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import numpy as np
def compute_accuracy(tp, tn, fn, fp): # only fit 2 cls condition
"""
Accuracy = (TP + TN) / (FP + FN + TP + TN)
"""
if tp + tn + fn + fp == 0:
return 0.0
return ((tp + tn) * 100) / float(tp + tn + fn + fp)
def compute_specificity(tn, fp):
"""
Precision = TN / (FN + TP)
"""
if tn + fp == 0:
return 0.0
return (tn * 100) / float(tn + fp)
def compute_sensitivity(tp, fn): # equal to recall
"""
Recall = TP / (FN + TP)
"""
if tp + fn == 0:
return 0.0
return (tp * 100) / float(tp + fn)
def compute_precision(tp, fp): # equal to Positive Predictive Value(PPV)
"""
Precision = TP / (FP + TP)
"""
if tp + fp == 0:
return 0.0
return (tp * 100) / float(tp + fp)
def compute_recall(tp, fn): # equal to Sensitivity
"""
Recall = TP / (FN + TP)
"""
if tp + fn == 0:
return 0.0
return (tp * 100) / float(tp + fn)
def compute_f1_score(tp, tn, fp, fn):
# calculates the F1 score
precision = compute_precision(tp, fp) / 100
recall = compute_recall(tp, fn) / 100
if precision + recall == 0:
return 0.0
f1_score = (2 * precision * recall) / (precision + recall)
return f1_score * 100
def compute_NPV(tn, fn): # Negative Predictive Value
"""
Negative Predictive Value = tn / (tn + fn)
"""
if tn + fn == 0:
return 0.0
return (tn * 100) / float(tn + fn)
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