diff --git a/learning.py b/learning.py
index 5f1ba596e..f5bc5d835 100644
--- a/learning.py
+++ b/learning.py
@@ -984,8 +984,8 @@ def flatten(seqs): return sum(seqs, [])
def err_ratio(predict, dataset, examples=None, verbose=0):
- """Return the proportion of the examples that are NOT correctly predicted."""
- """verbose - 0: No output; 1: Output wrong; 2 (or greater): Output correct"""
+ """Return the proportion of the examples that are NOT correctly predicted.
+ verbose - 0: No output; 1: Output wrong; 2 (or greater): Output correct"""
if examples is None:
examples = dataset.examples
if len(examples) == 0:
diff --git a/tests/test_csp.py b/tests/test_csp.py
index 5a10f5ce5..f303af6f9 100644
--- a/tests/test_csp.py
+++ b/tests/test_csp.py
@@ -1,5 +1,10 @@
import pytest
+from utils import failure_test
from csp import *
+import random
+
+
+random.seed("aima-python")
def test_csp_assign():
@@ -331,10 +336,12 @@ def test_backtracking_search():
def test_min_conflicts():
- random.seed("aima-python")
assert min_conflicts(australia)
- assert min_conflicts(usa)
assert min_conflicts(france)
+
+ tests = [(usa, None)] * 3
+ assert failure_test(min_conflicts, tests) > 1/3
+
australia_impossible = MapColoringCSP(list('RG'), 'SA: WA NT Q NSW V; NT: WA Q; NSW: Q V; T: ')
assert min_conflicts(australia_impossible, 1000) is None
@@ -351,7 +358,7 @@ def test_parse_neighbours():
def test_topological_sort():
root = 'NT'
Sort, Parents = topological_sort(australia,root)
-
+
assert Sort == ['NT','SA','Q','NSW','V','WA']
assert Parents['NT'] == None
assert Parents['SA'] == 'NT'
diff --git a/tests/test_learning.py b/tests/test_learning.py
index aff8903a4..8a21d6462 100644
--- a/tests/test_learning.py
+++ b/tests/test_learning.py
@@ -168,9 +168,13 @@ def test_decision_tree_learner():
def test_random_forest():
iris = DataSet(name="iris")
rF = RandomForest(iris)
- assert rF([5, 3, 1, 0.1]) == "setosa"
- assert rF([6, 5, 3, 1]) == "versicolor"
- assert rF([7.5, 4, 6, 2]) == "virginica"
+ 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_neural_network_learner():
diff --git a/utils.py b/utils.py
index d2720abe1..e5dbfd5cd 100644
--- a/utils.py
+++ b/utils.py
@@ -416,6 +416,16 @@ def open_data(name, mode='r'):
return open(aima_file)
+def failure_test(algorithm, tests):
+ """Grades the given algorithm based on how many tests it passes.
+ Most algorithms have arbitary output on correct execution, which is difficult
+ to check for correctness. On the other hand, a lot of algorithms output something
+ particular on fail (for example, False, or None).
+ tests is a list with each element in the form: (values, failure_output)."""
+ from statistics import mean
+ return mean(int(algorithm(x) != y) for x, y in tests)
+
+
# ______________________________________________________________________________
# Expressions
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