teezeit / tuning_xgboost Goto Github PK

The code is not working for a multi-class classification. Please check and correct the same.

`---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
in ()
----> 1 tuned_estimator = tuning_xgboost.grid_search_tuning(train[feature_names], target, hyperlist_to_try, booster, plotting = False)
2 tuned_parameter = tuned_estimator.get_params()

D:\Kartheek\ML Competetions\hackerearth\ML4-Predict Network Attacks\code\tuning_xgboost.py in grid_search_tuning(X, y, hyperparameter_grids, booster, gridsearch_params, verbose, plotting)
113
114 # Fit Gridsearch
--> 115 grid_result = GridSearchCV(**gridparams).fit(X,np.squeeze(y))
116 #save best result
117 best_estimator = grid_result.best_estimator_

~\AppData\Local\Continuum\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

~\AppData\Local\Continuum\anaconda3\lib\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:

~\AppData\Local\Continuum\anaconda3\lib\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

~\AppData\Local\Continuum\anaconda3\lib\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

~\AppData\Local\Continuum\anaconda3\lib\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)

~\AppData\Local\Continuum\anaconda3\lib\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):

~\AppData\Local\Continuum\anaconda3\lib\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):

~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in (.0)
129
130 def call(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
132
133 def len(self):

~\AppData\Local\Continuum\anaconda3\lib\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)
486 fit_time = time.time() - start_time
487 # _score will return dict if is_multimetric is True
--> 488 test_scores = _score(estimator, X_test, y_test, scorer, is_multimetric)
489 score_time = time.time() - start_time - fit_time
490 if return_train_score:

~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\model_selection_validation.py in _score(estimator, X_test, y_test, scorer, is_multimetric)
521 """
522 if is_multimetric:
--> 523 return _multimetric_score(estimator, X_test, y_test, scorer)
524 else:
525 if y_test is None:

~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\model_selection_validation.py in _multimetric_score(estimator, X_test, y_test, scorers)
551 score = scorer(estimator, X_test)
552 else:
--> 553 score = scorer(estimator, X_test, y_test)
554
555 if hasattr(score, 'item'):

~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\metrics\scorer.py in call(self, clf, X, y, sample_weight)
179 y_type = type_of_target(y)
180 if y_type not in ("binary", "multilabel-indicator"):
--> 181 raise ValueError("{0} format is not supported".format(y_type))
182
183 if is_regressor(clf):

ValueError: multiclass format is not supported
`