Agent Conversation: google/gemini-3.1-pro-preview

Model: google/gemini-3.1-pro-preview

Total steps: 7

Messages: 15

UserStep 0

Task: ts-imputation

Time Series Imputation: Custom Model Design

Objective

Design and implement a custom deep learning model for time series missing value imputation. Your code goes in the Model class in models/Custom.py. Three reference implementations (DLinear, TimesNet, PatchTST) are provided as read-only.

Evaluation

Trained and evaluated on three multivariate datasets with 25% random masking:

ETTh1 (7 variables)
Weather (21 variables)
ECL (321 variables)

All use seq_len=96. Metrics: MSE and MAE on masked regions only (lower is better).

Time-Series-Library/models/Custom.py [EDITABLE — entire file only]

     1: import torch
     2: import torch.nn as nn
     3: 
     4: 
     5: class Model(nn.Module):
     6:     """
     7:     Custom model for time series imputation.
     8: 
     9:     Forward signature: forward(x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None)
    10:     - x_enc: [batch, seq_len, enc_in] — input with masked values set to 0
    11:     - x_mark_enc: [batch, seq_len, time_features] — time feature encoding
    12:     - x_dec: not used for imputation (None)
    13:     - x_mark_dec: not used for imputation (None)
    14:     - mask: [batch, seq_len, enc_in] — binary mask (1=observed, 0=masked)
    15: 
    16:     Must return: [batch, seq_len, enc_in] — reconstructed sequence
    17:     """
    18: 
    19:     def __init__(self, configs):
    20:         super(Model, self).__init__()
    21:         self.task_name = configs.task_name
    22:         self.seq_len = configs.seq_len
    23:         self.pred_len = configs.seq_len  # For imputation, pred_len = seq_len
    24:         self.enc_in = configs.enc_in
    25:         # TODO: Define your model architecture here
    26: 
    27:     def imputation(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask):
    28:         """
    29:         Imputation: reconstruct missing values in the input sequence.
    30:         Input: x_enc [batch, seq_len, enc_in] with zeros at masked positions
    31:         Mask: [batch, seq_len, enc_in], 1=observed, 0=masked
    32:         Output: [batch, seq_len, enc_in]
    33:         """
    34:         # TODO: Implement your imputation logic
    35:         return x_enc  # Placeholder: return input as-is
    36: 
    37:     def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):
    38:         if self.task_name == 'imputation':
    39:             dec_out = self.imputation(x_enc, x_mark_enc, x_dec, x_mark_dec, mask)
    40:             return dec_out
    41:         return None

Time-Series-Library/models/DLinear.py [READ-ONLY — do not edit]

     1: import torch
     2: import torch.nn as nn
     3: import torch.nn.functional as F
     4: from layers.Autoformer_EncDec import series_decomp
     5: 
     6: 
     7: class Model(nn.Module):
     8:     """
     9:     Paper link: https://arxiv.org/pdf/2205.13504.pdf
    10:     """
    11: 
    12:     def __ini

