cluster visuals added

visualize_clusters.py

@@ -0,0 +1,103 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.manifold import TSNE
+from sklearn.preprocessing import MinMaxScaler
+import os
+
+INPUT_FILE = "wallet_dataset_labeled.csv"
+OUTPUT_DIR = "docs"
+RADAR_CHART_FILE = os.path.join(OUTPUT_DIR, "persona_radar_chart.png")
+TSNE_PLOT_FILE = os.path.join(OUTPUT_DIR, "clusters_tsne.png")
+
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+def load_data():
+    if not os.path.exists(INPUT_FILE):
+        print(f"Error: {INPUT_FILE} not found.")
+        return None
+    return pd.read_csv(INPUT_FILE)
+
+def plot_radar_chart(df):
+    print("Generating Radar Chart")
+    
+    features = [
+        'tx_count', 'active_days', 'total_gas_spent', 
+        'total_nft_volume_usd', 'dex_trades', 'total_traded_usd'
+    ]
+    
+    scaler = MinMaxScaler()
+    df_scaled = df.copy()
+    df_scaled[features] = scaler.fit_transform(df[features])
+    
+    persona_means = df_scaled.groupby('Persona')[features].mean()
+    
+    labels=np.array(features)
+    num_vars = len(labels)
+    angles = np.linspace(0, 2 * np.pi, num_vars, endpoint=False).tolist()
+    angles += angles[:1]
+    
+    fig, ax = plt.subplots(figsize=(10, 10), subplot_kw=dict(polar=True))
+    
+    colors = sns.color_palette("husl", len(persona_means))
+    
+    for idx, (persona, row) in enumerate(persona_means.iterrows()):
+        values = row.tolist()
+        values += values[:1] 
+        ax.plot(angles, values, color=colors[idx], linewidth=2, label=persona)
+        ax.fill(angles, values, color=colors[idx], alpha=0.1)
+    
+    ax.set_theta_offset(np.pi / 2)
+    ax.set_theta_direction(-1)
+    ax.set_xticks(angles[:-1])
+    ax.set_xticklabels(labels)
+    
+    plt.legend(loc='upper right', bbox_to_anchor=(1.3, 1.1))
+    plt.title("Persona Behavioral Fingerprints (Normalized)", y=1.08)
+    
+    plt.savefig(RADAR_CHART_FILE, bbox_inches='tight', dpi=300)
+    print(f"Saved radar chart to {RADAR_CHART_FILE}")
+    plt.close()
+
+def plot_tsne(df):
+    print("Generating t-SNE Plot (this may take a moment)...")
+    
+    feature_cols = [
+        'tx_count', 'active_days', 'avg_tx_per_day', 'total_gas_spent',
+        'total_nft_buys', 'total_nft_sells', 'total_nft_volume_usd',
+        'unique_nfts_owned', 'dex_trades', 'avg_trade_size_usd',
+        'total_traded_usd', 'erc20_receive_usd', 'erc20_send_usd',
+        'native_balance_delta'
+    ]
+    
+    X = df[feature_cols].fillna(0)
+    
+    tsne = TSNE(n_components=2, random_state=42, init='random', learning_rate='auto')
+    X_embedded = tsne.fit_transform(X)
+    
+    plt.figure(figsize=(12, 8))
+    sns.scatterplot(
+        x=X_embedded[:, 0], 
+        y=X_embedded[:, 1], 
+        hue=df['Persona'], 
+        palette='husl',
+        alpha=0.7
+    )
+    
+    plt.title("t-SNE Projection of Wallet Clusters")
+    plt.xlabel("Dimension 1")
+    plt.ylabel("Dimension 2")
+    plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
+    
+    plt.tight_layout()
+    plt.savefig(TSNE_PLOT_FILE, dpi=300)
+    print(f"Saved t-SNE plot to {TSNE_PLOT_FILE}")
+    plt.close()
+
+if __name__ == "__main__":
+    df = load_data()
+    if df is not None:
+        plot_radar_chart(df)
+        plot_tsne(df)
+        print("Visualization complete.")