sklearn 具有多个元素的数组的真值是不明确的.使用 a.any() 或 a.all() 错误 [英] Sklearn the truth value of an array with more than one element is ambiguous. Use a.any() or a.all() error
问题描述
我正在尝试在网上看到的训练数据集上的代码，但似乎无法解决上述错误.
I'm trying out a code on training datasets that I saw online, but can't seem to resolve the error as mentioned.
当我第一次运行代码时，我得到了上述错误:
When I first ran the code, I get the above error as such:
ValueError Traceback (most recent call last)
----> 2 knn_cv.fit(X_train, y_train)
<ipython-input-21-fb975450c609> in fit(self, X, y)
214 X = normalize(X, norm='l1', copy=False)
215
--> 216 cv = check_cv(self.cv, X, y)
/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py in
check_cv(cv, y, classifier)
1980
1981 if isinstance(cv, numbers.Integral):
-> 1982 if (classifier and (y is not None) and
1983 (type_of_target(y) in ('binary', 'multiclass'))):
1984 return StratifiedKFold(cv)
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
错误似乎在函数 check_cv 中，看起来 y_train 正在抛出布尔值，但我不确定如何修改它.我知道原因是and"语句，它通常是可修改的，但在这种情况下，错误存在于 check_cv 函数中，我不确定如何修改该语句.我尝试了使用 a.any() 或 a.all() 的建议操作，但每次都会抛出一个新错误.
The error seems to be in the function check_cv and looks like y_train is throwing the boolean, but I'm not exactly sure how to modify it. I know the cause is the 'and' statement which is usually modifiable but in this case the error resides within the check_cv function and I'm not sure on how to modify the statement. I tried the suggested action which was using a.any() or a.all() but it throws me a new error each time.
如果我使用y_train.any() 它给了我一个错误:
if I use y_train.any() it gives me an error:
269 if y.ndim > 2 or (y.dtype == object and len(y) and
270 not isinstance(y.flat[0], str)):
--> 271 return 'unknown' # [[[1, 2]]] or [obj_1] and not
["label_1"]
272
273 if y.ndim == 2 and y.shape[1] == 0:
TypeError: len() of unsized object
如果我使用 y_train.all()，它说类型错误:'KFold' 对象不可迭代
if I use y_train.all(), it says TypeError: 'KFold' object is not iterable
另一个查询建议将数组更改为列表，但它给了我np.array(y_train).tolist()
结果:TypeError: len() of unsized object
Another query suggested changing the array to a list, but it gives me
np.array(y_train).tolist()
result:
TypeError: len() of unsized object
也更新了 sklearn，但似乎没有修复错误.希望有人能解释什么是错的或者我如何修改代码(如果可能的话也解释一下.这部分代码我还是有点陌生)
Updated sklearn as well but doesn't seem to fix the error. Hoping someone can explain what's wrong or how I can modify the code (explanation as well if possible. I'm still a little unfamiliar with this part of the code)
使用 GoogleNews-vectors-negative300.bin.gz 创建的训练样本
training sample created using GoogleNews-vectors-negative300.bin.gz
y_train = array([ 3, 17, 14, 14, 5, 13,... 0, 1, 17, 16, 2])
y_train = array([ 3, 17, 14, 14, 5, 13,... 0, 1, 17, 16, 2])
y_train.shape() = (100,)
y_train.shape() = (100,)
X_train = <100x5100 '' 类型的稀疏矩阵以压缩稀疏行格式存储 10049 个元素>
X_train = <100x5100 sparse matrix of type '' with 10049 stored elements in Compressed Sparse Row format>
X = check_array(X_train, accept_sparse='csr', copy=True)
print(X)
(0, 679) 1.0
(0, 701) 1.0
(0, 1851) 2.0
(0, 1889) 1.0
(0, 2498) 1.0
(0, 2539) 1.0
(0, 2589) 1.0
(0, 2679) 1.0...
X.shape = (100, 5100)
我附上了代码的主要部分，如果你需要对整个事情的参考，我提供了下面的链接http://vene.ro/blog/word-movers-distance-in-python.html
I attached the main part of the code, if you need a reference to the whole thing, I've provided the link below http://vene.ro/blog/word-movers-distance-in-python.html
def fit(self, X, y):
if self.n_neighbors_try is None:
n_neighbors_try = range(1, 6)
else:
n_neighbors_try = self.n_neighbors_try
X = check_array(X, accept_sparse='csr', copy=True)
X = normalize(X, norm='l1', copy=False)
cv = check_cv(self.cv, X, y)
knn = KNeighborsClassifier(metric='precomputed', algorithm='brute')
scorer = check_scoring(knn, scoring=self.scoring)
scores = []
for train_ix, test_ix in cv:
dist = self._pairwise_wmd(X[test_ix], X[train_ix])
knn.fit(X[train_ix], y[train_ix])
scores.append([
scorer(knn.set_params(n_neighbors=k), dist, y[test_ix])
for k in n_neighbors_try
])
scores = np.array(scores)
self.cv_scores_ = scores
best_k_ix = np.argmax(np.mean(scores, axis=0))
best_k = n_neighbors_try[best_k_ix]
self.n_neighbors = self.n_neighbors_ = best_k
return super(WordMoversKNNCV, self).fit(X, y)
knn_cv = WordMoversKNNCV(cv=3,n_neighbors_try=range(1, 20),
W_embed=W_common, verbose=5, n_jobs=3)
knn_cv.fit(X_train, y_train.all())
根据作者的说法，我应该得到这个:
according to the author, I'm supposed to get this :
[Parallel(n_jobs=3)]: Done 12 tasks | elapsed: 30.8s
[Parallel(n_jobs=3)]: Done 34 out of 34 | elapsed: 2.0min finished
[Parallel(n_jobs=3)]: Done 12 tasks | elapsed: 25.7s
[Parallel(n_jobs=3)]: Done 33 out of 33 | elapsed: 2.9min finished
[Parallel(n_jobs=3)]: Done 12 tasks | elapsed: 53.3s
[Parallel(n_jobs=3)]: Done 33 out of 33 | elapsed: 2.0min finished
WordMoversKNNCV(W_embed=memmap([[ 0.04283, -0.01124, ..., -0.05679, -0.00763],
[ 0.02884, -0.05923, ..., -0.04744, 0.06698],
...,
[ 0.08428, -0.15534, ..., -0.01413, 0.04561],
[-0.02052, 0.08666, ..., 0.03659, 0.10445]]),
cv=3, n_jobs=3, n_neighbors_try=range(1, 20), scoring=None,
verbose=5)
推荐答案
您使用的 check_cv 错误.根据文档:-
You are using check_cv wrong. According to the documentation:-
check_cv(cv=’warn’, y=None, classifier=False):
cv : int,
cross-validation generator or an iterable, optional
y : array-like, optional
The target variable for supervised learning problems.
classifier : boolean, optional, default False
Whether the task is a classification task,
in which case stratified KFold will be used
所以它需要 y 和 estimator 输入.但是您提供的 X 和 y 是错误的.更改以下几行:
So it wants y and estimator in input. But you are providing X and y which is wrong. Change the below lines:
cv = check_cv(self.cv, X, y)
knn = KNeighborsClassifier(metric='precomputed', algorithm='brute')
到:
knn = KNeighborsClassifier(metric='precomputed', algorithm='brute')
cv = check_cv(self.cv, y, knn)
注意行的顺序.
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