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5f7b8bf 36ec76a 5f7b8bf b394447 5f7b8bf b1e5e3a 5f7b8bf 36ec76a b394447 5f7b8bf | 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 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 | import torch
import numpy as np
from sklearn.cluster import KMeans
################## Bayesian Functions Start ########################################################################
# helper function for determining state based on transit matrix
def get_traj_of_state(last_s, transit_p, centroids, centroid_std, sample_steps,
top_k=-1, temperature=1, future_action_enc_out=None,
embed_dim=768,
**kwargs):
# last_s: 1, embed_dim*bn*nvar
# centroids: num_centroids, embed_dim*bn*nvar
# future_action_enc_out: sample_steps, embed_dim*bn*action_nvar
# action_nvar < nvar, (action_nvar+phyio_nvar = nvar)
# currently, bn is always 1.
# init
temperature = min(max(1e-6, temperature), 2)
prev_ci = np.argmin(np.sqrt(np.sum((centroids - last_s.cpu().numpy())**2, axis=1)))
result_embeds = torch.zeros(1, sample_steps, last_s.shape[-1])
traj_log = 0
# generate across target steps
for ss in range(sample_steps):
# raw sampling
p = transit_p[prev_ci]
# up-weight the transition where the transited state representation is closer to the next step of the action.
if future_action_enc_out is not None:
action_emb = future_action_enc_out[ss] # embed_dim*bn*action_nvar
action_emb = action_emb.cpu().numpy()
centroids_action_emb = centroids[:, -future_action_enc_out.shape[-1]:] # num_centroids, embed_dim*bn*action_nvar
# compute distance, then apply min-max normalization.
action_distance = np.linalg.norm(centroids_action_emb - action_emb[None, :], axis=-1) # num_centroids
action_distance = (action_distance - action_distance.min()) / (action_distance.max() - action_distance.min() + 1e-8) # minmax norm
p = p * (1 - action_distance) # upweight the transition to states whose representation is more similar to the future action.
# apply temperature
p = p ** (1.0 / temperature)
# use top k token
if top_k > 0:
topk_idx = np.argsort(p)[-top_k:]
topk_p = p[topk_idx]
else: # use all token
topk_idx = np.arange(len(p))
topk_p = p
topk_p = topk_p / topk_p.sum() # make sure p sum to 1
# sampling
new_cidx = np.random.choice(np.arange(len(topk_idx)), p=topk_p) # sampling step
new_ci = topk_idx[new_cidx]
# update
# print(centroids.shape, centroid_std.keys())
# exit()
traj_log += np.log(topk_p[new_cidx] + 1e-12)
# result_embeds[:, ss, :] = torch.from_numpy(centroids[new_ci])
curr_scale = 0 if centroid_std.get(new_ci) is None else centroid_std[new_ci] # means there are less number of clusters than actual desired number of clusters
result_embeds[:, ss, :] = torch.from_numpy(
np.random.normal(loc=centroids[new_ci], scale=curr_scale)
)
prev_ci = new_ci
return result_embeds.float().to(last_s.device), traj_log # 1, 2048, dim
def quantile_traj_of_state(last_s, transit_p, centroids, centroid_std, sample_steps,
top_k=-1, temperature=1, num_traj=20, future_action_enc_out=None):
# initialize traj list
num_traj = int(min(100, max(0, num_traj)))
result_embeds_traj_log = list() # result_embeds, traj_log
# repeat for num_traj times
for _ in range(num_traj):
result_embeds, traj_log = get_traj_of_state(last_s, transit_p, centroids, centroid_std,
sample_steps, top_k=top_k, temperature=temperature,
future_action_enc_out=future_action_enc_out)
result_embeds_traj_log.append((result_embeds, traj_log))
result_embeds_traj_log.sort(key=lambda x: x[1], reverse=True)
# return result_embeds_traj_log[0][0] # 1, 2048, dim
# fuse each sampled traj, weighted by their total energy
total_p = torch.tensor([t[1] for t in result_embeds_traj_log]).float().to(result_embeds_traj_log[0][0].device)
total_p = torch.softmax(total_p, 0)[:, None, None, None]
total_traj = torch.stack([t[0] for t in result_embeds_traj_log]) # num_traj, 1, 2048, dim
return (total_traj * total_p).sum(dim=0) # 1, 2048, dim
# return total_traj.mean(dim=0) # 1, 2048, dim
# Helper function to fit new bayesian
def fit_observed_bayesian(observed_emebds, num_states=16,
original_knowledge=None, post_w=1.0,
):
# observed_emebds: N, embed_dim
# original_knowledge: (original_transit, original_centroids), ((3600, 3600), (3600, 768))
# return: regularized_transit_p, regularized_centroids
# only cluster based on physio channels, ignore action channels.
# when physio_channels are introduced, fit only on physio channels
# because we want to regularize the transit matrix based on physio states,
# and action channels may introduce extra noise for clustering.
reg_km = KMeans(
n_clusters=num_states,
random_state=42,
# n_init=10
n_init=1,
algorithm="elkan",
).fit(observed_emebds)
regularized_centroids = reg_km.cluster_centers_ # num_states, 768
observed_centroids = reg_km.labels_ # N
centroid_std = {
observed_centroids[-1]: [
(observed_emebds[-1] - regularized_centroids[observed_centroids[-1]])**2,
1 # counter
]
}
# identify prior transit
if original_knowledge is not None:
original_transit, original_centroids = original_knowledge
closest_prior_centroids = np.sum((regularized_centroids[:, None, :]-original_centroids[None, :, :])**2, axis=-1)
closest_prior_centroids = np.argmin(closest_prior_centroids, axis=-1) # num_states
prior_transit = original_transit[closest_prior_centroids, :][:, closest_prior_centroids] # num_states, num_states
prior_transit_p = (prior_transit+1e-8) / ((prior_transit+1e-8).sum(axis=1, keepdims=True))
else:
prior_transit_p, post_w = 0, 1.0
# fit expected bayesian transit matrix
posterior_transit = np.zeros((num_states, num_states))
for c_i in range(len(observed_centroids)-1):
curr_centoids_id = observed_centroids[c_i]
# update transit matrix
posterior_transit[observed_centroids[c_i], observed_centroids[c_i+1]] += 1
# update std stats
if centroid_std.get(curr_centoids_id) is None:
centroid_std[curr_centoids_id] = [0, 0]
centroid_std[curr_centoids_id][0] += ((observed_emebds[c_i] - regularized_centroids[curr_centoids_id])**2)
centroid_std[curr_centoids_id][1] += 1
# compute posterior probability
posterior_transit_p = (posterior_transit+1e-8) / ((posterior_transit+1e-8).sum(axis=-1, keepdims=True))
# clean up std
for std_k in centroid_std:
accum_centroids, centroid_num = centroid_std[std_k]
centroid_std[std_k] = np.sqrt(accum_centroids / centroid_num)
# aggregate
regularized_transit_p = (post_w*posterior_transit_p) + ((1-post_w)*prior_transit_p)
regularized_transit_p = (regularized_transit_p+1e-8) / ((regularized_transit_p+1e-8).sum(axis=-1, keepdims=True))
return regularized_transit_p, regularized_centroids, centroid_std
def bayesian_forecast(in_tensor, n_channels, physio_channels,
context_length=2048-16, pred_length=2048+16,
num_states=16, action_channels=[],
condition_bayes=False, num_traj_sampled=1,
latent_encoder=None, return_transit_matrix=False):
# in_tensor: 1, nvar, length
end_idx = in_tensor.shape[-1] if len(action_channels) < 1 else context_length
with torch.no_grad():
enc_out, ids_restore, masked_patches = latent_encoder.forward_encoder(in_tensor.clone()[:, :, :end_idx], masking=False)
# regularize transit and centroid then forecast
embed_dim = enc_out.shape[-1]
enc_out = enc_out.permute(1, 2, 0).flatten(start_dim=1) # L//patch_size + 1, embed_dim*bn*nvar
# adjust num state
curr_num_state = min(num_states, len(enc_out)-1)
# fit bayesian
bayesian_outpack = fit_observed_bayesian(
# enc_out[0, 1:, :].cpu().numpy(),
# enc_out[1:, :].cpu().numpy(),
enc_out[:, :].cpu().numpy(),
num_states=curr_num_state,
# num_states=(context_length // 16) // 2,
# original_knowledge=(transit_matrix, centroids),
post_w=1.0,
)
# extract core info
regularized_transit_p, regularized_centroids, centroid_std = bayesian_outpack[:3]
# regularized_transit_p, regularized_centroids = regularize_transit_centroids(enc_out[0, 1:, :].cpu().numpy(), transit_matrix, centroids)
future_action_enc_out = None
if len(action_channels) > 0:
with torch.no_grad():
future_action_enc_out, _, _ = latent_encoder.forward_encoder(in_tensor.clone()[:, action_channels, :], masking=False)
context_npatchs = context_length // latent_encoder.patch_size
future_action_enc_out = future_action_enc_out.permute(1, 2, 0).flatten(start_dim=1)[context_npatchs:context_npatchs+(pred_length//latent_encoder.patch_size)+1, :] # sample_steps, embed_dim*bn*action_nvar
appended_embeds = quantile_traj_of_state(
# enc_out[0, -1, :],
enc_out[-1:, :],
regularized_transit_p,
regularized_centroids,
centroid_std,
(pred_length // latent_encoder.patch_size)+1,
top_k=curr_num_state,
temperature=1.0,
num_traj=num_traj_sampled, # maybe increase this later
future_action_enc_out=future_action_enc_out if condition_bayes else None,
) # 1, pred_length//patch_size, dim
# decoding
# enc_with_append = torch.concatenate((enc_out, appended_embeds), dim=1) # bn*nvar, L//patch_size + 1 + pred_length//patch_size, embed_dim
enc_with_append = torch.concatenate((enc_out, appended_embeds[0]), dim=0) # L//patch_size + 1 + pred_length//patch_size, embed_dim*bn*nvar
enc_with_append = enc_with_append.reshape(enc_with_append.shape[0], embed_dim, -1).permute(2, 0, 1) # bn*nvar, L//patch_size + 1 + pred_length//patch_size, embed_dim
dec_out = latent_encoder.forward_decoder(enc_with_append, ids_restore, masked_patches) # bn*nvar, L
# dec_out = torch.concatenate((enc_out, appended_embeds), dim=1)
dec_out = dec_out.reshape(dec_out.shape[0], -1)
bn_nvar, total_L = dec_out.shape
bayesian_out = dec_out.reshape(1, n_channels, total_L)[0, physio_channels, context_length:context_length+pred_length] # bn, nvar, pred_length
# return
if return_transit_matrix:
return bayesian_out, regularized_transit_p, regularized_centroids, centroid_std, enc_out
return bayesian_out # bn, nvar, pred_length
################## Bayesian Functions End ########################################################################
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