feat: complete output schema + signal processor integration
- EventFlag: full spec Section 8.4 compliance * Fixed duplicate confidence kwarg * FlagType mapping updated for core EventType values (hack, whale, listing, etc.) - VelocityComputer: hype_velocity/pub_velocity now return -100 to +100 - SignalProcessor: * Populates contributing_events (fear_driver, greed_driver, velocity_driver) * EventFlag generation with all spec fields: detail_factor, base_impact, t_zero, decay_remaining, half_life_minutes, impact_duration_minutes, direction, is_scheduled, triggered_at, sources, details_extracted, flag_type, flags * _compute_detail_factor implementation per spec Section 6.1 * _map_event_to_flag_type covers core EventType values - ProcessedItem: added raw_text field (required for detail_factor) - NLP pipeline: passes raw_text when creating ProcessedItem - ScoringEngine: populates contributing_events at market/industry levels - All 46 core NLP unit tests pass - All 19 core crypto semantic tests + 6 calibration scenarios pass - Full pipeline integration test runs successfully with real e5-large-v2 encoder
This commit is contained in:
Codex
@@ -129,6 +129,7 @@ class NLPProcessingPipeline:
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source_type=payload.source_type.value,
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ingest_ts=payload.ingest_ts,
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publish_ts=payload.publish_ts,
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raw_text=payload.raw_text,
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entities=entities,
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sentiment_per_asset=sentiment_results,
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emotions_per_asset=emotion_results,
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@@ -109,6 +109,7 @@ class ProcessedItem(BaseModel):
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source_type: str
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ingest_ts: float
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publish_ts: Optional[float]
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raw_text: str = Field(..., description="Full normalized text content (from NormalizedPayload)")
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# NLP results
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entities: List[EntityExtraction] = Field(default_factory=list)
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@@ -146,6 +146,9 @@ class ScoringEngine:
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asset_signals, industry_signals
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)
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# Populate contributing_events at industry and market levels
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self._populate_contributing_events(market_signal, industry_signals)
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return SentimentOutput(
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timestamp=time.time(),
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market=market_signal,
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@@ -153,6 +156,33 @@ class ScoringEngine:
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assets=asset_signals
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)
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def _populate_contributing_events(self, market_signal: MarketSentiment, industry_signals: Dict[str, IndustrySentiment]) -> None:
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"""Populate contributing_events at market and industry levels"""
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# Market-level: aggregate top drivers across all assets
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fear_drivers = {}
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greed_drivers = {}
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velocity_drivers = {}
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for signal in market_signal.industry_breakdown.values():
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for k, v in signal.contributing_events.items():
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if k == "fear_driver":
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fear_drivers[v] = fear_drivers.get(v, 0) + 1
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elif k == "greed_driver":
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greed_drivers[v] = greed_drivers.get(v, 0) + 1
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elif k == "velocity_driver":
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velocity_drivers[v] = velocity_drivers.get(v, 0) + 1
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if fear_drivers:
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market_signal.contributing_events["fear_driver"] = max(fear_drivers, key=fear_drivers.get)
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if greed_drivers:
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market_signal.contributing_events["greed_driver"] = max(greed_drivers, key=greed_drivers.get)
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if velocity_drivers:
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market_signal.contributing_events["velocity_driver"] = max(velocity_drivers, key=velocity_drivers.get)
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# Industry-level: aggregate from assets
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# (Already done in aggregator.aggregate_industries)
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pass
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async def process_batch(
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self,
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items: List[ProcessedItem]
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@@ -81,6 +81,9 @@ class SignalProcessor:
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self.settings.scoring.parameters.fear_state.halflife_minutes
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)
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# Determine contributing events for interpretability
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contributing_events = self._determine_contributing_events(item, fear_state, greed_state)
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# Build asset sentiment
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asset_signals[asset_id] = AssetSentiment(
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asset_id=asset_id,
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@@ -100,7 +103,8 @@ class SignalProcessor:
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velocity=velocity,
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last_update_ts=item.processed_ts,
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contributing_sources=1,
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decay_factor=decay_factor
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decay_factor=decay_factor,
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contributing_events=contributing_events
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)
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# Update history
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@@ -178,16 +182,49 @@ class SignalProcessor:
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last_update_ts=item.processed_ts
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)
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def _compute_event_flags(self, asset_id: str, events: List[EventClassification]) -> List[EventFlag]:
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"""Convert event classifications to event flags"""
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def _compute_event_flags(self, asset_id: str, events: List[EventClassification], item: ProcessedItem) -> List[EventFlag]:
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"""Convert event classifications to event flags - per spec Section 8.4"""
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from sentiment_engine.schemas.output import EventFlag, FlagType
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flags = []
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for event in events:
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if asset_id in event.assets_involved or "MARKET" in event.assets_involved:
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# Determine flag type from event type
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flag_type = self._map_event_to_flag_type(event.event_type.value)
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# Determine sub-flags
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sub_flags = self._get_sub_flags(event.event_type.value)
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# Determine direction
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direction = self._get_event_direction(event.event_type.value)
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# Get base impact from catalogue
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base_impact = self._get_base_impact(event.event_type.value)
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# Get half-life and duration
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half_life = self._get_half_life(event.event_type.value)
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duration = self._get_impact_duration(event.event_type.value)
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flags.append(EventFlag(
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event_type=event.event_type.value,
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asset=asset_id,
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industry=self._get_asset_industry(asset_id),
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value=event.severity * 100,
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confidence=event.confidence,
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source_credibility=item.credibility.composite,
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num_sources=1, # Would be from fusion
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detail_factor=self._compute_detail_factor(item),
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base_impact=base_impact,
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t_zero=event.t_zero if hasattr(event, 't_zero') else item.publish_ts,
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decay_remaining=1.0,
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half_life_minutes=half_life,
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impact_duration_minutes=duration,
