Module 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__()Classes
class SeriesPipeline (processors=None)-
Expand source code
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
SeriesProcessorobjects sequentially.Parameters
processors:List[Union[SeriesProcessor, SeriesPipeline]], optional- List of
SeriesProcessororSeriesPipelineinstances 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.
Methods
def get_required_series(self)-
Expand source code
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
SeriesProcessorobjects of this processing pipeline.Returns
List[str]- List of all the required series names.
def append(self, processor)-
Expand source code
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
SeriesProcessorat the end of the pipeline.Parameters
processor:Union[SeriesProcessor, SeriesPipeline]- The
SeriesProcessororSeriesPipelinethat will be added to the end of the pipeline.
def insert(self, idx, processor)-
Expand source code
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
SeriesProcessorat 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
SeriesProcessororSeriesPipelinethat will be inserted.Note
If the given processor is aSeriesPipeline, 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)-
Expand source code
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
SeriesProcessorobjects 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.
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
datashould 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 loggingFileHandlerwill be used and the logging messages are only pushed to stdout. Otherwise, a loggingFileHandlerwill write the logged messages to the given file path.
Returns
Union[List[pd.Series], pd.DataFrame]- The preprocessed series.
Notes
- If a
logging_file_pathis provided, the execution (time) info can be retrieved by callinglogger.get_processor_logs(logging_file_path).
Be aware that thelogging_file_pathgets 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 newpd.Series(orpd.DataFrame) of that numpy array with a different (column) name. - If
func_outputis apd.Series, keep in mind that the input series gets transformed (i.e., replaced) in the pipeline with thefunc_outputwhen the series name is equal.
Raises
_ProcessingError- Error raised when a processing step fails.
def serialize(self, file_path)-
Expand source code
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
SeriesPipelineinstance.Notes
As we use Dill 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
SeriesProcessorwill be serialized.