129 lines
6.4 KiB
Python
129 lines
6.4 KiB
Python
from pathlib import Path
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import numpy as np
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import pandas as pd
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from sklearn.compose import ColumnTransformer
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from sklearn.ensemble import ExtraTreesRegressor, GradientBoostingRegressor, HistGradientBoostingRegressor, RandomForestRegressor
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from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
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from sklearn.pipeline import Pipeline
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from sklearn.preprocessing import OneHotEncoder, StandardScaler
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from sklearn.svm import SVR
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BASE_DIR = Path(__file__).resolve().parent
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DATA_PATH = Path('/root/.hermes-web-ui/upload/default/28752a235eb6ef9d.xlsx')
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OUT_DIR = BASE_DIR / 'outputs' / 'time_split_experiment'
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OUT_DIR.mkdir(parents=True, exist_ok=True)
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TARGET = '总骑行量'
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BASE_FEATURES = ['年', '季节', '月', '节假日', '星期', '工作日', '天气', '气温', '体感温度', '湿度', '风速']
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BASE_CAT = ['年', '季节', '月', '节假日', '星期', '工作日', '天气']
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def rmse(y_true, y_pred):
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return float(np.sqrt(mean_squared_error(y_true, y_pred)))
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def add_features(df, min_date):
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df = df.copy()
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df['日期数值'] = (df['日期'] - min_date).dt.days
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df['一年中的第几天'] = df['日期'].dt.dayofyear
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df['月内日'] = df['日期'].dt.day
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df['周序号'] = df['日期'].dt.isocalendar().week.astype(int)
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df['日期_sin'] = np.sin(2 * np.pi * df['一年中的第几天'] / 365.25)
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df['日期_cos'] = np.cos(2 * np.pi * df['一年中的第几天'] / 365.25)
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df['周序号_sin'] = np.sin(2 * np.pi * df['周序号'] / 52.18)
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df['月内日_sin'] = np.sin(2 * np.pi * df['月内日'] / 31)
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df['年内进度'] = df['一年中的第几天'] / 365.25
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df['季度内月份'] = ((df['月'] - 1) % 3) + 1
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df['是否月初'] = (df['月内日'] <= 7).astype(int)
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df['是否月末'] = (df['月内日'] >= 24).astype(int)
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df['日期分箱4'] = pd.qcut(df['日期数值'], q=4, labels=False, duplicates='drop').astype(int)
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df['日期分箱8'] = pd.qcut(df['日期数值'], q=8, labels=False, duplicates='drop').astype(int)
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return df
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def make_pipeline(model_name, estimator, features, categorical):
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numeric = [col for col in features if col not in categorical]
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need_scale = model_name == 'SVR'
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preprocessor = ColumnTransformer(
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transformers=[
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('cat', OneHotEncoder(handle_unknown='ignore', sparse_output=False), categorical),
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('num', StandardScaler() if need_scale else 'passthrough', numeric),
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],
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verbose_feature_names_out=False,
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)
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return Pipeline([('preprocess', preprocessor), ('model', estimator)])
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def main():
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daily = pd.read_excel(DATA_PATH, sheet_name='每天数据')
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future = pd.read_excel(DATA_PATH, sheet_name='预测集')
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daily['日期'] = pd.to_datetime(daily['日期'])
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future['日期'] = pd.to_datetime(future['日期'])
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daily = daily.sort_values('日期').reset_index(drop=True)
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future = future.sort_values('日期').reset_index(drop=True)
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min_date = daily['日期'].min()
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daily = add_features(daily, min_date)
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future = add_features(future, min_date)
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split_idx = int(len(daily) * 0.8)
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train = daily.iloc[:split_idx].copy()
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test = daily.iloc[split_idx:].copy()
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feature_schemes = {
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'12_日期数值': (['日期数值'] + BASE_FEATURES, BASE_CAT),
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'12_日期sin': (['日期_sin'] + BASE_FEATURES, BASE_CAT),
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'12_日期cos': (['日期_cos'] + BASE_FEATURES, BASE_CAT),
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'12_年内第几天': (['一年中的第几天'] + BASE_FEATURES, BASE_CAT),
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'12_年内进度': (['年内进度'] + BASE_FEATURES, BASE_CAT),
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'12_周序号': (['周序号'] + BASE_FEATURES, BASE_CAT + ['周序号']),
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'12_周序号sin': (['周序号_sin'] + BASE_FEATURES, BASE_CAT),
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'12_月内日': (['月内日'] + BASE_FEATURES, BASE_CAT + ['月内日']),
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'12_月内日sin': (['月内日_sin'] + BASE_FEATURES, BASE_CAT),
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'12_季度内月份': (['季度内月份'] + BASE_FEATURES, BASE_CAT + ['季度内月份']),
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'12_是否月初': (['是否月初'] + BASE_FEATURES, BASE_CAT + ['是否月初']),
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'12_是否月末': (['是否月末'] + BASE_FEATURES, BASE_CAT + ['是否月末']),
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'12_日期分箱4': (['日期分箱4'] + BASE_FEATURES, BASE_CAT + ['日期分箱4']),
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'12_日期分箱8': (['日期分箱8'] + BASE_FEATURES, BASE_CAT + ['日期分箱8']),
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}
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models = {
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'梯度提升树': lambda: GradientBoostingRegressor(random_state=42, n_estimators=300, learning_rate=0.05, max_depth=3),
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'随机森林': lambda: RandomForestRegressor(n_estimators=500, random_state=42, min_samples_leaf=2, n_jobs=-1),
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'极端随机树': lambda: ExtraTreesRegressor(n_estimators=500, random_state=42, min_samples_leaf=2, n_jobs=-1),
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'HistGBDT': lambda: HistGradientBoostingRegressor(random_state=42, max_iter=300, learning_rate=0.05, max_leaf_nodes=31),
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'SVR': lambda: SVR(C=200, gamma='scale', epsilon=50),
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}
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rows = []
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for scheme_name, (features, categorical) in feature_schemes.items():
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for model_name, factory in models.items():
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pipe = make_pipeline(model_name, factory(), features, categorical)
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pipe.fit(train[features], train[TARGET])
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pred = pipe.predict(test[features])
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future_pred = np.maximum(np.round(pipe.predict(future[features])), 0).astype(int)
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rows.append({
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'方案': scheme_name,
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'模型': model_name,
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'RMSE': rmse(test[TARGET], pred),
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'MAE': float(mean_absolute_error(test[TARGET], pred)),
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'R2': float(r2_score(test[TARGET], pred)),
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'未来预测最小值': int(future_pred.min()),
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'未来预测最大值': int(future_pred.max()),
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'未来预测均值': float(future_pred.mean()),
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})
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result = pd.DataFrame(rows)
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result.to_csv(OUT_DIR / 'extended_12_feature_nonlinear_comparison.csv', index=False, encoding='utf-8-sig')
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print('各模型最佳前5：')
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for model_name, group in result.groupby('模型'):
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print('\n[' + model_name + ']')
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print(group.sort_values('RMSE').head(5).to_string(index=False, float_format=lambda x: f'{x:.3f}'))
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print('\n总体前15：')
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print(result.sort_values('RMSE').head(15).to_string(index=False, float_format=lambda x: f'{x:.3f}'))
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print('\n保存：', OUT_DIR / 'extended_12_feature_nonlinear_comparison.csv')
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if __name__ == '__main__':
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main()
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