在 Spark 中使用 Pandas udf 与 Facebook 先知进行预测

Posted 2023-04-13

【中文标题】在 Spark 中使用 Pandas udf 与 Facebook 先知进行预测

【英文标题】：Forecasting with facebook prophet using Pandas udf in spark

【发布时间】：2020-04-29 18:26:31

【问题描述】：

我正在尝试在 Zeppelin 环境中在 Spark 中使用 Facebook 先知，并且我尝试按照 https://github.com/facebook/prophet/issues/517 的确切步骤进行操作，但是，我收到如下错误。我只是不确定我要在这里纠正什么或如何调试它。

我的数据包含一个名为 ds 的日期时间特征、我想要预测的数量 y 和 segment，我正在尝试为每个细分市场建立一个模型。

File"/hadoop14/yarn/nm/usercache/khasbab/appcache/application_1588090646020_2412/container_e168_1588090646020_2412_01_000001/py4j-0.10.7-src.zip/py4j/protocol.py", line 328, in get_return_value format(target_id, ".", name), value) py4j.protocol.Py4JJavaError: An error occurred while calling o3737.showString.

%livycd.pyspark

from pyspark.sql.types import StructType,StructField,StringType,TimestampType,ArrayType,DoubleType
from pyspark.sql.functions import current_date
from pyspark.sql.functions import pandas_udf, PandasUDFType
from fbprophet import Prophet
from datetime import datetime
import pandas as pd


result_schema = StructType([

    StructField('segment', StringType(), True),
    StructField('ds', TimestampType(), True),
    StructField('trend', ArrayType(DoubleType()), True),
    StructField('trend_upper', ArrayType(DoubleType()), True),
    StructField('trend_lower', ArrayType(DoubleType()), True),
    StructField('yearly', ArrayType(DoubleType()), True),
    StructField('yearly_upper', ArrayType(DoubleType()), True),
    StructField('yearly_lower', ArrayType(DoubleType()), True),
    StructField('yhat', ArrayType(DoubleType()), True),
    StructField('yhat_upper', ArrayType(DoubleType()), True),
    StructField('yhat_lower', ArrayType(DoubleType()), True),
    StructField('multiplicative_terms', ArrayType(DoubleType()), True),
    StructField('multiplicative_terms_upper', ArrayType(DoubleType()), True),
    StructField('multiplicative_terms_lower', ArrayType(DoubleType()), True),
    StructField('additive_terms', ArrayType(DoubleType()), True),
    StructField('additive_terms_upper', ArrayType(DoubleType()), True),
    StructField('additive_terms_lower', ArrayType(DoubleType()), True),

    ])

@pandas_udf(result_schema, PandasUDFType.GROUPED_MAP)
def forecast_loans(history_pd):

    # instantiate the model, configure the parameters
    model = Prophet(
        interval_width=0.95,
        growth='linear',
        daily_seasonality=False,
        weekly_seasonality=False,
        yearly_seasonality=True,
        seasonality_mode='multiplicative'
    )

    #history_pd['ds'] = pd.to_datetime(history_pd['ds'], errors = 'coerce', format = '%Y-%m-%d')
    #.apply(lambda x: datetime.strptime(x,'%Y-%m-%d')) 

    # fit the model
    model.fit(history_pd.loc[:,['ds','y']])

    # configure predictions
    future_pd = model.make_future_dataframe(
        periods=20,
        freq='W')

    # make predictions
    results_pd = model.predict(future_pd)

    # return predictions
    return pd.DataFrame(

        'segment':history_pd['segment'].values[0],
        'ds': [results_pd.loc[:,'ds'].values.tolist()],
        'trend': [results_pd.loc[:,'ds'].values.tolist()],
        'trend_upper': [results_pd.loc[:,'trend_upper'].values.tolist()],
        'trend_lower': [results_pd.loc[:,'trend_lower'].values.tolist()],
        'yearly': [results_pd.loc[:,'yearly'].values.tolist()],
        'yearly_upper': [results_pd.loc[:,'yearly_upper'].values.tolist()],
        'yearly_lower': [results_pd.loc[:,'yearly_lower'].values.tolist()],
        'yhat': [results_pd.loc[:,'yhat'].values.tolist()],
        'yhat_upper': [results_pd.loc[:,'yhat_upper'].values.tolist()],
        'yhat_lower': [results_pd.loc[:,'yhat_lower'].values.tolist()],
        'multiplicative_terms': [results_pd.loc[:,'multiplicative_terms'].values.tolist()],
        'multiplicative_terms_upper': [results_pd.loc[:,'multiplicative_terms_upper'].values.tolist()],
        'multiplicative_terms_lower': [results_pd.loc[:,'multiplicative_terms_lower'].values.tolist()],
        'additive_terms': [results_pd.loc[:,'additive_terms'].values.tolist()],
        'additive_terms_upper': [results_pd.loc[:,'additive_terms_upper'].values.tolist()],
        'additive_terms_lower': [results_pd.loc[:,'additive_terms_lower'].values.tolist()]