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direction=direction,
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is_scheduled=self._is_scheduled_event(event.event_type.value),
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triggered_at=datetime.now().timestamp(),
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sources=[item.source_id],
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details_extracted=event.key_details,
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flag_type=flag_type,
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flags=sub_flags,
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# Legacy compat (populated via validators)
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asset_id=asset_id,
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strength=event.severity * 100,
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confidence=event.confidence,
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first_seen_ts=datetime.now().timestamp(),
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last_seen_ts=datetime.now().timestamp(),
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source_count=1,
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@@ -195,6 +232,284 @@ class SignalProcessor:
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))
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return flags
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def _determine_contributing_events(self, item: ProcessedItem, fear_state: float, greed_state: float) -> Dict[str, str]:
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"""Determine top events driving each state metric"""
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contributing = {}
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# Fear driver
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fear_events = [e for e in item.events if e.event_type.value in ["hack", "liquidation", "regulatory", "manipulation", "delisting", "security_hack", "bankruptcy"]]
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if fear_events:
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top_fear = max(fear_events, key=lambda e: e.severity)
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contributing["fear_driver"] = top_fear.event_type.value
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# Greed driver
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greed_events = [e for e in item.events if e.event_type.value in ["listing", "upgrade", "partnership", "whale", "whale_accumulation", "etp_approval"]]
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if greed_events:
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top_greed = max(greed_events, key=lambda e: e.severity)
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contributing["greed_driver"] = top_greed.event_type.value
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# Velocity driver
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velocity_events = [e for e in item.events if e.event_type.value in ["viral_social_post", "breaking_news", "pump_coordination", "rumor_unconfirmed"]]
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if velocity_events:
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top_vel = max(velocity_events, key=lambda e: e.severity)
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contributing["velocity_driver"] = top_vel.event_type.value
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return contributing
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def _map_event_to_flag_type(self, event_type: str) -> 'FlagType':
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"""Map event type to FLAG_TYPE_FOR_EVENT"""
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from sentiment_engine.schemas.output import FlagType
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mapping = {
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# Core EventType values
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"listing": FlagType.FLAG_LISTING,
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"delisting": FlagType.FLAG_DELISTING,
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"hack": FlagType.FLAG_SECURITY_AUDIT_FAIL,
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"regulatory": FlagType.FLAG_REG_ENFORCEMENT,
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"governance": FlagType.FLAG_GOV_PROPOSAL_NEW,
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"upgrade": FlagType.FLAG_PROTOCOL_UPGRADE,
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"partnership": FlagType.FLAG_VERBAL_PARTNERSHIP,
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"earnings": FlagType.FLAG_VERBAL_EARNINGS_BEAT,
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"macro": FlagType.FLAG_CPI_RELEASE,
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"liquidation": FlagType.FLAG_TOKEN_UNLOCK, # closest match
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"whale": FlagType.FLAG_WHALE_ACCUMULATION,
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"manipulation": FlagType.FLAG_COORDINATED_MANIPULATION,
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# Extended catalogue types
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"token_unlock": FlagType.FLAG_TOKEN_UNLOCK,
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"token_burn": FlagType.FLAG_TOKEN_BURN,
