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https://github.com/ajayarunachalam/regressormetricgraphplot

Python package to simplify plotting of common evaluation metrics for regression models. Metrics included are pearson correlation coefficient (r), coefficient of determination (r-squared), mean squared error (mse), root mean squared error(rmse), root mean squared relative error (rmsre), mean absolute error (mae), mean absolute percentage error (mape), etc.
https://github.com/ajayarunachalam/regressormetricgraphplot

coefficient-of-determination correlation-coefficient evaluation-metrics graph mae metrics metrics-visualization mse plot python regression regression-testing rmse rmse-score rmsre

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Python package to simplify plotting of common evaluation metrics for regression models. Metrics included are pearson correlation coefficient (r), coefficient of determination (r-squared), mean squared error (mse), root mean squared error(rmse), root mean squared relative error (rmsre), mean absolute error (mae), mean absolute percentage error (mape), etc.

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README 

        
#  Common Evaluation metrics graph plot for Regression Problem



## Description



PyPI: https://pypi.org/project/regressormetricgraphplot/



Python implementations for comparing different Regression Models and Plotting with their most common evaluation metrics.



The purpose of this package is to help users plot the graph at ease with different widely used metrics for regression model evaluation for comparing them at a glance 





    





    Figure: Model evaluation plot with widely used metrics 




## Illustration Example



```python

# Importing libraries 

from sklearn.datasets import make_regression

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LinearRegression

from regressormetricgraphplot import *

%matplotlib inline

```



```python

#Let's load a simple dataset and make a train & test set :

X, y = make_regression(n_samples=1000, n_features=10, n_informative=7, n_targets=1, random_state=0)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=10)

```



```python

# Train the regressor and predict on test set 

# Fitting training set to linear regression model

lr = LinearRegression(n_jobs=-1)

lr.fit(X_train, y_train)

# Predicting

y_pred = lr.predict(X_test)

```



We can now use ``R2AndRMSE`` to compute & output R-squared, and Root Mean Square Error.



```python

# Metrics

CompareModels.R2AndRMSE(y_test=y_test, y_pred=y_pred)

```



Make object of the class ``CompareModels``



```python

plot = CompareModels()

```



We can now use ``add`` &  ``show`` method to add the built model & plot the graph at ease with all the evaluated metrics.



```python

plot.add(model_name='Linear Regression', y_test=y_test, y_pred=y_pred)

plot.show(figsize=(10, 5))

```





    




## Table of Contents

- [Compare Regression Model Metrics Plot](#regressormetricgraphplot)

  * [Installation](#installation)

  * [Examples](#examples)

    + [Demo](#Usage))

  * [Contact](#contact)



## Installation 

    $ pip install regressormetricgraphplot



         OR



    $ git clone https://github.com/ajayarunachalam/RegressorMetricGraphPlot

    $ cd RegressorMetricGraphPlot

    $ python setup.py install



## Notebook

    !pip install regressormetricgraphplot & import as 'from regressormetricgraphplot import *'



         OR



    !git clone https://github.com/ajayarunachalam/RegressorMetricGraphPlot.git

    cd RegressorMetricGraphPlot/



    Just replace the line 'from CompareModels import *' with 'from regressormetricgraphplot import CompareModels' 

Follow the rest as demonstrated in the demo example [here] -- (https://github.com/ajayarunachalam/RegressorMetricGraphPlot/blob/main/regressormetricgraphplot/demo.ipynb) 



## Installation with Anaconda



If you installed your Python with Anacoda you can run the following commands to get started:

```bash



# Clone the repository 

git clone https://github.com/ajayarunachalam/RegressorMetricGraphPlot.git

cd RegressorMetricGraphPlot

# Create new conda environment with Python 3.6

conda create --new your-env-name python=3.6

# Activate the environment

conda activate your-env-name

# Install conda dependencies

conda install --yes --file conda_requirements.txt

# Instal pip dependencies

pip install requirements.txt

```



## Examples

Navigate to the demo example in a form of iPython notebooks: -- [here](https://github.com/ajayarunachalam/RegressorMetricGraphPlot/blob/main/regressormetricgraphplot/demo.ipynb)



### Demo

     * demo.ipynb 



## Contact

If there's some implementation you would like to see here or add in some examples feel free to do so. You can reach me at [email](mailto:[email protected])