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import pytest
import math
from utils import open_data
def test_euclidean():
distance = euclidean_distance([1, 2], [3, 4])
assert round(distance, 2) == 2.83
distance = euclidean_distance([1, 2, 3], [4, 5, 6])
assert round(distance, 2) == 5.2
distance = euclidean_distance([0, 0, 0], [0, 0, 0])
assert distance == 0
def test_rms_error():
assert rms_error([2, 2], [2, 2]) == 0
assert rms_error((0, 0), (0, 1)) == math.sqrt(0.5)
assert rms_error((1, 0), (0, 1)) == 1
assert rms_error((0, 0), (0, -1)) == math.sqrt(0.5)
assert rms_error((0, 0.5), (0, -0.5)) == math.sqrt(0.5)
def test_manhattan_distance():
assert manhattan_distance([2, 2], [2, 2]) == 0
assert manhattan_distance([0, 0], [0, 1]) == 1
assert manhattan_distance([1, 0], [0, 1]) == 2
assert manhattan_distance([0, 0], [0, -1]) == 1
assert manhattan_distance([0, 0.5], [0, -0.5]) == 1
def test_mean_boolean_error():
assert mean_boolean_error([1, 1], [0, 0]) == 1
assert mean_boolean_error([0, 1], [1, 0]) == 1
assert mean_boolean_error([1, 1], [0, 1]) == 0.5
assert mean_boolean_error([0, 0], [0, 0]) == 0
assert mean_boolean_error([1, 1], [1, 1]) == 0
def test_mean_error():
assert mean_error([2, 2], [2, 2]) == 0
assert mean_error([0, 0], [0, 1]) == 0.5
assert mean_error([1, 0], [0, 1]) == 1
assert mean_error([0, 0], [0, -1]) == 0.5
assert mean_error([0, 0.5], [0, -0.5]) == 0.5
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([1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 4, 1, 0, 1]) == "mammal"
def test_naive_bayes():
iris = DataSet(name="iris")
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_k_nearest_neighbors():
iris = DataSet(name="iris")
kNN = NearestNeighborLearner(iris, k=3)
assert kNN([5, 3, 1, 0.1]) == "setosa"
assert kNN([5, 3, 1, 0.1]) == "setosa"
assert kNN([6, 5, 3, 1.5]) == "versicolor"
assert kNN([7.5, 4, 6, 2]) == "virginica"
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def test_truncated_svd():
test_mat = [[17, 0],
[0, 11]]
_, _, eival = truncated_svd(test_mat)
assert isclose(abs(eival[0]), 17)
assert isclose(abs(eival[1]), 11)
test_mat = [[17, 0],
[0, -34]]
_, _, eival = truncated_svd(test_mat)
assert isclose(abs(eival[0]), 34)
assert isclose(abs(eival[1]), 17)
test_mat = [[1, 0, 0, 0, 2],
[0, 0, 3, 0, 0],
[0, 0, 0, 0, 0],
[0, 2, 0, 0, 0]]
_, _, eival = truncated_svd(test_mat)
assert isclose(abs(eival[0]), 3)
assert isclose(abs(eival[1]), 5**0.5)
test_mat = [[3, 2, 2],
[2, 3, -2]]
_, _, eival = truncated_svd(test_mat)
assert isclose(abs(eival[0]), 5)
assert isclose(abs(eival[1]), 3)
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_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_neural_network_learner():
iris = DataSet(name="iris")
classes = ["setosa", "versicolor", "virginica"]
iris.classes_to_numbers(classes)
nNL = NeuralNetLearner(iris, [5], 0.15, 75)
tests = [([5.0, 3.1, 0.9, 0.1], 0),
([5.1, 3.5, 1.0, 0.0], 0),
([4.9, 3.3, 1.1, 0.1], 0),
([6.0, 3.0, 4.0, 1.1], 1),
([6.1, 2.2, 3.5, 1.0], 1),
([5.9, 2.5, 3.3, 1.1], 1),
([7.5, 4.1, 6.2, 2.3], 2),
([7.3, 4.0, 6.1, 2.4], 2),
([7.0, 3.3, 6.1, 2.5], 2)]
assert grade_learner(nNL, tests) >= 1/3
assert err_ratio(nNL, iris) < 0.21
def test_perceptron():
iris = DataSet(name="iris")
iris.classes_to_numbers()
classes_number = len(iris.values[iris.target])
perceptron = PerceptronLearner(iris)
([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_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 weight >= min_value and weight <= max_value
def test_adaboost():
iris = DataSet(name="iris")
iris.classes_to_numbers()
WeightedPerceptron = WeightedLearner(PerceptronLearner)
AdaboostLearner = AdaBoost(WeightedPerceptron, 5)
adaboost = AdaboostLearner(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(adaboost, tests) > 5/6
assert err_ratio(adaboost, iris) < 0.1