acryl-datahub-classify 0.0.6

Library to predict info types for DataHub

datahub-classify

Predict InfoTypes for DataHub.

Installation

python3 -m pip install --upgrade acryl-datahub-classify

API predict_infotypes

This API populates infotype proposal(s) for each input column by using metadata, values & confidence level threshold. Following are the input and output contract

API Input

API expects following parameters in the output

• column_infos - This is a list of ColumnInfo objects. Each ColumnInfo object contains metadata (col_name, description, datatype, etc) and values of a column.
• confidence_level_threshold - If the infotype prediction confidence is greater than the confidence threshold then the prediction is considered as a proposal. This is the common threshold for all infotypes.
• global_config - This dictionary contains configuration details about all supported infotypes. Refer section Infotype Configuration for more information.
• infotypes - This is a list of infotypes that is to be processed. This is an optional argument, if specified then it will override the default list of all supported infotypes. If user is interested in only few infotypes then this list can be specified with correct infotype names. Infotype names are case sensitive.

API Output

API returns a list of ColumnInfo objects of length same as input ColumnInfo objects list. A populated list of Infotype proposal(s), if any, is added in the ColumnInfo object itself with a variable name as infotype_proposals. The infotype_proposals list contains InfotypeProposal objects which has following information

• infotype - A proposed infotype name.
• confidence_level - Overall confidence of the infotype proposal.
• debug_info - confidence score of each prediction factor involved in the overall confidence score calculation. Refer section Debug Information for more information.

Convention: If infotype_proposals list is non-empty then it indicates that there is at least one infotype proposal with confidence greater than confidence_level_threshold.

Infotype Configuration

Infotype configuration is a dictionary with all infotypes at root level key. Each infotype has following configurable parameters (value of each parameter is a dictionary)

• Prediction_Factors_and_Weights - This is a dictionary that specifies the weight of each prediction factor which will be used in the final confidence calculation. Following are the prediction factors
  1. Name
  2. Description
  3. Datatype
  4. Values
• Name - regex list which is to be matched against column name
• Description - regex list which is to be matched against column description
• Datatype - list of datatypes to be matched against column datatype
• Values - this dictionary contains following information
  1. prediction_type - values evaluation model (regex/library)
  2. regex - regex list which is to be matched against column values
  3. library - library name which is to be used to evaluate column values

Sample Infotype Configuration Dictionary

{
    '<Infotype1>': {
        'Prediction_Factors_and_Weights': {
            'Name': 0.4,
            'Description': 0,
            'Datatype': 0,
            'Values': 0.6
        },
        'Name': { 'regex': [<regex patterns>] },
        'Description': { 'regex': [<regex patterns>] },
        'Datatype': { 'type': [<list of datatypes>] },
        'Values': {
            'prediction_type': 'regex/library',
            'regex': [<regex patterns>],
            'library': [<library name>]
        }
    },
    '<Infotype2>': {
    ..
    ..
    ..
    }
}

Debug Information

A debug information is associated with each infotype proposal, it provides details about confidence score from each prediction factor involved in overall confidence score calculation. This is a dictionary with following four prediction factors as key

• Name
• Description
• Datatype
• Values

{
    'Name': 0.4,
    'Description': 0.2,
    'Values': 0.6,
    'Datatype': 0.3
}

Supported Infotypes

1. Age
2. Gender
3. Person Name / Full Name
4. Email Address
5. Phone Number
6. Street Address
7. Credit-Debit Card Number
8. International Bank Account Number
9. Vehicle Identification Number
10. US Social Security Number
11. Ipv4 Address
12. Ipv6 Address
13. Swift Code
14. US Driving License Number

Assumptions

• If value prediction factor weight is non-zero (indicating values should be used for infotype inspection) then a minimum 50 non-null column values should be present.

Development

Set up your Python environment

cd datahub-classify
../gradlew :datahub-classify:installDev # OR pip install -e ".[dev]"
source venv/bin/activate

Runnning tests

pytest tests/ --capture=no --log-cli-level=DEBUG

Sanity check code before committing

# Assumes: pip install -e ".[dev]" and venv is activated
black src/ tests/
isort src/ tests/
flake8 src/ tests/
mypy src/ tests/

Build and Test

../gradlew :datahub-classify:build

You can also run these steps via the gradle build:

../gradlew :datahub-classify:lint
../gradlew :datahub-classify:lintFix
../gradlew :datahub-classify:testQuick