如何在结构化查询中使用scikit学习模型? [英] How to use scikit-learn model in structured query?

问题描述

我正在尝试将使用泡菜检索的scikit模型应用于结构化流数据帧的每一行.

I'm trying to apply a scikit model retrieved using a pickle to every row of a structured streaming dataframe.

我尝试使用pandas_udf(版本代码1)，它给了我这个错误:

I've tried using pandas_udf (version code 1), and it gives me this error:

AttributeError: 'numpy.ndarray' object has no attribute 'isnull'

代码:

inputPath = "/FileStore/df_training/streaming_df_1_nh_nd/"
from pyspark.sql import functions as f
from pyspark.sql.types import *

data_schema = data_spark_ts.schema

import pandas as pd

from pyspark.sql.functions import col, pandas_udf, PandasUDFType   # User Defines Functions for Pandas Dataframe
from pyspark.sql.types import LongType

get_prediction = pandas_udf(lambda x: gb2.predict(x), IntegerType())


streamingInputDF = (
  spark
    .readStream                       
    .schema(data_schema)               # Set the schema of the JSON data
    .option("maxFilesPerTrigger", 1)  # Treat a sequence of files as a stream by picking one file at a time
    .csv(inputPath)
    .fillna(0)
    .withColumn("prediction", get_prediction( f.struct([col(x) for x in data_spark.columns]) ))
)

display(streamingInputDF.select("prediction"))

我也尝试过使用普通的udf代替pandas_udf，它给了我这个错误:

I've tried also using a normal udf instead of the pandas_udf, and it gives me this error:

ValueError: Expected 2D array, got 1D array instead:
[.. ... .. ..]
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

我不知道如何重塑数据.

I don't know how to reshape my data.

我尝试应用的模型是通过以下方式检索的:

The model I try to apply is retrieved this way:

#load the pickle
import pickle
gb2 = None

with open('pickle_modello_unico.p', 'rb') as fp:
  gb2 = pickle.load(fp)

它的规格是这个:

GradientBoostingClassifier(criterion='friedman_mse', init=None,
              learning_rate=0.1, loss='deviance', max_depth=3,
              max_features=None, max_leaf_nodes=None,
              min_impurity_decrease=0.0, min_impurity_split=None,
              min_samples_leaf=1, min_samples_split=2,
              min_weight_fraction_leaf=0.0, n_estimators=300,
              n_iter_no_change=None, presort='auto', random_state=None,
              subsample=1.0, tol=0.0001, validation_fraction=0.1,
              verbose=0, warm_start=False)

有什么解决的办法吗?

Any help to solve this?

推荐答案

我解决了从pandas_udf返回pd.Series的问题.

I solved the issue returning a pd.Series from the pandas_udf.

这是工作代码:

inputPath = "/FileStore/df_training/streaming_df_1_nh_nd/"
from pyspark.sql import functions as f
from pyspark.sql.types import *

data_schema = data_spark_ts.schema

import pandas as pd

from pyspark.sql.functions import col, pandas_udf, PandasUDFType   # User Defines Functions for Pandas Dataframe
from pyspark.sql.types import LongType

get_prediction = pandas_udf(lambda x: pd.Series(gb2.predict(x)), StringType())


streamingInputDF = (
  spark
    .readStream                       
    .schema(data_schema)               # Set the schema of the JSON data
    .option("maxFilesPerTrigger", 1)  # Treat a sequence of files as a stream by picking one file at a time
    .csv(inputPath)
    .withColumn("prediction", get_prediction( f.struct([col(x) for x in data_spark.columns]) ))
)

display(streamingInputDF.select("prediction"))

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