Regression Metrics Calculation Made easy for tensorflow2 and scikit-learn

Related tags

Deep Learningmetricsscikit-learnkerastensorflow2regression-metrics
Overview

Regression Metrics

Installation

To install the package from the PyPi repository you can execute the following command:

pip install regressionmetrics

If you prefer, you can clone it and run the setup.py file. Use the following commands to get a copy from GitHub and install all dependencies:

git clone https://github.com/ashishpatel26/regressionmetrics.git
cd regressionmetrics
pip install .
  • Mean Absolute Error - sklearn, keras
  • Mean Square Error - sklearn, keras
  • Root Mean Square Error - sklearn, keras
  • Root Mean Square Logarithmic Error - sklearn, keras
  • Root Mean Square Logarithmic Error with negative value handle - sklearn
  • R2 Score - sklearn, keras
  • Adjusted R2 Score - sklearn, keras
  • Mean Absolute Percentage Error - sklearn, keras
  • Mean squared logarithmic Error - sklearn, keras
  • Symmetric mean absolute percentage error - sklearn, keras
  • Normalized Root Mean Squared Error - sklearn, keras

Usage

Usage with scikit learn :

from regressionmetrics.metrics import *

y_true = [3, 0.5, 2, 7]
y_pred = [2.5, 0.0, 2, -8]


print("R2Score: ",r2(y_true, y_pred))
print("Adjusted_R2_Score:",adj_r2(y_true, y_pred))
print("RMSE:", rmse(y_true, y_pred))
print("MAE:",mae(y_true, y_pred))
print("RMSLE with Neg Value:", rmsle_with_negval(y_true, y_pred))
print("MSE:", mse(y_true, y_pred))
print("MAPE: ", mape(y_true, y_pred))

Usage with Tensorflow keras:

from regressionmetrics.keras import *
import pandas as pd
import numpy as np

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

(x_train, y_train), (x_test, y_test) = tf.keras.datasets.boston_housing.load_data(path="boston_housing.npz", test_split=0.2, seed=113)

