smart_importer
==============
https://github.com/beancount/smart_importer
.. image:: https://github.com/beancount/smart_importer/actions/workflows/ci.yml/badge.svg?branch=main
:target: https://github.com/beancount/smart_importer/actions?query=branch%3Amain
Augments
`Beancount <http://furius.ca/beancount/>`__ importers
with machine learning functionality.
Status
------
Working protoype, development status: beta
Installation
------------
The ``smart_importer`` can be installed from PyPI:
.. code:: bash
pip install smart_importer
Quick Start
-----------
This package provides import hooks that can modify the imported entries. When
running the importer, the existing entries will be used as training data for a
machine learning model, which will then predict entry attributes.
The following example shows how to apply the ``PredictPostings`` hook to
an existing CSV importer:
.. code:: python
from beancount.ingest.importers import csv
from beancount.ingest.importers.csv import Col
from smart_importer import apply_hooks, PredictPostings
class MyBankImporter(csv.Importer):
'''Conventional importer for MyBank'''
def __init__(self, *, account):
super().__init__(
{Col.DATE: 'Date',
Col.PAYEE: 'Transaction Details',
Col.AMOUNT_DEBIT: 'Funds Out',
Col.AMOUNT_CREDIT: 'Funds In'},
account,
'EUR',
(
'Date, Transaction Details, Funds Out, Funds In'
)
)
CONFIG = [
apply_hooks(MyBankImporter(account='Assets:MyBank:MyAccount'), [PredictPostings()])
]
Documentation
-------------
This section explains in detail the relevant concepts and artifacts
needed for enhancing Beancount importers with machine learning.
Beancount Importers
~~~~~~~~~~~~~~~~~~~~
Let's assume you have created an importer for "MyBank" called
``MyBankImporter``:
.. code:: python
class MyBankImporter(importer.ImporterProtocol):
"""My existing importer"""
# the actual importer logic would be here...
Note:
This documentation assumes you already know how to create Beancount importers.
Relevant documentation can be found in the `beancount import documentation
<https://beancount.github.io/docs/importing_external_data.html>`__.
With the functionality of beancount.ingest, users can
write their own importers and use them to convert downloaded bank statements
into lists of Beancount entries.
An example is provided as part of beancount v2's source code under
`examples/ingest/office
<https://github.com/beancount/beancount/tree/v2/examples/ingest/office>`__.
smart_importer only works by appending onto incomplete single-legged postings
(i.e. It will not work by modifying postings with accounts like "Expenses:TODO").
The `extract` method in the importer should follow the
`latest interface <https://github.com/beancount/beancount/blob/v2/beancount/ingest/importer.py#L61>`__
and include an `existing_entries` argument.
Applying `smart_importer` hooks
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Any Beancount importer can be converted into a smart importer by applying one
of the following hooks:
* ``PredictPostings`` - predict the list of postings.
* ``PredictPayees``- predict the payee of the transaction.
* ``DuplicateDetector`` - detect duplicates
For example, to convert an existing ``MyBankImporter`` into a smart importer:
.. code:: python
from your_custom_importer import MyBankImporter
from smart_importer import apply_hooks, PredictPayees, PredictPostings
my_bank_importer = MyBankImporter('whatever', 'config', 'is', 'needed')
apply_hooks(my_bank_importer, [PredictPostings(), PredictPayees()])
CONFIG = [
my_bank_importer,
]
Note that the importer hooks need to be applied to an importer instance, as
shown above.
Specifying Training Data
~~~~~~~~~~~~~~~~~~~~~~~~
The ``smart_importer`` hooks need training data, i.e. an existing list of
transactions in order to be effective. Training data can be specified by
calling bean-extract with an argument that references existing Beancount
transactions, e.g., ``bean-extract -f existing_transactions.beancount``. When
using the importer in Fava, the existing entries are used as training data
automatically.
Usage with Fava
~~~~~~~~~~~~~~~
Smart importers play nice with `Fava <https://github.com/beancount/fava>`__.
This means you can use smart importers together with Fava in the exact same way
as you would do with a conventional importer. See `Fava's help on importers
<https://github.com/beancount/fava/blob/main/src/fava/help/import.md>`__ for more
information.
Development
-----------
Pull requests welcome!
Executing the Unit Tests
~~~~~~~~~~~~~~~~~~~~~~~~
Simply run (requires tox):
.. code:: bash
make test
Configuring Logging
~~~~~~~~~~~~~~~~~~~
Python's `logging` module is used by the smart_importer module.
The according log level can be changed as follows:
.. code:: python
import logging
logging.getLogger('smart_importer').setLevel(logging.DEBUG)
Using Tokenizer
~~~~~~~~~~~~~~~~~~
Custom tokenizers can let smart_importer support more languages, eg. Chinese.
