add test acc

app.py

@@ -143,7 +143,31 @@ def update_exemplars(df, rng, exemplars, lut):
     preds[dist_to_ex1 < dist_to_ex0] = 1
     print(preds.shape, labels_train.shape, np.sum(preds == labels_train))
     train_acc = np.sum(preds == labels_train) / len(labels_train)
-    return rng, exemplars, train_acc, 0. # score(embeds_train, exemplars, lut), score(embeds_test, exemplars, lut)
+    rng, test_acc = score(embeds_test, labels_test, rng, exemplars, lut)
+    return rng, exemplars, train_acc, test_acc
+
+def score(embeds, labels, rng, exemplars, lut):
+    quantized_embeds, closest_bin = quantize_embeds(embeds)
+    # closest bin is nexample X 512
+    # lut[0] is nvals X dims
+    # hd_embeds in nexample x 512 x dims
+    hd_embeds_per_pos = lut[0][closest_bin]
+    # bundle along pos dimension 512
+    # lut[1] is 512 x dims
+    xor = lambda a,b: a*(1.-b) + b*(1.-a)
+    hd_embeds = xor(lut[1][np.newaxis, ...], hd_embeds_per_pos)
+    hd_embeds = np.sum(hd_embeds, axis=1) / embeds.shape[-1]
+    hd_embeds[hd_embeds >= 0.5] = 1.
+    hd_embeds[hd_embeds < 0.5] = 0.
+    # hd_embeds_integer is nexample x dims
+    print(exemplars[0].shape, exemplars[1].shape, np.abs(exemplars[0] - exemplars[1]).sum())
+    preds = np.zeros(hd_embeds.shape[0])
+    dist_to_ex0 = np.abs(hd_embeds - exemplars[0][np.newaxis, ...]).sum(axis=-1)
+    dist_to_ex1 = np.abs(hd_embeds - exemplars[1][np.newaxis, ...]).sum(axis=-1)
+    preds[dist_to_ex1 < dist_to_ex0] = 1
+    print(preds.shape, labels.shape, np.sum(preds == labels), len(labels))
+    acc = np.sum(preds == labels) / len(labels)
+    return rng, acc
 
     
 with gr.Blocks(title="End-User Personalization") as demo: