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  1. RemoteTraceback                           Traceback (most recent call last)
  2. RemoteTraceback:
  3. """
  4. Traceback (most recent call last):
  5.  File "C:\Users\Jason\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py", line 350, in __call__
  6.    return self.func(*args, **kwargs)
  7.  File "C:\Users\Jason\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py", line 131, in __call__
  8.    return [func(*args, **kwargs) for func, args, kwargs in self.items]
  9.  File "C:\Users\Jason\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py", line 131, in <listcomp>
  10.    return [func(*args, **kwargs) for func, args, kwargs in self.items]
  11.  File "C:\Users\Jason\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 444, in _fit_and_score
  12.    estimator.set_params(**parameters)
  13.  File "C:\Users\Jason\Anaconda3\lib\site-packages\sklearn\base.py", line 274, in set_params
  14.    (key, self))
  15. ValueError: Invalid parameter c for estimator SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
  16.  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',
  17.  max_iter=-1, probability=False, random_state=None, shrinking=True,
  18.  tol=0.001, verbose=False). Check the list of available parameters with `estimator.get_params().keys()`.
  19.  
  20. During handling of the above exception, another exception occurred:
  21.  
  22. Traceback (most recent call last):
  23.  File "C:\Users\Jason\Anaconda3\lib\multiprocessing\pool.py", line 119, in worker
  24.    result = (True, func(*args, **kwds))
  25.  File "C:\Users\Jason\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py", line 359, in __call__
  26.    raise TransportableException(text, e_type)
  27. sklearn.externals.joblib.my_exceptions.TransportableException: TransportableException
  28. ___________________________________________________________________________
  29. ValueError                                         Mon Jun 25 19:45:44 2018
  30. PID: 5588                 Python 3.6.5: C:\Users\Jason\Anaconda3\python.exe
  31. ...........................................................................
  32. C:\Users\Jason\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self=<sklearn.externals.joblib.parallel.BatchedCalls object>)
  33.    126     def __init__(self, iterator_slice):
  34.    127         self.items = list(iterator_slice)
  35.    128         self._size = len(self.items)
  36.    129
  37.    130     def __call__(self):
  38. --> 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]
  39.        self.items = [(<function _fit_and_score>, (SVC(C=1.0, cache_size=200, class_weight=None, co...None, shrinking=True,
  40.  tol=0.001, verbose=False), array([[375, 84760, 970],
  41.       [270, 87607, 19... 1531],
  42.       [1270, 2817, 1531]], dtype=object), memmap([0, 0, 0, ..., 0, 2, 0], dtype=int64), {'score': make_scorer(accuracy_score)}, memmap([ 57335,  57337,  57344, ..., 574952, 574953, 574954]), array([     0,      1,      2, ...,  58765,  67140, 502152]), 0, {'c': 79.587450816311, 'class_weight': None, 'gamma': 0.18596042409118513, 'kernel': 'linear'}), {'error_score': 'raise', 'fit_params': {}, 'return_n_test_samples': True, 'return_parameters': False, 'return_times': True, 'return_train_score': 'warn'})]
  43.    132
  44.    133     def __len__(self):
  45.    134         return self._size
  46.    135
  47.  
  48. ...........................................................................
  49. C:\Users\Jason\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in <listcomp>(.0=<list_iterator object>)
  50.    126     def __init__(self, iterator_slice):
  51.    127         self.items = list(iterator_slice)
  52.    128         self._size = len(self.items)
  53.    129
  54.    130     def __call__(self):
  55. --> 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]
  56.        func = <function _fit_and_score>
  57.        args = (SVC(C=1.0, cache_size=200, class_weight=None, co...None, shrinking=True,
  58.  tol=0.001, verbose=False), array([[375, 84760, 970],
  59.       [270, 87607, 19... 1531],
  60.       [1270, 2817, 1531]], dtype=object), memmap([0, 0, 0, ..., 0, 2, 0], dtype=int64), {'score': make_scorer(accuracy_score)}, memmap([ 57335,  57337,  57344, ..., 574952, 574953, 574954]), array([     0,      1,      2, ...,  58765,  67140, 502152]), 0, {'c': 79.587450816311, 'class_weight': None, 'gamma': 0.18596042409118513, 'kernel': 'linear'})
  61.        kwargs = {'error_score': 'raise', 'fit_params': {}, 'return_n_test_samples': True, 'return_parameters': False, 'return_times': True, 'return_train_score': 'warn'}
  62.    132
  63.    133     def __len__(self):
  64.    134         return self._size
  65.    135
  66.  
  67. ...........................................................................
  68. C:\Users\Jason\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in _fit_and_score(estimator=SVC(C=1.0, cache_size=200, class_weight=None, co...None, shrinking=True,
  69.  tol=0.001, verbose=False), X=array([[375, 84760, 970],
  70.       [270, 87607, 19... 1531],
  71.       [1270, 2817, 1531]], dtype=object), y=memmap([0, 0, 0, ..., 0, 2, 0], dtype=int64), scorer={'score': make_scorer(accuracy_score)}, train=memmap([ 57335,  57337,  57344, ..., 574952, 574953, 574954]), test=array([     0,      1,      2, ...,  58765,  67140, 502152]), verbose=0, parameters={'c': 79.587450816311, 'class_weight': None, 'gamma': 0.18596042409118513, 'kernel': 'linear'}, fit_params={}, return_train_score='warn', return_parameters=False, return_n_test_samples=True, return_times=True, error_score='raise')
  72.    439                       for k, v in fit_params.items()])
  73.    440
  74.    441     test_scores = {}
  75.    442     train_scores = {}
  76.    443     if parameters is not None:
  77. --> 444         estimator.set_params(**parameters)
  78.        estimator.set_params = <bound method BaseEstimator.set_params of SVC(C=...one, shrinking=True,
  79.  tol=0.001, verbose=False)>
  80.        parameters = {'c': 79.587450816311, 'class_weight': None, 'gamma': 0.18596042409118513, 'kernel': 'linear'}
  81.    445
  82.    446     start_time = time.time()
  83.    447
  84.    448     X_train, y_train = _safe_split(estimator, X, y, train)
  85.  
  86. ...........................................................................
  87. C:\Users\Jason\Anaconda3\lib\site-packages\sklearn\base.py in set_params(self=SVC(C=1.0, cache_size=200, class_weight=None, co...None, shrinking=True,
  88.  tol=0.001, verbose=False), **params={'c': 79.587450816311, 'class_weight': None, 'gamma': 0.18596042409118513, 'kernel': 'linear'})
  89.    269             key, delim, sub_key = key.partition('__')
  90.    270             if key not in valid_params:
  91.    271                 raise ValueError('Invalid parameter %s for estimator %s. '
  92.    272                                  'Check the list of available parameters '
  93.    273                                  'with `estimator.get_params().keys()`.' %
  94. --> 274                                  (key, self))
  95.        key = 'c'
  96.        self = SVC(C=1.0, cache_size=200, class_weight=None, co...None, shrinking=True,
  97.  tol=0.001, verbose=False)
  98.    275
  99.    276             if delim:
  100.    277                 nested_params[key][sub_key] = value
  101.    278             else:
  102.  
  103. ValueError: Invalid parameter c for estimator SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
  104.  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',
  105.  max_iter=-1, probability=False, random_state=None, shrinking=True,
  106.  tol=0.001, verbose=False). Check the list of available parameters with `estimator.get_params().keys()`.
  107. ___________________________________________________________________________
  108. """
  109.  
  110. The above exception was the direct cause of the following exception:
  111.  
  112. TransportableException                    Traceback (most recent call last)
  113. ~\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in retrieve(self)
  114.     698                 if getattr(self._backend, 'supports_timeout', False):
  115. --> 699                     self._output.extend(job.get(timeout=self.timeout))
  116.     700                 else:
  117.  
  118. ~\Anaconda3\lib\multiprocessing\pool.py in get(self, timeout)
  119.     643         else:
  120. --> 644             raise self._value
  121.     645
  122.  
  123. TransportableException: TransportableException
  124. ___________________________________________________________________________
  125. ValueError                                         Mon Jun 25 19:45:44 2018
  126. PID: 5588                 Python 3.6.5: C:\Users\Jason\Anaconda3\python.exe
  127. ...........................................................................
  128. C:\Users\Jason\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self=<sklearn.externals.joblib.parallel.BatchedCalls object>)
  129.     126     def __init__(self, iterator_slice):
  130.     127         self.items = list(iterator_slice)
  131.     128         self._size = len(self.items)
  132.     129
  133.     130     def __call__(self):
  134. --> 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]
  135.         self.items = [(<function _fit_and_score>, (SVC(C=1.0, cache_size=200, class_weight=None, co...None, shrinking=True,
  136.   tol=0.001, verbose=False), array([[375, 84760, 970],
  137.        [270, 87607, 19... 1531],
  138.        [1270, 2817, 1531]], dtype=object), memmap([0, 0, 0, ..., 0, 2, 0], dtype=int64), {'score': make_scorer(accuracy_score)}, memmap([ 57335,  57337,  57344, ..., 574952, 574953, 574954]), array([     0,      1,      2, ...,  58765,  67140, 502152]), 0, {'c': 79.587450816311, 'class_weight': None, 'gamma': 0.18596042409118513, 'kernel': 'linear'}), {'error_score': 'raise', 'fit_params': {}, 'return_n_test_samples': True, 'return_parameters': False, 'return_times': True, 'return_train_score': 'warn'})]
  139.     132
  140.     133     def __len__(self):
  141.     134         return self._size
  142.     135
  143.  
  144. ...........................................................................
  145. C:\Users\Jason\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in <listcomp>(.0=<list_iterator object>)
  146.     126     def __init__(self, iterator_slice):
  147.     127         self.items = list(iterator_slice)
  148.     128         self._size = len(self.items)
  149.     129
  150.     130     def __call__(self):
  151. --> 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]
  152.         func = <function _fit_and_score>
  153.         args = (SVC(C=1.0, cache_size=200, class_weight=None, co...None, shrinking=True,
  154.   tol=0.001, verbose=False), array([[375, 84760, 970],
  155.        [270, 87607, 19... 1531],
  156.        [1270, 2817, 1531]], dtype=object), memmap([0, 0, 0, ..., 0, 2, 0], dtype=int64), {'score': make_scorer(accuracy_score)}, memmap([ 57335,  57337,  57344, ..., 574952, 574953, 574954]), array([     0,      1,      2, ...,  58765,  67140, 502152]), 0, {'c': 79.587450816311, 'class_weight': None, 'gamma': 0.18596042409118513, 'kernel': 'linear'})
  157.         kwargs = {'error_score': 'raise', 'fit_params': {}, 'return_n_test_samples': True, 'return_parameters': False, 'return_times': True, 'return_train_score': 'warn'}
  158.     132
  159.     133     def __len__(self):
  160.     134         return self._size
  161.     135
  162.  
  163. ...........................................................................
  164. C:\Users\Jason\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in _fit_and_score(estimator=SVC(C=1.0, cache_size=200, class_weight=None, co...None, shrinking=True,
  165.   tol=0.001, verbose=False), X=array([[375, 84760, 970],
  166.        [270, 87607, 19... 1531],
  167.        [1270, 2817, 1531]], dtype=object), y=memmap([0, 0, 0, ..., 0, 2, 0], dtype=int64), scorer={'score': make_scorer(accuracy_score)}, train=memmap([ 57335,  57337,  57344, ..., 574952, 574953, 574954]), test=array([     0,      1,      2, ...,  58765,  67140, 502152]), verbose=0, parameters={'c': 79.587450816311, 'class_weight': None, 'gamma': 0.18596042409118513, 'kernel': 'linear'}, fit_params={}, return_train_score='warn', return_parameters=False, return_n_test_samples=True, return_times=True, error_score='raise')
  168.     439                       for k, v in fit_params.items()])
  169.     440
  170.     441     test_scores = {}
  171.     442     train_scores = {}
  172.     443     if parameters is not None:
  173. --> 444         estimator.set_params(**parameters)
  174.         estimator.set_params = <bound method BaseEstimator.set_params of SVC(C=...one, shrinking=True,
  175.   tol=0.001, verbose=False)>
  176.         parameters = {'c': 79.587450816311, 'class_weight': None, 'gamma': 0.18596042409118513, 'kernel': 'linear'}
  177.     445
  178.     446     start_time = time.time()
  179.     447
  180.     448     X_train, y_train = _safe_split(estimator, X, y, train)
  181.  
  182. ...........................................................................
