examples/train_experiment/retweet_config.yaml

pipeline_config_id: runner_config

data:
  retweet:
    data_format: json
    train_dir: easytpp/retweet
    valid_dir: easytpp/retweet
    test_dir: easytpp/retweet
    data_specs:
      num_event_types: 3
      pad_token_id: 3
      padding_side: right
      truncation_side: right

NHP_train:
  base_config:
    stage: train
    backend: torch
    dataset_id: retweet
    runner_id: std_tpp
    model_id: NHP # model name
    base_dir: './checkpoints/'
  trainer_config:
    batch_size: 256
    max_epoch: 20
    shuffle: False
    optimizer: adam
    learning_rate: 1.e-3
    valid_freq: 1
    use_tfb: False
    metrics: [ 'acc', 'rmse' ]
    seed: 2019
    gpu: -1
  model_config:
    hidden_size: 64
    loss_integral_num_sample_per_step: 20
    thinning:
      num_seq: 10
      num_sample: 1
      num_exp: 500 # number of i.i.d. Exp(intensity_bound) draws at one time in thinning algorithm
      look_ahead_time: 10
      patience_counter: 5 # the maximum iteration used in adaptive thinning
      over_sample_rate: 5
      num_samples_boundary: 5
      dtime_max: 5
      num_step_gen: 1



SAHP_train:
  base_config:
    stage: train
    backend: torch
    dataset_id: taxi
    runner_id: std_tpp
    model_id: SAHP # model name
    base_dir: './checkpoints/'
  trainer_config:
    batch_size: 256
    max_epoch: 20
    shuffle: False
    optimizer: adam
    learning_rate: 1.e-3
    valid_freq: 1
    use_tfb: False
    metrics: [ 'acc', 'rmse' ]
    seed: 2019
    gpu: 0
  model_config:
    hidden_size: 32
    time_emb_size: 16
    num_layers: 2
    num_heads: 2
    loss_integral_num_sample_per_step: 20
    use_ln: False
    thinning:
      num_seq: 10
      num_sample: 1
      num_exp: 500 # number of i.i.d. Exp(intensity_bound) draws at one time in thinning algorithm
      look_ahead_time: 10
      patience_counter: 5 # the maximum iteration used in adaptive thinning
      over_sample_rate: 5
      num_samples_boundary: 5
      dtime_max: 5
      num_step_gen: 1



SAHP_gen:
  base_config:
    stage: gen
    backend: torch
    dataset_id: retweet
    runner_id: std_tpp
    model_id: SAHP # model name
    base_dir: './checkpoints/'
  trainer_config:
    batch_size: 256
    max_epoch: 1
  model_config:
    hidden_size: 16
    time_emb_size: 4
    num_layers: 2
    num_heads: 2
    loss_integral_num_sample_per_step: 20
    use_ln: False
    thinning:
      num_seq: 10
      num_sample: 1
      num_exp: 500 # number of i.i.d. Exp(intensity_bound) draws at one time in thinning algorithm
      look_ahead_time: 10
      patience_counter: 5 # the maximum iteration used in adaptive thinning
      over_sample_rate: 5
      num_samples_boundary: 5
      dtime_max: 5
      num_step_gen: 10

THP_train:
  base_config:
    stage: train
    backend: torch
    dataset_id: taxi
    runner_id: std_tpp
    model_id: THP # model name
    base_dir: './checkpoints/'
  trainer_config:
    batch_size: 256
    max_epoch: 30
    shuffle: False
    optimizer: adam
    learning_rate: 1.e-3
    valid_freq: 1
    use_tfb: False
    metrics: [ 'acc', 'rmse' ]
    seed: 2019
    gpu: -1
  model_config:
    hidden_size: 32
    time_emb_size: 16
    num_layers: 2
    num_heads: 2
    mc_num_sample_per_step: 20
    loss_integral_num_sample_per_step: 20
    use_ln: False
    thinning:
      num_seq: 10
      num_sample: 1
      num_exp: 500 # number of i.i.d. Exp(intensity_bound) draws at one time in thinning algorithm
      look_ahead_time: 10
      patience_counter: 5 # the maximum iteration used in adaptive thinning
      over_sample_rate: 5
      num_samples_boundary: 5
      dtime_max: 5
      num_step_gen: 1


THP_gen:
  base_config:
    stage: gen
    backend: torch
    dataset_id: retweet
    runner_id: std_tpp
    model_id: THP # model name
    base_dir: './checkpoints/'
  trainer_config:
    batch_size: 256
    max_epoch: 1
  model_config:
    hidden_size: 32
    time_emb_size: 16
    num_layers: 2
    num_heads: 2
    mc_num_sample_per_step: 20
    loss_integral_num_sample_per_step: 20
    use_ln: False
#    pretrained_model_dir: ./checkpoints/2694_4384867712_230603-160544/models/saved_model
    thinning:
      num_seq: 10
      num_sample: 1
      num_exp: 500 # number of i.i.d. Exp(intensity_bound) draws at one time in thinning algorithm
      look_ahead_time: 10
      patience_counter: 5 # the maximum iteration used in adaptive thinning
      over_sample_rate: 5
      num_samples_boundary: 5
      dtime_max: 5
      num_step_gen: 10

AttNHP_train:
  base_config:
    stage: train
    backend: torch
    dataset_id: taxi
    runner_id: std_tpp
    model_id: AttNHP # model name
    base_dir: './checkpoints/'
  trainer_config:
    batch_size: 256
    max_epoch: 200
    shuffle: False
    optimizer: adam
    learning_rate: 1.e-3
    valid_freq: 1
    use_tfb: False
    metrics: [ 'acc', 'rmse' ]
    seed: 2019
    gpu: -1
  model_config:
    hidden_size: 16
    time_emb_size: 4
    num_layers: 2
    num_heads: 2
    loss_integral_num_sample_per_step: 10
    use_ln: False
    thinning:
      num_seq: 2
      num_sample: 1
      num_exp: 50 # number of i.i.d. Exp(intensity_bound) draws at one time in thinning algorithm
      look_ahead_time: 10
      patience_counter: 5 # the maximum iteration used in adaptive thinning
      over_sample_rate: 5
      num_samples_boundary: 5
      dtime_max: 5
      num_step_gen: 1


AttNHP_gen:
  base_config:
    stage: gen
    backend: torch
    dataset_id: retweet
    runner_id: std_tpp
    model_id: AttNHP # model name
    base_dir: './checkpoints/'
  trainer_config:
    batch_size: 256
    max_epoch: 1
  model_config:
    hidden_size: 16
    time_emb_size: 4
    num_layers: 2
    num_heads: 2
    mc_num_sample_per_step: 20
    loss_integral_num_sample_per_step: 20
    use_ln: False
#    pretrained_model_dir: ./checkpoints/6934_4375315840_230603-222826/models/saved_model
    thinning:
      num_seq: 10
      num_sample: 1
      num_exp: 50 # number of i.i.d. Exp(intensity_bound) draws at one time in thinning algorithm
      look_ahead_time: 10
      patience_counter: 5 # the maximum iteration used in adaptive thinning
      over_sample_rate: 5
      num_samples_boundary: 5
      dtime_max: 5
      num_step_gen: 10