我如何使用 OneHotEncoder 和 ColumnTransformer - ValueError: could not convert string to float: 'low'

Question

我正在根据 uci ml repo 的汽车数据集制作 ML 项目。

这里有 6 个功能和 1 个标签....但问题是 6 个功能列是对象（字符串）形式...所以我如何使用 OneHotEncoder 和 ColumnTransformer 将其转换为浮点...附加蚂蚁答案中的示例代码...

下面的代码我用来转换..有人可以解释一下所有参数和它们的用途吗...

from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [3])], remainder='passthrough')
#print(ct)
X = np.array(ct.fit_transform(cars))
print(X)

使用此代码后我收到以下输出

[[1.0 0.0 1.0 ... 'small' 'low' 'unacc']
 [1.0 0.0 1.0 ... 'small' 'med' 'unacc']
 [1.0 0.0 1.0 ... 'small' 'high' 'unacc']
 ...
 [1.0 0.0 1.0 ... 'big' 'low' 'unacc']
 [1.0 0.0 1.0 ... 'big' 'med' 'good']
 [1.0 0.0 1.0 ... 'big' 'high' 'vgood']]

y 也一样

from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [6])], remainder='passthrough')
#print(ct)
y = np.array(ct.fit_transform(cars))
print(y)

而输出是

array([[0.0, 1.0, 1.0, ..., 'small', 'low', 'unacc'],
       [0.0, 1.0, 1.0, ..., 'small', 'med', 'unacc'],
       [0.0, 1.0, 1.0, ..., 'small', 'high', 'unacc'],
       ...,
       [0.0, 1.0, 1.0, ..., 'big', 'low', 'unacc'],
       [0.0, 1.0, 1.0, ..., 'big', 'med', 'good'],
       [0.0, 1.0, 1.0, ..., 'big', 'high', 'vgood']], dtype=object)

但是当我尝试训练模型时

from sklearn.model_selection import train_test_split
X_train, X_test, y_train , y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
y_test
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)

错误：

ValueError                                Traceback (most recent call last)
<ipython-input-127-19f7ef69ea28> in <module>
      1 from sklearn.linear_model import LinearRegression
      2 regressor = LinearRegression()
----> 3 regressor.fit(X_train, y_train)

c:\users\hp\appdata\local\programs\python\python37\lib\site-packages\sklearn\linear_model\_base.py in fit(self, X, y, sample_weight)
    517 
    518         X, y = self._validate_data(X, y, accept_sparse=accept_sparse,
--> 519                                    y_numeric=True, multi_output=True)
    520 
    521         if sample_weight is not None:

c:\users\hp\appdata\local\programs\python\python37\lib\site-packages\sklearn\base.py in _validate_data(self, X, y, reset, validate_separately, **check_params)
    431                 y = check_array(y, **check_y_params)
    432             else:
--> 433                 X, y = check_X_y(X, y, **check_params)
    434             out = X, y
    435 

c:\users\hp\appdata\local\programs\python\python37\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
     61             extra_args = len(args) - len(all_args)
     62             if extra_args <= 0:
---> 63                 return f(*args, **kwargs)
     64 
     65             # extra_args > 0

c:\users\hp\appdata\local\programs\python\python37\lib\site-packages\sklearn\utils\validation.py in check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)
    876                     ensure_min_samples=ensure_min_samples,
    877                     ensure_min_features=ensure_min_features,
--> 878                     estimator=estimator)
    879     if multi_output:
    880         y = check_array(y, accept_sparse='csr', force_all_finite=True,

c:\users\hp\appdata\local\programs\python\python37\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
     61             extra_args = len(args) - len(all_args)
     62             if extra_args <= 0:
---> 63                 return f(*args, **kwargs)
     64 
     65             # extra_args > 0

c:\users\hp\appdata\local\programs\python\python37\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)
    671                     array = array.astype(dtype, casting="unsafe", copy=False)
    672                 else:
--> 673                     array = np.asarray(array, order=order, dtype=dtype)
    674             except ComplexWarning as complex_warning:
    675                 raise ValueError("Complex data not supported\n"

c:\users\hp\appdata\local\programs\python\python37\lib\site-packages\numpy\core\_asarray.py in asarray(a, dtype, order, like)
    100         return _asarray_with_like(a, dtype=dtype, order=order, like=like)
    101 
--> 102     return array(a, dtype, copy=False, order=order)
    103 
    104 

ValueError: could not convert string to float: 'low'

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Answer 1

你可以用这个方法

import pandas as pd
temp = pd.DataFrame()
temp['something'] = [1, 2]
temp['something_else'] = [3, 4]
temp['first'] = ['car', 'aeroplace']
temp['second'] = ['hi', 'there']
temp = pd.concat([temp[['something', 'something_else']], pd.get_dummies(temp[['first', 'second']])], axis=1)