Delete odules/profiling.py

odules/profiling.py

@@ -1,86 +0,0 @@
-# modules/profiling.py
-
-# -*- coding: utf-8 -*-
-#
-# PROJECT:      CognitiveEDA v5.7 - The QuantumLeap Intelligence Platform
-#
-# DESCRIPTION:  A dedicated module for profiling and characterizing customer
-#               segments identified through clustering.
-
-import pandas as pd
-import plotly.express as px
-import plotly.graph_objects as go
-import logging
-
-def profile_clusters(df: pd.DataFrame, cluster_labels: pd.Series, numeric_cols: list, cat_cols: list) -> tuple:
-    """
-    Analyzes and profiles clusters to create meaningful business personas.
-
-    This function groups the data by cluster and calculates key statistics
-    for numeric and categorical features to describe each segment. It then
-    visualizes these differences.
-
-    Args:
-        df (pd.DataFrame): The feature-engineered DataFrame.
-        cluster_labels (pd.Series): The series of cluster labels from the K-Means model.
-        numeric_cols (list): List of numeric columns to profile (e.g., ['Total_Revenue']).
-        cat_cols (list): List of categorical columns to profile (e.g., ['City', 'Product']).
-
-    Returns:
-        A tuple containing:
-        - A markdown string with the detailed profile of each cluster.
-        - A Plotly Figure visualizing the differences between clusters.
-    """
-    if df.empty or cluster_labels.empty:
-        return "No data to profile.", go.Figure()
-
-    logging.info(f"Profiling {cluster_labels.nunique()} clusters...")
-    
-    profile_df = df.copy()
-    profile_df['Cluster'] = cluster_labels
-
-    # --- Generate Markdown Report ---
-    report_md = "### Cluster Persona Analysis\n\n"
-    
-    # Analyze numeric features by cluster
-    numeric_profile = profile_df.groupby('Cluster')[numeric_cols].mean().round(2)
-    
-    # Analyze categorical features by cluster (get the most frequent value - mode)
-    cat_profile_list = []
-    for col in cat_cols:
-        mode_series = profile_df.groupby('Cluster')[col].apply(lambda x: x.mode().iloc[0])
-        mode_df = mode_series.to_frame()
-        cat_profile_list.append(mode_df)
-    
-    full_profile = pd.concat([numeric_profile] + cat_profile_list, axis=1)
-
-    for cluster_id in sorted(profile_df['Cluster'].unique()):
-        report_md += f"#### Cluster {cluster_id}: The '{full_profile.loc[cluster_id, 'City']}' Persona\n"
-        
-        # Numeric Summary
-        for col in numeric_cols:
-            val = full_profile.loc[cluster_id, col]
-            report_md += f"- **Avg. {col.replace('_', ' ')}:** `{val:,.2f}`\n"
-        
-        # Categorical Summary
-        for col in cat_cols:
-            val = full_profile.loc[cluster_id, col]
-            report_md += f"- **Dominant {col}:** `{val}`\n"
-        report_md += "\n"
-
-    # --- Generate Visualization ---
-    # We'll visualize the average 'Total_Revenue' by 'City' for each cluster
-    # This directly tests our hypothesis that 'City' is the dominant feature.
-    vis_df = profile_df.groupby(['Cluster', 'City'])['Total_Revenue'].mean().reset_index()
-
-    fig = px.bar(
-        vis_df,
-        x='Cluster',
-        y='Total_Revenue',
-        color='City',
-        barmode='group',
-        title='<b>Cluster Profile: Avg. Total Revenue by City</b>',
-        labels={'Total_Revenue': 'Average Total Revenue ($)', 'Cluster': 'Customer Segment'}
-    )
-    
-    return report_md, fig