我有155个图像和8个类假设特征未在[0-1]范围内缩放。
网格搜索交叉验证建议我使用以下分数的线性内核和C=1000：
precision recall f1-score support
1 0.54 0.88 0.67 8
2 0.73 1.00 0.84 8
3 1.00 1.00 1.00 6
4 0.75 0.50 0.60 12
5 0.83 0.83 0.83 6
6 0.92 0.65 0.76 17
7 0.71 0.42 0.53 12
8 0.60 1.00 0.75 9
avg / total 0.77 0.73 0.72 78
但是当我尝试线性内核并且C=1000时，我获得：
precision recall f1-score support
1 0.00 0.00 0.00 0
2 1.00 0.70 0.82 10
3 1.00 1.00 1.00 13
4 0.73 0.58 0.65 19
5 1.00 0.95 0.97 19
6 0.96 0.88 0.92 25
7 0.82 0.67 0.73 27
8 0.70 1.00 0.82 16
avg / total 0.88 0.81 0.84 129
Confusion matrix:
[[ 0 0 0 0 0 0 0 0]
[ 0 7 0 0 0 0 3 0]
[ 0 0 13 0 0 0 0 0]
[ 2 0 0 11 0 1 0 5]
[ 0 0 0 1 18 0 0 0]
[ 0 0 0 0 0 22 1 2]
[ 6 0 0 3 0 0 18 0]
[ 0 0 0 0 0 0 0 16]]
为什么第1类全为零？
我还看到，使用rbf核，我有最好的结果，但在第一类中总是零：
precision recall f1-score support
1 0.00 0.00 0.00 0
2 1.00 1.00 1.00 10
3 1.00 1.00 1.00 13
4 0.94 0.89 0.92 19
5 1.00 0.95 0.97 19
6 0.93 1.00 0.96 25
7 1.00 0.78 0.88 27
8 1.00 1.00 1.00 16
avg / total 0.98 0.93 0.95 129
Confusion matrix:
[[ 0 0 0 0 0 0 0 0]
[ 0 10 0 0 0 0 0 0]
[ 0 0 13 0 0 0 0 0]
[ 1 0 0 17 0 1 0 0]
[ 0 0 0 1 18 0 0 0]
[ 0 0 0 0 0 25 0 0]
[ 5 0 0 0 0 1 21 0]
[ 0 0 0 0 0 0 0 16]]
最后，当我试图预测训练集的一些相同图像时
print(clf.predict(fv))
其中fv是图像特征向量：
[0.16666666666628771, 5.169878828456423e-26, 2.3475644278196356e-21, 1.0, 1.0000000000027285]
并将错误的类分配给特征向量！（即图像拥有4类，但预测（）结果为5类）
重新使用
图像集：https://docs.google.com/file/d/0ByS6Z5WRz-h2V3RkejFkb21Fb0E/edit?usp=sharing
功能图像集：https://docs.google.com/file/d/0ByS6Z5WRz-h2YlhuUmFBaElXVEE/edit?usp=sharing
完整代码：
import os
import glob
import numpy as np
from numpy import array
import cv2
target = [ 1,1,1,1,
1,1,1,1,1,1,1,
1,1,1,1,1,1,1,
1,2,2,2,2,2,2,
2,2,2,2,2,2,2,
2,2,2,2,3,3,3,
3,3,3,3,3,3,3,
3,3,3,4,4,4,4,
4,4,4,4,4,4,4,
4,4,4,4,4,4,4,
4,5,5,5,5,5,5,
5,5,5,5,5,5,5,
5,5,5,5,5,5,6,
6,6,6,6,6,6,6,
6,6,6,6,6,6,6,
6,6,6,6,6,6,6,
6,6,6,7,7,7,7,
7,7,7,7,7,7,7,
7,7,7,7,7,7,7,
7,7,7,7,7,7,7,
7,7,8,8,8,8,8,
8,8,8,8,8,8,8,
8,8,8,8]
features = [ [0.26912666717306399, 0.012738398606387012, 0.011347858467581035, 0.1896938013442868, 2.444553429782046]
,
[0.36793086934925351, 0.034364344308391102, 0.019054536791551006, 0.0076875387476751395, 3.03091214703604]
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[0.36793086934925351, 0.034364344308391102, 0.019054536791551006, 0.0076875387476751395, 3.03091214703604]
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[0.30406240228443038, 0.047100329090555518, 0.0049653458889261448, 0.0004618404341300081, 5.987025009738751]
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[0.36660353297714748, 0.034256126367653919, 0.01892501331178556, 0.007723901183105499, 3.0392760101225234]
,
[0.26708884220978957, 0.012126741224471632, 0.0063753119877062942, 0.0005937801528983894, 2.403113171408598]
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[0.27070254516425241, 0.01293684867974746, 0.01159661796151442, 0.008380724334031727, 2.4492688425144986]
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[0.27076540467770038, 0.012502407901054009, 0.011180048331833999, 0.0007116977225672878, 2.4068989750876266]
,
[0.22832314403919951, 0.010491475428909061, 0.0027317652016312271, 0.001417434443656981, 2.6271926274711968]
,
[0.22374814412737717, 0.0095258889624651646, 0.0040833924467236719, 0.1884906960716747, 2.5474055920602514]
,
[0.23860556210266026, 0.0067860933136106557, 0.0052050705189953389, 0.01498751040799334, 2.0545849084769694]
,
