---------------------------------------------------------------------------
RemoteTraceback Traceback (most recent call last)
RemoteTraceback:
"""
Traceback (most recent call last):
File "C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\mlens\externals\joblib\_parallel_backends.py", line 344, in __call__
return self.func(*args, **kwargs)
File "C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\mlens\externals\joblib\parallel.py", line 131, in __call__
return [func(*args, **kwargs) for func, args, kwargs in self.items]
File "C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\mlens\externals\joblib\parallel.py", line 131, in <listcomp>
return [func(*args, **kwargs) for func, args, kwargs in self.items]
File "C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\mlens\parallel\estimation.py", line 497, in fit_est
p = getattr(inst, attr)(xtest)
File "C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\sklearn\linear_model\stochastic_gradient.py", line 762, in predict_proba
self._check_proba()
File "C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\sklearn\linear_model\stochastic_gradient.py", line 724, in _check_proba
" loss=%r" % self.loss)
AttributeError: probability estimates are not available for loss='hinge'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\multiprocessing\pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\mlens\externals\joblib\_parallel_backends.py", line 353, in __call__
raise TransportableException(text, e_type)
mlens.externals.joblib.my_exceptions.TransportableException: TransportableException
___________________________________________________________________________
AttributeError Mon Jun 19 11:41:32 2017
PID: 5952Python 3.5.3: C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\python.exe
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\mlens\externals\joblib\parallel.py in __call__(self=<mlens.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_est>, (), {'attr': 'predict_proba', 'case': None, 'dir': r'C:\Users\ERNEST~1.CHO\AppData\Local\Temp\mlens_23m0k6kd', 'idx': ((0, 2598), (2598, 5195), 15), 'inst': SGDClassifier(alpha=0.0001, average=False, class...shuffle=True,
verbose=0, warm_start=False), 'inst_name': 'DvL_SGD_shortECH_elo{No,100,200}_SCALED_ResCorr', 'ivals': (0.1, 600), 'name': 'layer-5', 'pred': memmap([[ nan, nan, nan, ..., 0., 0., 0.... [ nan, nan, nan, ..., 0., 0., 0.]]), 'preprocess': False, ...})]
132
133 def __len__(self):
134 return self._size
135
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\mlens\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_est>
args = ()
kwargs = {'attr': 'predict_proba', 'case': None, 'dir': r'C:\Users\ERNEST~1.CHO\AppData\Local\Temp\mlens_23m0k6kd', 'idx': ((0, 2598), (2598, 5195), 15), 'inst': SGDClassifier(alpha=0.0001, average=False, class...shuffle=True,
verbose=0, warm_start=False), 'inst_name': 'DvL_SGD_shortECH_elo{No,100,200}_SCALED_ResCorr', 'ivals': (0.1, 600), 'name': 'layer-5', 'pred': memmap([[ nan, nan, nan, ..., 0., 0., 0.... [ nan, nan, nan, ..., 0., 0., 0.]]), 'preprocess': False, ...}
132
133 def __len__(self):
134 return self._size
135
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\mlens\parallel\estimation.py in fit_est(dir=r'C:\Users\ERNEST~1.CHO\AppData\Local\Temp\mlens_23m0k6kd', case=None, inst_name='DvL_SGD_shortECH_elo{No,100,200}_SCALED_ResCorr', inst=SGDClassifier(alpha=0.0001, average=False, class...shuffle=True,
verbose=0, warm_start=False), x=memmap([[ 0.],
[ 0.],
[ 0.],
...,
[ 0.],
[ 0.],
[ 0.]]), y=memmap([-1, 0, 0, ..., -1, 1, 1], dtype=int64), pred=memmap([[ nan, nan, nan, ..., 0., 0., 0.... [ nan, nan, nan, ..., 0., 0., 0.]]), idx=((0, 2598), (2598, 5195), 15), raise_on_exception=True, preprocess=False, name='layer-5', ivals=(0.1, 600), attr='predict_proba', scorer=None)
492 xtest, ytest = _slice_array(x, y, tei)
493
494 for tr_name, tr in tr_list:
495 xtest = tr.transform(xtest)
496
--> 497 p = getattr(inst, attr)(xtest)
p = undefined
inst = SGDClassifier(alpha=0.0001, average=False, class...shuffle=True,
verbose=0, warm_start=False)
attr = 'predict_proba'
xtest = memmap([[ 0.],
[ 0.],
[ 0.],
...,
[ 0.],
[ 0.],
[ 0.]])
498
499 # Assign predictions to matrix
500 _assign_predictions(pred, p, tei, col, n)
501
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\sklearn\linear_model\stochastic_gradient.py in predict_proba(self=SGDClassifier(alpha=0.0001, average=False, class...shuffle=True,
verbose=0, warm_start=False))
757
758 The justification for the formula in the loss="modified_huber"
759 case is in the appendix B in:
760 http://jmlr.csail.mit.edu/papers/volume2/zhang02c/zhang02c.pdf
761 """
--> 762 self._check_proba()
self._check_proba = <bound method SGDClassifier._check_proba of SGDC...huffle=True,
verbose=0, warm_start=False)>
763 return self._predict_proba
764
765 def _predict_proba(self, X):
766 if self.loss == "log":
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\sklearn\linear_model\stochastic_gradient.py in _check_proba(self=SGDClassifier(alpha=0.0001, average=False, class...shuffle=True,
verbose=0, warm_start=False))
719 def _check_proba(self):
720 check_is_fitted(self, "t_")
721
722 if self.loss not in ("log", "modified_huber"):
723 raise AttributeError("probability estimates are not available for"
--> 724 " loss=%r" % self.loss)
self.loss = 'hinge'
725
726 @property
727 def predict_proba(self):
728 """Probability estimates.
