逻辑回归的简单应用

JackYang
100 阅读3分钟

1.获取数据

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression

data = pd.read_csv(r'data\distance.csv')
data.head()
.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }
Unnamed: 0 A0 A1 A2 A3 x y z label
0 0 1016.931217 4782.857143 4552.962963 6298.994709 50 50 88 1
1 1 1338.424658 4920.136986 4109.178082 5943.561644 50 100 88 1
2 2 1783.790850 5033.464052 3605.882353 5619.150327 50 150 88 1
3 3 2253.617021 5218.563830 3085.159574 5322.819149 50 200 88 1
4 4 2727.142857 5370.285714 2600.142857 5033.238095 50 250 88 1

2. 基本数据处理

# 2.1 缺失值处理
data = data.replace(to_replace="?", value=np.NaN) # 把data中的 to_replece 值 替换为 value.
data = data.dropna()        # 删除有空值的行，默认 axis=0      
data.head()
.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }
Unnamed: 0 A0 A1 A2 A3 x y z label
0 0 1016.931217 4782.857143 4552.962963 6298.994709 50 50 88 1
1 1 1338.424658 4920.136986 4109.178082 5943.561644 50 100 88 1
2 2 1783.790850 5033.464052 3605.882353 5619.150327 50 150 88 1
3 3 2253.617021 5218.563830 3085.159574 5322.819149 50 200 88 1
4 4 2727.142857 5370.285714 2600.142857 5033.238095 50 250 88 1

3.确定特征值,目标值

data.columns    # 所有的列名

Index(['Unnamed: 0', 'A0', 'A1', 'A2', 'A3', 'x', 'y', 'z', 'label'], dtype='object')

x = data.iloc[:, 1:8]
x.head()
y = data["label"]
y.head()

0    1
1    1
2    1
3    1
4    1
Name: label, dtype: int64

x.head()
.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }
A0 A1 A2 A3 x y z
0 1016.931217 4782.857143 4552.962963 6298.994709 50 50 88
1 1338.424658 4920.136986 4109.178082 5943.561644 50 100 88
2 1783.790850 5033.464052 3605.882353 5619.150327 50 150 88
3 2253.617021 5218.563830 3085.159574 5322.819149 50 200 88
4 2727.142857 5370.285714 2600.142857 5033.238095 50 250 88 
x.shape[0]*0.75

486.0

4.分割数据

X_train, X_test, y_train, y_test = train_test_split(x, y, random_state=22) # 训练集和测试集按照 0.75 : 0.25 (随机)
X_train
.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }
A0 A1 A2 A3 x y z
327 2008.590604 4974.228188 3085.503356 5323.355705 50 200 88
56 3933.161290 2261.741935 5203.161290 3848.516129 350 150 88
242 6546.121495 4638.738318 4462.803738 869.252336 450 450 170
5 3212.800000 5665.200000 2149.911111 4832.577778 50 300 88
449 5138.976378 5117.716535 2554.251969 2446.771654 250 450 130
... ... ... ... ... ... ... ...
491 2979.007092 5374.609929 1948.226950 4928.297872 50 300 170
502 1375.707317 4091.414634 4167.268293 5628.926829 100 100 170
358 2200.423729 3178.559322 4556.694915 4955.677966 200 100 88
356 2788.269231 3631.730769 3693.750000 4229.326923 200 200 88
132 3312.545455 2428.227273 5024.227273 4202.136364 300 150 130

486 rows × 7 columns

X_test
.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }
A0 A1 A2 A3 x y z
623 3972.727273 1984.696970 4976.515152 3870.606061 350 150 200
389 5275.652174 3712.347826 4345.391304 1755.826087 400 350 88
551 5392.123288 3570.547945 4206.027397 1779.041096 400 350 170
617 3764.841270 2768.015873 4231.746032 3608.730159 300 200 200
130 3814.528302 3394.528302 4132.688679 3104.292453 300 250 130
... ... ... ... ... ... ... ...
568 1230.847458 4624.237288 4086.355932 6122.796610 50 100 200
427 2925.477707 4296.496815 2860.254777 4245.350318 150 250 130
485 6266.831683 4565.544554 4475.346535 568.910891 450 450 130
116 1969.612403 3129.302326 4966.899225 5711.046512 200 50 130
148 4686.401274 2586.019108 4689.681529 3138.057325 400 250 130

