def test_stopwordsremover(self): dataset = self.spark.createDataFrame([Row(input=["a", "panda"])]) stopWordRemover = StopWordsRemover(inputCol="input", outputCol="output") # Default self.assertEqual(stopWordRemover.getInputCol(), "input") transformedDF = stopWordRemover.transform(dataset) self.assertEqual(transformedDF.head().output, ["panda"]) self.assertEqual(type(stopWordRemover.getStopWords()), list) self.assertTrue( isinstance(stopWordRemover.getStopWords()[0], basestring)) # Custom stopwords = ["panda"] stopWordRemover.setStopWords(stopwords) self.assertEqual(stopWordRemover.getInputCol(), "input") self.assertEqual(stopWordRemover.getStopWords(), stopwords) transformedDF = stopWordRemover.transform(dataset) self.assertEqual(transformedDF.head().output, ["a"]) # with language selection stopwords = StopWordsRemover.loadDefaultStopWords("turkish") dataset = self.spark.createDataFrame( [Row(input=["acaba", "ama", "biri"])]) stopWordRemover.setStopWords(stopwords) self.assertEqual(stopWordRemover.getStopWords(), stopwords) transformedDF = stopWordRemover.transform(dataset) self.assertEqual(transformedDF.head().output, []) # with locale stopwords = ["BELKİ"] dataset = self.spark.createDataFrame([Row(input=["belki"])]) stopWordRemover.setStopWords(stopwords).setLocale("tr") self.assertEqual(stopWordRemover.getStopWords(), stopwords) transformedDF = stopWordRemover.transform(dataset) self.assertEqual(transformedDF.head().output, [])
def get_pd_keyword(self): df_spark = self.df_spark # Step 1. Text cleasing with punctuations REGEX = '[_,?\\-.!?@#$%^&*+\/\d]' df_spark = df_spark.withColumn( "description_clean", regexp_replace(df_spark.description, REGEX, ' ')) # Step 2. Tokenization # df_spark = df_spark.drop("description_token") tokenizer = Tokenizer(inputCol='description_clean', outputCol='description_token') df_spark = tokenizer.transform(df_spark) # Stemming # nltk.download('wordnet') lemmatizer = WordNetLemmatizer() def lemm_function(list): list_clean = [] for item in list: list_clean.append(lemmatizer.lemmatize(item)) return list_clean udf_lemm_function = F.udf(lemm_function, ArrayType(StringType())) df_spark = df_spark.withColumn( "description_lemm", udf_lemm_function(df_spark.description_token)) # Step 3. Remove stopword stopwords_list = StopWordsRemover.loadDefaultStopWords("english") stopwords_customize_list = ["app", "apps"] stopwords_list = np.append(stopwords_list, stopwords_customize_list) stopwords = StopWordsRemover(inputCol="description_lemm", outputCol="description_no_stop", stopWords=stopwords_list) stopwords.getStopWords() df_spark = stopwords.transform(df_spark) df_pd_desc_final = df_spark.toPandas() # ### Note: IDF vector must be trained with large corpus, otherwise lose the advance of IDF # get the "description" column joinF = lambda x: " ".join(x) df_pd_desc_final["description_final"] = df_pd_desc_final[ "description_no_stop"].apply(joinF) corpus_list = df_pd_desc_final["description_final"].tolist() df_pd_desc_final = get_tfidf(corpus_list, df_pd_desc_final, self.topn) return df_pd_desc_final
def test_stopwordsremover(self): dataset = self.spark.createDataFrame([Row(input=["a", "panda"])]) stopWordRemover = StopWordsRemover(inputCol="input", outputCol="output") # Default self.assertEqual(stopWordRemover.getInputCol(), "input") transformedDF = stopWordRemover.transform(dataset) self.assertEqual(transformedDF.head().output, ["panda"]) self.assertEqual(type(stopWordRemover.getStopWords()), list) self.assertTrue(isinstance(stopWordRemover.getStopWords()[0], basestring)) # Custom stopwords = ["panda"] stopWordRemover.setStopWords(stopwords) self.assertEqual(stopWordRemover.getInputCol(), "input") self.assertEqual(stopWordRemover.getStopWords(), stopwords) transformedDF = stopWordRemover.transform(dataset) self.assertEqual(transformedDF.head().output, ["a"]) # with language selection stopwords = StopWordsRemover.loadDefaultStopWords("turkish") dataset = self.spark.createDataFrame([Row(input=["acaba", "ama", "biri"])]) stopWordRemover.setStopWords(stopwords) self.assertEqual(stopWordRemover.getStopWords(), stopwords) transformedDF = stopWordRemover.transform(dataset) self.assertEqual(transformedDF.head().output, []) # with locale stopwords = ["BELKİ"] dataset = self.spark.createDataFrame([Row(input=["belki"])]) stopWordRemover.setStopWords(stopwords).setLocale("tr") self.assertEqual(stopWordRemover.getStopWords(), stopwords) transformedDF = stopWordRemover.transform(dataset) self.assertEqual(transformedDF.head().output, [])
def parse_data(path): spark = SparkSession.builder.appName("BigDataProject").getOrCreate() if (path == "../train.csv"): lcs = udf(lower_clean_str) rt = udf(rate_transform) # rews=udf(remove_extra_ws) #2/3 df = spark.read.csv(path, header=False, sep="\t") df = df.withColumn("_c1", lcs("_c1")) # df=df.withColumn("_c1",rews("_c1")) #3/3 expres = [split(col("_c1"), " ").alias("_c1")] df = df.withColumn("_c1", *expres) remover = StopWordsRemover(inputCol="_c1", outputCol="filtered") swlist = remover.getStopWords() swlist = swlist + list(set(stopwords.words('english'))) + [''] remover.setStopWords(swlist) #remover.transform(df).select("filtered") final = remover.transform(df.select("_c1")) df = df.withColumn('row_index', func.monotonically_increasing_id()) final = final.withColumn('row_index', func.monotonically_increasing_id()) final = final.join(df["row_index", "_c0"], on=["row_index"]).drop("row_index").drop("_c1") final = final.withColumn("_c0", rt("_c0")) # fdf.show() return final elif (path == "../test.csv"): lcs = udf(lower_clean_str) # rews=udf(remove_extra_ws) #2/3 df = spark.read.csv(path, header=False, sep="\t") initial = df df = df.withColumn("_c0", lcs("_c0")) # df=df.withColumn("_c1",rews("_c1")) #3/3 expres = [split(col("_c0"), " ").alias("_c0")] df = df.select(*expres) remover = StopWordsRemover(inputCol="_c0", outputCol="filtered") swlist = remover.getStopWords() swlist = swlist + list(set(stopwords.words('english'))) + [''] remover.setStopWords(swlist) remover.transform(df).select("filtered") final = remover.transform(df.select("_c0")) return final, initial else: print "Wrong File or Path" return -1
def set_pipeline(custom_stop_words=None): if not custom_stop_words: custom_stop_words = [] re_tokenizer = RegexTokenizer(inputCol="text", outputCol="raw_tokens", pattern="\\W") stop_words_remover = StopWordsRemover(inputCol="raw_tokens", outputCol="words") stop_words_remover.setStopWords(stop_words_remover.getStopWords() + custom_stop_words) cv = CountVectorizer(inputCol="words", outputCol="vectors") pipeline = Pipeline(stages=[re_tokenizer, stop_words_remover, cv]) return pipeline
def task_four(ngram): """ Set the ngram value :param ngram: :return: """ params = list(inspect.getargspec(task_four)) p = list(chain.from_iterable([i for i in params if i is not None])) param_values = {} if len(p) > 0: for i, v in enumerate(p): try: value = raw_input("Please enter a value for {} ==> ".format(v)) param_values.update({v: value}) except: pass ngram = param_values.get(p[0]) if int(ngram) == 2: # --- list of stopwords stopwords = { 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', 'her', 'hers', 'herself', 'it', 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 'can', 'will', 'just', 'don', 'should', 'now', ' a ', 'insured', 'sured', 'coverage', 'year', 'dob', 'insd', 'left' } # --- remove stop words REMOVER = StopWordsRemover() stopwords = REMOVER.getStopWords() REMOVER.setInputCol("inter_wordlist") REMOVER.setOutputCol("inter_wordlist_two") stpwrds_rmvd_sdf = REMOVER.transform(VECTOR_DATAFRAME) \ .select(["Claim_Id", "filename", "inter_wordlist_two"]) else: pass
