Apprendre Introduction to ML Pipelines in PySpark

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In the previous chapters you applied transformers one by one – fit, transform, fit, transform. For production workflows this is error-prone and hard to maintain. ML Pipelines chain multiple transformers and estimators into a single object that can be fit and applied in one step.

Pipeline Concepts

A Pipeline is a sequence of stages. Each stage is either:

a Transformer – has only transform(), takes a DataFrame and returns a DataFrame (e.g. VectorAssembler, OneHotEncoder);
an Estimator – has fit() which produces a Transformer (e.g. StringIndexer, StandardScaler, LogisticRegression).

When you call pipeline.fit(df), Spark runs each stage in order – fitting estimators and applying transformers – and returns a PipelineModel.

Building a Pipeline


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import urllib.request
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, floor, when
from pyspark.ml import Pipeline
from pyspark.ml.feature import StringIndexer, OneHotEncoder, VectorAssembler, StandardScaler

urllib.request.urlretrieve(
    "https://staging-content-media-cdn.codefinity.com/courses/aa80ac56-0d50-49e8-9231-2c2374cd3e9d/flights.csv",
    "flights.csv"
)

spark = SparkSession.builder \
    .appName("MLPipelines") \
    .master("local[*]") \
    .getOrCreate()

flights_df = spark.read.csv("flights.csv", header=True, inferSchema=True) \
    .fillna(0, subset=["DEPARTURE_DELAY", "ARRIVAL_DELAY", "DISTANCE", "SCHEDULED_TIME"])

flights_df = flights_df \
    .withColumn("LABEL", (col("ARRIVAL_DELAY") > 15).cast("integer")) \
    .withColumn("DEPARTURE_HOUR", floor(col("SCHEDULED_DEPARTURE") / 100).cast("integer"))

# Defining pipeline stages
indexer = StringIndexer(inputCol="AIRLINE", outputCol="AIRLINE_IDX")
encoder = OneHotEncoder(inputCol="AIRLINE_IDX", outputCol="AIRLINE_VEC")
assembler = VectorAssembler(
    inputCols=["DEPARTURE_DELAY", "DISTANCE", "DEPARTURE_HOUR", "AIRLINE_VEC"],
    outputCol="FEATURES_RAW"
)
scaler = StandardScaler(inputCol="FEATURES_RAW", outputCol="FEATURES", withMean=True, withStd=True)

# Assembling the pipeline
pipeline = Pipeline(stages=[indexer, encoder, assembler, scaler])

# Fitting and transforming in one step
pipeline_model = pipeline.fit(flights_df)
result_df = pipeline_model.transform(flights_df)

result_df.select("LABEL", "FEATURES").show(5, truncate=False)

Saving and Loading a Pipeline


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# Saving the fitted pipeline model to disk
pipeline_model.save("flights_pipeline_model")

# Loading it back for inference
from pyspark.ml import PipelineModel

loaded_model = PipelineModel.load("flights_pipeline_model")
result_df = loaded_model.transform(flights_df)

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Section 1. Chapitre 10

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Section 1. Chapitre 10