semi_supervised_classifiers.py

# Authors: Clay Woolam   <clay@woolam.org>
#          Oliver Rausch <rauscho@ethz.ch>
# License: BSD

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.svm import SVC
from sklearn.semi_supervised import LabelSpreading
from sklearn.semi_supervised import SelfTrainingClassifier
from sklearn.metrics import classification_report

iris = datasets.load_iris()
num_features = len(iris.data[0])
print("Number of features: {}".format(num_features))

y = iris.target

# step size in the mesh
h = 0.02

rng = np.random.RandomState(0)
y_rand = rng.rand(y.shape[0])
y_30 = np.copy(y)
y_30[y_rand < 0.3] = -1  # set random samples to be unlabeled
y_50 = np.copy(y)
y_50[y_rand < 0.5] = -1

counter = 0

for i in range(num_features):
    for j in range(num_features):
        if i < j:
            X = iris.data[:, [i,j]]
            feature_i = iris['feature_names'][i]
            feature_j = iris['feature_names'][j]
            # 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 = (LabelSpreading().fit(X, y_30), y_30, "Label Spreading 30% data")
            ls50 = (LabelSpreading().fit(X, y_50), y_50, "Label Spreading 50% data")
            ls100 = (LabelSpreading().fit(X, y), y, "Label Spreading 100% data")

            # the base classifier for self-training is identical to the SVC
            base_classifier = SVC(kernel="rbf", gamma=0.5, probability=True)
            st30 = (
                SelfTrainingClassifier(base_classifier).fit(X, y_30),
                y_30,
                "Self-training 30% data",
            )
            st50 = (
                SelfTrainingClassifier(base_classifier).fit(X, y_50),
                y_50,
                "Self-training 50% data",
            )

            rbf_svc = (SVC(kernel="rbf", gamma=0.5).fit(X, y), y, "SVC with rbf kernel")

            # create a mesh to plot in
            x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
            y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
            xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))

            color_map = {-1: (1, 1, 1), 0: (0, 0, 0.9), 1: (1, 0, 0), 2: (0.8, 0.6, 0)}

            classifiers = (ls30, st30, ls50, st50, ls100, rbf_svc)

            for k, (clf, y_train, title) in enumerate(classifiers):
                # Plot the decision boundary. For that, we will assign a color to each
                # point in the mesh [x_min, x_max]x[y_min, y_max].
                plt.subplot(3, 2, k + 1)
                Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])

                # Put the result into a color plot
                Z = Z.reshape(xx.shape)
                plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)
                plt.axis("off")

                # Plot also the training points
                colors = [color_map[y] for y in y_train]
                plt.scatter(X[:, 0], X[:, 1], c=colors, edgecolors="black")

                plt.title(title)

                y_pred = clf.predict(X)

                # classification report
                print("\n---------------------------------------------------"
                      "\nClassification Report for"
                      "\nFeatures: "+feature_i + " "+ feature_j +
                      "\nCLASSIFIER: {}"
                      "\n---------------------------------------------------".format(title))
                print(classification_report(y, y_pred))
                counter = counter + 1

print("\nCounter: ", counter)

# plt.suptitle("Unlabeled points are colored white", y=0.1)
# plt.show()