CODEWITHSUNDEEP
pythonnumpypandasscikit-learncross-validation
user3439537
user3439537

Reputation: 1

SKLearn Cross Validation Error -- Type Error

I'm attempting to implement cross validation on the results from my KNN classifier. I have used the following code, which returns a type error.

For context, I have already imported SciKit Learn, Numpy, and Pandas libraries.

from sklearn.cross_validation import cross_val_score, ShuffleSplit

n_samples = len(y)
knn = KNeighborsClassifier(3)
cv = ShuffleSplit(n_samples, n_iter=10, test_size=0.3, random_state=0)

test_scores = cross_val_score(knn, X, y, cv=cv)
test_scores.mean()

Returns:

    ---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-139-d8cc3ee0c29b> in <module>()
  7 cv = ShuffleSplit(n_samples, n_iter=10, test_size=0.3, random_state=0)
  8 
  9 test_scores = cross_val_score(knn, X, y, cv=cv)
 10 test_scores.mean()

//anaconda/lib/python2.7/site-packages/sklearn/cross_validation.pyc in     cross_val_score(estimator, X, y, scoring, cv, n_jobs, verbose, fit_params, score_func, pre_dispatch)
1150         delayed(_cross_val_score)(clone(estimator), X, y, scorer, train, test,
1151                                   verbose, fit_params)
1152         for train, test in cv)
1153     return np.array(scores)
1154 

//anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __call__(self, iterable)
515         try:
516             for function, args, kwargs in iterable:
517                 self.dispatch(function, args, kwargs)
518 
519             self.retrieve()
//anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in dispatch(self, func, args, kwargs)
310         """
311         if self._pool is None:
312             job = ImmediateApply(func, args, kwargs)
313             index = len(self._jobs)
314             if not _verbosity_filter(index, self.verbose):
//anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __init__(self, func, args, kwargs)
134         # Don't delay the application, to avoid keeping the input
135         # arguments in memory
136         self.results = func(*args, **kwargs)
137 
138     def get(self):

//anaconda/lib/python2.7/site-packages/sklearn/cross_validation.pyc in _cross_val_score(estimator, X, y, scorer, train, test, verbose, fit_params)
1056         y_test = None
1057     else:
1058         y_train = y[train]
1059         y_test = y[test]
1060     estimator.fit(X_train, y_train, **fit_params)

TypeError: only integer arrays with one element can be converted to an index

Upvotes: 0

Views: 1719

Answers (1)

eickenberg
eickenberg

Reputation: 14377

This is an error related to pandas. Scikit learn expects numpy arrays, sparse matrices or objects that behave similarly to these.

The main issue with pandas DataFrames is due to the fact that indexing with [...] chooses columns and not lines. Line indexing in pandas is done through DataFrame.loc[...]. This is unexpected behaviour for sklearn. The error probably came from line 1058, where the code is failing to extract the train sample.

To remedy this, if your y is one DataFrame column, try converting your column to array type

y = y.values

Otherwise pandas-sklearn is possibly an option.

Upvotes: 1

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