Classification tasks using SynapseML

Article
2025-04-23

This article shows how to perform a specific classification task with two methods. One method uses plain pyspark, and one method uses the synapseml library. Although the methods yield the same performance, they highlight the simplicity of synapseml as compared to pyspark.

The task described in this article predicts whether or not a specific customer review of book sold on Amazon is good (rating > 3) or bad, based on the review text. To build the task, you train LogisticRegression learners with different hyperparameters, and then choose the best model.

Prerequisites

Attach your notebook to a lakehouse. On the left side, you can select Add to add an existing lakehouse, or you can create a new lakehouse.

Setup

Import the necessary Python libraries, and get a Spark session:

from pyspark.sql import SparkSession

# Bootstrap Spark Session
spark = SparkSession.builder.getOrCreate()

Read the data

Download, and read in the data:

rawData = spark.read.parquet(
    "wasbs://[email protected]/BookReviewsFromAmazon10K.parquet"
)
rawData.show(5)

Extract features and process data

Real data has more complexity, compared to the dataset we downloaded earlier. A dataset often has features of multiple types - for example, text, numeric, and categorical. To show the difficulties of working with these datasets, add two numerical features to the dataset: the word count of the review and the mean word length:

from pyspark.sql.functions import udf
from pyspark.sql.types import *


def wordCount(s):
    return len(s.split())


def wordLength(s):
    import numpy as np

    ss = [len(w) for w in s.split()]
    return round(float(np.mean(ss)), 2)


wordLengthUDF = udf(wordLength, DoubleType())
wordCountUDF = udf(wordCount, IntegerType())

from synapse.ml.stages import UDFTransformer

wordLength = "wordLength"
wordCount = "wordCount"
wordLengthTransformer = UDFTransformer(
    inputCol="text", outputCol=wordLength, udf=wordLengthUDF
)
wordCountTransformer = UDFTransformer(
    inputCol="text", outputCol=wordCount, udf=wordCountUDF
)

from pyspark.ml import Pipeline

data = (
    Pipeline(stages=[wordLengthTransformer, wordCountTransformer])
    .fit(rawData)
    .transform(rawData)
    .withColumn("label", rawData["rating"] > 3)
    .drop("rating")
)

data.show(5)

Classify using pyspark

To choose the best LogisticRegression classifier using the pyspark library, you must explicitly perform these steps:

Process the features
- Tokenize the text column
- Hash the tokenized column into a vector using hashing
- Merge the numeric features with the vector
To process the label column, cast that column into the proper type
Train multiple LogisticRegression algorithms on the train dataset, with different hyperparameters
Compute the area under the ROC curve for each of the trained models, and select the model with the highest metric as computed on the test dataset
Evaluate the best model on the validation set

from pyspark.ml.feature import Tokenizer, HashingTF
from pyspark.ml.feature import VectorAssembler

# Featurize text column
tokenizer = Tokenizer(inputCol="text", outputCol="tokenizedText")
numFeatures = 10000
hashingScheme = HashingTF(
    inputCol="tokenizedText", outputCol="TextFeatures", numFeatures=numFeatures
)
tokenizedData = tokenizer.transform(data)
featurizedData = hashingScheme.transform(tokenizedData)

# Merge text and numeric features in one feature column
featureColumnsArray = ["TextFeatures", "wordCount", "wordLength"]
assembler = VectorAssembler(inputCols=featureColumnsArray, outputCol="features")
assembledData = assembler.transform(featurizedData)

# Select only columns of interest
# Convert rating column from boolean to int
processedData = assembledData.select("label", "features").withColumn(
    "label", assembledData.label.cast(IntegerType())
)

from pyspark.ml.evaluation import BinaryClassificationEvaluator
from pyspark.ml.classification import LogisticRegression

# Prepare data for learning
train, test, validation = processedData.randomSplit([0.60, 0.20, 0.20], seed=123)

# Train the models on the 'train' data
lrHyperParams = [0.05, 0.1, 0.2, 0.4]
logisticRegressions = [
    LogisticRegression(regParam=hyperParam) for hyperParam in lrHyperParams
]
evaluator = BinaryClassificationEvaluator(
    rawPredictionCol="rawPrediction", metricName="areaUnderROC"
)
metrics = []
models = []

# Select the best model
for learner in logisticRegressions:
    model = learner.fit(train)
    models.append(model)
    scoredData = model.transform(test)
    metrics.append(evaluator.evaluate(scoredData))
bestMetric = max(metrics)
bestModel = models[metrics.index(bestMetric)]

# Get AUC on the validation dataset
scoredVal = bestModel.transform(validation)
print(evaluator.evaluate(scoredVal))

Classify using SynapseML

The synapseml option involves simpler steps:

The TrainClassifier Estimator internally featurizes the data, as long as the columns selected in the train, test, validation dataset represent the features
The FindBestModel Estimator finds the best model from a pool of trained models. To do this, it finds the model that performs best on the test dataset given the specified metric
The ComputeModelStatistics Transformer computes the different metrics on a scored dataset (in our case, the validation dataset) at the same time

from synapse.ml.train import TrainClassifier, ComputeModelStatistics
from synapse.ml.automl import FindBestModel

# Prepare data for learning
train, test, validation = data.randomSplit([0.60, 0.20, 0.20], seed=123)

# Train the models on the 'train' data
lrHyperParams = [0.05, 0.1, 0.2, 0.4]
logisticRegressions = [
    LogisticRegression(regParam=hyperParam) for hyperParam in lrHyperParams
]
lrmodels = [
    TrainClassifier(model=lrm, labelCol="label", numFeatures=10000).fit(train)
    for lrm in logisticRegressions
]

# Select the best model
bestModel = FindBestModel(evaluationMetric="AUC", models=lrmodels).fit(test)


# Get AUC on the validation dataset
predictions = bestModel.transform(validation)
metrics = ComputeModelStatistics().transform(predictions)
print(
    "Best model's AUC on validation set = "
    + "{0:.2f}%".format(metrics.first()["AUC"] * 100)
)

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