KMeans

- implemented KMeans
- fixed non seeded rng
- fixed display exception with NoTargets
- added basic test cases to app

src/app.py

@@ -2,12 +2,14 @@ import random
 from typing import Any
 import numpy as np
 import sklearn
+import sklearn.cluster
 import sklearn.linear_model
 import sklearn.model_selection
 import sklearn.neural_network
 from learning.data import Dataset, TargetType
 from learning.supervised import LinearRegression, LogisticRegression, MultiLayerPerceptron
 from learning.ml import MLAlgorithm
+from learning.unsupervised import KMeans
 
 DATASET = "datasets/"
 REGRESSION = DATASET + "regression/"
@@ -75,6 +77,23 @@ def iris() -> tuple[Dataset, MLAlgorithm, Any]:
     size = [4, 3]
     return (ds, MultiLayerPerceptron(ds, size), sklearn.neural_network.MLPClassifier(size, 'relu'))
 
+# ********************
+# MultiLayerPerceptron
+# ********************
+
+def frogs_no_target() -> tuple[Dataset, MLAlgorithm, Any]:
+    ds = Dataset(CLASSIFICATION + "frogs.csv", "Species", TargetType.NoTarget)
+    ds.remove(["Family", "Genus", "RecordID", "Species"])
+    clusters = 10
+    return (ds, KMeans(ds, clusters), sklearn.cluster.KMeans(clusters))
+
+def iris_no_target() -> tuple[Dataset, MLAlgorithm, Any]:
+    ds = Dataset(CLASSIFICATION + "iris.csv", "Class", TargetType.NoTarget)
+    ds.remove(["Class"])
+    ds.normalize()
+    clusters = 3
+    return (ds, KMeans(ds, clusters), sklearn.cluster.KMeans(clusters))
+
 # ********************
 # Main & random
 # ********************
@@ -82,17 +101,24 @@ def iris() -> tuple[Dataset, MLAlgorithm, Any]:
 if __name__ == "__main__":
     np.set_printoptions(linewidth=np.inf, formatter={'float': '{:>10.5f}'.format})
     rand = random.randint(0, 4294967295)
+    #rand = 1997847910  # LiR for power_plant
+    #rand = 347617386   # LoR for electrical_grid
+    #rand = 1793295160  # MLP for iris
+    #rand = 885416001   # KMe for frogs_no_target
+
     np.random.seed(rand)
     print(f"Using seed: {rand}")
 
-    ds, ml, sk = electrical_grid()
-    ml.learn(10000, verbose=True)
+    ds, ml, sk = iris()
+
+    epochs, _, _ = ml.learn(1000, verbose=True)
     ml.display_results()
 
     np.random.seed(rand)
     learn, test, valid = ds.get_dataset()
+    sk.set_params(max_iter=epochs)
     sk.fit(learn.x, learn.y)
-    print(f"Sklearn    : {sk.score(test.x, test.y):0.5f}")
+    print(f"Sklearn    : {abs(sk.score(test.x, test.y)):0.5f}")
     print("========================")
 
     ml.plot()