diff --git a/pyproject.toml b/pyproject.toml index e223ca9..40ac6fa 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,6 +22,10 @@ dependencies = [ "structlog>=24.4.0", "defusedxml>=0.7.1", "trafilatura>=1.12.0", + "sentence-transformers>=5.2.2", + "numpy>=2.4.2", + "scipy>=1.17.0", + "networkx>=3.6.1", ] [project.optional-dependencies] @@ -91,6 +95,11 @@ module = [ "googleapiclient.*", "youtube_transcript_api.*", "trafilatura.*", + "sentence_transformers.*", + "sklearn.*", + "scipy.*", + "numpy.*", + "networkx.*", ] ignore_missing_imports = true diff --git a/src/intelstream/noosphere/__init__.py b/src/intelstream/noosphere/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/intelstream/noosphere/constants.py b/src/intelstream/noosphere/constants.py new file mode 100644 index 0000000..89bb5a5 --- /dev/null +++ b/src/intelstream/noosphere/constants.py @@ -0,0 +1,65 @@ +import enum +import math + +PHI: float = (1 + math.sqrt(5)) / 2 +GOLDEN_ANGLE: float = 2 * math.pi / (PHI**2) + +FIBONACCI_SEQ: list[int] = [1, 1, 2, 3, 5, 8, 13, 21, 34, 55] + +EMBEDDING_MODEL_MULTILINGUAL: str = "paraphrase-multilingual-MiniLM-L12-v2" +EMBEDDING_MODEL_ENGLISH: str = "all-MiniLM-L6-v2" +EMBEDDING_DIM: int = 384 + +MIN_MSG_THRESHOLD: int = 20 +OUTPUT_GAIN_RECOMPUTE_INTERVAL: float = 300.0 +HARD_COOLDOWN_SECONDS: float = 30.0 +DEFAULT_ANTHROPHONY_THRESHOLD: float = 0.15 +DEFAULT_GAIN_RATIO: float = 4.0 + +ARCHIVE_BASE_HALF_LIFE_HOURS: float = 168.0 +ARCHIVE_REFERENCE_EXTENSION: float = 1.5 +ARCHIVE_FIDELITY_FLOOR: float = 0.01 + + +class CrystalRoomMode(str, enum.Enum): + NUMBER_STATION = "number_station" + WHALE = "whale" + GHOST = "ghost" + + +class CrystalRoomState(str, enum.Enum): + OPEN = "open" + SEALED = "sealed" + BREATHING = "breathing" + + +class ComputationMode(str, enum.Enum): + SUBTRACTIVE = "subtractive" + BROADCAST = "broadcast" + RESONANT = "resonant" + STIGMERGIC = "stigmergic" + PARASITIC = "parasitic" + PARLIAMENTARY = "parliamentary" + INTEGRATIVE = "integrative" + CRYPTOBIOTIC = "cryptobiotic" + PROJECTIVE = "projective" + TOPOLOGICAL = "topological" + + +class PathologyType(str, enum.Enum): + CANCER = "non_terminating_pruning" + CYTOKINE_STORM = "receiver_saturation" + SEIZURE = "destructive_sync" + ANT_MILL = "positive_feedback_loop" + ADDICTION = "host_destructive_opt" + AUTOIMMUNE = "perpetual_non_consensus" + GROUPTHINK = "integration_no_diff" + COMA = "irreversible_suspension" + MISUNDERSTANDING = "irrecoverable_dim_loss" + SCHISM = "topological_damage" + + +class MessageClassification(str, enum.Enum): + ANTHROPHONY = "anthrophony" + BIOPHONY = "biophony" + GEOPHONY = "geophony" diff --git a/src/intelstream/noosphere/models.py b/src/intelstream/noosphere/models.py new file mode 100644 index 0000000..7c5ab8d --- /dev/null +++ b/src/intelstream/noosphere/models.py @@ -0,0 +1,130 @@ +from datetime import datetime + +from sqlalchemy import Float, ForeignKey, Integer, LargeBinary, String, Text, func +from sqlalchemy.orm import Mapped, mapped_column + +from intelstream.database.models import Base + + +class NoosphereGuildState(Base): + __tablename__ = "noosphere_guild_state" + id: Mapped[int] = mapped_column(primary_key=True) + guild_id: Mapped[str] = mapped_column(String(32), unique=True, index=True) + is_active: Mapped[bool] = mapped_column(default=True) + mode: Mapped[str] = mapped_column(String(32), default="integrative") + phi_phase: Mapped[float] = mapped_column(default=0.0) + cryptobiosis: Mapped[bool] = mapped_column(default=False) + cryptobiosis_snapshot: Mapped[str | None] = mapped_column(Text, nullable=True) + last_activity: Mapped[datetime] = mapped_column(default=func.now()) + created_at: Mapped[datetime] = mapped_column(default=func.now()) + + +class MessageEmbedding(Base): + __tablename__ = "noosphere_message_embeddings" + id: Mapped[int] = mapped_column(primary_key=True) + guild_id: Mapped[str] = mapped_column(String(32), index=True) + channel_id: Mapped[str] = mapped_column(String(32), index=True) + message_id: Mapped[str] = mapped_column(String(32), unique=True, index=True) + author_id: Mapped[str] = mapped_column(String(32), index=True) + embedding: Mapped[bytes] = mapped_column(LargeBinary) + sentiment: Mapped[float] = mapped_column(Float, default=0.0) + topic_cluster: Mapped[int | None] = mapped_column(Integer, nullable=True) + created_at: Mapped[datetime] = mapped_column(default=func.now()) + + +class EgregoreSnapshot(Base): + __tablename__ = "noosphere_egregore_snapshots" + id: Mapped[int] = mapped_column(primary_key=True) + guild_id: Mapped[str] = mapped_column(String(32), index=True) + channel_id: Mapped[str | None] = mapped_column(String(32), nullable=True, index=True) + semantic_coherence: Mapped[float] = mapped_column(Float) + topic_diversity: Mapped[float] = mapped_column(Float) + vocabulary_convergence: Mapped[float] = mapped_column(Float) + influence_score: Mapped[float] = mapped_column(Float) + created_at: Mapped[datetime] = mapped_column(default=func.now()) + + +class UserBioelectricState(Base): + __tablename__ = "noosphere_user_states" + id: Mapped[int] = mapped_column(primary_key=True) + guild_id: Mapped[str] = mapped_column(String(32), index=True) + user_id: Mapped[str] = mapped_column(String(32), index=True) + activity_frequency: Mapped[float] = mapped_column(Float) + topic_vector: Mapped[bytes] = mapped_column(LargeBinary) + sentiment_avg: Mapped[float] = mapped_column(Float, default=0.0) + engagement_level: Mapped[float] = mapped_column(Float, default=0.0) + last_active: Mapped[datetime] = mapped_column(default=func.now()) + created_at: Mapped[datetime] = mapped_column(default=func.now()) + + +class SoundscapeSnapshot(Base): + __tablename__ = "noosphere_soundscape" + id: Mapped[int] = mapped_column(primary_key=True) + guild_id: Mapped[str] = mapped_column(String(32), index=True) + geophony: Mapped[float] = mapped_column(Float) + biophony: Mapped[float] = mapped_column(Float) + anthrophony: Mapped[float] = mapped_column(Float) + bot_anthrophony: Mapped[float] = mapped_column(Float) + health_score: Mapped[float] = mapped_column(Float) + created_at: Mapped[datetime] = mapped_column(default=func.now()) + + +class AttractorSnapshot(Base): + __tablename__ = "noosphere_attractor" + id: Mapped[int] = mapped_column(primary_key=True) + guild_id: Mapped[str] = mapped_column(String(32), index=True) + fractal_dimension: Mapped[float | None] = mapped_column(Float, nullable=True) + lyapunov_exponent: Mapped[float | None] = mapped_column(Float, nullable=True) + curvature: Mapped[float | None] = mapped_column(Float, nullable=True) + created_at: Mapped[datetime] = mapped_column(default=func.now()) + + +class ArchiveEntry(Base): + __tablename__ = "noosphere_archive" + id: Mapped[int] = mapped_column(primary_key=True) + guild_id: Mapped[str] = mapped_column(String(32), index=True) + content: Mapped[str] = mapped_column(Text) + content_hash: Mapped[str] = mapped_column(String(64)) + fidelity: Mapped[float] = mapped_column(Float, default=1.0) + interaction_count: Mapped[int] = mapped_column(Integer, default=0) + last_interacted: Mapped[datetime] = mapped_column(default=func.now()) + dependencies: Mapped[str | None] = mapped_column(Text, nullable=True) + created_at: Mapped[datetime] = mapped_column(default=func.now()) + author_id: Mapped[str] = mapped_column(String(32)) + + +class ArchiveLink(Base): + __tablename__ = "noosphere_archive_links" + id: Mapped[int] = mapped_column(primary_key=True) + source_id: Mapped[int] = mapped_column(ForeignKey("noosphere_archive.id")) + target_id: Mapped[int] = mapped_column(ForeignKey("noosphere_archive.id")) + link_type: Mapped[str] = mapped_column(String(32)) + strength: Mapped[float] = mapped_column(Float, default=1.0) + created_at: Mapped[datetime] = mapped_column(default=func.now()) + + +class CrystalRoom(Base): + __tablename__ = "noosphere_crystal_rooms" + id: Mapped[int] = mapped_column(primary_key=True) + guild_id: Mapped[str] = mapped_column(String(32), index=True) + channel_id: Mapped[str] = mapped_column(String(32), unique=True) + mode: Mapped[str] = mapped_column(String(32), default="number_station") + is_sealed: Mapped[bool] = mapped_column(default=False) + tier: Mapped[str] = mapped_column(String(16), default="harmonic") + member_ids: Mapped[str] = mapped_column(Text) + sealed_at: Mapped[datetime | None] = mapped_column(nullable=True) + created_at: Mapped[datetime] = mapped_column(default=func.now()) + + +class GuildMetricsBaseline(Base): + __tablename__ = "noosphere_metrics_baseline" + id: Mapped[int] = mapped_column(primary_key=True) + guild_id: Mapped[str] = mapped_column(String(32), unique=True, index=True) + coherence_mean: Mapped[float] = mapped_column(Float, default=0.0) + coherence_var: Mapped[float] = mapped_column(Float, default=0.0) + convergence_mean: Mapped[float] = mapped_column(Float, default=0.0) + convergence_var: Mapped[float] = mapped_column(Float, default=0.0) + diversity_mean: Mapped[float] = mapped_column(Float, default=0.0) + diversity_var: Mapped[float] = mapped_column(Float, default=0.0) + sample_count: Mapped[int] = mapped_column(Integer, default=0) + updated_at: Mapped[datetime] = mapped_column(default=func.now()) diff --git a/src/intelstream/noosphere/shared/__init__.py b/src/intelstream/noosphere/shared/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/intelstream/noosphere/shared/archive_decay.py b/src/intelstream/noosphere/shared/archive_decay.py new file mode 100644 index 0000000..7e0a736 --- /dev/null +++ b/src/intelstream/noosphere/shared/archive_decay.py @@ -0,0 +1,48 @@ +from __future__ import annotations + +import math + +from intelstream.noosphere.constants import ( + ARCHIVE_BASE_HALF_LIFE_HOURS, + ARCHIVE_FIDELITY_FLOOR, + ARCHIVE_REFERENCE_EXTENSION, +) + + +def relevance_score( + entry_age_hours: float, + reference_count: int, + base_half_life: float = ARCHIVE_BASE_HALF_LIFE_HOURS, + extension_factor: float = ARCHIVE_REFERENCE_EXTENSION, + floor: float = ARCHIVE_FIDELITY_FLOOR, +) -> float: + effective_half_life = base_half_life * (extension_factor**reference_count) + if effective_half_life <= 0: + return floor + decay = 0.5 ** (entry_age_hours / effective_half_life) + return float(max(floor, decay)) + + +def compute_fidelity( + created_hours_ago: float, + interaction_timestamps_hours_ago: list[float], + reference_count: int, + base_half_life: float = ARCHIVE_BASE_HALF_LIFE_HOURS, + extension_factor: float = ARCHIVE_REFERENCE_EXTENSION, + boost: float = 0.3, + floor: float = ARCHIVE_FIDELITY_FLOOR, +) -> float: + effective_half_life = base_half_life * (extension_factor**reference_count) + if effective_half_life <= 0: + return floor + + decay_rate = math.log(2) / effective_half_life + base_fidelity = math.exp(-decay_rate * created_hours_ago) + + interaction_boost = 0.0 + for hours_since in interaction_timestamps_hours_ago: + if hours_since >= 0: + interaction_boost += boost * math.exp(-decay_rate * hours_since) + + total = base_fidelity + interaction_boost + return max(floor, min(1.0, total)) diff --git a/src/intelstream/noosphere/shared/baseline.py b/src/intelstream/noosphere/shared/baseline.py new file mode 100644 index 0000000..28055bf --- /dev/null +++ b/src/intelstream/noosphere/shared/baseline.py @@ -0,0 +1,48 @@ +from __future__ import annotations + +import math + + +class WelfordAccumulator: + """Numerically stable incremental mean/variance using Welford's algorithm.""" + + def __init__(self, mean: float = 0.0, variance: float = 0.0, count: int = 0) -> None: + self.mean = mean + self._m2 = variance * count if count > 0 else 0.0 + self.count = count + + def update(self, value: float) -> None: + self.count += 1 + delta = value - self.mean + self.mean += delta / self.count + delta2 = value - self.mean + self._m2 += delta * delta2 + + @property + def variance(self) -> float: + if self.count < 2: + return 0.0 + return self._m2 / self.count + + @property + def std(self) -> float: + return math.sqrt(max(self.variance, 1e-12)) + + def z_score(self, value: float) -> float: + if self.count < 2: + return 0.0 + s = self.std + if s < 1e-6: + return 0.0 + return (value - self.mean) / s + + @staticmethod + def sigmoid(z: float) -> float: + if z > 10: + return 1.0 + if z < -10: + return 0.0 + return 1.0 / (1.0 + math.exp(-z)) + + def normalize(self, value: float) -> float: + return self.sigmoid(self.z_score(value)) diff --git a/src/intelstream/noosphere/shared/data_models.py b/src/intelstream/noosphere/shared/data_models.py new file mode 100644 index 0000000..21cca1e --- /dev/null +++ b/src/intelstream/noosphere/shared/data_models.py @@ -0,0 +1,46 @@ +from __future__ import annotations + +import math +from dataclasses import dataclass, field +from datetime import datetime # noqa: TC003 + +import numpy as np # noqa: TC002 + +from intelstream.noosphere.constants import MessageClassification # noqa: TC001 + + +@dataclass +class ProcessedMessage: + guild_id: str + channel_id: str + user_id: str + message_id: str + content: str + timestamp: datetime + is_bot: bool + classification: MessageClassification + embedding: np.ndarray | None = field(default=None, repr=False) + topic_cluster: int | None = None + + +@dataclass +class CommunityStateVector: + guild_id: str + timestamp: datetime + semantic_coherence: float = 0.0 + vocab_convergence: float = 0.0 + topic_entropy: float = 0.0 + activity_rate: float = 0.0 + anthrophony_ratio: float = 0.0 + biophony_ratio: float = 0.0 + geophony_ratio: float = 0.0 + semantic_momentum: float = 0.0 + topic_churn: float = 0.0 + reply_depth: float = 0.0 + activity_entropy: float = 0.0 + egregore_index: float = 0.0 + sentiment_alignment: float = math.nan + interaction_modularity: float = math.nan + fractal_dimension: float = math.nan + lyapunov_exponent: float = math.nan + gromov_curvature: float = math.nan diff --git a/src/intelstream/noosphere/shared/egregore.py b/src/intelstream/noosphere/shared/egregore.py new file mode 100644 index 0000000..4d72a41 --- /dev/null +++ b/src/intelstream/noosphere/shared/egregore.py @@ -0,0 +1,74 @@ +from __future__ import annotations + +import numpy as np +import structlog + +from intelstream.noosphere.shared.baseline import WelfordAccumulator + +logger = structlog.get_logger(__name__) + +EI_WEIGHTS = (0.4, 0.3, 0.3) + + +def coherence_centroid(embeddings: np.ndarray) -> float: + if len(embeddings) < 2: + return 0.0 + centroid = embeddings.mean(axis=0) + norm = np.linalg.norm(centroid) + if norm < 1e-10: + return 0.0 + centroid = centroid / norm + sims = embeddings @ centroid + return float(sims.mean()) + + +def coherence_pairwise(embeddings: np.ndarray) -> float: + if len(embeddings) < 2: + return 0.0 + sim_matrix = embeddings @ embeddings.T + n = len(embeddings) + mask = ~np.eye(n, dtype=bool) + return float(sim_matrix[mask].mean()) + + +def topic_diversity(topic_counts: list[int]) -> float: + if not topic_counts: + return 0.0 + arr = np.array(topic_counts, dtype=float) + total = arr.sum() + if total <= 0: + return 0.0 + probs = arr / total + probs = probs[probs > 0] + n_topics = len(probs) + if n_topics <= 1: + return 0.0 + entropy = -float(np.sum(probs * np.log2(probs))) + return float(entropy / np.log2(n_topics)) + + +def vocabulary_convergence_jsd(dist_a: np.ndarray, dist_b: np.ndarray) -> float: + from scipy.spatial.distance import jensenshannon + + jsd = jensenshannon(dist_a, dist_b) ** 2 + return float(1.0 - jsd) + + +def compute_egregore_index( + coherence: float, + convergence: float, + concentration: float, + coherence_bl: WelfordAccumulator, + convergence_bl: WelfordAccumulator, + diversity_bl: WelfordAccumulator, +) -> float: + if coherence_bl.count < 2: + return 0.5 + + norm_coherence = coherence_bl.normalize(coherence) + norm_convergence = convergence_bl.normalize(convergence) + norm_concentration = 1.0 - diversity_bl.normalize(concentration) + + w1, w2, w3 = EI_WEIGHTS + raw = w1 * norm_coherence + w2 * norm_convergence + w3 * norm_concentration + return WelfordAccumulator.sigmoid((raw - 0.5) * 6) diff --git a/src/intelstream/noosphere/shared/embeddings.py b/src/intelstream/noosphere/shared/embeddings.py new file mode 100644 index 0000000..2dc4a29 --- /dev/null +++ b/src/intelstream/noosphere/shared/embeddings.py @@ -0,0 +1,56 @@ +from __future__ import annotations + +import asyncio +from functools import cached_property +from typing import TYPE_CHECKING + +import numpy as np +import structlog + +from intelstream.noosphere.constants import EMBEDDING_DIM, EMBEDDING_MODEL_MULTILINGUAL + +if TYPE_CHECKING: + from sentence_transformers import SentenceTransformer + +logger = structlog.get_logger(__name__) + + +class EmbeddingService: + def __init__(self, model_name: str = EMBEDDING_MODEL_MULTILINGUAL) -> None: + self._model_name = model_name + self._model: SentenceTransformer | None = None + + @cached_property + def dimension(self) -> int: + return EMBEDDING_DIM + + def _get_model(self) -> SentenceTransformer: + if self._model is None: + from sentence_transformers import SentenceTransformer + + logger.info("Loading embedding model", model=self._model_name) + self._model = SentenceTransformer(self._model_name) + return self._model + + async def embed(self, text: str) -> np.ndarray: + result: np.ndarray = (await self.embed_batch([text]))[0] + return result + + async def embed_batch(self, texts: list[str]) -> np.ndarray: + if not texts: + return np.empty((0, self.dimension), dtype=np.float32) + loop = asyncio.get_running_loop() + model = self._get_model() + embeddings: np.ndarray = await loop.run_in_executor( + None, lambda: model.encode(texts, normalize_embeddings=True, show_progress_bar=False) + ) + return embeddings.astype(np.float32) + + def embed_sync(self, texts: list[str]) -> np.ndarray: + if not texts: + return np.empty((0, self.dimension), dtype=np.float32) + model = self._get_model() + embeddings: np.ndarray = model.encode( + texts, normalize_embeddings=True, show_progress_bar=False + ) + return embeddings.astype(np.float32) diff --git a/src/intelstream/noosphere/shared/fibonacci.py b/src/intelstream/noosphere/shared/fibonacci.py new file mode 100644 index 0000000..81fde8b --- /dev/null +++ b/src/intelstream/noosphere/shared/fibonacci.py @@ -0,0 +1,35 @@ +from __future__ import annotations + +import math + +from intelstream.noosphere.constants import FIBONACCI_SEQ, GOLDEN_ANGLE + + +class FibonacciScheduler: + """Generates quasiperiodic intervals using Fibonacci sequence. + + Intervals cycle through Fibonacci numbers scaled by base_interval_minutes. + The starting point advances by the golden angle each cycle, preventing + exact repetition. + """ + + def __init__(self, base_interval_minutes: float = 1.0) -> None: + self._base = base_interval_minutes + self._index = 0 + self._phase = 0.0 + + def next_interval(self) -> float: + fib_value = FIBONACCI_SEQ[self._index % len(FIBONACCI_SEQ)] + interval = fib_value * self._base + self._index += 1 + self._phase = (self._phase + GOLDEN_ANGLE) % (2 * math.pi) + jitter = math.sin(self._phase) * 0.2 * interval + return max(0.5, interval + jitter) + + @property + def index(self) -> int: + return self._index + + @property + def phase(self) -> float: + return self._phase diff --git a/src/intelstream/noosphere/shared/metrics_computer.py b/src/intelstream/noosphere/shared/metrics_computer.py new file mode 100644 index 0000000..9719f78 --- /dev/null +++ b/src/intelstream/noosphere/shared/metrics_computer.py @@ -0,0 +1,170 @@ +from __future__ import annotations + +from datetime import UTC, datetime + +import numpy as np +import structlog + +from intelstream.noosphere.shared.baseline import WelfordAccumulator +from intelstream.noosphere.shared.data_models import CommunityStateVector, ProcessedMessage +from intelstream.noosphere.shared.soundscape import SoundscapeMonitor # noqa: TC001 + +logger = structlog.get_logger(__name__) + +EI_WEIGHT_COHERENCE = 0.4 +EI_WEIGHT_CONVERGENCE = 0.3 +EI_WEIGHT_CONCENTRATION = 0.3 + +MAX_MESSAGES_PER_GUILD = 5000 +MAX_EMBEDDINGS_PER_GUILD = 5000 + + +class MetricsComputer: + """Pre-computes CommunityStateVector on a schedule. + + Three-tier scheduling: + - Hourly: coherence (centroid), anthrophony ratio, activity entropy + - Daily: pairwise coherence, full state vector snapshot, baseline update + - Weekly: vocabulary convergence trends, EI trajectory + """ + + def __init__(self, soundscape: SoundscapeMonitor) -> None: + self._soundscape = soundscape + self._embeddings: dict[str, list[np.ndarray]] = {} + self._messages: dict[str, list[ProcessedMessage]] = {} + self._state_vectors: dict[str, CommunityStateVector] = {} + self._baselines: dict[str, dict[str, WelfordAccumulator]] = {} + + def ingest_message(self, message: ProcessedMessage) -> None: + guild_id = message.guild_id + msgs = self._messages.setdefault(guild_id, []) + msgs.append(message) + if len(msgs) > MAX_MESSAGES_PER_GUILD: + self._messages[guild_id] = msgs[-MAX_MESSAGES_PER_GUILD:] + + if message.embedding is not None: + embs = self._embeddings.setdefault(guild_id, []) + embs.append(message.embedding) + if len(embs) > MAX_EMBEDDINGS_PER_GUILD: + self._embeddings[guild_id] = embs[-MAX_EMBEDDINGS_PER_GUILD:] + + def get_baseline(self, guild_id: str, metric: str) -> WelfordAccumulator: + guild_baselines = self._baselines.setdefault(guild_id, {}) + return guild_baselines.setdefault(metric, WelfordAccumulator()) + + def compute_hourly(self, guild_id: str) -> CommunityStateVector: + messages = self._messages.get(guild_id, []) + embeddings = self._embeddings.get(guild_id, []) + + soundscape_state = self._soundscape.get_state(guild_id) + + coherence = self._compute_centroid_coherence(embeddings) + anthrophony_ratio = soundscape_state.anthrophony_ratio + biophony_ratio = soundscape_state.biophony_ratio + geophony_ratio = soundscape_state.geophony_ratio + activity_entropy = self._compute_activity_entropy(messages) + momentum = self._compute_semantic_momentum(embeddings) + + now = datetime.now(UTC) + vector = CommunityStateVector( + guild_id=guild_id, + timestamp=now, + semantic_coherence=coherence, + anthrophony_ratio=anthrophony_ratio, + biophony_ratio=biophony_ratio, + geophony_ratio=geophony_ratio, + activity_entropy=activity_entropy, + semantic_momentum=momentum, + ) + self._state_vectors[guild_id] = vector + return vector + + def compute_daily(self, guild_id: str) -> CommunityStateVector: + vector = self.compute_hourly(guild_id) + embeddings = self._embeddings.get(guild_id, []) + + if len(embeddings) >= 5: + pairwise = self._compute_pairwise_coherence(embeddings) + vector.semantic_coherence = pairwise + + coherence_bl = self.get_baseline(guild_id, "coherence") + coherence_bl.update(vector.semantic_coherence) + + egregore_index = self._compute_egregore_index(guild_id, vector) + vector.egregore_index = egregore_index + + self._state_vectors[guild_id] = vector + return vector + + def get_state_vector(self, guild_id: str) -> CommunityStateVector | None: + return self._state_vectors.get(guild_id) + + def _compute_centroid_coherence(self, embeddings: list[np.ndarray]) -> float: + if len(embeddings) < 2: + return 0.0 + matrix = np.stack(embeddings) + centroid = matrix.mean(axis=0) + norm = np.linalg.norm(centroid) + if norm < 1e-10: + return 0.0 + centroid = centroid / norm + sims = matrix @ centroid + return float(sims.mean()) + + def _compute_pairwise_coherence(self, embeddings: list[np.ndarray]) -> float: + if len(embeddings) < 2: + return 0.0 + matrix = np.stack(embeddings) + sim_matrix = matrix @ matrix.T + n = len(embeddings) + mask = ~np.eye(n, dtype=bool) + return float(sim_matrix[mask].mean()) + + def _compute_activity_entropy(self, messages: list[ProcessedMessage]) -> float: + if not messages: + return 0.0 + user_counts: dict[str, int] = {} + for msg in messages: + user_counts[msg.user_id] = user_counts.get(msg.user_id, 0) + 1 + + n_users = len(user_counts) + if n_users <= 1: + return 0.0 + + total = sum(user_counts.values()) + probs = np.array([c / total for c in user_counts.values()]) + probs = probs[probs > 0] + entropy = -float(np.sum(probs * np.log2(probs))) + max_entropy = np.log2(n_users) + if max_entropy < 1e-10: + return 0.0 + return float(entropy / max_entropy) + + def _compute_semantic_momentum(self, embeddings: list[np.ndarray]) -> float: + if len(embeddings) < 3: + return 0.0 + sims = [] + for i in range(len(embeddings) - 1): + sim = float(np.dot(embeddings[i], embeddings[i + 1])) + sims.append(sim) + return float(np.mean(sims)) + + def _compute_egregore_index(self, guild_id: str, vector: CommunityStateVector) -> float: + coherence_bl = self.get_baseline(guild_id, "coherence") + convergence_bl = self.get_baseline(guild_id, "convergence") + diversity_bl = self.get_baseline(guild_id, "diversity") + + if coherence_bl.count < 2: + return 0.5 + + norm_coherence = coherence_bl.normalize(vector.semantic_coherence) + norm_convergence = convergence_bl.normalize(vector.vocab_convergence) + norm_concentration = 1.0 - diversity_bl.normalize(vector.topic_entropy) + + raw = ( + EI_WEIGHT_COHERENCE * norm_coherence + + EI_WEIGHT_CONVERGENCE * norm_convergence + + EI_WEIGHT_CONCENTRATION * norm_concentration + ) + + return WelfordAccumulator.sigmoid((raw - 0.5) * 6) diff --git a/src/intelstream/noosphere/shared/output_governor.py b/src/intelstream/noosphere/shared/output_governor.py new file mode 100644 index 0000000..3393f8c --- /dev/null +++ b/src/intelstream/noosphere/shared/output_governor.py @@ -0,0 +1,93 @@ +from __future__ import annotations + +import random +import time + +import structlog + +from intelstream.noosphere.constants import ( + DEFAULT_ANTHROPHONY_THRESHOLD, + DEFAULT_GAIN_RATIO, + HARD_COOLDOWN_SECONDS, + MIN_MSG_THRESHOLD, + OUTPUT_GAIN_RECOMPUTE_INTERVAL, +) + +logger = structlog.get_logger(__name__) + + +class OutputGovernor: + """Controls all unprompted bot output via O(1) gain computation. + + Uses in-memory counters only -- no DB queries in the hot path. + Gain is recomputed every GAIN_RECOMPUTE_INTERVAL seconds from + cached message counts. + """ + + def __init__( + self, + threshold: float = DEFAULT_ANTHROPHONY_THRESHOLD, + gain_ratio: float = DEFAULT_GAIN_RATIO, + min_msg_threshold: int = MIN_MSG_THRESHOLD, + cooldown_seconds: float = HARD_COOLDOWN_SECONDS, + gain_recompute_interval: float = OUTPUT_GAIN_RECOMPUTE_INTERVAL, + ) -> None: + self._threshold = threshold + self._gain_ratio = gain_ratio + self._min_msg_threshold = min_msg_threshold + self._cooldown_seconds = cooldown_seconds + self._gain_recompute_interval = gain_recompute_interval + + self._bot_counts: dict[str, int] = {} + self._total_counts: dict[str, int] = {} + self._last_gain: dict[str, float] = {} + self._gain_updated: dict[str, float] = {} + self._last_response_time: dict[str, float] = {} + + def record_message(self, channel_id: str, *, is_bot: bool) -> None: + self._total_counts[channel_id] = self._total_counts.get(channel_id, 0) + 1 + if is_bot: + self._bot_counts[channel_id] = self._bot_counts.get(channel_id, 0) + 1 + + def get_gain(self, channel_id: str) -> float: + now = time.monotonic() + last_updated = self._gain_updated.get(channel_id, 0.0) + if now - last_updated < self._gain_recompute_interval: + return self._last_gain.get(channel_id, 1.0) + + total = self._total_counts.get(channel_id, 0) + if total < self._min_msg_threshold: + self._last_gain[channel_id] = 1.0 + self._gain_updated[channel_id] = now + return 1.0 + + bot = self._bot_counts.get(channel_id, 0) + ratio = bot / total + excess = max(0.0, ratio - self._threshold) + gain = max(0.1, 1.0 / (1.0 + excess * self._gain_ratio)) + + self._last_gain[channel_id] = gain + self._gain_updated[channel_id] = now + return gain + + def should_send(self, channel_id: str) -> bool: + now = time.monotonic() + last_response = self._last_response_time.get(channel_id, 0.0) + if now - last_response < self._cooldown_seconds: + return False + + gain = self.get_gain(channel_id) + if gain >= 1.0: + return True + + return random.random() < gain + + def record_response(self, channel_id: str) -> None: + self._last_response_time[channel_id] = time.monotonic() + + def reset_channel(self, channel_id: str) -> None: + self._bot_counts.pop(channel_id, None) + self._total_counts.pop(channel_id, None) + self._last_gain.pop(channel_id, None) + self._gain_updated.pop(channel_id, None) + self._last_response_time.pop(channel_id, None) diff --git a/src/intelstream/noosphere/shared/phi_parameter.py b/src/intelstream/noosphere/shared/phi_parameter.py new file mode 100644 index 0000000..7917719 --- /dev/null +++ b/src/intelstream/noosphere/shared/phi_parameter.py @@ -0,0 +1,69 @@ +from __future__ import annotations + +import math +from typing import ClassVar + +from intelstream.noosphere.constants import GOLDEN_ANGLE, PHI + + +class PhiParameter: + """Golden-ratio oscillator for mode balancing. + + The phase advances by the golden angle each tick. + Mode weights are derived from the phase's proximity + to Fibonacci fraction approximations of phi. + """ + + FIBONACCI_FRACTIONS: ClassVar[list[tuple[int, int]]] = [ + (1, 1), + (2, 1), + (3, 2), + (5, 3), + (8, 5), + (13, 8), + (21, 13), + (34, 21), + (55, 34), + ] + + def __init__(self) -> None: + self._phase = 0.0 + + @property + def phase(self) -> float: + return self._phase + + def advance(self) -> None: + self._phase = (self._phase + GOLDEN_ANGLE) % (2 * math.pi) + + def mode_weights(self) -> dict[str, float]: + proximity = self._fibonacci_proximity() + crystal_w = proximity**2 + quasicrystal_w = (1.0 - proximity) ** 2 + attractor_w = math.sin(self._phase) * 0.3 + 0.3 + ghost_w = math.cos(self._phase * PHI) * 0.2 + 0.2 + total = crystal_w + quasicrystal_w + attractor_w + ghost_w + if total < 1e-10: + return { + "crystal": 0.25, + "attractor": 0.25, + "quasicrystal": 0.25, + "ghost": 0.25, + } + return { + "crystal": crystal_w / total, + "attractor": attractor_w / total, + "quasicrystal": quasicrystal_w / total, + "ghost": ghost_w / total, + } + + def _fibonacci_proximity(self) -> float: + min_dist = float("inf") + for p, q in self.FIBONACCI_FRACTIONS: + frac_phase = (2 * math.pi * p / q) % (2 * math.pi) + dist = min( + abs(self._phase - frac_phase), + 2 * math.pi - abs(self._phase - frac_phase), + ) + min_dist = min(min_dist, dist) + return 1.0 - (min_dist / math.pi) diff --git a/src/intelstream/noosphere/shared/soundscape.py b/src/intelstream/noosphere/shared/soundscape.py new file mode 100644 index 0000000..14691a0 --- /dev/null +++ b/src/intelstream/noosphere/shared/soundscape.py @@ -0,0 +1,94 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import TYPE_CHECKING + +import structlog + +from intelstream.noosphere.constants import MessageClassification + +if TYPE_CHECKING: + from intelstream.noosphere.shared.data_models import ProcessedMessage + +logger = structlog.get_logger(__name__) + + +@dataclass +class SoundscapeState: + total: int = 0 + biophony: int = 0 + anthrophony: int = 0 + geophony: int = 0 + bot_anthrophony: int = 0 + unique_human_authors: set[str] | None = None + + def __post_init__(self) -> None: + if self.unique_human_authors is None: + self.unique_human_authors = set() + + @property + def biophony_ratio(self) -> float: + return self.biophony / self.total if self.total > 0 else 0.0 + + @property + def anthrophony_ratio(self) -> float: + return self.anthrophony / self.total if self.total > 0 else 0.0 + + @property + def geophony_ratio(self) -> float: + return self.geophony / self.total if self.total > 0 else 0.0 + + @property + def health_score(self) -> float: + denominator = self.biophony + self.anthrophony + if denominator == 0: + return 1.0 + return self.biophony / denominator + + @property + def voice_count(self) -> int: + return len(self.unique_human_authors) if self.unique_human_authors else 0 + + +class SoundscapeMonitor: + """Classifies messages and tracks acoustic ecology per guild.""" + + def __init__(self, bot_user_id: str | None = None) -> None: + self._bot_user_id = bot_user_id + self._states: dict[str, SoundscapeState] = {} + + def classify_message(self, message: ProcessedMessage) -> MessageClassification: + if message.is_bot: + return MessageClassification.ANTHROPHONY + return MessageClassification.BIOPHONY + + def classify_system_event(self) -> MessageClassification: + return MessageClassification.GEOPHONY + + def record_message(self, message: ProcessedMessage) -> None: + guild_id = message.guild_id + state = self._states.setdefault(guild_id, SoundscapeState()) + state.total += 1 + + classification = message.classification + if classification == MessageClassification.BIOPHONY: + state.biophony += 1 + if state.unique_human_authors is not None: + state.unique_human_authors.add(message.user_id) + elif classification == MessageClassification.ANTHROPHONY: + state.anthrophony += 1 + if self._bot_user_id and message.user_id == self._bot_user_id: + state.bot_anthrophony += 1 + elif classification == MessageClassification.GEOPHONY: + state.geophony += 1 + + def record_system_event(self, guild_id: str) -> None: + state = self._states.setdefault(guild_id, SoundscapeState()) + state.total += 1 + state.geophony += 1 + + def get_state(self, guild_id: str) -> SoundscapeState: + return self._states.get(guild_id, SoundscapeState()) + + def reset(self, guild_id: str) -> None: + self._states.pop(guild_id, None) diff --git a/tests/test_noosphere/__init__.py b/tests/test_noosphere/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_noosphere/test_archive_decay.py b/tests/test_noosphere/test_archive_decay.py new file mode 100644 index 0000000..4e481f0 --- /dev/null +++ b/tests/test_noosphere/test_archive_decay.py @@ -0,0 +1,65 @@ +from intelstream.noosphere.shared.archive_decay import compute_fidelity, relevance_score + + +class TestRelevanceScore: + def test_brand_new_entry(self) -> None: + score = relevance_score(entry_age_hours=0.0, reference_count=0) + assert abs(score - 1.0) < 1e-10 + + def test_one_half_life(self) -> None: + score = relevance_score(entry_age_hours=168.0, reference_count=0) + assert abs(score - 0.5) < 1e-5 + + def test_references_extend_half_life(self) -> None: + no_refs = relevance_score(entry_age_hours=168.0, reference_count=0) + with_refs = relevance_score(entry_age_hours=168.0, reference_count=3) + assert with_refs > no_refs + + def test_floor(self) -> None: + score = relevance_score(entry_age_hours=100000.0, reference_count=0) + assert score >= 0.01 + + def test_custom_half_life(self) -> None: + score = relevance_score(entry_age_hours=24.0, reference_count=0, base_half_life=24.0) + assert abs(score - 0.5) < 1e-5 + + +class TestComputeFidelity: + def test_new_entry_no_interactions(self) -> None: + fidelity = compute_fidelity( + created_hours_ago=0.0, + interaction_timestamps_hours_ago=[], + reference_count=0, + ) + assert abs(fidelity - 1.0) < 1e-5 + + def test_decays_over_time(self) -> None: + new = compute_fidelity(0.0, [], 0) + old = compute_fidelity(500.0, [], 0) + assert new > old + + def test_interactions_boost_fidelity(self) -> None: + without = compute_fidelity(200.0, [], 0) + with_interaction = compute_fidelity(200.0, [1.0, 5.0], 0) + assert with_interaction > without + + def test_references_slow_decay(self) -> None: + no_refs = compute_fidelity(300.0, [], 0) + with_refs = compute_fidelity(300.0, [], 5) + assert with_refs > no_refs + + def test_fidelity_capped_at_one(self) -> None: + fidelity = compute_fidelity( + created_hours_ago=0.0, + interaction_timestamps_hours_ago=[0.0, 0.0, 0.0, 0.0, 0.0], + reference_count=0, + ) + assert fidelity <= 1.0 + + def test_fidelity_floor(self) -> None: + fidelity = compute_fidelity( + created_hours_ago=1e6, + interaction_timestamps_hours_ago=[], + reference_count=0, + ) + assert fidelity >= 0.01 diff --git a/tests/test_noosphere/test_baseline.py b/tests/test_noosphere/test_baseline.py new file mode 100644 index 0000000..2ba28a1 --- /dev/null +++ b/tests/test_noosphere/test_baseline.py @@ -0,0 +1,59 @@ +from intelstream.noosphere.shared.baseline import WelfordAccumulator + + +class TestWelfordAccumulator: + def test_empty(self) -> None: + acc = WelfordAccumulator() + assert acc.count == 0 + assert acc.mean == 0.0 + assert acc.variance == 0.0 + assert acc.std > 0 # sqrt(1e-12) + + def test_single_value(self) -> None: + acc = WelfordAccumulator() + acc.update(5.0) + assert acc.count == 1 + assert acc.mean == 5.0 + assert acc.variance == 0.0 + + def test_known_sequence(self) -> None: + acc = WelfordAccumulator() + values = [2.0, 4.0, 4.0, 4.0, 5.0, 5.0, 7.0, 9.0] + for v in values: + acc.update(v) + assert acc.count == 8 + assert abs(acc.mean - 5.0) < 1e-10 + expected_var = sum((v - 5.0) ** 2 for v in values) / len(values) + assert abs(acc.variance - expected_var) < 1e-10 + + def test_z_score(self) -> None: + acc = WelfordAccumulator() + for v in [1.0, 2.0, 3.0, 4.0, 5.0]: + acc.update(v) + z = acc.z_score(3.0) + assert abs(z) < 1e-10 # mean is 3.0 + + def test_z_score_insufficient_data(self) -> None: + acc = WelfordAccumulator() + acc.update(1.0) + assert acc.z_score(5.0) == 0.0 + + def test_sigmoid(self) -> None: + assert abs(WelfordAccumulator.sigmoid(0.0) - 0.5) < 1e-10 + assert WelfordAccumulator.sigmoid(10.0) > 0.99 + assert WelfordAccumulator.sigmoid(-10.0) < 0.01 + assert WelfordAccumulator.sigmoid(100) == 1.0 + assert WelfordAccumulator.sigmoid(-100) == 0.0 + + def test_normalize(self) -> None: + acc = WelfordAccumulator() + for v in [0.0, 0.5, 1.0]: + acc.update(v) + result = acc.normalize(0.5) + assert abs(result - 0.5) < 1e-10 # mean is 0.5, so z=0, sigmoid=0.5 + + def test_init_with_existing_stats(self) -> None: + acc = WelfordAccumulator(mean=5.0, variance=2.0, count=10) + assert acc.count == 10 + assert acc.mean == 5.0 + assert abs(acc.variance - 2.0) < 1e-10 diff --git a/tests/test_noosphere/test_constants.py b/tests/test_noosphere/test_constants.py new file mode 100644 index 0000000..4b3a309 --- /dev/null +++ b/tests/test_noosphere/test_constants.py @@ -0,0 +1,58 @@ +import math + +from intelstream.noosphere.constants import ( + FIBONACCI_SEQ, + GOLDEN_ANGLE, + PHI, + ComputationMode, + CrystalRoomMode, + CrystalRoomState, + MessageClassification, + PathologyType, +) + + +class TestConstants: + def test_phi_value(self) -> None: + assert abs(PHI - 1.618033988749895) < 1e-10 + + def test_golden_angle_value(self) -> None: + expected = 2 * math.pi / (PHI**2) + assert abs(GOLDEN_ANGLE - expected) < 1e-10 + + def test_fibonacci_sequence(self) -> None: + assert FIBONACCI_SEQ == [1, 1, 2, 3, 5, 8, 13, 21, 34, 55] + for i in range(2, len(FIBONACCI_SEQ)): + assert FIBONACCI_SEQ[i] == FIBONACCI_SEQ[i - 1] + FIBONACCI_SEQ[i - 2] + + def test_computation_modes_count(self) -> None: + assert len(ComputationMode) == 10 + + def test_pathology_types_count(self) -> None: + assert len(PathologyType) == 10 + + def test_message_classification_values(self) -> None: + assert MessageClassification.ANTHROPHONY.value == "anthrophony" + assert MessageClassification.BIOPHONY.value == "biophony" + assert MessageClassification.GEOPHONY.value == "geophony" + + def test_crystal_room_mode_values(self) -> None: + assert len(CrystalRoomMode) == 3 + assert CrystalRoomMode.NUMBER_STATION.value == "number_station" + assert CrystalRoomMode.WHALE.value == "whale" + assert CrystalRoomMode.GHOST.value == "ghost" + + def test_crystal_room_state_values(self) -> None: + assert len(CrystalRoomState) == 3 + assert CrystalRoomState.OPEN.value == "open" + assert CrystalRoomState.SEALED.value == "sealed" + assert CrystalRoomState.BREATHING.value == "breathing" + + def test_enums_are_str_serializable(self) -> None: + import json + + assert json.dumps({"mode": ComputationMode.INTEGRATIVE}) == '{"mode": "integrative"}' + assert