机器学习sklearn（2）多元线性回归

from numpy import genfromtxt
import numpy as np
from sklearn import datasets, linear_model

datapath = r"C:\Users\meachine learn\delivery.csv"
#skip_header=0和skip_footer=0，表示不跳过任何行。
deliverydata = genfromtxt(datapath, delimiter=',',skip_header=2, usecols=(1,2,3))

print(deliverydata)

[[100.    4.    9.3]
 [ 50.    3.    4.8]
 [100.    4.    8.9]
 [100.    2.    6.5]
 [ 50.    2.    4.2]
 [ 80.    2.    6.2]
 [ 75.    3.    7.4]
 [ 65.    4.    6. ]
 [ 90.    3.    7.6]
 [ 90.    2.    6.1]]

x = deliverydata[:,:-1]
y = deliverydata[:,-1]

regr = linear_model.LinearRegression()
regr.fit(x,y)

LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)

print("coefficinet: ", regr.coef_)

coefficinet:  [0.0611346  0.92342537]

print("intercept: ", regr.intercept_)

intercept:  -0.868701466781709

根据模型进行预测

x_pred = [102, 6]
x_pred = np.array(x_pred).reshape(-1,2)   #把列表转换成矩阵形式
y_pred = regr.predict(x_pred)
print(y_pred)

[10.90757981]

特征量中存在类别特征时，需要先对类别特征进行one-hot编码

path = r"C:\Users\meachine learn\deliverydummydone.csv"
data = genfromtxt(path, delimiter=',')

print(data)

[[100.    4.    1.    9.3]
 [ 50.    3.    0.    4.8]
 [100.    4.    1.    8.9]
 [100.    2.    2.    6.5]
 [ 50.    2.    2.    4.2]
 [ 80.    2.    1.    6.2]
 [ 75.    3.    1.    7.4]
 [ 65.    4.    0.    6. ]
 [ 90.    3.    0.    7.6]]

#对第三列类别特征编码
from sklearn import preprocessing
enc = preprocessing.OneHotEncoder()
data_coder = data[:,2]
data_coder = np.array(data_coder).reshape(-1,1)
enc.fit(data_coder)

OneHotEncoder(categorical_features='all', dtype=<class 'numpy.float64'>,
       handle_unknown='error', n_values='auto', sparse=True)

data_2 = enc.transform(data_coder).toarray()

data_1 = np.array(data[:,0:2]).reshape(-1,2)
x = np.hstack((data_1, data_2))  #对特征量矩阵横向合并
print(x)

[[100.   4.   0.   1.   0.]
 [ 50.   3.   1.   0.   0.]
 [100.   4.   0.   1.   0.]
 [100.   2.   0.   0.   1.]
 [ 50.   2.   0.   0.   1.]
 [ 80.   2.   0.   1.   0.]
 [ 75.   3.   0.   1.   0.]
 [ 65.   4.   1.   0.   0.]
 [ 90.   3.   1.   0.   0.]]

y = data[:,-1]

regr = linear_model.LinearRegression()
regr.fit(x,y)

LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)

print("coefficinet: ", regr.coef_)

coefficinet:  [ 0.05553544  0.69257631 -0.17013278  0.57040007 -0.40026729]

print("intercept: ", regr.intercept_)

intercept:  0.19995688911881349