大lao们，能不能帮我看一下这串代码，决策树预测，但我看不太懂，帮我注解一下，提前万分感谢
这个决策树预测是放在flask中app.py中的，用的是xgboost模块，预测的是汽车的价格，代码可能有点残，我不知道该截多少，帮我稍微瞅一眼吧，可怜我这个决策树的菜鸟吧，
def rmse(y_true, y_pred):
return np.sqrt(mean_squared_error(y_true, y_pred))
all_y = dataset['最低指导价'].values
del dataset['最低指导价']
del dataset['最高指导价']
all_x = dataset.values
df_columns = dataset.columns.values
print('---> cv train to choose best_num_boost_round')
all_y = np.log1p(all_y)
dtrain = xgb.DMatrix(all_x, label=all_y, feature_names=df_columns)
xgb_params = {
'learning_rate': 0.01,
'max_depth': 4,
'eval_metric': 'rmse',
'objective': 'reg:linear',
'nthread': -1,
'silent': 1,
'booster': 'gbtree'
}
cv_result = xgb.cv(dict(xgb_params),
dtrain,
num_boost_round=4000,
early_stopping_rounds=100,
verbose_eval=100,
show_stdv=False,
)
best_num_boost_rounds = len(cv_result)
mean_train_logloss = cv_result.loc[best_num_boost_rounds -
11: best_num_boost_rounds - 1, 'train-rmse-mean'].mean()
mean_test_logloss = cv_result.loc[best_num_boost_rounds -
11: best_num_boost_rounds - 1, 'test-rmse-mean'].mean()
print('best_num_boost_rounds = {}'.format(best_num_boost_rounds))
print('mean_train_rmse = {:.7f} , mean_valid_rmse = {:.7f}\n'.format(
mean_train_logloss, mean_test_logloss))
print('---> training on total dataset to predict test and submit')
model = xgb.train(dict(xgb_params),
dtrain,
num_boost_round=best_num_boost_rounds)
# 特征重要程度
feature_importance = model.get_fscore()
feature_importance = sorted(
feature_importance.items(), key=lambda d: d[1], reverse=True)
print(feature_importance)
print(df_columns)
@app.route('/get_all_unique_values/' )
def get_all_unique_values(key):
values = list(set(ori_dataset[key]))
if '' in values:
values.remove('')
if '未知' in values:
values.remove('未知')
return jsonify(values)
@app.route(
'/predict_car_price////////' )
def predict_car_price(pinpai, pingfen, jibie, cheshenjiegou, fadongji, biansux, xuhanglich, diandongji):
"""预测汽车的价格"""
test_x = [
brand_map[pinpai],
float(pingfen),
jibie_map[jibie],
jiegou_map[cheshenjiegou],
fadongji_map(fadongji),
biansuxiang_map[biansux],
xuhang(xuhanglich),
diandongji_map(diandongji)
]
dtest = xgb.DMatrix(test_x, feature_names=df_columns)
predict_price = model.predict(dtest)[0]
predict_price = np.expm1(predict_price)
return jsonify({
'predict_price': float(predict_price)
})
你好，简单的为你写了一下代码注释，希望能帮到你：
# 导入numpy \xgboost \sklearn(主要是评价指标）\flask模块
import numpy as np
import xgboost as xgb
from sklearn.metrics import mean_squared_error
from flask import Flask, jsonify
# 定义 RMSE 评价指标的函数
def rmse(y_true, y_pred):
return np.sqrt(mean_squared_error(y_true, y_pred))
# 从数据集中提取所有的最低指导价，删除最低指导价和最高指导价列，并将剩余列保存到 all_x 中
all_y = dataset['最低指导价'].values
del dataset['最低指导价']
del dataset['最高指导价']
all_x = dataset.values
# 获取数据集的所有列名
df_columns = dataset.columns.values
# 使用自然对数转换所有的最低指导价（初步的数据处理）
all_y = np.log1p(all_y)
# 将数据集和标签转换为矩阵准备送入模型
dtrain = xgb.DMatrix(all_x, label=all_y, feature_names=df_columns)
# 定义 XGBoost 参数
xgb_params = {
'learning_rate': 0.01,
'max_depth': 4,
'eval_metric': 'rmse',
'objective': 'reg:linear',
'nthread': -1,
'silent': 1,
'booster': 'gbtree'
}
# 使用交叉验证来选择参数（主要是树的数量）
cv_result = xgb.cv(dict(xgb_params),
dtrain,
num_boost_round=4000,
early_stopping_rounds=100,
verbose_eval=100,
show_stdv=False,
)
best_num_boost_rounds = len(cv_result)
mean_train_logloss = cv_result.loc[best_num_boost_rounds -
11: best_num_boost_rounds - 1, 'train-rmse-mean'].mean()
mean_test_logloss = cv_result.loc[best_num_boost_rounds -
11: best_num_boost_rounds - 1, 'test-rmse-mean'].mean()
print('best_num_boost_rounds = {}'.format(best_num_boost_rounds))
# 输出训练误差和验证误差
print('mean_train_rmse = {:.7f} , mean_valid_rmse = {:.7f}\n'.format(
mean_train_logloss, mean_test_logloss))
# 训练模型获取特征重要性
model = xgb.train(dict(xgb_params),
dtrain,
num_boost_round=best_num_boost_rounds)
feature_importance = model.get_fscore()
feature_importance = sorted(
feature_importance.items(), key=lambda d: d[1], reverse=True)
print(feature_importance)
print(df_columns)
# 定义 Flask 的路由来处理请求
@app.route('/get_all_unique_values/<key>')
def get_all_unique_values(key):
values = list(set(ori_dataset[key]))
if '' in values:
values.remove('')
if '未知' in values:
values.remove('未知')
return jsonify(values)
代码的后半部分主要是调用了flask配置了接口，处理请求然后用训练好的模型进行预测。
https://blog.csdn.net/kaxiaokui/article/details/105156784
你把源码发给我测试一下
不知道你这个问题是否已经解决, 如果还没有解决的话: