And here is the error output, I think the reason for the error is n_jobs selection. Any thoughts about it?
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
~/anaconda3/lib/python3.6/site-packages/mlens/externals/joblib/_parallel_backends.py in __call__(self, *args, **kwargs)
349 try:
--> 350 return self.func(*args, **kwargs)
351 except KeyboardInterrupt:
~/anaconda3/lib/python3.6/site-packages/mlens/externals/joblib/parallel.py in __call__(self)
134 def __call__(self):
--> 135 return [func(*args, **kwargs) for func, args, kwargs in self.items]
136
~/anaconda3/lib/python3.6/site-packages/mlens/externals/joblib/parallel.py in <listcomp>(.0)
134 def __call__(self):
--> 135 return [func(*args, **kwargs) for func, args, kwargs in self.items]
136
~/anaconda3/lib/python3.6/site-packages/mlens/parallel/learner.py in __call__(self)
124 """Launch job"""
--> 125 return getattr(self, self.job)()
126
~/anaconda3/lib/python3.6/site-packages/mlens/parallel/learner.py in fit(self, path)
133
--> 134 self._fit(transformers)
135
~/anaconda3/lib/python3.6/site-packages/mlens/parallel/learner.py in _fit(self, transformers)
179 # Fit estimator
--> 180 self.estimator.fit(xtemp, ytemp)
181 self.fit_time_ = time() - t0
~/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py in fit(self, X, y, sample_weight)
315 tree = self._make_estimator(append=False,
--> 316 random_state=random_state)
317 trees.append(tree)
~/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/base.py in _make_estimator(self, append, random_state)
126 estimator.set_params(**dict((p, getattr(self, p))
--> 127 for p in self.estimator_params))
128
~/anaconda3/lib/python3.6/site-packages/sklearn/base.py in set_params(self, **params)
264 return self
--> 265 valid_params = self.get_params(deep=True)
266
~/anaconda3/lib/python3.6/site-packages/sklearn/base.py in get_params(self, deep)
240 finally:
--> 241 warnings.filters.pop(0)
242
IndexError: pop from empty list
During handling of the above exception, another exception occurred:
TransportableException Traceback (most recent call last)
~/anaconda3/lib/python3.6/site-packages/mlens/externals/joblib/parallel.py in retrieve(self)
702 if getattr(self._backend, 'supports_timeout', False):
--> 703 self._output.extend(job.get(timeout=self.timeout))
704 else:
~/anaconda3/lib/python3.6/multiprocessing/pool.py in get(self, timeout)
643 else:
--> 644 raise self._value
645
~/anaconda3/lib/python3.6/multiprocessing/pool.py in worker(inqueue, outqueue, initializer, initargs, maxtasks, wrap_exception)
118 try:
--> 119 result = (True, func(*args, **kwds))
120 except Exception as e:
~/anaconda3/lib/python3.6/site-packages/mlens/externals/joblib/_parallel_backends.py in __call__(self, *args, **kwargs)
358 text = format_exc(e_type, e_value, e_tb, context=10, tb_offset=1)
--> 359 raise TransportableException(text, e_type)
360
TransportableException: TransportableException
___________________________________________________________________________
IndexError Thu Jan 25 17:26:56 2018
PID: 3404 Python 3.6.3: /home/pyybor/anaconda3/bin/python
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/site-packages/mlens/externals/joblib/parallel.py in __call__(self=<mlens.externals.joblib.parallel.BatchedCalls object>)
130 def __init__(self, iterator_slice):
131 self.items = list(iterator_slice)
132 self._size = len(self.items)
133
134 def __call__(self):
--> 135 return [func(*args, **kwargs) for func, args, kwargs in self.items]
self.items = [(<mlens.parallel.learner.SubLearner object>, (), {})]
136
137 def __len__(self):
138 return self._size
139
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/site-packages/mlens/externals/joblib/parallel.py in <listcomp>(.0=<list_iterator object>)
130 def __init__(self, iterator_slice):
131 self.items = list(iterator_slice)
132 self._size = len(self.items)
133
134 def __call__(self):
--> 135 return [func(*args, **kwargs) for func, args, kwargs in self.items]
func = <mlens.parallel.learner.SubLearner object>
args = ()
kwargs = {}
136
137 def __len__(self):
138 return self._size
139
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/site-packages/mlens/parallel/learner.py in __call__(self=<mlens.parallel.learner.SubLearner object>)
120 else:
121 self.processing_index = ''
122
123 def __call__(self):
124 """Launch job"""
--> 125 return getattr(self, self.job)()
self = <mlens.parallel.learner.SubLearner object>
self.job = 'fit'
126
127 def fit(self, path=None):
128 """Fit sub-learner"""
129 if not path:
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/site-packages/mlens/parallel/learner.py in fit(self=<mlens.parallel.learner.SubLearner object>, path=[])
