Accelerating model creation and evaluation.

Related tags

Machine Learningpythondata-sciencemachine-learning
Overview

Emerald

EmeraldML

A machine learning library for streamlining the process of
(1) cleaning and splitting data,
(2) training, optimizing, and testing various models based on the task, and
(3) scoring and ranking them
during the exploratory phase for an elementary analysis of which models perform better for a specific dataset.

Installation

Dependencies

  • Python (>= 3.7)
  • NumPy (>= 1.21.2)
  • pandas (>= 1.3.3)
  • scikit-learn (>= 0.24.2)
  • statsmodels (>= 0.12.2)

User installation

pip install emeraldml

Development

Source code

You can check the latest sources with the command:

git clone https://github.com/yu3ufff/emeraldml.git

Demo

Getting the data:

import pandas as pd
audi = pd.read_csv('audi.csv')
audi.head()
|    | model   |   year |   price | transmission   |   mileage | fuelType   |   tax |   mpg |   engineSize |
|---:|:--------|-------:|--------:|:---------------|----------:|:-----------|------:|------:|-------------:|
|  0 | A1      |   2017 |   12500 | Manual         |     15735 | Petrol     |   150 |  55.4 |          1.4 |
|  1 | A6      |   2016 |   16500 | Automatic      |     36203 | Diesel     |    20 |  64.2 |          2   |
|  2 | A1      |   2016 |   11000 | Manual         |     29946 | Petrol     |    30 |  55.4 |          1.4 |
|  3 | A4      |   2017 |   16800 | Automatic      |     25952 | Diesel     |   145 |  67.3 |          2   |
|  4 | A3      |   2019 |   17300 | Manual         |      1998 | Petrol     |   145 |  49.6 |          1   |

Using EmeraldML:

import emerald
from emerald.boa import RegressionBoa

rboa = RegressionBoa(random_state=3)
rboa.hunt(data=audi, target='price')
rboa.ladder
[(OptimalRFRegressor, 0.9624889664024406),
 (OptimalDTRegressor, 0.9514992411732952),
 (OptimalKNRegressor, 0.9511411883559433),
 (OptimalLinearRegression, 0.8876961846248467),
 (OptimalABRegressor, 0.8491539140007975)]

for i in range(len(rboa)):
    print(rboa.model(i))
RandomForestRegressor(min_samples_split=5, n_estimators=500, random_state=3)
DecisionTreeRegressor(max_depth=15, min_samples_split=10, random_state=3)
KNeighborsRegressor(n_neighbors=3, p=1)
LinearRegression()
AdaBoostRegressor(learning_rate=0.1, n_estimators=100, random_state=3)
Owner
Yusuf
Yusuf
GitHub Repository
An implementation of Relaxed Linear Adversarial Concept Erasure (RLACE)

Background This repository contains an implementation of Relaxed Linear Adversarial Concept Erasure (RLACE). Given a dataset X of dense representation

Shauli Ravfogel 4 Apr 13, 2022
PyHarmonize: Adding harmony lines to recorded melodies in Python

PyHarmonize: Adding harmony lines to recorded melodies in Python About To use this module, the user provides a wav file containing a melody, the key i

Julian Kappler 2 May 20, 2022
Crunchdao - Python API for the Crunchdao machine learning tournament

Python API for the Crunchdao machine learning tournament Interact with the Crunc

3 Jan 19, 2022
An easier way to build neural search on the cloud

Jina is geared towards building search systems for any kind of data, including text, images, audio, video and many more. With the modular design & multi-layer abstraction, you can leverage the effici

Jina AI 17k Jan 01, 2023
Practical Time-Series Analysis, published by Packt

Practical Time-Series Analysis This is the code repository for Practical Time-Series Analysis, published by Packt. It contains all the supporting proj

Packt 325 Dec 23, 2022
K-Means clusternig example with Python and Scikit-learn

Unsupervised-Machine-Learning Flat Clustering K-Means clusternig example with Python and Scikit-learn Flat clustering Clustering algorithms group a se

Emin 1 Dec 13, 2021
Banpei is a Python package of the anomaly detection.

Banpei Banpei is a Python package of the anomaly detection. Anomaly detection is a technique used to identify unusual patterns that do not conform to

Hirofumi Tsuruta 282 Jan 03, 2023
Forecast dynamically at scale with this unique package. pip install scalecast

🌄 Scalecast: Dynamic Forecasting at Scale About This package uses a scaleable forecasting approach in Python with common scikit-learn and statsmodels

Michael Keith 158 Jan 03, 2023
Code Repository for Machine Learning with PyTorch and Scikit-Learn

Code Repository for Machine Learning with PyTorch and Scikit-Learn

Sebastian Raschka 1.4k Jan 03, 2023
Deep Survival Machines - Fully Parametric Survival Regression

Package: dsm Python package dsm provides an API to train the Deep Survival Machines and associated models for problems in survival analysis. The under

Carnegie Mellon University Auton Lab 10 Dec 30, 2022
Crypto-trading - ML techiques are used to forecast short term returns in 14 popular cryptocurrencies

Crypto-trading - ML techiques are used to forecast short term returns in 14 popular cryptocurrencies. We have amassed a dataset of millions of rows of high-frequency market data dating back to 2018 w

Panagiotis (Panos) Mavritsakis 4 Sep 22, 2022
CinnaMon is a Python library which offers a number of tools to detect, explain, and correct data drift in a machine learning system

CinnaMon is a Python library which offers a number of tools to detect, explain, and correct data drift in a machine learning system

Zelros 67 Dec 28, 2022
A basic Ray Tracer that exploits numpy arrays and functions to work fast.

Python-Fast-Raytracer A basic Ray Tracer that exploits numpy arrays and functions to work fast. The code is written keeping as much readability as pos

Rafael de la Fuente 393 Dec 27, 2022
Meerkat provides fast and flexible data structures for working with complex machine learning datasets.

Meerkat makes it easier for ML practitioners to interact with high-dimensional, multi-modal data. It provides simple abstractions for data inspection, model evaluation and model training supported by

Robustness Gym 115 Dec 12, 2022
Required for a machine learning pipeline data preprocessing and variable engineering script needs to be prepared

Feature-Engineering Required for a machine learning pipeline data preprocessing and variable engineering script needs to be prepared. When the dataset

kemalgunay 5 Apr 21, 2022
Python module for data science and machine learning users.

dsnk-distributions package dsnk distribution is a Python module for data science and machine learning that was created with the goal of reducing calcu

Emmanuel ASIFIWE 1 Nov 23, 2021
Bodywork deploys machine learning projects developed in Python, to Kubernetes.

Bodywork deploys machine learning projects developed in Python, to Kubernetes. It helps you to: serve models as microservices execute batch jobs run r

Bodywork Machine Learning 409 Jan 01, 2023
A python fast implementation of the famous SVD algorithm popularized by Simon Funk during Netflix Prize

âš¡ funk-svd funk-svd is a Python 3 library implementing a fast version of the famous SVD algorithm popularized by Simon Funk during the Neflix Prize co

Geoffrey Bolmier 171 Dec 19, 2022
Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application

Intel(R) Extension for Scikit-learn* Installation | Documentation | Examples | Support | FAQ With Intel(R) Extension for Scikit-learn you can accelera

Intel Corporation 858 Dec 25, 2022
LiuAlgoTrader is a scalable, multi-process ML-ready framework for effective algorithmic trading

LiuAlgoTrader is a scalable, multi-process ML-ready framework for effective algorithmic trading. The framework simplify development, testing, deployment, analysis and training algo trading strategies

Amichay Oren 458 Dec 24, 2022