class FormatModelTests(unittest.TestCase):
def setUp(self):
(self.train_x, self.test_x, self.train_y,
self.test_y), _, _ = load_abalone_data()
self.config = {
"trainable_rules": {
"base_model_name": "sklearn.tree.DecisionTreeClassifier",
"prune_branches_algorithms": [],
"prune_attributes": False,
"min_samples_leaf": 26,
"random_state": 1989,
},
}
trainer = TrainableRules(**self.config["trainable_rules"],
origin_config=self.config)
trainer.fit(self.test_x, self.test_y)
self.rules = trainer.rules
self.fm = FormatModel().load(
os.path.join(os.path.dirname(__file__), "model_jquery.asdf"))
self.maxDiff = None
def test_save_and_load(self):
fm1 = FormatModel()
fm1["js"] = self.rules
fm1["js2"] = self.rules
fm1["js3"] = self.rules
with tempfile.NamedTemporaryFile(prefix="lookout-") as f:
fm1.save(f.name)
fm2 = FormatModel().load(f.name)
compare_models(self, fm1, fm2)
def test_dump(self):
fm = FormatModel()
self.assertEqual(
fm.dump(),
"<unknown name>/[1, 0, 0] <unknown url> <unknown commit>")
DUMP = """style.format.analyzer.FormatAnalyzer/[3] https://github.com/jquery/jquery c2026b117d1ca5b2e42a52c7e2a8ae8988cf0d4b
# javascript
1269 rules, avg.len. 19.1""" # noqa
self.assertEqual(self.fm.dump(), DUMP)
def test_len(self):
fm = FormatModel()
self.assertEqual(len(fm), 0)
fm["js"] = self.rules
self.assertEqual(len(fm), 1)
fm["js2"] = self.rules
self.assertEqual(len(fm), 2)
def test_iter(self):
langs = set(self.fm.languages)
for item in self.fm:
self.assertIn(item, langs)
langs.remove(item)
self.assertEqual(len(langs), 0)
def test_dump(self):
fm = FormatModel()
self.assertEqual(
fm.dump(),
"<unknown name>/[1, 0, 0] <unknown url> <unknown commit>")
DUMP = """style.format.analyzer.FormatAnalyzer/[3] https://github.com/jquery/jquery c2026b117d1ca5b2e42a52c7e2a8ae8988cf0d4b
# javascript
1269 rules, avg.len. 19.1""" # noqa
self.assertEqual(self.fm.dump(), DUMP)
def test_dump(self):
fm = FormatModel()
self.assertEqual(fm.dump(),
"generic/[1, 0, 0] <unknown url> <unknown commit>")
DUMP = """code-format/[1] file:///var/folders/kw/93jybvs16_954hytgsq6ld7r0000gn/T/top-repos-quality-repos-jigt1n8g/jquery dae5f3ce3d2df27873d01f0d9682f6a91ad66b87
# javascript
1159 rules, avg.len. 12.7
## train
PPCR: 0.993413
### report
macro
{'f1-score': 0.7270769669476458,
'precision': 0.8106858458605273,
'recall': 0.7061608014058862,
'support': 163931}
micro
{'f1-score': 0.9704570825530253,
'precision': 0.9704570825530253,
'recall': 0.9704570825530253,
'support': 163931}
weighted
{'f1-score': 0.9682573644719648,
'precision': 0.9688067324990776,
'recall': 0.9704570825530253,
'support': 163931}
### report_full
macro
{'f1-score': 0.7207757082876136,
'precision': 0.8106858458605273,
'recall': 0.6958571203075536,
'support': 165018}
micro
{'f1-score': 0.9672502424387974,
'precision': 0.9704570825530253,
'recall': 0.9640645262941012,
'support': 165018}
weighted
{'f1-score': 0.964254372281313,
'precision': 0.967999533892513,
'recall': 0.9640645262941012,
'support': 165018}
## test
PPCR: 0.992673
### report
macro
{'f1-score': 0.670106403195044,
'precision': 0.7675483060510728,
'recall': 0.667193540547618,
'support': 39563}
micro
{'f1-score': 0.9646134014104087,
'precision': 0.9646134014104087,
'recall': 0.9646134014104087,
'support': 39563}
weighted
{'f1-score': 0.9623528642977015,
'precision': 0.964064574202937,
'recall': 0.9646134014104087,
'support': 39563}
### report_full
macro
{'f1-score': 0.6645299678762785,
'precision': 0.7675483060510728,
