def plot_tail_dist_loglog(seq, name=''):
"""
Plot the tail distribution of data
1-F(x), where F(x) is the empirical distribution of data
:param seq: array like
:param name: name of d
:return: void
"""
cdf = ECDF(seq)
# filter out the zero term
for x in cdf.x:
if x <= 0:
cdf.x = cdf.x[1:]
cdf.y = cdf.y[1:]
# eliminating the cdf = 1 term
cdf.x = cdf.x[:-1]
cdf.y = cdf.y[:-1]
plt.plot([math.log(elem) for elem in cdf.x],
[math.log(1 - elem) for elem in cdf.y],
label=name,
marker='<',
markerfacecolor='none',
markersize=1)
'feature #3': [10, 2, 1, 8]
}).describe().reset_index()
# Kolmogorov - Smirnov - we want to reject the null hypothesis, i.e. reject the possibility that the two samples are
# coming from the exact same distribution.
# If the K-S statistic is small or the p-value is high, then we cannot reject the hypothesis that the distributions
# of the two samples are the same.
ks_statistic, p_value = ks_2samp(false_around_gt, true_around_gt)
# fit an empirical ECDF
false_ecdf = ECDF(false_around_gt)
true_ecdf = ECDF(true_around_gt)
true_ecdf.x[0] = false_ecdf.x[0] = 0
if true_ecdf.x[-1] > false_ecdf.x[-1]:
false_ecdf.x = np.concatenate([false_ecdf.x, np.array([true_ecdf.x[-1]])])
false_ecdf.y = np.concatenate([false_ecdf.y, np.array([false_ecdf.y[-1]])])
else:
true_ecdf.x = np.concatenate([true_ecdf.x, np.array([false_ecdf.x[-1]])])
true_ecdf.y = np.concatenate([true_ecdf.y, np.array([true_ecdf.y[-1]])])
# qq plot
if len(false_ecdf.x) > len(true_ecdf.x):
# there are more false samples than true
true_interp = np.interp(false_ecdf.x, true_ecdf.x, true_ecdf.y)
false_interp = false_ecdf.y
else:
# there are more true samples than false
false_interp = np.interp(true_ecdf.x, false_ecdf.x, false_ecdf.y)
true_interp = true_ecdf.y
# dash visualization
