tsflex.processing.series_pipeline
SeriesPipeline class for time-series data (pre-)processing pipeline.
Expand source code"""SeriesPipeline class for time-series data (pre-)processing pipeline."""
from __future__ import annotations
__author__ = "Jonas Van Der Donckt, Emiel Deprost, Jeroen Van Der Donckt"
from pathlib import Path
from typing import Dict, List, Optional, Set, Union
import dill
import pandas as pd
from ..utils.data import flatten, series_dict_to_df, to_series_list
from ..utils.logging import add_logging_handler, delete_logging_handlers
from .logger import logger
from .series_processor import SeriesProcessor
class _ProcessingError(Exception):
pass
class SeriesPipeline:
"""Pipeline for applying ``SeriesProcessor`` objects sequentially.
Parameters
----------
processors : List[Union[SeriesProcessor, SeriesPipeline]], optional
List of ``SeriesProcessor`` or ``SeriesPipeline`` instances that will be applied
sequentially to the internal series dict, by default None.
**The processing steps will be executed in the same order as passed in this
list.**
"""
def __init__(
self, processors: Optional[List[Union[SeriesProcessor, SeriesPipeline]]] = None
):
self.processing_steps: List[SeriesProcessor] = [] # TODO: dit private of niet?
if processors is not None:
assert isinstance(processors, list)
self.processing_steps = list(
flatten(
[
p.processing_steps if isinstance(p, SeriesPipeline) else [p]
for p in processors
]
)
)
def get_required_series(self) -> List[str]:
"""Return all required series names for this pipeline.
Return the list of series names that are required in order to execute all the
``SeriesProcessor`` objects of this processing pipeline.
Returns
-------
List[str]
List of all the required series names.
"""
return list(
set(flatten(step.get_required_series() for step in self.processing_steps))
)
def append(self, processor: Union[SeriesProcessor, SeriesPipeline]) -> None:
"""Append a ``SeriesProcessor`` at the end of the pipeline.
Parameters
----------
processor : Union[SeriesProcessor, SeriesPipeline]
The ``SeriesProcessor`` or ``SeriesPipeline`` that will be added to the
end of the pipeline.
"""
if isinstance(processor, SeriesProcessor):
self.processing_steps.append(processor)
elif isinstance(processor, SeriesPipeline):
self.processing_steps.extend(processor.processing_steps)
else:
raise TypeError(
"Can only append SeriesProcessor or SeriesPipeline, "
+ f"not {type(processor)}"
)
def insert(
self, idx: int, processor: Union[SeriesProcessor, SeriesPipeline]
) -> None:
"""Insert a ``SeriesProcessor`` at the given index in the pipeline.
Parameters
----------
idx : int
The index where the given processor should be inserted in the pipeline.
Index 0 will insert the given processor at the front of the pipeline,
and index ``len(pipeline)`` is equivalent to appending the processor.
processor : Union[SeriesProcessor, SeriesPipeline]
The ``SeriesProcessor`` or ``SeriesPipeline`` that will be inserted.<br>
.. note::
If the given processor is a ``SeriesPipeline``, all its processors will
be inserted sequentially, starting from the given index.
"""
if isinstance(processor, SeriesProcessor):
self.processing_steps.insert(idx, processor)
elif isinstance(processor, SeriesPipeline):
for i, ps in enumerate(processor.processing_steps):
self.insert(idx + i, ps)
else:
raise TypeError(
"Can only insert a SeriesProcessor or SeriesPipeline, "
+ f"not {type(processor)}"
)
def process(
self,
data: Union[pd.Series, pd.DataFrame, List[Union[pd.Series, pd.DataFrame]]],
return_df: Optional[bool] = False,
return_all_series: Optional[bool] = True,
drop_keys: Optional[List[str]] = None,
copy: Optional[bool] = False,
logging_file_path: Optional[Union[str, Path]] = None,
) -> Union[List[pd.Series], pd.DataFrame]:
"""Execute all ``SeriesProcessor`` objects in pipeline sequentially.
Apply all the processing steps on passed Series list or DataFrame and return the
preprocessed Series list or DataFrame.
Parameters
----------
data : Union[pd.Series, pd.DataFrame, List[Union[pd.Series, pd.DataFrame]]]
Dataframe or Series or list thereof, with all the required data for the
processing steps. \n
**Remark**: each Series / DataFrame must have a ``pd.DatetimeIndex``.
**Remark**: we assume that each name / column is unique.
return_df : bool, optional
Whether the output needs to be a series list or a DataFrame, by default
False.
