code stringlengths 3 6.57k |
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np.array(range(len(degrees) |
np.ix_(not_pendant_inds, not_pendant_inds) |
np.argsort(degrees) |
plt.figure(figsize=(10, 5) |
sns.scatterplot(x=range(len(degrees) |
np.where(class_labels != "Unk") |
lse(adj, n_components, regularizer=None) |
pairplot(latent, labels=class_labels, title=embed) |
list(range(MIN_CLUSTERS, MAX_CLUSTERS + 1) |
len(k_list) |
binarize(adj) |
np.zeros(n_verts) |
print(run_name) |
print() |
ClusterModel(min_components=k, max_components=k, **gmm_params) |
gmm.fit(latent) |
gmm.predict(latent) |
gmm.model_.score(latent) |
base_dict.copy() |
out_dicts.append(temp_dict) |
gmm.model_.bic(latent) |
base_dict.copy() |
out_dicts.append(temp_dict) |
SBMEstimator(directed=True, loops=False) |
sbm.fit(bin_adj, y=pred_labels) |
sbm.score(bin_adj) |
base_dict.copy() |
out_dicts.append(temp_dict) |
DCSBMEstimator(directed=True, loops=False) |
dcsbm.fit(bin_adj, y=pred_labels) |
dcsbm.score(bin_adj) |
base_dict.copy() |
out_dicts.append(temp_dict) |
sub_ari(known_inds, class_labels, pred_labels) |
base_dict.copy() |
out_dicts.append(temp_dict) |
adjusted_rand_score(last_pred_labels, pred_labels) |
base_dict.copy() |
out_dicts.append(temp_dict) |
pairplot(latent, labels=pred_labels, title=run_name) |
stashfig("latent-" + save_name) |
clustergram(adj, class_labels, pred_labels) |
stashfig("clustergram-" + save_name) |
palplot(k, cmap="viridis") |
stashfig("palplot-" + save_name) |
Path("./maggot_models/notebooks/outs") |
Path(FNAME) |
str("colormap-" + save_name + ".json") |
open(filename, "w") |
json.dump(colormap, fout) |
pd.DataFrame(out_dicts) |
sns.FacetGrid(result_df, col="Metric", col_wrap=3, sharey=False, height=4) |
fg.map(sns.lineplot, "K", "Score") |
stashfig(f"metrics-{cluster}-{embed}-right-ad-PTR-raw") |
signal_flow(adj) |
np.zeros(k) |
np.unique(pred_labels) |
np.where(pred_labels == i) |
np.mean(node_signal_flow[inds]) |
SBMEstimator() |
fit(bin_adj, y=pred_labels) |
pd.DataFrame(data=block_probs, index=range(k) |
range(k) |
nx.from_pandas_adjacency(block_prob_df, create_using=nx.DiGraph) |
plt.figure(figsize=(10, 10) |
dict(zip(range(k) |
zip(cluster_mean_latent, mean_sf) |
nx.draw_networkx_nodes(block_g, pos=pos) |
nx.get_edge_attributes(block_g, "weight") |
nx.draw_networkx_edge_labels(block_g, pos, edge_labels=labels) |
mpl.colors.LogNorm(vmin=0.01, vmax=0.1) |
ScalarMappable(cmap="Reds", norm=norm) |
sm.to_rgba(np.array(list(labels.values() |
signal_flow_marginal(adj, pred_labels) |
signal_flow_marginal(adj, labels, col_wrap=5, palette="tab20") |
signal_flow(adj) |
np.unique(labels) |
np.where(labels == i) |
medians.append(np.median(sf[inds]) |
np.argsort(medians) |
pd.DataFrame() |
fg.map(sns.distplot, "Signal flow") |
np.linspace(-2.2, 2.2) |
fg.set(yticks=[], yticklabels=[]) |
plt.tight_layout() |
signal_flow_marginal(adj, class_labels) |
stashfig("known-class-sf-marginal") |
filterRemaining(remaining, environment) |
copy.copy(remaining) |
range(len(returned) |
any(not(r[e]==environment[e]) |
copy.copy(r['runs']) |
range(len(runs) |
any(not(u[e]==environment[e]) |
len(runs) |
copy.deepcopy(r) |
DataHandler(object) |
_load_data(im_fnames, add_channel_dim=True) |
cv2.imread(im_fnames[0], 0) |
np.zeros((len(im_fnames) |
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