Question

这是我在jupyternotebook中的代码我很困惑，为什么我的输入形状输入错误。在我的代码中失败的行是在下面给出的，通过打开数据集文件并用于分割可能的输出类高于50K或低于或等于50K。这个数据集略有不同每个数据点都是数字和字符串混合的意义

with open(input_file, 'r') as f:
    for line in f.readlines():
        if '?' in line:
            continue
        data = line[:-1].split(', ')

        if data[-1] == '<=50K' and count_lessthan50k < num_images_threshold:
            X.append(data)
            count_lessthan50k = count_lessthan50k + 1
        elif data[-1] == '>50K' and count_morethan50k <
num_images_threshold:
            X.append(data)
            count_morethan50k = count_morethan50k + 1
        if count_lessthan50k >= num_images_threshold and count_morethan50k>= num_images_threshold:
            break
X = np.array(X)

这是将字符串数据转换为数字数据

label_encoder = []
X_encoded = np.empty(X.shape)

for i, item in enumerate(X[0]):
    if item.isdigit():
        X_encoded[:, i] = X[:, i]
    else:
        label_encoder.append(preprocessing.LabelEncoder())
        X_encoded[:, i] = label_encoder[-1].fit_transform(X[:,i])


X = X_encoded[:, :-1].astype(int)
y = X_encoded[:, -1].astype(int)

交叉验证数据

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25,
                                                random_state=5)

classifier_gaussiannb = GaussianNB()
classifier_gaussiannb.fit(X_train, y_train)

y_test_pred = classifier_gaussiannb.predict(X_test)

在单个数据实例上测试编码

input_data = ['39', 'State-gov', '77516', 'Bachelors', '13','Never-married', 'Adm-clerical', 'Not-in-family', 'White','Male', '2174', '0', '40', 'United-States']



count = 0
input_data_encoded = [-1] * len(input_data)

for i,item in enumerate(input_data):
    if item.isdigit():
        input_data_encoded[i] = int(input_data[i])
    else:
        input_data_encoded[i] = int(label_encoder[count].transform(input_data[i]))
        count = count + 1

input_data_encoded = np.array(input_data_encoded)

我已经浏览了sklearn文档，但没有为我工作，任何帮助??

Answer 1

LabelEncoder transform()需要一次迭代所有样本进行转换，如documentation中所述： -

Transform labels to normalized encoding.

Parameters     y : array-like of shape [n_samples]
               Target values.

如果你想每次传递一个值，你需要将它包装在这样的列表中：

else:
    input_data_encoded[i] = int(label_encoder[count].transform([input_data[i]]))

注意input_data[i]附近的额外方括号。

输入形状不好（）LabelEncode？

1 个答案: