Question

有没有人有使用PySpark库训练的LDA模型的数据可视化示例（特别是使用pyLDAvis）？我已经看过很多GenSim和其他库的例子，但不是PySpark。具体来说，我想知道传递给pyLDAvis.prepare()函数的内容以及如何从我的lda模型中获取它。这是我的代码：

 from pyspark.mllib.clustering import LDA, LDAModel
 from pyspark.mllib.feature import IDF
 from pyspark.ml.feature import CountVectorizer
 from pyspark.mllib.linalg import Vectors

 vectorizer = CountVectorizer(inputCol="filtered1", outputCol="features").fit(filtered_final)
 countVectors = vectorizer.transform(filtered_final).select("status_id", "features")
 countVectors.show()
 frequencyVectors = countVectors.rdd.map(lambda vector: vector[1])
 frequencyDenseVectors = frequencyVectors.map(lambda vector: Vectors.dense(vector))
 idf = IDF().fit(frequencyDenseVectors)
 print('fitting complete')
 tfidf = idf.transform(frequencyDenseVectors)
 print("tf idf complete")
 #prepare corpus for LDA
 corpus = tfidf.map(lambda x: [1, x]).cache()
 #train LDA
 ldaModel = LDA.train(corpus, k = 15, maxIterations=100, optimizer="online", docConcentration=2.0, topicConcentration=3.0)
 print("lda model complete")

Answer 1

我还没有使用pyLDAvis来可视化pyspark的LDA，但这是一个示例，该示例说明了如何在没有特殊prepare的情况下将sklearn.prepare用于sklearn。

以下是pyLDAvis.prepare的源代码链接： https://github.com/bmabey/pyLDAvis/blob/master/pyLDAvis/_prepare.py

def prepare(topic_term_dists, doc_topic_dists, doc_lengths, vocab, term_frequency):
   """Transforms the topic model distributions and related corpus data into
   the data structures needed for the visualization.
    Parameters
    ----------
    topic_term_dists : array-like, shape (n_topics, n_terms)
        Matrix of topic-term probabilities. Where n_terms is len(vocab).
    doc_topic_dists : array-like, shape (n_docs, n_topics)
        Matrix of document-topic probabilities.
    doc_lengths : array-like, shape n_docs
        The length of each document, i.e. the number of words in each document.
        The order of the numbers should be consistent with the ordering of the
        docs in doc_topic_dists.
    vocab : array-like, shape n_terms
        List of all the words in the corpus used to train the model.
    term_frequency : array-like, shape n_terms
        The count of each particular term over the entire corpus. The ordering
        of these counts should correspond with `vocab` and topic_term_dists.

sklearn.decomposition.LatentDirichletAllocation的示例：

tfidf_vectorizer = TfidfVectorizer(max_df=0.95)
tfidf = tfidf_vectorizer.fit_transform(data)
lda = LatentDirichletAllocation(n_components=10)
lda.fit(tfidf)
topic_term_dists = lda.components_ / lda.components_.sum(axis=1)[:, None]
doc_lengths = tfidf.sum(axis=1).getA1()
term_frequency = tfidf.sum(axis=0).getA1()
lda_doc_topic_dists = lda.transform(tfidf)
doc_topic_dists = lda_doc_topic_dists / lda_doc_topic_dists.sum(axis=1)[:, None]
vocab = tfidf_vectorizer.get_feature_names()
lda_pyldavis = pyLDAvis.prepare(topic_term_dists, doc_topic_dists, doc_lengths, vocab, term_frequency)
pyLDAvis.display(lda_pyldavis)

Answer 2

我已经设法使pyspark的输出适合pyLDAvis。
以下代码需要一些清洁，但可以使用。

from pyspark.ml.feature import StopWordsRemover,Tokenizer, RegexTokenizer, CountVectorizer, IDF
from pyspark.sql.functions import udf, col, size, explode, regexp_replace, trim, lower, lit
from pyspark.sql.types import ArrayType, StringType, DoubleType, IntegerType, LongType
from pyspark.ml.clustering import LDA
import pyLDAvis


def format_data_to_pyldavis(df_filtered, count_vectorizer, transformed, lda_model):
    xxx = df_filtered.select((explode(df_filtered.words_filtered)).alias("words")).groupby("words").count()
    word_counts = {r['words']:r['count'] for r in xxx.collect()}
    word_counts = [word_counts[w] for w in count_vectorizer.vocabulary]


    data = {'topic_term_dists': np.array(lda_model.topicsMatrix().toArray()).T, 
            'doc_topic_dists': np.array([x.toArray() for x in transformed.select(["topicDistribution"]).toPandas()['topicDistribution']]),
            'doc_lengths': [r[0] for r in df_filtered.select(size(df_filtered.words_filtered)).collect()],
            'vocab': count_vectorizer.vocabulary,
            'term_frequency': word_counts}

    return data

def filter_bad_docs(data):
    bad = 0
    doc_topic_dists_filtrado = []
    doc_lengths_filtrado = []

    for x,y in zip(data['doc_topic_dists'], data['doc_lengths']):
        if np.sum(x)==0:
            bad+=1
        elif np.sum(x) != 1:
            bad+=1
        elif np.isnan(x).any():
            bad+=1
        else:
            doc_topic_dists_filtrado.append(x)
            doc_lengths_filtrado.append(y)

    data['doc_topic_dists'] = doc_topic_dists_filtrado
    data['doc_lengths'] = doc_lengths_filtrado

# This is the only part that you have to implement:
create a Spark Dataframe named df_filtered and it has the list of raw words.
It can be the output of StopWordsRemover

# WORD COUNT
count_vectorizer = CountVectorizer(inputCol="words_filtered", outputCol="features", minDF=0.05, maxDF=0.5)
count_vectorizer = count_vectorizer.fit(df_filtered)
df_counted = count_vectorizer.transform(df_filtered)

# TF-IDF
idf = IDF(inputCol="features", outputCol="features_tfidf")
idf_model = idf.fit(df_counted)
df_tfidf = idf_model.transform(df_counted)

# LDA
lda = LDA(k=2, maxIter=20, featuresCol='features_tfidf')
lda_model = lda.fit(df_tfidf)
transformed = lda_model.transform(df_tfidf)

# FORMAT DATA AND PASS IT TO PYLDAVIS
data = format_data_to_pyldavis(df_filtered, count_vectorizer, transformed, lda_model)
filter_bad_docs(data) # this is, because for some reason some docs apears with 0 value in all the vectors, or the norm is not 1, so I filter those docs.
py_lda_prepared_data = pyLDAvis.prepare(**data)
pyLDAvis.display(py_lda_prepared_data)

pyspark的pyLDAvis可视化生成LDA模型

2 个答案: