Update documentation

_sources/user_guide/run_eval.rst.txt

@@ -2,60 +2,64 @@
 Evaluate a Model
 ================================
 
-Step 1: Setup the data and model config files
+Step 1: Setup the config file
 ===============================================
 
-Same as in the training pipeline, firstly we need to initialize two config files: ``data_config.yaml`` and ``model_config.yaml``.
+Same as in the training pipeline, firstly we need to initialize the task configuration in the config file.
 
-``data_config.yaml`` is the same as in `Training Pipeline <./run_train_pipeline.html>`_ while we need to update the ``model_config.yaml``
-to let the model run the evaluation.
-
-
-model_config
+Similar to the setup in `Training Pipeline <./run_train_pipeline.html>`_, we set the `stage` to `eval` and pass the `pretrained_model_dir` to ``the model_config``
+Note that the *pretrained_model_dir* can be found in the log of the training process.
 
 .. code-block:: yaml
 
- RMTPP_gen:
- base_config:
- stage: gen
- backend: torch
- dataset_id: retweet
- runner_id: std_tpp
- base_dir: './checkpoints/'
- model_id: RMTPP
- model_config:
- hidden_size: 32
- time_emb_size: 16
- 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
- pretrained_model_dir: ./checkpoints/2555_4348724608_230603-155841/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: 1
-
-
-
-A complete example of these files can be seen at `examples/example_config`.
+ RMTPP_eval:
+ stage: eval
+ backend: torch
+ dataset_id: conttime
+ runner_id: std_tpp
+ base_config:
+ base_dir: './checkpoints/'
+ batch_size: 256
+ max_epoch: 10
+ shuffle: False
+ valid_freq: 1
+ use_tfb: False
+ metrics: [ 'acc', 'rmse' ]
+ model_config:
+ model_id: RMTPP # model name
+ 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
+ pretrained_model_dir: ./checkpoints/59618_4339156352_221128-142905/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
+
+
+
+A complete example of these files can be seen at `examples/example_config.yaml <https://github.com/ant-research/EasyTemporalPointProcess/blob/main/examples/configs/experiment_config.yaml>`_ .
 
 
 Step 2: Run the evaluation script
 =================================
 
 Same as in the training pipeline, we need to initialize a ``ModelRunner`` object to do the evaluation.
 
-The following code is an example, which is a copy from *examples/eval_nhp.py*.
+The following code is an example, which is a copy from `examples/train_nhp.py <https://github.com/ant-research/EasyTemporalPointProcess/blob/main/examples/train_nhp.py>`_ .
 
 
 .. code-block:: python
@@ -81,7 +85,7 @@ The following code is an example, which is a copy from *examples/eval_nhp.py*.
 
  model_runner = Runner.build_from_config(config)
 
- model_runner.evaluate()
+ model_runner.run()
 
 
  if __name__ == '__main__':

index.html

@@ -142,7 +142,7 @@ <h1><code class="docutils literal notranslate"><span class="pre">EasyTPP</span><
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="user_guide/run_eval.html">Model Prediction</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="user_guide/run_eval.html#step-1-setup-the-data-and-model-config-files">Step 1: Setup the data and model config files</a></li>
+<li class="toctree-l2"><a class="reference internal" href="user_guide/run_eval.html#step-1-setup-the-config-file">Step 1: Setup the config file</a></li>
 <li class="toctree-l2"><a class="reference internal" href="user_guide/run_eval.html#step-2-run-the-evaluation-script">Step 2: Run the evaluation script</a></li>
 <li class="toctree-l2"><a class="reference internal" href="user_guide/run_eval.html#checkout-the-output">Checkout the output</a></li>
 </ul>