SKLearn 多类分类器

Question

我编写了以下代码来从文件导入数据向量并测试 SVM classifier 的性能（使用 sklearn 和 python）。

然而，classifier 性能低于任何其他 classifier（例如，NNet 在测试数据上给出了 98% 的准确率，但这最多只能给出 92%）。以我的经验，SVM 应该为这种数据产生更好的结果。

我是不是做错了什么？

import numpy as np

def buildData(featureCols, testRatio):
    f = open("car-eval-data-1.csv")
    data = np.loadtxt(fname = f, delimiter = ',')

    X = data[:, :featureCols]  # select columns 0:featureCols-1
    y = data[:, featureCols]   # select column  featureCols 

    n_points = y.size
    print "Imported " + str(n_points) + " lines."

    ### split into train/test sets
    split = int((1-testRatio) * n_points)
    X_train = X[0:split,:]
    X_test  = X[split:,:]
    y_train = y[0:split]
    y_test  = y[split:]

    return X_train, y_train, X_test, y_test

def buildClassifier(features_train, labels_train):
    from sklearn import svm

    #clf = svm.SVC(kernel='linear',C=1.0, gamma=0.1)
    #clf = svm.SVC(kernel='poly', degree=3,C=1.0, gamma=0.1)
    clf = svm.SVC(kernel='rbf',C=1.0, gamma=0.1)
    clf.fit(features_train, labels_train)
    return clf

def checkAccuracy(clf, features, labels):
    from sklearn.metrics import accuracy_score

    pred = clf.predict(features)
    accuracy = accuracy_score(pred, labels)
    return accuracy

features_train, labels_train, features_test, labels_test = buildData(6, 0.3)
clf           = buildClassifier(features_train, labels_train)
trainAccuracy = checkAccuracy(clf, features_train, labels_train)
testAccuracy  = checkAccuracy(clf, features_test, labels_test)
print "Training Items: " + str(labels_train.size) + ", Test Items: " + str(labels_test.size)
print "Training Accuracy: " + str(trainAccuracy)
print "Test Accuracy: " + str(testAccuracy)

i = 0
while i < labels_test.size:
  pred = clf.predict(features_test[i])
  print "F(" + str(i) + ") : " + str(features_test[i]) + " label= " + str(labels_test[i]) + " pred= " + str(pred);
  i = i + 1

如果默认不进行多class class化怎么可能？

p.s。我的数据格式如下（最后一列是 class）：

2,2,2,2,2,1,0
2,2,2,2,1,2,0
0,2,2,5,2,2,3
2,2,2,4,2,2,1
2,2,2,4,2,0,0
2,2,2,4,2,1,1
2,2,2,4,1,2,1
0,2,2,5,2,2,3

Answer 1

好久才发现问题，现贴出来，以防有人需要。

问题是数据导入功能不会打乱数据。如果数据以某种方式排序，则存在使用某些数据训练分类器并使用完全不同的数据对其进行测试的风险。在 NNet 案例中，使用了自动打乱输入数据的 Matlab。

def buildData(filename, featureCols, testRatio):
f = open(filename)
data = np.loadtxt(fname = f, delimiter = ',')
np.random.shuffle(data)    # randomize the order

X = data[:, :featureCols]  # select columns 0:featureCols-1
y = data[:, featureCols]   # select column  featureCols 

n_points = y.size
print "Imported " + str(n_points) + " lines."

### split into train/test sets
split = int((1-testRatio) * n_points)
X_train = X[0:split,:]
X_test  = X[split:,:]
y_train = y[0:split]
y_test  = y[split:]

return X_train, y_train, X_test, y_test

SKLearn 多类分类器

SKLearn Multiclass Classifier

python

svm

scikit-learn