def tokenize_df(df): tokenizer = Tokenizer(inputCol="text", outputCol="vector") remover = StopWordsRemover() remover.setInputCol("vector") remover.setOutputCol("vector_no_stopw") stopwords = remover.getStopWords() stemmer = PorterStemmer() stemmer_udf = udf(lambda x: stem(x), ArrayType(StringType())) df = df.select(clean_text(col("text")).alias("text")) df = tokenizer.transform(df).select("vector") df = remover.transform(df).select("vector_no_stopw") df = (df .withColumn("vector_stemmed", stemmer_udf("vector_no_stopw")) .select("vector_stemmed") ) return df
def init_base_df(file_path=default_file_path): # Set legacy parsing as Spark 3.0+ cannot use 'E' for timestamp spark.sql("set spark.sql.legacy.timeParserPolicy=LEGACY") print("Loading", default_file_path) raw_df = ( spark.read.format("csv") .option("inferSchema", True) .load(file_path) .toDF("polarity", "tweet_id", "datetime", "query", "user", "text") ) # Parse string to timestamp time_parsed_df = raw_df.withColumn( "timestamp", to_timestamp("datetime", "EEE MMM dd HH:mm:ss zzz yyyy") ) df = time_parsed_df.drop("query").drop("datetime") # Shift polarity from a range of [0:4], to [-1:1] scaled_polarity_df = df.withColumn("sentiment", (col("polarity") / 2) - 1).drop( "polarity" ) clean_text_df = df.select(clean_text(col("text")).alias("text"), "tweet_id") tokenizer = Tokenizer(inputCol="text", outputCol="vector") vector_df = tokenizer.transform(clean_text_df).select("vector", "tweet_id") remover = StopWordsRemover() stopwords = remover.getStopWords() remover.setInputCol("vector") remover.setOutputCol("tokens") tokens_no_stopw_df = remover.transform(vector_df).select("tokens", "tweet_id") tweets_with_tokens_df = scaled_polarity_df.join(tokens_no_stopw_df, on=["tweet_id"]) return tweets_with_tokens_df
# Removing punctuation from pyspark.sql.functions import regexp_replace # Regular expression (REGEX) to match commas and hyphens REGEX = '[,\\-]' books = books.withColumn('text', regexp_replace(books.text, REGEX, ' ')) # Text to tokens from pyspark.ml.feature import Tokenizer books = Tokenizer(inputCol='text', outputCol='tokens').transform(books) # Remove stop words from pyspark.ml.feature import StopWordsRemover stopwords = StopWordsRemover() # Take a look at the list of stop words stopwords.getStopWords() # Spevify the input and output column names stopwords = stopwords.setInputCol('tokens').setOutputCol('words') books = stopwords.transform(books) # Feature hashing from pyspark.ml.feature import HashingTF hasher = HashingTF(inputCol='words', outputCol='hash', numFeatures=32) books = hasher.transoform(books) # Dealing with common words from pyspark.ml.feature import IDF books = IDF(inputCol='hash', outputCol='features').fit(books).transform(books) # Import the necessary functions from pyspark.sql.functions import regexp_replace
'''expres = [split(col("sentence"), " ").alias("sentence")] sentenceDataFrame = sentenceDataFrame.withColumn("sentence", *expres) remover = StopWordsRemover(inputCol="sentence", outputCol="filtered") swlist = remover.getStopWords() swlist.append("") remover.setStopWords(swlist) final = remover.transform(sentenceDataFrame.select("sentence"))''' tokenizer = Tokenizer(inputCol="sentence", outputCol="words") countTokens = udf(lambda words: len(words), IntegerType()) tokenized = tokenizer.transform(sentenceDataFrame) print(tokenized.columns) remover = StopWordsRemover(inputCol="words", outputCol="filtered") swlist = remover.getStopWords() swlist.append("") remover.setStopWords(swlist) tokenized = remover.transform(tokenized.select("words")) tokenized.select("sentence", "words")\ .withColumn("tokens", countTokens(col("words"))).show() hashingTF = HashingTF(inputCol="words", outputCol="rawFeatures") tf = hashingTF.transform(tokenized) tf.select('rawFeatures').take(2) idf = IDF(inputCol="rawFeatures", outputCol="features") idfModel = idf.fit(tf) tfidf = idfModel.transform(tf) print(tfidf.select("features").first()) spark.stop()
def Run_N_Grams(root_dir, file_name, n, app_name, extension_type, project_name): # type: (...) -> None """ Run the N-Grams language frequency algorithm. :param root_dir: the root directory :param file_name: the name of the csv file to be loaded :param n: the non-negative integer value for generating the N-Grams :param app_name: the name of the spark application :param extension_type: the file extension[.eml, .pdf, .doc, .docx, .rtf] :param project_name: the name of the project[SA_Claims, Personal_Umbrella] :return: None """ if os.getcwd() is not root_dir: print('changing directory: {}'.format(root_dir)) os.chdir(root_dir) # - list data containers raw_data = [] # holds the raw text data data_list = [] # holds the parsed text data row_lengths = [] # stores the row lengths # - read the csv file using the 'with' context manager if os.path.isfile(os.path.join(root_dir, file_name)): with open(os.path.join(root_dir, file_name)) as spark_file: for row in spark_file: data_list.append(tuple(row.strip('\n').split(',', 3)[1:])) row_lengths.append(len(row.rstrip('\n').split(',', 3)[1:])) raw_data.append(row) # - reset the column names claim_id = 'Claim_Id' filenames = 'filename' word_list = 'raw_wordlist' # - rename the columns in data_list data_list[0] = claim_id, filenames, word_list # - check to make sure row_length consists only of the value 3 try: if len(set(row_lengths)) == 1: print("Data successfully loaded") if len(set(row_lengths)) > 1: raise Exception("LoadDataException: Data not transformed properly.") except Exception as e: print(e) # - create a spark session spark = SparkSession.builder \ .appName(app_name) \ .getOrCreate() # - create a spark data frame sdf = spark.createDataFrame(data_list[1:], list(map(lambda x: str(x), [claim_id, filenames, word_list]))) # - setup tokenizer tokenizer = Tokenizer(inputCol='raw_wordlist', outputCol="inter_wordlist") # - create an output vector vector_df = tokenizer.transform(sdf).select(["Claim_Id", "filename", "inter_wordlist"]) # - stop words to be removed remover = StopWordsRemover() stopwords = remover.getStopWords() stopwords = {'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself' , 'she', 'her', 'hers', 'herself', 'it', 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that' , 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'an', 'the', 'and' , 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during' , 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once' , 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not' , 'only', 'own', 'same', 'so', 'than', 'too', 'very', 'can', 'will', 'just', 'don', 'should', 'now', ' a ', 'insured', 'sured', 'coverage', 'year', 'dob', 'insd', 'left'} # - create the N-gram ngram = NGram(n=n, inputCol="inter_wordlist", outputCol="wordlist") dev_sdf = ngram.transform(vector_df) dev_sdf = dev_sdf.where(size(col("wordlist")) >= n) filtered_sdf = dev_sdf.select("Claim_Id", "filename", explode(dev_sdf.inter_wordlist).alias('wordlist')) # - save to hive: Update the save name hdfs_location = '/bda/claimsops/data/' current_date = str(datetime.today())[:10].replace('-', '_') n_gram_str = '' if n == 2: n_gram_str = 'BiGrams' else: n_gram_str = str(n).upper() + 'Grams' hive_str = hdfs_location + project_name + '/' + extension_type + '_' + n_gram_str + '_' + current_date print(hive_str)
], ['cls','sent'] ) from pyspark.ml import Pipeline from pyspark.ml.feature import StringIndexer, HashingTF, StopWordsRemover, RegexTokenizer stopwords=list() _mystopwords=[u"나",u"너", u"우리"] for e in _mystopwords: stopwords.append(e) labelIndexer = StringIndexer(inputCol="cls", outputCol="label") regexTok = RegexTokenizer(inputCol="sent", outputCol="wordsRegex", pattern="\\s+") #tokenizer = Tokenizer(inputCol="sent", outputCol="words") stop = StopWordsRemover(inputCol="wordsRegex", outputCol="nostops") _stopwords=stop.getStopWords() for e in _stopwords: stopwords.append(e) stop.setStopWords(stopwords) hashingTF = HashingTF(inputCol="nostops", outputCol="features") pipeline = Pipeline(stages=[labelIndexer,regexTok,stop,hashingTF]) model=pipeline.fit(df) trainDf = model.transform(df) trainDf.select('cls','label','features').show() from pyspark.mllib.regression import LabeledPoint from pyspark.mllib.linalg import Vectors trainRdd = trainDf\
# MAGIC %md # MAGIC #### 3. Get insigts of Users # MAGIC Get all onwers (labeled as one as well as predicted as one) and get the words frequency # COMMAND ---------- from pyspark.ml.feature import StopWordsRemover df_all_owner = df_pets.select('text').union( pred_all.filter(F.col('prediction') == 1.0).select('text')) stopwords_custom = ['im', 'get', 'got', 'one', 'hes', 'shes', 'dog', 'dogs', 'cats', 'cat', 'kitty', 'much', 'really', 'love','like','dont','know','want','thin',\ 'see','also','never','go','ive'] remover1 = StopWordsRemover(inputCol="raw", outputCol="filtered") core = remover1.getStopWords() core = core + stopwords_custom remover = StopWordsRemover(inputCol="text", outputCol="filtered", stopWords=core) df_all_owner = remover.transform(df_all_owner) wc = df_all_owner.select('filtered').rdd.flatMap( lambda a: a.filtered).countByValue() # COMMAND ---------- df_all_owner.show(1) # COMMAND ----------
plt.axis("off") # Plot the word cloud: plot_word_cloud(tokenized, "words") # ### Remove common (stop) words from each review # Note that the ride reviews contain a number of common words such as "the" # that we do not expect to be relevant. # Use the # [StopWordsRemover](http://spark.apache.org/docs/latest/api/python/pyspark.ml.html#pyspark.ml.feature.StopWordsRemover) # class to remove these so-called *stop words*: from pyspark.ml.feature import StopWordsRemover remover = StopWordsRemover(inputCol="words", outputCol="relevant_words") remover.getStopWords()[:10] removed = remover.transform(tokenized) removed.select("words", "relevant_words").head(5) # Plot the word cloud: plot_word_cloud(removed, "relevant_words") # ### Count the frequency of words in each review # Use the # [CountVectorizer](http://spark.apache.org/docs/latest/api/python/pyspark.ml.html#pyspark.ml.feature.CountVectorizer) # class to compute the term frequency: from pyspark.ml.feature import CountVectorizer vectorizer = CountVectorizer(inputCol="relevant_words", outputCol="word_count_vector", vocabSize=100)
train_df = train_df.withColumn("numeric_label", udfNumericLabel("label")) test_df = test_df.withColumn("numeric_label", udfNumericLabel("label")) train_df = train_df.withColumnRenamed('label','text_label') train_df = train_df.withColumnRenamed('numeric_label','label') test_df = test_df.withColumnRenamed('label','text_label') test_df = test_df.withColumnRenamed('numeric_label','label') # Tockenize the doc into words tokenizer = Tokenizer(inputCol="doc",outputCol="words") wordsDataTrain = tokenizer.transform(train_df) wordsDataTest = tokenizer.transform(test_df) # Remove Stop Words remover = StopWordsRemover(inputCol="words",outputCol="filtered") sw =remover.getStopWords() sw.append('subject') sw.append('re') sw.append('email') remover.setStopWords(sw) cleanDataTrain = remover.transform(wordsDataTrain) cleanDataTest = remover.transform(wordsDataTest) # Made onegrams onegram = NGram(n=1, inputCol="filtered",outputCol="onegram") onegramedDataTrain=onegram.transform(cleanDataTrain) onegramedDataTest = onegram.transform(cleanDataTest) # Find hashed Term frequency value of word vector hashingTF = HashingTF(inputCol="onegram", outputCol="rawFeatures", numFeatures=100000) featurizedDataTrain = hashingTF.transform(onegramedDataTrain)
from pyspark.sql.functions import * from pyspark.sql.types import * tokenizer = Tokenizer(inputCol="cluster_text", outputCol="words") countTokens = udf(lambda words: len(words), IntegerType()) tokenized = tokenizer.transform(documents) # StopWordsRemover from pyspark.ml.feature import StopWordsRemover remover = StopWordsRemover(inputCol="words", outputCol="filtered") # Add stopwords to the existing list. add_stopwords = ["like", "-", "the", "to", "@", "get", "got", "i´m", "don´t"] newStopwords = remover.getStopWords() + add_stopwords remover.setStopWords(newStopwords) remover.getStopWords() # transform twitter text by removing stopwords tokenized = remover.transform(tokenized) # Explode and aggregate words tokenized = tokenized.withColumn("word", F.explode('filtered')) # Add counter column with 1 initiated. tokenized = tokenized.withColumn("count", F.lit(1)) # Count words under the same prediction(cluster) group. countedWords = tokenized.groupBy("prediction", "word")\ .agg(F.count("count").alias("wordCount"))#.orderBy("wordCount", ascending=False).show()
from pyspark.ml.feature import Tokenizer tokenizer = Tokenizer(inputCol="text", outputCol="vector") vector_df = tokenizer.transform(clean_text_df).select("vector") vector_df.printSchema() vector_df.show(10) """**3. Remove** **stop words**""" from pyspark.ml.feature import StopWordsRemover # Define a list of stop words or use default list remover = StopWordsRemover() stopwords = remover.getStopWords() # Display default list stopwords[:10] # Specify input/output columns remover.setInputCol("vector") remover.setOutputCol("Body_no_stopw") # Transform existing dataframe with the StopWordsRemover Body_no_stopw_df = remover.transform(vector_df).select("Body_no_stopw") # Display Body_no_stopw_df.printSchema() Body_no_stopw_df.show()
F.lower( F.col("tweet_text"))).withColumn( "tweet_text", F.trim(F.col("tweet_text")))) #============================================ # preprocessing #============================================ # 2.1. tokenize tokenizer = Tokenizer(inputCol="tweet_text", outputCol="tokens") # 2.2. remove stopwords stopword_remover = StopWordsRemover(inputCol="tokens", outputCol="remove_stop") stopwords_list = stopword_remover.getStopWords() stopwords_list = stopwords_list + more_stopwords stopword_remover.setStopWords(stopwords_list) #2.3. stemming # TODO: how to modify the stemming function into a transformer? stemmer = PorterStemmer() # more straightforward to use lambda stem_udf = F.udf(lambda l: [stemmer.stem(word) for word in l], returnType=ArrayType(StringType())) df_tokenized = tokenizer.transform(df_select_clean) df_rmstop = stopword_remover.transform(df_tokenized) df_stemmed = df_rmstop.withColumn("stemmed", stem_udf(F.col("remove_stop"))) # Load the trained LDAmodel