  183. C:\Users\Jason\Anaconda3\lib\site-packages\sklearn\base.py in set_params(self=SVC(C=1.0, cache_size=200, class_weight=None, co...None, shrinking=True,
  184.   tol=0.001, verbose=False), **params={'c': 79.587450816311, 'class_weight': None, 'gamma': 0.18596042409118513, 'kernel': 'linear'})
  185.     269             key, delim, sub_key = key.partition('__')
  186.     270             if key not in valid_params:
  187.     271                 raise ValueError('Invalid parameter %s for estimator %s. '
  188.     272                                  'Check the list of available parameters '
  189.     273                                  'with `estimator.get_params().keys()`.' %
  190. --> 274                                  (key, self))
  191.         key = 'c'
  192.         self = SVC(C=1.0, cache_size=200, class_weight=None, co...None, shrinking=True,
  193.   tol=0.001, verbose=False)
  194.     275
  195.     276             if delim:
  196.     277                 nested_params[key][sub_key] = value
  197.     278             else:
  198.  
  199. ValueError: Invalid parameter c for estimator SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
  200.   decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',
  201.   max_iter=-1, probability=False, random_state=None, shrinking=True,
  202.   tol=0.001, verbose=False). Check the list of available parameters with `estimator.get_params().keys()`.
  203. ___________________________________________________________________________
  204.  
  205. During handling of the above exception, another exception occurred:
  206.  
  207. JoblibValueError                          Traceback (most recent call last)
  208. <ipython-input-39-bf87e95432e2> in <module>()
  209.      32                               scoring='accuracy', cv=10, n_jobs=-1,
  210.      33                               random_state=0)
  211. ---> 34 randomcv.fit(x_tu, y_tu)
  212.  
  213. ~\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py in fit(self, X, y, groups, **fit_params)
  214.     637                                   error_score=self.error_score)
  215.     638           for parameters, (train, test) in product(candidate_params,
  216. --> 639                                                    cv.split(X, y, groups)))
  217.     640
  218.     641         # if one choose to see train score, "out" will contain train score info
  219.  
  220. ~\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self, iterable)
  221.     787                 # consumption.
  222.     788                 self._iterating = False
  223. --> 789             self.retrieve()
  224.     790             # Make sure that we get a last message telling us we are done
  225.     791             elapsed_time = time.time() - self._start_time
  226.  
  227. ~\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in retrieve(self)
  228.     738                     exception = exception_type(report)
  229.     739
  230. --> 740                     raise exception
  231.     741
  232.     742     def __call__(self, iterable):
  233.  
  234. JoblibValueError: JoblibValueError
  235. ___________________________________________________________________________
  236. Multiprocessing exception:
  237. ...........................................................................
  238. C:\Users\Jason\Anaconda3\lib\runpy.py in _run_module_as_main(mod_name='ipykernel_launcher', alter_argv=1)
  239.     188         sys.exit(msg)
  240.     189     main_globals = sys.modules["__main__"].__dict__
  241.     190     if alter_argv:
  242.     191         sys.argv[0] = mod_spec.origin
  243.     192     return _run_code(code, main_globals, None,
  244. --> 193                      "__main__", mod_spec)
  245.         mod_spec = ModuleSpec(name='ipykernel_launcher', loader=<_f...nda3\\lib\\site-packages\\ipykernel_launcher.py')
  246.    194
  247.    195 def run_module(mod_name, init_globals=None,
  248.    196                run_name=None, alter_sys=False):
  249.    197     """Execute a module's code without importing it
  250.  
  251. ...........................................................................
  252. C:\Users\Jason\Anaconda3\lib\runpy.py in _run_code(code=<code object <module> at 0x00000270451DCD20, fil...lib\site-packages\ipykernel_launcher.py", line 5>, run_globals={'__annotations__': {}, '__builtins__': <module 'builtins' (built-in)>, '__cached__': r'C:\Users\Jason\Anaconda3\lib\site-packages\__pycache__\ipykernel_launcher.cpython-36.pyc', '__doc__': 'Entry point for launching an IPython kernel.\n\nTh...orts until\nafter removing the cwd from sys.path.\n', '__file__': r'C:\Users\Jason\Anaconda3\lib\site-packages\ipykernel_launcher.py', '__loader__': <_frozen_importlib_external.SourceFileLoader object>, '__name__': '__main__', '__package__': '', '__spec__': ModuleSpec(name='ipykernel_launcher', loader=<_f...nda3\\lib\\site-packages\\ipykernel_launcher.py'), 'app': <module 'ipykernel.kernelapp' from 'C:\\Users\\J...a3\\lib\\site-packages\\ipykernel\\kernelapp.py'>, ...}, init_globals=None, mod_name='__main__', mod_spec=ModuleSpec(name='ipykernel_launcher', loader=<_f...nda3\\lib\\site-packages\\ipykernel_launcher.py'), pkg_name='', script_name=None)
  253.     80                        __cached__ = cached,
  254.     81                        __doc__ = None,
  255.     82                        __loader__ = loader,
  256.     83                        __package__ = pkg_name,
  257.     84                        __spec__ = mod_spec)
  258. ---> 85     exec(code, run_globals)
  259.        code = <code object <module> at 0x00000270451DCD20, fil...lib\site-packages\ipykernel_launcher.py", line 5>
  260.         run_globals = {'__annotations__': {}, '__builtins__': <module 'builtins' (built-in)>, '__cached__': r'C:\Users\Jason\Anaconda3\lib\site-packages\__pycache__\ipykernel_launcher.cpython-36.pyc', '__doc__': 'Entry point for launching an IPython kernel.\n\nTh...orts until\nafter removing the cwd from sys.path.\n', '__file__': r'C:\Users\Jason\Anaconda3\lib\site-packages\ipykernel_launcher.py', '__loader__': <_frozen_importlib_external.SourceFileLoader object>, '__name__': '__main__', '__package__': '', '__spec__': ModuleSpec(name='ipykernel_launcher', loader=<_f...nda3\\lib\\site-packages\\ipykernel_launcher.py'), 'app': <module 'ipykernel.kernelapp' from 'C:\\Users\\J...a3\\lib\\site-packages\\ipykernel\\kernelapp.py'>, ...}
  261.     86     return run_globals
  262.     87
  263.     88 def _run_module_code(code, init_globals=None,
  264.     89                     mod_name=None, mod_spec=None,
  265.  
  266. ...........................................................................
