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Update doc strings and fix
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Jactus
@@ -14,9 +14,11 @@ class Dataset(Serializable):
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def __init__(self, *args, **kwargs):
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"""
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init is designed to finish following steps
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init is designed to finish following steps:
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- setup data
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- The data related attributes' names should start with '_' so that it will not be saved on disk when serializing
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- initialize the state of the dataset(info to prepare the data)
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- The name of essential state for preparing data should not start with '_' so that it could be serialized on disk when serializing.
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@@ -29,11 +31,15 @@ class Dataset(Serializable):
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"""
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setup the data
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We split the setup_data function for following situation
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- 1) User have a Dataset object with learned status on disk
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- 2) User load the Dataset object from the disk(Note the init function is skiped)
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- 3) User call `setup_data` to load new data
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- 4) User prepare data for model based on previous status
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We split the setup_data function for following situation:
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- User have a Dataset object with learned status on disk
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- User load the Dataset object from the disk(Note the init function is skiped)
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- User call `setup_data` to load new data
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- User prepare data for model based on previous status
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"""
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pass
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@@ -41,8 +47,9 @@ class Dataset(Serializable):
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"""
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The type of dataset depends on the model. (It could be pd.DataFrame, pytorch.DataLoader, etc.)
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The parameters should specify the scope for the prepared data
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The method sould
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The method should:
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- process the data
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- return the processed data
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Returns
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@@ -55,11 +62,12 @@ class Dataset(Serializable):
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class DatasetH(Dataset):
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"""
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Dataset with Data(H)anler
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Dataset with Data(H)andler
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User should try to put the data preprocessing functions into handler.
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Only following data processing functions should be placed in Dataset
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Only following data processing functions should be placed in Dataset:
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- The processing is related to specific model.
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- The processing is related to data split
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"""
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@@ -81,21 +89,26 @@ class DatasetH(Dataset):
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Parameters
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----------
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handler : Union[dict, DataHandler]
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handler could be
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1) insntance of `DataHandler`
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2) config of `DataHandler`. Please refer to `DataHandler`
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handler could be:
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- insntance of `DataHandler`
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- config of `DataHandler`. Please refer to `DataHandler`
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segments : list
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Describe the options to segment the data.
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Here are some examples
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1) 'segments': {
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'train': ("2008-01-01", "2014-12-31"),
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'valid': ("2017-01-01", "2020-08-01",),
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'test': ("2015-01-01", "2016-12-31",),
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}
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2) 'segments': {
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'insample': ("2008-01-01", "2014-12-31"),
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'outsample': ("2017-01-01", "2020-08-01",),
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}
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Here are some examples:
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.. code-block::
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1) 'segments': {
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'train': ("2008-01-01", "2014-12-31"),
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'valid': ("2017-01-01", "2020-08-01",),
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'test': ("2015-01-01", "2016-12-31",),
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}
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2) 'segments': {
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'insample': ("2008-01-01", "2014-12-31"),
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'outsample': ("2017-01-01", "2020-08-01",),
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}
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"""
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self._handler = init_instance_by_config(handler, accept_types=DataHandler)
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self._segments = segments.copy()
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@@ -114,9 +127,11 @@ class DatasetH(Dataset):
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----------
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segments : Union[List[str], Tuple[str], str, slice]
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Describe the scope of the data to be prepared
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Here are some examples
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1) 'train'
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2) ['train', 'valid']
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Here are some examples:
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- 'train'
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- ['train', 'valid']
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col_set : str
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The col_set will be passed to self._handler when fetching data
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data_key: str
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@@ -41,7 +41,7 @@ class DataHandler(Serializable):
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Example of the data:
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The multi-index of the columns is optional.
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.. code-block::
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.. code-block:: python
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feature label
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$close $volume Ref($close, 1) Mean($close, 3) $high-$low LABEL0
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@@ -109,7 +109,8 @@ class DataHandler(Serializable):
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Parameters
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----------
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enable_cache : bool
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default value is false
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default value is false:
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- if `enable_cache` == True:
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the processed data will be saved on disk, and handler will load the cached data from the disk directly
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@@ -378,8 +379,10 @@ class DataHandlerLP(DataHandler):
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init_type : str
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The type `IT_*` listed above
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enable_cache : bool
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default value is false
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if `enable_cache` == True:
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default value is false:
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- if `enable_cache` == True:
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the processed data will be saved on disk, and handler will load the cached data from the disk directly
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when we call `init` next time
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"""
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@@ -39,14 +39,16 @@ class DataLoader(abc.ABC):
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pd.DataFrame:
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data load from the under layer source
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Example of the data:
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(The multi-index of the columns is optional.)
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feature label
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$close $volume Ref($close, 1) Mean($close, 3) $high-$low LABEL0
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datetime instrument
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2010-01-04 SH600000 81.807068 17145150.0 83.737389 83.016739 2.741058 0.0032
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SH600004 13.313329 11800983.0 13.313329 13.317701 0.183632 0.0042
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SH600005 37.796539 12231662.0 38.258602 37.919757 0.970325 0.0289
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Example of the data (The multi-index of the columns is optional.):
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.. code-block::
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feature label
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$close $volume Ref($close, 1) Mean($close, 3) $high-$low LABEL0
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datetime instrument
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2010-01-04 SH600000 81.807068 17145150.0 83.737389 83.016739 2.741058 0.0032
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SH600004 13.313329 11800983.0 13.313329 13.317701 0.183632 0.0042
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SH600005 37.796539 12231662.0 38.258602 37.919757 0.970325 0.0289
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"""
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pass
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@@ -55,7 +57,7 @@ class DLWParser(DataLoader):
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"""
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(D)ata(L)oader (W)ith (P)arser for features and names
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Extracting this class so that QlibDataLoader and other dataloaders(such as QdbDataLoader) can share the fields
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Extracting this class so that QlibDataLoader and other dataloaders(such as QdbDataLoader) can share the fields.
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"""
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def __init__(self, config: Tuple[list, tuple, dict]):
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@@ -65,14 +67,16 @@ class DLWParser(DataLoader):
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config : Tuple[list, tuple, dict]
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Config will be used to describe the fields and column names
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<config> := {
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"group_name1": <fields_info1>
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"group_name2": <fields_info2>
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}
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or
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<config> := <fields_info>
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.. code-block:: YAML
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<fields_info> := ["expr", ...] | (["expr", ...], ["col_name", ...])
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<config> := {
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"group_name1": <fields_info1>
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"group_name2": <fields_info2>
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}
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or
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<config> := <fields_info>
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<fields_info> := ["expr", ...] | (["expr", ...], ["col_name", ...])
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"""
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self.is_group = isinstance(config, dict)
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