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2f74c56 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 | # Portions of this file were developed with assistance from OpenAI ChatGPT/Codex and reviewed/modified by the author.
"""Training entry points for all Campus Triage models.
Portions of this file were developed with assistance from OpenAI ChatGPT/Codex and reviewed/modified by the author.
"""
from __future__ import annotations
import argparse
from pathlib import Path
import pandas as pd
from campus_triage.config import BASELINE_MODEL_PATH, CLASSICAL_MODEL_PATH, MODELS_DIR, TRANSFORMER_MODEL_DIR
from campus_triage.data import create_and_save_dataset, load_processed_splits
from campus_triage.models import (
build_baseline_model,
build_classical_model,
save_dual_classifier,
transformer_dependencies_available,
)
def train_baseline(train_dataframe: pd.DataFrame) -> Path:
"""Train and save the majority baseline."""
MODELS_DIR.mkdir(parents=True, exist_ok=True)
model = build_baseline_model(train_dataframe)
save_dual_classifier(model, str(BASELINE_MODEL_PATH))
return BASELINE_MODEL_PATH
def train_classical(train_dataframe: pd.DataFrame) -> Path:
"""Train and save the TF-IDF Logistic Regression model."""
MODELS_DIR.mkdir(parents=True, exist_ok=True)
model = build_classical_model(train_dataframe)
save_dual_classifier(model, str(CLASSICAL_MODEL_PATH))
return CLASSICAL_MODEL_PATH
def train_transformer_optional(train_dataframe: pd.DataFrame, validation_dataframe: pd.DataFrame, max_rows: int = 600) -> str:
"""Fine-tune lightweight DistilBERT models when optional dependencies are available."""
if not transformer_dependencies_available():
return "Skipped transformer training because optional dependencies are not installed."
from campus_triage.transformer_training import train_transformer_models
TRANSFORMER_MODEL_DIR.mkdir(parents=True, exist_ok=True)
train_transformer_models(train_dataframe, validation_dataframe, TRANSFORMER_MODEL_DIR, max_rows=max_rows)
return f"Saved transformer models to {TRANSFORMER_MODEL_DIR}"
def train_all(include_transformer: bool = False) -> list[str]:
"""Train all required models, with transformer training optional for laptop speed."""
if not Path("data/processed/train.csv").exists():
create_and_save_dataset()
train_dataframe, validation_dataframe, _ = load_processed_splits()
results = [
f"Saved baseline model to {train_baseline(train_dataframe)}",
f"Saved classical model to {train_classical(train_dataframe)}",
]
if include_transformer:
results.append(train_transformer_optional(train_dataframe, validation_dataframe))
else:
results.append("Skipped transformer training by default. Use --include-transformer to run it.")
return results
def parse_args() -> argparse.Namespace:
"""Parse command-line arguments."""
parser = argparse.ArgumentParser(description="Train Campus Triage models.")
parser.add_argument("--include-transformer", action="store_true", help="Fine-tune optional DistilBERT models.")
return parser.parse_args()
def main() -> None:
"""Run training from the command line."""
args = parse_args()
for message in train_all(include_transformer=args.include_transformer):
print(message)
if __name__ == "__main__":
main()
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