add script to evaluate patient statistics

mu_map/eval/dataset_stats.py

+import numpy as np
+
+from mu_map.data.prepare import headers
+from mu_map.data.split import split_csv
+
+
+def body_mass_index(weight: np.ndarray, size: np.ndarray) -> np.ndarray:
+    return weight / (size**2)
+
+
+if __name__ == "__main__":
+    import argparse
+    import pandas as pd
+
+    parser = argparse.ArgumentParser(
+        description="Evaluate dataset patient stats.",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument("--meta_csv", type=str, default="data/second/meta.csv")
+    parser.add_argument("--split_csv", type=str, default="data/second/split.csv")
+    args = parser.parse_args()
+    print(args)
+
+    data = pd.read_csv(args.meta_csv)
+    splits = split_csv(data, args.split_csv)
+
+    for split_name, split in splits.items():
+        print(f"{split_name[0].upper()}{split_name[1:]}:")
+
+        n_studies = len(split)
+        print(f" -    Studies: {n_studies}")
+        n_stress = len(split[split[headers.protocol] == "Stress"])
+        n_rest = len(split[split[headers.protocol] == "Rest"])
+        print(f"   -   Stress: {n_stress}")
+        print(f"   -     Rest: {n_rest}")
+        n_patients = len(split[headers.patient_id].unique())
+        _group = split.groupby(headers.patient_id).count()[headers.id]
+        n_studies_one = (_group == 1).sum()
+        n_studies_two = (_group == 2).sum()
+        n_studies_three = (_group == 3).sum()
+        print(
+            f" -   Patients: {n_patients}  [{n_studies_one}, {n_studies_two}, {n_studies_three}]"
+        )
+        _split = split.drop_duplicates(headers.patient_id)
+        n_males = len(_split[_split[headers.sex] == "M"])
+        n_females = len(_split[_split[headers.sex] == "F"])
+        print(f"   -        M: {n_males}")
+        print(f"   -        F: {n_females}")
+
+        age = split[headers.age]
+        height = split[headers.size]
+        weight = split[headers.weight]
+        bmi = body_mass_index(weight, height)
+
+        age = age.astype(int)
+        weight = weight.astype(int)
+        height = np.round(height * 100).astype(int)
+        bmi = np.round(bmi, 2)
+
+        for stat, label in [
+            (age, "Age"),
+            (weight, "Weight"),
+            (height, "Height"),
+            (bmi, "BMI"),
+        ]:
+            _min = f"{str(stat.min()):>5}"
+            _max = f"{str(stat.max()):>5}"
+            _mean = f"{stat.mean():.1f}"
+            _std = f"{stat.std():.1f}"
+            print(f" - {label:>10}: [{_min}, {_max}] - {_mean:>5}±{_std:>5}")