标签:statistics machine-learning prediction python
有谁知道python中是否有用于训练loglinear模型的现有软件包?我有一个包含2000个变量和1000条记录的数据集.我正在寻找使用对数线性模型来估计频率.
解决方法:
如果使用旧版本的SciPy(即0.10或更早版本),则可以使用scipy.maxentropy
(在NLP中,MaxEnt =最大熵建模=对数线性模型).在发布0.11.0版本时,该模块已从SciPy中删除,SciPy团队随后使用sklearn.linear_model.LogisticRegression替换了advised(请注意,both对数线性模型和逻辑回归是generalized linear models的示例,其中线性预测变量之间的关系).
使用SciPy的最大熵模块的Example(已在SciPy 0.11.0中删除):
#!/usr/bin/env python
""" Example use of the maximum entropy module:
Machine translation example -- English to French -- from the paper 'A
maximum entropy approach to natural language processing' by Berger et
al., 1996.
Consider the translation of the English word 'in' into French. We
notice in a corpus of parallel texts the following facts:
(1) p(dans) + p(en) + p(a) + p(au cours de) + p(pendant) = 1
(2) p(dans) + p(en) = 3/10
(3) p(dans) + p(a) = 1/2
This code finds the probability distribution with maximal entropy
subject to these constraints.
"""
__author__ = 'Ed Schofield'
__version__= '2.1'
from scipy import maxentropy
a_grave = u'\u00e0'
samplespace = ['dans', 'en', a_grave, 'au cours de', 'pendant']
def f0(x):
return x in samplespace
def f1(x):
return x=='dans' or x=='en'
def f2(x):
return x=='dans' or x==a_grave
f = [f0, f1, f2]
model = maxentropy.model(f, samplespace)
# Now set the desired feature expectations
K = [1.0, 0.3, 0.5]
model.verbose = True
# Fit the model
model.fit(K)
# Output the distribution
print "\nFitted model parameters are:\n" + str(model.params)
print "\nFitted distribution is:"
p = model.probdist()
for j in range(len(model.samplespace)):
x = model.samplespace[j]
print ("\tx = %-15s" %(x + ":",) + " p(x) = "+str(p[j])).encode('utf-8')
# Now show how well the constraints are satisfied:
print
print "Desired constraints:"
print "\tp['dans'] + p['en'] = 0.3"
print ("\tp['dans'] + p['" + a_grave + "'] = 0.5").encode('utf-8')
print
print "Actual expectations under the fitted model:"
print "\tp['dans'] + p['en'] =", p[0] + p[1]
print ("\tp['dans'] + p['" + a_grave + "'] = " + str(p[0]+p[2])).encode('utf-8')
# (Or substitute "x.encode('latin-1')" if you have a primitive terminal.)
其他意见:http://homepages.inf.ed.ac.uk/lzhang10/maxent.html
标签:statistics,machine-learning,prediction,python 来源: https://codeday.me/bug/20191031/1971937.html
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