def getCategoryByAgeQuantilesFast(sdf, queries, product, state, plot=False):
df = sdf.df
results = {}
for query in queries:
res = sdftools.categoryByAgeQuantiles(df,
sdf.schema,
query,
labels=True)
results.update(res)
if plot:
for key, df in results.items():
queryname, category, datatype = parseAgeQuantileKey(key,
fsname=False)
sdftools.print_item(
df.count(),
"Number of rows in the Spark DF before transforming to Pandas DF"
)
if datatype == "quantile_df":
age_quantile_pandas_df = df.toPandas()
saveloc = du.getdir(sdf.metric_save_location)
rp.age_quantile_lineplot(age_quantile_pandas_df, saveloc,
product, state)
else: # datatype == "survival_props"
pass
return results
def analyzeQuery(query, table_name, analysis, spark, geolevels, eps, buckets=default_buckets, schema="DHCP_HHGQ"):
"""
Main plotting fxn.
query : str, name of a valid query for the target experiment's schema
table_name : str, name of a table (used for file-naming conventions)
analysis : Analysis setuptools.setup object, organizes Analysis metadata
spark : SparkSession object, attached to analysis object
geolevels : [str, ...], geolevels to compute over for the current query
buckets : [(int,int), ...], list of mutually exclusive bucket boundaries for Tab(CEF) bucketing
schema : str, name of ../programs/schema/schemas/schemamaker.py schema associated with target data
Note, also, major control parameters hard-coded in getPaths for setting experiment ingest locations from s3.
"""
print(f"For table {table_name}, analyzing query {query} at geolevels {geolevels} with schema {schema}")
schema_name = schema
num_trials, paths, experiment_name, eps_str = getPathsAndName(schema_name, query, table_name, eps)
print(f"Passing paths to Analysis experiment maker: {paths}")
experiment = analysis.make_experiment(experiment_name, paths, schema_name=schema_name, dasruntype=AC.EXPERIMENT_FRAMEWORK_FLAT)
sdftools.print_item(experiment.__dict__, "Experiment Attributes")
spark_df = experiment.getDF()
print("df looks like:")
spark_df.show()
schema = experiment.schema
sdftools.print_item(spark_df, "Flat Experiment DF")
queries = [query]
spark_df = sdftools.aggregateGeolevels(spark, spark_df, geolevels)
spark_df = sdftools.answerQueries(spark_df, schema, queries)
spark_df = sdftools.getAvgAbsErrorByTrueCountRuns(spark_df, bins=[0,1,10,100,1000,10000]).persist()
missing_rows_pandas_df = sdftools.getMissingRowCounts(spark_df, schema, queries, groupby=[AC.GEOCODE, AC.GEOLEVEL, AC.PLB, AC.BUDGET_GROUP])
missing_rows_dict = defaultdict(int)
for index, row in missing_rows_pandas_df.iterrows():
#print(f"missing df row # {index} geolevel, sum(missing) = {row['geolevel']},{row['sum(missing)']}")
missing_rows_dict[row['geolevel']] = row['sum(missing)']
spark_df.show()
print("^^^^ with abs error, DF looks like ^^^^")
metric_name = "Avg( |q(MDF) - q(CEF)| )"
x_axis_variable_name = 'CEF Count, Binned'
pandas_df = spark_df.toPandas()
pandas_df = pandas_df.rename(columns={"abs_error":metric_name, "orig_count_bin":x_axis_variable_name})
plt.figure(1, figsize=(11,8.5))
plt.rc('axes', labelsize=8)
print(f"pandas df before plotting has cols: {pandas_df.columns.values}")
print(f"{x_axis_variable_name} column has distinct levels: {pandas_df[x_axis_variable_name].unique()}")
buckets = pandas_df[x_axis_variable_name].unique()
buckets = sorted(buckets, key=lambda bucket_name: largestIntInStr(bucket_name))
print(f"Sorted bucket names: {buckets}")
# Saving data frame
csv_savepath = experiment.save_location_linux + f"{experiment_name}.csv"
du.makePath(du.getdir(csv_savepath))
pandas_df.to_csv(csv_savepath, index=False)
makePlots(experiment, experiment_name, table_name, queries, x_axis_variable_name,
metric_name, geolevels, pandas_df, buckets,
schema_name, eps_str, missing_rows_dict, num_trials)
def getGeolevelTVDFast(sdf, product, state, plot=False):
sdf = sdf.geolevel_tvd(groupby=[AC.GEOLEVEL, AC.RUN_ID, AC.QUERY, AC.PLB])
if plot:
saveloc = du.getdir(sdf.metric_save_location)
sdftools.print_item(
sdf.df.count(),
"Number of rows in the Spark DF before transforming to Pandas DF")
geolevel_tvd_pandas_df = sdf.toPandas()
rp.geolevel_tvd_lineplot(geolevel_tvd_pandas_df, saveloc, product,
state)
rp.geolevel_tvd_heatmap(geolevel_tvd_pandas_df, saveloc, product,
state)
results = {'geolevel_tvd': sdf}
return results
def getGeolevelTVD(sdf, geolevels, queries, product, state, plot=False):
"""
Calculates the following:
0. Query answers at the specified geolevels
1. Per-geolevel TVD
"""
sdf = sdf.getGeolevels(geolevels).getQueryAnswers(queries)
sdf = sdf.geolevel_tvd(groupby=[AC.GEOLEVEL, AC.RUN_ID, AC.QUERY, AC.PLB])
if plot:
saveloc = du.getdir(sdf.metric_save_location)
geolevel_tvd_pandas_df = sdf.toPandas()
rp.geolevel_tvd_lineplot(geolevel_tvd_pandas_df, saveloc, product,
state)
rp.geolevel_tvd_heatmap(geolevel_tvd_pandas_df, saveloc, product,
state)
return sdf
queri = ["allraces"]
df_geolevel = sdftools.aggregateGeolevels(spark, df, geolevels)
df_table = sdftools.answerQueries(df_geolevel, schema, queri, labels=True).persist()
df_withmissingrows=sdftools.getFullWorkloadDF(df_table, schema, queri,groupby=[AC.GEOCODE, AC.GEOLEVEL, AC.RUN_ID, AC.PLB, AC.BUDGET_GROUP])
#print(df_withmissingrows.head(200))
sparse = sdftools.getCellSparsityByGroup(df_withmissingrows,schema,groupby=[AC.GEOCODE, AC.GEOLEVEL, AC.RUN_ID, AC.PLB, AC.BUDGET_GROUP,AC.QUERY])
zero=ReturnZeroCounts(df_withmissingrows, geolevels)
print("This is sparse:")
print(sparse.head(20))
