Update plot_rbf_parameters.py

zempleni · zempleni · commit 0e83dac51448 · 2022-03-09T19:00:02.000+01:00
move the imports closer to their first usage
diff --git a/examples/svm/plot_rbf_parameters.py b/examples/svm/plot_rbf_parameters.py
@@ -75,20 +75,12 @@
 
 """
 
-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):
@@ -106,6 +98,8 @@ 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
@@ -126,6 +120,8 @@ 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)
@@ -138,6 +134,10 @@ 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)
@@ -169,6 +169,8 @@ 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):