  267. C:\Users\Jason\Anaconda3\lib\site-packages\ipykernel_launcher.py in <module>()
  268.     11     # This is added back by InteractiveShellApp.init_path()
  269.     12     if sys.path[0] == '':
  270.     13         del sys.path[0]
  271.     14
  272.     15     from ipykernel import kernelapp as app
  273. ---> 16     app.launch_new_instance()
  274.  
  275. ...........................................................................
  276. C:\Users\Jason\Anaconda3\lib\site-packages\traitlets\config\application.py in launch_instance(cls=<class 'ipykernel.kernelapp.IPKernelApp'>, argv=None, **kwargs={})
  277.    653
  278.    654         If a global instance already exists, this reinitializes and starts it
  279.    655         """
  280.    656         app = cls.instance(**kwargs)
  281.    657         app.initialize(argv)
  282. --> 658         app.start()
  283.        app.start = <bound method IPKernelApp.start of <ipykernel.kernelapp.IPKernelApp object>>
  284.    659
  285.    660 #-----------------------------------------------------------------------------
  286.    661 # utility functions, for convenience
  287.    662 #-----------------------------------------------------------------------------
  288.  
  289. ...........................................................................
  290. C:\Users\Jason\Anaconda3\lib\site-packages\ipykernel\kernelapp.py in start(self=<ipykernel.kernelapp.IPKernelApp object>)
  291.    481         if self.poller is not None:
  292.    482             self.poller.start()
  293.    483         self.kernel.start()
  294.    484         self.io_loop = ioloop.IOLoop.current()
  295.    485         try:
  296. --> 486             self.io_loop.start()
  297.        self.io_loop.start = <bound method BaseAsyncIOLoop.start of <tornado.platform.asyncio.AsyncIOMainLoop object>>
  298.    487         except KeyboardInterrupt:
  299.    488             pass
  300.    489
  301.    490 launch_new_instance = IPKernelApp.launch_instance
  302.  
  303. ...........................................................................
  304. C:\Users\Jason\Anaconda3\lib\site-packages\tornado\platform\asyncio.py in start(self=<tornado.platform.asyncio.AsyncIOMainLoop object>)
  305.    122         except (RuntimeError, AssertionError):
  306.    123             old_loop = None
  307.    124         try:
  308.    125             self._setup_logging()
  309.    126             asyncio.set_event_loop(self.asyncio_loop)
  310. --> 127             self.asyncio_loop.run_forever()
  311.        self.asyncio_loop.run_forever = <bound method BaseEventLoop.run_forever of <_Win...EventLoop running=True closed=False debug=False>>
  312.    128         finally:
  313.    129             asyncio.set_event_loop(old_loop)
  314.    130
  315.    131     def stop(self):
  316.  
  317. ...........................................................................
  318. C:\Users\Jason\Anaconda3\lib\asyncio\base_events.py in run_forever(self=<_WindowsSelectorEventLoop running=True closed=False debug=False>)
  319.    417             sys.set_asyncgen_hooks(firstiter=self._asyncgen_firstiter_hook,
  320.    418                                    finalizer=self._asyncgen_finalizer_hook)
  321.    419         try:
  322.    420             events._set_running_loop(self)
  323.    421             while True:
  324. --> 422                 self._run_once()
  325.        self._run_once = <bound method BaseEventLoop._run_once of <_Windo...EventLoop running=True closed=False debug=False>>
  326.    423                 if self._stopping:
  327.    424                     break
  328.    425         finally:
  329.    426             self._stopping = False
  330.  
  331. ...........................................................................
  332. C:\Users\Jason\Anaconda3\lib\asyncio\base_events.py in _run_once(self=<_WindowsSelectorEventLoop running=True closed=False debug=False>)
  333.   1427                         logger.warning('Executing %s took %.3f seconds',
  334.   1428                                        _format_handle(handle), dt)
  335.   1429                 finally:
  336.   1430                     self._current_handle = None
  337.   1431             else:
  338. -> 1432                 handle._run()
  339.        handle._run = <bound method Handle._run of <Handle BaseAsyncIOLoop._handle_events(488, 1)>>
  340.   1433         handle = None  # Needed to break cycles when an exception occurs.
  341.   1434
  342.   1435     def _set_coroutine_wrapper(self, enabled):
  343.   1436         try:
  344.  
  345. ...........................................................................
  346. C:\Users\Jason\Anaconda3\lib\asyncio\events.py in _run(self=<Handle BaseAsyncIOLoop._handle_events(488, 1)>)
  347.    140             self._callback = None
  348.    141             self._args = None
  349.    142
  350.    143     def _run(self):
  351.    144         try:
  352. --> 145             self._callback(*self._args)
  353.        self._callback = <bound method BaseAsyncIOLoop._handle_events of <tornado.platform.asyncio.AsyncIOMainLoop object>>
  354.        self._args = (488, 1)
  355.    146         except Exception as exc:
  356.    147             cb = _format_callback_source(self._callback, self._args)
  357.    148             msg = 'Exception in callback {}'.format(cb)
  358.    149             context = {
  359.  
  360. ...........................................................................
  361. C:\Users\Jason\Anaconda3\lib\site-packages\tornado\platform\asyncio.py in _handle_events(self=<tornado.platform.asyncio.AsyncIOMainLoop object>, fd=488, events=1)
  362.    112             self.writers.remove(fd)
  363.    113         del self.handlers[fd]
  364.    114
  365.    115     def _handle_events(self, fd, events):
  366.    116         fileobj, handler_func = self.handlers[fd]
  367. --> 117         handler_func(fileobj, events)
  368.        handler_func = <function wrap.<locals>.null_wrapper>
  369.        fileobj = <zmq.sugar.socket.Socket object>
  370.        events = 1
  371.    118
  372.    119     def start(self):
  373.    120         try:
  374.    121             old_loop = asyncio.get_event_loop()
  375.  
  376. ...........................................................................
  377. C:\Users\Jason\Anaconda3\lib\site-packages\tornado\stack_context.py in null_wrapper(*args=(<zmq.sugar.socket.Socket object>, 1), **kwargs={})
  378.    271         # Fast path when there are no active contexts.
  379.    272         def null_wrapper(*args, **kwargs):
  380.    273             try:
  381.    274                 current_state = _state.contexts
  382.    275                 _state.contexts = cap_contexts[0]
  383. --> 276                 return fn(*args, **kwargs)
  384.        args = (<zmq.sugar.socket.Socket object>, 1)
  385.        kwargs = {}
  386.    277             finally:
  387.    278                 _state.contexts = current_state
  388.    279         null_wrapper._wrapped = True
  389.    280         return null_wrapper
  390.  
