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_splitdataset = 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)

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)

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)

It shows unhashable type: 'slice' for line model.fit(Xtrn, Ytrn[:,i])

How can it be avoided and made working?

Thanks!

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()

and use Xtrn_array and Ytrn_array when you fit your data to estimators.