load_series_from_multiple_providers(
[self.ab_producer, self.containing_b_producer],
None,
None,
[SensorTag("abba", None),
SensorTag("cba", None)],
))
self.assertEqual(series_collection[0].name, "ab.*")
self.assertEqual(series_collection[1].name, ".*b.*")
@pytest.mark.parametrize(
"provider,expected_params",
(
(
providers.RandomDataProvider(200, max_size=205),
{
"min_size": 200,
"max_size": 205
},
),
(
providers.InfluxDataProvider("measurement", value_name="Value"),
{
"measurement": "measurement",
"value_name": "Value"
},
),
),
)
def test_data_provider_serializations(provider: GordoBaseDataProvider,
assert len(os.listdir(output_dir)) > 0
model_output_dir = os.path.join(output_dir, tu.GORDO_SINGLE_TARGET)
assert os.path.isdir(model_output_dir)
model = serializer.load(model_output_dir)
assert isinstance(model, BaseEstimator)
@pytest.mark.parametrize(
"forwarder_args",
[["--influx-uri", tu.INFLUXDB_URI, "--forward-resampled-sensors"], None],
)
@pytest.mark.parametrize("output_dir", [tempfile.TemporaryDirectory(), None])
@pytest.mark.parametrize("data_provider",
[providers.RandomDataProvider(), None])
def test_client_cli_predict(influxdb, watchman_service, forwarder_args,
output_dir, data_provider):
"""
Test ability for client to get predictions via CLI
"""
runner = CliRunner()
args = [
"client",
"--metadata",
"key,value",
"--project",
tu.GORDO_PROJECT,
"predict",
"2016-01-01T00:00:00Z",
# Output directory should not be empty any longer
assert len(os.listdir(output_dir)) > 0
model_output_dir = os.path.join(output_dir, tu.GORDO_SINGLE_TARGET)
assert os.path.isdir(model_output_dir)
model = serializer.load(model_output_dir)
assert isinstance(model, BaseEstimator)
@pytest.mark.parametrize(
"forwarder_args",
[["--influx-uri", tu.INFLUXDB_URI, "--forward-resampled-sensors"], None],
)
@pytest.mark.parametrize("output_dir", [tempfile.TemporaryDirectory(), None])
@pytest.mark.parametrize("data_provider", [providers.RandomDataProvider(), None])
def test_client_cli_predict(
influxdb, watchman_service, forwarder_args, output_dir, data_provider
):
"""
Test ability for client to get predictions via CLI
"""
runner = CliRunner()
args = [
"client",
"--metadata",
"key,value",
"--project",
tu.GORDO_PROJECT,
"predict",
def test_client_cli_predict(
influxdb,
gordo_name,
watchman_service,
forwarder_args,
output_dir,
trained_model_directory,
use_parquet,
):
"""
Test ability for client to get predictions via CLI
"""
runner = CliRunner()
args = [
"client",
"--metadata",
"key,value",
"--project",
tu.GORDO_PROJECT,
"predict",
"--parquet" if use_parquet else "--no-parquet",
"2016-01-01T00:00:00Z",
"2016-01-01T01:00:00Z",
]
influx_client = client_utils.influx_client_from_uri(
uri=tu.INFLUXDB_URI, dataframe_client=True
)
query = """
SELECT *
FROM "resampled"
"""
# Do we have forwarder args?
if forwarder_args is not None:
args.extend(forwarder_args)
vals = influx_client.query(query)
# There is no data there before we start doing things
assert len(vals) == 0
# Should it write out the predictions to dataframes in an output directory?
if output_dir is not None:
args.extend(["--output-dir", output_dir.name])
# Do we have a data provider, POST else GET requests
args.extend(
["--data-provider", json.dumps(providers.RandomDataProvider().to_dict())]
)
# Run without any error
out = runner.invoke(cli.gordo, args=args)
assert out.exit_code == 0, f"{out.output}"
# If we activated forwarder and we had any actual data then there should
# be resampled values in the influx
if forwarder_args:
vals = influx_client.query(query)
assert len(vals) == 1
assert len(vals["resampled"]) == 48
influx_client.drop_measurement("resampled")
# Did it save dataframes to output dir if specified?
if output_dir is not None:
assert os.path.exists(
os.path.join(output_dir.name, f"{tu.GORDO_SINGLE_TARGET}.csv.gz")
)