minte9

learningjourney

K-Nearest

 
""" KNN Supervised machine learning (ML) algorithm
K is the number of nearest neighbors to use
Provide Training set of data points and Labels
Createa a KNN classifier with K=3
Predict the label of a new data point   
"""

from sklearn.neighbors import KNeighborsClassifier

X = [[0,0], [1,1], [2,2], [3,3]]    
y = [0, 1, 0, 1]

knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X, y)
            
prediction = knn.predict([[1,2]])   
print(prediction) # [0]

Pandas

 
"""KNN Fruit classification (height, width / type)
Learn a function f that maps any combination 
of height and width of a fruit to a (predicted) fruit type
"""

import pandas as pd
from sklearn.neighbors import KNeighborsClassifier

data = {
  'height': [
    3.91, 7.09, 10.48, 9.21, 7.95, 7.62, 7.95, 4.69, 7.50, 7.11, 
    4.15, 7.29, 8.49, 7.44, 7.86, 3.93, 4.40, 5.5, 8.10, 8.69
  ], 
  'width': [
     5.76, 7.69, 7.32, 7.20, 5.90, 7.51, 5.32, 6.19, 5.99, 7.02, 
     5.60, 8.38, 6.52, 7.89, 7.60, 6.12, 5.90, 4.5, 6.15, 5.82
  ],
  'fruit': [
    'Mandarin', 'Apple', 'Lemon', 'Lemon', 'Lemon', 'Apple', 'Mandarin', 
    'Mandarin', 'Lemon', 'Apple', 'Mandarin', 'Apple', 'Lemon', 'Apple', 
    'Apple', 'Apple', 'Mandarin', 'Lemon', 'Lemon', 'Lemon'
  ]
} 

df = pd.DataFrame(data) # transform dataset into a DataFrame
print(df)

X = df[['height', 'width']].values
y = df.fruit.values

knn = KNeighborsClassifier(n_neighbors=3) 
knn.fit(X, y)

prediction  = knn.predict([[9, 3]])
predictions = knn.predict([[9, 3], [4, 5], [2, 5], [8, 9], [5, 7]])

print(prediction)  # Lemon
print(predictions) # Lemon Mandarin Mandarin Apple Mandarin

Accuracy

 
"""KNN Evaluation
Evaluate the model on the training and test dataset
The score is the difference between actual and predicted labels
1.0 means the model correctly predicted all (100%)
"""

import pandas as pd
from sklearn.neighbors import KNeighborsClassifier
from sklearn import metrics

A = pd.DataFrame({ # training dataset
  'height': [
    3.91, 7.09, 10.48, 9.21, 7.95, 7.62, 7.95, 4.69, 7.50, 7.11, 
    4.15, 7.29, 8.49, 7.44, 7.86, 3.93, 4.40, 5.5, 8.10, 8.69
  ], 
  'width': [
     5.76, 7.69, 7.32, 7.20, 5.90, 7.51, 5.32, 6.19, 5.99, 7.02, 
     5.60, 8.38, 6.52, 7.89, 7.60, 6.12, 5.90, 4.5, 6.15, 5.82
  ],
  'fruit': [
    'Mandarin', 'Apple', 'Lemon', 'Lemon', 'Lemon', 'Apple', 'Mandarin', 
    'Mandarin', 'Lemon', 'Apple', 'Mandarin', 'Apple', 'Lemon', 'Apple', 
    'Apple', 'Apple', 'Mandarin', 'Lemon', 'Lemon', 'Lemon'
  ]
})

B = pd.DataFrame({   # test dataset
    'height': [4, 4.47, 6.49, 7.51, 8.34],
    'width':  [6.5, 7.13, 7, 5.01, 4.23],
    'fruit':  ['Mandarin', 'Mandarin', 'Apple', 'Lemon', 'Lemon']
})

X  = A[['height', 'width']].values
X2 = B[['height', 'width']].values
y  = A.fruit.values
y2 = B.fruit.values

knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X, y)

predictions = knn.predict(X)
score = metrics.accuracy_score(y, predictions) # evaluate on training dataset
print(score * 100) # 85%

predictions = knn.predict(X2)
score = metrics.accuracy_score(y2, predictions) # evaluate on test dataset
print(score * 100) # 100%

Score Graph

 
"""KNN plot score
Models between k=3 and k=7 perform optimally on the test set
They optimally balance overfitting and underfitting
"""
import pandas as pd
from sklearn.neighbors import KNeighborsClassifier
from sklearn import metrics
import matplotlib.pyplot as plt

A = pd.DataFrame({
  'height': [
    3.91, 7.09, 10.48, 9.21, 7.95, 7.62, 7.95, 4.69, 7.50, 7.11, 
    4.15, 7.29, 8.49, 7.44, 7.86, 3.93, 4.40, 5.5, 8.10, 8.69
  ], 
  'width': [
     5.76, 7.69, 7.32, 7.20, 5.90, 7.51, 5.32, 6.19, 5.99, 7.02, 
     5.60, 8.38, 6.52, 7.89, 7.60, 6.12, 5.90, 4.5, 6.15, 5.82
  ],
  'fruit': [
    'Mandarin', 'Apple', 'Lemon', 'Lemon', 'Lemon', 'Apple', 'Mandarin', 
    'Mandarin', 'Lemon', 'Apple', 'Mandarin', 'Apple', 'Lemon', 'Apple', 
    'Apple', 'Apple', 'Mandarin', 'Lemon', 'Lemon', 'Lemon'
  ]
})

B = pd.DataFrame({
    'height': [4, 4.47, 6.49, 7.51, 8.34],
    'width': [6.5, 7.13, 7, 5.01, 4.23],
    'fruit': ['Mandarin', 'Mandarin', 'Apple', 'Lemon', 'Lemon']
})

X  = A[['height', 'width']].values
X2 = B[['height', 'width']].values

y  = A.fruit.values
y2 = B.fruit.values

k = []
score = []
score2 = []

for i in range(len(X)):
    _k = i+1
    
    clf = KNeighborsClassifier(n_neighbors = _k)
    clf.fit(X, y)

    _score = metrics.accuracy_score(y, clf.predict(X))
    _score2 = metrics.accuracy_score(y2, clf.predict(X2))

    k.append(_k)
    score.append(_score * 100)
    score2.append(_score2 * 100)
    
    print(f'k={_k} | score: {score[i]} | score2: {score2[i]}')


# Plot train score
plt.scatter(k, score) #function
plt.plot(k, score, '-', label='train') #data points

# Plot test score
plt.scatter(k, score2)
plt.plot(k, score2, '-', label='test')

# Plot configurations
plt.axis([max(k),min(k)+1, 0, 100])
plt.xlabel('number of nearest neighbours (k)', size = 13)
plt.ylabel('accuracy score', size = 13)
plt.title('Model Performance vs Complexity', size = 20)
plt.legend()

# Output
plt.show()

Boundaries