Preparing data for LDA training with PySpark 1.6 - apache-spark

Question

I have a corpus of documents that I'm reading into a spark data frame.
I have tokeniked and vectorized the text and now I want to feed the vectorized data into an mllib LDA model. The LDA API docs seems to require the data to be:
rdd – RDD of documents, which are tuples of document IDs and term (word) count vectors. The term count vectors are “bags of words” with a fixed-size vocabulary (where the vocabulary size is the length of the vector). Document IDs must be unique and >= 0.
How can I get from my data frame to a suitable rdd?
from pyspark.mllib.clustering import LDA
from pyspark.ml.feature import Tokenizer
from pyspark.ml.feature import CountVectorizer
#read the data
tf = sc.wholeTextFiles("20_newsgroups/*")
#transform into a data frame
df = tf.toDF(schema=['file','text'])
#tokenize
tokenizer = Tokenizer(inputCol="text", outputCol="words")
tokenized = tokenizer.transform(df)
#vectorize
cv = CountVectorizer(inputCol="words", outputCol="vectors")
model = cv.fit(tokenized)
result = model.transform(tokenized)
#transform into a suitable rdd
myrdd = ?
#LDA
model = LDA.train(myrdd, k=2, seed=1)
PS : I'm using Apache Spark 1.6.3

Answer 1

Let's first organize imports, read the data, do some simple special characters removal and transform it into a DataFrame:
import re # needed to remove special character
from pyspark import Row
from pyspark.ml.feature import StopWordsRemover
from pyspark.ml.feature import Tokenizer, CountVectorizer
from pyspark.mllib.clustering import LDA
from pyspark.sql import functions as F
from pyspark.sql.types import StructType, StructField, LongType
pattern = re.compile('[\W_]+')
rdd = sc.wholeTextFiles("./data/20news-bydate/*/*/*") \
.mapValues(lambda x: pattern.sub(' ', x)).cache() # ref. https://stackoverflow.com/a/1277047/3415409
df = rdd.toDF(schema=['file', 'text'])
We will need to add an index to each Row. The following code snippet is inspired from this question about adding primary keys with Apache Spark :
row_with_index = Row(*["id"] + df.columns)
def make_row(columns):
def _make_row(row, uid):
row_dict = row.asDict()
return row_with_index(*[uid] + [row_dict.get(c) for c in columns])
return _make_row
f = make_row(df.columns)
indexed = (df.rdd
.zipWithUniqueId()
.map(lambda x: f(*x))
.toDF(StructType([StructField("id", LongType(), False)] + df.schema.fields)))
Once we have added the index, we can proceed to the features cleansing, extraction and transformation :
# tokenize
tokenizer = Tokenizer(inputCol="text", outputCol="tokens")
tokenized = tokenizer.transform(indexed)
# remove stop words
remover = StopWordsRemover(inputCol="tokens", outputCol="words")
cleaned = remover.transform(tokenized)
# vectorize
cv = CountVectorizer(inputCol="words", outputCol="vectors")
count_vectorizer_model = cv.fit(cleaned)
result = count_vectorizer_model.transform(cleaned)
Now, let's transform the results dataframe back to rdd
corpus = result.select(F.col('id').cast("long"), 'vectors').rdd \
.map(lambda x: [x[0], x[1]])
Our data is now ready to be trained :
# training data
lda_model = LDA.train(rdd=corpus, k=10, seed=12, maxIterations=50)
# extracting topics
topics = lda_model.describeTopics(maxTermsPerTopic=10)
# extraction vocabulary
vocabulary = count_vectorizer_model.vocabulary
We can print the topics descriptions now as followed :
for topic in range(len(topics)):
print("topic {} : ".format(topic))
words = topics[topic][0]
scores = topics[topic][1]
[print(vocabulary[words[word]], "->", scores[word]) for word in range(len(words))]
PS : This above code was tested with Spark 1.6.3.