无法在 pyspark 数据帧上使用 Sklearn 模型进行预测

Question

我已成功加载 sklearn 模型，但无法对 pyspark 数据帧进行预测。运行下面给定的代码时，出现下面提到的错误。请帮助我获取在 pyspark 上使用 sklearn 模型进行预测的代码。我也搜索过相关问题，但没有找到解决方案。

sc = spark.sparkContext
braodcast_model = sc.broadcast(loaded_model)
braodcast_model.value


#update prediction method
def predictor(cols):
    #call predict method for model
    return model.value.predict(*cols)

udf_predictor = udf(predictor, FloatType())

#apply the udf to dataframe
df_prediction = df.withColumn("prediction", udf_predictor(df.select(list_of_columns)))

我收到以下错误消息

TypeError: Invalid argument, not a string or column. For column literals, use 'lit', 'array',
'struct' or 'create_map' function.

Answer 1

我认为您在达到预期产出方面处于正确的轨道上。
我设法找到了解决此类问题的两种可能的解决方案：一种使用Spark UDF，另一种使用Pandas UDF。

火花UDF

from pyspark.sql.functions import udf

@udf('integer')
def predict_udf(*cols):
    return int(braodcast_model.value.predict((cols,)))

list_of_columns = df.columns
df_prediction = df.withColumn('prediction', predict_udf(*list_of_columns))

熊猫 UDF

import pandas as pd
from pyspark.sql.functions import pandas_udf

@pandas_udf('integer')
def predict_pandas_udf(*cols):
    X = pd.concat(cols, axis=1)
    return pd.Series(braodcast_model.value.predict(X))

list_of_columns = df.columns
df_prediction = df.withColumn('prediction', predict_pandas_udf(*list_of_columns))

可重现的例子

Spark 3.1.2在这里，我使用了带有、pandas==1.2.4和的 Databricks Community 集群pyarrow==4.0.0。
broadcasted_model是来自 scikit-learn 的简单逻辑回归，在乳腺癌数据集上进行训练。

import pandas as pd
import joblib
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
from pyspark.sql.functions import udf, pandas_udf


# load dataset
X, y = load_breast_cancer(return_X_y=True, as_frame=True)

# split in training and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=28)

# create a small pipeline with standardization and model
pipe = make_pipeline(StandardScaler(), LogisticRegression())

# save and reload the model
path = '/databricks/driver/test_model.joblib'
joblib.dump(model, path)
loaded_model = joblib.load(path)

# sample of unseen data
df = spark.createDataFrame(X_test.sample(50, random_state=42))

# create broadcasted model
sc = spark.sparkContext
braodcast_model = sc.broadcast(loaded_model)

然后我使用了上面说明的两种方法，您将看到两种情况下的输出df_prediction是相同的。