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MLE: return index for sorting distributions #184

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Jan 14, 2023
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MLE: return index for sorting distributions #184

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3 changes: 2 additions & 1 deletion preliz/internal/optimization.py
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Original file line number Diff line number Diff line change
Expand Up @@ -154,14 +154,15 @@ def fit_to_sample(selected_distributions, sample, x_min, x_max):
if dist.__class__.__name__ in ["BetaScaled", "TruncatedNormal"]:
update_bounds_beta_scaled(dist, x_min, x_max)

loss = np.inf
if dist._check_endpoints(x_min, x_max, raise_error=False):
dist._fit_mle(sample) # pylint:disable=protected-access
if dist.kind == "continuous":
loss = -dist.rv_frozen.logpdf(sample).sum()
else:
loss = -dist.rv_frozen.logpmf(sample).sum()

fitted.update(loss, dist)
fitted.update(loss, dist)

return fitted

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13 changes: 8 additions & 5 deletions preliz/unidimensional/mle.py
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Expand Up @@ -34,7 +34,8 @@ def mle(

Returns
-------
axes: matplotlib axes
idx : array with the indexes to sort ``distributions`` from best to worst match
axes : matplotlib axes
"""
sample = np.array(sample)
x_min = sample.min()
Expand All @@ -45,10 +46,12 @@ def mle(
if plot > len(distributions):
plot = len(distributions)

idx = np.argsort(fitted.losses)

if plot:
idx = np.argsort(fitted.losses)
idx = idx[:plot]
for dist in fitted.distributions[idx]:
plot_idx = idx[:plot]
for dist in fitted.distributions[plot_idx]:
if dist is not None:
ax = dist.plot_pdf(plot_kwargs)
return ax

return idx, ax