/usr/share/pyshared/statsmodels/sandbox/infotheo.py is in python-statsmodels 0.4.2-1.2.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 | """
Information Theoretic and Entropy Measures
References
----------
Golan, As. 2008. "Information and Entropy Econometrics -- A Review and
Synthesis." Foundations And Trends in Econometrics 2(1-2), 1-145.
Golan, A., Judge, G., and Miller, D. 1996. Maximum Entropy Econometrics.
Wiley & Sons, Chichester.
"""
#For MillerMadow correction
#Miller, G. 1955. Note on the bias of information estimates. Info. Theory
# Psychol. Prob. Methods II-B:95-100.
#For ChaoShen method
#Chao, A., and T.-J. Shen. 2003. Nonparametric estimation of Shannon's index of diversity when
#there are unseen species in sample. Environ. Ecol. Stat. 10:429-443.
#Good, I. J. 1953. The population frequencies of species and the estimation of population parameters.
#Biometrika 40:237-264.
#Horvitz, D.G., and D. J. Thompson. 1952. A generalization of sampling without replacement from a finute universe. J. Am. Stat. Assoc. 47:663-685.
#For NSB method
#Nemenman, I., F. Shafee, and W. Bialek. 2002. Entropy and inference, revisited. In: Dietterich, T.,
#S. Becker, Z. Gharamani, eds. Advances in Neural Information Processing Systems 14: 471-478.
#Cambridge (Massachusetts): MIT Press.
#For shrinkage method
#Dougherty, J., Kohavi, R., and Sahami, M. (1995). Supervised and unsupervised discretization of
#continuous features. In International Conference on Machine Learning.
#Yang, Y. and Webb, G. I. (2003). Discretization for naive-bayes learning: managing discretization
#bias and variance. Technical Report 2003/131 School of Computer Science and Software Engineer-
#ing, Monash University.
from scipy import maxentropy, stats
import numpy as np
from matplotlib import pyplot as plt
#TODO: change these to use maxentutils so that over/underflow is handled
#with the logsumexp.
from scipy.maxentropy import logsumexp as lse
def logsumexp(a, axis=None):
"""
Compute the log of the sum of exponentials log(e^{a_1}+...e^{a_n}) of a
Avoids numerical overflow.
Parameters
----------
a : array-like
The vector to exponentiate and sum
axis : int, optional
The axis along which to apply the operation. Defaults is None.
Returns
-------
sum(log(exp(a)))
Notes
-----
This function was taken from the mailing list
http://mail.scipy.org/pipermail/scipy-user/2009-October/022931.html
This should be superceded by the ufunc when it is finished.
"""
if axis is None:
# Use the scipy.maxentropy version.
return lse(a)
a = asarray(a)
shp = list(a.shape)
shp[axis] = 1
a_max = a.max(axis=axis)
s = log(exp(a - a_max.reshape(shp)).sum(axis=axis))
lse = a_max + s
return lse
def _isproperdist(X):
"""
Checks to see if `X` is a proper probability distribution
"""
X = np.asarray(X)
if not np.allclose(np.sum(X), 1) or not np.all(X>=0) or not np.all(X<=1):
return False
else:
return True
def discretize(X, method="ef", nbins=None):
"""
Discretize `X`
Parameters
----------
bins : int, optional
Number of bins. Default is floor(sqrt(N))
method : string
"ef" is equal-frequency binning
"ew" is equal-width binning
Examples
--------
"""
nobs = len(X)
if nbins == None:
nbins = np.floor(np.sqrt(nobs))
if method == "ef":
discrete = np.ceil(nbins * stats.rankdata(X)/nobs)
if method == "ew":
width = np.max(X) - np.min(X)
width = np.floor(width/nbins)
svec, ivec = stats.fastsort(X)
discrete = np.zeros(nobs)
binnum = 1
base = svec[0]
discrete[ivec[0]] = binnum
for i in xrange(1,nobs):
if svec[i] < base + width:
discrete[ivec[i]] = binnum
else:
base = svec[i]
binnum += 1
discrete[ivec[i]] = binnum
return discrete
#TODO: looks okay but needs more robust tests for corner cases
def logbasechange(a,b):
"""
There is a one-to-one transformation of the entropy value from
a log base b to a log base a :
H_{b}(X)=log_{b}(a)[H_{a}(X)]
Returns
-------
log_{b}(a)
"""
return np.log(b)/np.log(a)
def natstobits(X):
"""
Converts from nats to bits
"""
return logbasechange(np.e, 2) * X
def bitstonats(X):
"""
Converts from bits to nats
"""
return logbasechange(2, np.e) * X
#TODO: make this entropy, and then have different measures as
#a method
def shannonentropy(px, logbase=2):
"""
This is Shannon's entropy
Parameters
-----------
logbase, int or np.e
The base of the log
px : 1d or 2d array_like
Can be a discrete probability distribution, a 2d joint distribution,
or a sequence of probabilities.
