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Quickstart - Vowpal Wabbit on Tabular Data.md

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Classification - Adult Census using Vowpal Wabbit in SynapseML

In this example, we predict incomes from the Adult Census dataset using Vowpal Wabbit (VW) classifier in SynapseML. First, we read the data and split it into train and test sets as in this example.

data = spark.read.parquet(
    "wasbs://[email protected]/AdultCensusIncome.parquet"
)
data = data.select(["education", "marital-status", "hours-per-week", "income"])
train, test = data.randomSplit([0.75, 0.25], seed=123)
train.limit(10).toPandas()

Next, we define a pipeline that includes feature engineering and training of a VW classifier. We use a featurizer provided by VW that hashes the feature names. Note that VW expects classification labels being -1 or 1. Thus, the income category is mapped to this space before feeding training data into the pipeline.

from pyspark.sql.functions import when, col
from pyspark.ml import Pipeline
from synapse.ml.vw import VowpalWabbitFeaturizer, VowpalWabbitClassifier

# Define classification label
train = (
    train.withColumn("label", when(col("income").contains("<"), 0.0).otherwise(1.0))
    .repartition(1)
    .cache()
)
print(train.count())

# Specify featurizer
vw_featurizer = VowpalWabbitFeaturizer(
    inputCols=["education", "marital-status", "hours-per-week"], outputCol="features"
)

# Define VW classification model
args = "--loss_function=logistic --quiet --holdout_off"
vw_model = VowpalWabbitClassifier(
    featuresCol="features", labelCol="label", passThroughArgs=args, numPasses=10
)

# Create a pipeline
vw_pipeline = Pipeline(stages=[vw_featurizer, vw_model])

Then, we are ready to train the model by fitting the pipeline with the training data.

# Train the model
vw_trained = vw_pipeline.fit(train)

After the model is trained, we apply it to predict the income of each sample in the test set.

# Making predictions
test = test.withColumn("label", when(col("income").contains("<"), 0.0).otherwise(1.0))
prediction = vw_trained.transform(test)
prediction.limit(10).toPandas()

Finally, we evaluate the model performance using ComputeModelStatistics function which will compute confusion matrix, accuracy, precision, recall, and AUC by default for classification models.

from synapse.ml.train import ComputeModelStatistics

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