Grandient Regressor："TypeError:无法使用灵活类型执行reduce“

Question

我是python的新手，我正在尝试开发一个使用Gradient Boosting的程序。我有两个大的数据集，一个训练集和一个测试集，其中我有完全相同的列。我的目标是用训练集的信息来预测测试集的SeriousDlqin2yrs列。

这是我写的程序：

import numpy as np
import csv as csv
import pandas as pd
from sklearn import ensemble
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.utils import shuffle

# Load data

csv_file_object = csv.reader(open('cs-training-cleandata2NOLOG.csv', 'rb')) #Load in the training csv file
header = csv_file_object.next() #Skip the fist line as it is a header
train_data=[] #Creat a variable called 'train_data'
for row in csv_file_object: #Skip through each row in the csv file
train_data.append(row[1:]) #adding each row to the data variable
train_data = np.array(train_data) #Then convert from a list to an array

test_file_object = csv.reader(open('cs-test-cleandata2NOLOG.csv', 'rb')) #Load in the test csv file
header = test_file_object.next() #Skip the fist line as it is a header
test_data=[] #Creat a variable called 'test_data'
ids = []
for row in test_file_object: #Skip through each row in the csv file
ids.append(row[0])
test_data.append(row[1:]) #adding each row to the data variable
test_data = np.array(test_data) #Then convert from a list to an array

test_data = np.delete(test_data,[0],1) #remove SeriousDlqin2yrs

print 'Training '
# Fit regression model

clf = GradientBoostingRegressor(n_estimators=1000, min_samples_split=100, learning_rate=0.01)
clf = clf.fit(train_data[0::,1::],train_data[0::,0])

print 'Predicting'

output=clf.predict(test_data)

open_file_object = csv.writer(open("GradientBoostedRegression1.1.csv", "wb"))
open_file_object.writerow(["Id","Probability"])
open_file_object.writerows(zip(ids, output))

但是当我运行这个程序时，python给了我这样的答案：

Traceback (most recent call last):
  File "C:\Users\Paul HONORE\Dropbox\Research Study\Kaggle\Bank\GradientBoostedRegression1.1.py", line 64, in <module>
    clf = clf.fit(train_data[0::,1::],train_data[0::,0])
  File "C:\Python27\lib\site-packages\sklearn\ensemble\gradient_boosting.py", line 1126, in fit
    return super(GradientBoostingRegressor, self).fit(X, y)
  File "C:\Python27\lib\site-packages\sklearn\ensemble\gradient_boosting.py", line 595, in fit
    self.init_.fit(X, y)
  File "C:\Python27\lib\site-packages\sklearn\ensemble\gradient_boosting.py", line 69, in fit
    self.mean = np.mean(y)
  File "C:\Python27\lib\site-packages\numpy\core\fromnumeric.py", line 2716, in mean
out=out, keepdims=keepdims)
  File "C:\Python27\lib\site-packages\numpy\core\_methods.py", line 62, in _mean
ret = um.add.reduce(arr, axis=axis, dtype=dtype, out=out, keepdims=keepdims)
TypeError: cannot perform reduce with flexible type

我不知道它是从哪里来的，我读了很多关于这个问题的论文，但从来没有找到这个特定问题的解决方案。

提前感谢您的帮助。

Answer 1

我认为这个问题可以通过在数组函数中指定一个类型来解决。例如：

train_data = np.array(train_data, dtype = 'float_')