Returns
-----
For log base 2 (bits) given a discrete distribution
H(p) = sum(px * log2(1/px) = -sum(pk*log2(px)) = E[log2(1/p(X))]
For log base 2 (bits) given a joint distribution
H(px,py) = -sum_{k,j}*w_{kj}log2(w_{kj})
Notes
-----
shannonentropy(0) is defined as 0
"""
#TODO: haven't defined the px,py case?
px = np.asarray(px)
if not np.all(px <= 1) or not np.all(px >= 0):
raise ValueError, "px does not define proper distribution"
entropy = -np.sum(np.nan_to_num(px*np.log2(px)))
if logbase != 2:
return logbasechange(2,logbase) * entropy
else:
return entropy
# Shannon's information content
def shannoninfo(px, logbase=2):
"""
Shannon's information
Parameters
----------
px : float or array-like
`px` is a discrete probability distribution
Returns
-------
For logbase = 2
np.log2(px)
"""
px = np.asarray(px)
if not np.all(px <= 1) or not np.all(px >= 0):
raise ValueError, "px does not define proper distribution"
if logbase != 2:
return - logbasechange(2,logbase) * np.log2(px)
else:
return - np.log2(px)
def condentropy(px, py, pxpy=None, logbase=2):
"""
Return the conditional entropy of X given Y.
Parameters
----------
px : array-like
py : array-like
pxpy : array-like, optional
If pxpy is None, the distributions are assumed to be independent
and conendtropy(px,py) = shannonentropy(px)
logbase : int or np.e
Returns
-------
sum_{kj}log(q_{j}/w_{kj}
where q_{j} = Y[j]
and w_kj = X[k,j]
"""
if not _isproperdist(px) or not _isproperdist(py):
raise ValueError, "px or py is not a proper probability distribution"
if pxpy != None and not _isproperdist(pxpy):
raise ValueError, "pxpy is not a proper joint distribtion"
if pxpy == None:
pxpy = np.outer(py,px)
condent = np.sum(pxpy * np.nan_to_num(np.log2(py/pxpy)))
if logbase == 2:
return condent
else:
return logbasechange(2, logbase) * condent
def mutualinfo(px,py,pxpy, logbase=2):
"""
Returns the mutual information between X and Y.
Parameters
----------
px : array-like
Discrete probability distribution of random variable X
py : array-like
Discrete probability distribution of random variable Y
pxpy : 2d array-like
The joint probability distribution of random variables X and Y.
Note that if X and Y are independent then the mutual information
is zero.
logbase : int or np.e, optional
Default is 2 (bits)
Returns
-------
shannonentropy(px) - condentropy(px,py,pxpy)
"""
if not _isproperdist(px) or not _isproperdist(py):
raise ValueError, "px or py is not a proper probability distribution"
if pxpy != None and not _isproperdist(pxpy):
raise ValueError, "pxpy is not a proper joint distribtion"
if pxpy == None:
pxpy = np.outer(py,px)
return shannonentropy(px, logbase=logbase) - condentropy(px,py,pxpy,
logbase=logbase)
def corrent(px,py,pxpy,logbase=2):
"""
An information theoretic correlation measure.
Reflects linear and nonlinear correlation between two random variables
X and Y, characterized by the discrete probability distributions px and py
respectively.
Parameters
----------
px : array-like
Discrete probability distribution of random variable X
py : array-like
Discrete probability distribution of random variable Y
pxpy : 2d array-like, optional
Joint probability distribution of X and Y. If pxpy is None, X and Y
are assumed to be independent.
logbase : int or np.e, optional
Default is 2 (bits)
Returns
-------
mutualinfo(px,py,pxpy,logbase=logbase)/shannonentropy(py,logbase=logbase)
Notes
-----
This is also equivalent to
corrent(px,py,pxpy) = 1 - condent(px,py,pxpy)/shannonentropy(py)
"""
if not _isproperdist(px) or not _isproperdist(py):
raise ValueError, "px or py is not a proper probability distribution"
if pxpy != None and not _isproperdist(pxpy):
raise ValueError, "pxpy is not a proper joint distribtion"
if pxpy == None:
pxpy = np.outer(py,px)
return mutualinfo(px,py,pxpy,logbase=logbase)/shannonentropy(py,
logbase=logbase)
def covent(px,py,pxpy,logbase=2):
"""
An information theoretic covariance measure.
Reflects linear and nonlinear correlation between two random variables
X and Y, characterized by the discrete probability distributions px and py
respectively.
Parameters
----------
px : array-like
Discrete probability distribution of random variable X
py : array-like
Discrete probability distribution of random variable Y
pxpy : 2d array-like, optional
Joint probability distribution of X and Y. If pxpy is None, X and Y
are assumed to be independent.
logbase : int or np.e, optional
Default is 2 (bits)
Returns
-------
condent(px,py,pxpy,logbase=logbase) + condent(py,px,pxpy,
logbase=logbase)
Notes
-----
This is also equivalent to
covent(px,py,pxpy) = condent(px,py,pxpy) + condent(py,px,pxpy)
"""
if not _isproperdist(px) or not _isproperdist(py):
raise ValueError, "px or py is not a proper probability distribution"
if pxpy != None and not _isproperdist(pxpy):
raise ValueError, "pxpy is not a proper joint distribtion"
if pxpy == None:
pxpy = np.outer(py,px)
return condent(px,py,pxpy,logbase=logbase) + condent(py,px,pxpy,
logbase=logbase)
#### Generalized Entropies ####
def renyientropy(px,alpha=1,logbase=2,measure='R'):
"""
Renyi's generalized entropy
Parameters
----------
px : array-like
Discrete probability distribution of random variable X. Note that
px is assumed to be a proper probability distribution.
logbase : int or np.e, optional
Default is 2 (bits)
alpha : float or inf
The order of the entropy. The default is 1, which in the limit
is just Shannon's entropy. 2 is Renyi (Collision) entropy. If
the string "inf" or numpy.inf is specified the min-entropy is returned.
measure : str, optional
The type of entropy measure desired. 'R' returns Renyi entropy
measure. 'T' returns the Tsallis entropy measure.
Returns
-------
1/(1-alpha)*log(sum(px**alpha))
In the limit as alpha -> 1, Shannon's entropy is returned.
In the limit as alpha -> inf, min-entropy is returned.
"""
#TODO:finish returns
#TODO:add checks for measure
if not _isproperdist(px):
raise ValueError, "px is not a proper probability distribution"
alpha = float(alpha)
if alpha == 1:
genent = shannonentropy(px)
if logbase != 2:
return logbasechange(2, logbase) * genent
return genent
elif 'inf' in string(alpha).lower() or alpha == np.inf:
return -np.log(np.max(px))
# gets here if alpha != (1 or inf)
px = px**alpha
genent = np.log(px.sum())
if logbase == 2:
return 1/(1-alpha) * genent
else:
return 1/(1-alpha) * logbasechange(2, logbase) * genent
#TODO: before completing this, need to rethink the organization of
# (relative) entropy measures, ie., all put into one function
# and have kwdargs, etc.?
def gencrossentropy(px,py,pxpy,alpha=1,logbase=2, measure='T'):
"""
Generalized cross-entropy measures.
Parameters
----------
px : array-like
Discrete probability distribution of random variable X
py : array-like
Discrete probability distribution of random variable Y
pxpy : 2d array-like, optional
Joint probability distribution of X and Y. If pxpy is None, X and Y
are assumed to be independent.
logbase : int or np.e, optional
Default is 2 (bits)
measure : str, optional
The measure is the type of generalized cross-entropy desired. 'T' is
the cross-entropy version of the Tsallis measure. 'CR' is Cressie-Read
measure.
