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KNN classifier.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Oct 3 10:04:30 2022
@author: Mohammad Mehdi Keramati
"""
import pandas as pd
import numpy as np
from imblearn.over_sampling import RandomOverSampler
ros = RandomOverSampler(random_state=0)
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from tensorflow import keras
from keras.models import Model
from keras.layers import Dense, Conv1D,Conv2D, Flatten, Dropout
from sklearn.preprocessing import LabelEncoder
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import confusion_matrix,classification_report, precision_score
import matplotlib.pyplot as plt
import seaborn as sb
Data = pd.read_excel('C:/Users/Mehdi Keramati/Desktop/Medical data project- finalization/4- Feature selection based on N.N- effect of each combined feature/Data_combined_features_wisely_Final.xlsx', index_col=0)
# Counting before balancing
print(Data['Eligible'].value_counts())
print(Data['Participant_gender'].value_counts())
print(Data['Participant_ethnicity'].value_counts())
print(Data['Intervention_group'].value_counts())
print(Data['City_code'].value_counts())
print(Data['Village_StateCode'].value_counts())
print(Data['Village_Participant_Nubmer'].value_counts())
print(Data['Interviewer_Gender'].value_counts())
print(Data['Interviewer_Education'].value_counts())
print(Data['Interviewer_Ethnicity'].value_counts())
print(Data['FU_Month'].value_counts())
print(Data['Best_three'].value_counts())
print(Data['Best_four'].value_counts())
print(Data['Best_five'].value_counts())
print(Data['Best_six'].value_counts())
print(Data['Four_participant'].value_counts())
print(Data['Five_participant'].value_counts())
print(Data['Six_participant'].value_counts())
print(Data['Three_interviewer'].value_counts())
# Drop
Data=Data.drop(columns=['ID'])
Data=Data.drop(columns=['GCSID'])
Data=Data.drop(columns=['Village_Cluster'])
Data=Data.drop(columns=['Interviewer_WorkExperience'])
Data=Data.drop(columns=['Interviewer_age'])
X= np.array(Data.loc[:,'Best_five'])
y= np.array(Data.loc[:,'Quality score'])
#Balancing
def balancing(x,y):
X= np.array(x)
y= np.array(y)
X=pd.DataFrame(X, columns=['resampled'])
y=pd.DataFrame(y, columns=['Quality score resampled'])
y_resampled, X_resampled = ros.fit_resample(y, X)
Data_resampled= pd.concat([X_resampled,y_resampled], axis=1)
return Data_resampled, X_resampled, y_resampled
Data_resampled, X_resampled, y_resampled= balancing(X,y)
print(Data_resampled['resampled'].value_counts())
X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.33,stratify=X_resampled, random_state=1,shuffle=True)
# KNN (reversed)
knn= KNeighborsClassifier(n_neighbors=3,metric='minkowski',p=2)
knn.fit(y_train,X_train)
predict=knn.predict(y_test)
predict=pd.DataFrame(predict, columns=['Predict'])
print(classification_report(X_test,predict))
print(X_test.value_counts())
print(predict.value_counts())
Class_counter= predict.value_counts()
print(precision_score(X_test, predict,average='weighted', zero_division='warn'))
# Visualization
X_test=X_test.reset_index(drop=True)
y_test=y_test.reset_index(drop=True)
Data_test= pd.concat([X_test,y_test], axis=1)
sb.boxplot(x="resampled", y="Quality score resampled", showmeans=True, data=Data_test)
plt.show()
plt.figure()
predict=predict.reset_index(drop=True)
Data_predict= pd.concat([predict,y_test], axis=1)
plt.title('knn prediction')
sb.boxplot(x="Predict", y="Quality score resampled", showmeans=True, data=Data_predict)
plt.show()