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Improve optimization #76

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5 changes: 3 additions & 2 deletions choicemodels/mnl.py
Original file line number Diff line number Diff line change
Expand Up @@ -586,7 +586,7 @@ def mnl_loglik(beta, data, chosen, numalts, weights=None, lcgrad=False,
return -1 * loglik, -1 * gradarr


def mnl_estimate(data, chosen, numalts, GPU=False, coeffrange=(-1000, 1000),
def mnl_estimate(data, chosen, numalts, GPU=False, coeffrange=(None, None),
weights=None, lcgrad=False, beta=None):
"""
Calculate coefficients of the MNL model.
Expand Down Expand Up @@ -689,7 +689,7 @@ def mnl_estimate(data, chosen, numalts, GPU=False, coeffrange=(-1000, 1000),
beta,
args=args,
fprime=None,
factr=10,
factr=10e9,
approx_grad=False,
bounds=bounds)
logger.debug('finish: scipy optimization for MNL fit')
Expand All @@ -715,6 +715,7 @@ def mnl_estimate(data, chosen, numalts, GPU=False, coeffrange=(-1000, 1000),
aic = -2 * ll + 2 * len(beta)

log_likelihood = {
"model_converged": bfgs_result[2]['warnflag'] == 0,
'null': float(l0[0][0]),
'convergence': float(l1[0][0]),
'ratio': float((1 - (l1 / l0))[0][0]),
Expand Down
2 changes: 1 addition & 1 deletion choicemodels/tools/simulation.py
Original file line number Diff line number Diff line change
Expand Up @@ -176,7 +176,7 @@ def iterative_lottery_choices(
capacity, size = (alt_capacity, chooser_size)

len_choosers = len(choosers)
valid_choices = pd.Series()
valid_choices = pd.Series(dtype='float64')
iter = 0

while (len(valid_choices) < len_choosers):
Expand Down