model = keras.Sequential([
    layers.Dense(64, activation='relu', input_shape=(x_train.shape[1],)),
    layers.Dense(64, activation='relu'),
    layers.Dense(1)
])
model.compile(optimizer='rmsprop', loss='mse', metrics=[r2, mae, mse, rmse, mape, rmsle, nrmse])
model.fit(x_train, y_train, epochs=10, batch_size=32, validation_data=(x_test, y_test))
Epoch 1/10
 1/13 [=>............................] - ETA: 7s - loss: 1574.7567 - r2: 0.6597 - mae: 37.1803 - mse: 1574.7567 - rmse: 37.1802 - mape: 159.261313/13 [==============================] - 1s 15ms/step - loss: 270.0653 - r2: 0.9472 - mae: 11.5427 - mse: 270.0653 - rmse: 11.5427 - mape: 57.3519 - rmsle: 0.6445 - nrmse: 0.5735 - val_loss: 88.6351 - val_r2: 0.9727 - val_mae: 6.6028 - val_mse: 88.6351 - val_rmse: 6.6028 - val_mape: 29.6502 - val_rmsle: 0.3161 - val_nrmse: 0.2965
Epoch 2/10
 1/13 [=>............................] - ETA: 0s - loss: 74.6623 - r2: 0.9913 - mae: 5.5958 - mse: 74.6623 - rmse: 5.5958 - mape: 25.3655 - rmsl13/13 [==============================] - 0s 3ms/step - loss: 87.1876 - r2: 0.9856 - mae: 6.9466 - mse: 87.1876 - rmse: 6.9466 - mape: 33.4256 - rmsle: 0.3057 - nrmse: 0.3343 - val_loss: 81.7884 - val_r2: 0.9712 - val_mae: 6.6424 - val_mse: 81.7884 - val_rmse: 6.6424 - val_mape: 28.8687 - val_rmsle: 0.3334 - val_nrmse: 0.2887
Epoch 3/10
 1/13 [=>............................] - ETA: 0s - loss: 41.2790 - r2: 0.9722 - mae: 5.3798 - mse: 41.2790 - rmse: 5.3798 - mape: 28.7497 - rmsl13/13 [==============================] - 0s 3ms/step - loss: 103.6462 - r2: 0.9825 - mae: 7.1041 - mse: 103.6462 - rmse: 7.1041 - mape: 34.6278 - rmsle: 0.3231 - nrmse: 0.3463 - val_loss: 71.7539 - val_r2: 0.9769 - val_mae: 6.1455 - val_mse: 71.7539 - val_rmse: 6.1455 - val_mape: 27.5078 - val_rmsle: 0.2893 - val_nrmse: 0.2751
Epoch 4/10
 1/13 [=>............................] - ETA: 0s - loss: 113.6758 - r2: 0.9917 - mae: 6.6575 - mse: 113.6758 - rmse: 6.6575 - mape: 20.8683 - rm13/13 [==============================] - 0s 3ms/step - loss: 88.1601 - r2: 0.9823 - mae: 6.8479 - mse: 88.1601 - rmse: 6.8479 - mape: 32.5867 - rmsle: 0.3080 - nrmse: 0.3259 - val_loss: 63.3707 - val_r2: 0.9829 - val_mae: 6.0845 - val_mse: 63.3707 - val_rmse: 6.0845 - val_mape: 33.1628 - val_rmsle: 0.2747 - val_nrmse: 0.3316
Epoch 5/10
 1/13 [=>............................] - ETA: 0s - loss: 85.8188 - r2: 0.9893 - mae: 7.0097 - mse: 85.8188 - rmse: 7.0097 - mape: 34.8362 - rmsl13/13 [==============================] - 0s 3ms/step - loss: 82.3233 - r2: 0.9860 - mae: 6.5795 - mse: 82.3233 - rmse: 6.5795 - mape: 32.5198 - rmsle: 0.3105 - nrmse: 0.3252 - val_loss: 74.4783 - val_r2: 0.9813 - val_mae: 6.8936 - val_mse: 74.4783 - val_rmse: 6.8936 - val_mape: 41.9492 - val_rmsle: 0.3067 - val_nrmse: 0.4195
Epoch 7/10
 1/13 [=>............................] - ETA: 0s - loss: 105.6430 - r2: 0.9658 - mae: 9.4737 - mse: 105.6430 - rmse: 9.4737 - mape: 53.0854 - rm13/13 [==============================] - 0s 3ms/step - loss: 76.0740 - r2: 0.9856 - mae: 6.4234 - mse: 76.0740 - rmse: 6.4234 - mape: 31.8728 - rmsle: 0.2828 - nrmse: 0.3187 - val_loss: 104.1779 - val_r2: 0.9679 - val_mae: 7.5539 - val_mse: 104.1779 - val_rmse: 7.5539 - val_mape: 30.9401 - val_rmsle: 0.3692 - val_nrmse: 0.3094
Epoch 8/10
 1/13 [=>............................] - ETA: 0s - loss: 100.0114 - r2: 0.9833 - mae: 6.8492 - mse: 100.0114 - rmse: 6.8492 - mape: 27.9621 - rm13/13 [==============================] - 0s 4ms/step - loss: 68.4268 - r2: 0.9892 - mae: 5.9540 - mse: 68.4268 - rmse: 5.9540 - mape: 29.7586 - rmsle: 0.2623 - nrmse: 0.2976 - val_loss: 171.7968 - val_r2: 0.9412 - val_mae: 10.5855 - val_mse: 171.7968 - val_rmse: 10.5855 - val_mape: 47.9010 - val_rmsle: 0.7561 - val_nrmse: 0.4790
Epoch 9/10
 1/13 [=>............................] - ETA: 0s - loss: 291.8670 - r2: 0.9725 - mae: 13.9899 - mse: 291.8670 - rmse: 13.9899 - mape: 61.3658 - 13/13 [==============================] - 0s 3ms/step - loss: 92.3889 - r2: 0.9796 - mae: 6.8932 - mse: 92.3889 - rmse: 6.8932 - mape: 33.2856 - rmsle: 0.3333 - nrmse: 0.3329 - val_loss: 67.2208 - val_r2: 0.9808 - val_mae: 5.8498 - val_mse: 67.2208 - val_rmse: 5.8498 - val_mape: 26.4504 - val_rmsle: 0.2680 - val_nrmse: 0.2645
Epoch 10/10
 1/13 [=>............................] - ETA: 0s - loss: 97.0853 - r2: 0.9923 - mae: 5.9866 - mse: 97.0853 - rmse: 5.9866 - mape: 24.9878 - rmsl13/13 [==============================] - 0s 3ms/step - loss: 78.3823 - r2: 0.9856 - mae: 6.5958 - mse: 78.3823 - rmse: 6.5958 - mape: 32.8136 - rmsle: 0.3025 - nrmse: 0.3281 - val_loss: 69.5314 - val_r2: 0.9787 - val_mae: 6.8302 - val_mse: 69.5314 - val_rmse: 6.8302 - val_mape: 37.3933 - val_rmsle: 0.2974 - val_nrmse: 0.3739

😃 Thanks for reading and forking.

