From e121b02e3495018ea180d49b4ee99c179e0ac68f Mon Sep 17 00:00:00 2001 From: DXC Date: Mon, 29 Jun 2026 14:40:39 +0800 Subject: [PATCH] =?UTF-8?q?Step8/9=20UX:=20feature=5Fstart=20=E6=94=B9=20Q?= =?UTF-8?q?ComboBox=20+=20=E5=A4=9A=E6=BA=90=20CSV=20=E4=BC=98=E5=85=88?= =?UTF-8?q?=E5=9B=9E=E9=80=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- _smoke_test_step8_9_csv_combo.py | 228 +++++++++++++++++++++++ src/gui/panels/step8_ml_train_panel.py | 195 ++++++++++++++++--- src/gui/panels/step9_ml_predict_panel.py | 88 ++++++++- 3 files changed, 474 insertions(+), 37 deletions(-) create mode 100644 _smoke_test_step8_9_csv_combo.py diff --git a/_smoke_test_step8_9_csv_combo.py b/_smoke_test_step8_9_csv_combo.py new file mode 100644 index 0000000..a0eb81b --- /dev/null +++ b/_smoke_test_step8_9_csv_combo.py @@ -0,0 +1,228 @@ +# -*- coding: utf-8 -*- +""" +Smoke test: step8_ml_train_panel + step9_ml_predict_panel 的新 CSV→QComboBox 联动 +& update_from_config 多源文件优先回退 逻辑。 + +不真正 import 整个 panel（避免 osgeo 重依赖），只 import 类本身，验证： + 1) Step8MlTrainPanel.feature_start 变成 QComboBox + 2) Step8._on_training_csv_changed 正确读 CSV 表头 + 默认选中 374.285 + 3) Step8._resolve_training_csv_from_workdir 优先级（Step7 > 10_WaterIndex > Step6 > Step7 兜底） + 4) Step8.get_config / set_config / get_training_params 用 QComboBox API 不崩 + 5) Step9._resolve_latest_wqi_test_csv 找到正确的 WQI 测试集 + 6) Step9.update_from_config 优先级（WQI CSV > Step4 raw） + +用 offscreen 模式避免弹窗，完整可重复跑。 +""" +import os +import sys +import tempfile +from pathlib import Path + +# 避免 panel 顶部 osgeo 导入阻塞 +os.environ.setdefault("QT_QPA_PLATFORM", "offscreen") + +# 让 src 目录可 import +HERE = os.path.dirname(os.path.abspath(__file__)) +sys.path.insert(0, HERE) + +import pandas as pd +import numpy as np +from PyQt5.QtWidgets import QApplication + +# 必须先 create QApplication 才能 new widget +app = QApplication.instance() or QApplication(sys.argv) + + +# ============================================================ +# 准备 fixture：模拟工作目录 +# ============================================================ +def make_fake_csv(path, n_cols_50_band, extra_wqi_cols=None, n_rows=10): + """生成模拟 CSV：50 个纯数字波段列 + 任意额外 WQI 列。""" + band_cols = [374.285 + i for i in range(n_cols_50_band)] + # 浮点列名会被 pandas 自动转成 374.285 / 374.285001 / ... + cols = band_cols + (extra_wqi_cols or []) + data = np.random.RandomState(42).rand(n_rows, len(cols)) + df = pd.DataFrame(data, columns=cols) + df.to_csv(path, index=False) + return df + + +with tempfile.TemporaryDirectory() as tmp: + work_dir = tmp + print(f"[fixture] work_dir = {work_dir}") + + # 1) Step 6 原始光谱 + os.makedirs(os.path.join(work_dir, "6_Spectral_Feature_Extraction"), exist_ok=True) + step6_csv = os.path.join(work_dir, "6_Spectral_Feature_Extraction", "training_spectra.csv") + make_fake_csv(step6_csv, 50) + + # 2) Step 7 WQI 增强版 + os.makedirs(os.path.join(work_dir, "7_Water_Quality_Indices"), exist_ok=True) + step7_csv = os.path.join(work_dir, "7_Water_Quality_Indices", "training_spectra_indices.csv") + wqi_names = [f"WQI_Chla_{i}" for i in range(10)] + [f"WQI_TP_{i}" for i in range(5)] + make_fake_csv(step7_csv, 50, wqi_names) + + # 3) 10_WaterIndex_CSV 下的 training csv（较旧 mtime） + os.makedirs(os.path.join(work_dir, "10_WaterIndex_CSV"), exist_ok=True) + step10_csv = os.path.join(work_dir, "10_WaterIndex_CSV", "training_watercolor_summary.csv") + make_fake_csv(step10_csv, 50, wqi_names) + # 把 mtime 调成 3 年前，确认 7_Water_Quality_Indices 那个更新 + old_time = (pd.Timestamp.now() - pd.Timedelta(days=365 * 3)).timestamp() + os.utime(step10_csv, (old_time, old_time)) + + # 4) Step 4 raw sampling + os.makedirs(os.path.join(work_dir, "4_sampling"), exist_ok=True) + step4_csv = os.path.join(work_dir, "4_sampling", "sampling_spectra.csv") + make_fake_csv(step4_csv, 50) + + # 5) WQI 测试集（10_WaterIndex_CSV 下另一个 mtime 较新的 csv） + wqi_test_csv = os.path.join(work_dir, "10_WaterIndex_CSV", "sampling_watercolor_summary.csv") + make_fake_csv(wqi_test_csv, 50, wqi_names) + # 设为 1 小时前（mtime 比 step7_csv 新） + new_time = (pd.Timestamp.now() - pd.Timedelta(hours=1)).timestamp() + os.utime(wqi_test_csv, (new_time, new_time)) + + # 6) 9_ML_Prediction 输出目录 + os.makedirs(os.path.join(work_dir, "9_ML_Prediction"), exist_ok=True) + + # ============================================================ + # 测试 1：Step8 加载 + QComboBox 化验证 + # ============================================================ + from src.gui.panels.step8_ml_train_panel import Step8MlTrainPanel + from PyQt5.QtWidgets import QComboBox + + panel8 = Step8MlTrainPanel() + assert isinstance(panel8.feature_start, QComboBox), \ + f"FAIL: feature_start 应为 QComboBox，实际为 {type(panel8.feature_start)}" + print(f"[OK] Step8.feature_start 类型: {type(panel8.feature_start).__name__}") + + # ============================================================ + # 测试 2：CSV 变更槽 — 模拟用户选 WQI CSV，自动填表头 + # ============================================================ + panel8._on_training_csv_changed(step7_csv) + assert panel8.feature_start.count() > 0 + head_text = panel8.feature_start.currentText() + print(f"[OK] Step8 选中 step7_csv 后，feature_start.currentText() = {head_text!r}") + # 默认选中：第一个纯数字波段列 "374.285" + assert head_text.replace('.', '').lstrip('-').isdigit(), \ + f"FAIL: 默认应选数字波段，实际 = {head_text!r}" + print(f"[OK] 默认选中数字波段列: {head_text!r}") + + # 验证前 50 列已填充 + n_items = panel8.feature_start.count() + assert n_items >= 50, f"FAIL: 应至少 50 项，实际 {n_items}" + print(f"[OK] Step8.feature_start 填充了 {n_items} 个候选列（前 50 列）") + + # ============================================================ + # 测试 3：CSV 不存在时，combo 回到占位项 + # ============================================================ + panel8._on_training_csv_changed("/nonexistent/path.csv") + assert panel8.feature_start.count() == 1 + assert "请先选择训练 CSV" in panel8.feature_start.itemText(0) + print(f"[OK] Step8 CSV 不存在时，combo 回退到占位项: {panel8.feature_start.itemText(0)!r}") + + # ============================================================ + # 测试 4：get_config / set_config / get_training_params 用 QComboBox API 不崩 + # ============================================================ + panel8._on_training_csv_changed(step7_csv) + cfg = panel8.get_config() + assert 'feature_start_column' in cfg + assert isinstance(cfg['feature_start_column'], str) + assert cfg['feature_start_column'].replace('.', '').lstrip('-').isdigit() + print(f"[OK] Step8.get_config()['feature_start_column'] = {cfg['feature_start_column']!r}") + + # set_config 试一下（先选个不存在的列名，应该走兜底 addItem 分支） + panel8.set_config({'feature_start_column': '999.999_unknown'}) + # 重新触发 CSV 加载，验证回放不污染 + panel8._on_training_csv_changed(step7_csv) + panel8.set_config({'feature_start_column': '374.285'}) + assert panel8.feature_start.currentText() == '374.285' + print(f"[OK] Step8.set_config() 正确把 374.285 选中") + + # get_training_params + params = panel8.get_training_params() + assert 'feature_start' in params + assert isinstance(params['feature_start'], float) + print(f"[OK] Step8.get_training_params()['feature_start'] = {params['feature_start']}") + + # ============================================================ + # 测试 5：_resolve_training_csv_from_workdir 优先级 + # ============================================================ + panel8.work_dir = work_dir + resolved = panel8._resolve_training_csv_from_workdir() + expected = step7_csv.replace('\\', '/') + assert resolved == expected, f"FAIL: 应优先 step7, 实际 {resolved!r}, 期望 {expected!r}" + print(f"[OK] Step8._resolve_training_csv_from_workdir() = {resolved}") + print(f" 优先级 1: Step 7 WQI 增强版 ✓") + + # 模拟用户没跑过 Step 7（删掉 step7_csv 目录）— 但 10_WaterIndex_CSV 还在 + import shutil + shutil.rmtree(os.path.join(work_dir, "7_Water_Quality_Indices")) + resolved = panel8._resolve_training_csv_from_workdir() + # 用户明确提到 10_WaterIndex_CSV 目录下的结果是有效来源，应作为第二优先级 + expected_step10 = str(Path(step10_csv)).replace('\\', '/') + assert resolved == expected_step10, \ + f"FAIL: 应取 10_WaterIndex_CSV/*training*, 实际 {resolved!r}, 期望 {expected_step10!r}" + print(f"[OK] Step 7 缺省时，回退到 10_WaterIndex_CSV/*training*: {resolved}") + print(f" 优先级 2: 10_WaterIndex_CSV/*training* ✓") + + # 再删 10_WaterIndex_CSV 才回退到 Step 6 + shutil.rmtree(os.path.join(work_dir, "10_WaterIndex_CSV")) + resolved = panel8._resolve_training_csv_from_workdir() + assert resolved == step6_csv.replace('\\', '/'), \ + f"FAIL: 应回退 step6, 实际 {resolved!r}, 期望 {step6_csv!r}" + print(f"[OK] Step 7 / 10_WaterIndex_CSV 都缺省时，回退到 Step 6: {resolved}") + print(f" 优先级 3: Step 6 原始光谱 ✓") + + # ============================================================ + # 测试 6：Step9 加载 + # ============================================================ + from src.gui.panels.step9_ml_predict_panel import Step9MlPredictPanel + + panel9 = Step9MlPredictPanel() + print(f"[OK] Step9MlPredictPanel 实例化成功") + + # 重建 10_WaterIndex_CSV（WQI 测试集 fixture，test 5 中被删了） + os.makedirs(os.path.join(work_dir, "10_WaterIndex_CSV"), exist_ok=True) + wqi_test_csv = os.path.join(work_dir, "10_WaterIndex_CSV", "sampling_watercolor_summary.csv") + make_fake_csv(wqi_test_csv, 50, wqi_names) + new_time = (pd.Timestamp.now() - pd.Timedelta(hours=1)).timestamp() + os.utime(wqi_test_csv, (new_time, new_time)) + + # ============================================================ + # 测试 7：Step9._resolve_latest_wqi_test_csv 优先 10_WaterIndex_CSV + # ============================================================ + panel9.work_dir = work_dir + resolved = panel9._resolve_latest_wqi_test_csv() + # 应该是 sampling_watercolor_summary.csv（mtime 较新） + expected = wqi_test_csv.replace('\\', '/') + assert resolved == expected, \ + f"FAIL: 应取 mtime 最新的 wqi 测试集, 实际 {resolved!r}, 期望 {expected!r}" + print(f"[OK] Step9._resolve_latest_wqi_test_csv() = {resolved}") + print(f" 优先级：10_WaterIndex_CSV/sampling_watercolor_summary.csv ✓") + + # ============================================================ + # 测试 8：Step9.update_from_config 优先级：WQI CSV > Step4 raw + # ============================================================ + # 注：Step 4 回退路径依赖 main_window._panel_factory（offline 测试无 main_window， + # 故仅验证 WQI CSV 优先级主路径；Step 4 路径与原代码同源，逻辑没变） + # 有 WQI 时优先 WQI + os.makedirs(os.path.join(work_dir, "10_WaterIndex_CSV"), exist_ok=True) + wqi_test_csv2 = os.path.join(work_dir, "10_WaterIndex_CSV", "sampling_watercolor_summary.csv") + make_fake_csv(wqi_test_csv2, 50, wqi_names) + panel9.update_from_config(work_dir=work_dir, pipeline=None) + path = panel9.sampling_csv_file.get_path() + assert wqi_test_csv2.replace('\\', '/') in path, \ + f"FAIL: 应优先 WQI CSV, 实际 {path!r}" + print(f"[OK] Step9.update_from_config() 优先 WQI CSV: {path}") + + # ============================================================ + # 测试 9：work_dir 不存在时，所有 helper 安全返回 + # ============================================================ + panel9.work_dir = "" + assert panel9._resolve_latest_wqi_test_csv() == "" + print(f"[OK] work_dir 空时 _resolve_latest_wqi_test_csv 安全返回空串") + + print("\n" + "=" * 60) + print("ALL SMOKE TESTS PASSED") + print("=" * 60) diff --git a/src/gui/panels/step8_ml_train_panel.py b/src/gui/panels/step8_ml_train_panel.py index e1eb955..bc17fec 100644 --- a/src/gui/panels/step8_ml_train_panel.py +++ b/src/gui/panels/step8_ml_train_panel.py @@ -14,10 +14,12 @@ if _HERE not in sys.path: sys.path.insert(0, _HERE) from src.gui.panels._step_path_resolver import get_step_output_path, resolve_step_widget, resolve_subdir +import pandas as pd + from PyQt5.QtWidgets import ( QWidget, QVBoxLayout, QGroupBox, QFormLayout, QGridLayout, QHBoxLayout, QLabel, QLineEdit, QSpinBox, QCheckBox, - QPushButton, QFileDialog, QMessageBox, QSizePolicy, + QPushButton, QFileDialog, QMessageBox, QSizePolicy, QComboBox, ) from PyQt5.QtCore import Qt @@ -95,6 +97,9 @@ class Step8MlTrainPanel(QWidget): ) layout.addWidget(self.training_csv_file) + # 训练 CSV 选定后，自动刷新"特征起始列"下拉框的候选列 + self.training_csv_file.line_edit.textChanged.connect(self._on_training_csv_changed) + # 机器学习模型页面 self.ml_page = QWidget() self.create_ml_page() @@ -144,19 +149,22 @@ class Step8MlTrainPanel(QWidget): params_group = QGroupBox("训练参数") params_layout = QFormLayout() - self.feature_start = QLineEdit() - self.feature_start.setText("374.285004") + self.feature_start = QComboBox() + self.feature_start.setMinimumWidth(180) + self.feature_start.setStyleSheet(""" + QComboBox { + padding: 4px 8px; + border: 1px solid #C0C0C0; + border-radius: 4px; + min-height: 24px; + } + """) + # 初始空表：实际候选列由 _on_training_csv_changed 在用户选定 CSV 后填充； + # 此处先放一个占位项，防止未选 CSV 时下拉框完全空白（极端空状态视觉异常） + self.feature_start.addItem("（请先选择训练 CSV）", "") + self.feature_start.setCurrentIndex(0) params_layout.addRow("特征起始列:", self.feature_start) - # 特征起始列名提示：用记事本打开 training_spectra.csv 确认首个波长的精确表头 - feature_start_hint = QLabel( - "提示：请使用记事本打开 training_spectra.csv 确认首个波长的精确表头名称" - "（如 374.285 或 374.285004）并在此填入，避免因浮点精度差异导致列名匹配失败。" - ) - feature_start_hint.setWordWrap(True) - feature_start_hint.setStyleSheet("color: #666; font-size: 10px;") - params_layout.addRow(feature_start_hint) - self.cv_folds = QSpinBox() self.cv_folds.setRange(2, 10) self.cv_folds.setValue(3) @@ -318,6 +326,133 @@ class Step8MlTrainPanel(QWidget): return str(mw.work_dir) return "" + def _on_training_csv_changed(self, csv_path: str): + """训练 CSV 变更槽：自动读表头（前 50 列）填充 self.feature_start 下拉框。 + + 触发场景： + - 用户点"浏览..."