Question

我正在尝试从scikit-learn 0.16实现LogisticRegressionCV类，并且很难让它使用不同的评分函数。文档说要从sklearn.metrics传入其中一个评分函数，所以我尝试了以下代码：

from sklearn.linear_model import LogisticRegressionCV
from sklearn.metrics import log_loss

...

model_regression = LogisticRegressionCV(scoring=log_loss)
model_regression.fit(data_combined, winners_losers)

但是我在fit函数上遇到以下错误：

  File "C:\Anaconda3\lib\site-packages\sklearn\linear_model\logistic.py", line 1381, in fit
    for label in iter_labels
  File "C:\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py", line 659, in __call__
    self.dispatch(function, args, kwargs)
  File "C:\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py", line 406, in dispatch
    job = ImmediateApply(func, args, kwargs)
  File "C:\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py", line 140, in __init__
    self.results = func(*args, **kwargs)
  File "C:\Anaconda3\lib\site-packages\sklearn\linear_model\logistic.py", line 844, in _log_reg_scoring_path
    scores.append(scoring(log_reg, X_test, y_test))
  File "C:\Anaconda3\lib\site-packages\sklearn\metrics\classification.py", line 1403, in log_loss
    T = lb.fit_transform(y_true)
  File "C:\Anaconda3\lib\site-packages\sklearn\base.py", line 433, in fit_transform
    return self.fit(X, **fit_params).transform(X)
  File "C:\Anaconda3\lib\site-packages\sklearn\preprocessing\label.py", line 315, in fit
    self.y_type_ = type_of_target(y)
  File "C:\Anaconda3\lib\site-packages\sklearn\utils\multiclass.py", line 287, in type_of_target
    'got %r' % y)
ValueError: Expected array-like (array or non-string sequence), got LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr',
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0)

我在这里做错了什么？没有'scoring = log_loss'参数，那么函数工作正常，所以它必须与我如何传递函数有关？

Answer 1

它应该是scoring="log_loss"，一个字符串，而不是一个函数。如果要传递可调用对象，则需要具有不同的接口。请参阅docs。可调用者应该采用三个参数：拟合估计量，得分数据（X）和已知真实目标（y）。

Answer 2

要提供功能，您需要make_scorer包装器

import sklearn.metrics 

scorefunc = sklearn.metrics.accuracy_score  # Replace with custom
myscorer = sklearn.metrics.make_scorer(
         scorefunc,
         greater_is_better=True,
         needs_threshold=False # ... classification
)

LogisticRegressionCV(... scoring=myscorer,)

....作为旁注，如果sklearn的LogisticRegression主要是回归，并且新的LogisticClassification类包装了它，那将是很好的。它不可能提供回归误差，或者目前提供实值目标。（AFAIK）

如何在scikit-learn中的LogisticRegressionCV中实现不同的评分函数？

2 个答案: