ceja

PySpark phonetic, stemming, and string matching algorithms. Use the power of PySpark to run these algos on massive datasets!

Installation and basic usage

Run pip install ceja to install the library.

Import the functions with import ceja. After importing the code you can run functions like ceja.nysiis, ceja.jaro_winkler_similarity, etc.

Public interface summary

• Phonetic algorithms
  • nysiis
  • metaphone
  • match_rating_codex
• Stemming
  • porter_stem
• String similarity
  • damerau_levenshtein_distance
  • hamming_distance
  • jaro_similarity
  • jaro_winkler_similarity
  • match_rating_comparison

Phonetic algorithms

NYSIIS

data = [
    ("jellyfish",),
    ("li",),
    ("luisa",),
    (None,)
]
df = spark.createDataFrame(data, ["word"])
actual_df = df.withColumn("word_nysiis", ceja.nysiis(col("word")))
actual_df.show()

+---------+-----------+
|     word|word_nysiis|
+---------+-----------+
|jellyfish|      JALYF|
|       li|          L|
|    luisa|        LAS|
|     null|       null|
+---------+-----------+

Metaphone

data = [
    ("jellyfish",),
    ("li",),
    ("luisa",),
    ("Klumpz",),
    ("Clumps",),
    (None,)
]
df = spark.createDataFrame(data, ["word"])
actual_df = df.withColumn("word_metaphone", ceja.metaphone(col("word")))
actual_df.show()

+---------+--------------+
|     word|word_metaphone|
+---------+--------------+
|jellyfish|          JLFX|
|       li|             L|
|    luisa|            LS|
|   Klumpz|         KLMPS|
|   Clumps|         KLMPS|
|     null|          null|
+---------+--------------+

Match rating codex

data = [
    ("jellyfish",),
    ("li",),
    ("luisa",),
    (None,)
]
df = spark.createDataFrame(data, ["word"])
actual_df = df.withColumn("word_match_rating_codex", ceja.match_rating_codex(col("word")))
actual_df.show()

+---------+-----------------------+
|     word|word_match_rating_codex|
+---------+-----------------------+
|jellyfish|                 JLYFSH|
|       li|                      L|
|    luisa|                     LS|
|     null|                   null|
+---------+-----------------------+

Stemming algorithms

Porter stem

data = [
    ("chocolates",),
    ("chocolatey",),
    ("choco",),
    (None,)
]
df = spark.createDataFrame(data, ["word"])
actual_df = df.withColumn("word_porter_stem", ceja.porter_stem(col("word")))
actual_df.show()

+----------+----------------+
|      word|word_porter_stem|
+----------+----------------+
|chocolates|          chocol|
|chocolatey|      chocolatei|
|     choco|           choco|
|      null|            null|
+----------+----------------+

Similarity algorithms

Damerau Levenshtein Distance

data = [
    ("jellyfish", "smellyfish"),
    ("li", "lee"),
    ("luisa", "bruna"),
    (None, None)
]
df = spark.createDataFrame(data, ["word1", "word2"])
actual_df = df.withColumn("damerau_levenshtein_distance", ceja.damerau_levenshtein_distance(col("word1"), col("word2")))
actual_df.show()

+---------+----------+----------------------------+
|    word1|     word2|damerau_levenshtein_distance|
+---------+----------+----------------------------+
|jellyfish|smellyfish|                           2|
|       li|       lee|                           2|
|    luisa|     bruna|                           4|
|     null|      null|                        null|
+---------+----------+----------------------------+

Hamming distance

data = [
    ("jellyfish", "smellyfish"),
    ("li", "lee"),
    ("luisa", "bruna"),
    (None, None)
]
df = spark.createDataFrame(data, ["word1", "word2"])
actual_df = df.withColumn("hamming_distance", ceja.hamming_distance(col("word1"), col("word2")))
print("\nHamming distance")
actual_df.show()

+---------+----------+----------------+
|    word1|     word2|hamming_distance|
+---------+----------+----------------+
|jellyfish|smellyfish|               9|
|       li|       lee|               2|
|    luisa|     bruna|               4|
|     null|      null|            null|
+---------+----------+----------------+

Jaro similarity

data = [
    ("jellyfish", "smellyfish"),
    ("li", "lee"),
    ("luisa", "bruna"),
    ("hi", "colombia"),
    (None, None)
]
df = spark.createDataFrame(data, ["word1", "word2"])
actual_df = df.withColumn("jaro_similarity", ceja.jaro_similarity(col("word1"), col("word2")))
actual_df.show()

+---------+----------+---------------+
|    word1|     word2|jaro_similarity|
+---------+----------+---------------+
|jellyfish|smellyfish|      0.8962963|
|       li|       lee|      0.6111111|
|    luisa|     bruna|            0.6|
|       hi|  colombia|            0.0|
|     null|      null|           null|
+---------+----------+---------------+

Jaro Winkler similarity

data = [
    ("jellyfish", "smellyfish"),
    ("li", "lee"),
    ("luisa", "bruna"),
    (None, None)
]
df = spark.createDataFrame(data, ["word1", "word2"])
actual_df = df.withColumn("jaro_winkler_similarity", ceja.jaro_winkler_similarity(col("word1"), col("word2")))
actual_df.show()

+---------+----------+-----------------------+
|    word1|     word2|jaro_winkler_similarity|
+---------+----------+-----------------------+
|jellyfish|smellyfish|              0.8962963|
|       li|       lee|              0.6111111|
|    luisa|     bruna|                    0.6|
|     null|      null|                   null|
+---------+----------+-----------------------+

Match rating comparison

data = [
    ("mat", "matt"),
    ("there", "their"),
    ("luisa", "bruna"),
    (None, None)
]
df = spark.createDataFrame(data, ["word1", "word2"])
actual_df = df.withColumn("match_rating_comparison", ceja.match_rating_comparison(col("word1"), col("word2")))
actual_df.show()

+-----+-----+-----------------------+
|word1|word2|match_rating_comparison|
+-----+-----+-----------------------+
|  mat| matt|                   true|
|there|their|                   true|
|luisa|bruna|                  false|
| null| null|                   null|
+-----+-----+-----------------------+

Contributing

Contributions are welcome and encouraged. Feel free to open issues or send pull requests.

If you make a lot of good contributions, you'll be granted push access to the repo.

The best contributions to make would be implementing these functions as Spark native functions.