南大《探索数据的奥秘》课件示例代码笔记10

In [5]: import pandas as pd
import numpy as np
from scipy import stats
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn import metrics
my_iris=pd.read_csv('C:\Python\Scripts\my_data\iris.csv',sep=',',decimal='.',
header=None,
names=['sepal_length','sepal_width',
'petal_length','petal_width','target'])
feature_cols='petal_length'
#feature_cols='sepal_width'
x=my_iris[[feature_cols]]
y=np.array(my_iris['sepal_length'])
plt.plot(x,y,'o',alpha=0.5)
linreg=LinearRegression()
linreg.fit(x,y)
print('f(x) = ',linreg.intercept_,'+',linreg.coef_[0],'x')
pred_y=linreg.predict(x)
plt.plot(x,pred_y,'g',alpha=0.5)
plt.plot(np.array(x).mean(),y.mean(),'r*',ms=12)
plt.gca().set_xlabel(feature_cols)
plt.gca().set_ylabel('sepal_length')
print('RMSE = ',np.sqrt(metrics.mean_squared_error(y,pred_y)))
print('\n')
f(x) = 4.305565456292049 + 0.4091258984678836 x
RMSE = 0.40435105064202476

In [3]: print('r_square = ',linreg.score(x,y))
r_square = 0.7599553107783261
In [59]: print(my_iris[[feature_cols,'sepal_length']].corr())
print('\n')
r=np.array(my_iris[[feature_cols,'sepal_length']].corr()[['sepal_length']].
iloc(0)[0])
print('r = ',r)
print('square of r = ',r**2)
petal_length sepal_length
petal_length 1.000000 0.871754
sepal_length 0.871754 1.000000
r = [0.87175416]
square of r = [0.75995531]