If True the output series will be combined to a DataFrame with an outer
merge.
return_all_series : bool, optional
Whether the output needs to return all the series, by default True.
* If True the output will contain all series that were passed to this
method.
* If False the output will contain just the required series (see
``get_required_series``).
drop_keys : List[str], optional
Which keys should be dropped when returning the output, by default None.
copy : bool, optional
Whether the series in ``data`` should be copied, by default False.
logging_file_path : Union[str, Path], optional
The file path where the logged messages are stored, by default None.
If ``None``, then no logging ``FileHandler`` will be used and the logging
messages are only pushed to stdout. Otherwise, a logging ``FileHandler`` will
write the logged messages to the given file path.
Returns
-------
Union[List[pd.Series], pd.DataFrame]
The preprocessed series.
Notes
-----
* If a ``logging_file_path`` is provided, the execution (time) info can be
retrieved by calling ``logger.get_processor_logs(logging_file_path)``. <br>
Be aware that the ``logging_file_path`` gets cleared before the logger pushes
logged messages. Hence, one should use a separate logging file for each
constructed processing and feature instance with this library.
* If a series processor its function output is a ``np.ndarray``, the input series
dict (required dict for that function) must contain just 1 series! That series
its name and index are used to return a series dict. When a user does not want
a numpy array to replace its input series, it is his / her responsibility to
create a new ``pd.Series`` (or ``pd.DataFrame``) of that numpy array with a
different (column) name.
* If ``func_output`` is a ``pd.Series``, keep in mind that the input series gets
transformed (i.e., replaced) in the pipeline with the ``func_output`` when the
series name is equal.
Raises
------
_ProcessingError
Error raised when a processing step fails.
"""
# Delete other logging handlers
delete_logging_handlers(logger)
# Add logging handler (if path provided)
if logging_file_path:
f_handler = add_logging_handler(logger, logging_file_path)
# Convert the data to a series_dict
series_dict: Dict[str, pd.Series] = {}
for s in to_series_list(data):
# Assert the assumptions we make!
if len(s):
assert isinstance(s.index, pd.DatetimeIndex)
# TODO: also check monotonic increasing?
if s.name in self.get_required_series():
series_dict[str(s.name)] = s.copy() if copy else s
elif return_all_series:
# If all the series have to be returned
series_dict[str(s.name)] = s.copy() if copy else s
output_keys: Set[str] = set() # Maintain set of output series
for processor in self.processing_steps:
try:
processed_dict = processor(series_dict)
output_keys.update(processed_dict.keys())
series_dict.update(processed_dict)
except Exception as e:
# Close the file handler (this avoids PermissionError: [WinError 32])
if logging_file_path:
f_handler.close()
logger.removeHandler(f_handler)
raise _ProcessingError(
"Error while processing function {}:\n {}".format(
processor.name, str(e)
)
) from e
# Close the file handler (this avoids PermissionError: [WinError 32])
if logging_file_path:
f_handler.close()
logger.removeHandler(f_handler)
if not return_all_series:
# Return just the output series
output_dict = {key: series_dict[str(key)] for key in output_keys}
series_dict = output_dict
if drop_keys is not None:
# Drop the keys that should not be included in the output
output_dict = {
key: series_dict[key]
for key in set(series_dict.keys()).difference(drop_keys)
}
series_dict = output_dict
if return_df:
# We merge the series dict into a DataFrame
return series_dict_to_df(series_dict)
else:
return [s for s in series_dict.values()]
def serialize(self, file_path: Union[str, Path]) -> None:
"""Serialize this ``SeriesPipeline`` instance.
Notes
------
As we use [Dill](https://github.com/uqfoundation/dill){:target="_blank"} to
serialize, we can also serialize (decorator)functions which are defined in the
local scope, like lambdas.
Parameters
----------
file_path : Union[str, Path]
The path where the ``SeriesProcessor`` will be serialized.
"""
with open(file_path, "wb") as f:
dill.dump(self, f, recurse=True)
def __repr__(self) -> str:
"""Return formal representation of object."""
return "[\n" + "".join([f"\t{str(p)}\n" for p in self.processing_steps]) + "]"
def __str__(self) -> str:
"""Return informal representation of object."""
return self.__repr__() class SeriesPipeline (processors=None)
class SeriesPipeline:
"""Pipeline for applying ``SeriesProcessor`` objects sequentially.
Parameters
----------
processors : List[Union[SeriesProcessor, SeriesPipeline]], optional
List of ``SeriesProcessor`` or ``SeriesPipeline`` instances that will be applied
sequentially to the internal series dict, by default None.
**The processing steps will be executed in the same order as passed in this
list.**
"""
def __init__(
self, processors: Optional[List[Union[SeriesProcessor, SeriesPipeline]]] = None
):
self.processing_steps: List[SeriesProcessor] = [] # TODO: dit private of niet?
if processors is not None:
assert isinstance(processors, list)
self.processing_steps = list(
flatten(
[
p.processing_steps if isinstance(p, SeriesPipeline) else [p]
for p in processors
]
)
)
def get_required_series(self) -> List[str]:
"""Return all required series names for this pipeline.
Return the list of series names that are required in order to execute all the
``SeriesProcessor`` objects of this processing pipeline.
Returns
-------
List[str]
List of all the required series names.
"""
return list(
set(flatten(step.get_required_series() for step in self.processing_steps))
)
def append(self, processor: Union[SeriesProcessor, SeriesPipeline]) -> None:
"""Append a ``SeriesProcessor`` at the end of the pipeline.
Parameters
----------
processor : Union[SeriesProcessor, SeriesPipeline]
The ``SeriesProcessor`` or ``SeriesPipeline`` that will be added to the
end of the pipeline.
"""
if isinstance(processor, SeriesProcessor):
self.processing_steps.append(processor)
elif isinstance(processor, SeriesPipeline):
self.processing_steps.extend(processor.processing_steps)
else:
raise TypeError(
"Can only append SeriesProcessor or SeriesPipeline, "
+ f"not {type(processor)}"
)
def insert(
self, idx: int, processor: Union[SeriesProcessor, SeriesPipeline]
) -> None:
"""Insert a ``SeriesProcessor`` at the given index in the pipeline.
Parameters
----------
idx : int
The index where the given processor should be inserted in the pipeline.
Index 0 will insert the given processor at the front of the pipeline,
and index ``len(pipeline)`` is equivalent to appending the processor.
processor : Union[SeriesProcessor, SeriesPipeline]
The ``SeriesProcessor`` or ``SeriesPipeline`` that will be inserted.<br>
.. note::
If the given processor is a ``SeriesPipeline``, all its processors will
be inserted sequentially, starting from the given index.
"""
if isinstance(processor, SeriesProcessor):
self.processing_steps.insert(idx, processor)
elif isinstance(processor, SeriesPipeline):
for i, ps in enumerate(processor.processing_steps):
self.insert(idx + i, ps)
else:
raise TypeError(
"Can only insert a SeriesProcessor or SeriesPipeline, "
+ f"not {type(processor)}"
)
def process(
self,
data: Union[pd.Series, pd.DataFrame, List[Union[pd.Series, pd.DataFrame]]],
return_df: Optional[bool] = False,
return_all_series: Optional[bool] = True,
drop_keys: Optional[List[str]] = None,
copy: Optional[bool] = False,
logging_file_path: Optional[Union[str, Path]] = None,
) -> Union[List[pd.Series], pd.DataFrame]:
"""Execute all ``SeriesProcessor`` objects in pipeline sequentially.
Apply all the processing steps on passed Series list or DataFrame and return the
preprocessed Series list or DataFrame.
Parameters
----------
data : Union[pd.Series, pd.DataFrame, List[Union[pd.Series, pd.DataFrame]]]
Dataframe or Series or list thereof, with all the required data for the
processing steps. \n
**Remark**: each Series / DataFrame must have a ``pd.DatetimeIndex``.
**Remark**: we assume that each name / column is unique.
return_df : bool, optional
Whether the output needs to be a series list or a DataFrame, by default
False.
If True the output series will be combined to a DataFrame with an outer
merge.
return_all_series : bool, optional
Whether the output needs to return all the series, by default True.
* If True the output will contain all series that were passed to this
method.
* If False the output will contain just the required series (see
``get_required_series``).
drop_keys : List[str], optional
Which keys should be dropped when returning the output, by default None.
copy : bool, optional
Whether the series in ``data`` should be copied, by default False.
logging_file_path : Union[str, Path], optional
The file path where the logged messages are stored, by default None.
If ``None``, then no logging ``FileHandler`` will be used and the logging
messages are only pushed to stdout. Otherwise, a logging ``FileHandler`` will
write the logged messages to the given file path.
Returns
-------
Union[List[pd.Series], pd.DataFrame]
The preprocessed series.
