我想知道如何将朴素贝叶斯中的predict.proba结果转换为百分比。我已经尝试了一些但是失败了。我想让结果变成50％，100％。这是我的代码示例
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import confusion_matrix
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder
import itertools
plt.style.use('ggplot')
class bayesNaive:
def __init__(self, dataTrainInput):
self.data = pd.read_csv(dataTrainInput, delimiter=";", encoding="latin-1")
def encoderLabel(self):
self.lb = LabelEncoder()
df = pd.DataFrame(self.data,
columns=["laboratory_registration_id", "Albumin","Asam Urat", "Basofil", "Basofil Absolut","BE", "Berat Jenis", "Besi (Fe/iron)", "Eosinofil", "Eosinofil Absolut", "Eritrosit","Ferritin", "Free T4", "Glukosa Darah 2 jam PP", "Glukosa Darah Puasa","Glukosa Darah Sewaktu", "Hb-A1c", "Hematokrit", "Hemoglobin", "Kalium (K)","Klorida (Cl)", "Kolesterol HDL", "Kolesterol LDL", "Kolesterol Total", "Kreatinin","Leukosit", "Limfosit", "Limfosit Absolut", "MCH", "MCHC", "MCV", "Monosit","Monosit Absolut", "MPV", "Natrium (Na)", "Neutrofil Absolut", "Neutrofil Segmen","O2 Saturasi", "pCO2", "PDW", "pH", "pO2", "RDW-CV", "RDW-SD", "T CO2", "TIBC","T3 Total", "T4 Total", "Trigliserida", "Trombosit", "Troponin T", "TSH", "Ureum", "Age", "Gender", "Disease"])
data1 = self.data["Bakteri"]
data2 = self.data["Bilirubin"]
data3 = self.data["Blood"]
data5 = self.data["Epitel"]
data6 = self.data["Eritrosit Urin"]
data7 = self.data["Faktor Rheumatoid (RF)"]
data8 = self.data["Glukosa"]
data9 = self.data["HBsAg"]
data10 = self.data["Kejernihan"]
data11 = self.data["Keton"]
data12 = self.data["Kristal"]
data13 = self.data["Leukosit Urin"]
data14 = self.data["Nitrit"]
data15 = self.data["Protein"]
data16 = self.data["Silinder"]
data17 = self.data["Urobilinogen"]
data18 = self.data["Warna"]
x1 = self.lb.fit_transform(data1)
x2 = self.lb.fit_transform(data2)
x3 = self.lb.fit_transform(data3)
x5 = self.lb.fit_transform(data5)
x6 = self.lb.fit_transform(data6)
x7 = self.lb.fit_transform(data7)
x8 = self.lb.fit_transform(data8)
x9 = self.lb.fit_transform(data9)
x10 = self.lb.fit_transform(data10)
x11 = self.lb.fit_transform(data11)
x12 = self.lb.fit_transform(data12)
x13 = self.lb.fit_transform(data13)
x14 = self.lb.fit_transform(data14)
x15 = self.lb.fit_transform(data15)
x16 = self.lb.fit_transform(data16)
x17 = self.lb.fit_transform(data17)
x18 = self.lb.fit_transform(data18)
df1 = pd.DataFrame(x1, columns=['Bakteri'])
df2 = pd.DataFrame(x2, columns=['Bilirubin'])
df3 = pd.DataFrame(x3, columns=['Blood'])
df5 = pd.DataFrame(x5, columns=['Epitel'])
df6 = pd.DataFrame(x6, columns=['Eritrosit Urin'])
df7 = pd.DataFrame(x7, columns=['Faktor Rheumatoid (RF)'])
df8 = pd.DataFrame(x8, columns=['Glukosa'])
df9 = pd.DataFrame(x9, columns=['HBsAg'])
df10 = pd.DataFrame(x10, columns=['Kejernihan'])
df11 = pd.DataFrame(x11, columns=['Keton'])
df12 = pd.DataFrame(x12, columns=['Kristal'])
df13 = pd.DataFrame(x13, columns=['Leukosit Urin'])
df14 = pd.DataFrame(x14, columns=['Nitrit'])
df15 = pd.DataFrame(x15, columns=['Protein'])
df16 = pd.DataFrame(x16, columns=['Silinder'])
df17 = pd.DataFrame(x17, columns=['Urobilinogen'])
df18 = pd.DataFrame(x18, columns=['Warna'])
