Simple, light-weight config handling through python data classes with to/from JSON serialization/deserialization.

Overview

👩‍✈️ Coqpit

Simple, light-weight config handling through python data classes with to/from JSON serialization/deserialization.

Work in progress... 🌡️

Why I need this

What I need from a ML configuration library...

  1. Fixing a general config schema in Python to guide users about expected values.

    Python is good but not universal. Sometimes you train a ML model and use it on a different platform. So, you need your model configuration file importable by other programming languages.

  2. Simple dynamic value and type checking with default values.

    If you are a beginner in a ML project, it is hard to guess the right values for your ML experiment. Therefore it is important to have some default values and know what range and type of input are expected for each field.

  3. Ability to decompose large configs.

    As you define more fields for the training dataset, data preprocessing, model parameters, etc., your config file tends to get quite large but in most cases, they can be decomposed, enabling flexibility and readability.

  4. Inheritance and nested configurations.

    Simply helps to keep configurations consistent and easier to maintain.

  5. Ability to override values from the command line when necessary.

    For instance, you might need to define a path for your dataset, and this changes for almost every run. Then the user should be able to override this value easily over the command line.

    It also allows easy hyper-parameter search without changing your original code. Basically, you can run different models with different parameters just using command line arguments.

  6. Defining dynamic or conditional config values.

    Sometimes you need to define certain values depending on the other values. Using python helps to define the underlying logic for such config values.

  7. No dependencies

    You don't want to install a ton of libraries for just configuration management. If you install one, then it is better to be just native python.

🔍 Examples

👉 Serialization

import os
from dataclasses import asdict, dataclass, field
from coqpit import Coqpit, check_argument
from typing import List, Union


@dataclass
class SimpleConfig(Coqpit):
    val_a: int = 10
    val_b: int = None
    val_c: str = "Coqpit is great!"

    def check_values(self,):
        '''Check config fields'''
        c = asdict(self)
        check_argument('val_a', c, restricted=True, min_val=10, max_val=2056)
        check_argument('val_b', c, restricted=True, min_val=128, max_val=4058, allow_none=True)
        check_argument('val_c', c, restricted=True)


@dataclass
class NestedConfig(Coqpit):
    val_d: int = 10
    val_e: int = None
    val_f: str = "Coqpit is great!"
    sc_list: List[SimpleConfig] = None
    sc: SimpleConfig = SimpleConfig()
    union_var: Union[List[SimpleConfig], SimpleConfig] = field(default_factory=lambda: [SimpleConfig(),SimpleConfig()])

    def check_values(self,):
        '''Check config fields'''
        c = asdict(self)
        check_argument('val_d', c, restricted=True, min_val=10, max_val=2056)
        check_argument('val_e', c, restricted=True, min_val=128, max_val=4058, allow_none=True)
        check_argument('val_f', c, restricted=True)
        check_argument('sc_list', c, restricted=True, allow_none=True)
        check_argument('sc', c, restricted=True, allow_none=True)


if __name__ == '__main__':
    file_path = os.path.dirname(os.path.abspath(__file__))
    # init 🐸 dataclass
    config = NestedConfig()

    # save to a json file
    config.save_json(os.path.join(file_path, 'example_config.json'))
    # load a json file
    config2 = NestedConfig(val_d=None, val_e=500, val_f=None, sc_list=None, sc=None, union_var=None)
    # update the config with the json file.
    config2.load_json(os.path.join(file_path, 'example_config.json'))
    # now they should be having the same values.
    assert config == config2

    # pretty print the dataclass
    print(config.pprint())

    # export values to a dict
    config_dict = config.to_dict()
    # crate a new config with different values than the defaults
    config2 = NestedConfig(val_d=None, val_e=500, val_f=None, sc_list=None, sc=None, union_var=None)
    # update the config with the exported valuess from the previous config.
    config2.from_dict(config_dict)
    # now they should be having the same values.
    assert config == config2

👉 argparse handling and parsing.

import argparse
import os
from dataclasses import asdict, dataclass, field
from typing import List

from coqpit.coqpit import Coqpit, check_argument
import sys


@dataclass
class SimplerConfig(Coqpit):
    val_a: int = field(default=None, metadata={'help': 'this is val_a'})


@dataclass
class SimpleConfig(Coqpit):
    val_a: int = field(default=10,
                       metadata={'help': 'this is val_a of SimpleConfig'})
    val_b: int = field(default=None, metadata={'help': 'this is val_b'})
    val_c: str = "Coqpit is great!"
    mylist_with_default: List[SimplerConfig] = field(
        default_factory=lambda:
        [SimplerConfig(val_a=100),
         SimplerConfig(val_a=999)],
        metadata={'help': 'list of SimplerConfig'})

    # mylist_without_default: List[SimplerConfig] = field(default=None, metadata={'help': 'list of SimplerConfig'})  # NOT SUPPORTED YET!

    def check_values(self, ):
        '''Check config fields'''
        c = asdict(self)
        check_argument('val_a', c, restricted=True, min_val=10, max_val=2056)
        check_argument('val_b',
                       c,
                       restricted=True,
                       min_val=128,
                       max_val=4058,
                       allow_none=True)
        check_argument('val_c', c, restricted=True)


def main():
    file_path = os.path.dirname(os.path.abspath(__file__))

    # initial config
    config = SimpleConfig()
    print(config.pprint())

    # reference config that we like to match with the config above
    config_ref = SimpleConfig(val_a=222,
                              val_b=999,
                              val_c='this is different',
                              mylist_with_default=[
                                  SimplerConfig(val_a=222),
                                  SimplerConfig(val_a=111)
                              ])

    # create and init argparser with Coqpit
    parser = argparse.ArgumentParser()
    parser = config.init_argparse(parser)
    parser.print_help()
    args = parser.parse_args()

    # parse the argsparser
    config.from_argparse(args)
    config.pprint()
    # check the current config with the reference config
    assert config == config_ref


if __name__ == '__main__':
    sys.argv.extend(['--coqpit.val_a', '222'])
    sys.argv.extend(['--coqpit.val_b', '999'])
    sys.argv.extend(['--coqpit.val_c', 'this is different'])
    sys.argv.extend(['--coqpit.mylist_with_default.0.val_a', '222'])
    sys.argv.extend(['--coqpit.mylist_with_default.1.val_a', '111'])
    main()

🤸‍♀️ Merging coqpits

import os
from dataclasses import dataclass
from coqpit.coqpit import Coqpit, check_argument


@dataclass
class CoqpitA(Coqpit):
    val_a: int = 10
    val_b: int = None
    val_d: float = 10.21
    val_c: str = "Coqpit is great!"


@dataclass
class CoqpitB(Coqpit):
    val_d: int = 25
    val_e: int = 257
    val_f: float = -10.21
    val_g: str = "Coqpit is really great!"


if __name__ == '__main__':
    file_path = os.path.dirname(os.path.abspath(__file__))
    coqpita = CoqpitA()
    coqpitb = CoqpitB()
    coqpitb.merge(coqpita)
    print(coqpitb.val_a)
    print(coqpitb.pprint())
Comments
  • Allow file-like objects when saving and loading

    Allow file-like objects when saving and loading

    Allow users to save the configs to arbitrary locations through file-like objects. Would e.g. simplify coqui-ai/TTS#683 without adding an fsspec dependency to this library.

    opened by agrinh 6
  • Latest PR causes an issue when a `Serializable` has default None

    Latest PR causes an issue when a `Serializable` has default None

    https://github.com/coqui-ai/coqpit/blob/5379c810900d61ae19d79b73b03890fa103487dd/coqpit/coqpit.py#L539

    @reuben I am on it but if you have an easy fix go for it. Right now it breaks all the TTS trainings.

    opened by erogol 2
  • [feature request] change the `arg_perfix` of coqpit

    [feature request] change the `arg_perfix` of coqpit

    Is it possible to change the arg_perfix when using Coqpit object to another value / empty string? I see the option is supported in the code by changing arg_perfix, but not sure how to access it using the proposed API.

    Thanks for the package, looks very useful!

    opened by mosheman5 1
  • Setup CI to push new tags to PyPI automatically

    Setup CI to push new tags to PyPI automatically

    I'm gonna add a workflow to automatically upload new tags to PyPI. @erogol when you have a chance could you transfer the coqpit project on PyPI to the coqui user?[0] Then you can add your personal account as a maintainer also, so you don't have to change your local setup.

    In the mean time I'll iterate on testpypi.

    [0] https://pypi.org/user/coqui/

    opened by reuben 1
  • Fix rsetattr

    Fix rsetattr

    rsetattr() is updated to pass the new test cases below.

    I don't know if it is the right solution. It might be that rsetattr confuses when coqpit is used as a prefix.

    opened by erogol 0
  • [feature request] Warning when unexpected key is loaded but not present in class

    [feature request] Warning when unexpected key is loaded but not present in class

    Here is an toy scenario where it would be nice to have a warning

    from dataclasses import dataclass
    from coqpit import Coqpit
    
    @dataclass
    class SimpleConfig(Coqpit):
        val_a: int = 10
        val_b: int = None
    
    if __name__ == "__main__":
        config = SimpleConfig()
    
        tmp_config = config.to_dict()
        tmp_config["unknown_key"] = "Ignored value"
        config.from_dict(tmp_config)
        print(config.to_json())
    

    There the value of config.to_json() is

    {
        "val_a": 10,
        "val_b": null
    }
    

    Which is expected behaviour, but we should get a warning that some keys were ignored (IMO)

    feature request 
    opened by WeberJulian 6
  • [feature request] Add `is_defined`

    [feature request] Add `is_defined`

    Use coqpit.is_defined('field') to check if "field" in coqpit and coqpit.field is not None:

    It is a common condition when you parse out a coqpit object.

    feature request 
    opened by erogol 0
  • Allow grouping of argparse fields according to subclassing

    Allow grouping of argparse fields according to subclassing

    When using inheritance to extend config definitions the resulting ArgumentParser has all fields flattened out. It would be nice to group fields by class and allow some control over ordering.

    opened by reuben 2
Releases(v0.0.17)
Owner
Eren Gölge
AI researcher @Coqui.ai
Eren Gölge
🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams

🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams

Real-time water systems lab 416 Jan 06, 2023
This repository has datasets containing information of Uber pickups in NYC from April 2014 to September 2014 and January to June 2015. data Analysis , virtualization and some insights are gathered here

uber-pickups-analysis Data Source: https://www.kaggle.com/fivethirtyeight/uber-pickups-in-new-york-city Information about data set The dataset contain

B DEVA DEEKSHITH 1 Nov 03, 2021
customer churn prediction prevention in telecom industry using machine learning and survival analysis

Telco Customer Churn Prediction - Plotly Dash Application Description This dash application allows you to predict telco customer churn using machine l

Benaissa Mohamed Fayçal 3 Nov 20, 2021
Apache (Py)Spark type annotations (stub files).

PySpark Stubs A collection of the Apache Spark stub files. These files were generated by stubgen and manually edited to include accurate type hints. T

Maciej 114 Nov 22, 2022
Distributed scikit-learn meta-estimators in PySpark

sk-dist: Distributed scikit-learn meta-estimators in PySpark What is it? sk-dist is a Python package for machine learning built on top of scikit-learn

Ibotta 282 Dec 09, 2022
fastFM: A Library for Factorization Machines

Citing fastFM The library fastFM is an academic project. The time and resources spent developing fastFM are therefore justified by the number of citat

1k Dec 24, 2022
Real-time stream processing for python

Streamz Streamz helps you build pipelines to manage continuous streams of data. It is simple to use in simple cases, but also supports complex pipelin

Python Streamz 1.1k Dec 28, 2022
A Python package to preprocess time series

Disclaimer: This package is WIP. Do not take any APIs for granted. tspreprocess Time series can contain noise, may be sampled under a non fitting rate

Maximilian Christ 57 Dec 17, 2022
Python package for machine learning for healthcare using a OMOP common data model

This library was developed in order to facilitate rapid prototyping in Python of predictive machine-learning models using longitudinal medical data from an OMOP CDM-standard database.

Sontag Lab 75 Jan 03, 2023
Databricks Certified Associate Spark Developer preparation toolkit to setup single node Standalone Spark Cluster along with material in the form of Jupyter Notebooks.

Databricks Certification Spark Databricks Certified Associate Spark Developer preparation toolkit to setup single node Standalone Spark Cluster along

19 Dec 13, 2022
Predict the demand for electricity (R) - FRENCH

06.demand-electricity Predict the demand for electricity (R) - FRENCH Prédisez la demande en électricité Prérequis Pour effectuer ce projet, vous devr

1 Feb 13, 2022
Learn Machine Learning Algorithms by doing projects in Python and R Programming Language

Learn Machine Learning Algorithms by doing projects in Python and R Programming Language. This repo covers all aspect of Machine Learning Algorithms.

Ravi Chaubey 6 Oct 20, 2022
LILLIE: Information Extraction and Database Integration Using Linguistics and Learning-Based Algorithms

LILLIE: Information Extraction and Database Integration Using Linguistics and Learning-Based Algorithms Based on the work by Smith et al. (2021) Query

5 Aug 06, 2022
Toolkit for building machine learning models that generalize to unseen domains and are robust to privacy and other attacks.

Toolkit for Building Robust ML models that generalize to unseen domains (RobustDG) Divyat Mahajan, Shruti Tople, Amit Sharma Privacy & Causal Learning

Microsoft 149 Jan 06, 2023
LightGBM + Optuna: no brainer

AutoLGBM LightGBM + Optuna: no brainer auto train lightgbm directly from CSV files auto tune lightgbm using optuna auto serve best lightgbm model usin

Rishiraj Acharya 22 Dec 15, 2022
Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning

Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning. It features an imperative, define-by-run style user API.

7.4k Jan 04, 2023
虚拟货币(BTC、ETH)炒币量化系统项目。在一版本的基础上加入了趋势判断

🎉 第二版本 🎉 (现货趋势网格) 介绍 在第一版本的基础上 趋势判断,不在固定点位开单,选择更优的开仓点位 优势: 🎉 简单易上手 安全(不用将api_secret告诉他人) 如何启动 修改app目录下的authorization文件

幸福村的码农 250 Jan 07, 2023
This repo implements a Topological SLAM: Deep Visual Odometry with Long Term Place Recognition (Loop Closure Detection)

This repo implements a topological SLAM system. Deep Visual Odometry (DF-VO) and Visual Place Recognition are combined to form the topological SLAM system.

Best of Australian Centre for Robotic Vision (ACRV) 32 Jun 23, 2022
NCVX (NonConVeX): A User-Friendly and Scalable Package for Nonconvex Optimization in Machine Learning.

NCVX (NonConVeX): A User-Friendly and Scalable Package for Nonconvex Optimization in Machine Learning.

SUN Group @ UMN 28 Aug 03, 2022
A machine learning model for Covid case prediction

CovidcasePrediction A machine learning model for Covid case prediction Problem Statement Using regression algorithms we can able to track the active c

VijayAadhithya2019rit 1 Feb 02, 2022