def test_dist():
frame = BiFrame(DataFrame(adults01), DataFrame(adults02))
columns = ['age', 'education', 'relationship', 'salary', 'birth']
for col in columns:
bins, counts = frame.dist(col)
assert len(bins) == len(counts[0])
assert len(bins) == len(counts[1])
def test_to_html():
frame = BiFrame(DataFrame(adults01), DataFrame(adults02))
report = 'a.html'
frame.to_html(report, classifier='SVM', labels=['education'])
import os.path
assert os.path.isfile(report)
if os.path.exists(report):
os.remove(report)
def test_corr():
frame = BiFrame(DataFrame(adults01), DataFrame(adults02))
a_mi, b_mi = frame.corr()
from numpy import allclose, alltrue
assert allclose(a_mi, a_mi.T)
assert alltrue(a_mi >= 0.0)
assert alltrue(a_mi <= 1.0)
assert allclose(b_mi, b_mi.T)
assert alltrue(b_mi >= 0.0)
assert alltrue(b_mi <= 1.0)
def test_classify_multiple_classes():
frame = BiFrame(DataFrame(adults01), DataFrame(adults02))
matrix = frame.classify('education')
assert len(matrix) == 2
columns = ['11th', '7th-8th', '9th', 'Assoc-acdm', 'Assoc-voc', 'Bachelors',
'Doctorate', 'HS-grad', 'Masters', 'Some-college']
assert array_equal(matrix[0].columns, columns)
assert array_equal(matrix[0].index, columns)
assert array_equal(matrix[1].columns, columns)
assert array_equal(matrix[1].index, columns)
def test_describe():
frame = BiFrame(DataFrame(adults01), DataFrame(adults02))
desc = frame.describe()
from numpy import alltrue
columns = ['age', 'birth', 'education', 'relationship', 'salary']
# 'sex' not in adults01
assert array_equal(desc.columns, columns)
assert alltrue(desc >= 0.0)
assert alltrue(desc <= 1.0)
assert array_equal(desc.index, ['err', 'jsd'])
def main():
parser = argparse.ArgumentParser(
description='Evaluate the utility of synthesized dataset compared with '
'the source dataset.',
formatter_class=CustomFormatter,
add_help=False)
# positional arguments
parser.add_argument('source',
help='set file path of source (raw) dataset to be '
'compared with synthesized dataset, only support '
'CSV files')
parser.add_argument(
'target',
help='set file path of target (synthesized) dataset to '
'evaluate')
# optional arguments
group = parser.add_argument_group('general arguments')
group.add_argument("-h",
"--help",
action="help",
help="show this help message and exit")
group.add_argument('--na-values',
metavar='LIST',
help='set additional values to recognize as NA/NaN; ('
'default null values are from pandas.read_csv)')
group.add_argument('-o',
'--output',
metavar='FILE',
default='report.html',
help='set output path for evaluation report; (default '
'is "report.html" under current work directory)')
group = parser.add_argument_group('advanced arguments')
group.add_argument('--category',
metavar='LIST',
help='set categorical columns separated by a comma.')
group.add_argument(
'-t',
'--test',
help='set test dataset for classification or regression '
'task; (default take 20%% from source dataset)')
group.add_argument(
'--class-label',
metavar='LIST',
help='set column name as class label for classification '
'or regression task; supports one or multiple '
'columns (separated by comma)')
args = parser.parse_args()
start = time.time()
na_values = str_to_list(args.na_values)
class_labels = str_to_list(args.class_label)
categories = str_to_list(args.category)
# check kinds of parameters
args.output = os.path.join(os.getcwd(), args.output)
# if output folder not exists, then create it.
if not os.path.exists(os.path.dirname(args.output)):
os.makedirs(os.path.dirname(args.output))
def complement(attrs, full):
return set(attrs or []) - set(full)
# Initialization task:
source = read_data_from_csv(args.source,
na_values=na_values,
header='infer')
target = read_data_from_csv(args.target,
na_values=na_values,
header='infer')
test = read_data_from_csv(args.test) if args.test is not None else None
comp = complement(class_labels, source.columns)
if comp:
parser.exit(
message=f'--class-label(s): {comp} are not in source file.')
comp = complement(class_labels, target.columns)
if comp:
parser.exit(
message=f'--class-label(s): {comp} are not in target file.')
frame = BiFrame(source, target, categories=categories)
frame.to_html(buffer=args.output,
title='Data Utility Evaluation Report',
labels=class_labels,
test=test)
duration = time.time() - start
print(f'Evaluate dataset {args.source} and {args.target} and generate '
f'report at {args.output} in {round(duration, 2)} seconds.')
def test_classify_one_class():
frame = BiFrame(DataFrame(adults01), DataFrame(adults02))
matrix = frame.classify('salary')
assert len(matrix) == 2
assert array_equal(matrix[0].columns, ['<=50K', '>50K'])
assert array_equal(matrix[0].index, ['<=50K', '>50K'])