{{ message }}
Repository navigation
Expand file tree
/
Copy pathwindfm.py
More file actions
531 lines (423 loc) · 23.1 KB
/
Copy pathwindfm.py
File metadata and controls
531 lines (423 loc) · 23.1 KB
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
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
import numpy as np
import pandas as pd
import torch
from huggingface_hub import PyTorchModelHubMixin
import sys
from tqdm import trange
sys.path.append("../")
from model.module import *
class WindFMTokenizer(nn.Module, PyTorchModelHubMixin):
"""
WindFMTokenizer module for tokenizing input data using a hybrid quantization approach.
This tokenizer utilizes a combination of encoder and decoder Transformer blocks
along with the Binary Spherical Quantization (BSQuantizer) to compress and decompress input data.
Args:
d_in (int): Input dimension.
d_model (int): Model dimension.
n_heads (int): Number of attention heads.
ff_dim (int): Feed-forward dimension.
n_enc_layers (int): Number of encoder layers.
n_dec_layers (int): Number of decoder layers.
ffn_dropout_p (float): Dropout probability for feed-forward networks.
attn_dropout_p (float): Dropout probability for attention mechanisms.
resid_dropout_p (float): Dropout probability for residual connections.
s1_bits (int): Number of bits for the pre token in BSQuantizer.
s2_bits (int): Number of bits for the post token in BSQuantizer.
beta (float): Beta parameter for BSQuantizer.
gamma0 (float): Gamma0 parameter for BSQuantizer.
gamma (float): Gamma parameter for BSQuantizer.
zeta (float): Zeta parameter for BSQuantizer.
group_size (int): Group size parameter for BSQuantizer.
"""
def __init__(self, d_in, d_model, n_heads, ff_dim, n_enc_layers, n_dec_layers, ffn_dropout_p, attn_dropout_p, resid_dropout_p, s1_bits, s2_bits, beta, gamma0, gamma, zeta, group_size):
super().__init__()
self.d_in = d_in
self.d_model = d_model
self.n_heads = n_heads
self.ff_dim = ff_dim
self.enc_layers = n_enc_layers
self.dec_layers = n_dec_layers
self.ffn_dropout_p = ffn_dropout_p
self.attn_dropout_p = attn_dropout_p
self.resid_dropout_p = resid_dropout_p
self.s1_bits = s1_bits
self.s2_bits = s2_bits
self.codebook_dim = s1_bits + s2_bits # Total dimension of the codebook after quantization
self.embed = nn.Linear(self.d_in, self.d_model)
self.head = nn.Linear(self.d_model, self.d_in)
# Encoder Transformer Blocks
self.encoder = nn.ModuleList([
TransformerBlock(self.d_model, self.n_heads, self.ff_dim, self.ffn_dropout_p, self.attn_dropout_p, self.resid_dropout_p)
for _ in range(self.enc_layers - 1)
])
# Decoder Transformer Blocks
self.decoder = nn.ModuleList([
TransformerBlock(self.d_model, self.n_heads, self.ff_dim, self.ffn_dropout_p, self.attn_dropout_p, self.resid_dropout_p)
for _ in range(self.dec_layers - 1)
])
self.quant_embed = nn.Linear(in_features=self.d_model, out_features=self.codebook_dim) # Linear layer before quantization
self.post_quant_embed_pre = nn.Linear(in_features=self.s1_bits, out_features=self.d_model) # Linear layer after quantization (pre part - s1 bits)
self.post_quant_embed = nn.Linear(in_features=self.codebook_dim, out_features=self.d_model) # Linear layer after quantization (full codebook)
self.tokenizer = BSQuantizer(self.s1_bits, self.s2_bits, beta, gamma0, gamma, zeta, group_size) # BSQuantizer module
def forward(self, x):
"""
Forward pass of the WindFMTokenizer.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, seq_len, d_in).
Returns:
tuple: A tuple containing:
- tuple: (z_pre, z) - Reconstructed outputs from decoder with s1_bits and full codebook respectively,
both of shape (batch_size, seq_len, d_in).
- torch.Tensor: bsq_loss - Loss from the BSQuantizer.
- torch.Tensor: quantized - Quantized representation from BSQuantizer.
- torch.Tensor: z_indices - Indices from the BSQuantizer.