选了新 CSV + - 上游 panel_factory / set_config 注入路径 + + 默认选中规则： + 1) 优先 '374.285' 纯数字波段列（光谱起点；容忍 .285 / .285004 / .2850000001 等浮点变体） + 2) 兜底：列表里第一个纯数字波段列 + 3) 仍找不到：保留占位项 "（请先选择训练 CSV）" 不动 + + 注：nrows=1 即可读到全部表头，无需 load 整个文件 + """ + # 清空旧候选，保留占位策略 + self.feature_start.blockSignals(True) + try: + self.feature_start.clear() + if not csv_path or not os.path.isfile(csv_path): + self.feature_start.addItem("（请先选择训练 CSV）", "") + self.feature_start.setCurrentIndex(0) + return + + # 读取前 50 列表头 + try: + df_head = pd.read_csv(csv_path, nrows=0) + except Exception as e: + print(f"[Step8] 读取 CSV 表头失败: {e}") + self.feature_start.addItem("（CSV 读取失败）", "") + self.feature_start.setCurrentIndex(0) + return + + all_cols = list(df_head.columns) + head_cols = all_cols[:50] + if not head_cols: + self.feature_start.addItem("（CSV 无列）", "") + self.feature_start.setCurrentIndex(0) + return + + # 填充前 50 列到下拉框 + for col in head_cols: + self.feature_start.addItem(str(col), str(col)) + + # 默认选中规则：纯数字波段列（纯数字字符串视为波长） + default_idx = self._find_default_band_index(head_cols) + if default_idx is not None: + self.feature_start.setCurrentIndex(default_idx) + else: + self.feature_start.setCurrentIndex(0) + finally: + self.feature_start.blockSignals(False) + + def _find_default_band_index(self, columns): + """在前 50 列中找到默认要选中的波段列索引。 + + 优先级： + 1) 列名以 '374.285' 开头（覆盖 374.285 / 374.285004 / 374.2850001） + 2) 列名是纯数字（如 '443'）— 极少数仪器用整数波长 + 3) 第一个能被 float() 解析的纯数字列 + """ + # 1) 374.285 前缀 + for i, col in enumerate(columns): + if str(col).startswith("374.285"): + return i + # 2) 纯数字列（无小数点） + for i, col in enumerate(columns): + if str(col).replace('.', '').lstrip('-').isdigit() and '.' not in str(col): + return i + # 3) 任意可被 float 解析的列 + for i, col in enumerate(columns): + try: + float(str(col)) + return i + except (ValueError, TypeError): + continue + return None + + def _resolve_training_csv_from_workdir(self): + """根据工作目录智能挑选训练 CSV 路径。 + + 优先级（从高到低）： + 1) 7_Water_Quality_Indices/training_spectra_indices.csv（Step 7 WQI 增强版） + 2) 10_WaterIndex_CSV/*training* / *training*indices*.csv（用户自定义带指数汇总） + 3) 6_Spectral_Feature_Extraction/training_spectra.csv（Step 6 原始特征） + 4) 7_Water_Quality_Indices/ 下任意 *training*.csv + """ + work_dir = self._get_default_work_dir() + if not work_dir: + return "" + + from pathlib import Path + wd = Path(work_dir) + + # 1) Step 7 输出的训练 WQI 增强版 + step7_csv = wd / "7_Water_Quality_Indices" / "training_spectra_indices.csv" + if step7_csv.is_file(): + return str(step7_csv).replace('\\', '/') + + # 2) 10_WaterIndex_CSV 下任何带 "training" 关键词的 csv（用户在 Step 10 跑过训练集） + idx_dir = wd / "10_WaterIndex_CSV" + if idx_dir.is_dir(): + candidates = sorted( + idx_dir.glob("*training*.csv"), + key=lambda p: p.stat().st_mtime, + reverse=True, + ) + if candidates: + return str(candidates[0]).replace('\\', '/') + + # 3) Step 6 原始光谱特征 + step6_csv = wd / "6_Spectral_Feature_Extraction" / "training_spectra.csv" + if step6_csv.is_file(): + return str(step6_csv).replace('\\', '/') + + # 4) Step 7 目录下任何 training*.csv（兜底） + step7_dir = wd / "7_Water_Quality_Indices" + if step7_dir.is_dir(): + candidates = sorted( + step7_dir.glob("*training*.csv"), + key=lambda p: p.stat().st_mtime, + reverse=True, + ) + if candidates: + return str(candidates[0]).replace('\\', '/') + + return "" + def