Notes
-----
* If a ``logging_file_path`` is provided, the execution (time) info can be
retrieved by calling ``logger.get_processor_logs(logging_file_path)``. <br>
Be aware that the ``logging_file_path`` gets cleared before the logger pushes
logged messages. Hence, one should use a separate logging file for each
constructed processing and feature instance with this library.
* If a series processor its function output is a ``np.ndarray``, the input series
dict (required dict for that function) must contain just 1 series! That series
its name and index are used to return a series dict. When a user does not want
a numpy array to replace its input series, it is his / her responsibility to
create a new ``pd.Series`` (or ``pd.DataFrame``) of that numpy array with a
different (column) name.
* If ``func_output`` is a ``pd.Series``, keep in mind that the input series gets
transformed (i.e., replaced) in the pipeline with the ``func_output`` when the
series name is equal.
Raises
------
_ProcessingError
Error raised when a processing step fails.
"""
# Delete other logging handlers
delete_logging_handlers(logger)
# Add logging handler (if path provided)
if logging_file_path:
f_handler = add_logging_handler(logger, logging_file_path)
# Convert the data to a series_dict
series_dict: Dict[str, pd.Series] = {}
for s in to_series_list(data):
# Assert the assumptions we make!
if len(s):
assert isinstance(s.index, pd.DatetimeIndex)
# TODO: also check monotonic increasing?
if s.name in self.get_required_series():
series_dict[str(s.name)] = s.copy() if copy else s
elif return_all_series:
# If all the series have to be returned
series_dict[str(s.name)] = s.copy() if copy else s
output_keys: Set[str] = set() # Maintain set of output series
for processor in self.processing_steps:
try:
processed_dict = processor(series_dict)
output_keys.update(processed_dict.keys())
series_dict.update(processed_dict)
except Exception as e:
# Close the file handler (this avoids PermissionError: [WinError 32])
if logging_file_path:
f_handler.close()
logger.removeHandler(f_handler)
raise _ProcessingError(
"Error while processing function {}:\n {}".format(
processor.name, str(e)
)
) from e
# Close the file handler (this avoids PermissionError: [WinError 32])
if logging_file_path:
f_handler.close()
logger.removeHandler(f_handler)
if not return_all_series:
# Return just the output series
output_dict = {key: series_dict[str(key)] for key in output_keys}
series_dict = output_dict
if drop_keys is not None:
# Drop the keys that should not be included in the output
output_dict = {
key: series_dict[key]
for key in set(series_dict.keys()).difference(drop_keys)
}
series_dict = output_dict
if return_df:
# We merge the series dict into a DataFrame
return series_dict_to_df(series_dict)
else:
return [s for s in series_dict.values()]
def serialize(self, file_path: Union[str, Path]) -> None:
"""Serialize this ``SeriesPipeline`` instance.
Notes
------
As we use [Dill](https://github.com/uqfoundation/dill){:target="_blank"} to
serialize, we can also serialize (decorator)functions which are defined in the
local scope, like lambdas.
Parameters
----------
file_path : Union[str, Path]
The path where the ``SeriesProcessor`` will be serialized.
"""
with open(file_path, "wb") as f:
dill.dump(self, f, recurse=True)
def __repr__(self) -> str:
"""Return formal representation of object."""
return "[\n" + "".join([f"\t{str(p)}\n" for p in self.processing_steps]) + "]"
def __str__(self) -> str:
"""Return informal representation of object."""
return self.__repr__()Pipeline for applying SeriesProcessor objects sequentially.
processors : List[Union[SeriesProcessor, SeriesPipeline]], optional
SeriesProcessor or SeriesPipeline instances that will be applied sequentially to the internal series dict, by default None. The processing steps will be executed in the same order as passed in this list.
def get_required_series(self)
def get_required_series(self) -> List[str]:
"""Return all required series names for this pipeline.
Return the list of series names that are required in order to execute all the
``SeriesProcessor`` objects of this processing pipeline.
Returns
-------
List[str]
List of all the required series names.
"""
return list(
set(flatten(step.get_required_series() for step in self.processing_steps))
)Return all required series names for this pipeline.
Return the list of series names that are required in order to execute all the SeriesProcessor objects of this processing pipeline.
List[str]
def append(self, processor)
def append(self, processor: Union[SeriesProcessor, SeriesPipeline]) -> None:
"""Append a ``SeriesProcessor`` at the end of the pipeline.
