{"library":"sdmetrics","title":"SDMetrics","description":"SDMetrics is an open-source Python library developed by DataCebo (part of the Synthetic Data Vault project) for evaluating the quality and efficacy of synthetic datasets. It provides a variety of metrics to compare synthetic data against real data across aspects like quality, privacy, and utility, and includes tools for generating comprehensive visual reports. The library is model-agnostic, allowing evaluation of synthetic data generated by any model. The current version is 0.28.0, with active and frequent releases.","language":"python","status":"active","last_verified":"Thu Apr 16","install":{"commands":["pip install sdmetrics"],"cli":null},"imports":["from sdmetrics import load_demo","from sdmetrics.reports.single_table import QualityReport","from sdmetrics.single_column import CategoryCoverage"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import pandas as pd\nfrom sdmetrics import load_demo\nfrom sdmetrics.reports.single_table import QualityReport\n\n# Load demo data (real, synthetic, and metadata)\nreal_data, synthetic_data, metadata = load_demo(modality='single_table')\n\n# Or create your own dataframes and metadata\n# real_data = pd.DataFrame({'column1': [1, 2, 3], 'column2': ['A', 'B', 'C']})\n# synthetic_data = pd.DataFrame({'column1': [1, 2, 2], 'column2': ['A', 'C', 'B']})\n# metadata = {'columns': {'column1': {'sdtype': 'numerical'}, 'column2': {'sdtype': 'categorical'}}, 'primary_key': None}\n\n# Create a QualityReport\nreport = QualityReport()\n\n# Generate the report\nreport.generate(real_data, synthetic_data, metadata)\n\n# Print the overall quality score\nprint(f\"Overall Quality Score: {report.get_score():.2f}%\")\n\n# Get a visualization for a specific property (e.g., 'Column Shapes')\n# fig = report.get_visualization(property_name='Column Shapes')\n# fig.show()\n\n# Save the report\n# report.save(filepath='demo_data_quality_report.pkl')\n# To load later: loaded_report = QualityReport.load(filepath='demo_data_quality_report.pkl')","lang":"python","description":"This quickstart demonstrates how to load demo data, generate a single-table QualityReport, retrieve the overall score, and optionally visualize results. SDMetrics can also work with your own pandas DataFrames and metadata dictionaries.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}