testing.py
606143fb83d1master

testing.py
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			"""
			EE-311
			======

			Lab 4: support vector machines
			------------------------------

			created by Adrian Shajkofci and François marelli on 12.03.2020
			"""

			import unittest
			import numpy as np
			import homework

			from sklearn.datasets import load_iris
			from sklearn.svm import LinearSVC
			from sklearn.linear_model import LogisticRegression

			import importlib

			importlib.reload(homework)


			class TestFunctions(unittest.TestCase):
			def test_accuracy(self):
			np.random.seed(0)
			a = np.ones(10)
			b = np.zeros(10)
			c = np.concatenate([a, b], axis=0)
			d = np.concatenate([a, a], axis=0)
			f = homework.compute_accuracy
			np.testing.assert_array_equal(
			[
			f(a.copy(), a.copy()),
			f(a.copy(), b.copy()),
			f(c.copy(), d.copy()),
			f(d.copy(), c.copy()),
			],
			[1, 0, 0.5, 0.5],
			)

			def test_decision(self):
			data_X, data_y = load_iris(return_X_y=True)
			mask = (data_y == 0) \| (data_y == 1)
			data_X = data_X[mask]
			data_y = data_y[mask]
			data_X = data_X[:, 0:2]

			model = LinearSVC().fit(data_X, data_y)
			predict = model.predict(data_X)

			b = model.intercept_
			w = model.coef_.squeeze()

			np.testing.assert_array_equal(homework.smv_decision(w, b, data_X), predict)


			class TestTransform(unittest.TestCase):
			def setUp(self):
			data = np.load("data.npz")
			self.X = data["X"]
			self.Y = data["y"]

			def test_transform(self):
			X = homework.transform_space(self.X.copy())
			self.assertEqual(X.ndim, 2, "Transformed space should be 2D")
			score = LogisticRegression().fit(X, self.Y).score(X, self.Y)
			self.assertEqual(score, 1.0)


			if __name__ == "__main__":
			unittest.main()