AttributeError: probability estimates are not available for loss='hinge'
___________________________________________________________________________
"""
The above exception was the direct cause of the following exception:
TransportableException Traceback (most recent call last)
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\mlens\externals\joblib\parallel.py in retrieve(self)
681 if 'timeout' in getfullargspec(job.get).args:
--> 682 self._output.extend(job.get(timeout=self.timeout))
683 else:
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\multiprocessing\pool.py in get(self, timeout)
607 else:
--> 608 raise self._value
609
TransportableException: TransportableException
___________________________________________________________________________
AttributeError Mon Jun 19 11:41:32 2017
PID: 5952Python 3.5.3: C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\python.exe
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\mlens\externals\joblib\parallel.py in __call__(self=<mlens.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_est>, (), {'attr': 'predict_proba', 'case': None, 'dir': r'C:\Users\ERNEST~1.CHO\AppData\Local\Temp\mlens_23m0k6kd', 'idx': ((0, 2598), (2598, 5195), 15), 'inst': SGDClassifier(alpha=0.0001, average=False, class...shuffle=True,
verbose=0, warm_start=False), 'inst_name': 'DvL_SGD_shortECH_elo{No,100,200}_SCALED_ResCorr', 'ivals': (0.1, 600), 'name': 'layer-5', 'pred': memmap([[ nan, nan, nan, ..., 0., 0., 0.... [ nan, nan, nan, ..., 0., 0., 0.]]), 'preprocess': False, ...})]
132
133 def __len__(self):
134 return self._size
135
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\mlens\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_est>
args = ()
kwargs = {'attr': 'predict_proba', 'case': None, 'dir': r'C:\Users\ERNEST~1.CHO\AppData\Local\Temp\mlens_23m0k6kd', 'idx': ((0, 2598), (2598, 5195), 15), 'inst': SGDClassifier(alpha=0.0001, average=False, class...shuffle=True,
verbose=0, warm_start=False), 'inst_name': 'DvL_SGD_shortECH_elo{No,100,200}_SCALED_ResCorr', 'ivals': (0.1, 600), 'name': 'layer-5', 'pred': memmap([[ nan, nan, nan, ..., 0., 0., 0.... [ nan, nan, nan, ..., 0., 0., 0.]]), 'preprocess': False, ...}
132
133 def __len__(self):
134 return self._size
135
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\mlens\parallel\estimation.py in fit_est(dir=r'C:\Users\ERNEST~1.CHO\AppData\Local\Temp\mlens_23m0k6kd', case=None, inst_name='DvL_SGD_shortECH_elo{No,100,200}_SCALED_ResCorr', inst=SGDClassifier(alpha=0.0001, average=False, class...shuffle=True,
verbose=0, warm_start=False), x=memmap([[ 0.],
[ 0.],
[ 0.],
...,
[ 0.],
[ 0.],
[ 0.]]), y=memmap([-1, 0, 0, ..., -1, 1, 1], dtype=int64), pred=memmap([[ nan, nan, nan, ..., 0., 0., 0.... [ nan, nan, nan, ..., 0., 0., 0.]]), idx=((0, 2598), (2598, 5195), 15), raise_on_exception=True, preprocess=False, name='layer-5', ivals=(0.1, 600), attr='predict_proba', scorer=None)
492 xtest, ytest = _slice_array(x, y, tei)
493
494 for tr_name, tr in tr_list:
495 xtest = tr.transform(xtest)
496
--> 497 p = getattr(inst, attr)(xtest)
p = undefined
inst = SGDClassifier(alpha=0.0001, average=False, class...shuffle=True,
verbose=0, warm_start=False)
attr = 'predict_proba'
xtest = memmap([[ 0.],
[ 0.],
[ 0.],
...,
[ 0.],
[ 0.],
[ 0.]])
498
499 # Assign predictions to matrix
500 _assign_predictions(pred, p, tei, col, n)
501
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\sklearn\linear_model\stochastic_gradient.py in predict_proba(self=SGDClassifier(alpha=0.0001, average=False, class...shuffle=True,
verbose=0, warm_start=False))
757
758 The justification for the formula in the loss="modified_huber"
759 case is in the appendix B in:
760 http://jmlr.csail.mit.edu/papers/volume2/zhang02c/zhang02c.pdf
761 """
--> 762 self._check_proba()
self._check_proba = <bound method SGDClassifier._check_proba of SGDC...huffle=True,
verbose=0, warm_start=False)>
763 return self._predict_proba
764
765 def _predict_proba(self, X):
766 if self.loss == "log":
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\sklearn\linear_model\stochastic_gradient.py in _check_proba(self=SGDClassifier(alpha=0.0001, average=False, class...shuffle=True,
verbose=0, warm_start=False))
719 def _check_proba(self):
720 check_is_fitted(self, "t_")
721
722 if self.loss not in ("log", "modified_huber"):
723 raise AttributeError("probability estimates are not available for"
--> 724 " loss=%r" % self.loss)
self.loss = 'hinge'
725
726 @property
727 def predict_proba(self):
728 """Probability estimates.
AttributeError: probability estimates are not available for loss='hinge'
___________________________________________________________________________
During handling of the above exception, another exception occurred:
JoblibAttributeError Traceback (most recent call last)
<ipython-input-123-5c7ef2fc3ee9> in <module>()
1 name='myFirstEnsembleStack'
2 #FIT
----> 3 ensemble.fit(trainX, trainY)
4 #VALIDATE
5 predictions = model.predict(validX)
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\mlens\ensemble\base.py in fit(self, X, y)
714 X, y = X[idx], y[idx]
715
--> 716 self.scores_ = self.layers.fit(X, y)
717
718 return self
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\mlens\ensemble\base.py in fit(self, X, y, return_preds, **process_kwargs)
232 # Fit ensemble
233 try:
--> 234 processor.process()
235
236 if self.verbose:
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\mlens\parallel\manager.py in process(self)
216
217 for n, lyr in enumerate(self.layers.layers.values()):
--> 218 self._partial_process(n, lyr, parallel)
219
220 self.__fitted__ = 1
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\mlens\parallel\manager.py in _partial_process(self, n, lyr, parallel)
306 kwargs['P'] = self.job.P[n + 1]
307
--> 308 f(**kwargs)
309
310
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\mlens\parallel\estimation.py in fit(self, X, y, P, dir, parallel)
175 attr=pred_method,
176 scorer=self.scorer)
--> 177 for case, tri, tei, instance_list in self.e
178 for inst_name, instance in instance_list)
179
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\mlens\externals\joblib\parallel.py in __call__(self, iterable)
766 # consumption.
767 self._iterating = False
--> 768 self.retrieve()
769 # Make sure that we get a last message telling us we are done
770 elapsed_time = time.time() - self._start_time
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\mlens\externals\joblib\parallel.py in retrieve(self)
717 ensure_ready = self._managed_backend
718 backend.abort_everything(ensure_ready=ensure_ready)
--> 719 raise exception
720
721 def __call__(self, iterable):
JoblibAttributeError: JoblibAttributeError
___________________________________________________________________________
Multiprocessing exception:
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\runpy.py in _run_module_as_main(mod_name='ipykernel_launcher', alter_argv=1)
188 sys.exit(msg)
189 main_globals = sys.modules["__main__"].__dict__
190 if alter_argv:
191 sys.argv[0] = mod_spec.origin
192 return _run_code(code, main_globals, None,
--> 193 "__main__", mod_spec)
mod_spec = ModuleSpec(name='ipykernel_launcher', loader=<_f...flow\\lib\\site-packages\\ipykernel_launcher.py')
194
195 def run_module(mod_name, init_globals=None,
196 run_name=None, alter_sys=False):
197 """Execute a module's code without importing it
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\runpy.py in _run_code(code=<code object <module> at 0x000001FA07447F60, fil...lib\site-packages\ipykernel_launcher.py", line 5>, run_globals={'__builtins__': <module 'builtins' (built-in)>, '__cached__': r'C:\Users\ernest.chocholowski\AppData\Local\Conti...ges\__pycache__\ipykernel_launcher.cpython-35.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\ernest.chocholowski\AppData\Local\Conti...ensorflow\lib\site-packages\ipykernel_launcher.py', '__loader__': <_frozen_importlib_external.SourceFileLoader object>, '__name__': '__main__', '__package__': '', '__spec__': ModuleSpec(name='ipykernel_launcher', loader=<_f...flow\\lib\\site-packages\\ipykernel_launcher.py'), 'app': <module 'ipykernel.kernelapp' from 'C:\\Users\\e...ow\\lib\\site-packages\\ipykernel\\kernelapp.py'>, 'sys': <module 'sys' (built-in)>}, init_globals=None, mod_name='__main__', mod_spec=ModuleSpec(name='ipykernel_launcher', loader=<_f...flow\\lib\\site-packages\\ipykernel_launcher.py'), pkg_name='', script_name=None)
80 __cached__ = cached,
81 __doc__ = None,
82 __loader__ = loader,
83 __package__ = pkg_name,
84 __spec__ = mod_spec)
---> 85 exec(code, run_globals)
code = <code object <module> at 0x000001FA07447F60, fil...lib\site-packages\ipykernel_launcher.py", line 5>
run_globals = {'__builtins__': <module 'builtins' (built-in)>, '__cached__': r'C:\Users\ernest.chocholowski\AppData\Local\Conti...ges\__pycache__\ipykernel_launcher.cpython-35.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\ernest.chocholowski\AppData\Local\Conti...ensorflow\lib\site-packages\ipykernel_launcher.py', '__loader__': <_frozen_importlib_external.SourceFileLoader object>, '__name__': '__main__', '__package__': '', '__spec__': ModuleSpec(name='ipykernel_launcher', loader=<_f...flow\\lib\\site-packages\\ipykernel_launcher.py'), 'app': <module 'ipykernel.kernelapp' from 'C:\\Users\\e...ow\\lib\\site-packages\\ipykernel\\kernelapp.py'>, 'sys': <module 'sys' (built-in)>}
86 return run_globals
87
88 def _run_module_code(code, init_globals=None,
89 mod_name=None, mod_spec=None,
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\ipykernel_launcher.py in <module>()
11 # This is added back by InteractiveShellApp.init_path()
12 if sys.path[0] == '':
13 del sys.path[0]
14
15 from ipykernel import kernelapp as app
---> 16 app.launch_new_instance()
17
18
19
20
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\traitlets\config\application.py in launch_instance(cls=<class 'ipykernel.kernelapp.IPKernelApp'>, argv=None, **kwargs={})
653
654 If a global instance already exists, this reinitializes and starts it
655 """
656 app = cls.instance(**kwargs)
657 app.initialize(argv)
--> 658 app.start()
app.start = <bound method IPKernelApp.start of <ipykernel.kernelapp.IPKernelApp object>>
659
660 #-----------------------------------------------------------------------------
661 # utility functions, for convenience
662 #-----------------------------------------------------------------------------
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\ipykernel\kernelapp.py in start(self=<ipykernel.kernelapp.IPKernelApp object>)
472 return self.subapp.start()
473 if self.poller is not None:
474 self.poller.start()
475 self.kernel.start()
476 try:
--> 477 ioloop.IOLoop.instance().start()
478 except KeyboardInterrupt:
479 pass
480
481 launch_new_instance = IPKernelApp.launch_instance
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\zmq\eventloop\ioloop.py in start(self=<zmq.eventloop.ioloop.ZMQIOLoop object>)
172 )
173 return loop
174
175 def start(self):
176 try:
--> 177 super(ZMQIOLoop, self).start()
self.start = <bound method ZMQIOLoop.start of <zmq.eventloop.ioloop.ZMQIOLoop object>>
178 except ZMQError as e:
179 if e.errno == ETERM:
180 # quietly return on ETERM
181 pass
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\tornado\ioloop.py in start(self=<zmq.eventloop.ioloop.ZMQIOLoop object>)
883 self._events.update(event_pairs)
884 while self._events:
885 fd, events = self._events.popitem()
886 try:
887 fd_obj, handler_func = self._handlers[fd]
--> 888 handler_func(fd_obj, events)
handler_func = <function wrap.<locals>.null_wrapper>
fd_obj = <zmq.sugar.socket.Socket object>
events = 1
889 except (OSError, IOError) as e:
890 if errno_from_exception(e) == errno.EPIPE:
891 # Happens when the client closes the connection
892 pass
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\tornado\stack_context.py in null_wrapper(*args=(<zmq.sugar.socket.Socket object>, 1), **kwargs={})
272 # Fast path when there are no active contexts.
