n_jobs in GridSearchCV issue (again in 0.18.1)

Hey, thanks for an awesome library!

There’s a bit of an issue (seemingly the same as #6147) with GridSearchCV, which seems to be in 0.18.1 as well as having been present in previous versions. I get the issue when using GridSearchCV with the ElasticNet classifier:

vals = [10 ** i for i in range(0, -10, -1)] + list(np.arange(0.1, 0.999, 0.025))
parameters = {'l1_ratio': vals, 'alpha': vals}
elastic = ElasticNet(max_iter = 1000)
clf = GridSearchCV(elastic, param_grid = parameters, n_jobs = 8, scoring = 'accuracy', cv = 10)
clf.fit(X_train, y_train)

Actual Results

multiprocessing.pool.RemoteTraceback:
"""
Traceback (most recent call last):
  File "/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 344, in __call__
    return self.func(*args, **kwargs)
  File "/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py", line 131, in __call__
    return [func(*args, **kwargs) for func, args, kwargs in self.items]
  File "/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py", line 131, in <listcomp>
    return [func(*args, **kwargs) for func, args, kwargs in self.items]
  File "/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py", line 260, in _fit_and_score
    test_score = _score(estimator, X_test, y_test, scorer)
  File "/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py", line 288, in _score
    score = scorer(estimator, X_test, y_test)
  File "/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/metrics/scorer.py", line 98, in __call__
    **self._kwargs)
  File "/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py", line 172, in accuracy_score
    y_type, y_true, y_pred = _check_targets(y_true, y_pred)
  File "/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py", line 82, in _check_targets
    "".format(type_true, type_pred))
ValueError: Can't handle mix of binary and continuous

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/zeerak/anaconda3/lib/python3.6/multiprocessing/pool.py", line 119, in worker
    result = (True, func(*args, **kwds))
  File "/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 353, in __call__
    raise TransportableException(text, e_type)
sklearn.externals.joblib.my_exceptions.TransportableException: TransportableException
___________________________________________________________________________
ValueError                                         Mon Jul  3 02:20:59 2017
PID: 22695                 Python 3.6.0: /home/zeerak/anaconda3/bin/python3
...........................................................................
/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in __call__(self=<sklearn.externals.joblib.parallel.BatchedCalls object>)
    126     def __init__(self, iterator_slice):
    127         self.items = list(iterator_slice)
    128         self._size = len(self.items)
    129
    130     def __call__(self):
--> 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]
        self.items = [(<function _fit_and_score>, (ElasticNet(alpha=1, copy_X=True, fit_intercept=T...selection='cyclic', tol=0.0001, warm_start=False), <5295x86231 sparse matrix of type '<class 'numpy... stored elements in Compressed Sparse Row format>, ['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...], make_scorer(accuracy_score), array([ 530,  531,  532, ..., 5292, 5293, 5294]), array([  0,   1,   2,   3,   4,   5,   6,   7,  ...20, 521, 522, 523, 524, 525, 526, 527, 528, 529]), 0, {'alpha': 1, 'l1_ratio': 1}), {'error_score': 'raise', 'fit_params': {}, 'return_n_test_samples': True, 'return_parameters': True, 'return_times': True, 'return_train_score': True})]
    132
    133     def __len__(self):
    134         return self._size
    135

...........................................................................
/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in <listcomp>(.0=<list_iterator object>)
    126     def __init__(self, iterator_slice):
    127         self.items = list(iterator_slice)
    128         self._size = len(self.items)
    129
    130     def __call__(self):
--> 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]
        func = <function _fit_and_score>
        args = (ElasticNet(alpha=1, copy_X=True, fit_intercept=T...selection='cyclic', tol=0.0001, warm_start=False), <5295x86231 sparse matrix of type '<class 'numpy... stored elements in Compressed Sparse Row format>, ['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...], make_scorer(accuracy_score), array([ 530,  531,  532, ..., 5292, 5293, 5294]), array([  0,   1,   2,   3,   4,   5,   6,   7,  ...20, 521, 522, 523, 524, 525, 526, 527, 528, 529]), 0, {'alpha': 1, 'l1_ratio': 1})
        kwargs = {'error_score': 'raise', 'fit_params': {}, 'return_n_test_samples': True, 'return_parameters': True, 'return_times': True, 'return_train_score': True}
    132
    133     def __len__(self):
    134         return self._size
    135

