请问一下在机器学习PAI有没有Prophet训练 (ProphetTrainBatchOp)和Prophet预测 (ProphetPredictStreamOp)的python的代码示例？官网的示例不能正确运行。是docker环境,linux jupyter。

是的，机器学习PAI提供了ProphetTrainBatchOp和ProphetPredictStreamOp两个算子，可以用于Prophet模型的训练和预测。以下是Python代码示例，供您参考：

# 导入依赖库
import json
import os
from aliyun_utility import get_oss_instance
from odps import ODPS
from odps.df import DataFrame
from odps.inter import enter
from odps.ml import prophet_train_batch, prophet_predict_stream
from odps.udf import BaseUDTF
from fbprophet import Prophet
from fbprophet.diagnostics import cross_validation
from fbprophet.plot import plot_cross_validation_metric
import pandas as pd

# 从ODPS中读取训练数据
odps = enter()
train_table = odps.get_table('your_train_table_name')
train_df = DataFrame(train_table)

# 定义Prophet模型及训练参数
m = Prophet()
m.add_seasonality(name='monthly', period=30.5, fourier_order=5)
m.fit(train_df.to_pandas())

# 将Prophet模型保存到OSS中
model_path = 'oss://your-bucket/path/to/save/model'
model_file = 'prophet_model.pkl'
with open(model_file, 'wb') as f:
    pickle.dump(m, f)
oss = get_oss_instance()
oss.put_object_from_file(model_path, model_file)

# 使用ProphetPredictStreamOp进行预测
predict_table_name = 'your_predict_table_name'
predict_oss_path = 'oss://your-bucket/path/to/predict/data'
predict_oss_output_path = 'oss://your-bucket/path/to/save/predict/result'
predict_op = prophet_predict_stream(data=train_df,
                                    model_path=model_path,
                                    prediction_col='yhat',
                                    output_table=predict_table_name,
                                    output_oss_path=predict_oss_output_path)
predict_op.execute()

# 从ODPS中读取预测结果
predict_table = odps.get_table(predict_table_name)
predict_df = DataFrame(predict_table)
result = predict_df.to_pandas()

# 可视化预测结果
m.plot(result)

需要注意的是，ProphetTrainBatchOp和ProphetPredictStreamOp算子需要在ODPS中运行，需要先在ODPS客户端中安装fbprophet库。此外，Prophet模型的训练和预测也可以在本地环境中进行，只需将相关代码适当修改即可。