[0.32849751530034654, 0.0082079572128769367, 0.017950580842136479, 0.07211170619739862, 1.761646715256231]
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[0.3536962871782694, 0.04335618127793292, 0.0084705562859388305, 0.003939815915497741, 3.8626463078353632]
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[0.23642964900011443, 0.0060530993708264348, 0.0041172882106328976, 0.003276003276003276, 1.9809324414862304]
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[0.35468301957048581, 0.043735489028639378, 0.0085420200506240735, 0.00041124057573680605, 3.873602628153773]
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[0.35549112610207528, 0.043992218599656373, 0.0086354414147218166, 0.004276259969455286, 3.8781644572829106]
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[0.97303451800669749, 0.075165987107118692, 0.23350656471824954, 0.04989418850724402, 1.7845923298199189]
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[0.32292438991638828, 0.0078312712861588109, 0.018256154769458615, 0.05861489639723726, 1.754975905310628]
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[0.36415716731096714, 0.033783635359516562, 0.0087048690616182353, 0.0007989674881691353, 3.0382507494699778]
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[0.23247799686964493, 0.023970481957641395, 0.0020180739588722754, 0.2511737089201878, 4.987537342956105]
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[0.25249755819322928, 0.03355835554037629, 0.0024745974458906918, 0.49168600154679043, 6.286228850887637]
,
[0.25524836990657951, 0.035216193154545015, 0.0023524820730296808, 0.49272798742138363, 6.553001816315555]
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[0.25226043727172792, 0.033580607886770704, 0.002399474603048905, 0.4913428241631397, 6.310803986284148]
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[0.2552359153348957, 0.034993472521483299, 0.0024465696242431606, 0.49311565696302123, 6.488164071764478]
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[0.25249755819322928, 0.03355835554037629, 0.0024745974458906918, 0.49168600154679043, 6.286228850887637]
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[0.19296658297366265, 0.0073667093687413854, 0.0010128002719554498, 0.20292887029288703, 2.6022382484976103]
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[0.23130715659438109, 0.023652143308649062, 0.0020734509865596379, 0.2519981194170193, 4.96809084167716]
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[0.23646940610897133, 0.025909457534721684, 0.0019634358569802723, 0.25097465886939574, 5.263654156113397]
,
[0.61892415483059771, 0.1855733578950316, 0.024118739298890277, 0.00010742003920831431, 5.579333799263049]
,
[0.61892415483059771, 0.1855733578950316, 0.024118739298890277, 0.00010742003920831431, 5.579333799263049]
,
[0.62187109165606835, 0.18810005977070685, 0.060143785970969831, 0.005752046658462197, 5.609811692923419]
,
[0.64410628333823972, 0.20178318336365086, 0.039546324622261202, 8.006565383614564e-05, 5.609490756132282]
,
[0.6214309265075304, 0.18779664186718673, 0.061337975720487534, 0.006350402281839464, 5.608301926807521]
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,
[0.20123494853445922, 0.0069845347246147793, 0.0027020357704780201, 0.4106724003127443, 2.420576584506546]
,
[0.2015816556223165, 0.0070631416111702362, 0.0025149608542164329, 0.4106073986851143, 2.4300340608128606]
,
