scripts/staging/slicing/tests/regression/bd_spark_salary.py

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import sys

from pyspark import SparkConf, SparkContext
from pyspark.ml import Pipeline
from pyspark.ml.feature import StringIndexer, OneHotEncoderEstimator, VectorAssembler, IndexToString
from pyspark.ml.regression import LinearRegression
from pyspark.sql import SQLContext
import pyspark.sql.functions as sf
from pyspark.sql.functions import udf
from sklearn.model_selection import train_test_split

from slicing.spark_modules import spark_utils, join_data_parallel, union_data_parallel

binner = udf(lambda arg: int(int(arg) // 5))


if __name__ == "__main__":
    args = sys.argv
    if len(args) > 1:
        k = int(args[1])
        w = float(args[2].replace(',', '.'))
        alpha = int(args[3])
        if args[4] == "True":
            b_update = True
        else:
            b_update = False
        debug = args[5]
        loss_type = int(args[6])
        enumerator = args[7]
        dataset = args[8]
    else:
        k = 10
        w = 0.5
        alpha = 6
        b_update = True
        debug = True
        loss_type = 0
        dataset = 'slicing/datasets/parallel_data/salaries/rows1000.csv'
        enumerator = "join"

    conf = SparkConf().setAppName("salary_test").setMaster('local[4]')
    num_partitions = 8
    model_type = "regression"
    label = 'salary'
    sparkContext = SparkContext(conf=conf)
    sqlContext = SQLContext(sparkContext)
    fileRDD = sparkContext.textFile(dataset, num_partitions)
    header = fileRDD.first()
    head_split = header.split(",")
    fileRDD = fileRDD.filter(lambda line: line != header)
    data = fileRDD.map(lambda row: row.split(","))
    dataset_df = sqlContext.createDataFrame(data, head_split)

    cat_features = ["rank", "discipline", "sincephd_bin", "service_bin", "sex"]
    # initializing stages of main transformation pipeline
    stages = []
    dataset_df = dataset_df.withColumn("id", sf.monotonically_increasing_id())
    # bining numeric features by local binner udf function (specified for current dataset if needed)
    dataset_df = dataset_df.withColumn('sincephd_bin', binner(dataset_df['sincephd']))
    dataset_df = dataset_df.withColumn('service_bin', binner(dataset_df['service']))
    dataset_df = dataset_df.withColumn('model_type', sf.lit(0))
    dataset_df = dataset_df.drop('sincephd', 'service')
    dataset_df = dataset_df.withColumn('target', dataset_df[label].cast("int"))
    # hot encoding categorical features
    for feature in cat_features:
        string_indexer = StringIndexer(inputCol=feature, outputCol=feature + "_index")
        encoder = OneHotEncoderEstimator(inputCols=[string_indexer.getOutputCol()], outputCols=[feature + "_vec"])
        encoder.setDropLast(False)
        stages += [string_indexer, encoder]
    assembler_inputs = [feature + "_vec" for feature in cat_features]
    assembler = VectorAssembler(inputCols=assembler_inputs, outputCol="assembled_inputs")
    stages += [assembler]
    assembler_final = VectorAssembler(inputCols=["assembled_inputs"], outputCol="features")
    stages += [assembler_final]

    pipeline = Pipeline(stages=stages)
    pipeline_model = pipeline.fit(dataset_df)
    dataset_transformed = pipeline_model.transform(dataset_df)
    df_transform_fin = dataset_transformed.select('id', 'features', 'target', 'model_type').toPandas()

    cat = 0
    counter = 0
    decode_dict = {}
    for feature in cat_features:
        colIdx = dataset_transformed.select(feature, feature + "_index").distinct().rdd.collectAsMap()
        colIdx = {k: v for k, v in sorted(colIdx.items(), key=lambda item: item[1])}
        for item in colIdx:
            decode_dict[counter] = (cat, item, colIdx[item])
            counter = counter + 1
        cat = cat + 1

    train, test = train_test_split(df_transform_fin, test_size=0.3, random_state=0)
    train_df = sqlContext.createDataFrame(train)
    test_df = sqlContext.createDataFrame(test)
    lr = LinearRegression(featuresCol='features', labelCol='target', maxIter=10, regParam=0.3, elasticNetParam=0.8)
    lr_model = lr.fit(train_df)
    eval = lr_model.evaluate(test_df)
    f_l2 = eval.meanSquaredError
    pred = eval.predictions
    pred_df_fin = pred.withColumn('error', spark_utils.calc_loss(pred['target'], pred['prediction'], pred['model_type']))
    predictions = pred_df_fin.select('id', 'features', 'error').repartition(num_partitions)
    converter = IndexToString(inputCol='features', outputCol='cats')
    all_features = list(decode_dict.keys())
    if enumerator == "join":
        join_data_parallel.parallel_process(all_features, predictions, f_l2, sparkContext, debug=debug, alpha=alpha,
                                            k=k, w=w, loss_type=loss_type)
    elif enumerator == "union":
        union_data_parallel.parallel_process(all_features, predictions, f_l2, sparkContext, debug=debug, alpha=alpha,
                                             k=k, w=w, loss_type=loss_type)