Providing the full StackTrace here:[ code in previous email]
# Tuning hyper-parameters for precision
()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-85-7fedbaf85b7d> in<module>() 18 scoring='%s_weighted' % score)
19
---> 20clf.fit(Finaldata, y)
21
22 print("Best parameters set found on development set:")
D:\Anaconda\lib\site-packages\sklearn\grid_search.pyc infit(self, X, y) 730
731 """
--> 732return self._fit(X, y, ParameterGrid(self.param_grid))
733
734
D:\Anaconda\lib\site-packages\sklearn\grid_search.pyc in_fit(self, X, y,
parameter_iterable) 503 self.fit_params, return_parameters=True,
504 error_score=self.error_score)
--> 505for parameters in parameter_iterable
506 for train, test in cv)
507
D:\Anaconda\lib\site-packages\sklearn\externals\joblib\parallel.pyc
in__call__(self, iterable) 657 self._iterating= True
658 for function, args, kwargsin iterable:
--> 659self.dispatch(function, args, kwargs)
660
661 if pre_dispatch== "all" or n_jobs== 1:
D:\Anaconda\lib\site-packages\sklearn\externals\joblib\parallel.pyc indispatch(self, func,
args, kwargs) 404 """ 405 if self._poolis None:
--> 406job = ImmediateApply(func, args, kwargs)
407 index= len(self._jobs)
408 if not _verbosity_filter(index, self.verbose):
D:\Anaconda\lib\site-packages\sklearn\externals\joblib\parallel.pyc in__init__(self, func, args, kwargs) 138 # Don't delay the application,
to avoid keeping the input
139 # arguments in memory
--> 140self.results= func(*args, **kwargs)
141
142 def get(self):
D:\Anaconda\lib\site-packages\sklearn\cross_validation.pyc in_fit_and_score(estimator, X, y, scorer, train, test, verbose,
parameters, fit_params, return_train_score, return_parameters,
error_score) 1457 estimator.fit(X_train, **fit_params)
1458 else:
-> 1459estimator.fit(X_train, y_train, **fit_params)
1460
1461 except Exceptionas e:
D:\Anaconda\lib\site-packages\sklearn\linear_model\stochastic_gradient.pyc infit(self, X, y, coef_init, intercept_init, class_weight,
sample_weight) 562 loss=self.loss, learning_rate=self.learning_rate,
563 coef_init=coef_init,
intercept_init=intercept_init,
--> 564sample_weight=sample_weight) 565
566
D:\Anaconda\lib\site-packages\sklearn\linear_model\stochastic_gradient.pyc in_fit(self, X, y, alpha, C, loss, learning_rate, coef_init,
intercept_init, sample_weight) 401 self.classes_= None
402
--> 403X, y= check_X_y(X, y, 'csr', dtype=np.float64, order="C")
404 n_samples, n_features= X.shape
405
D:\Anaconda\lib\site-packages\sklearn\utils\validation.pyc incheck_X_y(X, y, accept_sparse, dtype, order, copy, force_all_finite,
ensure_2d, allow_nd, multi_output, ensure_min_samples,
ensure_min_features, y_numeric) 447 dtype=None) 448 else:
--> 449y = column_or_1d(y, warn=True)
450 _assert_all_finite(y)
451 if y_numericand y.dtype.kind== 'O':
D:\Anaconda\lib\site-packages\sklearn\utils\validation.pyc incolumn_or_1d(y,
warn) 483 return np.ravel(y)
484
--> 485raise ValueError("bad input shape {0}".format(shape))
486
487
ValueError: bad input shape (914551, 6)
On Mon, Dec 28, 2015 at 4:08 PM, Startup Hire
<blrstartuph...@gmail.com <mailto:blrstartuph...@gmail.com>> wrote:
Hi all,
Hope you are doing well.
I am working on fine tuning the following parameters in SGD
Classifier which I am using inside OneVsRest Classifier.
I am using GridSearch to use the same.
I have following questions:
1. How to use GridSearch to optimize OneVsRest Classifier?
2. Any reason why the below code does not work? Error is bad
input shape though the classifier.fit works find separately!
from sklearn.grid_search import GridSearchCV
# Set the parameters by cross-validation
tuned_parameters = [{'alpha': [0.001, 0.01,0.1,0.5] ,
'penalty': ['l1','l2','elasticnet'],
'loss':['log','modified_huber']}]
scores = ['precision', 'recall']
for score in scores:
print("# Tuning hyper-parameters for %s" % score)
print()
clf =
GridSearchCV(SGDClassifier(random_state=0,learning_rate='optimal',class_weight='auto',n_iter=100),
tuned_parameters, cv=5,
scoring='%s_weighted' % score)
clf.fit(Finaldata, y)
print("Best parameters set found on development set:")
print()
print(clf.best_params_)
print()
Regards,
Sanant
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