Clustering¶

Imports¶

In [1]:

import pandas as pd
import numpy as np

import matplotlib.pyplot as plt
import seaborn as sns

from sklearn import preprocessing
from sklearn.cluster import KMeans

from sklearn.metrics import confusion_matrix
from scipy.stats import mode

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn import preprocessing from sklearn.cluster import KMeans from sklearn.metrics import confusion_matrix from scipy.stats import mode

Load Dataset¶

In [2]:

import gdown

# Replace with your Google Drive shareable link
url = 'https://drive.google.com/file/d/13CJLrStKcEBjCZRAUbcYvMaiTfGeH1ug/view?usp=sharing'

# Convert to the direct download link
file_id = url.split('/d/')[1].split('/')[0]
direct_url = f'https://drive.google.com/uc?id={file_id}'

# Download
gdown.download(direct_url, 'seeds.txt', quiet=False)
!ls

import gdown # Replace with your Google Drive shareable link url = 'https://drive.google.com/file/d/13CJLrStKcEBjCZRAUbcYvMaiTfGeH1ug/view?usp=sharing' # Convert to the direct download link file_id = url.split('/d/')[1].split('/')[0] direct_url = f'https://drive.google.com/uc?id={file_id}' # Download gdown.download(direct_url, 'seeds.txt', quiet=False) !ls

Downloading...
From: https://drive.google.com/uc?id=13CJLrStKcEBjCZRAUbcYvMaiTfGeH1ug
To: /content/seeds.txt
100%|██████████| 9.30k/9.30k [00:00<00:00, 18.5MB/s]

sample_data  seeds.txt

In [3]:

columns = ["area", "perimeter", "compactness", "length", "width", "asymmetry", "groove", "class"]
df = pd.read_csv("./seeds.txt", names=columns, sep="\s+") # \s+ => any spaces

df.head(2)

columns = ["area", "perimeter", "compactness", "length", "width", "asymmetry", "groove", "class"] df = pd.read_csv("./seeds.txt", names=columns, sep="\s+") # \s+ => any spaces df.head(2)

Out[3]:

	area	perimeter	compactness	length	width	asymmetry	groove	class
0	15.26	14.84	0.8710	5.763	3.312	2.221	5.220	1
1	14.88	14.57	0.8811	5.554	3.333	1.018	4.956	1

In [4]:

X = df.drop(["class"], axis=1)

X = df.drop(["class"], axis=1)

Visualization¶

In [ ]:

for i in range(len(columns) - 1):
  for j in range(i+1, len(columns) - 1):
    sns.scatterplot(x=columns[i], y=columns[j], data=df, hue="class", palette="tab10", s=50)
    plt.show()

for i in range(len(columns) - 1): for j in range(i+1, len(columns) - 1): sns.scatterplot(x=columns[i], y=columns[j], data=df, hue="class", palette="tab10", s=50) plt.show()

Normalization¶

With distance-based models we should use MinMaxScalar

In [6]:

scaler = preprocessing.MinMaxScaler()

X_norm = pd.DataFrame(scaler.fit_transform(X), columns=X.columns)

X_norm.head(2)

scaler = preprocessing.MinMaxScaler() X_norm = pd.DataFrame(scaler.fit_transform(X), columns=X.columns) X_norm.head(2)

Out[6]:

	area	perimeter	compactness	length	width	asymmetry	groove
0	0.440982	0.502066	0.570780	0.486486	0.486101	0.189302	0.345150
1	0.405099	0.446281	0.662432	0.368806	0.501069	0.032883	0.215165

Clustering¶

In [7]:

k_means = KMeans(n_clusters=3)
k_means.fit(X_norm)

k_means = KMeans(n_clusters=3) k_means.fit(X_norm)

Out[7]:

KMeans(n_clusters=3)

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In [8]:

y_pred = k_means.labels_
y_true = df["class"] - 1

y_pred = k_means.labels_ y_true = df["class"] - 1

In [9]:

# map each cluster index to the correct label.
y_pred_remap = np.zeros_like(y_pred)
for cluster in np.unique(y_pred):
    mask = y_pred == cluster
    y_pred_remap[mask] = mode(y_true[mask], keepdims=True).mode[0]


# Build confusion matrix
cm = confusion_matrix(y_true, y_pred_remap)

# Plot
plt.figure(figsize=(5, 3))
sns.heatmap(cm, annot=True, fmt='d', cmap='RdPu')
plt.xlabel('Predicted Cluster')
plt.ylabel('True Cluster')
plt.title('Confusion Matrix: True Cluster vs Predicted Cluster K-Mean')
plt.show()

# map each cluster index to the correct label. y_pred_remap = np.zeros_like(y_pred) for cluster in np.unique(y_pred): mask = y_pred == cluster y_pred_remap[mask] = mode(y_true[mask], keepdims=True).mode[0] # Build confusion matrix cm = confusion_matrix(y_true, y_pred_remap) # Plot plt.figure(figsize=(5, 3)) sns.heatmap(cm, annot=True, fmt='d', cmap='RdPu') plt.xlabel('Predicted Cluster') plt.ylabel('True Cluster') plt.title('Confusion Matrix: True Cluster vs Predicted Cluster K-Mean') plt.show()