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#!/usr/bin/env python
"""Run a simple binary classification model over an ESDI dataset."""
import json
import logging
from argparse import ArgumentParser
from pathlib import Path
import datasets
import lightning as L
import numpy as np
import polars as pl
import torch
from MEDS_transformations import (
JoinCohortFntr,
NormalizeFntr,
SampleSubsequencesFntr,
TensorizeFntr,
TokenizeFntr,
)
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
from sample_ESDI_model.simple_model import LightningModel
logger = logging.getLogger(__name__)
def get_vocab(ds: datasets.Dataset) -> list[str]:
"""Get the size of the vocabulary.
TODO(mmd): Leverage the metadata file to get this.
"""
vocab = set()
for row in tqdm(ds, desc="Building vocabulary", total=len(ds)):
vocab.update([m["code"] for m in row["static_measurements"]])
for e in row["events"]:
vocab.update([m["code"] for m in e["measurements"]])
return sorted(list(vocab))
def get_norm_params(ds: datasets.Dataset) -> dict[str, tuple[float, float]]:
"""Get the means and standard deviations of observed code values.
TODO(mmd): Leverage the metadata file to get this.
"""
# stats will map code -> (count, sum, sum_sq)
stats = {}
def add_measurements(measurements):
for m in measurements:
code = m["code"]
value = m["numeric_value"]
if value is None or value is np.nan:
continue
if code not in stats:
stats[code] = (0, 0, 0)
count, sum_vals, sum_sq_vals = stats[code]
stats[code] = (count + 1, sum_vals + value, sum_sq_vals + value**2)
for row in tqdm(ds, desc="Measuring normalization parameters", total=len(ds)):
add_measurements(row["static_measurements"])
for e in row["events"]:
add_measurements(e["measurements"])
return {
code: (sum_vals / count, np.sqrt(sum_sq_vals / count - (sum_vals / count) ** 2))
for code, (count, sum_vals, sum_sq_vals) in stats.items()
}
def get_max_measurements(ds: datasets.Dataset) -> int:
"""Get the max number of measurements observed across all patients in the dataset.
TODO(mmd): Leverage the metadata file to get this.
"""
max_measurements = 0
for row in tqdm(ds, desc="Computing the max # of measurements", total=len(ds)):
max_measurements = max(max_measurements, len(row["static_measurements"]))
for e in row["events"]:
max_measurements = max(max_measurements, len(e["measurements"]))
return max_measurements
def main():
parser = ArgumentParser("Train a model over a binary classification task on an ESDI dataset.")
parser.add_argument("--dataset_path", type=str)
parser.add_argument("--task_df_path", type=str)
parser.add_argument("--output_path", type=str)
# Model params
parser.add_argument("--hidden_size", type=int, default=128)
parser.add_argument("--n_layers", type=int, default=2)
parser.add_argument("--max_seq_len", type=int, default=64)
# Training params
parser.add_argument("--n_epochs", type=int, default=10)
parser.add_argument("--batch_size", type=int, default=32)
parser.add_argument("--lr", type=float, default=5e-3)
args = parser.parse_args()
logger.info("Loading the core dataset...")
ds = datasets.load_dataset(
"parquet",
data_files={
sp: str(Path(args.dataset_path) / sp / "*.parquet") for sp in ("train", "tuning", "held_out")
},
)
logger.info("Loading the task dataframe...")
task_df = pl.read_parquet(args.task_df_path)
vocab_file = Path(args.output_path) / "vocab.json"
if vocab_file.exists():
logger.info("Loading vocab from disk...")
with open(vocab_file, "r") as f:
vocab = json.load(f)
else:
logger.info("Getting vocab...")
vocab = get_vocab(ds["train"])
with open(vocab_file, "w") as f:
json.dump(vocab, f)
idxmap = {code: i for i, code in enumerate(vocab)}
vocab_size = len(vocab)
norm_file = Path(args.output_path) / "norm_params.json"
if norm_file.exists():
logger.info("Loading normalization params from disk...")
with open(norm_file, "r") as f:
norm_params = json.load(f)
else:
logger.info("Getting normalization params...")
norm_params = get_norm_params(ds["train"])
with open(norm_file, mode="w") as f:
json.dump(norm_params, f)
max_measurements_file = Path(args.output_path) / "max_measurements.json"
if max_measurements_file.exists():
logger.info("Loading max # of measurements from disk...")
with open(max_measurements_file, "r") as f:
max_measurements = json.load(f)["max_measurements"]
else:
logger.info("Getting the max # of measurements...")
max_measurements = get_max_measurements(ds["train"])
with open(max_measurements_file, "w") as f:
json.dump({"max_measurements": max_measurements}, f)
logger.info("Applying transformations...")
transforms = [
JoinCohortFntr(task_df),
SampleSubsequencesFntr(
max_seq_len=args.max_seq_len, n_samples_per_patient=1, sample_strategy="to_end"
),
TokenizeFntr(vocab),
NormalizeFntr(norm_params),
]
for transform_fn in transforms:
ds = ds.map(transform_fn, batch_size=256, batched=True)
ds = ds.map(
TensorizeFntr(idxmap, pad_sequences_to=args.max_seq_len, pad_measurements_to=max_measurements),
batch_size=256,
batched=True,
remove_columns=["patient_id", "static_measurements", "events"]
)
train_dataloader = DataLoader(ds["train"], batch_size=args.batch_size, shuffle=True)
tuning_dataloader = DataLoader(ds["tuning"], batch_size=args.batch_size, shuffle=False)
held_out_dataloader = DataLoader(ds["held_out"], batch_size=args.batch_size, shuffle=False)
logger.info("Building the model...")
lit_model = LightningModel(
vocab_size=vocab_size,
hidden_size=args.hidden_size,
n_layers=args.n_layers,
lr=args.lr,
)
logger.info("Building the optimizer...")
logger.info("Training the model...")
trainer = L.Trainer(max_epochs=args.n_epochs, default_root_dir=args.output_path)
trainer.fit(lit_model, train_dataloader, tuning_dataloader)
logger.info("Evaluating the model...")
tuning_results = trainer.test(lit_model, tuning_dataloader)
held_out_results = trainer.test(lit_model, held_out_dataloader)
with open(Path(args.output_path) / "tuning_results.json", "w") as f:
json.dump(tuning_results, f)
with open(Path(args.output_path) / "held_out_results.json", "w") as f:
json.dump(held_out_results, f)
if __name__ == "__main__":
main()