Learning: Grade Learner (aimacode#496)

antmarakis · norvig · commit 17fac54ab3ca · 2017-04-13T22:48:20.000-07:00
* Add grade_learner

* Update test_learning.py
diff --git 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
@@ -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
