fig, axs = plt.subplots(len(model_paths), 1, figsize=(6, 6))
fig.subplots_adjust(top=0.875, bottom=0.075)
fig.suptitle(
f'{feature}:\nPosterior Probabilities of Mixture Model Components',
y=0.95,
size=10)
for i, model_path in enumerate(model_paths):
# Load model
with open('/'.join(['out', feature, model_path])) as file:
dist_names, params, params_fix, weights, name = json.load(file)
dists = [dists_dict[dist_name] for dist_name in dist_names]
mixmod = MixtureModel(dists, params, params_fix, weights, name)
# Create heatmap
data = rates.loc[rates[feature] != 0, feature].sort_values()
expts = mixmod.posterior(data)
# Plot heatmap
ax = axs[i]
ax.imshow(expts,
vmin=0,
vmax=1,
aspect='auto',
extent=[0, len(data), 0, len(mixmod.dists)])
ax.set_aspect(0.035 * len(data))
ax.set_title(mixmod.name, size=8)
ax.tick_params(labelsize=7.5)
ax.set_yticks([i + 0.5 for i in range(len(mixmod.dists))])
ax.set_yticklabels([dist.name for dist in dists])
if i != len(model_paths) - 1:
ax.tick_params(labelbottom=False)
rates = {}
counts = {}
fracs = {}
for feature in pics_lt:
# Load model, sorting components by scale and instantiating distributions from names
with open(f'../mixture_pic/out/{feature}/model_{model_paths[feature]}.json') as file:
model = json.load(file)
model_params, name = model[:-1], model[-1]
dist_names, params, params_fix, weights = [list(x) for x in zip(*sorted(zip(*model[:-1]), key=lambda y: y[1]['scale']))]
dists = [dists_dict[dist_name] for dist_name in dist_names]
mixmod = MixtureModel(dists, params, params_fix, weights, name)
# Remove extremes
raw = pics_lt[feature]
idx = np.flatnonzero(mixmod.posterior(raw)[-1] < thresh)
clean = raw[idx]
rates[feature] = (clean ** 2).groupby('block_id').mean()
# Count extremes
counts[feature] = len(raw) - len(clean)
fracs[feature] = (len(raw) - len(clean)) / (len(raw) - (raw == 0).sum()) # Fraction of non-zero contrasts
# Plot histograms of contrast counts in each block
y = clean.groupby('block_id').count().value_counts()
plt.bar(y.index, y)
plt.title(f'{feature}: Contrast Counts in Blocks')
plt.ylabel('Count')
plt.xlabel('Number of Contrasts in Block')
plt.xlim(0, 10)
plt.savefig(f'out/bar_{feature}.png')