...........................................................................
/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py in _fit_and_score(estimator=ElasticNet(alpha=1, copy_X=True, fit_intercept=T...selection='cyclic', tol=0.0001, warm_start=False), X=<5295x86231 sparse matrix of type '<class 'numpy... stored elements in Compressed Sparse Row format>, y=['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...], scorer=make_scorer(accuracy_score), train=array([ 530,  531,  532, ..., 5292, 5293, 5294]), test=array([  0,   1,   2,   3,   4,   5,   6,   7,  ...20, 521, 522, 523, 524, 525, 526, 527, 528, 529]), verbose=0, parameters={'alpha': 1, 'l1_ratio': 1}, fit_params={}, return_train_score=True, return_parameters=True, return_n_test_samples=True, return_times=True, error_score='raise')
    255                              " numeric value. (Hint: if using 'raise', please"
    256                              " make sure that it has been spelled correctly.)")
    257
    258     else:
    259         fit_time = time.time() - start_time
--> 260         test_score = _score(estimator, X_test, y_test, scorer)
        test_score = undefined
        estimator = ElasticNet(alpha=1, copy_X=True, fit_intercept=T...selection='cyclic', tol=0.0001, warm_start=False)
        X_test = <530x86231 sparse matrix of type '<class 'numpy.... stored elements in Compressed Sparse Row format>
        y_test = ['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...]
        scorer = make_scorer(accuracy_score)
    261         score_time = time.time() - start_time - fit_time
    262         if return_train_score:
    263             train_score = _score(estimator, X_train, y_train, scorer)
    264

...........................................................................
/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py in _score(estimator=ElasticNet(alpha=1, copy_X=True, fit_intercept=T...selection='cyclic', tol=0.0001, warm_start=False), X_test=<530x86231 sparse matrix of type '<class 'numpy.... stored elements in Compressed Sparse Row format>, y_test=['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...], scorer=make_scorer(accuracy_score))
    283 def _score(estimator, X_test, y_test, scorer):
    284     """Compute the score of an estimator on a given test set."""
    285     if y_test is None:
    286         score = scorer(estimator, X_test)
    287     else:
--> 288         score = scorer(estimator, X_test, y_test)
        score = undefined
        scorer = make_scorer(accuracy_score)
        estimator = ElasticNet(alpha=1, copy_X=True, fit_intercept=T...selection='cyclic', tol=0.0001, warm_start=False)
        X_test = <530x86231 sparse matrix of type '<class 'numpy.... stored elements in Compressed Sparse Row format>
        y_test = ['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...]
    289     if hasattr(score, 'item'):
    290         try:
    291             # e.g. unwrap memmapped scalars
    292             score = score.item()

...........................................................................
/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/metrics/scorer.py in __call__(self=make_scorer(accuracy_score), estimator=ElasticNet(alpha=1, copy_X=True, fit_intercept=T...selection='cyclic', tol=0.0001, warm_start=False), X=<530x86231 sparse matrix of type '<class 'numpy.... stored elements in Compressed Sparse Row format>, y_true=['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...], sample_weight=None)
     93             return self._sign * self._score_func(y_true, y_pred,
     94                                                  sample_weight=sample_weight,
     95                                                  **self._kwargs)
     96         else:
     97             return self._sign * self._score_func(y_true, y_pred,
---> 98                                                  **self._kwargs)
        self._kwargs = {}
     99
    100
    101 class _ProbaScorer(_BaseScorer):
    102     def __call__(self, clf, X, y, sample_weight=None):

...........................................................................
/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py in accuracy_score(y_true=['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...], y_pred=array([ 0.5320042,  0.5320042,  0.5320042,  0.53...  0.5320042,  0.5320042,  0.5320042,  0.5320042]), normalize=True, sample_weight=None)
    167     >>> accuracy_score(np.array([[0, 1], [1, 1]]), np.ones((2, 2)))
    168     0.5
    169     """
    170
    171     # Compute accuracy for each possible representation
--> 172     y_type, y_true, y_pred = _check_targets(y_true, y_pred)
        y_type = undefined
        y_true = ['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...]
        y_pred = array([ 0.5320042,  0.5320042,  0.5320042,  0.53...  0.5320042,  0.5320042,  0.5320042,  0.5320042])
    173     if y_type.startswith('multilabel'):
    174         differing_labels = count_nonzero(y_true - y_pred, axis=1)
    175         score = differing_labels == 0
    176     else:

...........................................................................
/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py in _check_targets(y_true=['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...], y_pred=array([ 0.5320042,  0.5320042,  0.5320042,  0.53...  0.5320042,  0.5320042,  0.5320042,  0.5320042]))
     77     if y_type == set(["binary", "multiclass"]):
     78         y_type = set(["multiclass"])
     79
     80     if len(y_type) > 1:
     81         raise ValueError("Can't handle mix of {0} and {1}"
---> 82                          "".format(type_true, type_pred))
        type_true = 'binary'
        type_pred = 'continuous'
     83
     84     # We can't have more than one value on y_type => The set is no more needed
     85     y_type = y_type.pop()
     86

ValueError: Can't handle mix of binary and continuous
___________________________________________________________________________
"""

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py", line 682, in retrieve
    self._output.extend(job.get(timeout=self.timeout))
  File "/home/zeerak/anaconda3/lib/python3.6/multiprocessing/pool.py", line 608, in get
    raise self._value
sklearn.externals.joblib.my_exceptions.TransportableException: TransportableException
___________________________________________________________________________
ValueError                                         Mon Jul  3 02:20:59 2017
PID: 22695                 Python 3.6.0: /home/zeerak/anaconda3/bin/python3
...........................................................................
/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in __call__(self=<sklearn.externals.joblib.parallel.BatchedCalls object>)
    126     def __init__(self, iterator_slice):
    127         self.items = list(iterator_slice)
    128         self._size = len(self.items)
    129
    130     def __call__(self):
--> 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]
        self.items = [(<function _fit_and_score>, (ElasticNet(alpha=1, copy_X=True, fit_intercept=T...selection='cyclic', tol=0.0001, warm_start=False), <5295x86231 sparse matrix of type '<class 'numpy... stored elements in Compressed Sparse Row format>, ['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...], make_scorer(accuracy_score), array([ 530,  531,  532, ..., 5292, 5293, 5294]), array([  0,   1,   2,   3,   4,   5,   6,   7,  ...20, 521, 522, 523, 524, 525, 526, 527, 528, 529]), 0, {'alpha': 1, 'l1_ratio': 1}), {'error_score': 'raise', 'fit_params': {}, 'return_n_test_samples': True, 'return_parameters': True, 'return_times': True, 'return_train_score': True})]
    132
    133     def __len__(self):
    134         return self._size
    135

...........................................................................
/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in <listcomp>(.0=<list_iterator object>)
    126     def __init__(self, iterator_slice):
    127         self.items = list(iterator_slice)
    128         self._size = len(self.items)
    129
    130     def __call__(self):
--> 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]
        func = <function _fit_and_score>
        args = (ElasticNet(alpha=1, copy_X=True, fit_intercept=T...selection='cyclic', tol=0.0001, warm_start=False), <5295x86231 sparse matrix of type '<class 'numpy... stored elements in Compressed Sparse Row format>, ['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...], make_scorer(accuracy_score), array([ 530,  531,  532, ..., 5292, 5293, 5294]), array([  0,   1,   2,   3,   4,   5,   6,   7,  ...20, 521, 522, 523, 524, 525, 526, 527, 528, 529]), 0, {'alpha': 1, 'l1_ratio': 1})
        kwargs = {'error_score': 'raise', 'fit_params': {}, 'return_n_test_samples': True, 'return_parameters': True, 'return_times': True, 'return_train_score': True}
    132
    133     def __len__(self):
    134         return self._size
    135