129 if not path:
130 path = self.path
131 t0 = time()
132 transformers = self._load_preprocess(path)
133
--> 134 self._fit(transformers)
self._fit = <bound method SubLearner._fit of <mlens.parallel.learner.SubLearner object>>
transformers = None
135
136 if self.out_array is not None:
137 self._predict(transformers, self.scorer is not None)
138
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/site-packages/mlens/parallel/learner.py in _fit(self=<mlens.parallel.learner.SubLearner object>, transformers=None)
175 t0 = time()
176 if transformers:
177 xtemp, ytemp = transformers.transform(xtemp, ytemp)
178
179 # Fit estimator
--> 180 self.estimator.fit(xtemp, ytemp)
self.estimator.fit = <bound method BaseForest.fit of RandomForestRegr... random_state=2017, verbose=0, warm_start=False)>
xtemp = array([[ 1.00000000e+00, 3.63000000e+02, 2....04, 2.66834227e-08,
5.41752459e+02]])
ytemp = array([ -1.95638686e-04, -3.79831391e-03, -2.9...9152394e-05, 3.94220996e-05, -8.21904840e-06])
181 self.fit_time_ = time() - t0
182
183 def _load_preprocess(self, path):
184 """Load preprocessing pipeline"""
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py in fit(self=RandomForestRegressor(bootstrap=True, criterion=..., random_state=2017, verbose=0, warm_start=False), X=array([[ 1.00000000e+00, 3.63000000e+02, 2....5e-08,
5.41752441e+02]], dtype=float32), y=array([[ -1.95638686e-04],
[ -3.79831391e... [ 3.94220996e-05],
[ -8.21904840e-06]]), sample_weight=None)
311 random_state.randint(MAX_INT, size=len(self.estimators_))
312
313 trees = []
314 for i in range(n_more_estimators):
315 tree = self._make_estimator(append=False,
--> 316 random_state=random_state)
random_state = <mtrand.RandomState object>
317 trees.append(tree)
318
319 # Parallel loop: we use the threading backend as the Cython code
320 # for fitting the trees is internally releasing the Python GIL
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/base.py in _make_estimator(self=RandomForestRegressor(bootstrap=True, criterion=..., random_state=2017, verbose=0, warm_start=False), append=False, random_state=<mtrand.RandomState object>)
122 Warning: This method should be used to properly instantiate new
123 sub-estimators.
124 """
125 estimator = clone(self.base_estimator_)
126 estimator.set_params(**dict((p, getattr(self, p))
--> 127 for p in self.estimator_params))
self.estimator_params = ('criterion', 'max_depth', 'min_samples_split', 'min_samples_leaf', 'min_weight_fraction_leaf', 'max_features', 'max_leaf_nodes', 'min_impurity_decrease', 'min_impurity_split', 'random_state')
128
129 if random_state is not None:
130 _set_random_states(estimator, random_state)
131
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/site-packages/sklearn/base.py in set_params(self=DecisionTreeRegressor(criterion='mse', max_depth...resort=False, random_state=None, splitter='best'), **params={'criterion': 'mse', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': 2017})
260 self
261 """
262 if not params:
263 # Simple optimization to gain speed (inspect is slow)
264 return self
--> 265 valid_params = self.get_params(deep=True)
valid_params = undefined
self.get_params = <bound method BaseEstimator.get_params of Decisi...esort=False, random_state=None, splitter='best')>
266
267 nested_params = defaultdict(dict) # grouped by prefix
268 for key, value in params.items():
269 key, delim, sub_key = key.partition('__')
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/site-packages/sklearn/base.py in get_params(self=DecisionTreeRegressor(criterion='mse', max_depth...resort=False, random_state=None, splitter='best'), deep=True)
236 value = getattr(self, key, None)
237 if len(w) and w[0].category == DeprecationWarning:
238 # if the parameter is deprecated, don't show it
239 continue
240 finally:
--> 241 warnings.filters.pop(0)
242
243 # XXX: should we rather test if instance of estimator?
244 if deep and hasattr(value, 'get_params'):
245 deep_items = value.get_params().items()
IndexError: pop from empty list
___________________________________________________________________________
During handling of the above exception, another exception occurred:
JoblibIndexError Traceback (most recent call last)
<ipython-input-49-f8f29c8588e9> in <module>()
----> 1 score_no_prep = evaluate_ensemble(None)
2 score_prep = evaluate_ensemble([1,3])
3 print("Test set score no feature propagation : %.3f" % score_no_prep)
4 print("Test set score with feature propagation: %.3f" % score_prep)
<ipython-input-46-a9bf13defd9a> in evaluate_ensemble(propagate_features)
2 """Wrapper for ensemble evaluation."""
