在 Python 中使用切片 [英] Using slices in Python
问题描述
我使用来自 UCI 存储库的数据集:http://archive.ics.uci.edu/ml/datasets/Energy+efficiency然后做下一步:
I use the dataset from UCI repo: http://archive.ics.uci.edu/ml/datasets/Energy+efficiency Then doing next:
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:
Then do the models and cross validate:
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)
我正在创建一个用于预测值的回归模型,但遇到了问题.代码如下:
I'm creating a regression model for predicting values but have a problems. Here is the code:
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)
它显示不可散列的类型:'slice' for line model.fit(Xtrn, Ytrn[:,i])
It shows unhashable type: 'slice' for line model.fit(Xtrn, Ytrn[:,i])
如何避免并使其发挥作用?
How can it be avoided and made working?
谢谢!
推荐答案
我想我以前也遇到过类似的问题!尝试将您的数据转换为 numpy 数组,然后再将它们提供给 sklearn
估计器.它很可能解决了散列问题.例如,您可以:
I think that I had a similar problem before! Try to convert your data to numpy arrays before feeding them to sklearn
estimators. It most probably solve the hashing problem. For instance, You can do:
Xtrn_array = Xtrn.as_matrix()
Ytrn_array = Ytrn.as_matrix()
并在将数据拟合到估算器时使用 Xtrn_array 和 Ytrn_array.
and use Xtrn_array and Ytrn_array when you fit your data to estimators.
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