module timeseries.utils
#
Short summary#
module mlinsights.timeseries.utils
Timeseries data manipulations.
Functions#
function |
truncated documentation |
---|---|
Builds standard X, y based in the given one. |
|
Checks that datasets (X, y) was built with function |
Documentation#
Timeseries data manipulations.
- mlinsights.timeseries.utils.build_ts_X_y(model, X, y, weights=None, same_rows=False)#
Builds standard X, y based in the given one.
- Parameters:
model – a timeseries model (
BaseTimeSeries
)X – times series, used as features, [n_obs, n_features], X may be empty (None)
y – timeseries (one single vector), [n_obs]
weights – weights None or array [n_obs]
same_rows – keep the same number of rows as the original datasets, use nan when no value is available
- Returns:
(X, y, weights): X is array of features [nrows, n_features + past] where nrows = n_obs + model.delay2 - model.past + 2, y is an array of targets [nrows], weights is None or array [nrows]
<<<
import numpy from mlinsights.timeseries import build_ts_X_y from mlinsights.timeseries.base import BaseTimeSeries X = numpy.arange(10).reshape(5, 2) y = numpy.arange(5) * 100 weights = numpy.arange(5) * 1000 bs = BaseTimeSeries(past=2) nx, ny, nw = build_ts_X_y(bs, X, y, weights) print('X=', X) print('y=', y) print('nx=', nx) print('ny=', ny)
>>>
X= [[0 1] [2 3] [4 5] [6 7] [8 9]] y= [ 0 100 200 300 400] nx= [[ 2 3 0 100] [ 4 5 100 200] [ 6 7 200 300]] ny= [[200] [300] [400]]
With
use_all_past=True
:<<<
import numpy from mlinsights.timeseries.base import BaseTimeSeries from mlinsights.timeseries import build_ts_X_y X = numpy.arange(10).reshape(5, 2) y = numpy.arange(5) * 100 weights = numpy.arange(5) * 1000 bs = BaseTimeSeries(past=2, use_all_past=True) nx, ny, nw = build_ts_X_y(bs, X, y, weights) print('X=', X) print('y=', y) print('nx=', nx) print('ny=', ny)
>>>
X= [[0 1] [2 3] [4 5] [6 7] [8 9]] y= [ 0 100 200 300 400] nx= [[ 0 1 2 3 0 100] [ 2 3 4 5 100 200] [ 4 5 6 7 200 300]] ny= [[200] [300] [400]]
- mlinsights.timeseries.utils.check_ts_X_y(model, X, y)#
Checks that datasets (X, y) was built with function
build_ts_X_y
.