学ぶ Classification with Logistic Regression

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Logistic Regression is the standard baseline for binary classification. Despite the name, it is a classification algorithm – it outputs a probability between 0 and 1 and classifies each row based on a threshold (default 0.5).

Building the Feature Pipeline


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

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

spark = SparkSession.builder \
    .appName("LogisticRegression") \
    .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("double")) \
    .withColumn("DEPARTURE_HOUR", floor(col("SCHEDULED_DEPARTURE") / 100).cast("integer")) \
    .withColumn("IS_WEEKEND", (col("DAY_OF_WEEK") >= 6).cast("integer"))

train_df, test_df = flights_df.randomSplit([0.8, 0.2], seed=42)

Training the Model


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# Defining pipeline stages
indexer = StringIndexer(inputCol="AIRLINE", outputCol="AIRLINE_IDX")
assembler = VectorAssembler(
    inputCols=["DEPARTURE_DELAY", "DISTANCE", "SCHEDULED_TIME", "DEPARTURE_HOUR", "IS_WEEKEND", "AIRLINE_IDX"],
    outputCol="FEATURES_RAW"
)
scaler = StandardScaler(inputCol="FEATURES_RAW", outputCol="FEATURES", withMean=True, withStd=True)
lr = LogisticRegression(featuresCol="FEATURES", labelCol="LABEL", maxIter=10)

pipeline = Pipeline(stages=[indexer, assembler, scaler, lr])
model = pipeline.fit(train_df)

Generating Predictions


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predictions = model.transform(test_df)

# Showing label, probability, and prediction for each row
predictions.select("LABEL", "probability", "prediction").show(5)

The probability column is a vector of two values – the probability of class 0 and class 1. The prediction column is the final binary output.

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