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Version: 1.0.4

Binary Classification with VowpalWabbit on Criteo Dataset

SparkML Vector input​

Read dataset​

import pyspark.sql.types as T

schema = T.StructType(
    [
        T.StructField("label", T.IntegerType(), True),
        *[T.StructField("i" + str(i), T.IntegerType(), True) for i in range(1, 13)],
        *[T.StructField("s" + str(i), T.StringType(), True) for i in range(26)],
    ]
)

df = (
    spark.read.format("csv")
    .option("header", False)
    .option("delimiter", "\t")
    .schema(schema)
    .load("wasbs://publicwasb@mmlspark.blob.core.windows.net/criteo_day0_1k.csv.gz")
)
# print dataset basic info
print("records read: " + str(df.count()))
print("Schema: ")
df.printSchema()
display(df)

Use VowpalWabbitFeaturizer to convert data features into vector​

from synapse.ml.vw import VowpalWabbitFeaturizer

featurizer = VowpalWabbitFeaturizer(
    inputCols=[
        *["i" + str(i) for i in range(1, 13)],
        *["s" + str(i) for i in range(26)],
    ],
    outputCol="features",
)

df = featurizer.transform(df).select("label", "features")

Split the dataset into train and test​

train, test = df.randomSplit([0.85, 0.15], seed=1)

Model Training​

from synapse.ml.vw import VowpalWabbitClassifier

model = VowpalWabbitClassifier(
    numPasses=20,
    labelCol="label",
    featuresCol="features",
    passThroughArgs="--holdout_off --loss_function logistic",
).fit(train)

Model Prediction​

predictions = model.transform(test)
display(predictions)
from synapse.ml.train import ComputeModelStatistics

metrics = ComputeModelStatistics(
    evaluationMetric="classification", labelCol="label", scoredLabelsCol="prediction"
).transform(predictions)
display(metrics)