我正在使用anaconda-navigator - > python3.6
当我运行以下代码时,我收到此错误:
from sklearn.metrics import make_scorer
from sklearn.tree import DecisionTreeRegressor
from sklearn.grid_search import GridSearchCV
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]. """
# Create cross-validation sets from the training data
# sklearn version 0.18: ShuffleSplit(n_splits=10, test_size=0.1, train_size=None, random_state=None)
# sklearn versiin 0.17: ShuffleSplit(n, n_iter=10, test_size=0.1, train_size=None, random_state=None)
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(1,10)}
# TODO: Transform 'performance_metric' into a scoring function using 'make_scorer'
scoring_fnc = make_scorer(performance_metric)
# TODO: Create the grid search cv object --> GridSearchCV()
# Make sure to include the right parameters in the object:
# (estimator, param_grid, scoring, cv) which have values 'regressor', 'params', 'scoring_fnc', and 'cv_sets' respectively.
grid = GridSearchCV(regressor, params, scoring_fnc, cv=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_`
# Fit the training data to the model using grid search
reg = fit_model(X_train, y_train)
# Produce the value for 'max_depth'
print ("Parameter 'max_depth' is {} for the optimal model.".format(reg.get_params()['max_depth']))
以下是错误消息:
ValueError Traceback (most recent call last)
<ipython-input-12-05857a84a7c5> 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-11-2c0c19498236> in fit_model(X, y)
26 # (estimator, param_grid, scoring, cv) which have values 'regressor', 'params', 'scoring_fnc', and 'cv_sets' respectively.
27
---> 28 grid = GridSearchCV(regressor, params, scoring_fnc, cv=cv_sets)
29
30 # Fit the grid search object to the data to compute the optimal model
~/anaconda3/lib/python3.6/site-packages/sklearn/grid_search.py in __init__(self, estimator, param_grid, scoring, fit_params, n_jobs, iid, refit, cv, verbose, pre_dispatch, error_score)
819 refit, cv, verbose, pre_dispatch, error_score)
820 self.param_grid = param_grid
--> 821 _check_param_grid(param_grid)
822
823 def fit(self, X, y=None):
~/anaconda3/lib/python3.6/site-packages/sklearn/grid_search.py in _check_param_grid(param_grid)
349 if True not in check:
350 raise ValueError("Parameter values for parameter ({0}) need "
--> 351 "to be a sequence.".format(name))
352
353 if len(v) == 0:
ValueError: Parameter values for parameter (max_depth) need to be a sequence.
答案 0 :(得分:0)
grid_search.py会检查:
check = [isinstance(v, k) for k in (list, tuple, np.ndarray)]
好像你不能使用范围。我会试试这个:
params = {'max_depth': np.arange(1,10)}
或没有numpy:
params = {'max_depth': [x for x in range(1,10)]}