273 def null_wrapper(*args, **kwargs):
274 try:
275 current_state = _state.contexts
276 _state.contexts = cap_contexts[0]
--> 277 return fn(*args, **kwargs)
args = (<zmq.sugar.socket.Socket object>, 1)
kwargs = {}
278 finally:
279 _state.contexts = current_state
280 null_wrapper._wrapped = True
281 return null_wrapper
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\zmq\eventloop\zmqstream.py in _handle_events(self=<zmq.eventloop.zmqstream.ZMQStream object>, fd=<zmq.sugar.socket.Socket object>, events=1)
435 # dispatch events:
436 if events & IOLoop.ERROR:
437 gen_log.error("got POLLERR event on ZMQStream, which doesn't make sense")
438 return
439 if events & IOLoop.READ:
--> 440 self._handle_recv()
self._handle_recv = <bound method ZMQStream._handle_recv of <zmq.eventloop.zmqstream.ZMQStream object>>
441 if not self.socket:
442 return
443 if events & IOLoop.WRITE:
444 self._handle_send()
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\zmq\eventloop\zmqstream.py in _handle_recv(self=<zmq.eventloop.zmqstream.ZMQStream object>)
467 gen_log.error("RECV Error: %s"%zmq.strerror(e.errno))
468 else:
469 if self._recv_callback:
470 callback = self._recv_callback
471 # self._recv_callback = None
--> 472 self._run_callback(callback, msg)
self._run_callback = <bound method ZMQStream._run_callback of <zmq.eventloop.zmqstream.ZMQStream object>>
callback = <function wrap.<locals>.null_wrapper>
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>]
473
474 # self.update_state()
475
476
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\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={})
409 close our socket."""
410 try:
411 # Use a NullContext to ensure that all StackContexts are run
412 # inside our blanket exception handler rather than outside.
413 with stack_context.NullContext():
--> 414 callback(*args, **kwargs)
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 = {}
415 except:
416 gen_log.error("Uncaught exception, closing connection.",
417 exc_info=True)
418 # Close the socket on an uncaught exception from a user callback
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\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={})
272 # Fast path when there are no active contexts.
273 def null_wrapper(*args, **kwargs):
274 try:
275 current_state = _state.contexts
276 _state.contexts = cap_contexts[0]
--> 277 return fn(*args, **kwargs)
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 = {}
278 finally:
279 _state.contexts = current_state
280 null_wrapper._wrapped = True
281 return null_wrapper
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\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>])
278 if self.control_stream:
279 self.control_stream.on_recv(self.dispatch_control, copy=False)
280
281 def make_dispatcher(stream):
282 def dispatcher(msg):
--> 283 return self.dispatch_shell(stream, msg)
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>]
284 return dispatcher
285
286 for s in self.shell_streams:
287 s.on_recv(make_dispatcher(s), copy=False)
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\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': "name='myFirstEnsembleStack'\n#FIT\nensemble.fit(tr...le = 'Confusion matrix '+name, cmap=plt.cm.Blues)", 'silent': False, 'stop_on_error': True, 'store_history': True, 'user_expressions': {}}, 'header': {'date': datetime.datetime(2017, 6, 19, 9, 40, 45, 594708, tzinfo=datetime.timezone.utc), 'msg_id': 'B35BF61F0FC7495E81324089522085DE', 'msg_type': 'execute_request', 'session': '22FA7B0EA7F24B29818F71DDB9C1C26E', 'username': 'username', 'version': '5.0'}, 'metadata': {}, 'msg_id': 'B35BF61F0FC7495E81324089522085DE', 'msg_type': 'execute_request', 'parent_header': {}})
230 self.log.warn("Unknown message type: %r", msg_type)
231 else:
232 self.log.debug("%s: %s", msg_type, msg)
233 self.pre_handler_hook()
234 try:
--> 235 handler(stream, idents, msg)
handler = <bound method Kernel.execute_request of <ipykernel.ipkernel.IPythonKernel object>>
stream = <zmq.eventloop.zmqstream.ZMQStream object>
idents = [b'22FA7B0EA7F24B29818F71DDB9C1C26E']
msg = {'buffers': [], 'content': {'allow_stdin': True, 'code': "name='myFirstEnsembleStack'\n#FIT\nensemble.fit(tr...le = 'Confusion matrix '+name, cmap=plt.cm.Blues)", 'silent': False, 'stop_on_error': True, 'store_history': True, 'user_expressions': {}}, 'header': {'date': datetime.datetime(2017, 6, 19, 9, 40, 45, 594708, tzinfo=datetime.timezone.utc), 'msg_id': 'B35BF61F0FC7495E81324089522085DE', 'msg_type': 'execute_request', 'session': '22FA7B0EA7F24B29818F71DDB9C1C26E', 'username': 'username', 'version': '5.0'}, 'metadata': {}, 'msg_id': 'B35BF61F0FC7495E81324089522085DE', 'msg_type': 'execute_request', 'parent_header': {}}
236 except Exception:
237 self.log.error("Exception in message handler:", exc_info=True)
238 finally:
239 self.post_handler_hook()
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\ipykernel\kernelbase.py in execute_request(self=<ipykernel.ipkernel.IPythonKernel object>, stream=<zmq.eventloop.zmqstream.ZMQStream object>, ident=[b'22FA7B0EA7F24B29818F71DDB9C1C26E'], parent={'buffers': [], 'content': {'allow_stdin': True, 'code': "name='myFirstEnsembleStack'\n#FIT\nensemble.fit(tr...le = 'Confusion matrix '+name, cmap=plt.cm.Blues)", 'silent': False, 'stop_on_error': True, 'store_history': True, 'user_expressions': {}}, 'header': {'date': datetime.datetime(2017, 6, 19, 9, 40, 45, 594708, tzinfo=datetime.timezone.utc), 'msg_id': 'B35BF61F0FC7495E81324089522085DE', 'msg_type': 'execute_request', 'session': '22FA7B0EA7F24B29818F71DDB9C1C26E', 'username': 'username', 'version': '5.0'}, 'metadata': {}, 'msg_id': 'B35BF61F0FC7495E81324089522085DE', 'msg_type': 'execute_request', 'parent_header': {}})
394 if not silent:
395 self.execution_count += 1
396 self._publish_execute_input(code, parent, self.execution_count)
397
398 reply_content = self.do_execute(code, silent, store_history,
--> 399 user_expressions, allow_stdin)
user_expressions = {}
allow_stdin = True
400
401 # Flush output before sending the reply.
402 sys.stdout.flush()
403 sys.stderr.flush()
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\ipykernel\ipkernel.py in do_execute(self=<ipykernel.ipkernel.IPythonKernel object>, code="name='myFirstEnsembleStack'\n#FIT\nensemble.fit(tr...le = 'Confusion matrix '+name, cmap=plt.cm.Blues)", silent=False, store_history=True, user_expressions={}, allow_stdin=True)
191
192 self._forward_input(allow_stdin)
193
194 reply_content = {}
195 try:
--> 196 res = shell.run_cell(code, store_history=store_history, silent=silent)
res = undefined
shell.run_cell = <bound method ZMQInteractiveShell.run_cell of <ipykernel.zmqshell.ZMQInteractiveShell object>>
code = "name='myFirstEnsembleStack'\n#FIT\nensemble.fit(tr...le = 'Confusion matrix '+name, cmap=plt.cm.Blues)"
store_history = True
silent = False
197 finally:
198 self._restore_input()
199
200 if res.error_before_exec is not None:
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\ipykernel\zmqshell.py in run_cell(self=<ipykernel.zmqshell.ZMQInteractiveShell object>, *args=("name='myFirstEnsembleStack'\n#FIT\nensemble.fit(tr...le = 'Confusion matrix '+name, cmap=plt.cm.Blues)",), **kwargs={'silent': False, 'store_history': True})
528 )
529 self.payload_manager.write_payload(payload)
530
531 def run_cell(self, *args, **kwargs):
532 self._last_traceback = None
--> 533 return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
self.run_cell = <bound method ZMQInteractiveShell.run_cell of <ipykernel.zmqshell.ZMQInteractiveShell object>>
args = ("name='myFirstEnsembleStack'\n#FIT\nensemble.fit(tr...le = 'Confusion matrix '+name, cmap=plt.cm.Blues)",)
kwargs = {'silent': False, 'store_history': True}
534
535 def _showtraceback(self, etype, evalue, stb):
536 # try to preserve ordering of tracebacks and print statements
537 sys.stdout.flush()
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\IPython\core\interactiveshell.py in run_cell(self=<ipykernel.zmqshell.ZMQInteractiveShell object>, raw_cell="name='myFirstEnsembleStack'\n#FIT\nensemble.fit(tr...le = 'Confusion matrix '+name, cmap=plt.cm.Blues)", store_history=True, silent=False, shell_futures=True)
2678 self.displayhook.exec_result = result
2679
2680 # Execute the user code
2681 interactivity = "none" if silent else self.ast_node_interactivity
2682 has_raised = self.run_ast_nodes(code_ast.body, cell_name,
-> 2683 interactivity=interactivity, compiler=compiler, result=result)
interactivity = 'last_expr'
compiler = <IPython.core.compilerop.CachingCompiler object>
2684
2685 self.last_execution_succeeded = not has_raised
2686
2687 # Reset this so later displayed values do not modify the
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\IPython\core\interactiveshell.py in run_ast_nodes(self=<ipykernel.zmqshell.ZMQInteractiveShell object>, nodelist=[<_ast.Assign object>, <_ast.Expr object>, <_ast.Assign object>, <_ast.Assign object>, <_ast.Expr object>, <_ast.Assign object>, <_ast.Assign object>, <_ast.Expr object>], cell_name='<ipython-input-123-5c7ef2fc3ee9>', interactivity='last', compiler=<IPython.core.compilerop.CachingCompiler object>, result=<ExecutionResult object at 1fa487693c8, executio..._before_exec=None error_in_exec=None result=None>)
2782
2783 try:
2784 for i, node in enumerate(to_run_exec):
2785 mod = ast.Module([node])
2786 code = compiler(mod, cell_name, "exec")
-> 2787 if self.run_code(code, result):
self.run_code = <bound method InteractiveShell.run_code of <ipykernel.zmqshell.ZMQInteractiveShell object>>
code = <code object <module> at 0x000001FA44EFD780, file "<ipython-input-123-5c7ef2fc3ee9>", line 3>
result = <ExecutionResult object at 1fa487693c8, executio..._before_exec=None error_in_exec=None result=None>
2788 return True
2789
2790 for i, node in enumerate(to_run_interactive):
2791 mod = ast.Interactive([node])
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\IPython\core\interactiveshell.py in run_code(self=<ipykernel.zmqshell.ZMQInteractiveShell object>, code_obj=<code object <module> at 0x000001FA44EFD780, file "<ipython-input-123-5c7ef2fc3ee9>", line 3>, result=<ExecutionResult object at 1fa487693c8, executio..._before_exec=None error_in_exec=None result=None>)
2842 outflag = True # happens in more places, so it's easier as default
2843 try:
2844 try:
2845 self.hooks.pre_run_code_hook()
2846 #rprint('Running code', repr(code_obj)) # dbg
-> 2847 exec(code_obj, self.user_global_ns, self.user_ns)
code_obj = <code object <module> at 0x000001FA44EFD780, file "<ipython-input-123-5c7ef2fc3ee9>", line 3>
self.user_global_ns = {'DRAW': home_player_1_overall_rating away_player...
12592 1.174520 -0.002704 -0.544673 0 , 'DRAWvsLOSE': home_player_1_overall_rating away_player...
14518 1.281230 0.783397 -2.543106 -1 , 'DecisionTreeClassifier': <class 'sklearn.tree.tree.DecisionTreeClassifier'>, 'EnsembleTransformer': <class 'mlens.preprocessing.ensemble_transformer.EnsembleTransformer'>, 'Evaluator': <class 'mlens.model_selection.model_selection.Evaluator'>, 'ExtraTreesClassifier': <class 'sklearn.ensemble.forest.ExtraTreesClassifier'>, 'GaussianNB': <class 'sklearn.naive_bayes.GaussianNB'>, 'GradientBoostingClassifier': <class 'sklearn.ensemble.gradient_boosting.GradientBoostingClassifier'>, 'Imputer': <class 'sklearn.preprocessing.imputation.Imputer'>, 'In': ['', '# SQL\nimport sqlite3\n# Data Manipulation\nimport ...matches_df[NoScalling]\n\nscaled_features_df.head()', "WIN = scaled_features_df[scaled_features_df['RES...d_features_df[scaled_features_df['RESULT'] == -1]", 'print("WIN_LEN:",len(WIN))\nprint("DRAW_LEN:",len(DRAW))\nprint("LOSE_LEN:",len(LOSE))', 'print("WIN_LEN:",len(WIN//2))\nprint("DRAW_LEN:",len(DRAW))\nprint("LOSE_LEN:",len(LOSE))', 'print("WIN_LEN:",len(WIN//2))\nprint("DRAW_LEN:",len(DRAW))\nprint("LOSE_LEN:",len(LOSE))', 'print("WIN_LEN:",len(WIN)//2)\nprint("DRAW_LEN:",len(DRAW))\nprint("LOSE_LEN:",len(LOSE))', 'msk = np.random.rand(len(df)) < 0.5', 'msk = np.random.rand(len(WIN)) < 0.5', 'len(msk)', 'print("WIN_LEN:",len(WIN))\nprint("DRAW_LEN:",len(DRAW))\nprint("LOSE_LEN:",len(LOSE))', '#gets a random 80% of the entire set\nWIN_one = W...the dataset\nWIN_two = WIN.loc[~WIN.isin(WIN_one)]', '#gets a random 80% of the entire set\nWIN_one = W...ortion of the dataset\nWIN_two = WIN.loc[~WIN_one]', 'WIN.head()', 'WIN.RESULT.describe()', 'WIN.head()', "WIN_one.to_dict('l')", "WIN_one.to_dict('l')\nWIN.isin(WIN_one.to_dict('l'))", '# SQL\nimport sqlite3\n# Data Manipulation\nimport ...matches_df[NoScalling]\n\nscaled_features_df.head()', "WIN_one.to_dict('l')", ...], ...}
self.user_ns = {'DRAW': home_player_1_overall_rating away_player...
12592 1.174520 -0.002704 -0.544673 0 , 'DRAWvsLOSE': home_player_1_overall_rating away_player...
14518 1.281230 0.783397 -2.543106 -1 , 'DecisionTreeClassifier': <class 'sklearn.tree.tree.DecisionTreeClassifier'>, 'EnsembleTransformer': <class 'mlens.preprocessing.ensemble_transformer.EnsembleTransformer'>, 'Evaluator': <class 'mlens.model_selection.model_selection.Evaluator'>, 'ExtraTreesClassifier': <class 'sklearn.ensemble.forest.ExtraTreesClassifier'>, 'GaussianNB': <class 'sklearn.naive_bayes.GaussianNB'>, 'GradientBoostingClassifier': <class 'sklearn.ensemble.gradient_boosting.GradientBoostingClassifier'>, 'Imputer': <class 'sklearn.preprocessing.imputation.Imputer'>, 'In': ['', '# SQL\nimport sqlite3\n# Data Manipulation\nimport ...matches_df[NoScalling]\n\nscaled_features_df.head()', "WIN = scaled_features_df[scaled_features_df['RES...d_features_df[scaled_features_df['RESULT'] == -1]", 'print("WIN_LEN:",len(WIN))\nprint("DRAW_LEN:",len(DRAW))\nprint("LOSE_LEN:",len(LOSE))', 'print("WIN_LEN:",len(WIN//2))\nprint("DRAW_LEN:",len(DRAW))\nprint("LOSE_LEN:",len(LOSE))', 'print("WIN_LEN:",len(WIN//2))\nprint("DRAW_LEN:",len(DRAW))\nprint("LOSE_LEN:",len(LOSE))', 'print("WIN_LEN:",len(WIN)//2)\nprint("DRAW_LEN:",len(DRAW))\nprint("LOSE_LEN:",len(LOSE))', 'msk = np.random.rand(len(df)) < 0.5', 'msk = np.random.rand(len(WIN)) < 0.5', 'len(msk)', 'print("WIN_LEN:",len(WIN))\nprint("DRAW_LEN:",len(DRAW))\nprint("LOSE_LEN:",len(LOSE))', '#gets a random 80% of the entire set\nWIN_one = W...the dataset\nWIN_two = WIN.loc[~WIN.isin(WIN_one)]', '#gets a random 80% of the entire set\nWIN_one = W...ortion of the dataset\nWIN_two = WIN.loc[~WIN_one]', 'WIN.head()', 'WIN.RESULT.describe()', 'WIN.head()', "WIN_one.to_dict('l')", "WIN_one.to_dict('l')\nWIN.isin(WIN_one.to_dict('l'))", '# SQL\nimport sqlite3\n# Data Manipulation\nimport ...matches_df[NoScalling]\n\nscaled_features_df.head()', "WIN_one.to_dict('l')", ...], ...}
2848 finally:
2849 # Reset our crash handler in place
2850 sys.excepthook = old_excepthook
2851 except SystemExit as e:
...........................................................................
