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AI02, Clustering

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Implement with sklearn

Clustering through K-Means algorithm

# [0] : importing modules
from sklearn import datasets
from sklearn import metrics
from sklearn import cluster
import numpy as np

# [1] : loading dataset
iris = datasets.load_iris()
X, y = iris.data, iris.target

# [2] : clustering
clustering = cluster.KMeans(n_clusters=3)
clustering.fit(X)
y_pred = clustering.predict(X)

# [3] : correction assigned different integer values to the groups
idx_0, idx_1, idx_2 = (np.where(y_pred == n) for n in range(3))
y_pred[idx_0], y_pred[idx_1], y_pred[idx_2] = 2, 0, 1

# [4] : summarize the overlaps between the supervised and unsupervised classification
metrics.confusion_matrix(y, y_pred)
OUTPUT 
array([[50,  0,  0],
       [ 0, 48,  2],
       [ 0, 14, 36]], dtype=int64)
VISUALIZATION 
# [0] : importing modules
from sklearn import datasets
from sklearn import metrics
from sklearn import cluster
import numpy as np
import matplotlib.pyplot as plt

# [1] : loading dataset
iris = datasets.load_iris()
X, y = iris.data, iris.target

# [2] : clustering
n_clusters = 3
clustering = cluster.KMeans(n_clusters)
clustering.fit(X)
y_pred = clustering.predict(X)

# [3] : correction assigned different integer values to the groups
idx_0, idx_1, idx_2 = (np.where(y_pred == n) for n in range(3))
y_pred[idx_0], y_pred[idx_1], y_pred[idx_2] = 2, 0, 1

# [4] : summarize the overlaps between the supervised and unsupervised classification
metrics.confusion_matrix(y, y_pred)


# Visualization
N = X.shape[1]
fig, axes = plt.subplots(N, N, figsize=(12, 12), sharex=True, sharey=True)  
colors = ["coral", "blue", "green"]    
markers = ["^", "v", "o"]   
for m in range(N):   
    for n in range(N):   
        for p in range(n_clusters):  
            mask = y_pred == p   
            axes[m, n].scatter(X[:, m][mask], X[:, n][mask], s=30, marker=markers[p], color=colors[p], alpha=0.25)   
             
    axes[N-1, m].set_xlabel(iris.feature_names[m], fontsize=16)   
    axes[m, 0].set_ylabel(iris.feature_names[m], fontsize=16)

다운로드

Details code[1]

iris dataset

import pandas as pd
from sklearn import datasets

iris = datasets.load_iris()
iris.feature_names.append('target_names')

df1 = pd.DataFrame(iris.data)
df2 = pd.DataFrame(iris.target)
df = pd.concat([df1,df2], axis=1)
df.columns = iris.feature_names

print(df)

     s.length (cm)  s.width (cm)  ...  p.width (cm)  target_names
0              5.1           3.5  ...           0.2             0
1              4.9           3.0  ...           0.2             0
2              4.7           3.2  ...           0.2             0
3              4.6           3.1  ...           0.2             0
4              5.0           3.6  ...           0.2             0
..             ...           ...  ...           ...           ...
145            6.7           3.0  ...           2.3             2
146            6.3           2.5  ...           1.9             2
147            6.5           3.0  ...           2.0             2
148            6.2           3.4  ...           2.3             2
149            5.9           3.0  ...           1.8             2

[150 rows x 5 columns]
Details code[2]

INPUT

# [0] : importing modules
from sklearn import datasets
from sklearn import metrics
from sklearn import cluster
import numpy as np

# [1] : loading dataset
iris = datasets.load_iris()
X, y = iris.data, iris.target

# [2] : clustering
clustering = cluster.KMeans(n_clusters=3)
clustering.fit(X)

OUTPUT

KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
       n_clusters=3, n_init=10, n_jobs=None, precompute_distances='auto',
       random_state=None, tol=0.0001, verbose=0)
Details code[3] 
# [0] : importing modules
from sklearn import datasets
from sklearn import metrics
from sklearn import cluster
import numpy as np

# [1] : loading dataset
iris = datasets.load_iris()
X, y = iris.data, iris.target

# [2] : clustering
clustering = cluster.KMeans(n_clusters=3)
clustering.fit(X)
y_pred = clustering.predict(X)

y_pred[::8]

OUTPUT : array([1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0]) 

y[::8]

OUTPUT : array([0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2])

Details code[4]

On the above confusion matrix matrix, the diagonals correspond to the number of samples that are correctly classified for each level of the category variable, and the off-diagonal elements are the number of incorrectly classified samples. More specifically, the element of the confusion matrix C is the number of samples of category i that were categorized as j.




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