RandomizedSearchCV和GridSearchCV，在调用fit方法的时候产生'list' object has no attribute 'values'错误之处理方法

【pyhon 版本 3.5.0 skit-learn版本<0.18.1>】

昨天发现的问题，RandomizedSearchCV怎么都调不通：

# Split the dataset in two equal partsX_train, X_test, y_train, y_test = train_test_split(    data,label, test_size=0.25, random_state=0) # Set the parameters by cross-validationtuned_parameters = [{'n_neighbors': range(2,7)},                     {'leaf_size':range(9,100,3)},                     {'p':range(1,5)}] svr=KNeighborsClassifier() scores = ['precision', 'recall'] for score in scores:    print("# Tuning hyper-parameters for %s" % score)    print()     labels=y_train.values    aa    c, r = labels.shape    labels = labels.reshape(c,)     clf = RandomizedSearchCV(svr, tuned_parameters,cv=5,n_jobs=-1,verbose=3)#    clf = GridSearchCV(svr, tuned_parameters,cv=5,n_jobs=-1,verbose=3)clf.fit(X_train, labels)

报错如下：

 

 File "C:\ProgramData\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 102, in execfile    exec(compile(f.read(), filename, 'exec'), namespace)   File "C:/Users/gzhuangzhongyi/Desktop/NetEase/test/RandomSearchCV_Functional.py", line 46, in <module>    clf.fit(X_train, labels)   File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py", line 1190, in fit    return self._fit(X, y, groups, sampled_params)   File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py", line 564, in _fit    for parameters in parameter_iterable   File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py", line 758, in __call__    while self.dispatch_one_batch(iterator):   File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py", line 603, in dispatch_one_batch    tasks = BatchedCalls(itertools.islice(iterator, batch_size))   File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py", line 127, in __init__    self.items = list(iterator_slice)   File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py", line 557, in <genexpr>    )(delayed(_fit_and_score)(clone(base_estimator), X, y, self.scorer_,   File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py", line 230, in __iter__    for v in self.param_distributions.values()]) AttributeError: 'list' object has no attribute 'values'

 

经过查看fit方法，发现无论如何调整fit方法的参数，都没法运行。

但是如果换成GridSearchCV就可以运行。

经过查看类实现，发现两种类调用了相同的，fit方法，但是，fit方法有隐含传入的参数：

   

sampled_params = ParameterSampler(self.param_distributions,                                          self.n_iter,                                          random_state=self.random_state)        return self._fit(X, y, groups, sampled_params)

其中，sampled_params为传入参数之采样。

其传入参数在初始化的时候传入：

 

clf = RandomizedSearchCV(svr, tuned_parameters,cv=5,n_jobs=-1,verbose=3)

而，这个参数由：

tuned_parameters = [{'n_neighbors': range(2,7)},                     {'leaf_size':range(9,100,3)},                     {'p':range(1,5)}]

语句设定，这里有三个字典。而正确的是：

 

tuned_parameters = [{'n_neighbors': range(2,7),                     'leaf_size':range(9,100,3),                     'p':range(1,5)}]

Grid的时候会遍历字典中所有参数的组合，所以字典的划分不重要。

 for p in self.param_grid:            # Always sort the keys of a dictionary, for reproducibility            items = sorted(p.items())            if not items:                yield {}            else:                keys, values = zip(*items)                for v in product(*values):                    params = dict(zip(keys, v))                    yield params

但是Randomlize,当传入字典的时候，会作为带分布的进行处理，对字典取值

# Always sort the keys of a dictionary, for reproducibility            items = sorted(self.param_distributions.items())            for _ in six.moves.range(self.n_iter):                params = dict()                for k, v in items:                    if hasattr(v, "rvs"):                        if sp_version < (0, 16):                            params[k] = v.rvs()                        else:                            params[k] = v.rvs(random_state=rnd)                    else:                        params[k] = v[rnd.randint(len(v))]                yield params

Random会检查传入的参数，如果可以遍历就认为是分布。

于是传入作为fit的参数集的时候，不是作为可遍历的对象的字典，可以.values，而是一个一个把分布元素组合成字典的list，但因为传入的不是一个分布而是一个list，所以不能对分布取值。

上面的两段函数GridSearchCV产生的参数集：

RandomizeSearchCV产生的参数集因为debug调不出来，无法展示。