x = StandardScaler().fit_transform(vectors)
y = np.load(f"../vectorize/paths/~tienda1~miembros~editar.jsp_y.npy")
vis = Visualizer()
for eps in np.linspace(0.4, 3.2, 8):
clf = DBSCAN(eps=eps, min_samples=3)
y_pred = clf.fit_predict(x)
y_pred += (1 if -1 in y_pred else 1)
counts = np.bincount(y_pred)
n_clusters_ = len(set(y_pred))
sorted_count_index = np.argsort(counts)
all = len(y)
axis_x = []
axis_y = []
for c in sorted_count_index:
picked = counts[c]
right = np.sum(y[y_pred == c] == 1)
acc = right / picked
ratio = picked / all
axis_x.append(ratio)
axis_y.append(acc)
vis.line(X=axis_x,
Y=axis_y,
win=eps,
opts={
'title': f'EPS {round(eps, 2)} {n_clusters_} clusters',
'xlabel': 'cluster percentile',
'ylabel': 'anonymous sample percentile',
'markers': True,
'markersize': 5
})