Parameters
----------
processor : Union[SeriesProcessor, SeriesPipeline]
The ``SeriesProcessor`` or ``SeriesPipeline`` that will be added to the
end of the pipeline.
"""
if isinstance(processor, SeriesProcessor):
self.processing_steps.append(processor)
elif isinstance(processor, SeriesPipeline):
self.processing_steps.extend(processor.processing_steps)
else:
raise TypeError(
"Can only append SeriesProcessor or SeriesPipeline, "
+ f"not {type(processor)}"
)Append a SeriesProcessor at the end of the pipeline.
processor : Union[SeriesProcessor, SeriesPipeline]
SeriesProcessor or SeriesPipeline that will be added to the end of the pipeline.
def insert(self, idx, processor)
def insert(
self, idx: int, processor: Union[SeriesProcessor, SeriesPipeline]
) -> None:
"""Insert a ``SeriesProcessor`` at the given index in the pipeline.
Parameters
----------
idx : int
The index where the given processor should be inserted in the pipeline.
Index 0 will insert the given processor at the front of the pipeline,
and index ``len(pipeline)`` is equivalent to appending the processor.
processor : Union[SeriesProcessor, SeriesPipeline]
The ``SeriesProcessor`` or ``SeriesPipeline`` that will be inserted.<br>
.. note::
If the given processor is a ``SeriesPipeline``, all its processors will
be inserted sequentially, starting from the given index.
"""
if isinstance(processor, SeriesProcessor):
self.processing_steps.insert(idx, processor)
elif isinstance(processor, SeriesPipeline):
for i, ps in enumerate(processor.processing_steps):
self.insert(idx + i, ps)
else:
raise TypeError(
"Can only insert a SeriesProcessor or SeriesPipeline, "
+ f"not {type(processor)}"
)Insert a SeriesProcessor at the given index in the pipeline.
idx : int
len(pipeline) is equivalent to appending the processor.
processor : Union[SeriesProcessor, SeriesPipeline]
SeriesProcessor or SeriesPipeline that will be inserted.
Note
If the given processor is a
SeriesPipeline
, all its processors will be inserted sequentially, starting from the given index.
def process(self, data, return_df=False, return_all_series=True, drop_keys=None, copy=False, logging_file_path=None)
def process(
self,
data: Union[pd.Series, pd.DataFrame, List[Union[pd.Series, pd.DataFrame]]],
return_df: Optional[bool] = False,
return_all_series: Optional[bool] = True,
drop_keys: Optional[List[str]] = None,
copy: Optional[bool] = False,
logging_file_path: Optional[Union[str, Path]] = None,
) -> Union[List[pd.Series], pd.DataFrame]:
"""Execute all ``SeriesProcessor`` objects in pipeline sequentially.
Apply all the processing steps on passed Series list or DataFrame and return the
preprocessed Series list or DataFrame.
Parameters
----------
data : Union[pd.Series, pd.DataFrame, List[Union[pd.Series, pd.DataFrame]]]
Dataframe or Series or list thereof, with all the required data for the
processing steps. \n
**Remark**: each Series / DataFrame must have a ``pd.DatetimeIndex``.
**Remark**: we assume that each name / column is unique.
return_df : bool, optional
Whether the output needs to be a series list or a DataFrame, by default
False.
If True the output series will be combined to a DataFrame with an outer
merge.
return_all_series : bool, optional
Whether the output needs to return all the series, by default True.
* If True the output will contain all series that were passed to this
method.
* If False the output will contain just the required series (see
``get_required_series``).
drop_keys : List[str], optional
Which keys should be dropped when returning the output, by default None.
copy : bool, optional
Whether the series in ``data`` should be copied, by default False.
logging_file_path : Union[str, Path], optional
The file path where the logged messages are stored, by default None.
If ``None``, then no logging ``FileHandler`` will be used and the logging
messages are only pushed to stdout. Otherwise, a logging ``FileHandler`` will
write the logged messages to the given file path.
Returns
-------
Union[List[pd.Series], pd.DataFrame]
The preprocessed series.
Notes
-----
* If a ``logging_file_path`` is provided, the execution (time) info can be
retrieved by calling ``logger.get_processor_logs(logging_file_path)``. <br>
Be aware that the ``logging_file_path`` gets cleared before the logger pushes
logged messages. Hence, one should use a separate logging file for each
constructed processing and feature instance with this library.
