ExpandingWindowFeatures#
ExpandingWindowFeatures adds expanding window features to the dataframe.
Window features are the result of applying an aggregation operation (e.g., mean,
min, max, etc.) to a variable over a past window. For an expanding window feature,
the window spans from the start of the data up to, but not including, the
time point at which the feature is being computed.
For example, the mean value of all the data prior to the current value is an expanding window feature. The maximum value of all the rows prior to the current row is another expanding window feature.
When forecasting the future values of a variable, the past values of that variable are
likely to be predictive. To capitalize on the past values of a variable, we can simply
lag features with LagFeatures. We can also create features that summarise all
the past values into a single quantity utilising ExpandingWindowFeatures.
ExpandingWindowFeatures works on top of pandas.expanding, pandas.aggregate
and pandas.shift.
ExpandingWindowFeatures uses pandas.aggregate to perform the mathematical
operations over the expanding window. Therefore, you can use any operation supported
by pandas. For supported aggregation functions, see Expanding Window
Functions.
With pandas.shift, ExpandingWindowFeatures lags the result of the expanding
window operation. This is useful to ensure that only the information known at predict
time is used to compute the window feature. So if at predict time we only know
the value of a feature at the previous time period and before that, then we should lag the
the window feature by 1 period. If at predict time we only know the value of a feature
from 2 weeks ago and before that, then we should lag the window feature column by 2 weeks.
ExpandingWindowFeatures uses a default lag of one period.
ExpandingWindowFeatures will add the new variables with a representative
name to the original dataframe. It also has the methods fit() and transform()
that make it compatible with the Scikit-learn’s Pipeline and cross-validation
functions.
Note that to be compatible with ExpandingWindowFeatures the dataframe’s
index must have unique values and no NaN.
Examples#
Let’s create a toy dataset to demonstrate the functionality of ExpandingWindowFeatures.
The dataframe contains 3 numerical variables, a categorical variable, and a datetime
index.
import pandas as pd
X = {"ambient_temp": [31.31, 31.51, 32.15, 32.39, 32.62, 32.5, 32.52, 32.68],
"module_temp": [49.18, 49.84, 52.35, 50.63, 49.61, 47.01, 46.67, 47.52],
"irradiation": [0.51, 0.79, 0.65, 0.76, 0.42, 0.49, 0.57, 0.56],
"color": ["green"] * 4 + ["blue"] * 4,
}
X = pd.DataFrame(X)
X.index = pd.date_range("2020-05-15 12:00:00", periods=8, freq="15min")
X.head()
Below we see the output of our toy dataframe:
ambient_temp module_temp irradiation color
2020-05-15 12:00:00 31.31 49.18 0.51 green
2020-05-15 12:15:00 31.51 49.84 0.79 green
2020-05-15 12:30:00 32.15 52.35 0.65 green
2020-05-15 12:45:00 32.39 50.63 0.76 green
2020-05-15 13:00:00 32.62 49.61 0.42 blue
Now we will create expanding window features from the numerical variables. In functions,
we indicate all the operations that we want to perform over those windows. In
our example below, we want to calculate the mean and the standard deviation of the
data within those windows and also find the maximum value within the windows.
from feature_engine.timeseries.forecasting import ExpandingWindowFeatures
win_f = ExpandingWindowFeatures(functions=["mean", "max", "std"])
X_tr = win_f.fit_transform(X)
X_tr.head()
We can find the window features on the right side of the dataframe.
ambient_temp module_temp irradiation color \
2020-05-15 12:00:00 31.31 49.18 0.51 green
2020-05-15 12:15:00 31.51 49.84 0.79 green
2020-05-15 12:30:00 32.15 52.35 0.65 green
2020-05-15 12:45:00 32.39 50.63 0.76 green
2020-05-15 13:00:00 32.62 49.61 0.42 blue
ambient_temp_expanding_mean ambient_temp_expanding_max \
2020-05-15 12:00:00 NaN NaN
2020-05-15 12:15:00 31.310000 31.31
2020-05-15 12:30:00 31.410000 31.51
2020-05-15 12:45:00 31.656667 32.15
2020-05-15 13:00:00 31.840000 32.39
ambient_temp_expanding_std module_temp_expanding_mean \
2020-05-15 12:00:00 NaN NaN
2020-05-15 12:15:00 NaN 49.180000
2020-05-15 12:30:00 0.141421 49.510000
2020-05-15 12:45:00 0.438786 50.456667
2020-05-15 13:00:00 0.512640 50.500000
module_temp_expanding_max module_temp_expanding_std \
2020-05-15 12:00:00 NaN NaN
2020-05-15 12:15:00 49.18 NaN
2020-05-15 12:30:00 49.84 0.466690
2020-05-15 12:45:00 52.35 1.672553
2020-05-15 13:00:00 52.35 1.368381
irradiation_expanding_mean irradiation_expanding_max \
2020-05-15 12:00:00 NaN NaN
2020-05-15 12:15:00 0.5100 0.51
2020-05-15 12:30:00 0.6500 0.79
2020-05-15 12:45:00 0.6500 0.79
2020-05-15 13:00:00 0.6775 0.79
irradiation_expanding_std
2020-05-15 12:00:00 NaN
2020-05-15 12:15:00 NaN
2020-05-15 12:30:00 0.197990
2020-05-15 12:45:00 0.140000
2020-05-15 13:00:00 0.126853
The variables used as input for the window features are stored in the variables_
attribute of the ExpandingWindowFeatures.