return pd.concat([df1, df2, df3, df5, df6, df7, df8, df9, df10, df11, df12, df13, df14, df15, df16, df17, df18, df], axis=1)
def split_label(self):
res = self.encoderLabel()
X = res.iloc[:, :-1].values
Y = res['Disease'].values
return X, Y
def test_split(self):
X, Y = self.split_label()
X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size=0.2, random_state=42)
return X_train, X_test, Y_train, Y_test
def Classify_lab(self):
try:
X_train, X_test, Y_train, Y_test = self.test_split()
# print(X_test.shape)
model = GaussianNB()
model_train = model.fit(X_train, Y_train)
model_score = model_train.score(X_test, Y_test)
accuracy = round(model_score * 100, 2)
predicted = model_train.predict(X_test)
Y_prob = model_train.predict_proba(X_test)
report=classification_report(Y_test, predicted)
conf_m = confusion_matrix(Y_test, predicted)
# "precision: tp/(tp+fp)"
# "recall: tp/(tp+fn)"
# "f1-score: (2xprecisionxrecall)/(precision+recall)"
# print(test.shape)
test.columns = ["Bakteri", "Bilirubin", "Blood", "Epitel","Eritrosit Urin","Faktor Rheumatoid (RF)", "Glukosa", "HBsAg", "Kejernihan", "Keton", "Kristal", "Leukosit Urin", "Nitrit", "Protein", "Silinder", "Urobilinogen", "Warna","laboratory_registration_id", "Albumin", "Asam Urat", "Basofil", "Basofil Absolut","BE", "Berat Jenis", "Besi (Fe/iron)", "Eosinofil", "Eosinofil Absolut", "Eritrosit","Ferritin", "Free T4", "Glukosa Darah 2 jam PP", "Glukosa Darah Puasa","Glukosa Darah Sewaktu", "Hb-A1c", "Hematokrit", "Hemoglobin", "Kalium (K)","Klorida (Cl)", "Kolesterol HDL", "Kolesterol LDL", "Kolesterol Total", "Kreatinin","Leukosit", "Limfosit", "Limfosit Absolut", "MCH", "MCHC", "MCV", "Monosit","Monosit Absolut", "MPV", "Natrium (Na)", "Neutrofil Absolut", "Neutrofil Segmen","O2 Saturasi", "pCO2", "PDW", "pH", "pO2", "RDW-CV", "RDW-SD", "T CO2", "TIBC","T3 Total", "T4 Total", "Trigliserida", "Trombosit", "Troponin T", "TSH", "Ureum","Age", "Gender"]
labels = pd.DataFrame(predicted)
# print(Y_prob)
print("bulatan")
# Y_prob[:, 1:3] = np.around(Y_prob[:,1:3],decimals=1)
probability = pd.DataFrame(Y_prob)
labels.columns = ["Disease"]
probability.columns = ["Diabetes mellitus", "Ginjal","Jantung", "Thalassemia"]
result = pd.concat([test, labels, probability], axis=1)
# Z = self.decodeLabel(X_test)
# print("Ini", Z)
# exit()
return [result, str(accuracy), report, conf_m]
except ValueError as v:
print(v)
except FileNotFoundError:
print('File not found')
if __name__ == '__main__':
dataTrainInput = "perc4.csv"
obj = bayesNaive(dataTrainInput)
print(obj.Classify_lab())
在Classify_lab（）中，我预测了其他目标的概率
Y_prob = model_train.predict_proba(X_test)
然后，我创建数据框
probability = pd.DataFrame(Y_prob)
运行概率，我会得到这个结果
Diabetes mellitus ... Thalassemia
0 1.000000e+00 ... 0.000000e+00
1 5.693959e-28 ... 0.000000e+00
2 1.610343e-182 ... 0.000000e+00
3 4.347851e-04 ... 9.949097e-01
4 2.611833e-24 ... 0.000000e+00
5 6.281686e-32 ... 1.000000e+00
6 1.000000e+00 ... 3.472943e-99
7 2.580374e-132 ... 0.000000e+00
8 1.457926e-26 ... 1.000000e+00
9 1.000000e+00 ... 0.000000e+00
10 4.993317e-33 ... 1.000000e+00
如果我使用HTML运行概率，我将得到此结果
非常感谢您...
答案 0 :(得分：0)
NB的总概率为1。在您看到的情况下，无论那里有1.0，对于其余列，该概率都是0或非常接近0。您可以简单地从返回的数组中取出最大值并乘以100。