3 ens = build_ensemble(True, propagate_features)
----> 4 ens.fit(X.iloc[:75].values, Y.iloc[:75].values)
5 pred = ens.predict(X.iloc[75:2000].values)
6 #print(pred[:5])
~/anaconda3/lib/python3.6/site-packages/mlens/ensemble/base.py in fit(self, X, y, **kwargs)
514 self._id_train.fit(X)
515
--> 516 out = self._backend.fit(X, y, **kwargs)
517 if out is not self._backend:
518 # fit_transform
~/anaconda3/lib/python3.6/site-packages/mlens/ensemble/base.py in fit(self, X, y, **kwargs)
156 with ParallelProcessing(self.backend, self.n_jobs,
157 max(self.verbose - 4, 0)) as manager:
--> 158 out = manager.stack(self, 'fit', X, y, **kwargs)
159
160 if self.verbose:
~/anaconda3/lib/python3.6/site-packages/mlens/parallel/backend.py in stack(self, caller, job, X, y, path, return_preds, wart_start, split, **kwargs)
653 job=job, X=X, y=y, path=path, warm_start=wart_start,
654 return_preds=return_preds, split=split, stack=True)
--> 655 return self.process(caller=caller, out=out, **kwargs)
656
657 def process(self, caller, out, **kwargs):
~/anaconda3/lib/python3.6/site-packages/mlens/parallel/backend.py in process(self, caller, out, **kwargs)
698 self.job.clear()
699
--> 700 self._partial_process(task, parallel, **kwargs)
701
702 if task.name in return_names:
~/anaconda3/lib/python3.6/site-packages/mlens/parallel/backend.py in _partial_process(self, task, parallel, **kwargs)
719 self._gen_prediction_array(task, self.job.job, self.__threading__)
720
--> 721 task(self.job.args(**kwargs), parallel=parallel)
722
723 if not task.__no_output__ and getattr(task, 'n_feature_prop', 0):
~/anaconda3/lib/python3.6/site-packages/mlens/parallel/layer.py in __call__(self, args, parallel)
150
151 parallel(delayed(sublearner, not _threading)()
--> 152 for learner in self.learners
153 for sublearner in learner(args, 'main'))
154
~/anaconda3/lib/python3.6/site-packages/mlens/externals/joblib/parallel.py in __call__(self, iterable)
791 # consumption.
792 self._iterating = False
--> 793 self.retrieve()
794 # Make sure that we get a last message telling us we are done
795 elapsed_time = time.time() - self._start_time
~/anaconda3/lib/python3.6/site-packages/mlens/externals/joblib/parallel.py in retrieve(self)
742 exception = exception_type(report)
743
--> 744 raise exception
745
746 def __call__(self, iterable):
JoblibIndexError: JoblibIndexError
___________________________________________________________________________
Multiprocessing exception:
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/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...b/python3.6/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
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/runpy.py in _run_code(code=<code object <module> at 0x7fde65662420, file "/...3.6/site-packages/ipykernel_launcher.py", line 5>, run_globals={'__annotations__': {}, '__builtins__': <module 'builtins' (built-in)>, '__cached__': '/home/pyybor/anaconda3/lib/python3.6/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__': '/home/pyybor/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py', '__loader__': <_frozen_importlib_external.SourceFileLoader object>, '__name__': '__main__', '__package__': '', '__spec__': ModuleSpec(name='ipykernel_launcher', loader=<_f...b/python3.6/site-packages/ipykernel_launcher.py'), 'app': <module 'ipykernel.kernelapp' from '/home/pyybor.../python3.6/site-packages/ipykernel/kernelapp.py'>, ...}, init_globals=None, mod_name='__main__', mod_spec=ModuleSpec(name='ipykernel_launcher', loader=<_f...b/python3.6/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 0x7fde65662420, file "/...3.6/site-packages/ipykernel_launcher.py", line 5>
run_globals = {'__annotations__': {}, '__builtins__': <module 'builtins' (built-in)>, '__cached__': '/home/pyybor/anaconda3/lib/python3.6/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__': '/home/pyybor/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py', '__loader__': <_frozen_importlib_external.SourceFileLoader object>, '__name__': '__main__', '__package__': '', '__spec__': ModuleSpec(name='ipykernel_launcher', loader=<_f...b/python3.6/site-packages/ipykernel_launcher.py'), 'app': <module 'ipykernel.kernelapp' from '/home/pyybor.../python3.6/site-packages/ipykernel/kernelapp.py'>, ...}
86 return run_globals
87
88 def _run_module_code(code, init_globals=None,
89 mod_name=None, mod_spec=None,
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/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()
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/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 #-----------------------------------------------------------------------------
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/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
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/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
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/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
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/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
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/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()
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/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
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/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
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/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
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/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)
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/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': 'score_no_prep = evaluate_ensemble(None)\nscore_pr...ore with feature propagation: %.3f" % score_prep)', 'silent': False, 'stop_on_error': True, 'store_history': True, 'user_expressions': {}}, 'header': {'date': datetime.datetime(2018, 1, 25, 17, 26, 56, 820132, tzinfo=tzlocal()), 'msg_id': '97F4C39E677248CE8B483E3539F82292', 'msg_type': 'execute_request', 'session': 'B2C82C6B86CB489C834937CEBD68684B', 'username': 'username', 'version': '5.0'}, 'metadata': {}, 'msg_id': '97F4C39E677248CE8B483E3539F82292', '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'B2C82C6B86CB489C834937CEBD68684B']
msg = {'buffers': [], 'content': {'allow_stdin': True, 'code': 'score_no_prep = evaluate_ensemble(None)\nscore_pr...ore with feature propagation: %.3f" % score_prep)', 'silent': False, 'stop_on_error': True, 'store_history': True, 'user_expressions': {}}, 'header': {'date': datetime.datetime(2018, 1, 25, 17, 26, 56, 820132, tzinfo=tzlocal()), 'msg_id': '97F4C39E677248CE8B483E3539F82292', 'msg_type': 'execute_request', 'session': 'B2C82C6B86CB489C834937CEBD68684B', 'username': 'username', 'version': '5.0'}, 'metadata': {}, 'msg_id': '97F4C39E677248CE8B483E3539F82292', '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()
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/site-packages/ipykernel/kernelbase.py in execute_request(self=<ipykernel.ipkernel.IPythonKernel object>, stream=<zmq.eventloop.zmqstream.ZMQStream object>, ident=[b'B2C82C6B86CB489C834937CEBD68684B'], parent={'buffers': [], 'content': {'allow_stdin': True, 'code': 'score_no_prep = evaluate_ensemble(None)\nscore_pr...ore with feature propagation: %.3f" % score_prep)', 'silent': False, 'stop_on_error': True, 'store_history': True, 'user_expressions': {}}, 'header': {'date': datetime.datetime(2018, 1, 25, 17, 26, 56, 820132, tzinfo=tzlocal()), 'msg_id': '97F4C39E677248CE8B483E3539F82292', 'msg_type': 'execute_request', 'session': 'B2C82C6B86CB489C834937CEBD68684B', 'username': 'username', 'version': '5.0'}, 'metadata': {}, 'msg_id': '97F4C39E677248CE8B483E3539F82292', '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()
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/site-packages/ipykernel/ipkernel.py in do_execute(self=<ipykernel.ipkernel.IPythonKernel object>, code='score_no_prep = evaluate_ensemble(None)\nscore_pr...ore with feature propagation: %.3f" % score_prep)', 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 = 'score_no_prep = evaluate_ensemble(None)\nscore_pr...ore with feature propagation: %.3f" % score_prep)'
store_history = True
silent = False
197 finally:
198 self._restore_input()
199
200 if res.error_before_exec is not None:
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/site-packages/ipykernel/zmqshell.py in run_cell(self=<ipykernel.zmqshell.ZMQInteractiveShell object>, *args=('score_no_prep = evaluate_ensemble(None)\nscore_pr...ore with feature propagation: %.3f" % score_prep)',), **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 = ('score_no_prep = evaluate_ensemble(None)\nscore_pr...ore with feature propagation: %.3f" % score_prep)',)
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()
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py in run_cell(self=<ipykernel.zmqshell.ZMQInteractiveShell object>, raw_cell='score_no_prep = evaluate_ensemble(None)\nscore_pr...ore with feature propagation: %.3f" % score_prep)', store_history=True, silent=False, shell_futures=True)
2693 self.displayhook.exec_result = result
2694
2695 # Execute the user code
2696 interactivity = "none" if silent else self.ast_node_interactivity
2697 has_raised = self.run_ast_nodes(code_ast.body, cell_name,
-> 2698 interactivity=interactivity, compiler=compiler, result=result)
interactivity = 'last_expr'
compiler = <IPython.core.compilerop.CachingCompiler object>
2699
2700 self.last_execution_succeeded = not has_raised
2701
2702 # Reset this so later displayed values do not modify the
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py in run_ast_nodes(self=<ipykernel.zmqshell.ZMQInteractiveShell object>, nodelist=[<_ast.Assign object>, <_ast.Assign object>, <_ast.Expr object>, <_ast.Expr object>], cell_name='<ipython-input-49-f8f29c8588e9>', interactivity='last', compiler=<IPython.core.compilerop.CachingCompiler object>, result=<ExecutionResult object at 7fde107b1d30, executi..._before_exec=None error_in_exec=None result=None>)
2797
2798 try:
2799 for i, node in enumerate(to_run_exec):
2800 mod = ast.Module([node])
2801 code = compiler(mod, cell_name, "exec")
-> 2802 if self.run_code(code, result):
self.run_code = <bound method InteractiveShell.run_code of <ipykernel.zmqshell.ZMQInteractiveShell object>>
code = <code object <module> at 0x7fde1829bf60, file "<ipython-input-49-f8f29c8588e9>", line 1>
result = <ExecutionResult object at 7fde107b1d30, executi..._before_exec=None error_in_exec=None result=None>
2803 return True
2804
2805 for i, node in enumerate(to_run_interactive):
2806 mod = ast.Interactive([node])
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py in run_code(self=<ipykernel.zmqshell.ZMQInteractiveShell object>, code_obj=<code object <module> at 0x7fde1829bf60, file "<ipython-input-49-f8f29c8588e9>", line 1>, result=<ExecutionResult object at 7fde107b1d30, executi..._before_exec=None error_in_exec=None result=None>)
