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test_basic_transformers.py
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test_basic_transformers.py
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import csv
import os
import shutil
import sys
import tempfile
import unittest
from pyspark import StorageLevel
from pyspark.sql import Row
from sourced.ml.utils.engine import create_engine
from sourced.ml.utils.spark import SparkDefault
from sourced.ml.transformers import ParquetSaver, ParquetLoader, Collector, First, \
Identity, FieldsSelector, Repartitioner, DzhigurdaFiles, CsvSaver, Rower, \
PartitionSelector, Sampler, Distinct, Cacher, Ignition, HeadFiles, LanguageSelector, \
UastExtractor, UastDeserializer, UastRow2Document, RepositoriesFilter
from sourced.ml.tests.models import PARQUET_DIR, SIVA_DIR
class BasicTransformerTests(unittest.TestCase):
@classmethod
@unittest.skipUnless(sys.version_info < (3, 7), "Python 3.7 is not yet supported")
def setUpClass(cls):
cls.engine = create_engine("test_with_engine", SIVA_DIR, "siva")
cls.spark = cls.engine.session
cls.data = ParquetLoader(session=cls.spark, paths=PARQUET_DIR).execute().rdd.coalesce(1)
def test_repartitioner(self):
partitions = 2
# coalesce without shuffle cannot make more partitions, only concatenate them
# it is a shuffle flag check.
repartitioned_data = Repartitioner(partitions, shuffle=False)(self.data)
self.assertEqual(1, repartitioned_data.getNumPartitions())
repartitioned_data = Repartitioner(partitions, shuffle=True)(self.data)
self.assertEqual(partitions, repartitioned_data.getNumPartitions())
repartitioned_data = Repartitioner.maybe(partitions, shuffle=True, multiplier=2)(self.data)
self.assertEqual(partitions * 2, repartitioned_data.getNumPartitions())
repartitioned_data = Repartitioner.maybe(None, shuffle=False)(self.data)
self.assertEqual(1, repartitioned_data.getNumPartitions())
repartitioned_data = Repartitioner.maybe(partitions, keymap=lambda x: x[0])(self.data)
self.assertEqual(repartitioned_data.count(), 6)
def test_partition_selector(self):
partitioned_data = PartitionSelector(partition_index=0)(self.data)
self.assertEqual(partitioned_data.count(), 6)
def test_sampler(self):
sampled_data = Sampler()(self.data)
self.assertEqual(sampled_data.count(), 2)
def test_parquet_loader(self):
# load parquet and check number of rows
loader = ParquetLoader(session=self.spark, paths=(PARQUET_DIR, PARQUET_DIR))
data = loader.execute()
self.assertEqual(data.count(), 6 * 2)
loader = ParquetLoader(session=self.spark, paths=PARQUET_DIR)
data = loader.execute()
self.assertEqual(data.count(), 6)
self.assertEqual(loader.paths, PARQUET_DIR)
self.assertNotIn("session", loader.__getstate__())
with self.assertRaises(ValueError):
loader = ParquetLoader(session=self.spark, paths=None)
data = loader.execute()
def test_rower(self):
rows = [("get_user", 3)]
df = self.spark.createDataFrame(rows, ["identifier", "frequency"])
data = Rower(lambda x: {"identifier": x[0], "frequency": x[1]})(df.rdd)
self.assertEqual(data.count(), 1)
self.assertEqual(data.collect()[0].identifier, "get_user")
self.assertEqual(data.collect()[0].frequency, 3)
def test_dzhigurda(self):
self.assertEqual(DzhigurdaFiles(0)(self.engine.repositories).count(), 325)
self.assertEqual(DzhigurdaFiles(10)(self.engine.repositories).count(), 3490)
self.assertEqual(DzhigurdaFiles(-1)(self.engine.repositories).count(), 27745)
def test_identity(self):
# load parquet
loader = ParquetLoader(session=self.spark, paths=PARQUET_DIR)
data = loader.execute()
# check that identity returns the same RDD
data_identity = Identity()(data)
self.assertEqual(data_identity.count(), 6)
self.assertEqual(data_identity, data)
def test_distinct(self):
rows = [("foo_bar", 3), ("baz", 5), ("foo_bar", 3)]
df = self.spark.createDataFrame(rows, ["identifier", "frequency"])
self.assertEqual(set(rows), set(Distinct()(df).collect()))
def test_cacher(self):
persistence = SparkDefault.STORAGE_LEVEL
cacher = Cacher(persistence)
cached_data = cacher(self.data)
self.assertTrue(cached_data.is_cached)
self.assertEqual(cacher.persistence, getattr(StorageLevel, persistence))
self.assertIn("head", cacher.__getstate__())
