def test_clear(self):
processor = MagicMock()
exp, path, wait = (
self._create_run(processor),
self._random_path(),
self._random_wait(),
)
var = FloatSeries(exp, path)
var.clear(wait=wait)
processor.enqueue_operation.assert_called_once_with(ClearFloatLog(path), wait)
def test_log_value_errors(self):
processor = MagicMock()
exp, path = self._create_run(processor), self._random_path()
attr = FloatSeries(exp, path)
with self.assertRaises(ValueError):
attr.log(["str", 5])
with self.assertRaises(ValueError):
attr.log([5, 10], step=10)
with self.assertRaises(TypeError):
attr.log(5, step="str")
with self.assertRaises(TypeError):
attr.log(5, timestamp="str")
def test_log_with_timestamp(self):
value_step_and_expected = [
(13, 5.3, LogFloats.ValueType(13, None, 5.3)),
(15.3, 10, LogFloats.ValueType(15.3, None, 10)),
]
for value, ts, expected in value_step_and_expected:
processor = MagicMock()
exp, path, wait = (
self._create_run(processor),
self._random_path(),
self._random_wait(),
)
var = FloatSeries(exp, path)
var.log(value, timestamp=ts, wait=wait)
processor.enqueue_operation.assert_called_once_with(
LogFloats(path, [expected]), wait
)
def test_log(self):
value_and_expected = [
(13, [LogFloats.ValueType(13, None, self._now())]),
(15.3, [LogFloats.ValueType(15.3, None, self._now())]),
(
[1, 9, 7],
[
LogFloats.ValueType(1, None, self._now()),
LogFloats.ValueType(9, None, self._now()),
LogFloats.ValueType(7, None, self._now()),
],
),
(
(1, 9, 7),
[
LogFloats.ValueType(1, None, self._now()),
LogFloats.ValueType(9, None, self._now()),
LogFloats.ValueType(7, None, self._now()),
],
),
(
{1, 9, 7},
[
LogFloats.ValueType(1, None, self._now()),
LogFloats.ValueType(9, None, self._now()),
LogFloats.ValueType(7, None, self._now()),
],
),
]
for value, expected in value_and_expected:
processor = MagicMock()
exp, path, wait = (
self._create_run(processor),
self._random_path(),
self._random_wait(),
)
var = FloatSeries(exp, path)
var.log(value, wait=wait)
processor.enqueue_operation.assert_called_once_with(
LogFloats(path, expected), wait
)
def test_log_with_step(self):
value_step_and_expected = [
(13, 5.3, LogFloats.ValueType(13, 5.3, self._now())),
(15.3, 10, LogFloats.ValueType(15.3, 10, self._now())),
([13], 5.3, LogFloats.ValueType(13, 5.3, self._now())),
((13,), 5.3, LogFloats.ValueType(13, 5.3, self._now())),
({13}, 5.3, LogFloats.ValueType(13, 5.3, self._now())),
]
for value, step, expected in value_step_and_expected:
processor = MagicMock()
exp, path, wait = (
self._create_run(processor),
self._random_path(),
self._random_wait(),
)
var = FloatSeries(exp, path)
var.log(value, step=step, wait=wait)
processor.enqueue_operation.assert_called_once_with(
LogFloats(path, [expected]), wait
)
def test_log(self):
exp, path = self._create_run(), self._random_path()
var = FloatSeries(exp, path)
var.log([val for val in range(0, 5000)])
self.assertEqual(4999, var.fetch_last())
values = list(var.fetch_values()["value"].array)
expected = list(range(0, 5000))
self.assertEqual(len(set(expected)), len(set(values)))
def test_assign(self):
value = FloatSeriesVal([17, 3.6], min=0, max=100, unit="%")
expected = [
LogFloats.ValueType(17, None, self._now()),
LogFloats.ValueType(3.6, None, self._now()),
]
processor = MagicMock()
exp, path, wait = (
self._create_run(processor),
self._random_path(),
self._random_wait(),
)
var = FloatSeries(exp, path)
var.assign(value, wait=wait)
self.assertEqual(3, processor.enqueue_operation.call_count)
processor.enqueue_operation.assert_has_calls(
[
call(ConfigFloatSeries(path, min=0, max=100, unit="%"), False),
call(ClearFloatLog(path), False),
call(LogFloats(path, expected), wait),
]
)
def log(
self,
value,
step: Optional[float] = None,
timestamp: Optional[float] = None,
wait: bool = False,
**kwargs,
) -> None:
"""Logs the provided value or a collection of values.
Available for following field types (`Field types docs page`_):
* `FloatSeries`
* `StringSeries`
* `FileSeries`
Args:
value: Value or collection of values to be added to the field.
step (float or int, optional, default is None): Index of the log entry being appended.
Must be strictly increasing.
Defaults to `None`.
timestamp(float or int, optional): Time index of the log entry being appended in form of Unix time.
If `None` current time (`time.time()`) will be used as a timestamp.
Defaults to `None`.
wait (bool, optional): If `True` the client will wait to send all tracked metadata to the server.
This makes the call synchronous.
Defaults to `False`.
.. _Field types docs page:
https://docs.neptune.ai/api-reference/field-types
"""
verify_type("step", step, (int, float, type(None)))
verify_type("timestamp", timestamp, (int, float, type(None)))
with self._run.lock():
attr = self._run.get_attribute(self._path)
if not attr:
if is_collection(value):
if value:
first_value = next(iter(value))
else:
raise ValueError(
"Cannot deduce value type: `value` cannot be empty"
)
else:
first_value = value
if is_float(first_value):
attr = FloatSeries(self._run, parse_path(self._path))
elif is_string(first_value):
attr = StringSeries(self._run, parse_path(self._path))
elif FileVal.is_convertable(first_value):
attr = FileSeries(self._run, parse_path(self._path))
elif is_float_like(first_value):
attr = FloatSeries(self._run, parse_path(self._path))
elif is_string_like(first_value):
attr = StringSeries(self._run, parse_path(self._path))
else:
raise TypeError("Value of unsupported type {}".format(
type(first_value)))
self._run.set_attribute(self._path, attr)
attr.log(value,
step=step,
timestamp=timestamp,
wait=wait,
**kwargs)
def test_get(self):
exp, path = self._create_run(), self._random_path()
var = FloatSeries(exp, path)
var.log(5)
var.log(34)
self.assertEqual(34, var.fetch_last())
def test_log_type_error(self):
values = [["text"], [[]], [5, ""], "string", None]
for value in values:
with self.assertRaises(Exception):
FloatSeries(MagicMock(), MagicMock()).log(value)
def test_assign_type_error(self):
values = [["text"], 55, "string", None]
for value in values:
with self.assertRaises(Exception):
FloatSeries(MagicMock(), MagicMock()).assign(value)