將數據划分為訓練集和測試集；縮放特征區間

導入葡萄酒數據：

import numpy as np
import pandas as pd

df_wine = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data", header=None)
df_wine.columns = ["class label", "alcohol",
                   "malic acid", "ash",
                   "alcalinity of ash", "magnesium",
                   "total phenols", "flavanoids",
                   "nonflavaniod phenols", "proanthocyanins",
                   "color intensity", "hue",
                   "OD280/OD315 of diluted wines", "proline"]
# 查看類標
print("class label:", np.unique(df_wine["class label"]))
print(df_wine.head())

運行結果：

 

划分訓練集和測試集：

 　　我們可以使用 sklearn.model_selection 中的 train_test_split 划分數據，test_size用來設置測試數據的比例，random_state用來

設置隨機數是否保持一致。

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

　　這里如果你用的是 sklearn.cross_validation 的 train_test_split ,那么代碼是會報警告的，由於版本的更新，推薦使用上面的代碼。

 

特征縮放：

　　特征縮放我們可以采用歸一化和標准化兩者方法

# 特征縮放：歸一化
from sklearn.preprocessing import MinMaxScaler
mms = MinMaxScaler()
x_train_norm = mms.fit_transform(x_train)
x_test_norm = mms.transform(x_test)
print(x_test_norm, "\n")

# 特征縮放：標准化
from sklearn.preprocessing import StandardScaler
stdsc = StandardScaler()
x_train_std = stdsc.fit_transform(x_train)
x_test_std = stdsc.transform(x_test)
print(x_test_std)