python - TypeError：单例数组数组（0.5）不能被视为有效集合

Question

我有以下错误，但我真的不明白它为什么会发生以及如何解决它。

这是我的代码：

from sklearn import naive_bayes, tree, metrics
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import numpy as np

我首先加载了虹膜数据，然后获取了其对应的标签。

# get all data
iris = load_iris()

# get raw data and label
labeled_data = list(zip(iris_data, iris_labels))

X, y = np.arange(10).reshape((5, 2)), range(5)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=25)

然后我运行一个分类器。
# 运行指定的分类器 def run_classifier(classifier, training, testing): classifier.fit(*training)

    # classifier.fit(X_train, y_train, batch_size=10, epochs=100)

    expect = testing[1]
    predict = classifier.predict(testing[0])

    return expect, predict

我使用朴素贝叶斯和决策树进行分类。
# 收集关于训练大小高原的数据 def simulation(): # 在测试数据大小范围内取得进展 nb_acc = [] tree_acc = [] training_fracs = [x/1000 for x in range(500, 850, 25)]

    for i in training_fracs:
        nb = naive_bayes.CategoricalNB()
        dt = tree.DecisionTreeClassifier()
        training, testing = train_test_split(i)

        nb_expect, nb_predict = run_classifier(nb, training, testing)
        dt_expect, dt_predict = run_classifier(dt, training, testing)

        nb_acc.append(metrics.accuracy_score(nb_expect, nb_predict))
        tree_acc.append(metrics.accuracy_score(dt_expect, dt_predict))

    return nb_acc, tree_acc, training_fracs

nb_acc, tree_acc, training_fracs = simulate()

print(f"Naive Bayes accuracy @ 50% training: {nb_acc[0]}")
print(f"Decision Tree accuracy @ 50% training: {tree_acc[0]}")

>     ---------------------------------------------------------------------------
>     TypeError                                 Traceback (most recent call last)
>     <ipython-input-541-1b565a23fd50> in <module>
>          28     return nb_acc, tree_acc, training_fracs
>          29 
>     ---> 30 nb_acc, tree_acc, training_fracs = simulate()
>          31 
>          32 print(f"Naive Bayes accuracy @ 50% training: {nb_acc[0]}")
>     
>     <ipython-input-541-1b565a23fd50> in simulate()
>          18         nb = naive_bayes.CategoricalNB()
>          19         dt = tree.DecisionTreeClassifier()
>     ---> 20         training, testing = train_test_split(i)
>          21 
>          22         nb_expect, nb_predict = run_classifier(nb, training, testing)
>     
>     ~\anaconda3\lib\site-packages\sklearn\model_selection\_split.py in train_test_split(test_size, train_size, random_state, shuffle,
> stratify, *arrays)
>        2170         raise ValueError("At least one array required as input")
>        2171 
>     -> 2172     arrays = indexable(*arrays)
>        2173 
>        2174     n_samples = _num_samples(arrays[0])
>     
>     ~\anaconda3\lib\site-packages\sklearn\utils\validation.py in indexable(*iterables)
>         297     """
>         298     result = [_make_indexable(X) for X in iterables]
>     --> 299     check_consistent_length(*result)
>         300     return result
>         301 
>     
>     ~\anaconda3\lib\site-packages\sklearn\utils\validation.py in check_consistent_length(*arrays)
>         257     """
>         258 
>     --> 259     lengths = [_num_samples(X) for X in arrays if X is not None]
>         260     uniques = np.unique(lengths)
>         261     if len(uniques) > 1:
>     
>     ~\anaconda3\lib\site-packages\sklearn\utils\validation.py in <listcomp>(.0)
>         257     """
>         258 
>     --> 259     lengths = [_num_samples(X) for X in arrays if X is not None]
>         260     uniques = np.unique(lengths)
>         261     if len(uniques) > 1:
>     
>     ~\anaconda3\lib\site-packages\sklearn\utils\validation.py in _num_samples(x)
>         200     if hasattr(x, 'shape') and x.shape is not None:
>         201         if len(x.shape) == 0:
>     --> 202             raise TypeError("Singleton array %r cannot be considered"
>         203                             " a valid collection." % x)
>         204         # Check that shape is returning an integer or default to len
>     
>     TypeError: Singleton array array(0.5) cannot be considered a valid collection.

标签： pythonmachine-learningscikit-learn