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XBRL-AI

The purpuse off this project is to apply XBRL in AI and Machine learning, by usage of Python.

The project will be divided into 3 parts:

  1. Generic XBRL to AI.
  • xbrl_ai.py
  1. XBRL in local GAAP including
  • Danish GAAP og Danish IFRS (with extension) to AI
    • xbrl_ai_dk.py
  • German GAAP
    • xbrl_ai_de.py
  1. Sample of Machine Learning implementation based on this project
  • test_xbrl_ai_dk.py
  • test_xbrl_ai_de.py
  • test_xbrl_ai_us.py

Why this project?

Working with machine learning basicly comes down to one thing: y = f(X), y is what we want to predict, X is the input and f is the machine learning model. Unfortunately X hardly ever fits into to f. If we want to fit e.g. an XBRL-instance into to f, we need to prepare the data. XBRL needs a good representation to fit into AI and Machine learning.

Creating a good and standardized representation of XBRL into AI and Machine learning are the main purpose of this project.

Getting started

To see an example of how one could use xbrl-ai start by creating a conda environment from the cloned yaml file:

>> conda env create -f environment.yml

now activate the environment by using

>> activate xbrl_ai

for windows, or

>> source activate xbrl_ai

for linux.

You can now run the test_xbrl_ai_dk.py.

Installation

Download the files and run the following command from the folder:

  • Using pip:
>> pip install .
  • Using setuptools:
>> python setup.py install