script for generating test data

src/generate_test_csv.py

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+import random
+import csv
+import string
+from pathlib import Path
+
+# Constants
+SAMPLES_PER_CLASS = 10
+CURRENT_DIR = Path(__file__).resolve().parent
+PROJECT_ROOT = CURRENT_DIR.parent
+IMAGE_DIR = PROJECT_ROOT / "data" / "images"
+TRAIN_CSV_PATH = PROJECT_ROOT / "data" / "emr_records.csv"
+OUTPUT_CSV = PROJECT_ROOT / "data" / "test_samples.csv"
+LABELS = ["COVID", "NORMAL", "VIRAL PNEUMONIA"]
+
+alt_symptoms = [
+    "The patient has noted intermittent chest pressure and occasional shortness of breath.",
+    "A gradual onset of dry cough with mild respiratory discomfort has been documented.",
+    "Reported complaints include mild fatigue and sporadic episodes of wheezing.",
+    "Mild respiratory symptoms have progressed over several days.",
+    "Episodes of throat irritation and general malaise observed.",
+]
+
+alt_diagnosis = [
+    "Clinical features are suggestive of a nonspecific viral etiology.",
+    "Diagnosis remains unclear pending further laboratory confirmation.",
+    "Preliminary indicators fall into a diagnostic grey area.",
+    "No definitive pattern observed; further evaluation is warranted.",
+    "Presentation overlaps multiple pulmonary conditions.",
+]
+
+alt_noise = [
+    "Patient remains oriented with stable hemodynamics.",
+    "No remarkable family history or chronic illness reported.",
+    "Nutritional intake and sleep patterns appear adequate.",
+    "No prior admissions or surgical history disclosed.",
+    "Standard precautions have been advised post-evaluation.",
+]
+
+
+def random_token():
+    prefix = "TEST"
+    letters = "".join(random.choices(string.ascii_uppercase, k=2))
+    digits = "".join(random.choices(string.digits, k=2))
+    return f"{prefix}-{letters}{digits}"
+
+
+def get_oxygen(label):
+    if label == "NORMAL":
+        return random.randint(94, 100)
+    elif label == "VIRAL PNEUMONIA":
+        return random.randint(90, 96)
+    else:
+        return random.randint(87, 94)
+
+
+def get_temp(label):
+    if label == "NORMAL":
+        return round(random.uniform(97.5, 99.0), 1)
+    else:
+        return round(random.uniform(98.8, 102.5), 1)
+
+
+def get_age():
+    return random.randint(18, 85)
+
+
+def get_days():
+    return random.randint(1, 10)
+
+
+def build_alt_emr(label):
+    pid = random_token()
+    age = f"{get_age()} years old"
+    days = get_days()
+    temp = get_temp(label)
+    oxygen = get_oxygen(label)
+
+    sent_intro = f"Patient {pid}, a {age} individual presented after experiencing symptoms for approximately {days} days."
+    sent_vitals = f"Vital measurements include a body temperature of {temp}°F and an oxygen saturation level of {oxygen}%."
+
+    body = [
+        sent_intro,
+        random.choice(alt_symptoms),
+        sent_vitals,
+        random.choice(alt_diagnosis),
+    ]
+
+    if random.random() < 0.3:
+        if label == "COVID":
+            body.append("Anosmia has been intermittently observed over recent days.")
+        elif label == "VIRAL PNEUMONIA":
+            body.append("Radiographic evidence reveals dispersed infiltrative patterns.")
+        elif label == "NORMAL":
+            body.append("There are currently no active complaints from the patient.")
+
+    # inject 1-2 neutral clinical observations
+    if random.random() < 0.9:
+        body.insert(random.randint(1, len(body)), random.choice(alt_noise))
+    if random.random() < 0.5:
+        body.insert(random.randint(1, len(body)), random.choice(alt_noise))
+
+    random.shuffle(body[1:])  # Keep the first sentence intact
+    return " ".join(body)
+
+
+def get_training_image_set():
+    if not TRAIN_CSV_PATH.exists():
+        raise FileNotFoundError(f"Training CSV not found at {TRAIN_CSV_PATH}")
+    with open(TRAIN_CSV_PATH, newline="") as f:
+        reader = csv.DictReader(f)
+        return set(row["image_path"].strip() for row in reader)
+
+
+def generate_test_csv():
+    training_images = get_training_image_set()
+    records = []
+
+    for label in LABELS:
+        label_dir = IMAGE_DIR / label
+        image_files = sorted([
+            f for f in label_dir.glob("*") if f.suffix.lower() in [".png", ".jpg", ".jpeg"]
+        ])
+        unseen_images = [
+            f for f in image_files
+            if str(f.relative_to(PROJECT_ROOT)) not in training_images
+        ]
+
+        if len(unseen_images) < SAMPLES_PER_CLASS:
+            raise ValueError(f"Not enough unseen images in {label_dir}."
+                             f"Needed {SAMPLES_PER_CLASS}, found {len(unseen_images)}")
+        sampled_images = random.sample(unseen_images, SAMPLES_PER_CLASS)
+
+        for img_path in sampled_images:
+            relative_path = str(img_path.relative_to(PROJECT_ROOT))
+            text = build_alt_emr(label)
+            records.append([text, relative_path, label])
+
+    random.shuffle(records)
+    with open(OUTPUT_CSV, "w", newline="") as f:
+        writer = csv.writer(f)
+        writer.writerow(["text", "image_path", "label"])
+        writer.writerows(records)
+
+    print(f"✅ test CSV file generated: {OUTPUT_CSV}")
+    print(f"📦 Total samples: {len(records)} (10 per class)")
+
+
+if __name__ == "__main__":
+    generate_test_csv()