json.dumps({"cls": MessageClassification.BIOPHONY}) == '{"cls": "biophony"}' + assert json.dumps({"path": PathologyType.CANCER}) == '{"path": "non_terminating_pruning"}' + assert json.dumps({"rm": CrystalRoomMode.WHALE}) == '{"rm": "whale"}' + assert json.dumps({"rs": CrystalRoomState.SEALED}) == '{"rs": "sealed"}' diff --git a/tests/test_noosphere/test_data_models.py b/tests/test_noosphere/test_data_models.py new file mode 100644 index 0000000..7e4f031 --- /dev/null +++ b/tests/test_noosphere/test_data_models.py @@ -0,0 +1,55 @@ +import math +from datetime import UTC, datetime + +import numpy as np + +from intelstream.noosphere.constants import MessageClassification +from intelstream.noosphere.shared.data_models import CommunityStateVector, ProcessedMessage + + +class TestProcessedMessage: + def test_create(self) -> None: + msg = ProcessedMessage( + guild_id="g1", + channel_id="ch1", + user_id="u1", + message_id="m1", + content="hello", + timestamp=datetime.now(UTC), + is_bot=False, + classification=MessageClassification.BIOPHONY, + ) + assert msg.embedding is None + assert msg.topic_cluster is None + + def test_with_embedding(self) -> None: + emb = np.random.randn(384).astype(np.float32) + msg = ProcessedMessage( + guild_id="g1", + channel_id="ch1", + user_id="u1", + message_id="m1", + content="hello", + timestamp=datetime.now(UTC), + is_bot=False, + classification=MessageClassification.BIOPHONY, + embedding=emb, + ) + assert msg.embedding is not None + assert msg.embedding.shape == (384,) + + +class TestCommunityStateVector: + def test_defaults(self) -> None: + v = CommunityStateVector(guild_id="g1", timestamp=datetime.now(UTC)) + assert v.semantic_coherence == 0.0 + assert v.egregore_index == 0.0 + assert v.anthrophony_ratio == 0.0 + + def test_phase4_fields_default_to_nan(self) -> None: + v = CommunityStateVector(guild_id="g1", timestamp=datetime.now(UTC)) + assert math.isnan(v.sentiment_alignment) + assert math.isnan(v.interaction_modularity) + assert math.isnan(v.fractal_dimension) + assert math.isnan(v.lyapunov_exponent) + assert math.isnan(v.gromov_curvature) diff --git a/tests/test_noosphere/test_egregore.py b/tests/test_noosphere/test_egregore.py new file mode 100644 index 0000000..bc2d2b7 --- /dev/null +++ b/tests/test_noosphere/test_egregore.py @@ -0,0 +1,100 @@ +import numpy as np + +from intelstream.noosphere.shared.baseline import WelfordAccumulator +from intelstream.noosphere.shared.egregore import ( + coherence_centroid, + coherence_pairwise, + compute_egregore_index, + topic_diversity, +) + + +class TestCoherenceCentroid: + def test_empty(self) -> None: + assert coherence_centroid(np.empty((0, 384))) == 0.0 + + def test_single(self) -> None: + emb = np.random.randn(1, 384).astype(np.float32) + assert coherence_centroid(emb) == 0.0 + + def test_identical_embeddings(self) -> None: + v = np.random.randn(384).astype(np.float32) + v = v / np.linalg.norm(v) + embs = np.tile(v, (10, 1)) + result = coherence_centroid(embs) + assert abs(result - 1.0) < 1e-5 + + def test_orthogonal_embeddings(self) -> None: + embs = np.eye(10, 384, dtype=np.float32) + for i in range(10): + embs[i] /= np.linalg.norm(embs[i]) + result = coherence_centroid(embs) + assert result < 0.5 + + +class TestCoherencePairwise: + def test_identical(self) -> None: + v = np.random.randn(384).astype(np.float32) + v = v / np.linalg.norm(v) + embs = np.tile(v, (10, 1)) + result = coherence_pairwise(embs) + assert abs(result - 1.0) < 1e-5 + + def test_empty(self) -> None: + assert coherence_pairwise(np.empty((0, 384))) == 0.0 + + +class TestTopicDiversity: + def test_empty(self) -> None: + assert topic_diversity([]) == 0.0 + + def test_single_topic(self) -> None: + assert topic_diversity([100]) == 0.0 + + def test_uniform_distribution(self) -> None: + counts = [10, 10, 10, 10] + result = topic_diversity(counts) + assert abs(result - 1.0) < 1e-10 + + def test_skewed_distribution(self) -> None: + counts = [100, 1, 1, 1] + result = topic_diversity(counts) + assert result < 0.5 + + def test_two_topics_equal(self) -> None: + counts = [50, 50] + result = topic_diversity(counts) + assert abs(result - 1.0) < 1e-10 + + +class TestComputeEgregoreIndex: + def test_insufficient_data(self) -> None: + bl = WelfordAccumulator() + bl.update(0.5) # only one sample + result = compute_egregore_index(0.5, 0.5, 0.5, bl, bl, bl) + assert result == 0.5 + + def test_with_baseline(self) -> None: + coherence_bl = WelfordAccumulator() + convergence_bl = WelfordAccumulator() + diversity_bl = WelfordAccumulator() + for v in [0.3, 0.4, 0.5, 0.6, 0.7]: + coherence_bl.update(v) + convergence_bl.update(v) + diversity_bl.update(v) + + result = compute_egregore_index(0.5, 0.5, 0.5, coherence_bl, convergence_bl, diversity_bl) + assert 0.0 < result < 1.0 + + def test_high_coherence_increases_ei(self) -> None: + coherence_bl = WelfordAccumulator() + convergence_bl = WelfordAccumulator() + diversity_bl = WelfordAccumulator() + for v in [0.3, 0.4, 0.5, 0.6, 0.7]: + coherence_bl.update(v) + convergence_bl.update(v) + diversity_bl.update(v) + + low = compute_egregore_index(0.3, 0.5, 0.5, coherence_bl, convergence_bl, diversity_bl) + high = compute_egregore_index(0.9, 0.5, 0.5, coherence_bl, convergence_bl, diversity_bl) + assert high > low diff --git a/tests/test_noosphere/test_fibonacci.py b/tests/test_noosphere/test_fibonacci.py new file mode 100644 index 0000000..c90ed57 --- /dev/null +++ b/tests/test_noosphere/test_fibonacci.py @@ -0,0 +1,45 @@ +from intelstream.noosphere.shared.fibonacci import FibonacciScheduler + + +class TestFibonacciScheduler: + def test_first_interval(self) -> None: + scheduler = FibonacciScheduler(base_interval_minutes=1.0) + interval = scheduler.next_interval() + assert interval >= 0.5 + + def test_intervals_increase(self) -> None: + scheduler = FibonacciScheduler(base_interval_minutes=1.0) + intervals = [scheduler.next_interval() for _ in range(10)] + for i in range(1, 8): + assert intervals[i] > 0 + + def test_index_advances(self) -> None: + scheduler = FibonacciScheduler() + assert scheduler.index == 0 + scheduler.next_interval() + assert scheduler.index == 1 + + def test_phase_advances(self) -> None: + scheduler = FibonacciScheduler() + assert scheduler.phase == 0.0 + scheduler.next_interval() + assert scheduler.phase > 0.0 + + def test_minimum_interval(self) -> None: + scheduler = FibonacciScheduler(base_interval_minutes=0.01) + for _ in range(20): + interval = scheduler.next_interval() + assert interval >= 0.5 + + def test_base_interval_scaling(self) -> None: + slow = FibonacciScheduler(base_interval_minutes=10.0) + fast = FibonacciScheduler(base_interval_minutes=1.0) + slow_first = slow.next_interval() + fast_first = fast.next_interval() + assert slow_first > fast_first + + def test_wraps_around_sequence(self) -> None: + scheduler = FibonacciScheduler(base_interval_minutes=1.0) + for _ in range(15): + interval = scheduler.next_interval() + assert interval > 0 diff --git a/tests/test_noosphere/test_metrics_computer.py b/tests/test_noosphere/test_metrics_computer.py new file mode 100644 index 0000000..b19e576 --- /dev/null +++ b/tests/test_noosphere/test_metrics_computer.py @@ -0,0 +1,139 @@ +from datetime import UTC, datetime + +import numpy as np + +from intelstream.noosphere.constants import MessageClassification +from intelstream.noosphere.shared.data_models import ProcessedMessage +from intelstream.noosphere.shared.metrics_computer import ( + MAX_EMBEDDINGS_PER_GUILD, + MAX_MESSAGES_PER_GUILD, + MetricsComputer, +) +from intelstream.noosphere.shared.soundscape import SoundscapeMonitor + + +def _make_message( + guild_id: str = "g1", + user_id: str = "u1", + is_bot: bool = False, + embedding: np.ndarray | None = None, +) -> ProcessedMessage: + return ProcessedMessage( + guild_id=guild_id, + channel_id="ch1", + user_id=user_id, + message_id=f"m_{user_id}_{id(embedding)}", + content="test", + timestamp=datetime.now(UTC), + is_bot=is_bot, + classification=MessageClassification.BIOPHONY + if not is_bot + else MessageClassification.ANTHROPHONY, + embedding=embedding, + ) + + +class TestMetricsComputer: + def test_empty_guild(self) -> None: + soundscape = SoundscapeMonitor() + mc = MetricsComputer(soundscape) + vector = mc.compute_hourly("g1") + assert vector.semantic_coherence == 0.0 + assert vector.activity_entropy == 0.0 + + def test_ingest_and_compute(self) -> None: + soundscape = SoundscapeMonitor() + mc = MetricsComputer(soundscape) + + rng = np.random.default_rng(42) + base_vec = rng.standard_normal(384).astype(np.float32) + base_vec /= np.linalg.norm(base_vec) + + for i in range(10): + noise = rng.standard_normal(384).astype(np.float32) * 0.1 + emb = base_vec + noise + emb = emb / np.linalg.norm(emb) + msg = _make_message(user_id=f"u{i}", embedding=emb) + mc.ingest_message(msg) + + vector = mc.compute_hourly("g1") + assert vector.semantic_coherence > 0.5 + + def test_activity_entropy_single_user(self) -> None: + soundscape = SoundscapeMonitor() + mc = MetricsComputer(soundscape) + for _ in range(5): + mc.ingest_message(_make_message(user_id="u1")) + vector = mc.compute_hourly("g1") + assert vector.activity_entropy == 0.0 + + def test_activity_entropy_uniform(self) -> None: + soundscape = SoundscapeMonitor() + mc = MetricsComputer(soundscape) + for i in range(5): + mc.ingest_message(_make_message(user_id=f"u{i}")) + vector = mc.compute_hourly("g1") + assert abs(vector.activity_entropy - 1.0) < 1e-5 + + def test_semantic_momentum(self) -> None: + soundscape = SoundscapeMonitor() + mc = MetricsComputer(soundscape) + + rng = np.random.default_rng(42) + v = rng.standard_normal(384).astype(np.float32) + v /= np.linalg.norm(v) + for i in range(10): + mc.ingest_message(_make_message(user_id=f"u{i}", embedding=v.copy())) + + vector = mc.compute_hourly("g1") + assert abs(vector.semantic_momentum - 1.0) < 1e-5 + + def test_daily_updates_baseline(self) -> None: + soundscape = SoundscapeMonitor() + mc = MetricsComputer(soundscape) + + rng = np.random.default_rng(42) + for i in range(10): + emb = rng.standard_normal(384).astype(np.float32) + emb /= np.linalg.norm(emb) + mc.ingest_message(_make_message(user_id=f"u{i}", embedding=emb)) + + mc.compute_daily("g1") + bl = mc.get_baseline("g1", "coherence") + assert bl.count == 1 + + def test_egregore_index_default(self) -> None: + soundscape = SoundscapeMonitor() + mc = MetricsComputer(soundscape) + + rng = np.random.default_rng(42) + for i in range(10): + emb = rng.standard_normal(384).astype(np.float32) + emb /= np.linalg.norm(emb) + mc.ingest_message(_make_message(user_id=f"u{i}", embedding=emb)) + + vector = mc.compute_daily("g1") + assert vector.egregore_index == 0.5 + + def test_get_state_vector(self) -> None: + soundscape = SoundscapeMonitor() + mc = MetricsComputer(soundscape) + assert mc.get_state_vector("g1") is None + mc.compute_hourly("g1") + assert mc.get_state_vector("g1") is not None + + def test_memory_bounds_messages(self) -> None: + soundscape = SoundscapeMonitor() + mc = MetricsComputer(soundscape) + for i in range(MAX_MESSAGES_PER_GUILD + 100): + mc.ingest_message(_make_message(user_id=f"u{i}")) + assert len(mc._messages["g1"]) == MAX_MESSAGES_PER_GUILD + + def test_memory_bounds_embeddings(self) -> None: + soundscape = SoundscapeMonitor() + mc = MetricsComputer(soundscape) + rng = np.random.default_rng(42) + for i in range(MAX_EMBEDDINGS_PER_GUILD + 100): + emb = rng.standard_normal(384).astype(np.float32) + mc.ingest_message(_make_message(user_id=f"u{i}", embedding=emb)) + assert len(mc._embeddings["g1"]) == MAX_EMBEDDINGS_PER_GUILD diff --git a/tests/test_noosphere/test_models.py b/tests/test_noosphere/test_models.py new file mode 100644 index 0000000..5c38483 --- /dev/null +++ b/tests/test_noosphere/test_models.py @@ -0,0 +1,164 @@ +import pytest +from sqlalchemy import select +from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine + +from intelstream.database.models import Base +from intelstream.noosphere.models import ( + ArchiveEntry, + ArchiveLink, + EgregoreSnapshot, + GuildMetricsBaseline, + MessageEmbedding, + NoosphereGuildState, +) + + +@pytest.fixture +async def session(): + engine = create_async_engine("sqlite+aiosqlite:///:memory:") + async with engine.begin() as conn: + await conn.run_sync(Base.metadata.create_all) + async_session = async_sessionmaker(engine, class_=AsyncSession, expire_on_commit=False) + async with async_session() as session: + yield session + await engine.dispose() + + +class TestNoosphereGuildState: + async def test_create_guild_state(self, session: AsyncSession) -> None: + state = NoosphereGuildState(guild_id="123", mode="integrative", phi_phase=0.5) + session.add(state) + await session.commit() + + result = await session.execute( + select(NoosphereGuildState).where(NoosphereGuildState.guild_id == "123") + ) + saved = result.scalar_one() + assert saved.guild_id == "123" + assert saved.mode == "integrative" + assert saved.phi_phase == 0.5 + assert saved.is_active is True + assert saved.cryptobiosis is False + + +class TestMessageEmbedding: + async def test_create_embedding(self, session: AsyncSession) -> None: + import numpy as np + + emb_data = np.random.randn(384).astype(np.float32).tobytes() + embedding = MessageEmbedding( + guild_id="123", + channel_id="456", + message_id="789", + author_id="111", + embedding=emb_data, + sentiment=0.5, + ) + session.add(embedding) + await session.commit() + + result = await session.execute( + select(MessageEmbedding).where(MessageEmbedding.message_id == "789") + ) + saved = result.scalar_one() + assert saved.guild_id == "123" + assert saved.sentiment == 0.5 + restored = np.frombuffer(saved.embedding, dtype=np.float32) + assert restored.shape == (384,) + + +class TestEgregoreSnapshot: + async def test_create_snapshot(self, session: AsyncSession) -> None: + snapshot = EgregoreSnapshot( + guild_id="123", + semantic_coherence=0.6, + topic_diversity=0.8, + vocabulary_convergence=0.4, + influence_score=0.1, + ) + session.add(snapshot) + await session.commit() + + result = await session.execute( + select(EgregoreSnapshot).where(EgregoreSnapshot.guild_id == "123") + ) + saved = result.scalar_one() + assert saved.semantic_coherence == 0.6 + + async def test_channel_scoped_snapshot(self, session: AsyncSession) -> None: + snapshot = EgregoreSnapshot( + guild_id="123", + channel_id="456", + semantic_coherence=0.7, + topic_diversity=0.5, + vocabulary_convergence=0.3, + influence_score=0.2, + ) + session.add(snapshot) + await session.commit() + + result = await session.execute( + select(EgregoreSnapshot).where(EgregoreSnapshot.channel_id == "456") + ) + saved = result.scalar_one() + assert saved.channel_id == "456" + + +class TestArchiveEntry: + async def test_create_entry(self, session: AsyncSession) -> None: + entry = ArchiveEntry( + guild_id="123", + content="Test knowledge", + content_hash="abc123", + fidelity=1.0, + author_id="user1", + ) + session.add(entry) + await session.commit() + + result = await session.execute(select(ArchiveEntry).where(ArchiveEntry.guild_id == "123")) + saved = result.scalar_one() + assert saved.content == "Test knowledge" + assert saved.fidelity == 1.0 + + +class TestArchiveLink: + async def test_create_link(self, session: AsyncSession) -> None: + entry1 = ArchiveEntry(guild_id="123", content="A", content_hash="a1", author_id="u1") + entry2 = ArchiveEntry(guild_id="123", content="B", content_hash="b2", author_id="u2") + session.add_all([entry1, entry2]) + await session.flush() + + link = ArchiveLink( + source_id=entry1.id, + target_id=entry2.id, + link_type="supports", + strength=0.8, + ) + session.add(link) + await session.commit() + + result = await session.execute( + select(ArchiveLink).where(ArchiveLink.link_type == "supports") + ) + saved = result.scalar_one() + assert saved.strength == 0.8 + + +class TestGuildMetricsBaseline: + async def