def populate_init_data():
# Initialize data store with default computing
ht_res = apply_clustering()
rt_res = rating_clustering(INIT_THRESHOLD_VAL)
return {
"ht": {"data": ht_res[0].to_json(), "pct": ht_res[1].to_json()},
"rt": {
"data": rt_res[0].to_json(),
"p_val": rt_res[1],
"pct_d": rt_res[2].to_json(),
},
}
def update_ds(n_clicks, clustering_type, cur_ds, thr):
# Apply algorithm and only apply computing once upon button=click,save it for figure loading
if n_clicks:
if clustering_type == "ht-cluster":
# Apply KMeans and update datastore
ht_res = apply_clustering()
cur_ds.update(ht={"data": ht_res[0].to_json(), "pct": ht_res[1].to_json()})
elif clustering_type == "rating-cluster":
rt_res = rating_clustering(thr)
cur_ds.update(
rt={
"data": rt_res[0].to_json(),
"p_val": rt_res[1],
"pct_d": rt_res[2].to_json(),
}
)
else:
return cur_ds
return cur_ds
return cur_ds