from typing import Callable, List, Union
import pandas as pd
from feature_engine._docstrings.fit_attributes import (
_feature_names_in_docstring,
_n_features_in_docstring,
)
from feature_engine._docstrings.init_parameters.all_trasnformers import (
_drop_original_docstring,
_missing_values_docstring,
_variables_numerical_docstring,
)
from feature_engine._docstrings.methods import (
_fit_not_learn_docstring,
_fit_transform_docstring,
)
from feature_engine._docstrings.substitute import Substitution
from feature_engine.timeseries.forecasting.base_forecast_transformers import (
BaseForecastTransformer,
)
[文档]@Substitution(
variables=_variables_numerical_docstring,
missing_values=_missing_values_docstring,
drop_original=_drop_original_docstring,
feature_names_in_=_feature_names_in_docstring,
n_features_in_=_n_features_in_docstring,
fit=_fit_not_learn_docstring,
fit_transform=_fit_transform_docstring,
)
class WindowFeatures(BaseForecastTransformer):
"""
WindowFeatures adds new features to a dataframe based on window operations. Window
operations are operations that perform an aggregation over a sliding partition of
past values. A window feature is, in other words, a feature created after computing
statistics (e.g., mean, min, max, etc.) using a window over the past data. For
example, the mean value of the previous 3 months of data is a window feature. The
maximum value of the previous three rows of data is another window feature.
WindowFeatures uses pandas functions `rolling()`, `agg()` and `shift()`. With
`rolling()`, it creates rolling windows. With `agg()` it applies multiple functions
within those windows. With `shift()` it allocates the values to the correct rows.
For supported aggregation functions, see Rolling Window
`Functions <https://pandas.pydata.org/docs/reference/window.html>`_.
With pandas `rolling()` we can perform rolling operations over 1 window size at a
time. WindowFeatures builds on top of pandas `rolling()` in that new features can
be derived from multiple window sizes, and the created features will be
automatically concatenated to the original dataframe.
To be compatible with WindowFeatures, the dataframe's index must have unique values
and no missing data.
WindowFeatures works only with numerical variables. You can pass a list of variables
to use as input for the windows. Alternatively, WindowFeatures will automatically
select all numerical variables in the training set.
More details in the :ref:`User Guide <window_features>`.
Parameters
----------
{variables}
window: int, offset, BaseIndexer subclass, or list, default=3
Size of the moving window. If an integer, the fixed number of observations used
for each window. If an offset (recommended), the time period of each window. It
can also take a function. See parameter `windows` in pandas `rolling()`
documentation for more details.
In addition to pandas normal input values, `window` can also take a list with
the above specified values, in which case, features will be created for each
one of the windows specified in the list.
min_periods: int, default None.
Minimum number of observations in the window required to have a value;
otherwise, the result is np.nan. See parameter `min_periods` in pandas
`rolling()` documentation for more details.
functions: string or list of strings, default = 'mean'
The functions to apply within the window. Valid functions can be found
`here <https://pandas.pydata.org/docs/reference/window.html>`_.
periods: int, list of ints, default=1
Number of periods to shift. Can be a positive integer. See param `periods` in
pandas `shift()`.
freq: str, list of str, default=None
Offset to use from the tseries module or time rule. See parameter `freq` in
pandas `shift()`.
sort_index: bool, default=True
Whether to order the index of the dataframe before creating the features.
{missing_values}
{drop_original}
drop_na: bool, default=False.
Whether the NAN introduced in the lag features should be removed.
Attributes
----------
variables_:
The group of variables that will be used to create the window features.
{feature_names_in_}
{n_features_in_}
Methods
-------
{fit}
transform:
Add window features.
transform_x_y:
Remove rows with missing data from X and y.
{fit_transform}
See Also
--------
pandas.rolling
pandas.aggregate
pandas.shift
Examples
--------
>>> import pandas as pd
>>> from feature_engine.timeseries.forecasting import WindowFeatures
>>> X = pd.DataFrame(dict(date = ["2022-09-18",
>>> "2022-09-19",
>>> "2022-09-20",
>>> "2022-09-21",
>>> "2022-09-22"],
>>> x1 = [1,2,3,4,5],
>>> x2 = [6,7,8,9,10]
>>> ))
>>> wf = WindowFeatures(window = 2)
>>> wf.fit_transform(X)
date x1 x2 x1_window_2_mean x2_window_2_mean
0 2022-09-18 1 6 NaN NaN
1 2022-09-19 2 7 NaN NaN
2 2022-09-20 3 8 1.5 6.5
3 2022-09-21 4 9 2.5 7.5
4 2022-09-22 5 10 3.5 8.5
"""
def __init__(
self,
variables: Union[None, int, str, List[Union[str, int]]] = None,
window: Union[str, int, Callable, List[int], List[str]] = 3,
min_periods: Union[int, None] = None,
functions: Union[str, List[str]] = "mean",
periods: int = 1,
freq: Union[str, None] = None,
sort_index: bool = True,
missing_values: str = "raise",
drop_original: bool = False,
drop_na: bool = False,
) -> None:
if isinstance(window, list) and len(window) != len(set(window)):
raise ValueError(f"There are duplicated windows in the list: {window}")
if not isinstance(functions, (str, list)) or not all(
isinstance(val, str) for val in functions
):
raise ValueError(
f"functions must be a string or a list of strings. "
f"Got {functions} instead."
)
if isinstance(functions, list) and len(functions) != len(set(functions)):
raise ValueError(f"There are duplicated functions in the list: {functions}")
if not isinstance(periods, int) or periods < 1:
raise ValueError(
f"periods must be a positive integer. Got {periods} instead."
)
super().__init__(variables, missing_values, drop_original, drop_na)
self.window = window
self.min_periods = min_periods
self.functions = functions
self.periods = periods
self.freq = freq
self.sort_index = sort_index
def _get_new_features_name(self) -> List:
"""Get names of the lag features."""
if not isinstance(self.functions, list):
functions_ = [self.functions]
else:
functions_ = self.functions
if isinstance(self.window, list):
feature_names = [
f"{feature}_window_{win}_{agg}"
for win in self.window
for feature in self.variables_
for agg in functions_
]
else:
feature_names = [
f"{feature}_window_{self.window}_{agg}"
for feature in self.variables_
for agg in functions_
]
return feature_names
