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[Fix] complexity_coarsegraining(): fix method #892

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merged 12 commits into from
Sep 22, 2023
Merged

[Fix] complexity_coarsegraining(): fix method #892

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00f73cc
adjust delay
DominiqueMakowski Aug 29, 2023
1570a10
change coarsegraining
DominiqueMakowski Aug 30, 2023
a12ce58
entropy_sample: return phi in info dict
DominiqueMakowski Aug 31, 2023
820d0d9
return more info in entropy_sample
DominiqueMakowski Aug 31, 2023
487407d
Update utils_complexity_coarsegraining.py
DominiqueMakowski Sep 4, 2023
c5e95d3
Merge branch 'dev' into fix_entropymultiscale
DominiqueMakowski Sep 13, 2023
6fbef91
rename .utils to .utils_entropy
DominiqueMakowski Sep 15, 2023
41b923a
move entropy_approximate internals and add entropy_quadratic
DominiqueMakowski Sep 16, 2023
ec67af6
add to docs
DominiqueMakowski Sep 16, 2023
19d5487
Update entropy_svd.py
DominiqueMakowski Sep 16, 2023
067f1cc
fix scikit-learn #910
DominiqueMakowski Sep 21, 2023
a7382fd
Update entropy_quadratic.py
DominiqueMakowski Sep 21, 2023
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40 changes: 28 additions & 12 deletions neurokit2/complexity/entropy_multiscale.py
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Original file line number Diff line number Diff line change
Expand Up @@ -249,10 +249,12 @@ def entropy_multiscale(
if "delay" in kwargs:
kwargs.pop("delay")

# Parameters selection
# Default parameters
algorithm = entropy_sample
refined = False
coarsegraining = "nonoverlapping"

# Parameters adjustement for variants
if method in ["MSEn", "SampEn"]:
pass # The default arguments are good
elif method in ["MSApEn", "ApEn", "MSPEn", "PEn", "MSWPEn", "WPEn"]:
Expand Down Expand Up @@ -326,13 +328,9 @@ def entropy_multiscale(
info["Value"] = np.array(
[
_entropy_multiscale(
coarse=complexity_coarsegraining(
signal,
scale=scale,
method=coarsegraining,
show=False,
**kwargs,
),
signal,
scale=scale,
coarsegraining=coarsegraining,
algorithm=algorithm,
dimension=dimension,
tolerance=info["Tolerance"],
Expand Down Expand Up @@ -378,13 +376,31 @@ def _entropy_multiscale_plot(mse, info):
# =============================================================================
# Methods
# =============================================================================
def _entropy_multiscale(coarse, algorithm, dimension, tolerance, refined=False, **kwargs):
def _entropy_multiscale(
signal,
scale,
coarsegraining,
algorithm,
dimension,
tolerance,
refined=False,
**kwargs,
):
"""Wrapper function that works both on 1D and 2D coarse-grained (for composite)"""

# Get coarse-grained signal
coarse = complexity_coarsegraining(signal, scale=scale, method=coarsegraining)

# Get delay
delay = 1 # If non-overlapping
if coarsegraining in ["rolling", "interpolate"]:
delay = scale

# For 1D coarse-graining
if coarse.ndim == 1:
return algorithm(
coarse,
delay=1,
delay=delay,
dimension=dimension,
tolerance=tolerance,
**kwargs,
Expand All @@ -398,7 +414,7 @@ def _entropy_multiscale(coarse, algorithm, dimension, tolerance, refined=False,
[
algorithm(
coarse[i],
delay=1,
delay=delay,
dimension=dimension,
tolerance=tolerance,
**kwargs,
Expand All @@ -412,7 +428,7 @@ def _entropy_multiscale(coarse, algorithm, dimension, tolerance, refined=False,
[
_phi(
coarse[i],
delay=1,
delay=delay,
dimension=dimension,
tolerance=tolerance,
approximate=False,
Expand Down
32 changes: 24 additions & 8 deletions neurokit2/complexity/utils_complexity_coarsegraining.py
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Original file line number Diff line number Diff line change
@@ -1,12 +1,14 @@
# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import numpy as np
import scipy.ndimage.filters

from ..signal import signal_interpolate
from .utils_complexity_embedding import complexity_embedding


def complexity_coarsegraining(signal, scale=2, method="nonoverlapping", show=False, **kwargs):
def complexity_coarsegraining(
signal, scale=2, method="nonoverlapping", show=False, **kwargs
):
"""**Coarse-graining of a signal**

The goal of coarse-graining is to represent the signal at a different "scale". The
Expand Down Expand Up @@ -184,11 +186,16 @@ def complexity_coarsegraining(signal, scale=2, method="nonoverlapping", show=Fal
# Relying on scipy is a fast alternative to:
# pd.Series(signal).rolling(window=scale).mean().values[scale-1::]
# https://stackoverflow.com/questions/13728392/moving-average-or-running-mean
coarse = scipy.ndimage.filters.uniform_filter1d(signal, size=scale, mode="nearest")
coarse = coarse[scale - 1 : :]
# coarse = scipy.ndimage.filters.uniform_filter1d(
# signal, size=scale, mode="nearest"
# )
# coarse = coarse[scale - 1 : :]
coarse = complexity_embedding(signal, dimension=scale, delay=1).mean(axis=1)
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I updated the coarsegraining procedure but it doesn't seem to have entirely solved the issue:

image

@hsyu001 let's just make sure that we have the same sample entropy results: would you mind computing SampEn with these parameters using your own algorithm:

signal = [1, 2, 3, 5, 3, 1, 2, 4, 5, 7, 3, 2, 6, 2, 4, 8, 2]
tol = 2

With NK, this gives:

nk.entropy_sample(signal, dimension=2, delay=3, tolerance=tol)
> (0.2831469172863898,
 {'Dimension': 2, 'Delay': 3, 'Tolerance': 2.0034572195207527})

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elif method == "timeshift":
coarse = np.transpose(np.reshape(signal[: scale * (n // scale)], (n // scale, scale)))
coarse = np.transpose(
np.reshape(signal[: scale * (n // scale)], (n // scale, scale))
)

else:
raise ValueError("Unknown `method`: {}".format(method))
Expand All @@ -204,8 +211,15 @@ def complexity_coarsegraining(signal, scale=2, method="nonoverlapping", show=Fal
def _complexity_show(signal, coarse, method="nonoverlapping"):
plt.plot(signal, linewidth=1.5)
if method == "nonoverlapping":
plt.plot(np.linspace(0, len(signal), len(coarse)), coarse, color="red", linewidth=0.75)
plt.scatter(np.linspace(0, len(signal), len(coarse)), coarse, color="red", linewidth=0.5)
plt.plot(
np.linspace(0, len(signal), len(coarse)),
coarse,
color="red",
linewidth=0.75,
)
plt.scatter(
np.linspace(0, len(signal), len(coarse)), coarse, color="red", linewidth=0.5
)
elif method == "timeshift":
for i in range(len(coarse)):
plt.plot(
Expand All @@ -215,7 +229,9 @@ def _complexity_show(signal, coarse, method="nonoverlapping"):
linewidth=0.75,
)
else:
plt.plot(np.linspace(0, len(signal), len(coarse)), coarse, color="red", linewidth=1)
plt.plot(
np.linspace(0, len(signal), len(coarse)), coarse, color="red", linewidth=1
)
plt.title(f'Coarse-graining using method "{method}"')


Expand Down
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