spread_min of hyperectangle_csr is not respected (Nested config problem?)

[dpaetzel]

The spread_min setting is somehow not respected. For example,

import xcsf
import json

model = xcsf.XCS(max_trials=10, condition={"args": {"spread_min": 0.5}})

pop = json.loads(model.json())["classifiers"]

spreads = [rule["condition"]["spread"][0] for rule in pop]
print(np.min(spreads))

prints 0.10028… but I would expect this to be a number greater than 0.5.

Interestingly, the spread_min does not get propagated to the internal_params upon initialization

print(model.get_params()["condition"]["args"]["spread_min"])
# 0.5
print(model.internal_params()["condition"]["args"]["spread_min"])
# 0.1

and also not after during fitting

import numpy as np
X = np.random.random((100, 1))
y = np.random.random(100)
model.fit(X, y)
print(model.internal_params()["condition"]["args"]["spread_min"])
# 0.1

Since eta is not propagated through either, this may have something to do with the nested dict in the condition parameter?

model = xcsf.XCS(max_trials=10, condition={"args": {"eta": 0.5}})
print(model.get_params()["condition"]["args"]["eta"])
# 0.5
print(model.internal_params()["condition"]["args"]["eta"])
# 0

[rpreen]

I believe this is because the condition dict doesn't include the type: if you use the following it will set the parameters:

model = xcsf.XCS(max_trials=10, condition={"type": "hyperrectangle_csr", "args": {"spread_min": 0.5}})

[rpreen]

So probably there should be some better input checking to test for this...

[dpaetzel]

Thank you!