You might also like...
Hitters Linear Regression - Hitters Linear Regression With Python
Hitters Linear Regression - Hitters Linear Regression With Python

Hitters_Linear_Regression Kullanacağımız veri seti Carnegie Mellon Üniversitesi'

AyseBuyukcelik 2 Jan 26, 2022
A set of tools for creating and testing machine learning features, with a scikit-learn compatible API

Feature Forge This library provides a set of tools that can be useful in many machine learning applications (classification, clustering, regression, e

Machinalis 380 Nov 5, 2022
Using python and scikit-learn to make stock predictions

MachineLearningStocks in python: a starter project and guide EDIT as of Feb 2021: MachineLearningStocks is no longer actively maintained MachineLearni

Robert Martin 1.3k Dec 29, 2022
A real-time speech emotion recognition application using Scikit-learn and gradio
A real-time speech emotion recognition application using Scikit-learn and gradio

Speech-Emotion-Recognition-App A real-time speech emotion recognition application using Scikit-learn and gradio. Requirements librosa==0.6.3 numpy sou

Son Tran 6 Oct 4, 2022
Python package for Bayesian Machine Learning with scikit-learn API
Python package for Bayesian Machine Learning with scikit-learn API

Python package for Bayesian Machine Learning with scikit-learn API Installing & Upgrading package pip install https://github.com/AmazaspShumik/sklearn

Amazasp Shaumyan 482 Jan 4, 2023
A scikit-learn compatible neural network library that wraps PyTorch

A scikit-learn compatible neural network library that wraps PyTorch. Resources Documentation Source Code Examples To see more elaborate examples, look

null 4.9k Dec 31, 2022
scikit-learn: machine learning in Python
scikit-learn: machine learning in Python

scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license. The project was started

scikit-learn 52.5k Jan 8, 2023
A scikit-learn compatible neural network library that wraps PyTorch

A scikit-learn compatible neural network library that wraps PyTorch. Resources Documentation Source Code Examples To see more elaborate examples, look

null 3.8k Feb 13, 2021
A scikit-learn compatible neural network library that wraps PyTorch

A scikit-learn compatible neural network library that wraps PyTorch. Resources Documentation Source Code Examples To see more elaborate examples, look

null 4.9k Jan 3, 2023
Comments
  • Very nice toolkit

    Very nice toolkit

    This isn't really an issue. I wanted to thank you for sharing such a nice toolkit for regression tasks with tensorflow

    Do you have a similar toolkit for classification?

    opened by happypanda5 0
Releases(v1.4.0)

  • v1.4.0(Oct 30, 2021)

    • Changelog for v1.4.0 (2022-01-13)
    • Name clashes resolved with keras names
    • Changelog for v1.3.0 (2021-11-18)
    • new regresson metrics are added with details explaination
    • Changelog for v1.2.0 (2021-10-31)
    • Adjusted r2 score error solved
    • Changelog for v1.1.0 (2021-10-31)
    • SomeError solved
    • Changelog for v1.0.0 (2021-10-31)
    • regressionmetrics package first release 1.0.0.
    Source code(tar.gz)
    Source code(zip)
Owner
Ashish Patel
AI Researcher & Senior Data Scientist at Softweb Solutions Avnet Solutions(Fortune 500) | Rank 3 Kaggle Kernel Master
Ashish Patel
GitHub Repository https://pypi.org/project/regressionmetrics/
Code for CMaskTrack R-CNN (proposed in Occluded Video Instance Segmentation)

CMaskTrack R-CNN for OVIS This repo serves as the official code release of the CMaskTrack R-CNN model on the Occluded Video Instance Segmentation data

Q . J . Y 61 Nov 25, 2022
Symmetry and Uncertainty-Aware Object SLAM for 6DoF Object Pose Estimation

SUO-SLAM This repository hosts the code for our CVPR 2022 paper "Symmetry and Uncertainty-Aware Object SLAM for 6DoF Object Pose Estimation". ArXiv li

Robot Perception & Navigation Group (RPNG) 97 Jan 03, 2023
Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures: A Case Study on Time-Series Classification

PPML-TSA This repository provides all code necessary to reproduce the results reported in our paper Evaluating Privacy-Preserving Machine Learning in

Dominik 1 Mar 08, 2022
This project is based on RIFE and aims to make RIFE more practical for users by adding various features and design new models

