Functions — github.com/GestaltCogTeam/BasicTS

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Functions713 in github.com/GestaltCogTeam/BasicTS

⨍ Functions 713◇ Types & classes 201↳ Endpoints 1

Methodforward

(self, x: torch.Tensor)

src/basicts/models/MTSMixer/arch/mtsmixer_layers.py:60

Methodforward

(self, x: torch.Tensor)

src/basicts/models/MTSMixer/arch/mtsmixer_layers.py:82

Methodforward

(self,x: torch.Tensor)

src/basicts/models/MTSMixer/arch/mtsmixer_layers.py:99

Methodforward

Forward pass of the MTSMixer model. Args: inputs (torch.Tensor): Input tensor with shape [batch_size, input_len, num_fea

src/basicts/models/MTSMixer/arch/mtsmixer_arch.py:27

Methodforward

Forward function of HI. Args: inputs (torch.Tensor): shape = [B, L_in, N] Returns: torch.Tensor: model predi

src/basicts/models/HI/arch/hi_arch.py:39

Methodforward

(self, x: torch.Tensor)

src/basicts/models/DUET/arch/linear_extractor_cluster.py:133

Methodforward

Args: x: tensor shape [batch_size, input_size] loss_coef: a scalar - multiplier on load-balancing losses Ret

src/basicts/models/DUET/arch/linear_extractor_cluster.py:293

Methodforward

(self, x: torch.Tensor)

src/basicts/models/DUET/arch/mahalanobis_mask.py:57

Methodforward

Args: inputs (torch.Tensor): Input tensor with shape [batch_size, input_len, num_features]. Returns: Dict[st

src/basicts/models/DUET/arch/duet_arch.py:64

Methodforward

Forward pass of the FreTS model. Args: inputs (torch.Tensor): Input tensor of shape [batch_size, input_len, num_features

src/basicts/models/FreTS/arch/frets_arch.py:36

Methodforward

(self, x: torch.Tensor)

src/basicts/models/FreTS/arch/frets_arch.py:91

Methodforward

Forward pass of the SegRNN model. Args: inputs (torch.Tensor): [batch_size, input_len, num_features] Returns:

src/basicts/models/SegRNN/arch/segrnn_arch.py:48

Methodforward

(self, inputs)

src/basicts/models/TiDE/arch/tide_arch.py:31

Methodforward

Feed forward of TiDE. Args: inputs: Input data with shape: [batch_size, input_len, num_features] inputs_timestamps: I

src/basicts/models/TiDE/arch/tide_arch.py:87

Methodforward

( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None,

src/basicts/models/Informer/arch/encoder.py:25

Methodforward

(self, x: torch.Tensor)

src/basicts/models/Informer/arch/conv.py:25

Methodforward

Feed forward of Informer. Args: inputs: Input data with shape: [batch_size, input_len, num_features] targets: Future

src/basicts/models/Informer/arch/informer_arch.py:97

Methodforward

Args: inputs (Tensor): Input data with shape: [batch_size, input_len, num_features] inputs_std (Tensor): Input stand

src/basicts/models/NonstationaryTransformer/arch/ns_transformer_arch.py:77

Methodforward

Args: inputs (Tensor): Input data with shape: [batch_size, input_len, num_features] inputs_timestamps (Tensor): Inpu

src/basicts/models/NonstationaryTransformer/arch/ns_transformer_arch.py:165

Methodforward

Args: inputs (Tensor): Input data with shape: [batch_size, input_len, num_features] inputs_timestamps (Tensor): Inpu

src/basicts/models/NonstationaryTransformer/arch/ns_transformer_arch.py:214

Methodforward

Forward pass of the NonstationaryTransformerForReconstruction model. Args: inputs (Tensor): Input data with shape: [batc

src/basicts/models/NonstationaryTransformer/arch/ns_transformer_arch.py:254

Methodforward

Forward pass of the De-stationary Attention Layer. Args: hidden_states (torch.Tensor): Input tensor of shape [batch_size,