C:\Users\ernest.chocholowski\Desktop\GIT\SoccerAnalysis\<ipython-input-123-5c7ef2fc3ee9> in <module>()
1
2
----> 3
4 name='myFirstEnsembleStack'
5 #FIT
6 ensemble.fit(trainX, trainY)
7 #VALIDATE
8 predictions = model.predict(validX)
9 validset_acc = round(accuracy_score(validY, predictions) * 100, 2)
10 print(validset_acc)
11 #CONF MATRIX
12 conf_matrix = confusion_matrix(validY, predictions)
13 classes=['Lose', 'Draw', 'Win']
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\mlens\ensemble\base.py in fit(self=SequentialEnsemble(array_check=2, backend='multi... scorer=None, shuffle=False, verbose=False), X=array([[-0.04586072, 0.22469482, -1.22123789, .... 1.3245056 ,
1.30746492, -2.9315475 ]]), y=array([ 1, -1, -1, ..., -1, 1, 1], dtype=int64))
711 if self.shuffle:
712 r = check_random_state(self.random_state)
713 idx = r.permutation(X.shape[0])
714 X, y = X[idx], y[idx]
715
--> 716 self.scores_ = self.layers.fit(X, y)
self.scores_ = None
self.layers.fit = <bound method LayerContainer.fit of LayerContain...jobs=-1, raise_on_exception=True, verbose=False)>
X = array([[-0.04586072, 0.22469482, -1.22123789, .... 1.3245056 ,
1.30746492, -2.9315475 ]])
y = array([ 1, -1, -1, ..., -1, 1, 1], dtype=int64)
717
718 return self
719
720 def predict(self, X):
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\mlens\ensemble\base.py in fit(self=LayerContainer(backend='multiprocessing',
..._jobs=-1, raise_on_exception=True, verbose=False), X=array([[-0.04586072, 0.22469482, -1.22123789, .... 1.3245056 ,
1.30746492, -2.9315475 ]]), y=array([ 1, -1, -1, ..., -1, 1, 1], dtype=int64), return_preds=None, **process_kwargs={})
229 processor = ParallelProcessing(self)
230 processor.initialize('fit', X, y, **process_kwargs)
231
232 # Fit ensemble
233 try:
--> 234 processor.process()
processor.process = <bound method ParallelProcessing.process of <mlens.parallel.manager.ParallelProcessing object>>
235
236 if self.verbose:
237 print_time(t0, "Fit complete", file=pout, flush=True)
238
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\mlens\parallel\manager.py in process(self=<mlens.parallel.manager.ParallelProcessing object>)
213 mmap_mode='w+',
214 verbose=self.layers.verbose,
215 backend=self.layers.backend) as parallel:
216
217 for n, lyr in enumerate(self.layers.layers.values()):
--> 218 self._partial_process(n, lyr, parallel)
self._partial_process = <bound method ParallelProcessing._partial_proces...lens.parallel.manager.ParallelProcessing object>>
n = 3
lyr = Layer(cls='blend', cls_kwargs=None,
estimator...se_on_exception=True, scorer=None, verbose=False)
parallel = Parallel(n_jobs=-1)
219
220 self.__fitted__ = 1
221
222 def get_preds(self, n=-1, dtype=np.float, order='C'):
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\mlens\parallel\manager.py in _partial_process(self=<mlens.parallel.manager.ParallelProcessing object>, n=3, lyr=Layer(cls='blend', cls_kwargs=None,
estimator...se_on_exception=True, scorer=None, verbose=False), parallel=Parallel(n_jobs=-1))
303 if 'X' in fargs:
304 kwargs['X'] = self.job.P[n]
305 if 'P' in fargs:
306 kwargs['P'] = self.job.P[n + 1]
307
--> 308 f(**kwargs)
f = <bound method BaseEstimator.fit of <mlens.parallel.blend.Blender object>>
kwargs = {'P': memmap([[ nan, nan, nan, ..., 0., 0., 0.... [ nan, nan, nan, ..., 0., 0., 0.]]), 'X': memmap([[ 0.],
[ 0.],
[ 0.],
...,
[ 0.],
[ 0.],
[ 0.]]), 'dir': r'C:\Users\ERNEST~1.CHO\AppData\Local\Temp\mlens_23m0k6kd', 'parallel': Parallel(n_jobs=-1), 'y': memmap([ 1, -1, -1, ..., -1, 1, 1], dtype=int64)}
309
310
311 ###############################################################################
312 class ParallelEvaluation(object):
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\mlens\parallel\estimation.py in fit(self=<mlens.parallel.blend.Blender object>, X=memmap([[ 0.],
[ 0.],
[ 0.],
...,
[ 0.],
[ 0.],
[ 0.]]), y=memmap([-1, 0, 0, ..., -1, 1, 1], dtype=int64), P=memmap([[ nan, nan, nan, ..., 0., 0., 0.... [ nan, nan, nan, ..., 0., 0., 0.]]), dir=r'C:\Users\ERNEST~1.CHO\AppData\Local\Temp\mlens_23m0k6kd', parallel=Parallel(n_jobs=-1))
172 raise_on_exception=self.raise_,
173 preprocess=preprocess,
174 ivals=self.ivals,
175 attr=pred_method,
176 scorer=self.scorer)
--> 177 for case, tri, tei, instance_list in self.e
self.e = [(None, (0, 2598), (2598, 5195), [('DvL_GNB_shortECH_elo{No,100,200}_SCALED_ResCorr', GaussianNB(priors=None)), ('DvL_GradientBoosting_shortECH_elo{No,100,200}_SCALED_ResCorr', GradientBoostingClassifier(criterion='friedman_m... subsample=1.0, verbose=0, warm_start=False)), ('DvL_KNN_shortECH_elo{No,100,200}_SCALED_ResCorr', KNeighborsClassifier(algorithm='auto', leaf_size...n_neighbors=3, p=2,
weights='uniform')), ('DvL_RandomForest_balanced_shortECH_elo{No,100,200}_SCALED_ResCorr', RandomForestClassifier(bootstrap=True, class_wei...te=None, verbose=0,
warm_start=False)), ('DvL_RandomForest_shortECH_elo{No,100,200}_SCALED_ResCorr', RandomForestClassifier(bootstrap=True, class_wei... random_state=None, verbose=0, warm_start=False)), ('DvL_SGD_shortECH_elo{No,100,200}_SCALED_ResCorr', SGDClassifier(alpha=0.0001, average=False, class...shuffle=True,
verbose=0, warm_start=False)), ('DvL_SVC_shortECH_elo{No,100,200}_SCALED_ResCorr', SVC(C=1.0, cache_size=200, class_weight=None, co...None, shrinking=True,
tol=0.001, verbose=False)), ('DvL_linSVC_shortECH_elo{No,100,200}_SCALED_ResCorr', LinearSVC(C=1.0, class_weight=None, dual=True, f...', random_state=None, tol=0.0001,
verbose=0)), ('DvL_logreg_balanced_shortECH_elo{No,100,200}_SCALED_ResCorr', LogisticRegression(C=1.0, class_weight='balanced...linear', tol=0.0001, verbose=0, warm_start=False)), ('DvL_logreg_shortECH_elo{No,100,200}_SCALED_ResCorr', LogisticRegression(C=1.0, class_weight=None, dua...ol=0.0001,
verbose=0, warm_start=False)), ('DvL_xTrees_balanced_shortECH_elo{No,100,200}_SCALED_ResCorr', ExtraTreesClassifier(bootstrap=False, class_weig..., random_state=None, verbose=0, warm_start=False)), ('DvL_xTrees_shortECH_elo{No,100,200}_SCALED_ResCorr', ExtraTreesClassifier(bootstrap=False, class_weig...ate=None,
verbose=0, warm_start=False)), ('WvD_GNB_shortECH_elo{No,100,200}_SCALED_ResCorr', GaussianNB(priors=None)), ('WvD_GradientBoosting_shortECH_elo{No,100,200}_SCALED_ResCorr', GradientBoostingClassifier(criterion='friedman_m... subsample=1.0, verbose=0, warm_start=False)), ('WvD_KNN_shortECH_elo{No,100,200}_SCALED_ResCorr', KNeighborsClassifier(algorithm='auto', leaf_size...n_neighbors=3, p=2,
weights='uniform')), ('WvD_RandomForest_balanced_shortECH_elo{No,100,200}_SCALED_ResCorr', RandomForestClassifier(bootstrap=True, class_wei...te=None, verbose=0,
warm_start=False)), ('WvD_RandomForest_shortECH_elo{No,100,200}_SCALED_ResCorr', RandomForestClassifier(bootstrap=True, class_wei... random_state=None, verbose=0, warm_start=False)), ('WvD_SGD_shortECH_elo{No,100,200}_SCALED_ResCorr', SGDClassifier(alpha=0.0001, average=False, class...shuffle=True,
verbose=0, warm_start=False)), ('WvD_SVC_shortECH_elo{No,100,200}_SCALED_ResCorr', SVC(C=1.0, cache_size=200, class_weight=None, co...None, shrinking=True,
tol=0.001, verbose=False)), ('WvD_linSVC_shortECH_elo{No,100,200}_SCALED_ResCorr', LinearSVC(C=1.0, class_weight=None, dual=True, f...', random_state=None, tol=0.0001,
verbose=0)), ...])]
