def test_logger_can_record_attributes_from_an_object():
obj = ObjToLog()
logger = log.Logger(obj, ["a"])
obj.a = 3
logger.record()
obj.a = 4
logger.record()
expected = pd.DataFrame.from_dict({"a": [3, 4]})
assert logger.to_df().equals(expected)
def test_logger_can_record_attributes_from_an_object() -> None:
obj = ObjToLog("obj")
logger = log.Logger(obj, ["unit0_act", "unit1_act", "avg_act"])
logger.record()
expected = pd.DataFrame.from_dict({
"act": [0.3, 0.5, None],
"avg_act": [None, None, 0.4],
"unit": [0, 1, None]
})
expected["time"] = 0
assert logger.to_df().equals(expected)
def _add_loggers(self, obj: log.ObservableMixin) -> None:
"""Instantiates loggers for an observable object.
We assume the object has a "spec" attribute that specifies which
attributes to log at each frequency, as well as a "name" attribute.
"""
for freq_name, freq in events.Frequency.registry.items():
attrs_to_log = obj.spec.attrs_to_log(freq)
if attrs_to_log:
logger = log.Logger(obj, attrs_to_log, freq)
self.loggers.append(logger)
self.objs["{0}_{1}_logger".format(obj.name,
freq_name)] = logger
def new_layer(self,
name: str,
size: int,
spec: specs.LayerSpec = None) -> None:
"""Adds a new layer to the network.
Args:
name: The name of the layer.
size: How many units the layer should have.
spec: The layer specification.
Raises:
spec.ValidationError: If the spec contains an invalid parameter
value.
"""
if spec is not None:
spec.validate()
lr = layer.Layer(name, size, spec)
self.layers.append(lr)
self.objs[lr.name] = lr
if lr.spec.log_on_cycle != ():
self.cycle_loggers.append(log.Logger(lr, lr.spec.log_on_cycle))
def test_logger_has_a_name_property():
obj = ObjToLog()
logger = log.Logger(obj, ["a"])
logger.name = "obj"
assert logger.name == "obj"