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"token_mint": FlagType.FLAG_TOKEN_MINT,
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"staking_reward": FlagType.FLAG_STAKING_REWARD,
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"staking_slashing": FlagType.FLAG_STAKING_SLASHING,
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"hard_fork": FlagType.FLAG_HARD_FORK,
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"soft_fork": FlagType.FLAG_SOFT_FORK,
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"protocol_upgrade": FlagType.FLAG_PROTOCOL_UPGRADE,
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"airdrop": FlagType.FLAG_AIRDROP,
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"bridge_integration": FlagType.FLAG_BRIDGE_INTEGRATION,
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"api_deprecation": FlagType.FLAG_API_DEPRECATION,
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"api_limit_change": FlagType.FLAG_API_LIMIT_CHANGE,
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"feature_release": FlagType.FLAG_FEATURE_RELEASE,
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"performance_degradation": FlagType.FLAG_PERFORMANCE_DEGRADATION,
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"security_audit_pass": FlagType.FLAG_SECURITY_AUDIT_PASS,
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"security_audit_fail": FlagType.FLAG_SECURITY_AUDIT_FAIL,
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"gov_proposal_new": FlagType.FLAG_GOV_PROPOSAL_NEW,
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"gov_vote_success": FlagType.FLAG_GOV_VOTE_SUCCESS,
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"gov_vote_failed": FlagType.FLAG_GOV_VOTE_FAILED,
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"gov_vote_ran_away": FlagType.FLAG_GOV_VOTE_RAN_AWAY,
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"gov_quorum_miss": FlagType.FLAG_GOV_QUORUM_MISS,
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"dao_deployment": FlagType.FLAG_DAO_DEPLOYMENT,
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"gov_delay_change": FlagType.FLAG_GOV_DELAY_CHANGE,
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"halving": FlagType.FLAG_HALVING,
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"etp_approval": FlagType.FLAG_ETP_APPROVAL,
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"etp_rejection": FlagType.FLAG_ETP_REJECTION,
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"whale_accumulation": FlagType.FLAG_WHALE_ACCUMULATION,
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"whale_distribution": FlagType.FLAG_WHALE_DISTRIBUTION,
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"exchange_halt": FlagType.FLAG_EXCHANGE_HALT,
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"withdrawal_suspend": FlagType.FLAG_WITHDRAWAL_SUSPEND,
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"liquidity_migration": FlagType.FLAG_LIQUIDITY_MIGRATION,
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"mm_program_change": FlagType.FLAG_MM_PROGRAM_CHANGE,
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"reg_clarity_positive": FlagType.FLAG_REG_CLARITY_POSITIVE,
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"reg_clarity_negative": FlagType.FLAG_REG_CLARITY_NEGATIVE,
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"reg_enforcement": FlagType.FLAG_REG_ENFORCEMENT,
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"reg_investigation": FlagType.FLAG_REG_INVESTIGATION,
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"reg_compliance_issue": FlagType.FLAG_REG_COMPLIANCE_ISSUE,
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"tax_treatment_change": FlagType.FLAG_TAX_TREATMENT_CHANGE,
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"litigation_filed": FlagType.FLAG_LITIGATION_FILED,
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"litigation_settled": FlagType.FLAG_LITIGATION_SETTLED,
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"bankruptcy": FlagType.FLAG_BANKRUPTCY,
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"default": FlagType.FLAG_DEFAULT,
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"pump_coordination": FlagType.FLAG_PUMP_COORDINATION,
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"promotion": FlagType.FLAG_PROMOTION,
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"criticism": FlagType.FLAG_CRITICISM,
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"echo_chamber": FlagType.FLAG_ECHO_CHAMBER,
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"bot_activity": FlagType.FLAG_BOT_ACTIVITY,
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"fear_keyword_spike": FlagType.FLAG_FEAR_KEYWORD_SPIKE,
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"greed_keyword_spike": FlagType.FLAG_GREED_KEYWORD_SPIKE,
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"panic_keyword_spike": FlagType.FLAG_PANIC_KEYWORD_SPIKE,
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"uncertainty_keyword_spike": FlagType.FLAG_UNCERTAINTY_KEYWORD_SPIKE,
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"confidence_keyword_spike": FlagType.FLAG_CONFIDENCE_KEYWORD_SPIKE,
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"fed_rate_cut": FlagType.FLAG_FED_RATE_CUT,
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"fed_rate_hike": FlagType.FLAG_FED_RATE_HIKE,
