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Functions713 in github.com/GestaltCogTeam/BasicTS

⨍ Functions 713◇ Types & classes 201↳ Endpoints 1

↓ 1 callersMethod_count_parameters

Count parameters of the model.

src/basicts/runners/basicts_runner.py:870

↓ 1 callersMethod_denormalize

(self, x: torch.Tensor)

src/basicts/modules/norm/revin.py:78

↓ 1 callersMethod_eval_on_horizons

(self, forward_return: Dict[str, Any])

src/basicts/runners/basicts_runner.py:814

↓ 1 callersMethod_fft_for_period

(self, x: torch.Tensor)

src/basicts/models/TimesNet/arch/times_block.py:30

↓ 1 callersMethod_gates_to_load

Compute the true load per expert, given the gates. The load is the number of examples for which the corresponding gate is >0. Args:

src/basicts/models/DUET/arch/linear_extractor_cluster.py:198

↓ 1 callersMethod_get_ckpt_save_dir

Get checkpoint save dir. The format is "{exp_name}/{model_name}_{time}" Returns: checkpoint save dir (str)

src/basicts/runners/basicts_runner.py:1126

↓ 1 callersMethod_get_context

Update context with selected attention scores.

src/basicts/modules/transformer/attentions/prob_attention.py:60

↓ 1 callersMethod_get_md5

Get MD5 value of training-dependent part of config.

src/basicts/configs/base_config.py:354

↓ 1 callersMethod_get_statistics

(self, x: torch.Tensor)

src/basicts/modules/norm/revin.py:58

↓ 1 callersMethod_get_valid_end_idx

(self, seq: np.ndarray, sample_len: int)

src/basicts/data/blast.py:145

↓ 1 callersMethod_init_params

(self)

src/basicts/modules/norm/revin.py:51

↓ 1 callersMethod_init_train

(self)

src/basicts/runners/basicts_runner.py:203

↓ 1 callersMethod_init_weights

(self)

src/basicts/modules/embed/tst_embed.py:55

↓ 1 callersMethod_init_weights

(self)

src/basicts/models/TimesNet/arch/conv_block.py:24

↓ 1 callersMethod_init_weights

(self)

src/basicts/models/TimesNet/arch/conv_block.py:60

↓ 1 callersMethod_load_estimator

(self, runner: "BasicTSRunner")

src/basicts/runners/callback/selective_learning.py:109

↓ 1 callersMethod_load_model_resume

Load last checkpoint in checkpoint save dir to resume training. Load model state dict. Load optimizer state dict. Load start

src/basicts/runners/basicts_runner.py:925

↓ 1 callersMethod_mixup

Mixup augmentation. Returns: np.ndarray: mixed sequence

src/basicts/data/blast.py:123

↓ 1 callersMethod_normalize

(self, x: torch.Tensor)

src/basicts/modules/norm/revin.py:67

↓ 1 callersMethod_optimizer_step

(self)

src/basicts/runners/basicts_runner.py:829

↓ 1 callersMethod_padding_with_nan

Pad the sequence with NaN values. Args: seq (np.ndarray): input sequence Returns: np.ndarray: padde

src/basicts/data/blast.py:196

↓ 1 callersMethod_prepare_multi_scale_inputs

( self, inputs: torch.Tensor, inputs_timestamps: Optional[torch.Tensor] =

src/basicts/models/TimeMixer/arch/timemixer_arch.py:68

↓ 1 callersMethod_prepare_multi_scale_inputs

(self, inputs: torch.Tensor)

src/basicts/models/TimeKAN/arch/timekan_arch.py:36

↓ 1 callersMethod_prob_in_top_k

Helper function to NoisyTopKGating. Computes the probability that value is in top k, given different random noise. This gives us a way

src/basicts/models/DUET/arch/linear_extractor_cluster.py:208

↓ 1 callersMethod_prob_qk

Probabilistic sparse attention calculation.

src/basicts/modules/transformer/attentions/prob_attention.py:35

↓ 1 callersMethod_repeat_kv

Repeat key/value for GQA.

