Subtask 2

In the starter kit, you will find the files required to train a baseline model using BART.

Environment Setup

We have provided a Conda environment file environment.yml. To install:

conda env create -f environment.yml
conda activate myenv

Verify that it was installed:

conda env list

Next, copy the dataset files train.jsonl and dev.jsonl to the data if they are not already there.

Dataset

Our dataset consists of many JSON records. The records contain these (and more) properties:

• document is the original document
• vars is the first occurrence of each variable
• var_mentions is each unique mention of a variable
• var_mentions_to_first_var is a mapping of variable mentions to their first occurrence
• obj_declaration is the objective declaration
• const_declarations contains a list of the constraint declarations
• spans correspond to the entities of the LP problem (i.e. the output of the first subtask). Please note that the tokens and spans annotations are provided in the Spacy v3 format
• tokens correspond to the tokens found in the document
• order_mapping is a mapping of each variable mention to an index corresponding to its column in the canonical form

The constraints may have various types. The limit (if given) is the constant term. In the table, we denote the limit as $c$. The terms dictionary will attach a number to each variable, representing the constant multiplying it, which we denote here as $a_1, ..., a_n$.

Constraint Type	Mathematical representation
sum	$x + y \le c$
upperbound	$x \le c$
lowerbound	$x \ge c$
linear	$a_1 x + a_2 y \le c$
ratio	$x \le c (x+y)$
xby	$x \le a y$
xy	$x \le y$

For this task, there are 3 inputs:

1. Problem Description
2. Entities (tokens and spans)
3. Order mapping of variable mentions

Here is an example of a data sample for subtask 2:

{
   "-804075997":{
      "document":"A hotel employs cleaners and receptionists. Cleaners earn $500 per week and receptionists earn $350 per week. The hotel requires a minimum of 100 workers of whom at least 20 must be receptionists. To keep the hotel clean and running smoothly, the number of receptionists should be at least a third of the number of cleaners. The hotel wants to keep the weekly wage bill below $30000. Formulate a LP to minimize the wage bill.",
      "vars":[
         "cleaners",
         "receptionists"
      ],
      "var_mentions":[
         "cleaners",
         "receptionists",
         "Cleaners",
         "receptionists",
         "receptionists",
         "receptionists",
         "cleaners"
      ],
      "params":[
         "500",
         "350",
         "third"
      ],
      "var_mention_to_first_var":{
         "Cleaners":"cleaners",
         "receptionists":"receptionists",
         "cleaners":"cleaners"
      },
      "first_var_to_mentions":{
         "cleaners":[
            "cleaners",
            "Cleaners",
            "cleaners"
         ],
         "receptionists":[
            "receptionists",
            "receptionists",
            "receptionists",
            "receptionists"
         ]
      },
      "obj_declaration":{
         "type":"objective",
         "direction":"minimize",
         "name":"wage bill",
         "terms":{
            "Cleaners":"500",
            "receptionists":"350"
         }
      },
      "const_declarations":[
         {
            "type":"sum",
            "direction":"minimum",
            "limit":"100",
            "operator":"GREATER_OR_EQUAL"
         },
         {
            "type":"lowerbound",
            "direction":"at least",
            "limit":"20",
            "var":"receptionists",
            "operator":"GREATER_OR_EQUAL"
         },
         {
            "type":"xby",
            "x_var":"receptionists",
            "direction":"at least",
            "param":"third",
            "y_var":"cleaners",
            "operator":"GREATER_OR_EQUAL"
         },
         {
            "type":"linear",
            "direction":"below",
            "limit":"30000",
            "terms":{
               "Cleaners":"500",
               "receptionists":"350"
            },
            "operator":"LESS_OR_EQUAL"
         }
      ],
      "spans":[
         {
            "text":"cleaners",
            "start":16,
            "token_start":3,
            "token_end":3,
            "end":24,
            "type":"span",
            "label":"VAR"
         },
         {
            "text":"receptionists",
            "start":29,
            "token_start":5,
            "token_end":5,
            "end":42,
            "type":"span",
            "label":"VAR"
         },
         ...
      ],
      "tokens":[
         ...
         {
            "text":"cleaners",
            "start":16,
            "end":24,
            "id":3,
            "ws":true,
            "disabled":false
         }, 
         ...
         {
            "text":"receptionists",
            "start":29,
            "end":42,
            "id":5,
            "ws":false,
            "disabled":false
         }, 
         ...
      ],
      "_input_hash":-804075997,
      "order_mapping":{
         "cleaners":0,
         "receptionists":1,
         "Cleaners":0
      }
   }
}

Training

The subfolder ./configs should contain the configuration file for setting up the model configuration and the training hyperparameters. The configuration file baseline.json corresponds to the baseline model for subtask 2. To run the training with our configuration:

python train.py --config configs/baseline.json

The important parameters here are use_copy, per_declaration, and use_prompt.

• use_copy uses a copy mechanism that computes $P_\text{copy}$ over the input tokens.
• per_declaration controls each training data sample to correspond to a single declaration of a given LP problem instead of the entire formulation (i.e. all declarations in the problem).
• use_prompt uses a declaration prompt to focus the generation. For example, the <OBJ_DIR> is used as a prompt for generating the objective declaration.

Note that beam search is available as an alternative to greedy search in decoding; however, we found that greedy search worked better.

Testing

To evaluate the model:

python test.py --gpu <gpu id> --checkpoint <checkpoint.mdl> --test-file <test.jsonl>

More details about scoring can be found in the notebooks folder here. For reference, our baseline model achieves a per-declaration accuracy of Acc = 0.60 on the test set. Note however that the test set is held out and will not be shared to participants.

Extending and Modifying the Code

You are permitted to modify the model code and training code. However, you are not permitted to modify the file scoring.py, which is used to evaluate and score models using the shared canonical format. In the testing code, you are allowed to modify the data loaders and parsers if you choose a different model output format than what the baseline uses. Precisely, the file parsers.py is for parsing model outputs into the canonical form. If you need to make a custom parser for your own model outputs, you can use your own classes or extend parsers.py. Make sure that your parser uses the order_mapping dictionary to correctly map outputs to their respective columns in the canonical output.

You will also need to submit the conda environment file that you use so we can run your code. If you are extending the baseline code and need other Python libraries or different versions, please update the environment.yml by exporting your environment with conda env export > environment.yml.

Submission Guidelines

We expect that your model prediction will be converted to a CanonicalForm object in the evaluation loop. Take a look at test.py to see how we score our model, and modify the code so that your model can be used.

Each participating team is required to include the following in their submission folder.

1. The training and evaluation code
2. The trained model checkpoint
3. The Conda environment file environment.yml or pip environment file environment.txt.