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"cpi_release": FlagType.FLAG_CPI_RELEASE,
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"cpi_surprise_high": FlagType.FLAG_CPI_SURPRISE_HIGH,
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"cpi_surprise_low": FlagType.FLAG_CPI_SURPRISE_LOW,
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"gdp_release": FlagType.FLAG_GDP_RELEASE,
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"employment_release": FlagType.FLAG_EMPLOYMENT_RELEASE,
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"central_bank_speech_hawkish": FlagType.FLAG_CENTRAL_BANK_SPEECH_HAWKISH,
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"central_bank_speech_dove": FlagType.FLAG_CENTRAL_BANK_SPEECH_DOVE,
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"geopolitical_tension": FlagType.FLAG_GEOPOLITICAL_TENSION,
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"geopolitical_resolution": FlagType.FLAG_GEOPOLITICAL_RESOLUTION,
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"echo_chamber_detected": FlagType.FLAG_ECHO_CHAMBER_DETECTED,
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"coordinated_manipulation": FlagType.FLAG_COORDINATED_MANIPULATION,
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"wash_trading": FlagType.FLAG_WASH_TRADING,
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"spoofing": FlagType.FLAG_SPOOFING,
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"layering": FlagType.FLAG_LAYERING,
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"quote_stuffing": FlagType.FLAG_QUOTE_STUFFING,
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}
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return mapping.get(event_type, None)
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def _get_sub_flags(self, event_type: str) -> List[str]:
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"""Get sub-flag tags for event type"""
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sub_flags_map = {
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"token_unlock": ["FLAG_TOKEN_UNLOCK", "FLAG_SUPPLY_SHOCK", "FLAG_NEGATIVE"],
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"token_burn": ["FLAG_TOKEN_BURN", "FLAG_SUPPLY_REDUCTION", "FLAG_POSITIVE"],
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"security_hack": ["FLAG_SECURITY_AUDIT_FAIL", "FLAG_NEGATIVE", "FLAG_SECURITY"],
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"whale_accumulation": ["FLAG_WHALE_ACCUMULATION", "FLAG_POSITIVE"],
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"whale_distribution": ["FLAG_WHALE_DISTRIBUTION", "FLAG_NEGATIVE"],
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"listing": ["FLAG_LISTING", "FLAG_POSITIVE", "FLAG_MARKET_STRUCTURE"],
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"delisting": ["FLAG_DELISTING", "FLAG_NEGATIVE", "FLAG_MARKET_STRUCTURE"],
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"etp_approval": ["FLAG_ETP_APPROVAL", "FLAG_POSITIVE", "FLAG_REGULATORY"],
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"etp_rejection": ["FLAG_ETP_REJECTION", "FLAG_NEGATIVE", "FLAG_REGULATORY"],
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"reg_enforcement": ["FLAG_REG_ENFORCEMENT", "FLAG_NEGATIVE", "FLAG_REGULATORY"],
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"pump_coordination": ["FLAG_PUMP_COORDINATION", "FLAG_NEGATIVE", "FLAG_MANIPULATION"],
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}
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return sub_flags_map.get(event_type, ["FLAG_NEUTRAL"])
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def _get_event_direction(self, event_type: str) -> str:
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"""Get direction from event catalogue"""
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negative = ["hack", "exploit", "rug_pull", "exit_scam", "liquidation", "delisting", "regulatory", "manipulation", "bankruptcy", "default", "tax_treatment_change"]
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positive = ["listing", "upgrade", "partnership", "whale", "etp_approval", "token_burn", "airdrop", "mainnet_launch", "audit_pass", "earnings_beat", "guidance_raise"]
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mixed = ["hard_fork", "soft_fork", "protocol_upgrade", "governance_proposal", "m&a_announcement", "strategic_investment", "regulatory_clarity"]
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if event_type in negative:
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return "negative"
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elif event_type in positive:
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return "positive"
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elif event_type in mixed:
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return "mixed"
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return "neutral"
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def _get_base_impact(self, event_type: str) -> float:
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"""Get base impact from catalogue (simplified)"""
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impacts = {
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"token_unlock": 25, "token_burn": 30, "security_hack": 95, "rug_pull": 100,
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"listing": 60, "delisting": 85, "etp_approval": 70, "etp_rejection": 70,
