def unwrap_self_peek_model_queue(PGE):
MAXFEV = 100
if PGE.remote_eval == True:
MAXFEV = 2
pos, modl = -1, None
while True:
try:
val = PGE.peek_in_queue.get()
if val is None:
print("Val None Breaking Peek Processor")
break;
pos = val[0]
modl = val[1]
# print("GOT HERE 1:", pos)
passed = False
passed = evaluate.eval_model(modl, PGE.vars, PGE.X_peek, PGE.Y_peek, PGE.err_method, MAXFEV=MAXFEV)
if not passed:
# print("Failed!!", pos, modl.error)
PGE.peek_out_queue.put( (pos, modl.error, modl.exception) )
else:
# print("Passed!!", pos, modl.id)
vals = [ (k,v) for (k,v) in modl.params.valuesdict().items() ]
# print("GOT HERE 2:", pos)
ret_data = {
'score': modl.score,
'r2': modl.r2,
'evar': modl.evar,
'aic': modl.aic,
'bic': modl.bic,
'chisqr': modl.chisqr,
'redchi': modl.redchi,
'params': vals,
'nfev': modl.fit_result.nfev
}
# print("GOT HERE A:", pos)
PGE.peek_out_queue.put( (pos, None, ret_data) )
# print("GOT HERE B:", pos)
except Exception as e:
print("peek breaking!", e, "\n ", pos, modl.id, modl.expr, val)
break
def unwrap_self_eval_model_queue(PGE):
MAXFEV = 100
if PGE.remote_eval == True:
MAXFEV = 2
while True:
try:
val = PGE.eval_in_queue.get()
if val is None:
print("Val None Breaking Eval Processor")
break;
pos = val[0]
modl = val[1]
passed = False
passed = evaluate.eval_model(modl, PGE.vars, PGE.X_train, PGE.Y_train, PGE.err_method, MAXFEV=MAXFEV)
if not passed:
PGE.eval_out_queue.put( (pos, modl.error, modl.exception) )
else:
vals = [ (k,v) for (k,v) in modl.params.valuesdict().items() ]
# vals = modl.params.valuesdict()
ret_data = {
'score': modl.score,
'r2': modl.r2,
'evar': modl.evar,
'aic': modl.aic,
'bic': modl.bic,
'chisqr': modl.chisqr,
'redchi': modl.redchi,
'params': vals,
'nfev': modl.fit_result.nfev
}
PGE.eval_out_queue.put( (pos, None, ret_data) )
except Exception as e:
print("eval breaking!", e, "\n ", pos, modl.expr, passed, modl.params.valuesdict())
break
def eval_models_local(self, models, peek=False, progress=False):
if self.workers > 0:
self.eval_models_multiprocess(models, peek, progress)
else:
L = len(models)
ppp = L / 10
PPP = ppp
if progress:
which = "peek'n" if peek else "eval'n"
print(" ", which, L, ppp, " ", end="", flush=True)
for i,modl in enumerate(models):
if progress and i >= PPP:
print('.',end="",flush=True)
PPP += ppp
passed = evaluate.eval_model(modl, self.vars, self.X_train, self.Y_train, self.err_method)
if not passed or modl.error is not None:
info = "{:5d} ERROR ".format(modl.id)
print(info, modl.expr, modl.jac, file=self.logs["evals"])
else:
if peek:
modl.peek_score = modl.score
modl.peek_r2 = modl.r2
modl.peek_evar = modl.evar
modl.peek_aic = modl.aic
modl.peek_bic = modl.bic
modl.peek_chisqr = modl.chisqr
modl.peek_redchi = modl.redchi
modl.peek_nfev = dat['nfev']
self.peek_nfev += modl.peek_nfev
self.peekd_models += 1
modl.peeked = True
else:
modl.eval_nfev += dat['nfev']
self.eval_nfev += modl.eval_nfev
self.evald_models += 1
modl.evaluated = True
# info = "{:5d} {:5d} ".format(modl.id, modl.eval_nfev)
# print(info, modl.expr, modl.jac, file=self.logs["evals"])
if modl.parent_id >= 0:
parent = self.models[modl.parent_id]
modl.improve_score = parent.score - modl.score
modl.improve_r2 = modl.r2 - parent.r2
modl.improve_evar = modl.evar - parent.evar
modl.improve_aic = parent.aic - modl.aic
modl.improve_bic = parent.bic - modl.bic
modl.improve_redchi = parent.redchi - modl.redchi
else:
# should probaly normalized this across the initial population and permenately set
modl.improve_score = -0.000001 * modl.score
modl.improve_r2 = -0.000001 * modl.r2
modl.improve_evar = -0.000001 * modl.evar
modl.improve_aic = -0.000001 * modl.aic
modl.improve_bic = -0.000001 * modl.bic
modl.improve_redchi = -0.000001 * modl.redchi
if progress:
print("")