* If a series processor its function output is a ``np.ndarray``, the input series
dict (required dict for that function) must contain just 1 series! That series
its name and index are used to return a series dict. When a user does not want
a numpy array to replace its input series, it is his / her responsibility to
create a new ``pd.Series`` (or ``pd.DataFrame``) of that numpy array with a
different (column) name.
* If ``func_output`` is a ``pd.Series``, keep in mind that the input series gets
transformed (i.e., replaced) in the pipeline with the ``func_output`` when the
series name is equal.
Raises
------
_ProcessingError
Error raised when a processing step fails.
"""
# Delete other logging handlers
delete_logging_handlers(logger)
# Add logging handler (if path provided)
if logging_file_path:
f_handler = add_logging_handler(logger, logging_file_path)
# Convert the data to a series_dict
series_dict: Dict[str, pd.Series] = {}
for s in to_series_list(data):
# Assert the assumptions we make!
if len(s):
assert isinstance(s.index, pd.DatetimeIndex)
# TODO: also check monotonic increasing?
if s.name in self.get_required_series():
series_dict[str(s.name)] = s.copy() if copy else s
elif return_all_series:
# If all the series have to be returned
series_dict[str(s.name)] = s.copy() if copy else s
output_keys: Set[str] = set() # Maintain set of output series
for processor in self.processing_steps:
try:
processed_dict = processor(series_dict)
output_keys.update(processed_dict.keys())
series_dict.update(processed_dict)
except Exception as e:
# Close the file handler (this avoids PermissionError: [WinError 32])
if logging_file_path:
f_handler.close()
logger.removeHandler(f_handler)
raise _ProcessingError(
"Error while processing function {}:\n {}".format(
processor.name, str(e)
)
) from e
# Close the file handler (this avoids PermissionError: [WinError 32])
if logging_file_path:
f_handler.close()
logger.removeHandler(f_handler)
if not return_all_series:
# Return just the output series
output_dict = {key: series_dict[str(key)] for key in output_keys}
series_dict = output_dict
if drop_keys is not None:
# Drop the keys that should not be included in the output
output_dict = {
key: series_dict[key]
for key in set(series_dict.keys()).difference(drop_keys)
}
series_dict = output_dict
if return_df:
# We merge the series dict into a DataFrame
return series_dict_to_df(series_dict)
else:
return [s for s in series_dict.values()]Execute all SeriesProcessor objects in pipeline sequentially.
Apply all the processing steps on passed Series list or DataFrame and return the preprocessed Series list or DataFrame.
Parametersdata : Union[pd.Series, pd.DataFrame, List[Union[pd.Series, pd.DataFrame]]]
Dataframe or Series or list thereof, with all the required data for the processing steps.
Remark: each Series / DataFrame must have a pd.DatetimeIndex. Remark: we assume that each name / column is unique.
return_df : bool, optional
return_all_series : bool, optional
get_required_series).
drop_keys : List[str], optional
copy : bool, optional
data should be copied, by default False.
logging_file_path : Union[str, Path], optional
None, then no logging FileHandler will be used and the logging messages are only pushed to stdout. Otherwise, a logging FileHandler will write the logged messages to the given file path.
Union[List[pd.Series], pd.DataFrame]
logging_file_path is provided, the execution (time) info can be retrieved by calling logger.get_processor_logs(logging_file_path). logging_file_path gets cleared before the logger pushes logged messages. Hence, one should use a separate logging file for each constructed processing and feature instance with this library.np.ndarray, the input series dict (required dict for that function) must contain just 1 series! That series its name and index are used to return a series dict. When a user does not want a numpy array to replace its input series, it is his / her responsibility to create a new pd.Series (or pd.DataFrame) of that numpy array with a different (column) name.func_output is a pd.Series, keep in mind that the input series gets transformed (i.e., replaced) in the pipeline with the func_output when the series name is equal._ProcessingError
def serialize(self, file_path)
def serialize(self, file_path: Union[str, Path]) -> None:
"""Serialize this ``SeriesPipeline`` instance.
Notes
------
As we use [Dill](https://github.com/uqfoundation/dill){:target="_blank"} to
serialize, we can also serialize (decorator)functions which are defined in the
local scope, like lambdas.
Parameters
----------
file_path : Union[str, Path]
The path where the ``SeriesProcessor`` will be serialized.
"""
with open(file_path, "wb") as f:
dill.dump(self, f, recurse=True)Serialize this SeriesPipeline instance.
As we use Dill to serialize, we can also serialize (decorator)functions which are defined in the local scope, like lambdas.
Parametersfile_path : Union[str, Path]
SeriesProcessor will be serialized.
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