If you looking for Chinese tokenizer, you can follow this example:
First make sure that `jieba` is installed in your python environment:
.. code:: bash
pip install jieba
In your importer code, you can then pass `jieba` to be used as tokenizer:
.. code:: python
from smart_importer import PredictPostings
import jieba
jieba.initialize()
tokenizer = lambda s: list(jieba.cut(s))
predictor = PredictPostings(string_tokenizer=tokenizer)
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"description": "smart_importer\n==============\n\nhttps://github.com/beancount/smart_importer\n\n.. image:: https://github.com/beancount/smart_importer/actions/workflows/ci.yml/badge.svg?branch=main\n :target: https://github.com/beancount/smart_importer/actions?query=branch%3Amain\n\nAugments\n`Beancount <http://furius.ca/beancount/>`__ importers\nwith machine learning functionality.\n\n\nStatus\n------\n\nWorking protoype, development status: beta\n\n\nInstallation\n------------\n\nThe ``smart_importer`` can be installed from PyPI:\n\n.. code:: bash\n\n pip install smart_importer\n\n\nQuick Start\n-----------\n\nThis package provides import hooks that can modify the imported entries. When\nrunning the importer, the existing entries will be used as training data for a\nmachine learning model, which will then predict entry attributes.\n\nThe following example shows how to apply the ``PredictPostings`` hook to\nan existing CSV importer:\n\n.. code:: python\n\n from beancount.ingest.importers import csv\n from beancount.ingest.importers.csv import Col\n\n from smart_importer import apply_hooks, PredictPostings\n\n\n class MyBankImporter(csv.Importer):\n '''Conventional importer for MyBank'''\n\n def __init__(self, *, account):\n super().__init__(\n {Col.DATE: 'Date',\n Col.PAYEE: 'Transaction Details',\n Col.AMOUNT_DEBIT: 'Funds Out',\n Col.AMOUNT_CREDIT: 'Funds In'},\n account,\n 'EUR',\n (\n 'Date, Transaction Details, Funds Out, Funds In'\n )\n )\n\n\n CONFIG = [\n apply_hooks(MyBankImporter(account='Assets:MyBank:MyAccount'), [PredictPostings()])\n ]\n\n\nDocumentation\n-------------\n\nThis section explains in detail the relevant concepts and artifacts\nneeded for enhancing Beancount importers with machine learning.\n\n\nBeancount Importers\n~~~~~~~~~~~~~~~~~~~~\n\nLet's assume you have created an importer for \"MyBank\" called\n``MyBankImporter``:\n\n.. code:: python\n\n class MyBankImporter(importer.ImporterProtocol):\n \"\"\"My existing importer\"\"\"\n # the actual importer logic would be here...\n\nNote:\nThis documentation assumes you already know how to create Beancount importers.\nRelevant documentation can be found in the `beancount import documentation\n<https://beancount.github.io/docs/importing_external_data.html>`__.\nWith the functionality of beancount.ingest, users can\nwrite their own importers and use them to convert downloaded bank statements\ninto lists of Beancount entries.\nAn example is provided as part of beancount v2's source code under\n`examples/ingest/office\n<https://github.com/beancount/beancount/tree/v2/examples/ingest/office>`__.\n\nsmart_importer only works by appending onto incomplete single-legged postings\n(i.e. It will not work by modifying postings with accounts like \"Expenses:TODO\").\nThe `extract` method in the importer should follow the\n`latest interface <https://github.com/beancount/beancount/blob/v2/beancount/ingest/importer.py#L61>`__\nand include an `existing_entries` argument.\n\nApplying `smart_importer` hooks\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nAny Beancount importer can be converted into a smart importer by applying one\nof the following hooks:\n\n* ``PredictPostings`` - predict the list of postings.\n* ``PredictPayees``- predict the payee of the transaction.\n* ``DuplicateDetector`` - detect duplicates\n\nFor example, to convert an existing ``MyBankImporter`` into a smart importer:\n\n.. code:: python\n\n from your_custom_importer import MyBankImporter\n from smart_importer import apply_hooks, PredictPayees, PredictPostings\n\n my_bank_importer = MyBankImporter('whatever', 'config', 'is', 'needed')\n apply_hooks(my_bank_importer, [PredictPostings(), PredictPayees()])\n\n CONFIG = [\n my_bank_importer,\n ]\n\nNote that the importer hooks need to be applied to an importer instance, as\nshown above.\n\n\nSpecifying Training Data\n~~~~~~~~~~~~~~~~~~~~~~~~\n\nThe ``smart_importer`` hooks need training data, i.e. an existing list of\ntransactions in order to be effective. Training data can be specified by\ncalling bean-extract with an argument that references existing Beancount\ntransactions, e.g., ``bean-extract -f existing_transactions.beancount``. When\nusing the importer in Fava, the existing entries are used as training data\nautomatically.\n\n\nUsage with Fava\n~~~~~~~~~~~~~~~\n\nSmart importers play nice with `Fava <https://github.com/beancount/fava>`__.\nThis means you can use smart importers together with Fava in the exact same way\nas you would do with a conventional importer. See `Fava's help on importers\n<https://github.com/beancount/fava/blob/main/src/fava/help/import.md>`__ for more\ninformation.\n\n\nDevelopment\n-----------\n\nPull requests welcome!\n\n\nExecuting the Unit Tests\n~~~~~~~~~~~~~~~~~~~~~~~~\n\nSimply run (requires tox):\n\n.. code:: bash\n\n make test\n\n\nConfiguring Logging\n~~~~~~~~~~~~~~~~~~~\n\nPython's `logging` module is used by the smart_importer module.\nThe according log level can be changed as follows:\n\n\n.. code:: python\n\n import logging\n logging.getLogger('smart_importer').setLevel(logging.DEBUG)\n\n\nUsing Tokenizer\n~~~~~~~~~~~~~~~~~~\n\nCustom tokenizers can let smart_importer support more languages, eg. 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