def test_singlecolumns_partialnullspartialdistinct(self):
data = pd.DataFrame()
# create and assign columns to df
l1 = [chr(i) for i in range(100)]
l2 = [i for i in range(100)]
l3 = [i / 0.7 for i in range(100)]
for i in range(20):
l1[i] = ""
l2[i] = 0
l3[i] = 0.
for i in range(20, 40):
l1[i] = "zzzzz"
l2[i] = 500
l3[i] = 402.2
random.shuffle(l1)
random.shuffle(l2)
random.shuffle(l3)
data["c1"] = l1
data["c2"] = l2
data["c3"] = l3
df = self.spark.createDataFrame(data)
df = df.withColumn("c1", replace_empty_with_null(df["c1"]))
df = df.withColumn("c2", replace_0_with_null(df["c2"]))
df = df.withColumn("c3", replace_0dot_with_null(df["c3"]))
r1, r2, r3 = deduplication_approximated(["c1", "c2", "c3"], df)
self.assertAlmostEqual(r1, 61.0, delta=3.)
self.assertAlmostEqual(r2, 61.0, delta=3.)
self.assertAlmostEqual(r3, 61.0, delta=3.)
def test_singlecolumns_alldifferent(self):
data = pd.DataFrame()
data["c1"] = [chr(i) for i in range(100)]
data["c2"] = [i for i in range(100)]
data["c3"] = [i / 0.7 for i in range(100)]
df = self.spark.createDataFrame(data)
r1, r2, r3 = deduplication_approximated(["c1", "c2", "c3"], df)
self.assertAlmostEqual(r1, 100.0, delta=5.)
self.assertAlmostEqual(r2, 100.0, delta=5.)
self.assertAlmostEqual(r3, 100.0, delta=5.)
def test_singlecolumns_allsame(self):
data = pd.DataFrame()
data["c1"] = [chr(0) for _ in range(100)]
data["c2"] = [10 for _ in range(100)]
data["c3"] = [20 / 0.7 for _ in range(100)]
df = self.spark.createDataFrame(data)
r1, r2, r3 = deduplication_approximated(["c1", "c2", "c3"], df)
self.assertAlmostEqual(r1, 1.0, delta=0.05)
self.assertAlmostEqual(r2, 1.0, delta=0.05)
self.assertAlmostEqual(r3, 1.0, delta=0.05)
def test_singlecolumns_empty(self):
data = pd.DataFrame()
data["c1"] = []
data["c2"] = []
schema = [
StructField("c1", IntegerType(), True),
StructField("c2", StringType(), True)
]
df = self.spark.createDataFrame(data, StructType(schema))
r1, r2 = deduplication_approximated(["c1", "c2"], df)
self.assertAlmostEqual(r1, 100., delta=5.)
self.assertAlmostEqual(r2, 100., delta=5.)
def test_singlecolumns_allnull(self):
data = pd.DataFrame()
data["c1"] = [chr(i) for i in range(100)]
data["c2"] = [i for i in range(100)]
data["c3"] = [i / 0.7 for i in range(100)]
df = self.spark.createDataFrame(data)
df = df.withColumn("c1", replace_every_string_with_null(df["c1"]))
df = df.withColumn("c2", replace_every_int_with_null(df["c2"]))
df = df.withColumn("c3", replace_every_float_with_null(df["c3"]))
r1, r2, r3 = deduplication_approximated(["c1", "c2", "c3"], df)
self.assertEqual(r1, 0.0)
self.assertEqual(r2, 0.0)
self.assertEqual(r3, 0.0)
def test_singlecolumns_partial(self):
data = pd.DataFrame()
# create and assign columns to df
l1 = [chr(i) for i in range(100)]
l2 = [i for i in range(100)]
l3 = [i / 0.7 for i in range(100)]
for i in range(20):
l1[i] = ""
l2[i] = 0
l3[i] = 0.
random.shuffle(l1)
random.shuffle(l2)
random.shuffle(l3)
data["c1"] = l1
data["c2"] = l2
data["c3"] = l3
df = self.spark.createDataFrame(data)
r1, r2, r3 = deduplication_approximated(["c1", "c2", "c3"], df)
self.assertAlmostEqual(r1, 81.0, delta=4.)
self.assertAlmostEqual(r2, 81.0, delta=4.)
self.assertAlmostEqual(r3, 81.0, delta=4.)
#!/usr/bin/python3
from pyspark.sql import SparkSession
from haychecker.dhc.metrics import deduplication_approximated
spark = SparkSession.builder.appName(
"Deduplication_approximated_example").getOrCreate()
df = spark.read.format("csv").option(
"header", "true").load("examples/resources/employees.csv")
df.show()
r1, r2 = deduplication_approximated(["title", "city"], df)
print(
"Deduplication_approximated title: {}, deduplication_approximated city: {}"
.format(r1, r2))
task1 = deduplication_approximated(["title", "city"])
task2 = deduplication_approximated(["lastName"])
task3 = task1.add(task2)
result = task3.run(df)
r1, r2 = result[0]["scores"]
r3 = result[1]["scores"][0]
print(
"Deduplication_approximated title: {}, deduplication_approximated city: {}, "
"deduplication_approximated lastName: {}".format(r1, r2, r3))
