Generate mock documents in various formats (CSV, DOCX, PDF, TXT, and more) that embed seed data and can be used to test data classification software.
Project description
Mockingbird: Generate mock documents for data classification
About
Mockingbird is a Python library for generating mock documents in various formats. It accepts user-defined data, and embeds it into documents generated in many different formats. Developers can use Mockingbird to quickly generate datasets, with particular use for validating the efficacy of a data classification software.
Installation
The easiest way to install Mockingbird is by using pip
:
pip install mockingbird
For local development, clone the repository and run pip install .
Getting Started
Mockingbird can run as a functional Python library or as a CLI.
CLI Usage
Once installed with pip, unix-like systems can use the command mockingbird_cli --h
to access Mockingbird's
command line interface. Some sample CLI calls are:
mockingbird_cli --type dry -o ./output/dry_test/
mockingbird_cli --type csv -i ./samples/csv_sample.csv -o ./output/csv/
mockingbird_cli --type csv_curl -i <curl'able URL> -o ./output/csv_curl/
mockingbird_cli --type mockaroo -i ./samples/sample_schema.json --mockaroo_api <mockaroo API> -o ./output/mockaroo
As a Python Library
Starting from Code
Mockingbird functions as a fully functional Python library. A basic example generating documents using mock-data is demonstrated below. In this example, key-value pairs are inserted as strings mapping to a list of strings.
from mockingbird import Mockingbird
# Spawn a new Mockingbird session
fab = Mockingbird()
# Set which file extensions to output
fab.set_file_extensions(["html", "docx", "yaml", "xlsx", "odt"])
# Input the data we want to test / inject into the documents
fab.add_sensitive_data(keyword="ssn", entries=["000-000-0000", "999-999-9999"])
fab.add_sensitive_data(keyword="dob", entries=["01/01/1991", "02/02/1992"])
# Generate and save the fabricated documents
fab.save(save_path="./output_basic/")
fab.dump_meta_data(output_file="./output_basic/meta_data.json")
Starting from CSV
Mockingbird can be started using a CSV file, treating the column headers as keywords, and the remaining rows as entries.
The CSV's are expected to be structured as the following,
FILE: mockingbird_data.csv
ssn, dob
000-000-000, 01/01/1991
999-999-999, 02/02/1992
from mockingbird.mb_wrappers import MockingbirdFromCSV
# This effectively loads files from the csv and generates a session using each column
fab = MockingbirdFromCSV("csv_sample.csv")
fab.set_all_extensions()
fab.save(save_path="./output_csv/")
fab.dump_meta_data(output_file="./output_csv/meta_data.json")
Optionally, multiple keywords can be defined in the CSV header file, which Mockingbird will split up into separate
keywords. For example, rather than just testing the keyword ssn
, we can test ssn
and social security number
.
Multiple keywords can be defined in the CSV file by using ;
as a delimiter.
For example,
FILE: mockingbird_data.csv
ssn;social security number,dob;date of birth;birth
000-000-000, 01/01/1991
999-999-999, 02/02/1992
This will generate documents for each keyword in each column header.
Starting Using Mockaroo
Using a Mockaroo API key, we can request mocked data using json requests from Mockaroo's servers. Currently, the request has to be saved to a json file on disk, and loaded during runtime. More documentation can be found at Mockaroo's Website, but below is a json-example.
FILE: mockaroo_request.json
[
{
"name": "ssn;social security;social",
"type": "SSN"
},
{
"name": "cc;credit card",
"type": "Credit Card #"
},
{
"name": "phone;phone-number;number",
"type": "Phone"
},
{
"name": "name;fullname;full name",
"type": "Full Name"
}
]
In code, Mockingbird can use this request as a json-payload,
import json
from mockingbird.mb_wrappers import MockingbirdFromMockaroo
with open("mockaroo_request.json") as json_file:
schema_request = json.load(json_file)
fab = MockingbirdFromMockaroo(api_key="MOCKAROO_API_KEY", schema_request=schema_request)
fab.set_all_extensions()
fab.save(save_path="./output_mockaroo/")
fab.dump_meta_data(output_file="./output_mockaroo/meta_data.json")
License
Licensed under the Apache License, Version 2.0. See LICENSE for the full license text.
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