68 lines
3.1 KiB
Python
68 lines
3.1 KiB
Python
import pandas as pd
|
||
import numpy as np
|
||
import xgboost as xgb
|
||
from sklearn.model_selection import train_test_split
|
||
from sklearn.metrics import mean_squared_error, r2_score
|
||
import joblib
|
||
from logzero import logger
|
||
|
||
def time_series_to_supervised(data, columns, n_in=24, n_out=1,dropnan=True):
|
||
"""
|
||
:param data:作为列表或2D NumPy数组的观察序列。需要。
|
||
:param n_in:作为输入的滞后观察数（X）。值可以在[1..len（数据）]之间可选。默认为1。
|
||
:param n_out:作为输出的观测数量（y）。值可以在[0..len（数据）]之间。可选的。默认为1。
|
||
:param dropnan:Boolean是否删除具有NaN值的行。可选的。默认为True。
|
||
:return:
|
||
"""
|
||
logger.info(f"正在处理训练数据：size：{data.shape}")
|
||
n_vars = 1 if type(data) is list else data.shape[1]
|
||
df = pd.DataFrame(data)
|
||
origNames = columns
|
||
cols, names = list(), list()
|
||
cols.append(df.shift(0))
|
||
names += [('%s' % origNames[j]) for j in range(n_vars)]
|
||
n_in = max(1, n_in)
|
||
for i in range(n_in-1, 0, -1):
|
||
time = '(t-%d)' % i
|
||
cols.append(df.shift(i))
|
||
names += [('%s%s' % (origNames[j], time)) for j in range(n_vars)]
|
||
n_out = max(n_out, 0)
|
||
for i in range(1, n_out+1):
|
||
time = '(t+%d)' % i
|
||
cols.append(df.shift(-i))
|
||
names += [('%s%s' % (origNames[j], time)) for j in range(n_vars)]
|
||
agg = pd.concat(cols, axis=1)
|
||
agg.columns = names
|
||
if dropnan:
|
||
agg.dropna(inplace=True)
|
||
return agg
|
||
|
||
def train_model(train_data: pd.DataFrame):
|
||
"""训练模型的函数，需要根据模型类型实际调整
|
||
Args:
|
||
data (pd.DataFrame): 训练集
|
||
"""
|
||
# 特征和输出列名，需要根据业务场景灵活处理
|
||
fea_cols = train_data.columns[:-1].tolist()
|
||
out_cols = train_data.columns[-1:].tolist()
|
||
logger.info(f"特征列: {fea_cols}, 输出列: {out_cols}")
|
||
X = train_data[fea_cols]
|
||
y = train_data[out_cols]
|
||
train_X,test_X,train_y,test_y = train_test_split(X, y, test_size=0.2, random_state=42)
|
||
valid_X,test_X,valid_y,test_y = train_test_split(test_X, test_y, test_size=0.5, random_state=42)
|
||
# 参数
|
||
other_params = {'learning_rate': 0.1, 'n_estimators': 150, 'max_depth': 10, 'min_child_weight': 1, 'seed': 0, 'subsample': 0.8, 'colsample_bytree': 0.8, 'gamma': 0, 'reg_alpha': 0, 'reg_lambda': 1}
|
||
print(train_X.shape, train_y.shape)
|
||
gbm = xgb.XGBRegressor(objective='reg:squarederror', early_stopping_rounds=20,**other_params)
|
||
gbm.fit(train_X.values, train_y.values, eval_set=[(valid_X.values, valid_y.values)])
|
||
y_pred = gbm.predict(test_X.values)
|
||
logger.info(f"Root Mean Squared Error on Test set: {np.sqrt(mean_squared_error(test_y, y_pred))}")
|
||
logger.info(f"R2 score on Test set: {r2_score(test_y, y_pred)}")
|
||
joblib.dump(gbm, './models/carbon_pred.joblib')
|
||
logger.info(f"save_path: ./models/carbon_pred.joblib")
|
||
|
||
if __name__ == '__main__':
|
||
data = pd.read_csv('./data/carbon_data_hourly.csv', index_col=0)
|
||
agg = time_series_to_supervised(data.values, data.columns, 24, 1)
|
||
train_model(agg)
|