def time_series_has_duplicate(x):
"""
:param x: the time series to calculate the feature of
:type x: pandas.Series
:return: the value of this feature
:return type: bool
"""
return ts_feature_calculators.has_duplicate(x)
def time_series_has_duplicate(x):
"""
Checks if any value in x occurs more than once
:param x: the time series to calculate the feature of
:type x: pandas.Series
:return: the value of this feature
:return type: bool
"""
return ts_feature_calculators.has_duplicate(x)
def TS_features2(signal):
duplicate = ts.has_duplicate(signal) # t.f
duplicate_max = ts.has_duplicate_max(signal) # t.f
duplicate_min = ts.has_duplicate_min(signal) # t.f
kurtosis = ts.kurtosis(signal)
longest_strike_above = ts.longest_strike_above_mean(signal)
longest_strike_below = ts.longest_strike_below_mean(signal)
return duplicate, duplicate_max, duplicate_min, kurtosis, longest_strike_above, longest_strike_below
def time_series_has_duplicate(x):
"""
序列x是否存在重复值，存在为false，否则为true
Checks if any value in x occurs more than once
:param x: the time series to calculate the feature of
:type x: pandas.Series
:return: the value of this feature
:return type: int
"""
return ts_feature_calculators.has_duplicate(x)
def check_for_duplicate(mag):
"""Checks if any val in mag repeats.
1 if True, 0 if False
rtype: int
"""
check = str(ts.has_duplicate(mag))
if check == 'False':
val = 0.0
else:
val = 1.0
return val
def extract_features(data):
day = 24 * 60
return list(
numpy.nan_to_num(
numpy.array([
feature.symmetry_looking(data, [{
'r': 0.3
}])[0][1],
feature.variance_larger_than_standard_deviation(data).bool(),
feature.ratio_beyond_r_sigma(data, 2),
feature.has_duplicate_max(data),
feature.has_duplicate_min(data),
feature.has_duplicate(data),
feature.agg_autocorrelation(numpy.array(data.value),
[{
'f_agg': 'mean',
'maxlag': day
}])[0][1],
feature.partial_autocorrelation(data, [{
'lag': day
}])[0][1],
feature.abs_energy(numpy.array(data.value)),
feature.mean_change(data),
feature.mean_second_derivative_central(data),
feature.median(data),
float(feature.mean(data)),
float(feature.standard_deviation(data)),
float(feature.longest_strike_below_mean(data)),
float(feature.longest_strike_above_mean(data)),
int(feature.number_peaks(data, 10)),
feature.linear_trend(numpy.array(data.value), [{
'attr': 'rvalue'
}])[0][1],
feature.c3(data, day),
float(feature.maximum(data)),
float(feature.minimum(data))
])))
