59 lines
2.0 KiB
Python
59 lines
2.0 KiB
Python
# -*- coding: utf-8 -*-
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from __future__ import absolute_import
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from packtml.clustering import KNNClassifier
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from packtml.utils.plotting import add_decision_boundary_to_axis
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import StandardScaler
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from sklearn.metrics import accuracy_score
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from sklearn.datasets import load_iris
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from matplotlib import pyplot as plt
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from matplotlib.colors import ListedColormap
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import sys
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# #############################################################################
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# Create a classification sub-dataset using iris
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iris = load_iris()
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X = iris.data[:, :2] # just use the first two dimensions
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y = iris.target
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# split data
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X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
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# scale the data
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scaler = StandardScaler()
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X_train = scaler.fit_transform(X_train)
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X_test = scaler.transform(X_test)
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# #############################################################################
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# Fit a k-nearest neighbor model and get predictions
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k=10
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clf = KNNClassifier(X_train, y_train, k=k)
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pred = clf.predict(X_test)
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clf_accuracy = accuracy_score(y_test, pred)
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print("Test accuracy: %.3f" % clf_accuracy)
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# #############################################################################
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# Visualize difference in classes (this is from the scikit-learn KNN
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# plotting example:
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# http://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html#sphx-glr-auto-examples-neighbors-plot-classification-py)
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xx, yy, _ = add_decision_boundary_to_axis(estimator=clf, axis=plt,
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nclasses=3, X_data=X_test)
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# Plot also the training points
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plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test,
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cmap=ListedColormap(['#FF0000', '#00FF00', '#0000FF']),
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edgecolor='k', s=20)
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plt.xlim(xx.min(), xx.max())
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plt.ylim(yy.min(), yy.max())
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plt.title("3-Class classification (k=%i)" % k)
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# if we're supposed to save it, do so INSTEAD OF showing it
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if len(sys.argv) > 1:
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plt.savefig(sys.argv[1])
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else:
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plt.show()
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