Remove old configs and HDBScan

config/2d-noise_extracted_1.json

-{
-	"dimensions": [
-		{
-			"kind": "OSC",
-			"equation": {
-				"function": "sin",
-				"amplitude": 0.7,
-				"frequency": 2.7
-			},
-			"noise": {
-				"mean": 0.0,
-				"std": 0.1
-			},
-			"anomalies": [
-				{
-					"kind": "NOISE",
-					"std": 0.1,
-					"mean": 0.0,
-					"length": 5,
-					"position": "middle"
-				}
-			]
-		},
-		{
-			"kind": "CORRELATION",
-			"dimension": 0,
-			"equation": {
-				"step": 0.03,
-				"sign": -1
-			},
-			"noise": {
-				"mean": 0.0,
-				"std": 0.1
-			},
-			"anomalies": []
-		}
-	],
-	"length": 1000
-}

config/2d-noise_extracted_2.json

-{
-	"dimensions": [
-		{
-			"kind": "OSC",
-			"equation": {
-				"function": "covercosine",
-				"amplitude": 1.5,
-				"frequency": 2.1
-			},
-			"noise": {
-				"mean": 0.0,
-				"std": 0.1
-			},
-			"anomalies": [
-				{
-					"kind": "NOISE",
-					"std": 0.1,
-					"mean": 0.0,
-					"length": 5,
-					"position": "end"
-				}
-			]
-		},
-		{
-			"kind": "CORRELATION",
-			"dimension": 0,
-			"equation": {
-				"step": 0.1,
-				"sign": 1
-			},
-			"noise": {
-				"mean": 0.0,
-				"std": 0.1
-			},
-			"anomalies": [
-				{
-					"kind": "NOISE",
-					"std": 0.07,
-					"mean": 0.02,
-					"length": 5,
-					"position": "start"
-				}
-			]
-		}
-	],
-	"length": 1000
-}

config/2d-noise_extracted_3.json

-{
-	"dimensions": [
-		{
-			"kind": "OSC",
-			"equation": {
-				"function": "coversine",
-				"amplitude": 0.7,
-				"frequency": 1.3
-			},
-			"noise": {
-				"mean": 0.0,
-				"std": 0.1
-			},
-			"anomalies": [
-				{
-					"kind": "NOISE",
-					"std": 0.1,
-					"mean": -0.01,
-					"length": 5,
-					"position": "end"
-				}
-			]
-		},
-		{
-			"kind": "CORRELATION",
-			"dimension": 0,
-			"equation": {
-				"step": 0.05,
-				"sign": -1
-			},
-			"noise": {
-				"mean": 0.0,
-				"std": 0.1
-			},
-			"anomalies": [
-				{
-					"kind": "NOISE",
-					"std": 0.03,
-					"mean": -0.02,
-					"length": 5,
-					"position": "start"
-				}
-			]
-		}
-	],
-	"length": 1000
-}

split/base.py

@@ -9,7 +9,7 @@ import numpy as np
 import pandas as pd
 import seaborn as sns
 
-from sklearn.cluster import KMeans, HDBSCAN
+from sklearn.cluster import KMeans#, HDBSCAN
 from sklearn.exceptions import ConvergenceWarning
 from sklearn.metrics import silhouette_score
 
@@ -46,10 +46,10 @@ class BaseSplitter:
         x = self._compute_correlations(w_df)
 
         for i in range(1, len(w_df.columns)):
-            # km = KMeans(n_clusters=i)
-            # clusters = km.fit_predict(x)
-            hdb_scan = HDBSCAN(min_cluster_size=2, allow_single_cluster=True)
-            clusters = hdb_scan.fit_predict(x)
+            km = KMeans(n_clusters=i)
+            clusters = km.fit_predict(x)
+            # hdb_scan = HDBSCAN(min_cluster_size=2, allow_single_cluster=True)
+            # clusters = hdb_scan.fit_predict(x)
 
             if len(np.unique(clusters)) == 1:
                 score = 0.5