code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
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def gen_dataset_cache(self, cache_path: Union[str, Path], instruments, fields, freq, inst_processors=[]):
"""gen_dataset_cache
.. note:: This function does not consider the cache read write lock. Please
acquire the lock outside this function
The format the cache contains 3 parts(fo... | gen_dataset_cache
.. note:: This function does not consider the cache read write lock. Please
acquire the lock outside this function
The format the cache contains 3 parts(followed by typical filename).
- index : cache/d41366901e25de3ec47297f12e2ba11d.index
- The conte... | gen_dataset_cache | python | microsoft/qlib | qlib/data/cache.py | https://github.com/microsoft/qlib/blob/master/qlib/data/cache.py | MIT |
def send_request(self, request_type, request_content, msg_queue, msg_proc_func=None):
"""Send a certain request to server.
Parameters
----------
request_type : str
type of proposed request, 'calendar'/'instrument'/'feature'.
request_content : dict
records... | Send a certain request to server.
Parameters
----------
request_type : str
type of proposed request, 'calendar'/'instrument'/'feature'.
request_content : dict
records the information of the request.
msg_proc_func : func
the function to process... | send_request | python | microsoft/qlib | qlib/data/client.py | https://github.com/microsoft/qlib/blob/master/qlib/data/client.py | MIT |
def request_callback(*args):
"""callback_wrapper
:param *args: args[0] is the response content
"""
# args[0] is the response content
self.logger.debug("receive data and enter queue")
msg = dict(args[0])
if msg["detailed_info"] is not N... | callback_wrapper
:param *args: args[0] is the response content
| request_callback | python | microsoft/qlib | qlib/data/client.py | https://github.com/microsoft/qlib/blob/master/qlib/data/client.py | MIT |
def calendar(self, start_time=None, end_time=None, freq="day", future=False):
"""Get calendar of certain market in given time range.
Parameters
----------
start_time : str
start of the time range.
end_time : str
end of the time range.
freq : str
... | Get calendar of certain market in given time range.
Parameters
----------
start_time : str
start of the time range.
end_time : str
end of the time range.
freq : str
time frequency, available: year/quarter/month/week/day.
future : bool
... | calendar | python | microsoft/qlib | qlib/data/data.py | https://github.com/microsoft/qlib/blob/master/qlib/data/data.py | MIT |
def locate_index(
self, start_time: Union[pd.Timestamp, str], end_time: Union[pd.Timestamp, str], freq: str, future: bool = False
):
"""Locate the start time index and end time index in a calendar under certain frequency.
Parameters
----------
start_time : pd.Timestamp
... | Locate the start time index and end time index in a calendar under certain frequency.
Parameters
----------
start_time : pd.Timestamp
start of the time range.
end_time : pd.Timestamp
end of the time range.
freq : str
time frequency, available:... | locate_index | python | microsoft/qlib | qlib/data/data.py | https://github.com/microsoft/qlib/blob/master/qlib/data/data.py | MIT |
def _get_calendar(self, freq, future):
"""Load calendar using memcache.
Parameters
----------
freq : str
frequency of read calendar file.
future : bool
whether including future trading day.
Returns
-------
list
list of... | Load calendar using memcache.
Parameters
----------
freq : str
frequency of read calendar file.
future : bool
whether including future trading day.
Returns
-------
list
list of timestamps.
dict
dict compose... | _get_calendar | python | microsoft/qlib | qlib/data/data.py | https://github.com/microsoft/qlib/blob/master/qlib/data/data.py | MIT |
def instruments(market: Union[List, str] = "all", filter_pipe: Union[List, None] = None):
"""Get the general config dictionary for a base market adding several dynamic filters.
Parameters
----------
market : Union[List, str]
str:
market/industry/index shortna... | Get the general config dictionary for a base market adding several dynamic filters.
Parameters
----------
market : Union[List, str]
str:
market/industry/index shortname, e.g. all/sse/szse/sse50/csi300/csi500.
list:
["ID1", "ID2"]. A list o... | instruments | python | microsoft/qlib | qlib/data/data.py | https://github.com/microsoft/qlib/blob/master/qlib/data/data.py | MIT |
def period_feature(
self,
instrument,
field,
start_index: int,
end_index: int,
cur_time: pd.Timestamp,
period: Optional[int] = None,
) -> pd.Series:
"""
get the historical periods data series between `start_index` and `end_index`
Param... |
get the historical periods data series between `start_index` and `end_index`
Parameters
----------
start_index: int
start_index is a relative index to the latest period to cur_time
end_index: int
end_index is a relative index to the latest period to cur... | period_feature | python | microsoft/qlib | qlib/data/data.py | https://github.com/microsoft/qlib/blob/master/qlib/data/data.py | MIT |
def _uri(
self,
instruments,
fields,
start_time=None,
end_time=None,
freq="day",
disk_cache=1,
inst_processors=[],
**kwargs,
):
"""Get task uri, used when generating rabbitmq task in qlib_server
Parameters
----------
... | Get task uri, used when generating rabbitmq task in qlib_server
Parameters
----------
instruments : list or dict
list/dict of instruments or dict of stockpool config.
fields : list
list of feature instances.
start_time : str
start of the time ... | _uri | python | microsoft/qlib | qlib/data/data.py | https://github.com/microsoft/qlib/blob/master/qlib/data/data.py | MIT |
def get_instruments_d(instruments, freq):
"""
Parse different types of input instruments to output instruments_d
Wrong format of input instruments will lead to exception.
"""
if isinstance(instruments, dict):
if "market" in instruments:
# dict of stoc... |
Parse different types of input instruments to output instruments_d
Wrong format of input instruments will lead to exception.
| get_instruments_d | python | microsoft/qlib | qlib/data/data.py | https://github.com/microsoft/qlib/blob/master/qlib/data/data.py | MIT |
def get_column_names(fields):
"""
Get column names from input fields
"""
if len(fields) == 0:
raise ValueError("fields cannot be empty")
column_names = [str(f) for f in fields]
return column_names |
Get column names from input fields
| get_column_names | python | microsoft/qlib | qlib/data/data.py | https://github.com/microsoft/qlib/blob/master/qlib/data/data.py | MIT |
def dataset_processor(instruments_d, column_names, start_time, end_time, freq, inst_processors=[]):
"""
Load and process the data, return the data set.
- default using multi-kernel method.
"""
normalize_column_names = normalize_cache_fields(column_names)
# One process fo... |
Load and process the data, return the data set.
- default using multi-kernel method.
| dataset_processor | python | microsoft/qlib | qlib/data/data.py | https://github.com/microsoft/qlib/blob/master/qlib/data/data.py | MIT |
def inst_calculator(inst, start_time, end_time, freq, column_names, spans=None, g_config=None, inst_processors=[]):
"""
Calculate the expressions for **one** instrument, return a df result.
If the expression has been calculated before, load from cache.
return value: A data frame with in... |
Calculate the expressions for **one** instrument, return a df result.
If the expression has been calculated before, load from cache.
return value: A data frame with index 'datetime' and other data columns.
| inst_calculator | python | microsoft/qlib | qlib/data/data.py | https://github.com/microsoft/qlib/blob/master/qlib/data/data.py | MIT |
def load_calendar(self, freq, future):
"""Load original calendar timestamp from file.
Parameters
----------
freq : str
frequency of read calendar file.
future: bool
Returns
----------
list
list of timestamps
"""
try... | Load original calendar timestamp from file.
Parameters
----------
freq : str
frequency of read calendar file.
future: bool
Returns
----------
list
list of timestamps
| load_calendar | python | microsoft/qlib | qlib/data/data.py | https://github.com/microsoft/qlib/blob/master/qlib/data/data.py | MIT |
def __init__(self, align_time: bool = True):
"""
Parameters
----------
align_time : bool
Will we align the time to calendar
the frequency is flexible in some dataset and can't be aligned.
For the data with fixed frequency with a shared calendar, the al... |
Parameters
----------
align_time : bool
Will we align the time to calendar
the frequency is flexible in some dataset and can't be aligned.
For the data with fixed frequency with a shared calendar, the align data to the calendar will provides following benefit... | __init__ | python | microsoft/qlib | qlib/data/data.py | https://github.com/microsoft/qlib/blob/master/qlib/data/data.py | MIT |
def multi_cache_walker(instruments, fields, start_time=None, end_time=None, freq="day"):
"""
This method is used to prepare the expression cache for the client.
Then the client will load the data from expression cache by itself.
"""
instruments_d = DatasetProvider.get_instrument... |
This method is used to prepare the expression cache for the client.
Then the client will load the data from expression cache by itself.
| multi_cache_walker | python | microsoft/qlib | qlib/data/data.py | https://github.com/microsoft/qlib/blob/master/qlib/data/data.py | MIT |
def cache_walker(inst, start_time, end_time, freq, column_names):
"""
If the expressions of one instrument haven't been calculated before,
calculate it and write it into expression cache.
"""
for field in column_names:
ExpressionD.expression(inst, field, start_time, ... |
If the expressions of one instrument haven't been calculated before,
calculate it and write it into expression cache.
| cache_walker | python | microsoft/qlib | qlib/data/data.py | https://github.com/microsoft/qlib/blob/master/qlib/data/data.py | MIT |
def features(
self,
instruments,
fields,
start_time=None,
end_time=None,
freq="day",
disk_cache=None,
inst_processors=[],
):
"""
Parameters
----------
disk_cache : int
whether to skip(0)/use(1)/replace(2) dis... |
Parameters
----------
disk_cache : int
whether to skip(0)/use(1)/replace(2) disk_cache
This function will try to use cache method which has a keyword `disk_cache`,
and will use provider method if a type error is raised because the DatasetD instance
is a pro... | features | python | microsoft/qlib | qlib/data/data.py | https://github.com/microsoft/qlib/blob/master/qlib/data/data.py | MIT |
def _uri(self, type, **kwargs):
"""_uri
The server hope to get the uri of the request. The uri will be decided
by the dataprovider. For ex, different cache layer has different uri.
:param type: The type of resource for the uri
:param **kwargs:
"""
if type == "cal... | _uri
The server hope to get the uri of the request. The uri will be decided
by the dataprovider. For ex, different cache layer has different uri.
:param type: The type of resource for the uri
:param **kwargs:
| _uri | python | microsoft/qlib | qlib/data/data.py | https://github.com/microsoft/qlib/blob/master/qlib/data/data.py | MIT |
def __init__(self, fstart_time=None, fend_time=None, keep=False):
"""Init function for filter base class.
Filter a set of instruments based on a certain rule within a certain period assigned by fstart_time and fend_time.
Parameters
----------
fstart_time: str
the... | Init function for filter base class.
Filter a set of instruments based on a certain rule within a certain period assigned by fstart_time and fend_time.
Parameters
----------
fstart_time: str
the time for the filter rule to start filter the instruments.
fend_time:... | __init__ | python | microsoft/qlib | qlib/data/filter.py | https://github.com/microsoft/qlib/blob/master/qlib/data/filter.py | MIT |
def _getTimeBound(self, instruments):
"""Get time bound for all instruments.
Parameters
----------
instruments: dict
the dict of instruments in the form {instrument_name => list of timestamp tuple}.
Returns
----------
pd.Timestamp, pd.Timestamp
... | Get time bound for all instruments.
Parameters
----------
instruments: dict
the dict of instruments in the form {instrument_name => list of timestamp tuple}.
Returns
----------
pd.Timestamp, pd.Timestamp
the lower time bound and upper time bound ... | _getTimeBound | python | microsoft/qlib | qlib/data/filter.py | https://github.com/microsoft/qlib/blob/master/qlib/data/filter.py | MIT |
def _toSeries(self, time_range, target_timestamp):
"""Convert the target timestamp to a pandas series of bool value within a time range.
Make the time inside the target_timestamp range TRUE, others FALSE.
Parameters
----------
time_range : D.calendar
the time ran... | Convert the target timestamp to a pandas series of bool value within a time range.
Make the time inside the target_timestamp range TRUE, others FALSE.
Parameters
----------
time_range : D.calendar
the time range of the instruments.
target_timestamp : list
... | _toSeries | python | microsoft/qlib | qlib/data/filter.py | https://github.com/microsoft/qlib/blob/master/qlib/data/filter.py | MIT |
def _filterSeries(self, timestamp_series, filter_series):
"""Filter the timestamp series with filter series by using element-wise AND operation of the two series.
Parameters
----------
timestamp_series : pd.Series
the series of bool value indicating existing time.
fi... | Filter the timestamp series with filter series by using element-wise AND operation of the two series.
Parameters
----------
timestamp_series : pd.Series
the series of bool value indicating existing time.
filter_series : pd.Series
the series of bool value indicati... | _filterSeries | python | microsoft/qlib | qlib/data/filter.py | https://github.com/microsoft/qlib/blob/master/qlib/data/filter.py | MIT |
def _toTimestamp(self, timestamp_series):
"""Convert the timestamp series to a list of tuple (timestamp, timestamp) indicating a continuous range of TRUE.
Parameters
----------
timestamp_series: pd.Series
the series of bool value after being filtered.
Returns
... | Convert the timestamp series to a list of tuple (timestamp, timestamp) indicating a continuous range of TRUE.
Parameters
----------
timestamp_series: pd.Series
the series of bool value after being filtered.
Returns
----------
list
the list of tup... | _toTimestamp | python | microsoft/qlib | qlib/data/filter.py | https://github.com/microsoft/qlib/blob/master/qlib/data/filter.py | MIT |
def __call__(self, instruments, start_time=None, end_time=None, freq="day"):
"""Call this filter to get filtered instruments list"""
self.filter_freq = freq
return self.filter_main(instruments, start_time, end_time) | Call this filter to get filtered instruments list | __call__ | python | microsoft/qlib | qlib/data/filter.py | https://github.com/microsoft/qlib/blob/master/qlib/data/filter.py | MIT |
def filter_main(self, instruments, start_time=None, end_time=None):
"""Implement this method to filter the instruments.
Parameters
----------
instruments: dict
input instruments to be filtered.
start_time: str
start of the time range.
end_time: st... | Implement this method to filter the instruments.
Parameters
----------
instruments: dict
input instruments to be filtered.
start_time: str
start of the time range.
end_time: str
end of the time range.
