[Python] Get the clusters from agglomerative for a given truncation

From the example of scikit-learn using scipy, (only changing the truncate_mode from 'level' to 'lastp'),

import numpy as np

from matplotlib import pyplot as plt
from scipy.cluster.hierarchy import dendrogram
from sklearn.datasets import load_iris
from sklearn.cluster import AgglomerativeClustering


def plot_dendrogram(model, **kwargs):
# Create linkage matrix and then plot the dendrogram

# create the counts of samples under each node
counts = np.zeros(model.children_.shape[0])
n_samples = len(model.labels_)
for i, merge in enumerate(model.children_):
current_count = 0
for child_idx in merge:
if child_idx < n_samples:
current_count += 1 # leaf node
else:
current_count += counts[child_idx - n_samples]
counts = current_count

linkage_matrix = np.column_stack(
[model.children_, model.distances_, counts]
).astype(float)

# Plot the corresponding dendrogram
dendrogram(linkage_matrix, **kwargs)


iris = load_iris()
X = iris.data

# setting distance_threshold=0 ensures we compute the full tree.
model = AgglomerativeClustering(distance_threshold=0, n_clusters=None)

model = model.fit(X)
plt.title("Hierarchical Clustering Dendrogram")
# plot the top three levels of the dendrogram
plot_dendrogram(model, truncate_mode="lastp", p=10)
plt.xlabel("Number of points in node (or index of point if no parenthesis).")
plt.show()


I get,



Now, how can I get the elements that belong to each of the clusters for that specific truncation? I would like to know the indices of 21, 17, 12 etc elements that constitute each of those clusters.

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