AssistantStep 1edit

Tool Input

1{
2  "start_line": 1,
3  "op": "replace",
4  "end_line": 41,
5  "filename": "models/Custom.py",
6  "content": "import torch\nimport torch.nn as nn\nfrom layers.Embed import PositionalEmbedding\n\nclass CustomPatchEmbedding(nn.Module):\n    def __init__(self, d_model, patch_len, stride, dropout):\n        super().__init__()\n        self.patch_len = patch_len\n        self.stride = stride\n        self.value_embedding = nn.Linear(patch_len * 2, d_model, bias=False)\n        self.position_embedding = PositionalEmbedding(d_model)\n        self.dropout = nn.Dropout(dropout)\n\n    def forward(self, x, mask):\n        # x, mask: [sz, 1, seq_len]\n        padding = self.stride\n        x = nn.functional.pad(x, (padding, 0))\n        mask = nn.functional.pad(mask, (padding, 0), value=1)\n\n        x = x.unfold(dimension=-1, size=self.patch_len, step=self.stride)\n        mask = mask.unfold(dimension=-1, size=self.patch_len, step=self.stride)\n\n        x = x.squeeze(1) # [sz, patch_num, patch_len]\n        mask = mask.squeeze(1)\n\n        x_cat = torch.cat([x, mask], dim=-1) # [sz, patch_num, patch_len * 2]\n\n        x_emb = self.value_embedding(x_cat) + self.position_embedding(x_cat)\n        return self.dropout(x_emb)\n\nclass Model(nn.Module):\n    def __init__(self, configs):\n        super(Model, self).__init__()\n        self.task_name = configs.task_name\n        self.seq_len = configs.seq_len\n        self.pred_len = configs.seq_len\n        self.enc_in = configs.enc_in\n        self.d_model = configs.d_model\n        \n        self.patch_len = 16\n        self.stride = 8\n        self.num_iterations = 2  # Multiple refinement steps\n        \n        # Fallback for number of heads\n        n_heads = configs.n_heads\n        if self.d_model % n_heads != 0:\n            n_heads = 1\n            \n        self.patch_embedding = CustomPatchEmbedding(self.d_model, self.patch_len, self.stride, configs.dropout)\n        \n        encoder_layer = nn.TransformerEncoderLayer(\n            d_model=self.d_model, \n            nhead=n_heads, \n            dim_feedforward=configs.d_ff, \n            dropout=configs.dropout, \n            activation='gelu', \n            batch_first=True\n        )\n        self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=configs.e_layers)\n        \n        self.mix_channels = nn.MultiheadAttention(embed_dim=self.d_model, num_heads=n_heads, batch_first=True, dropout=configs.dropout)\n        self.channel_norm = nn.LayerNorm(self.d_model)\n        \n        self.step_embeddings = nn.Parameter(torch.randn(self.num_iterations, 1, 1, self.d_model))\n        \n        self.head_nf = self.d_model * int((self.seq_len - self.patch_len) / self.stride + 2)\n        self.head = nn.Sequential(\n            nn.Flatten(start_dim=-2),\n            nn.Linear(self.head_nf, self.seq_len),\n            nn.Dropout(configs.dropout)\n        )\n\n    def imputation(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask):\n        B, L, D = x_enc.shape\n        \n        means = torch.sum(x_enc, dim=1) / (torch.sum(mask == 1, dim=1) + 1e-5)\n        means = means.unsqueeze(1).detach()\n        x_enc = x_enc - means\n        x_enc = x_enc.masked_fill(mask == 0, 0)\n        \n        stdev = torch.sqrt(torch.sum(x_enc * x_enc, dim=1) / (torch.sum(mask == 1, dim=1) + 1e-5) + 1e-5)\n        stdev = stdev.unsqueeze(1).detach()\n        x_enc /= stdev\n\n        x_current = x_enc.clone()\n        m_in = mask.permute(0, 2, 1).reshape(-1, 1, L)\n        \n        for i in range(self.num_iterations):\n            x_in = x_current.permute(0, 2, 1).reshape(-1, 1, L)\n            \n            enc_out = self.patch_embedding(x_in, m_in)\n            num_patches = enc_out.shape[1]\n            \n            enc_out = enc_out + self.step_embeddings[i]\n            enc_out = self.encoder(enc_out)\n            \n            enc_out = enc_out.reshape(B, self.enc_in, num_patches, self.d_model)\n            enc_out = enc_out.permute(0, 2, 1, 3).reshape(B * num_patches, self.enc_in, self.d_model)\n            \n            mix_out, _ = self.mix_channels(enc_out, enc_out, enc_out)\n            enc_out = self.channel_norm(enc_out + mix_out)\n            \n            enc_out = enc_out.reshape(B, num_patches, self.enc_in, self.d_model).permute(0, 2, 1, 3).reshape(B * self.enc_in, num_patches, self.d_model)\n            \n            dec_out = self.head(enc_out)\n            dec_out = dec_out.reshape(B, self.enc_in, L).permute(0, 2, 1)\n            \n            if i < self.num_iterations - 1:\n                x_current = x_enc * mask + dec_out * (1 - mask)\n\n        dec_out = dec_out * (stdev[:, 0, :].unsqueeze(1).repeat(1, L, 1))\n        dec_out = dec_out + (means[:, 0, :].unsqueeze(1).repeat(1, L, 1))\n        \n        return dec_out\n\n    def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):\n        if self.task_name == 'imputation':\n            dec_out = self.imputation(x_enc, x_mark_enc, x_dec, x_mark_dec, mask)\n            return dec_out\n        return None"
7}

Tool ResultStep 1

ERROR: Package 'models' is not in allowed packages

AssistantStep 2edit

Tool Input

1{
2  "start_line": 1,
3  "end_line": 41,
4  "op": "replace",
5  "filename": "Time-Series-Library/models/Custom.py",
6  "content": "import torch\nimport torch.nn as nn\nfrom layers.Embed import PositionalEmbedding\n\nclass CustomPatchEmbedding(nn.Module):\n    def __init__(self, d_model, patch_len, stride, dropout):\n        super().__init__()\n        self.patch_len = patch_len\n        self.stride = stride\n        self.value_embedding = nn.Linear(patch_len * 2, d_model, bias=False)\n        self.position_embedding = PositionalEmbedding(d_model)\n        self.dropout = nn.Dropout(dropout)\n\n    def forward(self, x, mask):\n        # x, mask: [sz, 1, seq_len]\n        padding = self.stride\n        x = nn.functional.pad(x, (padding, 0))\n        mask = nn.functional.pad(mask, (padding, 0), value=1)\n\n        x = x.unfold(dimension=-1, size=self.patch_len, step=self.stride)\n        mask = mask.unfold(dimension=-1, size=self.patch_len, step=self.stride)\n\n        x = x.squeeze(1) # [sz, patch_num, patch_len]\n        mask = mask.squeeze(1)\n\n        x_cat = torch.cat([x, mask], dim=-1) # [sz, patch_num, patch_len * 2]\n\n        x_emb = self.value_embedding(x_cat) + self.position_embedding(x_cat)\n        return self.dropout(x_emb)\n\nclass Model(nn.Module):\n    def __init__(self, configs):\n        super(Model, self).__init__()\n        self.task_name = configs.task_name\n        self.seq_len = configs.seq_len\n        self.pred_len = configs.seq_len\n        self.enc_in = configs.enc_in\n        self.d_model = configs.d_model\n        \n        self.patch_len = 16\n        self.stride = 8\n        self.num_iterations = 2  # Multiple refinement steps\n        \n        # Fallback for number of heads\n        n_heads = configs.n_heads\n        if self.d_model % n_heads != 0:\n            n_heads = 1\n            \n        self.patch_embedding = CustomPatchEmbedding(self.d_model, self.patch_len, self.stride, configs.dropout)\n        \n        encoder_layer = nn.TransformerEncoderLayer(\n            d_model=self.d_model, \n            nhead=n_heads, \n            dim_feedforward=configs.d_ff, \n            dropout=configs.dropout, \n            activation='gelu', \n            batch_first=True\n        )\n        self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=configs.e_layers)\n        \n        self.mix_channels = nn.MultiheadAttention(embed_dim=self.d_model, num_heads=n_heads, batch_first=True, dropout=configs.dropout)\n        self.channel_norm = nn.LayerNorm(self.d_model)\n        \n        self.step_embeddings = nn.Parameter(torch.randn(self.num_iterations, 1, 1, self.d_model))\n        \n        self.head_nf = self.d_model * int((self.seq_len - self.patch_len) / self.stride + 2)\n        self.head = nn.Sequential(\n            nn.Flatten(start_dim=-2),\n            nn.Linear(self.head_nf, self.seq_len),\n            nn.Dropout(configs.dropout)\n        )\n\n    def imputation(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask):\n        B, L, D = x_enc.shape\n        \n        means = torch.sum(x_enc, dim=1) / (torch.sum(mask == 1, dim=1) + 1e-5)\n        means = means.unsqueeze(1).detach()\n        x_enc = x_enc - means\n        x_enc = x_enc.masked_fill(mask == 0, 0)\n        \n        stdev = torch.sqrt(torch.sum(x_enc * x_enc, dim=1) / (torch.sum(mask == 1, dim=1) + 1e-5) + 1e-5)\n        stdev = stdev.unsqueeze(1).detach()\n        x_enc /= stdev\n\n        x_current = x_enc.clone()\n        m_in = mask.permute(0, 2, 1).reshape(-1, 1, L)\n        \n        for i in range(self.num_iterations):\n            x_in = x_current.permute(0, 2, 1).reshape(-1, 1, L)\n            \n            enc_out = self.patch_embedding(x_in, m_in)\n            num_patches = enc_out.shape[1]\n            \n            enc_out = enc_out + self.step_embeddings[i]\n            enc_out = self.encoder(enc_out)\n            \n            enc_out = enc_out.reshape(B, self.enc_in, num_patches, self.d_model)\n            enc_out = enc_out.permute(0, 2, 1, 3).reshape(B * num_patches, self.enc_in, self.d_model)\n            \n            mix_out, _ = self.mix_channels(enc_out, enc_out, enc_out)\n            enc_out = self.channel_norm(enc_out + mix_out)\n            \n            enc_out = enc_out.reshape(B, num_patches, self.enc_in, self.d_model).permute(0, 2, 1, 3).reshape(B * self.enc_in, num_patches, self.d_model)\n            \n            dec_out = self.head(enc_out)\n            dec_out = dec_out.reshape(B, self.enc_in, L).permute(0, 2, 1)\n            \n            if i < self.num_iterations - 1:\n                x_current = x_enc * mask + dec_out * (1 - mask)\n\n        dec_out = dec_out * (stdev[:, 0, :].unsqueeze(1).repeat(1, L, 1))\n        dec_out = dec_out + (means[:, 0, :].unsqueeze(1).repeat(1, L, 1))\n        \n        return dec_out\n\n    def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):\n        if self.task_name == 'imputation':\n            dec_out = self.imputation(x_enc, x_mark_enc, x_dec, x_mark_dec, mask)\n            return dec_out\n        return None\n"
7}

Tool ResultStep 2

OK: Replaced lines 1..41 with 120 line(s) in Time-Series-Library/models/Custom.py. Editable range: entire file.

[Current file: Time-Series-Library/models/Custom.py | editable: 1–120 | total: 120 lines]
     1  import torch
     2  import torch.nn as nn
     3  from layers.Embed import PositionalEmbedding
     4  
     5  class CustomPatchEmbedding(nn.Module):
     6      def __init__(self, d_model, patch_len, stride, dropout):
     7          super().__init__()
     8          self.patch_len = patch_len
     9          self.stride = stride
    10          self.value_embedding = nn.Linear(patch_len * 2, d_model, bias=False)
    11          self.position_embedding = PositionalEmbedding(d_model)
    12          self.dropout = nn.Dropout(dropout)
    13  
    14      def forward(self, x, mask):
    15          # x, mask: [sz, 1, seq_len]
    16          padding = self.stride
    17          x = nn.functional.pad(x, (padding, 0))
    18          mask = nn.functional.pad(mask, (padding, 0),

AssistantStep 3test

Tool ResultStep 3

[Test #1] (2 tests remaining; call submit(n=1) to submit this result)

### ETTh1 (scripts/etth1.sh)
Training metrics (last epochs):
TRAIN_METRICS epoch=6 train_loss=0.0932487 vali_loss=0.0800568 test_loss=0.0686298
TRAIN_METRICS epoch=7 train_loss=0.0914097 vali_loss=0.0769945 test_loss=0.0654639
TRAIN_METRICS epoch=8 train_loss=0.0901238 vali_loss=0.0765355 test_loss=0.0645929
TRAIN_METRICS epoch=9 train_loss=0.0899475 vali_loss=0.0760268 test_loss=0.0657365
TRAIN_METRICS epoch=10 train_loss=0.0895045 vali_loss=0.0751887 test_loss=0.0639567
Test results (ETTh1):
  MSE: 0.064165, MAE: 0.171364

### Weather (scripts/weather.sh)
Training metrics (last epochs):
TRAIN_METRICS epoch=2 train_loss=0.4374132 vali_loss=0.3697360 test_loss=0.1108314
TRAIN_METRICS epoch=3 train_loss=0.4494066 vali_loss=0.3624937 test_loss=0.1093204
TRAIN_METRICS epoch=4 train_loss=0.4327891 vali_loss=0.3662713 test_loss=0.1094134
TRAIN_METRICS epoch=5 train_loss=0.4134600 vali_loss=0.3703071 test_loss=0.1090000
T

AssistantStep 4edit

Tool Input

1{
2  "end_line": 120,
3  "content": "import torch\nimport torch.nn as nn\nfrom layers.Embed import PositionalEmbedding\n\nclass CustomPatchEmbedding(nn.Module):\n    def __init__(self, d_model, patch_len, stride, dropout):\n        super().__init__()\n        self.patch_len = patch_len\n        self.stride = stride\n        self.padding = stride\n        self.value_embedding = nn.Linear(patch_len * 2, d_model, bias=False)\n        self.position_embedding = PositionalEmbedding(d_model)\n        self.dropout = nn.Dropout(dropout)\n\n    def forward(self, x, mask):\n        x = nn.functional.pad(x, (0, self.padding), mode='replicate')\n        mask = nn.functional.pad(mask, (0, self.padding), mode='replicate')\n\n        x = x.unfold(dimension=-1, size=self.patch_len, step=self.stride)\n        mask = mask.unfold(dimension=-1, size=self.patch_len, step=self.stride)\n\n        x = x.squeeze(1)\n        mask = mask.squeeze(1)\n\n        x_cat = torch.cat([x, mask], dim=-1)\n\n        x_emb = self.value_embedding(x_cat) + self.position_embedding(x_cat)\n        return self.dropout(x_emb)\n\nclass Model(nn.Module):\n    def __init__(self, configs):\n        super(Model, self).__init__()\n        self.task_name = configs.task_name\n        self.seq_len = configs.seq_len\n        self.pred_len = configs.seq_len\n        self.enc_in = configs.enc_in\n        self.d_model = configs.d_model\n        \n        self.patch_len = 16\n        self.stride = 8\n        self.num_iterations = 2\n        \n        n_heads = configs.n_heads\n        if self.d_model % n_heads != 0:\n            n_heads = 1\n            \n        self.patch_embedding = CustomPatchEmbedding(self.d_model, self.patch_len, self.stride, configs.dropout)\n        \n        encoder_layer = nn.TransformerEncoderLayer(\n            d_model=self.d_model, \n            nhead=n_heads, \n            dim_feedforward=configs.d_ff, \n            dropout=configs.dropout, \n            activation='gelu', \n            batch_first=True\n        )\n        self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=configs.e_layers)\n        \n        self.step_embeddings = nn.Parameter(torch.randn(self.num_iterations, 1, 1, self.d_model))\n        \n        self.head_nf = self.d_model * int((self.seq_len - self.patch_len) / self.stride + 2)\n        self.head = nn.Sequential(\n            nn.Flatten(start_dim=-2),\n            nn.Linear(self.head_nf, self.seq_len),\n            nn.Dropout(configs.dropout)\n        )\n\n    def imputation(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask):\n        B, L, D = x_enc.shape\n        \n        means = torch.sum(x_enc, dim=1) / (torch.sum(mask == 1, dim=1) + 1e-5)\n        means = means.unsqueeze(1).detach()\n        x_enc = x_enc - means\n        x_enc = x_enc.masked_fill(mask == 0, 0)\n        \n        stdev = torch.sqrt(torch.sum(x_enc * x_enc, dim=1) / (torch.sum(mask == 1, dim=1) + 1e-5) + 1e-5)\n        stdev = stdev.unsqueeze(1).detach()\n        x_enc /= stdev\n\n        x_current = x_enc.clone()\n        m_in = mask.permute(0, 2, 1).reshape(-1, 1, L)\n        \n        for i in range(self.num_iterations):\n            x_in = x_current.permute(0, 2, 1).reshape(-1, 1, L)\n            \n            enc_out = self.patch_embedding(x_in, m_in)\n            \n            enc_out = enc_out + self.step_embeddings[i]\n            enc_out = self.encoder(enc_out)\n            \n            dec_out = self.head(enc_out)\n            dec_out = dec_out.reshape(B, self.enc_in, L).permute(0, 2, 1)\n            \n            if i < self.num_iterations - 1:\n                x_current = x_enc * mask + dec_out * (1 - mask)\n\n        dec_out = dec_out * (stdev[:, 0, :].unsqueeze(1).repeat(1, L, 1))\n        dec_out = dec_out + (means[:, 0, :].unsqueeze(1).repeat(1, L, 1))\n        \n        return dec_out\n\n    def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):\n        if self.task_name == 'imputation':\n            dec_out = self.imputation(x_enc, x_mark_enc, x_dec, x_mark_dec, mask)\n            return dec_out\n        return None",
4  "filename": "Time-Series-Library/models/Custom.py",
5  "start_line": 1,
6  "op": "replace"
7}

Tool ResultStep 4

OK: Replaced lines 1..120 with 106 line(s) in Time-Series-Library/models/Custom.py. Editable range: entire file.

[Current file: Time-Series-Library/models/Custom.py | editable: 1–106 | total: 106 lines]
     1  import torch
     2  import torch.nn as nn
     3  from layers.Embed import PositionalEmbedding
     4  
     5  class CustomPatchEmbedding(nn.Module):
     6      def __init__(self, d_model, patch_len, stride, dropout):
     7          super().__init__()
     8          self.patch_len = patch_len
     9          self.stride = stride
    10          self.padding = stride
    11          self.value_embedding = nn.Linear(patch_len * 2, d_model, bias=False)
    12          self.position_embedding = PositionalEmbedding(d_model)
    13          self.dropout = nn.Dropout(dropout)
    14  
    15      def forward(self, x, mask):
    16          x = nn.functional.pad(x, (0, self.padding), mode='replicate')
    17          mask = nn.functional.pad(mask, (0, self.padding), mode='replicat

AssistantStep 5test

Tool ResultStep 5

[Test #2] (1 test remaining; call submit(n=2) to submit this result)

### ETTh1 (scripts/etth1.sh)
Training metrics (last epochs):
TRAIN_METRICS epoch=6 train_loss=0.1043212 vali_loss=0.0954621 test_loss=0.0819705
TRAIN_METRICS epoch=7 train_loss=0.1037652 vali_loss=0.0932917 test_loss=0.0796512
TRAIN_METRICS epoch=8 train_loss=0.1026268 vali_loss=0.0927561 test_loss=0.0784908
TRAIN_METRICS epoch=9 train_loss=0.1031907 vali_loss=0.0925089 test_loss=0.0786852
TRAIN_METRICS epoch=10 train_loss=0.1029212 vali_loss=0.0924250 test_loss=0.0783484
Test results (ETTh1):
  MSE: 0.078893, MAE: 0.186583

### Weather (scripts/weather.sh)
Training metrics (last epochs):
TRAIN_METRICS epoch=6 train_loss=0.2370697 vali_loss=0.1528074 test_loss=0.0502007
TRAIN_METRICS epoch=7 train_loss=0.2148363 vali_loss=0.1589199 test_loss=0.0483600
TRAIN_METRICS epoch=8 train_loss=0.2191332 vali_loss=0.1422937 test_loss=0.0484339
TRAIN_METRICS epoch=9 train_loss=0.2386333 vali_loss=0.1506638 test_loss=0.0477611
TR

AssistantStep 6edit

Tool Input

1{
2  "filename": "Time-Series-Library/models/Custom.py",
3  "op": "replace",
4  "start_line": 83,
5  "end_line": 95,
6  "content": "        for i in range(self.num_iterations):\n            x_in = x_current.permute(0, 2, 1).reshape(-1, 1, L)\n            \n            enc_out = self.patch_embedding(x_in, m_in)\n            \n            enc_out = enc_out + self.step_embeddings[i]\n            enc_out = self.encoder(enc_out)\n            \n            dec_out = self.head(enc_out)\n            dec_out = dec_out.reshape(B, self.enc_in, L).permute(0, 2, 1)\n            \n            if i < self.num_iterations - 1:\n                x_current = x_enc * mask + dec_out.detach() * (1 - mask)"
7}

Tool ResultStep 6

OK: Replaced lines 83..95 with 13 line(s) in Time-Series-Library/models/Custom.py. Editable range: entire file.

[Current file: Time-Series-Library/models/Custom.py | editable: 1–106 | total: 106 lines]
     1  import torch
     2  import torch.nn as nn
     3  from layers.Embed import PositionalEmbedding
     4  
     5  class CustomPatchEmbedding(nn.Module):
     6      def __init__(self, d_model, patch_len, stride, dropout):
     7          super().__init__()
     8          self.patch_len = patch_len
     9          self.stride = stride
    10          self.padding = stride
    11          self.value_embedding = nn.Linear(patch_len * 2, d_model, bias=False)
    12          self.position_embedding = PositionalEmbedding(d_model)
    13          self.dropout = nn.Dropout(dropout)
    14  
    15      def forward(self, x, mask):
    16          x = nn.functional.pad(x, (0, self.padding), mode='replicate')
    17          mask = nn.functional.pad(mask, (0, self.padding), mode='replicate

AssistantStep 7test

Tool ResultStep 7

[Test #3] (0 tests remaining)

### ETTh1 (scripts/etth1.sh)
Training metrics (last epochs):
TRAIN_METRICS epoch=6 train_loss=0.1092691 vali_loss=0.0979588 test_loss=0.0859527
TRAIN_METRICS epoch=7 train_loss=0.1085234 vali_loss=0.0973159 test_loss=0.0848848
TRAIN_METRICS epoch=8 train_loss=0.1074949 vali_loss=0.0962957 test_loss=0.0836497
TRAIN_METRICS epoch=9 train_loss=0.1080065 vali_loss=0.0958103 test_loss=0.0836215
TRAIN_METRICS epoch=10 train_loss=0.1076801 vali_loss=0.0957179 test_loss=0.0835722
Test results (ETTh1):
  MSE: 0.084009, MAE: 0.192850

### Weather (scripts/weather.sh)
Training metrics (last epochs):
TRAIN_METRICS epoch=6 train_loss=0.2212915 vali_loss=0.1324073 test_loss=0.0446284
TRAIN_METRICS epoch=7 train_loss=0.2007329 vali_loss=0.1383821 test_loss=0.0433393
TRAIN_METRICS epoch=8 train_loss=0.2061286 vali_loss=0.1239769 test_loss=0.0433362
TRAIN_METRICS epoch=9 train_loss=0.2268175 vali_loss=0.1312260 test_loss=0.0431845
TRAIN_METRICS epoch=10 train_loss=0.19530