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Replace histogram feature implementation #167

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Sep 12, 2024
Merged

Replace histogram feature implementation #167

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69be8f0
Replace histogram feature implementation to histogram mode.
mbarandas Sep 11, 2024
4c33138
Minor adjustments for PEP compliance.
dmfolgado Sep 11, 2024
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12 changes: 6 additions & 6 deletions tests/test_calc_features.py
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Original file line number Diff line number Diff line change
Expand Up @@ -75,7 +75,7 @@ def test_input_list_window_multi_axis_multi(self):
)
np.testing.assert_array_equal(
features0.shape,
(16, 495),
(16, 468),
)

def test_input_array_window_tosplit_axis_single(self):
Expand All @@ -92,7 +92,7 @@ def test_input_array_window_tosplit_axis_single(self):

np.testing.assert_array_equal(
features1.shape,
(16, 165),
(16, 156),
)

def test_input_series_window_tosplit_axis_single(self):
Expand All @@ -108,7 +108,7 @@ def test_input_series_window_tosplit_axis_single(self):

np.testing.assert_array_equal(
features2.shape,
(16, 165),
(16, 156),
)

def test_input_dataframe_window_single_axis_multi(self):
Expand Down Expand Up @@ -152,7 +152,7 @@ def test_input_series_window_single_axis_single(self):

np.testing.assert_array_equal(
features5.shape,
(1, 40),
(1, 31),
)

def test_input_array_window_single_axis_single(self):
Expand Down Expand Up @@ -183,14 +183,14 @@ def test_personal_features(self):

np.testing.assert_array_equal(
features7.shape,
(1, 169),
(1, 160),
)

def test_get_number_features(self):
feature_sets_size = [get_number_features(get_features_by_domain(domain)) for domain in domains]
np.testing.assert_array_equal(
feature_sets_size,
[40, 14, 124, 6], # 184 is the total
[31, 14, 124, 6], # 184 is the total
)

def test_dataset_extractor(self):
Expand Down
36 changes: 20 additions & 16 deletions tests/test_features.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,38 +34,42 @@ class TestFeatures(unittest.TestCase):
# ############################################### STATISTICAL FEATURES ############################################### #
def test_hist(self):
np.testing.assert_almost_equal(
hist(const0, 10, 5)["values"],
(0.0, 0.0, 0.0, 0.0, 0.0, 20.0, 0.0, 0.0, 0.0, 0.0),
hist_mode(const0, 10),
0.050000000000000044,
decimal=5,
)
np.testing.assert_almost_equal(
hist(const1, 10, 5)["values"],
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 20.0, 0.0, 0.0, 0.0),
hist_mode(const1, 10),
1.05,
decimal=5,
)
np.testing.assert_almost_equal(
hist(constNeg, 10, 5)["values"],
(0.0, 0.0, 0.0, 0.0, 20.0, 0.0, 0.0, 0.0, 0.0, 0.0),
hist_mode(constNeg, 10),
-0.95,
decimal=5,
)
np.testing.assert_almost_equal(
hist(constF, 10, 5)["values"],
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 20.0, 0.0, 0.0),
hist_mode(constF, 10),
2.55,
decimal=5,
)
np.testing.assert_almost_equal(
hist(lin, 10, 5)["values"],
(0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 2),
hist_mode(lin, 10),
0.95,
)
np.testing.assert_almost_equal(
hist(wave, 10, 5)["values"],
(0.0, 0.0, 0.0, 0.0, 499, 496, 5, 0.0, 0.0, 0.0),
hist_mode(wave, 10),
-0.9,
decimal=5,
)
np.testing.assert_almost_equal(
hist(offsetWave, 10, 5)["values"],
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 499, 496, 5, 0.0),
hist_mode(offsetWave, 10),
1.1,
decimal=5,
)
np.testing.assert_almost_equal(
hist(noiseWave, 10, 5)["values"],
(0.0, 0.0, 0.0, 48, 446, 450, 56, 0.0, 0.0, 0.0),
hist_mode(noiseWave, 10),
-0.8862517157830269,
decimal=5,
)

Expand Down
11 changes: 5 additions & 6 deletions tests/tests_tools/test_features.json
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Original file line number Diff line number Diff line change
Expand Up @@ -430,14 +430,13 @@
"tag": "eeg",
"use": "yes"
},
"Histogram": {
"Histogram mode": {
"complexity": "log",
"description": "Computes histogram of the signal.",
"function": "tsfel.hist",
"n_features": "nbins",
"description": "Computes the mode of the signal's histogram.",
"function": "tsfel.hist_mode",
"n_features": 1,
"parameters": {
"nbins": 10,
"r": 1
"nbins": 10
},
"use": "yes"
},
Expand Down
11 changes: 5 additions & 6 deletions tsfel/feature_extraction/features.json
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Original file line number Diff line number Diff line change
Expand Up @@ -355,15 +355,14 @@
"use": "yes",
"tag": "eeg"
},
"Histogram": {
"Histogram mode": {
"complexity": "log",
"description": "Computes histogram of the signal.",
"function": "tsfel.hist",
"description": "Computes the mode of the signal's histogram.",
"function": "tsfel.hist_mode",
"parameters": {
"nbins": 10,
"r": 1
"nbins": 10
},
"n_features": "nbins",
"n_features": 1,
"use": "yes"
},
"Interquartile range": {
Expand Down
43 changes: 13 additions & 30 deletions tsfel/feature_extraction/features.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,9 +13,6 @@
+ " data points."
)

future_warn_flag = False
warnings.simplefilter("once", FutureWarning)

# ############################################# TEMPORAL DOMAIN ##################################################### #


Expand Down Expand Up @@ -473,45 +470,31 @@ def entropy(signal, prob="standard"):


@set_domain("domain", "statistical")
def hist(signal, nbins=10, r=1):
"""Computes histogram of the signal.
def hist_mode(signal, nbins=10):
"""Compute the mode of a histogram using a given number of (linearly spaced)
bins.

Feature computational cost: 1

Parameters
----------
signal : nd-array
Input from histogram is computed
signal : np.ndarray
Input signal from which the histogram is computed.
nbins : int
The number of equal-width bins in the given range
r : float
The lower(-r) and upper(r) range of the bins
The number of equal-width bins in the given range, by default 10.

Returns
-------
nd-array
The values of the histogram
float
The mode of the histogram (the midpoint of the bin with the highest
count).
"""
global future_warn_flag

if not future_warn_flag:
warnings.warn(
(
"The histogram feature was deprecated in version 0.1.8 and will be replaced by the mode of histogram in 0.1.9."
" From then on, only a single feature value will be returned."
),
FutureWarning,
)
future_warn_flag = True

# TODO: r value must be revised!
histsig, bin_edges = np.histogram(signal, bins=nbins, range=[-r, r])

names = [
str(np.around(bin_edges[i], 2)) + ":" + str(np.around(bin_edges[i + 1], 2)) for i in range(len(bin_edges) - 1)
]
hist_values, bin_edges = np.histogram(signal, bins=nbins)
max_bin_idx = np.argmax(hist_values)
mode_value = (bin_edges[max_bin_idx] + bin_edges[max_bin_idx + 1]) / 2.0

return {"names": names, "values": histsig}
return mode_value


@set_domain("domain", "statistical")
Expand Down
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