def fit(self, X, y=None):
n_samples = X.shape[0]
if n_samples > self.min_split_samples:
cluster = GaussianCluster(min_components=1,
max_components=2,
n_init=20)
cluster.fit(X)
self.model_ = cluster
else:
self.pred_labels_ = np.zeros(X.shape[0])
self.left_ = None
self.right_ = None
self.model_ = None
return self
# recurse
if cluster.n_components_ != 1:
pred_labels = cluster.predict(X)
self.pred_labels_ = pred_labels
indicator = pred_labels == 0
self.X_left_ = X[indicator, :]
self.X_right_ = X[~indicator, :]
split_left = PartitionCluster()
self.left_ = split_left.fit(self.X_left_)
split_right = PartitionCluster()
self.right_ = split_right.fit(self.X_right_)
else:
self.pred_labels_ = np.zeros(X.shape[0])
self.left_ = None
self.right_ = None
self.model_ = None
return self
def test_mug2vec():
graphs, labels = generate_data()
mugs = mug2vec(pass_to_ranks=None)
xhat = mugs.fit_transform(graphs)
gmm = GaussianCluster(5)
gmm.fit(xhat, labels)
assert_equal(gmm.n_components_, 2)
def estimate_assignments(graph,
n_communities,
n_components=None,
method="gc",
metric=None):
"""Given a graph and n_comunities, sweeps over covariance structures
Not deterministic
Not using graph bic or mse to calculate best
1. Does an embedding on the raw graph
2. GaussianCluster on the embedding. This will sweep covariance structure for the
given n_communities
3. Returns n_parameters based on the number used in GaussianCluster
method can be "gc" or "bc"
method
"gc" : use graspy GaussianCluster
this defaults to full covariance
"bc" : tommyclust with defaults
so sweep covariance, agglom, linkage
"bc-metric" : tommyclust with custom metric
still sweep everything
"bc-none" : mostly for testing, should behave just like GaussianCluster
"""
embed_graph = graph.copy()
latent = AdjacencySpectralEmbed(
n_components=n_components).fit_transform(embed_graph)
if isinstance(latent, tuple):
latent = np.concatenate(latent, axis=1)
if method == "gc":
gc = GaussianCluster(
min_components=n_communities,
max_components=n_communities,
covariance_type="all",
)
vertex_assignments = gc.fit_predict(latent)
n_params = gc.model_._n_parameters()
elif method == "bc":
vertex_assignments, n_params = brute_cluster(latent, [n_communities])
elif method == "bc-metric":
vertex_assignments, n_params = brute_cluster(latent, [n_communities],
metric=metric)
elif method == "bc-none":
vertex_assignments, n_params = brute_cluster(
latent,
[n_communities],
affinities=["none"],
linkages=["none"],
covariance_types=["full"],
)
else:
raise ValueError("Unspecified clustering method")
return (vertex_assignments, n_params)
def fit_and_score(X_train, X_test, k, **kws):
gc = GaussianCluster(min_components=k, max_components=k, **kws)
gc.fit(X_train)
model = gc.model_
train_bic = model.bic(X_train)
train_lik = model.score(X_train)
test_bic = model.bic(X_test)
test_lik = model.score(X_test)
bic = model.bic(np.concatenate((X_train, X_test), axis=0))
res = {
"train_bic": -train_bic,
"train_lik": train_lik,
"test_bic": -test_bic,
"test_lik": test_lik,
"bic": -bic,
"lik": train_lik + test_lik,
"k": k,
"model": gc.model_,
}
return res, model
def fit(self, X, y=None):
n_samples = X.shape[0]
self.n_samples_ = n_samples
if n_samples > self.min_split_samples:
cluster = GaussianCluster(min_components=1,
max_components=2,
n_init=40)
cluster.fit(X)
