Regression Metrics Calculation Made easy for tensorflow2 and scikit-learn

Last update: Dec 16, 2022

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 Kullanacağımız veri seti Carnegie Mellon Üniversitesi'

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

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

1.3k Dec 29, 2022

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

6 Oct 4, 2022

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

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

4.9k Dec 31, 2022

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

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

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

4.9k Jan 3, 2023

Comments

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)

Regression Metrics Calculation Made easy for tensorflow2 and scikit-learn

Related tags

Overview

Regression Metrics

Installation

Usage

😃 Thanks for reading and forking.

You might also like...

Hitters Linear Regression - Hitters Linear Regression With Python

A set of tools for creating and testing machine learning features, with a scikit-learn compatible API

Using python and scikit-learn to make stock predictions

A real-time speech emotion recognition application using Scikit-learn and gradio

Python package for Bayesian Machine Learning with scikit-learn API

A scikit-learn compatible neural network library that wraps PyTorch

scikit-learn: machine learning in Python

A scikit-learn compatible neural network library that wraps PyTorch

A scikit-learn compatible neural network library that wraps PyTorch

Comments

Very nice toolkit

Releases(v1.4.0)

v1.4.0(Oct 30, 2021)

Owner

Ashish Patel

All the essential resources and template code needed to understand and practice data structures and algorithms in python with few small projects to demonstrate their practical application.

Pixel-wise segmentation on VOC2012 dataset using pytorch.

The Submission for SIMMC 2.0 Challenge 2021

Semi-supevised Semantic Segmentation with High- and Low-level Consistency

Single-Stage 6D Object Pose Estimation, CVPR 2020

Qt-GUI implementation of the YOLOv5 algorithm (ver.6 and ver.5)

This repository includes the official project for the paper: TransMix: Attend to Mix for Vision Transformers.

Combinatorial model of ligand-receptor binding

AFL binary instrumentation

Theory-inspired Parameter Control Benchmarks for Dynamic Algorithm Configuration

A fast and easy to use, moddable, Python based Minecraft server!

YOLOX Win10 Project

FCN (Fully Convolutional Network) is deep fully convolutional neural network architecture for semantic pixel-wise segmentation

Welcome to The Eigensolver Quantum School, a quantum computing crash course designed by students for students.

Image Super-Resolution Using Very Deep Residual Channel Attention Networks

GrailQA: Strongly Generalizable Question Answering

Code for MarioNette: Self-Supervised Sprite Learning, in NeurIPS 2021

Reverse engineering Rosetta 2 in M1 Mac

System Combination for Grammatical Error Correction Based on Integer Programming

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.