55 lines
1.7 KiB
Python
55 lines
1.7 KiB
Python
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Fri Mar 22 23:55:53 2019
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Matrice de confusion et calcul des métriques
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@author: François Pelletier
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"""
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import numpy as np
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def confusion_matrix(obs_labels,pred_labels):
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"""
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Retourne la matrice de confusion
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Prend en entrée deux vecteurs d'étiquettes: observations et prédictions
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Retourne une matrice NumPy
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"""
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unique_obs_labels=np.unique(obs_labels)
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nb_unique_obs_labels=(unique_obs_labels.shape)[0]
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confusion_matrix = np.zeros((nb_unique_obs_labels,nb_unique_obs_labels))
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for observed,predicted in zip(obs_labels,pred_labels):
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confusion_matrix[observed][predicted] += 1
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return confusion_matrix
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def prediction_metrics(cm,obs_labels,pred_labels):
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"""
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Cette fonction retourne les métriques accuracy, precision et recall
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Elle prend en entrée la matrice de confusion et les vecteurs d'étiquettes: observations et prédictions
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accuracy=(tp+tn)/all
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precision=tp/(tp+fp)
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recall=tp/(tp+fn)
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"""
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accuracy = (obs_labels == pred_labels).sum() / float(len(obs_labels))
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precision=[]
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recall=[]
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for label_num in np.unique(obs_labels):
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precision.append(cm[label_num,label_num] / sum(cm[:,label_num]))
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recall.append(cm[label_num,label_num] / sum(cm[label_num,:]))
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return accuracy, precision, recall
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def print_prediction_metrics(cm,accuracy,precision,recall):
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"""
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Cette fonction imprime la matrice de confusion et les métriques
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"""
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print("Matrice de confusion:")
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print(cm)
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print("\nAccuracy:")
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print(accuracy)
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print("\nPrecision:")
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print(precision)
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print("\nRecall")
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print(recall)
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