Now, it saves kmeans parameters on the json file.

Mathias
1 parent 3b7e63994c
Showing 1 changed file with 13 additions and 4 deletions Side-by-side Diff
scripts/evaluations/clustering.py
@@ -9,6 +9,7 @@
 import time
 import pickle
 import csv
+import json
  
 from sklearn.preprocessing import LabelEncoder
 from sklearn.metrics.pairwise import pairwise_distances
  
@@ -31,11 +32,19 @@
     num_classes = len(label_encoder.classes_)
  
     # Compute KMEANS clustering on data
+    kmeans_parameters = {
+        "n_clusters": num_classes,
+        "n_init": 100,
+        "tol": 10-6,
+        "algorithm": "elkan"
+    }
+    with open(os.path.join(outdir, f"{args.prefix}kmeans_parameters.json"), "w") as f:
+        json.dump(kmeans_parameters, f)
+
+    # Save parameters
+    
     estimator = KMeans(
-        n_clusters=num_classes,
-        n_init=100,
-        tol=10-6,
-        algorithm="elkan"
+        **kmeans_parameters
     )
     estimator.fit(feats)
     print(f"Kmeans: processed {estimator.n_iter_} iterations - intertia={estimator.inertia_}")
...	...	@@ -9,6 +9,7 @@
9	9	import time
10	10	import pickle
11	11	import csv
	12	+import json
12	13
13	14	from sklearn.preprocessing import LabelEncoder
14	15	from sklearn.metrics.pairwise import pairwise_distances
15	16
...	...	@@ -31,11 +32,19 @@
31	32	num_classes = len(label_encoder.classes_)
32	33
33	34	# Compute KMEANS clustering on data
	35	+ kmeans_parameters = {
	36	+ "n_clusters": num_classes,
	37	+ "n_init": 100,
	38	+ "tol": 10-6,
	39	+ "algorithm": "elkan"
	40	+ }
	41	+ with open(os.path.join(outdir, f"{args.prefix}kmeans_parameters.json"), "w") as f:
	42	+ json.dump(kmeans_parameters, f)
	43	+
	44	+ # Save parameters
	45	+
34	46	estimator = KMeans(
35		- n_clusters=num_classes,
36		- n_init=100,
37		- tol=10-6,
38		- algorithm="elkan"
	47	+ **kmeans_parameters
39	48	)
40	49	estimator.fit(feats)
41	50	print(f"Kmeans: processed {estimator.n_iter_} iterations - intertia={estimator.inertia_}")