ValueError：发现样本数量不一致的数组

Question

这是我的代码：

import pandas as pa
from sklearn.linear_model import Perceptron
from sklearn.metrics import accuracy_score

def get_accuracy(X_train, y_train, y_test):
    perceptron = Perceptron(random_state=241)
    perceptron.fit(X_train, y_train)
    result = accuracy_score(y_train, y_test)
    return result

test_data = pa.read_csv("C:/Users/Roman/Downloads/perceptron-test.csv")
test_data.columns = ["class", "f1", "f2"]
train_data = pa.read_csv("C:/Users/Roman/Downloads/perceptron-train.csv")
train_data.columns = ["class", "f1", "f2"]

accuracy = get_accuracy(train_data[train_data.columns[1:]], train_data[train_data.columns[0]], test_data[test_data.columns[0]])
print(accuracy)

我不明白为什么会出现此错误：

Traceback (most recent call last):
  File "C:/Users/Roman/PycharmProjects/data_project-1/lecture_2_perceptron.py", line 35, in <module>
    accuracy = get_accuracy(train_data[train_data.columns[1:]], 
train_data[train_data.columns[0]], test_data[test_data.columns[0]])
  File "C:/Users/Roman/PycharmProjects/data_project-1/lecture_2_perceptron.py", line 22, in get_accuracy
    result = accuracy_score(y_train, y_test)
  File "C:\Users\Roman\AppData\Roaming\Python\Python35\site-packages\sklearn\metrics\classification.py", line 172, in accuracy_score
    y_type, y_true, y_pred = _check_targets(y_true, y_pred)
  File "C:\Users\Roman\AppData\Roaming\Python\Python35\site-packages\sklearn\metrics\classification.py", line 72, in _check_targets
    check_consistent_length(y_true, y_pred)
  File "C:\Users\Roman\AppData\Roaming\Python\Python35\site-packages\sklearn\utils\validation.py", line 176, in check_consistent_length
    "%s" % str(uniques))
ValueError: Found arrays with inconsistent numbers of samples: [199 299]

我想通过获取此类错误的方法 accuracy_score 来获取准确性。我用谷歌搜索找不到任何可以帮助我的东西。谁能解释一下会发生什么？

Answer 1

sklearn.metrics.accuracy_score() 接受 y_true 和 y_pred 参数。也就是说，对于相同的数据集（可能是测试集），它想知道基本事实和模型预测的值。这将允许它评估您的模型与假设的完美模型相比的表现。

在您的代码中，您传递了两个不同数据集的真实结果变量。这些结果都是事实，绝不反映您的模型对观察结果进行正确分类的能力！

更新您的get_accuracy() 函数以将X_test 作为参数，我认为这更符合您的意图：

def get_accuracy(X_train, y_train, X_test, y_test):
    perceptron = Perceptron(random_state=241)
    perceptron.fit(X_train, y_train)
    pred_test = perceptron.predict(X_test)
    result = accuracy_score(y_test, pred_test)
    return result