David Blanc Brioir
6af52866ed
Phase 3 - State Transformation: - transform_state() function with alpha/beta parameters - compute_adaptive_params() for dynamic transformation - StateTransformer class for state management Phase 4 - Occasion Logger: - OccasionLog dataclass for structured logging - OccasionLogger for JSON file storage - Profile evolution tracking and statistics Phase 5 - Occasion Manager: - Full cycle: Prehension → Concrescence → Satisfaction - Search integration (thoughts, library) - State creation and logging orchestration Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
141 lines
4.5 KiB
Python
141 lines
4.5 KiB
Python
#!/usr/bin/env python3
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"""Tests pour Phase 3 - Transformation d'état."""
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import numpy as np
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import pytest
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from ..state_transformation import (
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transform_state,
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compute_adaptive_params,
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StateTransformer
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)
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class TestTransformState:
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"""Tests de la fonction de transformation."""
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def test_transform_preserves_norm(self):
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"""Le vecteur transformé doit rester normalisé."""
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s_prev = np.random.randn(1024)
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s_prev = s_prev / np.linalg.norm(s_prev)
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occasion = np.random.randn(1024)
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occasion = occasion / np.linalg.norm(occasion)
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s_new = transform_state(s_prev, occasion)
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assert abs(np.linalg.norm(s_new) - 1.0) < 0.001
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def test_high_alpha_preserves_identity(self):
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"""Alpha élevé = peu de changement."""
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s_prev = np.random.randn(1024)
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s_prev = s_prev / np.linalg.norm(s_prev)
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occasion = np.random.randn(1024)
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occasion = occasion / np.linalg.norm(occasion)
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s_new = transform_state(s_prev, occasion, alpha=0.99, beta=0.01)
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similarity = np.dot(s_prev, s_new)
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assert similarity > 0.98, f"Trop de changement: similarity={similarity}"
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def test_low_alpha_allows_change(self):
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"""Alpha bas = plus de changement."""
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s_prev = np.random.randn(1024)
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s_prev = s_prev / np.linalg.norm(s_prev)
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# Occasion très différente
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occasion = -s_prev + 0.1 * np.random.randn(1024)
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occasion = occasion / np.linalg.norm(occasion)
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s_new = transform_state(s_prev, occasion, alpha=0.5, beta=0.5)
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similarity = np.dot(s_prev, s_new)
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assert similarity < 0.9, f"Pas assez de changement: similarity={similarity}"
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def test_identical_occasion_increases_identity(self):
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"""Si l'occasion est identique à l'état, l'identité est renforcée."""
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s_prev = np.random.randn(1024)
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s_prev = s_prev / np.linalg.norm(s_prev)
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s_new = transform_state(s_prev, s_prev.copy(), alpha=0.85, beta=0.15)
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# Doit rester très similaire
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similarity = np.dot(s_prev, s_new)
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assert similarity > 0.99
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class TestAdaptiveParams:
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"""Tests des paramètres adaptatifs."""
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def test_default_params(self):
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"""Paramètres par défaut."""
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alpha, beta = compute_adaptive_params({})
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assert abs(alpha + beta - 1.0) < 0.001
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assert 0.8 < alpha < 0.9
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assert 0.1 < beta < 0.2
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def test_more_thoughts_increases_beta(self):
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"""Plus de pensées = plus de beta."""
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alpha1, beta1 = compute_adaptive_params({'thoughts_created': 0})
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alpha2, beta2 = compute_adaptive_params({'thoughts_created': 5})
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assert beta2 > beta1
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assert alpha2 < alpha1
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def test_timer_reduces_intensity(self):
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"""Timer = moins d'intensité."""
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alpha_user, beta_user = compute_adaptive_params({
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'trigger_type': 'user',
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'thoughts_created': 3
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})
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alpha_timer, beta_timer = compute_adaptive_params({
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'trigger_type': 'timer',
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'thoughts_created': 3
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})
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assert beta_timer < beta_user
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assert alpha_timer > alpha_user
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def test_params_sum_to_one(self):
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"""Alpha + beta = 1 toujours."""
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test_cases = [
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{'thoughts_created': 0},
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{'thoughts_created': 10},
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{'trigger_type': 'timer'},
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{'trigger_type': 'user', 'trigger_content': 'x' * 300},
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]
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for case in test_cases:
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alpha, beta = compute_adaptive_params(case)
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assert abs(alpha + beta - 1.0) < 0.001, f"Cas: {case}"
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class TestStateTransformer:
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"""Tests du StateTransformer (nécessite Weaviate)."""
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@pytest.fixture
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def transformer(self):
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"""Créer un transformer sans modèle (tests unitaires)."""
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return StateTransformer(embedding_model=None)
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def test_get_current_state_id(self, transformer):
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"""Test de récupération de l'ID courant."""
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# Ce test nécessite Weaviate
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state_id = transformer.get_current_state_id()
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assert isinstance(state_id, int)
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# -1 si pas d'état, sinon >= 0
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assert state_id >= -1
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@pytest.mark.skip(reason="Nécessite Weaviate avec S(0)")
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def test_get_state_vector(self, transformer):
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"""Test de récupération du vecteur d'état."""
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vector = transformer.get_state_vector(0)
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if vector is not None:
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assert len(vector) == 1024
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assert abs(np.linalg.norm(vector) - 1.0) < 0.01
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if __name__ == "__main__":
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pytest.main([__file__, "-v"])
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