Python: k-Nearest Neighbors

Utilizzo l’environment conda py3

~$ conda activate py3

kNN

Parameters

• k
• Distance Metric

# lib
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline

from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import classification_report,confusion_matrix

# df
df = pd.read_csv("Classified Data",index_col=0)
df.head()

	WTT	PTI	EQW	SBI	LQE	QWG	FDJ	PJF	HQE	NXJ	TARGET CLASS
0	0.913917	1.162073	0.567946	0.755464	0.780862	0.352608	0.759697	0.643798	0.879422	1.231409	1
1	0.635632	1.003722	0.535342	0.825645	0.924109	0.648450	0.675334	1.013546	0.621552	1.492702	0
2	0.721360	1.201493	0.921990	0.855595	1.526629	0.720781	1.626351	1.154483	0.957877	1.285597	0
3	1.234204	1.386726	0.653046	0.825624	1.142504	0.875128	1.409708	1.380003	1.522692	1.153093	1
4	1.279491	0.949750	0.627280	0.668976	1.232537	0.703727	1.115596	0.646691	1.463812	1.419167	1

Scaling data

# standardize variables per il kNN
# StandardScaler() object
scaler = StandardScaler()
# Fit scaler to the features
scaler.fit(df.drop('TARGET CLASS',axis=1))
# .transform() method to transform the features to a scaled version
scaled_features = scaler.transform(df.drop('TARGET CLASS',axis=1))
# Convert the scaled features to a dataframe
df_feat = pd.DataFrame(scaled_features,columns=df.columns[:-1])
df_feat.head()

	WTT	PTI	EQW	SBI	LQE	QWG	FDJ	PJF	HQE	NXJ
0	-0.123542	0.185907	-0.913431	0.319629	-1.033637	-2.308375	-0.798951	-1.482368	-0.949719	-0.643314
1	-1.084836	-0.430348	-1.025313	0.625388	-0.444847	-1.152706	-1.129797	-0.202240	-1.828051	0.636759
2	-0.788702	0.339318	0.301511	0.755873	2.031693	-0.870156	2.599818	0.285707	-0.682494	-0.377850
3	0.982841	1.060193	-0.621399	0.625299	0.452820	-0.267220	1.750208	1.066491	1.241325	-1.026987
4	1.139275	-0.640392	-0.709819	-0.057175	0.822886	-0.936773	0.596782	-1.472352	1.040772	0.276510

Training model kNN

# train test
X = df_feat
y = df['TARGET CLASS']

X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.30,random_state=101)

# X_train, X_test, y_train, y_test = train_test_split(df.drop('TARGET CLASS',axis=1),df['TARGET CLASS'],test_size=0.30,random_state=101)

# kNN
knn = KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train,y_train)

KNeighborsClassifier(n_neighbors=1)

# predictions
pred = knn.predict(X_test)

# confusion matrix
print(confusion_matrix(y_test,pred))

[[151   8]
 [ 15 126]]

# metrics report
print(classification_report(y_test,pred))

              precision    recall  f1-score   support

           0       0.91      0.95      0.93       159
           1       0.94      0.89      0.92       141

    accuracy                           0.92       300
   macro avg       0.92      0.92      0.92       300
weighted avg       0.92      0.92      0.92       300

Best k-Value Model

# iterate models and find best k
error_rate = []

for i in range(1,60):
    
    knn = KNeighborsClassifier(n_neighbors=i)
    knn.fit(X_train,y_train)
    pred_i = knn.predict(X_test)
    error_rate.append(np.mean(pred_i != y_test))

# plot trend k-errors
plt.figure(figsize=(10,6))
plt.plot(range(1,60),error_rate,color='blue',linestyle='dashed', marker='o',markerfacecolor='red', markersize=10)
plt.title('Error Rate vs. K Value')
plt.xlabel('K')
plt.ylabel('Error Rate')

Text(0, 0.5, 'Error Rate')

# model comparisons: k = 1
knn = KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train,y_train)
pred = knn.predict(X_test)
print('K = 1')
print('\nConfusion Matrix:')
print(confusion_matrix(y_test,pred))
print('\nClassification metrics:')
print(classification_report(y_test,pred))

K = 1

Confusion Matrix:
[[151   8]
 [ 15 126]]

Classification metrics:
              precision    recall  f1-score   support

           0       0.91      0.95      0.93       159
           1       0.94      0.89      0.92       141

    accuracy                           0.92       300
   macro avg       0.92      0.92      0.92       300
weighted avg       0.92      0.92      0.92       300

# model comparisons: k = 40
knn = KNeighborsClassifier(n_neighbors=40)
knn.fit(X_train,y_train)
pred = knn.predict(X_test)
print('K = 40')
print('\nConfusion Matrix:')
print(confusion_matrix(y_test,pred))
print('\nClassification metrics:')
print(classification_report(y_test,pred))

K = 40

Confusion Matrix:
[[154   5]
 [  7 134]]

Classification metrics:
              precision    recall  f1-score   support

           0       0.96      0.97      0.96       159
           1       0.96      0.95      0.96       141

    accuracy                           0.96       300
   macro avg       0.96      0.96      0.96       300
weighted avg       0.96      0.96      0.96       300