WQ_GUI/src/gui/panels/step8_ml_train_panel.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Step8 面板 - 机器学习建模
"""

import os
import sys
from pathlib import Path

# 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:
    sys.path.insert(0, _HERE)
from src.gui.panels._step_path_resolver import get_step_output_path, resolve_step_widget, resolve_subdir

from PyQt5.QtWidgets import (
    QWidget, QVBoxLayout, QGroupBox, QFormLayout, QGridLayout,
    QHBoxLayout, QLabel, QLineEdit, QSpinBox, QCheckBox,
    QPushButton, QFileDialog, QMessageBox, QSizePolicy,
)
from PyQt5.QtCore import Qt

from src.gui.components.custom_widgets import FileSelectWidget
from src.gui.styles import ModernStylesheet


# ============================================================
# 中文映射表（内部键名 -> 显示文本）
# ============================================================

# 预处理方法：内部键 -> 显示文本
PREPROC_CHINESE = {
    'None': '无 (None)',
    'MMS': '最小-最大归一化 (MMS)',
    'SS': '标度化 (SS)',
    'SNV': '标准正态变换 (SNV)',
    'MA': '移动平均 (MA)',
    'SG': 'Savitzky-Golay (SG)',
    'MSC': '多元散射校正 (MSC)',
    'D1': '一阶导数 (D1)',
    'D2': '二阶导数 (D2)',
    'DT': '去趋势 (DT)',
    'CT': '中心化 (CT)',
}

# 模型类型：内部键 -> 显示文本
MODEL_CHINESE = {
    # 线性模型
    'LinearRegression': '多元线性回归 (MLR)',
    'Ridge': '岭回归 (Ridge)',
    'Lasso': '套索回归 (Lasso)',
    'ElasticNet': '弹性网络 (ElasticNet)',
    'PLS': '偏最小二乘 (PLSR)',
    # 树模型
    'DecisionTree': '决策树 (CART)',
    'RF': '随机森林 (RF)',
    'ExtraTrees': '极端随机树 (ET)',
    'XGBoost': '极值梯度提升 (XGBoost)',
    'LightGBM': '轻量梯度提升 (LightGBM)',
    'CatBoost': '类别梯度提升 (CatBoost)',
    # 集成学习
    'GradientBoosting': '梯度提升树 (GBDT)',
    'AdaBoost': '自适应提升 (AdaBoost)',
    # 其他模型
    'SVR': '支持向量回归 (SVR)',
    'KNN': 'K近邻回归 (KNN)',
    'MLP': '多层感知机 (BP神经网络)',
}

# 数据划分方法：内部键 -> 显示文本
SPLIT_CHINESE = {
    'spxy': 'SPXY 算法 (考量X-Y空间)',
    'ks': 'KS 算法 (考量X空间)',
    'random': '随机划分 (Random)',
}


class Step8MlTrainPanel(QWidget):
    """步骤8：机器学习建模"""
    def __init__(self, parent=None):
        super().__init__(parent)
        self.init_ui()

    def init_ui(self):
        layout = QVBoxLayout()

        # 标题


        # 训练数据文件（用于独立运行）
        self.training_csv_file = FileSelectWidget(
            "训练数据:",
            "CSV Files (*.csv);;All Files (*.*)"
        )
        layout.addWidget(self.training_csv_file)

        # 机器学习模型页面
        self.ml_page = QWidget()
        self.create_ml_page()
        layout.addWidget(self.ml_page)

        # ==========================================
        # 卡片 3：输出与执行
        # ==========================================
        output_group = QGroupBox("🚀 输出与执行")
        output_layout = QVBoxLayout()
        output_layout.setSpacing(16)
        output_layout.setContentsMargins(20, 24, 20, 20)

        # 输出文件路径 (改为文件夹模式)
        self.output_path = FileSelectWidget(
            "模型输出目录:",
            "Directories"
        )
        self.output_path.browse_btn.clicked.disconnect()
        self.output_path.browse_btn.clicked.connect(self.browse_output_path)
        output_layout.addWidget(self.output_path)

        # 完美对齐的底部按钮栏（已彻底移除多余的启用复选框）
        action_layout = QHBoxLayout()
        action_layout.addStretch()

        self.run_btn = QPushButton("独立运行步骤")
        self.run_btn.setStyleSheet(ModernStylesheet.get_button_stylesheet('primary'))
        self.run_btn.setMinimumWidth(140)
        self.run_btn.clicked.connect(self._on_run_single_clicked)
        action_layout.addWidget(self.run_btn)

        output_layout.addLayout(action_layout)
        output_group.setLayout(output_layout)

        # 将打包好的输出卡片添加到主 layout 中
        layout.addWidget(output_group)

        layout.addStretch()
        self.setLayout(layout)

    def create_ml_page(self):
        """创建机器学习模型页面"""
        layout = QVBoxLayout()

        # 参数设置
        params_group = QGroupBox("训练参数")
        params_layout = QFormLayout()

        self.feature_start = QLineEdit()
        self.feature_start.setText("374.285004")
        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)
        params_layout.addRow("交叉验证折数:", self.cv_folds)

        params_group.setLayout(params_layout)
        layout.addWidget(params_group)

        # 预处理方法 - 多选
        preproc_group = QGroupBox("预处理方法 (可多选)")
        preproc_layout = QVBoxLayout()

        preproc_grid = QGridLayout()
        self.preproc_checkboxes = {}
        preproc_methods = ['None', 'MMS', 'SS', 'SNV', 'MA', 'SG', 'MSC', 'D1', 'D2', 'DT', 'CT']

        for i, method in enumerate(preproc_methods):
            checkbox = QCheckBox(PREPROC_CHINESE.get(method, method))
            checkbox.setChecked(False)
            self.preproc_checkboxes[method] = checkbox
            preproc_grid.addWidget(checkbox, i // 4, i % 4)

        button_layout = QHBoxLayout()
        button_layout.setContentsMargins(0, 10, 0, 0)  # 顶部增加边距
        button_layout.setSpacing(10)  # 按钮之间的间距

        select_all_btn = QPushButton("全选")
        deselect_all_btn = QPushButton("全不选")

        # 应用统一样式和自适应宽度策略
        for btn in [select_all_btn, deselect_all_btn]:
            base_style = ModernStylesheet.get_button_stylesheet('normal')
            # 增加 padding 确保文字不被截断
            btn.setStyleSheet(base_style + "\nQPushButton { padding-left: 14px; padding-right: 14px; }")
            btn.setFixedHeight(30)
            btn.setSizePolicy(QSizePolicy.Minimum, QSizePolicy.Fixed)

        select_all_btn.clicked.connect(lambda: self._toggle_checkboxes(self.preproc_checkboxes, True))
        deselect_all_btn.clicked.connect(lambda: self._toggle_checkboxes(self.preproc_checkboxes, False))
        button_layout.addWidget(select_all_btn)
        button_layout.addWidget(deselect_all_btn)
        button_layout.addStretch()

        preproc_layout.addLayout(preproc_grid)
        preproc_layout.addLayout(button_layout)
        preproc_group.setLayout(preproc_layout)
        layout.addWidget(preproc_group)

        # 模型选择 - 多选
        model_group = QGroupBox("模型类型 (可多选)")
        model_layout = QVBoxLayout()

        model_grid = QGridLayout()
        self.model_checkboxes = {}

        model_groups = [
            ("【线性模型】", ['LinearRegression', 'Ridge', 'Lasso', 'ElasticNet', 'PLS']),
            ("【树模型】", ['DecisionTree', 'RF', 'ExtraTrees', 'XGBoost', 'LightGBM', 'CatBoost']),
            ("【集成学习】", ['GradientBoosting', 'AdaBoost']),
            ("【其他模型】", ['SVR', 'KNN', 'MLP'])
        ]

        row = 0
        for group_name, models in model_groups:
            group_label = QLabel(f"<b>{group_name}</b>")
            group_label.setStyleSheet(
                f"background-color: {ModernStylesheet.COLORS['hover']}; "
                f"padding: 5px; border: 1px solid {ModernStylesheet.COLORS['border_light']}; "
                f"border-radius: 3px;"
            )
            model_grid.addWidget(group_label, row, 0, 1, 4)
            row += 1

            for i, model in enumerate(models):
                checkbox = QCheckBox(MODEL_CHINESE.get(model, model))
                checkbox.setChecked(False)
                self.model_checkboxes[model] = checkbox
                model_grid.addWidget(checkbox, row, i % 4)
                if (i + 1) % 4 == 0:
                    row += 1

            row += 1

        model_button_layout = QHBoxLayout()
        model_button_layout.setContentsMargins(0, 10, 0, 0)
        model_button_layout.setSpacing(10)

        model_select_all = QPushButton("全选")
        model_deselect_all = QPushButton("全不选")

        for btn in [model_select_all, model_deselect_all]:
            base_style = ModernStylesheet.get_button_stylesheet('normal')
            btn.setStyleSheet(base_style + "\nQPushButton { padding-left: 14px; padding-right: 14px; }")
            btn.setFixedHeight(30)
            btn.setSizePolicy(QSizePolicy.Minimum, QSizePolicy.Fixed)

        model_select_all.clicked.connect(lambda: self._toggle_checkboxes(self.model_checkboxes, True))
        model_deselect_all.clicked.connect(lambda: self._toggle_checkboxes(self.model_checkboxes, False))
        model_button_layout.addWidget(model_select_all)
        model_button_layout.addWidget(model_deselect_all)
        model_button_layout.addStretch()

        model_layout.addLayout(model_grid)
        model_layout.addLayout(model_button_layout)
        model_group.setLayout(model_layout)
        layout.addWidget(model_group)

        # 数据划分方法 - 多选
        split_group = QGroupBox("数据划分方法 (可多选)")
        split_layout = QVBoxLayout()

        split_grid = QGridLayout()
        self.split_checkboxes = {}
        split_methods = ['spxy', 'ks', 'random']

        for i, method in enumerate(split_methods):
            checkbox = QCheckBox(SPLIT_CHINESE.get(method, method))
            checkbox.setChecked(False)
            self.split_checkboxes[method] = checkbox
            split_grid.addWidget(checkbox, 0, i)

        split_button_layout = QHBoxLayout()
        split_button_layout.setContentsMargins(0, 10, 0, 0)
        split_button_layout.setSpacing(10)

        split_select_all = QPushButton("全选")
        split_deselect_all = QPushButton("全不选")

        for btn in [split_select_all, split_deselect_all]:
            base_style = ModernStylesheet.get_button_stylesheet('normal')
            btn.setStyleSheet(base_style + "\nQPushButton { padding-left: 14px; padding-right: 14px; }")
            btn.setFixedHeight(30)
            btn.setSizePolicy(QSizePolicy.Minimum, QSizePolicy.Fixed)

        split_select_all.clicked.connect(lambda: self._toggle_checkboxes(self.split_checkboxes, True))
        split_deselect_all.clicked.connect(lambda: self._toggle_checkboxes(self.split_checkboxes, False))
        split_button_layout.addWidget(split_select_all)
        split_button_layout.addWidget(split_deselect_all)
        split_button_layout.addStretch()

        split_layout.addLayout(split_grid)
        split_layout.addLayout(split_button_layout)
        split_group.setLayout(split_layout)
        layout.addWidget(split_group)

        self.ml_page.setLayout(layout)

    def _toggle_checkboxes(self, checkboxes_dict, checked):
        """统一设置checkbox状态"""
        for checkbox in checkboxes_dict.values():
            checkbox.setChecked(checked)

    def _get_default_work_dir(self):
        """获取 work_dir，优先用 panel 自身缓存的，否则尝试从主窗口取"""
        if hasattr(self, 'work_dir') and self.work_dir:
            return str(self.work_dir)
        mw = self.window()
        if mw and hasattr(mw, 'work_dir') and mw.work_dir:
            return str(mw.work_dir)
        return ""