"""
z = self.embed(x)
for layer in self.encoder:
z = layer(z)
z = self.quant_embed(z) # (B, T, codebook)
bsq_loss, quantized, z_indices = self.tokenizer(z)
quantized_pre = quantized[:, :, :self.s1_bits] # Extract the first part of quantized representation (s1_bits)
z_pre = self.post_quant_embed_pre(quantized_pre)
z = self.post_quant_embed(quantized)
# Decoder layers (for pre part - s1 bits)
for layer in self.decoder:
z_pre = layer(z_pre)
z_pre = self.head(z_pre)
# Decoder layers (for full codebook)
for layer in self.decoder:
z = layer(z)
z = self.head(z)
return (z_pre, z), bsq_loss, quantized, z_indices
def indices_to_bits(self, x, half=False):
"""
Converts indices to bit representations and scales them.
Args:
x (torch.Tensor): Indices tensor.
half (bool, optional): Whether to process only half of the codebook dimension. Defaults to False.
Returns:
torch.Tensor: Bit representation tensor.
"""
if half:
x1 = x[0] # Assuming x is a tuple of indices if half is True
x2 = x[1]
mask = 2 ** torch.arange(self.codebook_dim//2, device=x1.device, dtype=torch.long) # Create a mask for bit extraction
x1 = (x1.unsqueeze(-1) & mask) != 0 # Extract bits for the first half
x2 = (x2.unsqueeze(-1) & mask) != 0 # Extract bits for the second half
x = torch.cat([x1, x2], dim=-1) # Concatenate the bit representations
else:
mask = 2 ** torch.arange(self.codebook_dim, device=x.device, dtype=torch.long) # Create a mask for bit extraction
x = (x.unsqueeze(-1) & mask) != 0 # Extract bits
x = x.float() * 2 - 1 # Convert boolean to bipolar (-1, 1)
q_scale = 1. / (self.codebook_dim ** 0.5) # Scaling factor
x = x * q_scale
return x
def encode(self, x, half=False):
"""
Encodes the input data into quantized indices.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, seq_len, d_in).
half (bool, optional): Whether to use half quantization in BSQuantizer. Defaults to False.
Returns:
torch.Tensor: Quantized indices from BSQuantizer.
"""
z = self.embed(x)
for layer in self.encoder:
z = layer(z)
z = self.quant_embed(z)
bsq_loss, quantized, z_indices = self.tokenizer(z, half)
return z_indices
def decode(self, x, half=False):
"""
Decodes quantized indices back to the input data space.
Args:
x (torch.Tensor): Quantized indices tensor.
half (bool, optional): Whether the indices were generated with half quantization. Defaults to False.
Returns:
torch.Tensor: Reconstructed output tensor of shape (batch_size, seq_len, d_in).
"""
quantized = self.indices_to_bits(x, half)
z = self.post_quant_embed(quantized)
for layer in self.decoder:
z = layer(z)
z = self.head(z)
return z
class WindFM(nn.Module, PyTorchModelHubMixin):
"""
WindFM Model.
Args:
s1_bits (int): Number of bits for pre tokens.
s2_bits (int): Number of bits for post tokens.
n_layers (int): Number of Transformer blocks.
d_model (int): Dimension of the model's embeddings and hidden states.
n_heads (int): Number of attention heads in the MultiheadAttention layers.
ff_dim (int): Dimension of the feedforward network in the Transformer blocks.
ffn_dropout_p (float): Dropout probability for the feedforward network.
attn_dropout_p (float): Dropout probability for the attention layers.
resid_dropout_p (float): Dropout probability for residual connections.
token_dropout_p (float): Dropout probability for token embeddings.
learn_te (bool): Whether to use learnable temporal embeddings.
"""
def __init__(self, s1_bits, s2_bits, n_layers, d_model, n_heads, ff_dim, ffn_dropout_p, attn_dropout_p, resid_dropout_p, token_dropout_p, learn_te):
super().__init__()
self.s1_bits = s1_bits
self.s2_bits = s2_bits
self.n_layers = n_layers
self.d_model = d_model
self.n_heads = n_heads
self.learn_te = learn_te
self.ff_dim = ff_dim
self.ffn_dropout_p = ffn_dropout_p
self.attn_dropout_p = attn_dropout_p
self.resid_dropout_p = resid_dropout_p
self.token_dropout_p = token_dropout_p
self.s1_vocab_size = 2 ** self.s1_bits
self.token_drop = nn.Dropout(self.token_dropout_p)
self.embedding = HierarchicalEmbedding(self.s1_bits, self.s2_bits, self.d_model)
self.time_emb = FourierTemporalEmbedding(self.d_model)
self.transformer = nn.ModuleList([
TransformerBlock(self.d_model, self.n_heads, self.ff_dim, self.ffn_dropout_p, self.attn_dropout_p, self.resid_dropout_p)
for _ in range(self.n_layers)
])
self.norm = RMSNorm(self.d_model)
self.dep_layer = DependencyAwareLayer(self.d_model)
self.head = DualHead(self.s1_bits, self.s2_bits, self.d_model)
self.apply(self._init_weights)
def _init_weights(self, module):
if isinstance(module, nn.Linear):
nn.init.xavier_normal_(module.weight)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
nn.init.normal_(module.weight, mean=0, std=self.embedding.d_model ** -0.5)
elif isinstance(module, nn.LayerNorm):
nn.init.ones_(module.weight)
nn.init.zeros_(module.bias)
elif isinstance(module, RMSNorm):
nn.init.ones_(module.weight)
def forward(self, s1_ids, s2_ids, stamp=None, padding_mask=None, use_teacher_forcing=False, s1_targets=None):
"""
Args:
s1_ids (torch.Tensor): Input tensor of s1 token IDs. Shape: [batch_size, seq_len]
s2_ids (torch.Tensor): Input tensor of s2 token IDs. Shape: [batch_size, seq_len]
stamp (torch.Tensor, optional): Temporal stamp tensor. Shape: [batch_size, seq_len]. Defaults to None.
padding_mask (torch.Tensor, optional): Mask for padding tokens. Shape: [batch_size, seq_len]. Defaults to None.
use_teacher_forcing (bool, optional): Whether to use teacher forcing for s1 decoding. Defaults to False.
s1_targets (torch.Tensor, optional): Target s1 token IDs for teacher forcing. Shape: [batch_size, seq_len]. Defaults to None.
Returns:
Tuple[torch.Tensor, torch.Tensor]:
- s1 logits: Logits for s1 token predictions. Shape: [batch_size, seq_len, s1_vocab_size]
- s2_logits: Logits for s2 token predictions, conditioned on s1. Shape: [batch_size, seq_len, s2_vocab_size]
"""
x = self.embedding([s1_ids, s2_ids])
if stamp is not None:
time_embedding = self.time_emb(stamp)
x = x + time_embedding
x = self.token_drop(x)
for layer in self.transformer:
x = layer(x, key_padding_mask=padding_mask)
x = self.norm(x)
s1_logits = self.head(x)
if use_teacher_forcing:
sibling_embed = self.embedding.emb_s1(s1_targets)
else:
s1_probs = F.softmax(s1_logits.detach(), dim=-1)
sample_s1_ids = torch.multinomial(s1_probs.view(-1, self.s1_vocab_size), 1).view(s1_ids.shape)
sibling_embed = self.embedding.emb_s1(sample_s1_ids)
x2 = self.dep_layer(x, sibling_embed, key_padding_mask=padding_mask) # Dependency Aware Layer: Condition on s1 embeddings
s2_logits = self.head.cond_forward(x2)
return s1_logits, s2_logits
def decode_s1(self, s1_ids, s2_ids, stamp=None, padding_mask=None):
"""
Decodes only the s1 tokens.
This method performs a forward pass to predict only s1 tokens. It returns the s1 logits
and the context representation from the Transformer, which can be used for subsequent s2 decoding.
Args:
s1_ids (torch.Tensor): Input tensor of s1 token IDs. Shape: [batch_size, seq_len]
s2_ids (torch.Tensor): Input tensor of s2 token IDs. Shape: [batch_size, seq_len]
stamp (torch.Tensor, optional): Temporal stamp tensor. Shape: [batch_size, seq_len]. Defaults to None.
padding_mask (torch.Tensor, optional): Mask for padding tokens. Shape: [batch_size, seq_len]. Defaults to None.
Returns:
Tuple[torch.Tensor, torch.Tensor]:
- s1 logits: Logits for s1 token predictions. Shape: [batch_size, seq_len, s1_vocab_size]
- context: Context representation from the Transformer. Shape: [batch_size, seq_len, d_model]
"""
x = self.embedding([s1_ids, s2_ids])
if stamp is not None:
time_embedding = self.time_emb(stamp)
x = x + time_embedding
x = self.token_drop(x)
for layer in self.transformer:
x = layer(x, key_padding_mask=padding_mask)
x = self.norm(x)
s1_logits = self.head(x)
return s1_logits, x
def decode_s2(self, context, s1_ids, padding_mask=None):
"""
Decodes the s2 tokens, conditioned on the context and s1 tokens.
This method decodes s2 tokens based on a pre-computed context representation (typically from `decode_s1`)
and the s1 token IDs. It uses the dependency-aware layer and the conditional s2 head to predict s2 tokens.
Args:
context (torch.Tensor): Context representation from the transformer (output of decode_s1).
Shape: [batch_size, seq_len, d_model]
s1_ids (torch.torch.Tensor): Input tensor of s1 token IDs. Shape: [batch_size, seq_len]
padding_mask (torch.Tensor, optional): Mask for padding tokens. Shape: [batch_size, seq_len]. Defaults to None.
Returns:
torch.Tensor: s2 logits. Shape: [batch_size, seq_len, s2_vocab_size]
"""
sibling_embed = self.embedding.emb_s1(s1_ids)
x2 = self.dep_layer(context, sibling_embed, key_padding_mask=padding_mask)
return self.head.cond_forward(x2)
def top_k_top_p_filtering(
logits,
top_k: int = 0,
top_p: float = 1.0,
filter_value: float = -float("Inf"),
min_tokens_to_keep: int = 1,
):
"""Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
Args:
logits: logits distribution shape (batch size, vocabulary size)
if top_k > 0: keep only top k tokens with highest probability (top-k filtering).
if top_p < 1.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
Make sure we keep at least min_tokens_to_keep per batch example in the output
From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
"""
if top_k > 0:
top_k = min(max(top_k, min_tokens_to_keep), logits.size(-1)) # Safety check
# Remove all tokens with a probability less than the last token of the top-k
indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
logits[indices_to_remove] = filter_value
return logits
if top_p < 1.0:
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
# Remove tokens with cumulative probability above the threshold (token with 0 are kept)
sorted_indices_to_remove = cumulative_probs > top_p
if min_tokens_to_keep > 1:
# Keep at least min_tokens_to_keep (set to min_tokens_to_keep-1 because we add the first one below)
sorted_indices_to_remove[..., :min_tokens_to_keep] = 0
# Shift the indices to the right to keep also the first token above the threshold
sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
sorted_indices_to_remove[..., 0] = 0
# scatter sorted tensors to original indexing
indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
logits[indices_to_remove] = filter_value
return logits
def sample_from_logits(logits, temperature=1.0, top_k=None, top_p=None, sample_logits=True):
logits = logits / temperature
if top_k is not None or top_p is not None:
if top_k > 0 or top_p < 1.0:
logits = top_k_top_p_filtering(logits, top_k=top_k, top_p=top_p)
probs = F.softmax(logits, dim=-1)
if not sample_logits:
_, x = top_k(probs, k=1, dim=-1)
else:
x = torch.multinomial(probs, num_samples=1)
return x
def auto_regressive_inference(tokenizer, model, x, x_stamp, y_stamp, max_lookback, pred_len, clip=5, T=1.0, top_k=0, top_p=0.99, sample_count=5, verbose=False):
with torch.no_grad():
batch_size = x.size(0)
initial_seq_len = x.size(1)
full_seq_len = initial_seq_len + pred_len
device = x.device
x = torch.clip(x, -clip, clip)
x_repeated = x.unsqueeze(1).repeat(1, sample_count, 1, 1).reshape(-1, x.size(1), x.size(2))
x_stamp_repeated = x_stamp.unsqueeze(1).repeat(1, sample_count, 1, 1).reshape(-1, x_stamp.size(1), x_stamp.size(2))
y_stamp_repeated = y_stamp.unsqueeze(1).repeat(1, sample_count, 1, 1).reshape(-1, y_stamp.size(1), y_stamp.size(2))
full_stamps = torch.cat([x_stamp_repeated, y_stamp_repeated], dim=1).to(device)
initial_x_token = tokenizer.encode(x_repeated, half=True)
full_x_token_s1 = torch.zeros(
(batch_size * sample_count, full_seq_len),
dtype=initial_x_token[0].dtype,
device=device
)
full_x_token_s2 = torch.zeros(
(batch_size * sample_count, full_seq_len),
dtype=initial_x_token[1].dtype,
device=device
)
full_x_token_s1[:, :initial_seq_len] = initial_x_token[0]
full_x_token_s2[:, :initial_seq_len] = initial_x_token[1]
if verbose:
ran = trange
else:
ran = range
for i in ran(pred_len):
current_pos = initial_seq_len + i
start_idx = max(0, current_pos - max_lookback)
end_idx = current_pos
input_tokens_s1 = full_x_token_s1[:, start_idx:end_idx]
input_tokens_s2 = full_x_token_s2[:, start_idx:end_idx]
current_stamp = full_stamps[:, start_idx:end_idx, :]
s1_logits, context = model.decode_s1(input_tokens_s1, input_tokens_s2, current_stamp)
s1_logits = s1_logits[:, -1, :]
sample_pre = sample_from_logits(s1_logits, temperature=T, top_k=top_k, top_p=top_p, sample_logits=True)
s2_logits = model.decode_s2(context, sample_pre)
s2_logits = s2_logits[:, -1, :]
sample_post = sample_from_logits(s2_logits, temperature=T, top_k=top_k, top_p=top_p, sample_logits=True)
full_x_token_s1[:, current_pos] = sample_pre.squeeze(1)
full_x_token_s2[:, current_pos] = sample_post.squeeze(1)
final_tokens_to_decode = [
full_x_token_s1[:, -max_lookback:].contiguous(),
full_x_token_s2[:, -max_lookback:].contiguous()
]
z = tokenizer.decode(final_tokens_to_decode, half=True)
z = z.reshape(batch_size, sample_count, z.size(1), z.size(2))
preds = z.cpu().numpy()
return preds
def calc_time_stamps(x_timestamp):
time_df = pd.DataFrame()
time_df['minute'] = x_timestamp.dt.minute
time_df['hour'] = x_timestamp.dt.hour
time_df['weekday'] = x_timestamp.dt.weekday
time_df['day'] = x_timestamp.dt.day
time_df['month'] = x_timestamp.dt.month
return time_df
class WindFMPredictor:
def __init__(self, model, tokenizer, device="cuda:0", max_context=512, clip=5):
self.tokenizer = tokenizer
self.model = model
self.max_context = max_context
self.clip = clip
self.feature_cols = ['wind_speed', 'wind_direction', 'power', 'density', 'temperature', 'pressure']
self.time_cols = ['minute', 'hour', 'weekday', 'day', 'month']
self.device = device
self.tokenizer = self.tokenizer.to(self.device)
self.model = self.model.to(self.device)
def generate(self, x, x_stamp, y_stamp, pred_len, T, top_k, top_p, sample_count, verbose):
x_tensor = torch.from_numpy(np.array(x).astype(np.float32)).to(self.device)
x_stamp_tensor = torch.from_numpy(np.array(x_stamp).astype(np.float32)).to(self.device)
y_stamp_tensor = torch.from_numpy(np.array(y_stamp).astype(np.float32)).to(self.device)
preds = auto_regressive_inference(self.tokenizer, self.model, x_tensor, x_stamp_tensor, y_stamp_tensor, self.max_context, pred_len,
self.clip, T, top_k, top_p, sample_count, verbose)
preds = preds[:, -pred_len:, :]
return preds
def predict(self, df, x_timestamp, y_timestamp, pred_len, T=1.0, top_k=0, top_p=0.9, sample_count=1, verbose=True):
if not isinstance(df, pd.DataFrame):
raise ValueError("Input must be a pandas DataFrame.")
if not all(col in df.columns for col in self.feature_cols):
raise ValueError(f"Feature columns {self.feature_cols} not found in DataFrame.")
df = df.copy()
if df[self.feature_cols].isnull().values.any():
raise ValueError("Input DataFrame contains NaN values in price or volume columns.")
x_time_df = calc_time_stamps(x_timestamp)
y_time_df = calc_time_stamps(y_timestamp)
x = df[self.feature_cols].values.astype(np.float32)
x_stamp = x_time_df.values.astype(np.float32)
y_stamp = y_time_df.values.astype(np.float32)
x_mean, x_std = np.mean(x, axis=0), np.std(x, axis=0)
x = (x - x_mean) / (x_std + 1e-6)
x = np.clip(x, -self.clip, self.clip)
x = x[np.newaxis, :]
x_stamp = x_stamp[np.newaxis, :]
y_stamp = y_stamp[np.newaxis, :]
preds = self.generate(x, x_stamp, y_stamp, pred_len, T, top_k, top_p, sample_count, verbose)
preds = preds.squeeze(0)
preds = preds * (x_std + 1e-6) + x_mean
# take the power prediction
preds = preds[:, -pred_len:, 2]
preds = np.swapaxes(preds, 0, 1) # (pred_len, sample_count)
pred_df = pd.DataFrame(preds, columns=[f'pred-{i}' for i in range(preds.shape[1])], index=y_timestamp)
return pred_df
You can’t perform that action at this time.