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add experiment to run retweet experiment
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‎examples/benchmark_script.py‎

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‎examples/train_experiment/retweet_config.yaml‎

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pipeline_config_id: runner_config
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data:
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retweet:
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data_format: json
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train_dir: easytpp/retweet
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valid_dir: easytpp/retweet
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test_dir: easytpp/retweet
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data_specs:
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num_event_types: 3
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pad_token_id: 3
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padding_side: right
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truncation_side: right
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NHP_train:
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base_config:
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stage: train
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backend: torch
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dataset_id: retweet
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runner_id: std_tpp
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model_id: NHP # model name
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base_dir: './checkpoints/'
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trainer_config:
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batch_size: 256
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max_epoch: 20
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shuffle: False
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optimizer: adam
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learning_rate: 1.e-3
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valid_freq: 1
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use_tfb: False
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metrics: [ 'acc', 'rmse' ]
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seed: 2019
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gpu: -1
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model_config:
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hidden_size: 64
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loss_integral_num_sample_per_step: 20
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thinning:
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num_seq: 10
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num_sample: 1
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num_exp: 500 # number of i.i.d. Exp(intensity_bound) draws at one time in thinning algorithm
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look_ahead_time: 10
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patience_counter: 5 # the maximum iteration used in adaptive thinning
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over_sample_rate: 5
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num_samples_boundary: 5
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dtime_max: 5
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num_step_gen: 1
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SAHP_train:
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base_config:
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stage: train
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backend: torch
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dataset_id: taxi
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runner_id: std_tpp
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model_id: SAHP # model name
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base_dir: './checkpoints/'
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trainer_config:
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batch_size: 256
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max_epoch: 20
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shuffle: False
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optimizer: adam
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learning_rate: 1.e-3
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valid_freq: 1
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use_tfb: False
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metrics: [ 'acc', 'rmse' ]
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seed: 2019
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gpu: 0
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model_config:
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hidden_size: 32
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time_emb_size: 16
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num_layers: 2
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num_heads: 2
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loss_integral_num_sample_per_step: 20
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use_ln: False
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thinning:
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num_seq: 10
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num_sample: 1
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num_exp: 500 # number of i.i.d. Exp(intensity_bound) draws at one time in thinning algorithm
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look_ahead_time: 10
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patience_counter: 5 # the maximum iteration used in adaptive thinning
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over_sample_rate: 5
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num_samples_boundary: 5
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dtime_max: 5
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num_step_gen: 1
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SAHP_gen:
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base_config:
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stage: gen
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backend: torch
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dataset_id: retweet
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runner_id: std_tpp
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model_id: SAHP # model name
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base_dir: './checkpoints/'
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trainer_config:
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batch_size: 256
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max_epoch: 1
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model_config:
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hidden_size: 16
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time_emb_size: 4
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num_layers: 2
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num_heads: 2
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loss_integral_num_sample_per_step: 20
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use_ln: False
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thinning:
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num_seq: 10
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num_sample: 1
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num_exp: 500 # number of i.i.d. Exp(intensity_bound) draws at one time in thinning algorithm
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look_ahead_time: 10
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patience_counter: 5 # the maximum iteration used in adaptive thinning
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over_sample_rate: 5
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num_samples_boundary: 5
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dtime_max: 5
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num_step_gen: 10
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THP_train:
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base_config:
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stage: train
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backend: torch
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dataset_id: taxi
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runner_id: std_tpp
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model_id: THP # model name
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base_dir: './checkpoints/'
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trainer_config:
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batch_size: 256
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max_epoch: 30
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shuffle: False
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optimizer: adam
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learning_rate: 1.e-3
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valid_freq: 1
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use_tfb: False
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metrics: [ 'acc', 'rmse' ]
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seed: 2019
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gpu: -1
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model_config:
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hidden_size: 32
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time_emb_size: 16