...........................................................................
/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py in _fit_and_score(estimator=ElasticNet(alpha=1, copy_X=True, fit_intercept=T...selection='cyclic', tol=0.0001, warm_start=False), X=<5295x86231 sparse matrix of type '<class 'numpy... stored elements in Compressed Sparse Row format>, y=['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...], scorer=make_scorer(accuracy_score), train=array([ 530,  531,  532, ..., 5292, 5293, 5294]), test=array([  0,   1,   2,   3,   4,   5,   6,   7,  ...20, 521, 522, 523, 524, 525, 526, 527, 528, 529]), verbose=0, parameters={'alpha': 1, 'l1_ratio': 1}, fit_params={}, return_train_score=True, return_parameters=True, return_n_test_samples=True, return_times=True, error_score='raise')
    255                              " numeric value. (Hint: if using 'raise', please"
    256                              " make sure that it has been spelled correctly.)")
    257
    258     else:
    259         fit_time = time.time() - start_time
--> 260         test_score = _score(estimator, X_test, y_test, scorer)
        test_score = undefined
        estimator = ElasticNet(alpha=1, copy_X=True, fit_intercept=T...selection='cyclic', tol=0.0001, warm_start=False)
        X_test = <530x86231 sparse matrix of type '<class 'numpy.... stored elements in Compressed Sparse Row format>
        y_test = ['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...]
        scorer = make_scorer(accuracy_score)
    261         score_time = time.time() - start_time - fit_time
    262         if return_train_score:
    263             train_score = _score(estimator, X_train, y_train, scorer)
    264

...........................................................................
/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py in _score(estimator=ElasticNet(alpha=1, copy_X=True, fit_intercept=T...selection='cyclic', tol=0.0001, warm_start=False), X_test=<530x86231 sparse matrix of type '<class 'numpy.... stored elements in Compressed Sparse Row format>, y_test=['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...], scorer=make_scorer(accuracy_score))
    283 def _score(estimator, X_test, y_test, scorer):
    284     """Compute the score of an estimator on a given test set."""
    285     if y_test is None:
    286         score = scorer(estimator, X_test)
    287     else:
--> 288         score = scorer(estimator, X_test, y_test)
        score = undefined
        scorer = make_scorer(accuracy_score)
        estimator = ElasticNet(alpha=1, copy_X=True, fit_intercept=T...selection='cyclic', tol=0.0001, warm_start=False)
        X_test = <530x86231 sparse matrix of type '<class 'numpy.... stored elements in Compressed Sparse Row format>
        y_test = ['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...]
    289     if hasattr(score, 'item'):
    290         try:
    291             # e.g. unwrap memmapped scalars
    292             score = score.item()

...........................................................................
/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/metrics/scorer.py in __call__(self=make_scorer(accuracy_score), estimator=ElasticNet(alpha=1, copy_X=True, fit_intercept=T...selection='cyclic', tol=0.0001, warm_start=False), X=<530x86231 sparse matrix of type '<class 'numpy.... stored elements in Compressed Sparse Row format>, y_true=['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...], sample_weight=None)
     93             return self._sign * self._score_func(y_true, y_pred,
     94                                                  sample_weight=sample_weight,
     95                                                  **self._kwargs)
     96         else:
     97             return self._sign * self._score_func(y_true, y_pred,
---> 98                                                  **self._kwargs)
        self._kwargs = {}
     99
    100
    101 class _ProbaScorer(_BaseScorer):
    102     def __call__(self, clf, X, y, sample_weight=None):

...........................................................................
/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py in accuracy_score(y_true=['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...], y_pred=array([ 0.5320042,  0.5320042,  0.5320042,  0.53...  0.5320042,  0.5320042,  0.5320042,  0.5320042]), normalize=True, sample_weight=None)
    167     >>> accuracy_score(np.array([[0, 1], [1, 1]]), np.ones((2, 2)))
    168     0.5
    169     """
    170
    171     # Compute accuracy for each possible representation
--> 172     y_type, y_true, y_pred = _check_targets(y_true, y_pred)
        y_type = undefined
        y_true = ['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...]
        y_pred = array([ 0.5320042,  0.5320042,  0.5320042,  0.53...  0.5320042,  0.5320042,  0.5320042,  0.5320042])
    173     if y_type.startswith('multilabel'):
    174         differing_labels = count_nonzero(y_true - y_pred, axis=1)
    175         score = differing_labels == 0
    176     else:

