69 lines
3.3 KiB
Python
69 lines
3.3 KiB
Python
import numpy as np
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import pandas as pd
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from decimal import Decimal, ROUND_HALF_UP
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class Calculate():
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def __init__(self) -> None:
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self.Loss, self.Accuracy, self.Precision, self.Recall, self.F1, self.AUC = 0, 0, 0, 0, 0, 0
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self.Loss_Record, self.Accuracy_Record, self.Precision_Record, self.Recall_Record, self.F1_Record, self.AUC_Record = [], [], [], [], [], []
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self.History = []
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pass
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def Append_numbers(self, Loss, Accuracy, Precision, Recall, AUC, F1):
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self.Loss_Record.append(Loss)
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self.Accuracy_Record.append(Accuracy)
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self.Precision_Record.append(Precision)
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self.Recall_Record.append(Recall)
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self.F1_Record.append(F1)
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self.AUC_Record.append(AUC)
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pass
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def Construction_To_DataFrame(self, Loss, Accuracy, Precision, Recall, F1, AUC):
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DataFrame = pd.DataFrame(
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{
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"loss" : "{:.2f}".format(Loss),
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"precision" : "{:.2f}".format(Precision * 100),
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"recall" : "{:.2f}".format(Recall * 100),
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"accuracy" : "{:.2f}".format(Accuracy * 100),
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"f1" : "{:.2f}".format(F1 * 100),
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"AUC" : "{:.2f}".format(AUC * 100)
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}, index = [0]
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)
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self.History.append(DataFrame)
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return DataFrame
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def Calculate_Mean(self):
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Loss_Mean = np.mean(self.Loss_Record)
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Accuracy_Mean = np.mean(self.Accuracy_Record)
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Precision_Mean = np.mean(self.Precision_Record)
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Recall_Mean = np.mean(self.Recall_Record)
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F1_Mean = np.mean(self.F1_Record)
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AUC_Mean = np.mean(self.AUC_Record)
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Mean_DataFram = self.Construction_To_DataFrame(Loss_Mean, Accuracy_Mean, Precision_Mean, Recall_Mean, F1_Mean, AUC_Mean)
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return Mean_DataFram
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def Calculate_Std(self):
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Loss_Std = Decimal(str(np.std(self.Loss_Record))).quantize(Decimal('0.01'), rounding=ROUND_HALF_UP)
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Accuracy_Std = Decimal(str(np.std(self.Accuracy_Record))).quantize(Decimal('0.01'), rounding=ROUND_HALF_UP)
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Precision_Std = Decimal(str(np.std(self.Precision_Record))).quantize(Decimal('0.01'), rounding=ROUND_HALF_UP)
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Recall_Std = Decimal(str(np.std(self.Recall_Record))).quantize(Decimal('0.01'), rounding=ROUND_HALF_UP)
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F1_Std = Decimal(str(np.std(self.F1_Record))).quantize(Decimal('0.01'), rounding=ROUND_HALF_UP)
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AUC_Std = Decimal(str(np.std(self.AUC_Record))).quantize(Decimal('0.01'), rounding=ROUND_HALF_UP)
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Std_DataFram = self.Construction_To_DataFrame(Loss_Std, Accuracy_Std, Precision_Std, Recall_Std, F1_Std, AUC_Std)
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return Std_DataFram
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def Output_Style(self):
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Result = pd.DataFrame(
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{
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"loss" : "{}%±{}".format(self.History[0]["loss"][0], self.History[1]["loss"][0]),
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"precision" : "{}%±{}".format(self.History[0]["precision"][0], self.History[1]["precision"][0]),
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"recall" : "{}%±{}".format(self.History[0]["recall"][0], self.History[1]["recall"][0]),
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"accuracy" : "{}%±{}".format(self.History[0]["accuracy"][0], self.History[1]["accuracy"][0]),
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"f1" : "{}%±{}".format(self.History[0]["f1"][0], self.History[1]["f1"][0]),
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"AUC" : "{}%±{}".format(self.History[0]["AUC"][0], self.History[1]["AUC"][0])
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}, index = [0]
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)
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return Result |