2857 outflag = True # happens in more places, so it's easier as default
2858 try:
2859 try:
2860 self.hooks.pre_run_code_hook()
2861 #rprint('Running code', repr(code_obj)) # dbg
-> 2862 exec(code_obj, self.user_global_ns, self.user_ns)
code_obj = <code object <module> at 0x7fde1829bf60, file "<ipython-input-49-f8f29c8588e9>", line 1>
self.user_global_ns = {'DataFrame': <class 'pandas.core.frame.DataFrame'>, 'In': ['', "import numpy as np\nimport pandas as pd\ndf = pd.r...ay'],1)\nX_test = df_test.drop(['Day'],1)\nY = df.y", 'from sklearn.linear_model import LinearRegressio...elf).predict(X)\n return 1 * (p > p.mean())', 'import numpy as np\nfrom pandas import DataFrame\n...mport load_iris\n\nseed = 2017\nnp.random.seed(seed)', 'from mlens.ensemble import SuperLearner\nfrom skl... ensemble.add_meta(MyClass())\n return ensemble', 'import sklearn as sk\nsk.__version__', "get_ipython().system('pip install -U sklearn')", 'base = build_ensemble(False,[1, 3])\nbase.fit(X, ...[:5]\nprint("Input to meta learner :\\n %r" % pred)', 'def evaluate_ensemble(propagate_features):\n "...(mean_squared_error(pred, Y.iloc[75:200].values))', 'score_no_prep = evaluate_ensemble(None)\nscore_pr...ore with feature propagation: %.3f" % score_prep)', 'score_no_prep = evaluate_ensemble(None)\nscore_pr...ore with feature propagation: %.3f" % score_prep)', 'score_no_prep = evaluate_ensemble(None)\nscore_pr...ore with feature propagation: %.3f" % score_prep)', 'score_no_prep = evaluate_ensemble(None)\nscore_pr...ore with feature propagation: %.3f" % score_prep)', 'def evaluate_ensemble(propagate_features):\n "...mean_squared_error(pred, Y.iloc[75:2000].values))', 'score_no_prep = evaluate_ensemble(None)\nscore_pr...ore with feature propagation: %.3f" % score_prep)', 'def evaluate_ensemble(propagate_features):\n "...mean_squared_error(pred, Y.iloc[75:2000].values))', 'score_no_prep = evaluate_ensemble(None)\nscore_pr...ore with feature propagation: %.3f" % score_prep)', 'def evaluate_ensemble(propagate_features):\n "...mean_squared_error(pred, Y.iloc[75:2000].values))', 'score_no_prep = evaluate_ensemble(None)\nscore_pr...ore with feature propagation: %.3f" % score_prep)', 'def evaluate_ensemble(propagate_features):\n "...mean_squared_error(pred, Y.iloc[75:2000].values))', ...], 'LinearRegression': <class 'sklearn.linear_model.base.LinearRegression'>, 'MyClass': <class '__main__.MyClass'>, 'Out': {5: '0.19.1', 22: '0.19.1', 30: '0.19.1', 37: '0.19.1', 40: '0.19.1', 43: '0.19.1'}, 'RandomForestRegressor': <class 'sklearn.ensemble.forest.RandomForestRegressor'>, 'SVC': <class 'sklearn.svm.classes.SVC'>, 'SuperLearner': <class 'mlens.ensemble.super_learner.SuperLearner'>, 'X': Market Stock x0 x1 ...24e-05 100.000000
[623817 rows x 13 columns], 'X_test': Market Stock x0 x1 ...603e-05 104.118389
[640430 rows x 13 columns], ...}
self.user_ns = {'DataFrame': <class 'pandas.core.frame.DataFrame'>, 'In': ['', "import numpy as np\nimport pandas as pd\ndf = pd.r...ay'],1)\nX_test = df_test.drop(['Day'],1)\nY = df.y", 'from sklearn.linear_model import LinearRegressio...elf).predict(X)\n return 1 * (p > p.mean())', 'import numpy as np\nfrom pandas import DataFrame\n...mport load_iris\n\nseed = 2017\nnp.random.seed(seed)', 'from mlens.ensemble import SuperLearner\nfrom skl... ensemble.add_meta(MyClass())\n return ensemble', 'import sklearn as sk\nsk.__version__', "get_ipython().system('pip install -U sklearn')", 'base = build_ensemble(False,[1, 3])\nbase.fit(X, ...[:5]\nprint("Input to meta learner :\\n %r" % pred)', 'def evaluate_ensemble(propagate_features):\n "...(mean_squared_error(pred, Y.iloc[75:200].values))', 'score_no_prep = evaluate_ensemble(None)\nscore_pr...ore with feature propagation: %.3f" % score_prep)', 'score_no_prep = evaluate_ensemble(None)\nscore_pr...ore with feature propagation: %.3f" % score_prep)', 'score_no_prep = evaluate_ensemble(None)\nscore_pr...ore with feature propagation: %.3f" % score_prep)', 'score_no_prep = evaluate_ensemble(None)\nscore_pr...ore with feature propagation: %.3f" % score_prep)', 'def evaluate_ensemble(propagate_features):\n "...mean_squared_error(pred, Y.iloc[75:2000].values))', 'score_no_prep = evaluate_ensemble(None)\nscore_pr...ore with feature propagation: %.3f" % score_prep)', 'def evaluate_ensemble(propagate_features):\n "...mean_squared_error(pred, Y.iloc[75:2000].values))', 'score_no_prep = evaluate_ensemble(None)\nscore_pr...ore with feature propagation: %.3f" % score_prep)', 'def evaluate_ensemble(propagate_features):\n "...mean_squared_error(pred, Y.iloc[75:2000].values))', 'score_no_prep = evaluate_ensemble(None)\nscore_pr...ore with feature propagation: %.3f" % score_prep)', 'def evaluate_ensemble(propagate_features):\n "...mean_squared_error(pred, Y.iloc[75:2000].values))', ...], 'LinearRegression': <class 'sklearn.linear_model.base.LinearRegression'>, 'MyClass': <class '__main__.MyClass'>, 'Out': {5: '0.19.1', 22: '0.19.1', 30: '0.19.1', 37: '0.19.1', 40: '0.19.1', 43: '0.19.1'}, 'RandomForestRegressor': <class 'sklearn.ensemble.forest.RandomForestRegressor'>, 'SVC': <class 'sklearn.svm.classes.SVC'>, 'SuperLearner': <class 'mlens.ensemble.super_learner.SuperLearner'>, 'X': Market Stock x0 x1 ...24e-05 100.000000
[623817 rows x 13 columns], 'X_test': Market Stock x0 x1 ...603e-05 104.118389
[640430 rows x 13 columns], ...}
2863 finally:
2864 # Reset our crash handler in place
2865 sys.excepthook = old_excepthook
2866 except SystemExit as e:
...........................................................................
