diff --git a/learning.py b/learning.py
index 8347fbbef..fffbccf83 100644
--- a/learning.py
+++ b/learning.py
@@ -908,6 +908,16 @@ def score(learner, size):
return [(size, mean([score(learner, size) for t in range(trials)]))
for size in sizes]
+
+def grade_learner(predict, tests):
+ """Grades the given learner based on how many tests it passes.
+ tests is a list with each element in the form: (values, output)."""
+ correct = 0
+ for t in tests:
+ if predict(t[0]) == t[1]:
+ correct += 1
+ return correct
+
# ______________________________________________________________________________
# The rest of this file gives datasets for machine learning problems.
diff --git a/tests/test_learning.py b/tests/test_learning.py
index 1b4b825c1..1bac9a4cc 100644
--- a/tests/test_learning.py
+++ b/tests/test_learning.py
@@ -1,19 +1,19 @@
from learning import parse_csv, weighted_mode, weighted_replicate, DataSet, \
PluralityLearner, NaiveBayesLearner, NearestNeighborLearner, \
NeuralNetLearner, PerceptronLearner, DecisionTreeLearner, \
- euclidean_distance
+ euclidean_distance, grade_learner
from utils import DataFile
def test_euclidean():
- distance = euclidean_distance([1,2], [3,4])
+ distance = euclidean_distance([1, 2], [3, 4])
assert round(distance, 2) == 2.83
- distance = euclidean_distance([1,2,3], [4,5,6])
+ distance = euclidean_distance([1, 2, 3], [4, 5, 6])
assert round(distance, 2) == 5.2
- distance = euclidean_distance([0,0,0], [0,0,0])
+ distance = euclidean_distance([0, 0, 0], [0, 0, 0])
assert distance == 0
@@ -24,7 +24,7 @@ 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():
@@ -47,25 +47,25 @@ def test_naive_bayes():
# Discrete
nBD = NaiveBayesLearner(iris)
- assert nBD([5,3,1,0.1]) == "setosa"
+ assert nBD([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"
- assert kNN([6,5,3,1.5]) == "versicolor"
- assert kNN([7.5,4,6,2]) == "virginica"
+ 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_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"
+ 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_neural_network_learner():
@@ -75,14 +75,11 @@ def test_neural_network_learner():
iris.classes_to_numbers(classes)
nNL = NeuralNetLearner(iris, [5], 0.15, 75)
- pred1 = nNL([5,3,1,0.1])
- pred2 = nNL([6,3,3,1.5])
- pred3 = nNL([7.5,4,6,2])
+ tests = [([5, 3, 1, 0.1], 0),
+ ([6, 3, 3, 1.5], 1),
+ ([7.5, 4, 6, 2], 2)]
- # NeuralNetLearner might be wrong. If it is, check if prediction is in range.
- assert pred1 == 0 or pred1 in range(len(classes))
- assert pred2 == 1 or pred2 in range(len(classes))
- assert pred3 == 2 or pred3 in range(len(classes))
+ assert grade_learner(nNL, tests) >= 2
def test_perceptron():
@@ -92,11 +89,8 @@ def test_perceptron():
classes_number = len(iris.values[iris.target])
perceptron = PerceptronLearner(iris)
- pred1 = perceptron([5,3,1,0.1])
- pred2 = perceptron([6,3,4,1])
- pred3 = perceptron([7.5,4,6,2])
-
- # PerceptronLearner might be wrong. If it is, check if prediction is in range.
- assert pred1 == 0 or pred1 in range(classes_number)
- assert pred2 == 1 or pred2 in range(classes_number)
- assert pred3 == 2 or pred3 in range(classes_number)
+ tests = [([5, 3, 1, 0.1], 0),
+ ([6, 3, 4, 1.1], 1),
+ ([7.5, 4, 6, 2], 2)]
+
+ assert grade_learner(perceptron, tests) >= 2
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