值错误：数组长度与索引长度不匹配

Question

我正在为像kaggle这样的比赛练习，我一直在尝试使用XGBoost，并试图让自己熟悉python第三方库，如pandas和numpy。

我一直在审查这个叫做桑坦德客户满意度分类的特殊竞争对手的脚本，我一直在修改不同的分叉脚本，以便对它们进行实验。

下面是一个修改过的脚本，我正试图通过它实现XGBoost：

import pandas as pd
from sklearn import cross_validation as cv
import xgboost as xgb

df_train = pd.read_csv("/Users/pavan7vasan/Desktop/Machine_Learning/Project datasets/Santander_Customer_Satisfaction/train.csv")
df_test  = pd.read_csv("/Users/pavan7vasan/Desktop/Machine_Learning/Project Datasets/Santander_Customer_Satisfaction/test.csv")   

df_train = df_train.replace(-999999,2)

id_test = df_test['ID']
y_train = df_train['TARGET'].values
X_train = df_train.drop(['ID','TARGET'], axis=1).values
X_test = df_test.drop(['ID'], axis=1).values

X_train, X_test, y_train, y_test = cv.train_test_split(X_train, y_train, random_state=1301, test_size=0.4)

clf = xgb.XGBClassifier(objective='binary:logistic',
                missing=9999999999,
                max_depth = 7,
                n_estimators=200,
                learning_rate=0.1, 
                nthread=4,
                subsample=1.0,
                colsample_bytree=0.5,
                min_child_weight = 3,
                reg_alpha=0.01,
                seed=7)

clf.fit(X_train, y_train, early_stopping_rounds=50, eval_metric="auc", eval_set=[(X_train, y_train), (X_test, y_test)])
y_pred = clf.predict_proba(X_test)

print("Cross validating and checking the score...")
scores = cv.cross_val_score(clf, X_train, y_train) 
'''
test = []
result = []
for each in id_test:
    test.append(each)
for each in y_pred[:,1]:
    result.append(each)

print len(test)
print len(result)
'''
submission = pd.DataFrame({"ID":id_test, "TARGET":y_pred[:,1]})
#submission = pd.DataFrame({"ID":test, "TARGET":result})
submission.to_csv("submission_XGB_Pavan.csv", index=False)

这是stacktrace：

Traceback (most recent call last):
  File "/Users/pavan7vasan/Documents/workspace/Machine_Learning_Project/Kaggle/XG_Boost.py", line 45, in <module>
submission = pd.DataFrame({"ID":id_test, "TARGET":y_pred[:,1]})
  File "/anaconda/lib/python2.7/site-packages/pandas/core/frame.py", line 214, in __init__
mgr = self._init_dict(data, index, columns, dtype=dtype)
  File "/anaconda/lib/python2.7/site-packages/pandas/core/frame.py", line 341, in _init_dict
dtype=dtype)
  File "/anaconda/lib/python2.7/site-packages/pandas/core/frame.py", line 4798, in _arrays_to_mgr
index = extract_index(arrays)
  File "/anaconda/lib/python2.7/site-packages/pandas/core/frame.py", line 4856, in extract_index
raise ValueError(msg)
ValueError: array length 30408 does not match index length 75818

我已经尝试了基于搜索不同解决方案的解决方案，但我无法找出错误所在。我做错什么了？请告诉我

Answer 1

问题是您定义的X_test是@maxymoo提到的两倍。首先你把它定义为

X_test = df_test.drop(['ID'], axis=1).values

然后你可以重新定义它：

X_train, X_test, y_train, y_test = cv.train_test_split(X_train, y_train, random_state=1301, test_size=0.4)

这意味着现在X_test的大小等于0.4*len(X_train)。之后：

y_pred = clf.predict_proba(X_test)

您已经对X_train的那一部分进行了预测，并试图用它和初始的id_test创建数据帧，初始的X_test具有原始的X_test长度。
你可以在train_test_split中使用X_fit和X_eval，而不要隐藏初始X_train和X_test，因为对于你的cross_validation你也有不同的X_train，这意味着你得不到正确的答案，或者你的^}在公共/私人评分中不准确。