@GaelVaroquaux Yup, it only occurs with n_jobs > 1. Fixing the errors with the invalid y does not change it. However, the scoring fixed it. Had missed that I hadn't updated the scoring.

@jnothman Thanks for that, this was the issue! Not entirely sure why this brought out the issue though.

fitted_models = {}

Loop through model pipelines, tuning each one and saving it to fitted_models

for name, pipeline in pipelines.items():
# Create cross-validation object from pipeline and hyperparameters
model = GridSearchCV(pipeline, hyperparameters[name], cv = 10, n_jobs=-1)

# Fit model on X_train, y_train
model.fit(X_train, y_train)

# Store model in fitted_models[name] 
fitted_models[name] = model

# Print '{name} has been fitted'
print('{0} has been fitted'.format(name))

It gives following error:
RemoteTraceback Traceback (most recent call last)
RemoteTraceback:
"""
Traceback (most recent call last):
File "C:\Users\HP\Anaconda3\lib\site-packages\sklearn\externals\joblib_parallel_backends.py", line 350, in call
return self.func(*args, **kwargs)
File "C:\Users\HP\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py", line 131, in call
return [func(*args, **kwargs) for func, args, kwargs in self.items]
File "C:\Users\HP\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py", line 131, in
return [func(*args, **kwargs) for func, args, kwargs in self.items]
File "C:\Users\HP\Anaconda3\lib\site-packages\sklearn\model_selection_validation.py", line 458, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\HP\Anaconda3\lib\site-packages\sklearn\pipeline.py", line 248, in fit
Xt, fit_params = self._fit(X, y, **fit_params)
File "C:\Users\HP\Anaconda3\lib\site-packages\sklearn\pipeline.py", line 213, in _fit
**fit_params_steps[name])
File "C:\Users\HP\Anaconda3\lib\site-packages\sklearn\externals\joblib\memory.py", line 362, in call
return self.func(*args, **kwargs)
File "C:\Users\HP\Anaconda3\lib\site-packages\sklearn\pipeline.py", line 581, in _fit_transform_one
res = transformer.fit_transform(X, y, **fit_params)
File "C:\Users\HP\Anaconda3\lib\site-packages\sklearn\base.py", line 520, in fit_transform
return self.fit(X, y, **fit_params).transform(X)
File "C:\Users\HP\Anaconda3\lib\site-packages\sklearn\preprocessing\data.py", line 590, in fit
return self.partial_fit(X, y)
File "C:\Users\HP\Anaconda3\lib\site-packages\sklearn\preprocessing\data.py", line 612, in partial_fit
warn_on_dtype=True, estimator=self, dtype=FLOAT_DTYPES)
File "C:\Users\HP\Anaconda3\lib\site-packages\sklearn\utils\validation.py", line 453, in check_array
_assert_all_finite(array)
File "C:\Users\HP\Anaconda3\lib\site-packages\sklearn\utils\validation.py", line 44, in _assert_all_finite
" or a value too large for %r." % X.dtype)
ValueError: Input contains NaN, infinity or a value too large for dtype('float64').

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "C:\Users\HP\Anaconda3\lib\multiprocessing\pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "C:\Users\HP\Anaconda3\lib\site-packages\sklearn\externals\joblib_parallel_backends.py", line 359, in call
raise TransportableException(text, e_type)
sklearn.externals.joblib.my_exceptions.TransportableException: TransportableException

ValueError Wed Oct 3 16:16:44 2018
PID: 7136 Python 3.6.5: C:\Users\HP\Anaconda3\python.exe
...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in call(self=<sklearn.externals.joblib.parallel.BatchedCalls object>)
126 def init(self, iterator_slice):
127 self.items = list(iterator_slice)
128 self._size = len(self.items)
129
130 def call(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
self.items = [(, (Pipeline(memory=None,
steps=[('standardscal...0.0001,
verbose=0, warm_start=False))]), Token Number AGE ... ... 1

[97 rows x 449 columns], 54 0
18 0
64 0
103 1
85 0
45 ...9 0
Name: ATTRITION, Length: 97, dtype: int64, {'score': }, array([10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 2..., 87, 88, 89, 90, 91, 92, 93, 94, 95,
96]), array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11]), 0, {'logisticregression__C': 0.001}), {'error_score': 'raise', 'fit_params': {}, 'return_n_test_samples': True, 'return_parameters': False, 'return_times': True, 'return_train_score': 'warn'})]
132
133 def len(self):
134 return self._size
135

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in (.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 =
args = (Pipeline(memory=None,
steps=[('standardscal...0.0001,
verbose=0, warm_start=False))]), Token Number AGE ... ... 1

[97 rows x 449 columns], 54 0
18 0
64 0
103 1
85 0
45 ...9 0
Name: ATTRITION, Length: 97, dtype: int64, {'score': }, array([10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 2..., 87, 88, 89, 90, 91, 92, 93, 94, 95,
96]), array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11]), 0, {'logisticregression__C': 0.001})
kwargs = {'error_score': 'raise', 'fit_params': {}, 'return_n_test_samples': True, 'return_parameters': False, 'return_times': True, 'return_train_score': 'warn'}
132
133 def len(self):
134 return self._size
135

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\model_selection_validation.py in _fit_and_score(estimator=Pipeline(memory=None,
steps=[('standardscal...0.0001,
verbose=0, warm_start=False))]), X= Token Number AGE ... ... 1

[97 rows x 449 columns], y=54 0
18 0
64 0
103 1
85 0
45 ...9 0
Name: ATTRITION, Length: 97, dtype: int64, scorer={'score': }, train=array([10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 2..., 87, 88, 89, 90, 91, 92, 93, 94, 95,
96]), test=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11]), verbose=0, parameters={'logisticregression__C': 0.001}, fit_params={}, return_train_score='warn', return_parameters=False, return_n_test_samples=True, return_times=True, error_score='raise')
453
454 try:
455 if y_train is None:
456 estimator.fit(X_train, **fit_params)
457 else:
--> 458 estimator.fit(X_train, y_train, **fit_params)
estimator.fit = <bound method Pipeline.fit of Pipeline(memory=No....0001,
verbose=0, warm_start=False))])>
X_train = Token Number AGE ... ... 1

[86 rows x 449 columns]
y_train = 8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64
fit_params = {}
459
460 except Exception as e:
461 # Note fit time as time until error
462 fit_time = time.time() - start_time

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\pipeline.py in fit(self=Pipeline(memory=None,
steps=[('standardscal...0.0001,
verbose=0, warm_start=False))]), X= Token Number AGE ... ... 1

[86 rows x 449 columns], y=8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64, **fit_params={})
243 Returns
244 -------
245 self : Pipeline
246 This estimator
247 """
--> 248 Xt, fit_params = self._fit(X, y, **fit_params)
Xt = undefined
fit_params = {}
self._fit = <bound method Pipeline._fit of Pipeline(memory=N....0001,
verbose=0, warm_start=False))])>
X = Token Number AGE ... ... 1

[86 rows x 449 columns]
y = 8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64
249 if self._final_estimator is not None:
250 self._final_estimator.fit(Xt, y, **fit_params)
251 return self
252

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\pipeline.py in _fit(self=Pipeline(memory=None,
steps=[('standardscal...0.0001,
verbose=0, warm_start=False))]), X= Token Number AGE ... ... 1

[86 rows x 449 columns], y=8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64, **fit_params={})
208 else:
209 cloned_transformer = clone(transformer)
210 # Fit or load from cache the current transfomer
211 Xt, fitted_transformer = fit_transform_one_cached(
212 cloned_transformer, None, Xt, y,
--> 213 **fit_params_steps[name])
fit_params_steps = {'logisticregression': {}, 'standardscaler': {}}
name = 'standardscaler'
214 # Replace the transformer of the step with the fitted
215 # transformer. This is necessary when loading the transformer
216 # from the cache.
217 self.steps[step_idx] = (name, fitted_transformer)

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\externals\joblib\memory.py in call(self=NotMemorizedFunc(func=<function _fit_transform_one at 0x000001FC877EC8C8>), *args=(StandardScaler(copy=True, with_mean=True, with_std=True), None, Token Number AGE ... ... 1

[86 rows x 449 columns], 8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64), **kwargs={})
357 # Should be a light as possible (for speed)
358 def init(self, func):
359 self.func = func
360
361 def call(self, *args, **kwargs):
--> 362 return self.func(*args, **kwargs)
self.func =
args = (StandardScaler(copy=True, with_mean=True, with_std=True), None, Token Number AGE ... ... 1

[86 rows x 449 columns], 8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64)
kwargs = {}
363
364 def call_and_shelve(self, *args, **kwargs):
365 return NotMemorizedResult(self.func(*args, **kwargs))
366

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\pipeline.py in _fit_transform_one(transformer=StandardScaler(copy=True, with_mean=True, with_std=True), weight=None, X= Token Number AGE ... ... 1

[86 rows x 449 columns], y=8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64, **fit_params={})
576
577
578 def _fit_transform_one(transformer, weight, X, y,
579 **fit_params):
580 if hasattr(transformer, 'fit_transform'):
--> 581 res = transformer.fit_transform(X, y, **fit_params)
res = undefined
transformer.fit_transform = <bound method TransformerMixin.fit_transform of ...Scaler(copy=True, with_mean=True, with_std=True)>
X = Token Number AGE ... ... 1

[86 rows x 449 columns]
y = 8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64
fit_params = {}
582 else:
583 res = transformer.fit(X, y, **fit_params).transform(X)
584 # if we have a weight for this transformer, multiply output
585 if weight is None:

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\base.py in fit_transform(self=StandardScaler(copy=True, with_mean=True, with_std=True), X= Token Number AGE ... ... 1

[86 rows x 449 columns], y=8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64, **fit_params={})
515 if y is None:
516 # fit method of arity 1 (unsupervised transformation)
517 return self.fit(X, **fit_params).transform(X)
518 else:
519 # fit method of arity 2 (supervised transformation)
--> 520 return self.fit(X, y, **fit_params).transform(X)
self.fit = <bound method StandardScaler.fit of StandardScaler(copy=True, with_mean=True, with_std=True)>
X = Token Number AGE ... ... 1

[86 rows x 449 columns]
y = 8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64
fit_params.transform = undefined
521
522
523 class DensityMixin(object):
524 """Mixin class for all density estimators in scikit-learn."""

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\preprocessing\data.py in fit(self=StandardScaler(copy=True, with_mean=True, with_std=True), X= Token Number AGE ... ... 1

[86 rows x 449 columns], y=8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64)
585 y : Passthrough for Pipeline compatibility.
586 """
587
588 # Reset internal state before fitting
589 self._reset()
--> 590 return self.partial_fit(X, y)
self.partial_fit = <bound method StandardScaler.partial_fit of Stan...Scaler(copy=True, with_mean=True, with_std=True)>
X = Token Number AGE ... ... 1

[86 rows x 449 columns]
y = 8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64
591
592 def partial_fit(self, X, y=None):
593 """Online computation of mean and std on X for later scaling.
594 All of X is processed as a single batch. This is intended for cases

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\preprocessing\data.py in partial_fit(self=StandardScaler(copy=True, with_mean=True, with_std=True), X= Token Number AGE ... ... 1

[86 rows x 449 columns], y=8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64)
607 used for later scaling along the features axis.
608
609 y : Passthrough for Pipeline compatibility.
610 """
611 X = check_array(X, accept_sparse=('csr', 'csc'), copy=self.copy,
--> 612 warn_on_dtype=True, estimator=self, dtype=FLOAT_DTYPES)
self = StandardScaler(copy=True, with_mean=True, with_std=True)
613
614 # Even in the case of with_mean=False, we update the mean anyway
615 # This is needed for the incremental computation of the var
616 # See incr_mean_variance_axis and _incremental_mean_variance_axis

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_array(array=array([[2.0287700e+05, 4.6000000e+01, 2.3000000e...0000e+00,
0.0000000e+00, 1.0000000e+00]]), accept_sparse=('csr', 'csc'), dtype=<class 'numpy.float64'>, order=None, copy=True, force_all_finite=True, ensure_2d=True, allow_nd=False, ensure_min_samples=1, ensure_min_features=1, warn_on_dtype=True, estimator=StandardScaler(copy=True, with_mean=True, with_std=True))
448 array = array.astype(np.float64)
449 if not allow_nd and array.ndim >= 3:
450 raise ValueError("Found array with dim %d. %s expected <= 2."
451 % (array.ndim, estimator_name))
452 if force_all_finite:
--> 453 _assert_all_finite(array)
array = array([[2.0287700e+05, 4.6000000e+01, 2.3000000e...0000e+00,
0.0000000e+00, 1.0000000e+00]])
454
455 shape_repr = _shape_repr(array.shape)
456 if ensure_min_samples > 0:
457 n_samples = _num_samples(array)

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\utils\validation.py in _assert_all_finite(X=array([[2.0287700e+05, 4.6000000e+01, 2.3000000e...0000e+00,
0.0000000e+00, 1.0000000e+00]]))
39 # everything is finite; fall back to O(n) space np.isfinite to prevent
40 # false positives from overflow in sum method.
41 if (X.dtype.char in np.typecodes['AllFloat'] and not np.isfinite(X.sum())
42 and not np.isfinite(X).all()):
43 raise ValueError("Input contains NaN, infinity"
---> 44 " or a value too large for %r." % X.dtype)
X.dtype = dtype('float64')
45
46
47 def assert_all_finite(X):
48 """Throw a ValueError if X contains NaN or infinity.

ValueError: Input contains NaN, infinity or a value too large for dtype('float64').

"""

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

TransportableException Traceback (most recent call last)
~\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in retrieve(self)
698 if getattr(self._backend, 'supports_timeout', False):
--> 699 self._output.extend(job.get(timeout=self.timeout))
700 else:

~\Anaconda3\lib\multiprocessing\pool.py in get(self, timeout)
643 else:
--> 644 raise self._value
645

TransportableException: TransportableException

ValueError Wed Oct 3 16:16:44 2018
PID: 7136 Python 3.6.5: C:\Users\HP\Anaconda3\python.exe
...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in call(self=<sklearn.externals.joblib.parallel.BatchedCalls object>)
126 def init(self, iterator_slice):
127 self.items = list(iterator_slice)
128 self._size = len(self.items)
129
130 def call(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
self.items = [(, (Pipeline(memory=None,
steps=[('standardscal...0.0001,
verbose=0, warm_start=False))]), Token Number AGE ... ... 1

[97 rows x 449 columns], 54 0
18 0
64 0
103 1
85 0
45 ...9 0
Name: ATTRITION, Length: 97, dtype: int64, {'score': }, array([10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 2..., 87, 88, 89, 90, 91, 92, 93, 94, 95,
96]), array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11]), 0, {'logisticregression__C': 0.001}), {'error_score': 'raise', 'fit_params': {}, 'return_n_test_samples': True, 'return_parameters': False, 'return_times': True, 'return_train_score': 'warn'})]
132
133 def len(self):
134 return self._size
135

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in (.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 =
args = (Pipeline(memory=None,
steps=[('standardscal...0.0001,
verbose=0, warm_start=False))]), Token Number AGE ... ... 1

