adding entropy to feats

setup.py

@@ -19,7 +19,6 @@
     python_requires='>=3.6',
     install_requires=[
         "arch>=4.11",
-        "entropy @ https://github.com/raphaelvallat/entropy/tarball/tag/v0.1.1",
         "pandas>=1.0.5",
         "scikit-learn>=0.23.1",
         "statsmodels>=0.11.1",

tsfeatures/tsfeatures.py

@@ -13,7 +13,6 @@
 
 from arch import arch_model
 from collections import ChainMap
-from entropy import spectral_entropy
 from functools import partial
 from itertools import groupby
 from math import log, e
@@ -29,11 +28,10 @@
 from typing import List, Dict, Optional, Callable
 
 from .utils import (
-    poly, embed, scalets,
-    terasvirta_test,
-    hurst_exponent, ur_pp,
-    lambda_coef_var,
-    FREQS
+    embed, FREQS, hurst_exponent,
+    lambda_coef_var, poly,
+    scalets, spectral_entropy,
+    terasvirta_test, ur_pp
 )
 
 

tsfeatures/utils.py

@@ -4,6 +4,8 @@
 import numpy as np
 import statsmodels.api as sm
 
+from scipy.signal import periodogram, welch
+
 np.seterr(divide='ignore', invalid='ignore')
 
 ################################################################################
@@ -245,6 +247,84 @@ def lambda_coef_var(lambda_par: float, x: np.array, period: int = 2):
 
     return value
 
+def spectral_entropy(x, sf, method='fft', nperseg=None, normalize=False):
+    """Spectral Entropy.
+    TAKEN FROM https://github.com/raphaelvallat/entropy/blob/master/entropy/entropy.py
+    DUE TO INSTALATION ISSUES.
+
+    Parameters
+    ----------
+    x : list or np.array
+        One-dimensional time series of shape (n_times)
+    sf : float
+        Sampling frequency, in Hz.
+    method : str
+        Spectral estimation method:
+        * ``'fft'`` : Fourier Transform (:py:func:`scipy.signal.periodogram`)
+        * ``'welch'`` : Welch periodogram (:py:func:`scipy.signal.welch`)
+    nperseg : int or None
+        Length of each FFT segment for Welch method.
+        If None (default), uses scipy default of 256 samples.
+    normalize : bool
+        If True, divide by log2(psd.size) to normalize the spectral entropy
+        between 0 and 1. Otherwise, return the spectral entropy in bit.
+
+    Returns
+    -------
+    se : float
+        Spectral Entropy
+
+    Notes
+    -----
+    Spectral Entropy is defined to be the Shannon entropy of the power
+    spectral density (PSD) of the data:
+    .. math:: H(x, sf) =  -\\sum_{f=0}^{f_s/2} P(f) \\log_2[P(f)]
+    Where :math:`P` is the normalised PSD, and :math:`f_s` is the sampling
+    frequency.
+
+    References
+    ----------
+    Inouye, T. et al. (1991). Quantification of EEG irregularity by
+    use of the entropy of the power spectrum. Electroencephalography
+    and clinical neurophysiology, 79(3), 204-210.
+    https://en.wikipedia.org/wiki/Spectral_density
+    https://en.wikipedia.org/wiki/Welch%27s_method
+
+    Examples
+    --------
+    Spectral entropy of a pure sine using FFT
+    >>> from entropy import spectral_entropy
+    >>> import numpy as np
+    >>> sf, f, dur = 100, 1, 4
+    >>> N = sf * dur # Total number of discrete samples
+    >>> t = np.arange(N) / sf # Time vector
+    >>> x = np.sin(2 * np.pi * f * t)
+    >>> np.round(spectral_entropy(x, sf, method='fft'), 2)
+    0.0
+    Spectral entropy of a random signal using Welch's method
+    >>> from entropy import spectral_entropy
+    >>> import numpy as np
+    >>> np.random.seed(42)
+    >>> x = np.random.rand(3000)
+    >>> spectral_entropy(x, sf=100, method='welch')
+    6.980045662371389
+    Normalized spectral entropy
+    >>> spectral_entropy(x, sf=100, method='welch', normalize=True)
+    0.9955526198316071
+    """
+    x = np.array(x)
+    # Compute and normalize power spectrum
+    if method == 'fft':
+        _, psd = periodogram(x, sf)
+    elif method == 'welch':
+        _, psd = welch(x, sf, nperseg=nperseg)
+    psd_norm = np.divide(psd, psd.sum())
+    se = -np.multiply(psd_norm, np.log2(psd_norm)).sum()
+    if normalize:
+        se /= np.log2(psd_norm.size)
+    return se
+
+
 ################################################################################
 ####### TS #####################################################################
 ################################################################################