def test_prune_can_remove_outliers_leaving_missing_example_data():
examples = [{'data': {}, 'conclusion': 'happy'}] * 99
examples += [{'data': {'weather': 'rainy'}, 'conclusion': 'sad'}]
tree = build(examples)
tree = prune(examples, tree, threshold=0.1)
assert (tree.decide({}) == {'happy': 1.0})
assert (tree.decide({'weather': 'rainy'}) == {'happy': 1.0})
def test_prune_removes_outliers():
examples = [{'data': {'weather': 'sunny'}, 'conclusion': 'happy'}] * 99
examples += [{'data': {'weather': 'rainy'}, 'conclusion': 'sad'}]
tree = build(examples)
tree = prune(examples, tree, threshold=0.1)
assert (tree.decide({'weather': 'sunny'}) == {'happy': 1.0})
assert (tree.decide({'weather': 'rainy'}) == {'happy': 1.0})
def test_no_pruning_unless_gain_is_less_than_threshold():
examples = [{'data': {'weather': 'sunny'}, 'conclusion': 'happy'}] * 99
examples += [{'data': {'weather': 'rainy'}, 'conclusion': 'sad'}]
tree = build(examples)
tree = prune(examples, tree, threshold=0.01)
assert (tree.decide({'weather': 'sunny'}) == {'happy': 1.0})
assert (tree.decide({'weather': 'rainy'}) == {'sad': 1.0})
def test_prune_does_nothing_on_decisive_tree():
examples = [{
'data': {
'weather': 'sunny'
},
'conclusion': 'happy'
}, {
'data': {
'weather': 'rainy'
},
'conclusion': 'sad'
}]
tree = build(examples)
tree = prune(examples, tree)
assert (tree.decide({'weather': 'sunny'}) == {'happy': 1.0})
assert (tree.decide({'weather': 'rainy'}) == {'sad': 1.0})
def test_can_prune_tree_with_missing_example_data():
examples = [{
'data': {
'weather': 'sunny'
},
'conclusion': 'happy'
}, {
'data': {},
'conclusion': 'cheerful'
}, {
'data': {
'weather': 'rainy'
},
'conclusion': 'sad'
}]
tree = build(examples)
tree = prune(examples, tree, threshold=0.1)
assert (tree.decide({'weather': 'sunny'}) == {'happy': 1.0})
# assert(tree.decide({}) == {'cheerful': 1.0})
assert (tree.decide({'weather': 'rainy'}) == {'sad': 1.0})