sklearnAssertionError：不等于自定義估算器的容差

出于學習原因，我正在使用 scikit-learn 界面創建自定義分類器。所以，我想出了以下代碼：

import numpy as np
from sklearn.utils.estimator_checks import check_estimator
from sklearn.base import BaseEstimator, ClassifierMixin, check_X_y
from sklearn.utils.validation import check_array, check_is_fitted, check_random_state

class TemplateEstimator(BaseEstimator, ClassifierMixin):
  def __init__(self, threshold=0.5, random_state=None):
    self.threshold = threshold
    self.random_state = random_state

  def fit(self, X, y):
    self.random_state_ = check_random_state(self.random_state)
    X, y = check_X_y(X, y)
    self.classes_ = np.unique(y)
    self.fitted_ = True
    return self
  
  def predict(self, X):
    check_is_fitted(self)
    X = check_array(X)

    y_hat = self.random_state_.choice(self.classes_, size=X.shape[0])
    return y_hat

check_estimator(TemplateEstimator())

這個分類器只是做隨機猜測。我盡力遵循 scikit-learn 檔案和指南來開發自己的估算器。但是，我收到以下錯誤：

AssertionError: 
Arrays are not equal
Classifier cant predict when only one class is present.
Mismatched elements: 10 / 10 (100%)
Max absolute difference: 1.
Max relative difference: 1.
 x: array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
 y: array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])

我不能確定，但??我猜是隨機性（即self.random_state_）導致了錯誤。我正在使用 sklearn 版本1.0.2。

uj5u.com熱心網友回復：

首先要注意的是，如果使用parametrize_with_checkswithpytest而不是check_estimator. 它看起來像：

@parametrize_with_checks([TemplateEstimator()])
def test_sklearn_compatible_estimator(estimator, check):
    check(estimator)

如果你用 pytest 運行它，你會得到一個包含以下失敗測驗的輸出：

FAILED ../../../../tmp/1.py::test_sklearn_compatible_estimator[TemplateEstimator()-check_pipeline_consistency] - AssertionError: 
FAILED ../../../../tmp/1.py::test_sklearn_compatible_estimator[TemplateEstimator()-check_classifiers_train] - AssertionError
FAILED ../../../../tmp/1.py::test_sklearn_compatible_estimator[TemplateEstimator()-check_classifiers_train(readonly_memmap=True)] - AssertionError
FAILED ../../../../tmp/1.py::test_sklearn_compatible_estimator[TemplateEstimator()-check_classifiers_train(readonly_memmap=True,X_dtype=float32)] - AssertionError
FAILED ../../../../tmp/1.py::test_sklearn_compatible_estimator[TemplateEstimator()-check_classifiers_regression_target] - AssertionError: Did not raise: [<class 'ValueErr...
FAILED ../../../../tmp/1.py::test_sklearn_compatible_estimator[TemplateEstimator()-check_methods_sample_order_invariance] - AssertionError: 
FAILED ../../../../tmp/1.py::test_sklearn_compatible_estimator[TemplateEstimator()-check_methods_subset_invariance] - AssertionError: 

其中一些測驗檢查某些輸出一致性，這與您的情況無關，因為您回傳隨機值。在這種情況下，您需要設定non_deterministic estimator tag. 其他一些測驗，例如check_classifiers_regression_target檢查您是否進行了正確的驗證并引發了正確的錯誤，而您卻沒有。所以你要么需要解決這個問題，要么添加no_validation標簽。另一個問題是check_classifier_train檢查您的模型是否為給定問題提供合理的輸出。但是由于您回傳的是隨機值，因此不滿足這些條件。您可以設定poor_score估算器標簽以跳過它。

您可以通過將其添加到您的估算器來添加這些標簽：

class TemplateEstimator(BaseEstimator, ClassifierMixin):
    ...
    def _more_tags(self):
        return {
            "non_deterministic": True,
            "no_validation": True,
            "poor_score": True,
        }

但即便如此，如果您使用mainscikit-learn 的分支或 nightly 構建，兩個測驗也會失敗。我相信這需要修復，我已經為它打開了一個問題。您可以通過在標簽中將這些測驗設定為預期失敗來避免這些失敗。最后，您的估算器將如下所示：

import numpy as np
from sklearn.utils.estimator_checks import parametrize_with_checks
from sklearn.base import BaseEstimator, ClassifierMixin, check_X_y
from sklearn.utils.validation import check_array, check_is_fitted, check_random_state


class TemplateEstimator(BaseEstimator, ClassifierMixin):
    def __init__(self, threshold=0.5, random_state=None):
        self.threshold = threshold
        self.random_state = random_state

    def fit(self, X, y):
        self.random_state_ = check_random_state(self.random_state)
        X, y = check_X_y(X, y)
        self.classes_ = np.unique(y)
        self.fitted_ = True
        return self

    def predict(self, X):
        check_is_fitted(self)
        X = check_array(X)

        y_hat = self.random_state_.choice(self.classes_, size=X.shape[0])
        return y_hat

    def _more_tags(self):
        return {
            "non_deterministic": True,
            "no_validation": True,
            "poor_score": True,
            "_xfail_checks": {
                "check_methods_sample_order_invariance": "This test shouldn't be running at all!",
                "check_methods_subset_invariance": "This test shouldn't be running at all!",
            },
        }


@parametrize_with_checks([TemplateEstimator()])
def test_sklearn_compatible_estimator(estimator, check):
    check(estimator)

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