/home/pyybor/g_ch/<ipython-input-49-f8f29c8588e9> in <module>()
----> 1 score_no_prep = evaluate_ensemble(None)
2 score_prep = evaluate_ensemble([1,3])
3 print("Test set score no feature propagation : %.3f" % score_no_prep)
4 print("Test set score with feature propagation: %.3f" % score_prep)
...........................................................................
/home/pyybor/g_ch/<ipython-input-46-a9bf13defd9a> in evaluate_ensemble(propagate_features=None)
1 def evaluate_ensemble(propagate_features):
2 """Wrapper for ensemble evaluation."""
3 ens = build_ensemble(True, propagate_features)
----> 4 ens.fit(X.iloc[:75].values, Y.iloc[:75].values)
5 pred = ens.predict(X.iloc[75:2000].values)
6 #print(pred[:5])
7 return np.sqrt(mean_squared_error(pred, Y.iloc[75:2000].values))
8
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/site-packages/mlens/ensemble/base.py in fit(self=SuperLearner(array_check=2, backend=None, folds=...scorer=None, shuffle=False,
verbose=False), X=array([[ 1.00000000e+00, 3.63000000e+02, 2....04, 2.66834227e-08,
5.41752459e+02]]), y=array([ -1.95638686e-04, -3.79831391e-03, -2.9...9152394e-05, 3.94220996e-05, -8.21904840e-06]), **kwargs={})
511 X, y = check_inputs(X, y, self.array_check)
512
513 if self.model_selection:
514 self._id_train.fit(X)
515
--> 516 out = self._backend.fit(X, y, **kwargs)
out = undefined
self._backend.fit = <bound method Sequential.fit of Sequential(backe...rmers=[])],
verbose=0)],
verbose=False)>
X = array([[ 1.00000000e+00, 3.63000000e+02, 2....04, 2.66834227e-08,
5.41752459e+02]])
y = array([ -1.95638686e-04, -3.79831391e-03, -2.9...9152394e-05, 3.94220996e-05, -8.21904840e-06])
kwargs = {}
517 if out is not self._backend:
518 # fit_transform
519 return out
520 else:
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/site-packages/mlens/ensemble/base.py in fit(self=Sequential(backend='threading', dtype=<class 'nu...ormers=[])],
verbose=0)],
verbose=False), X=array([[ 1.00000000e+00, 3.63000000e+02, 2....04, 2.66834227e-08,
5.41752459e+02]]), y=array([ -1.95638686e-04, -3.79831391e-03, -2.9...9152394e-05, 3.94220996e-05, -8.21904840e-06]), **kwargs={})
153
154 f, t0 = print_job(self, "Fitting")
155
156 with ParallelProcessing(self.backend, self.n_jobs,
157 max(self.verbose - 4, 0)) as manager:
--> 158 out = manager.stack(self, 'fit', X, y, **kwargs)
out = undefined
manager.stack = <bound method ParallelProcessing.stack of <mlens.parallel.backend.ParallelProcessing object>>
self = Sequential(backend='threading', dtype=<class 'nu...ormers=[])],
verbose=0)],
verbose=False)
X = array([[ 1.00000000e+00, 3.63000000e+02, 2....04, 2.66834227e-08,
5.41752459e+02]])
y = array([ -1.95638686e-04, -3.79831391e-03, -2.9...9152394e-05, 3.94220996e-05, -8.21904840e-06])
kwargs = {}
159
160 if self.verbose:
161 print_time(t0, "{:<35}".format("Fit complete"), file=f)
162
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/site-packages/mlens/parallel/backend.py in stack(self=<mlens.parallel.backend.ParallelProcessing object>, caller=Sequential(backend='threading', dtype=<class 'nu...ormers=[])],
verbose=0)],
verbose=False), job='fit', X=array([[ 1.00000000e+00, 3.63000000e+02, 2....04, 2.66834227e-08,
5.41752459e+02]]), y=array([ -1.95638686e-04, -3.79831391e-03, -2.9...9152394e-05, 3.94220996e-05, -8.21904840e-06]), path=None, return_preds=False, wart_start=False, split=True, **kwargs={})
650 Prediction array(s).
651 """
652 out = self.initialize(
653 job=job, X=X, y=y, path=path, warm_start=wart_start,
654 return_preds=return_preds, split=split, stack=True)
--> 655 return self.process(caller=caller, out=out, **kwargs)
self.process = <bound method ParallelProcessing.process of <mlens.parallel.backend.ParallelProcessing object>>
caller = Sequential(backend='threading', dtype=<class 'nu...ormers=[])],
verbose=0)],
verbose=False)
out = {}
kwargs = {}
656
657 def process(self, caller, out, **kwargs):
658 """Process job.
