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#!/usr/bin/env python3
"""
Visualizzazione 3D dello spazio Feature Typology
(mean_consistency, max_affinity, logit_influence)
"""
import json
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
def create_3d_visualization():
"""Crea visualizzazione 3D dello spazio feature typology"""
print("=" * 60)
print("VISUALIZZAZIONE 3D FEATURE TYPOLOGY SPACE")
print("=" * 60)
# Carica dati
print("\n📊 Caricamento dati...")
try:
with open("output/feature_typology.json", 'r', encoding='utf-8') as f:
typology = json.load(f)
print(f" ✅ Caricati {len(typology)} feature")
except FileNotFoundError:
print(" ❌ File feature_typology.json non trovato!")
print(" Esegui prima: python anthropological_basic.py")
return
# Estrai coordinate e tipi
coords = np.array([v['coordinates'] for v in typology.values()])
types = [v['type'] for v in typology.values()]
# Statistiche per tipo
type_counts = {}
for t in types:
type_counts[t] = type_counts.get(t, 0) + 1
print("\n📈 Distribuzione per tipo:")
for t, count in sorted(type_counts.items()):
pct = count / len(types) * 100
print(f" {t.capitalize()}: {count} ({pct:.1f}%)")
# Colori per tipo
color_map = {
'generalist': '#1f77b4', # blu
'specialist': '#ff7f0e', # arancione
'computational': '#2ca02c', # verde
'hybrid': '#d62728' # rosso
}
colors = [color_map.get(t, '#888888') for t in types]
# Crea plot 3D
print("\n🎨 Creazione visualizzazione 3D...")
fig = plt.figure(figsize=(14, 10))
ax = fig.add_subplot(111, projection='3d')
# Scatter plot principale
scatter = ax.scatter(coords[:, 0], coords[:, 1], coords[:, 2],
c=colors, alpha=0.6, s=20, edgecolors='w', linewidth=0.5)
# Labels e titolo
ax.set_xlabel('Mean Consistency\n(Generalizability)', fontsize=11, labelpad=10)
ax.set_ylabel('Max Affinity\n(Specialization)', fontsize=11, labelpad=10)
ax.set_zlabel('Logit Influence\n(Causal Importance)', fontsize=11, labelpad=10)
ax.set_title('Feature Typology Space\n3D Behavioral Coordinates',
fontsize=14, fontweight='bold', pad=20)
# Griglia
ax.grid(True, alpha=0.3)
# Legenda
from matplotlib.patches import Patch
legend_elements = []
for t in sorted(color_map.keys()):
if t in type_counts:
count = type_counts[t]
pct = count / len(types) * 100
label = f"{t.capitalize()} ({count}, {pct:.1f}%)"
legend_elements.append(Patch(facecolor=color_map[t], label=label))
ax.legend(handles=legend_elements, loc='upper left',
frameon=True, framealpha=0.9, fontsize=10)
# Imposta limiti degli assi
ax.set_xlim([0, 1])
ax.set_ylim([0, 1])
# zlim dipende dai dati (logit_influence può essere >1)
# Angolo di visualizzazione ottimale
ax.view_init(elev=20, azim=45)
plt.tight_layout()
# Salva
output_path = 'output/feature_typology_3d.png'
plt.savefig(output_path, dpi=150, bbox_inches='tight')
print(f" ✅ Salvata: {output_path}")
# Salva anche versione interattiva (opzionale)
try:
output_path_svg = 'output/feature_typology_3d.svg'
plt.savefig(output_path_svg, format='svg', bbox_inches='tight')
print(f" ✅ Salvata versione SVG: {output_path_svg}")
except:
pass
# Analisi addizionale: centroidi per tipo
print("\n📍 Centroidi per tipo (mean coordinates):")
for t in sorted(color_map.keys()):
if t in type_counts:
type_coords = coords[[i for i, typ in enumerate(types) if typ == t]]
if len(type_coords) > 0:
centroid = type_coords.mean(axis=0)
print(f" {t.capitalize()}:")
print(f" Mean Consistency: {centroid[0]:.3f}")
print(f" Max Affinity: {centroid[1]:.3f}")
print(f" Logit Influence: {centroid[2]:.3f}")
print("\n✅ Visualizzazione 3D completata!")
return fig
def create_2d_projections():
"""Crea proiezioni 2D degli assi principali"""
print("\n📊 Creazione proiezioni 2D...")
with open("output/feature_typology.json", 'r', encoding='utf-8') as f:
typology = json.load(f)
coords = np.array([v['coordinates'] for v in typology.values()])
types = [v['type'] for v in typology.values()]
color_map = {
'generalist': '#1f77b4',
'specialist': '#ff7f0e',
'computational': '#2ca02c',
'hybrid': '#d62728'
}
colors = [color_map.get(t, '#888888') for t in types]
# Crea figura con 3 subplots
fig, axes = plt.subplots(1, 3, figsize=(18, 5))
# Plot 1: Mean Consistency vs Max Affinity
axes[0].scatter(coords[:, 0], coords[:, 1], c=colors, alpha=0.6, s=30, edgecolors='w', linewidth=0.5)
axes[0].set_xlabel('Mean Consistency (Generalizability)', fontsize=10)
axes[0].set_ylabel('Max Affinity (Specialization)', fontsize=10)
axes[0].set_title('Consistency vs Affinity', fontsize=11, fontweight='bold')
axes[0].grid(True, alpha=0.3)
axes[0].set_xlim([0, 1])
axes[0].set_ylim([0, 1])
# Plot 2: Mean Consistency vs Logit Influence
axes[1].scatter(coords[:, 0], coords[:, 2], c=colors, alpha=0.6, s=30, edgecolors='w', linewidth=0.5)
axes[1].set_xlabel('Mean Consistency (Generalizability)', fontsize=10)
axes[1].set_ylabel('Logit Influence (Causal Importance)', fontsize=10)
axes[1].set_title('Consistency vs Influence', fontsize=11, fontweight='bold')
axes[1].grid(True, alpha=0.3)
axes[1].set_xlim([0, 1])
# Plot 3: Max Affinity vs Logit Influence
axes[2].scatter(coords[:, 1], coords[:, 2], c=colors, alpha=0.6, s=30, edgecolors='w', linewidth=0.5)
axes[2].set_xlabel('Max Affinity (Specialization)', fontsize=10)
axes[2].set_ylabel('Logit Influence (Causal Importance)', fontsize=10)
axes[2].set_title('Affinity vs Influence', fontsize=11, fontweight='bold')
axes[2].grid(True, alpha=0.3)
axes[2].set_xlim([0, 1])
# Legenda comune
from matplotlib.patches import Patch
legend_elements = [Patch(facecolor=color_map[t], label=t.capitalize())
for t in sorted(color_map.keys())]
fig.legend(handles=legend_elements, loc='upper center', ncol=4,
frameon=True, framealpha=0.9, fontsize=10, bbox_to_anchor=(0.5, 0.98))
plt.tight_layout(rect=[0, 0, 1, 0.96])
output_path = 'output/feature_typology_2d_projections.png'
plt.savefig(output_path, dpi=150, bbox_inches='tight')
print(f" ✅ Salvata: {output_path}")
return fig
if __name__ == "__main__":
print("🎨 Avvio visualizzazione Feature Typology Space...")
try:
# Visualizzazione 3D
fig_3d = create_3d_visualization()
# Proiezioni 2D
fig_2d = create_2d_projections()
print("\n🏆 COMPLETATO!")
print(" Visualizzazioni salvate in output/")
except Exception as e:
print(f"\n❌ Errore: {e}")
import traceback
traceback.print_exc()
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