kmeans clustering works and returns centroids and labeled data

app.py

@@ -36,6 +36,7 @@ def index():
     # parse arguments
     algorithm = options[request.json["algorithm"]]
     args = request.json["arguments"]
+    args["algorithm"] = request.json["algorithm"]
 
     # using the iris data set for every algorithm
     X, y = iris()
@@ -48,4 +49,4 @@ def index():
 
 
 if __name__ == "__main__":
-    app.run(debug=False)
+    app.run(debug=True)

cluster/clusterer.py

@@ -7,7 +7,6 @@ import numpy as np
 @dataclass
 class Clusterer:
     cluster_func: Callable
-    options: dict
 
     def eval(
         self,
@@ -19,9 +18,3 @@ class Clusterer:
     @classmethod
     def from_dict(cls, dct: dict):
         return cls(**dct)
-
-    def to_dict(self):
-        return {
-            "cluster_method": self.cluster_func.__name__,
-            "options": self.options,
-        }

cluster/kmeans.py

@@ -61,6 +61,6 @@ class Kmeans(Clusterer):
         return {
             "k": self.k,
             "max_iter": self.max_iter,
-            "centroids": self.centroids,
-            "clusters": self.clusters,
+            "centroids": self.centroids.tolist(),
+            "clusters": self.clusters.tolist(),
         }

cluster/main.py

@@ -1,4 +1,3 @@
-from sklearn.model_selection import train_test_split
 import numpy as np
 
 from cluster.clusterer import Clusterer
@@ -11,9 +10,11 @@ def main(
     y: np.array,
     args: dict,
 ) -> dict:
-    cluster_alg: Clusterer = clustering_methods[args["algorithm"]]
+    cluster_func = args.pop("algorithm")
+    cluster_alg: Clusterer = clustering_methods[cluster_func]
 
-    alg = cluster_alg.from_dict(args)
-    alg.build(X)
+    cluster_args: dict = {"cluster_func": cluster_func} | args
+    alg = cluster_alg.from_dict(cluster_args)
 
+    alg.build(X)
     return alg.to_dict()

cluster/opts.py

@@ -4,6 +4,6 @@ from cluster.kmeans import Kmeans
 
 
 clustering_methods: dict[str, Clusterer] = {
-    "kmeans": Kmeans,
-    "kmedoids": Kmedoids,
+    "kmeans-clustering": Kmeans,
+    "kmedoids-clustering": Kmedoids,
 }

example/kmeans.py

@@ -1,10 +1,10 @@
 import requests
 import json
 
-ENDPOINT: str = "https://data-mining-from-scratch-backend.onrender.com/"
+ENDPOINT: str = "http://127.0.0.1:5000/"
 
 request_params = {
-    "algorithm": "kmeans",
+    "algorithm": "kmeans-clustering",
     "arguments": {
         "k": 3,
         "max_iter": 10,

example/neural_network.py

@@ -3,7 +3,9 @@ import seaborn as sns
 import requests
 import json
 
-ENDPOINT: str = "https://data-mining-from-scratch-backend.onrender.com/"
+# ENDPOINT: str = "https://data-mining-from-scratch-backend.onrender.com/"
+
+ENDPOINT: str = "http://127.0.0.1:5000/"
 
 request_params = {
     "algorithm": "neural-network",