659
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/site-packages/mlens/parallel/backend.py in process(self=<mlens.parallel.backend.ParallelProcessing object>, caller=Sequential(backend='threading', dtype=<class 'nu...ormers=[])],
verbose=0)],
verbose=False), out=None, **kwargs={})
695 backend=self.backend) as parallel:
696
697 for task in caller:
698 self.job.clear()
699
--> 700 self._partial_process(task, parallel, **kwargs)
self._partial_process = <bound method ParallelProcessing._partial_proces...lens.parallel.backend.ParallelProcessing object>>
task = Layer(backend='threading', dtype=<class 'numpy.f..._exception=True, transformers=[])],
verbose=0)
parallel = Parallel(n_jobs=-1)
kwargs = {}
701
702 if task.name in return_names:
703 out.append(self.get_preds(dtype=_dtype(task)))
704
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/site-packages/mlens/parallel/backend.py in _partial_process(self=<mlens.parallel.backend.ParallelProcessing object>, task=Layer(backend='threading', dtype=<class 'numpy.f..._exception=True, transformers=[])],
verbose=0), parallel=Parallel(n_jobs=-1), **kwargs={})
716 task.setup(self.job.predict_in, self.job.y, self.job.job)
717
718 if not task.__no_output__:
719 self._gen_prediction_array(task, self.job.job, self.__threading__)
720
--> 721 task(self.job.args(**kwargs), parallel=parallel)
task = Layer(backend='threading', dtype=<class 'numpy.f..._exception=True, transformers=[])],
verbose=0)
self.job.args = <bound method Job.args of <mlens.parallel.backend.Job object>>
kwargs = {}
parallel = Parallel(n_jobs=-1)
722
723 if not task.__no_output__ and getattr(task, 'n_feature_prop', 0):
724 self._propagate_features(task)
725
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/site-packages/mlens/parallel/layer.py in __call__(self=Layer(backend='threading', dtype=<class 'numpy.f..._exception=True, transformers=[])],
verbose=0), args={'auxiliary': {'P': None, 'X': array([[ 1.00000000e+00, 3.63000000e+02, 2....04, 2.66834227e-08,
5.41752459e+02]]), 'y': array([ -1.95638686e-04, -3.79831391e-03, -2.9...9152394e-05, 3.94220996e-05, -8.21904840e-06])}, 'dir': [('randomforestregressor-2.0.0', <mlens.parallel.learner.IndexedEstimator object>), ('randomforestregressor-2.0.2', <mlens.parallel.learner.IndexedEstimator object>), ('randomforestregressor-1.0.2', <mlens.parallel.learner.IndexedEstimator object>)], 'job': 'fit', 'main': {'P': array([[ -6.91946599e-36, 6.62666976e-01],
....72381141e-05, 6.66164560e-05]], dtype=float32), 'X': array([[ 1.00000000e+00, 3.63000000e+02, 2....04, 2.66834227e-08,
5.41752459e+02]]), 'y': array([ -1.95638686e-04, -3.79831391e-03, -2.9...9152394e-05, 3.94220996e-05, -8.21904840e-06])}}, parallel=Parallel(n_jobs=-1))
147 if self.verbose >= 2:
148 safe_print(msg.format('Learners ...'), file=f, end=e2)
149 t1 = time()
150
151 parallel(delayed(sublearner, not _threading)()
--> 152 for learner in self.learners
self.learners = [Learner(attr='predict', backend='threading', dty...=False,
raise_on_exception=True, scorer=None), Learner(attr='predict', backend='threading', dty...=False,
raise_on_exception=True, scorer=None)]
153 for sublearner in learner(args, 'main'))
154
155 if self.verbose >= 2:
156 print_time(t1, 'done', file=f)
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/site-packages/mlens/externals/joblib/parallel.py in __call__(self=Parallel(n_jobs=-1), iterable=<generator object Layer.__call__.<locals>.<genexpr>>)
788 if pre_dispatch == "all" or n_jobs == 1:
789 # The iterable was consumed all at once by the above for loop.
790 # No need to wait for async callbacks to trigger to
791 # consumption.
