import pytest

import math

import random

from utils import open_data

from learning import *

random.seed("aima-python")

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_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_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_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) > 4/6

assert err_ratio(adaboost, iris) < 0.25