  391. ...........................................................................
  392. C:\Users\Jason\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py in _handle_events(self=<zmq.eventloop.zmqstream.ZMQStream object>, fd=<zmq.sugar.socket.Socket object>, events=1)
  393.    445             return
  394.    446         zmq_events = self.socket.EVENTS
  395.    447         try:
  396.    448             # dispatch events:
  397.    449             if zmq_events & zmq.POLLIN and self.receiving():
  398. --> 450                 self._handle_recv()
  399.        self._handle_recv = <bound method ZMQStream._handle_recv of <zmq.eventloop.zmqstream.ZMQStream object>>
  400.    451                 if not self.socket:
  401.    452                     return
  402.    453             if zmq_events & zmq.POLLOUT and self.sending():
  403.    454                 self._handle_send()
  404.  
  405. ...........................................................................
  406. C:\Users\Jason\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py in _handle_recv(self=<zmq.eventloop.zmqstream.ZMQStream object>)
  407.    475             else:
  408.    476                 raise
  409.    477         else:
  410.    478             if self._recv_callback:
  411.    479                 callback = self._recv_callback
  412. --> 480                 self._run_callback(callback, msg)
  413.        self._run_callback = <bound method ZMQStream._run_callback of <zmq.eventloop.zmqstream.ZMQStream object>>
  414.        callback = <function wrap.<locals>.null_wrapper>
  415.        msg = [<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>]
  416.    481        
  417.    482
  418.    483     def _handle_send(self):
  419.    484         """Handle a send event."""
  420.  
  421. ...........................................................................
  422. C:\Users\Jason\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py in _run_callback(self=<zmq.eventloop.zmqstream.ZMQStream object>, callback=<function wrap.<locals>.null_wrapper>, *args=([<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>],), **kwargs={})
  423.    427         close our socket."""
  424.    428         try:
  425.    429             # Use a NullContext to ensure that all StackContexts are run
  426.    430             # inside our blanket exception handler rather than outside.
  427.    431             with stack_context.NullContext():
  428. --> 432                 callback(*args, **kwargs)
  429.        callback = <function wrap.<locals>.null_wrapper>
  430.        args = ([<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>],)
  431.        kwargs = {}
  432.    433         except:
  433.    434             gen_log.error("Uncaught exception in ZMQStream callback",
  434.    435                           exc_info=True)
  435.    436             # Re-raise the exception so that IOLoop.handle_callback_exception
  436.  
  437. ...........................................................................
  438. C:\Users\Jason\Anaconda3\lib\site-packages\tornado\stack_context.py in null_wrapper(*args=([<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>],), **kwargs={})
  439.    271         # Fast path when there are no active contexts.
  440.    272         def null_wrapper(*args, **kwargs):
  441.    273             try:
  442.    274                 current_state = _state.contexts
  443.    275                 _state.contexts = cap_contexts[0]
  444. --> 276                 return fn(*args, **kwargs)
  445.        args = ([<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>],)
  446.        kwargs = {}
  447.    277             finally:
  448.    278                 _state.contexts = current_state
  449.    279         null_wrapper._wrapped = True
  450.    280         return null_wrapper
  451.  
  452. ...........................................................................
  453. C:\Users\Jason\Anaconda3\lib\site-packages\ipykernel\kernelbase.py in dispatcher(msg=[<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>])
  454.    278         if self.control_stream:
  455.    279             self.control_stream.on_recv(self.dispatch_control, copy=False)
  456.    280
  457.    281         def make_dispatcher(stream):
  458.    282             def dispatcher(msg):
  459. --> 283                 return self.dispatch_shell(stream, msg)
  460.        msg = [<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>]
  461.    284             return dispatcher
  462.    285
  463.    286         for s in self.shell_streams:
  464.    287             s.on_recv(make_dispatcher(s), copy=False)
  465.  
  466. ...........................................................................
  467. C:\Users\Jason\Anaconda3\lib\site-packages\ipykernel\kernelbase.py in dispatch_shell(self=<ipykernel.ipkernel.IPythonKernel object>, stream=<zmq.eventloop.zmqstream.ZMQStream object>, msg={'buffers': [], 'content': {'allow_stdin': True, 'code': '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', 'silent': False, 'stop_on_error': True, 'store_history': True, 'user_expressions': {}}, 'header': {'date': datetime.datetime(2018, 6, 25, 10, 45, 43, 857285, tzinfo=tzutc()), 'msg_id': 'd87de63984dd402f89f3c778fcf06aeb', 'msg_type': 'execute_request', 'session': '0e67879809694dd1b02d2ffe8f9eccf2', 'username': 'username', 'version': '5.2'}, 'metadata': {}, 'msg_id': 'd87de63984dd402f89f3c778fcf06aeb', 'msg_type': 'execute_request', 'parent_header': {}})
  468.     228             self.log.warn("Unknown message type: %r", msg_type)
  469.     229         else:
  470.     230             self.log.debug("%s: %s", msg_type, msg)
  471.     231             self.pre_handler_hook()
  472.     232             try:
  473. --> 233                 handler(stream, idents, msg)
  474.         handler = <bound method Kernel.execute_request of <ipykernel.ipkernel.IPythonKernel object>>
  475.         stream = <zmq.eventloop.zmqstream.ZMQStream object>
  476.         idents = [b'0e67879809694dd1b02d2ffe8f9eccf2']
  477.         msg = {'buffers': [], 'content': {'allow_stdin': True, 'code': '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', 'silent': False, 'stop_on_error': True, 'store_history': True, 'user_expressions': {}}, 'header': {'date': datetime.datetime(2018, 6, 25, 10, 45, 43, 857285, tzinfo=tzutc()), 'msg_id': 'd87de63984dd402f89f3c778fcf06aeb', 'msg_type': 'execute_request', 'session': '0e67879809694dd1b02d2ffe8f9eccf2', 'username': 'username', 'version': '5.2'}, 'metadata': {}, 'msg_id': 'd87de63984dd402f89f3c778fcf06aeb', 'msg_type': 'execute_request', 'parent_header': {}}
  478.     234             except Exception:
  479.     235                 self.log.error("Exception in message handler:", exc_info=True)
  480.     236             finally:
  481.     237                 self.post_handler_hook()
  482.  
