def cut_straight(dendrogram: np.ndarray, n_clusters: Optional[int] = None, threshold: Optional[float] = None,
sort_clusters: bool = True, return_dendrogram: bool = False) \
-> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]:
"""Cut a dendrogram and return the corresponding clustering.
Parameters
----------
dendrogram:
Dendrogram.
n_clusters :
Number of clusters (optional).
The number of clusters can be larger than n_clusters in case of equal heights in the dendrogram.
threshold :
Threshold on height (optional).
If both n_clusters and threshold are ``None``, n_clusters is set to 2.
sort_clusters :
If ``True``, sorts clusters in decreasing order of size.
return_dendrogram :
If ``True``, returns the dendrogram formed by the clusters up to the root.
Returns
-------
labels : np.ndarray
Cluster of each node.
dendrogram_aggregate : np.ndarray
Dendrogram starting from clusters (leaves = clusters).
Example
-------
>>> from sknetwork.hierarchy import cut_straight
>>> dendrogram = np.array([[0, 1, 0, 2], [2, 3, 1, 3]])
>>> cut_straight(dendrogram)
array([0, 0, 1])
"""
check_dendrogram(dendrogram)
n = dendrogram.shape[0] + 1
if return_dendrogram and not np.all(np.diff(dendrogram[:, 2]) >= 0):
raise ValueError(
"The third column of the dendrogram must be non-decreasing.")
cluster = {i: [i] for i in range(n)}
if n_clusters is None:
if threshold is None:
n_clusters = 2
else:
n_clusters = n
else:
check_n_clusters(n_clusters, n, n_min=1)
cut = np.sort(dendrogram[:, 2])[n - n_clusters]
if threshold is not None:
cut = max(cut, threshold)
for t in range(n - 1):
i = int(dendrogram[t][0])
j = int(dendrogram[t][1])
if dendrogram[t][2] < cut and i in cluster and j in cluster:
cluster[n + t] = cluster.pop(i) + cluster.pop(j)
return get_labels(dendrogram, cluster, sort_clusters, return_dendrogram)
def cut_straight(
dendrogram: np.ndarray,
n_clusters: int = 2,
sort_clusters: bool = True,
return_dendrogram: bool = False
) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]:
"""Cut a dendrogram and return the corresponding clustering.
Parameters
----------
dendrogram:
Dendrogram
n_clusters :
Number of clusters.
sort_clusters :
If ``True``, sorts clusters in decreasing order of size.
return_dendrogram :
If ``True``, returns the dendrogram formed by the clusters up to the root.
Returns
-------
labels : np.ndarray
Cluster of each node.
dendrogram_aggregate : np.ndarray
Dendrogram starting from clusters (leaves = clusters).
Example
-------
>>> from sknetwork.hierarchy import cut_straight
>>> dendrogram = np.array([[0, 1, 0, 2], [2, 3, 1, 3]])
>>> cut_straight(dendrogram)
array([0, 0, 1])
"""
check_dendrogram(dendrogram)
n = dendrogram.shape[0] + 1
check_n_clusters(n_clusters, n, n_min=1)
if return_dendrogram and not np.all(np.diff(dendrogram[:, 2]) >= 0):
raise ValueError(
"The third column of the dendrogram must be non-decreasing.")
cluster = {i: [i] for i in range(n)}
cut = np.sort(dendrogram[:, 2])[n - n_clusters]
for t in range(n - 1):
i = int(dendrogram[t][0])
j = int(dendrogram[t][1])
if dendrogram[t][2] < cut and i in cluster and j in cluster:
cluster[n + t] = cluster.pop(i) + cluster.pop(j)
return get_labels(dendrogram, cluster, sort_clusters, return_dendrogram)
def cut_balanced(
dendrogram: np.ndarray,
max_cluster_size: int = 20,
sort_clusters: bool = True,
return_dendrogram: bool = False
) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]:
"""Cuts a dendrogram with a constraint on the cluster size and returns the corresponding clustering.
Parameters
----------
dendrogram:
Dendrogram
max_cluster_size :
Maximum size of each cluster.
sort_clusters :
If ``True``, sort labels in decreasing order of cluster size.
return_dendrogram :
If ``True``, returns the dendrogram formed by the clusters up to the root.
Returns
-------
labels : np.ndarray
Label of each node.
dendrogram_aggregate : np.ndarray
Dendrogram starting from clusters (leaves = clusters).
Example
-------
>>> from sknetwork.hierarchy import cut_balanced
>>> dendrogram = np.array([[0, 1, 0, 2], [2, 3, 1, 3]])
>>> cut_balanced(dendrogram, 2)
array([0, 0, 1])
"""
check_dendrogram(dendrogram)
n = dendrogram.shape[0] + 1
if max_cluster_size < 2 or max_cluster_size > n:
raise ValueError(
"The maximum cluster size must be between 2 and the number of nodes."
)
cluster = {i: [i] for i in range(n)}
for t in range(n - 1):
i = int(dendrogram[t][0])
j = int(dendrogram[t][1])
if i in cluster and j in cluster and len(cluster[i]) + len(
cluster[j]) <= max_cluster_size:
cluster[n + t] = cluster.pop(i) + cluster.pop(j)
return get_labels(dendrogram, cluster, sort_clusters, return_dendrogram)