python - 如何解决 "IndexError: too many indices for array"

Question

下面的代码给出了以下错误“IndexError:数组索引太多”。我对机器学习很陌生，所以我不知道如何解决这个问题。任何形式的帮助将不胜感激。

train = pandas.read_csv("D:/...input/train.csv")


xTrain = train.iloc[:,0:54]
yTrain = train.iloc[:,54:]


from sklearn.cross_validation import cross_val_score
clf = LogisticRegression(multi_class='multinomial')
scores = cross_val_score(clf, xTrain, yTrain, cv=10, scoring='accuracy')
print('****Results****')
print(scores.mean())

Answer 1

使用 Pandas Dataframe 逐步解释 ML(机器学习)代码:

1. 将预测变量和目标列分别分成 X 和 y。

2. 拆分训练数据 (X_train,y_train) 和测试数据 (X_test,y_test)。

3. 计算交叉验证的 AUC(曲线下面积)。由于 y_train 出现错误“IndexError:数组索引过多”，因为它期望是一维数组，但获取的二维数组不匹配。将代码 'y_train' 替换为 y_train['y'] 后，代码就像 Charm 一样工作。

<小时/>

   # Importing Packages :

   import pandas as pd

   from sklearn.model_selection import cross_val_score

   from sklearn.model_selection import StratifiedShuffleSplit

   # Seperating Predictor and Target Columns into X and y Respectively :
   # df -> Dataframe extracted from CSV File

   data_X = df.drop(['y'], axis=1) 
   data_y = pd.DataFrame(df['y'])

   # Making a Stratified Shuffle Split of Train and Test Data (test_size=0.3 Denotes 30 % Test Data and Remaining 70% Train Data) :

   rs = StratifiedShuffleSplit(n_splits=2, test_size=0.3,random_state=2)       
   rs.get_n_splits(data_X,data_y)

   for train_index, test_index in rs.split(data_X,data_y):

       # Splitting Training and Testing Data based on Index Values :

       X_train,X_test = data_X.iloc[train_index], data_X.iloc[test_index]
       y_train,y_test = data_y.iloc[train_index], data_y.iloc[test_index]

       # Calculating 5-Fold Cross-Validated AUC (cv=5) - Error occurs due to Dimension of **y_train** in this Line :

       classify_cross_val_score = cross_val_score(classify, X_train, y_train, cv=5, scoring='roc_auc').mean()

       print("Classify_Cross_Val_Score ",classify_cross_val_score) # Error at Previous Line.

       # Worked after Replacing 'y_train' with y_train['y'] in above Line 
       # where y is the ONLY Column (or) Series Present in the Pandas Data frame 
       # (i.e) Target variable for Prediction :

       classify_cross_val_score = cross_val_score(classify, X_train, y_train['y'], cv=5, scoring='roc_auc').mean()

       print("Classify_Cross_Val_Score ",classify_cross_val_score)

       print(y_train.shape)

       print(y_train['y'].shape)

输出:

    Classify_Cross_Val_Score  0.7021433588790991
    (31647, 1) # 2-D
    (31647,)   # 1-D

注意:从 sklearn.model_selection 导入 cross_val_score。 cross_val_score 已导入 来自 sklearn.model_selection 和 不是来自已弃用的 sklearn.cross_validation。