src/basicts/modules/transformer/attentions/multi_head_attention.py:47

↓ 1 callersMethod_sample_valid_seq

Randomly sample a valid sequence from the dataset. Returns: np.ndarray: a valid sequence

src/basicts/data/blast.py:103

↓ 1 callersMethod_save_metrics

Save metrics results to self.ckpt_save_dir/test_metrics.json

src/basicts/runners/basicts_runner.py:890

↓ 1 callersMethod_save_results

Save the test results to disk. Args: batch_idx (int): The index of the current batch. batch_data (Dict[np.nd

src/basicts/runners/basicts_runner.py:1057

↓ 1 callersMethod_serialize

(self)

src/basicts/configs/base_config.py:235

↓ 1 callersMethod_serialize_obj

(self, obj: object)

src/basicts/configs/base_config.py:296

↓ 1 callersMethod_shape_q

(self, x: torch.Tensor, seq_len: int)

src/basicts/modules/transformer/attentions/multi_head_attention.py:41

↓ 1 callersMethod_time_delay_aggregation

:param x: [batch_size, n_heads, head_dim, seq_len] :param corr: [batch_size, n_heads, head_dim, seq_len] :return: [batch_s

src/basicts/modules/transformer/attentions/auto_correlation.py:31

↓ 1 callersMethod_train_loop

Train loop.

src/basicts/runners/basicts_runner.py:404

↓ 1 callersFunctionadd_temporal_features

Add time of day and day of week as features to the data.

scripts/data_preparation/METR-LA/generate_training_data.py:43

↓ 1 callersFunctionadd_temporal_features

Add time of day and day of week as features to the data.

scripts/data_preparation/ETTh1/generate_training_data.py:48

↓ 1 callersFunctionadd_temporal_features

Add time of day and day of week as features to the data.

scripts/data_preparation/Illness/generate_training_data.py:47

↓ 1 callersFunctionadd_temporal_features

Add time of day and day of week as features to the data.

scripts/data_preparation/ETTh2/generate_training_data.py:48

↓ 1 callersFunctionadd_temporal_features

Add time of day and day of week as features to the data.

scripts/data_preparation/BeijingAirQuality/generate_training_data.py:47

↓ 1 callersFunctionadd_temporal_features

Add time of day and day of week as features to the data.

scripts/data_preparation/JiNan/generate_training_data.py:49

↓ 1 callersFunctionadd_temporal_features

Add time of day and day of week as features to the data.

scripts/data_preparation/Traffic/generate_training_data.py:47

↓ 1 callersFunctionadd_temporal_features

Add time of day and day of week as features to the data.

scripts/data_preparation/GlobalTemp/generate_training_data.py:54

↓ 1 callersFunctionadd_temporal_features

Add time of day and day of week as features to the data.

scripts/data_preparation/PEMS04/generate_training_data.py:46

↓ 1 callersFunctionadd_temporal_features

Add time of day and day of week as features to the data.

scripts/data_preparation/GlobalWind/generate_training_data.py:55

↓ 1 callersFunctionadd_temporal_features

Add time of day and day of week as features to the data.

scripts/data_preparation/ExchangeRate/generate_training_data.py:47

↓ 1 callersFunctionadd_temporal_features

Add time of day and day of week as features to the data.

scripts/data_preparation/GBA/generate_training_data.py:53

↓ 1 callersFunctionadd_temporal_features

Add time of day and day of week as features to the data.

scripts/data_preparation/SD/generate_training_data.py:53

↓ 1 callersFunctionadd_temporal_features

Add time of day and day of week as features to the data.

scripts/data_preparation/CA/generate_training_data.py:53

↓ 1 callersFunctionadd_temporal_features

Add time of day and day of week as features to the data.

scripts/data_preparation/PEMS08/generate_training_data.py:46

↓ 1 callersFunctionadd_temporal_features

Add time of day and day of week as features to the data.

scripts/data_preparation/ETTm2/generate_training_data.py:48

↓ 1 callersFunctionadd_temporal_features

Add time of day and day of week as features to the data.

scripts/data_preparation/Weather/generate_training_data.py:47

↓ 1 callersFunctionadd_temporal_features

Add time of day and day of week as features to the data.

scripts/data_preparation/GLA/generate_training_data.py:53

↓ 1 callersFunctionadd_temporal_features

Add time of day and day of week as features to the data.

scripts/data_preparation/PEMS-BAY/generate_training_data.py:43

↓ 1 callersFunctionadd_temporal_features

Add time of day and day of week as features to the data.

scripts/data_preparation/PEMS07/generate_training_data.py:46

↓ 1 callersFunctionadd_temporal_features

Add time of day and day of week as features to the data.

