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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: Inputs: Outputs: User defined functions: tpf = true_positive / [true_positive + false_negative] 2.resultsMETRICS-ROC-AND-AUC
->scipy.io for loading the data from .mat files
->matplotlib.pyplot for plotting the roc curve
->numpy for calculating the area under the curve
actual.mat :data file containning the actuals labels
predicted.mat :data file containning classifier's output[in a range of [0,1]]
->Plot displaying the ROC_CURVE
->AUC[the area under the ROC_CURVE is printed
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.
fpf = false_positive / [false_positive + true_negative]
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.
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