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Все не работает в соответствии с официальным обзором.ipynb

Я пытаюсь построить модель в локальном режиме на pyspark.

 import pyspark
from pyspark.ml.feature import VectorAssembler
from synapse.ml.lightgbm import LightGBMClassificationModel, LightGBMClassifier
spark = pyspark.sql.SparkSession.builder.appName("MyApp") 
            .config("spark.jars.packages", "com.microsoft.azure:synapseml_2.12:0.9.4") 
            .config("spark.jars.repositories", "https://mmlspark.azureedge.net/maven") 
            .getOrCreate()

##build modeling data
non_feats = [i for i in df.columns if '__val_col' in i.lower()]
non_feats.extend(['index','label','fold'])
feature_cols = [i for i in df.columns if i not in non_feats] 
featurizer = VectorAssembler(
inputCols = feature_cols,
outputCol = 'features',handleInvalid='keep')
spark_df = df.to_spark() #will not run on koalas or pandas on pysaprk dataframes. This step is necessary
modeling_data = featurizer.transform(spark_df)

##build the model
model=LightGBMClassifier(objective='binary',isUnbalance=True,metric='auc',
    earlyStoppingRound=10,featuresShapCol='shap',validationIndicatorCol='__val_col0',labelCol='label',
   )
 

После этого я могу успешно обучить модель (я думаю!)

 model.fit(dataset=modeling_data)
output: LightGBMClassifier_867a1dd2276d
 

Проблемы:

1. В модели нет saveNativeModel атрибута, доступного.

 model.saveNativeModel("./lgbmclassifier.model") 
output: AttributeError: 'LightGBMClassifier' object has no attribute 'saveNativeModel'
 

2. Модель не загружается после ее сохранения .save (это единственный доступный атрибут сохранения)

 model.save("./lgbmclassifier_test.model")
LightGBMClassificationModel.loadNativeModelFromFile("./lgbmclassifier_test.model")
 

 output:
[LightGBM] [Fatal] Model file doesn't specify the number of classes
---------------------------------------------------------------------------
Py4JJavaError                             Traceback (most recent call last)
/tmp/ipykernel_268437/1849142297.py in <module>
----> 1 LightGBMClassificationModel.loadNativeModelFromFile("./lgbmclassifier_test.model")

/tmp/spark-5794a877-8405-42db-b23c-33bfa7d0eacd/userFiles-9a0cfba3-8761-4c76-9d83-ae455b04cb37/com.microsoft.azure_synapseml-lightgbm_2.12-0.9.4.jar/synapse/ml/lightgbm/LightGBMClassificationModel.py in loadNativeModelFromFile(filename)
     19         ctx = SparkContext._active_spark_context
     20         loader = ctx._jvm.com.microsoft.azure.synapse.ml.lightgbm.LightGBMClassificationModel
---> 21         java_model = loader.loadNativeModelFromFile(filename)
     22         return JavaParams._from_java(java_model)
     23 

~/miniconda3/envs/pyspark/lib/python3.9/site-packages/py4j/java_gateway.py in __call__(self, *args)
   1307 
   1308         answer = self.gateway_client.send_command(command)
-> 1309         return_value = get_return_value(
   1310             answer, self.gateway_client, self.target_id, self.name)
   1311 

~/miniconda3/envs/pyspark/lib/python3.9/site-packages/pyspark/sql/utils.py in deco(*a, **kw)
    109     def deco(*a, **kw):
    110         try:
--> 111             return f(*a, **kw)
    112         except py4j.protocol.Py4JJavaError as e:
    113             converted = convert_exception(e.java_exception)

~/miniconda3/envs/pyspark/lib/python3.9/site-packages/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    324             value = OUTPUT_CONVERTER[type](answer[2:], gateway_client)
    325             if answer[1] == REFERENCE_TYPE:
--> 326                 raise Py4JJavaError(
    327                     "An error occurred while calling {0}{1}{2}.n".
    328                     format(target_id, ".", name), value)

