async def update(self, repo: Repo):
db = self.parent.db
# Store feature data
feature_cols = self.parent.FEATURE_COLS
feature_data = OrderedDict.fromkeys(feature_cols)
feature_data.update(repo.features(feature_cols))
await db.execute(
"INSERT OR REPLACE INTO features (src_url, "
+ ", ".join(feature_cols)
+ ") "
"VALUES(?, " + ", ".join("?" * len(feature_cols)) + ")",
[repo.src_url] + list(feature_data.values()),
)
# Store prediction
prediction = repo.prediction()
if prediction:
prediction_cols = self.parent.PREDICTION_COLS
prediction_data = OrderedDict.fromkeys(prediction_cols)
prediction_data.update(prediction.dict())
await db.execute(
"INSERT OR REPLACE INTO prediction (src_url, "
+ ", ".join(prediction_cols)
+ ") "
"VALUES(?, " + ", ".join("?" * len(prediction_cols)) + ")",
[repo.src_url] + list(prediction_data.values()),
)
# Store classification
if repo.classified():
await db.execute(
"INSERT OR REPLACE INTO classification "
"(src_url, classification) VALUES(?, ?)",
[repo.src_url, repo.classification()],
)
async def update(self, repo: Repo):
db = self.parent.db
# Store feature data
feature_cols = self.parent.FEATURE_COLS
feature_data = OrderedDict.fromkeys(feature_cols)
feature_data.update(repo.features(feature_cols))
await db.execute(
"INSERT OR REPLACE INTO features (key, " +
", ".join(feature_cols) + ") "
"VALUES(?, " + ", ".join("?" * len(feature_cols)) + ")",
[repo.key] + list(feature_data.values()),
)
# Store prediction
try:
prediction = repo.prediction("target_name")
prediction_cols = self.parent.PREDICTION_COLS
prediction_data = OrderedDict.fromkeys(prediction_cols)
prediction_data.update(prediction.dict())
await db.execute(
"INSERT OR REPLACE INTO prediction (key, " +
", ".join(prediction_cols) + ") "
"VALUES(?, " + ", ".join("?" * len(prediction_cols)) + ")",
[repo.key] + list(prediction_data.values()),
)
except KeyError:
pass
class TestRepo(unittest.TestCase):
def setUp(self):
self.null = Repo("null")
self.full = Repo(
"full",
data=dict(
features=dict(dead="beef"),
extra=dict(extra="read all about it"),
),
extra=dict(half=True),
)
def test_dict(self):
data = self.full.dict()
self.assertIn("extra", data)
def test_repr(self):
repr(self.full)
def test_str(self):
self.full.prediction = RepoPrediction()
self.assertIn("Undetermined", str(self.full))
self.full.data.prediction = RepoPrediction(value="Good")
self.assertIn("Good", str(self.full))
self.full.extra.update(dict(hi=5))
self.assertIn("5", str(self.full))
self.full.extra = dict()
self.assertNotIn("5", str(self.full))
def test_merge(self):
null = Repo("null")
null.merge(self.full)
self.assertIn("half", null.extra)
self.assertTrue(null.extra["half"])
def test_src_url(self):
return self.full.data.src_url
def test_evaluated(self):
old_last_updated = self.full.data.last_updated
results = {"new": "feature"}
self.full.evaluated({"feed": "face"})
self.assertIn("feed", self.full.data.features)
self.assertEqual("face", self.full.data.features["feed"])
self.full.evaluated(results, overwrite=True)
self.assertEqual(self.full.data.features, results)
self.assertNotEqual(old_last_updated, self.full.data.last_updated)
def test_features(self):
self.assertIn("dead", self.full.features())
self.assertIn("dead", self.full.features(["dead"]))
self.assertFalse(self.full.features(["dead", "beaf"]))
def test_predicted(self):
old_prediction = self.full.data.prediction
old_last_updated = self.full.data.last_updated
self.full.predicted("feed", 1.00)
self.assertNotEqual(old_prediction, self.full.data.prediction)
self.assertNotEqual(old_last_updated, self.full.data.last_updated)
def test_prediction(self):
self.full.predicted("feed", 1.00)
self.assertTrue(self.full.prediction())
class TestRepo(unittest.TestCase):
def setUp(self):
self.null = Repo('null')
self.full = Repo('full',
data=dict(features=dict(dead='beef'),
extra=dict(extra='read all about it')),
extra=dict(half=True))
def test_dict(self):
data = self.full.dict()
self.assertIn('extra', data)
def test_repr(self):
repr(self.full)
def test_str(self):
self.full.prediction = RepoPrediction()
self.assertIn('Undetermined', str(self.full))
self.full.data.prediction = RepoPrediction(classification='Good')
self.assertIn('Good', str(self.full))
self.full.data.classification = 'Great'
self.assertIn('Great', str(self.full))
self.full.extra.update(dict(hi=5))
self.assertIn('5', str(self.full))
self.full.extra = dict()
self.assertNotIn('5', str(self.full))
def test_merge(self):
null = Repo('null')
null.merge(self.full)
self.assertIn('half', null.extra)
self.assertTrue(null.extra['half'])
def test_src_url(self):
return self.full.data.src_url
def test_evaluated(self):
old_last_updated = self.full.data.last_updated
results = {'new': 'feature'}
self.full.evaluated({'feed': 'face'})
self.assertIn('feed', self.full.data.features)
self.assertEqual('face', self.full.data.features['feed'])
self.full.evaluated(results, overwrite=True)
self.assertEqual(self.full.data.features, results)
self.assertNotEqual(old_last_updated, self.full.data.last_updated)
def test_features(self):
self.assertIn('dead', self.full.features())
self.assertIn('dead', self.full.features(['dead']))
self.assertFalse(self.full.features(['dead', 'beaf']))
def test_predicted(self):
old_prediction = self.full.data.prediction
old_last_updated = self.full.data.last_updated
self.full.predicted('feed', 1.00)
self.assertNotEqual(old_prediction, self.full.data.prediction)
self.assertNotEqual(old_last_updated, self.full.data.last_updated)
def test_prediction(self):
self.full.predicted('feed', 1.00)
self.assertTrue(self.full.prediction())
def test_classify(self):
self.full.classify('face')
self.assertEqual(self.full.data.classification, 'face')
def test_classified(self):
self.full.classify('')
self.assertFalse(self.full.classified())
self.full.classify(True)
self.assertTrue(self.full.classified())
def test_classification(self):
self.full.classify(True)
self.assertTrue(self.full.classification())
self.full.classify('')
with self.assertRaisesRegex(ValueError, 'Unclassified'):
self.full.classification()