print("This is zero")
print(zero.head(20))
csv_savepath = save_location_linux + f"origtable.csv"
csv_savepath2 = save_location_linux + f"missingrows.csv"
du.makePath(du.getdir(csv_savepath))
du.makePath(du.getdir(csv_savepath2))
pandas_df_table=df_table.toPandas()
pandas_df_table.to_csv(csv_savepath, index=False)
pandas_dfmissing=df_withmissingrows.toPandas()
pandas_dfmissing.to_csv(csv_savepath2, index=False)
# df_geolevel = sdftools.aggregateGeolevels(spark, df, geolevels)
# races_names1=['White','Black or African American','American Indian and Alaskan Native']
# races_names2=['Asian','Native Hawaiian and Other Pacific Islander','Some Other Race']
# white1=['Aian']
# buckets=[(0,0),(1,10),(11,100),(100,1000),(1000,10000),(10000,float('inf'))]
# path = du.addslash(save_location)
# plt.figure(figsize=(11,8.5))
#'detailed'
'cenrace'
]
sdftools.show(df, "df with geolevel crosswalk columns")
sdftools.show(df, "df with geolevel crosswalk columns")
df = sdftools.aggregateGeolevels(spark, df, geolevels)
sdftools.show(df, "df after geolevel aggregation", 1000)
qdf = sdftools.answerQuery(df, schema, "total", labels=False, merge_dims=False)
sdftools.show(qdf, "Query df with the query 'total'", 1000)
rdd = sdftools.getRowGroupsAsRDD(qdf, groupby=[AC.GEOLEVEL, AC.QUERY])
#sdftools.show(rdd.collect(), "Row groups")
path = save_location_linux + "Gel.csv"
q = qdf.toPandas()
du.makePath(du.getdir(path))
q.to_csv(path, index=False)
def Qualbins(rows, column, bins):
#only works if bins > 2
pandas_df = pandas.DataFrame(rows)
q = 1/bins
p = bins+1
for i in range (1, p):
k=str(i)
pandas_df['Bin'+k]=(np.quantile(pandas_df[column],q)>=pandas_df[column])&(pandas_df[column]>=np.quantile(pandas_df[column],q-1/bins))
q=q+1/bins
rows = pandas_df.to_dict('records')
def MattsMetrics(query,
table_name,
analysis,
spark,
geolevels,
schema="DHCP_HHGQ"):
"""
This function computes metrics for MAE, MALPE, CoV, RMS, MAPE, and percent thresholds"
"""
print(
f"For table {table_name}, analyzing query {query} at geolevels {geolevels} with schema {schema}"
)
schema_name = schema
paths, experiment_name, eps_str = getPathsAndName(schema_name)
experiment = analysis.make_experiment(
experiment_name,
paths,
schema_name=schema_name,
dasruntype=AC.EXPERIMENT_FRAMEWORK_FLAT)
sdftools.print_item(experiment.__dict__, "Experiment Attributes")
spark_df = experiment.getDF()
print("df looks like:")
spark_df.show()
schema = experiment.schema
sdftools.print_item(spark_df, "Flat Experiment DF")
queries = [query]
spark_df = sdftools.aggregateGeolevels(spark, spark_df, geolevels)
spark_df = sdftools.answerQueries(spark_df, schema, queries)
spark_df = sdftools.getFullWorkloadDF(
spark_df,
schema,
queries,
groupby=[AC.GEOCODE, AC.GEOLEVEL, AC.RUN_ID, AC.PLB, AC.BUDGET_GROUP])
#spark_df.show(spark_df.count(), False)
# AC.PRIV means "protected via the differential privacy routines in this code base" variable to be renamed after P.L.94-171 production
spark_df = sdftools.getL1(spark_df,
colname="L1",
col1=AC.PRIV,
col2=AC.ORIG)
spark_df = sdftools.getL2(spark_df,
colname="L2",
col1=AC.PRIV,
col2=AC.ORIG)
# apply bin functions for particular tables
if (table_name in table_bucket_list1):
spark_df = sdftools.getCountBins(
spark_df,
column=AC.ORIG,
bins=[0, 1000, 5000, 10000, 50000, 100000]).persist()
if (table_name in table_bucket_list2):
spark_df = sdftools.getCountBins(spark_df,
column=AC.ORIG,
bins=[0, 10, 100]).persist()
# This finds overall metrics
spark_df.show(100, False)
if table_name not in (table_list_3_plus_list_age):
for g in geolevels:
spark_df1 = spark_df[spark_df['geolevel'] ==
g] # Separate data for each geolevel
if table_name in table_default_no_bucket: # If data is not in buckets
bucket_size = "NA"
metrics_result = sdftools.metrics_with_popbucket(spark_df1,
bucket_size,
spark,
key="A")
file_name = f"{table_name}_{g}.csv"
if table_name in table_bucket_list2: # if data is bucketed in 3 buckets,
bucket_size = default_buckets2
print("BUCKET SIZE IS:", bucket_size)
metrics_result = sdftools.metrics_with_popbucket(spark_df1,
bucket_size,
spark,
key="B")
file_name = f"{table_name}_{g}.csv"
if table_name in table_bucket_list1: # Table 1 and 2, six buckets
bucket_size = default_buckets1
print("BUCKET SIZE IS:", bucket_size)
metrics_result = sdftools.metrics_with_popbucket(spark_df1,
bucket_size,
spark,
key="B")
file_name = f"{table_name}_{g}.csv"
if table_name in table_list_3geolevels: #three geolevels, state, county, place, Tables 10,14,18,22
metrics_result = sdftools.metrics_with_3geolevels(
spark_df, spark, geolevels)
file_name = f"{table_name}.csv"
if table_name in table_list_age: # Tables 32-35
if table_name in table_age_bracket1:
metrics_result = sdftools.metrics_with_age(spark_df,
spark,
age_range_list,
key="A")
if table_name in table_age_bracket2:
metrics_result = sdftools.metrics_with_age(spark_df,
spark,
age_range_list,
key="B")
if table_name in table_age_bracket3:
metrics_result = sdftools.metrics_with_age(spark_df,
spark,
age_range_list2,
key="A")
if table_name in table_age_bracket4:
metrics_result = sdftools.metrics_with_age(spark_df,
spark,
age_range_list2,
key="B")
file_name = f"{table_name}.csv"
pandas_df = metrics_result.toPandas()
csv_savepath = experiment.save_location_linux + file_name
du.makePath(du.getdir(csv_savepath))
pandas_df.to_csv(csv_savepath, index=False)
def analyzeQuery(query, table_name, analysis, spark, geolevels, buckets=default_buckets, schema="DHCP_HHGQ"):
"""
Main plotting fxn.
query : str, name of a valid query for the target experiment's schema
table_name : str, name of a table (used for file-naming conventions)
analysis : Analysis setuptools.setup object, organizes Analysis metadata
spark : SparkSession object, attached to analysis object
geolevels : [str, ...], geolevels to compute over for the current query
buckets : [(int,int), ...], list of mutually exclusive bucket boundaries for Tab(CEF) bucketing
Note, also, major control parameters hard-coded in getPaths for setting experiment ingest locations from s3.
"""
print(f"For table {table_name}, analyzing query {query} at geolevels {geolevels} with schema {schema}")
schema_name = schema
paths, experiment_name, eps_str = getPathsAndName(schema_name)
experiment = analysis.make_experiment(experiment_name, paths, schema_name=schema_name, dasruntype=AC.EXPERIMENT_FRAMEWORK_FLAT)
sdftools.print_item(experiment.__dict__, "Experiment Attributes")
spark_df = experiment.getDF()
print("df looks like:")
spark_df.show()
schema = experiment.schema
sdftools.print_item(spark_df, "Flat Experiment DF")
queries = [query]
#y=sdftools.getAnswers(spark,df,geolevels,schema,queries)
# Old approach to computing df with abs diff, bucketed by true count:
#sparkDFWithAbsDiff = getSparkDFWithAbsDiff(spark, spark_df, geolevels, queries, schema)
#getSignedErrorByTrueCountRuns(spark_df, bins=[0,1,10,100,1000,10000]):
#rdd = sdftools.getRowGroupsAsRDD(sparkDFWithAbsDiff, groupby=[AC.GEOLEVEL, AC.QUERY])
#rdd = rdd.flatMapValues(lambda rows: sepBounds(rows, 'orig', buckets)).persist()
#rdd = rdd.map(lambda row: Row(**row[1]))
#spark_df = rdd.toDF().persist()
# New (actually preexisting) approach to computing spark_df with abs diff, bucketed by true count:
# (avoids pandas dfs inside mappers, which is RAM-hungry)
spark_df = sdftools.aggregateGeolevels(spark, spark_df, geolevels)
spark_df = sdftools.answerQueries(spark_df, schema, queries)
spark_df = sdftools.getFullWorkloadDF(spark_df, schema, queries,groupby=[AC.GEOCODE, AC.GEOLEVEL, AC.RUN_ID, AC.PLB, AC.BUDGET_GROUP])
spark_df = sdftools.getAvgAbsErrorByTrueCountRuns(spark_df, bins=[0,1,10,100,1000,10000]).persist()
spark_df.show()
print("^^^^ with abs error, DF looks like ^^^^")
metric_name = "Avg( |q(MDF) - q(CEF)| )"
x_axis_variable_name = 'CEF Count, Binned'
# spark_df = spark_df.groupby(['geocode','geolevel','level','Bin0','Bin1','Bin2','Bin3','Bin4','Bin5']).avg()
# Below spark_df has cols: geocode, geolevel, run_id, plb, budget_group, query, orig_count_bin, signed_error, re
#spark_df = spark_df.groupby(['geocode', 'geolevel', 'plb', 'budget_group', 'query', 'orig_count_bin']).avg()
#print("^^^^ after averaging, spark_df looks like ^^^^")
pandas_df = spark_df.toPandas()
#pandas_df = pandas_df.rename(columns={"avg(signed_error)":metric_name, "avg(orig)":"orig"})
#pandas_df[x_axis_variable_name] = pandas_df.apply(lambda row: binIndexToInteger(row, buckets), axis=1)
#pandas_df = pandas_df.rename(columns={"avg(signed_error)":metric_name, "avg(orig_count_bin)":"orig"})
pandas_df = pandas_df.rename(columns={"abs_error":metric_name, "orig_count_bin":x_axis_variable_name})
plt.figure(1, figsize=(11,8.5))
plt.rc('axes', labelsize=8)
print(f"pandas df before plotting has cols: {pandas_df.columns.values}")
print(f"{x_axis_variable_name} column has distinct levels: {pandas_df[x_axis_variable_name].unique()}")
buckets = pandas_df[x_axis_variable_name].unique()
buckets = sorted(buckets, key=lambda bucket_name: largestIntInStr(bucket_name))
print(f"Sorted bucket names: {buckets}")
new_bucket_order = [0,1,2,3,5,4] # Apply ordering system to make 10000+ the last bucket
buckets = [buckets[i] for i in new_bucket_order]
print(f"Sorted bucket names: {buckets}")
"""
print(pandas_df.head(30))
print(f"pandas_df headers: {list(pandas_df.columns.values)}")
tmpDf = pandas_df[[x_axis_variable_name, 'orig', metric_name]]
print("tmpDf looks like:")
with pandas.option_context('display.max_rows', None, 'display.max_columns', None):
print(tmpDf)
print("^^^^ pandas df looks like ^^^^")
print("And first 3 rows:")
print(pandas_df.iloc[:3])
#print(df.dtypes)
print("And first 100 rows, subset to Bins:")
print(pandas_df.iloc[0:101,3:9])
print(pandas_df.iloc[0:101,-1])
"""
# Saving data frame
csv_savepath = experiment.save_location_linux + f"Executive_Priority_Tabulations_#1_{experiment_name}_{table_name}_{query}.csv"
du.makePath(du.getdir(csv_savepath))
pandas_df.to_csv(csv_savepath, index=False)
makePlots(experiment, experiment_name, table_name, queries, x_axis_variable_name,
metric_name, geolevels, pandas_df, buckets,
schema_name, eps_str)
def MattsMetrics(query,
table_name,
analysis,
spark,
geolevels,
key,
agekey,
sexkey,
bucketkey,
schema="DHCP_HHGQ"):
"""
This function computes metrics for MAE, MALPE, CoV, RMS, MAPE, and percent thresholds"
"""
print(
f"For table {table_name}, analyzing query {query} at geolevels {geolevels} with schema {schema}"
)
schema_name = schema
paths, experiment_name, eps_str = getPathsAndName(schema_name)
experiment = analysis.make_experiment(
experiment_name,
