DOC Update notebook style for plot_bayesian_ridge_curvefit (#22916)

2357juan · Juan Gomez · web-flow · commit 24ef3881d755 · 2022-03-30T15:43:33.000+02:00
Co-authored-by: Juan Gomez &lt;jgomez75@gatech.edu&gt;
diff --git a/examples/linear_model/plot_bayesian_ridge_curvefit.py b/examples/linear_model/plot_bayesian_ridge_curvefit.py
@@ -28,34 +28,38 @@
 
 # Author: Yoshihiro Uchida <nimbus1after2a1sun7shower@gmail.com>
 
+# %%
+# Generate sinusoidal data with noise
+# -----------------------------------
 import numpy as np
-import matplotlib.pyplot as plt
-
-from sklearn.linear_model import BayesianRidge
 
 
 def func(x):
     return np.sin(2 * np.pi * x)
 
 
-# #############################################################################
-# Generate sinusoidal data with noise
 size = 25
 rng = np.random.RandomState(1234)
 x_train = rng.uniform(0.0, 1.0, size)
 y_train = func(x_train) + rng.normal(scale=0.1, size=size)
 x_test = np.linspace(0.0, 1.0, 100)
 
 
-# #############################################################################
+# %%
 # Fit by cubic polynomial
+# -----------------------
+from sklearn.linear_model import BayesianRidge
+
 n_order = 3
 X_train = np.vander(x_train, n_order + 1, increasing=True)
 X_test = np.vander(x_test, n_order + 1, increasing=True)
+reg = BayesianRidge(tol=1e-6, fit_intercept=False, compute_score=True)
 
-# #############################################################################
+# %%
 # Plot the true and predicted curves with log marginal likelihood (L)
-reg = BayesianRidge(tol=1e-6, fit_intercept=False, compute_score=True)
+# -------------------------------------------------------------------
+import matplotlib.pyplot as plt
+
 fig, axes = plt.subplots(1, 2, figsize=(8, 4))
 for i, ax in enumerate(axes):
     # Bayesian ridge regression with different initial value pairs