Returns
----------
... | filter_main | python | microsoft/qlib | qlib/data/filter.py | https://github.com/microsoft/qlib/blob/master/qlib/data/filter.py | MIT |
def __init__(self, name_rule_re, fstart_time=None, fend_time=None):
"""Init function for name filter class
Parameters
----------
name_rule_re: str
regular expression for the name rule.
"""
super(NameDFilter, self).__init__(fstart_time, fend_time)
self... | Init function for name filter class
Parameters
----------
name_rule_re: str
regular expression for the name rule.
| __init__ | python | microsoft/qlib | qlib/data/filter.py | https://github.com/microsoft/qlib/blob/master/qlib/data/filter.py | MIT |
def __init__(self, rule_expression, fstart_time=None, fend_time=None, keep=False):
"""Init function for expression filter class
Parameters
----------
fstart_time: str
filter the feature starting from this time.
fend_time: str
filter the feature ending by ... | Init function for expression filter class
Parameters
----------
fstart_time: str
filter the feature starting from this time.
fend_time: str
filter the feature ending by this time.
rule_expression: str
an input expression for the rule.
| __init__ | python | microsoft/qlib | qlib/data/filter.py | https://github.com/microsoft/qlib/blob/master/qlib/data/filter.py | MIT |
def __call__(self, df: pd.DataFrame, instrument, *args, **kwargs):
"""
process the data
NOTE: **The processor could change the content of `df` inplace !!!!! **
User should keep a copy of data outside
Parameters
----------
df : pd.DataFrame
The raw_df... |
process the data
NOTE: **The processor could change the content of `df` inplace !!!!! **
User should keep a copy of data outside
Parameters
----------
df : pd.DataFrame
The raw_df of handler or result from previous processor.
| __call__ | python | microsoft/qlib | qlib/data/inst_processor.py | https://github.com/microsoft/qlib/blob/master/qlib/data/inst_processor.py | MIT |
def _load_internal(self, instrument, start_index, end_index, *args):
"""
To avoid error raised by bool type input, we transform the data into float32.
"""
series = self.feature.load(instrument, start_index, end_index, *args)
# TODO: More precision types should be configurable
... |
To avoid error raised by bool type input, we transform the data into float32.
| _load_internal | python | microsoft/qlib | qlib/data/ops.py | https://github.com/microsoft/qlib/blob/master/qlib/data/ops.py | MIT |
def __init__(self, feature, freq, func):
"""
Resampling the data to target frequency.
The resample function of pandas is used.
- the timestamp will be at the start of the time span after resample.
Parameters
----------
feature : Expression
An express... |
Resampling the data to target frequency.
The resample function of pandas is used.
- the timestamp will be at the start of the time span after resample.
Parameters
----------
feature : Expression
An expression for calculating the feature
freq : str
... | __init__ | python | microsoft/qlib | qlib/data/ops.py | https://github.com/microsoft/qlib/blob/master/qlib/data/ops.py | MIT |
def register(self, ops_list: List[Union[Type[ExpressionOps], dict]]):
"""register operator
Parameters
----------
ops_list : List[Union[Type[ExpressionOps], dict]]
- if type(ops_list) is List[Type[ExpressionOps]], each element of ops_list represents the operator class, which ... | register operator
Parameters
----------
ops_list : List[Union[Type[ExpressionOps], dict]]
- if type(ops_list) is List[Type[ExpressionOps]], each element of ops_list represents the operator class, which should be the subclass of `ExpressionOps`.
- if type(ops_list) is Lis... | register | python | microsoft/qlib | qlib/data/ops.py | https://github.com/microsoft/qlib/blob/master/qlib/data/ops.py | MIT |
def __init__(
self,
instruments=None,
start_time=None,
end_time=None,
data_loader: Union[dict, str, DataLoader] = None,
init_data=True,
fetch_orig=True,
):
"""
Parameters
----------
instruments :
The stock list to re... |
Parameters
----------
instruments :
The stock list to retrieve.
start_time :
start_time of the original data.
end_time :
end_time of the original data.
data_loader : Union[dict, str, DataLoader]
data loader to load the data... | __init__ | python | microsoft/qlib | qlib/data/dataset/handler.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/handler.py | MIT |
def config(self, **kwargs):
"""
configuration of data.
# what data to be loaded from data source
This method will be used when loading pickled handler from dataset.
The data will be initialized with different time range.
"""
attr_list = {"instruments", "start_ti... |
configuration of data.
# what data to be loaded from data source
This method will be used when loading pickled handler from dataset.
The data will be initialized with different time range.
| config | python | microsoft/qlib | qlib/data/dataset/handler.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/handler.py | MIT |
def setup_data(self, enable_cache: bool = False):
"""
Set Up the data in case of running initialization for multiple time
It is responsible for maintaining following variable
1) self._data
Parameters
----------
enable_cache : bool
default value is fa... |
Set Up the data in case of running initialization for multiple time
It is responsible for maintaining following variable
1) self._data
Parameters
----------
enable_cache : bool
default value is false:
- if `enable_cache` == True:
... | setup_data | python | microsoft/qlib | qlib/data/dataset/handler.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/handler.py | MIT |
def fetch(
self,
selector: Union[pd.Timestamp, slice, str, pd.Index] = slice(None, None),
level: Union[str, int] = "datetime",
col_set: Union[str, List[str]] = CS_ALL,
squeeze: bool = False,
proc_func: Callable = None,
) -> pd.DataFrame:
"""
fetch data... |
fetch data from underlying data source
Design motivation:
- providing a unified interface for underlying data.
- Potential to make the interface more friendly.
- User can improve performance when fetching data in this extra layer
Parameters
----------
s... | fetch | python | microsoft/qlib | qlib/data/dataset/handler.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/handler.py | MIT |
def get_cols(self, col_set=CS_ALL) -> list:
"""
get the column names
Parameters
----------
col_set : str
select a set of meaningful columns.(e.g. features, columns)
Returns
-------
list:
list of column names
"""
df... |
get the column names
Parameters
----------
col_set : str
select a set of meaningful columns.(e.g. features, columns)
Returns
-------
list:
list of column names
| get_cols | python | microsoft/qlib | qlib/data/dataset/handler.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/handler.py | MIT |
def get_range_selector(self, cur_date: Union[pd.Timestamp, str], periods: int) -> slice:
"""
get range selector by number of periods
Args:
cur_date (pd.Timestamp or str): current date
periods (int): number of periods
"""
trading_dates = self._data.index.u... |
get range selector by number of periods
Args:
cur_date (pd.Timestamp or str): current date
periods (int): number of periods
| get_range_selector | python | microsoft/qlib | qlib/data/dataset/handler.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/handler.py | MIT |
def get_range_iterator(
self, periods: int, min_periods: Optional[int] = None, **kwargs
) -> Iterator[Tuple[pd.Timestamp, pd.DataFrame]]:
"""
get an iterator of sliced data with given periods
Args:
periods (int): number of periods.
min_periods (int): minimum ... |
get an iterator of sliced data with given periods
Args:
periods (int): number of periods.
min_periods (int): minimum periods for sliced dataframe.
kwargs (dict): will be passed to `self.fetch`.
| get_range_iterator | python | microsoft/qlib | qlib/data/dataset/handler.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/handler.py | MIT |
def __init__(
self,
instruments=None,
start_time=None,
end_time=None,
data_loader: Union[dict, str, DataLoader] = None,
infer_processors: List = [],
learn_processors: List = [],
shared_processors: List = [],
process_type=PTYPE_A,
drop_raw=F... |
Parameters
----------
infer_processors : list
- list of <description info> of processors to generate data for inference
- example of <description info>:
.. code-block::
1) classname & kwargs:
{
"c... | __init__ | python | microsoft/qlib | qlib/data/dataset/handler.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/handler.py | MIT |
def fit(self):
"""
fit data without processing the data
"""
for proc in self.get_all_processors():
with TimeInspector.logt(f"{proc.__class__.__name__}"):
proc.fit(self._data) |
fit data without processing the data
| fit | python | microsoft/qlib | qlib/data/dataset/handler.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/handler.py | MIT |
def _is_proc_readonly(proc_l: List[processor_module.Processor]):
"""
NOTE: it will return True if `len(proc_l) == 0`
"""
for p in proc_l:
if not p.readonly():
return False
return True |
NOTE: it will return True if `len(proc_l) == 0`
| _is_proc_readonly | python | microsoft/qlib | qlib/data/dataset/handler.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/handler.py | MIT |
def process_data(self, with_fit: bool = False):
"""
process_data data. Fun `processor.fit` if necessary
Notation: (data) [processor]
# data processing flow of self.process_type == DataHandlerLP.PTYPE_I
.. code-block:: text
(self._data)-[shared_processors]-(_share... |
process_data data. Fun `processor.fit` if necessary
Notation: (data) [processor]
# data processing flow of self.process_type == DataHandlerLP.PTYPE_I
.. code-block:: text
(self._data)-[shared_processors]-(_shared_df)-[learn_processors]-(_learn_df)
... | process_data | python | microsoft/qlib | qlib/data/dataset/handler.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/handler.py | MIT |
def setup_data(self, init_type: str = IT_FIT_SEQ, **kwargs):
"""
Set up the data in case of running initialization for multiple time
Parameters
----------
init_type : str
The type `IT_*` listed above.
enable_cache : bool
default value is false:
... |
Set up the data in case of running initialization for multiple time
Parameters
----------
init_type : str
The type `IT_*` listed above.
enable_cache : bool
default value is false:
- if `enable_cache` == True:
the processed d... | setup_data | python | microsoft/qlib | qlib/data/dataset/handler.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/handler.py | MIT |
def fetch(
self,
selector: Union[pd.Timestamp, slice, str] = slice(None, None),
level: Union[str, int] = "datetime",
col_set=DataHandler.CS_ALL,
data_key: DATA_KEY_TYPE = DK_I,
squeeze: bool = False,
proc_func: Callable = None,
) -> pd.DataFrame:
"""
... |
fetch data from underlying data source
Parameters
----------
selector : Union[pd.Timestamp, slice, str]
describe how to select data by index.
level : Union[str, int]
which index level to select the data.
col_set : str
select a set of ... | fetch | python | microsoft/qlib | qlib/data/dataset/handler.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/handler.py | MIT |
def get_cols(self, col_set=DataHandler.CS_ALL, data_key: DATA_KEY_TYPE = DK_I) -> list:
"""
get the column names
Parameters
----------
col_set : str
select a set of meaningful columns.(e.g. features, columns).
data_key : DATA_KEY_TYPE
the data to ... |
get the column names
Parameters
----------
col_set : str
select a set of meaningful columns.(e.g. features, columns).
data_key : DATA_KEY_TYPE
the data to fetch: DK_*.
Returns
-------
list:
list of column names
... | get_cols | python | microsoft/qlib | qlib/data/dataset/handler.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/handler.py | MIT |
def cast(cls, handler: "DataHandlerLP") -> "DataHandlerLP":
"""
Motivation
- A user creates a datahandler in his customized package. Then he wants to share the processed handler to
other users without introduce the package dependency and complicated data processing logic.
- Th... |
Motivation
- A user creates a datahandler in his customized package. Then he wants to share the processed handler to
other users without introduce the package dependency and complicated data processing logic.
- This class make it possible by casting the class to DataHandlerLP and onl... | cast | python | microsoft/qlib | qlib/data/dataset/handler.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/handler.py | MIT |
def load(self, instruments, start_time=None, end_time=None) -> pd.DataFrame:
"""
load the data as pd.DataFrame.
Example of the data (The multi-index of the columns is optional.):
.. code-block:: text
feature ... |
load the data as pd.DataFrame.
Example of the data (The multi-index of the columns is optional.):
.. code-block:: text
feature label
$close $vol... | load | python | microsoft/qlib | qlib/data/dataset/loader.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/loader.py | MIT |
def __init__(self, config: Union[list, tuple, dict]):
"""
Parameters
----------
config : Union[list, tuple, dict]
Config will be used to describe the fields and column names
.. code-block::
<config> := {
"group_name1": <fields... |
Parameters
----------
config : Union[list, tuple, dict]
Config will be used to describe the fields and column names
.. code-block::
<config> := {
"group_name1": <fields_info1>
"group_name2": <fields_info2>
... | __init__ | python | microsoft/qlib | qlib/data/dataset/loader.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/loader.py | MIT |
def load_group_df(
self,
instruments,
exprs: list,
names: list,
start_time: Union[str, pd.Timestamp] = None,
end_time: Union[str, pd.Timestamp] = None,
gp_name: str = None,
) -> pd.DataFrame:
"""
load the dataframe for specific group
P... |
load the dataframe for specific group
Parameters
----------
instruments :
the instruments.
exprs : list
the expressions to describe the content of the data.
names : list
the name of the data.
Returns
-------
p... | load_group_df | python | microsoft/qlib | qlib/data/dataset/loader.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/loader.py | MIT |
def __init__(
self,
config: Tuple[list, tuple, dict],
filter_pipe: List = None,
swap_level: bool = True,
freq: Union[str, dict] = "day",
inst_processors: Union[dict, list] = None,
):
"""
Parameters
----------
config : Tuple[list, tuple,... |
Parameters
----------
config : Tuple[list, tuple, dict]
Please refer to the doc of DLWParser
filter_pipe :
Filter pipe for the instruments
swap_level :
Whether to swap level of MultiIndex
freq: dict or str
If type(config) ... | __init__ | python | microsoft/qlib | qlib/data/dataset/loader.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/loader.py | MIT |
def __init__(self, config: Union[dict, str, pd.DataFrame], join="outer"):
"""
Parameters
----------
config : dict
{fields_group: <path or object>}
join : str
How to align different dataframes
"""
self._config = config # using "_" to avoid ... |
Parameters
----------
config : dict
{fields_group: <path or object>}
join : str
How to align different dataframes
| __init__ | python | microsoft/qlib | qlib/data/dataset/loader.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/loader.py | MIT |
def __init__(self, dataloader_l: List[Dict], join="left") -> None:
"""
Parameters
----------
dataloader_l : list[dict]
A list of dataloader, for exmaple
.. code-block:: python
nd = NestedDataLoader(
dataloader_l=[
... |
Parameters
----------
dataloader_l : list[dict]
A list of dataloader, for exmaple
.. code-block:: python
nd = NestedDataLoader(
dataloader_l=[
{
"class": "qlib.contrib.data.loader.... | __init__ | python | microsoft/qlib | qlib/data/dataset/loader.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/loader.py | MIT |
def __init__(self, handler_config: dict, fetch_kwargs: dict = {}, is_group=False):
"""
Parameters
----------
handler_config : dict
handler_config will be used to describe the handlers
.. code-block::
<handler_config> := {
"gro... |
Parameters
----------
handler_config : dict
handler_config will be used to describe the handlers
.. code-block::
<handler_config> := {
"group_name1": <handler>
"group_name2": <handler>
}
... | __init__ | python | microsoft/qlib | qlib/data/dataset/loader.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/loader.py | MIT |
def get_group_columns(df: pd.DataFrame, group: Union[Text, None]):
"""
get a group of columns from multi-index columns DataFrame
Parameters
----------
df : pd.DataFrame
with multi of columns.
