将数据划分为训练集和测试集；缩放特征区间

导入葡萄酒数据：

 1 import numpy as np
 2 import pandas as pd
 3 
 4 df_wine = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data", header=None)
 5 df_wine.columns = ["class label", "alcohol",
 6                    "malic acid", "ash",
 7                    "alcalinity of ash", "magnesium",
 8                    "total phenols", "flavanoids",
 9                    "nonflavaniod phenols", "proanthocyanins",
10                    "color intensity", "hue",
11                    "OD280/OD315 of diluted wines", "proline"]
12 # 查看类标
13 print("class label:", np.unique(df_wine["class label"]))
14 print(df_wine.head())

运行结果：

 

划分训练集和测试集：

 　　我们可以使用 sklearn.model_selection 中的 train_test_split 划分数据，test_size用来设置测试数据的比例，random_state用来

设置随机数是否保持一致。

1 from sklearn.model_selection import train_test_split
2 # import warnings
3 # warnings.filterwarnings('ignore')
4 x, y = df_wine.iloc[:, 1:].values, df_wine.iloc[:, 0].values
5 x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=0)

　　这里如果你用的是 sklearn.cross_validation 的 train_test_split ,那么代码是会报警告的，由于版本的更新，推荐使用上面的代码。

 

特征缩放：

　　特征缩放我们可以采用归一化和标准化两者方法

 1 # 特征缩放：归一化
 2 from sklearn.preprocessing import MinMaxScaler
 3 mms = MinMaxScaler()
 4 x_train_norm = mms.fit_transform(x_train)
 5 x_test_norm = mms.transform(x_test)
 6 print(x_test_norm, "\n")
 7 
 8 # 特征缩放：标准化
 9 from sklearn.preprocessing import StandardScaler
10 stdsc = StandardScaler()
11 x_train_std = stdsc.fit_transform(x_train)
12 x_test_std = stdsc.transform(x_test)
13 print(x_test_std)

 

转载于:https://www.cnblogs.com/dan-baishucaizi/p/9107858.html