Add Ikario Architecture v2 - Phases 1-8 complete
Implements the processual architecture based on Whitehead's Process Philosophy and Peirce's Semiotics. Core paradigm: "L'espace latent pense. Le LLM traduit." (The latent space thinks. The LLM translates.) Phase 1-4: Core semiotic cycle - StateTensor 8x1024 (8 Peircean dimensions) - Dissonance computation with hard negatives - Fixation via 4 Peircean methods (Tenacity, Authority, A Priori, Science) - LatentEngine orchestrating the full cycle Phase 5: StateToLanguage - LLM as pure translator (zero-reasoning, T=0) - Projection on interpretable directions - Reasoning markers detection (Amendment #4) Phase 6: Vigilance - x_ref (David) as guard-rail, NOT attractor - Drift detection per dimension and globally - Alerts: ok, warning, critical Phase 7: Autonomous Daemon - Two modes: CONVERSATION (always verbalize), AUTONOMOUS (~1000 cycles/day) - Amendment #5: 50% probability on unresolved impacts - TriggerGenerator with weighted random selection Phase 8: Integration & Metrics - ProcessMetrics for daily/weekly reports - Health status monitoring - Integration tests validating all modules 297 tests passing, version 0.7.0 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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ikario_processual/tests/test_state_tensor.py
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297
ikario_processual/tests/test_state_tensor.py
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#!/usr/bin/env python3
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"""
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Tests pour StateTensor - Tenseur d'état 8×1024.
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Exécuter: pytest ikario_processual/tests/test_state_tensor.py -v
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"""
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import numpy as np
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import pytest
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from datetime import datetime
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# Import du module à tester
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent.parent.parent))
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from ikario_processual.state_tensor import (
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StateTensor,
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TensorDimension,
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DIMENSION_NAMES,
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EMBEDDING_DIM,
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)
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class TestStateTensorBasic:
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"""Tests de base pour StateTensor."""
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def test_create_empty_tensor(self):
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"""Test création d'un tenseur vide."""
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tensor = StateTensor(
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state_id=0,
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timestamp=datetime.now().isoformat(),
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)
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assert tensor.state_id == 0
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assert tensor.firstness.shape == (EMBEDDING_DIM,)
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assert tensor.valeurs.shape == (EMBEDDING_DIM,)
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assert np.all(tensor.firstness == 0)
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def test_create_with_values(self):
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"""Test création avec valeurs."""
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firstness = np.random.randn(EMBEDDING_DIM)
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firstness = firstness / np.linalg.norm(firstness)
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tensor = StateTensor(
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state_id=1,
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timestamp=datetime.now().isoformat(),
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firstness=firstness,
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)
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assert np.allclose(tensor.firstness, firstness)
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assert np.isclose(np.linalg.norm(tensor.firstness), 1.0)
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def test_to_matrix(self):
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"""Test conversion en matrice."""
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tensor = StateTensor(
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state_id=0,
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timestamp=datetime.now().isoformat(),
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)
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matrix = tensor.to_matrix()
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assert matrix.shape == (8, EMBEDDING_DIM)
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def test_to_flat(self):
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"""Test aplatissement."""
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tensor = StateTensor(
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state_id=0,
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timestamp=datetime.now().isoformat(),
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)
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flat = tensor.to_flat()
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assert flat.shape == (8 * EMBEDDING_DIM,)
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assert flat.shape == (8192,)
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def test_dimension_names(self):
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"""Test que toutes les dimensions sont présentes."""
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expected = [
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"firstness", "secondness", "thirdness", "dispositions",
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"orientations", "engagements", "pertinences", "valeurs"
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]
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assert DIMENSION_NAMES == expected
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assert len(DIMENSION_NAMES) == 8
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class TestStateTensorOperations:
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"""Tests des opérations sur StateTensor."""
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def test_copy(self):
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"""Test copie profonde."""
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original = StateTensor(
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state_id=1,
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timestamp=datetime.now().isoformat(),
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firstness=np.random.randn(EMBEDDING_DIM),
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)
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copied = original.copy()
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# Modifier l'original ne doit pas affecter la copie
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original.firstness[0] = 999.0
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assert copied.firstness[0] != 999.0
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def test_set_dimension(self):
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"""Test modification d'une dimension."""
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tensor = StateTensor(
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state_id=0,
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timestamp=datetime.now().isoformat(),
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)
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new_vec = np.random.randn(EMBEDDING_DIM)
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tensor.set_dimension(TensorDimension.VALEURS, new_vec)
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# Doit être normalisé
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assert np.isclose(np.linalg.norm(tensor.valeurs), 1.0)
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def test_get_dimension(self):
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"""Test récupération d'une dimension."""
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tensor = StateTensor(
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state_id=0,
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timestamp=datetime.now().isoformat(),
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)
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vec = tensor.get_dimension(TensorDimension.FIRSTNESS)
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assert vec.shape == (EMBEDDING_DIM,)
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def test_to_dict(self):
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"""Test conversion en dictionnaire."""
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tensor = StateTensor(
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state_id=5,
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timestamp="2026-02-01T12:00:00",
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trigger_type="user",
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trigger_content="Hello",
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)
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d = tensor.to_dict()
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assert d["state_id"] == 5
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assert d["trigger_type"] == "user"
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assert "firstness" not in d # Vecteurs pas dans properties
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def test_get_vectors_dict(self):
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"""Test récupération des vecteurs pour Weaviate."""
