def get_features_from_one_signal(X, sample_rate=50):
assert X.ndim == 1, "Expected single signal in feature extraction"
mean = np.mean(X)
stdev = np.std(X)
abs_energy = fc.abs_energy(X)
sum_of_changes = fc.absolute_sum_of_changes(X)
autoc = fc.autocorrelation(X, sample_rate)
count_above_mean = fc.count_above_mean(X)
count_below_mean = fc.count_below_mean(X)
kurtosis = fc.kurtosis(X)
longest_above = fc.longest_strike_above_mean(X)
zero_crossing = fc.number_crossing_m(X, mean)
num_peaks = fc.number_peaks(X, int(sample_rate / 10))
sample_entropy = fc.sample_entropy(X)
spectral_density = fc.spkt_welch_density(X, [{
"coeff": 1
}, {
"coeff": 2
}, {
"coeff": 3
}, {
"coeff": 4
}, {
"coeff": 5
}, {
"coeff": 6
}])
c, v = zip(*spectral_density)
v = np.asarray(v)
return [
mean, stdev, abs_energy, sum_of_changes, autoc, count_above_mean,
count_below_mean, kurtosis, longest_above, zero_crossing, num_peaks,
sample_entropy, v[0], v[1], v[2], v[3], v[4], v[5]
]
def sample_entropy(mag):
"""Returns sample entropy: http://en.wikipedia.org/wiki/Sample_Entropy
rtype: float
"""
entropy = ts.sample_entropy(mag)
return entropy
def TS_features11(signal):
percentage_of_reoccurring = ts.percentage_of_reoccurring_datapoints_to_all_datapoints(
signal)
percentage_of_reoccurring_values = ts.percentage_of_reoccurring_values_to_all_values(
signal)
ratio_value_number = ts.ratio_value_number_to_time_series_length(signal)
sample_entropy = ts.sample_entropy(signal)
skewness = ts.skewness(signal)
return percentage_of_reoccurring, percentage_of_reoccurring_values, ratio_value_number, sample_entropy, skewness
def scalar_feature_extraction(column):
retval = np.zeros([1, 10], dtype=float)
retval[0][0] = tffe.count_above_mean(column.values)
retval[0][1] = tffe.mean(column.values)
retval[0][2] = tffe.maximum(column.values)
retval[0][3] = tffe.median(column.values)
retval[0][4] = tffe.minimum(column.values)
retval[0][5] = tffe.sample_entropy(column.values)
if (isNaN(retval[0][5])):
retval[0][5] = 0
retval[0][6] = tffe.skewness(column.values)
retval[0][7] = tffe.variance(column.values)
retval[0][8] = tffe.longest_strike_above_mean(column.values)
retval[0][9] = tffe.longest_strike_below_mean(column.values)
return retval
def entropy(self):
return np.array([
tcal.sample_entropy(self.data[i, :]) for i in range(len(self.data))
])
def sample_entropy(current_observation: pd.DataFrame, raw_key: str):
return tsf.sample_entropy(current_observation[raw_key])
Real_output = pd.read_csv(r"Real_output.csv")
dataset = Real_output.drop(columns='Class', axis=1)
##Create dataframe for feature selection
feat_dataset = pd.DataFrame()
#Calculated columns
feat_dataset['CGM_Min'] = dataset.min(axis=1)
feat_dataset['CGM_Max'] = dataset.max(axis=1)
#Reset index after merging different files into one
feat_dataset.reset_index(drop=True, inplace=True)
##Populate feature characteristics
##ENTROPY
feat_dataset['CGM_Entropy'] = np.nan
