Lære Tokenization and TF-IDF with MLlib

Sveip for å vise menyen

After tokenization and stop word removal, you need to convert token lists into numeric vectors. TF-IDF (Term Frequency–Inverse Document Frequency) is the standard approach: it weights each word by how often it appears in a document relative to how common it is across all documents.

HashingTF

HashingTF maps each token to a fixed-size vector using a hashing trick. Each position in the vector represents a hash bucket, and the value is the term frequency:


              1234567891011121314151617181920212223242526
            
import urllib.request
from pyspark.sql import SparkSession
from pyspark.ml.feature import Tokenizer, StopWordsRemover, HashingTF, IDF

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

text_df = spark.createDataFrame([
    (1, "flight delayed due to bad weather conditions"),
    (2, "technical issue caused significant departure delay"),
    (3, "flight cancelled due to severe weather"),
    (4, "late aircraft arrival caused departure delay"),
    (5, "air traffic control delay affected departure time"),
], ["id", "description"])

# Tokenizing and removing stop words
tokenized_df = Tokenizer(inputCol="description", outputCol="tokens").transform(text_df)
filtered_df = StopWordsRemover(inputCol="tokens", outputCol="filtered_tokens").transform(tokenized_df)

# Computing term frequencies
hashing_tf = HashingTF(inputCol="filtered_tokens", outputCol="raw_features", numFeatures=20)
tf_df = hashing_tf.transform(filtered_df)

tf_df.select("filtered_tokens", "raw_features").show(truncate=False)

IDF

IDF down-weights terms that appear in many documents - common words get a lower score even if they appear frequently in one document:


              123456
            
# Fitting IDF on the full corpus and transforming
idf = IDF(inputCol="raw_features", outputCol="tfidf_features")
idf_model = idf.fit(tf_df)
tfidf_df = idf_model.transform(tf_df)

tfidf_df.select("filtered_tokens", "tfidf_features").show(truncate=False)

The resulting tfidf_features column is a sparse vector – each document is now represented as a numeric feature vector ready for model input.

Alt var klart?

Takk for tilbakemeldingene dine!

Seksjon 1. Kapittel 6

Spør AI

Spør om hva du vil, eller prøv ett av de foreslåtte spørsmålene for å starte chatten vår

Seksjon 1. Kapittel 6