  483. ...........................................................................
  484. C:\Users\Jason\Anaconda3\lib\site-packages\ipykernel\kernelbase.py in execute_request(self=<ipykernel.ipkernel.IPythonKernel object>, stream=<zmq.eventloop.zmqstream.ZMQStream object>, ident=[b'0e67879809694dd1b02d2ffe8f9eccf2'], parent={'buffers': [], 'content': {'allow_stdin': True, 'code': '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', 'silent': False, 'stop_on_error': True, 'store_history': True, 'user_expressions': {}}, 'header': {'date': datetime.datetime(2018, 6, 25, 10, 45, 43, 857285, tzinfo=tzutc()), 'msg_id': 'd87de63984dd402f89f3c778fcf06aeb', 'msg_type': 'execute_request', 'session': '0e67879809694dd1b02d2ffe8f9eccf2', 'username': 'username', 'version': '5.2'}, 'metadata': {}, 'msg_id': 'd87de63984dd402f89f3c778fcf06aeb', 'msg_type': 'execute_request', 'parent_header': {}})
  485.     394         if not silent:
  486.     395             self.execution_count += 1
  487.     396             self._publish_execute_input(code, parent, self.execution_count)
  488.     397
  489.     398         reply_content = self.do_execute(code, silent, store_history,
  490. --> 399                                         user_expressions, allow_stdin)
  491.         user_expressions = {}
  492.         allow_stdin = True
  493.     400
  494.     401         # Flush output before sending the reply.
  495.     402         sys.stdout.flush()
  496.     403         sys.stderr.flush()
  497.  
  498. ...........................................................................
  499. C:\Users\Jason\Anaconda3\lib\site-packages\ipykernel\ipkernel.py in do_execute(self=<ipykernel.ipkernel.IPythonKernel object>, code='# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', silent=False, store_history=True, user_expressions={}, allow_stdin=True)
  500.     203
  501.     204         self._forward_input(allow_stdin)
  502.     205
  503.     206         reply_content = {}
  504.     207         try:
  505. --> 208             res = shell.run_cell(code, store_history=store_history, silent=silent)
  506.         res = undefined
  507.         shell.run_cell = <bound method ZMQInteractiveShell.run_cell of <ipykernel.zmqshell.ZMQInteractiveShell object>>
  508.         code = '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)'
  509.         store_history = True
  510.         silent = False
  511.     209         finally:
  512.     210             self._restore_input()
  513.     211
  514.     212         if res.error_before_exec is not None:
  515.  
  516. ...........................................................................
  517. C:\Users\Jason\Anaconda3\lib\site-packages\ipykernel\zmqshell.py in run_cell(self=<ipykernel.zmqshell.ZMQInteractiveShell object>, *args=('# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)',), **kwargs={'silent': False, 'store_history': True})
  518.     532             )
  519.     533         self.payload_manager.write_payload(payload)
  520.     534
  521.     535     def run_cell(self, *args, **kwargs):
  522.     536         self._last_traceback = None
  523. --> 537         return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
  524.         self.run_cell = <bound method ZMQInteractiveShell.run_cell of <ipykernel.zmqshell.ZMQInteractiveShell object>>
  525.         args = ('# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)',)
  526.         kwargs = {'silent': False, 'store_history': True}
  527.     538
  528.     539     def _showtraceback(self, etype, evalue, stb):
  529.     540         # try to preserve ordering of tracebacks and print statements
  530.     541         sys.stdout.flush()
  531.  
  532. ...........................................................................
  533. C:\Users\Jason\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py in run_cell(self=<ipykernel.zmqshell.ZMQInteractiveShell object>, raw_cell='# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', store_history=True, silent=False, shell_futures=True)
  534.    2657         -------
  535.    2658         result : :class:`ExecutionResult`
  536.    2659         """
  537.   2660         try:
  538.   2661             result = self._run_cell(
  539. -> 2662                 raw_cell, store_history, silent, shell_futures)
  540.        raw_cell = '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)'
  541.        store_history = True
  542.        silent = False
  543.        shell_futures = True
  544.   2663         finally:
  545.   2664             self.events.trigger('post_execute')
  546.   2665             if not silent:
  547.   2666                 self.events.trigger('post_run_cell', result)
  548.  
  549. ...........................................................................
  550. C:\Users\Jason\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py in _run_cell(self=<ipykernel.zmqshell.ZMQInteractiveShell object>, raw_cell='# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', store_history=True, silent=False, shell_futures=True)
  551.   2780                 self.displayhook.exec_result = result
  552.   2781
  553.   2782                 # Execute the user code
  554.   2783                 interactivity = 'none' if silent else self.ast_node_interactivity
  555.   2784                 has_raised = self.run_ast_nodes(code_ast.body, cell_name,
  556. -> 2785                    interactivity=interactivity, compiler=compiler, result=result)
  557.        interactivity = 'last_expr'
  558.        compiler = <IPython.core.compilerop.CachingCompiler object>
  559.   2786                
  560.   2787                 self.last_execution_succeeded = not has_raised
  561.   2788                 self.last_execution_result = result
  562.   2789
  563.  
  564. ...........................................................................
  565. C:\Users\Jason\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py in run_ast_nodes(self=<ipykernel.zmqshell.ZMQInteractiveShell object>, nodelist=[<_ast.Assign object>, <_ast.Assign object>, <_ast.Assign object>, <_ast.Expr object>], cell_name='<ipython-input-39-bf87e95432e2>', interactivity='last', compiler=<IPython.core.compilerop.CachingCompiler object>, result=<ExecutionResult object at 27003f62e48, executio...rue silent=False shell_futures=True> result=None>)
  566.   2904                     return True
  567.   2905
  568.   2906             for i, node in enumerate(to_run_interactive):
  569.   2907                 mod = ast.Interactive([node])
  570.   2908                 code = compiler(mod, cell_name, "single")
  571. -> 2909                 if self.run_code(code, result):
  572.        self.run_code = <bound method InteractiveShell.run_code of <ipykernel.zmqshell.ZMQInteractiveShell object>>
  573.        code = <code object <module> at 0x0000027003EFDC00, file "<ipython-input-39-bf87e95432e2>", line 34>
  574.        result = <ExecutionResult object at 27003f62e48, executio...rue silent=False shell_futures=True> result=None>
  575.   2910                     return True
  576.   2911
  577.   2912             # Flush softspace
  578.   2913             if softspace(sys.stdout, 0):
  579.  
