import pytest from deep_learning4e import PerceptronLearner from learning4e import * random.seed("aima-python") def test_exclude(): iris = DataSet(name='iris', exclude=[3]) assert iris.inputs == [0, 1, 2] def test_parse_csv(): iris = open_data('iris.csv').read() assert parse_csv(iris)[0] == [5.1, 3.5, 1.4, 0.2, 'setosa'] def test_weighted_mode(): assert weighted_mode('abbaa', [1, 2, 3, 1, 2]) == 'b' def test_weighted_replicate(): assert weighted_replicate('ABC', [1, 2, 1], 4) == ['A', 'B', 'B', 'C'] def test_means_and_deviation(): iris = DataSet(name='iris') means, deviations = iris.find_means_and_deviations() assert round(means['setosa'][0], 3) == 5.006 assert round(means['versicolor'][0], 3) == 5.936 assert round(means['virginica'][0], 3) == 6.588 assert round(deviations['setosa'][0], 3) == 0.352 assert round(deviations['versicolor'][0], 3) == 0.516 assert round(deviations['virginica'][0], 3) == 0.636 def test_plurality_learner(): zoo = DataSet(name='zoo') pl = PluralityLearner(zoo) assert pl.predict([1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 4, 1, 0, 1]) == 'mammal' def test_k_nearest_neighbors(): iris = DataSet(name='iris') knn = NearestNeighborLearner(iris, k=3) assert knn.predict([5, 3, 1, 0.1]) == 'setosa' assert knn.predict([6, 5, 3, 1.5]) == 'versicolor' assert knn.predict([7.5, 4, 6, 2]) == 'virginica' def test_decision_tree_learner(): iris = DataSet(name='iris') dtl = DecisionTreeLearner(iris) assert dtl.predict([5, 3, 1, 0.1]) == 'setosa' assert dtl.predict([6, 5, 3, 1.5]) == 'versicolor' assert dtl.predict([7.5, 4, 6, 2]) == 'virginica' def test_svc(): iris = DataSet(name='iris') classes = ['setosa', 'versicolor', 'virginica'] iris.classes_to_numbers(classes) n_samples, n_features = len(iris.examples), iris.target X, y = (np.array([x[:n_features] for x in iris.examples]), np.array([x[n_features] for x in iris.examples])) svm = MultiClassLearner(SVC()).fit(X, y) assert svm.predict([[5.0, 3.1, 0.9, 0.1]]) == 0 assert svm.predict([[5.1, 3.5, 1.0, 0.0]]) == 0 assert svm.predict([[4.9, 3.3, 1.1, 0.1]]) == 0 assert svm.predict([[6.0, 3.0, 4.0, 1.1]]) == 1 assert svm.predict([[6.1, 2.2, 3.5, 1.0]]) == 1 assert svm.predict([[5.9, 2.5, 3.3, 1.1]]) == 1 assert svm.predict([[7.5, 4.1, 6.2, 2.3]]) == 2 assert svm.predict([[7.3, 4.0, 6.1, 2.4]]) == 2 assert svm.predict([[7.0, 3.3, 6.1, 2.5]]) == 2 def test_information_content(): assert information_content([]) == 0 assert information_content([4]) == 0 assert information_content([5, 4, 0, 2, 5, 0]) > 1.9 assert information_content([5, 4, 0, 2, 5, 0]) < 2 assert information_content([1.5, 2.5]) > 0.9 assert information_content([1.5, 2.5]) < 1.0 def test_random_forest(): iris = DataSet(name='iris') rf = RandomForest(iris) tests = [([5.0, 3.0, 1.0, 0.1], 'setosa'), ([5.1, 3.3, 1.1, 0.1], 'setosa'), ([6.0, 5.0, 3.0, 1.0], 'versicolor'), ([6.1, 2.2, 3.5, 1.0], 'versicolor'), ([7.5, 4.1, 6.2, 2.3], 'virginica'), ([7.3, 3.7, 6.1, 2.5], 'virginica')] assert grade_learner(rf, tests) >= 1 / 3 def test_random_weights(): min_value = -0.5 max_value = 0.5 num_weights = 10 test_weights = random_weights(min_value, max_value, num_weights) assert len(test_weights) == num_weights for weight in test_weights: assert min_value <= weight <= max_value def test_ada_boost(): iris = DataSet(name='iris') classes = ['setosa', 'versicolor', 'virginica'] iris.classes_to_numbers(classes) wl = WeightedLearner(PerceptronLearner(iris)) ab = ada_boost(iris, wl, 5) tests = [([5, 3, 1, 0.1], 0), ([5, 3.5, 1, 0], 0), ([6, 3, 4, 1.1], 1), ([6, 2, 3.5, 1], 1), ([7.5, 4, 6, 2], 2), ([7, 3, 6, 2.5], 2)] assert grade_learner(ab, tests) > 2 / 3 assert err_ratio(ab, iris) < 0.25 if __name__ == "__main__": pytest.main()