[97 rows x 449 columns], 54 0
18 0
64 0
103 1
85 0
45 ...9 0
Name: ATTRITION, Length: 97, dtype: int64, {'score': }, array([10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 2..., 87, 88, 89, 90, 91, 92, 93, 94, 95,
96]), array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11]), 0, {'logisticregression__C': 0.001})
kwargs = {'error_score': 'raise', 'fit_params': {}, 'return_n_test_samples': True, 'return_parameters': False, 'return_times': True, 'return_train_score': 'warn'}
132
133 def len(self):
134 return self._size
135

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\model_selection_validation.py in _fit_and_score(estimator=Pipeline(memory=None,
steps=[('standardscal...0.0001,
verbose=0, warm_start=False))]), X= Token Number AGE ... ... 1

[97 rows x 449 columns], y=54 0
18 0
64 0
103 1
85 0
45 ...9 0
Name: ATTRITION, Length: 97, dtype: int64, scorer={'score': }, train=array([10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 2..., 87, 88, 89, 90, 91, 92, 93, 94, 95,
96]), test=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11]), verbose=0, parameters={'logisticregression__C': 0.001}, fit_params={}, return_train_score='warn', return_parameters=False, return_n_test_samples=True, return_times=True, error_score='raise')
453
454 try:
455 if y_train is None:
456 estimator.fit(X_train, **fit_params)
457 else:
--> 458 estimator.fit(X_train, y_train, **fit_params)
estimator.fit = <bound method Pipeline.fit of Pipeline(memory=No....0001,
verbose=0, warm_start=False))])>
X_train = Token Number AGE ... ... 1

[86 rows x 449 columns]
y_train = 8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64
fit_params = {}
459
460 except Exception as e:
461 # Note fit time as time until error
462 fit_time = time.time() - start_time

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\pipeline.py in fit(self=Pipeline(memory=None,
steps=[('standardscal...0.0001,
verbose=0, warm_start=False))]), X= Token Number AGE ... ... 1

[86 rows x 449 columns], y=8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64, **fit_params={})
243 Returns
244 -------
245 self : Pipeline
246 This estimator
247 """
--> 248 Xt, fit_params = self._fit(X, y, **fit_params)
Xt = undefined
fit_params = {}
self._fit = <bound method Pipeline._fit of Pipeline(memory=N....0001,
verbose=0, warm_start=False))])>
X = Token Number AGE ... ... 1

[86 rows x 449 columns]
y = 8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64
249 if self._final_estimator is not None:
250 self._final_estimator.fit(Xt, y, **fit_params)
251 return self
252

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\pipeline.py in _fit(self=Pipeline(memory=None,
steps=[('standardscal...0.0001,
verbose=0, warm_start=False))]), X= Token Number AGE ... ... 1

[86 rows x 449 columns], y=8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64, **fit_params={})
208 else:
209 cloned_transformer = clone(transformer)
210 # Fit or load from cache the current transfomer
211 Xt, fitted_transformer = fit_transform_one_cached(
212 cloned_transformer, None, Xt, y,
--> 213 **fit_params_steps[name])
fit_params_steps = {'logisticregression': {}, 'standardscaler': {}}
name = 'standardscaler'
214 # Replace the transformer of the step with the fitted
215 # transformer. This is necessary when loading the transformer
216 # from the cache.
217 self.steps[step_idx] = (name, fitted_transformer)

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\externals\joblib\memory.py in call(self=NotMemorizedFunc(func=<function _fit_transform_one at 0x000001FC877EC8C8>), *args=(StandardScaler(copy=True, with_mean=True, with_std=True), None, Token Number AGE ... ... 1

[86 rows x 449 columns], 8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64), **kwargs={})
357 # Should be a light as possible (for speed)
358 def init(self, func):
359 self.func = func
360
361 def call(self, *args, **kwargs):
--> 362 return self.func(*args, **kwargs)
self.func =
args = (StandardScaler(copy=True, with_mean=True, with_std=True), None, Token Number AGE ... ... 1

[86 rows x 449 columns], 8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64)
kwargs = {}
363
364 def call_and_shelve(self, *args, **kwargs):
365 return NotMemorizedResult(self.func(*args, **kwargs))
366

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\pipeline.py in _fit_transform_one(transformer=StandardScaler(copy=True, with_mean=True, with_std=True), weight=None, X= Token Number AGE ... ... 1

[86 rows x 449 columns], y=8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64, **fit_params={})
576
577
578 def _fit_transform_one(transformer, weight, X, y,
579 **fit_params):
580 if hasattr(transformer, 'fit_transform'):
--> 581 res = transformer.fit_transform(X, y, **fit_params)
res = undefined
transformer.fit_transform = <bound method TransformerMixin.fit_transform of ...Scaler(copy=True, with_mean=True, with_std=True)>
X = Token Number AGE ... ... 1

[86 rows x 449 columns]
y = 8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64
fit_params = {}
582 else:
583 res = transformer.fit(X, y, **fit_params).transform(X)
584 # if we have a weight for this transformer, multiply output
585 if weight is None:

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\base.py in fit_transform(self=StandardScaler(copy=True, with_mean=True, with_std=True), X= Token Number AGE ... ... 1

[86 rows x 449 columns], y=8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64, **fit_params={})
515 if y is None:
516 # fit method of arity 1 (unsupervised transformation)
517 return self.fit(X, **fit_params).transform(X)
518 else:
519 # fit method of arity 2 (supervised transformation)
--> 520 return self.fit(X, y, **fit_params).transform(X)
self.fit = <bound method StandardScaler.fit of StandardScaler(copy=True, with_mean=True, with_std=True)>
X = Token Number AGE ... ... 1

[86 rows x 449 columns]
y = 8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64
fit_params.transform = undefined
521
522
523 class DensityMixin(object):
524 """Mixin class for all density estimators in scikit-learn."""

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\preprocessing\data.py in fit(self=StandardScaler(copy=True, with_mean=True, with_std=True), X= Token Number AGE ... ... 1

[86 rows x 449 columns], y=8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64)
585 y : Passthrough for Pipeline compatibility.
586 """
587
588 # Reset internal state before fitting
589 self._reset()
--> 590 return self.partial_fit(X, y)
self.partial_fit = <bound method StandardScaler.partial_fit of Stan...Scaler(copy=True, with_mean=True, with_std=True)>
X = Token Number AGE ... ... 1

[86 rows x 449 columns]
y = 8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64
591
592 def partial_fit(self, X, y=None):
593 """Online computation of mean and std on X for later scaling.
594 All of X is processed as a single batch. This is intended for cases

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\preprocessing\data.py in partial_fit(self=StandardScaler(copy=True, with_mean=True, with_std=True), X= Token Number AGE ... ... 1

[86 rows x 449 columns], y=8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64)
607 used for later scaling along the features axis.
608
609 y : Passthrough for Pipeline compatibility.
610 """
611 X = check_array(X, accept_sparse=('csr', 'csc'), copy=self.copy,
--> 612 warn_on_dtype=True, estimator=self, dtype=FLOAT_DTYPES)
self = StandardScaler(copy=True, with_mean=True, with_std=True)
613
614 # Even in the case of with_mean=False, we update the mean anyway
615 # This is needed for the incremental computation of the var
616 # See incr_mean_variance_axis and _incremental_mean_variance_axis

