Simple FeatureRepMix Example

This example demonstrates how to use the FeatureRepMix on segmented data.

Although not shown here, FeatureRepMix can be used with Pype in place of FeatureRep. See API documentation for an example.

Out:

After segmentation:
X: [[[ 0.  1.  2.  3.]
  [ 4.  5.  6.  7.]
  [ 8.  9. 10. 11.]]]
y:  [0.]
After column-wise feature extraction:
   a_min_0  b_min_1  c_min_2  c_min_3  d_max_0  d_max_1  e_max_2  e_max_3
0      0.0      1.0      2.0      3.0      8.0      9.0     10.0     11.0

# Author: Matthias Gazzari
# License: BSD

from seglearn.transform import Segment, FeatureRep, FeatureRepMix
from seglearn.feature_functions import minimum, maximum
from seglearn.base import TS_Data

import numpy as np
import pandas as pd

# Single multivariate time series with 3 samples of 4 variables
X = [np.array([[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]])]
# Time series target
y = [np.array([True, False, False])]

segment = Segment(width=3, overlap=1)
X, y, _ = segment.fit_transform(X, y)

print('After segmentation:')
print("X:", X)
print("y: ", y)

union = FeatureRepMix([
    ('a', FeatureRep(features={'min': minimum}), 0),
    ('b', FeatureRep(features={'min': minimum}), 1),
    ('c', FeatureRep(features={'min': minimum}), [2, 3]),
    ('d', FeatureRep(features={'max': maximum}), slice(0, 2)),
    ('e', FeatureRep(features={'max': maximum}), [False, False, True, True]),
])

X = union.fit_transform(X, y)
print('After column-wise feature extraction:')
df = pd.DataFrame(data=X, columns=union.f_labels)
print(df)