ValueError：拟合逻辑回归模型时输入形状错误

Question

我目前正在从头开始学习逻辑回归。我正在创建一个基于我制作的 Iris 数据集的更改形式的逻辑回归模型。这是我的代码的摘录：

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

df = pd.read_excel('Iris_Dataset.xlsx')
df = df.dropna()

colour = pd.get_dummies(df.colour, drop_first = True)
species = pd.get_dummies(df.species, drop_first = True)
df = pd.concat([df, colour, species], axis = 1)
df = df.drop(['colour', 'species'], axis = 1)

x = df.drop(["versicolor", "virginica"], axis = 1)
y = pd.concat([df.versicolor, df.virginica], axis = 1)

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state = 1)

model = LogisticRegression()

model.fit(x_train, y_train)

由于某种原因，在最后一条语句(model.fit(x_train, y_train)) 中，我收到以下错误：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-2-0c77380c6ea5> in <module>()
     18 model = LogisticRegression()
     19 
---> 20 model.fit(x_train, y_train)

/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py in column_or_1d(y, warn)
    795         return np.ravel(y)
    796 
--> 797     raise ValueError("bad input shape {0}".format(shape))
    798 
    799 

ValueError: bad input shape (86, 2)

无论我尝试什么，我都无法理解错误的含义，也无法理解为什么会出现此错误。请帮我解决这个问题。

顺便说一下，这里是数据集。它采用谷歌表格的形式，但我在 Microsoft Excel 中复制粘贴了与 Iris_Dataset.xlsx 相同的数据集（我无法弄清楚如何直接共享 excel 文件）： https://docs.google.com/spreadsheets/d/18zkZvPQ5q_ExaWu4dywsHtpO9D6ECdmXzvZahPryiqU/edit?usp=sharing

提前致谢。

编辑：所以我尝试了其他方法，这次我只将颜色列转换为虚拟值并保持物种完好无损。代码如下：

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

df = pd.read_excel('Iris_Dataset.xlsx')
df = df.dropna()

colour = pd.get_dummies(df.colour, drop_first = True)
df = pd.concat([df, colour], axis = 1)
df = df.drop(['colour'], axis = 1)
    
x = df.drop(["species"], axis = 1)
y = df.species

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state = 1)
    
model = LogisticRegression()

model.fit(x_train, y_train)

我只是为了颜色而这样做的唯一原因是，当我尝试没有虚拟值的情况下（我试图将“物种”列作为一个整体而不是我之前所做的传递）时，我得到了错误：could not convert string to float: 'violet'

所以执行上述代码后，我得到了输出，但拟合过程在输出的同时给出了警告消息：

/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=None, max_iter=100,
                   multi_class='auto', n_jobs=None, penalty='l2',
                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,
                   warm_start=False)

后面的代码虽然有些流畅，但我想知道消息的含义，或者有什么办法可以避免它？

Answer 1

您正在输入一个包含两列 y 的数据框作为您在 LogisticRegression.fit() 中的目标向量，这是不可能的。目标向量的形状必须为 (n_samples,)，在数据帧中是单列。

这是文档中的一个示例：

from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
X, y = load_iris(return_X_y=True)
clf = LogisticRegression(random_state=0).fit(X, y)
clf.predict(X[:2, :])

clf.predict_proba(X[:2, :])


clf.score(X, y)

打印y，你会看到它是一个一维数组。

https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html