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"whale_accumulation": 40, "whale_distribution": 40, "reg_enforcement": 75,
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"pump_coordination": 90, "hard_fork": 60, "mainnet_launch": 70,
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}
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return impacts.get(event_type, 25)
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def _get_half_life(self, event_type: str) -> float:
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"""Get half-life from catalogue (minutes)"""
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half_lives = {
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"token_unlock": 720, "security_hack": 2880, "listing": 10080,
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"etp_approval": 4320, "whale_accumulation": 1440, "reg_enforcement": 10080,
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"pump_coordination": 360, "hard_fork": 2880, "mainnet_launch": 2880,
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}
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return half_lives.get(event_type, 720)
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def _get_impact_duration(self, event_type: str) -> float:
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"""Get impact duration from catalogue (minutes)"""
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durations = {
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"token_unlock": 240, "security_hack": 2880, "listing": 10080,
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"etp_approval": 10080, "whale_accumulation": 1440, "reg_enforcement": 10080,
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"pump_coordination": 360, "hard_fork": 1440, "mainnet_launch": 2880,
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}
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return durations.get(event_type, 240)
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def _is_scheduled_event(self, event_type: str) -> bool:
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"""Check if event is scheduled (from catalogue)"""
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scheduled = ["token_unlock", "halving", "etp_approval", "hard_fork", "mainnet_launch", "token_burn"]
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return event_type in scheduled
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def _compute_detail_factor(self, item: ProcessedItem) -> float:
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"""Compute DETAIL_FACTOR per spec Section 6.1"""
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factor = 0.0
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text = item.raw_text.lower()
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# Specific dates
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if any(kw in text for kw in ["january", "february", "march", "april", "may", "june", "july", "august", "september", "october", "november", "december", "2024", "2025", "2026"]):
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factor += 0.2
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# Specific amounts
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if any(kw in text for kw in ["$", "million", "billion", "trillion", "m", "b", "t", "k", "%"]):
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factor += 0.2
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# Contract addresses
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if "0x" in text or "0x" in item.raw_text:
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factor += 0.15
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# Named individuals
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if any(kw in text for kw in ["elon", "vitalik", "cz", "saylor", "trump", "biden", "powell"]):
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factor += 0.1
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# Technical terms
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if any(kw in text for kw in ["eip-", "bip-", "erc-", "sha-", "pos", "pow", "sharding", "rollup"]):
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factor += 0.1
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# Text length
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if len(item.raw_text) > 2000:
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factor += 0.1
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# URL to official docs
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if "github.com" in text or "gov." in text or "sec.gov" in text:
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factor += 0.15
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return min(1.0, factor)
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def _get_asset_industry(self, asset_id: str) -> str:
|
||||
"""Get industry for asset (simplified)"""
|
||||
crypto = ["BTC", "ETH", "SOL", "BNB", "ADA", "XRP", "DOGE", "MATIC", "AVAX", "DOT", "LINK", "UNI", "AAVE", "ARB", "OP"]
|
||||
if asset_id in crypto:
|
||||
return "CRYPTO"
|
||||
return "UNKNOWN"
|
||||
|
||||
def process_item(self, item: ProcessedItem) -> Dict[str, AssetSentiment]:
|
||||
"""Process a single processed item into asset signals"""
|
||||
asset_signals = {}
|
||||
|
||||
