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ai_data_validation [2025/05/25 20:05] – [**DataValidation.py** Class Documentation] eagleeyenebulaai_data_validation [2025/05/25 20:10] (current) – [Extensions & Best Practices] eagleeyenebula
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 ===== DataValidation.py Class Documentation ===== ===== DataValidation.py Class Documentation =====
  
-The **DataValidation** class is the backbone of this system. It includes a static method `validate` that performs all logic for checking data consistency and logging the results.+The **DataValidation** class is the backbone of this system. It includes a static method **validate** that performs all logic for checking data consistency and logging the results.
  
 === Class Design === === Class Design ===
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-===== Template Integration ===== 
- 
-The accompanying HTML template (`ai_data_validation.html`) is designed to integrate results from the validation system into web dashboards or front-end applications. It provides a framework to display validation results in real-time, including warnings, statistics, and visual feedback. 
- 
-=== Sample Template Structure === 
- 
-```html 
-<!DOCTYPE html> 
-<html> 
-<head> 
-    <title>Data Validation Results</title> 
-</head> 
-<body> 
-    <h1>Data Validation Summary</h1> 
-    <div id="validation-status"> 
-        Validation Status: <span style="color: green;">Passed</span> 
-    </div> 
-    <div id="validation-errors"> 
-        <h2>Validation Errors:</h2> 
-        <ul> 
-            <li>No errors detected.</li> 
-        </ul> 
-    </div> 
-    <footer> 
-        <p>Generated by AI Data Validation System</p> 
-    </footer> 
-</body> 
-</html> 
-``` 
- 
-Key Features of Template: 
-  * **Dynamic Status Update**: Displays whether the validation passed or failed using a clear color-coded visual. 
-  * **Error Reporting**: Lists all encountered validation errors (if any). 
-  * **Extensibility**: Can be dynamically updated via backend values using Flask, Django, or other frameworks. 
- 
---- 
  
 ===== Advanced Usage Examples ===== ===== Advanced Usage Examples =====
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 Expand the basic validation to enforce uniform data type rules. For example, ensuring all elements are integers: Expand the basic validation to enforce uniform data type rules. For example, ensuring all elements are integers:
- +<code> 
-```python+python
 class DataTypeValidation(DataValidation): class DataTypeValidation(DataValidation):
     @staticmethod     @staticmethod
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 if not DataTypeValidation.validate(data): if not DataTypeValidation.validate(data):
     print("Failed Validation: Non-integer found.")     print("Failed Validation: Non-integer found.")
-```+</code>
  
 ==== 2. Threshold-based Validation ==== ==== 2. Threshold-based Validation ====
  
 Check if numeric data values lie within a specific range: Check if numeric data values lie within a specific range:
- +<code> 
-```python+python
 class ThresholdValidation(DataValidation): class ThresholdValidation(DataValidation):
     @staticmethod     @staticmethod
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 if not ThresholdValidation.validate(data, 0, 100): if not ThresholdValidation.validate(data, 0, 100):
     print("Failed Validation: Data out of acceptable range.")     print("Failed Validation: Data out of acceptable range.")
-```+</code>
  
 ==== 3. JSON Schema Validation ==== ==== 3. JSON Schema Validation ====
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 For structured datasets, integrate JSON schema validation using libraries like `jsonschema`: For structured datasets, integrate JSON schema validation using libraries like `jsonschema`:
  
-```python+<code> 
 +python
 import jsonschema import jsonschema
 from jsonschema import validate from jsonschema import validate
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             logging.error(f"Schema validation failed: {err}")             logging.error(f"Schema validation failed: {err}")
             return False             return False
 +</code>
 # Sample JSON and Schema # Sample JSON and Schema
 +<code>
 data = {"name": "John", "age": 30} data = {"name": "John", "age": 30}
 schema = { schema = {
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 if JsonSchemaValidation.validate(data, schema): if JsonSchemaValidation.validate(data, schema):
     print("JSON Schema Validated Successfully")     print("JSON Schema Validated Successfully")
-``` +</code>
- +
---- +
 ===== Extensions & Best Practices ===== ===== Extensions & Best Practices =====
  
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   * Use detailed logging to ensure traceability.   * Use detailed logging to ensure traceability.
   * Modularize validation logic for usability across pipelines.   * Modularize validation logic for usability across pipelines.
- 
---- 
  
 ===== Conclusion ===== ===== Conclusion =====
  
 The **AI Data Validation** system is both flexible and powerful, enabling basic to advanced data integrity checks. Its integration into web-based systems and extensibility make it an essential component in data pipelines. The **AI Data Validation** system is both flexible and powerful, enabling basic to advanced data integrity checks. Its integration into web-based systems and extensibility make it an essential component in data pipelines.
ai_data_validation.1748203545.txt.gz · Last modified: 2025/05/25 20:05 by eagleeyenebula