178 for inst_name, instance in instance_list)
179
180 else:
181 parallel(delayed(_fit)(dir=dir,
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\mlens\externals\joblib\parallel.py in __call__(self=Parallel(n_jobs=-1), iterable=<generator object BaseEstimator.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=-1)>
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:
---------------------------------------------------------------------------
AttributeError Mon Jun 19 11:41:32 2017
PID: 5952Python 3.5.3: C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\python.exe
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\mlens\externals\joblib\parallel.py in __call__(self=<mlens.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_est>, (), {'attr': 'predict_proba', 'case': None, 'dir': r'C:\Users\ERNEST~1.CHO\AppData\Local\Temp\mlens_23m0k6kd', 'idx': ((0, 2598), (2598, 5195), 15), 'inst': SGDClassifier(alpha=0.0001, average=False, class...shuffle=True,
verbose=0, warm_start=False), 'inst_name': 'DvL_SGD_shortECH_elo{No,100,200}_SCALED_ResCorr', 'ivals': (0.1, 600), 'name': 'layer-5', 'pred': memmap([[ nan, nan, nan, ..., 0., 0., 0.... [ nan, nan, nan, ..., 0., 0., 0.]]), 'preprocess': False, ...})]
132
133 def __len__(self):
134 return self._size
135
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\mlens\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_est>
args = ()
kwargs = {'attr': 'predict_proba', 'case': None, 'dir': r'C:\Users\ERNEST~1.CHO\AppData\Local\Temp\mlens_23m0k6kd', 'idx': ((0, 2598), (2598, 5195), 15), 'inst': SGDClassifier(alpha=0.0001, average=False, class...shuffle=True,
verbose=0, warm_start=False), 'inst_name': 'DvL_SGD_shortECH_elo{No,100,200}_SCALED_ResCorr', 'ivals': (0.1, 600), 'name': 'layer-5', 'pred': memmap([[ nan, nan, nan, ..., 0., 0., 0.... [ nan, nan, nan, ..., 0., 0., 0.]]), 'preprocess': False, ...}
132
133 def __len__(self):
134 return self._size
135
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\mlens\parallel\estimation.py in fit_est(dir=r'C:\Users\ERNEST~1.CHO\AppData\Local\Temp\mlens_23m0k6kd', case=None, inst_name='DvL_SGD_shortECH_elo{No,100,200}_SCALED_ResCorr', inst=SGDClassifier(alpha=0.0001, average=False, class...shuffle=True,
verbose=0, warm_start=False), x=memmap([[ 0.],
[ 0.],
[ 0.],
...,
[ 0.],
[ 0.],
[ 0.]]), y=memmap([-1, 0, 0, ..., -1, 1, 1], dtype=int64), pred=memmap([[ nan, nan, nan, ..., 0., 0., 0.... [ nan, nan, nan, ..., 0., 0., 0.]]), idx=((0, 2598), (2598, 5195), 15), raise_on_exception=True, preprocess=False, name='layer-5', ivals=(0.1, 600), attr='predict_proba', scorer=None)
492 xtest, ytest = _slice_array(x, y, tei)
493
494 for tr_name, tr in tr_list:
495 xtest = tr.transform(xtest)
496
--> 497 p = getattr(inst, attr)(xtest)
p = undefined
inst = SGDClassifier(alpha=0.0001, average=False, class...shuffle=True,
verbose=0, warm_start=False)
attr = 'predict_proba'
xtest = memmap([[ 0.],
[ 0.],
[ 0.],
...,
[ 0.],
[ 0.],
[ 0.]])
498
499 # Assign predictions to matrix
500 _assign_predictions(pred, p, tei, col, n)
501
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\sklearn\linear_model\stochastic_gradient.py in predict_proba(self=SGDClassifier(alpha=0.0001, average=False, class...shuffle=True,
verbose=0, warm_start=False))
757
758 The justification for the formula in the loss="modified_huber"
759 case is in the appendix B in:
760 http://jmlr.csail.mit.edu/papers/volume2/zhang02c/zhang02c.pdf
761 """
--> 762 self._check_proba()
self._check_proba = <bound method SGDClassifier._check_proba of SGDC...huffle=True,
verbose=0, warm_start=False)>
763 return self._predict_proba
764
765 def _predict_proba(self, X):
766 if self.loss == "log":
...........................................................................
C:\Users\ernest.chocholowski\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-packages\sklearn\linear_model\stochastic_gradient.py in _check_proba(self=SGDClassifier(alpha=0.0001, average=False, class...shuffle=True,
verbose=0, warm_start=False))
719 def _check_proba(self):
720 check_is_fitted(self, "t_")
721
722 if self.loss not in ("log", "modified_huber"):
723 raise AttributeError("probability estimates are not available for"
--> 724 " loss=%r" % self.loss)
self.loss = 'hinge'
725
726 @property
727 def predict_proba(self):
728 """Probability estimates.
AttributeError: probability estimates are not available for loss='hinge'
___________________________________________________________________________