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Improving feature names #42

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merged 6 commits into from
Sep 28, 2018
Merged

Improving feature names #42

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064225b
Attempt at improving `get_feature_names` (so that `extract_features` …
jbschiratti Jul 12, 2018
1fa9f34
* Specific feature names for `compute_pow_freq_bands` : added
jbschiratti Sep 20, 2018
bf9d818
Fixes for Travis.
jbschiratti Sep 20, 2018
80dfa84
Added fit method to FeatureFunctionExtractor (does not extract features
jbschiratti Sep 27, 2018
a71fe05
Added test for feature names with `compute_pow_freq_bands`.
jbschiratti Sep 27, 2018
8748c87
Allow `freq_bands` parameter (in `compute_pow_freq_bands` and
jbschiratti Sep 28, 2018
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36 changes: 36 additions & 0 deletions mne_features/tests/test_univariate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@
import numpy as np
from numpy.testing import assert_equal, assert_almost_equal, assert_raises

from mne_features.feature_extraction import extract_features
from mne_features.univariate import (_slope_lstsq, _accumulate_std,
_accumulate_min, _accumulate_max,
compute_mean, compute_variance,
Expand Down Expand Up @@ -178,6 +179,40 @@ def test_pow_freq_bands():
ratios='only'), expected_ratios)


def test_feature_names_pow_freq_bands():
_data = data[:, :3, :] # keep only 3 channels for the sake of simplicity
selected_funcs = ['pow_freq_bands']
fb = np.array([[4., 8.], [30., 70.]])
ratios_col_names = ['ch0_0_1', 'ch0_1_0', 'ch1_0_1', 'ch1_1_0',
'ch2_0_1', 'ch2_1_0']
pow_col_names = ['ch0_0', 'ch0_1', 'ch1_0', 'ch1_1', 'ch2_0', 'ch2_1']
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no way to have more explicit names likes alpha, beta etc?

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We could improve that but, at some point, the user would need to name the frequency bands he wishes to use. What about allowing the freq_bands parameter in compute_pow_freq_bands to be a dict as the one below?

freq_bands = {'delta': [0.5, 4], 
              'theta': [4, 8], 
              'alpha': [8, 13], 
              'beta': [13, 30], 
              'low-gamma': [30, 70], 
              'high-gamma': [70, 100]}


# With `ratios = 'only'`:
df_only = extract_features(
_data, sfreq, selected_funcs,
funcs_params={'pow_freq_bands__ratios': 'only',
'pow_freq_bands__freq_bands': fb},
return_as_df=True)
assert_equal(df_only.columns.get_level_values(1).values, ratios_col_names)

# With `ratios = 'all'`:
df_all = extract_features(
_data, sfreq, selected_funcs,
funcs_params={'pow_freq_bands__ratios': 'all',
'pow_freq_bands__freq_bands': fb},
return_as_df=True)
assert_equal(df_all.columns.get_level_values(1).values,
pow_col_names + ratios_col_names)

# With `ratios = None`:
df = extract_features(
_data, sfreq, selected_funcs,
funcs_params={'pow_freq_bands__ratios': None,
'pow_freq_bands__freq_bands': fb},
return_as_df=True)
assert_equal(df.columns.get_level_values(1).values, pow_col_names)


def test_hjorth_mobility_spect():
expected = 0.005 * (5 ** 2) + 0.00125 * (33 ** 2)
assert_almost_equal(compute_hjorth_mobility_spect(sfreq, data_sin),
Expand Down Expand Up @@ -368,6 +403,7 @@ def test_shape_output_teager_kaiser_energy():
test_samp_entropy()
test_decorr_time()
test_pow_freq_bands()
test_feature_names_pow_freq_bands()
test_hjorth_mobility_spect()
test_hjorth_complexity_spect()
test_hjorth_mobility()
Expand Down