我正在尝试在RF分类器上执行网格搜索，其中评分函数是sklearn.metrics模块中的precision_score。这是代码。
from sklearn.metrics import precision_score
param_grid = {'n_estimators': [51, 101, 201, 301, 501],
'max_depth': [3, 5, 10, None],
'min_samples_split': [2, 5, 10],
'criterion': ['gini', 'entropy'],
'bootstrap': [True, False]}
def fit_gridCV_RFclassifier(param_grid):
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier()
clf = GridSearchCV(estimator=rf, param_grid=param_grid,
cv=5, scoring=precision_score,
refit=True)
clf.fit(train_X, train_y)
return clf
gridsearch_rf = fit_gridCV_RFclassifier(param_grid)
运行该功能时，出现以下错误
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-34-6f91362a017c> in <module>()
----> 1 gridsearch_rf = fit_gridCV_RFclassifier(param_grid)
<ipython-input-33-974d026d5dc8> in fit_gridCV_RFclassifier(param_grid)
11 scoring=precision_score,
12 cv=5, refit=True)
---> 13 clf.fit(train_X, train_y)
14 return clf
/anaconda/lib/python2.7/site-packages/sklearn/grid_search.pyc in fit(self, X, y)
594
595 """
--> 596 return self._fit(X, y, ParameterGrid(self.param_grid))
597
598
/anaconda/lib/python2.7/site-packages/sklearn/grid_search.pyc in _fit(self, X, y, parameter_iterable)
376 train, test, self.verbose, parameters,
377 self.fit_params, return_parameters=True)
--> 378 for parameters in parameter_iterable
379 for train, test in cv)
380
/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __call__(self, iterable)
651 self._iterating = True
652 for function, args, kwargs in iterable:
--> 653 self.dispatch(function, args, kwargs)
654
655 if pre_dispatch == "all" or n_jobs == 1:
/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in dispatch(self, func, args, kwargs)
398 """
399 if self._pool is None:
--> 400 job = ImmediateApply(func, args, kwargs)
401 index = len(self._jobs)
402 if not _verbosity_filter(index, self.verbose):
/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __init__(self, func, args, kwargs)
136 # Don't delay the application, to avoid keeping the input
137 # arguments in memory
--> 138 self.results = func(*args, **kwargs)
139
140 def get(self):
/anaconda/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)
1238 else:
1239 estimator.fit(X_train, y_train, **fit_params)
-> 1240 test_score = _score(estimator, X_test, y_test, scorer)
1241 if return_train_score:
1242 train_score = _score(estimator, X_train, y_train, scorer)
/anaconda/lib/python2.7/site-packages/sklearn/cross_validation.pyc in _score(estimator, X_test, y_test, scorer)
1294 score = scorer(estimator, X_test)
1295 else:
-> 1296 score = scorer(estimator, X_test, y_test)
1297 if not isinstance(score, numbers.Number):
1298 raise ValueError("scoring must return a number, got %s (%s) instead."
/anaconda/lib/python2.7/site-packages/sklearn/metrics/metrics.pyc in precision_score(y_true, y_pred, labels, pos_label, average, sample_weight)
1883 average=average,
1884 warn_for=('precision',),
-> 1885 sample_weight=sample_weight)
1886 return p
1887
/anaconda/lib/python2.7/site-packages/sklearn/metrics/metrics.pyc in precision_recall_fscore_support(y_true, y_pred, beta, labels, pos_label, average, warn_for, sample_weight)
1667 raise ValueError("beta should be >0 in the F-beta score")
1668
-> 1669 y_type, y_true, y_pred = _check_clf_targets(y_true, y_pred)
1670
1671 label_order = labels # save this for later
/anaconda/lib/python2.7/site-packages/sklearn/metrics/metrics.pyc in _check_clf_targets(y_true, y_pred)
107 y_pred : array or indicator matrix
108 """
--> 109 y_true, y_pred = check_arrays(y_true, y_pred, allow_lists=True)
110 type_true = type_of_target(y_true)
111 type_pred = type_of_target(y_pred)
/anaconda/lib/python2.7/site-packages/sklearn/utils/validation.pyc in check_arrays(*arrays, **options)
252 if size != n_samples:
253 raise ValueError("Found array with dim %d. Expected %d"
--> 254 % (size, n_samples))
255
256 if not allow_lists or hasattr(array, "shape"):
ValueError: Found array with dim 317760. Expected 51
似乎错误来自评分功能。任何帮助，将不胜感激。感谢。
我的scikit-learn版本：0.15.2
答案 0 :(得分：4)
“得分”参数需要（docs）
评分：字符串，可调用或无，可选，默认值：无
A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y).
“precision_score”函数具有不同的签名。你应该做的只是给一个字符串，因为“精度”是内置指标之一（docs）：
clf = GridSearchCV(estimator=rf, param_grid=param_grid,
cv=5, scoring="precision",
refit=True)