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_array(array=array([[2.0287700e+05, 4.6000000e+01, 2.3000000e...0000e+00,
0.0000000e+00, 1.0000000e+00]]), accept_sparse=('csr', 'csc'), dtype=<class 'numpy.float64'>, order=None, copy=True, force_all_finite=True, ensure_2d=True, allow_nd=False, ensure_min_samples=1, ensure_min_features=1, warn_on_dtype=True, estimator=StandardScaler(copy=True, with_mean=True, with_std=True))
448 array = array.astype(np.float64)
449 if not allow_nd and array.ndim >= 3:
450 raise ValueError("Found array with dim %d. %s expected <= 2."
451 % (array.ndim, estimator_name))
452 if force_all_finite:
--> 453 _assert_all_finite(array)
array = array([[2.0287700e+05, 4.6000000e+01, 2.3000000e...0000e+00,
0.0000000e+00, 1.0000000e+00]])
454
455 shape_repr = _shape_repr(array.shape)
456 if ensure_min_samples > 0:
457 n_samples = _num_samples(array)

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\utils\validation.py in _assert_all_finite(X=array([[2.0287700e+05, 4.6000000e+01, 2.3000000e...0000e+00,
0.0000000e+00, 1.0000000e+00]]))
39 # everything is finite; fall back to O(n) space np.isfinite to prevent
40 # false positives from overflow in sum method.
41 if (X.dtype.char in np.typecodes['AllFloat'] and not np.isfinite(X.sum())
42 and not np.isfinite(X).all()):
43 raise ValueError("Input contains NaN, infinity"
---> 44 " or a value too large for %r." % X.dtype)
X.dtype = dtype('float64')
45
46
47 def assert_all_finite(X):
48 """Throw a ValueError if X contains NaN or infinity.

ValueError: Input contains NaN, infinity or a value too large for dtype('float64').

During handling of the above exception, another exception occurred:

JoblibValueError Traceback (most recent call last)
in ()
7
8 # Fit model on X_train, y_train
----> 9 model.fit(X_train, y_train)
10
11 # Store model in fitted_models[name]

~\Anaconda3\lib\site-packages\sklearn\model_selection_search.py in fit(self, X, y, groups, **fit_params)
637 error_score=self.error_score)
638 for parameters, (train, test) in product(candidate_params,
--> 639 cv.split(X, y, groups)))
640
641 # if one choose to see train score, "out" will contain train score info

~\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in call(self, iterable)
787 # consumption.
788 self._iterating = False
--> 789 self.retrieve()
790 # Make sure that we get a last message telling us we are done
791 elapsed_time = time.time() - self._start_time

~\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in retrieve(self)
738 exception = exception_type(report)
739
--> 740 raise exception
741
742 def call(self, iterable):

JoblibValueError: JoblibValueError

Multiprocessing exception:
...........................................................................
C:\Users\HP\Anaconda3\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...nda3\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\HP\Anaconda3\lib\runpy.py in run_code(code=<code object at 0x0000020E99CFDD20, fil...lib\site-packages\ipykernel_launcher.py", line 5>, run_globals={'annotations': {}, 'builtins': <module 'builtins' (built-in)>, 'cached': r'C:\Users\HP\Anaconda3\lib\site-packages_pycache\ipykernel_launcher.cpython-36.pyc', 'doc': 'Entry point for launching an IPython kernel.\n\nTh...orts until\nafter removing the cwd from sys.path.\n', 'file': r'C:\Users\HP\Anaconda3\lib\site-packages\ipykernel_launcher.py', 'loader': <_frozen_importlib_external.SourceFileLoader object>, 'name': 'main', 'package': '', 'spec': ModuleSpec(name='ipykernel_launcher', loader=<_f...nda3\lib\site-packages\ipykernel_launcher.py'), 'app': <module 'ipykernel.kernelapp' from 'C:\Users\H...a3\lib\site-packages\ipykernel\kernelapp.py'>, ...}, init_globals=None, mod_name='main', mod_spec=ModuleSpec(name='ipykernel_launcher', loader=<f...nda3\lib\site-packages\ipykernel_launcher.py'), pkg_name='', script_name=None)
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 at 0x0000020E99CFDD20, fil...lib\site-packages\ipykernel_launcher.py", line 5>
run_globals = {'annotations': {}, 'builtins': <module 'builtins' (built-in)>, 'cached': r'C:\Users\HP\Anaconda3\lib\site-packages_pycache\ipykernel_launcher.cpython-36.pyc', 'doc': 'Entry point for launching an IPython kernel.\n\nTh...orts until\nafter removing the cwd from sys.path.\n', 'file': r'C:\Users\HP\Anaconda3\lib\site-packages\ipykernel_launcher.py', 'loader': <_frozen_importlib_external.SourceFileLoader object>, 'name': 'main', 'package': '', 'spec': ModuleSpec(name='ipykernel_launcher', loader=<_f...nda3\lib\site-packages\ipykernel_launcher.py'), 'app': <module 'ipykernel.kernelapp' from 'C:\Users\H...a3\lib\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,

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\ipykernel_launcher.py in ()
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()

...........................................................................
C:\Users\HP\Anaconda3\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\HP\Anaconda3\lib\site-packages\ipykernel\kernelapp.py in start(self=<ipykernel.kernelapp.IPKernelApp object>)
481 if self.poller is not None:
482 self.poller.start()
483 self.kernel.start()
484 self.io_loop = ioloop.IOLoop.current()
485 try:
--> 486 self.io_loop.start()
self.io_loop.start = <bound method BaseAsyncIOLoop.start of <tornado.platform.asyncio.AsyncIOMainLoop object>>
487 except KeyboardInterrupt:
488 pass
489
490 launch_new_instance = IPKernelApp.launch_instance

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\tornado\platform\asyncio.py in start(self=<tornado.platform.asyncio.AsyncIOMainLoop object>)
122 except (RuntimeError, AssertionError):
123 old_loop = None
124 try:
125 self._setup_logging()
126 asyncio.set_event_loop(self.asyncio_loop)
--> 127 self.asyncio_loop.run_forever()
self.asyncio_loop.run_forever = <bound method BaseEventLoop.run_forever of <_Win...EventLoop running=True closed=False debug=False>>
128 finally:
129 asyncio.set_event_loop(old_loop)
130
131 def stop(self):

...........................................................................
C:\Users\HP\Anaconda3\lib\asyncio\base_events.py in run_forever(self=<_WindowsSelectorEventLoop running=True closed=False debug=False>)
417 sys.set_asyncgen_hooks(firstiter=self._asyncgen_firstiter_hook,
418 finalizer=self._asyncgen_finalizer_hook)
419 try:
420 events._set_running_loop(self)
421 while True:
--> 422 self._run_once()
self._run_once = <bound method BaseEventLoop._run_once of <_Windo...EventLoop running=True closed=False debug=False>>
423 if self._stopping:
424 break
425 finally:
426 self._stopping = False

...........................................................................
C:\Users\HP\Anaconda3\lib\asyncio\base_events.py in _run_once(self=<_WindowsSelectorEventLoop running=True closed=False debug=False>)
1427 logger.warning('Executing %s took %.3f seconds',
1428 _format_handle(handle), dt)
1429 finally:
1430 self._current_handle = None
1431 else:
-> 1432 handle._run()
handle._run = <bound method Handle._run of <Handle BaseAsyncIOLoop._handle_events(580, 1)>>
1433 handle = None # Needed to break cycles when an exception occurs.
1434
1435 def _set_coroutine_wrapper(self, enabled):
1436 try:

...........................................................................
C:\Users\HP\Anaconda3\lib\asyncio\events.py in _run(self=<Handle BaseAsyncIOLoop._handle_events(580, 1)>)
140 self._callback = None
141 self._args = None
142
143 def _run(self):
144 try:
--> 145 self._callback(*self._args)
self._callback = <bound method BaseAsyncIOLoop._handle_events of <tornado.platform.asyncio.AsyncIOMainLoop object>>
self._args = (580, 1)
146 except Exception as exc:
147 cb = _format_callback_source(self._callback, self._args)
148 msg = 'Exception in callback {}'.format(cb)
149 context = {

