def load_data_for_model(doc: Document, plots: TrainTestPlots, logdir: Path) -> None:
last_epoch = 0
while doc.session_context:
try:
path = logdir / 'metrics' / f'epoch_{last_epoch}'
new_metrics = load_metrics(str(path))
last_epoch += 1
doc.add_next_tick_callback(partial(update_aggregated_metrics, plots, new_metrics))
except OSError:
time.sleep(1)
logger.info("Connection closed")
def load_profiling_data(doc: Document, plots: ProfilingPlots, logdir: Path, model_name: str) -> None:
last_epoch = 0
while doc.session_context:
try:
path = logdir / 'metrics' / f'epoch_{last_epoch}'
new_metrics = load_metrics(str(path))
last_epoch += 1
doc.add_next_tick_callback(partial(update_profiling_metrics, plots, new_metrics))
METRICS[model_name] = new_metrics
except OSError:
time.sleep(1)
logger.info("Connection closed")
def modify_doc(doc: Document):
plot = figure(x_axis_type='datetime')
# _source = ColumnDataSource(data=data_holder.get_data())
def callback():
d = data_holder.get_data()
# d = {'a': [(1,123), (2,130), (3, 133)], 'b':[(4,123),(5,130),(6,133)]}
for k_vtlist, color in zip(d.items(), Spectral4):
k, vt_list = k_vtlist
v, t = zip(*vt_list)
plot.circle(t, v, legend=k, color=color)
doc.add_next_tick_callback(callback)
doc.add_root(plot)
def plot(self, metrics: typing.List[typing.Dict[str, np.ndarray]],
plot: Figure, doc: Document):
# Override the plot method to perform plotting
def get_metrics() -> typing.Iterable[typing.Tuple[str, np.ndarray]]:
"""Method to get the metrics in the required format
Yields:
typing.Tuple[str, np.ndarray] -- the metric for a specific axis
"""
for i, agent_metrics in enumerate(metrics):
for axis, metric in zip(("x", "y"), self._metrics):
yield f"{axis}{i}", metric.select(agent_metrics)
# Add a callback that will update the plot with the new data on the next tick
doc.add_next_tick_callback(
partial(self._source.stream, dict(get_metrics())))