scripts/data_preparation/Electricity/generate_training_data.py:47

↓ 1 callersFunctionadd_temporal_features

Add time of day and day of week as features to the data.

scripts/data_preparation/ETTm1/generate_training_data.py:48

↓ 1 callersFunctionadd_temporal_features

Add time of day and day of week as features to the data.

scripts/data_preparation/PEMS03/generate_training_data.py:46

↓ 1 callersMethodalign_series

Pad series to the same length.

scripts/data_preparation/UEA/generate_training_data.py:122

↓ 1 callersMethodapply_rope

Applies Rotary Position Embedding to the query and key tensors. Args: q (`torch.Tensor`): The query tensor. k (`torch

src/basicts/modules/transformer/rope.py:33

↓ 1 callersFunctioncalculate_scaled_laplacian

Scale the normalized Laplacian for use in Chebyshev polynomials. Rescale the Laplacian matrix such that its eigenvalues are within the range

src/basicts/utils/adjacent_matrix_norm.py:30

↓ 1 callersFunctioncalculate_symmetric_message_passing_adj

Calculate the renormalized message-passing adjacency matrix as proposed in GCN. The message-passing adjacency matrix is defined as A' = D^{-

src/basicts/utils/adjacent_matrix_norm.py:60

↓ 1 callersMethodcheb_polynomial

(self, laplacian: torch.Tensor)

src/basicts/models/StemGNN/arch/stemgnn_arch.py:170

↓ 1 callersMethodcombine

Sum together the expert output, weighted by the gates. The slice corresponding to a particular batch element `b` is computed as the su

src/basicts/models/DUET/arch/linear_extractor_cluster.py:77

↓ 1 callersMethodcompl_mul1d

( self, order: str, x: torch.Tensor, weights_real: torch.Tenso

src/basicts/models/FiLM/arch/film_arch.py:82

↓ 1 callersMethodcross_attn_forward

Forward pass of the cross-attention layer. Args: hidden_states (torch.Tensor): The input hidden states.

src/basicts/modules/transformer/decoder.py:502

↓ 1 callersMethodcurriculum_learning

Curriculum learning schedule. Args: epoch (int): Current epoch. Returns: int: Curriculum learning l

src/basicts/runners/callback/curriculum_learrning.py:47

↓ 1 callersMethoddispatch

Create one input Tensor for each expert. The `Tensor` for an expert `i` contains the slices of `inp` corresponding to the batch elemen

src/basicts/models/DUET/arch/linear_extractor_cluster.py:60

↓ 1 callersMethodexpert_to_gates

Gate values corresponding to the examples in the per-expert `Tensor`s. Returns: a list of `num_experts` one-dimensional `Tensor`s wi

src/basicts/models/DUET/arch/linear_extractor_cluster.py:103

↓ 1 callersFunctiongenerate_gaussian_noise_sequence

()

scripts/data_preparation/Gaussian/simulate_data.py:13

↓ 1 callersFunctiongenerate_pulse_sequence

()

scripts/data_preparation/Pulse/simulate_data.py:15

↓ 1 callersFunctionget_adjacency_matrix

Generate adjacency matrix. Args: distance_df_filename (str): path of the csv file contains edges information num_of_vertices (int

scripts/data_preparation/JiNan/generate_adj_mx.py:8

↓ 1 callersFunctionget_adjacency_matrix

Generate adjacency matrix. Args: distance_df_filename (str): path of the csv file contains edges information num_of_vertices (int

scripts/data_preparation/PEMS04/generate_adj_mx.py:8

↓ 1 callersFunctionget_adjacency_matrix

Generate adjacency matrix. Args: distance_df_filename (str): path of the csv file contains edges information num_of_vertices (int

scripts/data_preparation/PEMS08/generate_adj_mx.py:8

↓ 1 callersFunctionget_adjacency_matrix

Generate adjacency matrix. Args: distance_df_filename (str): path of the csv file contains edges information num_of_vertices (int

scripts/data_preparation/PEMS07/generate_adj_mx.py:8

↓ 1 callersFunctionget_adjacency_matrix

Generate adjacency matrix. Args: distance_df_filename (str): path of the csv file contains edges information num_of_vertices (int

scripts/data_preparation/PEMS03/generate_adj_mx.py:8

↓ 1 callersFunctionget_ckpt_config_dict

()

server/engine/utils.py:36

↓ 1 callersMethodget_seq_length

Returns the sequence length of the cached states. A layer index can be optionally passed.

src/basicts/modules/transformer/kv_cache.py:79

↓ 1 callersMethodlatent_correlation_layer

(self, x: torch.Tensor)

src/basicts/models/StemGNN/arch/stemgnn_arch.py:185

↓ 1 callersFunctionload_and_preprocess_data

Load and preprocess raw data, selecting the specified channel(s).

scripts/data_preparation/METR-LA/generate_training_data.py:36

↓ 1 callersFunctionload_and_preprocess_data

Load and preprocess raw data, selecting the specified channel(s).

scripts/data_preparation/ETTh1/generate_training_data.py:37

↓ 1 callersFunctionload_and_preprocess_data

Load and preprocess raw data, selecting the specified channel(s).

scripts/data_preparation/Illness/generate_training_data.py:37

↓ 1 callersFunctionload_and_preprocess_data

Load and preprocess raw data, selecting the specified channel(s).

scripts/data_preparation/ETTh2/generate_training_data.py:37

↓ 1 callersFunctionload_and_preprocess_data

Load and preprocess raw data, selecting the specified channel(s).

scripts/data_preparation/BeijingAirQuality/generate_training_data.py:34

↓ 1 callersFunctionload_and_preprocess_data

Load and preprocess raw data, selecting the specified channel(s).

scripts/data_preparation/JiNan/generate_training_data.py:40

↓ 1 callersFunctionload_and_preprocess_data

Load and preprocess raw data, selecting the specified channel(s).

scripts/data_preparation/Traffic/generate_training_data.py:37

↓ 1 callersFunctionload_and_preprocess_data

Load and preprocess raw data, selecting the specified channel(s).

scripts/data_preparation/GlobalTemp/generate_training_data.py:38

↓ 1 callersFunctionload_and_preprocess_data

Load and preprocess raw data, selecting the specified channel(s).

scripts/data_preparation/PEMS04/generate_training_data.py:37

↓ 1 callersFunctionload_and_preprocess_data

Load and preprocess raw data, selecting the specified channel(s).

scripts/data_preparation/Pulse/generate_training_data.py:31

↓ 1 callersFunctionload_and_preprocess_data

Load and preprocess raw data, selecting the specified channel(s).

scripts/data_preparation/GlobalWind/generate_training_data.py:38

↓ 1 callersFunctionload_and_preprocess_data

Load and preprocess raw data, selecting the specified channel(s).

scripts/data_preparation/ExchangeRate/generate_training_data.py:37

↓ 1 callersFunctionload_and_preprocess_data

Load and preprocess raw data, selecting the specified channel(s).

scripts/data_preparation/GBA/generate_training_data.py:41

↓ 1 callersFunctionload_and_preprocess_data

Load and preprocess raw data, selecting the specified channel(s).

scripts/data_preparation/SD/generate_training_data.py:41

↓ 1 callersFunctionload_and_preprocess_data

Load and preprocess raw data, selecting the specified channel(s).

scripts/data_preparation/CA/generate_training_data.py:41

↓ 1 callersFunctionload_and_preprocess_data

Load and preprocess raw data, selecting the specified channel(s).

scripts/data_preparation/PEMS08/generate_training_data.py:37

↓ 1 callersFunctionload_and_preprocess_data

Load and preprocess raw data, selecting the specified channel(s).

scripts/data_preparation/ETTm2/generate_training_data.py:37

↓ 1 callersFunctionload_and_preprocess_data

Load and preprocess raw data, selecting the specified channel(s).

scripts/data_preparation/Weather/generate_training_data.py:37

↓ 1 callersFunctionload_and_preprocess_data

Load and preprocess raw data, selecting the specified channel(s).

scripts/data_preparation/GLA/generate_training_data.py:41

↓ 1 callersFunctionload_and_preprocess_data

Load and preprocess raw data, selecting the specified channel(s).

scripts/data_preparation/PEMS-BAY/generate_training_data.py:36

↓ 1 callersFunctionload_and_preprocess_data

Load and preprocess raw data, selecting the specified channel(s).

scripts/data_preparation/PEMS07/generate_training_data.py:37

↓ 1 callersFunctionload_and_preprocess_data

Load and preprocess raw data, selecting the specified channel(s).

scripts/data_preparation/Electricity/generate_training_data.py:37

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