The ai_data_monitoring_reporing.py module provides tools for monitoring data quality and generating comprehensive reports for data-related processes. It is designed to assist developers and data scientists in identifying data quality issues, ensuring consistency, and documenting processing outcomes. This helps in maintaining high-quality datasets and tracking transformations within machine learning pipelines or ETL workflows.
The accompanying ai_data_monitoring_reporing.html file integrates interactive examples and visual templates for report generation and monitoring results.
This module makes it easy to:
The quality of data directly impacts the performance of artificial intelligence and machine learning models. The DataMonitoringReporting class provides utilities to monitor datasets for quality issues and generate user-friendly reports summarizing data processing or transformation steps. With its extensible architecture, this module can be tailored for workflows of varying complexity.
The module has two primary functionalities:
1. Data Quality Monitoring:
2. Report Generation:
The ai_data_monitoring_reporting.py module was designed to:
By summarizing both issues and progress, this module is an essential tool for pipeline observability and governance.
The DataMonitoringReporting module includes the following core features:
1. Detect missing values and calculate dataset coverage (% completeness).
2. Automated string-based summary reports for processed datasets or workflows.
3. Logs all actions, including data quality checks and report generation results, for thorough traceability.
4. Easily integrates into existing pipelines as a monitoring or reporting component.
5. Can be extended to generate reports in various formats like JSON, HTML, or Markdown.
The DataMonitoringReporting class provides two core methods:
Monitors the quality of a dataset by calculating the total number of data points, missing values, and the completeness percentage.
Generates a textual summary of the processed dataset.
The workflow is as follows:
This method performs an analysis to detect:
The output is a dictionary summarizing quality statistics:
python
{
"total_values": 1000,
"missing_values": 50,
"coverage": 95.0
}
The generate_report method creates a simple, string-based summary indicating the total number of data points processed. This can be extended to include additional details or formatted outputs (e.g., Markdown, HTML).
Example Report:
plaintext Data Report: Processed 950 data points successfully.
The module uses Python's logging library to provide detailed logs at various levels:
Example log outputs:
plaintext
INFO:root:Monitoring data quality...
INFO:root:Data quality report: {'total_values': 1000, 'missing_values': 50, 'coverage': 95.0}
INFO:root:Generating data processing report...
Run the following command to install optional dependencies:
bash pip install pandas
Below are examples demonstrating the module's core functionalities.
Monitoring and generating a report for a simple dataset.
python from ai_data_monitoring_reporing import DataMonitoringReporting
# Sample dataset
data = [1, 2, None, 4, None, 5]
# Monitor quality
quality_report = DataMonitoringReporting.monitor_data_quality(data)
print("Quality Report:", quality_report)
# Generate report
report = DataMonitoringReporting.generate_report(data) print(report)
Output:
plaintext
Quality Report: {'total_values': 6, 'missing_values': 2, 'coverage': 66.66666666666666}
Data Report: Processed 6 data points successfully.
Analyze datasets for additional metrics like unique values, data type distributions, or outliers.
python
import pandas as pd
class ExtendedDataMonitoringReporting(DataMonitoringReporting):
@staticmethod
def extended_data_quality(data):
report = DataMonitoringReporting.monitor_data_quality(data)
report.update({
"unique_values": len(set(data)),
"data_types": {type(item).__name__ for item in data if item is not None}
})
return report
# Example usage
extended_monitor = ExtendedDataMonitoringReporting() data = [1, 2, None, 4, 4, 5] detailed_quality_report = extended_monitor.extended_data_quality(data) print(detailed_quality_report)
Output:
plaintext
{
'total_values': 6,
'missing_values': 1,
'coverage': 83.33,
'unique_values': 5,
'data_types': {'int'}
}
—
Extend the generate_report method to produce Markdown-based formatted outputs.
python
class MarkdownReport(DataMonitoringReporting):
@staticmethod
def generate_markdown_report(data):
report = DataMonitoringReporting.monitor_data_quality(data)
return (f"# Data Quality Report\n\n"
f"- **Total Values:** {report['total_values']}\n"
f"- **Missing Values:** {report['missing_values']}\n"
f"- **Coverage:** {report['coverage']}%\n")
# Example usage
markdown_report = MarkdownReport.generate_markdown_report(data)
print(markdown_report)
Output:
markdown
# Data Quality Report
- **Total Values:** 6 - **Missing Values:** 2 - **Coverage:** 66.67%
—
Integrate monitoring and reporting into a data preprocessing pipeline.
python
class DataPipeline:
def __init__(self, data):
self.data = data
def run_pipeline(self):
monitor = DataMonitoringReporting()
quality_report = monitor.monitor_data_quality(self.data)
logging.info(f"Pipeline Quality Report: {quality_report}")
return monitor.generate_report(self.data)
# Example usage
pipeline = DataPipeline(data=[1, 2, 3, None, 5, None]) print(pipeline.run_pipeline())
1. Monitor Regularly:
2. Understand Coverage:
3. Customize Reports for Stakeholders:
4. Automate Logs:
The DataMonitoringReporting module can be extended in many ways:
1. Add logic to detect outliers or invalid data types.
2. Include customizable data validation checks.
3. Generate reports in JSON, HTML templates, or dashboards.
This module can integrate seamlessly with:
The DataMonitoringReporting module offers an efficient way to ensure data quality and generate process documentation. With its logging, monitoring, and reporting capabilities, it is a valuable tool for maintaining high standards in machine learning pipelines and data workflows. Users can extend it for custom validations or integrate it into ETL pipelines for end-to-end governance.