given
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import make_scorer
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import precision_score
def fit_model(X, y):
""" Performs grid search over the 'max_depth' parameter for a
decision tree regressor trained on the input data [X, y]. """
print('The scikit-learn version is {}.'.format(sklearn.__version__))
# Create cross-validation sets from the training data
print(X.shape[0])
cv_sets = ShuffleSplit(X.shape[0], n_iter = 10, test_size = 0.20, random_state = 0)
# TODO: Create a decision tree regressor object
regressor = DecisionTreeRegressor()
# TODO: Create a dictionary for the parameter 'max_depth' with a range from 1 to 10
params = { 'max_depth': range(1, 11) }
# TODO: Transform 'performance_metric' into a scoring function using 'make_scorer'
scoring_fnc = make_scorer(precision_score)
# TODO: Create the grid search object
grid = GridSearchCV(regressor, params, scoring_fnc, cv_sets)
# Fit the grid search object to the data to compute the optimal model
grid = grid.fit(X, y)
# Return the optimal model after fitting the data
return grid.best_estimator_The scikit-learn version is 0.17.1.
391
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-26-192f7c286a58> in <module>()
1 # Fit the training data to the model using grid search
----> 2 reg = fit_model(X_train, y_train)
3
4 # Produce the value for 'max_depth'
5 print "Parameter 'max_depth' is {} for the optimal model.".format(reg.get_params()['max_depth'])
<ipython-input-25-d72c80aed4d6> in fit_model(X, y)
27
28 # Fit the grid search object to the data to compute the optimal model
---> 29 grid = grid.fit(X, y)
30
31 # Return the optimal model after fitting the data
/Users/mariogintili/anaconda2/lib/python2.7/site-packages/sklearn/grid_search.pyc in fit(self, X, y)
802
803 """
--> 804 return self._fit(X, y, ParameterGrid(self.param_grid))
805
806
/Users/mariogintili/anaconda2/lib/python2.7/site-packages/sklearn/grid_search.pyc in _fit(self, X, y, parameter_iterable)
551 self.fit_params, return_parameters=True,
552 error_score=self.error_score)
--> 553 for parameters in parameter_iterable
554 for train, test in cv)
555
/Users/mariogintili/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __call__(self, iterable)
798 # was dispatched. In particular this covers the edge
799 # case of Parallel used with an exhausted iterator.
--> 800 while self.dispatch_one_batch(iterator):
801 self._iterating = True
802 else:
/Users/mariogintili/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in dispatch_one_batch(self, iterator)
656 return False
657 else:
--> 658 self._dispatch(tasks)
659 return True
660
/Users/mariogintili/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in _dispatch(self, batch)
564
565 if self._pool is None:
--> 566 job = ImmediateComputeBatch(batch)
567 self._jobs.append(job)
568 self.n_dispatched_batches += 1
/Users/mariogintili/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __init__(self, batch)
178 # Don't delay the application, to avoid keeping the input
179 # arguments in memory
--> 180 self.results = batch()
181
182 def get(self):
/Users/mariogintili/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __call__(self)
70
71 def __call__(self):
---> 72 return [func(*args, **kwargs) for func, args, kwargs in self.items]
73
74 def __len__(self):
/Users/mariogintili/anaconda2/lib/python2.7/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)
1515 fit_params = fit_params if fit_params is not None else {}
1516 fit_params = dict([(k, _index_param_value(X, v, train))
-> 1517 for k, v in fit_params.items()])
1518
1519 if parameters is not None:
AttributeError: 'ShuffleSplit' object has no attribute 'items'
Thank you :)