"""
if __name__ == "__main__":
print "From Golan (2008) \"Information and Entropy Econometrics -- A Review \
and Synthesis"
print "Table 3.1"
# Examples from Golan (2008)
X = [.2,.2,.2,.2,.2]
Y = [.322,.072,.511,.091,.004]
for i in X:
print shannoninfo(i)
for i in Y:
print shannoninfo(i)
print shannonentropy(X)
print shannonentropy(Y)
p = [1e-5,1e-4,.001,.01,.1,.15,.2,.25,.3,.35,.4,.45,.5]
plt.subplot(111)
plt.ylabel("Information")
plt.xlabel("Probability")
x = np.linspace(0,1,100001)
plt.plot(x, shannoninfo(x))
# plt.show()
plt.subplot(111)
plt.ylabel("Entropy")
plt.xlabel("Probability")
x = np.linspace(0,1,101)
plt.plot(x, map(shannonentropy, zip(x,1-x)))
# plt.show()
# define a joint probability distribution
# from Golan (2008) table 3.3
w = np.array([[0,0,1./3],[1/9.,1/9.,1/9.],[1/18.,1/9.,1/6.]])
# table 3.4
px = w.sum(0)
py = w.sum(1)
H_X = shannonentropy(px)
H_Y = shannonentropy(py)
H_XY = shannonentropy(w)
H_XgivenY = condentropy(px,py,w)
H_YgivenX = condentropy(py,px,w)
# note that cross-entropy is not a distance measure as the following shows
D_YX = logbasechange(2,np.e)*stats.entropy(px, py)
D_XY = logbasechange(2,np.e)*stats.entropy(py, px)
I_XY = mutualinfo(px,py,w)
print "Table 3.3"
print H_X,H_Y, H_XY, H_XgivenY, H_YgivenX, D_YX, D_XY, I_XY
print "discretize functions"
X=np.array([21.2,44.5,31.0,19.5,40.6,38.7,11.1,15.8,31.9,25.8,20.2,14.2,
24.0,21.0,11.3,18.0,16.3,22.2,7.8,27.8,16.3,35.1,14.9,17.1,28.2,16.4,
16.5,46.0,9.5,18.8,32.1,26.1,16.1,7.3,21.4,20.0,29.3,14.9,8.3,22.5,
12.8,26.9,25.5,22.9,11.2,20.7,26.2,9.3,10.8,15.6])
discX = discretize(X)
#CF: R's infotheo
#TODO: compare to pyentropy quantize?
print
print "Example in section 3.6 of Golan, using table 3.3"
print "Bounding errors using Fano's inequality"
print "H(P_{e}) + P_{e}log(K-1) >= H(X|Y)"
print "or, a weaker inequality"
print "P_{e} >= [H(X|Y) - 1]/log(K)"
print "P(x) = %s" % px
print "X = 3 has the highest probability, so this is the estimate Xhat"
pe = 1 - px[2]
print "The probability of error Pe is 1 - p(X=3) = %0.4g" % pe
H_pe = shannonentropy([pe,1-pe])
print "H(Pe) = %0.4g and K=3" % H_pe
print "H(Pe) + Pe*log(K-1) = %0.4g >= H(X|Y) = %0.4g" % \
(H_pe+pe*np.log2(2), H_XgivenY)
print "or using the weaker inequality"
print "Pe = %0.4g >= [H(X) - 1]/log(K) = %0.4g" % (pe, (H_X - 1)/np.log2(3))
print "Consider now, table 3.5, where there is additional information"
print "The conditional probabilities of P(X|Y=y) are "
w2 = np.array([[0.,0.,1.],[1/3.,1/3.,1/3.],[1/6.,1/3.,1/2.]])
print w2
# not a proper distribution?
print "The probability of error given this information is"
print "Pe = [H(X|Y) -1]/log(K) = %0.4g" % ((np.mean([0,shannonentropy(w2[1]),shannonentropy(w2[2])])-1)/np.log2(3))
print "such that more information lowers the error"
### Stochastic processes
markovchain = np.array([[.553,.284,.163],[.465,.312,.223],[.420,.322,.258]])
|