CLN Uses public API instead of deprecated modules in examples (scikit-learn#15390)

Author: thomasjpfan

examples/gaussian_process/plot_gpc.py

@@ -30,7 +30,7 @@
 
 from matplotlib import pyplot as plt
 
-from sklearn.metrics.classification import accuracy_score, log_loss
+from sklearn.metrics import accuracy_score, log_loss
 from sklearn.gaussian_process import GaussianProcessClassifier
 from sklearn.gaussian_process.kernels import RBF
 

examples/semi_supervised/plot_label_propagation_digits.py

@@ -25,7 +25,7 @@ class will be very good.
 from scipy import stats
 
 from sklearn import datasets
-from sklearn.semi_supervised import label_propagation
+from sklearn.semi_supervised import LabelSpreading
 
 from sklearn.metrics import confusion_matrix, classification_report
 
@@ -52,7 +52,7 @@ class will be very good.
 
 # #############################################################################
 # Learn with LabelSpreading
-lp_model = label_propagation.LabelSpreading(gamma=.25, max_iter=20)
+lp_model = LabelSpreading(gamma=.25, max_iter=20)
 lp_model.fit(X, y_train)
 predicted_labels = lp_model.transduction_[unlabeled_set]
 true_labels = y[unlabeled_set]

examples/semi_supervised/plot_label_propagation_digits_active_learning.py

@@ -28,7 +28,7 @@
 from scipy import stats
 
 from sklearn import datasets
-from sklearn.semi_supervised import label_propagation
+from sklearn.semi_supervised import LabelSpreading
 from sklearn.metrics import classification_report, confusion_matrix
 
 digits = datasets.load_digits()
@@ -54,7 +54,7 @@
     y_train = np.copy(y)
     y_train[unlabeled_indices] = -1
 
-    lp_model = label_propagation.LabelSpreading(gamma=0.25, max_iter=20)
+    lp_model = LabelSpreading(gamma=0.25, max_iter=20)
     lp_model.fit(X, y_train)
 
     predicted_labels = lp_model.transduction_[unlabeled_indices]

examples/semi_supervised/plot_label_propagation_structure.py

@@ -17,7 +17,7 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
-from sklearn.semi_supervised import label_propagation
+from sklearn.semi_supervised import LabelSpreading
 from sklearn.datasets import make_circles
 
 # generate ring with inner box
@@ -30,7 +30,7 @@
 
 # #############################################################################
 # Learn with LabelSpreading
-label_spread = label_propagation.LabelSpreading(kernel='knn', alpha=0.8)
+label_spread = LabelSpreading(kernel='knn', alpha=0.8)
 label_spread.fit(X, labels)
 
 # #############################################################################

examples/semi_supervised/plot_label_propagation_versus_svm_iris.py

@@ -19,7 +19,7 @@
 import matplotlib.pyplot as plt
 from sklearn import datasets
 from sklearn import svm
-from sklearn.semi_supervised import label_propagation
+from sklearn.semi_supervised import LabelSpreading
 
 rng = np.random.RandomState(0)
 
@@ -37,11 +37,9 @@
 y_50[rng.rand(len(y)) < 0.5] = -1
 # we create an instance of SVM and fit out data. We do not scale our
 # data since we want to plot the support vectors
-ls30 = (label_propagation.LabelSpreading().fit(X, y_30),
-        y_30)
-ls50 = (label_propagation.LabelSpreading().fit(X, y_50),
-        y_50)
-ls100 = (label_propagation.LabelSpreading().fit(X, y), y)
+ls30 = (LabelSpreading().fit(X, y_30), y_30)
+ls50 = (LabelSpreading().fit(X, y_50), y_50)
+ls100 = (LabelSpreading().fit(X, y), y)
 rbf_svc = (svm.SVC(kernel='rbf', gamma=.5).fit(X, y), y)
 
 # create a mesh to plot in