def test_learn_with_id(client, app, regression, lin_reg):
client.post('/api/predict',
data=json.dumps({
'id': 42,
'features': {
'x': 1
}
}),
content_type='application/json')
# Check the sample has been stored
with app.app_context():
shelf = storage.get_db()
assert sorted(
shelf['#42'].keys()) == ['features', 'model', 'prediction']
assert shelf['#42']['features'] == {'x': 1}
r = client.post('/api/learn',
data=json.dumps({
'id': 42,
'ground_truth': True
}),
content_type='application/json')
assert r.status_code == 201
# Check the sample has now been removed
with app.app_context():
shelf = storage.get_db()
assert '#42' not in shelf
def test_delete_model(client, app, regression):
# Upload a model
model = linear_model.LinearRegression()
client.post('/api/model/healthy-banana', data=pickle.dumps(model))
with app.app_context():
assert 'models/healthy-banana' in storage.get_db()
# Delete it
client.delete('/api/model/healthy-banana')
with app.app_context():
assert 'models/healthy-banana' not in storage.get_db()
def test_model_upload(client, app, regression):
# Instantiate a model
model = linear_model.LinearRegression()
probe = uuid.uuid4()
model.probe = probe
# Upload the model
r = client.post('/api/model/healthy-banana', data=pickle.dumps(model))
assert r.status_code == 201
assert r.json == {'name': 'healthy-banana'}
# Check that the model has been added to the shelf
with app.app_context():
shelf = storage.get_db()
assert isinstance(shelf['models/healthy-banana'], linear_model.LinearRegression)
assert shelf['models/healthy-banana'].probe == probe
# Check that the model can be retrieved via the API with it's name
model = pickle.loads(client.get('/api/model/healthy-banana').get_data())
assert isinstance(model, linear_model.LinearRegression)
assert model.probe == probe
# Check that the model can be retrieved via the API by default
model = pickle.loads(client.get('/api/model').get_data())
assert isinstance(model, linear_model.LinearRegression)
assert model.probe == probe
def test_predict_with_id(client, app, regression, lin_reg):
r = client.post('/api/predict',
data=json.dumps({'features': {}, 'id': '90210'}),
content_type='application/json'
)
assert r.status_code == 201
assert r.json == {'model': 'lin-reg', 'prediction': 0}
with app.app_context():
shelf = storage.get_db()
assert '#90210' in shelf
def test_init(client, app):
r = client.post('/api/init',
data=json.dumps({'flavor': 'regression'}),
content_type='application/json')
assert r.status_code == 201
with app.app_context():
assert storage.get_db()['flavor'].name == 'regression'
assert client.get('/api/init').json == {
'storage': app.config['STORAGE_BACKEND'],
'flavor': 'regression',
'creme_version': creme.__version__
}
def test_add_model(app):
runner = app.test_cli_runner()
# Pickle a model
model = linear_model.LinearRegression()
probe = uuid.uuid4()
model.probe = probe
with open('tmp.pkl', 'wb') as f:
pickle.dump(model, f)
# Add the model to the shelf through the CLI
result = runner.invoke(cli.add_model, ['tmp.pkl', '--name', 'banana'])
assert result.exit_code == 0
# Check that the model has been added to the shelf
with app.app_context():
db = storage.get_db()
assert isinstance(db['models/banana'], linear_model.LinearRegression)
assert db['models/banana'].probe == probe
# Delete the pickle
os.remove('tmp.pkl')