我刚开始学习机器学习，正在使用 Python（3.6）、Pandas、Numpy 和 SKLearn 进行一个项目。

我的 DataFrame 是这样的：

discount   tax   total   subtotal   productid
3         0     20       13        002
10        3     106      94        003
46.49     6     21       20        004

我进行分类的方法如下：

df_full = pd.read_excel('input/Potential_Learning_Patterns.xlsx', sheet_name=0)
df_full.head()
# 转换为数值型
df_full['discount'] = pd.to_numeric(df_full['discount'], errors='coerce')
df_full['productdiscount'] = pd.to_numeric(df_full['discount'], errors='coerce')
df_full['Class'] = ((df_full['discount'] > 20) & 
                    (df_full['tax'] == 0) & 
                    (df_full['productdiscount'] > 20) & 
                    (df_full['total'] > 100)).astype(int)
print (df_full)
# 从整个数据集中获取一些样本数据
data = df_full.sample(frac = 0.1, random_state = 1)
print(data.shape)
data.isnull().sum()
# 将 Excel 数据转换为矩阵
columns = "invoiceid locationid timestamp customerid discount tax total subtotal productid quantity productprice productdiscount invoice_products_id producttax invoice_payments_id paymentmethod paymentdetails amount Class(0/1) Class".split()
X = pd.DataFrame.as_matrix(data, columns=columns)
Y = data.Class
# temp = np.array(temp).reshape((len(temp), 1)
Y = Y.values.reshape(Y.shape[0], 1)
X.shape
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.06)
X_test, X_dev, Y_test, Y_dev = train_test_split(X_test, Y_test, test_size = .5)
# 检查训练集和其他集合中是否有分类值 - 0/1
np.where(Y_train == 1)
np.where(Y_test == 1)
np.where(Y_dev == 1)
# 确定数据集中欺诈案例的数量
Fraud = data[data['Class'] == 1]
Valid = data[data['Class'] == 0]
# 计算欺诈和有效案例的百分比
outlier_fraction = len(Fraud) / float(len(Valid))
print(outlier_fraction)
print('Fraud Cases : {}'.format(len(Fraud)))
print('Valid Cases : {}'.format(len(Valid)))
# 相关矩阵
corrmat = data.corr()
fig = plt.figure( figsize = (12, 9))
sns.heatmap(corrmat, vmax = .8, square = True)
plt.show()

我应用reshape的方法如下：

# 获取数据框中的所有列
columns = data.columns.tolist()
# 过滤不需要的数据列
columns = [c for c in columns if c not in ["Class"] ]
# 存储我们要预测的变量
target = "Class"
for column in data.columns:
    if data[column].dtype == type(object):
        le = LabelEncoder()
        data[column] = le.fit_transform(data[column])
        X = data[column]
    X = data[column]
    Y = data[target]
    # 打印 X 和 Y 的形状
    print(X.shape)
    print(Y.shape)
    # 定义一个随机状态
    state = 1
# 定义异常检测方法
classifiers = {
    "Isolation Forest": IsolationForest(max_samples=len(X), 
                                         contamination=outlier_fraction, 
                                         random_state=state),
    "Local Outlier Factor": LocalOutlierFactor(
    n_neighbors = 20, 
    contamination = outlier_fraction)}
# 拟合模型
n_outliers = len(Fraud)
for i, (clf_name, clf) in enumerate(classifiers.items()):
    # 拟合数据并标记异常值
    if clf_name == "Local Outlier Factor":
        y_pred = clf.fit_predict(X)
        scores_pred = clf.negative_outlier_factor_
    else:
        clf.fit(X)
        scores_pred = clf.decision_function(X)
        y_pred = clf.predict(X)
    # 将预测值重塑为0表示有效，1表示欺诈
    y_pred[y_pred == 1] = 0
    y_pred[y_pred == -1] = 1
    n_errors = (y_pred != Y).sum()
    # 运行分类指标
    print('{}:{}'.format(clf_name, n_errors))
    print(accuracy_score(Y, y_pred ))
    print(classification_report(Y, y_pred ))

代码在重塑样本和目标之前运行正常。但是当我尝试对分类器使用fit方法时，它返回了一个错误，如下所示：

ValueError: Expected 2D array, got 1D array instead: array=[1 0]. Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

我刚开始学习机器学习，我在这里做错了什么？我有多个特征，我如何正确地重塑我的样本数组？

请帮助我！提前感谢！

回答：

在下面的循环中，你在每次循环迭代中用单个列（Series）覆盖了变量X：

for column in data.columns:
    if data[column].dtype == type(object):
        le = LabelEncoder()
        data[column] = le.fit_transform(data[column])
        X = data[column]
    X = data[column]
        #  <------- 注意：
    Y = data[target]

实际上，你可以在循环之后定义X和Y如下：

X = data.drop(target, 1)
Y = data[target]

绝大多数sklearn方法都接受 pandas DataFrames 和 Series 作为输入数据集…