How to resolve "IndexError: too many indices for array"

Question

My code below is giving me the following error "IndexError: too many indices for array". I am quite new to machine learning so I do not have any idea about how to solve this. Any kind of help would be appreciated.

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

Step by Step Explanation of ML (Machine Learning) Code with Pandas Dataframe :

1. Seperating Predictor and Target Columns into X and y Respectively.

2. Splitting Training data (X_train,y_train) and Testing Data (X_test,y_test).

3. Calculating Cross-Validated AUC (Area Under the Curve). Got an Error “IndexError: too many indices for array” due to y_train since it was expecting a 1-D Array but Fetched 2-D Array which is a Mismatch. After Replacing the code 'y_train' with y_train['y'] code worked like a Charm.

---

   # 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)

Output :

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

Note : from sklearn.model_selection import cross_val_score. cross_val_score has been imported from sklearn.model_selection and NOT from sklearn.cross_validation which is Deprecated.

Answer 2

The error code you're getting is basically saying you've declared contents for your array that don't fit it. I can't see the declaration of your array but I'm assuming it's one dimensional and the program is objecting to you treating it like a 2 dimensional one.

Just check your declarations are correct and also test the code by printing the values after you've set them to double check they are what you intend them to be.

There are a few existing questions on this subject already so i'll just link one that might be helpful here: IndexError: too many indices. Numpy Array with 1 row and 2 columns