TypeError: HiPlot generation failing when optimizing TransitionChoice

[akorb]

Steps to reproduce

Execute the script below.

Observed Results

• TypeError: Object of type int64 is not JSON serializable

Expected Results

• plot.html which shows the HiPlot of the optimization.

Relevant Code

from hiplot import Experiment
import nevergrad as ng
from nevergrad.optimization.callbacks import ParametersLogger
from math import cos


parametrization = ng.p.Instrumentation(
    ng.p.TransitionChoice(10)
)

optimizer = ng.optimizers.NGOpt(parametrization=parametrization, budget=10)

logger = ParametersLogger("log.json")
optimizer.register_callback("tell",  logger)

recommendation = optimizer.minimize(cos)
print("Recommendation: ", recommendation.value)

exp: Experiment = logger.to_hiplot_experiment()
exp.to_html("plot.html")

Notes

The reason that it crashes is that exp.datapoints[0].values['0.indices#0'] is a np.int64 instead of a simple native int.

TransitionChoice has the layer _datalayers.Int. And its _layered_get_value function returns an np.int64.

nevergrad/nevergrad/parametrization/_layering.py

Lines 269 to 285 in 8403d6c

	def _layered_get_value(self) -> np.ndarray:
	if self._cache is not None:
	return self._cache
	bounds = self._layers[0].bounds # type: ignore
	out = super()._layered_get_value()
	if not self.deterministic:
	out += self.random_state.rand(*out.shape) - 0.5
	out = np.round(out).astype(int)
	# make sure rounding does not reach beyond the bounds
	eps = 1e-12
	if bounds[0] is not None:
	out = np.maximum(_to_int(bounds[0] + 0.5 - eps), out)
	if bounds[1] is not None:
	out = np.minimum(_to_int(bounds[1] - 0.5 + eps), out)
	# return out
	self._cache = out
	return self._cache

Not sure if this is the source of the problem, I'm not familiar with the guts of nevergrad