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# modules/clustering.py
# -*- coding: utf-8 -*-
#
# PROJECT: CognitiveEDA v5.7 - The QuantumLeap Intelligence Platform
#
# DESCRIPTION: Specialized module for K-Means clustering. This version is
# updated to return the cluster labels for downstream profiling.
import logging
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
def perform_clustering(df: pd.DataFrame, numeric_cols: list, n_clusters: int):
"""
Performs K-Means clustering, generates an Elbow plot for optimal K,
visualizes the clusters via PCA, and returns the cluster labels.
Args:
df (pd.DataFrame): The input DataFrame.
numeric_cols (list): A list of numeric columns to use for clustering.
n_clusters (int): The number of clusters (k) to create.
Returns:
A tuple containing:
- fig_cluster (go.Figure): Plot of the clustered data in 2D PCA space.
- fig_elbow (go.Figure): The Elbow Method plot for determining optimal k.
- summary (str): A markdown summary of the methodology.
- labels (pd.Series): The cluster label assigned to each data point.
"""
if len(numeric_cols) < 2:
empty_fig = go.Figure()
return empty_fig, empty_fig, "Clustering requires at least 2 numeric features.", pd.Series()
cluster_data = df[numeric_cols].dropna()
if len(cluster_data) < n_clusters:
empty_fig = go.Figure()
return empty_fig, empty_fig, f"Not enough data ({len(cluster_data)}) for {n_clusters} clusters.", pd.Series()
scaler = StandardScaler()
scaled_data = scaler.fit_transform(cluster_data)
# --- Elbow Method Plot ---
wcss = []
k_range = range(1, 11)
for i in k_range:
kmeans_elbow = KMeans(n_clusters=i, init='k-means++', random_state=42, n_init='auto')
kmeans_elbow.fit(scaled_data)
wcss.append(kmeans_elbow.inertia_)
fig_elbow = go.Figure()
fig_elbow.add_trace(go.Scatter(x=list(k_range), y=wcss, mode='lines+markers'))
fig_elbow.update_layout(title='<b>💡 The Elbow Method for Optimal K</b>',
xaxis_title='Number of Clusters (K)',
yaxis_title='Within-Cluster Sum of Squares (WCSS)')
# --- K-Means Clustering & Visualization ---
kmeans = KMeans(n_clusters=n_clusters, init='k-means++', random_state=42, n_init='auto').fit(scaled_data)
labels = pd.Series(kmeans.labels_, name='Cluster_Labels', index=cluster_data.index)
pca = PCA(n_components=2)
components = pca.fit_transform(scaled_data)
# Create a DataFrame for plotting
plot_df = pd.DataFrame(components, columns=['PCA1', 'PCA2'], index=cluster_data.index)
plot_df['Cluster'] = labels.astype(str)
fig_cluster = px.scatter(
plot_df, x='PCA1', y='PCA2', color='Cluster',
title=f"<b>K-Means Clustering Visualization (K={n_clusters})</b>",
labels={'PCA1': 'Principal Component 1', 'PCA2': 'Principal Component 2'},
color_discrete_sequence=px.colors.qualitative.Vivid
)
explained_variance = pca.explained_variance_ratio_.sum() * 100
summary = (f"**Features Used:** `{len(numeric_cols)}` | **Clusters (K):** `{n_clusters}`\n\n"
f"PCA explains **{explained_variance:.2f}%** of variance.")
# --- MODIFIED RETURN ---
return fig_cluster, fig_elbow, summary, labels |