{
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{
"cell_type": "code",
"execution_count": null,
"id": "afdb8408-8328-49ec-95ad-1ad1b45217f8",
"metadata": {},
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"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"from sdv.single_table import CTGANSynthesizer\n",
"from sdv.metadata import SingleTableMetadata\n",
"from sdmetrics.reports.single_table import QualityReport\n",
"from sdmetrics.reports.single_table import DiagnosticReport\n",
"from table_evaluator import TableEvaluator"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1eb44865-7c03-46df-9780-aca27a6d0494",
"metadata": {},
"outputs": [],
"source": [
"#creating a combination of real data\n",
"\n",
"# real_data = pd.read_csv('Datasets/Preprocessed_Datasets/doS_attacks.csv')\n",
"# brute = pd.read_csv('Datasets/Preprocessed_Datasets/bruteforce_attacks.csv')\n",
"# goldenEye = real_data[real_data.Label=='DoS attacks-GoldenEye']\n",
"# slowloris = real_data[real_data.Label=='DoS attacks-Slowloris']\n",
"# hulk = real_data[real_data.Label=='DoS attacks-Hulk']\n",
"# hulk = hulk.iloc[:300000, :]\n",
"# slowHTTPtest = real_data[real_data.Label=='DoS attacks-SlowHTTPTest']\n",
"# loicHTTp = real_data[real_data.Label=='DDoS attacks-LOIC-HTTP']\n",
"# loicHTTp = loicHTTp.iloc[:300000, :]\n",
"# hoic = real_data[real_data.Label=='DDOS attack-HOIC']\n",
"# hoic = hoic.iloc[:300000, :]\n",
"# bot = pd.read_csv('Datasets/Preprocessed_Datasets/bot_attacks.csv')\n",
"# infilteration = pd.read_csv('Datasets/Preprocessed_Datasets/infilteration_attacks.csv')\n",
"# benign = pd.read_csv('Datasets/Preprocessed_Datasets/benign.csv')\n",
"# benign = benign.iloc[:300000, :]\n",
"# ftp = brute[brute.Label=='FTP-BruteForce']\n",
"# ssh = brute[brute.Label=='SSH-Bruteforce']\n",
"\n",
"# real_all_in_one = pd.concat([goldenEye, slowloris, hulk, slowHTTPtest, loicHTTp, hoic, bot, infilteration, benign, ftp, ssh], ignore_index=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "79233ab4-949f-40bb-ae7b-f338d5076a13",
"metadata": {},
"outputs": [],
"source": [
"# reading all csv files in the directory\n",
"\n",
"folder_path = 'RTVAE_Results'\n",
"csv_files = [file for file in os.listdir(folder_path) if file.endswith('.csv')]\n",
"dfs = []\n",
"for file in csv_files:\n",
" file_path = os.path.join(folder_path, file)\n",
" dfs.append(pd.read_csv(file_path))\n",
" \n",
"synthetic_all_in_one = pd.concat(dfs, ignore_index=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "40d00c4b-31a6-4a88-8b53-9d212d4f807e",
"metadata": {},
"outputs": [],
"source": [
"#savign real and synthetic data\n",
"# real_all_in_one.to_csv('TVAE_Results/real_all_in_one.csv', index=False)\n",
"synthetic_all_in_one.to_csv('RTVAE_Results/RTVAE_synthetic_all.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a7e4c7ea-8b6d-4537-83ed-cda57173a704",
"metadata": {},
"outputs": [],
"source": [
"#ensuring that everthing went well\n",
"\n",
"synthetic_data = synthetic_all_in_one \n",
"print(synthetic_data.shape)\n",
"synthetic_data.dropna(inplace=True)\n",
"print(synthetic_data.shape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ae9bd1d9-52d7-4703-890c-a2588a15b417",
"metadata": {},
"outputs": [],
"source": [
"#Loading real and sytnehtic data for evaluation\n",
"\n",
"real_data = pd.read_csv('TVAE_Results/real_all_in_one.csv')\n",
"synthetic_data = pd.read_csv('TabFairGAN_Results/TabFairGAN_synthetic_all.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "549bce4a-e6ea-457d-91a9-dfa9f95fe073",
"metadata": {},
"outputs": [],
"source": [
"def get_data_info(df):\n",
" \"\"\"Crates the categorical columns, continuous columns, and metadata of a dataframe.\n",
"\n",
" Args:\n",
" df (pandas.Dataframe): The input dataframe containing continuous and categorical values.\n",
"\n",
" Returns:\n",
" list: the list of categorical column names. Specifically, columns with only 4 uniques values\n",
" list: The list of continuous column names.\n",
" metadata: The metadata of the dataframe. for more informatin visit https://docs.sdv.dev/sdv/reference/metadata-spec/single-table-metadata-json\n",
" \"\"\"\n",
" #createing \n",
" categorical_columns = ['Label']\n",
" continuous_columns = []\n",
" for i in df.columns:\n",
" if i not in categorical_columns:\n",
" continuous_columns.append(i)\n",
" \n",
" #creating metadat\n",
" metadata = SingleTableMetadata()\n",
" metadata.detect_from_dataframe(df)\n",
" \n",
" for column in categorical_columns:\n",
" metadata.update_column(\n",
" column_name = column,\n",
" sdtype = 'categorical'\n",
" )\n",
" \n",
" for column in continuous_columns:\n",
" metadata.update_column(\n",
" column_name = column,\n",
" sdtype = 'numerical' \n",
" )\n",
" # validating metadata\n",
" metadata.validate()\n",
" metadata.validate_data(data=real_data)\n",
" \n",
" return categorical_columns, continuous_columns, metadata\n",
"\n",
"\n",
"categorical_columns, continuous_columns, metadata = get_data_info(real_data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fc6db1ff-e2fc-4817-87d1-22743468b7b8",
"metadata": {},
"outputs": [],
"source": [
"# evaluating synthetic data with table_evaluator cumulative sum per features and distribution\n",
"table_evaluator = TableEvaluator(real_data, synthetic_data, cat_cols = categorical_columns)\n",
"table_evaluator.visual_evaluation()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7bc79e6f-0232-4e2b-9b19-b2fd8825a687",
"metadata": {},
"outputs": [],
"source": [
"#saving and visualizing column pair trend and column shapes\n",
"metadata = metadata.to_dict()\n",
"my_report = QualityReport()\n",
"my_report.generate(real_data, synthetic_data, metadata)\n",
"my_report.save(filepath='RTVAE_Results/quality.pkl')\n",
"my_report.get_visualization(property_name='Column Pair Trends')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9332967a-f289-454a-9e20-ff73c7f9bbcf",
"metadata": {},
"outputs": [],
"source": [
"#saving and visualiztation data validity\n",
"my_report = DiagnosticReport()\n",
"my_report.generate(real_data, synthetic_data, metadata)\n",
"my_report.save(filepath='RTVAE_Results/diagnostic.pkl')\n",
"my_report.get_visualization('Data Validity')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ad3689d9-2361-4dfa-8efb-8d72d2859b7a",
"metadata": {},
"outputs": [],
"source": []
}
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