def test_operation(operation, args):
auger_operation = MockHelpers.called_with(AugerA2ML, operation,
monkeypatch)
google_operation = MockHelpers.called_with(GoogleA2ML, operation,
monkeypatch)
#run operation for a single provider
ctx.config.set('providers', ['auger'])
a2ml = A2ML(ctx)
getattr(a2ml, operation)(*args)
assert auger_operation.times == 1
assert google_operation.times == 0
for arg in range(len(args)):
assert auger_operation.args[arg + 1] == args[arg]
#run operation for multiple providers
if operation != 'deploy' and operation != 'predict':
ctx.config.set('providers', ['auger', 'google'])
auger_operation.reset()
google_operation.reset()
a2ml = A2ML(ctx)
getattr(a2ml, operation)(*args)
assert auger_operation.times == 1
assert google_operation.times == 1
for arg in range(len(args)):
assert auger_operation.args[arg + 1] == args[arg]
assert google_operation.args[arg + 1] == args[arg]
def cmdl(ctx, provider, filename, model_id, threshold, locally, output):
"""Predict with deployed model."""
ctx.setup_logger(format='')
A2ML(ctx, provider).predict(filename,
model_id,
threshold=threshold,
locally=locally,
output=output)
def test_init(self):
fulldir = os.getcwd() + "/tests/test_google"
os.chdir(fulldir)
# load config(s) from test app
ctx = Context()
a2ml = A2ML(ctx)
assert len(a2ml.runner.providers) == 3
assert isinstance(a2ml.runner.providers[0], GoogleA2ML)
def test_init(self):
print("Current directory: {}".format(os.getcwd()))
fulldir = os.getcwd() + "/tests/test_app"
os.chdir(fulldir)
# load config(s) from test app
ctx = Context()
a2ml = A2ML(ctx)
assert len(a2ml.runner.providers) == 3
assert isinstance(a2ml.runner.providers[0], GoogleA2ML)
def test_import_server(self):
from a2ml.api.a2ml import A2ML
ctx = Context(path=os.path.join(
os.environ.get('A2ML_PROJECT_PATH', ''), 'cli-integration-test'),
debug=True)
provider = "auger"
ctx.config.set('config', 'providers', [provider])
ctx.config.set('config', 'use_server', True)
A2ML(ctx, provider).import_data()
def test_init_a2ml(self, project, ctx, monkeypatch):
init_auger = MockHelpers.count_calls(
AugerA2ML, "__init__", monkeypatch)
init_google = MockHelpers.count_calls(
GoogleA2ML, "__init__", monkeypatch)
ctx.config.set('config', 'providers', 'auger')
a2ml = A2ML(ctx)
assert len(a2ml.runner.providers) == 1
assert isinstance(a2ml.runner.providers['auger'], AugerA2ML)
assert init_auger.times == 1
assert init_google.times == 0
# modify config on the fly
ctx.config.set('config', 'providers', ['auger','google'])
init_auger.reset()
init_google.reset()
a2ml = A2ML(ctx)
assert len(a2ml.runner.providers) == 2
assert isinstance(a2ml.runner.providers['auger'], AugerA2ML)
assert isinstance(a2ml.runner.providers['google'], GoogleA2ML)
assert init_auger.times == 1
assert init_google.times == 1
def test_init_a2ml(self, monkeypatch):
init_auger = MockHelpers.count_calls(AugerA2ML, "__init__",
monkeypatch)
init_google = MockHelpers.count_calls(GoogleA2ML, "__init__",
monkeypatch)
self.ctx.config['config'].providers = 'auger'
a2ml = A2ML(self.ctx)
assert len(a2ml.runner.providers) == 1
assert isinstance(a2ml.runner.providers[0], AugerA2ML)
assert init_auger.times == 1
assert init_google.times == 0
# modify config on the fly
self.ctx.config['config'].providers = ['auger', 'google']
init_auger.reset()
init_google.reset()
a2ml = A2ML(self.ctx)
assert len(a2ml.runner.providers) == 2
assert isinstance(a2ml.runner.providers[0], AugerA2ML)
assert isinstance(a2ml.runner.providers[1], GoogleA2ML)
assert init_auger.times == 1
assert init_google.times == 1
def test_operation(operation, args):
auger_operation = MockHelpers.called_with(AugerA2ML, operation,
monkeypatch)
google_operation = MockHelpers.called_with(GoogleA2ML, operation,
monkeypatch)
#run operation for a single provider
self.ctx.config['config'].providers = ['auger']
a2ml = A2ML(self.ctx)
getattr(a2ml, operation)(*args)
assert auger_operation.times == 1
assert google_operation.times == 0
for arg in range(len(args)):
assert auger_operation.args[arg + 1] == args[arg]
#run operation for multiple providers
self.ctx.config['config'].providers = ['auger', 'google']
auger_operation.reset()
google_operation.reset()
a2ml = A2ML(self.ctx)
getattr(a2ml, operation)(*args)
assert auger_operation.times == 1
assert google_operation.times == 1
for arg in range(len(args)):
assert auger_operation.args[arg + 1] == args[arg]
assert google_operation.args[arg + 1] == args[arg]
def import_data_task(params):
ctx = create_context(params)
return A2ML(ctx).import_data()
def cmdl(ctx, model_id, locally):
"""Deploy trained model."""
ctx.setup_logger(format='')
A2ML(ctx).deploy(model_id, locally)
def cmdl(ctx, run_id, provider):
"""Evaluate models after training."""
ctx.setup_logger(format='')
A2ML(ctx, provider).evaluate(run_id)
def cmdl(ctx, provider, model_id, locally, review):
"""Deploy trained model."""
ctx.setup_logger(format='')
A2ML(ctx, provider).deploy(model_id, locally, review)
def cmdl(ctx):
"""Import data for training."""
ctx.setup_logger(format='')
A2ML(ctx).import_data()
def cmdl(ctx, source, provider):
"""Import data for training."""
ctx.setup_logger(format='')
A2ML(ctx, provider).import_data(source)
def deploy_task(params):
return with_context(
params,
lambda ctx: A2ML(ctx).deploy(*params['args'], **params['kwargs']))
def train_task(params):
return with_context(
params,
lambda ctx: A2ML(ctx).train(*params['args'], **params['kwargs']))
def cmdl(ctx):
"""Review specified model info."""
ctx.setup_logger(format='')
A2ML(ctx).review()
def predict_task(params):
ctx = create_context(params)
return A2ML(ctx).predict(params.get('filename'), params.get('model_id'),
params.get('threshold'))
def cmdl(ctx, filename, model_id, threshold, locally):
"""Predict with deployed model."""
ctx.setup_logger(format='')
A2ML(ctx).predict(filename, model_id, threshold, locally)
def cmdl(ctx):
"""Evaluate models after training."""
ctx.setup_logger(format='')
A2ML(ctx).evaluate()
def import_data_task(params):
return with_context(
params,
lambda ctx: A2ML(ctx).import_data(*params['args'], **params['kwargs']))
def train_task(params):
ctx = create_context(params)
return A2ML(ctx).train()
def evaluate_task(params):
return with_context(
params,
lambda ctx: A2ML(ctx).evaluate(*params['args'], **params['kwargs']))
def evaluate_task(params):
ctx = create_context(params)
return A2ML(ctx).evaluate()
def predict_task(params):
return with_context(
params,
lambda ctx: A2ML(ctx).predict(*params['args'], **params['kwargs']))
def deploy_task(params):
ctx = create_context(params)
return A2ML(ctx).deploy(params.get('model_id'))
def cmdl(ctx, provider):
"""Train the model."""
ctx.setup_logger(format='')
A2ML(ctx, provider).train()
def actuals_task(params):
return with_context(
params,
lambda ctx: A2ML(ctx).actuals(*params['args'], **params['kwargs']))
