Using slices in Python

Question

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)

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!

try to change `model.fit(Xtrn, Ytrn[:,i]) ` to `model.fit(Xtrn, Ytrn[:i])`, remove comma — Jahongir Rahmonov, Aug 29 '16 at 16:57
please post the entire traceback and/or post complete runnable code so I can get the traceback myself. — Tadhg McDonald-Jensen, Aug 29 '16 at 16:59
I can reproduce the error message `TypeError: unhashable type: 'slice'` if `Ytrn` is a DataFrame. In that case, you need to use `Ytrn.iloc[:, i]` instead of `Ytrn[:, i]`. See http://stackoverflow.com/q/34215501/190597. — unutbu, Aug 29 '16 at 17:15
thanks, iloc works. but then it goes: Unknown label type: array([ 29.9 , 26.84, 28.07, 39.89, 14.32, 11.33, 15.55, 10.39]). Is it because 2-d array? — Keithx, Aug 29 '16 at 18:19
That look like a 1D array. Please post a runnable example which generates the error so we don't have to guess. — unutbu, Aug 29 '16 at 19:24
Did you try converting them to numpy arrays before fitting your data to sklearn? — MhFarahani, Aug 30 '16 at 18:27

score 1 · Accepted Answer · answered Aug 30 '16 at 18:04

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.

Using slices in Python

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