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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 | import numpy as np
import torch
import pandas as pd
import os
import sys
import json
import pickle
# Metrics
from sdmetrics import load_demo
from sdmetrics.single_table import LogisticDetection
from sdv.metadata import SingleTableMetadata
from matplotlib import pyplot as plt
import argparse
import warnings
warnings.filterwarnings("ignore")
def eval_detection(syn_data, real_data, domain_dict):
metadata = SingleTableMetadata()
metadata.detect_from_dataframe(real_data)
for col, _ in domain_dict.items():
if domain_dict[col]['type'] == 'discrete':
metadata.update_column(
column_name=col,
sdtype='categorical',
)
else:
metadata.update_column(
column_name=col,
sdtype='numerical',
)
metadata.remove_primary_key()
score = LogisticDetection.compute(
real_data=real_data,
synthetic_data=syn_data,
metadata=metadata
)
print(f'score: {score}')
return score
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__':
parser = argparse.ArgumentParser()
parser.add_argument('--dataname', type=str, default='adult')
parser.add_argument('--model', type=str, default='real')
args = parser.parse_args()
dataname = args.dataname
model = args.model
syn_path = f'synthetic/{dataname}/{model}.csv'
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.generate(new_real_data, new_syn_data, metadata)
score = LogisticDetection.compute(
real_data=new_real_data,
synthetic_data=new_syn_data,
metadata=metadata
)
print(f'{dataname}, {model}: {score}')
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