src/basicts/models/NonstationaryTransformer/arch/ns_transformer_layers.py:30

Methodforward

Forward pass of the NonstationaryTransformerEncoderLayer. Args: hidden_states (torch.Tensor): The input

src/basicts/models/NonstationaryTransformer/arch/ns_transformer_layers.py:101

Methodforward

Forward pass of the NonstationaryTransformerDecoderLayer. Args: hidden_states (torch.Tensor): The input

src/basicts/models/NonstationaryTransformer/arch/ns_transformer_layers.py:154

Methodforward

(self, x: torch.Tensor, stats: torch.Tensor)

src/basicts/models/NonstationaryTransformer/arch/ns_transformer_layers.py:239

Methodforward

Args: inputs (Tensor): Input data with shape: [batch_size, input_len, num_features] inputs_timestamps (Tensor): Inpu

src/basicts/models/iTransformer/arch/itransformer_arch.py:52

Methodforward

Forward pass of iTransformerForForecasting. Args: inputs (Tensor): Input data with shape: [batch_size, input_len, num_fe

src/basicts/models/iTransformer/arch/itransformer_arch.py:87

Methodforward

Args: inputs (Tensor): Input data with shape: [batch_size, input_len, num_features] inputs_timestamps (Tensor): Inpu

src/basicts/models/iTransformer/arch/itransformer_arch.py:129

Methodforward

Args: inputs (Tensor): Input data with shape: [batch_size, input_len, num_features] inputs_timestamps (Tensor): Inpu

src/basicts/models/iTransformer/arch/itransformer_arch.py:170

Methodforward

Feed forward of NLinear. Args: inputs (torch.Tensor): input data with shape [batch_size, input_len, num_features] Return

src/basicts/models/NLinear/arch/nlinear_arch.py:19

Methodforward

Forward pass of SOFTS model. Args: inputs (`torch.Tensor`): input tensor of shape [batch_size, seq_len, num_features]

src/basicts/models/SOFTS/arch/softs_arch.py:50

Methodforward

(self, inputs: torch.Tensor)

src/basicts/models/SOFTS/arch/star.py:19

Methodforward

(self, x: torch.Tensor)

src/basicts/models/TimeKAN/arch/timekan_layers.py:29

Methodforward

(self, level_list: List[torch.Tensor])

src/basicts/models/TimeKAN/arch/timekan_layers.py:133

Methodforward

(self, level_list: List[torch.Tensor])

src/basicts/models/TimeKAN/arch/timekan_layers.py:167

Methodforward

Forward pass of the M_KAN layer. Args: x (torch.Tensor): Input tensor of shape [batch_size, seq_len, hidden_size].

src/basicts/models/TimeKAN/arch/timekan_layers.py:195

Methodforward

Forward pass of TimeKAN model. Args: inputs (torch.Tensor): Input tensor of shape (batch_size, input_len, num_features).

src/basicts/models/TimeKAN/arch/timekan_arch.py:48

Methodfrom_json

Load config from a json file. Args: json_file_path (str): json file path

src/basicts/configs/base_config.py:221

Functiongenerate_adj_jinan

(distance_df_filename, graph_file_path, num_of_vertices)

scripts/data_preparation/JiNan/generate_adj_mx.py:65

Functiongenerate_adj_pems03

(distance_df_filename, graph_file_path, num_of_vertices)

scripts/data_preparation/PEMS04/generate_adj_mx.py:65

Functiongenerate_adj_pems03

(distance_df_filename, graph_file_path, num_of_vertices)

scripts/data_preparation/PEMS08/generate_adj_mx.py:65

Functiongenerate_adj_pems03

(distance_df_filename, graph_file_path, num_of_vertices)

scripts/data_preparation/PEMS07/generate_adj_mx.py:65

Functiongenerate_adj_pems03

(distance_df_filename, graph_file_path, num_of_vertices)

