sklearn：sklearn.preprocessing DeprecationWarning 数组

Question

首先，我查看了所有相关问题。 给出了非常相似的问题。
所以我遵循了链接中的建议，但没有一个对我有用。
Data Conversion Error while applying a function to each row in pandas Python
Getting deprecation warning in Sklearn over 1d array, despite not having a 1D array

我也尝试按照错误消息进行操作，但也没有用。

代码如下所示：

# Importing the libraries
import numpy as np
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:, 1:2].values
y = dataset.iloc[:, 2].values

# avoid DataConversionError
X = X.astype(float)
y = y.astype(float)


## Attempt to avoid DeprecationWarning for sklearn.preprocessing
#X = X.reshape(-1,1)                  # attempt 1
#X = np.array(X).reshape((len(X), 1)) # attempt 2
#X = np.array([X])                    # attempt 3


# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
sc_y = StandardScaler()
X = sc_X.fit_transform(X)
y = sc_y.fit_transform(y)

# Fitting SVR to the dataset
from sklearn.svm import SVR
regressor = SVR(kernel = 'rbf')
regressor.fit(X, y)

# Predicting a new result
y_pred = regressor.predict(sc_X.transform(np.array([6.5])))
y_pred = sc_y.inverse_transform(y_pred)

数据如下：

Position,Level,Salary
Business Analyst,1,45000
Junior Consultant,2,50000
Senior Consultant,3,60000
Manager,4,80000
Country Manager,5,110000
Region Manager,6,150000
Partner,7,200000
Senior Partner,8,300000
C-level,9,500000
CEO,10,1000000

完整的错误日志如下所示：

/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
  DeprecationWarning)

我只使用第二列和第三列，因此第一列不需要一个热编码。唯一的问题是 DeprecationWarning。

我尝试了所有给出的建议，但没有一个奏效。
因此，我们将非常感谢您的帮助。

Answer 1

这很奇怪。下面是我用来摆脱弃用警告的代码，稍微修改了您适合 StandardScaler() 并调用 transform() 的方式。该解决方案涉及根据警告消息费力地重塑和拆散阵列。不确定这是否是最好的方法，但它删除了警告。

# Importing the libraries
import numpy as np
import pandas as pd
from io import StringIO
from sklearn.preprocessing import StandardScaler

# Setting up data string to be read in as a .csv
data = StringIO("""Position,Level,Salary
Business Analyst,1,45000
Junior Consultant,2,50000
Senior Consultant,3,60000
Manager,4,80000
Country Manager,5,110000
Region Manager,6,150000
Partner,7,200000
Senior Partner,8,300000
C-level,9,500000
CEO,10,1000000""")

dataset = pd.read_csv(data)

# Importing the dataset
#dataset = pd.read_csv('Position_Salaries.csv')

# Deprecation warnings call for reshaping of single feature arrays with reshape(-1,1)
X = dataset.iloc[:, 1:2].values.reshape(-1,1)
y = dataset.iloc[:, 2].values.reshape(-1,1)

# avoid DataConversionError
X = X.astype(float)
y = y.astype(float)

#sc_X = StandardScaler()
#sc_y = StandardScaler()
X_scaler = StandardScaler().fit(X)
y_scaler = StandardScaler().fit(y)

X_scaled = X_scaler.transform(X)
y_scaled = y_scaler.transform(y)

# Fitting SVR to the dataset
from sklearn.svm import SVR
regressor = SVR(kernel = 'rbf')

# One of the warnings called for ravel()
regressor.fit(X_scaled, y_scaled.ravel())

# Predicting a new result
# The warnings called for single samples to reshaped with reshape(1,-1)
X_new = np.array([6.5]).reshape(1,-1)
X_new_scaled = X_scaler.transform(X_new)
y_pred = regressor.predict(X_new_scaled)
y_pred = y_scaler.inverse_transform(y_pred)