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()