scripts/data_preparation/PEMS03/generate_adj_mx.py:65

Functionget_baseline_config_dict

()

server/engine/utils.py:29

Functionget_dataset_name

Extract the dataset name from the configuration dictionary. Args: cfg (Dict): Configuration dictionary. Returns: str: T

src/basicts/utils/config.py:4

Methodget_laplacian

(self, graph: torch.Tensor, normalize: bool)

src/basicts/models/StemGNN/arch/stemgnn_arch.py:159

Functionget_regular_settings

Get the regular settings for a dataset. Args: dataset_name (str): Name of the dataset. Returns: dict: Regular s

src/basicts/utils/serialization.py:12

Methodget_weight

Get the weight of the forward return

src/basicts/runners/taskflow/classification_taskflow.py:37

Methodget_weight

Get the weight of the forward return

src/basicts/runners/taskflow/imputation_taskflow.py:47

Methodget_weight

Get the weight of the forward return

src/basicts/runners/taskflow/forecasting_taskflow.py:46

Methodgraph_fft

(self, inputs, eigenvectors)

src/basicts/models/StemGNN/arch/stemgnn_arch.py:216

Functioninference

(input_data: InputData)

server/http_server.py:21

Methodinference

The complete inference process. Args: ckpt_path (str, optional): Path to the checkpoint file. Defaults to None.

src/basicts/runners/basicts_runner.py:355

Methodinverse_transform

Reverse the Z-score normalization to recover the original data scale. This method transforms the normalized data back to its origina

src/basicts/scaler/z_score_scaler.py:98

Methodinverse_transform

Reverse the min-max normalization to recover the original data scale. This method transforms the normalized data back to its origina

src/basicts/scaler/min_max_scaler.py:91

Methodlaunch_evaluation

Launches the evaluation process. This method initializes the runner specified in the configuration, sets up logging, and sta

src/basicts/launcher.py:61

Functionload_adj

Load and preprocess an adjacency matrix. Args: dataset_name (str): Name of the dataset. adj_type (str): Type of adjacency ma

src/basicts/utils/serialization.py:78

Functionmasked_corr

Calculate the Masked Pearson Correlation Coefficient between the predicted and target values, while ignoring the entries in the target tensor

src/basicts/metrics/corr.py:4

Functionmasked_huber

Calculate the Masked Huber Loss between predicted and target values, ignoring entries in the target tensor that match the specified null valu

src/basicts/metrics/huber.py:5

Functionmasked_mae

Calculate the Masked Mean Absolute Error (MAE) between the predicted and target values, while ignoring the entries in the target tensor that

src/basicts/metrics/mae.py:4

Functionmasked_mape

Calculate the Masked Mean Absolute Percentage Error (MAPE) between predicted and target values, ignoring entries that are either zero or matc

src/basicts/metrics/mape.py:4

Functionmasked_r2

Calculate the Masked R square between the predicted and target values, while ignoring the entries in the target tensor that match the specifi

src/basicts/metrics/r_square.py:4

Functionmasked_rmse

Calculate the Masked Root Mean Squared Error (RMSE) between predicted and target values, ignoring entries in the target tensor that match the

src/basicts/metrics/rmse.py:6

Functionmasked_smape

Calculate the Masked Symmetric Mean Absolute Percentage Error (SMAPE) between predicted and target values, ignoring entries that are either z

src/basicts/metrics/smape.py:5

Functionmasked_wape

Calculate the Masked Weighted Absolute Percentage Error (WAPE) between predicted and target values, ignoring entries in the target tensor tha

src/basicts/metrics/wape.py:4

Methodon_backward

(self, runner, *args, **kwargs)

src/basicts/runners/callback/callback.py:39

Methodon_backward

(self, runner: "BasicTSRunner", loss: torch.Tensor)

src/basicts/runners/callback/grad_accumulation.py:29

Methodon_compute_loss

(self, runner, *args, **kwargs)