test_create_baseline(self, session: AsyncSession) -> None: + baseline = GuildMetricsBaseline( + guild_id="123", + coherence_mean=0.5, + coherence_var=0.01, + sample_count=10, + ) + session.add(baseline) + await session.commit() + + result = await session.execute( + select(GuildMetricsBaseline).where(GuildMetricsBaseline.guild_id == "123") + ) + saved = result.scalar_one() + assert saved.coherence_mean == 0.5 + assert saved.sample_count == 10 diff --git a/tests/test_noosphere/test_output_governor.py b/tests/test_noosphere/test_output_governor.py new file mode 100644 index 0000000..ae0b901 --- /dev/null +++ b/tests/test_noosphere/test_output_governor.py @@ -0,0 +1,65 @@ +from intelstream.noosphere.shared.output_governor import OutputGovernor + + +class TestOutputGovernor: + def test_gain_defaults_to_one(self) -> None: + gov = OutputGovernor() + assert gov.get_gain("ch1") == 1.0 + + def test_gain_stays_one_below_threshold(self) -> None: + gov = OutputGovernor(min_msg_threshold=5) + for _ in range(3): + gov.record_message("ch1", is_bot=False) + gov.record_message("ch1", is_bot=True) + assert gov.get_gain("ch1") == 1.0 + + def test_gain_reduces_when_bot_dominates(self) -> None: + gov = OutputGovernor(min_msg_threshold=5, gain_recompute_interval=0) + for _ in range(5): + gov.record_message("ch1", is_bot=True) + for _ in range(5): + gov.record_message("ch1", is_bot=False) + gain = gov.get_gain("ch1") + assert gain < 1.0 + + def test_gain_minimum_floor(self) -> None: + gov = OutputGovernor(min_msg_threshold=5, gain_recompute_interval=0) + for _ in range(50): + gov.record_message("ch1", is_bot=True) + gain = gov.get_gain("ch1") + assert gain >= 0.1 + + def test_should_send_respects_cooldown(self) -> None: + gov = OutputGovernor(cooldown_seconds=10.0) + gov.record_response("ch1") + assert gov.should_send("ch1") is False + + def test_should_send_after_cooldown(self) -> None: + gov = OutputGovernor(cooldown_seconds=0.0) + gov.record_response("ch1") + assert gov.should_send("ch1") is True + + def test_reset_channel(self) -> None: + gov = OutputGovernor(min_msg_threshold=5, gain_recompute_interval=0) + for _ in range(10): + gov.record_message("ch1", is_bot=True) + gov.reset_channel("ch1") + assert gov.get_gain("ch1") == 1.0 + + def test_gain_cached_within_interval(self) -> None: + gov = OutputGovernor(min_msg_threshold=5, gain_recompute_interval=300) + for _ in range(20): + gov.record_message("ch1", is_bot=False) + gain1 = gov.get_gain("ch1") + for _ in range(20): + gov.record_message("ch1", is_bot=True) + gain2 = gov.get_gain("ch1") + assert gain1 == gain2 + + def test_independent_channels(self) -> None: + gov = OutputGovernor(min_msg_threshold=5, gain_recompute_interval=0) + for _ in range(10): + gov.record_message("ch1", is_bot=True) + for _ in range(10): + gov.record_message("ch2", is_bot=False) + assert gov.get_gain("ch1") < gov.get_gain("ch2") diff --git a/tests/test_noosphere/test_phi_parameter.py b/tests/test_noosphere/test_phi_parameter.py new file mode 100644 index 0000000..2373953 --- /dev/null +++ b/tests/test_noosphere/test_phi_parameter.py @@ -0,0 +1,49 @@ +import math + +from intelstream.noosphere.shared.phi_parameter import PhiParameter + + +class TestPhiParameter: + def test_initial_phase(self) -> None: + phi = PhiParameter() + assert phi.phase == 0.0 + + def test_advance_changes_phase(self) -> None: + phi = PhiParameter() + phi.advance() + assert phi.phase > 0.0 + + def test_phase_stays_in_range(self) -> None: + phi = PhiParameter() + for _ in range(1000): + phi.advance() + assert 0.0 <= phi.phase < 2 * math.pi + + def test_mode_weights_sum_to_one(self) -> None: + phi = PhiParameter() + for _ in range(20): + phi.advance() + weights = phi.mode_weights() + total = sum(weights.values()) + assert abs(total - 1.0) < 1e-10 + + def test_mode_weights_keys(self) -> None: + phi = PhiParameter() + weights = phi.mode_weights() + assert set(weights.keys()) == {"crystal", "attractor", "quasicrystal", "ghost"} + + def test_mode_weights_non_negative(self) -> None: + phi = PhiParameter() + for _ in range(50): + phi.advance() + weights = phi.mode_weights() + for w in weights.values(): + assert w >= 0.0 + + def test_never_repeats_exactly(self) -> None: + phi = PhiParameter() + phases = [] + for _ in range(100): + phi.advance() + phases.append(phi.phase) + assert len({round(p, 10) for p in phases}) == 100 diff --git a/tests/test_noosphere/test_soundscape.py b/tests/test_noosphere/test_soundscape.py new file mode 100644 index 0000000..953aa2a --- /dev/null +++ b/tests/test_noosphere/test_soundscape.py @@ -0,0 +1,97 @@ +from datetime import UTC, datetime + +from intelstream.noosphere.constants import MessageClassification +from intelstream.noosphere.shared.data_models import ProcessedMessage +from intelstream.noosphere.shared.soundscape import SoundscapeMonitor, SoundscapeState + + +def _make_message( + guild_id: str = "g1", + user_id: str = "u1", + is_bot: bool = False, + classification: MessageClassification = MessageClassification.BIOPHONY, +) -> ProcessedMessage: + return ProcessedMessage( + guild_id=guild_id, + channel_id="ch1", + user_id=user_id, + message_id="m1", + content="hello", + timestamp=datetime.now(UTC), + is_bot=is_bot, + classification=classification, + ) + + +class TestSoundscapeState: + def test_empty_state(self) -> None: + state = SoundscapeState() + assert state.biophony_ratio == 0.0 + assert state.anthrophony_ratio == 0.0 + assert state.health_score == 1.0 + assert state.voice_count == 0 + + def test_health_score_all_human(self) -> None: + state = SoundscapeState(total=10, biophony=10) + assert state.health_score == 1.0 + + def test_health_score_mixed(self) -> None: + state = SoundscapeState(total=10, biophony=7, anthrophony=3) + assert abs(state.health_score - 0.7) < 1e-10 + + +class TestSoundscapeMonitor: + def test_classify_human_message(self) -> None: + monitor = SoundscapeMonitor() + msg = _make_message(is_bot=False) + assert monitor.classify_message(msg) == MessageClassification.BIOPHONY + + def test_classify_bot_message(self) -> None: + monitor = SoundscapeMonitor() + msg = _make_message(is_bot=True) + assert monitor.classify_message(msg) == MessageClassification.ANTHROPHONY + + def test_record_human_messages(self) -> None: + monitor = SoundscapeMonitor() + for i in range(5): + msg = _make_message(user_id=f"u{i}") + monitor.record_message(msg) + + state = monitor.get_state("g1") + assert state.total == 5 + assert state.biophony == 5 + assert state.voice_count == 5 + + def test_record_bot_messages(self) -> None: + monitor = SoundscapeMonitor(bot_user_id="bot1") + msg = _make_message( + user_id="bot1", + is_bot=True, + classification=MessageClassification.ANTHROPHONY, + ) + monitor.record_message(msg) + + state = monitor.get_state("g1") + assert state.anthrophony == 1 + assert state.bot_anthrophony == 1 + + def test_record_system_event(self) -> None: + monitor = SoundscapeMonitor() + monitor.record_system_event("g1") + state = monitor.get_state("g1") + assert state.geophony == 1 + assert state.total == 1 + + def test_reset(self) -> None: + monitor = SoundscapeMonitor() + monitor.record_message(_make_message()) + monitor.reset("g1") + state = monitor.get_state("g1") + assert state.total == 0 + + def test_separate_guilds(self) -> None: + monitor = SoundscapeMonitor() + monitor.record_message(_make_message(guild_id="g1")) + monitor.record_message(_make_message(guild_id="g2")) + assert monitor.get_state("g1").total == 1 + assert monitor.get_state("g2").total == 1 diff --git a/uv.lock b/uv.lock index 7ebc22d..9d81e6f 100644 --- a/uv.lock +++ b/uv.lock @@ -415,6 +415,18 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/0a/4c/925909008ed5a988ccbb72dcc897407e5d6d3bd72410d69e051fc0c14647/charset_normalizer-3.4.4-py3-none-any.whl", hash = "sha256:7a32c560861a02ff789ad905a2fe94e3f840803362c84fecf1851cb4cf3dc37f", size = 53402, upload-time = 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