browse_output_path(self): """浏览输出模型目录""" work_dir = getattr(self, 'work_dir', "") @@ -342,7 +477,8 @@ class Step8MlTrainPanel(QWidget): ] config = { - 'feature_start_column': self.feature_start.text(), + # QComboBox 适配：currentText() 拿用户选的列名（与原 QLineEdit.text() 语义等价） + 'feature_start_column': self.feature_start.currentText(), 'preprocessing_methods': preprocessing_methods if preprocessing_methods else ['None'], 'model_names': model_names if model_names else ['SVR'], 'split_methods': split_methods if split_methods else ['random'], @@ -359,7 +495,15 @@ class Step8MlTrainPanel(QWidget): def set_config(self, config): """设置配置""" if 'feature_start_column' in config: - self.feature_start.setText(str(config['feature_start_column'])) + # QComboBox 适配：先 findText 匹配候选；找不到时把值原样塞进去（兜底） + target = str(config['feature_start_column']) + idx = self.feature_start.findText(target) + if idx >= 0: + self.feature_start.setCurrentIndex(idx) + else: + # 配置回放时 CSV 可能尚未选定，候选列表为空；先插一项保留语义 + self.feature_start.addItem(target, target) + self.feature_start.setCurrentIndex(self.feature_start.count() - 1) if 'cv_folds' in config: self.cv_folds.setValue(config['cv_folds']) if 'preprocessing_methods' in config: @@ -393,20 +537,15 @@ class Step8MlTrainPanel(QWidget): else: self.work_dir = None - # 1. 强制读 Step 6 的 training_spectra.csv（光谱特征提取结果） - # 修复张冠李戴：原链路 STEP_DATA_SOURCE['training_spectra_csv'] → step5_clean_panel - # 错误地指向了 Step 5 的 processed_data.csv（纯清洗数据，不含光谱特征）， - # 实际 ML 训练需要的特征数据来自 Step 6 的 6_Spectral_Feature_Extraction/training_spectra.csv - main_window = self.window() + # 1. 智能挑选训练 CSV（不再"强制"读 Step 6，而是优先 WQI 增强版） + # 优先级：Step 7 WQI > 10_WaterIndex_CSV/*training* > Step 6 原始光谱 > Step 7 兜底 + # 修复目标：用户跑过 Step 7 后再回到 Step 8，UI 默认应指向带指数的训练集， + # 否则训练好的模型有 95 维（50 波段 + 45 WQI），下次回放变成只 50 维训练，特征维数错位。 existing_training_csv = self.training_csv_file.get_path() if not existing_training_csv or not existing_training_csv.strip(): - if self.work_dir: - step6_dir = resolve_subdir(self.work_dir, 'spectral_feature') - step6_training_csv = os.path.join( - step6_dir, 'training_spectra.csv' - ).replace('\\', '/') - if step6_training_csv: - self.training_csv_file.set_path(step6_training_csv) + candidate = self._resolve_training_csv_from_workdir() + if candidate: + self.training_csv_file.set_path(candidate) # 2. 自动填充输出目录为 8_Machine_Learning_Models if self.work_dir: @@ -448,7 +587,9 @@ class Step8MlTrainPanel(QWidget): """获取模型训练参数""" return { 'pipeline_type': 'machine_learning', - 'feature_start': float(self.feature_start.text()), + # QComboBox 适配：currentText() 取列名；下拉项里就是"374.285004" 等纯数字波段名 + # （与原 QLineEdit 中 "374.285004" 字符串保持完全一致，后端 float() 解析不变） + 'feature_start': float(self.feature_start.currentText()), 'cv_folds': self.cv_folds.value(), 'preprocess_methods': [method for method, cb in self.preproc_checkboxes.items() if cb.isChecked()], 'model_types': [model for model, cb in self.model_checkboxes.items() if cb.isChecked()], diff --git a/src/gui/panels/step9_ml_predict_panel.py b/src/gui/panels/step9_ml_predict_panel.py index db3fae0..e320212 100644 --- a/src/gui/panels/step9_ml_predict_panel.py +++ b/src/gui/panels/step9_ml_predict_panel.py @@ -8,6 +8,8 @@ import os import sys from pathlib import Path +import pandas as pd + # 路径归一化 helper（与 pipeline.get_step_output_dir 互为表里） _HERE = os.path.dirname(os.path.abspath(__file__)) if _HERE not in sys.path: @@ -333,24 +335,90 @@ class Step9MlPredictPanel(QWidget): result[name] = self.external_models_dict[name] return result + def _resolve_latest_wqi_test_csv(self): + """在工作目录中智能挑选"最新生成的、含 WQI 指数的测试集 CSV"。 + + 返回：找到则返回文件路径字符串；找不到返回 ""。 + + 搜索策略（按优先级递减，命中即返回）： + 1) 10_WaterIndex_CSV/*.csv — Step 10 输出目录（用户在 Step 10 跑过的产品） + 2) 7_Water_Quality_Indices/*sampling*.csv / *test*.csv — 用户手动对采样点算过 WQI + 3) work_dir 下任何 *indices*.csv / *wqi*.csv（不区分大小写） + 4) work_dir 下任何 > 60 列的 csv（启发式：50 波段 + > 10 WQI 指数列） + 5) 兜底空串，调用方回退到 Step 4 sampling_spectra.csv + + 多个候选时按 mtime 倒序选"最新生成的"。 + """ + work_dir = self._get_default_work_dir() + if not work_dir: + return "" + + wd = Path(work_dir) + found = [] + + # 1) 10_WaterIndex_CSV 下所有 csv（Step 10 输出） + idx_dir = wd / "10_WaterIndex_CSV" + if idx_dir.is_dir(): + found.extend(idx_dir.glob("*.csv")) + + # 2) 7_Water_Quality_Indices 下与采样/测试相关的 csv + qa_dir = wd / "7_Water_Quality_Indices" + if qa_dir.is_dir(): + for pattern in ("*sampling*.csv", "*test*.csv", "*predict*.csv"): + found.extend(qa_dir.glob(pattern)) + + # 3) work_dir 直接子树下含 indices/wqi 关键词的 csv + for keyword in ("*indices*.csv", "*wqi*.csv", "*WQI*.csv"): + found.extend(wd.rglob(keyword)) + + # 4) 启发式：> 60 列的 csv（50 波段 + 至少 10 个指数） + try: + for csv in wd.rglob("*.csv"): + if csv in found: + continue + try: + head = pd.read_csv(csv, nrows=0) + if head.shape[1] > 60: + found.append(csv) + except Exception: + pass # 读取失败就跳过，不影响其它候选 + except Exception: + pass + + if not found: + return "" + + # 去重 + 按 mtime 倒序排 + uniq = {p.resolve(): p for p in found}.values() + sorted_paths = sorted(uniq, key=lambda p: p.stat().st_mtime, reverse=True) + return str(sorted_paths[0]).replace('\\', '/') + def update_from_config(self, work_dir=None, pipeline=None): if work_dir: self.work_dir = work_dir main_window = self.window() factory = getattr(main_window, '_panel_factory', None) if main_window else None - if not factory: return - # 1. 拿第 4 步的采样光谱 - step4_panel = factory.get_panel('step4_sampling') - if step4_panel and hasattr(step4_panel, 'output_file'): - path = step4_panel.output_file.get_path() - if path: self.sampling_csv_file.set_path(path) + # 1. 智能挑选采样 CSV：优先"含 WQI 指数的测试集"（防止特征维度与训练时不匹配） + # 修复目标：用户在 Step 8 用 95 维 (50+45 WQI) 训练 → Step 9 默认读 50 维 raw sampling + # 时 inference_batch.preprocess_spectra 会触发"自动特征补全"逻辑；但若用户已经在 + # Step 10/手工把指数算到 CSV 里了，应该直接用那个文件（少走内存补全、避免 band 列顺序漂移） + wqi_test_csv = self._resolve_latest_wqi_test_csv() + if wqi_test_csv: + self.sampling_csv_file.set_path(wqi_test_csv) + elif factory: + # 兜底：拿第 4 步的纯原始采样光谱（旧行为保留） + step4_panel = factory.get_panel('step4_sampling') + if step4_panel and hasattr(step4_panel, 'output_file'): + path = step4_panel.output_file.get_path() + if path: self.sampling_csv_file.set_path(path) # 2. 拿第 8 步的模型目录 - step8_panel = factory.get_panel('step8_ml_train') - if step8_panel and hasattr(step8_panel, 'output_path'): - path = step8_panel.output_path.get_path() - if path: self.models_dir_file.set_path(path) + if factory: + step8_panel = factory.get_panel('step8_ml_train') + if step8_panel and hasattr(step8_panel, 'output_path'): + path = step8_panel.output_path.get_path() + if path: self.models_dir_file.set_path(path) # 3. 生成第 9 步的输出目录 if hasattr(self, 'work_dir') and self.work_dir: