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c4ac745 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 | import numpy as np
import pandas as pd
import os
import json
# Metrics
from sdmetrics.reports.single_table import QualityReport, DiagnosticReport
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--dataname', type=str, default='adult')
parser.add_argument('--model', type=str, default='tabsyn')
parser.add_argument('--path', type=str, default = None, help='The file path of the synthetic data')
args = parser.parse_args()
def reorder(real_data, syn_data, info):
num_col_idx = info['num_col_idx']
cat_col_idx = info['cat_col_idx']
target_col_idx = info['target_col_idx']
task_type = info['task_type']
if task_type == 'regression':
num_col_idx += target_col_idx
else:
cat_col_idx += target_col_idx
real_num_data = real_data[num_col_idx]
real_cat_data = real_data[cat_col_idx]
new_real_data = pd.concat([real_num_data, real_cat_data], axis=1)
new_real_data.columns = range(len(new_real_data.columns))
syn_num_data = syn_data[num_col_idx]
syn_cat_data = syn_data[cat_col_idx]
new_syn_data = pd.concat([syn_num_data, syn_cat_data], axis=1)
new_syn_data.columns = range(len(new_syn_data.columns))
metadata = info['metadata']
columns = metadata['columns']
metadata['columns'] = {}
inverse_idx_mapping = info['inverse_idx_mapping']
for i in range(len(new_real_data.columns)):
if i < len(num_col_idx):
metadata['columns'][i] = columns[num_col_idx[i]]
else:
metadata['columns'][i] = columns[cat_col_idx[i-len(num_col_idx)]]
return new_real_data, new_syn_data, metadata
if __name__ == '__main__':
dataname = args.dataname
model = args.model
if not args.path:
syn_path = f'synthetic/{dataname}/{model}.csv'
else:
syn_path = args.path
real_path = f'synthetic/{dataname}/real.csv'
data_dir = f'data/{dataname}'
print(syn_path)
with open(f'{data_dir}/info.json', 'r') as f:
info = json.load(f)
syn_data = pd.read_csv(syn_path)
real_data = pd.read_csv(real_path)
save_dir = f'eval/density/{dataname}/{model}'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
real_data.columns = range(len(real_data.columns))
syn_data.columns = range(len(syn_data.columns))
metadata = info['metadata']
metadata['columns'] = {int(key): value for key, value in metadata['columns'].items()}
new_real_data, new_syn_data, metadata = reorder(real_data, syn_data, info)
qual_report = QualityReport()
qual_report.generate(new_real_data, new_syn_data, metadata)
diag_report = DiagnosticReport()
diag_report.generate(new_real_data, new_syn_data, metadata)
quality = qual_report.get_properties()
diag = diag_report.get_properties()
Shape = quality['Score'][0]
Trend = quality['Score'][1]
with open(f'{save_dir}/quality.txt', 'w') as f:
f.write(f'{Shape}\n')
f.write(f'{Trend}\n')
Quality = (Shape + Trend) / 2
shapes = qual_report.get_details(property_name='Column Shapes')
trends = qual_report.get_details(property_name='Column Pair Trends')
coverages = diag_report.get_details('Coverage')
shapes.to_csv(f'{save_dir}/shape.csv')
trends.to_csv(f'{save_dir}/trend.csv')
coverages.to_csv(f'{save_dir}/coverage.csv')
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