cacher = Cacher.maybe(persistence=None)
uncached_data = cacher(self.data)
self.assertEqual(uncached_data, self.data)
cacher = Cacher.maybe(persistence)
cached_data = cacher(self.data)
self.assertTrue(cached_data.is_cached)
cached_data = Cacher.maybe(persistence)(self.data)
self.assertFalse(cached_data.unpersist().is_cached)
def test_ignition(self):
start_point = Ignition(self.engine)
columns = start_point(self).columns
self.assertNotIn("engine", start_point.__getstate__())
self.assertEqual(columns, ["id", "urls", "is_fork", "repository_path"])
def test_repositories_filter(self):
start_point = Ignition(self.engine)
repos = start_point.link(RepositoriesFilter(".*antoniolg.*")).link(Collector()).execute()
self.assertEqual(len(repos), 1)
self.assertEqual(repos[0].id, "github.com/antoniolg/androidmvp.git")
def test_head_files(self):
df = HeadFiles()(self.engine.repositories)
df_as_dict = df.first().asDict()
keys = set(df_as_dict.keys())
self.assertIn("commit_hash", keys)
self.assertIn("path", keys)
self.assertIn("content", keys)
self.assertIn("reference_name", keys)
def test_uast_extractor(self):
df = HeadFiles()(self.engine.repositories)
df_uast = UastExtractor()(df)
self.assertIn("uast", df_uast.columns)
def test_uast_deserializer(self):
df = HeadFiles()(self.engine.repositories)
df_uast = UastExtractor()(df)
r2d = UastRow2Document()
row_uast = r2d.documentize(df_uast.first())
uasts_empty = list(UastDeserializer().deserialize_uast(df.first()))
uasts = list(UastDeserializer().deserialize_uast(row_uast))
self.assertEqual(len(uasts_empty), 0)
self.assertGreater(len(uasts), 0)
def test_csv_saver(self):
with tempfile.TemporaryDirectory() as tmpdir:
dirname = tmpdir
# load and save data
rows = [("Alice", 1)]
df = self.spark.createDataFrame(rows, ["name", "age"])
CsvSaver(dirname)(df.rdd)
# read saved data and check it
for root, d, files in os.walk(dirname):
for f in files:
filename = os.path.join(root, f)
if filename.endswith(".csv"):
with open(filename) as f:
reader = csv.reader(f)
next(reader)
data = [r for r in reader]
self.assertEqual(len(data), 1)
self.assertEqual(data[0][0], rows[0][0])
self.assertEqual(int(data[0][1]), rows[0][1])
def test_parquet_saver(self):
with tempfile.TemporaryDirectory() as tmpdir:
dirname = tmpdir
try:
# load and save data
rows = [("Alice", 1)]
df = self.spark.createDataFrame(rows, ["name", "age"])
ParquetSaver(dirname + "/", explain=True)(df.rdd)
ParquetSaver(dirname + "2/")(df.rdd)
# read saved data and check it
data = ParquetLoader(session=self.spark, paths=dirname).execute()
self.assertEqual(data.count(), 1)
finally:
shutil.rmtree(dirname)
def test_collector(self):
data = ParquetLoader(session=self.spark, paths=PARQUET_DIR).link(Collector()) \
.execute()
self.assertEqual(len(data), 6)
def test_first(self):
row = ParquetLoader(session=self.spark, paths=PARQUET_DIR).link(First()) \
.execute()
self.assertIsInstance(row, Row)
def test_field_selector(self):
rows = [("Alice", 1)]
df = self.spark.createDataFrame(rows, ["name", "age"])
# select field "name"
row = FieldsSelector(fields=["name"], explain=True)(df.rdd).first()
self.assertFalse(hasattr(row, "age"))
self.assertTrue(hasattr(row, "name"))
# select field "age"
row = FieldsSelector(fields=["age"])(df.rdd).first()
self.assertTrue(hasattr(row, "age"))
self.assertFalse(hasattr(row, "name"))
# select field "name" and "age"
row = FieldsSelector(fields=["name", "age"])(df.rdd).first()
self.assertTrue(hasattr(row, "age"))
self.assertTrue(hasattr(row, "name"))
def test_language_selector(self):
language_selector = LanguageSelector(languages=["XML", "YAML"], blacklist=True)
df = language_selector(HeadFiles()(self.engine.repositories).classify_languages())
langs = [x.lang for x in df.select("lang").distinct().collect()]
self.assertEqual(langs, ["Markdown", "Gradle", "Text", "INI",
"Batchfile", "Python", "Java", "Shell"])
language_selector = LanguageSelector(languages=["Python", "Java"], blacklist=False)
df = language_selector(HeadFiles()(self.engine.repositories).classify_languages())
langs = [x.lang for x in df.select("lang").distinct().collect()]
self.assertEqual(langs, ["Python", "Java"])
if __name__ == '__main__':
unittest.main()