paths,
schema_name=schema_name,
dasruntype=AC.EXPERIMENT_FRAMEWORK_FLAT)
sdftools.print_item(experiment.__dict__, "Experiment Attributes")
spark_df = experiment.getDF()
print("df looks like:")
spark_df.show()
schema = experiment.schema
sdftools.print_item(spark_df, "Flat Experiment DF")
queries = [query]
spark_df = sdftools.aggregateGeolevels(spark, spark_df, geolevels)
spark_df = sdftools.answerQueries(spark_df, schema, queries)
spark_df = sdftools.getFullWorkloadDF(
spark_df,
schema,
queries,
groupby=[AC.GEOCODE, AC.GEOLEVEL, AC.RUN_ID, AC.PLB, AC.BUDGET_GROUP])
#spark_df.show(spark_df.count(), False)
# AC.PRIV means "protected via the differential privacy routines in this code base" variable to be renamed after P.L.94-171 production
spark_df = sdftools.getL1(spark_df,
colname="L1",
col1=AC.PRIV,
col2=AC.ORIG)
spark_df = sdftools.getL2(spark_df,
colname="L2",
col1=AC.PRIV,
col2=AC.ORIG)
# apply bin functions for particular tables
if (table_name in table_bucket_list1):
spark_df = sdftools.getCountBins(
spark_df,
column=AC.ORIG,
bins=[0, 1000, 5000, 10000, 50000, 100000]).persist()
if (table_name in table_bucket_list2):
spark_df = sdftools.getCountBins(spark_df,
column=AC.ORIG,
bins=[0, 10, 100]).persist()
# This finds overall metrics
spark_df.show(100, False)
metrics_result = sdftools.combined_metrics(spark_df, spark, geolevels,
agekey, sexkey, bucketkey, key)
file_name = f"{table_name}.csv"
pandas_df = metrics_result.toPandas()
csv_savepath = experiment.save_location_linux + file_name
du.makePath(du.getdir(csv_savepath))
pandas_df.to_csv(csv_savepath, index=False)
def MattsMetrics(query,
table_name,
analysis,
spark,
geolevels,
buckets=default_buckets,
schema="DHCP_HHGQ"):
"""
This function computes metrics for MAE, MALPE, CoV, RMS, MAPE, and percent thresholds"
"""
print(
f"For table {table_name}, analyzing query {query} at geolevels {geolevels} with schema {schema}"
)
schema_name = schema
paths, experiment_name, eps_str = getPathsAndName(schema_name)
experiment = analysis.make_experiment(
experiment_name,
paths,
schema_name=schema_name,
dasruntype=AC.EXPERIMENT_FRAMEWORK_FLAT)
sdftools.print_item(experiment.__dict__, "Experiment Attributes")
spark_df = experiment.getDF()
print("df looks like:")
spark_df.show()
schema = experiment.schema
sdftools.print_item(spark_df, "Flat Experiment DF")
queries = [query]
spark_df = sdftools.aggregateGeolevels(spark, spark_df, geolevels)
spark_df = sdftools.answerQueries(spark_df, schema, queries)
#spark_df.show(spark_df.count(), False)
# AC.PRIV means "protected via the differential privacy routines in this code base" variable to be renamed after P.L.94-171 production
spark_df = sdftools.getL1(spark_df,
colname="L1",
col1=AC.PRIV,
col2=AC.ORIG)
spark_df = sdftools.getL2(spark_df,
colname="L2",
col1=AC.PRIV,
col2=AC.ORIG)
# apply bin functions for particular tables
if (table_name in table_default_bucket_list):
spark_df = sdftools.getCountBins(
spark_df,
column=AC.ORIG,
bins=[0, 1000, 5000, 10000, 50000, 100000]).persist()
if (table_name in table_default_bucket_list2):
spark_df = sdftools.getCountBins(spark_df,
column=AC.ORIG,
bins=[0, 10, 100]).persist()
# This finds overall metrics
#spark_df.show(spark_df.count(), False)
for g in geolevels:
spark_df = spark_df[spark_df['geolevel'] == g]
print("This has all levels")
spark_df.show(150, False)
metrics_dataframe = sdftools.mattsmetrics(spark_df, spark)
Counts = spark_df.count()
print("Counts are", Counts)
newRow = spark.createDataFrame([(Counts, "Counts")])
metrics_dataframe = metrics_dataframe.union(newRow)
pandas_df = metrics_dataframe.toPandas()
csv_savepath = experiment.save_location_linux + f"{table_name}_{g}.csv"
du.makePath(du.getdir(csv_savepath))
pandas_df.to_csv(csv_savepath, index=False)
if table_name in table_default_bucket_list2: # If data needs bucketing
for b in default_buckets2: # calculate Metrics at each bucket
subset_sparkdf = spark_df[spark_df['orig_count_bin'] ==
b] #subset into bins
subset_sparkdf = subset_sparkdf.subtract(
subset_sparkdf.filter(subset_sparkdf.level.rlike("Not"))
) # Removes instances of Not Hispanic..from dataframe
subset_sparkdf.show(100, False)
print("Make sure its bucketed and without 'Not' values")
subset_metrics = sdftools.mattsmetrics(subset_sparkdf, spark)
Counts = subset_sparkdf.count()
newRow = spark.createDataFrame([(b, "Bucket")])
newRow1 = spark.createDataFrame([(Counts, "Counts")])
subset_metrics = subset_metrics.union(newRow).union(newRow1)
pandas_df = subset_metrics.toPandas()
csv_savepath = experiment.save_location_linux + f"{table_name}_{g}_{b}.csv"
du.makePath(du.getdir(csv_savepath))
pandas_df.to_csv(csv_savepath, index=False)
df.write.partitionBy(partitionby).option("header", "true").format("csv").save(path)
path = analysis.save_location_s3 + "toy_data_df_results_by_partition.csv"
sdftools.show(path, "S3 location of the toy df analysis results in csv form, partitioned by columns")
# can be a list; spark will split by each column requested, in the order specified
partitionby = ['query', 'geocode']
df.write.partitionBy(partitionby).option("header", "true").format("csv").save(path)
### Saving Spark DF as csv locally (via Pandas DF)
path = analysis.save_location_linux + "toy_data_df_results.csv"
sdftools.show(path, "Linux location of the toy df analysis results in csv form")
pdf = df.toPandas()
sdftools.show(pdf, "The Pandas DF based on the Spark DF")
# create the directory locally, otherwise pandas_df.to_csv will throw an error
sdftools.show(du.getdir(path), "The directory being created to house the pandas df data")
du.makePath(du.getdir(path))
pdf.to_csv(path, index=False)
### Saving Pandas DF to S3
# save locally and then copy to s3
path_linux = analysis.save_location_linux + "toy_data_pandas_df_results.csv"
du.makePath(du.getdir(path))
pdf.to_csv(path_linux, index=False)
path_s3 = analysis.save_location_s3 + "toy_data_pandas_df_results.csv"
s3.put_s3url(path_s3, path_linux)
sdftools.show(analysis.save_location_s3, "S3 Analysis Results Save Location")
def MattsMetrics(query,
table_name,
analysis,
spark,
geolevels,
buckets=default_buckets,
schema="DHCP_HHGQ"):
"""
This function computes metrics for MAE, MALPE, CoV, RMS, MAPE, and percent thresholds"
"""
print(
f"For table {table_name}, analyzing query {query} at geolevels {geolevels} with schema {schema}"
)
schema_name = schema
paths, experiment_name, eps_str = getPathsAndName(schema_name)
experiment = analysis.make_experiment(
experiment_name,
paths,
schema_name=schema_name,
dasruntype=AC.EXPERIMENT_FRAMEWORK_FLAT)
sdftools.print_item(experiment.__dict__, "Experiment Attributes")
spark_df = experiment.getDF()
print("df looks like:")
spark_df.show()
schema = experiment.schema
sdftools.print_item(spark_df, "Flat Experiment DF")
queries = [query]
spark_df = sdftools.aggregateGeolevels(spark, spark_df, geolevels)
spark_df = sdftools.answerQueries(spark_df, schema, queries)
spark_df = sdftools.getFullWorkloadDF(
spark_df,
schema,
queries,
groupby=[AC.GEOCODE, AC.GEOLEVEL, AC.RUN_ID, AC.PLB, AC.BUDGET_GROUP])
#spark_df.show(spark_df.count(), False)
# AC.PRIV means "protected via the differential privacy routines in this code base" variable to be renamed after P.L.94-171 production
spark_df = sdftools.getL1(spark_df,
colname="L1",
col1=AC.PRIV,
col2=AC.ORIG)
spark_df = sdftools.getL2(spark_df,
colname="L2",
col1=AC.PRIV,
col2=AC.ORIG)
# apply bin functions for particular tables
if (table_name in table_default_bucket_list):
spark_df = sdftools.getCountBins(
spark_df,
column=AC.ORIG,
bins=[0, 1000, 5000, 10000, 50000, 100000]).persist()
if (table_name in table_default_bucket_list2):
spark_df = sdftools.getCountBins(spark_df,
column=AC.ORIG,
bins=[0, 10, 100]).persist()
# This finds overall metrics
spark_df.show(100, False)
for g in geolevels:
spark_df1 = spark_df[spark_df['geolevel'] ==
g] # Separate data for each geolevel
if table_name in table_default_no_bucket: # If data is not in buckets
if table_name in table_race_query: # Table 17, 18, 21 and others
print("no buckets, with race query")
spark_df2 = spark_df1.subtract(
spark_df1.filter(spark_df1.level.rlike("Not")))
spark_df2.show(100, False)
print("Make sure 'Not' values are removed")
metrics_dataframe = sdftools.mattsmetrics(spark_df2, spark)
Counts = spark_df2.count()
print("Counts are", Counts)
newRow = spark.createDataFrame([(Counts, "Counts")])
metrics_dataframe = metrics_dataframe.union(newRow)
pandas_df = metrics_dataframe.toPandas()
csv_savepath = experiment.save_location_linux + f"{table_name}_{g}.csv"
du.makePath(du.getdir(csv_savepath))
pandas_df.to_csv(csv_savepath, index=False)
else:
print("no buckets, without race query")
spark_df1.show(100, False)
spark_df2 = spark_df1.subtract(
spark_df1.filter(spark_df1.level.rlike("Not")))
print("with Not removed")
spark_df2.show(100, False)
metrics_dataframe = sdftools.mattsmetrics(spark_df2, spark)
Counts = spark_df2.count()
print("Counts are", Counts)
newRow = spark.createDataFrame([(Counts, "Counts")])
metrics_dataframe = metrics_dataframe.union(newRow)
pandas_df = metrics_dataframe.toPandas()
csv_savepath = experiment.save_location_linux + f"{table_name}_{g}.csv"
du.makePath(du.getdir(csv_savepath))
pandas_df.to_csv(csv_savepath, index=False)
if table_name in table_age_bracket1:
print("Data is in age brackets, 0 to 17, 18 to 64, 65+")
spark_df1.show(100, False)
for age_range in age_range_list:
subset_sparkdf1 = spark_df1.filter(
spark_df1.level.rlike(age_range))
subset_sparkdf1.show(100, False)
metrics_dataframe = sdftools.mattsmetrics(
subset_sparkdf1, spark)
#subset_sparkdf1.show(100, False)
Counts = subset_sparkdf1.count()
print("Counts are", Counts)
newRow = spark.createDataFrame([(Counts, "Counts")])
metrics_dataframe = metrics_dataframe.union(newRow)
pandas_df = metrics_dataframe.toPandas()
csv_savepath = experiment.save_location_linux + f"{table_name}_{g}_{age_range}.csv"
du.makePath(du.getdir(csv_savepath))
pandas_df.to_csv(csv_savepath, index=False)
if table_name in table_age_bracket2:
print("Data is age buckets, with sex query")
spark_df1.show(100, False)
for sexlevel in sex_range_list:
subset_sparkdf1 = spark_df1.filter(
