【机器学习】特征数据预处理-标准化和归一化

加载数据

import pandas as pd
import numpy as np

df = pd.read_csv(
'../data/wine_data.csv',  #葡萄酒数据集
header=None,     #用哪行当做列名，我们自己来指定
usecols=[0,1,2]  #返回一个子集，我们拿部分特征举例就可以了
)

df.columns=['Class label', 'Alcohol', 'Malic acid']

df.head()

在数据中，Alcohol和Malic acid 衡量的标准应该是不同的，特征之间数值差异较大

标准化和归一化

from sklearn.preprocessing import StandardScaler,MinMaxScaler

std_scale = StandardScaler().fit(df[['Alcohol', 'Malic acid']])
df_std = std_scale.transform(df[['Alcohol', 'Malic acid']])

minmax_scale = MinMaxScaler().fit(df[['Alcohol', 'Malic acid']])
df_minmax = minmax_scale.transform(df[['Alcohol', 'Malic acid']])

print('Mean after standardization:\nAlcohol={:.2f}, Malic acid={:.2f}'
.format(df_std[:,0].mean(), df_std[:,1].mean()))
print('\nStandard deviation after standardization:\nAlcohol={:.2f}, Malic acid={:.2f}'
.format(df_std[:,0].std(), df_std[:,1].std()))

Mean after standardization:
Alcohol=-0.00, Malic acid=-0.00

Standard deviation after standardization:
Alcohol=1.00, Malic acid=1.00

print('Min-value after min-max scaling:\nAlcohol={:.2f}, Malic acid={:.2f}'
.format(df_minmax[:,0].min(), df_minmax[:,1].min()))
print('\nMax-value after min-max scaling:\nAlcohol={:.2f}, Malic acid={:.2f}'
.format(df_minmax[:,0].max(), df_minmax[:,1].max()))

Min-value after min-max scaling:
Alcohol=0.00, Malic acid=0.00

Max-value after min-max scaling:
Alcohol=1.00, Malic acid=1.00

%matplotlib inline
from matplotlib import pyplot as plt

def plot():
plt.figure(figsize=(8,6))

plt.scatter(df['Alcohol'], df['Malic acid'],
color='green', label='input scale', alpha=0.5)

plt.scatter(df_std[:,0], df_std[:,1], color='red',
label='Standardized [$N  (\mu=0, \; \sigma=1)$]', alpha=0.3)

plt.scatter(df_minmax[:,0], df_minmax[:,1],
color='blue', label='min-max scaled [min=0, max=1]', alpha=0.3)

plt.title('Alcohol and Malic Acid content of the wine dataset')
plt.xlabel('Alcohol')
plt.ylabel('Malic Acid')
plt.legend(loc='upper left')
plt.grid()

plt.tight_layout()

plot()
plt.show()

我们将原始的和变换后都放到了同一个图上，再看看数据是否被打乱了

fig, ax = plt.subplots(3, figsize=(6,14))

for a,d,l in zip(range(len(ax)),
(df[['Alcohol', 'Malic acid']].values, df_std, df_minmax),
('Input scale',
'Standardized [$N  (\mu=0, \; \sigma=1)$]',
'min-max scaled [min=0, max=1]')
):
for i,c in zip(range(1,4), ('red', 'blue', 'green')):
ax[a].scatter(d[df['Class label'].values == i, 0],
d[df['Class label'].values == i, 1],
alpha=0.5,
color=c,
label='Class %s' %i
)
ax[a].set_title(l)
ax[a].set_xlabel('Alcohol')
ax[a].set_ylabel('Malic Acid')
ax[a].legend(loc='upper left')
ax[a].grid()

plt.tight_layout()

plt.show()

标准化处理对PCA主成分分析的影响

from sklearn.model_selection import train_test_split

X_wine = df.values[:,1:]
y_wine = df.values[:,0]

X_train, X_test, y_train, y_test = train_test_split(X_wine, y_wine,
test_size=0.30, random_state=12345)

from sklearn import preprocessing

std_scale = preprocessing.StandardScaler().fit(X_train)
X_train_std = std_scale.transform(X_train)
X_test_std = std_scale.transform(X_test)

from sklearn.decomposition import PCA

# on non-standardized data
pca = PCA(n_components=2).fit(X_train)
X_train = pca.transform(X_train)
X_test = pca.transform(X_test)

# om standardized data
pca_std = PCA(n_components=2).fit(X_train_std)
X_train_std = pca_std.transform(X_train_std)
X_test_std = pca_std.transform(X_test_std)

接下来看看效果

%matplotlib inline

from matplotlib import pyplot as plt

fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10,4))

for l,c,m in zip(range(1,4), ('blue', 'red', 'green'), ('^', 's', 'o')):
ax1.scatter(X_train[y_train==l, 0], X_train[y_train==l, 1],
color=c,
label='class %s' %l,
alpha=0.5,
marker=m
)

for l,c,m in zip(range(1,4), ('blue', 'red', 'green'), ('^', 's', 'o')):
ax2.scatter(X_train_std[y_train==l, 0], X_train_std[y_train==l, 1],
color=c,
label='class %s' %l,
alpha=0.5,
marker=m
)

ax1.set_title('Transformed NON-standardized training dataset after PCA')
ax2.set_title('Transformed standardized training dataset after PCA')

for ax in (ax1, ax2):

ax.set_xlabel('1st principal component')
ax.set_ylabel('2nd principal component')
ax.legend(loc='upper right')
ax.grid()
plt.tight_layout()

plt.show()

可以清晰的看到经过标准化的数据可分性更强

• 点赞
• 收藏
• 分享
• 文章举报