    )
    #return pd.concat([pd.DataFrame(results_pd),pd.DataFrame(history_pd[['segment']].values[0])], axis = 1)




results =df3.groupBy('segment').apply(forecast_loans)


results.show()

【参考方案1】：

我已按照Pandas scalar UDF failing, IllegalArgumentException 的建议将我的代码调整为以下代码并降级为 pyarrow 0.14 并且一切正常！我相信将 pyarrow 降级到 0.14 是 spark 2.x 版本的关键，正如 *** 上所评论的那样。

评论说“问题不在于pyarrow的新版本，是spark必须升级并与pyarrow兼容。（恐怕我们必须等待spark 3.0才能使用最新的pyarrow）”

%livycd.pyspark

from pyspark.sql.types import StructType,StructField,StringType,TimestampType,ArrayType,DoubleType
from pyspark.sql.functions import current_date
from pyspark.sql.functions import pandas_udf, PandasUDFType
from fbprophet import Prophet
from datetime import datetime
import pandas as pd


result_schema = StructType([

    StructField('segment', StringType(), True),
    StructField('ds', TimestampType(), True),
    StructField('trend', DoubleType(), True),
    StructField('trend_upper', DoubleType(), True),
    StructField('trend_lower', DoubleType(), True),
    StructField('yearly', DoubleType(), True),
    StructField('yearly_upper', DoubleType(), True),
    StructField('yearly_lower', DoubleType(), True),
    StructField('yhat', DoubleType(), True),
    StructField('yhat_upper', DoubleType(), True),
    StructField('yhat_lower', DoubleType(), True),
    StructField('multiplicative_terms', DoubleType(), True),
    StructField('multiplicative_terms_upper', DoubleType(), True),
    StructField('multiplicative_terms_lower', DoubleType(), True),
    StructField('additive_terms', DoubleType(), True),
    StructField('additive_terms_upper', DoubleType(), True),
    StructField('additive_terms_lower', DoubleType(), True),

    ])


@pandas_udf(result_schema, PandasUDFType.GROUPED_MAP)
def forecast_loans(df):

    def prophet_model(df,test_start_date):

        df['ds'] = pd.to_datetime(df['ds'])

        # train
        ts_train = (df
                    .query('ds < @test_start_date')
                    .sort_values('ds')
                    )
        # test
        ts_test = (df
                   .query('ds >= @test_start_date')
                   .sort_values('ds')
                   .drop('y', axis=1)
                   )

        print(ts_test.columns)

        # instantiate the model, configure the parameters
        model = Prophet(
            interval_width=0.95,
            growth='linear',
            daily_seasonality=False,
            weekly_seasonality=False,
            yearly_seasonality=True,
            seasonality_mode='multiplicative'
        )

        # fit the model

        model.fit(ts_train.loc[:,['ds','y']])

        # configure predictions
        future_pd = model.make_future_dataframe(
            periods=len(ts_test),
            freq='W')

        # make predictions
        results_pd = model.predict(future_pd)
        results_pd = pd.concat([results_pd,df['segment']],axis = 1)

        return pd.DataFrame(results_pd, columns = result_schema.fieldNames())

    # return predictions
    return prophet_model(df, test_start_date= '2019-03-31')




results =df3.groupBy('segment').apply(forecast_loans)

【参考方案2】：

假设您使用的是 Spark 2.3.x 或 2.4.x 并且 PyArrow >= 0.15.0，则有一个已知的compatibility issue between PyArrow and Spark。

最简单的解决方案是设置环境变量ARROW_PRE_0_15_IPC_FORMAT=1。 Spark 文档建议将其设置在 conf/spark-env.sh 中，但您可以在 Linux shell 中进行设置，也可以在 Python 脚本或 shell 中创建 spark_session 之前进行设置。

import os
os.ENVIRON["ARROW_PRE_0_15_IPC_FORMAT"] = "1"
spark_session = ...

或者，如果您可以选择 PyArrow，您可以降级，如另一个答案中所述。