使用scikit_learn和特征矩阵的奇怪卡方结果

时间:2012-10-20 09:41:04

标签: python classification scikit-learn chi-squared

我正在使用scikit学习计算基本的卡方统计量(sklearn.feature_selection.chi2(X,y)):

def chi_square(feat,target):
"""   """
from sklearn.feature_selection import chi2
ch,pval =  chi2(feat,target)
return ch,pval



chisq,p = chi_square(feat_mat,target_sc)
print(chisq)
print("**********************")
print(p)

我有1500个样本,45个功能,4个类。输入是具有1500x45的特征矩阵和具有1500个组件的目标阵列。特征矩阵不稀疏。当我运行程序并使用45个组件打印arrray“chisq”时,我可以看到组件13具有负值并且p = 1.如何可能?或者它是什么意思或我正在做的最大错误是什么?

我附上了chisq和p的打印输出:

[  9.17099260e-01   3.77439701e+00   5.35004211e+01   2.17843312e+03
   4.27047184e+04   2.23204883e+01   6.49985540e-01   2.02132664e-01
   1.57324454e-03   2.16322638e-01   1.85592258e+00   5.70455805e+00
   1.34911126e-02  -1.71834753e+01   1.05112366e+00   3.07383691e-01
   5.55694752e-02   7.52801686e-01   9.74807972e-01   9.30619466e-02
   4.52669897e-02   1.08348058e-01   9.88146259e-03   2.26292358e-01
   5.08579194e-02   4.46232554e-02   1.22740419e-02   6.84545170e-02
   6.71339545e-03   1.33252061e-02   1.69296016e-02   3.81318236e-02
   4.74945604e-02   1.59313146e-01   9.73037448e-03   9.95771327e-03
   6.93777954e-02   3.87738690e-02   1.53693158e-01   9.24603716e-04
   1.22473138e-01   2.73347277e-01   1.69060817e-02   1.10868365e-02
   8.62029628e+00]

**********************

[  8.21299526e-01   2.86878266e-01   1.43400668e-11   0.00000000e+00
   0.00000000e+00   5.59436980e-05   8.84899894e-01   9.77244281e-01
   9.99983411e-01   9.74912223e-01   6.02841813e-01   1.26903019e-01
   9.99584918e-01   1.00000000e+00   7.88884155e-01   9.58633878e-01
   9.96573548e-01   8.60719653e-01   8.07347364e-01   9.92656816e-01
   9.97473024e-01   9.90817144e-01   9.99739526e-01   9.73237195e-01
   9.96995722e-01   9.97526259e-01   9.99639669e-01   9.95333185e-01
   9.99853998e-01   9.99592531e-01   9.99417113e-01   9.98042114e-01
   9.97286030e-01   9.83873717e-01   9.99745466e-01   9.99736512e-01
   9.95239765e-01   9.97992843e-01   9.84693908e-01   9.99992525e-01
   9.89010468e-01   9.64960636e-01   9.99418323e-01   9.99690553e-01
   3.47893682e-02]

1 个答案:

答案 0 :(得分:1)

如果你在the code defining中加入一些印刷语句 chi2

def chi2(X, y):
    X = atleast2d_or_csr(X)
    Y = LabelBinarizer().fit_transform(y)
    if Y.shape[1] == 1:
        Y = np.append(1 - Y, Y, axis=1)
    observed = safe_sparse_dot(Y.T, X)          # n_classes * n_features
    print(repr(observed))
    feature_count = array2d(X.sum(axis=0))
    class_prob = array2d(Y.mean(axis=0))
    expected = safe_sparse_dot(class_prob.T, feature_count)
    print(repr(expected))
    return stats.chisquare(observed, expected)

你会发现expected最终会产生一些负面影响 值。

import numpy as np
import sklearn.feature_selection as FS

x = np.array([-0.23918515, -0.29967287, -0.33007592, 0.07383528, -0.09205183,
              -0.12548226, 0.04770942, -0.54318463, -0.16833203, -0.00332341,
              0.0179646, -0.0526383, 0.04288736, -0.27427317, -0.16136621,
              -0.09228812, -0.2255725, -0.03744027, 0.02953499, -0.17387492])

y = np.array([1, 2, 2, 1, 1, 1, 1, 3, 1, 1, 3, 2, 2, 1, 1, 2, 1, 2, 1, 1],
             dtype = 'int64')

FS.chi2(x.reshape(-1,1),y)

产量

observed:
array([[-1.31238179],
       [-0.76922812],
       [-0.52522003]])

expected:
array([[-1.56409796],
       [-0.78204898],
       [-0.26068299]])
然后调用

stats.chisquared(observed, expected)。那里,observed 并假设expected是类别的频率。它们应该都是 非负数,因为频率是非负数。

我对scikits不够熟悉 - 学会建议如何解决您的问题,但是您发送给chi2数据似乎是错误的排序,因为expected应该是非负面的。

(例如,可能是上面的x值都应该是正数并代表观测频率吗?)