416 lines
16 KiB
Python
416 lines
16 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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Step8 面板 - 机器学习建模
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"""
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import os
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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,
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)
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from PyQt5.QtCore import Qt
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from src.gui.components.custom_widgets import FileSelectWidget
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from src.gui.styles import ModernStylesheet
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# ============================================================
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# 中文映射表（内部键名 -> 显示文本）
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# ============================================================
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# 预处理方法：内部键 -> 显示文本
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PREPROC_CHINESE = {
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'None': '无 (None)',
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'MMS': '最小-最大归一化 (MMS)',
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'SS': '标度化 (SS)',
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'SNV': '标准正态变换 (SNV)',
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'MA': '移动平均 (MA)',
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'SG': 'Savitzky-Golay (SG)',
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'MSC': '多元散射校正 (MSC)',
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'D1': '一阶导数 (D1)',
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'D2': '二阶导数 (D2)',
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'DT': '去趋势 (DT)',
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'CT': '中心化 (CT)',
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}
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# 模型类型：内部键 -> 显示文本
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MODEL_CHINESE = {
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# 线性模型
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'LinearRegression': '多元线性回归 (MLR)',
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'Ridge': '岭回归 (Ridge)',
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'Lasso': '套索回归 (Lasso)',
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'ElasticNet': '弹性网络 (ElasticNet)',
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'PLS': '偏最小二乘 (PLSR)',
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# 树模型
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'DecisionTree': '决策树 (CART)',
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'RF': '随机森林 (RF)',
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'ExtraTrees': '极端随机树 (ET)',
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'XGBoost': '极值梯度提升 (XGBoost)',
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'LightGBM': '轻量梯度提升 (LightGBM)',
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'CatBoost': '类别梯度提升 (CatBoost)',
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# 集成学习
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'GradientBoosting': '梯度提升树 (GBDT)',
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'AdaBoost': '自适应提升 (AdaBoost)',
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# 其他模型
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'SVR': '支持向量回归 (SVR)',
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'KNN': 'K近邻回归 (KNN)',
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'MLP': '多层感知机 (BP神经网络)',
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}
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# 数据划分方法：内部键 -> 显示文本
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SPLIT_CHINESE = {
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'spxy': 'SPXY 算法 (考量X-Y空间)',
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'ks': 'KS 算法 (考量X空间)',
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'random': '随机划分 (Random)',
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}
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class Step8MlTrainPanel(QWidget):
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"""步骤8：机器学习建模"""
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def __init__(self, parent=None):
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super().__init__(parent)
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self.init_ui()
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def init_ui(self):
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layout = QVBoxLayout()
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# 标题
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# 训练数据文件（用于独立运行）
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self.training_csv_file = FileSelectWidget(
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"训练数据:",
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"CSV Files (*.csv);;All Files (*.*)"
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)
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layout.addWidget(self.training_csv_file)
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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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layout.addWidget(self.ml_page)
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# 输出文件路径
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self.output_path = FileSelectWidget(
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"输出文件:",
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"CSV Files (*.csv);;All Files (*.*)",
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mode="save"
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)
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self.output_path.line_edit.setPlaceholderText("自动生成，或手动指定输出文件路径...")
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self.output_path.browse_btn.clicked.disconnect()
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self.output_path.browse_btn.clicked.connect(self.browse_output_path)
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layout.addWidget(self.output_path)
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# 启用步骤
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self.enable_checkbox = QCheckBox("启用此步骤")
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self.enable_checkbox.setChecked(False)
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layout.addWidget(self.enable_checkbox)
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# 独立运行按钮
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self.run_btn = QPushButton("独立运行此步骤")
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self.run_btn.setStyleSheet(ModernStylesheet.get_button_stylesheet('success'))
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self.run_btn.clicked.connect(self.run_step)
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layout.addWidget(self.run_btn)
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layout.addStretch()
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self.setLayout(layout)
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def create_ml_page(self):
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"""创建机器学习模型页面"""
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layout = QVBoxLayout()
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# 参数设置
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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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params_layout.addRow("特征起始列:", self.feature_start)
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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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params_layout.addRow("交叉验证折数:", self.cv_folds)
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params_group.setLayout(params_layout)
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layout.addWidget(params_group)
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# 预处理方法 - 多选
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preproc_group = QGroupBox("预处理方法 (可多选)")
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preproc_layout = QVBoxLayout()
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preproc_grid = QGridLayout()
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self.preproc_checkboxes = {}
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preproc_methods = ['None', 'MMS', 'SS', 'SNV', 'MA', 'SG', 'MSC', 'D1', 'D2', 'DT', 'CT']
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for i, method in enumerate(preproc_methods):
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checkbox = QCheckBox(PREPROC_CHINESE.get(method, method))
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checkbox.setChecked(False)
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self.preproc_checkboxes[method] = checkbox
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preproc_grid.addWidget(checkbox, i // 4, i % 4)
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button_layout = QHBoxLayout()
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select_all_btn = QPushButton("全选")
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deselect_all_btn = QPushButton("全不选")
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select_all_btn.clicked.connect(lambda: self._toggle_checkboxes(self.preproc_checkboxes, True))
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deselect_all_btn.clicked.connect(lambda: self._toggle_checkboxes(self.preproc_checkboxes, False))
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button_layout.addWidget(select_all_btn)
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button_layout.addWidget(deselect_all_btn)
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button_layout.addStretch()
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preproc_layout.addLayout(preproc_grid)
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preproc_layout.addLayout(button_layout)
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preproc_group.setLayout(preproc_layout)
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layout.addWidget(preproc_group)
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# 模型选择 - 多选
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model_group = QGroupBox("模型类型 (可多选)")
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model_layout = QVBoxLayout()
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model_grid = QGridLayout()
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self.model_checkboxes = {}
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model_groups = [
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("【线性模型】", ['LinearRegression', 'Ridge', 'Lasso', 'ElasticNet', 'PLS']),
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("【树模型】", ['DecisionTree', 'RF', 'ExtraTrees', 'XGBoost', 'LightGBM', 'CatBoost']),
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("【集成学习】", ['GradientBoosting', 'AdaBoost']),
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("【其他模型】", ['SVR', 'KNN', 'MLP'])
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]
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row = 0
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for group_name, models in model_groups:
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group_label = QLabel(f"<b>{group_name}</b>")
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group_label.setStyleSheet(
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f"background-color: {ModernStylesheet.COLORS['hover']}; "
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f"padding: 5px; border: 1px solid {ModernStylesheet.COLORS['border_light']}; "
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f"border-radius: 3px;"
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)
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model_grid.addWidget(group_label, row, 0, 1, 4)
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row += 1
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for i, model in enumerate(models):
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checkbox = QCheckBox(MODEL_CHINESE.get(model, model))
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checkbox.setChecked(False)
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self.model_checkboxes[model] = checkbox
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model_grid.addWidget(checkbox, row, i % 4)
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if (i + 1) % 4 == 0:
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row += 1
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row += 1
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model_button_layout = QHBoxLayout()
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model_select_all = QPushButton("全选")
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model_deselect_all = QPushButton("全不选")
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model_select_all.clicked.connect(lambda: self._toggle_checkboxes(self.model_checkboxes, True))
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model_deselect_all.clicked.connect(lambda: self._toggle_checkboxes(self.model_checkboxes, False))
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model_button_layout.addWidget(model_select_all)
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model_button_layout.addWidget(model_deselect_all)
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model_button_layout.addStretch()
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model_layout.addLayout(model_grid)
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model_layout.addLayout(model_button_layout)
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model_group.setLayout(model_layout)
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layout.addWidget(model_group)
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# 数据划分方法 - 多选
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split_group = QGroupBox("数据划分方法 (可多选)")
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split_layout = QVBoxLayout()
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split_grid = QGridLayout()
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self.split_checkboxes = {}
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split_methods = ['spxy', 'ks', 'random']
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for i, method in enumerate(split_methods):
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checkbox = QCheckBox(SPLIT_CHINESE.get(method, method))
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checkbox.setChecked(False)
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self.split_checkboxes[method] = checkbox
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split_grid.addWidget(checkbox, 0, i)
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split_button_layout = QHBoxLayout()
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split_select_all = QPushButton("全选")
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split_deselect_all = QPushButton("全不选")
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split_select_all.clicked.connect(lambda: self._toggle_checkboxes(self.split_checkboxes, True))
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split_deselect_all.clicked.connect(lambda: self._toggle_checkboxes(self.split_checkboxes, False))
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split_button_layout.addWidget(split_select_all)
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split_button_layout.addWidget(split_deselect_all)
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split_button_layout.addStretch()
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split_layout.addLayout(split_grid)
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split_layout.addLayout(split_button_layout)
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split_group.setLayout(split_layout)
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layout.addWidget(split_group)
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self.ml_page.setLayout(layout)
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def _toggle_checkboxes(self, checkboxes_dict, checked):
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"""统一设置checkbox状态"""
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for checkbox in checkboxes_dict.values():
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checkbox.setChecked(checked)
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def _get_default_work_dir(self):
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"""获取 work_dir，优先用 panel 自身缓存的，否则尝试从主窗口取"""
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if hasattr(self, 'work_dir') and self.work_dir:
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return str(self.work_dir)
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mw = self.window()
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if mw and hasattr(mw, 'work_dir') and mw.work_dir:
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return str(mw.work_dir)
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return ""
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def browse_output_path(self):
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"""浏览输出文件路径（保存对话框）"""
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current = self.output_path.get_path().strip()
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if current:
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initial_dir = os.path.dirname(current)
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initial_file = os.path.basename(current)
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else:
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initial_dir = ""
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initial_file = ""
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if not initial_dir or not os.path.isdir(initial_dir):
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# 默认定位到 indices 目录
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work_dir = self._get_default_work_dir()
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initial_dir = os.path.join(work_dir, "6_water_quality_indices") if work_dir else ""
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if initial_dir and not os.path.isdir(initial_dir):
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os.makedirs(initial_dir, exist_ok=True)
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file_path, _ = QFileDialog.getSaveFileName(
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self, "保存输出文件", os.path.join(initial_dir, initial_file) if initial_file else initial_dir,
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"CSV Files (*.csv);;All Files (*.*)"
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)
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if file_path:
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self.output_path.set_path(file_path)
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def get_config(self):
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"""获取配置"""
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preprocessing_methods = [
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method for method, checkbox in self.preproc_checkboxes.items()
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if checkbox.isChecked()
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]
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model_names = [
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model for model, checkbox in self.model_checkboxes.items()
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if checkbox.isChecked()
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]
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split_methods = [
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method for method, checkbox in self.split_checkboxes.items()
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if checkbox.isChecked()
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]
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config = {
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'feature_start_column': self.feature_start.text(),
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'preprocessing_methods': preprocessing_methods if preprocessing_methods else ['None'],
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'model_names': model_names if model_names else ['SVR'],
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'split_methods': split_methods if split_methods else ['random'],
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'cv_folds': self.cv_folds.value()
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}
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training_csv_path = self.training_csv_file.get_path()
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if training_csv_path:
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config['training_csv_path'] = training_csv_path
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output_path = self.output_path.get_path()
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if output_path:
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config['output_path'] = output_path
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return config
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def set_config(self, config):
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"""设置配置"""
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if 'feature_start_column' in config:
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self.feature_start.setText(str(config['feature_start_column']))
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if 'cv_folds' in config:
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self.cv_folds.setValue(config['cv_folds'])
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if 'preprocessing_methods' in config:
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methods = config['preprocessing_methods']
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for method, checkbox in self.preproc_checkboxes.items():
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checkbox.setChecked(method in methods)
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if 'model_names' in config:
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models = config['model_names']
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for model, checkbox in self.model_checkboxes.items():
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checkbox.setChecked(model in models)
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if 'split_methods' in config:
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methods = config['split_methods']
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for method, checkbox in self.split_checkboxes.items():
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checkbox.setChecked(method in methods)
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if 'training_csv_path' in config:
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self.training_csv_file.set_path(config['training_csv_path'])
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if 'output_path' in config:
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self.output_path.set_path(config['output_path'])
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def update_from_config(self, work_dir=None, pipeline=None):
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"""从全局配置自动填充训练数据和输出路径
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Args:
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work_dir: 工作目录路径
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pipeline: Pipeline 实例（未使用，保留接口兼容性）
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"""
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if work_dir:
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self.work_dir = work_dir
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elif hasattr(self, 'work_dir') and self.work_dir:
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pass
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else:
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self.work_dir = None
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# 1. 尝试从 Step5 界面读取训练数据路径，并确保为绝对路径
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main_window = self.window()
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if hasattr(main_window, 'step5_panel'):
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# 优先直接从 Step5 的输出 widget 读取
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step5_output = main_window.step5_panel.output_file.get_path()
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if step5_output:
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# 若为相对路径，使用 work_dir 合成为绝对路径
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if not os.path.isabs(step5_output):
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step5_output = os.path.join(self.work_dir or '', step5_output).replace('\\', '/')
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self.training_csv_file.set_path(step5_output)
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elif hasattr(main_window, 'step5_panel') and hasattr(main_window.step5_panel, 'get_config'):
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# 回退：从 Step5 的 config 字典中查找可能的键名
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step5_cfg = main_window.step5_panel.get_config()
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step5_csv = (
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step5_cfg.get('training_csv_path')
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or step5_cfg.get('output_file')
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or step5_cfg.get('csv_path')
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or step5_cfg.get('output_csv')
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)
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if step5_csv:
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# 若为相对路径，使用 work_dir 合成为绝对路径
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if not os.path.isabs(step5_csv):
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step5_csv = os.path.join(self.work_dir or '', step5_csv).replace('\\', '/')
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self.training_csv_file.set_path(step5_csv)
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# 2. 自动填充输出文件路径（基于工作目录和输入文件名）
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# 输入是 training_spectra.csv → 输出 {work_dir}/6_water_quality_indices/training_spectra_indices.csv
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# 输入是 sampling_spectra.csv → 输出 {work_dir}/6_water_quality_indices/sampling_spectra_indices.csv
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if self.work_dir:
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indices_dir = os.path.join(self.work_dir, "6_water_quality_indices")
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os.makedirs(indices_dir, exist_ok=True)
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training_csv = self.training_csv_file.get_path()
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if training_csv:
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basename = os.path.splitext(os.path.basename(training_csv))[0]
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output_file = f"{basename}_indices.csv"
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else:
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output_file = "water_quality_indices.csv"
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output_path = os.path.join(indices_dir, output_file).replace('\\', '/')
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self.output_path.set_path(output_path)
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else:
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self.output_path.set_path("")
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def run_step(self):
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"""独立运行步骤8"""
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training_csv_path = self.training_csv_file.get_path()
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if not training_csv_path:
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QMessageBox.warning(self, "输入错误", "请选择训练数据CSV文件！")
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return
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main_window = self.window()
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if hasattr(main_window, 'run_single_step'):
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config = {'step8_ml_train': self.get_config()}
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main_window.run_single_step('step8_ml_train', config)
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def get_training_params(self):
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"""获取模型训练参数"""
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return {
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'pipeline_type': 'machine_learning',
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'feature_start': float(self.feature_start.text()),
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'cv_folds': self.cv_folds.value(),
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'preprocess_methods': [method for method, cb in self.preproc_checkboxes.items() if cb.isChecked()],
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'model_types': [model for model, cb in self.model_checkboxes.items() if cb.isChecked()],
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'split_methods': [method for method, cb in self.split_checkboxes.items() if cb.isChecked()]
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}
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