Question

如何在并行执行程序中转换此代码末尾提供的for循环，以使循环的每次迭代都并行执行，而不是顺序执行，即fold_list中的每个值都应用于与当前内核不同的内核上？

def ROEM(predictions, userCol= "USER_ID", itemCol= "SERVICE_ROUTE_ID", ratingCol= "ENGAGEMENT_SCORE"):
              #Creates table that can be queried
              predictions.createOrReplaceTempView("predictions")

              #Sum of total number of plays of all songs
              denominator = predictions.groupBy().sum(ratingCol).collect()[0][0]

              #Calculating rankings of songs predictions by user
              spark.sql("SELECT " + userCol + " , " + ratingCol + " , PERCENT_RANK() OVER (PARTITION BY " + userCol + " ORDER BY prediction DESC) AS rank FROM predictions").createOrReplaceTempView("rankings")

              #Multiplies the rank of each song by the number of plays and adds the products together
              numerator = spark.sql('SELECT SUM(' + ratingCol + ' * rank) FROM rankings').collect()[0][0]

              performance = numerator/denominator

              return performance

# Split the data into training and test sets
(training, test) = df.randomSplit([0.8, 0.2])

#Building 5 folds within the training set.
train1, train2, train3, train4, train5 = training.randomSplit([0.2, 0.2, 0.2, 0.2, 0.2], seed = 1)
fold1 = train2.union(train3).union(train4).union(train5)
fold2 = train3.union(train4).union(train5).union(train1)
fold3 = train4.union(train5).union(train1).union(train2)
fold4 = train5.union(train1).union(train2).union(train3)
fold5 = train1.union(train2).union(train3).union(train4)

foldlist = [(fold1, train1), (fold2, train2), (fold3, train3), (fold4, train4), (fold5, train5)]

# Empty list to fill with ROEMs from each model
ROEMS = []

# Loops through all models and all folds
for model in model_list:
    for ft_pair in foldlist:

        # Fits model to fold within training data
        fitted_model = model.fit(ft_pair[0])

        # Generates predictions using fitted_model on respective CV test data
        predictions = fitted_model.transform(ft_pair[1])

        # Generates and prints a ROEM metric CV test data
        r = ROEM(predictions)
        print ("ROEM: ", r)

    # Fits model to all of training data and generates preds for test data
    v_fitted_model = model.fit(training)
    v_predictions = v_fitted_model.transform(test)
    v_ROEM = ROEM(v_predictions)

    # Adds validation ROEM to ROEM list
    ROEMS.append(v_ROEM)
    print ("Validation ROEM: ", v_ROEM)

将Python并行循环到并行Spark执行器

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