group : str
the name of the feature group, i.e. the first level value of the group inde... |
get a group of columns from multi-index columns DataFrame
Parameters
----------
df : pd.DataFrame
with multi of columns.
group : str
the name of the feature group, i.e. the first level value of the group index.
| get_group_columns | python | microsoft/qlib | qlib/data/dataset/processor.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/processor.py | MIT |
def fit(self, df: pd.DataFrame = None):
"""
learn data processing parameters
Parameters
----------
df : pd.DataFrame
When we fit and process data with processor one by one. The fit function reiles on the output of previous
processor, i.e. `df`.
"... |
learn data processing parameters
Parameters
----------
df : pd.DataFrame
When we fit and process data with processor one by one. The fit function reiles on the output of previous
processor, i.e. `df`.
| fit | python | microsoft/qlib | qlib/data/dataset/processor.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/processor.py | MIT |
def __init__(
self,
start_time: Optional[Union[pd.Timestamp, str]] = None,
end_time: Optional[Union[pd.Timestamp, str]] = None,
freq: str = "day",
):
"""
Parameters
----------
start_time : Optional[Union[pd.Timestamp, str]]
The data must st... |
Parameters
----------
start_time : Optional[Union[pd.Timestamp, str]]
The data must start earlier (or equal) than `start_time`
None indicates data will not be filtered based on `start_time`
end_time : Optional[Union[pd.Timestamp, str]]
similar to star... | __init__ | python | microsoft/qlib | qlib/data/dataset/processor.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/processor.py | MIT |
def fetch(
self,
selector: Union[pd.Timestamp, slice, str, list] = slice(None, None),
level: Union[str, int] = "datetime",
col_set: Union[str, List[str]] = DataHandler.CS_ALL,
fetch_orig: bool = True,
proc_func: Callable = None,
**kwargs,
) -> pd.DataFrame:
... | fetch data from the data storage
Parameters
----------
selector : Union[pd.Timestamp, slice, str]
describe how to select data by index
level : Union[str, int]
which index level to select the data
- if level is None, apply selector to df directly
... | fetch | python | microsoft/qlib | qlib/data/dataset/storage.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/storage.py | MIT |
def _fetch_hash_df_by_stock(self, selector, level):
"""fetch the data with stock selector
Parameters
----------
selector : Union[pd.Timestamp, slice, str]
describe how to select data by index
level : Union[str, int]
which index level to select the data
... | fetch the data with stock selector
Parameters
----------
selector : Union[pd.Timestamp, slice, str]
describe how to select data by index
level : Union[str, int]
which index level to select the data
- if level is None, apply selector to df directly
... | _fetch_hash_df_by_stock | python | microsoft/qlib | qlib/data/dataset/storage.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/storage.py | MIT |
def get_level_index(df: pd.DataFrame, level: Union[str, int]) -> int:
"""
get the level index of `df` given `level`
Parameters
----------
df : pd.DataFrame
data
level : Union[str, int]
index level
Returns
-------
int:
The level index in the multiple index
... |
get the level index of `df` given `level`
Parameters
----------
df : pd.DataFrame
data
level : Union[str, int]
index level
Returns
-------
int:
The level index in the multiple index
| get_level_index | python | microsoft/qlib | qlib/data/dataset/utils.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/utils.py | MIT |
def fetch_df_by_index(
df: pd.DataFrame,
selector: Union[pd.Timestamp, slice, str, list, pd.Index],
level: Union[str, int],
fetch_orig=True,
) -> pd.DataFrame:
"""
fetch data from `data` with `selector` and `level`
selector are assumed to be well processed.
`fetch_df_by_index` is only r... |
fetch data from `data` with `selector` and `level`
selector are assumed to be well processed.
`fetch_df_by_index` is only responsible for get the right level
Parameters
----------
selector : Union[pd.Timestamp, slice, str, list]
selector
level : Union[int, str]
the level t... | fetch_df_by_index | python | microsoft/qlib | qlib/data/dataset/utils.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/utils.py | MIT |
def convert_index_format(df: Union[pd.DataFrame, pd.Series], level: str = "datetime") -> Union[pd.DataFrame, pd.Series]:
"""
Convert the format of df.MultiIndex according to the following rules:
- If `level` is the first level of df.MultiIndex, do nothing
- If `level` is the second level of df.M... |
Convert the format of df.MultiIndex according to the following rules:
- If `level` is the first level of df.MultiIndex, do nothing
- If `level` is the second level of df.MultiIndex, swap the level of index.
NOTE:
the number of levels of df.MultiIndex should be 2
Parameters
---... | convert_index_format | python | microsoft/qlib | qlib/data/dataset/utils.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/utils.py | MIT |
def init_task_handler(task: dict) -> DataHandler:
"""
initialize the handler part of the task **inplace**
Parameters
----------
task : dict
the task to be handled
Returns
-------
Union[DataHandler, None]:
returns
"""
# avoid recursive import
from .handler im... |
initialize the handler part of the task **inplace**
Parameters
----------
task : dict
the task to be handled
Returns
-------
Union[DataHandler, None]:
returns
| init_task_handler | python | microsoft/qlib | qlib/data/dataset/utils.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/utils.py | MIT |
def setup_data(self, **kwargs):
"""
Setup the data.
We split the setup_data function for following situation:
- User have a Dataset object with learned status on disk.
- User load the Dataset object from the disk.
- User call `setup_data` to load new data.
- ... |
Setup the data.
We split the setup_data function for following situation:
- User have a Dataset object with learned status on disk.
- User load the Dataset object from the disk.
- User call `setup_data` to load new data.
- User prepare data for model based on previo... | setup_data | python | microsoft/qlib | qlib/data/dataset/__init__.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/__init__.py | MIT |
def prepare(self, **kwargs) -> object:
"""
The type of dataset depends on the model. (It could be pd.DataFrame, pytorch.DataLoader, etc.)
The parameters should specify the scope for the prepared data
The method should:
- process the data
- return the processed data
... |
The type of dataset depends on the model. (It could be pd.DataFrame, pytorch.DataLoader, etc.)
The parameters should specify the scope for the prepared data
The method should:
- process the data
- return the processed data
Returns
-------
object:
... | prepare | python | microsoft/qlib | qlib/data/dataset/__init__.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/__init__.py | MIT |
def __init__(
self,
handler: Union[Dict, DataHandler],
segments: Dict[Text, Tuple],
fetch_kwargs: Dict = {},
**kwargs,
):
"""
Setup the underlying data.
Parameters
----------
handler : Union[dict, DataHandler]
handler could... |
Setup the underlying data.
Parameters
----------
handler : Union[dict, DataHandler]
handler could be:
- instance of `DataHandler`
- config of `DataHandler`. Please refer to `DataHandler`
segments : dict
Describe the options to... | __init__ | python | microsoft/qlib | qlib/data/dataset/__init__.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/__init__.py | MIT |
def config(self, handler_kwargs: dict = None, **kwargs):
"""
Initialize the DatasetH
Parameters
----------
handler_kwargs : dict
Config of DataHandler, which could include the following arguments:
- arguments of DataHandler.conf_data, such as 'instrument... |
Initialize the DatasetH
Parameters
----------
handler_kwargs : dict
Config of DataHandler, which could include the following arguments:
- arguments of DataHandler.conf_data, such as 'instruments', 'start_time' and 'end_time'.
kwargs : dict
... | config | python | microsoft/qlib | qlib/data/dataset/__init__.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/__init__.py | MIT |
def setup_data(self, handler_kwargs: dict = None, **kwargs):
"""
Setup the Data
Parameters
----------
handler_kwargs : dict
init arguments of DataHandler, which could include the following arguments:
- init_type : Init Type of Handler
- enab... |
Setup the Data
Parameters
----------
handler_kwargs : dict
init arguments of DataHandler, which could include the following arguments:
- init_type : Init Type of Handler
- enable_cache : whether to enable cache
| setup_data | python | microsoft/qlib | qlib/data/dataset/__init__.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/__init__.py | MIT |
def _prepare_seg(self, slc, **kwargs):
"""
Give a query, retrieve the according data
Parameters
----------
slc : please refer to the docs of `prepare`
NOTE: it may not be an instance of slice. It may be a segment of `segments` from `def prepare`
"""
... |
Give a query, retrieve the according data
Parameters
----------
slc : please refer to the docs of `prepare`
NOTE: it may not be an instance of slice. It may be a segment of `segments` from `def prepare`
| _prepare_seg | python | microsoft/qlib | qlib/data/dataset/__init__.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/__init__.py | MIT |
def prepare(
self,
segments: Union[List[Text], Tuple[Text], Text, slice, pd.Index],
col_set=DataHandler.CS_ALL,
data_key=DataHandlerLP.DK_I,
**kwargs,
) -> Union[List[pd.DataFrame], pd.DataFrame]:
"""
Prepare the data for learning and inference.
Param... |
Prepare the data for learning and inference.
Parameters
----------
segments : Union[List[Text], Tuple[Text], Text, slice]
Describe the scope of the data to be prepared
Here are some examples:
- 'train'
- ['train', 'valid']
col_... | prepare | python | microsoft/qlib | qlib/data/dataset/__init__.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/__init__.py | MIT |
def _get_extrema(segments, idx: int, cmp: Callable, key_func=pd.Timestamp):
"""it will act like sort and return the max value or None"""
candidate = None
for k, seg in segments.items():
point = seg[idx]
if point is None:
# None indicates unbounded, return ... | it will act like sort and return the max value or None | _get_extrema | python | microsoft/qlib | qlib/data/dataset/__init__.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/__init__.py | MIT |
def __init__(
self,
data: pd.DataFrame,
start,
end,
step_len: int,
fillna_type: str = "none",
dtype=None,
flt_data=None,
):
"""
Build a dataset which looks like torch.data.utils.Dataset.
Parameters
----------
da... |
Build a dataset which looks like torch.data.utils.Dataset.
Parameters
----------
data : pd.DataFrame
The raw tabular data whose index order is <"datetime", "instrument">
start :
The indexable start time
end :
The indexable end time
... | __init__ | python | microsoft/qlib | qlib/data/dataset/__init__.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/__init__.py | MIT |
def build_index(data: pd.DataFrame) -> Tuple[pd.DataFrame, dict]:
"""
The relation of the data
Parameters
----------
data : pd.DataFrame
A DataFrame with index in order <instrument, datetime>
RSQR5 RESI5 WVMA5 LABEL0
... |
The relation of the data
Parameters
----------
data : pd.DataFrame
A DataFrame with index in order <instrument, datetime>
RSQR5 RESI5 WVMA5 LABEL0
instrument datetime
SH600000 2017-01-03 0.016389 ... | build_index | python | microsoft/qlib | qlib/data/dataset/__init__.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/__init__.py | MIT |
def _get_indices(self, row: int, col: int) -> np.array:
"""
get series indices of self.data_arr from the row, col indices of self.idx_df
Parameters
----------
row : int
the row in self.idx_df
col : int
the col in self.idx_df
Returns
... |
get series indices of self.data_arr from the row, col indices of self.idx_df
Parameters
----------
row : int
the row in self.idx_df
col : int
the col in self.idx_df
Returns
-------
np.array:
The indices of data of the... | _get_indices | python | microsoft/qlib | qlib/data/dataset/__init__.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/__init__.py | MIT |
def _get_row_col(self, idx) -> Tuple[int]:
"""
get the col index and row index of a given sample index in self.idx_df
Parameters
----------
idx :
the input of `__getitem__`
Returns
-------
Tuple[int]:
the row and col index
... |
get the col index and row index of a given sample index in self.idx_df
Parameters
----------
idx :
the input of `__getitem__`
Returns
-------
Tuple[int]:
the row and col index
| _get_row_col | python | microsoft/qlib | qlib/data/dataset/__init__.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/__init__.py | MIT |
def __getitem__(self, idx: Union[int, Tuple[object, str], List[int]]):
"""
# We have two method to get the time-series of a sample
tsds is a instance of TSDataSampler
# 1) sample by int index directly
tsds[len(tsds) - 1]
# 2) sample by <datetime,instrument> index
... |
# We have two method to get the time-series of a sample
tsds is a instance of TSDataSampler
# 1) sample by int index directly
tsds[len(tsds) - 1]
# 2) sample by <datetime,instrument> index
tsds['2016-12-31', "SZ300315"]
# The return value will be similar to th... | __getitem__ | python | microsoft/qlib | qlib/data/dataset/__init__.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/__init__.py | MIT |
def _prepare_seg(self, slc: slice, **kwargs) -> TSDataSampler:
"""
split the _prepare_raw_seg is to leave a hook for data preprocessing before creating processing data
NOTE: TSDatasetH only support slc segment on datetime !!!
