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
Apple-voice-recognition - Machine Learning

Apple-voice-recognition Machine Learning How does Siri work? Siri is based on large-scale Machine Learning systems that employ many aspects of data sc

Harshith VH 1 Oct 22, 2021
A framework for building (and incrementally growing) graph-based data structures used in hierarchical or DAG-structured clustering and nearest neighbor search

A framework for building (and incrementally growing) graph-based data structures used in hierarchical or DAG-structured clustering and nearest neighbor search

Nicholas Monath 31 Nov 03, 2022
Home repository for the Regularized Greedy Forest (RGF) library. It includes original implementation from the paper and multithreaded one written in C++, along with various language-specific wrappers.

Regularized Greedy Forest Regularized Greedy Forest (RGF) is a tree ensemble machine learning method described in this paper. RGF can deliver better r

RGF-team 363 Dec 14, 2022
Credit Card Fraud Detection, used the credit card fraud dataset from Kaggle

Credit Card Fraud Detection, used the credit card fraud dataset from Kaggle

Sean Zahller 1 Feb 04, 2022
Apache Liminal is an end-to-end platform for data engineers & scientists, allowing them to build, train and deploy machine learning models in a robust and agile way

Apache Liminals goal is to operationalise the machine learning process, allowing data scientists to quickly transition from a successful experiment to an automated pipeline of model training, validat

The Apache Software Foundation 121 Dec 28, 2022
Machine-learning-dell - Repositório com as atividades desenvolvidas no curso de Machine Learning

📚 Descrição Neste curso da Dell aprofundamos nossos conhecimentos em Machine Learning. 🖥️ Aulas (Em curso) 1.1 - Python aplicado a Data Science 1.2

Claudia dos Anjos 1 Jan 05, 2022
A model to predict steering torque fully end-to-end

torque_model The torque model is a spiritual successor to op-smart-torque, which was a project to train a neural network to control a car's steering f

Shane Smiskol 4 Jun 03, 2022
Python package for concise, transparent, and accurate predictive modeling

Python package for concise, transparent, and accurate predictive modeling. All sklearn-compatible and easy to use. 📚 docs • 📖 demo notebooks Modern

Chandan Singh 983 Jan 01, 2023
Contains an implementation (sklearn API) of the algorithm proposed in "GENDIS: GEnetic DIscovery of Shapelets" and code to reproduce all experiments.

GENDIS GENetic DIscovery of Shapelets In the time series classification domain, shapelets are small subseries that are discriminative for a certain cl

IDLab Services 90 Oct 28, 2022
GroundSeg Clustering Optimized Kdtree

ground seg and clustering based on kitti velodyne data, and a additional optimized kdtree for knn and radius nn search

2 Dec 02, 2021
Bottleneck a collection of fast, NaN-aware NumPy array functions written in C.

Bottleneck Bottleneck is a collection of fast, NaN-aware NumPy array functions written in C. As one example, to check if a np.array has any NaNs using

Python for Data 835 Dec 27, 2022
To design and implement the Identification of Iris Flower species using machine learning using Python and the tool Scikit-Learn.

To design and implement the Identification of Iris Flower species using machine learning using Python and the tool Scikit-Learn.

Astitva Veer Garg 1 Jan 11, 2022
Accelerating model creation and evaluation.

EmeraldML A machine learning library for streamlining the process of (1) cleaning and splitting data, (2) training, optimizing, and testing various mo

Yusuf 0 Dec 06, 2021
Steganography is the art of hiding the fact that communication is taking place, by hiding information in other information.

Steganography is the art of hiding the fact that communication is taking place, by hiding information in other information.

Priyansh Sharma 7 Nov 09, 2022
MiniTorch - a diy teaching library for machine learning engineers

This repo is the full student code for minitorch. It is designed as a single repo that can be completed part by part following the guide book. It uses

1.1k Jan 07, 2023
Machine Learning Model to predict the payment date of an invoice when it gets created in the system.

Payment-Date-Prediction Machine Learning Model to predict the payment date of an invoice when it gets created in the system.

15 Sep 09, 2022
This is a Cricket Score Predictor that predicts the first innings score of a T20 Cricket match using Machine Learning

This is a Cricket Score Predictor that predicts the first innings score of a T20 Cricket match using Machine Learning. It is a Web Application.

Developer Junaid 3 Aug 04, 2022
The code from the Machine Learning Bookcamp book and a free course based on the book

The code from the Machine Learning Bookcamp book and a free course based on the book

Alexey Grigorev 5.5k Jan 09, 2023
Python implementation of the rulefit algorithm

RuleFit Implementation of a rule based prediction algorithm based on the rulefit algorithm from Friedman and Popescu (PDF) The algorithm can be used f

Christoph Molnar 326 Jan 02, 2023
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