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============================================
Training a Model & Configuration Explanation
============================================
This tutorial shows how one can use ``EasyTPP`` to train the implemented models.
In principle, firstly we need to initialize a config yaml file, containing all the input configuration to guide the training and eval process. The overall structure of a config file is shown as below:
.. code-block:: yaml
pipeline_config_id: .. # name of the config for guiding the pipeline
data:
[Dataset ID]: # name of the dataset, e.g, taxi
....
[EXPERIMENT ID]: # name of the experiment to run
base_config:
....
model_config:
...
After the config file is setup, we can run the script, by specifying the `config directory` and `experiment id`, to start the pipeline. We currently provide a preset script at `examples/train_nhp.py`.
Step 1: Setup the config file containing data and model configs
================================================================
To be specific, one needs to define the following entries in the config file:
- **pipeline_config_id**: registered name of EasyTPP.Config objects, such as `runner_config` or `hpo_runner_config`. By reading this, the corresponding configuration class will be loaded for constructing the pipeline.
.. code-block:: yaml
pipeline_config_id: runner_config
- **data**: dataset specifics. One can put multiple dataset specifics in the config file, but only one will be used in one experiment.
- *[DATASET ID]*: name of the dataset, e.g., taxi.
- *train_dir, valid_dir, test_dir*: directory of the datafile. For the moment we only accept pkl file (please see `Dataset <./dataset.html>`_ for details)
- *data_spec*: define the event type information.
.. code-block:: yaml
data:
taxi:
data_format: pkl
train_dir: ../data/taxi/train.pkl
valid_dir: ../data/taxi/dev.pkl
test_dir: ../data/taxi/test.pkl
data_spec：
num_event_types: 7 # num of types excluding pad events.
pad_token_id: 6 # event type index for pad events
padding_side: right # pad at the right end of the sequence
truncation_side: right # truncate at the right end of the sequence
max_len: 100 # max sequence length used as model input
- **[EXPERIMENT ID]**: name of the experiment to run in the pipeline. It contains two blocks of configs:
*base_config* contains the pipeline framework related specifications.
.. code-block:: yaml
base_config:
stage: train # train, eval and generate
backend: tensorflow # tensorflow and torch
dataset_id: conttime # name of the dataset
runner_id: std_tpp # registered name of the pipeline runner
model_id: RMTPP # model name # registered name of the implemented model
base_dir: './checkpoints/' # base dir to save the logs and models.
*model_config* contains the model related specifications.
.. code-block:: yaml
model_config:
hidden_size: 32
time_emb_size: 16
num_layers: 2
num_heads: 2
mc_num_sample_per_step: 20
sharing_param_layer: False
loss_integral_num_sample_per_step: 20
dropout: 0.0
use_ln: False
thinning_params: # thinning algorithm for event sampling
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
*trainer_config* contains the training related specifications.
.. code-block:: yaml
trainer_config: # trainer arguments
seed: 2019
gpu: 0
batch_size: 256
max_epoch: 10
shuffle: False
optimizer: adam
learning_rate: 1.e-3
valid_freq: 1
use_tfb: False
metrics: ['acc', 'rmse']
A complete example of these files can be seen at *examples/example_config*.
Step 2: Run the training script
===============================================
To run the training process, we simply need to call two functions:
1. ``Config``: it reads the directory of the configs specified in Step 1 and do some processing to form a complete configuration.
2. ``Runner``: it reads the configuration and setups the whole pipeline for training, evaluation and generation.
The following code is an example, which is a copy from *examples/train_nhp.py*.
.. code-block:: python
import argparse
from easy_tpp.config_factory import Config
from easy_tpp.runner import Runner
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--config_dir', type=str, required=False, default='configs/experiment_config.yaml',
help='Dir of configuration yaml to train and evaluate the model.')
parser.add_argument('--experiment_id', type=str, required=False, default='RMTPP_train',
help='Experiment id in the config file.')
args = parser.parse_args()
config = Config.build_from_yaml_file(args.config_dir, experiment_id=args.experiment_id)
model_runner = Runner.build_from_config(config)
model_runner.run()
if __name__ == '__main__':
main()
Checkout the output
========================
During training, the log, the best model based on valid set performance, the complete configuration file are all saved. The directory of the saved files is specified in 'base' of ``model_config.yaml``, i.e.,
In the `./checkpoints/` folder, one find the correct subfolder by concatenating the 'experiment_id' and running timestamps. Inside that subfolder, there is a complete configuration file, e.g., ``NHP_train_output.yaml`` that records all the information used in the pipeline. The
.. code-block:: yaml
data_config:
train_dir: ../data/conttime/train.pkl
valid_dir: ../data/conttime/dev.pkl
test_dir: ../data/conttime/test.pkl
specs:
num_event_types_pad: 6
num_event_types: 5
event_pad_index: 5
data_format: pkl
base_config:
stage: train
backend: tensorflow
dataset_id: conttime
runner_id: std_tpp
model_id: RMTPP
base_dir: ./checkpoints/
exp_id: RMTPP_train
log_folder: ./checkpoints/98888_4299965824_221205-153425
saved_model_dir: ./checkpoints/98888_4299965824_221205-153425/models/saved_model
saved_log_dir: ./checkpoints/98888_4299965824_221205-153425/log
output_config_dir: ./checkpoints/98888_4299965824_221205-153425/RMTPP_train_output.yaml
model_config:
hidden_size: 32
time_emb_size: 16
num_layers: 2
num_heads: 2
mc_num_sample_per_step: 20
sharing_param_layer: false
loss_integral_num_sample_per_step: 20
dropout: 0.0
use_ln: false
seed: 2019
gpu: 0
thinning_params:
num_seq: 10
num_sample: 1
num_exp: 500
look_ahead_time: 10
patience_counter: 5
over_sample_rate: 5
num_samples_boundary: 5
dtime_max: 5
num_step_gen: 1
trainer:
batch_size: 256
max_epoch: 10
shuffle: false
optimizer: adam
learning_rate: 0.001
valid_freq: 1
use_tfb: false
metrics:
- acc
- rmse
seq_pad_end: true
is_training: true
num_event_types_pad: 6
num_event_types: 5
event_pad_index: 5
model_id: RMTPP
If we set ``use_tfb`` to ``true``, it means we can launch the tensorboard to track the training process, one
can see `Running Tensorboard <../advanced/tensorboard.html>`_ for details. |