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num_layers: 2
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num_heads: 2
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mc_num_sample_per_step: 20
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loss_integral_num_sample_per_step: 20
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use_ln: False
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thinning:
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num_seq: 10
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num_sample: 1
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num_exp: 500 # number of i.i.d. Exp(intensity_bound) draws at one time in thinning algorithm
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look_ahead_time: 10
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patience_counter: 5 # the maximum iteration used in adaptive thinning
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over_sample_rate: 5
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num_samples_boundary: 5
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dtime_max: 5
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num_step_gen: 1
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THP_gen:
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base_config:
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stage: gen
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backend: torch
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dataset_id: retweet
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runner_id: std_tpp
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model_id: THP # model name
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base_dir: './checkpoints/'
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trainer_config:
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batch_size: 256
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max_epoch: 1
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model_config:
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hidden_size: 32
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time_emb_size: 16
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num_layers: 2
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num_heads: 2
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mc_num_sample_per_step: 20
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loss_integral_num_sample_per_step: 20
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use_ln: False
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# pretrained_model_dir: ./checkpoints/2694_4384867712_230603-160544/models/saved_model
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thinning:
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num_seq: 10
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num_sample: 1
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num_exp: 500 # number of i.i.d. Exp(intensity_bound) draws at one time in thinning algorithm
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look_ahead_time: 10
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patience_counter: 5 # the maximum iteration used in adaptive thinning
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over_sample_rate: 5
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num_samples_boundary: 5
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dtime_max: 5
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num_step_gen: 10
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AttNHP_train:
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base_config:
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stage: train
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backend: torch
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dataset_id: taxi
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runner_id: std_tpp
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model_id: AttNHP # model name
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base_dir: './checkpoints/'
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trainer_config:
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batch_size: 256
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max_epoch: 200
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shuffle: False
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optimizer: adam
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learning_rate: 1.e-3
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valid_freq: 1
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use_tfb: False
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metrics: [ 'acc', 'rmse' ]
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seed: 2019
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gpu: -1
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model_config:
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hidden_size: 16
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time_emb_size: 4
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num_layers: 2
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num_heads: 2
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loss_integral_num_sample_per_step: 10
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use_ln: False
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thinning:
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num_seq: 2
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num_sample: 1
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num_exp: 50 # number of i.i.d. Exp(intensity_bound) draws at one time in thinning algorithm
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look_ahead_time: 10
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patience_counter: 5 # the maximum iteration used in adaptive thinning
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over_sample_rate: 5
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num_samples_boundary: 5
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dtime_max: 5
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num_step_gen: 1
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AttNHP_gen:
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base_config:
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stage: gen
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backend: torch
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dataset_id: retweet
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runner_id: std_tpp
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model_id: AttNHP # model name
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base_dir: './checkpoints/'
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trainer_config:
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batch_size: 256
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max_epoch: 1
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model_config:
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hidden_size: 16
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time_emb_size: 4
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num_layers: 2
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num_heads: 2
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mc_num_sample_per_step: 20
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loss_integral_num_sample_per_step: 20
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use_ln: False
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# pretrained_model_dir: ./checkpoints/6934_4375315840_230603-222826/models/saved_model
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thinning:
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num_seq: 10
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num_sample: 1
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num_exp: 50 # number of i.i.d. Exp(intensity_bound) draws at one time in thinning algorithm
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look_ahead_time: 10
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patience_counter: 5 # the maximum iteration used in adaptive thinning
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over_sample_rate: 5
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num_samples_boundary: 5
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dtime_max: 5
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num_step_gen: 10

‎examples/train_experiment/run_retweet.py‎

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import argparse
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from easy_tpp.config_factory import Config
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from easy_tpp.runner import Runner
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument('--config_dir', type=str, required=False, default='retweet_config.yaml',
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help='Dir of configuration yaml to train and evaluate the model.')
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parser.add_argument('--experiment_id', type=str, required=False, default='NHP_train',
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help='Experiment id in the config file.')
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args = parser.parse_args()
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config = Config.build_from_yaml_file(args.config_dir, experiment_id=args.experiment_id)
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model_runner = Runner.build_from_config(config)
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model_runner.run()
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if __name__ == '__main__':
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main()

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