"""
dtype = kwargs.pop("dtype", None)
if not i... |
split the _prepare_raw_seg is to leave a hook for data preprocessing before creating processing data
NOTE: TSDatasetH only support slc segment on datetime !!!
| _prepare_seg | python | microsoft/qlib | qlib/data/dataset/__init__.py | https://github.com/microsoft/qlib/blob/master/qlib/data/dataset/__init__.py | MIT |
def _freq_file(self) -> str:
"""the freq to read from file"""
if not hasattr(self, "_freq_file_cache"):
freq = Freq(self.freq)
if freq not in self.support_freq:
# NOTE: uri
# 1. If `uri` does not exist
# - Get the `min_uri` ... | the freq to read from file | _freq_file | python | microsoft/qlib | qlib/data/storage/file_storage.py | https://github.com/microsoft/qlib/blob/master/qlib/data/storage/file_storage.py | MIT |
def __delitem__(self, i) -> None:
"""
Raises
------
ValueError
If the data(storage) does not exist, raise ValueError
"""
raise NotImplementedError(
"Subclass of CalendarStorage must implement `__delitem__(i: int)`/`__delitem__(s: slice)` method"
... |
Raises
------
ValueError
If the data(storage) does not exist, raise ValueError
| __delitem__ | python | microsoft/qlib | qlib/data/storage/storage.py | https://github.com/microsoft/qlib/blob/master/qlib/data/storage/storage.py | MIT |
def __getitem__(self, i) -> CalVT:
"""
Raises
------
ValueError
If the data(storage) does not exist, raise ValueError
"""
raise NotImplementedError(
"Subclass of CalendarStorage must implement `__getitem__(i: int)`/`__getitem__(s: slice)` method... |
Raises
------
ValueError
If the data(storage) does not exist, raise ValueError
| __getitem__ | python | microsoft/qlib | qlib/data/storage/storage.py | https://github.com/microsoft/qlib/blob/master/qlib/data/storage/storage.py | MIT |
def rebase(self, start_index: int = None, end_index: int = None):
"""Rebase the start_index and end_index of the FeatureStorage.
start_index and end_index are closed intervals: [start_index, end_index]
Examples
---------
.. code-block::
feature:
... | Rebase the start_index and end_index of the FeatureStorage.
start_index and end_index are closed intervals: [start_index, end_index]
Examples
---------
.. code-block::
feature:
3 4
4 5
... | rebase | python | microsoft/qlib | qlib/data/storage/storage.py | https://github.com/microsoft/qlib/blob/master/qlib/data/storage/storage.py | MIT |
def __getitem__(self, s: slice) -> pd.Series:
"""x.__getitem__(slice(start: int, stop: int, step: int)) <==> x[start:stop:step]
Returns
-------
pd.Series(values, index=pd.RangeIndex(start, len(values))
""" | x.__getitem__(slice(start: int, stop: int, step: int)) <==> x[start:stop:step]
Returns
-------
pd.Series(values, index=pd.RangeIndex(start, len(values))
| __getitem__ | python | microsoft/qlib | qlib/data/storage/storage.py | https://github.com/microsoft/qlib/blob/master/qlib/data/storage/storage.py | MIT |
def __getitem__(self, i) -> Union[Tuple[int, float], pd.Series]:
"""x.__getitem__(y) <==> x[y]
Notes
-------
if data(storage) does not exist:
if isinstance(i, int):
return (None, None)
if isinstance(i, slice):
# return empty pd.Se... | x.__getitem__(y) <==> x[y]
Notes
-------
if data(storage) does not exist:
if isinstance(i, int):
return (None, None)
if isinstance(i, slice):
# return empty pd.Series
return pd.Series(dtype=np.float32)
| __getitem__ | python | microsoft/qlib | qlib/data/storage/storage.py | https://github.com/microsoft/qlib/blob/master/qlib/data/storage/storage.py | MIT |
def begin_task_train(task_config: dict, experiment_name: str, recorder_name: str = None) -> Recorder:
"""
Begin task training to start a recorder and save the task config.
Args:
task_config (dict): the config of a task
experiment_name (str): the name of experiment
recorder_name (str... |
Begin task training to start a recorder and save the task config.
Args:
task_config (dict): the config of a task
experiment_name (str): the name of experiment
recorder_name (str): the given name will be the recorder name. None for using rid.
Returns:
Recorder: the model re... | begin_task_train | python | microsoft/qlib | qlib/model/trainer.py | https://github.com/microsoft/qlib/blob/master/qlib/model/trainer.py | MIT |
def end_task_train(rec: Recorder, experiment_name: str) -> Recorder:
"""
Finish task training with real model fitting and saving.
Args:
rec (Recorder): the recorder will be resumed
experiment_name (str): the name of experiment
Returns:
Recorder: the model recorder
"""
w... |
Finish task training with real model fitting and saving.
Args:
rec (Recorder): the recorder will be resumed
experiment_name (str): the name of experiment
Returns:
Recorder: the model recorder
| end_task_train | python | microsoft/qlib | qlib/model/trainer.py | https://github.com/microsoft/qlib/blob/master/qlib/model/trainer.py | MIT |
def task_train(task_config: dict, experiment_name: str, recorder_name: str = None) -> Recorder:
"""
Task based training, will be divided into two steps.
Parameters
----------
task_config : dict
The config of a task.
experiment_name: str
The name of experiment
recorder_name: ... |
Task based training, will be divided into two steps.
Parameters
----------
task_config : dict
The config of a task.
experiment_name: str
The name of experiment
recorder_name: str
The name of recorder
Returns
----------
Recorder: The instance of the recorder... | task_train | python | microsoft/qlib | qlib/model/trainer.py | https://github.com/microsoft/qlib/blob/master/qlib/model/trainer.py | MIT |
def __init__(
self,
experiment_name: Optional[str] = None,
train_func: Callable = task_train,
call_in_subproc: bool = False,
default_rec_name: Optional[str] = None,
):
"""
Init TrainerR.
Args:
experiment_name (str, optional): the default n... |
Init TrainerR.
Args:
experiment_name (str, optional): the default name of experiment.
train_func (Callable, optional): default training method. Defaults to `task_train`.
call_in_subproc (bool): call the process in subprocess to force memory release
| __init__ | python | microsoft/qlib | qlib/model/trainer.py | https://github.com/microsoft/qlib/blob/master/qlib/model/trainer.py | MIT |
def train(self, tasks: list, train_func: Callable = None, experiment_name: str = None, **kwargs) -> List[Recorder]:
"""
Given a list of `tasks` and return a list of trained Recorder. The order can be guaranteed.
Args:
tasks (list): a list of definitions based on `task` dict
... |
Given a list of `tasks` and return a list of trained Recorder. The order can be guaranteed.
Args:
tasks (list): a list of definitions based on `task` dict
train_func (Callable): the training method which needs at least `tasks` and `experiment_name`. None for the default trainin... | train | python | microsoft/qlib | qlib/model/trainer.py | https://github.com/microsoft/qlib/blob/master/qlib/model/trainer.py | MIT |
def end_train(self, models: list, **kwargs) -> List[Recorder]:
"""
Set STATUS_END tag to the recorders.
Args:
models (list): a list of trained recorders.
Returns:
List[Recorder]: the same list as the param.
"""
if isinstance(models, Recorder):
... |
Set STATUS_END tag to the recorders.
Args:
models (list): a list of trained recorders.
Returns:
List[Recorder]: the same list as the param.
| end_train | python | microsoft/qlib | qlib/model/trainer.py | https://github.com/microsoft/qlib/blob/master/qlib/model/trainer.py | MIT |
def __init__(
self, experiment_name: str = None, train_func=begin_task_train, end_train_func=end_task_train, **kwargs
):
"""
Init TrainerRM.
Args:
experiment_name (str): the default name of experiment.
train_func (Callable, optional): default train method. De... |
Init TrainerRM.
Args:
experiment_name (str): the default name of experiment.
train_func (Callable, optional): default train method. Defaults to `begin_task_train`.
end_train_func (Callable, optional): default end_train method. Defaults to `end_task_train`.
| __init__ | python | microsoft/qlib | qlib/model/trainer.py | https://github.com/microsoft/qlib/blob/master/qlib/model/trainer.py | MIT |
def end_train(self, models, end_train_func=None, experiment_name: str = None, **kwargs) -> List[Recorder]:
"""
Given a list of Recorder and return a list of trained Recorder.
This class will finish real data loading and model fitting.
Args:
models (list): a list of Recorder,... |
Given a list of Recorder and return a list of trained Recorder.
This class will finish real data loading and model fitting.
Args:
models (list): a list of Recorder, the tasks have been saved to them
end_train_func (Callable, optional): the end_train method which needs a... | end_train | python | microsoft/qlib | qlib/model/trainer.py | https://github.com/microsoft/qlib/blob/master/qlib/model/trainer.py | MIT |
def __init__(
self,
experiment_name: str = None,
task_pool: str = None,
train_func=task_train,
skip_run_task: bool = False,
default_rec_name: Optional[str] = None,
):
"""
Init TrainerR.
Args:
experiment_name (str): the default name... |
Init TrainerR.
Args:
experiment_name (str): the default name of experiment.
task_pool (str): task pool name in TaskManager. None for use same name as experiment_name.
train_func (Callable, optional): default training method. Defaults to `task_train`.
ski... | __init__ | python | microsoft/qlib | qlib/model/trainer.py | https://github.com/microsoft/qlib/blob/master/qlib/model/trainer.py | MIT |
def train(
self,
tasks: list,
train_func: Callable = None,
experiment_name: str = None,
before_status: str = TaskManager.STATUS_WAITING,
after_status: str = TaskManager.STATUS_DONE,
default_rec_name: Optional[str] = None,
**kwargs,
) -> List[Recorder]:... |
Given a list of `tasks` and return a list of trained Recorder. The order can be guaranteed.
This method defaults to a single process, but TaskManager offered a great way to parallel training.
Users can customize their train_func to realize multiple processes or even multiple machines.
... | train | python | microsoft/qlib | qlib/model/trainer.py | https://github.com/microsoft/qlib/blob/master/qlib/model/trainer.py | MIT |
def end_train(self, recs: list, **kwargs) -> List[Recorder]:
"""
Set STATUS_END tag to the recorders.
Args:
recs (list): a list of trained recorders.
Returns:
List[Recorder]: the same list as the param.
"""
if isinstance(recs, Recorder):
... |
Set STATUS_END tag to the recorders.
Args:
recs (list): a list of trained recorders.
Returns:
List[Recorder]: the same list as the param.
| end_train | python | microsoft/qlib | qlib/model/trainer.py | https://github.com/microsoft/qlib/blob/master/qlib/model/trainer.py | MIT |
def worker(
self,
train_func: Callable = None,
experiment_name: str = None,
):
"""
The multiprocessing method for `train`. It can share a same task_pool with `train` and can run in other progress or other machines.
Args:
train_func (Callable): the trainin... |
The multiprocessing method for `train`. It can share a same task_pool with `train` and can run in other progress or other machines.
Args:
train_func (Callable): the training method which needs at least `tasks` and `experiment_name`. None for the default training method.
experim... | worker | python | microsoft/qlib | qlib/model/trainer.py | https://github.com/microsoft/qlib/blob/master/qlib/model/trainer.py | MIT |
def __init__(
self,
experiment_name: str = None,
task_pool: str = None,
train_func=begin_task_train,
end_train_func=end_task_train,
skip_run_task: bool = False,
**kwargs,
):
"""
Init DelayTrainerRM.
Args:
experiment_name (s... |
Init DelayTrainerRM.
Args:
experiment_name (str): the default name of experiment.
task_pool (str): task pool name in TaskManager. None for use same name as experiment_name.
train_func (Callable, optional): default train method. Defaults to `begin_task_train`.
... | __init__ | python | microsoft/qlib | qlib/model/trainer.py | https://github.com/microsoft/qlib/blob/master/qlib/model/trainer.py | MIT |
def train(self, tasks: list, train_func=None, experiment_name: str = None, **kwargs) -> List[Recorder]:
"""
Same as `train` of TrainerRM, after_status will be STATUS_PART_DONE.
Args:
tasks (list): a list of definition based on `task` dict
train_func (Callable): the train... |
Same as `train` of TrainerRM, after_status will be STATUS_PART_DONE.
Args:
tasks (list): a list of definition based on `task` dict
train_func (Callable): the train method which need at least `tasks` and `experiment_name`. Defaults to None for using self.train_func.
... | train | python | microsoft/qlib | qlib/model/trainer.py | https://github.com/microsoft/qlib/blob/master/qlib/model/trainer.py | MIT |
def end_train(self, recs, end_train_func=None, experiment_name: str = None, **kwargs) -> List[Recorder]:
"""
Given a list of Recorder and return a list of trained Recorder.
This class will finish real data loading and model fitting.
Args:
recs (list): a list of Recorder, the... |
Given a list of Recorder and return a list of trained Recorder.
This class will finish real data loading and model fitting.
Args:
recs (list): a list of Recorder, the tasks have been saved to them.
end_train_func (Callable, optional): the end_train method which need at ... | end_train | python | microsoft/qlib | qlib/model/trainer.py | https://github.com/microsoft/qlib/blob/master/qlib/model/trainer.py | MIT |
def worker(self, end_train_func=None, experiment_name: str = None):
"""
The multiprocessing method for `end_train`. It can share a same task_pool with `end_train` and can run in other progress or other machines.
Args:
end_train_func (Callable, optional): the end_train method which n... |
The multiprocessing method for `end_train`. It can share a same task_pool with `end_train` and can run in other progress or other machines.
Args:
end_train_func (Callable, optional): the end_train method which need at least `recorders` and `experiment_name`. Defaults to None for using self... | worker | python | microsoft/qlib | qlib/model/trainer.py | https://github.com/microsoft/qlib/blob/master/qlib/model/trainer.py | MIT |
def __call__(self, ensemble_dict: dict) -> pd.DataFrame:
"""using sample:
from qlib.model.ens.ensemble import AverageEnsemble
pred_res['new_key_name'] = AverageEnsemble()(predict_dict)
Parameters
----------
ensemble_dict : dict
Dictionary you want to ensemble... | using sample:
from qlib.model.ens.ensemble import AverageEnsemble
pred_res['new_key_name'] = AverageEnsemble()(predict_dict)
Parameters
----------
ensemble_dict : dict
Dictionary you want to ensemble
Returns
-------
pd.DataFrame
T... | __call__ | python | microsoft/qlib | qlib/model/ens/ensemble.py | https://github.com/microsoft/qlib/blob/master/qlib/model/ens/ensemble.py | MIT |
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