162 rows × 7 columns

y_train

327    0
56     1
242    1
5      1
449    0
      ..
491    0
502    0
358    0
356    0
132    1
Name: label, Length: 486, dtype: int64

y_test

623    0
389    0
551    0
617    0
130    1
      ..
568    0
427    0
485    0
116    1
148    1
Name: label, Length: 162, dtype: int64

5.特征工程(标准化)

transfer = StandardScaler()                     #  实例化对象
X_train = transfer.fit_transform(X_train)       # 标准化
X_test = transfer.fit_transform(X_test)

X_train

array([[-1.43796501,  0.86810151, -0.54520456, ..., -1.55143148,        -0.39370337, -1.39230246],
       [ 0.05371734, -1.15687309,  1.03849334, ...,  0.73802915,        -0.77946917, -1.39230246],
       [ 2.07895166,  0.61764551,  0.48481435, ...,  1.50118269,         1.53512564,  0.55690142],
       ...,
       [-1.28928037, -0.47243389,  0.55503119, ..., -0.40670117,        -1.16523498, -1.39230246],
       [-0.83365733, -0.13412409, -0.09032518, ..., -0.40670117,        -0.39370337, -1.39230246],
       [-0.4273051 , -1.03258542,  0.9046769 , ...,  0.35645237,        -0.77946917, -0.39392974]])

X_test

array([[ 0.20623287, -1.62114508,  1.00301367, ...,  0.89636437,        -0.76011455,  1.22013336],
       [ 1.18389765, -0.15251848,  0.47513551, ...,  1.30426052,         0.80852949, -1.42227529],
       [ 1.27129309, -0.2730585 ,  0.35857018, ...,  1.30426052,         0.80852949,  0.51234533],
       ...,
       [ 1.92764066,  0.57275967,  0.58383134, ...,  1.71215667,         1.59285151, -0.43137205],
       [-1.29682755, -0.64814879,  0.99497082, ..., -0.32732407,        -1.54443657, -0.43137205],
       [ 0.74174642, -1.10997832,  0.76310322, ...,  1.30426052,         0.02420747, -0.43137205]])

6.机器学习(逻辑回归)

estimator = LogisticRegression()
estimator.fit(X_train, y_train)
estimator

LogisticRegression()

7.模型评估

y_predict = estimator.predict(X_test)
y_predict

array([0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0,
       1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1,
       0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1,
       0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1,
       0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0,
       1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0,
       0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0,
       1, 1, 1, 1, 0, 1, 1, 0], dtype=int64)

estimator.score(X_test, y_test)

0.5864197530864198

X_test

array([[ 0.20623287, -1.62114508,  1.00301367, ...,  0.89636437,        -0.76011455,  1.22013336],
       [ 1.18389765, -0.15251848,  0.47513551, ...,  1.30426052,         0.80852949, -1.42227529],
       [ 1.27129309, -0.2730585 ,  0.35857018, ...,  1.30426052,         0.80852949,  0.51234533],
       ...,
       [ 1.92764066,  0.57275967,  0.58383134, ...,  1.71215667,         1.59285151, -0.43137205],
       [-1.29682755, -0.64814879,  0.99497082, ..., -0.32732407,        -1.54443657, -0.43137205],
       [ 0.74174642, -1.10997832,  0.76310322, ...,  1.30426052,         0.02420747, -0.43137205]])

y_test

623    0
389    0
551    0
617    0
130    1
      ..
568    0
427    0
485    0
116    1
148    1
Name: label, Length: 162, dtype: int64

from sklearn.metrics import precision_score, recall_score, f1_score

precision = precision_score(np.array(y_test), np.array(y_predict))
recall = recall_score(y_test, y_predict)
f1 = f1_score(y_test, y_predict)

print(precision)
print(recall)
print(f1)

0.6049382716049383
0.5833333333333334
0.5939393939393939

总结

逻辑回归的准确率、召回率和F1-socre的分数相对较大，模型预测的结果不好。

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