CPM 项目描述 CPM（Chinese Pretrained Models）模型是北京智源人工智能研究院和清华大学发布的中文大规模预训练模型。官方发布了三种规模的模型，参数量分别为109M、334M、2.6B，用户需申请与通过审核，方可下载。 由于原项目需要考虑大模型的训练和使用，需要安装较为复杂

hzwer 190 Jan 08, 2023
EMNLP 2021 - Frustratingly Simple Pretraining Alternatives to Masked Language Modeling

Frustratingly Simple Pretraining Alternatives to Masked Language Modeling This is the official implementation for "Frustratingly Simple Pretraining Al

Atsuki Yamaguchi 31 Nov 18, 2022
the official implementation of the paper "Isometric Multi-Shape Matching" (CVPR 2021)

Isometric Multi-Shape Matching (IsoMuSh) Paper-CVF | Paper-arXiv | Video | Code Citation If you find our work useful in your research, please consider

Maolin Gao 9 Jul 17, 2022
Auxiliary data to the CHIIR paper Searching to Learn with Instructional Scaffolding

Searching to Learn with Instructional Scaffolding This is the data and analysis code for the paper "Searching to Learn with Instructional Scaffolding"

Arthur Câmara 2 Mar 02, 2022
Generative Handwriting using LSTM Mixture Density Network with TensorFlow

Generative Handwriting Demo using TensorFlow An attempt to implement the random handwriting generation portion of Alex Graves' paper. See my blog post

hardmaru 686 Nov 24, 2022
Deep Reinforcement Learning based autonomous navigation for quadcopters using PPO algorithm.

PPO-based Autonomous Navigation for Quadcopters This repository contains an implementation of Proximal Policy Optimization (PPO) for autonomous naviga

Bilal Kabas 16 Nov 11, 2022
FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective

FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective Official implementation of "FL-WBC: Enhan

Jingwei Sun 26 Nov 28, 2022
Repositório criado para abrigar os notebooks com a listas de exercícios propostos pelo professor Gustavo Guanabara do canal Curso em Vídeo do YouTube durante o Curso de Python 3

Curso em Vídeo - Exercícios de Python 3 Sobre o repositório Este repositório contém os notebooks com a listas de exercícios propostos pelo professor G

João Pedro Pereira 9 Oct 15, 2022
Companion repository to the paper accepted at the 4th ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities

Transfer learning approach to bicycle sharing systems station location planning using OpenStreetMap Companion repository to the paper accepted at the

Politechnika Wrocławska - repozytorium dla informatyków 4 Oct 24, 2022
Pytorch implementation code for [Neural Architecture Search for Spiking Neural Networks]

Neural Architecture Search for Spiking Neural Networks Pytorch implementation code for [Neural Architecture Search for Spiking Neural Networks] (https

Intelligent Computing Lab at Yale University 28 Nov 18, 2022
Godot RL Agents is a fully Open Source packages that allows video game creators

Godot RL Agents The Godot RL Agents is a fully Open Source packages that allows video game creators, AI researchers and hobbiest the opportunity to le

Edward Beeching 326 Dec 30, 2022
Code for the paper "Unsupervised Contrastive Learning of Sound Event Representations", ICASSP 2021.

Unsupervised Contrastive Learning of Sound Event Representations This repository contains the code for the following paper. If you use this code or pa

Eduardo Fonseca 81 Dec 22, 2022
For holding anime-related object classification and detection models

Animesion An end-to-end framework for anime-related object classification, detection, segmentation, and other models. Update: 01/22/2020. Due to time-

Edwin Arkel Rios 72 Nov 30, 2022
Implementation of TimeSformer, a pure attention-based solution for video classification

TimeSformer - Pytorch Implementation of TimeSformer, a pure and simple attention-based solution for reaching SOTA on video classification.

Phil Wang 602 Jan 03, 2023
A Deep Reinforcement Learning Framework for Stock Market Trading

DQN-Trading This is a framework based on deep reinforcement learning for stock market trading. This project is the implementation code for the two pap

61 Jan 01, 2023
Python implementation of cover trees, near-drop-in replacement for scipy.spatial.kdtree

This is a Python implementation of cover trees, a data structure for finding nearest neighbors in a general metric space (e.g., a 3D box with periodic

Patrick Varilly 28 Nov 25, 2022
SOLOv2 on onnx & tensorRT

SOLOv2.tensorRT: NOTE: code based on WXinlong/SOLO add support to TensorRT inference onnxruntime tensorRT full_dims and dynamic shape postprocess with

47 Nov 26, 2022