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主要思想：将多分类问题转换为多个二分类问题。
import pandas as pd
import matplotlib.pyplot as plt
#由于数据集中每列数据没有标签，因此需要先手动添加，且用空格来隔开
columns = [
'mpg', 'cylinders', 'displacement', 'horsepower', 'weight', 'acceleration',
'model year', 'origin', 'car name'
]
cars = pd.read_table('./data/auto-mpg.data', delim_whitespace=True, names = columns)
print(cars.head())
mpg cylinders displacement horsepower weight acceleration model year \
0 18.0 8 307.0 130.0 3504.0 12.0 70
1 15.0 8 350.0 165.0 3693.0 11.5 70
2 18.0 8 318.0 150.0 3436.0 11.0 70
3 16.0 8 304.0 150.0 3433.0 12.0 70
4 17.0 8 302.0 140.0 3449.0 10.5 70
origin car name
0 1 chevrolet chevelle malibu
1 1 buick skylark 320
2 1 plymouth satellite
3 1 amc rebel sst
4 1 ford torino
pandas.get_dummies()函数生成多分类标签
dummy_cylinders = pd.get_dummies(cars['cylinders'], prefix='cyl')
#print(dummy_cylinders.head())
cars = pd.concat([cars, dummy_cylinders], axis=1)
#print(cars.head())
dummy_years = pd.get_dummies(cars['model year'], prefix='year')
cars = pd.concat([cars, dummy_years], axis=1)
cars = cars.drop('model year', axis=1)
cars = cars.drop('cylinders', axis=1)
print(cars.head())
mpg displacement horsepower weight acceleration origin \
0 18.0 307.0 130.0 3504.0 12.0 1
1 15.0 350.0 165.0 3693.0 11.5 1
2 18.0 318.0 150.0 3436.0 11.0 1
3 16.0 304.0 150.0 3433.0 12.0 1
4 17.0 302.0 140.0 3449.0 10.5 1
car name cyl_3 cyl_4 cyl_5 ... year_73 year_74 \
0 chevrolet chevelle malibu 0 0 0 ... 0 0
1 buick skylark 320 0 0 0 ... 0 0
2 plymouth satellite 0 0 0 ... 0 0
3 amc rebel sst 0 0 0 ... 0 0
4 ford torino 0 0 0 ... 0 0
year_75 year_76 year_77 year_78 year_79 year_80 year_81 year_82
0 0 0 0 0 0 0 0 0
1 0 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0 0
4 0 0 0 0 0 0 0 0
[5 rows x 25 columns]
import numpy as np
shuffled_rows = np.random.permutation(cars.index)
shuffled_cars = cars.iloc[shuffled_rows]
highest_train_row = int(cars.shape[0] * .70)
train = shuffled_cars.iloc[0:highest_train_row]
test = shuffled_cars.iloc[highest_train_row:]
from sklearn.linear_model import LogisticRegression
unique_origins = cars['origin'].unique()
unique_origins.sort()
models = {}
features = [c for c in train.columns if c.startswith('cyl') or c.startswith('year')]
for origin in unique_origins:
model = LogisticRegression()
X_train = train[features]
y_train = train['origin'] == origin
model.fit(X_train, y_train)
models[origin] = model
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\linear_model\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.
FutureWarning)
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\linear_model\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.
FutureWarning)
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\linear_model\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.
FutureWarning)
testing_probs = pd.DataFrame(columns=unique_origins)
print(testing_probs)
for origin in unique_origins:
# Select testing features.
X_test = test[features]
# Compute probability of observation being in the origin.
testing_probs[origin] = models[origin].predict_proba(X_test)[:,1]
print(testing_probs)
Empty DataFrame
Columns: [1, 2, 3]
Index: []
1 2 3
0 0.582297 0.126099 0.306066
1 0.817356 0.067122 0.129694
2 0.272403 0.469606 0.262321
3 0.582297 0.126099 0.306066
4 0.365031 0.282050 0.337488
5 0.321619 0.321747 0.347881
6 0.265345 0.497703 0.247373
7 0.272403 0.469606 0.262321
8 0.836376 0.072543 0.104897
9 0.582297 0.126099 0.306066
10 0.323216 0.487932 0.189434
11 0.821074 0.068240 0.123570
12 0.817356 0.067122 0.129694
13 0.327120 0.325627 0.335424
14 0.316809 0.166039 0.514284
15 0.959333 0.031030 0.026298
16 0.323235 0.412184 0.256623
17 0.582297 0.126099 0.306066
18 0.973135 0.014010 0.037249
19 0.264071 0.355003 0.384353
20 0.323216 0.487932 0.189434
21 0.265345 0.497703 0.247373
22 0.582297 0.126099 0.306066
23 0.323235 0.412184 0.256623
24 0.582297 0.126099 0.306066
25 0.854075 0.086848 0.074716
26 0.316809 0.166039 0.514284
27 0.836376 0.072543 0.104897
28 0.264071 0.355003 0.384353
29 0.582297 0.126099 0.306066
.. ... ... ...
90 0.351290 0.340231 0.295533
91 0.818445 0.126277 0.061284
92 0.772058 0.077050 0.148501
93 0.382735 0.385388 0.224242
94 0.959333 0.031030 0.026298
95 0.975043 0.022176 0.021483
96 0.967840 0.024734 0.025550
97 0.958345 0.040700 0.017952
98 0.382735 0.385388 0.224242
99 0.957909 0.034598 0.024356
100 0.321619 0.321747 0.347881
101 0.958345 0.040700 0.017952
102 0.272403 0.469606 0.262321
103 0.316809 0.166039 0.514284
104 0.316809 0.166039 0.514284
105 0.975043 0.022176 0.021483
106 0.582297 0.126099 0.306066
107 0.323235 0.412184 0.256623
108 0.975043 0.022176 0.021483
109 0.814029 0.029313 0.228263
110 0.316809 0.166039 0.514284
111 0.821074 0.068240 0.123570
112 0.959333 0.031030 0.026298
113 0.323235 0.412184 0.256623
114 0.817356 0.067122 0.129694
115 0.382735 0.385388 0.224242
116 0.814029 0.029313 0.228263
117 0.265345 0.497703 0.247373
118 0.323216 0.487932 0.189434
119 0.836376 0.072543 0.104897
[120 rows x 3 columns]