diff --git a/.vscode/launch.json b/.vscode/launch.json
new file mode 100644
index 0000000..6b76b4f
--- /dev/null
+++ b/.vscode/launch.json
@@ -0,0 +1,15 @@
+{
+ // Use IntelliSense to learn about possible attributes.
+ // Hover to view descriptions of existing attributes.
+ // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+ "version": "0.2.0",
+ "configurations": [
+ {
+ "name": "Python Debugger: Current File",
+ "type": "debugpy",
+ "request": "launch",
+ "program": "${file}",
+ "console": "integratedTerminal"
+ }
+ ]
+}
\ No newline at end of file
diff --git a/development-code/bitbirchX/.idea/workspace.xml b/development-code/bitbirchX/.idea/workspace.xml
new file mode 100644
index 0000000..a2ef5ee
--- /dev/null
+++ b/development-code/bitbirchX/.idea/workspace.xml
@@ -0,0 +1,93 @@
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\ No newline at end of file
diff --git a/development-code/bitbirch_dev.py b/development-code/bitbirch_dev.py
index 4caf6e5..19017c7 100644
--- a/development-code/bitbirch_dev.py
+++ b/development-code/bitbirch_dev.py
@@ -26,8 +26,11 @@
### Joel Nothman
### License: BSD 3 clause
+import time
import numpy as np
from scipy import sparse
+from sklearn.cluster import AgglomerativeClustering, KMeans
+from sklearn.metrics import pairwise_distances_argmin
def jt_distances(X):
"""Calculates the matrix of Tanimoto distances
@@ -439,6 +442,9 @@ class BitBirch():
subcluster_labels_ : ndarray
Labels assigned to the centroids of the subclusters after
they are clustered globally.
+
+ labels_ : ndarray of shape (n_samples,)
+ Array of labels assigned to the input data.
Notes
-----
@@ -464,6 +470,8 @@ def __init__(
n_clusters=3,
compute_labels=True,
copy=True,
+ perform_clustering=False,
+ clustering_type=""
):
self.threshold = threshold
self.branching_factor = branching_factor
@@ -472,6 +480,8 @@ def __init__(
self.copy = copy
self.index_tracker = 0
self.first_call = True
+ self.perform_clustering = perform_clustering
+ self.clustering_type = clustering_type
def fit(self, X, y=None):
"""
@@ -557,8 +567,8 @@ def _fit(self, X, partial):
self.subcluster_centers_ = centroids
self._n_features_out = self.subcluster_centers_.shape[0]
- # TODO: Incorporate global_clustering option
- #self._global_clustering(X)
+ if(self.perform_clustering):
+ self._global_clustering(X)
self.first_call = False
return self
@@ -581,3 +591,26 @@ def _get_leaves(self):
def retrieveVal(self):
print()
+ def _global_clustering(self, X):
+ """
+ Global clustering for the subclusters obtained after fitting
+ """
+ clusters = self.n_clusters
+ centroids = self.subcluster_centers_
+ clustering_type = self.clustering_type
+ compute_labels = (X is not None) and self.compute_labels
+
+ if clustering_type == "kmeans" and isinstance(clusters, int):
+ clusterer = KMeans(n_clusters=clusters)
+ self.subcluster_labels_ = clusterer.fit_predict(centroids)
+ elif clustering_type == "hierarchical" and isinstance(clusters, int):
+ clusterer = AgglomerativeClustering(n_clusters=clusters)
+ self.subcluster_labels_ = clusterer.fit_predict(centroids)
+ else:
+ # n_clusters is None and/or clustering_type == "" (skip global clustering)
+ self.subcluster_labels_ = np.arange(len(centroids))
+ return
+
+ if compute_labels:
+ argmin = pairwise_distances_argmin(X, centroids)
+ self.labels_ = self.subcluster_labels_[argmin]
\ No newline at end of file
diff --git a/jt_fit_label.txt b/jt_fit_label.txt
new file mode 100644
index 0000000..2aa3190
--- /dev/null
+++ b/jt_fit_label.txt
@@ -0,0 +1,2 @@
+ 1000 0.386942
+ 2000 0.20582
\ No newline at end of file