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Add Tests for learning.py by antmarakis · Pull Request #376 · aimacode/aima-python · GitHub

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Add Tests for learning.py #376

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norvig merged 1 commit into from
Mar 22, 2017
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Add Tests for learning.py #376

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42 changes: 36 additions & 6 deletions tests/test_learning.py
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Original file line number Diff line number Diff line change
@@ -1,11 +1,13 @@
from learning import parse_csv, weighted_mode, weighted_replicate, DataSet, \
PluralityLearner, NaiveBayesLearner, NearestNeighborLearner
PluralityLearner, NaiveBayesLearner, NearestNeighborLearner, \
NeuralNetLearner, PerceptronLearner, DecisionTreeLearner
from utils import DataFile



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 All @@ -20,18 +22,46 @@ def test_plurality_learner():
zoo = DataSet(name="zoo")

pL = PluralityLearner(zoo)
assert pL([]) == "mammal"
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")

nB = NaiveBayesLearner(iris)
assert nB([5, 3, 1, 0.1]) == "setosa"
assert nB([5,3,1,0.1]) == "setosa"


def test_k_nearest_neighbors():
iris = DataSet(name="iris")

kNN = NearestNeighborLearner(iris, k=3)
assert kNN([5, 3, 1, 0.1]) == "setosa"
kNN = NearestNeighborLearner(iris,k=3)
assert kNN([5,3,1,0.1]) == "setosa"

def test_decision_tree_learner():
iris = DataSet(name="iris")

dTL = DecisionTreeLearner(iris)
assert dTL([5,3,1,0.1]) == "setosa"


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")
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))
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