From a20339d35e5b6cdc4a8b78bc5e37cd7dd88b39b3 Mon Sep 17 00:00:00 2001 From: Huarch Date: Mon, 20 Jul 2026 13:47:39 +0800 Subject: [PATCH] Update prediction results workflow --- app/prediction.py | 259 +++++++++++++++++++++------------------ app/routes.py | 7 -- example.xlsx | Bin 32492 -> 36183 bytes requirements.txt | 4 +- static/css/app.css | 2 +- static/js/dashboard.js | 9 -- templates/home.html | 18 ++- templates/login.html | 2 +- templates/result.html | 66 ++-------- tests/test_prediction.py | 79 ++++++++++-- 10 files changed, 239 insertions(+), 207 deletions(-) diff --git a/app/prediction.py b/app/prediction.py index 19aad27..127c026 100644 --- a/app/prediction.py +++ b/app/prediction.py @@ -61,6 +61,16 @@ PIPE_AGE_COLUMN = "管龄（年）" LEGACY_PIPE_AGE_COLUMN = "管龄" STATUS_COLUMN = "状态" EVENT_AGE_COLUMN = "事件/观察管龄（年）" +COLUMN_ALIASES = { + "管径（mm）": "管径", + "管径（mm)": "管径", + "流速（m/s）": "流速", + "压力（MPa)": "压力", + "压力（MPa）": "压力", + "温度（℃）": "温度", + "年均降雨量（mm）": "降雨量", + "降雨量（mm）": "降雨量", +} INPUT_COLUMNS = [ ID_COLUMN, PIPE_AGE_COLUMN, @@ -68,6 +78,7 @@ INPUT_COLUMNS = [ EVENT_AGE_COLUMN, *FEATURES, ] +CHART_DISPLAY_LIMIT = 10 MATERIAL_COLUMN = "管材" MATERIAL_CODE_OPTIONS = [ (1, "镀锌"), @@ -103,6 +114,7 @@ MATERIAL_ALIAS_TO_CODE.update( ) SUPPORTED_EXTENSIONS = {".csv", ".xls", ".xlsx"} CHINESE_FONT_PROP = None +DEFECT_GRADE_VALUES = {"无": 0.0, "轻度": 1.0, "中度": 3.0, "严重": 5.0} class PredictionError(Exception): @@ -119,12 +131,35 @@ class PredictionArtifacts: excel_path: Path image_path: Path image_filename: str - importance_filename: str | None sample_count: int summary_rows: list[dict[str, Any]] analysis_text: str +class ModelBundleAdapter: + """Expose a bundled preprocessor and survival model as one predictor.""" + + def __init__(self, preprocessor, model) -> None: + self.preprocessor = preprocessor + self.model = model + + def _transform(self, frame: pd.DataFrame): + values = frame[FEATURES].copy() + values[MATERIAL_COLUMN] = values[MATERIAL_COLUMN].map( + lambda value: f"M{int(value)}" if pd.notna(value) else value + ) + values["位置"] = values["位置"].map( + lambda value: f"L{int(value)}" if pd.notna(value) else value + ) + return self.preprocessor.transform(values.to_numpy(dtype=object)).astype(np.float32) + + def predict_survival_function(self, frame: pd.DataFrame): + return self.model.predict_survival_function(self._transform(frame)) + + def predict(self, frame: pd.DataFrame): + return self.model.predict(self._transform(frame)) + + def configure_matplotlib_fonts(): for font_path in CHINESE_FONT_FILES: path = Path(font_path) @@ -159,6 +194,10 @@ def load_model(model_path: str): if not os.path.exists(model_path): raise FileNotFoundError(f"未找到模型文件: {model_path}") model = joblib.load(model_path) + if isinstance(model, dict): + if not {"preprocessor", "model"}.issubset(model): + raise ValueError("模型包缺少 preprocessor 或 model。") + model = ModelBundleAdapter(model["preprocessor"], model["model"]) feature_names = getattr(model, "feature_names_in_", None) if feature_names is not None and list(feature_names) != FEATURES: raise ValueError( @@ -195,17 +234,25 @@ def read_input_file(path: Path, suffix: str) -> pd.DataFrame: if suffix == ".csv": df = pd.read_csv(path, dtype={ID_COLUMN: "string"}) else: - df = pd.read_excel(path, dtype={ID_COLUMN: "string"}) + try: + df = pd.read_excel(path, sheet_name="Template", dtype={ID_COLUMN: "string"}) + except ValueError: + df = pd.read_excel(path, dtype={ID_COLUMN: "string"}) + df["_excel_row"] = df.index + 2 except Exception as exc: logging.exception("文件解析失败: %s", exc) raise PredictionError("文件解析失败，请检查编码或表格格式。") - return normalize_input_columns(df) + df = normalize_input_columns(df) + if suffix in {".xls", ".xlsx"}: + df = fill_defects_from_detail_sheet(path, df) + return df def normalize_input_columns(df: pd.DataFrame) -> pd.DataFrame: + aliases = dict(COLUMN_ALIASES) if PIPE_AGE_COLUMN not in df.columns and LEGACY_PIPE_AGE_COLUMN in df.columns: - return df.rename(columns={LEGACY_PIPE_AGE_COLUMN: PIPE_AGE_COLUMN}) - return df + aliases[LEGACY_PIPE_AGE_COLUMN] = PIPE_AGE_COLUMN + return df.rename(columns={name: aliases[name] for name in df.columns if name in aliases}) def normalize_id_value(value: Any, fallback: str) -> str: @@ -216,6 +263,79 @@ def normalize_id_value(value: Any, fallback: str) -> str: return str(value) +def defect_grade_value(value: Any) -> float | None: + if pd.isna(value): + return None + text = str(value).strip() + if text == "": + return None + if text in DEFECT_GRADE_VALUES: + return DEFECT_GRADE_VALUES[text] + try: + return float(text) + except ValueError: + return None + + +def weighted_defect_score(values: list[Any], weights: list[float]) -> float | None: + scores = [defect_grade_value(value) for value in values] + if any(score is None for score in scores): + return None + return round(sum(score * weight for score, weight in zip(scores, weights)), 3) + + +def numeric_or_none(value: Any) -> float | None: + try: + if pd.isna(value): + return None + return float(value) + except (TypeError, ValueError): + return None + + +def fill_defects_from_detail_sheet(path: Path, df: pd.DataFrame) -> pd.DataFrame: + if "结构缺陷" not in df.columns or "功能缺陷" not in df.columns or "_excel_row" not in df.columns: + return df + + structure = pd.to_numeric(df["结构缺陷"], errors="coerce") + function = pd.to_numeric(df["功能缺陷"], errors="coerce") + needs_fill = structure.isna() | function.isna() + df["结构缺陷"] = structure + df["功能缺陷"] = function + if not needs_fill.any(): + return df + + try: + from openpyxl import load_workbook + + workbook = load_workbook(path, data_only=False, read_only=True) + defect_sheet = workbook["缺陷计算"] + except Exception as exc: + logging.info("未能读取缺陷计算工作表，跳过缺陷自动计算: %s", exc) + return df + + for index in df.index[needs_fill]: + excel_row = int(df.at[index, "_excel_row"]) + if pd.isna(df.at[index, "结构缺陷"]): + value = numeric_or_none(defect_sheet.cell(excel_row, 5).value) + if value is None: + value = weighted_defect_score( + [defect_sheet.cell(excel_row, column).value for column in (2, 3, 4)], + [0.5, 0.4, 0.1], + ) + df.at[index, "结构缺陷"] = value + if pd.isna(df.at[index, "功能缺陷"]): + value = numeric_or_none(defect_sheet.cell(excel_row, 10).value) + if value is None: + value = weighted_defect_score( + [defect_sheet.cell(excel_row, column).value for column in (6, 7, 8, 9)], + [0.5, 0.2, 0.2, 0.1], + ) + df.at[index, "功能缺陷"] = value + + return df + + def validate_input_frame(df: pd.DataFrame) -> None: if df.empty: raise PredictionError("上传文件没有可预测的数据。") @@ -288,99 +408,6 @@ def make_analysis_text(summary_rows: list[dict[str, Any]]) -> str: ) -def compute_feature_importance(model, x_test: pd.DataFrame) -> np.ndarray | None: - try: - importances = model.feature_importances_ - except Exception: - importances = None - - if importances is not None and len(importances) == len(FEATURES): - vals = np.asarray(importances, dtype=float) - else: - try: - baseline = np.asarray(model.predict(x_test), dtype=float) - except Exception as exc: - logging.exception("特征重要性基线预测失败: %s", exc) - return None - - rng = np.random.default_rng(42) - vals = np.zeros(len(FEATURES), dtype=float) - for j, feat in enumerate(FEATURES): - diffs = [] - for _ in range(5): - x_perm = x_test.copy() - x_perm[feat] = rng.permutation(x_perm[feat].to_numpy()) - try: - perm_pred = np.asarray(model.predict(x_perm), dtype=float) - except Exception: - perm_pred = baseline - diffs.append(float(np.mean(np.abs(perm_pred - baseline)))) - vals[j] = float(np.mean(diffs)) if diffs else 0.0 - - vals = np.clip(vals, a_min=0.0, a_max=None) - total = float(vals.sum()) - if total > 0: - vals = vals / total - return vals - - -def render_importance_chart(values: np.ndarray, save_path: Path) -> None: - from matplotlib.colors import LinearSegmentedColormap - - keep = values >= 5e-4 - if not bool(np.any(keep)): - keep = np.ones_like(values, dtype=bool) - kept_feats = [FEATURES[i] for i in range(len(FEATURES)) if keep[i]] - kept_vals = values[keep] - - order = np.argsort(kept_vals) - sorted_feats = [kept_feats[k] for k in order] - sorted_vals = kept_vals[order] - n = len(sorted_vals) - - fig, ax = plt.subplots(figsize=(9, max(3.0, 0.62 * n + 1.6))) - cmap = LinearSegmentedColormap.from_list("brand", ["#7fb2e6", "#005EB8", "#0c4188"]) - colors = cmap(np.linspace(0.15, 1.0, n)) if n else None - y_pos = np.arange(n) - bars = ax.barh(y_pos, sorted_vals, color=colors, height=0.66, edgecolor="white", linewidth=0.8, zorder=3) - - font_kwargs = chinese_font_kwargs() - ax.set_xlabel("相对重要性", fontsize=11, color="#475569", **font_kwargs) - ax.set_title("模型输入因素重要性排序", fontsize=15, fontweight="bold", color="#0f172a", pad=14, **font_kwargs) - ax.set_yticks(y_pos) - ax.set_yticklabels(sorted_feats, **font_kwargs) - ax.grid(axis="x", color="#e2e8f0", linewidth=1, zorder=0) - ax.set_axisbelow(True) - for spine in ("top", "right", "left"): - ax.spines[spine].set_visible(False) - ax.spines["bottom"].set_color("#cbd5e1") - ax.tick_params(axis="y", length=0, labelsize=11) - ax.tick_params(axis="x", colors="#94a3b8", labelsize=9) - if CHINESE_FONT_PROP: - for label in [*ax.get_yticklabels(), *ax.get_xticklabels()]: - label.set_fontproperties(CHINESE_FONT_PROP) - - max_val = float(sorted_vals.max()) if n else 0.0 - for bar, value in zip(bars, sorted_vals): - ax.text( - bar.get_width() + max_val * 0.012, - bar.get_y() + bar.get_height() / 2, - f"{value:.1%}", - va="center", - ha="left", - fontsize=10, - fontweight="bold", - color="#1e293b", - **font_kwargs, - ) - if max_val > 0: - ax.set_xlim(0, max_val * 1.18) - - fig.tight_layout() - fig.savefig(save_path, dpi=160, bbox_inches="tight") - plt.close(fig) - - def run_prediction(uploaded: FileStorage, user_id: int, model) -> PredictionArtifacts: original_filename = uploaded.filename or "" if not original_filename: @@ -409,15 +436,6 @@ def run_prediction(uploaded: FileStorage, user_id: int, model) -> PredictionArti image_path = IMAGE_DIR / image_filename summary_rows, summary_sheet_rows = render_survival_chart(df, curves, image_path) - importance_filename = None - try: - importance_values = compute_feature_importance(model, x_test) - if importance_values is not None: - importance_filename = f"importance_{user_id}_{run_id}.png" - render_importance_chart(importance_values, IMAGE_DIR / importance_filename) - except Exception as exc: - logging.exception("生成特征重要性图失败: %s", exc) - safe_stem = Path(saved_filename).stem excel_path = user_dir / f"{safe_stem}_pre.xlsx" write_prediction_workbook(excel_path, curves, summary_rows, summary_sheet_rows) @@ -427,7 +445,6 @@ def run_prediction(uploaded: FileStorage, user_id: int, model) -> PredictionArti excel_path=excel_path, image_path=image_path, image_filename=image_filename, - importance_filename=importance_filename, sample_count=len(summary_rows), summary_rows=summary_rows, analysis_text=make_analysis_text(summary_rows), @@ -441,13 +458,17 @@ def render_survival_chart(df: pd.DataFrame, curves, image_path: Path) -> tuple[l plt.figure(figsize=(10, 5.6)) summary_rows: list[dict[str, Any]] = [] summary_sheet_rows: list[dict[str, Any]] = [] + display_note = f"说明：图中仅展示前{CHART_DISPLAY_LIMIT}条管道的示例数据，不足{CHART_DISPLAY_LIMIT}条则全部展示；完整结果请下载电子表格。" for i, curve in enumerate(curves): times = [float(x) for x in list(curve.x)] probs = [float(y) for y in list(curve.y)] pipe_id = normalize_id_value(df.iloc[i][ID_COLUMN], f"Pipe_{i+1:03d}") - pipe_age = f"{df.iloc[i][PIPE_AGE_COLUMN]} 年" if PIPE_AGE_COLUMN in df.columns and pd.notna(df.iloc[i][PIPE_AGE_COLUMN]) else "-" - health_probability = interpolate_probability(times, probs, 10) + raw_pipe_age = df.iloc[i][PIPE_AGE_COLUMN] if PIPE_AGE_COLUMN in df.columns else None + pipe_age_value = float(raw_pipe_age) if pd.notna(raw_pipe_age) else 0.0 + pipe_age = f"{raw_pipe_age} 年" if pd.notna(raw_pipe_age) else "-" + health_probability = interpolate_probability(times, probs, pipe_age_value) + health_risk = 1.0 - health_probability remaining_life = estimate_remaining_life(times, probs) grade_label, grade_desc, grade_class = grade_info(health_probability) @@ -456,6 +477,7 @@ def render_survival_chart(df: pd.DataFrame, curves, image_path: Path) -> tuple[l "pipe_id": pipe_id, "pipe_age": pipe_age, "health_probability": health_probability, + "health_risk": health_risk, "remaining_life": remaining_life, "grade_label": grade_label, "grade_desc": grade_desc, @@ -466,23 +488,25 @@ def render_survival_chart(df: pd.DataFrame, curves, image_path: Path) -> tuple[l { ID_COLUMN: pipe_id, PIPE_AGE_COLUMN: pipe_age, - "健康等级": grade_label, + "健康风险值": health_risk, } ) - plt.step(times, probs, where="post", linewidth=2, label=pipe_id) + if i < CHART_DISPLAY_LIMIT: + plt.step(times, probs, where="post", linewidth=2, label=pipe_id) font_kwargs = chinese_font_kwargs() plt.xlabel("管龄（年）", **font_kwargs) plt.ylabel("健康风险", **font_kwargs) - plt.title("预测分析图", **font_kwargs) + plt.title("管道的剩余寿命分析图", **font_kwargs) + plt.figtext(0.5, 0.02, display_note, ha="center", fontsize=9, color="#475569", **font_kwargs) plt.grid(alpha=0.18) - if len(summary_rows) <= 12: + if min(len(summary_rows), CHART_DISPLAY_LIMIT) <= 12: plt.legend(loc="best", fontsize=8, prop=CHINESE_FONT_PROP) ax = plt.gca() if CHINESE_FONT_PROP: for label in [*ax.get_xticklabels(), *ax.get_yticklabels()]: label.set_fontproperties(CHINESE_FONT_PROP) - plt.tight_layout() + plt.tight_layout(rect=(0, 0.07, 1, 1)) plt.savefig(image_path, dpi=160, bbox_inches="tight") plt.close() return summary_rows, summary_sheet_rows @@ -511,7 +535,7 @@ def write_prediction_workbook( ] with pd.ExcelWriter(excel_path, engine="openpyxl") as writer: - summary_df = pd.DataFrame(summary_sheet_rows, columns=[ID_COLUMN, PIPE_AGE_COLUMN, "健康等级"]) + summary_df = pd.DataFrame(summary_sheet_rows, columns=[ID_COLUMN, PIPE_AGE_COLUMN, "健康风险值"]) sample_df = pd.DataFrame(sample_data_rows, columns=sample_columns) if ID_COLUMN in summary_df.columns: summary_df[ID_COLUMN] = summary_df[ID_COLUMN].astype("string") @@ -520,6 +544,9 @@ def write_prediction_workbook( sample_df.to_excel(writer, sheet_name="样本数据", index=False) sample_worksheet = writer.book["样本数据"] + summary_worksheet = writer.book["结果摘要"] + for cell in summary_worksheet["C"][1:]: + cell.number_format = "0.0%" sample_worksheet.freeze_panes = "A2" sample_worksheet.auto_filter.ref = sample_worksheet.dimensions sample_worksheet.column_dimensions["A"].width = 12 diff --git a/app/routes.py b/app/routes.py index 20a3ebe..54004dd 100644 --- a/app/routes.py +++ b/app/routes.py @@ -130,16 +130,10 @@ def password_reset_users(): def prediction_result_payload(artifacts, record: UploadRecord) -> dict: image_url = url_for("static", filename=f"images/{artifacts.image_filename}") - importance_url = ( - url_for("static", filename=f"images/{artifacts.importance_filename}") - if artifacts.importance_filename - else None - ) return { "original_filename": artifacts.original_filename, 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(min-width:768px){.md\:flex{display:flex}.md\:hidden{display:none}.md\:w-auto{width:auto}.md\:grid-cols-\[minmax\(0\2c 1fr\)_auto\]{grid-template-columns:minmax(0,1fr) auto}.md\:flex-nowrap{flex-wrap:nowrap}.md\:items-center{align-items:center}.md\:px-14{padding-left:3.5rem;padding-right:3.5rem}}@media (min-width:1024px){.lg\:col-start-1{grid-column-start:1}.lg\:col-start-2{grid-column-start:2}.lg\:row-start-1{grid-row-start:1}.lg\:row-start-2{grid-row-start:2}.lg\:flex{display:flex}.lg\:hidden{display:none}.lg\:grid-cols-\[1\.4fr_1fr\]{grid-template-columns:1.4fr 1fr}.lg\:grid-cols-\[minmax\(0\2c 1fr\)_360px\]{grid-template-columns:minmax(0,1fr) 360px}.lg\:flex-row{flex-direction:row}.lg\:items-end{align-items:flex-end}.lg\:justify-between{justify-content:space-between}.lg\:px-8{padding-left:2rem;padding-right:2rem}.lg\:py-8{padding-top:2rem;padding-bottom:2rem}.lg\:text-\[40px\]{font-size:40px}}@media (min-width:1280px){.xl\:left-auto{left:auto}.xl\:right-auto{right:auto}.xl\:w-\[320px\]{width:320px}} \ No newline at end of file diff --git a/static/js/dashboard.js b/static/js/dashboard.js index 12db5f4..3a36d5d 100644 --- a/static/js/dashboard.js +++ b/static/js/dashboard.js @@ -15,8 +15,6 @@ const inlineResult = document.getElementById('inlineResult'); const resultPlaceholder = document.getElementById('resultPlaceholder'); const resultContent = document.getElementById('resultContent'); const resultImage = document.getElementById('resultImage'); -const resultImportanceWrap = document.getElementById('resultImportanceWrap'); -const resultImportanceImage = document.getElementById('resultImportanceImage'); const excelBtn = document.getElementById('excelBtn'); const resultPageBtn = document.getElementById('resultPageBtn'); let selectedFile = null; @@ -69,13 +67,6 @@ function updateSelectedFile(file) { function renderResult(data) { resultImage.src = data.image_url; - if (data.importance_url) { - resultImportanceImage.src = data.importance_url; - resultImportanceWrap.classList.remove('hidden'); - } else { - resultImportanceWrap.classList.add('hidden'); - resultImportanceImage.removeAttribute('src'); - } excelBtn.href = data.excel_url; resultPageBtn.href = data.result_url; resultPlaceholder.classList.add('hidden'); diff --git a/templates/home.html b/templates/home.html index 77b3ec8..15727cf 100644 --- a/templates/home.html +++ b/templates/home.html @@ -7,7 +7,7 @@

管道健康状态与剩余寿命评估

-

上传标准数据文件，系统将生成生存概率曲线、健康等级摘要、剩余寿命判断和电子表格预测报告。

+

上传标准数据文件，系统将生成管道的剩余寿命分析图、健康等级摘要、剩余寿命判断和电子表格预测报告。

download @@ -59,7 +59,7 @@
monitoring
暂无预测结果
-
完成一次分析后，将生成生存概率阶梯图、摘要和电子表格报告。
+
完成一次分析后，将生成管道的剩余寿命分析图、摘要和电子表格报告。
@@ -81,15 +81,11 @@ -
-
-
-
生存概率阶梯图
- 预测图 -
-
© {{ now_year() }} 供水管道健康评估系统
diff --git a/templates/result.html b/templates/result.html index 08ca52a..3cfd0ae 100644 --- a/templates/result.html +++ b/templates/result.html @@ -23,25 +23,21 @@
-
-
+
+

管道剩余寿命动态评估

-

生存概率随时间变化的阶梯曲线。

+

管道剩余寿命随时间变化的分析曲线。

-
- 生存概率阶梯图 -
-
- 分析说明：{{ result.analysis_text }} +
+ 管道的剩余寿命分析图
-
{% else %}
diff --git a/tests/test_prediction.py b/tests/test_prediction.py index 15e8727..0156937 100644 --- a/tests/test_prediction.py +++ b/tests/test_prediction.py @@ -7,8 +7,10 @@ from unittest.mock import patch import pandas as pd from openpyxl import load_workbook +from openpyxl import Workbook from app.prediction import ( + CHART_DISPLAY_LIMIT, FEATURES, ID_COLUMN, INPUT_COLUMNS, @@ -70,7 +72,8 @@ class PredictionHelpersTest(unittest.TestCase): worksheet = workbook["Template"] headers = [cell.value for cell in next(worksheet.iter_rows(min_row=1, max_row=1))] - self.assertEqual(headers, INPUT_COLUMNS) + self.assertEqual(len(headers), len(INPUT_COLUMNS)) + self.assertEqual(set(headers), set(INPUT_COLUMNS)) def test_read_input_file_preserves_text_pipe_ids(self) -> None: with TemporaryDirectory() as temp_dir: @@ -81,6 +84,36 @@ class PredictionHelpersTest(unittest.TestCase): self.assertEqual(df[ID_COLUMN].tolist(), ["00123"]) + def test_read_input_file_calculates_defects_from_template_detail_sheet(self) -> None: + with TemporaryDirectory() as temp_dir: + output_path = Path(temp_dir) / "input.xlsx" + workbook = Workbook() + template = workbook.active + template.title = "Template" + template.append(INPUT_COLUMNS) + template.append(["001", 5, 0, 5, 1, 100, 1.2, 0.4, 20, 800, 1, "=缺陷计算!E2", "=缺陷计算!J2"]) + + detail = workbook.create_sheet("缺陷计算") + detail.append([ + ID_COLUMN, + "泄漏\n权重0.5", + "腐蚀\n权重0.4", + "管瘤\n权重0.1", + "结构性缺陷值", + "气囊\n权重0.5", + "杂质\n权重0.2", + "异物\n权重0.2", + "不明连接\n权重0.1", + "功能性缺陷值", + ]) + detail.append(["001", "严重", "中度", "轻度", None, "轻度", "无", "中度", "严重", None]) + workbook.save(output_path) + + df = read_input_file(output_path, ".xlsx") + + self.assertAlmostEqual(float(df.loc[0, "结构缺陷"]), 3.8) + self.assertAlmostEqual(float(df.loc[0, "功能缺陷"]), 1.6) + def test_prepare_model_features_maps_material_aliases_to_codes(self) -> None: rows = [] for value in ["镀锌", "2-钢塑", 13]: @@ -110,8 +143,8 @@ class PredictionHelpersTest(unittest.TestCase): ] summary_rows = [{"pipe_id": "P001"}, {"pipe_id": "P002"}] summary_sheet_rows = [ - {ID_COLUMN: "P001", PIPE_AGE_COLUMN: "10 年", "健康概率": 0.7, "预计剩余寿命": 2.0, "健康等级": "IV级"}, - {ID_COLUMN: "P002", PIPE_AGE_COLUMN: "12 年", "健康概率": 0.6, "预计剩余寿命": 1.5, "健康等级": "III级"}, + {ID_COLUMN: "P001", PIPE_AGE_COLUMN: "10 年", "健康风险值": 0.3}, + {ID_COLUMN: "P002", PIPE_AGE_COLUMN: "12 年", "健康风险值": 0.4}, ] with TemporaryDirectory() as temp_dir: @@ -122,7 +155,7 @@ class PredictionHelpersTest(unittest.TestCase): self.assertEqual(workbook.sheet_names, ["结果摘要", "样本数据"]) summary_data = pd.read_excel(output_path, sheet_name="结果摘要") - self.assertEqual(summary_data.columns.tolist(), [ID_COLUMN, PIPE_AGE_COLUMN, "健康等级"]) + self.assertEqual(summary_data.columns.tolist(), [ID_COLUMN, PIPE_AGE_COLUMN, "健康风险值"]) sample_data = pd.read_excel(output_path, sheet_name="样本数据") self.assertEqual(sample_data.columns.tolist(), ["管龄（年）", "P001", "P002"]) @@ -140,7 +173,7 @@ class PredictionHelpersTest(unittest.TestCase): def test_prediction_workbook_writes_pipe_ids_as_excel_text(self) -> None: curves = [DummyCurve([1], [0.9])] summary_rows = [{"pipe_id": "00123"}] - summary_sheet_rows = [{ID_COLUMN: "00123", PIPE_AGE_COLUMN: "10 年", "健康等级": "V级"}] + summary_sheet_rows = [{ID_COLUMN: "00123", PIPE_AGE_COLUMN: "10 年", "健康风险值": 0.1}] with TemporaryDirectory() as temp_dir: output_path = Path(temp_dir) / "prediction.xlsx" @@ -161,13 +194,45 @@ class PredictionHelpersTest(unittest.TestCase): with TemporaryDirectory() as temp_dir: output_path = Path(temp_dir) / "chart.png" - with patch("app.prediction.plt.xlabel") as xlabel, patch("app.prediction.plt.ylabel") as ylabel: - render_survival_chart(df, [DummyCurve([1, 2], [0.9, 0.7])], output_path) + with ( + patch("app.prediction.plt.xlabel") as xlabel, + patch("app.prediction.plt.ylabel") as ylabel, + patch("app.prediction.plt.figtext") as figtext, + patch("app.prediction.plt.title") as title, + ): + summary_rows, _ = render_survival_chart(df, [DummyCurve([1, 10, 12], [0.9, 0.7, 0.4])], output_path) + + self.assertAlmostEqual(summary_rows[0]["health_probability"], 0.4) + self.assertAlmostEqual(summary_rows[0]["health_risk"], 0.6) + self.assertEqual(summary_rows[0]["grade_label"], "II级") xlabel.assert_called_once() self.assertEqual(xlabel.call_args.args[0], "管龄（年）") ylabel.assert_called_once() self.assertEqual(ylabel.call_args.args[0], "健康风险") + title.assert_called_once() + self.assertEqual(title.call_args.args[0], "管道的剩余寿命分析图") + figtext.assert_called_once() + self.assertIn(f"前{CHART_DISPLAY_LIMIT}条管道的示例数据", figtext.call_args.args[2]) + + def test_survival_chart_only_plots_first_ten_pipes(self) -> None: + sample_count = CHART_DISPLAY_LIMIT + 2 + df = pd.DataFrame( + { + ID_COLUMN: [f"P{i:03d}" for i in range(sample_count)], + PIPE_AGE_COLUMN: [12] * sample_count, + } + ) + curves = [DummyCurve([1, 10, 12], [0.9, 0.7, 0.4]) for _ in range(sample_count)] + + with TemporaryDirectory() as temp_dir: + output_path = Path(temp_dir) / "chart.png" + with patch("app.prediction.plt.step") as step, patch("app.prediction.plt.legend"): + summary_rows, summary_sheet_rows = render_survival_chart(df, curves, output_path) + + self.assertEqual(step.call_count, CHART_DISPLAY_LIMIT) + self.assertEqual(len(summary_rows), sample_count) + self.assertEqual(len(summary_sheet_rows), sample_count) if __name__ == "__main__":