spark_df1.level.rlike(sexlevel))
for age_range in age_range_list:
subset_sparkdf2 = subset_sparkdf1.filter(
subset_sparkdf1.level.rlike(age_range))
subset_sparkdf2.show(100, False)
metrics_dataframe = sdftools.mattsmetrics(
subset_sparkdf2, spark)
#subset_sparkdf1.show(100, False)
Counts = subset_sparkdf2.count()
print("Counts are", Counts)
newRow = spark.createDataFrame([(Counts, "Counts")])
metrics_dataframe = metrics_dataframe.union(newRow)
pandas_df = metrics_dataframe.toPandas()
csv_savepath = experiment.save_location_linux + f"{table_name}_{g}_{age_range}_{sexlevel}.csv"
du.makePath(du.getdir(csv_savepath))
pandas_df.to_csv(csv_savepath, index=False)
if table_name in table_age_bracket3:
print("Data is in age brackets of 5 year age groups")
spark_df1.show(100, False)
for age_range in age_range_list2:
subset_sparkdf1 = spark_df1.filter(
spark_df1.level.rlike(age_range))
subset_sparkdf1.show(100, False)
metrics_dataframe = sdftools.mattsmetrics(
subset_sparkdf1, spark)
#subset_sparkdf1.show(100, False)
Counts = subset_sparkdf1.count()
print("Counts are", Counts)
newRow = spark.createDataFrame([(Counts, "Counts")])
metrics_dataframe = metrics_dataframe.union(newRow)
pandas_df = metrics_dataframe.toPandas()
csv_savepath = experiment.save_location_linux + f"{table_name}_{g}_{age_range}.csv"
du.makePath(du.getdir(csv_savepath))
pandas_df.to_csv(csv_savepath, index=False)
if table_name in table_age_bracket4:
print("Data is age buckets of 5 year age groups, with sex query")
spark_df1.show(100, False)
for sexlevel in sex_range_list:
subset_sparkdf1 = spark_df1.filter(
spark_df1.level.rlike(sexlevel))
for age_range in age_range_list2:
subset_sparkdf2 = subset_sparkdf1.filter(
subset_sparkdf1.level.rlike(age_range))
subset_sparkdf2.show(100, False)
metrics_dataframe = sdftools.mattsmetrics(
subset_sparkdf2, spark)
#subset_sparkdf1.show(100, False)
Counts = subset_sparkdf2.count()
print("Counts are", Counts)
newRow = spark.createDataFrame([(Counts, "Counts")])
metrics_dataframe = metrics_dataframe.union(newRow)
pandas_df = metrics_dataframe.toPandas()
csv_savepath = experiment.save_location_linux + f"{table_name}_{g}_{age_range}_{sexlevel}.csv"
du.makePath(du.getdir(csv_savepath))
pandas_df.to_csv(csv_savepath, index=False)
if table_name in table_default_bucket_list2: # If data is in buckets [0,10],[10,100),[100+)
print("data is bucketed and treated accordingly")
#if table_name in table_race_query:
for b in default_buckets2: # calculate Metrics at each bucket
print("Bucket is:", b)
subset_sparkdf = spark_df1[spark_df1['orig_count_bin'] ==
b] #subset into bins
subset_sparkdf.show(100, False)
print("Bucketed data")
subset_sparkdf1 = subset_sparkdf.subtract(
subset_sparkdf.filter(subset_sparkdf.level.rlike("Not")))
subset_sparkdf1.show(100, False)
print("Make sure its bucketed and 'Not' values are removed")
subset_metrics = sdftools.mattsmetrics(subset_sparkdf1, spark)
Counts = subset_sparkdf1.count()
newRow = spark.createDataFrame([(b, "Bucket")])
newRow1 = spark.createDataFrame([(Counts, "Counts")])
subset_metrics = subset_metrics.union(newRow).union(newRow1)
pandas_df = subset_metrics.toPandas()
csv_savepath = experiment.save_location_linux + f"{table_name}_{g}_{b}.csv"
du.makePath(du.getdir(csv_savepath))
pandas_df.to_csv(csv_savepath, index=False)
if table_name in table_default_bucket_list: # If data is in buckets [0,1000],[1000,5000),etc. Table 1 and 2
print("data is bucketed and treated accordingly")
#if table_name in table_race_query:
for b in default_buckets: # calculate Metrics at each bucket
print("Bucket is:", b)
subset_sparkdf = spark_df1[spark_df1['orig_count_bin'] ==
b] #subset into bins
subset_sparkdf.show(100, False)
print("Bucketed data")
subset_sparkdf1 = subset_sparkdf.subtract(
subset_sparkdf.filter(subset_sparkdf.level.rlike("Not")))
subset_sparkdf1.show(100, False)
print("Make sure its bucketed and 'Not' values are removed")
subset_metrics = sdftools.mattsmetrics(subset_sparkdf1, spark)
Counts = subset_sparkdf1.count()
newRow = spark.createDataFrame([(b, "Bucket")])
newRow1 = spark.createDataFrame([(Counts, "Counts")])
subset_metrics = subset_metrics.union(newRow).union(newRow1)
pandas_df = subset_metrics.toPandas()
csv_savepath = experiment.save_location_linux + f"{table_name}_{g}_{b}.csv"
du.makePath(du.getdir(csv_savepath))
pandas_df.to_csv(csv_savepath, index=False)
def analyzeQuery(query,
table_name,
analysis,
spark,
geolevels,
buckets=default_buckets,
schema="DHCP_HHGQ"):
"""
Main plotting fxn.
query : str, name of a valid query for the target experiment's schema
table_name : str, name of a table (used for file-naming conventions)
analysis : Analysis setuptools.setup object, organizes Analysis metadata
spark : SparkSession object, attached to analysis object
geolevels : [str, ...], geolevels to compute over for the current query
buckets : [(int,int), ...], list of mutually exclusive bucket boundaries for Tab(CEF) bucketing
Note, also, major control parameters hard-coded in getPaths for setting experiment ingest locations from s3.
