请问一下在机器学习PAI有没有Prophet训练 和Prophet预测的python的代码示例？

以下是使用 Prophet 进行时间序列预测的 Python 代码示例,可以试一下:

import pandas as pd
from fbprophet import Prophet

# 读取数据
data = pd.read_csv('data.csv')

# 转换日期列格式
data['ds'] = pd.to_datetime(data['ds'])

# 创建 Prophet 模型实例
model = Prophet()

# 训练模型
model.fit(data)

# 创建预测时间段
future = model.make_future_dataframe(periods=30)

# 进行预测
forecast = model.predict(future)

# 打印预测结果
print(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']])

PAI支持Prophet模型的训练和预测，您可以通过以下示例代码实现：

Prophet训练：

import os
import sys
import argparse
from fcntl import fcntl, F_GETFL, F_SETFL
from os import O_NONBLOCK, read
from ai_flow import init_ai_flow_context, PythonObjectExecutor
from ai_flow.graph.edge import JobControlEdge, JobSchedulingRule
from ai_flow.graph.node import JobNode
from ai_flow.graph.workflow import Workflow
from prophet import ProphetTrainBatchOp
from ai_flow_plugins.job_plugin.job_config import JobConfig

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--start', type=int, default=0)
    args = parser.parse_args()

    with open('/paddle/ai_flow/job_id', 'r') as f:
        job_id = int(f.read())

    init_ai_flow_context()

    # set up prophet training job using ProphetTrainBatchOp
    prophet_op = ProphetTrainBatchOp(name='prophet_train', dt_col='date',
                                     target_col='target', freq='D',
                                     prediction_length=7)

    prophet_op.set_inputs({'data': '/pfs/data/bike_sharing_hourly.csv'})
    prophet_op.set_outputs(['m', 'samples'])

    prophet_node = JobNode(
        name='prophet_train',
        executor=PythonObjectExecutor(python_object=prophet_op),
        job_config=JobConfig(job_id=job_id, job_name='prophet_train', properties={}),
        common_job_parameters={'resource': {'cpu': 1, 'memory': '2Gi'}})

    # set up workflow for prophet training
    workflow = Workflow(workflow_name='prophet_workflow')

    ctrl_edge = JobControlEdge(
        name='prophet_train_edge',
        upstream_node_id=None,
        downstream_node_id=prophet_node.node_id,
        action_config=JobSchedulingRule()
    )

    workflow.add_edge(ctrl_edge)
    workflow.add_node(prophet_node)

    # start the workflow
    workflow.submit_workflow()

Prophet预测：

import os
import sys
import argparse
from fcntl import fcntl, F_GETFL, F_SETFL
from os import O_NONBLOCK, read
from ai_flow import init_ai_flow_context, PythonObjectExecutor
from ai_flow.graph.edge import JobControlEdge, JobSchedulingRule
from ai_flow.graph.node import JobNode
from ai_flow.graph.workflow import Workflow
from prophet import ProphetPredictStreamOp
from ai_flow_plugins.job_plugin.job_config import JobConfig

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--start', type=int, default=0)
    args = parser.parse_args()

    with open('/paddle/ai_flow/job_id', 'r') as f:
        job_id = int(f.read())

    init_ai_flow_context()

    # set up prophet prediction job using ProphetPredictStreamOp
    prophet_op = ProphetPredictStreamOp(name='prophet_predict',
                                      freq='D', prediction_length=7)
    prophet_op.set_inputs({'m': '/pfs/modelfile/prophet_m.pkl', 'samples': '/pfs/inputdata/samples'})
    prophet_op.set_outputs({'output': '/pfs/outputfile/predictions'})

    prophet_node = JobNode(
        name='prophet_predict',
        executor=PythonObjectExecutor(python_object=prophet_op),
        job_config=JobConfig(job_id=job_id, job_name='prophet_predict', properties={}),
        common_job_parameters={'resource': {'cpu': 1, 'memory': '2Gi'}})

    # set up workflow for prophet prediction
    workflow = Workflow(workflow_name='prophet_predict_workflow')

    ctrl_edge = JobControlEdge(
        name='prophet_predict_edge',
        upstream_node_id=None,
        downstream_node_id=prophet_node.node_id,
        action_config=JobSchedulingRule()
    )

    workflow.add_edge(ctrl_edge)
    workflow.add_node(prophet_node)

    # start the workflow
    workflow.submit_workflow()

需要注意的是，在代码示例中，您需要替换数据路径、模型路径和输出路径等相关参数，使其适配您的环境。