def test_simplepipe():
df = SPARK_SESSION.sparkContext.\
parallelize([['this is a test'], ['this is another test']]).\
toDF(schema=types.StructType().add('sentence', types.StringType()))
pl = feature.Tokenizer().setInputCol('sentence') | \
feature.CountVectorizer() | \
feature.IDF()
pl_model = pl.fit(df)
pl_model.transform(df).count()
def test_ml_pipe():
df = sc. \
parallelize([Row(sentence='this is a test', label=0.),
Row(sentence='this is another test', label=1.)]). \
toDF()
pl = feature.Tokenizer().setInputCol('sentence') | feature.CountVectorizer()
ml = pl | classification.LogisticRegression()
ml_model = ml.fit(df)
assert_equal(ml_model.transform(df).count(), 2)
def test_stackedml_pipe():
df = SPARK_SESSION.sparkContext. \
parallelize([Row(sentence='this is a test', label=0.),
Row(sentence='this is another test', label=1.)]).\
toDF()
pl = feature.Tokenizer().setInputCol(
'sentence') | feature.CountVectorizer()
ml = pl | (classification.LogisticRegression(),) | feature.VectorAssembler() | \
classification.\
RandomForestClassifier()
ml_model = ml.fit(df)
assert_equal(ml_model.transform(df).count(), 2)
def logistic_regression_text(df, input_col):
"""
Runs a logistic regression for input (text) DataFrame.
:param df: Pyspark dataframe to analyze
:param input_col: Column to predict
:return: DataFrame with logistic regression and prediction run.
"""
assert_spark_df(df)
pl = feature.Tokenizer().setInputCol(input_col) | feature.CountVectorizer()
ml = pl | classification.LogisticRegression()
ml_model = ml.fit(df)
df_model = ml_model.transform(df)
return df_model, ml_model
def canonicaltokens(df, inputColumn, outputColumn):
"""
turn input column of strings into canonical format as output column of tokens
return as output column added to the dataframe
"""
newname = df.withColumn("cleanname", \
f.regexp_replace(f.regexp_replace(f.rtrim(f.ltrim(f.col(inputColumn))), \
" (\w) (\w) ", "$1$2"), "(\w) (\w) (\w)$", "$1$2$3"))
newtokenizer = mlf.Tokenizer(inputCol="cleanname", outputCol="words")
chtokenized = newtokenizer.transform(newname).drop("cleanname")
stopwordremover = mlf.StopWordsRemover(inputCol="words", outputCol=outputColumn)
canonicalname = stopwordremover.transform(chtokenized).drop("words")
return canonicalname
def test_multi_model_pipe():
df = SPARK_SESSION.sparkContext. \
parallelize([Row(sentence='this is a test', label=0.),
Row(sentence='this is another test', label=1.)]).\
toDF()
pl = feature.Tokenizer().setInputCol(
'sentence') | feature.CountVectorizer()
models = (classification.LogisticRegression(),
classification.RandomForestClassifier(),
classification.LogisticRegression().setElasticNetParam(0.2),
classification.GBTClassifier())
ml = pl | models | feature.VectorAssembler().setOutputCol('final_features') | \
classification.LogisticRegression()
ml_model = ml.fit(df)
assert_equal(ml_model.transform(df).count(), 2)
def n_gram(df, input_col, n=2):
"""
Converts the input array of strings inside of a Spark DF into an array of n-grams.
:param df: Pyspark dataframe to analyze
:param input_col: Column to analyzer.
:param n: number of elements per n-gram >=1.
:return: Spark DataFrame with n-grams calculated.
"""
is_dataframe(df)
tokenizer = feature.Tokenizer().setInputCol(input_col) | feature.StopWordsRemover()
count = feature.CountVectorizer()
gram = feature.NGram(n=n) | feature.CountVectorizer()
tf = tokenizer | (count, gram) | feature.VectorAssembler()
tfidf = tf | feature.IDF().setOutputCol('features')
tfidf_model = tfidf.fit(df)
df_model = tfidf_model.transform(df)
return df_model, tfidf_model
def getTFIDF(closest):
grouped_clusters = closest.groupBy("prediction")\
.agg(F.collect_list("split_aspect").alias("text"))\
.withColumn("text", F.concat_ws(" ", "text"))
tokenizer = feat.Tokenizer(inputCol="text", outputCol="words")
wordsData = tokenizer.transform(grouped_clusters)
# get term freqs (using count vectorizer because it does hash the words and we can revert back to words from idx)
cv = feat.CountVectorizer(inputCol="words", outputCol="rawFeatures").fit(wordsData)
featurizedData = cv.transform(wordsData)
# save vocab object
vocab = cv.vocabulary
# compute idf
idf = feat.IDF(inputCol="rawFeatures", outputCol="features")
idfModel = idf.fit(featurizedData)
tfidf = idfModel.transform(featurizedData)
return tfidf, vocab
def test_unigram_and_bigram():
df = SPARK_SESSION.sparkContext. \
parallelize([['this is the best sentence ever'],
['this is however the worst sentence available']]). \
toDF(schema=types.StructType().add('sentence', types.StringType()))
import requests
stop_words = requests.get(
'http://ir.dcs.gla.ac.uk/resources/linguistic_utils/stop_words'
).text.split()
tokenizer = feature.Tokenizer().setInputCol(
'sentence') | feature.StopWordsRemover(stopWords=stop_words)
unigram = feature.CountVectorizer()
bigram = feature.NGram() | feature.CountVectorizer()
trigram = feature.NGram(n=3) | feature.CountVectorizer()
tf = tokenizer | (unigram, bigram, trigram) | feature.VectorAssembler()
tfidf = tf | feature.IDF().setOutputCol('features')
tfidf_model = tfidf.fit(df)
assert_equal(
tfidf_model.transform(df).select('sentence', 'features').count(), 2)
def create_vocab(df): """Create a vocabulary from a dataframe. Also removes some special tokens. Args: df: A dataframe with columns'processed_abstract' and 'processed_full_text' Return: vocab: A wordlist sorted by frequency """ concat_udf = F.udf( lambda cols: " ".join([col for col in cols]), spark_types.StringType()) df = df.withColumn( 'all_text', concat_udf(F.array( 'processed_abstract', 'processed_full_text'))) tokenizer = ml_feature.Tokenizer( inputCol='all_text', outputCol='tokens') df = tokenizer.transform(df) cv = ml_feature.CountVectorizer( inputCol='tokens', outputCol='vectors', vocabSize=200000) cv_model = cv.fit(df) # wrd_list is sorted by frequency vocab = cv_model.vocabulary vocab.remove(SENT_START) vocab.remove(SENT_END) vocab.remove(SEC_START) vocab.remove(SEC_END) return vocab
