feat: generate embedding from index and initialization

examples/index_fit_example.py

@@ -0,0 +1,40 @@
+#!/usr/bin/env python
+
+from sklearn.decomposition import PCA
+import umap
+from sklearn.datasets import fetch_openml
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# install hnswlib with pip install hnswlib
+# or update to use any other index (e.g., nndescent)
+import hnswlib
+
+sns.set(context="paper", style="white")
+
+mnist = fetch_openml("mnist_784", version=1)
+
+sample_size = 70000
+
+# create a knn index
+knn_index = hnswlib.Index(space='l2', dim=mnist.data.shape[1])
+knn_index.init_index(max_elements=sample_size, ef_construction=100, M=16)
+knn_index.add_items(mnist.data.values[:sample_size])
+
+knn_indices, knn_dists = knn_index.knn_query(mnist.data.values[:sample_size], k=15)
+
+
+pca_init = PCA(n_components=2).fit_transform(mnist.data.values[:sample_size])
+
+reducer = umap.UMAP(random_state=42, n_neighbors=15, 
+                    precomputed_knn=(knn_indices, knn_dists), 
+                    verbose=True, n_jobs=-1, init=pca_init, metric="euclidean")
+embedding = reducer.fit_transform_index()
+
+fig, ax = plt.subplots(figsize=(12, 10))
+color = mnist.target[:sample_size].astype(int)
+plt.scatter(embedding[:, 0], embedding[:, 1], c=color, cmap="Spectral", s=0.1)
+plt.setp(ax, xticks=[], yticks=[])
+plt.title("MNIST data embedded into two dimensions by UMAP", fontsize=18)
+
+plt.show()