Revert "Update plot_rbf_parameters.py"

zempleni · zempleni · commit 74751fb3601f · 2022-03-09T20:47:42.000+01:00
This reverts commit 0e83dac.
diff --git a/examples/svm/plot_rbf_parameters.py b/examples/svm/plot_rbf_parameters.py
@@ -75,12 +75,20 @@
 
 """
 
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.colors import Normalize
+
+from sklearn.svm import SVC
+from sklearn.preprocessing import StandardScaler
+from sklearn.datasets import load_iris
+from sklearn.model_selection import StratifiedShuffleSplit
+from sklearn.model_selection import GridSearchCV
+
 # %%
 # Utility class to move the midpoint of a colormap to be around
 # the values of interest.
 
-import numpy as np
-from matplotlib.colors import Normalize
 
 class MidpointNormalize(Normalize):
     def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False):
@@ -98,8 +106,6 @@ def __call__(self, value, clip=None):
 #
 # dataset for grid search
 
-from sklearn.datasets import load_iris
-
 iris = load_iris()
 X = iris.data
 y = iris.target
@@ -120,8 +126,6 @@ def __call__(self, value, clip=None):
 # instead of fitting the transformation on the training set and
 # just applying it on the test set.
 
-from sklearn.preprocessing import StandardScaler
-
 scaler = StandardScaler()
 X = scaler.fit_transform(X)
 X_2d = scaler.fit_transform(X_2d)
@@ -134,10 +138,6 @@ def __call__(self, value, clip=None):
 # 10 is often helpful. Using a basis of 2, a finer
 # tuning can be achieved but at a much higher cost.
 
-from sklearn.svm import SVC
-from sklearn.model_selection import StratifiedShuffleSplit
-from sklearn.model_selection import GridSearchCV
-
 C_range = np.logspace(-2, 10, 13)
 gamma_range = np.logspace(-9, 3, 13)
 param_grid = dict(gamma=gamma_range, C=C_range)
@@ -169,8 +169,6 @@ def __call__(self, value, clip=None):
 #
 # draw visualization of parameter effects
 
-import matplotlib.pyplot as plt
-
 plt.figure(figsize=(8, 6))
 xx, yy = np.meshgrid(np.linspace(-3, 3, 200), np.linspace(-3, 3, 200))
 for (k, (C, gamma, clf)) in enumerate(classifiers):
