fix: conditional import of multicore tsne

sdcat/cluster/cluster.py

@@ -13,10 +13,16 @@
 from umap import UMAP
 from hdbscan import HDBSCAN
 from sklearn.metrics.pairwise import cosine_similarity
+from sklearn.preprocessing import MinMaxScaler
 from sdcat.logger import info, warn, debug, err
 from sdcat.cluster.utils import cluster_grid, crop_square_image
 from sdcat.cluster.embedding import fetch_embedding, has_cached_embedding, compute_norm_embedding
 
+if find_spec("multicore_tsne"):
+    from multicore_tsne import MulticoreTSNE as TSNE
+else:
+    from sklearn.manifold import TSNE
+
 if find_spec("cuml"):
     info('=======> USING GPU for HDBSCAN AND UMAP <=========')
     from cuml.cluster import HDBSCAN as cuHDBSCAN  # pylint: disable=E0611, E0401
@@ -74,10 +80,6 @@ def _run_hdbscan_assign(
     # Get the number of samples which is the number of rows in the dataframe - this is used mostly for calculating coverage
     num_samples = df.shape[0]
 
-    # from sklearn.manifold import TSNE
-    from sklearn.preprocessing import MinMaxScaler
-    from MulticoreTSNE import MulticoreTSNE as TSNE
-
     tsne = TSNE(n_components=2, perplexity=40, metric="cosine", n_jobs=8, random_state=42, verbose=True)
     embedding = tsne.fit_transform(df.values)
     x = MinMaxScaler().fit_transform(embedding) # scale the embedding to 0-1