Py4JJavaError: An error occurred while calling z:com.microsoft.azure.synapse.ml.lightgbm.LightGBMClassificationModel.loadNativeModelFromFile.
: java.lang.Exception: Booster LoadFromString call failed in LightGBM with error: Model file doesn't specify the number of classes
    at com.microsoft.azure.synapse.ml.lightgbm.LightGBMUtils$.validate(LightGBMUtils.scala:24)
    at com.microsoft.azure.synapse.ml.lightgbm.booster.BoosterHandler$.com$microsoft$azure$synapse$ml$lightgbm$booster$BoosterHandler$createBoosterPtrFromModelString(LightGBMBooster.scala:46)
    at com.microsoft.azure.synapse.ml.lightgbm.booster.BoosterHandler.<init>(LightGBMBooster.scala:63)
    at com.microsoft.azure.synapse.ml.lightgbm.booster.LightGBMBooster.boosterHandler$lzycompute(LightGBMBooster.scala:236)
    at com.microsoft.azure.synapse.ml.lightgbm.booster.LightGBMBooster.boosterHandler(LightGBMBooster.scala:230)
    at com.microsoft.azure.synapse.ml.lightgbm.booster.LightGBMBooster.numClasses$lzycompute(LightGBMBooster.scala:500)
    at com.microsoft.azure.synapse.ml.lightgbm.booster.LightGBMBooster.numClasses(LightGBMBooster.scala:500)
    at com.microsoft.azure.synapse.ml.lightgbm.LightGBMClassificationModel$.loadNativeModelFromFile(LightGBMClassifier.scala:199)
    at com.microsoft.azure.synapse.ml.lightgbm.LightGBMClassificationModel.loadNativeModelFromFile(LightGBMClassifier.scala)
    at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.base/java.lang.reflect.Method.invoke(Method.java:566)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
    at py4j.Gateway.invoke(Gateway.java:282)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:182)
    at py4j.ClientServerConnection.run(ClientServerConnection.java:106)
    at java.base/java.lang.Thread.run(Thread.java:829)
 

Env информация:

• Версия SynapseML: как проверить версию? В synapse нет .version, когда я его импортирую. Но, я думаю, это 0.9.4, поскольку это jar, который я предоставляю («com.microsoft.azure: synapseml_2.12: 0.9.4») при создании контекста.
• Версия Spark: 3.2.0
• Платформа Spark: локальная [*]

Не могли бы вы помочь мне понять, что делать после обучения модели. Я ищу save load и predict возможности

Вы должны сохранить файловый файл, а затем перейти в песочницу:

 gbm.save_model('/tmp/%s'%(gbm_name))

storage_account_name = "name"
storage_account_key = "key"
container_name = "container_name"
directory_name = "ml_path"

service_client = DataLakeServiceClient(account_url="{}://{}.dfs.core.windows.net".format(
        "https", storage_account_name), credential=storage_account_key)

file_system_client = service_client.get_file_system_client(file_system=container_name)

dir_client = file_system_client.get_directory_client(directory_name)

dir_client.create_directory()

local_path = '/tmp/%s'%(gbm_name)

file_client = dir_client.create_file(gbm_name)

f = open(local_path,'rb')

file_contents = f.read()

file_client.upload_data(file_contents, overwrite=True)

f.close()
 

Затем вы можете загружать и оценивать с помощью sparkmml.

 from mmlspark.lightgbm import LightGBMClassificationModel
gbm_spark = LightGBMClassificationModel().loadNativeModelFromFile('directory_name/gbm_name')

featurizer = VectorAssembler(
    handleInvalid = "keep",
    inputCols=feature_cols,
    outputCol='features'
)
score_data= featurizer.transform(var_score)
scored_data = gbm_spark.transform(score_data)
 

Надеюсь, это вам поможет.