# Experiments 

This module contains the code to run the experiments. Each Python file contains the code to run the experiments for a specific method. For example, the `./bilstm.py` file contains the following experiments: 

```Python   

# runs experiments on the n2c2 corpus using the BiLSTM model in a passive learning setting

bilstm_passive_learning_n2c2() 

# runs experiments on the n2c2 corpus using the BiLSTM model in an active learning setting

bilstm_active_learning_n2c2() 

# runs experiments on the DDI corpus using the BiLSTM model in a passive learning setting

bilstm_passive_learning_ddi()

# runs experiments on the DDI corpus using the BiLSTM model in an active learning setting

bilstm_active_learning_ddi()

```

The function `bilstm_active_learning_n2c2()` will run the active learning experiment with the BiLSTM model on the n2c2 corpus by executing the following code: 

```Python

# Base Dependencies

# -----------------

from copy import deepcopy

from pathlib import Path

from os.path import join as pjoin

# Local Dependencies

# ------------------

from training.config import BaalExperimentConfig

from training.bilstm import BilstmTrainer

from utils import set_seed

# 3rd-Party Dependencies

# ----------------------

from datasets import load_from_disk

# Constants

# ----------

from constants import (

    N2C2_HF_TRAIN_PATH,

    N2C2_HF_TEST_PATH,

    N2C2_REL_TYPES,

    BAAL_QUERY_STRATEGIES,

    EXP_RANDOM_SEEDS

)

MODEL_NAME = "bilstm"

REPETITIONS = 5

INITIAL_REPETITION = 0

FINAL_REPETITION = INITIAL_REPETITION + REPETITIONS

# Experiment configuration

config = BaalExperimentConfig(

    max_epoch=15,

    batch_size=32

)

# Repetitions of the experiment

for i in range(INITIAL_REPETITION, FINAL_REPETITION):

    # set random seed 

    set_seed(EXP_RANDOM_SEEDS[i])

    # for each relation type

    for rel_type in N2C2_REL_TYPES:

        # load datasets

        train_dataset = load_from_disk(

            Path(pjoin(N2C2_HF_TRAIN_PATH, MODEL_NAME, rel_type))

        )

        test_dataset = load_from_disk(

            Path(pjoin(N2C2_HF_TEST_PATH, MODEL_NAME, rel_type))

        )

        # create trainer

        trainer = BilstmTrainer(

            dataset="n2c2",

            train_dataset=train_dataset,

            test_dataset=test_dataset,

            relation_type=rel_type,

        )

        # for each query strategy

        for query_strategy in BAAL_QUERY_STRATEGIES:

            exp_config = deepcopy(config)

            trainer.train_active_learning(query_strategy, exp_config)

```