for i in range(len(dataset)):
feat_dataset['CGM_Entropy'][i] = ts.sample_entropy(
np.array(dataset.iloc[i, :]))
##RMS
feat_dataset['CGM_RMS'] = np.nan
for i in range(len(dataset)):
feat_dataset['CGM_RMS'][i] = np.sqrt(np.mean(dataset.iloc[i, :]**2))
#Correlation
feat_dataset['CGM_Correlation'] = np.nan
for i in range(len(dataset)):
feat_dataset['CGM_Correlation'][i] = ts.autocorrelation(
np.array(dataset.iloc[i, :]), 1)
##Number_of_Peaks
feat_dataset['CGM_Peaks'] = np.nan
for i in range(len(dataset)):
feat_dataset['CGM_Peaks'][i] = ts.number_peaks(
np.array(dataset.iloc[i, :]), 2)
#CGM Velocity
def feat_extraction(dataset):
feat_dataset = pd.DataFrame(index=np.arange(len(dataset)))
#Calculated columns
feat_dataset['CGM_Min'] = dataset.min(axis=1)
feat_dataset['CGM_Max'] = dataset.max(axis=1)
##ENTROPY
feat_dataset['CGM_Entropy'] = np.nan
for i in range(len(dataset)):
feat_dataset['CGM_Entropy'][i] = ts.sample_entropy(
np.array(dataset.iloc[i, :]))
##RMS
feat_dataset['CGM_RMS'] = np.nan
for i in range(len(dataset)):
feat_dataset['CGM_RMS'][i] = np.sqrt(np.mean(dataset.iloc[i, :]**2))
#Correlation
feat_dataset['CGM_Correlation'] = np.nan
for i in range(len(dataset)):
feat_dataset['CGM_Correlation'][i] = ts.autocorrelation(
np.array(dataset.iloc[i, :]), 1)
##Number_of_Peaks
feat_dataset['CGM_Peaks'] = np.nan
for i in range(len(dataset)):
feat_dataset['CGM_Peaks'][i] = ts.number_peaks(
np.array(dataset.iloc[i, :]), 2)
#CGM Velocity
feat_dataset['CGM_Velocity'] = np.nan
for i in range(len(dataset)):
c_list = dataset.loc[i, :].tolist()
sum_ = []
for j in range(1, len(c_list)):
sum_.append(abs(c_list[j] - c_list[j - 1]))
feat_dataset['CGM_Velocity'][i] = np.round(np.mean(sum_), 2)
#MinMax
feat_dataset['CGM_MinMax'] = np.nan
feat_dataset[
'CGM_MinMax'] = feat_dataset['CGM_Max'] - feat_dataset['CGM_Min']
##SKewness
feat_dataset['CGM_Skewness'] = np.nan
for i in range(len(dataset)):
feat_dataset['CGM_Skewness'][i] = ts.skewness(dataset.loc[i, :])
#CGM_Displacement
feat_dataset['CGM_Displacement'] = np.nan
for i in range(len(dataset)):
c_list = dataset.loc[i, :].tolist()
sum_ = []
for j in range(1, len(c_list)):
sum_.append(abs(c_list[j] - c_list[j - 1]))
feat_dataset['CGM_Displacement'][i] = np.round(np.sum(sum_), 2)
#CGM_Kurtosis
feat_dataset['CGM_Kurtosis'] = np.nan
for i in range(len(dataset)):
feat_dataset['CGM_Kurtosis'][i] = ts.kurtosis(
np.array(dataset.iloc[i, :]))
#Recurr
feat_dataset['CGM_Recur'] = np.nan
for i in range(len(dataset)):
feat_dataset['CGM_Recur'][
i] = ts.ratio_value_number_to_time_series_length(
np.array(dataset.iloc[i, :]))
#Remove calculated columns
del feat_dataset['CGM_Max']
del feat_dataset['CGM_Min']
feat_dataset = feat_dataset[[
'CGM_Entropy', 'CGM_RMS', 'CGM_Correlation', 'CGM_Peaks',
'CGM_Velocity', 'CGM_MinMax', 'CGM_Skewness', 'CGM_Displacement',
'CGM_Kurtosis', 'CGM_Recur'
]]
return feat_dataset