"""
print(
f"For table {table_name}, analyzing query {query} at geolevels {geolevels} with schema {schema}"
)
schema_name = schema
paths, experiment_name = getPathsAndName(schema_name)
experiment = analysis.make_experiment(
experiment_name,
paths,
schema_name=schema_name,
dasruntype=AC.EXPERIMENT_FRAMEWORK_FLAT)
sdftools.print_item(experiment.__dict__, "Experiment Attributes")
df = experiment.getDF()
if TEST:
df = df.limit(TEST_NUM)
print("df looks like:")
df.show()
schema = experiment.schema
sdftools.print_item(df, "Flat Experiment DF")
queries = [query]
#y=sdftools.getAnswers(spark,df,geolevels,schema,queries)
rddWithAbsDiff = getRddWithAbsDiff(spark, df, geolevels, queries, schema)
rddWithAbsDiff = sdftools.getFullWorkloadDF(
rddWithAbsDiff,
schema,
queri,
groupby=[AC.GEOCODE, AC.GEOLEVEL, AC.RUN_ID, AC.PLB, AC.BUDGET_GROUP])
rdd = sdftools.getRowGroupsAsRDD(rddWithAbsDiff,
groupby=[AC.GEOLEVEL, AC.QUERY])
rdd = rdd.flatMapValues(
lambda rows: sepBounds(rows, 'orig', buckets)).persist()
rdd = rdd.map(lambda row: Row(**row[1]))
df = rdd.toDF().persist()
metric_name = "Avg( |q(MDF) - q(CEF)| )"
x_axis_variable_name = 'CEF Count, Binned'
df = df.groupby([
'geocode', 'geolevel', 'level', 'Bin0', 'Bin1', 'Bin2', 'Bin3', 'Bin4',
'Bin5'
]).avg()
pandas_df = df.toPandas()
pandas_df = pandas_df.rename(columns={
"avg(abs diff)": metric_name,
"avg(orig)": "orig"
})
pandas_df[x_axis_variable_name] = pandas_df.apply(
lambda row: binIndexToInteger(row, buckets), axis=1)
plt.figure(1, figsize=(11, 8.5))
plt.rc('axes', labelsize=8)
"""
print(pandas_df.head(30))
print(f"pandas_df headers: {list(pandas_df.columns.values)}")
tmpDf = pandas_df[[x_axis_variable_name, 'orig', metric_name]]
print("tmpDf looks like:")
with pandas.option_context('display.max_rows', None, 'display.max_columns', None):
print(tmpDf)
print("^^^^ pandas df looks like ^^^^")
print("And first 3 rows:")
print(pandas_df.iloc[:3])
#print(df.dtypes)
print("And first 100 rows, subset to Bins:")
print(pandas_df.iloc[0:101,3:9])
print(pandas_df.iloc[0:101,-1])
"""
# Saving data frame
csv_savepath = experiment.save_location_linux + f"Executive_Priority_Tabulations_#1_{experiment_name}.csv"
du.makePath(du.getdir(csv_savepath))
pandas_df.to_csv(csv_savepath, index=False)
makePlots(experiment, experiment_name, table_name, queries,
x_axis_variable_name, metric_name, geolevels, pandas_df, buckets,
schema_name)
def makePlots(experiment, experiment_name, table_name, queries,
x_axis_variable_name, metric_name, geolevels, pandas_df, buckets,
schema_name):
EPT = table_name[:4] + "_" + schema_name
for query in queries:
for geolevel in geolevels:
subsetted_df = pandas_df[pandas_df['geolevel'] == geolevel]
race_iterates = major_omb_race_names if (
"EPT3" in table_name and "allraces *" in query) else [""]
race_iterates = ["2+ Races"] if (race_iterates == [""]
and "tomr *" in query) else [""]
race_iterates = major_combination_race_names if (
"EPT3" in table_name
and query.str.contains("combo")) else race_iterates
print(
f"table {table_name}, query {query}, geolevel {geolevel}, race_iterates {race_iterates})"
)
# TODO: graph titling/subsetting for distinct queries is getting convoluted. Re-factor?
for race in race_iterates:
plotting_df = subsetted_df
if race in major_omb_race_names:
plotting_df = subsetted_df[
subsetted_df['level'].str.contains(race)]
if race in major_combination_race_names:
plotting_df = subsetted_df[
subsetted_df['level'].str.contains(race)]
bucket_counts = {}
for bindex, bucket in enumerate(buckets):
trueRows = plotting_df[plotting_df[f"Bin{bindex}"] == True]
bucket_counts[bucket] = trueRows.shape[0]
print(
f"Geolevel {geolevel} has bucket_counts: {bucket_counts}")
bucket_names = [
str(bucket) for bucket in buckets
if bucket_counts[bucket] != 0
]
if (np.array(list(bucket_counts.values())) > 0).any():
graph_title = f"Average L1 Error (over trials) for {query}\nGeolevel: {geolevel}"
if race in major_omb_race_names:
graph_title += f"Race Alone: {race.title()}"
if race == "2+ Races":
graph_title += f"2+ Races"
if race in major_combination_race_names:
graph_title += f"Race: {race}"
graph_title += f"\n(binned by CEF count)\nDisclosure Prohibited - Title 13 U.S.C."
# Scatterplot strips superimposed on violin plots
sns.set(style="whitegrid")
sns.violinplot(x=x_axis_variable_name, y=metric_name, data=plotting_df, order=bucket_names,
inner = None, color="0.8") \
.set_title(graph_title)
sns.stripplot(x=x_axis_variable_name, y=metric_name, data=plotting_df, order=bucket_names) \
.set_title(graph_title)
plot_savepath = f"{experiment.save_location_linux}plots/{EPT}/scatteredViolin/{experiment_name}_"
plot_savepath += f"{table_name}_{query.replace(' ', '_')}_{geolevel}_{race.replace(' ','_')}.pdf"
du.makePath(du.getdir(plot_savepath))
print(
f"Saving scatterstrips w/ violins for query {query}, geolevel {geolevel}, & race {race} to: {plot_savepath}"
)
plt.savefig(plot_savepath)
plt.clf()
else:
print(
f"No observations for {table_name}, {query}, {geolevel}, {race}. Plot not generated."
