在Python中使用切片

Question

我使用来自UCI repo: http://archive.ics.uci.edu/ml/datasets/Energy+efficiency的数据集然后做下一步:

from pandas import *
from sklearn.neighbors import KNeighborsRegressor
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.svm import SVR
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score
from sklearn.cross_validation import train_test_split
dataset = read_excel('/Users/Half_Pint_boy/Desktop/ENB2012_data.xlsx')
dataset = dataset.drop(['X1','X4'], axis=1)
trg = dataset[['Y1','Y2']]
trn = dataset.drop(['Y1','Y2'], axis=1)

然后做模型并交叉验证:

models = [LinearRegression(), 
      RandomForestRegressor(n_estimators=100, max_features ='sqrt'), 
      KNeighborsRegressor(n_neighbors=6),
      SVR(kernel='linear'), 
      LogisticRegression() 
     ]
Xtrn, Xtest, Ytrn, Ytest = train_test_split(trn, trg, test_size=0.4)

我正在创建一个预测值的回归模型，但有一个问题。下面是代码:

TestModels = DataFrame()
tmp = {}
for model in models:
    m = str(model)
    tmp['Model'] = m[:m.index('(')]    
for i in range(Ytrn.shape[1]):
    model.fit(Xtrn, Ytrn[:,i]) 
    tmp[str(i+1)] = r2_score(Ytest[:,0], model.predict(Xtest))
    TestModels = TestModels.append([tmp])
    TestModels.set_index('Model', inplace=True)

它显示了线模型的不可哈希类型:'slice'。适合(Xtrn Ytrn[:,我])

如何避免并使其发挥作用?

谢谢!

Answer 1

我想我以前也遇到过类似的问题!在将数据提供给sklearn估计器之前，尝试将数据转换为numpy数组。它很可能解决了哈希问题。例如:

Xtrn_array = Xtrn.as_matrix() 
Ytrn_array = Ytrn.as_matrix()

和使用Xtrn_array和Ytrn_array当你拟合你的数据估计。