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tensor = StateTensor(
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state_id=0,
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timestamp=datetime.now().isoformat(),
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)
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vectors = tensor.get_vectors_dict()
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assert len(vectors) == 8
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assert "firstness" in vectors
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assert "valeurs" in vectors
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assert len(vectors["firstness"]) == EMBEDDING_DIM
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class TestStateTensorAggregation:
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"""Tests des opérations d'agrégation."""
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def test_weighted_mean_two_tensors(self):
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"""Test moyenne pondérée de 2 tenseurs."""
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t1 = StateTensor(
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state_id=1,
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timestamp=datetime.now().isoformat(),
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)
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t2 = StateTensor(
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state_id=2,
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timestamp=datetime.now().isoformat(),
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)
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# Initialiser avec des vecteurs aléatoires normalisés
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for dim_name in DIMENSION_NAMES:
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v1 = np.random.randn(EMBEDDING_DIM)
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v1 = v1 / np.linalg.norm(v1)
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setattr(t1, dim_name, v1)
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v2 = np.random.randn(EMBEDDING_DIM)
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v2 = v2 / np.linalg.norm(v2)
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setattr(t2, dim_name, v2)
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# Moyenne 50/50
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result = StateTensor.weighted_mean([t1, t2], [0.5, 0.5])
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# Résultat doit être normalisé
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for dim_name in DIMENSION_NAMES:
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vec = getattr(result, dim_name)
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assert np.isclose(np.linalg.norm(vec), 1.0, atol=1e-5)
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def test_blend(self):
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"""Test blend 70/30."""
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t1 = StateTensor(state_id=1, timestamp=datetime.now().isoformat())
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t2 = StateTensor(state_id=2, timestamp=datetime.now().isoformat())
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# Initialiser
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for dim_name in DIMENSION_NAMES:
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v1 = np.random.randn(EMBEDDING_DIM)
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v1 = v1 / np.linalg.norm(v1)
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setattr(t1, dim_name, v1)
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v2 = np.random.randn(EMBEDDING_DIM)
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v2 = v2 / np.linalg.norm(v2)
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setattr(t2, dim_name, v2)
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result = StateTensor.blend(t1, t2, alpha=0.7)
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assert result is not None
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assert result.state_id == -1 # Non défini
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def test_from_matrix(self):
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"""Test création depuis matrice."""
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matrix = np.random.randn(8, EMBEDDING_DIM)
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tensor = StateTensor.from_matrix(
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matrix=matrix,
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state_id=10,
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timestamp="2026-02-01T12:00:00"
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)
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assert tensor.state_id == 10
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assert np.allclose(tensor.firstness, matrix[0])
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assert np.allclose(tensor.valeurs, matrix[7])
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def test_from_matrix_wrong_shape(self):
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"""Test erreur si matrice mauvaise forme."""
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matrix = np.random.randn(4, EMBEDDING_DIM) # 4 au lieu de 8
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with pytest.raises(ValueError):
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StateTensor.from_matrix(matrix, state_id=0, timestamp="")
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class TestStateTensorDistance:
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"""Tests de calcul de distance entre tenseurs."""
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def test_distance_to_self_is_zero(self):
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"""Distance à soi-même = 0."""
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tensor = StateTensor(state_id=0, timestamp=datetime.now().isoformat())
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for dim_name in DIMENSION_NAMES:
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v = np.random.randn(EMBEDDING_DIM)
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v = v / np.linalg.norm(v)
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setattr(tensor, dim_name, v)
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flat = tensor.to_flat()
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distance = np.linalg.norm(flat - flat)
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assert distance == 0.0
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def test_normalized_distance(self):
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"""Test distance normalisée entre 2 tenseurs."""
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t1 = StateTensor(state_id=1, timestamp=datetime.now().isoformat())
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t2 = StateTensor(state_id=2, timestamp=datetime.now().isoformat())
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for dim_name in DIMENSION_NAMES:
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v1 = np.random.randn(EMBEDDING_DIM)
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v1 = v1 / np.linalg.norm(v1)
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setattr(t1, dim_name, v1)
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v2 = np.random.randn(EMBEDDING_DIM)
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v2 = v2 / np.linalg.norm(v2)
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setattr(t2, dim_name, v2)
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diff = t1.to_flat() - t2.to_flat()
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distance = np.linalg.norm(diff) / np.linalg.norm(t2.to_flat())
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# Distance normalisée doit être > 0 et finie
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assert distance > 0
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assert np.isfinite(distance)
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class TestStateTensorSerialization:
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"""Tests de sérialisation."""
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def test_from_dict_roundtrip(self):
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"""Test aller-retour dict."""
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original = StateTensor(
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state_id=42,
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timestamp="2026-02-01T12:00:00",
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previous_state_id=41,
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trigger_type="user",
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trigger_content="Test message",
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embedding_model="BAAI/bge-m3",
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)
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# Simuler les vecteurs
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vectors = {}
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for dim_name in DIMENSION_NAMES:
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v = np.random.randn(EMBEDDING_DIM)
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v = v / np.linalg.norm(v)
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setattr(original, dim_name, v)
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vectors[dim_name] = v.tolist()
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# Convertir et recréer
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props = original.to_dict()
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reconstructed = StateTensor.from_dict(props, vectors)
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assert reconstructed.state_id == 42
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assert reconstructed.trigger_type == "user"
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assert np.allclose(reconstructed.firstness, original.firstness)
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if __name__ == "__main__":
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pytest.main([__file__, "-v"])
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