python - 如何使用 scikit 正确进行一种热编码？

Question

我的功能之一是可以采用 29 种不同状态的分类变量。我正在尝试使用一种热编码来转换它，以便我可以使用此功能构建预测模型。以下是我的代码:

enc = preprocessing.OneHotEncoder()
enc.fit([[0], [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28]]) 

subredditCategory = []
        if row[1] == 'Art':
            subredditCategory.append(0)
        elif row[1] == 'AskReddit':
            subredditCategory.append(1)
        elif row[1] == 'askscience':
            subredditCategory.append(2)
        elif row[1] == 'aww':
            subredditCategory.append(3)
        elif row[1] == 'books':
            subredditCategory.append(4)
        elif row[1] == 'creepy':
            subredditCategory.append(5)
        elif row[1] == 'dataisbeautiful':
            subredditCategory.append(6)
        elif row[1] == 'DIY':
            subredditCategory.append(7)
        elif row[1] == 'Documentaries':
            subredditCategory.append(8)
        elif row[1] == 'EarthPorn':
            subredditCategory.append(9)
        elif row[1] == 'explainlikeimfive':
            subredditCategory.append(10)
        elif row[1] == 'food':
            subredditCategory.append(11)
        elif row[1] == 'funny':
            subredditCategory.append(12)
        elif row[1] == 'gaming':
            subredditCategory.append(13)
        elif row[1] == 'gifs':
            subredditCategory.append(14)
        elif row[1] == 'history':
            subredditCategory.append(15)
        elif row[1] == 'jokes':
            subredditCategory.append(16)
        elif row[1] == 'LifeProTips':
            subredditCategory.append(17)
        elif row[1] == 'movies':
            subredditCategory.append(18)
        elif row[1] == 'music':
            subredditCategory.append(19)
        elif row[1] == 'pics':
            subredditCategory.append(20)
        elif row[1] == 'science':
            subredditCategory.append(21)
        elif row[1] == 'ShowerThoughts':
            subredditCategory.append(22)
        elif row[1] == 'space':
            subredditCategory.append(23)
        elif row[1] == 'sports':
            subredditCategory.append(24)
        elif row[1] == 'tifu':
            subredditCategory.append(25)
        elif row[1] == 'todayilearned':
            subredditCategory.append(26)
        elif row[1] == 'videos':
            subredditCategory.append(27)
        elif row[1] == 'worldnews':
            subredditCategory.append(28)

sub = enc.transform([subredditCategory]).toarray()

        features.append([row[2], row[3], row[6], row[8], sub])
        labels.append(row[9])

但是当我尝试使用特征和标签来训练模型时，如下所示:

clf = tree.DecisionTreeClassifier()
clf = clf.fit(features, labels)

我收到以下运行时崩溃错误:

ValueError: setting an array element with a sequence.

这就是生成的 clf.fit 行。不确定我做错了什么 - 有什么想法吗？

Answer 1

我相信，当您拥有分类数据时，您还需要利用 LabelBinarizer或LabelEncoder .

您可以按如下方式使用 LabelEncoder:

encoder = sklearn.preprocessing.OneHotEncoder()
label_encoder = sklearn.preprocessing.LabelEncoder()
data_labels_encoded = label_encoder.fit_transform(data['category_feature'])
data['category_feature'] = data_label_encoded
feature = encoder.fit_transform(data[['category_feature']].as_matrix())

您可以按如下方式使用 LabelBinarizer:

lb = preprocessing.LabelBinarizer()
feature = lb.fit_transform(data['category_feature'])

我觉得后者是更好的方法，但这可能是情况。