'recall': 0.6569592424077594,
'support': 39855}
micro
{'f1-score': 0.961066760683976,
'precision': 0.9646134014104087,
'recall': 0.9575461046292811,
'support': 39855}
weighted
{'f1-score': 0.9579239894541836,
'precision': 0.9627543953777487,
'recall': 0.9575461046292811,
'support': 39855}""" # noqa
self.assertEqual(self.fm.dump(), DUMP)
class FormatModelTests(unittest.TestCase):
def setUp(self):
(self.train_x, self.test_x, self.train_y,
self.test_y), _, _ = load_abalone_data()
self.config = {
"trainable_rules": {
"base_model_name": "sklearn.tree.DecisionTreeClassifier",
"prune_branches_algorithms": [],
"prune_attributes": False,
"min_samples_leaf": 26,
"random_state": 1989,
},
}
trainer = TrainableRules(**self.config["trainable_rules"],
origin_config=self.config)
trainer.fit(self.test_x, self.test_y)
self.rules = trainer.rules
self.fm = FormatModel().load(
os.path.join(os.path.dirname(__file__), "model_jquery.asdf"))
self.maxDiff = None
def test_save_and_load(self):
fm1 = FormatModel()
fm1["js"] = self.rules
fm1["js2"] = self.rules
fm1["js3"] = self.rules
with tempfile.NamedTemporaryFile(prefix="lookout-") as f:
fm1.save(f.name)
fm2 = FormatModel().load(f.name)
compare_models(self, fm1, fm2)
def test_dump(self):
fm = FormatModel()
self.assertEqual(fm.dump(),
"generic/[1, 0, 0] <unknown url> <unknown commit>")
DUMP = """code-format/[1] file:///var/folders/kw/93jybvs16_954hytgsq6ld7r0000gn/T/top-repos-quality-repos-jigt1n8g/jquery dae5f3ce3d2df27873d01f0d9682f6a91ad66b87
# javascript
1159 rules, avg.len. 12.7
## train
PPCR: 0.993413
### report
macro
{'f1-score': 0.7270769669476458,
'precision': 0.8106858458605273,
'recall': 0.7061608014058862,
'support': 163931}
micro
{'f1-score': 0.9704570825530253,
'precision': 0.9704570825530253,
'recall': 0.9704570825530253,
'support': 163931}
weighted
{'f1-score': 0.9682573644719648,
'precision': 0.9688067324990776,
'recall': 0.9704570825530253,
'support': 163931}
### report_full
macro
{'f1-score': 0.7207757082876136,
'precision': 0.8106858458605273,
'recall': 0.6958571203075536,
'support': 165018}
micro
{'f1-score': 0.9672502424387974,
'precision': 0.9704570825530253,
'recall': 0.9640645262941012,
'support': 165018}
weighted
{'f1-score': 0.964254372281313,
'precision': 0.967999533892513,
'recall': 0.9640645262941012,
'support': 165018}
## test
PPCR: 0.992673
### report
macro
{'f1-score': 0.670106403195044,
'precision': 0.7675483060510728,
'recall': 0.667193540547618,
'support': 39563}
micro
{'f1-score': 0.9646134014104087,
'precision': 0.9646134014104087,
'recall': 0.9646134014104087,
'support': 39563}
weighted
{'f1-score': 0.9623528642977015,
'precision': 0.964064574202937,
'recall': 0.9646134014104087,
'support': 39563}
### report_full
macro
{'f1-score': 0.6645299678762785,
'precision': 0.7675483060510728,
'recall': 0.6569592424077594,
'support': 39855}
micro
{'f1-score': 0.961066760683976,
'precision': 0.9646134014104087,
'recall': 0.9575461046292811,
'support': 39855}
weighted
{'f1-score': 0.9579239894541836,
'precision': 0.9627543953777487,
'recall': 0.9575461046292811,
'support': 39855}""" # noqa
self.assertEqual(self.fm.dump(), DUMP)
def test_len(self):
fm = FormatModel()
self.assertEqual(len(fm), 0)
fm["js"] = self.rules
self.assertEqual(len(fm), 1)
fm["js2"] = self.rules
self.assertEqual(len(fm), 2)
def test_iter(self):
langs = set(self.fm.languages)
for item in self.fm:
self.assertIn(item, langs)
langs.remove(item)
self.assertEqual(len(langs), 0)