srilakshmi-thota / metrics-roc-and-auc

Python code to obtain metrics like receiver operating characteristics (ROC) curve and area under the curve (AUC) from scratch without using in-built functions.

METRICS-ROC-AND-AUC

Python code to obtain metrics like receiver operating characteristics (ROC) curve and area under the curve (AUC) from scratch without using in-built functions.

Libraries used:
->scipy.io for loading the data from .mat files
->matplotlib.pyplot for plotting the roc curve
->numpy for calculating the area under the curve

Inputs:
actual.mat :data file containning the actuals labels
predicted.mat :data file containning classifier's output(in a range of [0,1])

Outputs:
->Plot displaying the ROC_CURVE
->AUC(the area under the ROC_CURVE is printed

User defined functions:
1.confusion_metrics
Inputs : labels,predictions,threshold
Ouputs : tpf,fpf
This function essentially compares the labels(actual values) and checks whether the predictions(classifier output) is satisfying the condition of threshold and accordingly updates the values of true_positive,false_positive,true_negative,false_negative.

tpf = true_positive / (true_positive + false_negative)
fpf = false_positive / (false_positive + true_negative)

2.results
Inputs : labels,predictions
Outputs : Plot displaying the ROC_CURVE,Printing the AUC value
->This function takes the labels and the predictions and calls the confusion metrics function for all the values of thresholds ranging from 0 to 1 by increementing by a step size of 0.0002.And finally plots the ROC_curve by plotting tpf along Yaxis and fpf along Xaxis.
->Uses the trapz function from numpy library to calculate the area by integrating along the given axis using the composite trapezoidal rule.