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/
Morphable Detector for Object Detection on Demand

Morphable Detector for Object Detection on Demand (ICCV 2021) PyTorch implementation of the paper Morphable Detector for Object Detection on Demand. I

9 Feb 23, 2022
automatic color-grading

color-matcher Description color-matcher enables color transfer across images which comes in handy for automatic color-grading of photographs, painting

hahnec 168 Jan 05, 2023
Labels4Free: Unsupervised Segmentation using StyleGAN

Labels4Free: Unsupervised Segmentation using StyleGAN ICCV 2021 Figure: Some segmentation masks predicted by Labels4Free Framework on real and synthet

70 Dec 23, 2022
Project repo for Learning Category-Specific Mesh Reconstruction from Image Collections

Learning Category-Specific Mesh Reconstruction from Image Collections Angjoo Kanazawa*, Shubham Tulsiani*, Alexei A. Efros, Jitendra Malik University

438 Dec 22, 2022
This implements one of result networks from Large-scale evolution of image classifiers

Exotic structured image classifier This implements one of result networks from Large-scale evolution of image classifiers by Esteban Real, et. al. Req

54 Nov 25, 2022
Code for "LASR: Learning Articulated Shape Reconstruction from a Monocular Video". CVPR 2021.

LASR Installation Build with conda conda env create -f lasr.yml conda activate lasr # install softras cd third_party/softras; python setup.py install;

Google 157 Dec 26, 2022
This PyTorch package implements MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation (NAACL 2022).

MoEBERT This PyTorch package implements MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation (NAACL 2022). Installation Create an

Simiao Zuo 34 Dec 24, 2022
Pytorch implementation of Masked Auto-Encoder

Masked Auto-Encoder (MAE) Pytorch implementation of Masked Auto-Encoder: Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick

Jiyuan 22 Dec 13, 2022
Analysis code and Latex source of the manuscript describing the conditional permutation test of confounding bias in predictive modelling.

Git repositoty of the manuscript entitled Statistical quantification of confounding bias in predictive modelling by Tamas Spisak The manuscript descri

PNI - Predictive Neuroimaging Lab, University Hospital Essen, Germany 0 Nov 22, 2021
ScaleNet: A Shallow Architecture for Scale Estimation

ScaleNet: A Shallow Architecture for Scale Estimation Repository for the code of ScaleNet paper: "ScaleNet: A Shallow Architecture for Scale Estimatio

Axel Barroso 34 Nov 09, 2022
Code for A Volumetric Transformer for Accurate 3D Tumor Segmentation

VT-UNet This repo contains the supported pytorch code and configuration files to reproduce 3D medical image segmentaion results of VT-UNet. Environmen

Himashi Amanda Peiris 114 Dec 20, 2022
Multi-resolution SeqMatch based long-term Place Recognition

MRS-SLAM for long-term place recognition In this work, we imply an multi-resolution sambling based visual place recognition method. This work is based

METASLAM 6 Dec 06, 2022
AMTML-KD: Adaptive Multi-teacher Multi-level Knowledge Distillation

AMTML-KD: Adaptive Multi-teacher Multi-level Knowledge Distillation

Frank Liu 26 Oct 13, 2022
Measure WWjj polarization fraction

WlWl Polarization Measure WWjj polarization fraction Paper: arXiv:2109.09924 Notice: This code can only be used for the inference process, if you want

4 Apr 10, 2022
Simple and understandable swin-transformer OCR project

swin-transformer-ocr ocr with swin-transformer Overview Simple and understandable swin-transformer OCR project. The model in this repository heavily r

Ha YongWook 67 Dec 31, 2022
The code is an implementation of Feedback Convolutional Neural Network for Visual Localization and Segmentation.

Feedback Convolutional Neural Network for Visual Localization and Segmentation The code is an implementation of Feedback Convolutional Neural Network

19 Dec 04, 2022
Official Pytorch implementation of Online Continual Learning on Class Incremental Blurry Task Configuration with Anytime Inference (ICLR 2022)

The Official Implementation of CLIB (Continual Learning for i-Blurry) Online Continual Learning on Class Incremental Blurry Task Configuration with An

NAVER AI 34 Oct 26, 2022
The Python ensemble sampling toolkit for affine-invariant MCMC

emcee The Python ensemble sampling toolkit for affine-invariant MCMC emcee is a stable, well tested Python implementation of the affine-invariant ense

Dan Foreman-Mackey 1.3k Dec 31, 2022
This Jupyter notebook shows one way to implement a simple first-order low-pass filter on sampled data in discrete time.

How to Implement a First-Order Low-Pass Filter in Discrete Time We often teach or learn about filters in continuous time, but then need to implement t

Joshua Marshall 4 Aug 24, 2022
Seeing Dynamic Scene in the Dark: High-Quality Video Dataset with Mechatronic Alignment (ICCV2021)

Seeing Dynamic Scene in the Dark: High-Quality Video Dataset with Mechatronic Alignment This is a pytorch project for the paper Seeing Dynamic Scene i

DV Lab 21 Nov 28, 2022