[0.70115857527896985, 0.35625759453714789, 0.028386898853323388, 0.001234186979327368, 12.446918085552586]
,
[0.68366020888533297, 0.2387861974848598, 0.04047049559400958, 0.0725675987982436, 6.011803834536788]
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[0.70115857527896985, 0.35625759453714789, 0.028386898853323388, 0.001234186979327368, 12.446918085552586]
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[0.71378846605495283, 0.37185054375086962, 0.078338189105938844, 0.4899937460913071, 12.727628852581882]
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[0.72219309919241148, 0.37567368174335658, 0.029371875736917675, 0.48066298342541436, 12.21840343375]
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[0.84033907078880576, 0.29025638999406633, 0.090118665350957639, 0.00013319126265316994, 4.572824986179928]
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[0.85542893012496013, 0.29898764801731947, 0.17279563533793374, 0.0005314202205393915, 4.543371196521408]
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[0.68653873117620423, 0.24135977292901584, 0.031609483792605572, 0.4553053169259345, 6.032229402405299]
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[0.68937407444389065, 0.2429428175127194, 0.031783181019183315, 0.07118412046543464, 6.017180801429501]
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[0.66262362984605561, 0.22830191525650573, 0.027222059698182095, 0.4712353884941554, 6.170703008647743]
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[0.85191326598415906, 0.0066280315423251869, 0.18568977018064967, 0.24070082098793744, 1.211324246965761]
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[0.41763663758743241, 0.0042550997098748248, 0.01052268995786553, 0.000998003992015968, 1.3702049090803978]
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[0.16628339469915165, 0.0020643314471593802, 1.4502279324313873e-05, 0.14276914653343373, 1.7519319117125625]
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[0.49053398407596349, 0.13596678236015974, 0.0068673835378752004, 0.5062523683213338, 7.054927383254023]
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[0.51893243284454049, 0.14486098229876093, 0.007011404157031503, 0.0013995801259622112, 6.503015780005906]
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[0.51611281879296478, 0.14397569681830566, 0.0063953861901166996, 0.0024067388688327317, 6.552602133840095]
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[0.52265570318341037, 0.14786059553298658, 0.021856594872657918, 0.002438599547117227, 6.567632701584826]
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[0.30079480228240624, 0.022512205511218238, 0.00042758792096778651, 0.016516516516516516, 2.990535008572801]
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[0.30656959740479811, 0.025225633729599333, 0.00052074639660009423, 0.014692653673163419, 3.1500163953105362]
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############################ PREDICTION TEST 1 IMAGE ################
print("TRY IMAGE")
import numpy as np
from sklearn import svm, metrics
X = features
y = target
from sklearn.svm import SVC
C = 1000.0
clf = svm.SVC(kernel='rbf', C=C).fit(X, y)
#svm.SVC(kernel='linear', C=C).fit(X, y) #SVC()
#clf.fit(X, y)
print("predizione")
#fv is class 8 but show me 5
fv = [0.16666666666628771, 5.169878828456423e-26, 2.584939414228212e-22, 1.0, 1.0000000000027285]
print(fv)
