如何计算多类分类的加权准确性

Question

我对不平衡类进行多类分类。我正在使用SGDClassifier(), GradientBoostingClassifier(), RandomForestClassifier(), and LogisticRegression()和class_weight='balanced'.比较结果。需要计算精度。我尝试了以下方法来计算加权准确性：

n_samples = len(y_train)
weights_cof = float(n_samples)/(n_classes*np.bincount(data[target_label].as_matrix().astype(int))[1:])
sample_weights = np.ones((n_samples,n_classes)) * weights_cof
print accuracy_score(y_test, y_pred, sample_weight=sample_weights)

y_train是一个二进制数组。所以sample_weights的形状与y_train（n_samples, n_classes）相同。运行脚本时，收到以下错误：

更新：

 Traceback (most recent call last):
  File "C:Program Files (x86)JetBrainsPyCharm Community Edition 2016.3.2helperspydevpydevd.py", line 1596, in <module>
    globals = debugger.run(setup['file'], None, None, is_module)
  File "C:Program Files (x86)JetBrainsPyCharm Community Edition 2016.3.2helperspydevpydevd.py", line 974, in run
    pydev_imports.execfile(file, globals, locals)  # execute the script
  File "D:/Destiny/DestinyScripts/MainLocationAware.py", line 424, in <module>
    predict_country(featuresDF, score, featuresLabel, country_sample_size, 'gbc')
  File "D:/Destiny/DestinyScripts/MainLocationAware.py", line 313, in predict_country
    print accuracy_score(y_test, y_pred, sample_weight=sample_weights)
  File "C:ProgramDataAnaconda2libsite-packagessklearnmetricsclassification.py", line 183, in accuracy_score
    return _weighted_sum(score, sample_weight, normalize)
  File "C:ProgramDataAnaconda2libsite-packagessklearnmetricsclassification.py", line 108, in _weighted_sum
    return np.average(sample_score, weights=sample_weight)
  File "C:ProgramDataAnaconda2libsite-packagesnumpylibfunction_base.py", line 1124, in average
    "Axis must be specified when shapes of a and weights "
TypeError: Axis must be specified when shapes of a and weights differ.

Answer 1

该错误似乎表明sample_weights和y_test/y_pred数组的形状不同。基本上，该方法创建一个带有y_test == y_pred的布尔数组，并将其与sample_weights一起传递给np.average。该方法中的第一个检查是确保输入的数组和权重是相同的形状，在这种情况下显然它们不是。

更新

您的评论"sample_weights、y_test和y_pred具有相同的形状（n_samples、n_classes）"暴露了问题。根据accuracy_score的文档，y_pred和y_true（在你的情况下y_test和y_pred）应该是一维的。您是否可能使用一个热编码标签？如果是这样，则应将它们转换为单值标签，然后再次尝试准确性分数。