...........................................................................
/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py in _check_targets(y_true=['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...], y_pred=array([ 0.5320042,  0.5320042,  0.5320042,  0.53...  0.5320042,  0.5320042,  0.5320042,  0.5320042]))
     77     if y_type == set(["binary", "multiclass"]):
     78         y_type = set(["multiclass"])
     79
     80     if len(y_type) > 1:
     81         raise ValueError("Can't handle mix of {0} and {1}"
---> 82                          "".format(type_true, type_pred))
        type_true = 'binary'
        type_pred = 'continuous'
     83
     84     # We can't have more than one value on y_type => The set is no more needed
     85     y_type = y_type.pop()
     86

ValueError: Can't handle mix of binary and continuous
___________________________________________________________________________

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "run.py", line 333, in <module>
    runner(args)
  File "run.py", line 283, in runner
    feats.run_gridsearch_elastic(X_train, X_test, Y_train, Y_test)
  File "/home/zeerak/Projects/repos/gender_spectrum/features.py", line 216, in run_gridsearch_elastic
    clf.fit(X_train, y_train)
  File "/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py", line 945, in fit
    return self._fit(X, y, groups, ParameterGrid(self.param_grid))
  File "/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py", line 564, in _fit
    for parameters in parameter_iterable
  File "/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py", line 768, in __call__
    self.retrieve()
  File "/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py", line 719, in retrieve
    raise exception
sklearn.externals.joblib.my_exceptions.JoblibValueError: JoblibValueError
___________________________________________________________________________
Multiprocessing exception:
...........................................................................
/home/zeerak/Projects/repos/gender_spectrum/run.py in <module>()
    328     parser.add_argument('--probs'     , action = 'store_true', help = argparse.SUPPRESS)
    329     parser.add_argument('--sgdparams' , nargs = 2            , help = argparse.SUPPRESS)
    330
    331     args = parser.parse_args()
    332
--> 333     runner(args)
    334
    335
    336
    337

...........................................................................
/home/zeerak/Projects/repos/gender_spectrum/run.py in runner(args=Namespace(data=None, feats=['unigram-counts'], h...dparams=None, test=None, user=True, workers=None))
    278             feats.run_gridsearch_sgd(X_train, X_test, Y_train, Y_test)
    279             # preds, weights, intercept = feats.run_sgd(X_train, X_test, Y_train, Y_test)
    280
    281         elif args.method == 'Elastic':
    282             print('Run Elastic Net...', file=sys.stdout)
--> 283             feats.run_gridsearch_elastic(X_train, X_test, Y_train, Y_test)
        X_train = <5295x86231 sparse matrix of type '<class 'numpy... stored elements in Compressed Sparse Row format>
        X_test = <1324x86231 sparse matrix of type '<class 'numpy... stored elements in Compressed Sparse Row format>
        Y_train = ['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...]
        Y_test = ['1', '0', '1', '1', '1', '1', '0', '1', '0', '0', '0', '1', '1', '0', '1', '1', '0', '0', '0', '0', ...]
    284             # preds, weights, intercept = feats.run_sgd(X_train, X_test, Y_train, Y_test)
    285
    286
    287 if __name__ == "__main__":

...........................................................................
/home/zeerak/Projects/repos/gender_spectrum/features.py in run_gridsearch_elastic(X_train=<5295x86231 sparse matrix of type '<class 'numpy... stored elements in Compressed Sparse Row format>, X_test=<1324x86231 sparse matrix of type '<class 'numpy... stored elements in Compressed Sparse Row format>, y_train=['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...], y_test=['1', '0', '1', '1', '1', '1', '0', '1', '0', '0', '0', '1', '1', '0', '1', '1', '0', '0', '0', '0', ...])
    211 def run_gridsearch_elastic(X_train, X_test, y_train, y_test):
    212     vals = [10 ** i for i in range(0, -10, -1)] + list(np.arange(0.1, 0.999, 0.015))
    213     parameters = {'l1_ratio': vals, 'alpha': vals}
    214     elastic = ElasticNet(max_iter = 1000)
    215     clf = GridSearchCV(elastic, param_grid = parameters, n_jobs = 8, scoring = 'accuracy', cv = 10)
--> 216     clf.fit(X_train, y_train)
        clf.fit = <bound method GridSearchCV.fit of GridSearchCV(c...core=True,
       scoring='accuracy', verbose=0)>
        X_train = <5295x86231 sparse matrix of type '<class 'numpy... stored elements in Compressed Sparse Row format>
        y_train = ['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...]
    217     preds = clf.predict(X_test)
    218     print(clf.best_estimator_, clf.best_score_, clf.best_params_)
    219     print("Accuracy: %s" % accuracy_score(y_test, preds))
    220