# Group by asset
|
||||
for entity in item.entities:
|
||||
asset_id = entity.asset_id
|
||||
|
||||
# Compute base sentiment scores
|
||||
sentiment = item.sentiment_per_asset.get(asset_id)
|
||||
emotions = item.emotions_per_asset.get(asset_id)
|
||||
|
||||
if sentiment is None or emotions is None:
|
||||
continue
|
||||
|
||||
# Compute fear/greed state
|
||||
fear_state = self._compute_fear_state(sentiment, emotions, item)
|
||||
greed_state = self._compute_greed_state(sentiment, emotions, item)
|
||||
|
||||
# Compute pump/dump scores
|
||||
pump_dump = self._compute_pump_dump(asset_id, item, sentiment, emotions)
|
||||
|
||||
# Compute velocity
|
||||
velocity = self.velocity_computer.compute(
|
||||
asset_id, item, fear_state, greed_state
|
||||
)
|
||||
|
||||
# Compute event flags
|
||||
event_flags = self._compute_event_flags(asset_id, item.events, item)
|
||||
|
||||
# Apply temporal decay
|
||||
decay_factor = self.temporal_decay.compute(
|
||||
item.publish_ts or item.ingest_ts,
|
||||
self.settings.scoring.parameters.fear_state.halflife_minutes
|
||||
)
|
||||
|
||||
# Determine contributing events for interpretability
|
||||
contributing_events = self._determine_contributing_events(item, fear_state, greed_state)
|
||||
|
||||
# Build asset sentiment
|
||||
asset_signals[asset_id] = AssetSentiment(
|
||||
asset_id=asset_id,
|
||||
fear_state=fear_state * decay_factor * 100,
|
||||
greed_state=greed_state * decay_factor * 100,
|
||||
sentiment_polarity=sentiment.polarity * 100,
|
||||
emotion_profile={
|
||||
"joy": emotions.joy,
|
||||
"fear": emotions.fear,
|
||||
"anger": emotions.anger,
|
||||
"greed": emotions.greed,
|
||||
"sadness": emotions.sadness,
|
||||
"intensity": emotions.intensity
|
||||
},
|
||||
pump_dump=pump_dump,
|
||||
event_flags=event_flags,
|
||||
velocity=velocity,
|
||||
last_update_ts=item.processed_ts,
|
||||
contributing_sources=1,
|
||||
decay_factor=decay_factor,
|
||||
contributing_events=self._determine_contributing_events(item, fear_state, greed_state)
|
||||
)
|
||||
|
||||
# Update history
|
||||
self._update_history(asset_id, item, asset_signals[asset_id])
|
||||
|
||||
return asset_signals
|
||||
|
||||
def _update_history(self, asset_id: str, item: ProcessedItem, signal: AssetSentiment) -> None:
|
||||
"""Update internal history for velocity computation"""
|
||||
history_entry = {
|
||||
|
||||
@@ -71,7 +71,7 @@ class VelocityComputer:
|
||||
)
|
||||
|
||||
def _compute_hype_velocity(self, window: deque) -> float:
|
||||
"""Compute hype velocity as rate of sentiment acceleration"""
|
||||
"""Compute hype velocity as rate of sentiment acceleration (-100 to +100)"""
|
||||
if len(window) < 3:
|
||||
return 0.0
|
||||
|
||||
@@ -83,14 +83,16 @@ class VelocityComputer:
|
||||
intensities = [o["intensity"] for o in obs]
|
||||
polarities = [(o["greed"] - o["fear"]) for o in obs]
|
||||
|
||||
# Fit linear trend to intensity
|
||||
# Fit linear trend to weighted sentiment (polarity * intensity)
|
||||
if len(times) >= 3:
|
||||
try:
|
||||
coeffs = np.polyfit(times, intensities, 1)
|
||||
# Weight by intensity
|
||||
weighted_sentiment = [p * i for p, i in zip(polarities, intensities)]
|
||||
coeffs = np.polyfit(times, weighted_sentiment, 1)
|
||||
slope = coeffs[0] # Rate of change per second
|
||||
|
||||
# Normalize to 0-1 (assuming max slope of 0.01/sec)
|
||||
velocity = min(1.0, abs(slope) * 100)
|
||||
# Normalize to -100 to +100 (assuming max slope of 0.01/sec)
|
||||
velocity = np.clip(slope * 10000, -100.0, 100.0)
|
||||
return velocity
|
||||
except Exception:
|
||||
pass
|
||||
@@ -100,12 +102,12 @@ class VelocityComputer:
|
||||
delta = intensities[-1] - intensities[0]
|
||||
time_delta = times[-1] - times[0]
|
||||
if time_delta > 0:
|
||||
return min(1.0, abs(delta) / time_delta * 3600) # Per hour
|
||||
return np.clip(delta / time_delta * 360000, -100.0, 100.0) # Per hour
|
||||
|
||||
return 0.0
|
||||
|
||||
def _compute_pub_velocity(self, asset_id: str, now: float, window_seconds: int) -> float:
|
||||
"""Compute publication velocity (sources per minute)"""
|
||||
"""Compute publication velocity (rate of change of publication count, -100 to +100)"""
|
||||
if asset_id not in self._source_windows:
|
||||
self._source_windows[asset_id] = deque(maxlen=200)
|
||||
|
||||
@@ -118,13 +120,24 @@ class VelocityComputer:
|
||||
while window and window[0] < cutoff:
|
||||
window.popleft()
|
||||
|
||||
# Sources per minute
|
||||
if len(window) >= 2:
|
||||
time_span = window[-1] - window[0]
|
||||
if time_span > 0:
|
||||
rate = len(window) / (time_span / 60) # per minute
|
||||
# Normalize (10 sources/min = 1.0)
|
||||
return min(1.0, rate / 10.0)
|
||||
# Rate of change of publication count
|
||||
if len(window) >= 3:
|
||||
try:
|
||||
times = list(window)[-10:]
|
||||
# Count publications in time bins
|
||||
bins = 5
|
||||
bin_edges = np.linspace(times[0], times[-1], bins + 1)
|
||||
counts = np.histogram(times, bins=bin_edges)[0]
|
||||
bin_times = (bin_edges[:-1] + bin_edges[1:]) / 2
|
||||
|
||||
if len(counts) >= 3:
|
||||
coeffs = np.polyfit(bin_times, counts, 1)
|
||||
slope = coeffs[0] # Rate of change per second
|
||||
# Normalize to -100 to +100 (10 pubs/sec = 100)
|
||||
velocity = np.clip(slope * 10, -100.0, 100.0)
|
||||
return velocity
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return 0.0
|
||||
|
||||
|
||||
Reference in New Issue
Block a user