)
data = [{
"Area": 'State',
'Value': no_state
}, {
"Area": 'County',
'Value': no_county
}, {
"Area": 'Tract',
'Value': no_tract
}, {
"Area": 'Place',
'Value': no_place
}, {
"Area": 'Block Group',
'Value': no_group
}, {
"Area": 'Block',
'Value': no_block
}, {
"Area": 'SLDL',
'Value': no_sldl
}, {
"Area": 'SLDU',
'Value': no_sldu
}]
df = spark.createDataFrame(data)
pandas_df = df.toPandas()
csv_savepath = "s3://uscb-decennial-ite-das/rao001/Geolevel_counts.csv"
du.makePath(du.getdir(csv_savepath))
pandas_df.to_csv(csv_savepath, index=False)
print("This is q")
q.show(10)
q=q.groupby(['geolevel','run_id']).sum()
columnstodrop=['sum(diff)','sum(sum(orig))','sum(sum(priv))','sum(MDF/sum)','sum(CEF/sum)','sum(difference)']
print("this is q2")
q=q.drop(*columnstodrop)
q.show(10)
z=q.groupby(['geolevel']).avg()
print("this is z")
z.show(10)
return q,z
get,get2=NEbias(spark,df,geolevels,queries,schema)
path=save_location_linux+"NEbias.csv"
pdf=get.toPandas()
du.makePath(du.getdir(path))
pdf.to_csv(path,index=False)
path2=save_location_linux+"NEbias_av.csv"
pdf2=get2.toPandas()
du.makePath(du.getdir(path2))
pdf2.to_csv(path2,index=False)
get3,get4=MMbias(get)
path3=save_location_linux+"MMbias.csv"
pdf3=get3.toPandas()
du.makePath(du.getdir(path3))
pdf3.to_csv(path3,index=False)
path4=save_location_linux+"MMbias_av.csv"
pdf4=get4.toPandas()
def makePlots(experiment, experiment_name, table_name, queries, x_axis_variable_name,
metric_name, geolevels, pandas_df, buckets,
schema_name, eps_str):
EPT = table_name[:4]+"_"+schema_name
for query in queries:
for geolevel in geolevels:
subsetted_df = pandas_df[pandas_df['geolevel'] == geolevel]
race_iterates = major_omb_race_names if ("EPT3" in table_name and "allraces *" in query) else [""]
race_iterates = race_combo_names if (race_iterates == [""] and "racecombos *" in query) else race_iterates
race_iterates = ["2+ Races"] if (race_iterates == [""] and "tomr *" in query) else race_iterates
print(f"table {table_name}, query {query}, geolevel {geolevel}, race_iterates {race_iterates})")
# TODO: graph titling/subsetting for distinct queries is getting convoluted. Re-factor?
for race in race_iterates:
plotting_df = subsetted_df
if race in major_omb_race_names:
plotting_df = subsetted_df[subsetted_df['level'].str.contains(race)]
if race in race_combo_names:
plotting_df = subsetted_df[subsetted_df['level'].str.contains(race)]
bucket_counts = {}
for bucket in buckets:
trueRows = plotting_df[plotting_df[x_axis_variable_name] == bucket]
bucket_counts[bucket] = trueRows.shape[0]
print(f"Geolevel {geolevel} has bucket_counts: {bucket_counts}")
bucket_names = [str(bucket) for bucket in buckets if bucket_counts[bucket] != 0]
if (np.array(list(bucket_counts.values())) > 0).any():
graph_title = f"Average L1 Error (over trials) for {query}\nGeolevel: {geolevel}"
if race in major_omb_race_names:
graph_title += f"\n Race Alone: {race.title()}"
if race in race_combo_names:
graph_title += f"\n Race Combination: {race.title()}"
if race == "2+ Races":
graph_title += f"\n 2+ Races"
graph_title += f"\n(binned by CEF count)\nDisclosure Prohibited - Title 13 U.S.C."
pandas.set_option('display.max_columns', None)
pandas.set_option('display.max_rows', None)
#print(f"Before plotting, plotting_df looks like:")
#print(plotting_df)
#print(f"Feeding plotting_df to seaborn with xvar {x_axis_variable_name} & yvar {metric_name}")
#print(f"And bucket_names are: {bucket_names}")
# Scatterplot strips superimposed on violin plots
sns.set(style="whitegrid")
ax = sns.violinplot(x=x_axis_variable_name, y=metric_name, data=plotting_df, order=bucket_names,
inner = None, color="0.8") \
.set_title(graph_title)
maxVal = plotting_df[metric_name].max()
minVal = plotting_df[metric_name].min()
print(f"maxVal type, val: {type(maxVal)}, {maxVal}")
print(f"minVal type, val: {type(minVal)}, {minVal}")
if abs(maxVal - minVal) < 0.1:
#print(f"violin ax has type {type(ax)} & methods: {dir(ax)}")
#print(f"violin ax.axes has type {type(ax.axes)} & methods: {dir(ax.axes)}")
ax.axes.set(ylim=(minVal-10, maxVal+10))
ax = sns.stripplot(x=x_axis_variable_name, y=metric_name, data=plotting_df, order=bucket_names) \
.set_title(graph_title)
#if geolevel == C.US:
# #print(f"strip ax has type {type(ax)} & methods: {dir(ax)}")
# ax.axes.set(ylim=(plotting_df[x_axis_variable_name].min - 10,plotting_df[x_axis_variable_name].max + 10))
plot_savepath = f"{experiment.save_location_linux}plots/{EPT}/epsilon{eps_str}/scatteredViolin/{experiment_name}_"
plot_savepath += f"{table_name}_{query.replace(' ', '_')}_{geolevel}_{race.replace(' ','_')}.pdf"
du.makePath(du.getdir(plot_savepath))
print(f"Saving scatterstrips w/ violins for query {query}, geolevel {geolevel}, & race {race} to: {plot_savepath}")
plt.savefig(plot_savepath)
plt.clf()
else:
print(f"No observations for {table_name}, {query}, {geolevel}, {race}. Plot not generated.")
