KNN.py

import pandas as pd 
import matplotlib.pyplot as plt
from pandas import Series, DataFrame
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score
from sklearn.metrics import roc_auc_score
from lightgbm import LGBMClassifier
from imblearn.over_sampling import SMOTE
from sklearn.neighbors import KNeighborsClassifier

k_list = range(1,101)
accuracies_before_OverSampling = []
accuracies_after_OverSampling = []

df = pd.read_csv('creditcard.csv')
X = df.iloc[:,:-1]
y = df.iloc[:,-1]
smote = SMOTE(random_state=0)
# devide train, test -> over Sampling
print("------------- 오버 샘플링 나중 ----------------")
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.25,random_state=10)
X_train_over,y_train_over = smote.fit_resample(X_train,y_train)
for k in k_list:
    classifier = KNeighborsClassifier(n_neighbors = k)
    classifier.fit(X_train_over, y_train_over)
    # print(classifier.score(X_test, y_test))
    accuracies_before_OverSampling.append(classifier.score(X_test, y_test))
plt.plot(k_list, accuracies_before_OverSampling)
plt.xlabel("k")
plt.ylabel("Validation Accuracy")
plt.title("Breast Cancer Classifier Accuracy")
plt.show()

# over Sampling  -> devide train, test
print("------------- 오버 샘플링 먼저 ----------------")
smote2 = SMOTE(random_state=0)
X_over,y_over = smote.fit_resample(X,y)
X_train, X_test, y_train, y_test = train_test_split(X_over,y_over,test_size=0.25,random_state=10)
for k in k_list:
    classifier = KNeighborsClassifier(n_neighbors = k)
    classifier.fit(X_train, y_train)
    # print(classifier.score(X_test, y_test))
    accuracies_after_OverSampling.append(classifier.score(X_test, y_test))
plt.plot(k_list, accuracies_after_OverSampling)
plt.xlabel("k")
plt.ylabel("Validation Accuracy")
plt.title("Breast Cancer Classifier Accuracy")
plt.show()