找到最近的文本

Question

我是个新用户。我想比较两种不同的数据(包含新闻信息)的文本作为推荐。

我可以很容易地用Python完成这一任务：

def get_recommendations(title, cosine_sim, indices):
    idx = indices[title]
    # Get the pairwsie similarity scores
    sim_scores = list(enumerate(cosine_sim[idx]))
    print(sim_scores)
    # Sort the movies based on the similarity scores
    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
    # Get the scores for 10 most similar movies
    sim_scores = sim_scores[1:11]
    talk_indices = [i[0] for i in sim_scores]
    # Return the top 10 most
    return ted['News Data'].iloc[talk_indices]

indices = pd.Series(det.index, index=det['Unnamed: 0']).drop_duplicates()

transcripts = det['News Data']
transcripts2 = ted['News Data']

tfidf = TfidfVectorizer(stop_words='english')
tfidf_matrix = tfidf.fit_transform(transcripts)
tfidf_matrixx = tfidf.transform(transcripts2)
cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrixx)

print(get_recommendations(0, cosine_sim, indices))

当我转到火星雨，我得到了非常不同的结果时，计算TF-国防军。我知道，需要计算余弦相似度才能提出“新闻”建议。

我正在使用下面的Pyspark进行tfidf计算：

df1 = sqlContext.read.format('com.databricks.spark.csv').options(header='true', inferschema='true').load('bbcclear.csv')
df2 = sqlContext.read.format('com.databricks.spark.csv').options(header='true', inferschema='true').load('yenisafakcategorypredict.csv')

# tokenize
tokenizer = Tokenizer().setInputCol("News Data").setOutputCol("word")
wordsData = tokenizer.transform(df2)
wordsData2 = tokenizer.transform(df1)

# vectorize
vectorizer = CountVectorizer(inputCol='word', outputCol='vectorizer').fit(wordsData)
wordsData = vectorizer.transform(wordsData)
wordsData2 = vectorizer.transform(wordsData2)

# calculate scores
idf = IDF(inputCol="vectorizer", outputCol="tfidf_features")
idf_model = idf.fit(wordsData)
wordsData = idf_model.transform(wordsData)

idf_model = idf.fit(wordsData2)
wordsData2 = idf_model.transform(wordsData2)

如何使用上面获得的ID-以色列国防军计算余弦相似度来提出建议？

Answer 1

下面是我的PoC作业中TF-国防军在星火中使用的一个例子.我强烈推荐使用高级NLP框架，如BERT than TF-下手，以获得有意义的相似性。

样本数据集：

df = spark.createDataFrame(
    [
        ["cricket sport team player"],
        ["global politics"],
        ["football sport player team"],
    ],
    ["news"]
)

+--------------------------+
|news                      |
+--------------------------+
|cricket sport team player |
|global politics           |
|football sport player team|
+--------------------------+

TF-下手矢量化和余弦相似度计算：

regex_tokenizer = RegexTokenizer(gaps=False, pattern="\w+", inputCol="news", outputCol="tokens")
count_vectorizer = CountVectorizer(inputCol="tokens", outputCol="tf")
idf = IDF(inputCol="tf", outputCol="idf")
tf_idf_pipeline = Pipeline(stages=[regex_tokenizer, count_vectorizer, idf])
df = tf_idf_pipeline.fit(df).transform(df).drop("news", "tokens", "tf")
df = df.crossJoin(df.withColumnRenamed("idf", "idf2"))

@F.udf(returnType=FloatType())
def cos_sim(u, v):
  return float(u.dot(v) / (u.norm(2) * v.norm(2)))
# 

df.withColumn("cos_sim", cos_sim(F.col("idf"), F.col("idf2")))

+--------------------+--------------------+----------+
|                 idf|                idf2|   cos_sim|
+--------------------+--------------------+----------+
|(7,[0,1,2,4],[0.2...|(7,[0,1,2,4],[0.2...|       1.0|
|(7,[0,1,2,4],[0.2...|(7,[5,6],[0.69314...|       0.0|
|(7,[0,1,2,4],[0.2...|(7,[0,1,2,3],[0.2...|0.34070355|
|(7,[5,6],[0.69314...|(7,[0,1,2,4],[0.2...|       0.0|
|(7,[5,6],[0.69314...|(7,[5,6],[0.69314...|       1.0|
|(7,[5,6],[0.69314...|(7,[0,1,2,3],[0.2...|       0.0|
|(7,[0,1,2,3],[0.2...|(7,[0,1,2,4],[0.2...|0.34070355|
|(7,[0,1,2,3],[0.2...|(7,[5,6],[0.69314...|       0.0|
|(7,[0,1,2,3],[0.2...|(7,[0,1,2,3],[0.2...|       1.0|
+--------------------+--------------------+----------+