尝试实现逻辑回归，但 gridsearchCV 显示输入变量的样本数量不一致：[60000, 60001]答案

【问题标题】：Trying to implement logistic regression but gridsearchCV shows input variables with inconsistent numbers of samples: [60000, 60001]尝试实现逻辑回归，但 gridsearchCV 显示输入变量的样本数量不一致：[60000, 60001]
【发布时间】：2020-11-14 02:40:12
【问题描述】：

尝试实现逻辑回归，但 gridsearchCV 显示输入变量的样本数量不一致：[60000, 60001] 这是我在 python 3 环境中的代码：

import joblib
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV
import warnings
warnings.filterwarnings('ignore', category=FutureWarning)
warnings.filterwarnings('ignore', category=DeprecationWarning)

tr_features = pd.read_csv('/home/pranjal/PycharmProjects/train_features.csv')
tr_labels = pd.read_csv('/home/pranjal/PycharmProjects/train_labels.csv', header=None)


        

lr = LogisticRegression()
parameters = {
    'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]
}

cv = GridSearchCV(lr, parameters, cv=5)
cv.fit(tr_features, tr_labels.values.ravel())

print_results(cv)

输出运行时错误如下：

ValueError                                Traceback (most recent call last)
<ipython-input-20-c836a092d0ab> in <module>
      5 
      6 cv = GridSearchCV(lr, parameters, cv=5)
----> 7 cv.fit(tr_features, tr_labels.values.ravel())
      8 
      9 print_results(cv)

/home/pranjal/snap/jupyter/common/lib/python3.7/site-packages/sklearn/utils/validation.py in inner_f(*args, **kwargs)
     71                           FutureWarning)
     72         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 73         return f(**kwargs)
     74     return inner_f
     75 

/home/pranjal/snap/jupyter/common/lib/python3.7/site-packages/sklearn/model_selection/_search.py in fit(self, X, y, groups, **fit_params)
    674             refit_metric = 'score'
    675 
--> 676         X, y, groups = indexable(X, y, groups)
    677         fit_params = _check_fit_params(X, fit_params)
    678 

/home/pranjal/snap/jupyter/common/lib/python3.7/site-packages/sklearn/utils/validation.py in indexable(*iterables)
    291     """
    292     result = [_make_indexable(X) for X in iterables]
--> 293     check_consistent_length(*result)
    294     return result
    295 

/home/pranjal/snap/jupyter/common/lib/python3.7/site-packages/sklearn/utils/validation.py in check_consistent_length(*arrays)
    255     if len(uniques) > 1:
    256         raise ValueError("Found input variables with inconsistent numbers of"
--> 257                          " samples: %r" % [int(l) for l in lengths])
    258 
    259 

ValueError: Found input variables with inconsistent numbers of samples: [60000, 60001]

请帮我调试这段代码

【问题讨论】：

标签： python machine-learning scikit-learn logistic-regression grid-search

【解决方案1】：

Sklearn 需要(n_samples, n_columns) 的数据形状。当您在 numpy 数组上使用 ravel 时，生成的形状为 (n_samples,)。将其重塑为(n_samples, n_columns)。如果这不起作用，您可以尝试对cv.fit() 中的输入使用相同的数据类型，即

cv.fit(tr_features, tr_labels)

这样特征和标签都是数据框。

【讨论】：