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metrics_sklearn.py
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from sklearn.metrics import accuracy_score,f1_score,recall_score,precision_score
from sklearn.metrics import classification_report
def classification2(reference_list, prediciton_list):
# micro_accuracy = accuracy_score(reference_list, prediciton_list)
micro_precision = precision_score(reference_list, prediciton_list)
micro_recall = recall_score(reference_list, prediciton_list)
micro_f1 = f1_score(reference_list, prediciton_list)
return micro_precision, micro_recall, micro_f1
def classificationN(reference_list, prediciton_list):
# micro_accuracy = accuracy_score(reference_list, prediciton_list)
micro_precision = precision_score(reference_list, prediciton_list, average="micro")
micro_recall = recall_score(reference_list, prediciton_list, average="micro")
micro_f1 = f1_score(reference_list, prediciton_list, average="micro")
macro_precision = precision_score(reference_list, prediciton_list, average="macro")
macro_recall = recall_score(reference_list, prediciton_list, average="macro")
macro_f1 = f1_score(reference_list, prediciton_list, average="macro")
weighted_precision = precision_score(reference_list, prediciton_list, average="weighted")
weighted_recall = recall_score(reference_list, prediciton_list, average="weighted")
weighted_f1 = f1_score(reference_list, prediciton_list, average="weighted")
return (micro_precision, micro_recall, micro_f1), (macro_precision, macro_recall, macro_f1), (weighted_precision, weighted_recall, weighted_f1)
def classificationM(reference_list, prediciton_list):
print(classification_report(reference_list, prediciton_list))
# micro_accuracy = accuracy_score(reference_list, prediciton_list)
micro_precision = precision_score(reference_list, prediciton_list, average="micro")
micro_recall = recall_score(reference_list, prediciton_list, average="micro")
micro_f1 = f1_score(reference_list, prediciton_list, average="micro")
macro_precision = precision_score(reference_list, prediciton_list, average="macro")
macro_recall = recall_score(reference_list, prediciton_list, average="macro")
macro_f1 = f1_score(reference_list, prediciton_list, average="macro")
weighted_precision = precision_score(reference_list, prediciton_list, average="weighted")
weighted_recall = recall_score(reference_list, prediciton_list, average="weighted")
weighted_f1 = f1_score(reference_list, prediciton_list, average="weighted")
return (micro_precision, micro_recall, micro_f1), (macro_precision, macro_recall, macro_f1), (weighted_precision, weighted_recall, weighted_f1)
def main():
reference_list = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
prediciton_list = [0, 0, 1, 1, 1, 0, 0, 1, 1, 1]
print(classification2(reference_list, prediciton_list)) # (0.5, 0.5, 0.6, 0.5454545454545454)
print('-'*100)
reference_list = [1, 1, 2, 2, 2, 3, 3, 3, 3, 3]
prediciton_list = [1, 2, 2, 2, 3, 1, 2, 3, 3, 3]
print(classificationN(reference_list, prediciton_list))
print('-'*100)
reference_list = [[1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 1, 0], [1, 0, 1]]
prediciton_list = [[1, 0, 0], [1, 0, 0], [1, 1, 1], [1, 0, 0], [0, 1, 1]]
print(classificationM(reference_list, prediciton_list))
if __name__ == '__main__':
main()