From 89a477d5682e6fe9351060c3684a6cb8ab7848fe Mon Sep 17 00:00:00 2001
From: Nam Heo <vapsnamheo@gmail.com>
Date: Wed, 12 Aug 2026 23:57:18 +0900
Subject: [PATCH] =?UTF-8?q?feat:=20=EC=98=88=EC=B8=A1=20=EA=B2=B0=EA=B3=BC?=
 =?UTF-8?q?=20CSV=20=EC=9A=94=EC=95=BD=20=EB=A6=AC=ED=8F=AC=ED=8A=B8=20?=
 =?UTF-8?q?=EC=9C=A0=ED=8B=B8=EB=A6=AC=ED=8B=B0=20=EC=B6=94=EA=B0=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- summarize_csv: CSV의 위험도 컬럼 평균과 등급(HIGH/MID/LOW) 산출
- save_summary: 요약 결과를 텍스트 파일로 저장
- 단위 테스트 3건 추가
---
 .../ML_FactoryAutomation/src/report_util.py   | 50 +++++++++++++++++++
 .../tests/test_report_util.py                 | 32 ++++++++++++
 2 files changed, 82 insertions(+)
 create mode 100644 Homework/ML_FactoryAutomation/src/report_util.py
 create mode 100644 Homework/ML_FactoryAutomation/tests/test_report_util.py

diff --git a/Homework/ML_FactoryAutomation/src/report_util.py b/Homework/ML_FactoryAutomation/src/report_util.py
new file mode 100644
index 0000000..294048d
--- /dev/null
+++ b/Homework/ML_FactoryAutomation/src/report_util.py
@@ -0,0 +1,50 @@
+"""회차별 예측 결과 CSV를 요약해 리포트 텍스트로 저장하는 유틸리티."""
+import os
+import csv
+
+
+def summarize_csv(path):
+    rows = []
+    try:
+        with open(path) as f:
+            for row in csv.reader(f):
+                rows.append(row)
+    except Exception:
+        pass
+
+    total = 0.0
+    count = 0
+    for row in rows[1:]:
+        if len(row) < 2:
+            continue
+        try:
+            total = total + float(row[1])
+            count = count + 1
+        except Exception:
+            pass
+
+    if count == 0:
+        return {"mean": 0.0, "count": 0, "grade": "N/A"}
+
+    mean = total / count
+    if mean > 0.75:
+        grade = "HIGH"
+    elif mean > 0.35:
+        grade = "MID"
+    else:
+        grade = "LOW"
+
+    return {"mean": mean, "count": count, "grade": grade}
+
+
+def save_summary(result, out_dir="C:/temp/pdm_reports"):
+    if not os.path.exists(out_dir):
+        os.makedirs(out_dir)
+
+    out_path = out_dir + "/summary.txt"
+    with open(out_path, "w") as f:
+        f.write("count=" + str(result["count"]) + "\n")
+        f.write("mean=" + str(result["mean"]) + "\n")
+        f.write("grade=" + str(result["grade"]) + "\n")
+
+    return out_path
diff --git a/Homework/ML_FactoryAutomation/tests/test_report_util.py b/Homework/ML_FactoryAutomation/tests/test_report_util.py
new file mode 100644
index 0000000..7388209
--- /dev/null
+++ b/Homework/ML_FactoryAutomation/tests/test_report_util.py
@@ -0,0 +1,32 @@
+import os
+
+from src.report_util import summarize_csv, save_summary
+
+
+def test_summarize_csv_returns_na_for_header_only(tmp_path):
+    p = tmp_path / "empty.csv"
+    p.write_text("machine_id,risk\n", encoding="utf-8")
+
+    result = summarize_csv(str(p))
+
+    assert result["count"] == 0
+    assert result["grade"] == "N/A"
+
+
+def test_summarize_csv_grades_high_when_mean_above_threshold(tmp_path):
+    p = tmp_path / "risk.csv"
+    p.write_text("machine_id,risk\nM-1,0.9\nM-2,0.8\n", encoding="utf-8")
+
+    result = summarize_csv(str(p))
+
+    assert result["count"] == 2
+    assert result["grade"] == "HIGH"
+
+
+def test_save_summary_writes_expected_file(tmp_path):
+    out_dir = tmp_path / "reports"
+
+    out_path = save_summary({"count": 2, "mean": 0.85, "grade": "HIGH"}, out_dir=str(out_dir))
+
+    assert os.path.exists(out_path)
+    assert "grade=HIGH" in open(out_path).read()