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\tornado\platform\asyncio.py in _handle_events(self=<tornado.platform.asyncio.AsyncIOMainLoop object>, fd=580, events=1)
112 self.writers.remove(fd)
113 del self.handlers[fd]
114
115 def _handle_events(self, fd, events):
116 fileobj, handler_func = self.handlers[fd]
--> 117 handler_func(fileobj, events)
handler_func = <function wrap..null_wrapper>
fileobj = <zmq.sugar.socket.Socket object>
events = 1
118
119 def start(self):
120 try:
121 old_loop = asyncio.get_event_loop()

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\tornado\stack_context.py in null_wrapper(*args=(<zmq.sugar.socket.Socket object>, 1), **kwargs={})
271 # Fast path when there are no active contexts.
272 def null_wrapper(*args, **kwargs):
273 try:
274 current_state = _state.contexts
275 _state.contexts = cap_contexts[0]
--> 276 return fn(*args, **kwargs)
args = (<zmq.sugar.socket.Socket object>, 1)
kwargs = {}
277 finally:
278 _state.contexts = current_state
279 null_wrapper._wrapped = True
280 return null_wrapper

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py in _handle_events(self=<zmq.eventloop.zmqstream.ZMQStream object>, fd=<zmq.sugar.socket.Socket object>, events=1)
445 return
446 zmq_events = self.socket.EVENTS
447 try:
448 # dispatch events:
449 if zmq_events & zmq.POLLIN and self.receiving():
--> 450 self._handle_recv()
self._handle_recv = <bound method ZMQStream._handle_recv of <zmq.eventloop.zmqstream.ZMQStream object>>
451 if not self.socket:
452 return
453 if zmq_events & zmq.POLLOUT and self.sending():
454 self._handle_send()

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py in _handle_recv(self=<zmq.eventloop.zmqstream.ZMQStream object>)
475 else:
476 raise
477 else:
478 if self._recv_callback:
479 callback = self._recv_callback
--> 480 self._run_callback(callback, msg)
self._run_callback = <bound method ZMQStream._run_callback of <zmq.eventloop.zmqstream.ZMQStream object>>
callback = <function wrap..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>]
481
482
483 def _handle_send(self):
484 """Handle a send event."""

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py in _run_callback(self=<zmq.eventloop.zmqstream.ZMQStream object>, callback=<function wrap..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={})
427 close our socket."""
428 try:
429 # Use a NullContext to ensure that all StackContexts are run
430 # inside our blanket exception handler rather than outside.
431 with stack_context.NullContext():
--> 432 callback(*args, **kwargs)
callback = <function wrap..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 = {}
433 except:
434 gen_log.error("Uncaught exception in ZMQStream callback",
435 exc_info=True)
436 # Re-raise the exception so that IOLoop.handle_callback_exception

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\tornado\stack_context.py in null_wrapper(*args=([<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>],), **kwargs={})
271 # Fast path when there are no active contexts.
272 def null_wrapper(*args, **kwargs):
273 try:
274 current_state = _state.contexts
275 _state.contexts = cap_contexts[0]
--> 276 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 = {}
277 finally:
278 _state.contexts = current_state
279 null_wrapper._wrapped = True
280 return null_wrapper

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\ipykernel\kernelbase.py in dispatcher(msg=[<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>])
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\HP\Anaconda3\lib\site-packages\ipykernel\kernelbase.py in dispatch_shell(self=<ipykernel.ipkernel.IPythonKernel object>, stream=<zmq.eventloop.zmqstream.ZMQStream object>, msg={'buffers': [], 'content': {'allow_stdin': True, 'code': "fitted_models = {}\n\n# Loop through model pipelin...ed'\n print('{0} has been fitted'.format(name))", 'silent': False, 'stop_on_error': True, 'store_history': True, 'user_expressions': {}}, 'header': {'date': datetime.datetime(2018, 10, 3, 23, 16, 31, 739419, tzinfo=tzutc()), 'msg_id': '2a12f227dc194643a2f3e2c845579a14', 'msg_type': 'execute_request', 'session': '8c19fc225dc641549355ba37ad32a56d', 'username': 'username', 'version': '5.2'}, 'metadata': {}, 'msg_id': '2a12f227dc194643a2f3e2c845579a14', 'msg_type': 'execute_request', 'parent_header': {}})
228 self.log.warn("Unknown message type: %r", msg_type)
229 else:
230 self.log.debug("%s: %s", msg_type, msg)
231 self.pre_handler_hook()
232 try:
--> 233 handler(stream, idents, msg)
handler = <bound method Kernel.execute_request of <ipykernel.ipkernel.IPythonKernel object>>
stream = <zmq.eventloop.zmqstream.ZMQStream object>
idents = [b'8c19fc225dc641549355ba37ad32a56d']
msg = {'buffers': [], 'content': {'allow_stdin': True, 'code': "fitted_models = {}\n\n# Loop through model pipelin...ed'\n print('{0} has been fitted'.format(name))", 'silent': False, 'stop_on_error': True, 'store_history': True, 'user_expressions': {}}, 'header': {'date': datetime.datetime(2018, 10, 3, 23, 16, 31, 739419, tzinfo=tzutc()), 'msg_id': '2a12f227dc194643a2f3e2c845579a14', 'msg_type': 'execute_request', 'session': '8c19fc225dc641549355ba37ad32a56d', 'username': 'username', 'version': '5.2'}, 'metadata': {}, 'msg_id': '2a12f227dc194643a2f3e2c845579a14', 'msg_type': 'execute_request', 'parent_header': {}}
234 except Exception:
235 self.log.error("Exception in message handler:", exc_info=True)
236 finally:
237 self.post_handler_hook()

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\ipykernel\kernelbase.py in execute_request(self=<ipykernel.ipkernel.IPythonKernel object>, stream=<zmq.eventloop.zmqstream.ZMQStream object>, ident=[b'8c19fc225dc641549355ba37ad32a56d'], parent={'buffers': [], 'content': {'allow_stdin': True, 'code': "fitted_models = {}\n\n# Loop through model pipelin...ed'\n print('{0} has been fitted'.format(name))", 'silent': False, 'stop_on_error': True, 'store_history': True, 'user_expressions': {}}, 'header': {'date': datetime.datetime(2018, 10, 3, 23, 16, 31, 739419, tzinfo=tzutc()), 'msg_id': '2a12f227dc194643a2f3e2c845579a14', 'msg_type': 'execute_request', 'session': '8c19fc225dc641549355ba37ad32a56d', 'username': 'username', 'version': '5.2'}, 'metadata': {}, 'msg_id': '2a12f227dc194643a2f3e2c845579a14', '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\HP\Anaconda3\lib\site-packages\ipykernel\ipkernel.py in do_execute(self=<ipykernel.ipkernel.IPythonKernel object>, code="fitted_models = {}\n\n# Loop through model pipelin...ed'\n print('{0} has been fitted'.format(name))", silent=False, store_history=True, user_expressions={}, allow_stdin=True)
203
204 self._forward_input(allow_stdin)
205
206 reply_content = {}
207 try:
--> 208 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 = "fitted_models = {}\n\n# Loop through model pipelin...ed'\n print('{0} has been fitted'.format(name))"
store_history = True
silent = False
209 finally:
210 self._restore_input()
211
212 if res.error_before_exec is not None:

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\ipykernel\zmqshell.py in run_cell(self=<ipykernel.zmqshell.ZMQInteractiveShell object>, *args=("fitted_models = {}\n\n# Loop through model pipelin...ed'\n print('{0} has been fitted'.format(name))",), **kwargs={'silent': False, 'store_history': True})
532 )
533 self.payload_manager.write_payload(payload)
534
535 def run_cell(self, *args, **kwargs):
536 self._last_traceback = None
--> 537 return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
self.run_cell = <bound method ZMQInteractiveShell.run_cell of <ipykernel.zmqshell.ZMQInteractiveShell object>>
args = ("fitted_models = {}\n\n# Loop through model pipelin...ed'\n print('{0} has been fitted'.format(name))",)
kwargs = {'silent': False, 'store_history': True}
538
539 def _showtraceback(self, etype, evalue, stb):
540 # try to preserve ordering of tracebacks and print statements
541 sys.stdout.flush()

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py in run_cell(self=<ipykernel.zmqshell.ZMQInteractiveShell object>, raw_cell="fitted_models = {}\n\n# Loop through model pipelin...ed'\n print('{0} has been fitted'.format(name))", store_history=True, silent=False, shell_futures=True)
2657 -------
2658 result : :class:ExecutionResult
2659 """
2660 try:
2661 result = self._run_cell(
-> 2662 raw_cell, store_history, silent, shell_futures)
raw_cell = "fitted_models = {}\n\n# Loop through model pipelin...ed'\n print('{0} has been fitted'.format(name))"
store_history = True
silent = False
shell_futures = True
2663 finally:
2664 self.events.trigger('post_execute')
2665 if not silent:
2666 self.events.trigger('post_run_cell', result)

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py in _run_cell(self=<ipykernel.zmqshell.ZMQInteractiveShell object>, raw_cell="fitted_models = {}\n\n# Loop through model pipelin...ed'\n print('{0} has been fitted'.format(name))", store_history=True, silent=False, shell_futures=True)
2780 self.displayhook.exec_result = result
2781
2782 # Execute the user code
2783 interactivity = 'none' if silent else self.ast_node_interactivity
2784 has_raised = self.run_ast_nodes(code_ast.body, cell_name,
-> 2785 interactivity=interactivity, compiler=compiler, result=result)
interactivity = 'last_expr'
compiler = <IPython.core.compilerop.CachingCompiler object>
2786
2787 self.last_execution_succeeded = not has_raised
2788 self.last_execution_result = result
2789

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py in run_ast_nodes(self=<ipykernel.zmqshell.ZMQInteractiveShell object>, nodelist=[<_ast.Assign object>, <_ast.For object>], cell_name='', interactivity='none', compiler=<IPython.core.compilerop.CachingCompiler object>, result=<ExecutionResult object at 20ea44e4e80, executio...rue silent=False shell_futures=True> result=None>)
2898
2899 try:
2900 for i, node in enumerate(to_run_exec):
2901 mod = ast.Module([node])
2902 code = compiler(mod, cell_name, "exec")
-> 2903 if self.run_code(code, result):
self.run_code = <bound method InteractiveShell.run_code of <ipykernel.zmqshell.ZMQInteractiveShell object>>
code = <code object at 0x0000020EA2696660, file "", line 4>
result = <ExecutionResult object at 20ea44e4e80, executio...rue silent=False shell_futures=True> result=None>
2904 return True
2905
2906 for i, node in enumerate(to_run_interactive):
2907 mod = ast.Interactive([node])

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py in run_code(self=<ipykernel.zmqshell.ZMQInteractiveShell object>, code_obj=<code object at 0x0000020EA2696660, file "", line 4>, result=<ExecutionResult object at 20ea44e4e80, executio...rue silent=False shell_futures=True> result=None>)
2958 outflag = True # happens in more places, so it's easier as default
2959 try:
2960 try:
2961 self.hooks.pre_run_code_hook()
2962 #rprint('Running code', repr(code_obj)) # dbg
-> 2963 exec(code_obj, self.user_global_ns, self.user_ns)
code_obj = <code object at 0x0000020EA2696660, file "", line 4>
self.user_global_ns = {'GradientBoostingClassifier': <class 'sklearn.ensemble.gradient_boosting.GradientBoostingClassifier'>, 'GridSearchCV': <class 'sklearn.model_selection._search.GridSearchCV'>, 'In': ['', 'from future import print_function\n\n# NumPy f...rn for easier visualization\nimport seaborn as sns', "df = pd.read_csv('C:/Users/HP/Documents/Copy of Attrition & MCARES Data.csv')", "df = pd.read_csv('C:/Users/HP/Documents/Copy of Attrition & MCARES Data.xlsx')", "df = pd.read_csv('C:/Users/HP/Documents/Copy of Attrition & MCARES Data.xlsx')", 'import pandas as pd', "df = pd.read_csv('C:/Users/HP/Documents/Copy of Attrition & MCARES Data.xlsx')", "df = pd.read_csv('C:/Users/HP/Documents/Copy of Attrition & MCARES Data.csv')", 'df.shape', 'df.dtypes', 'df.head(10)', 'df.tail(10)', "df.hist(figsize=(10,10), xrot=-45, edgecolor='black')", 'plt.show()', 'df.describe()', "df.describe(include=['object'])", 'categorical_features = df.select_dtypes(include=...s.countplot(y = cat_feat, data=df)\n plt.show()', "sns.violinplot(y='ATTRITION', x = 'self satisfaction', data=df)", "sns.violinplot(y='ATTRITION', x='YEARS IN CURRENT ROLE',data=df)", "df.groupby('ATTRITION').mean()", ...], 'LogisticRegression': <class 'sklearn.linear_model.logistic.LogisticRegression'>, 'Out': {8: (122, 104), 9: Token Number int64
AGE ...action float64
Length: 104, dtype: object, 10: Token Number AGE F16-Leave F17-Leave PERM....01 4.179333

[10 rows x 104 columns], 11: Token Number AGE F16-Leave F17-Leave PE...121 4.206000

[10 rows x 104 columns], 12: array([[<matplotlib.axes._subplots.AxesSubplot o...ect at 0x0000020EA1F31AC8>]],
dtype=object), 14: Token Number AGE F16-Leave F1...00 4.330000 4.010000e+00 4.498400 , 15: PERMANENT CURRENT DEPARTMENT GEN...vision
freq 102 122 , 17: <matplotlib.axes._subplots.AxesSubplot object>, 18: <matplotlib.axes._subplots.AxesSubplot object>, 19: Token Number AGE F16-Leave ... 4.076316 3.868500 4.01 4.219077 , ...}, 'RandomForestClassifier': <class 'sklearn.ensemble.forest.RandomForestClassifier'>, 'StandardScaler': <class 'sklearn.preprocessing.data.StandardScaler'>, 'X': Token Number AGE F16-Leave F17-Leave J... 1

[122 rows x 449 columns], 'X_test': Token Number AGE F16-Leave F17-Leave J... 1

[25 rows x 449 columns], 'X_train': Token Number AGE F16-Leave F17-Leave J... 1

[97 rows x 449 columns], ...}
self.user_ns = {'GradientBoostingClassifier': <class 'sklearn.ensemble.gradient_boosting.GradientBoostingClassifier'>, 'GridSearchCV': <class 'sklearn.model_selection._search.GridSearchCV'>, 'In': ['', 'from future import print_function\n\n# NumPy f...rn for easier visualization\nimport seaborn as sns', "df = pd.read_csv('C:/Users/HP/Documents/Copy of Attrition & MCARES Data.csv')", "df = pd.read_csv('C:/Users/HP/Documents/Copy of Attrition & MCARES Data.xlsx')", "df = pd.read_csv('C:/Users/HP/Documents/Copy of Attrition & MCARES Data.xlsx')", 'import pandas as pd', "df = pd.read_csv('C:/Users/HP/Documents/Copy of Attrition & MCARES Data.xlsx')", "df = pd.read_csv('C:/Users/HP/Documents/Copy of Attrition & MCARES Data.csv')", 'df.shape', 'df.dtypes', 'df.head(10)', 'df.tail(10)', "df.hist(figsize=(10,10), xrot=-45, edgecolor='black')", 'plt.show()', 'df.describe()', "df.describe(include=['object'])", 'categorical_features = df.select_dtypes(include=...s.countplot(y = cat_feat, data=df)\n plt.show()', "sns.violinplot(y='ATTRITION', x = 'self satisfaction', data=df)", "sns.violinplot(y='ATTRITION', x='YEARS IN CURRENT ROLE',data=df)", "df.groupby('ATTRITION').mean()", ...], 'LogisticRegression': <class 'sklearn.linear_model.logistic.LogisticRegression'>, 'Out': {8: (122, 104), 9: Token Number int64
AGE ...action float64
Length: 104, dtype: object, 10: Token Number AGE F16-Leave F17-Leave PERM....01 4.179333

[10 rows x 104 columns], 11: Token Number AGE F16-Leave F17-Leave PE...121 4.206000

[10 rows x 104 columns], 12: array([[<matplotlib.axes._subplots.AxesSubplot o...ect at 0x0000020EA1F31AC8>]],
dtype=object), 14: Token Number AGE F16-Leave F1...00 4.330000 4.010000e+00 4.498400 , 15: PERMANENT CURRENT DEPARTMENT GEN...vision
freq 102 122 , 17: <matplotlib.axes._subplots.AxesSubplot object>, 18: <matplotlib.axes._subplots.AxesSubplot object>, 19: Token Number AGE F16-Leave ... 4.076316 3.868500 4.01 4.219077 , ...}, 'RandomForestClassifier': <class 'sklearn.ensemble.forest.RandomForestClassifier'>, 'StandardScaler': <class 'sklearn.preprocessing.data.StandardScaler'>, 'X': Token Number AGE F16-Leave F17-Leave J... 1

[122 rows x 449 columns], 'X_test': Token Number AGE F16-Leave F17-Leave J... 1

[25 rows x 449 columns], 'X_train': Token Number AGE F16-Leave F17-Leave J... 1

[97 rows x 449 columns], ...}
2964 finally:
2965 # Reset our crash handler in place
2966 sys.excepthook = old_excepthook
2967 except SystemExit as e:

...........................................................................
C:\Users\HP\Documents<ipython-input-63-67076fd7d5b3> in ()
4 for name, pipeline in pipelines.items():
5 # Create cross-validation object from pipeline and hyperparameters
6 model = GridSearchCV(pipeline, hyperparameters[name], cv = 10, n_jobs=-1)
7
8 # Fit model on X_train, y_train
----> 9 model.fit(X_train, y_train)
10
11 # Store model in fitted_models[name]
12 fitted_models[name] = model
13

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\model_selection_search.py in fit(self=GridSearchCV(cv=10, error_score='raise',
...ain_score='warn',
scoring=None, verbose=0), X= Token Number AGE F16-Leave F17-Leave J... 1

[97 rows x 449 columns], y=54 0
18 0
64 0
103 1
85 0
45 ...9 0
Name: ATTRITION, Length: 97, dtype: int64, groups=None, **fit_params={})
634 return_train_score=self.return_train_score,
635 return_n_test_samples=True,
636 return_times=True, return_parameters=False,
637 error_score=self.error_score)
638 for parameters, (train, test) in product(candidate_params,
--> 639 cv.split(X, y, groups)))
cv.split = <bound method StratifiedKFold.split of Stratifie...d(n_splits=10, random_state=None, shuffle=False)>
X = Token Number AGE F16-Leave F17-Leave J... 1

[97 rows x 449 columns]
y = 54 0
18 0
64 0
103 1
85 0
45 ...9 0
Name: ATTRITION, Length: 97, dtype: int64
groups = None
640
641 # if one choose to see train score, "out" will contain train score info
642 if self.return_train_score:
643 (train_score_dicts, test_score_dicts, test_sample_counts, fit_time,

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in call(self=Parallel(n_jobs=-1), iterable=<generator object BaseSearchCV.fit..>)
784 if pre_dispatch == "all" or n_jobs == 1:
785 # The iterable was consumed all at once by the above for loop.
786 # No need to wait for async callbacks to trigger to
787 # consumption.
788 self._iterating = False
--> 789 self.retrieve()
self.retrieve = <bound method Parallel.retrieve of Parallel(n_jobs=-1)>
790 # Make sure that we get a last message telling us we are done
791 elapsed_time = time.time() - self._start_time
792 self._print('Done %3i out of %3i | elapsed: %s finished',
793 (len(self._output), len(self._output),

Sub-process traceback:

ValueError Wed Oct 3 16:16:44 2018
PID: 7136 Python 3.6.5: C:\Users\HP\Anaconda3\python.exe
...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in call(self=<sklearn.externals.joblib.parallel.BatchedCalls object>)
126 def init(self, iterator_slice):
127 self.items = list(iterator_slice)
128 self._size = len(self.items)
129
130 def call(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
self.items = [(, (Pipeline(memory=None,
steps=[('standardscal...0.0001,
verbose=0, warm_start=False))]), Token Number AGE ... ... 1

[97 rows x 449 columns], 54 0
18 0
64 0
103 1
85 0
45 ...9 0
Name: ATTRITION, Length: 97, dtype: int64, {'score': }, array([10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 2..., 87, 88, 89, 90, 91, 92, 93, 94, 95,
96]), array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11]), 0, {'logisticregression__C': 0.001}), {'error_score': 'raise', 'fit_params': {}, 'return_n_test_samples': True, 'return_parameters': False, 'return_times': True, 'return_train_score': 'warn'})]
132
133 def len(self):
134 return self._size
135

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in (.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 =
args = (Pipeline(memory=None,
steps=[('standardscal...0.0001,
verbose=0, warm_start=False))]), Token Number AGE ... ... 1

[97 rows x 449 columns], 54 0
18 0
64 0
103 1
85 0
45 ...9 0
Name: ATTRITION, Length: 97, dtype: int64, {'score': }, array([10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 2..., 87, 88, 89, 90, 91, 92, 93, 94, 95,
96]), array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11]), 0, {'logisticregression__C': 0.001})
kwargs = {'error_score': 'raise', 'fit_params': {}, 'return_n_test_samples': True, 'return_parameters': False, 'return_times': True, 'return_train_score': 'warn'}
132
133 def len(self):
134 return self._size
135

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\model_selection_validation.py in _fit_and_score(estimator=Pipeline(memory=None,
steps=[('standardscal...0.0001,
verbose=0, warm_start=False))]), X= Token Number AGE ... ... 1

[97 rows x 449 columns], y=54 0
18 0
64 0
103 1
85 0
45 ...9 0
Name: ATTRITION, Length: 97, dtype: int64, scorer={'score': }, train=array([10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 2..., 87, 88, 89, 90, 91, 92, 93, 94, 95,
96]), test=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11]), verbose=0, parameters={'logisticregression__C': 0.001}, fit_params={}, return_train_score='warn', return_parameters=False, return_n_test_samples=True, return_times=True, error_score='raise')
453
454 try:
455 if y_train is None:
456 estimator.fit(X_train, **fit_params)
457 else:
--> 458 estimator.fit(X_train, y_train, **fit_params)
estimator.fit = <bound method Pipeline.fit of Pipeline(memory=No....0001,
verbose=0, warm_start=False))])>
X_train = Token Number AGE ... ... 1