  580. ...........................................................................
  581. C:\Users\Jason\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py in run_code(self=<ipykernel.zmqshell.ZMQInteractiveShell object>, code_obj=<code object <module> at 0x0000027003EFDC00, file "<ipython-input-39-bf87e95432e2>", line 34>, result=<ExecutionResult object at 27003f62e48, executio...rue silent=False shell_futures=True> result=None>)
  582.   2958         outflag = True  # happens in more places, so it's easier as default
  583.   2959         try:
  584.   2960             try:
  585.   2961                 self.hooks.pre_run_code_hook()
  586.   2962                 #rprint('Running code', repr(code_obj)) # dbg
  587. -> 2963                 exec(code_obj, self.user_global_ns, self.user_ns)
  588.        code_obj = <code object <module> at 0x0000027003EFDC00, file "<ipython-input-39-bf87e95432e2>", line 34>
  589.        self.user_global_ns = {'DecisionTreeClassifier': <class 'sklearn.tree.tree.DecisionTreeClassifier'>, 'GaussianNB': <class 'sklearn.naive_bayes.GaussianNB'>, 'GridSearchCV': <class 'sklearn.model_selection._search.GridSearchCV'>, 'In': ['', 'import pandas as pd\nimport numpy as np\nimport ma...andomForestClassifier\nfrom sklearn.svm import SVC', 'train = pd.read_csv("h1b_train.csv").dropna()\ntu...pna()\ntest = pd.read_csv("h1b_test.csv").dropna()', 'data_cls_tr = train[["CASE_STATUS", "SOC_NAME", ...(x_tu[:,2])\ny_tu = y_tu_label.fit_transform(y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', ...], 'KNeighborsClassifier': <class 'sklearn.neighbors.classification.KNeighborsClassifier'>, 'LabelEncoder': <class 'sklearn.preprocessing.label.LabelEncoder'>, 'Lasso': <class 'sklearn.linear_model.coordinate_descent.Lasso'>, 'LinearRegression': <class 'sklearn.linear_model.base.LinearRegression'>, 'LogisticRegression': <class 'sklearn.linear_model.logistic.LogisticRegression'>, 'MultinomialNB': <class 'sklearn.naive_bayes.MultinomialNB'>, ...}
  590.        self.user_ns = {'DecisionTreeClassifier': <class 'sklearn.tree.tree.DecisionTreeClassifier'>, 'GaussianNB': <class 'sklearn.naive_bayes.GaussianNB'>, 'GridSearchCV': <class 'sklearn.model_selection._search.GridSearchCV'>, 'In': ['', 'import pandas as pd\nimport numpy as np\nimport ma...andomForestClassifier\nfrom sklearn.svm import SVC', 'train = pd.read_csv("h1b_train.csv").dropna()\ntu...pna()\ntest = pd.read_csv("h1b_test.csv").dropna()', 'data_cls_tr = train[["CASE_STATUS", "SOC_NAME", ...(x_tu[:,2])\ny_tu = y_tu_label.fit_transform(y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', '# x_train, x_test, y_train, y_test = train_test_...         random_state=0)\nrandomcv.fit(x_tu, y_tu)', ...], 'KNeighborsClassifier': <class 'sklearn.neighbors.classification.KNeighborsClassifier'>, 'LabelEncoder': <class 'sklearn.preprocessing.label.LabelEncoder'>, 'Lasso': <class 'sklearn.linear_model.coordinate_descent.Lasso'>, 'LinearRegression': <class 'sklearn.linear_model.base.LinearRegression'>, 'LogisticRegression': <class 'sklearn.linear_model.logistic.LogisticRegression'>, 'MultinomialNB': <class 'sklearn.naive_bayes.MultinomialNB'>, ...}
  591.   2964             finally:
  592.   2965                 # Reset our crash handler in place
  593.   2966                 sys.excepthook = old_excepthook
  594.   2967         except SystemExit as e:
  595.  
  596. ...........................................................................
  597. C:\Users\Jason\<ipython-input-39-bf87e95432e2> in <module>()
  598.     29               "class_weight": ["balanced", None]}
  599.     30
  600.     31 randomcv = RandomizedSearchCV(estimator=classifier, param_distributions=parameters,
  601.     32                               scoring='accuracy', cv=10, n_jobs=-1,
  602.     33                               random_state=0)
  603. ---> 34 randomcv.fit(x_tu, y_tu)
  604.  
  605. ...........................................................................
  606. C:\Users\Jason\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py in fit(self=RandomizedSearchCV(cv=10, error_score='raise',
  607. ...rain_score='warn', scoring='accuracy', verbose=0), X=array([[375, 84760, 970],
  608.       [270, 87607, 19... 1531],
  609.       [1270, 2817, 1531]], dtype=object), y=array([0, 0, 0, ..., 0, 2, 0], dtype=int64), groups=None, **fit_params={})
  610.    634                                   return_train_score=self.return_train_score,
  611.    635                                   return_n_test_samples=True,
  612.    636                                   return_times=True, return_parameters=False,
  613.    637                                   error_score=self.error_score)
  614.    638           for parameters, (train, test) in product(candidate_params,
  615. --> 639                                                    cv.split(X, y, groups)))
  616.        cv.split = <bound method StratifiedKFold.split of Stratifie...d(n_splits=10, random_state=None, shuffle=False)>
  617.        X = array([[375, 84760, 970],
  618.       [270, 87607, 19... 1531],
  619.       [1270, 2817, 1531]], dtype=object)
  620.        y = array([0, 0, 0, ..., 0, 2, 0], dtype=int64)
  621.        groups = None
  622.    640
  623.    641         # if one choose to see train score, "out" will contain train score info
  624.    642         if self.return_train_score:
  625.    643             (train_score_dicts, test_score_dicts, test_sample_counts, fit_time,
  626.  
  627. ...........................................................................
  628. C:\Users\Jason\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self=Parallel(n_jobs=-1), iterable=<generator object BaseSearchCV.fit.<locals>.<genexpr>>)
  629.    784             if pre_dispatch == "all" or n_jobs == 1:
  630.    785                 # The iterable was consumed all at once by the above for loop.
  631.    786                 # No need to wait for async callbacks to trigger to
  632.    787                 # consumption.
  633.    788                 self._iterating = False
  634. --> 789             self.retrieve()
  635.        self.retrieve = <bound method Parallel.retrieve of Parallel(n_jobs=-1)>
  636.    790             # Make sure that we get a last message telling us we are done
  637.    791             elapsed_time = time.time() - self._start_time
  638.    792             self._print('Done %3i out of %3i | elapsed: %s finished',
  639.    793                         (len(self._output), len(self._output),
  640.  
  641. ---------------------------------------------------------------------------
  642. Sub-process traceback:
  643. ---------------------------------------------------------------------------
  644. ValueError                                         Mon Jun 25 19:45:44 2018
  645. PID: 5588                 Python 3.6.5: C:\Users\Jason\Anaconda3\python.exe
  646. ...........................................................................
  647. C:\Users\Jason\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self=<sklearn.externals.joblib.parallel.BatchedCalls object>)
  648.    126     def __init__(self, iterator_slice):
  649.    127         self.items = list(iterator_slice)
  650.    128         self._size = len(self.items)
  651.    129
  652.    130     def __call__(self):
  653. --> 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]
  654.        self.items = [(<function _fit_and_score>, (SVC(C=1.0, cache_size=200, class_weight=None, co...None, shrinking=True,
  655.  tol=0.001, verbose=False), array([[375, 84760, 970],
  656.       [270, 87607, 19... 1531],
  657.       [1270, 2817, 1531]], dtype=object), memmap([0, 0, 0, ..., 0, 2, 0], dtype=int64), {'score': make_scorer(accuracy_score)}, memmap([ 57335,  57337,  57344, ..., 574952, 574953, 574954]), array([     0,      1,      2, ...,  58765,  67140, 502152]), 0, {'c': 79.587450816311, 'class_weight': None, 'gamma': 0.18596042409118513, 'kernel': 'linear'}), {'error_score': 'raise', 'fit_params': {}, 'return_n_test_samples': True, 'return_parameters': False, 'return_times': True, 'return_train_score': 'warn'})]
  658.    132
  659.    133     def __len__(self):
  660.    134         return self._size
  661.    135
  662.  
  663. ...........................................................................
  664. C:\Users\Jason\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in <listcomp>(.0=<list_iterator object>)
  665.    126     def __init__(self, iterator_slice):
  666.    127         self.items = list(iterator_slice)
  667.    128         self._size = len(self.items)
  668.    129
  669.    130     def __call__(self):
  670. --> 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]
  671.        func = <function _fit_and_score>
  672.        args = (SVC(C=1.0, cache_size=200, class_weight=None, co...None, shrinking=True,
  673.  tol=0.001, verbose=False), array([[375, 84760, 970],
  674.       [270, 87607, 19... 1531],
  675.       [1270, 2817, 1531]], dtype=object), memmap([0, 0, 0, ..., 0, 2, 0], dtype=int64), {'score': make_scorer(accuracy_score)}, memmap([ 57335,  57337,  57344, ..., 574952, 574953, 574954]), array([     0,      1,      2, ...,  58765,  67140, 502152]), 0, {'c': 79.587450816311, 'class_weight': None, 'gamma': 0.18596042409118513, 'kernel': 'linear'})
  676.        kwargs = {'error_score': 'raise', 'fit_params': {}, 'return_n_test_samples': True, 'return_parameters': False, 'return_times': True, 'return_train_score': 'warn'}
  677.    132
  678.    133     def __len__(self):
  679.    134         return self._size
  680.    135
  681.  
  682. ...........................................................................
  683. C:\Users\Jason\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in _fit_and_score(estimator=SVC(C=1.0, cache_size=200, class_weight=None, co...None, shrinking=True,
  684.  tol=0.001, verbose=False), X=array([[375, 84760, 970],
  685.       [270, 87607, 19... 1531],
  686.       [1270, 2817, 1531]], dtype=object), y=memmap([0, 0, 0, ..., 0, 2, 0], dtype=int64), scorer={'score': make_scorer(accuracy_score)}, train=memmap([ 57335,  57337,  57344, ..., 574952, 574953, 574954]), test=array([     0,      1,      2, ...,  58765,  67140, 502152]), verbose=0, parameters={'c': 79.587450816311, 'class_weight': None, 'gamma': 0.18596042409118513, 'kernel': 'linear'}, fit_params={}, return_train_score='warn', return_parameters=False, return_n_test_samples=True, return_times=True, error_score='raise')
  687.    439                       for k, v in fit_params.items()])
  688.    440
  689.    441     test_scores = {}
  690.    442     train_scores = {}
  691.    443     if parameters is not None:
  692. --> 444         estimator.set_params(**parameters)
  693.        estimator.set_params = <bound method BaseEstimator.set_params of SVC(C=...one, shrinking=True,
  694.  tol=0.001, verbose=False)>
  695.        parameters = {'c': 79.587450816311, 'class_weight': None, 'gamma': 0.18596042409118513, 'kernel': 'linear'}
  696.    445
  697.    446     start_time = time.time()
  698.    447
  699.    448     X_train, y_train = _safe_split(estimator, X, y, train)
  700.  
  701. ...........................................................................
  702. C:\Users\Jason\Anaconda3\lib\site-packages\sklearn\base.py in set_params(self=SVC(C=1.0, cache_size=200, class_weight=None, co...None, shrinking=True,
  703.  tol=0.001, verbose=False), **params={'c': 79.587450816311, 'class_weight': None, 'gamma': 0.18596042409118513, 'kernel': 'linear'})
  704.    269             key, delim, sub_key = key.partition('__')
  705.    270             if key not in valid_params:
  706.    271                 raise ValueError('Invalid parameter %s for estimator %s. '
  707.    272                                  'Check the list of available parameters '
  708.    273                                  'with `estimator.get_params().keys()`.' %
  709. --> 274                                  (key, self))
  710.        key = 'c'
  711.        self = SVC(C=1.0, cache_size=200, class_weight=None, co...None, shrinking=True,
  712.  tol=0.001, verbose=False)
  713.    275
  714.    276             if delim:
  715.    277                 nested_params[key][sub_key] = value
  716.    278             else:
  717.  
  718. ValueError: Invalid parameter c for estimator SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
  719.  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',
  720.  max_iter=-1, probability=False, random_state=None, shrinking=True,
  721.  tol=0.001, verbose=False). Check the list of available parameters with `estimator.get_params().keys()`.
  722. ___________________________________________________________________________
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