win_f.variables_
['ambient_temp', 'module_temp', 'irradiation']
We can obtain the names of the variables in the returned dataframe using the get_feature_names_out() method:
win_f.get_feature_names_out()
['ambient_temp',
'module_temp',
'irradiation',
'color',
'ambient_temp_expanding_mean',
'ambient_temp_expanding_max',
'ambient_temp_expanding_std',
'module_temp_expanding_mean',
'module_temp_expanding_max',
'module_temp_expanding_std',
'irradiation_expanding_mean',
'irradiation_expanding_max',
'irradiation_expanding_std']
We can get the names of the expanding window features created for the variable “irradiation” as follows:
win_f.get_feature_names_out(["irradiation"])
['irradiation_expanding_mean',
'irradiation_expanding_max',
'irradiation_expanding_std']
Working with pandas series#
If your time series is a pandas Series instead of a pandas Dataframe, you need to
transform it into a dataframe before using ExpandingWindowFeatures.
The following is a pandas Series:
X['ambient_temp']
2020-05-15 12:00:00 31.31
2020-05-15 12:15:00 31.51
2020-05-15 12:30:00 32.15
2020-05-15 12:45:00 32.39
2020-05-15 13:00:00 32.62
2020-05-15 13:15:00 32.50
2020-05-15 13:30:00 32.52
2020-05-15 13:45:00 32.68
Freq: 15T, Name: ambient_temp, dtype: float64
We can use ExpandingWindowFeatures to create, for example, 2 new expanding window
features by finding the mean and maximum value of a pandas Series if we convert
it to a pandas Dataframe using the method to_frame():
win_f = ExpandingWindowFeatures(functions=["mean", "max"])
X_tr = win_f.fit_transform(X['ambient_temp'].to_frame())
X_tr.head()
ambient_temp ambient_temp_expanding_mean \
2020-05-15 12:00:00 31.31 NaN
2020-05-15 12:15:00 31.51 31.310000
2020-05-15 12:30:00 32.15 31.410000
2020-05-15 12:45:00 32.39 31.656667
2020-05-15 13:00:00 32.62 31.840000
ambient_temp_expanding_max
2020-05-15 12:00:00 NaN
2020-05-15 12:15:00 31.31
2020-05-15 12:30:00 31.51
2020-05-15 12:45:00 32.15
2020-05-15 13:00:00 32.39
And if we do not want the original values of time series in the returned dataframe, we just need to remember to drop the original series after the transformation:
win_f = WindowFeatures(
functions=["mean", "max"],
drop_original=True,
)
X_tr = win_f.fit_transform(X['ambient_temp'].to_frame())
X_tr.head()
ambient_temp_expanding_mean ambient_temp_expanding_max
2020-05-15 12:00:00 NaN NaN
2020-05-15 12:15:00 31.310000 31.31
2020-05-15 12:30:00 31.410000 31.51
2020-05-15 12:45:00 31.656667 32.15
2020-05-15 13:00:00 31.840000 32.39
Getting the name of the variables#
We can easily obtain the name of the original and new variables with the method
get_feature_names_out. By using the method with the default parameters, we obtain
all the features in the output dataframe.
win_f = ExpandingWindowFeatures()
win_f.fit(X)
win_f.get_feature_names_out()
['ambient_temp',
'module_temp',
'irradiation',
'color',
'ambient_temp_expanding_mean',
'module_temp_expanding_mean',
'irradiation_expanding_mean']
Alternatively, we can obtain the names of the expanding window features created from one or more input features as follows:
win_f.get_feature_names_out(input_features=["irradiation"])
['irradiation_expanding_mean']