Step8/9 UX: feature_start 改 QComboBox + 多源 CSV 优先回退
This commit is contained in:
228
_smoke_test_step8_9_csv_combo.py
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228
_smoke_test_step8_9_csv_combo.py
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# -*- coding: utf-8 -*-
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"""
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Smoke test: step8_ml_train_panel + step9_ml_predict_panel 的新 CSV→QComboBox 联动
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& update_from_config 多源文件优先回退 逻辑。
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不真正 import 整个 panel（避免 osgeo 重依赖），只 import 类本身，验证：
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1) Step8MlTrainPanel.feature_start 变成 QComboBox
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2) Step8._on_training_csv_changed 正确读 CSV 表头 + 默认选中 374.285
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3) Step8._resolve_training_csv_from_workdir 优先级（Step7 > 10_WaterIndex > Step6 > Step7 兜底）
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4) Step8.get_config / set_config / get_training_params 用 QComboBox API 不崩
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5) Step9._resolve_latest_wqi_test_csv 找到正确的 WQI 测试集
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6) Step9.update_from_config 优先级（WQI CSV > Step4 raw）
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用 offscreen 模式避免弹窗，完整可重复跑。
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"""
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import os
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import sys
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import tempfile
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from pathlib import Path
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# 避免 panel 顶部 osgeo 导入阻塞
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os.environ.setdefault("QT_QPA_PLATFORM", "offscreen")
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# 让 src 目录可 import
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HERE = os.path.dirname(os.path.abspath(__file__))
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sys.path.insert(0, HERE)
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import pandas as pd
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import numpy as np
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from PyQt5.QtWidgets import QApplication
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# 必须先 create QApplication 才能 new widget
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app = QApplication.instance() or QApplication(sys.argv)
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# ============================================================
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# 准备 fixture：模拟工作目录
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# ============================================================
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def make_fake_csv(path, n_cols_50_band, extra_wqi_cols=None, n_rows=10):
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"""生成模拟 CSV：50 个纯数字波段列 + 任意额外 WQI 列。"""
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band_cols = [374.285 + i for i in range(n_cols_50_band)]
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# 浮点列名会被 pandas 自动转成 374.285 / 374.285001 / ...
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cols = band_cols + (extra_wqi_cols or [])
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data = np.random.RandomState(42).rand(n_rows, len(cols))
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df = pd.DataFrame(data, columns=cols)
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df.to_csv(path, index=False)
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return df
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with tempfile.TemporaryDirectory() as tmp:
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work_dir = tmp
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print(f"[fixture] work_dir = {work_dir}")
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# 1) Step 6 原始光谱
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os.makedirs(os.path.join(work_dir, "6_Spectral_Feature_Extraction"), exist_ok=True)
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step6_csv = os.path.join(work_dir, "6_Spectral_Feature_Extraction", "training_spectra.csv")
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make_fake_csv(step6_csv, 50)
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# 2) Step 7 WQI 增强版
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os.makedirs(os.path.join(work_dir, "7_Water_Quality_Indices"), exist_ok=True)
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step7_csv = os.path.join(work_dir, "7_Water_Quality_Indices", "training_spectra_indices.csv")
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wqi_names = [f"WQI_Chla_{i}" for i in range(10)] + [f"WQI_TP_{i}" for i in range(5)]
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make_fake_csv(step7_csv, 50, wqi_names)
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# 3) 10_WaterIndex_CSV 下的 training csv（较旧 mtime）
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os.makedirs(os.path.join(work_dir, "10_WaterIndex_CSV"), exist_ok=True)
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step10_csv = os.path.join(work_dir, "10_WaterIndex_CSV", "training_watercolor_summary.csv")
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make_fake_csv(step10_csv, 50, wqi_names)
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# 把 mtime 调成 3 年前，确认 7_Water_Quality_Indices 那个更新
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old_time = (pd.Timestamp.now() - pd.Timedelta(days=365 * 3)).timestamp()
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os.utime(step10_csv, (old_time, old_time))
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# 4) Step 4 raw sampling
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os.makedirs(os.path.join(work_dir, "4_sampling"), exist_ok=True)
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step4_csv = os.path.join(work_dir, "4_sampling", "sampling_spectra.csv")
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make_fake_csv(step4_csv, 50)
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# 5) WQI 测试集（10_WaterIndex_CSV 下另一个 mtime 较新的 csv）
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wqi_test_csv = os.path.join(work_dir, "10_WaterIndex_CSV", "sampling_watercolor_summary.csv")
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make_fake_csv(wqi_test_csv, 50, wqi_names)
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# 设为 1 小时前（mtime 比 step7_csv 新）
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new_time = (pd.Timestamp.now() - pd.Timedelta(hours=1)).timestamp()
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os.utime(wqi_test_csv, (new_time, new_time))
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# 6) 9_ML_Prediction 输出目录
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os.makedirs(os.path.join(work_dir, "9_ML_Prediction"), exist_ok=True)
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# ============================================================
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# 测试 1：Step8 加载 + QComboBox 化验证
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# ============================================================
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from src.gui.panels.step8_ml_train_panel import Step8MlTrainPanel
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from PyQt5.QtWidgets import QComboBox
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panel8 = Step8MlTrainPanel()
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assert isinstance(panel8.feature_start, QComboBox), \
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f"FAIL: feature_start 应为 QComboBox，实际为 {type(panel8.feature_start)}"
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print(f"[OK] Step8.feature_start 类型: {type(panel8.feature_start).__name__}")
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# ============================================================
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# 测试 2：CSV 变更槽 — 模拟用户选 WQI CSV，自动填表头
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# ============================================================
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panel8._on_training_csv_changed(step7_csv)
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assert panel8.feature_start.count() > 0
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head_text = panel8.feature_start.currentText()
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print(f"[OK] Step8 选中 step7_csv 后，feature_start.currentText() = {head_text!r}")
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# 默认选中：第一个纯数字波段列 "374.285"
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assert head_text.replace('.', '').lstrip('-').isdigit(), \
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f"FAIL: 默认应选数字波段，实际 = {head_text!r}"
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print(f"[OK] 默认选中数字波段列: {head_text!r}")
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# 验证前 50 列已填充
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n_items = panel8.feature_start.count()
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assert n_items >= 50, f"FAIL: 应至少 50 项，实际 {n_items}"
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print(f"[OK] Step8.feature_start 填充了 {n_items} 个候选列（前 50 列）")
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# ============================================================
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# 测试 3：CSV 不存在时，combo 回到占位项
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# ============================================================
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panel8._on_training_csv_changed("/nonexistent/path.csv")
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assert panel8.feature_start.count() == 1
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assert "请先选择训练 CSV" in panel8.feature_start.itemText(0)
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print(f"[OK] Step8 CSV 不存在时，combo 回退到占位项: {panel8.feature_start.itemText(0)!r}")
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# ============================================================
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# 测试 4：get_config / set_config / get_training_params 用 QComboBox API 不崩
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# ============================================================
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panel8._on_training_csv_changed(step7_csv)
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cfg = panel8.get_config()
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assert 'feature_start_column' in cfg
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assert isinstance(cfg['feature_start_column'], str)
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assert cfg['feature_start_column'].replace('.', '').lstrip('-').isdigit()
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print(f"[OK] Step8.get_config()['feature_start_column'] = {cfg['feature_start_column']!r}")
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# set_config 试一下（先选个不存在的列名，应该走兜底 addItem 分支）
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panel8.set_config({'feature_start_column': '999.999_unknown'})
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# 重新触发 CSV 加载，验证回放不污染
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panel8._on_training_csv_changed(step7_csv)
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panel8.set_config({'feature_start_column': '374.285'})
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assert panel8.feature_start.currentText() == '374.285'
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print(f"[OK] Step8.set_config() 正确把 374.285 选中")
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# get_training_params
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params = panel8.get_training_params()
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assert 'feature_start' in params
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assert isinstance(params['feature_start'], float)
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print(f"[OK] Step8.get_training_params()['feature_start'] = {params['feature_start']}")
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# ============================================================
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# 测试 5：_resolve_training_csv_from_workdir 优先级
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# ============================================================
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panel8.work_dir = work_dir
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resolved = panel8._resolve_training_csv_from_workdir()
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expected = step7_csv.replace('\\', '/')
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assert resolved == expected, f"FAIL: 应优先 step7, 实际 {resolved!r}, 期望 {expected!r}"
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print(f"[OK] Step8._resolve_training_csv_from_workdir() = {resolved}")
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print(f" 优先级 1: Step 7 WQI 增强版 ✓")
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# 模拟用户没跑过 Step 7（删掉 step7_csv 目录）— 但 10_WaterIndex_CSV 还在
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import shutil
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shutil.rmtree(os.path.join(work_dir, "7_Water_Quality_Indices"))
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resolved = panel8._resolve_training_csv_from_workdir()
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# 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
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expected_step10 = str(Path(step10_csv)).replace('\\', '/')
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assert resolved == expected_step10, \
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f"FAIL: 应取 10_WaterIndex_CSV/*training*, 实际 {resolved!r}, 期望 {expected_step10!r}"
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print(f"[OK] Step 7 缺省时，回退到 10_WaterIndex_CSV/*training*: {resolved}")
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print(f" 优先级 2: 10_WaterIndex_CSV/*training* ✓")
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# 再删 10_WaterIndex_CSV 才回退到 Step 6
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shutil.rmtree(os.path.join(work_dir, "10_WaterIndex_CSV"))
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resolved = panel8._resolve_training_csv_from_workdir()
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assert resolved == step6_csv.replace('\\', '/'), \
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f"FAIL: 应回退 step6, 实际 {resolved!r}, 期望 {step6_csv!r}"
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print(f"[OK] Step 7 / 10_WaterIndex_CSV 都缺省时，回退到 Step 6: {resolved}")
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print(f" 优先级 3: Step 6 原始光谱 ✓")
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# ============================================================
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# 测试 6：Step9 加载
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# ============================================================
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from src.gui.panels.step9_ml_predict_panel import Step9MlPredictPanel
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panel9 = Step9MlPredictPanel()
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print(f"[OK] Step9MlPredictPanel 实例化成功")
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# 重建 10_WaterIndex_CSV（WQI 测试集 fixture，test 5 中被删了）
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os.makedirs(os.path.join(work_dir, "10_WaterIndex_CSV"), exist_ok=True)
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wqi_test_csv = os.path.join(work_dir, "10_WaterIndex_CSV", "sampling_watercolor_summary.csv")
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make_fake_csv(wqi_test_csv, 50, wqi_names)
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new_time = (pd.Timestamp.now() - pd.Timedelta(hours=1)).timestamp()
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os.utime(wqi_test_csv, (new_time, new_time))
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# ============================================================
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# 测试 7：Step9._resolve_latest_wqi_test_csv 优先 10_WaterIndex_CSV
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# ============================================================
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panel9.work_dir = work_dir
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resolved = panel9._resolve_latest_wqi_test_csv()
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# 应该是 sampling_watercolor_summary.csv（mtime 较新）
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expected = wqi_test_csv.replace('\\', '/')
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assert resolved == expected, \
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f"FAIL: 应取 mtime 最新的 wqi 测试集, 实际 {resolved!r}, 期望 {expected!r}"
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print(f"[OK] Step9._resolve_latest_wqi_test_csv() = {resolved}")
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print(f" 优先级：10_WaterIndex_CSV/sampling_watercolor_summary.csv ✓")
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# ============================================================
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# 测试 8：Step9.update_from_config 优先级：WQI CSV > Step4 raw
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# ============================================================
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# 注：Step 4 回退路径依赖 main_window._panel_factory（offline 测试无 main_window，
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# 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）
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# 有 WQI 时优先 WQI
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os.makedirs(os.path.join(work_dir, "10_WaterIndex_CSV"), exist_ok=True)
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wqi_test_csv2 = os.path.join(work_dir, "10_WaterIndex_CSV", "sampling_watercolor_summary.csv")
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make_fake_csv(wqi_test_csv2, 50, wqi_names)
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panel9.update_from_config(work_dir=work_dir, pipeline=None)
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path = panel9.sampling_csv_file.get_path()
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assert wqi_test_csv2.replace('\\', '/') in path, \
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f"FAIL: 应优先 WQI CSV, 实际 {path!r}"
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print(f"[OK] Step9.update_from_config() 优先 WQI CSV: {path}")
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# ============================================================
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# 测试 9：work_dir 不存在时，所有 helper 安全返回
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# ============================================================
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panel9.work_dir = ""
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assert panel9._resolve_latest_wqi_test_csv() == ""
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print(f"[OK] work_dir 空时 _resolve_latest_wqi_test_csv 安全返回空串")
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print("\n" + "=" * 60)
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print("ALL SMOKE TESTS PASSED")
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print("=" * 60)
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@ -14,10 +14,12 @@ if _HERE not in sys.path:
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sys.path.insert(0, _HERE)
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from src.gui.panels._step_path_resolver import get_step_output_path, resolve_step_widget, resolve_subdir
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import pandas as pd
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from PyQt5.QtWidgets import (
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QWidget, QVBoxLayout, QGroupBox, QFormLayout, QGridLayout,
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QHBoxLayout, QLabel, QLineEdit, QSpinBox, QCheckBox,
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QPushButton, QFileDialog, QMessageBox, QSizePolicy,
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QPushButton, QFileDialog, QMessageBox, QSizePolicy, QComboBox,
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)
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from PyQt5.QtCore import Qt
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@ -95,6 +97,9 @@ class Step8MlTrainPanel(QWidget):
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)
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layout.addWidget(self.training_csv_file)
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# 训练 CSV 选定后，自动刷新"特征起始列"下拉框的候选列
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self.training_csv_file.line_edit.textChanged.connect(self._on_training_csv_changed)
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# 机器学习模型页面
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self.ml_page = QWidget()
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self.create_ml_page()
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@ -144,19 +149,22 @@ class Step8MlTrainPanel(QWidget):
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params_group = QGroupBox("训练参数")
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params_layout = QFormLayout()
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self.feature_start = QLineEdit()
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self.feature_start.setText("374.285004")
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self.feature_start = QComboBox()
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self.feature_start.setMinimumWidth(180)
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self.feature_start.setStyleSheet("""
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QComboBox {
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padding: 4px 8px;
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border: 1px solid #C0C0C0;
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border-radius: 4px;
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min-height: 24px;
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}
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""")
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# 初始空表：实际候选列由 _on_training_csv_changed 在用户选定 CSV 后填充；
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# 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）
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self.feature_start.addItem("（请先选择训练 CSV）", "")
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self.feature_start.setCurrentIndex(0)
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params_layout.addRow("特征起始列:", self.feature_start)
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# 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
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feature_start_hint = QLabel(
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"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"
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"（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。"
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)
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feature_start_hint.setWordWrap(True)
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feature_start_hint.setStyleSheet("color: #666; font-size: 10px;")
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params_layout.addRow(feature_start_hint)
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self.cv_folds = QSpinBox()
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self.cv_folds.setRange(2, 10)
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self.cv_folds.setValue(3)
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@ -318,6 +326,133 @@ class Step8MlTrainPanel(QWidget):
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return str(mw.work_dir)
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return ""
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def _on_training_csv_changed(self, csv_path: str):
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"""训练 CSV 变更槽：自动读表头（前 50 列）填充 self.feature_start 下拉框。
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触发场景：
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- 用户点"浏览..."选了新 CSV
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- 上游 panel_factory / set_config 注入路径
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默认选中规则：
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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）
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2) 兜底：列表里第一个纯数字波段列
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3) 仍找不到：保留占位项 "（请先选择训练 CSV）" 不动
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注：nrows=1 即可读到全部表头，无需 load 整个文件
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"""
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# 清空旧候选，保留占位策略
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self.feature_start.blockSignals(True)
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try:
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self.feature_start.clear()
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if not csv_path or not os.path.isfile(csv_path):
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self.feature_start.addItem("（请先选择训练 CSV）", "")
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self.feature_start.setCurrentIndex(0)
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return
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# 读取前 50 列表头
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try:
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df_head = pd.read_csv(csv_path, nrows=0)
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except Exception as e:
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print(f"[Step8] 读取 CSV 表头失败: {e}")
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self.feature_start.addItem("（CSV 读取失败）", "")
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self.feature_start.setCurrentIndex(0)
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return
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all_cols = list(df_head.columns)
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head_cols = all_cols[:50]
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if not head_cols:
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self.feature_start.addItem("（CSV 无列）", "")
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self.feature_start.setCurrentIndex(0)
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return
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# 填充前 50 列到下拉框
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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()],
|
||||
|
||||
@ -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:
|
||||
|
||||
Reference in New Issue
Block a user