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KLDivergence.py
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import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
import numpy as np
from sklearn.neighbors import KernelDensity
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import LeaveOneOut
from scipy.stats import entropy
from scipy import stats
def KDE1V(x, variable_name, bw_type="grid", plot="T"):
if bw_type == "grid":
bandwidths = 10 ** np.linspace(-1, 1, 100)
grid = GridSearchCV(
KernelDensity(kernel="gaussian"),
{"bandwidth": bandwidths},
cv=LeaveOneOut(),
)
grid.fit(x[:, None])
bw = grid.best_params_["bandwidth"]
# instantiate and fit the KDE model
kde = KernelDensity(bandwidth=bw, kernel="gaussian")
kde.fit(x[:, None])
if variable_name == "AvgDeg":
xmin = 0
xmax = 3
if variable_name == "Ng1/N":
xmin = 0
xmax = 1
if variable_name == "Ng2/N":
xmin = 0
xmax = 1
X = np.mgrid[xmin:xmax:100j]
positions = np.vstack([X.ravel()])
gdens = np.exp(kde.score_samples(positions.T))
elif bw_type == "silverman":
if variable_name == "AvgDeg":
xmin = 0
xmax = 3
if variable_name == "Ng1/N":
xmin = 0
xmax = 1
if variable_name == "Ng2/N":
xmin = 0
xmax = 1
X = np.mgrid[xmin:xmax:100j]
positions = np.vstack([X.ravel()])
# print("=====")
# print(x.std())
kde = stats.gaussian_kde(x)
kde.set_bandwidth(bw_method="silverman")
gdens = kde(positions).T
else:
print("Wrong bw_type")
# if plot == 'T':
# fig = plt.figure(figsize=(12,10))
# ax = fig.add_subplot(111)
# ax.imshow(np.rot90(Z), cmap=plt.get_cmap('viridis'),
# extent=[xmin, xmax, ymin, ymax])
# ax.scatter(x, y, c='red', s=20, edgecolor='red')
# #ax.set_aspect('auto')
# plt.show()
# else:
# pass
return gdens
# 1 variables KLD
def KLD1V(gdens1, gdens2):
if (0 in gdens1) or (0 in gdens2):
gdens1 = [gd + 1e-100 for gd in gdens1]
gdens2 = [gd + 1e-100 for gd in gdens2]
if entropy(pk=gdens1, qk=gdens2, base=2) >= entropy(pk=gdens2, qk=gdens1, base=2):
return entropy(pk=gdens2, qk=gdens1, base=2)
else:
return entropy(pk=gdens1, qk=gdens2, base=2)
""" Old version of KLD1V
def KLD1V(gdens1, gdens2):
return entropy(pk=gdens1, qk=gdens2,base=2)
"""
"""
def KDE3V(x, y, z, bw_type = 'grid', plot='T'):
xyz = np.vstack([x,y,z])
if bw_type == 'grid':
bandwidths = 10 ** np.linspace(-1, 1, 100)
grid = GridSearchCV(KernelDensity(kernel='gaussian'),
{'bandwidth': bandwidths},
cv=LeaveOneOut())
grid.fit(xyz.T)
bw = grid.best_params_['bandwidth']
elif bw_type == 'silverman':
d = xyz.shape[0]
n = xyz.shape[1]
bw = (n * (d + 2) / 4.)**(-1. / (d + 4))
else:
print('Wrong bw_type')
# instantiate and fit the KDE model
kde = KernelDensity(bandwidth=bw, kernel='gaussian')
kde.fit(xyz.T)
# xmin = x.min()
# xmax = x.max()
# ymin = y.min()
# ymax = y.max()
# zmin = z.min()
# zmax = z.max()
xmin = 0
xmax = 3
ymin = 0
ymax = 1
zmin = 0
zmax = 1
X, Y, Z = np.mgrid[xmin:xmax:100j, ymin:ymax:100j, zmin:zmax:100j]
positions = np.vstack([X.ravel(), Y.ravel(), Z.ravel()])
gdens = np.exp(kde.score_samples(positions.T))
return gdens
# 2 variables KLD
def KLD3V(gdens1, gdens2):
return entropy(pk=gdens1, qk=gdens2,base=2)
def KDE2V(x, y, bw_type = 'grid', plot='T'):
xy = np.vstack([x,y])
if bw_type == 'grid':
bandwidths = 10 ** np.linspace(-1, 1, 100)
grid = GridSearchCV(KernelDensity(kernel='gaussian'),
{'bandwidth': bandwidths},
cv=LeaveOneOut())
grid.fit(xy.T)
bw = grid.best_params_['bandwidth']
elif bw_type == 'silverman':
d = xy.shape[0]
n = xy.shape[1]
bw = (n * (d + 2) / 4.)**(-1. / (d + 4))
else:
print('Wrong bw_type')
# instantiate and fit the KDE model
kde = KernelDensity(bandwidth=bw, kernel='gaussian')
kde.fit(xy.T)
# xmin = x.min()
# xmax = x.max()
# ymin = y.min()
# ymax = y.max()
# xmin = 0
# xmax = 3
# ymin = 0
# ymax = y.max()+5
xmin = 0
xmax = 3
ymin = 0
ymax = 1
X, Y = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
positions = np.vstack([X.ravel(), Y.ravel()])
gdens = np.exp(kde.score_samples(positions.T))
Z = np.reshape(np.exp(kde.score_samples(positions.T)), X.shape)
if plot == 'T':
fig = plt.figure(figsize=(12,10))
ax = fig.add_subplot(111)
ax.imshow(np.rot90(Z), cmap=plt.get_cmap('viridis'),
extent=[xmin, xmax, ymin, ymax])
ax.scatter(x, y, c='red', s=20, edgecolor='red')
#ax.set_aspect('auto')
plt.show()
else:
pass
return gdens
# 2 variables KLD
def KLD2V(gdens1, gdens2):
return entropy(pk=gdens1, qk=gdens2,base=2)
def KDE1V(x, variable_name, bw_type = 'grid', plot='T'):
if bw_type == 'grid':
bandwidths = 10 ** np.linspace(-1, 1, 100)
grid = GridSearchCV(KernelDensity(kernel='gaussian'),
{'bandwidth': bandwidths},
cv=LeaveOneOut())
grid.fit(x[:, None])
bw = grid.best_params_['bandwidth']
elif bw_type == 'silverman':
d = x[:, None].shape[0]
n = x[:, None].shape[1]
bw = (n * (d + 2) / 4.)**(-1. / (d + 4))
else:
print('Wrong bw_type')
# instantiate and fit the KDE model
kde = KernelDensity(bandwidth=bw, kernel='gaussian')
kde.fit(x[:, None])
# xmin = x.min()
# xmax = x.max()
if variable_name == 'AvgDeg':
xmin = 0
xmax = 3
if variable_name == 'Ng1/N':
xmin = 0
xmax = 1
if variable_name == 'Ng2/N':
xmin = 0
xmax = 1
X= np.mgrid[xmin:xmax:100j]
positions = np.vstack([X.ravel()])
gdens = np.exp(kde.score_samples(positions.T))
# if plot == 'T':
# fig = plt.figure(figsize=(12,10))
# ax = fig.add_subplot(111)
# ax.imshow(np.rot90(Z), cmap=plt.get_cmap('viridis'),
# extent=[xmin, xmax, ymin, ymax])
# ax.scatter(x, y, c='red', s=20, edgecolor='red')
# #ax.set_aspect('auto')
# plt.show()
# else:
# pass
return gdens
# 1 variables KLD
def KLD1V(gdens1, gdens2):
return entropy(pk=gdens1, qk=gdens2,base=2)
"""