AutoLogging-ML 0.1.1

This is a python package useful for the automated logging and visulaization of metrics for machine learning tasks.

This project is about a python package for automated logging and visualization of metrics of classfication and regression algorithms in machine learning.

Features

• The module covers both regression and classification tasks.
• The module integrates a wide range of metrics related to classification and regression
• The module can provide a barplot of the specified metrics from the specified subset of data
• The module can provide the confusion matrix for all the different ML algorithms

Functions

• Train and log for classification. All the classifiers are trained on the datasets and the results (accuracy, precision, recall, F1) are logged onto a dataframe which is displayed to the user. This function applies around 12 different classification algorithms as mentioned below:- 'svm-linear' 'svm-rbf' 'svm-poly' 'knn' 'naive bayes' 'decision tree' 'random forest' 'adaboost' 'gradient boost' 'xgboost' 'logistic regression' 'bagging classifier'

• Get confusion matrix. This function helps to get the confusion matrices for all the classification algorithms on the specified set (training/validation)

• Display metric plots. This function plots a barplot for the comparative analysis of the classication algorithms on the specified metric and on the specified subset.

• Train and log for regression. All the regressors are trained on the datasets and the results (mae, mse, msle, median error, mape, max error) are logged onto a dataframe which is displayed to the user. This function applies around 11 different regression algorithms as mentioned below:- 'linear regression' 'sgd regression' 'ridge regression' 'elastic net' 'decision tree regression' 'random forest regression' 'adaboost regression' 'gradient boost regression' 'xgboost regression' 'bagging regression' 'hist gradient boosting regression'

Syntax

from AutoLogging_ML import AutoLogger

train_and_log_classification

a= AutoLogger.train_and_log_classification(x_train,x_test,y_train,y_test) print(a)

a stores the dataframe comprising of all the metric values for the training and validation datasets.

get_metric_plots_classification

AutoLogger.get_metric_plots_classification(a,subset='None',metric='None')

returns the barplot of all the algorithms on the mentioned metric and mentioned subset.

subset= 'training' , 'validation'

metric= 'accuracy' , 'precision', 'recall', 'f1', 'msle', 'median absolute error', 'maximum error'

get_confusion_matrix

AutoLogger.get_confusion_matrix(a,subset='None')

returns the confusion matrix of all the classification algorithms on the specified subset

subset= 'training'. 'validation'

train_and_log_regression

b= AutoLogger.train_and_log_regression(x_train, x_test, y_train, y_test)

print(b)

b stores the dataframe comprising of all the metric values for the training and validation datasets.

get_metric_plots_regression

AutoLogger.get_metric_plots_regression(b,subset=None,metric=None)

returns the barplot of all the algorithms on the mentioned metric and mentioned subset.

subset= 'training' , 'validation'

metric= 'mae' , 'mse', 'mape', 'r2', 'msle', 'median absolute error', 'maximum error'