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FEAT: Support exog features in TimeMixer #1088

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FEAT: Support exog features in TimeMixer #1088

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d9e08f9
Fix bug for futr_exog
marcopeix Jul 29, 2024
eddd1af
Fix how futr_exog are selected
marcopeix Jul 29, 2024
7c81b17
Fix nbdev version to avoid bug in linting check
marcopeix Jul 29, 2024
0ce93fe
Revert changes in env yaml and fix nbdev in lint task
marcopeix Jul 29, 2024
8ae7894
Fix conflicts
marcopeix Jul 29, 2024
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40 changes: 29 additions & 11 deletions nbs/models.timemixer.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -36,8 +36,6 @@
"source": [
"#| export\n",
"\n",
"import numpy as np\n",
"\n",
"import torch\n",
"import torch.nn as nn\n",
"\n",
Expand Down Expand Up @@ -464,7 +462,6 @@
" down_sampling_window: int = 2,\n",
" down_sampling_method: str = 'avg',\n",
" use_norm: bool = True,\n",
" decoder_input_size_multiplier: float = 0.5,\n",
" loss = MAE(),\n",
" valid_loss = None,\n",
" max_steps: int = 1000,\n",
Expand Down Expand Up @@ -509,10 +506,6 @@
" lr_scheduler_kwargs=lr_scheduler_kwargs,\n",
" **trainer_kwargs)\n",
" \n",
" self.label_len = int(np.ceil(input_size * decoder_input_size_multiplier))\n",
" if (self.label_len >= input_size) or (self.label_len <= 0):\n",
" raise Exception(f'Check decoder_input_size_multiplier={decoder_input_size_multiplier}, range (0,1)')\n",
" \n",
" self.h = h\n",
" self.input_size = input_size\n",
" self.e_layers = e_layers\n",
Expand Down Expand Up @@ -659,9 +652,9 @@
" if self.channel_independence == 1:\n",
" B, T, N = x_enc.size()\n",
" x_mark_dec = x_mark_dec.repeat(N, 1, 1)\n",
" self.x_mark_dec = self.enc_embedding(None, x_mark_dec)\n",
" self.x_mark_dec = self.enc_embedding(None, x_mark_dec) \n",
" else:\n",
" self.x_mark_dec = self.enc_embedding(None, x_mark_dec)\n",
" self.x_mark_dec = self.enc_embedding(x_enc, x_mark_dec) #MODIFIED\n",
"\n",
" x_enc, x_mark_enc = self.__multi_scale_process_inputs(x_enc, x_mark_enc)\n",
"\n",
Expand Down Expand Up @@ -736,8 +729,8 @@
" futr_exog = windows_batch['futr_exog']\n",
"\n",
" if self.futr_exog_size > 0:\n",
" x_mark_enc = futr_exog[:,:self.input_size,:]\n",
" x_mark_dec = futr_exog[:,-(self.label_len+self.h):,:]\n",
" x_mark_enc = futr_exog[:, :, :self.input_size]\n",
" x_mark_dec = futr_exog[:, -(self.h):, :]\n",
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" else:\n",
" x_mark_enc = None\n",
" x_mark_dec = None\n",
Expand Down Expand Up @@ -878,6 +871,31 @@
"forecasts = fcst.predict(futr_df=Y_test_df)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| eval: false\n",
"\n",
"Y_train_df = AirPassengersPanel[AirPassengersPanel.ds<AirPassengersPanel['ds'].values[-12]].reset_index(drop=True) # 132 train\n",
"Y_test_df = AirPassengersPanel[AirPassengersPanel.ds>=AirPassengersPanel['ds'].values[-12]].reset_index(drop=True) # 12 test\n",
"\n",
"model = TimeMixer(h=12,\n",
" input_size=24,\n",
" n_series=2,\n",
" futr_exog_list=['trend'],\n",
" loss=MSE(),\n",
" valid_loss=MAE(),\n",
" early_stop_patience_steps=3,\n",
" batch_size=32)\n",
"\n",
"fcst = NeuralForecast(models=[model], freq='M')\n",
"fcst.fit(df=Y_train_df, static_df=AirPassengersStatic, val_size=12)\n",
"forecasts = fcst.predict(futr_df=Y_test_df)"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand Down
19 changes: 5 additions & 14 deletions neuralforecast/models/timemixer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,8 +5,6 @@
'PastDecomposableMixing', 'TimeMixer']

# %% ../../nbs/models.timemixer.ipynb 3
import numpy as np

import torch
import torch.nn as nn

Expand Down Expand Up @@ -386,7 +384,6 @@ def __init__(
down_sampling_window: int = 2,
down_sampling_method: str = "avg",
use_norm: bool = True,
decoder_input_size_multiplier: float = 0.5,
loss=MAE(),
valid_loss=None,
max_steps: int = 1000,
Expand All @@ -404,7 +401,7 @@ def __init__(
optimizer_kwargs=None,
lr_scheduler=None,
lr_scheduler_kwargs=None,
**trainer_kwargs,
**trainer_kwargs
):

super(TimeMixer, self).__init__(
Expand All @@ -431,15 +428,9 @@ def __init__(
optimizer_kwargs=optimizer_kwargs,
lr_scheduler=lr_scheduler,
lr_scheduler_kwargs=lr_scheduler_kwargs,
**trainer_kwargs,
**trainer_kwargs
)

self.label_len = int(np.ceil(input_size * decoder_input_size_multiplier))
if (self.label_len >= input_size) or (self.label_len <= 0):
raise Exception(
f"Check decoder_input_size_multiplier={decoder_input_size_multiplier}, range (0,1)"
)

self.h = h
self.input_size = input_size
self.e_layers = e_layers
Expand Down Expand Up @@ -618,7 +609,7 @@ def forecast(self, x_enc, x_mark_enc, x_mark_dec):
x_mark_dec = x_mark_dec.repeat(N, 1, 1)
self.x_mark_dec = self.enc_embedding(None, x_mark_dec)
else:
self.x_mark_dec = self.enc_embedding(None, x_mark_dec)
self.x_mark_dec = self.enc_embedding(x_enc, x_mark_dec) # MODIFIED

x_enc, x_mark_enc = self.__multi_scale_process_inputs(x_enc, x_mark_enc)

Expand Down Expand Up @@ -704,8 +695,8 @@ def forward(self, windows_batch):
futr_exog = windows_batch["futr_exog"]

if self.futr_exog_size > 0:
x_mark_enc = futr_exog[:, : self.input_size, :]
x_mark_dec = futr_exog[:, -(self.label_len + self.h) :, :]
x_mark_enc = futr_exog[:, :, : self.input_size]
x_mark_dec = futr_exog[:, -(self.h) :, :]
else:
x_mark_enc = None
x_mark_dec = None
Expand Down
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