pepred 0.1.0

PE prediction model (batch scoring and evaluation).

Batch PE Risk Prediction

This tool runs the Pre-Eclampsia (PE) risk prediction model on a CSV file. Each row represents a patient, and the script outputs a new CSV with an added score column (predicted probability).

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1. Input CSV Format

Your input CSV must contain column names that appear in Features.csv.

Core numeric features

• age – years
• ht – height in centimeters
• wt – weight in kilograms
• interval – inter-pregnancy interval
• last.ga – gestational age at last delivery (weeks)
• (full model only) ga, pappa, plgf, utpi, map, plgf.machine

Optional raw-unit features

Instead of writing the base units, you may provide:

• ht_ft, ht_in → height in feet and inches (converted to ht in cm)
• wt_lb → weight in pounds (converted to wt in kg)
• ga_weeks → gestational age in weeks (converted to ga in days; full model only)

If both raw-unit and base-unit columns exist, the script prefers:

• ht_ft / ht_in over ht
• wt_lb over wt
• ga_weeks over ga

Categorical features

Use the category names defined in Features.csv → valid_options (e.g., White, Spontaneous, No, etc.), or provide numeric codes 0/1/2 matching those options.

Missing values are allowed and will be imputed automatically using the median (or median category) from Features.csv.

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2. Running From the Terminal

Assuming your script is named main.py:

Partial model, case 3

python main.py \
  --input data/input.csv \
  --output results/output.csv \
  --case 3

Full model, case 3

python main.py \
  --input data/input_full.csv \
  --output results/output_full.csv \
  --case 3 \
  --full

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3. Running a Different CSV File

Step 1: Add your CSV

Place your file in the data/ folder, for example:

data/new_patients.csv

Step 2: Edit launch.json (VS Code)

Modify the arguments:

• Change data/input.csv → data/new_patients.csv
• Change results/output.csv → results/new_output.csv (optional)
• Change 3 → 1 if you want to run --case 1
• Add --full to use the full model

Step 3: Run

In VS Code:

• Open the Run/Debug panel
• Select Python: Run main.py
• Click Run

The output CSV will be saved at the path specified in --output, containing all original columns plus the new score column.

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