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# 全局静态变量值存储类
class StaticValue:
# SHAP抽样数量
SAMPLE_NUM = 20
# 超参数文本框的最大组件数量
MAX_PARAMS_NUM = 60
# 颜色和标签显示的最大组件数量
MAX_NUM = 20
# 随机种子 (数据集切分+模型训练)
RANDOM_STATE = 123
# 参数类型
INT = "int"
FLOAT = "float"
BOOL = "bool"
STR = "str"
# 画图颜色组重复次数
COLOR_ITER_NUM = 3
# 颜色组
COLORS = [
"#ca5353",
"#c874a5",
"#b674c8",
"#8274c8",
"#748dc8",
"#74acc8",
"#74c8b7",
"#74c88d",
"#a6c874",
"#e0e27e",
"#df9b77",
"#404040",
"#999999",
"#d4d4d4"
] * COLOR_ITER_NUM
COLORS_0 = [
"#8074C8",
"#7895C1",
"#A8CBDF",
"#992224",
"#B54764",
"#E3625D",
"#EF8B67",
"#F0C284"
] * COLOR_ITER_NUM
COLORS_1 = [
"#4A5F7E",
"#719AAC",
"#72B063",
"#94C6CD",
"#B8DBB3",
"#E29135"
] * COLOR_ITER_NUM
COLORS_2 = [
"#4485C7",
"#D4562E",
"#DBB428",
"#682487",
"#84BA42",
"#7ABBDB",
"#A51C36"
] * COLOR_ITER_NUM
COLORS_3 = [
"#8074C8",
"#7895C1",
"#A8CBDF",
"#F5EBAE",
"#F0C284",
"#EF8B67",
"#E3625D",
"#B54764"
] * COLOR_ITER_NUM
COLORS_4 = [
"#979998",
"#C69287",
"#E79A90",
"#EFBC91",
"#E4CD87",
"#FAE5BB",
"#DDDDDF"
] * COLOR_ITER_NUM
COLORS_5 = [
"#91CCC0",
"#7FABD1",
"#F7AC53",
"#EC6E66",
"#B5CE4E",
"#BD7795",
"#7C7979"
] * COLOR_ITER_NUM
COLORS_6 = [
"#E9687A",
"#F58F7A",
"#FDE2D8",
"#CFCFD0",
"#B6B3D6"
] * COLOR_ITER_NUM
# 文件路径相关静态变量存储类
class FilePath:
png_base = "./buffer/{}.png"
excel_base = "./buffer/{}.xlsx"
# [绘图]
display_dataset = "current_excel_data"
data_distribution_plot = "data_distribution_plot"
descriptive_indicators_plot = "descriptive_indicators_plot"
heatmap_plot = "heatmap_plot"
learning_curve_plot = "learning_curve_plot"
shap_beeswarm_plot = "shap_beeswarm_plot"
data_fit_plot = "data_fit_plot"
waterfall_plot = "waterfall_plot"
force_plot = "force_plot"
dependence_plot = "dependence_plot"
# 绘图Step 15:在这里添加新的绘图方法名称
# 模型名称静态变量存储类
class MN: # ModelName
classification = "classification"
regression = "regression"
# [模型]
linear_regressor = "linear regressor"
polynomial_regressor = "polynomial regressor"
logistic_classifier = "logistic classifier"
decision_tree_classifier = "decision tree classifier"
random_forest_classifier = "random forest classifier"
random_forest_regressor = "random forest regressor"
xgboost_classifier = "xgboost classifier"
lightGBM_classifier = "lightGBM classifier"
gradient_boosting_regressor = "gradient boosting regressor"
svm_classifier = "svm classifier"
svm_regressor = "svm regressor"
knn_classifier = "knn classifier"
knn_regressor = "knn regressor"
naive_bayes_classifier = "naive bayes classifier"
# 模型Step 4:在这里添加新的模型名称
# [绘图]
data_distribution = "data_distribution"
descriptive_indicators = "descriptive_indicators"
heatmap = "heatmap"
learning_curve = "learning_curve"
shap_beeswarm = "shap_beeswarm"
data_fit = "data_fit"
