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refactor(step6): 步骤6机器学习建模UI汉化 + 默认全不选 + 底层反向映射清洗

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DXC
2026-05-10 14:58:57 +08:00
parent 14278739bf
commit f96c55f361
2 changed files with 179 additions and 11 deletions
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117
src/core/steps/modeling_step.py
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@ -14,6 +14,114 @@ import pandas as pd
import numpy as np
# ============================================================
# 汉化 -> 英文 反向映射字典（UI 复选框显示文本 -> 底层算法键名）
# ============================================================
# 模型名称：中文 (缩写) -> 英文键名
MODEL_NAME_MAP = {
"多元线性回归 (MLR)": "LinearRegression",
"岭回归 (Ridge)": "Ridge",
"套索回归 (Lasso)": "Lasso",
"弹性网络 (ElasticNet)": "ElasticNet",
"偏最小二乘 (PLSR)": "PLS",
"决策树 (CART)": "DecisionTree",
"随机森林 (RF)": "RF",
"极端随机树 (ET)": "ExtraTrees",
"极值梯度提升 (XGBoost)": "XGBoost",
"轻量梯度提升 (LightGBM)": "LightGBM",
"类别梯度提升 (CatBoost)": "CatBoost",
"梯度提升树 (GBDT)": "GradientBoosting",
"自适应提升 (AdaBoost)": "AdaBoost",
"支持向量回归 (SVR)": "SVR",
"K近邻回归 (KNN)": "KNN",
"多层感知机 (BP神经网络)": "MLP",
}
# 预处理方法：各种可能的中文变体 -> 标准键名
PREPROC_NAME_MAP = {
# 无处理
"无 (None)": "None",
"None": "None",
# MMS
"最小-最大归一化 (MMS)": "MMS",
"MMS": "MMS",
# SS
"标度化 (SS)": "SS",
"SS": "SS",
# SNV
"标准正态变换 (SNV)": "SNV",
"SNV": "SNV",
# MA
"移动平均 (MA)": "MA",
"MA": "MA",
# SG
"Savitzky-Golay (SG)": "SG",
"SG": "SG",
# MSC
"多元散射校正 (MSC)": "MSC",
"MSC": "MSC",
# D1
"一阶导数 (D1)": "D1",
"D1": "D1",
# D2
"二阶导数 (D2)": "D2",
"D2": "D2",
# DT
"去趋势 (DT)": "DT",
"DT": "DT",
# CT
"中心化 (CT)": "CT",
"CT": "CT",
}
# 数据划分方法：各种可能的中文变体 -> 标准键名
SPLIT_NAME_MAP = {
"SPXY 算法 (考量X-Y空间)": "spxy",
"spxy": "spxy",
"KS 算法 (考量X空间)": "ks",
"ks": "ks",
"随机划分 (Random)": "random",
"random": "random",
}
def _normalize_model_names(model_names: List[str]) -> List[str]:
"""清洗模型名称列表：将汉化显示文本还原为英文键名"""
result = []
for name in model_names:
if name in MODEL_NAME_MAP:
result.append(MODEL_NAME_MAP[name])
else:
# 已经是英文键名，直接保留
result.append(name)
return result
def _normalize_preprocessing_methods(methods: List[str]) -> List[str]:
"""清洗预处理方法列表：将汉化显示文本还原为标准键名"""
result = []
for method in methods:
if method in PREPROC_NAME_MAP:
result.append(PREPROC_NAME_MAP[method])
else:
# 已经是标准键名，直接保留
result.append(method)
return result
def _normalize_split_methods(methods: List[str]) -> List[str]:
"""清洗数据划分方法列表：将汉化显示文本还原为标准键名"""
result = []
for method in methods:
if method in SPLIT_NAME_MAP:
result.append(SPLIT_NAME_MAP[method])
else:
# 已经是标准键名，直接保留
result.append(method)
return result
class ModelingStep:
"""建模步骤"""
@ -75,6 +183,15 @@ class ModelingStep:
if split_methods is None:
split_methods = ["spxy", "ks", "random"]
# ---- 汉化清洗：将 UI 传来的中文/混合名称转换为底层英文键名 ----
preprocessing_methods = _normalize_preprocessing_methods(preprocessing_methods)
model_names = _normalize_model_names(model_names)
split_methods = _normalize_split_methods(split_methods)
print(f"[参数清洗] 预处理方法: {preprocessing_methods}")
print(f"[参数清洗] 模型名称: {model_names}")
print(f"[参数清洗] 划分方法: {split_methods}")
modeler = WaterQualityModelingBatch(str(output_dir))
modeler.train_models_batch(
csv_path=training_csv_path,

73
src/gui/panels/step6_panel.py
View File

@ -17,6 +17,57 @@ 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 Step6Panel(QWidget):
"""步骤6：机器学习建模"""
def __init__(self, parent=None):
@ -54,7 +105,7 @@ class Step6Panel(QWidget):
# 启用步骤
self.enable_checkbox = QCheckBox("启用此步骤")
self.enable_checkbox.setChecked(True)
self.enable_checkbox.setChecked(False)
layout.addWidget(self.enable_checkbox)
# 独立运行按钮
@ -95,8 +146,8 @@ class Step6Panel(QWidget):
preproc_methods = ['None', 'MMS', 'SS', 'SNV', 'MA', 'SG', 'MSC', 'D1', 'D2', 'DT', 'CT']
for i, method in enumerate(preproc_methods):
checkbox = QCheckBox(method)
checkbox.setChecked(True)
checkbox = QCheckBox(PREPROC_CHINESE.get(method, method))
checkbox.setChecked(False)
self.preproc_checkboxes[method] = checkbox
preproc_grid.addWidget(checkbox, i // 4, i % 4)
@ -122,10 +173,10 @@ class Step6Panel(QWidget):
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'])
("【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
@ -140,8 +191,8 @@ class Step6Panel(QWidget):
row += 1
for i, model in enumerate(models):
checkbox = QCheckBox(model)
checkbox.setChecked(model in ['SVR', 'RF', 'Ridge', 'Lasso'])
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:
@ -172,8 +223,8 @@ class Step6Panel(QWidget):
split_methods = ['spxy', 'ks', 'random']
for i, method in enumerate(split_methods):
checkbox = QCheckBox(method)
checkbox.setChecked(True)
checkbox = QCheckBox(SPLIT_CHINESE.get(method, method))
checkbox.setChecked(False)
self.split_checkboxes[method] = checkbox
split_grid.addWidget(checkbox, 0, i)