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Merged
norvig merged 1 commit into from
Jun 6, 2017
Merged

Learning Tests (Fixing Build) #534

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68 changes: 35 additions & 33 deletions tests/test_learning.py
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Original file line number Diff line number Diff line change
@@ -1,10 +1,13 @@

import pytest
import math
import random
from utils import DataFile
from learning import *


random.seed("aima-python")


def test_euclidean():
distance = euclidean_distance([1, 2], [3, 4])
assert round(distance, 2) == 2.83
Expand All @@ -15,6 +18,34 @@ def test_euclidean():
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])
Expand All @@ -23,7 +54,7 @@ def test_exclude():

def test_parse_csv():
Iris = DataFile('iris.csv').read()
assert parse_csv(Iris)[0] == [5.1, 3.5, 1.4, 0.2,'setosa']
assert parse_csv(Iris)[0] == [5.1, 3.5, 1.4, 0.2, 'setosa']


def test_weighted_mode():
Expand Down Expand Up @@ -74,39 +105,11 @@ def test_naive_bayes():
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([5, 3, 1, 0.1]) == "setosa"
assert kNN([6, 5, 3, 1.5]) == "versicolor"
assert kNN([7.5, 4, 6, 2]) == "virginica"

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_decision_tree_learner():
iris = DataSet(name="iris")
Expand All @@ -118,7 +121,7 @@ def test_decision_tree_learner():

def test_neural_network_learner():
iris = DataSet(name="iris")
classes = ["setosa","versicolor","virginica"]
classes = ["setosa", "versicolor", "virginica"]
iris.classes_to_numbers(classes)
nNL = NeuralNetLearner(iris, [5], 0.15, 75)
tests = [([5, 3, 1, 0.1], 0),
Expand Down Expand Up @@ -154,4 +157,3 @@ def test_random_weights():
assert len(test_weights) == num_weights
for weight in test_weights:
assert weight >= min_value and weight <= max_value