waterfall = "waterfall"
force = "force"
dependence = "dependence"
# 绘图Step 4:在这里添加新的绘图方法名称
# 组件标签名称静态变量存储类
class LN: # LabelName
choose_dataset_radio = "选择所需数据源 [必选]"
display_total_col_num_text = "总列数"
display_total_row_num_text = "总行数"
display_na_list_text = "存在缺失值的列"
del_all_na_col_button = "删除所有存在缺失值的列 [可选]"
display_duplicate_num_text = "重复的行数"
del_col_checkboxgroup = "选择所需删除的列"
del_col_button = "删除 [可选]"
remain_row_slider = "保留的行数"
remain_row_button = "保留 [可选]"
del_duplicate_button = "删除所有重复行 [可选]"
encode_label_checkboxgroup = "选择所需标签编码的字符型数值列"
display_encode_label_dataframe = "标签编码信息"
encode_label_button = "字符型转数值型 [可选]"
change_data_type_to_float_button = "将所有数据强制转换为浮点型（除第1列以外）[必选]"
standardize_data_checkboxgroup = "选择所需标准化的列"
standardize_data_button = "标准化 [可选]"
select_as_y_radio = "选择因变量 [必选]"
choose_assign_radio = "选择任务类型（同时会根据任务类型将第1列数据强制转换）[必选]"
train_size_textbox = "分割出的训练集所占比例"
model_optimize_radio = "选择超参数优化方法"
model_train_input_params_dataframe = "超参数列表"
model_train_button = "训练"
model_train_params_dataframe = "训练后的模型参数"
model_train_metrics_dataframe = "训练后的模型指标"
select_as_model_radio = "选择所需训练的模型"
# [模型]
linear_regression_model_radio = "选择线性回归的模型"
naive_bayes_classification_model_radio = "选择朴素贝叶斯分类的模型"
# 模型Step 5:在这里添加新的模型额外组件名称
title_name_textbox = "标题"
x_label_textbox = "x 轴名称"
y_label_textbox = "y 轴名称"
colors = ["颜色 {}".format(i) for i in range(StaticValue.MAX_NUM)]
labels = ["图例 {}".format(i) for i in range(StaticValue.MAX_NUM)]
# [绘图]
heatmap_is_rotate = "x轴标签是否旋转"
heatmap_checkboxgroup = "选择所需绘制系数热力图的列"
heatmap_button = "绘制系数热力图"
data_distribution_radio = "选择所需绘制数据分布图的列"
data_distribution_is_rotate = "x轴标签是否旋转"
data_distribution_button = "绘制数据分布图"
descriptive_indicators_checkboxgroup = "选择所需绘制箱线统计图的列"
descriptive_indicators_is_rotate = "x轴标签是否旋转"
descriptive_indicators_button = "绘制箱线统计图"
learning_curve_checkboxgroup = "选择所需绘制学习曲线图的模型"
learning_curve_button = "绘制学习曲线图"
shap_beeswarm_radio = "选择所需绘制特征蜂群图的模型"
shap_beeswarm_type = "选择图像类型"
shap_beeswarm_button = "绘制特征蜂群图"
data_fit_checkboxgroup = "选择所需绘制数据拟合图的模型"
data_fit_button = "绘制数据拟合图"
waterfall_radio = "选择所需绘制特征瀑布图的模型"
waterfall_number = "输入相关特征的变量索引"
waterfall_button = "绘制特征瀑布图"
force_radio = "选择所需绘制特征力图的模型"
force_number = "输入相关特征的变量索引"
force_button = "绘制特征力图"
dependence_radio = "选择所需绘制特征依赖图的模型"
dependence_col = "选择相应的列"
dependence_button = "绘制特征依赖图"
# 绘图Step 5:在这里添加新的绘图方法相关组件名称
data_distribution_plot = "数据分布图"
descriptive_indicators_plot = "箱线统计图"
heatmap_plot = "系数热力图"
learning_curve_plot = "学习曲线图"
shap_beeswarm_plot = "特征蜂群图"
data_fit_plot = "数据拟合图"
waterfall_plot = "特征瀑布图"
force_plot = "特征力图"
dependence_plot = "特征依赖图"
# 绘图Step 6:在这里添加新的绘图方法名称
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