``Qlib`` has a complete system to tracking all the information as well as artifacts generated during training, inference and evaluation phase. For more information about how ``Qlib`` handles this, please refer to the related document: `Recorder: Experiment Management <../component/recorder.html>`_.
Complete Example
===================
@@ -35,8 +35,9 @@ Below is a typical config file of ``qrun``.
.. code-block:: YAML
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market csi300
benchmark: &benchmark SH000300
data_handler_config: &data_handler_config
@@ -100,12 +101,16 @@ After saving the config into `configuration.yaml`, users could start the workflo
.. code-block:: bash
qrun -c configuration.yaml
qrun configuration.yaml
.. note::
`qrun` will be placed in your $PATH directory when installing ``Qlib``.
.. note::
The symbol `&` in `yaml` file stands for an anchor of a field, which is useful when another fields include this parameter as part of the value. Taking the configuration file above as an example, users can directly change the value of `market` and `benchmark` without traversing the entire configuration file.
Configuration File
===================
@@ -114,17 +119,15 @@ Let's get into details of ``qrun`` in this section.
Before using ``qrun``, users need to prepare a configuration file. The following content shows how to prepare each part of the configuration file.
Qlib Data Section
Qlib Init Section
--------------------
At first, the configuration file needs to contain several basic parameters about the data, which will be used for qlib initialization, data handling and backtest.
At first, the configuration file needs to contain several basic parameters which will be used for qlib initialization.
.. code-block:: YAML
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market csi300
benchmark: &benchmark SH000300
The meaning of each field is as follows:
@@ -139,34 +142,14 @@ The meaning of each field is as follows:
The value of `region` should be aligned with the data stored in `provider_uri`.
- `market`
Type: str. Index name, the default value is `csi500`.
- `benchmark`
Type: str, list or pandas.Series. Stock index symbol, the default value is `SH000905`.
Task Section
--------------------
.. note::
* If `benchmark` is str, it will use the daily change as the 'bench'.
* If `benchmark` is list, it will use the daily average change of the stock pool in the list as the 'bench'.
* If `benchmark` is pandas.Series, whose `index` is trading date and the value T is the change from T-1 to T, it will be directly used as the 'bench'. An example is as following:
.. code-block:: python
print(D.features(D.instruments('csi500'), ['$close/Ref($close, 1)-1'])['$close/Ref($close, 1)-1'].head())
2017-01-04 0.011693
2017-01-05 0.000721
2017-01-06 -0.004322
2017-01-09 0.006874
2017-01-10 -0.003350
.. note::
The symbol `&` in `yaml` file stands for an anchor of a field, which is useful when another fields include this parameter as part of the value. Taking the configuration file above as an example, users can directly change the value of `market` and `benchmark` without traversing the entire configuration file.
The `task` field in the configuration corresponds to a `task`, which contains the parameters of three different subsections: `Model`, `Dataset` and `Record`.
Model Section
--------------------
~~~~~~~~~~~~~~~~~~~~
In the `task` field, the `model` section describes the parameters of the model to be used for training and inference. For more information about the base ``Model`` class, please refer to `Qlib Model <../component/model.html>`_.
@@ -202,7 +185,7 @@ The meaning of each field is as follows:
``Qlib`` provides a util named: ``init_instance_by_config`` to initialize any class inside ``Qlib`` with the configuration includes the fields: `class`, `module_path` and `kwargs`.
Dataset Section
--------------------
~~~~~~~~~~~~~~~~~~~~
The `dataset` field describes the parameters for the ``Dataset`` module in ``Qlib`` as well those for the module ``DataHandler``. For more information about the ``Dataset`` module, please refer to `Qlib Model <../component/data.html#dataset>`_.
@@ -237,9 +220,9 @@ Here is the configuration for the ``Dataset`` module which will take care of dat
test: [2017-01-01, 2020-08-01]
Record Section
--------------------
~~~~~~~~~~~~~~~~~~~~
The `record` field is about the parameters the ``Record`` module in ``Qlib``. ``Record`` is responsible for generating certain analysis and evaluation results such as `prediction`, `information Coefficient (IC)` and `backtest`.
The `record` field is about the parameters the ``Record`` module in ``Qlib``. ``Record`` is responsible for tracking training process and results such as `information Coefficient (IC)` and `backtest` in a standard format.
The following script is the configuration of `backtest` and the `strategy` used in `backtest`:

17
docs/start/integration.rst
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@@ -19,8 +19,8 @@ The Custom models need to inherit `qlib.model.base.Model <../reference/api.html#
- Override the `__init__` method
- ``Qlib`` passes the initialized parameters to the \_\_init\_\_ method.
- The parameter must be consistent with the hyperparameters in the configuration file.
- Code Example: In the following example, the hyperparameter filed of the configuration file should contain parameters such as `loss:mse`.
- The hyperparameters of model in the configuration must be consistent with those defined in the `__init__` method.
- Code Example: In the following example, the hyperparameters of model in the configuration file should contain parameters such as `loss:mse`.
.. code-block:: Python
def __init__(self, loss='mse', **kwargs):
@@ -31,9 +31,9 @@ The Custom models need to inherit `qlib.model.base.Model <../reference/api.html#
self._model = None
- Override the `fit` method
- ``Qlib`` calls the fit method to train the model
- The parameters must include training feature `dataset`.
- The parameters could include some optional parameters with default values, such as `num_boost_round = 1000` for `GBDT`.
- ``Qlib`` calls the fit method to train the model.
- The parameters must include training feature `dataset`, which is designed in the interface.
- The parameters could include some `optional` parameters with default values, such as `num_boost_round = 1000` for `GBDT`.
- Code Example: In the following example, `num_boost_round = 1000` is an optional parameter.
.. code-block:: Python
@@ -69,7 +69,7 @@ The Custom models need to inherit `qlib.model.base.Model <../reference/api.html#
)
- Override the `predict` method
- The parameters must include training feature `dataset`, which will be userd to get the test dataset.
- The parameters must include the parameter `dataset`, which will be userd to get the test dataset.
- Return the `prediction score`.
- Please refer to `Model API <../reference/api.html#module-qlib.model.base>`_ for the parameter types of the fit method.
- Code Example: In the following example, users need to use `LightGBM` to predict the label(such as `preds`) of test data `x_test` and return it.
@@ -81,8 +81,9 @@ The Custom models need to inherit `qlib.model.base.Model <../reference/api.html#
x_test = dataset.prepare("test", col_set="feature", data_key=DataHandlerLP.DK_I)
return pd.Series(self.model.predict(x_test.values), index=x_test.index)
- Override the `finetune` method
- The parameters must include training feature `dataset`.
- Override the `finetune` method (Optional)
- This method is optional to the users, and when users one to use this method on their own models, they should inherit the ``ModelFT`` base class, which includes the interface of `finetune`.
- The parameters must include the parameter `dataset`.
- Code Example: In the following example, users will use `LightGBM` as the model and finetune it.
.. code-block:: Python

5
examples/benchmarks/ALSTM/workflow_config_alstm.yaml
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@@ -1,5 +1,6 @@
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market csi300
benchmark: &benchmark SH000300
data_handler_config: &data_handler_config

5
examples/benchmarks/CatBoost/workflow_config_catboost.yaml
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@@ -1,5 +1,6 @@
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market csi300
benchmark: &benchmark SH000300
data_handler_config: &data_handler_config

5
examples/benchmarks/GATs/workflow_config_gats.yaml
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@@ -1,5 +1,6 @@
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market csi300
benchmark: &benchmark SH000300
data_handler_config: &data_handler_config

5
examples/benchmarks/GRU/workflow_config_gru.yaml
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@@ -1,5 +1,6 @@
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market csi300
benchmark: &benchmark SH000300
data_handler_config: &data_handler_config

5
examples/benchmarks/LSTM/workflow_config_lstm.yaml
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@@ -1,5 +1,6 @@
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market csi300
benchmark: &benchmark SH000300
data_handler_config: &data_handler_config

5
examples/benchmarks/LightGBM/workflow_config_lightgbm.yaml
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@@ -1,5 +1,6 @@
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market csi300
benchmark: &benchmark SH000300
data_handler_config: &data_handler_config

5
examples/benchmarks/Linear/workflow_config_linear.yaml
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@@ -1,5 +1,6 @@
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market csi300
benchmark: &benchmark SH000300
data_handler_config: &data_handler_config

5
examples/benchmarks/MLP/workflow_config_mlp.yaml
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@@ -1,5 +1,6 @@
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market csi300
benchmark: &benchmark SH000300
data_handler_config: &data_handler_config

5
examples/benchmarks/SFM/workflow_config_sfm.yaml
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@@ -1,5 +1,6 @@
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market csi300
benchmark: &benchmark SH000300
data_handler_config: &data_handler_config

10
examples/benchmarks/TFT/workflow_config_tft.yaml
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@@ -1,7 +1,8 @@
sys:
rel_path: .
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market csi300
benchmark: &benchmark SH000300
data_handler_config: &data_handler_config
@@ -46,6 +47,11 @@ task:
- class: SignalRecord
module_path: qlib.workflow.record_temp
kwargs: {}
- class: SigAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
ana_long_short: False
ann_scaler: 252
- class: PortAnaRecord
module_path: qlib.workflow.record_temp
kwargs:

5
examples/benchmarks/XGBoost/workflow_config_xgboost.yaml
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@@ -1,5 +1,6 @@
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market csi300
benchmark: &benchmark SH000300
data_handler_config: &data_handler_config

25
qlib/model/base.py
View File

@@ -27,13 +27,32 @@ class Model(BaseModel):
.. note::
The the attribute names of learned model should `not` start with '_'. So that the model could be
The attribute names of learned model should `not` start with '_'. So that the model could be
dumped to disk.
Parameters
----------
dataset : Dataset
dataset will generate the processed data from model training.
The following code example shows how to retrieve `x_train`, `y_train` and `w_train` from the `dataset`:
.. code-block:: Python
# get features and labels
df_train, df_valid = dataset.prepare(
["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
)
x_train, y_train = df_train["feature"], df_train["label"]
x_valid, y_valid = df_valid["feature"], df_valid["label"]
# get weights
try:
wdf_train, wdf_valid = dataset.prepare(["train", "valid"], col_set=["weight"], data_key=DataHandlerLP.DK_L)
w_train, w_valid = wdf_train["weight"], wdf_valid["weight"]
except KeyError as e:
w_train = pd.DataFrame(np.ones_like(y_train.values), index=y_train.index)
w_valid = pd.DataFrame(np.ones_like(y_valid.values), index=y_valid.index)
"""
raise NotImplementedError()
@@ -45,6 +64,10 @@ class Model(BaseModel):
----------
dataset : Dataset
dataset will generate the processed dataset from model training.
Returns
-------
Prediction results with certain type such as `pandas.Series`.
"""
raise NotImplementedError()

14
qlib/model/trainer.py
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@@ -6,29 +6,29 @@ from qlib.workflow import R
from qlib.workflow.record_temp import SignalRecord
def task_train(config: dict, experiment_name):
def task_train(task_config: dict, experiment_name):
"""
task based training
Parameters
----------
config : dict
A dict describing the training process
task_config : dict
A dict describes a task setting.
"""
# model initiaiton
model = init_instance_by_config(config.get("task")["model"])
dataset = init_instance_by_config(config.get("task")["dataset"])
model = init_instance_by_config(task_config["model"])
dataset = init_instance_by_config(task_config["dataset"])
# start exp
with R.start(experiment_name=experiment_name):
# train model
R.log_params(**flatten_dict(config.get("task")))
R.log_params(**flatten_dict(task_config))
model.fit(dataset)
recorder = R.get_recorder()
# generate records: prediction, backtest, and analysis
for record in config.get("task")["record"]:
for record in task_config.get["record"]:
if record["class"] == SignalRecord.__name__:
srconf = {"model": model, "dataset": dataset, "recorder": recorder}
record["kwargs"].update(srconf)

43
qlib/workflow/__init__.py
View File

@@ -90,7 +90,11 @@ class QlibRecorder:
def search_records(self, experiment_ids, **kwargs):
"""
Get a pandas DataFrame of records that fit the search criteria. Here is the example code of the method:
Get a pandas DataFrame of records that fit the search criteria.
The arguments of this function are not set to be rigid, and they will be different with different implementation of
``ExpManager`` in ``Qlib``. ``Qlib`` now provides an implementation of ``ExpManager`` with mlflow, and here is the
example code of the this method with the ``MLflowExpManager``:
.. code-block:: Python
@@ -139,7 +143,8 @@ class QlibRecorder:
If user doesn't provide the id or name of the experiment, this method will try to retrieve the default experiment and
list all the recorders of the default experiment. If the default experiment doesn't exist, the method will first
create the default experiment, and then create a new recorder under it.
create the default experiment, and then create a new recorder under it. (More information about the default experiment
can be found `here <../component/recorder.html#qlib.workflow.exp.Experiment>`_).
Here is the example code:
@@ -168,27 +173,27 @@ class QlibRecorder:
- If '`create`' is True:
- If ``R``'s running:
- If `active experiment` exists:
- no id or name specified, return the active experiment.
- if id or name is specified, return the specified experiment. If no such exp found, create a new experiment with given id or name, and the experiment is set to be running.
- if id or name is specified, return the specified experiment. If no such exp found, create a new experiment with given id or name, and the experiment is set to be active.
- If ``R``'s not running:
- If `active experiment` not exists:
- no id or name specified, create a default experiment, and the experiment is set to be running.
- no id or name specified, create a default experiment, and the experiment is set to be active.
- if id or name is specified, return the specified experiment. If no such exp found, create a new experiment with given name or the default experiment, and the experiment is set to be running.
- if id or name is specified, return the specified experiment. If no such exp found, create a new experiment with given name or the default experiment, and the experiment is set to be active.
- Else If '`create`' is False:
- If ``R``'s running:
- If ``active experiment` exists:
- no id or name specified, return the active experiment.
- if id or name is specified, return the specified experiment. If no such exp found, raise Error.
- If ``R``'s not running:
- If `active experiment` not exists:
- no id or name specified. If the default experiment exists, return it, otherwise, raise Error.
@@ -272,13 +277,13 @@ class QlibRecorder:
"""
Method for retrieving a recorder.
- If ``R``'s running:
- If `active recorder` exists:
- no id or name specified, return the active recorder.
- if id or name is specified, return the specified recorder.
- If ``R``'s not running:
- If `active recorder` not exists:
- no id or name specified, raise Error.
@@ -351,8 +356,8 @@ class QlibRecorder:
from a local file/directory, or directly saving objects. User can use valid python's keywords arguments
to specify the object to be saved as well as its name (name: value).
- If R's running: it will save the objects through the running recorder.
- If R's not running: the system will create a default experiment, and a new recorder and save objects under it.
- If `active recorder` exists: it will save the objects through the active recorder.
- If `active recorder` not exists: the system will create a default experiment, and a new recorder and save objects under it.
.. note::
@@ -384,8 +389,8 @@ class QlibRecorder:
"""
Method for logging parameters during an experiment. In addition to using ``R``, one can also log to a specific recorder after getting it with `get_recorder` API.
- If R's running: it will log parameters through the running recorder.
- If R's not running: the system will create a default experiment as well as a new recorder, and log parameters under it.
- If `active recorder` exists: it will log parameters through the active recorder.
- If `active recorder` not exists: the system will create a default experiment as well as a new recorder, and log parameters under it.
Here are some use cases:
@@ -409,8 +414,8 @@ class QlibRecorder:
"""
Method for logging metrics during an experiment. In addition to using ``R``, one can also log to a specific recorder after getting it with `get_recorder` API.
- If R's running: it will log metrics through the running recorder.
- If R's not running: the system will create a default experiment as well as a new recorder, and log metrics under it.
- If `active recorder` exists: it will log metrics through the active recorder.
- If `active recorder` not exists: the system will create a default experiment as well as a new recorder, and log metrics under it.
Here are some use cases:
@@ -434,8 +439,8 @@ class QlibRecorder:
"""
Method for setting tags for a recorder. In addition to using ``R``, one can also set the tag to a specific recorder after getting it with `get_recorder` API.
- If R's running: it will set tags through the running recorder.
- If R's not running: the system will create a default experiment as well as a new recorder, and set the tags under it.
- If `active recorder` exists: it will set tags through the active recorder.
- If `active recorder` not exists: the system will create a default experiment as well as a new recorder, and set the tags under it.
Here are some use cases:

6
qlib/workflow/cli.py
View File

@@ -49,13 +49,11 @@ def workflow(config_path, experiment_name="workflow", uri_folder="mlruns"):
# config the `sys` section
sys_config(config, config_path)
provider_uri = config.get("provider_uri")
region = config.get("region")
exp_manager = C["exp_manager"]
exp_manager["kwargs"]["uri"] = "file:" + str(Path(os.getcwd()).resolve() / uri_folder)
qlib.init(provider_uri=provider_uri, region=region, exp_manager=exp_manager)
qlib.init(**config.get("qlib_init"), exp_manager=exp_manager)
task_train(config, experiment_name=experiment_name)
task_train(config.get("task"), experiment_name=experiment_name)
# function to run worklflow by config

12
qlib/workflow/exp.py
View File

@@ -114,24 +114,24 @@ class Experiment:
* If `create` is True:
* If R's running:
* If `active recorder` exists:
* no id or name specified, return the active recorder.
* if id or name is specified, return the specified recorder. If no such exp found, create a new recorder with given id or name, and the recorder shoud be running.
* if id or name is specified, return the specified recorder. If no such exp found, create a new recorder with given id or name, and the recorder shoud be active.
* If R's not running:
* If `active recorder` not exists:
* no id or name specified, create a new recorder.
* if id or name is specified, return the specified experiment. If no such exp found, create a new recorder with given id or name, and the recorder shoud be running.
* if id or name is specified, return the specified experiment. If no such exp found, create a new recorder with given id or name, and the recorder shoud be active.
* Else If `create` is False:
* If R's running:
* If `active recorder` exists:
* no id or name specified, return the active recorder.
* if id or name is specified, return the specified recorder. If no such exp found, raise Error.
* If R's not running:
* If `active recorder` not exists:
* no id or name specified, raise Error.
* if id or name is specified, return the specified recorder. If no such exp found, raise Error.

20
qlib/workflow/expm.py
View File

@@ -23,12 +23,12 @@ class ExpManager:
def __init__(self, uri, default_exp_name):
self.uri = uri
self.default_exp_name = default_exp_name
self.active_experiment = None # only one experiment can running each time
self.active_experiment = None # only one experiment can active each time
def start_exp(self, experiment_name=None, recorder_name=None, uri=None, **kwargs):
"""
Start an experiment. This method includes first get_or_create an experiment, and then
set it to be running.
set it to be active.
Parameters
----------
@@ -47,7 +47,7 @@ class ExpManager:
def end_exp(self, recorder_status: str = Recorder.STATUS_S, **kwargs):
"""
End an running experiment.
End an active experiment.
Parameters
----------
@@ -90,7 +90,7 @@ class ExpManager:
def get_exp(self, experiment_id=None, experiment_name=None, create: bool = True):
"""
Retrieve an experiment. This method includes getting an active experiment, and get_or_create a specific experiment.
The returned experiment will be running.
The returned experiment will be active.
When user specify experiment id and name, the method will try to return the specific experiment.
When user does not provide recorder id or name, the method will try to return the current active experiment.
@@ -99,24 +99,24 @@ class ExpManager:
* If `create` is True:
* If R's running:
* If `active experiment` exists:
* no id or name specified, return the active experiment.
* if id or name is specified, return the specified experiment. If no such exp found, create a new experiment with given id or name, and the experiment is set to be running.
* if id or name is specified, return the specified experiment. If no such exp found, create a new experiment with given id or name, and the experiment is set to be active.
* If R's not running:
* If `active experiment` not exists:
* no id or name specified, create a default experiment.
* if id or name is specified, return the specified experiment. If no such exp found, create a new experiment with given id or name, and the experiment is set to be running.
* if id or name is specified, return the specified experiment. If no such exp found, create a new experiment with given id or name, and the experiment is set to be active.
* Else If `create` is False:
* If R's running:
* If `active experiment` exists:
* no id or name specified, return the active experiment.
* if id or name is specified, return the specified experiment. If no such exp found, raise Error.
* If R's not running:
* If `active experiment` not exists:
* no id or name specified. If the default experiment exists, return it, otherwise, raise Error.
* if id or name is specified, return the specified experiment. If no such exp found, raise Error.