print(clf.predict([fv]))
############### METRICS ##########
# We learn the digits on the first half of the digits
# Now predict the value of the digit on the second half:
import matplotlib.pyplot as plt
expected = y[26:]
predicted = clf.predict(X[26:])
print("expected")
print(len(expected))
print("predicted")
print(len(predicted))
print "Classification report for classifier %s:n%sn" % (
clf, metrics.classification_report(expected, predicted))
print "Confusion matrix:n%s" % metrics.confusion_matrix(expected, predicted)
在完整的数据集上训练模型，然后计算训练集的一个子集上的分数，即数据集的所有末尾，除了26个第一个样本，它包括来自类0的整个样本集。
你不能用这种方式评估模型：你需要随机洗牌数据，然后在训练模型之前拆分训练集和测试集（否则整个数据集就是训练集，你没有单独的测试集）。如果你这样做：
import numpy as np
from sklearn import svm, metrics
from sklearn.cross_validation import train_test_split
from sklearn.svm import SVC
X = features
y = target
X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size=0.25, random_state=42)
C = 1000.0
clf = svm.SVC(kernel='rbf', C=C).fit(X_train, y_train)
y_predicted = clf.predict(X_test)
print "Classification report for classifier %s:n%sn" % (
clf, metrics.classification_report(y_test, y_predicted))
print "Confusion matrix:n%s" % metrics.confusion_matrix(y_test, y_predicted)
print "Predicting on 1 sample"
print "Input features:"
fv = [0.16666666666628771, 5.169878828456423e-26, 2.584939414228212e-22, 1.0, 1.0000000000027285]
print fv
print "Predicted class index:"
print clf.predict([fv])
您将得到以下输出：
Classification report for classifier SVC(C=1000.0, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0, kernel=rbf, max_iter=-1, probability=False, shrinking=True,
tol=0.001, verbose=False):
precision recall f1-score support
1 0.50 0.25 0.33 4
2 0.75 1.00 0.86 6
3 1.00 1.00 1.00 2
4 0.75 1.00 0.86 3
5 1.00 0.88 0.93 8
6 1.00 1.00 1.00 5
7 0.75 0.75 0.75 8
8 1.00 1.00 1.00 3
avg / total 0.84 0.85 0.83 39
Confusion matrix:
[[1 1 0 0 0 0 2 0]
[0 6 0 0 0 0 0 0]
[0 0 2 0 0 0 0 0]
[0 0 0 3 0 0 0 0]
[0 0 0 1 7 0 0 0]
[0 0 0 0 0 5 0 0]
[1 1 0 0 0 0 6 0]
[0 0 0 0 0 0 0 3]]
Predicting on 1 sample
Input features:
[0.1666666666662877, 5.169878828456423e-26, 2.584939414228212e-22, 1.0, 1.0000000000027285]
Predicted class index:
[5]
当然，这是一个单一的随机训练/测试分割，由于你的数据集很小，你得到的分数的估计值会有很大的方差。您可以通过迭代交叉验证来计算该模型类和参数集的预期平均分数的估计值：
from sklearn.cross_validation import ShuffleSplit
from sklearn.cross_validation import cross_val_score
from scipy.stats import sem
params = dict(kernel='rbf', C=1000)
clf = svm.SVC(**params)
cv = ShuffleSplit(X.shape[0], n_iter=50)
cv_scores = cross_val_score(clf, X, y, cv=cv)
将输出：
print "Cross Validated test scores for SVC with params {0} on full dataset:".format(params)
print "Mean: {0:.3} +/-{1:.3}".format(np.mean(cv_scores), sem(cv_scores))
print "Standard deviation: {0:.3}".format(np.std(cv_scores))
Cross Validated test scores for SVC with params {'kernel': 'rbf', 'C': 1000} on full dataset:
Mean: 0.834 +/-0.0125
Standard deviation: 0.0872
因此，你可以合理地预期总体上有83%的预测准确率（或者更高一点，因为CV程序低估了一点）。
如果你想显著提高这一级别的性能，我的第一个建议是收集更多标记的样本，以获得更大的数据集。
第二个建议是通过对原始图像应用小的扰动（例如，小的平移、旋转和一点均匀的随机噪声）来从现有图像中生成更多的标记数据，以便通过提取那些附加样本的特征来从现有数据中生成更多标记数据。
编辑：针对补充问题：
我还遗漏了8/10个图像样本，因为我认为它们不属于任何类别。
您可能应该为不属于以前其他类的所有图像添加一个名为"其他"的附加类别。
我应该为每个类添加一个新的类，并通过小的平移旋转创建新的样本吗？
不，目标是通过在现有类中构造新的样本来为每个类添加更多样本，从而提高现有类的分类精度。
我得到了这个错误：TypeError:init（）在cv=ShuffleSplit（X.shape[0]，n_iter=50）这一行得到了一个意外的关键字参数"n_iter"
n_iter是0.13版本中的新名称。0.12是n_iterations:
http://scikit-learn.org/0.12/modules/generated/sklearn.cross_validation.ShuffleSplit.html