pred_labels = cluster.predict(X)
self.pred_labels_ = pred_labels
self.model_ = cluster
if cluster.n_components_ != 1:
indicator = pred_labels == 0
self.X_children_ = (X[indicator, :], X[~indicator, :])
children = []
for i, X_child in enumerate(self.X_children_):
child = DivisiveCluster(name=self.name + str(i),
parent=self)
child = child.fit(X_child)
children.append(child)
self.children = children
return self
def fit(self, X, y=None):
n_samples = X.shape[0]
self.n_samples_ = n_samples
self.cum_dist_ = 0
if n_samples > self.min_split_samples:
if self.cluster_method == "graspy-gmm":
cluster = GaussianCluster(
min_components=1,
max_components=2,
n_init=self.n_init,
covariance_type="all",
)
elif self.cluster_method == "auto-gmm":
cluster = AutoGMMCluster(
min_components=1, max_components=2, max_agglom_size=None
)
elif self.cluster_method == "vmm":
# cluster = VonMisesFisherMixture(n)
pass
else:
raise ValueError(f"`cluster_method` must be one of {valid_methods}")
cluster.fit(X)
pred_labels = cluster.predict(X)
self.pred_labels_ = pred_labels
self.model_ = cluster
if hasattr(cluster, "bic_"):
bics = cluster.bic_
self.bics_ = bics
bic_ratio = bics.loc[2].min() / bics.loc[1].min()
self.bic_ratio_ = bic_ratio
if cluster.n_components_ != 1: # recurse
indicator = pred_labels == 0
self.X_children_ = (X[indicator, :], X[~indicator, :])
children = []
for i, X_child in enumerate(self.X_children_):
child = DivisiveCluster(
name=self.name + str(i),
parent=self,
min_split_samples=self.min_split_samples,
n_init=self.n_init,
cluster_method=self.cluster_method,
)
child = child.fit(X_child)
children.append(child)
self.children = children
return self
n = 100 d = 3 np.random.seed(3) X1 = np.random.normal(0.5, 0.5, size=(n, d)) X2 = np.random.normal(-0.5, 0.5, size=(n, d)) X3 = np.random.normal(0.8, 0.6, size=(n, d)) X4 = np.random.uniform(0.2, 0.3, size=(n, d)) X = np.vstack((X1, X2, X3, X4)) pairplot(X) np.random.seed(3) gclust = GaussianCluster(min_components=2, max_components=2, n_init=1, max_iter=100) gclust.fit(X) bic1 = gclust.bic_ np.random.seed(3) gclust = GaussianCluster(min_components=2, max_components=2, n_init=50, max_iter=100) gclust.fit(X) bic2 = gclust.bic_ # we'd hope that bic2 is a little bit lower than bic1 print(bic1) print(bic2)
#- BIC
bic_ = 2 * likeli - temp_n_params * np.log(n)
#- ARI
ari_ = ari(true_labels, temp_c_hat)
return [combo, likeli, ari_, bic_]
np.random.seed(16661)
A = binarize(right_adj)
X_hat = np.concatenate(ASE(n_components=3).fit_transform(A), axis=1)
n, d = X_hat.shape
gclust = GCLUST(max_components=15)
est_labels = gclust.fit_predict(X_hat)
loglikelihoods = [np.sum(gclust.model_.score_samples(X_hat))]
combos = [None]
aris = [ari(right_labels, est_labels)]
bic = [gclust.model_.bic(X_hat)]
unique_labels = np.unique(est_labels)
class_idx = np.array([np.where(est_labels == u)[0] for u in unique_labels])
for k in range(len(unique_labels)):
for combo in list(combinations(np.unique(est_labels), k + 1)):
combo = np.array(list(combo)).astype(int)
combos.append(combo)
if normalize:
sums = X.sum(axis=1)
sums[sums == 0] = 1
X = X / sums[:, None]
if log_cluster:
X = np.log10(X + 1)
agmm = AutoGMMCluster(**cluster_kws)
pred_labels = agmm.fit_predict(X)
results = agmm.results_
fg_col_meta[i]["pred_labels"] = pred_labels
fg_autoclusters.append(agmm)
ggmm = GaussianCluster(min_components=10,
max_components=40,
n_init=20,
covariance_type="diag")
ggmm.fit(X)
fg_graspyclusters.append(ggmm)
gbics = ggmm.bic_
fig, ax = plt.subplots(1, 1, figsize=(10, 5))
scatter_kws = {"s": 30}
best_results = results.groupby("n_components").min()
best_results = best_results.reset_index()
sns.scatterplot(
data=best_results,
y="bic/aic",
x="n_components",
label="AutoGMM",
max_components = 35
comps = list(range(min_components, max_components))
cluster_kws = dict(min_components=min_components,
max_components=max_components,
covariance_type="full")
n_sims = 25
n_components_range = list(range(3, 25))
bic_results = []
for i, n_components in tqdm(enumerate(n_components_range)):
ase = AdjacencySpectralEmbed(n_components=n_components)
latent = ase.fit_transform(embed_graph)
latent = np.concatenate(latent, axis=-1)
for _ in range(n_sims):
cluster = GaussianCluster(**cluster_kws)
cluster.fit(latent)
bics = cluster.bic_
bics["n_clusters"] = bics.index
bics["n_components"] = n_components
bic_results.append(bics)
#%%
result_df = pd.concat(bic_results, axis=0)
result_df.rename(columns={"full": "bic"}, inplace=True)
plt.figure(figsize=(15, 10))
sns.lineplot(data=result_df, x="n_clusters", y="bic", hue="n_components")
save("clustering_june_bics")
# #%%
embed = "LSE"
cluster = "GMM"
lse_latent = lse(adj, 4, regularizer=None)
latent = lse_latent
pairplot(latent, labels=simple_class_labels, title=embed)
for k in range(MIN_CLUSTERS, MAX_CLUSTERS + 1):
run_name = f"k = {k}, {cluster}, {embed}, right hemisphere (A to D), PTR, raw"
print(run_name)
print()
# Cluster
gmm = GaussianCluster(min_components=k, max_components=k, **gmm_params)
gmm.fit(latent)
pred_labels = gmm.predict(latent)
# ARI
base_dict = {
"K": k,
"Cluster": cluster,
"Embed": embed,
"Method": f"{cluster} o {embed}",
"Score": gmm.model_.score(latent),
}
mb_ari = sub_ari(known_inds, mb_labels, pred_labels)
mb_ari_dict = base_dict.copy()
mb_ari_dict["ARI"] = mb_ari
mb_ari_dict["Metric"] = "MB ARI"
n = 1000
pi = 0.9
A, counts = generate_cyclops(X, n, pi, None)
c = [0] * counts[0]
c += [1] * counts[1]
ase = ASE(n_components=3)
X_hat = ase.fit_transform(A)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(X_hat[:, 0], X_hat[:, 1], X_hat[:, 2], c=c)
gclust = GCLUST(max_components=4)
c_hat = gclust.fit_predict(X_hat)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(X_hat[:, 0], X_hat[:, 1], X_hat[:, 2], c=c_hat)
def quadratic(data, params):
if data.ndim == 1:
sum_ = np.sum(data[:-1]**2 * params[:-1]) + params[-1]
return sum_
elif data.ndim == 2:
sums = np.sum(data[:, :-1]**2 * params[:-1], axis=1) + params[-1]
return sums
else:
def lse(adj, n_components, regularizer=None):
if PTR:
adj = pass_to_ranks(adj)
lap = to_laplace(adj, form="R-DAD")
ase = AdjacencySpectralEmbed(n_components=n_components)
latent = ase.fit_transform(lap)
latent = np.concatenate(latent, axis=-1)
return latent
n_components = None
k = 30
latent = lse(adj, n_components, regularizer=None)
gmm = GaussianCluster(min_components=k, max_components=k)
pred_labels = gmm.fit_predict(latent)
stacked_barplot(pred_labels, class_labels, palette="tab20")
# %% [markdown]
# # verify on sklearn toy dataset
from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans
X, y = make_blobs(n_samples=200, n_features=3, centers=None, cluster_std=3)
