Skip to main content
Version: 1.0.6

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)