SYMBOL INDEX (85 symbols across 22 files)

FILE: .github/workflows/json_to_md.py
  function to_markdown (line 5) | def to_markdown(data):
  function json_to_markdown (line 38) | def json_to_markdown(json_fp, md_fp):

FILE: madewithml/data.py
  function load_data (line 14) | def load_data(dataset_loc: str, num_samples: int = None) -> Dataset:
  function stratify_split (line 30) | def stratify_split(
  function clean_text (line 77) | def clean_text(text: str, stopwords: List = STOPWORDS) -> str:
  function tokenize (line 104) | def tokenize(batch: Dict) -> Dict:
  function preprocess (line 118) | def preprocess(df: pd.DataFrame, class_to_index: Dict) -> Dict:
  class CustomPreprocessor (line 137) | class CustomPreprocessor:
    method __init__ (line 140) | def __init__(self, class_to_index={}):
    method fit (line 144) | def fit(self, ds):
    method transform (line 150) | def transform(self, ds):

FILE: madewithml/evaluate.py
  function get_overall_metrics (line 23) | def get_overall_metrics(y_true: np.ndarray, y_pred: np.ndarray) -> Dict:...
  function get_per_class_metrics (line 43) | def get_per_class_metrics(y_true: np.ndarray, y_pred: np.ndarray, class_...
  function nlp_llm (line 68) | def nlp_llm(x):  # pragma: no cover, eval workload
  function short_text (line 77) | def short_text(x):  # pragma: no cover, eval workload
  function get_slice_metrics (line 82) | def get_slice_metrics(y_true: np.ndarray, y_pred: np.ndarray, ds: Datase...
  function evaluate (line 109) | def evaluate(

FILE: madewithml/models.py
  class FinetunedLLM (line 11) | class FinetunedLLM(nn.Module):
    method __init__ (line 12) | def __init__(self, llm, dropout_p, embedding_dim, num_classes):
    method forward (line 21) | def forward(self, batch):
    method predict (line 29) | def predict(self, batch):
    method predict_proba (line 36) | def predict_proba(self, batch):
    method save (line 42) | def save(self, dp):
    method load (line 53) | def load(cls, args_fp, state_dict_fp):

FILE: madewithml/predict.py
  function decode (line 23) | def decode(indices: Iterable[Any], index_to_class: Dict) -> List:
  function format_prob (line 36) | def format_prob(prob: Iterable, index_to_class: Dict) -> Dict:
  class TorchPredictor (line 52) | class TorchPredictor:
    method __init__ (line 53) | def __init__(self, preprocessor, model):
    method __call__ (line 58) | def __call__(self, batch):
    method predict_proba (line 62) | def predict_proba(self, batch):
    method get_preprocessor (line 66) | def get_preprocessor(self):
    method from_checkpoint (line 70) | def from_checkpoint(cls, checkpoint):
  function predict_proba (line 77) | def predict_proba(
  function get_best_run_id (line 102) | def get_best_run_id(experiment_name: str = "", metric: str = "", mode: s...
  function get_best_checkpoint (line 122) | def get_best_checkpoint(run_id: str) -> TorchCheckpoint:  # pragma: no c...
  function predict (line 137) | def predict(

FILE: madewithml/serve.py
  class ModelDeployment (line 24) | class ModelDeployment:
    method __init__ (line 25) | def __init__(self, run_id: str, threshold: int = 0.9):
    method _index (line 34) | def _index(self) -> Dict:
    method _run_id (line 44) | def _run_id(self) -> Dict:
    method _evaluate (line 49) | async def _evaluate(self, request: Request) -> Dict:
    method _predict (line 55) | async def _predict(self, request: Request):

FILE: madewithml/train.py
  function train_step (line 36) | def train_step(
  function eval_step (line 71) | def eval_step(
  function train_loop_per_worker (line 101) | def train_loop_per_worker(config: dict) -> None:  # pragma: no cover, te...
  function train_model (line 152) | def train_model(

FILE: madewithml/tune.py
  function tune_models (line 30) | def tune_models(

FILE: madewithml/utils.py
  function set_seeds (line 16) | def set_seeds(seed: int = 42):
  function load_dict (line 27) | def load_dict(path: str) -> Dict:
  function save_dict (line 41) | def save_dict(d: Dict, path: str, cls: Any = None, sortkeys: bool = Fals...
  function pad_array (line 58) | def pad_array(arr: np.ndarray, dtype=np.int32) -> np.ndarray:
  function collate_fn (line 76) | def collate_fn(batch: Dict[str, np.ndarray]) -> Dict[str, torch.Tensor]:...
  function get_run_id (line 94) | def get_run_id(experiment_name: str, trial_id: str) -> str:  # pragma: n...
  function dict_to_list (line 109) | def dict_to_list(data: Dict, keys: List[str]) -> List[Dict[str, Any]]:

FILE: notebooks/clear_cell_nums.py
  function clear_execution_numbers (line 6) | def clear_execution_numbers(nb_path):

FILE: tests/code/conftest.py
  function dataset_loc (line 7) | def dataset_loc():
  function preprocessor (line 12) | def preprocessor():

FILE: tests/code/test_data.py
  function df (line 9) | def df():
  function class_to_index (line 16) | def class_to_index():
  function test_load_data (line 21) | def test_load_data(dataset_loc):
  function test_stratify_split (line 27) | def test_stratify_split():
  function test_clean_text (line 45) | def test_clean_text(text, sw, clean_text):
  function test_preprocess (line 49) | def test_preprocess(df, class_to_index):
  function test_fit_transform (line 55) | def test_fit_transform(dataset_loc, preprocessor):

FILE: tests/code/test_predict.py
  function test_decode (line 4) | def test_decode():
  function test_format_prob (line 9) | def test_format_prob():

FILE: tests/code/test_train.py
  function test_train_model (line 10) | def test_train_model(dataset_loc):

FILE: tests/code/test_tune.py
  function test_tune_models (line 10) | def test_tune_models(dataset_loc):

FILE: tests/code/test_utils.py
  function test_set_seed (line 12) | def test_set_seed():
  function test_save_and_load_dict (line 23) | def test_save_and_load_dict():
  function test_pad_array (line 32) | def test_pad_array():
  function test_collate_fn (line 38) | def test_collate_fn():
  function test_dict_to_list (line 61) | def test_dict_to_list(d, keys, list):

FILE: tests/code/utils.py
  function generate_experiment_name (line 6) | def generate_experiment_name(prefix: str = "test") -> str:
  function delete_experiment (line 10) | def delete_experiment(experiment_name: str) -> None:

FILE: tests/data/conftest.py
  function pytest_addoption (line 6) | def pytest_addoption(parser):
  function df (line 14) | def df(request):

FILE: tests/data/test_dataset.py
  function test_dataset (line 1) | def test_dataset(df):

FILE: tests/model/conftest.py
  function pytest_addoption (line 7) | def pytest_addoption(parser):
  function run_id (line 12) | def run_id(request):
  function predictor (line 17) | def predictor(run_id):

FILE: tests/model/test_behavioral.py
  function test_invariance (line 15) | def test_invariance(input_a, input_b, label, predictor):
  function test_directional (line 39) | def test_directional(input, label, predictor):
  function test_mft (line 62) | def test_mft(input, label, predictor):

FILE: tests/model/utils.py
  function get_label (line 6) | def get_label(text, predictor):