geounits = [
testdata_random_geounit_generator(x, schema, density=0.00001, scale=10)
for x in geocodes
]
sdftools.print_item(geounits, "Random Geounit Data")
rdd = spark.sparkContext.parallelize(geounits).persist()
sdftools.print_item(rdd, "Parallelized RDD data")
df = rdd.flatMap(lambda node: mappers.getSparseDF_mapper(node, schema)
).map(lambda row: Row(**row)).toDF().persist()
sdftools.print_item(df, "DF of Random Geounit Data")
df = df.withColumn("STATE", sf.col("geocode")[0:2]).persist()
sdftools.print_item(df, "DF with STATE code")
df = sdftools.aggregateGeolevels(spark, df, 'STATE')
sdftools.print_item(df, "Aggregated to the STATE geolevel")
query = 'sex * age'
df = sdftools.answerQuery(df, schema, query, labels=False)
sdftools.print_item(df, "Answering the sex query")
groupby = ['geocode', 'geolevel']
rdd = sdftools.getRowGroupsAsRDD(df, groupby)
df = rdd.flatMapValues(prob_vector_mapper).map(
lambda row: Row(**row[1])).toDF()
df = df.withColumn('age', sf.col('age').cast("int")).persist()
df = df.sort(['geocode', 'age', 'sex']).persist()
sdftools.print_item(df, f"Prob vector for {query} query", show=1000)
geocode_dict = {3: 'block', 2: 'county'}
# build geounits
geounits = toytools.getToyGeounitData(schema, geocodes, geocode_dict)
rdd = spark.sparkContext.parallelize(geounits).persist()
sdftools.print_item(rdd.take(1), "One of the toy example geounits")
# use Analysis to transform the rdd of geounitnodes into a spark dataframe
df = datatools.rdd2df(rdd, schema)
sdftools.print_item(df, "Toy example DF", 300)
# perform analyses
# L1
# 'priv' means "protected via the differential privacy routines in this code base" variable to be renamed after P.L.94-171 production
df = sdftools.getL1(df, colname="L1_cell", col1='priv', col2='orig')
sdftools.print_item(df, "Toy example L1", 300)
# adding in a simple row-counting column
df = df.withColumn("row_count", sf.lit(1)).persist()
sdftools.print_item(df, "Totals + rowcounter column")
# total within each geocode
df = sdftools.answerQuery(df, schema, "total", labels=False)
sdftools.print_item(df, "Totals within each geounit", 300)
# L1 of total
df = sdftools.getL1(df, colname="L1_total", col1="priv", col2="orig")
sdftools.print_item(df, "Totals + rowcounter column + L1")
df = sdftools.aggregateGeolevels(spark, df, geolevels)
sdftools.print_item(df, "Geolevel DF")
####################
# Answering Queries
####################
queries = [
"total", "sex", "sex * hispanic", "votingage * sex * hispanic",
"numraces"
]
for query in queries:
querydf = sdftools.answerQuery(df,
schema,
query,
labels=False,
merge_dims=False)
sdftools.print_item(
querydf, f"Query DF for {query}, labels=False, merge_dims=False",
2000)
querydf = sdftools.answerQuery(df,
schema,
query,
labels=False,
merge_dims=True)
sdftools.print_item(
querydf, f"Query DF for {query}, labels=False, merge_dims=True",
2000)
