程序运行之后出现这样的错误：
plt.tight_layout()使用
该怎么解决！
Traceback (most recent call last):
File "D:\Program Files\Python310\lib\code.py", line 90, in runcode
exec(code, self.locals)
File "<input>", line 1, in <module>
File "D:\PyCharm2022\PyCharm 2022.1.3\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 198, in runfile
pydev_imports.execfile(filename, global_vars, local_vars) # execute the script
File "D:\PyCharm2022\PyCharm 2022.1.3\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "E:\PyCharm\Python project\demo\5.3..py", line 110, in <module>
plt.tight_layout(1, rect=(0, 0, 1, 0.95))
TypeError: tight_layout() takes 0 positional arguments but 1 positional argument (and 1 keyword-only argument) were given
代码如下：
import numpy as np
from sklearn.linear_model import LinearRegression, RidgeCV, LassoCV, ElasticNetCV
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import Pipeline
from sklearn.exceptions import ConvergenceWarning
import matplotlib as mpl
import matplotlib.pyplot as plt
import warnings
# import seaborn
def xss(y, y_hat):
y = y.ravel()
y_hat = y_hat.ravel()
# Version 1
tss = ((y - np.average(y)) ** 2).sum()
rss = ((y_hat - y) ** 2).sum()
ess = ((y_hat - np.average(y)) ** 2).sum()
r2 = 1 - rss / tss
# print 'RSS:', rss, '\t ESS:', ess
# print 'TSS:', tss, 'RSS + ESS = ', rss + ess
tss_list.append(tss)
rss_list.append(rss)
ess_list.append(ess)
ess_rss_list.append(rss + ess)
# Version 2
# tss = np.var(y)
# rss = np.average((y_hat - y) ** 2)
# r2 = 1 - rss / tss
corr_coef = np.corrcoef(y, y_hat)[0, 1]
return r2, corr_coef
if __name__ == "__main__":
warnings.filterwarnings(action='ignore', category=ConvergenceWarning)
np.random.seed(0)
np.set_printoptions(linewidth=300, suppress=True)
N = 9
x = np.linspace(0, 6, N) + np.random.randn(N)
x = np.sort(x)
y = x**2 - 4*x - 3 + np.random.randn(N)
x.shape = -1, 1
y.shape = -1, 1
models = [Pipeline([
('poly', PolynomialFeatures()),
('linear', LinearRegression(fit_intercept=False))]),
Pipeline([
('poly', PolynomialFeatures()),
('linear', RidgeCV(alphas=np.logspace(-3, 2, 10), fit_intercept=False))]),
Pipeline([
('poly', PolynomialFeatures()),
('linear', LassoCV(alphas=np.logspace(-3, 2, 10), fit_intercept=False))]),
Pipeline([
('poly', PolynomialFeatures()),
('linear', ElasticNetCV(alphas=np.logspace(-3, 2, 10), l1_ratio=[.1, .5, .7, .9, .95, .99, 1],
fit_intercept=False))])
]
mpl.rcParams['font.sans-serif'] = ['simHei']
mpl.rcParams['axes.unicode_minus'] = False
plt.figure(figsize=(15, 10), facecolor='w')
d_pool = np.arange(1, N, 1) # 阶
m = d_pool.size
clrs = [] # 颜色
for c in np.linspace(16711680, 255, m, dtype=int):
clrs.append('#%06x' % c)
line_width = np.linspace(5, 2, m) * 0.7
titles = '线性回归', 'Ridge回归', 'LASSO', 'ElasticNet'
tss_list = []
rss_list = []
ess_list = []
ess_rss_list = []
for t in range(4):
model = models[t]
plt.subplot(2, 2, t+1)
plt.plot(x, y, 'ro', markersize=7, zorder=N, mec='k')
for i, d in enumerate(d_pool):
model.set_params(poly__degree=d)
model.fit(x, y.ravel())
lin = model.get_params('linear')['linear']
output = '%s：%d阶，系数为：' % (titles[t], d)
if hasattr(lin, 'alpha_'):
idx = output.find('系数')
output = output[:idx] + ('alpha=%.6f，' % lin.alpha_) + output[idx:]
if hasattr(lin, 'l1_ratio_'): # 根据交叉验证结果，从输入l1_ratio(list)中选择的最优l1_ratio_(float)
idx = output.find('系数')
output = output[:idx] + ('l1_ratio=%.6f，' % lin.l1_ratio_) + output[idx:]
print(output, lin.coef_.ravel())
x_hat = np.linspace(x.min(), x.max(), num=100)
x_hat.shape = -1, 1
y_hat = model.predict(x_hat)
s = model.score(x, y)
r2, corr_coef = xss(y, model.predict(x))
# print 'R2和相关系数：', r2, corr_coef
# print 'R2：', s, '\n'
z = N - 1 if (d == 2) else 0
label = '%d阶，$R^2$=%.3f' % (d, s)
if hasattr(lin, 'l1_ratio_'):
label += '，L1 ratio=%.2f' % lin.l1_ratio_
plt.plot(x_hat, y_hat, color=clrs[i], lw=line_width[i], alpha=0.75, label=label, zorder=z)
plt.legend(loc='upper left')
plt.grid(True)
plt.title(titles[t], fontsize=18)
plt.xlabel('X', fontsize=16)
plt.ylabel('Y', fontsize=16)
plt.tight_layout(1, rect=(0, 0, 1, 0.95))
plt.suptitle('多项式曲线拟合比较', fontsize=22)
plt.show()
y_max = max(max(tss_list), max(ess_rss_list)) * 1.05
plt.figure(figsize=(9, 7), facecolor='w')
t = np.arange(len(tss_list))
plt.plot(t, tss_list, 'ro-', lw=2, label='TSS(Total Sum of Squares)', mec='k')
plt.plot(t, ess_list, 'mo-', lw=1, label='ESS(Explained Sum of Squares)', mec='k')
plt.plot(t, rss_list, 'bo-', lw=1, label='RSS(Residual Sum of Squares)', mec='k')
plt.plot(t, ess_rss_list, 'go-', lw=2, label='ESS+RSS', mec='k')
plt.ylim((0, y_max))
plt.legend(loc='center right')
plt.xlabel('实验：线性回归/Ridge/LASSO/Elastic Net', fontsize=15)
plt.ylabel('XSS值', fontsize=15)
plt.title('总平方和TSS=？', fontsize=18)
plt.grid(True)
plt.show()
