File size: 3,662 Bytes
78aefa8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 | """Clustering unsupervised (K-Means, GMM) + validasi klaster."""
import numpy as np
from sklearn.cluster import KMeans
from sklearn.metrics import (
adjusted_rand_score,
calinski_harabasz_score,
davies_bouldin_score,
normalized_mutual_info_score,
silhouette_score,
)
from sklearn.mixture import GaussianMixture
import config
def run_kmeans(X, k=None, random_state=None):
k = config.N_CLUSTERS_FINAL if k is None else k
random_state = config.RANDOM_STATE if random_state is None else random_state
model = KMeans(n_clusters=k, random_state=random_state, n_init=20)
labels = model.fit_predict(X)
return labels, model
def run_gmm(X, k=None, random_state=None):
k = config.N_CLUSTERS_FINAL if k is None else k
random_state = config.RANDOM_STATE if random_state is None else random_state
model = GaussianMixture(
n_components=k, random_state=random_state, n_init=10, covariance_type="full"
)
labels = model.fit_predict(X)
return labels, model
# Pipeline utama akan membaca dictionary ini secara dinamis
# Sekarang hanya terdaftar dua algoritma
CLUSTERING_METHODS = {
"kmeans": run_kmeans,
"gmm": run_gmm,
}
def internal_validation(X, labels):
"""
Metrik validasi klaster TANPA label ground truth. Dipakai sebagai bukti
separabilitas kelas (pengganti akurasi supervised SVM/KNN), sesuai
Tahap 5 metode proposal ("validasi separabilitas dan evaluasi kinerja fitur").
- silhouette_score: [-1, 1], makin tinggi makin baik (klaster rapat & terpisah)
- davies_bouldin_score: >= 0, makin RENDAH makin baik
- calinski_harabasz_score: >= 0, makin tinggi makin baik
"""
n_unique = len(np.unique(labels))
if n_unique < 2 or n_unique >= len(X):
return {
"silhouette_score": float("nan"),
"davies_bouldin_score": float("nan"),
"calinski_harabasz_score": float("nan"),
"note": "Klaster tidak valid untuk dihitung metriknya (n_unique={})".format(
n_unique
),
}
return {
"silhouette_score": float(silhouette_score(X, labels)),
"davies_bouldin_score": float(davies_bouldin_score(X, labels)),
"calinski_harabasz_score": float(calinski_harabasz_score(X, labels)),
}
def _cluster_purity(true_labels, pred_labels):
"""Purity = proporsi anggota klaster yang berasal dari kelas mayoritasnya."""
true_labels = np.asarray(true_labels)
pred_labels = np.asarray(pred_labels)
total = len(true_labels)
correct = 0
for cluster_id in np.unique(pred_labels):
mask = pred_labels == cluster_id
if mask.sum() == 0:
continue
values, counts = np.unique(true_labels[mask], return_counts=True)
correct += counts.max()
return correct / float(total)
def external_validation(true_labels, pred_labels):
"""
Metrik validasi klaster MENGGUNAKAN label ground truth opsional (dari
pelabelan awal tim peneliti). Ini BUKAN supervised training - label hanya
dipakai sebagai pembanding pasca-hoc untuk mengukur kesesuaian klaster
otomatis dengan penilaian pakar (dosen Peternakan).
- adjusted_rand_score (ARI): [-1, 1], 1 = identik dengan ground truth
- normalized_mutual_info_score (NMI): [0, 1], makin tinggi makin baik
- purity: [0, 1], makin tinggi makin baik
"""
return {
"adjusted_rand_index": float(adjusted_rand_score(true_labels, pred_labels)),
"normalized_mutual_info": float(
normalized_mutual_info_score(true_labels, pred_labels)
),
"purity": float(_cluster_purity(true_labels, pred_labels)),
} |