def test_perceptron(): iris = DataSet(name="iris") classes = ["setosa", "versicolor", "virginica"] iris.classes_to_numbers() perceptron = PerceptronLearner(iris) # PerceptronLearner might be wrong. Just check if prediction is in range assert perceptron([5, 3, 1, 0.1]) in range(len(classes))
def test_neural_network_learner(): iris = DataSet(name="iris") classes = ["setosa", "versicolor", "virginica"] iris.classes_to_numbers() nNL = NeuralNetLearner(iris) # NeuralNetLearner might be wrong. Just check if prediction is in range assert nNL([5, 3, 1, 0.1]) in range(len(classes))
def test_perceptron(): iris = DataSet(name="iris") iris.classes_to_numbers() classes_number = len(iris.values[iris.target]) perceptron = PerceptronLearner(iris) tests = [([5, 3, 1, 0.1], 0), ([6, 3, 4, 1.1], 1), ([7.5, 4, 6, 2], 2)] assert grade_learner(perceptron, tests) >= 2
def test_neural_network_learner(): iris = DataSet(name="iris") classes = ["setosa", "versicolor", "virginica"] iris.classes_to_numbers(classes) nNL = NeuralNetLearner(iris, [5], 0.15, 75) tests = [([5, 3, 1, 0.1], 0), ([6, 3, 3, 1.5], 1), ([7.5, 4, 6, 2], 2)] assert grade_learner(nNL, tests) >= 2
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_perceptron(): iris = DataSet(name="iris") iris.classes_to_numbers() classes_number = len(iris.values[iris.target]) perceptron = PerceptronLearner(iris) 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(perceptron, tests) > 1/2 assert err_ratio(perceptron, iris) < 0.4
def test_naive_bayes(): iris = DataSet(name='iris') # discrete nbd = NaiveBayesLearner(iris, continuous=False) assert nbd([5, 3, 1, 0.1]) == 'setosa' assert nbd([6, 3, 4, 1.1]) == 'versicolor' assert nbd([7.7, 3, 6, 2]) == 'virginica' # continuous nbc = NaiveBayesLearner(iris, continuous=True) assert nbc([5, 3, 1, 0.1]) == 'setosa' assert nbc([6, 5, 3, 1.5]) == 'versicolor' assert nbc([7, 3, 6.5, 2]) == 'virginica' # simple data1 = 'a' * 50 + 'b' * 30 + 'c' * 15 dist1 = CountingProbDist(data1) data2 = 'a' * 30 + 'b' * 45 + 'c' * 20 dist2 = CountingProbDist(data2) data3 = 'a' * 20 + 'b' * 20 + 'c' * 35 dist3 = CountingProbDist(data3) dist = { ('First', 0.5): dist1, ('Second', 0.3): dist2, ('Third', 0.2): dist3 } nbs = NaiveBayesLearner(dist, simple=True) assert nbs('aab') == 'First' assert nbs(['b', 'b']) == 'Second' assert nbs('ccbcc') == 'Third'
def test_decision_tree_learner(): iris = DataSet(name="iris") dTL = DecisionTreeLearner(iris) assert dTL([5, 3, 1, 0.1]) == "setosa" assert dTL([6, 5, 3, 1.5]) == "versicolor" assert dTL([7.5, 4, 6, 2]) == "virginica"
def test_k_nearest_neighbors(): iris = DataSet(name="iris") kNN = NearestNeighborLearner(iris, k=3) assert kNN([5, 3, 1, 0.1]) == "setosa" assert kNN([6, 5, 3, 1.5]) == "versicolor" assert kNN([7.5, 4, 6, 2]) == "virginica"
def test_neural_network_learner(): iris = DataSet(name="iris") classes = ["setosa","versicolor","virginica"] iris.classes_to_numbers(classes) nNL = NeuralNetLearner(iris, [5], 0.15, 75) 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(nNL, tests) >= 2/3 assert err_ratio(nNL, iris) < 0.25
def show_iris(i=0, j=1, k=2): """Plots the iris dataset in a 3D plot. The three axes are given by i, j and k, which correspond to three of the four iris features.""" from mpl_toolkits.mplot3d import Axes3D plt.rcParams.update(plt.rcParamsDefault) fig = plt.figure() ax = fig.add_subplot(111, projection='3d') iris = DataSet(name="iris") buckets = iris.split_values_by_classes() features = ["Sepal Length", "Sepal Width", "Petal Length", "Petal Width"] f1, f2, f3 = features[i], features[j], features[k] a_setosa = [v[i] for v in buckets["setosa"]] b_setosa = [v[j] for v in buckets["setosa"]] c_setosa = [v[k] for v in buckets["setosa"]] a_virginica = [v[i] for v in buckets["virginica"]] b_virginica = [v[j] for v in buckets["virginica"]] c_virginica = [v[k] for v in buckets["virginica"]] a_versicolor = [v[i] for v in buckets["versicolor"]] b_versicolor = [v[j] for v in buckets["versicolor"]] c_versicolor = [v[k] for v in buckets["versicolor"]] for c, m, sl, sw, pl in [('b', 's', a_setosa, b_setosa, c_setosa), ('g', '^', a_virginica, b_virginica, c_virginica), ('r', 'o', a_versicolor, b_versicolor, c_versicolor)]: ax.scatter(sl, sw, pl, c=c, marker=m) ax.set_xlabel(f1) ax.set_ylabel(f2) ax.set_zlabel(f3) plt.show()
def test_naive_bayes(): iris = DataSet(name="iris") # Discrete nBD = NaiveBayesLearner(iris) assert nBD([5, 3, 1, 0.1]) == "setosa" assert nBD([6, 5, 3, 1.5]) == "versicolor" assert nBD([7, 3, 6.5, 2]) == "virginica" # Continuous nBC = NaiveBayesLearner(iris, continuous=True) assert nBC([5, 3, 1, 0.1]) == "setosa" assert nBC([6, 5, 3, 1.5]) == "versicolor" assert nBC([7, 3, 6.5, 2]) == "virginica"
def test_k_nearest_neighbors(): iris = DataSet(name="iris") kNN = NearestNeighborLearner(iris, k=3) assert kNN([5, 3, 1, 0.1]) == "setosa"
def test_naive_bayes(): iris = DataSet(name="iris") nB = NaiveBayesLearner(iris) assert nB([5, 3, 1, 0.1]) == "setosa"
def test_plurality_learner(): zoo = DataSet(name="zoo") pL = PluralityLearner(zoo) assert pL([]) == "mammal"
def test_decision_tree_learner(): iris = DataSet(name="iris") dTL = DecisionTreeLearner(iris) assert dTL([5, 3, 1, 0.1]) == "setosa"
def test_exclude(): iris = DataSet(name='iris', exclude=[3]) assert iris.inputs == [0, 1, 2]