我在用磁盘上保存的随机森林模型和使用完全相同的数据集进行预测时，很难再现结果。换句话说，我用数据集a训练一个模型，并将它保存在本地机器上，然后我加载它并用它来预测数据集b，每次预测数据集b我都会得到不同的结果。
我知道随机森林分类器中涉及的随机性，但是据我所知，这种随机性是在训练过程中产生的，一旦创建了模型，如果使用相同的数据进行预测，预测就不会改变。
培训脚本具有以下结构：

df_train = spark.read.format("csv") \
      .option('header', 'true') \
      .option('inferSchema', 'true') \
      .option('delimiter', ';') \
      .load("C:\2020_05.csv") 

# The problem seems to be related to the StringIndexer/One-Hot Encoding

# If I remove all categorical variables the results can be reproduced

categorical_variables = []
for variable in df_train.dtypes:
    if variable[1] == 'string' :
       categorical_variables.append(variable[0])

indexers = [StringIndexer(inputCol=col, outputCol=col+"_indexed") for col in categorical_variables]

for indexer in indexers:
    df_train =indexer.fit(df_train).transform(df_train)
    df_train = df_train.drop(indexer.getInputCol())

indexed_cols = []
for variable in df_train.columns:
    if variable.endswith("_indexed"):
        indexed_cols.append(variable)

encoders = []
for variable in indexed_cols:
    inputCol = variable
    outputCol = variable.replace("_indexed", "_encoded")
    one_hot_encoder_estimator_train = OneHotEncoderEstimator(inputCols=[inputCol], outputCols=[outputCol])

    encoder_model_train = one_hot_encoder_estimator_train.fit(df_train)
    df_train = encoder_model_train.transform(df_train)
    df_train = df_train.drop(inputCol)

inputCols = [x for x in df_train.columns if x != "id" and x != "churn"]

vector_assembler_train = VectorAssembler(
      inputCols=inputCols,
      outputCol='features',
      handleInvalid='keep'
)

df_train = vector_assembler_train.transform(df_train)

df_train = df_train.select('churn', 'features', 'id')

df_train_1 = df_train.filter(df_train['churn'] == 0).sample(withReplacement=False, fraction=0.3, seed=7)
df_train_2 = df_train.filter(df_train['churn'] == 1).sample(withReplacement=True, fraction=20.0, seed=7)
df_train = df_train_1.unionAll(df_train_2) 

rf = RandomForestClassifier(labelCol="churn", featuresCol="features")
  paramGrid = ParamGridBuilder() \
      .addGrid(rf.numTrees, [100]) \
      .addGrid(rf.maxDepth, [15]) \
      .addGrid(rf.maxBins, [32]) \
      .addGrid(rf.featureSubsetStrategy, ['onethird']) \
      .addGrid(rf.subsamplingRate, [1.0])\
      .addGrid(rf.minInfoGain, [0.0])\
      .addGrid(rf.impurity, ['gini']) \
      .addGrid(rf.minInstancesPerNode, [1]) \
      .addGrid(rf.seed, [10]) \
  .build()

  evaluator = BinaryClassificationEvaluator(
      labelCol="churn")

  crossval = CrossValidator(estimator=rf,
                            estimatorParamMaps=paramGrid,
                            evaluator=evaluator,
                            numFolds=3)
  model = crossval.fit(df_train)
  model.save("C:/myModel")

测试脚本如下：

df_test = spark.read.format("csv") \
      .option('header', 'true') \
      .option('inferSchema', 'true') \
      .option('delimiter', ';') \
      .load("C:\2020_06.csv")

# The problem seems to be related to the StringIndexer/One-Hot Encoding

# If I remove all categorical variables the results can be reproduced

categorical_variables = []
for variable in df_test.dtypes:
    if variable[1] == 'string' :
       categorical_variables.append(variable[0])

indexers = [StringIndexer(inputCol=col, outputCol=col+"_indexed") for col in categorical_variables]

for indexer in indexers:
    df_test =indexer.fit(df_test).transform(df_test)
    df_test = df_test.drop(indexer.getInputCol())

indexed_cols = []
for variable in df_test.columns:
    if variable.endswith("_indexed"):
        indexed_cols.append(variable)

encoders = []
for variable in indexed_cols:
    inputCol = variable
    outputCol = variable.replace("_indexed", "_encoded")
    one_hot_encoder_estimator_test = OneHotEncoderEstimator(inputCols=[inputCol], outputCols=[outputCol])

    encoder_model_test= one_hot_encoder_estimator_test.fit(df_test)
    df_test= encoder_model_test.transform(df_test)
    df_test= df_test.drop(inputCol)

inputCols = [x for x in df_test.columns if x != "id" and x != "churn"]

vector_assembler_test = VectorAssembler(
      inputCols=inputCols,
      outputCol='features',
      handleInvalid='keep'
)

df_test = vector_assembler_test.transform(df_test)

df_test = df_test.select('churn', 'features', 'id')

model = CrossValidatorModel.load("C:/myModel")

result = model.transform(df_test)

areaUnderROC = evaluator.evaluate(result)

tp = result.filter("prediction == 1.0 AND churn == 1").count()
tn = result.filter("prediction == 0.0 AND churn == 0").count()
fp = result.filter("prediction == 1.0 AND churn == 0").count()
fn = result.filter("prediction == 0.0 AND churn == 1").count()

每次我运行测试脚本时，auc和混淆矩阵总是不同的。我在windows10机器上使用spark2.4.5和python3.7。如有任何建议或想法，我们将不胜感激。
编辑：问题与stringindexer/one热编码步骤有关。当我只使用数值变量时，我就能重现结果。这个问题仍然悬而未决，因为我无法解释为什么会发生这种情况。