src/basicts/runners/callback/callback.py:36

Methodon_compute_loss

(self, runner: "BasicTSRunner", **kwargs)

src/basicts/runners/callback/selective_learning.py:71

Methodon_compute_loss

(self, runner: "BasicTSRunner", **kwargs)

src/basicts/runners/callback/add_aux_loss.py:22

Methodon_compute_loss

(self, runner: "BasicTSRunner", **kwargs)

src/basicts/runners/callback/curriculum_learrning.py:33

Methodon_epoch_end

(self, runner, *args, **kwargs)

src/basicts/runners/callback/callback.py:15

Methodon_epoch_end

(self, runner: "BasicTSRunner", **kwargs)

src/basicts/runners/callback/selective_learning.py:102

Methodon_epoch_start

(self, runner, *args, **kwargs)

src/basicts/runners/callback/callback.py:12

Methodon_inference_end

Callback at the end of inference.

src/basicts/runners/basicts_runner.py:747

Methodon_inference_start

Callback at the start of inference.

src/basicts/runners/basicts_runner.py:741

Methodon_optimizer_step

(self, runner, *args, **kwargs)

src/basicts/runners/callback/callback.py:42

Methodon_optimizer_step

(self, runner: "BasicTSRunner")

src/basicts/runners/callback/clip_grad.py:44

Methodon_step_end

(self, runner, *args, **kwargs)

src/basicts/runners/callback/callback.py:21

Methodon_step_start

(self, runner, *args, **kwargs)

src/basicts/runners/callback/callback.py:18

Methodon_test_end

(self, runner, *args, **kwargs)

src/basicts/runners/callback/callback.py:33

Methodon_test_start

(self, runner, *args, **kwargs)

src/basicts/runners/callback/callback.py:30

Methodon_train_begin

(self, runner)

src/basicts/models/Koopa/callback/koopa_mask_init.py:28

Methodon_train_end

(self, runner, *args, **kwargs)

src/basicts/runners/callback/callback.py:9

Methodon_train_start

(self, runner, *args, **kwargs)

src/basicts/runners/callback/callback.py:6

Methodon_train_start

(self, runner: "BasicTSRunner")

src/basicts/runners/callback/no_bp.py:15

Methodon_train_start

(self, runner: "BasicTSRunner")

src/basicts/runners/callback/grad_accumulation.py:22

Methodon_train_start

(self, runner: "BasicTSRunner")

src/basicts/runners/callback/clip_grad.py:38

Methodon_train_start

(self, runner: "BasicTSRunner")

src/basicts/runners/callback/selective_learning.py:64

Methodon_train_start

(self, runner: 'BasicTSRunner')

src/basicts/runners/callback/early_stopping.py:22

Methodon_train_start

(self, runner: "BasicTSRunner")

src/basicts/runners/callback/curriculum_learrning.py:30

Methodon_validate_end

(self, runner, *args, **kwargs)

src/basicts/runners/callback/callback.py:27

Methodon_validate_end

(self, runner: 'BasicTSRunner', train_step: int, train_epoch: Optional[int] = None)

src/basicts/runners/callback/early_stopping.py:25

Methodon_validate_start

(self, runner, *args, **kwargs)

src/basicts/runners/callback/callback.py:24

Methodplt_meters

Plot the specified type of meters in tensorboard. Args: meter_type (str): meter type. step (int): Global step value t

src/basicts/utils/meter_pool.py:85

Methodpostprocess

Run the task flow

src/basicts/runners/taskflow/classification_taskflow.py:30

Methodpostprocess

Run the task flow

src/basicts/runners/taskflow/imputation_taskflow.py:39

Methodpostprocess

Run the task flow

src/basicts/runners/taskflow/forecasting_taskflow.py:36

Methodpreprocess

Run the task flow

src/basicts/runners/taskflow/classification_taskflow.py:16

Methodpreprocess

Run the task flow

src/basicts/runners/taskflow/imputation_taskflow.py:16

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