792 self._iterating = False
--> 793 self.retrieve()
self.retrieve = <bound method Parallel.retrieve of Parallel(n_jobs=-1)>
794 # Make sure that we get a last message telling us we are done
795 elapsed_time = time.time() - self._start_time
796 self._print('Done %3i out of %3i | elapsed: %s finished',
797 (len(self._output), len(self._output),
---------------------------------------------------------------------------
Sub-process traceback:
---------------------------------------------------------------------------
IndexError Thu Jan 25 17:26:56 2018
PID: 3404 Python 3.6.3: /home/pyybor/anaconda3/bin/python
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/site-packages/mlens/externals/joblib/parallel.py in __call__(self=<mlens.externals.joblib.parallel.BatchedCalls object>)
130 def __init__(self, iterator_slice):
131 self.items = list(iterator_slice)
132 self._size = len(self.items)
133
134 def __call__(self):
--> 135 return [func(*args, **kwargs) for func, args, kwargs in self.items]
self.items = [(<mlens.parallel.learner.SubLearner object>, (), {})]
136
137 def __len__(self):
138 return self._size
139
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/site-packages/mlens/externals/joblib/parallel.py in <listcomp>(.0=<list_iterator object>)
130 def __init__(self, iterator_slice):
131 self.items = list(iterator_slice)
132 self._size = len(self.items)
133
134 def __call__(self):
--> 135 return [func(*args, **kwargs) for func, args, kwargs in self.items]
func = <mlens.parallel.learner.SubLearner object>
args = ()
kwargs = {}
136
137 def __len__(self):
138 return self._size
139
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/home/pyybor/anaconda3/lib/python3.6/site-packages/mlens/parallel/learner.py in __call__(self=<mlens.parallel.learner.SubLearner object>)
120 else:
121 self.processing_index = ''
122
123 def __call__(self):
124 """Launch job"""
--> 125 return getattr(self, self.job)()
self = <mlens.parallel.learner.SubLearner object>
self.job = 'fit'
126
127 def fit(self, path=None):
128 """Fit sub-learner"""
129 if not path:
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/site-packages/mlens/parallel/learner.py in fit(self=<mlens.parallel.learner.SubLearner object>, path=[])
129 if not path:
130 path = self.path
131 t0 = time()
132 transformers = self._load_preprocess(path)
133
--> 134 self._fit(transformers)
self._fit = <bound method SubLearner._fit of <mlens.parallel.learner.SubLearner object>>
transformers = None
135
136 if self.out_array is not None:
137 self._predict(transformers, self.scorer is not None)
138
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/home/pyybor/anaconda3/lib/python3.6/site-packages/mlens/parallel/learner.py in _fit(self=<mlens.parallel.learner.SubLearner object>, transformers=None)
175 t0 = time()
176 if transformers:
177 xtemp, ytemp = transformers.transform(xtemp, ytemp)
178
179 # Fit estimator
--> 180 self.estimator.fit(xtemp, ytemp)
self.estimator.fit = <bound method BaseForest.fit of RandomForestRegr... random_state=2017, verbose=0, warm_start=False)>
xtemp = array([[ 1.00000000e+00, 3.63000000e+02, 2....04, 2.66834227e-08,
5.41752459e+02]])
ytemp = array([ -1.95638686e-04, -3.79831391e-03, -2.9...9152394e-05, 3.94220996e-05, -8.21904840e-06])
181 self.fit_time_ = time() - t0
182
183 def _load_preprocess(self, path):
184 """Load preprocessing pipeline"""
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py in fit(self=RandomForestRegressor(bootstrap=True, criterion=..., random_state=2017, verbose=0, warm_start=False), X=array([[ 1.00000000e+00, 3.63000000e+02, 2....5e-08,
5.41752441e+02]], dtype=float32), y=array([[ -1.95638686e-04],
[ -3.79831391e... [ 3.94220996e-05],
[ -8.21904840e-06]]), sample_weight=None)
311 random_state.randint(MAX_INT, size=len(self.estimators_))
312
313 trees = []
314 for i in range(n_more_estimators):
315 tree = self._make_estimator(append=False,
--> 316 random_state=random_state)
random_state = <mtrand.RandomState object>
317 trees.append(tree)
318
319 # Parallel loop: we use the threading backend as the Cython code
320 # for fitting the trees is internally releasing the Python GIL
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/base.py in _make_estimator(self=RandomForestRegressor(bootstrap=True, criterion=..., random_state=2017, verbose=0, warm_start=False), append=False, random_state=<mtrand.RandomState object>)
122 Warning: This method should be used to properly instantiate new
123 sub-estimators.
124 """
125 estimator = clone(self.base_estimator_)
126 estimator.set_params(**dict((p, getattr(self, p))
--> 127 for p in self.estimator_params))
self.estimator_params = ('criterion', 'max_depth', 'min_samples_split', 'min_samples_leaf', 'min_weight_fraction_leaf', 'max_features', 'max_leaf_nodes', 'min_impurity_decrease', 'min_impurity_split', 'random_state')
128
129 if random_state is not None:
130 _set_random_states(estimator, random_state)
131
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/site-packages/sklearn/base.py in set_params(self=DecisionTreeRegressor(criterion='mse', max_depth...resort=False, random_state=None, splitter='best'), **params={'criterion': 'mse', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': 2017})
260 self
261 """
262 if not params:
263 # Simple optimization to gain speed (inspect is slow)
264 return self
--> 265 valid_params = self.get_params(deep=True)
valid_params = undefined
self.get_params = <bound method BaseEstimator.get_params of Decisi...esort=False, random_state=None, splitter='best')>
266
267 nested_params = defaultdict(dict) # grouped by prefix
268 for key, value in params.items():
269 key, delim, sub_key = key.partition('__')
...........................................................................
/home/pyybor/anaconda3/lib/python3.6/site-packages/sklearn/base.py in get_params(self=DecisionTreeRegressor(criterion='mse', max_depth...resort=False, random_state=None, splitter='best'), deep=True)
236 value = getattr(self, key, None)
237 if len(w) and w[0].category == DeprecationWarning:
238 # if the parameter is deprecated, don't show it
239 continue
240 finally:
--> 241 warnings.filters.pop(0)
242
243 # XXX: should we rather test if instance of estimator?
244 if deep and hasattr(value, 'get_params'):
245 deep_items = value.get_params().items()
IndexError: pop from empty list
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