    def browse_output_path(self):
        """浏览输出模型目录"""
        work_dir = getattr(self, 'work_dir', "")
        initial_dir = os.path.join(work_dir, '8_Machine_Learning_Models') if work_dir else ""
        dir_path = QFileDialog.getExistingDirectory(self, "选择模型输出目录", initial_dir)
        if dir_path:
            self.output_path.set_path(dir_path)

    def get_config(self):
        """获取配置"""
        preprocessing_methods = [
            method for method, checkbox in self.preproc_checkboxes.items()
            if checkbox.isChecked()
        ]
        model_names = [
            model for model, checkbox in self.model_checkboxes.items()
            if checkbox.isChecked()
        ]
        split_methods = [
            method for method, checkbox in self.split_checkboxes.items()
            if checkbox.isChecked()
        ]

        config = {
            'feature_start_column': self.feature_start.text(),
            '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'],
            'cv_folds': self.cv_folds.value()
        }
        training_csv_path = self.training_csv_file.get_path()
        if training_csv_path:
            config['training_csv_path'] = training_csv_path
        output_path = self.output_path.get_path()
        if output_path:
            config['output_path'] = output_path
        return config

    def set_config(self, config):
        """设置配置"""
        if 'feature_start_column' in config:
            self.feature_start.setText(str(config['feature_start_column']))
        if 'cv_folds' in config:
            self.cv_folds.setValue(config['cv_folds'])
        if 'preprocessing_methods' in config:
            methods = config['preprocessing_methods']
            for method, checkbox in self.preproc_checkboxes.items():
                checkbox.setChecked(method in methods)
        if 'model_names' in config:
            models = config['model_names']
            for model, checkbox in self.model_checkboxes.items():
                checkbox.setChecked(model in models)
        if 'split_methods' in config:
            methods = config['split_methods']
            for method, checkbox in self.split_checkboxes.items():
                checkbox.setChecked(method in methods)
        if 'training_csv_path' in config:
            self.training_csv_file.set_path(config['training_csv_path'])
        if 'output_path' in config:
            self.output_path.set_path(config['output_path'])

    def update_from_config(self, work_dir=None, pipeline=None):
        """从全局配置自动填充训练数据和输出路径

        Args:
            work_dir: 工作目录路径
            pipeline: Pipeline 实例（未使用，保留接口兼容性）
        """
        if work_dir:
            self.work_dir = work_dir
        elif hasattr(self, 'work_dir') and self.work_dir:
            pass
        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()
        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)

        # 2. 自动填充输出目录为 8_Machine_Learning_Models
        if self.work_dir:
            import os
            models_dir = os.path.join(self.work_dir, "8_Machine_Learning_Models").replace('\\', '/')
            os.makedirs(models_dir, exist_ok=True)
            self.output_path.set_path(models_dir)
        else:
            self.output_path.set_path("")

    def _on_run_single_clicked(self):
        """通过 EventBus 发布单步执行请求（解耦面板与 PipelineExecutor）。"""
        from src.gui.core.event_bus import global_event_bus

        training_csv_path = self.training_csv_file.get_path()
        if not training_csv_path:
            QMessageBox.warning(self, "输入错误", "请选择训练数据CSV文件！")
            return

        config = {'step8_ml_train': self.get_config()}
        global_event_bus.publish('RequestRunSingleStep', {
            'step_name': 'step8_ml_train',
            'config': config,
        })

    def run_step(self):
        """独立运行步骤8（旧版 parent 链上溯方式，保留兼容）。"""
        training_csv_path = self.training_csv_file.get_path()
        if not training_csv_path:
            QMessageBox.warning(self, "输入错误", "请选择训练数据CSV文件！")
            return

        main_window = self.window()
        if hasattr(main_window, 'run_single_step'):
            config = {'step8_ml_train': self.get_config()}
            main_window.run_single_step('step8_ml_train', config)

    def get_training_params(self):
        """获取模型训练参数"""
        return {
            'pipeline_type': 'machine_learning',
            'feature_start': float(self.feature_start.text()),
            '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()],
            'split_methods': [method for method, cb in self.split_checkboxes.items() if cb.isChecked()]
        }