Added a t-SNE plot

clusttraj/io.py

@@ -559,7 +559,7 @@ def parse_args(args: argparse.Namespace) -> ClustOptions:
         "plot": bool(args.plot),
         "evo_name": basenameout + "_evo.pdf" if args.plot else None,
         "dendrogram_name": basenameout + "_dendrogram.pdf" if args.plot else None,
-        "mds_name": basenameout + ".pdf" if args.plot else None,
+        "mds_name": basenameout + "_mds.pdf" if args.plot else None,
         "trajfile": args.trajectory_file,
         "min_rmsd": args.min_rmsd,
         "method": args.method,

clusttraj/main.py

@@ -9,7 +9,7 @@
 from typing import List
 from .io import Logger, configure_runtime, save_clusters_config
 from .distmat import get_distmat
-from .plot import plot_clust_evo, plot_dendrogram, plot_mds
+from .plot import plot_clust_evo, plot_dendrogram, plot_mds, plot_tsne
 from .classify import classify_structures, classify_structures_silhouette
 from .metrics import compute_metrics
 
@@ -66,6 +66,8 @@ def main(args: List[str] = None) -> None:
 
         plot_mds(clust_opt, clusters, distmat)
 
+        plot_tsne(clust_opt, clusters, distmat)
+
     # print the cluster sizes
     outclust_str = f"A total {len(clusters)} snapshots were read and {max(clusters)} cluster(s) was(were) found.\n"
     outclust_str += "The cluster sizes are:\nCluster\tSize\n"

clusttraj/metrics.py

@@ -11,33 +11,30 @@
 def compute_metrics(
 	clust_opt: ClustOptions, distmat: np.ndarray, z_matrix: np.ndarray, clusters: np.ndarray
 ) -> Tuple[np.float64, np.float64, np.float64, np.float64]:
-	"""Compute the Cophenetic Correlation Coefficient and Silhouette, 
-	Calinski Harabasz and Davies-Bouldin scores.
+	"""Compute metrics to assess the performance of the clustering procedure.
 
 	Args:
         clust_opt (ClustOptions): The clustering options.
         z_matrix (np.ndarray): The Z-matrix from hierarchical clustering procedure.
 
     Returns:
-        None
+        ss (np.float64): The silhouette score.
+        ch (np.float64): The Calinski Harabasz score.
+        db (np.float64): The Davies-Bouldin score.
+        cpcc (np.float64): The cophenetic correlation coefficient.
 	"""
 
 	# Compute the silhouette score
 	ss = silhouette_score(squareform(distmat), clusters, metric="precomputed")
 
 	# Compute the Calinski Harabasz score
-
 	ch = calinski_harabasz_score(squareform(distmat), clusters)
 
 	# Compute the Davies-Bouldin score
-
 	db = davies_bouldin_score(squareform(distmat), clusters)
 
 	# Compute the cophenetic correlation coefficient 
+	cpcc = cophenet(z_matrix)[0]
 
-	# cpcc_coef, a = cophenet(z_matrix)
-	cpcc_coef = cophenet(z_matrix)[0]
-	# cpcc_coef = result[0]
-
-	return ss, ch, db, cpcc_coef
+	return ss, ch, db, cpcc
 

clusttraj/plot.py

@@ -102,12 +102,69 @@ def plot_mds(clust_opt: ClustOptions, clusters: np.ndarray, distmat: np.ndarray)
         right=False,
         labelbottom=False,
         labelleft=False,
-    )
+    ) 
 
     # Scatter plot the coordinates with cluster colors
     plt.scatter(
         coords[:, 0], coords[:, 1], marker="o", c=clusters, cmap=plt.cm.nipy_spectral
     )
 
+    plt.title('MDS Visualization')
+
     # Save the plot
     plt.savefig(clust_opt.mds_name, bbox_inches="tight")
+
+
+def plot_tsne(clust_opt: ClustOptions, clusters: np.ndarray, distmat: np.ndarray) -> None:
+    """Plot the t-distributed Stochastic Neighbor Embedding 2D plot of the clustering.
+
+    Args:
+        clust_opt (ClustOptions): The clustering options.
+        clusters (np.ndarray): The cluster labels.
+        distmat (np.ndarray): The distance matrix.
+
+    Returns:
+        None
+    """
+
+    # Initialize the tSNE model
+    tsne = manifold.TSNE(
+         n_components=2, 
+         perplexity=30, 
+         learning_rate=200,
+         random_state=666,
+         n_jobs=clust_opt.n_workers,
+    )
+
+    # Perform the t-SNE and get the 2D representation 
+    coords = tsne.fit_transform(squareform(distmat))
+
+    # Define a list of unique colors for each cluster
+    unique_clusters = np.unique(clusters)
+    colors = plt.cm.tab20(np.linspace(0, 1, len(unique_clusters)))
+
+    # Create a new figure
+    plt.figure()
+
+    # Configure tick parameters
+    plt.tick_params(
+        axis="both",
+        which="both",
+        bottom=False,
+        top=False,
+        left=False,
+        right=False,
+        labelbottom=False,
+        labelleft=False,
+    ) 
+
+    # Create a scatter plot with different colors for each cluster
+    for i, cluster in enumerate(unique_clusters):
+        cluster_data = coords[clusters == cluster]
+        plt.scatter(cluster_data[:, 0], cluster_data[:, 1], color=colors[i])
+
+    plt.title('t-SNE Visualization')
+
+    # Save the plot
+    plt.savefig(clust_opt.mds_name[:-7] + "tsne.pdf", bbox_inches="tight")
+