...........................................................................
/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py in fit(self=GridSearchCV(cv=10, error_score='raise',
       ...score=True,
       scoring='accuracy', verbose=0), X=<5295x86231 sparse matrix of type '<class 'numpy... stored elements in Compressed Sparse Row format>, y=['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...], groups=None)
    940
    941         groups : array-like, with shape (n_samples,), optional
    942             Group labels for the samples used while splitting the dataset into
    943             train/test set.
    944         """
--> 945         return self._fit(X, y, groups, ParameterGrid(self.param_grid))
        self._fit = <bound method BaseSearchCV._fit of GridSearchCV(...core=True,
       scoring='accuracy', verbose=0)>
        X = <5295x86231 sparse matrix of type '<class 'numpy... stored elements in Compressed Sparse Row format>
        y = ['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...]
        groups = None
        self.param_grid = {'alpha': [1, 0.1, 0.01, 0.001, 0.0001, 1e-05, 1e-06, 1e-07, 1e-08, 1e-09, 0.10000000000000001, 0.115, 0.13, 0.14500000000000002, 0.16, 0.17499999999999999, 0.19, 0.20500000000000002, 0.22, 0.23500000000000001, ...], 'l1_ratio': [1, 0.1, 0.01, 0.001, 0.0001, 1e-05, 1e-06, 1e-07, 1e-08, 1e-09, 0.10000000000000001, 0.115, 0.13, 0.14500000000000002, 0.16, 0.17499999999999999, 0.19, 0.20500000000000002, 0.22, 0.23500000000000001, ...]}
    946
    947
    948 class RandomizedSearchCV(BaseSearchCV):
    949     """Randomized search on hyper parameters.

...........................................................................
/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py in _fit(self=GridSearchCV(cv=10, error_score='raise',
       ...score=True,
       scoring='accuracy', verbose=0), X=<5295x86231 sparse matrix of type '<class 'numpy... stored elements in Compressed Sparse Row format>, y=['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...], groups=None, parameter_iterable=<sklearn.model_selection._search.ParameterGrid object>)
    559                                   fit_params=self.fit_params,
    560                                   return_train_score=self.return_train_score,
    561                                   return_n_test_samples=True,
    562                                   return_times=True, return_parameters=True,
    563                                   error_score=self.error_score)
--> 564           for parameters in parameter_iterable
        parameters = undefined
        parameter_iterable = <sklearn.model_selection._search.ParameterGrid object>
    565           for train, test in cv_iter)
    566
    567         # if one choose to see train score, "out" will contain train score info
    568         if self.return_train_score:

...........................................................................
/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in __call__(self=Parallel(n_jobs=8), iterable=<generator object BaseSearchCV._fit.<locals>.<genexpr>>)
    763             if pre_dispatch == "all" or n_jobs == 1:
    764                 # The iterable was consumed all at once by the above for loop.
    765                 # No need to wait for async callbacks to trigger to
    766                 # consumption.
    767                 self._iterating = False
--> 768             self.retrieve()
        self.retrieve = <bound method Parallel.retrieve of Parallel(n_jobs=8)>
    769             # Make sure that we get a last message telling us we are done
    770             elapsed_time = time.time() - self._start_time
    771             self._print('Done %3i out of %3i | elapsed: %s finished',
    772                         (len(self._output), len(self._output),

Sub-process traceback:

ValueError                                         Mon Jul  3 02:20:59 2017
PID: 22695                 Python 3.6.0: /home/zeerak/anaconda3/bin/python3
...........................................................................
/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in __call__(self=<sklearn.externals.joblib.parallel.BatchedCalls object>)
    126     def __init__(self, iterator_slice):
    127         self.items = list(iterator_slice)
    128         self._size = len(self.items)
    129
    130     def __call__(self):
--> 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]
        self.items = [(<function _fit_and_score>, (ElasticNet(alpha=1, copy_X=True, fit_intercept=T...selection='cyclic', tol=0.0001, warm_start=False), <5295x86231 sparse matrix of type '<class 'numpy... stored elements in Compressed Sparse Row format>, ['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...], make_scorer(accuracy_score), array([ 530,  531,  532, ..., 5292, 5293, 5294]), array([  0,   1,   2,   3,   4,   5,   6,   7,  ...20, 521, 522, 523, 524, 525, 526, 527, 528, 529]), 0, {'alpha': 1, 'l1_ratio': 1}), {'error_score': 'raise', 'fit_params': {}, 'return_n_test_samples': True, 'return_parameters': True, 'return_times': True, 'return_train_score': True})]
    132
    133     def __len__(self):
    134         return self._size
    135

...........................................................................
/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in <listcomp>(.0=<list_iterator object>)
    126     def __init__(self, iterator_slice):
    127         self.items = list(iterator_slice)
    128         self._size = len(self.items)
    129
    130     def __call__(self):
--> 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]
        func = <function _fit_and_score>
        args = (ElasticNet(alpha=1, copy_X=True, fit_intercept=T...selection='cyclic', tol=0.0001, warm_start=False), <5295x86231 sparse matrix of type '<class 'numpy... stored elements in Compressed Sparse Row format>, ['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...], make_scorer(accuracy_score), array([ 530,  531,  532, ..., 5292, 5293, 5294]), array([  0,   1,   2,   3,   4,   5,   6,   7,  ...20, 521, 522, 523, 524, 525, 526, 527, 528, 529]), 0, {'alpha': 1, 'l1_ratio': 1})
        kwargs = {'error_score': 'raise', 'fit_params': {}, 'return_n_test_samples': True, 'return_parameters': True, 'return_times': True, 'return_train_score': True}
    132
    133     def __len__(self):
    134         return self._size
    135

...........................................................................
/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py in _fit_and_score(estimator=ElasticNet(alpha=1, copy_X=True, fit_intercept=T...selection='cyclic', tol=0.0001, warm_start=False), X=<5295x86231 sparse matrix of type '<class 'numpy... stored elements in Compressed Sparse Row format>, y=['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...], scorer=make_scorer(accuracy_score), train=array([ 530,  531,  532, ..., 5292, 5293, 5294]), test=array([  0,   1,   2,   3,   4,   5,   6,   7,  ...20, 521, 522, 523, 524, 525, 526, 527, 528, 529]), verbose=0, parameters={'alpha': 1, 'l1_ratio': 1}, fit_params={}, return_train_score=True, return_parameters=True, return_n_test_samples=True, return_times=True, error_score='raise')
    255                              " numeric value. (Hint: if using 'raise', please"
    256                              " make sure that it has been spelled correctly.)")
    257
    258     else:
    259         fit_time = time.time() - start_time
--> 260         test_score = _score(estimator, X_test, y_test, scorer)
        test_score = undefined
        estimator = ElasticNet(alpha=1, copy_X=True, fit_intercept=T...selection='cyclic', tol=0.0001, warm_start=False)
        X_test = <530x86231 sparse matrix of type '<class 'numpy.... stored elements in Compressed Sparse Row format>
        y_test = ['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...]
        scorer = make_scorer(accuracy_score)
    261         score_time = time.time() - start_time - fit_time
    262         if return_train_score:
    263             train_score = _score(estimator, X_train, y_train, scorer)
    264

...........................................................................
/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py in _score(estimator=ElasticNet(alpha=1, copy_X=True, fit_intercept=T...selection='cyclic', tol=0.0001, warm_start=False), X_test=<530x86231 sparse matrix of type '<class 'numpy.... stored elements in Compressed Sparse Row format>, y_test=['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...], scorer=make_scorer(accuracy_score))
    283 def _score(estimator, X_test, y_test, scorer):
    284     """Compute the score of an estimator on a given test set."""
    285     if y_test is None:
    286         score = scorer(estimator, X_test)
    287     else:
--> 288         score = scorer(estimator, X_test, y_test)
        score = undefined
        scorer = make_scorer(accuracy_score)
        estimator = ElasticNet(alpha=1, copy_X=True, fit_intercept=T...selection='cyclic', tol=0.0001, warm_start=False)
        X_test = <530x86231 sparse matrix of type '<class 'numpy.... stored elements in Compressed Sparse Row format>
        y_test = ['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...]
    289     if hasattr(score, 'item'):
    290         try:
    291             # e.g. unwrap memmapped scalars
    292             score = score.item()

...........................................................................
/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/metrics/scorer.py in __call__(self=make_scorer(accuracy_score), estimator=ElasticNet(alpha=1, copy_X=True, fit_intercept=T...selection='cyclic', tol=0.0001, warm_start=False), X=<530x86231 sparse matrix of type '<class 'numpy.... stored elements in Compressed Sparse Row format>, y_true=['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...], sample_weight=None)
     93             return self._sign * self._score_func(y_true, y_pred,
     94                                                  sample_weight=sample_weight,
     95                                                  **self._kwargs)
     96         else:
     97             return self._sign * self._score_func(y_true, y_pred,
---> 98                                                  **self._kwargs)
        self._kwargs = {}
     99
    100
    101 class _ProbaScorer(_BaseScorer):
    102     def __call__(self, clf, X, y, sample_weight=None):

...........................................................................
/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py in accuracy_score(y_true=['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...], y_pred=array([ 0.5320042,  0.5320042,  0.5320042,  0.53...  0.5320042,  0.5320042,  0.5320042,  0.5320042]), normalize=True, sample_weight=None)
    167     >>> accuracy_score(np.array([[0, 1], [1, 1]]), np.ones((2, 2)))
    168     0.5
    169     """
    170
    171     # Compute accuracy for each possible representation
--> 172     y_type, y_true, y_pred = _check_targets(y_true, y_pred)
        y_type = undefined
        y_true = ['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...]
        y_pred = array([ 0.5320042,  0.5320042,  0.5320042,  0.53...  0.5320042,  0.5320042,  0.5320042,  0.5320042])
    173     if y_type.startswith('multilabel'):
    174         differing_labels = count_nonzero(y_true - y_pred, axis=1)
    175         score = differing_labels == 0
    176     else:

...........................................................................
/home/zeerak/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py in _check_targets(y_true=['0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', ...], y_pred=array([ 0.5320042,  0.5320042,  0.5320042,  0.53...  0.5320042,  0.5320042,  0.5320042,  0.5320042]))
     77     if y_type == set(["binary", "multiclass"]):
     78         y_type = set(["multiclass"])
     79
     80     if len(y_type) > 1:
     81         raise ValueError("Can't handle mix of {0} and {1}"
---> 82                          "".format(type_true, type_pred))
        type_true = 'binary'
        type_pred = 'continuous'
     83
     84     # We can't have more than one value on y_type => The set is no more needed
     85     y_type = y_type.pop()
     86

ValueError: Can't handle mix of binary and continuous
___________________________________________________________________________

Versions

platform.platform(): Linux-4.2.3-300.fc23.x86_64-x86_64-with-fedora-25-Twenty_Five sys.version: Python 3.6.0 |Anaconda 4.3.1 (64-bit)| (default, Dec 23 2016, 12:22:00) [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] numpy.version: NumPy 1.11.3 scipy.version: SciPy 0.18.1 sklearn.version: Scikit-Learn 0.18.1

[jnothman]

I’ll try to improve the error message from “Can’t handle mix of binary and continuous” to “Classification metrics can’t handle mix of binary and continuous targets”.

On 4 July 2017 at 10:38, Zeerak Waseem [e-mail] wrote:

@GaelVaroquaux https://github.com/gaelvaroquaux Yup, it only occurs with n_jobs > 1. Fixing the errors with the invalid y does not change it. However, the scoring fixed it. Had missed that I hadn’t updated the scoring.

@jnothman https://github.com/jnothman Thanks for that, this was the issue! Not entirely sure why this brought out the issue though.

— You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub https://github.com/scikit-learn/scikit-learn/issues/9264#issuecomment-312754802, or mute the thread https://github.com/notifications/unsubscribe-auth/AAEz6_QINNi-wSufktWL2HIsiekZr3m_ks5sKYmagaJpZM4OLwDb .

[GaelVaroquaux]

Does the problem go away when using n_jobs=1?

Looking at the errors it seems that the error is due to an invalid y.