import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, accuracy_score
from sklearn.calibration import CalibratedClassifierCV
from sklearn import model_selection
from sklearn.metrics import make_scorer
from sklearn.decomposition import PCA, FastICA
from sklearn.pipeline import Pipeline

feature_len = 36

clf = RandomForestClassifier(n_estimators = 200, random_state = 1, class_weight = 'balanced')
params = {'clf__max_features': ['auto', 'log2'],
                 'dm_reduce__n_components': np.arange(5, feature_len, np.around(feature_len/5))}
jobs = 1
scorer = make_scorer(accuracy_score)

Xtest = np.random.random((179,feature_len))
ytest = [['Win','Defeat','Draw'][np.random.randint(3)] for _ in range(179)]
cv_sets_test = model_selection.StratifiedShuffleSplit(n_splits = 5, test_size = 0.20, random_state = 5)
cv_sets_test.get_n_splits(Xtest, ytest)

pca = PCA()
dm_reduction = pca

#Define pipeline of dm reduction and classifier
estimators = [('dm_reduce', dm_reduction), ('clf', clf)]
pipeline = Pipeline(estimators)

#Grid search over pipeline and return best classifier
grid_obj = model_selection.GridSearchCV(pipeline, param_grid = params, scoring = scorer, cv = cv_sets_test, n_jobs = jobs)
grid_obj.fit(Xtest, ytest)
best_pipe = grid_obj.best_estimator_

TypeError                                 Traceback (most recent call last)
<ipython-input-11-3543a8fcf60e> in <module>()
     31 #Grid search over pipeline and return best classifier
     32 grid_obj = model_selection.GridSearchCV(pipeline, param_grid = params, scoring = scorer, cv = cv_sets_test, n_jobs = jobs)
---> 33 grid_obj.fit(Xtest, ytest)
     34 best_pipe = grid_obj.best_estimator_

~/Library/Python/3.5/lib/python/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

~/Library/Python/3.5/lib/python/site-packages/sklearn/externals/joblib/parallel.py in __call__(self, iterable)
    777             # was dispatched. In particular this covers the edge
    778             # case of Parallel used with an exhausted iterator.
--> 779             while self.dispatch_one_batch(iterator):
    780                 self._iterating = True
    781             else:

~/Library/Python/3.5/lib/python/site-packages/sklearn/externals/joblib/parallel.py in dispatch_one_batch(self, iterator)
    623                 return False
    624             else:
--> 625                 self._dispatch(tasks)
    626                 return True
    627 

~/Library/Python/3.5/lib/python/site-packages/sklearn/externals/joblib/parallel.py in _dispatch(self, batch)
    586         dispatch_timestamp = time.time()
    587         cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self)
--> 588         job = self._backend.apply_async(batch, callback=cb)
    589         self._jobs.append(job)
    590 

~/Library/Python/3.5/lib/python/site-packages/sklearn/externals/joblib/_parallel_backends.py in apply_async(self, func, callback)
    109     def apply_async(self, func, callback=None):
    110         """Schedule a func to be run"""
--> 111         result = ImmediateResult(func)
    112         if callback:
    113             callback(result)

~/Library/Python/3.5/lib/python/site-packages/sklearn/externals/joblib/_parallel_backends.py in __init__(self, batch)
    330         # Don't delay the application, to avoid keeping the input
    331         # arguments in memory
--> 332         self.results = batch()
    333 
    334     def get(self):

~/Library/Python/3.5/lib/python/site-packages/sklearn/externals/joblib/parallel.py in __call__(self)
    129 
    130     def __call__(self):
--> 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]
    132 
    133     def __len__(self):

~/Library/Python/3.5/lib/python/site-packages/sklearn/externals/joblib/parallel.py in <listcomp>(.0)
    129 
    130     def __call__(self):
--> 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]
    132 
    133     def __len__(self):

~/Library/Python/3.5/lib/python/site-packages/sklearn/model_selection/_validation.py in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, error_score)
    456             estimator.fit(X_train, **fit_params)
    457         else:
--> 458             estimator.fit(X_train, y_train, **fit_params)
    459 
    460     except Exception as e:

~/Library/Python/3.5/lib/python/site-packages/sklearn/pipeline.py in fit(self, X, y, **fit_params)
    246             This estimator
    247         """
--> 248         Xt, fit_params = self._fit(X, y, **fit_params)
    249         if self._final_estimator is not None:
    250             self._final_estimator.fit(Xt, y, **fit_params)

~/Library/Python/3.5/lib/python/site-packages/sklearn/pipeline.py in _fit(self, X, y, **fit_params)
    211                 Xt, fitted_transformer = fit_transform_one_cached(
    212                     cloned_transformer, None, Xt, y,
--> 213                     **fit_params_steps[name])
    214                 # Replace the transformer of the step with the fitted
    215                 # transformer. This is necessary when loading the transformer

~/Library/Python/3.5/lib/python/site-packages/sklearn/externals/joblib/memory.py in __call__(self, *args, **kwargs)
    360 
    361     def __call__(self, *args, **kwargs):
--> 362         return self.func(*args, **kwargs)
    363 
    364     def call_and_shelve(self, *args, **kwargs):

~/Library/Python/3.5/lib/python/site-packages/sklearn/pipeline.py in _fit_transform_one(transformer, weight, X, y, **fit_params)
    579                        **fit_params):
    580     if hasattr(transformer, 'fit_transform'):
--> 581         res = transformer.fit_transform(X, y, **fit_params)
    582     else:
    583         res = transformer.fit(X, y, **fit_params).transform(X)

~/Library/Python/3.5/lib/python/site-packages/sklearn/decomposition/pca.py in fit_transform(self, X, y)
    346 
    347         """
--> 348         U, S, V = self._fit(X)
    349         U = U[:, :self.n_components_]
    350 

~/Library/Python/3.5/lib/python/site-packages/sklearn/decomposition/pca.py in _fit(self, X)
    390         # Call different fits for either full or truncated SVD
    391         if svd_solver == 'full':
--> 392             return self._fit_full(X, n_components)
    393         elif svd_solver in ['arpack', 'randomized']:
    394             return self._fit_truncated(X, n_components, svd_solver)

~/Library/Python/3.5/lib/python/site-packages/sklearn/decomposition/pca.py in _fit_full(self, X, n_components)
    439         # The sigma2 maximum likelihood (cf. eq. 12.46)
    440         if n_components < min(n_features, n_samples):
--> 441             self.noise_variance_ = explained_variance_[n_components:].mean()
    442         else:
    443             self.noise_variance_ = 0.

TypeError: slice indices must be integers or None or have an __index__ method