[86 rows x 449 columns]
y_train = 8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64
fit_params = {}
459
460 except Exception as e:
461 # Note fit time as time until error
462 fit_time = time.time() - start_time

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\pipeline.py in fit(self=Pipeline(memory=None,
steps=[('standardscal...0.0001,
verbose=0, warm_start=False))]), X= Token Number AGE ... ... 1

[86 rows x 449 columns], y=8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64, **fit_params={})
243 Returns
244 -------
245 self : Pipeline
246 This estimator
247 """
--> 248 Xt, fit_params = self._fit(X, y, **fit_params)
Xt = undefined
fit_params = {}
self._fit = <bound method Pipeline._fit of Pipeline(memory=N....0001,
verbose=0, warm_start=False))])>
X = Token Number AGE ... ... 1

[86 rows x 449 columns]
y = 8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64
249 if self._final_estimator is not None:
250 self._final_estimator.fit(Xt, y, **fit_params)
251 return self
252

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\pipeline.py in _fit(self=Pipeline(memory=None,
steps=[('standardscal...0.0001,
verbose=0, warm_start=False))]), X= Token Number AGE ... ... 1

[86 rows x 449 columns], y=8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64, **fit_params={})
208 else:
209 cloned_transformer = clone(transformer)
210 # Fit or load from cache the current transfomer
211 Xt, fitted_transformer = fit_transform_one_cached(
212 cloned_transformer, None, Xt, y,
--> 213 **fit_params_steps[name])
fit_params_steps = {'logisticregression': {}, 'standardscaler': {}}
name = 'standardscaler'
214 # Replace the transformer of the step with the fitted
215 # transformer. This is necessary when loading the transformer
216 # from the cache.
217 self.steps[step_idx] = (name, fitted_transformer)

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\externals\joblib\memory.py in call(self=NotMemorizedFunc(func=<function _fit_transform_one at 0x000001FC877EC8C8>), *args=(StandardScaler(copy=True, with_mean=True, with_std=True), None, Token Number AGE ... ... 1

[86 rows x 449 columns], 8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64), **kwargs={})
357 # Should be a light as possible (for speed)
358 def init(self, func):
359 self.func = func
360
361 def call(self, *args, **kwargs):
--> 362 return self.func(*args, **kwargs)
self.func =
args = (StandardScaler(copy=True, with_mean=True, with_std=True), None, Token Number AGE ... ... 1

[86 rows x 449 columns], 8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64)
kwargs = {}
363
364 def call_and_shelve(self, *args, **kwargs):
365 return NotMemorizedResult(self.func(*args, **kwargs))
366

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\pipeline.py in _fit_transform_one(transformer=StandardScaler(copy=True, with_mean=True, with_std=True), weight=None, X= Token Number AGE ... ... 1

[86 rows x 449 columns], y=8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64, **fit_params={})
576
577
578 def _fit_transform_one(transformer, weight, X, y,
579 **fit_params):
580 if hasattr(transformer, 'fit_transform'):
--> 581 res = transformer.fit_transform(X, y, **fit_params)
res = undefined
transformer.fit_transform = <bound method TransformerMixin.fit_transform of ...Scaler(copy=True, with_mean=True, with_std=True)>
X = Token Number AGE ... ... 1

[86 rows x 449 columns]
y = 8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64
fit_params = {}
582 else:
583 res = transformer.fit(X, y, **fit_params).transform(X)
584 # if we have a weight for this transformer, multiply output
585 if weight is None:

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\base.py in fit_transform(self=StandardScaler(copy=True, with_mean=True, with_std=True), X= Token Number AGE ... ... 1

[86 rows x 449 columns], y=8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64, **fit_params={})
515 if y is None:
516 # fit method of arity 1 (unsupervised transformation)
517 return self.fit(X, **fit_params).transform(X)
518 else:
519 # fit method of arity 2 (supervised transformation)
--> 520 return self.fit(X, y, **fit_params).transform(X)
self.fit = <bound method StandardScaler.fit of StandardScaler(copy=True, with_mean=True, with_std=True)>
X = Token Number AGE ... ... 1

[86 rows x 449 columns]
y = 8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64
fit_params.transform = undefined
521
522
523 class DensityMixin(object):
524 """Mixin class for all density estimators in scikit-learn."""

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\preprocessing\data.py in fit(self=StandardScaler(copy=True, with_mean=True, with_std=True), X= Token Number AGE ... ... 1

[86 rows x 449 columns], y=8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64)
585 y : Passthrough for Pipeline compatibility.
586 """
587
588 # Reset internal state before fitting
589 self._reset()
--> 590 return self.partial_fit(X, y)
self.partial_fit = <bound method StandardScaler.partial_fit of Stan...Scaler(copy=True, with_mean=True, with_std=True)>
X = Token Number AGE ... ... 1

[86 rows x 449 columns]
y = 8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64
591
592 def partial_fit(self, X, y=None):
593 """Online computation of mean and std on X for later scaling.
594 All of X is processed as a single batch. This is intended for cases

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\preprocessing\data.py in partial_fit(self=StandardScaler(copy=True, with_mean=True, with_std=True), X= Token Number AGE ... ... 1

[86 rows x 449 columns], y=8 0
77 0
90 0
32 0
60 0
39 ...9 0
Name: ATTRITION, Length: 86, dtype: int64)
607 used for later scaling along the features axis.
608
609 y : Passthrough for Pipeline compatibility.
610 """
611 X = check_array(X, accept_sparse=('csr', 'csc'), copy=self.copy,
--> 612 warn_on_dtype=True, estimator=self, dtype=FLOAT_DTYPES)
self = StandardScaler(copy=True, with_mean=True, with_std=True)
613
614 # Even in the case of with_mean=False, we update the mean anyway
615 # This is needed for the incremental computation of the var
616 # See incr_mean_variance_axis and _incremental_mean_variance_axis

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_array(array=array([[2.0287700e+05, 4.6000000e+01, 2.3000000e...0000e+00,
0.0000000e+00, 1.0000000e+00]]), accept_sparse=('csr', 'csc'), dtype=<class 'numpy.float64'>, order=None, copy=True, force_all_finite=True, ensure_2d=True, allow_nd=False, ensure_min_samples=1, ensure_min_features=1, warn_on_dtype=True, estimator=StandardScaler(copy=True, with_mean=True, with_std=True))
448 array = array.astype(np.float64)
449 if not allow_nd and array.ndim >= 3:
450 raise ValueError("Found array with dim %d. %s expected <= 2."
451 % (array.ndim, estimator_name))
452 if force_all_finite:
--> 453 _assert_all_finite(array)
array = array([[2.0287700e+05, 4.6000000e+01, 2.3000000e...0000e+00,
0.0000000e+00, 1.0000000e+00]])
454
455 shape_repr = _shape_repr(array.shape)
456 if ensure_min_samples > 0:
457 n_samples = _num_samples(array)

...........................................................................
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\utils\validation.py in _assert_all_finite(X=array([[2.0287700e+05, 4.6000000e+01, 2.3000000e...0000e+00,
0.0000000e+00, 1.0000000e+00]]))
39 # everything is finite; fall back to O(n) space np.isfinite to prevent
40 # false positives from overflow in sum method.
41 if (X.dtype.char in np.typecodes['AllFloat'] and not np.isfinite(X.sum())
42 and not np.isfinite(X).all()):
43 raise ValueError("Input contains NaN, infinity"
---> 44 " or a value too large for %r." % X.dtype)
X.dtype = dtype('float64')
45
46
47 def assert_all_finite(X):
48 """Throw a ValueError if X contains NaN or infinity.

ValueError: Input contains NaN, infinity or a value too large for dtype('float64').

plz someone help me Its urgent