# y = y.astype(int).astype(str)
data_df = pd.DataFrame(
data=np.concatenate((X, y[:, np.newaxis]), axis=-1),
columns=("Dim 0", "Dim 1", "Dim 2", "Labels"),
side=side,
n_components=agmm_model.n_components,
covariance_type=agmm_model.covariance_type,
model=agmm_model,
embed=embed,
labels=labels,
)
rows.append(row)
# cluster using GaussianCluster
method = "GClust"
gclust = GaussianCluster(
min_components=2,
max_components=10,
n_init=200,
covariance_type="full",
max_iter=200,
tol=0.001,
init_params="kmeans",
)
gclust.fit(embed)
gclust_model = gclust.model_
gclust_results = gclust.bic_.copy()
gclust_results.index.name = "n_components"
gclust_results.reset_index(inplace=True)
gclust_results = pd.melt(
gclust_results,
id_vars="n_components",
var_name="covariance_type",
value_name="bic/aic",
)
def cluster_func(k, seed):
np.random.seed(seed)
run_name = f"k = {k}, {cluster}, {embed}, right hemisphere (A to D), PTR, raw"
print(run_name)
print()
# Cluster
gmm = GaussianCluster(min_components=k, max_components=k, **gmm_params)
gmm.fit(latent)
pred_labels = gmm.predict(latent)
# ARI
base_dict = {
"K": k,
"Cluster": cluster,
"Embed": embed,
"Method": f"{cluster} o {embed}",
"Score": gmm.model_.score(latent),
}
mb_ari = sub_ari(known_inds, mb_labels, pred_labels)
mb_ari_dict = base_dict.copy()
mb_ari_dict["ARI"] = mb_ari
mb_ari_dict["Metric"] = "MB ARI"
out_dicts.append(mb_ari_dict)
simple_ari = sub_ari(known_inds, simple_class_labels, pred_labels)
simple_ari_dict = base_dict.copy()
simple_ari_dict["ARI"] = simple_ari
simple_ari_dict["Metric"] = "Simple ARI"
out_dicts.append(simple_ari_dict)
full_ari = adjusted_rand_score(class_labels, pred_labels)
full_ari_dict = base_dict.copy()
full_ari_dict["ARI"] = full_ari
full_ari_dict["Metric"] = "Full ARI"
out_dicts.append(full_ari_dict)
save_name = f"k{k}-{cluster}-{embed}-right-ad-PTR-raw"
# Plot embedding
pairplot(latent, labels=pred_labels, title=run_name)
# stashfig("latent-" + save_name)
# Plot everything else
prob_df = get_sbm_prob(adj, pred_labels)
block_sum_df = get_block_edgesums(adj, pred_labels, prob_df.columns.values)
clustergram(adj, latent, prob_df, block_sum_df, simple_class_labels,
pred_labels)
plt.suptitle(run_name, fontsize=40)
stashfig("clustergram-" + save_name)
# output skeletons
_, colormap, pal = stashskel(save_name,
skeleton_labels,
pred_labels,
palette="viridis",
multiout=True)
sns.set_context("talk")
palplot(k, cmap="viridis")
stashfig("palplot-" + save_name)
# save dict colormapping
filename = (Path("./maggot_models/notebooks/outs") / Path(FNAME) /
str("colormap-" + save_name + ".json"))
with open(filename, "w") as fout:
json.dump(colormap, fout)
stashskel(save_name,
skeleton_labels,
pred_labels,
palette="viridis",
multiout=False)
def crossval_cluster(
embed,
left_inds,
right_inds,
R,
min_clusters=2,
max_clusters=15,
n_init=25,
left_pair_inds=None,
right_pair_inds=None,
):
left_embed = embed[left_inds]
right_embed = embed[right_inds]
print("Running left/right clustering with cross-validation\n")
currtime = time.time()
rows = []
for k in tqdm(range(min_clusters, max_clusters)):
# train left, test right
# TODO add option for AutoGMM as well, might as well check
left_gc = GaussianCluster(min_components=k,
max_components=k,
n_init=n_init)
left_gc.fit(left_embed)
model = left_gc.model_
train_left_bic = model.bic(left_embed)
train_left_lik = model.score(left_embed)
test_left_bic = model.bic(right_embed @ R.T)
test_left_lik = model.score(right_embed @ R.T)
# train right, test left
right_gc = GaussianCluster(min_components=k,
max_components=k,
n_init=n_init)
right_gc.fit(right_embed)
model = right_gc.model_
train_right_bic = model.bic(right_embed)
train_right_lik = model.score(right_embed)
test_right_bic = model.bic(left_embed @ R)
test_right_lik = model.score(left_embed @ R)
left_row = {
"k": k,
"contra_bic": -test_left_bic,
"contra_lik": test_left_lik,
"ipsi_bic": -train_left_bic,
"ipsi_lik": train_left_lik,
"cluster": left_gc,
"train": "left",
"n_components": n_components,
}
right_row = {
"k": k,
"contra_bic": -test_right_bic,
"contra_lik": test_right_lik,
"ipsi_bic": -train_right_bic,
"ipsi_lik": train_right_lik,
"cluster": right_gc,
"train": "right",
"n_components": n_components,
}
# pairedness computation, if available
if left_pair_inds is not None and right_pair_inds is not None:
# TODO double check this is right
pred_left = left_gc.predict(embed[left_pair_inds])
pred_right = right_gc.predict(embed[right_pair_inds])
pness, _, _ = compute_pairedness_bipartite(pred_left, pred_right)
left_row["pairedness"] = pness
right_row["pairedness"] = pness
ari = adjusted_rand_score(pred_left, pred_right)
left_row["ARI"] = ari
right_row["ARI"] = ari
rows.append(left_row)
rows.append(right_row)
results = pd.DataFrame(rows)
print(f"{time.time() - currtime} elapsed")
return results
Rs = []
n_components = embed[0].shape[1]
print(n_components)
print()
train_embed = np.concatenate(
(embed[0][:, :n_components], embed[1][:, :n_components]), axis=-1)
R, _ = orthogonal_procrustes(train_embed[lp_inds], train_embed[rp_inds])
Rs.append(R)
left_embed = train_embed[left_inds]
left_embed = left_embed @ R
right_embed = train_embed[right_inds]
for k in tqdm(range(2, 15)):
# train left, test right
left_gc = GaussianCluster(min_components=k,
max_components=k,
n_init=n_init)
left_gc.fit(left_embed)
model = left_gc.model_
train_left_bic = model.bic(left_embed)
train_left_lik = model.score(left_embed)
test_left_bic = model.bic(right_embed)
test_left_lik = model.score(right_embed)
row = {
"k": k,
"contra_bic": test_left_bic,
"contra_lik": test_left_lik,
"ipsi_bic": train_left_bic,
"ipsi_lik": train_left_lik,
"cluster": left_gc,
def brute_graspy_cluster(Ns,
x,
covariance_types,
ks,
c_true,
savefigs=None,
graphList=None):
if graphList != None and 'all_bics' in graphList:
_, ((ax0, ax1), (ax2, ax3)) = plt.subplots(2,
2,
sharey='row',
sharex='col',
figsize=(10, 10))
titles = ['full', 'tied', 'diag', 'spherical']
best_bic = -np.inf
for N in Ns:
bics = np.zeros([len(ks), len(covariance_types), N])
aris = np.zeros([len(ks), len(covariance_types), N])
for i in np.arange(N):
graspy_gmm = GaussianCluster(min_components=ks[0],
max_components=ks[len(ks) - 1],
covariance_type=covariance_types,
random_state=i)
c_hat, ari = graspy_gmm.fit_predict(x, y=c_true)
bic_values = -graspy_gmm.bic_.values
ari_values = graspy_gmm.ari_.values
bics[:, :, i] = bic_values
aris[:, :, i] = ari_values
bic = bic_values.max()
if bic > best_bic:
idx = np.argmax(bic_values)
idxs = np.unravel_index(idx, bic_values.shape)
best_ari_bic = ari
best_bic = bic
best_k_bic = ks[idxs[0]]
best_cov_bic = titles[3 - idxs[1]]
best_c_hat_bic = c_hat
max_bics = np.amax(bics, axis=2)
title = 'N=' + str(N)
if graphList != None and 'all_bics' in graphList:
ax0.plot(np.arange(1, len(ks) + 1), max_bics[:, 3])
ax1.plot(np.arange(1, len(ks) + 1), max_bics[:, 2], label=title)
ax2.plot(np.arange(1, len(ks) + 1), max_bics[:, 1])
ax3.plot(np.arange(1, len(ks) + 1), max_bics[:, 0])
if graphList != None and 'best_bic' in graphList:
#Plot with best BIC*********************************
if c_true is None:
best_ari_bic_str = 'NA'
else:
best_ari_bic_str = '%1.3f' % best_ari_bic
fig_bestbic = plt.figure(figsize=(8, 8))
ax_bestbic = fig_bestbic.add_subplot(1, 1, 1)
#ptcolors = [colors[i] for i in best_c_hat_bic]
ax_bestbic.scatter(x[:, 0], x[:, 1], c=best_c_hat_bic)
#mncolors = [colors[i] for i in np.arange(best_k_bic)]
mncolors = [i for i in np.arange(best_k_bic)]
ax_bestbic.set_title(
"py(agg-gmm) BIC %3.0f from " % best_bic + str(best_cov_bic) +
" k=" + str(best_k_bic) + ' ari=' +
best_ari_bic_str) # + "iter=" + str(best_iter_bic))
ax_bestbic.set_xlabel("First feature")
ax_bestbic.set_ylabel("Second feature")
if savefigs is not None:
plt.savefig(savefigs + '_python_bestbic.jpg')
if graphList != None and 'all_bics' in graphList:
#plot of all BICS*******************************
titles = ['full', 'tied', 'diag', 'spherical']
#ax0.set_title(titles[0],fontsize=20,fontweight='bold')
#ax0.set_ylabel('BIC',fontsize=20)
ax0.locator_params(axis='y', tight=True, nbins=4)
ax0.set_yticklabels(ax0.get_yticks(), fontsize=14)
#ax1.set_title(titles[1],fontsize=20,fontweight='bold')
legend = ax1.legend(loc='best', title='Number of\nRuns', fontsize=12)
plt.setp(legend.get_title(), fontsize=14)
#ax2.set_title(titles[2],fontsize=20,fontweight='bold')
#ax2.set_xlabel('Number of components',fontsize=20)
ax2.set_xticks(np.arange(0, 21, 4))
ax2.set_xticklabels(ax2.get_xticks(), fontsize=14)
#ax2.set_ylabel('BIC',fontsize=20)
ax2.locator_params(axis='y', tight=True, nbins=4)
ax2.set_yticklabels(ax2.get_yticks(), fontsize=14)
#ax3.set_title(titles[3],fontsize=20,fontweight='bold')
#ax3.set_xlabel('Number of components',fontsize=20)
ax3.set_xticks(np.arange(0, 21, 4))
ax3.set_xticklabels(ax3.get_xticks(), fontsize=14)
if savefigs is not None:
plt.savefig('.\\figures\\25_6_19_paperv2\\' + savefigs +
'_graspy_bicplot2.jpg')
plt.show()
return best_c_hat_bic, best_cov_bic, best_k_bic, best_ari_bic, best_bic
concatenate_latent = np.concatenate(list(latent), axis=-1)
concatenate_latent.shape
pairplot(concatenate_latent, labels=unknown)
#%%
g = graphs[0]
classes = [meta["Class"] for node, meta in g.nodes(data=True)]
classes = np.array(classes)
unknown = classes == "Other"
plot_unknown = np.tile(unknown, n_graphs)
pairplot(plot_latent, labels=plot_unknown, alpha=0.3, legend_name="Unknown")
clust_latent = np.concatenate(list(latent), axis=-1)
clust_latent.shape
#%%
gc = GaussianCluster(min_components=2, max_components=15, covariance_type="all")
filterwarnings("ignore")
n_init = 50
sim_mat = np.zeros((n_verts, n_verts))
for i in tqdm(range(n_init)):
assignments = gc.fit_predict(clust_latent)
for c in np.unique(assignments):
inds = np.where(assignments == c)[0]
sim_mat[np.ix_(inds, inds)] += 1
sim_mat -= np.diag(np.diag(sim_mat))
sim_mat = sim_mat / n_init
heatmap(sim_mat)
