def test_Size(self):
result = self.AnalysisRunner.onData(self.df).addAnalyzer(Size()).run()
# result_df = result.select('value').collect()
result_df = AnalyzerContext.successMetricsAsDataFrame(
self.spark, result)
result_df_row = result_df.select("value").collect()
self.assertEqual(result_df_row, [Row(value=3.0)])
def test_KLLSketch(self):
result = (self.AnalysisRunner.onData(self.df).addAnalyzer(
KLLSketch("b", KLLParameters(self.spark, 2, 0.64, 2))).run())
result_df = AnalyzerContext.successMetricsAsDataFrame(
self.spark, result)
result_df.show()
return result_df.select("value").collect()
def Correlation(self, column1, column2, where=None):
result = self.AnalysisRunner.onData(self.df).addAnalyzer(
Correlation(column1, column2, where)).run()
result_df = AnalyzerContext.successMetricsAsDataFrame(
self.spark, result)
AnalyzerContext.successMetricsAsJson(self.spark, result)
return result_df.select("value").collect()
def Completeness(self, column, where=None):
result = self.AnalysisRunner.onData(self.df).addAnalyzer(
Completeness(column, where)).run()
result_df = AnalyzerContext.successMetricsAsDataFrame(
self.spark, result)
result_json = AnalyzerContext.successMetricsAsJson(self.spark, result)
df_from_json = self.spark.read.json(self.sc.parallelize([result_json]))
self.assertEqual(
df_from_json.select("value").collect(),
result_df.select("value").collect())
return result_df.select("value").collect()
def test_PatternMatch(self):
result = (self.AnalysisRunner.onData(self.df).addAnalyzer(
PatternMatch(column="a", pattern_regex="ba(r|z)")).run())
result_df = AnalyzerContext.successMetricsAsDataFrame(
self.spark, result)
result_json = AnalyzerContext.successMetricsAsJson(self.spark, result)
df_from_json = self.spark.read.json(self.sc.parallelize([result_json]))
self.assertEqual(
df_from_json.select("value").collect(),
result_df.select("value").collect())
self.assertEqual(result_df.select("value").collect(), [Row(value=0.0)])
def ApproxQuantile(self, column, quantile, where=None):
relativeError: float = 0.01
result = (self.AnalysisRunner.onData(self.df).addAnalyzer(
ApproxQuantile(column, quantile, relativeError, where)).run())
result_df = AnalyzerContext.successMetricsAsDataFrame(
self.spark, result)
result_json = AnalyzerContext.successMetricsAsJson(self.spark, result)
df_from_json = self.spark.read.json(self.sc.parallelize([result_json]))
self.assertEqual(
df_from_json.select("value").collect(),
result_df.select("value").collect())
return result_df.select("value").collect()
def Histogram_maxBins(self,
column,
binningUdf=None,
maxDetailBins: int = None,
where: str = None):
result = (self.AnalysisRunner.onData(self.df).addAnalyzer(
Histogram(column, binningUdf, maxDetailBins, where)).run())
result_df = AnalyzerContext.successMetricsAsDataFrame(
self.spark, result)
result_json = AnalyzerContext.successMetricsAsJson(self.spark, result)
df_from_json = self.spark.read.json(self.sc.parallelize([result_json]))
self.assertEqual(
df_from_json.select("value").collect(),
result_df.select("value").collect())
return result_df.select("value").collect()