feat(governance): implement publication eligibility gates, proportional dynamic redistribution, CI workflow, and 20/20 QA suite

core/ranking_engine.py

@@ -1,14 +1,19 @@
 """
 core/ranking_engine.py
 Ranks artists according to HIFFI formula stages:
-- Recent Momentum (35%)
-- View Velocity (30%)
-- Audience Reach (20%)
-- Consistency (15%)
-Calculates Breakout Velocity Index (BVI, 0-100) and breakout_rank
-with a 10,000 subscriber floor to prevent phantom accounts from ranking.
+- YouTube Production Baseline: Momentum (35%), Velocity (30%), Reach (20%), Consistency (15%)
+- Breakout Velocity Index (BVI, 0-100) with 10k subscriber floor
+- Multi-Platform HIFFI-XP Composite (0-100):
+  * YouTube Production Score (50%)
+  * Spotify Monthly Listeners (25%)
+  * Spotify Daily Change / Velocity (5%)
+  * Instagram Followers (20%)
+  With zero-imputation compliance and proportional weight redistribution.
+- Publication Eligibility Gates: Decoupled public chart assigning consecutive ranks
+  while enforcing multi-platform evidence tiers for Top 10 and Top 50.
 """
 
+from typing import Optional
 import numpy as np
 import pandas as pd
 
@@ -41,7 +46,6 @@ class HIFFIRankingEngine:
         """
         out_bvi = pd.Series(np.nan, index=df.index, dtype=float)
 
-        # Qualification Gate: Must have real baseline audience (>= 10,000 subs)
         valid_mask = (
             df["subscriber_count"].notna()
             & (df["subscriber_count"] >= 10000)
@@ -59,21 +63,13 @@ class HIFFIRankingEngine:
         daily_vel = subset["daily_view_velocity"].astype(float)
         momentum = subset["momentum_30d"].fillna(0.0).clip(lower=-100.0, upper=300.0)
 
-        # 1. Audience Velocity Ratio: Views per day relative to subscriber base
         v_aud = daily_vel / np.maximum(subs, 1000.0)
-
-        # 2. Scaled Momentum Factor: Map [-100, 300] to [0, 1]
         m_signal = (momentum + 100.0) / 400.0
-
-        # 3. Log-compress velocity to manage power-law peaks
         log_v_aud = np.log10(1.0 + v_aud)
-
-        # 4. Emergence Dampener: inverse log weight to favor emerging accounts
         w_emerge = 1.0 / (1.0 + np.log10(1.0 + np.maximum(subs, 1.0)))
 
         raw_bvi = (0.65 * log_v_aud + 0.35 * m_signal) * w_emerge
 
-        # Min-max scale to 0-100 across valid records
         bvi_min = raw_bvi.min()
         bvi_max = raw_bvi.max()
 
@@ -90,9 +86,9 @@ class HIFFIRankingEngine:
         df: pd.DataFrame,
         min_confidence: float = 0.50,
     ) -> pd.DataFrame:
+        """Computes baseline YouTube production ranking."""
         out = df.copy()
 
-        # Handle confidence score defensively
         if "confidence_score" in out.columns:
             out["confidence_score"] = pd.to_numeric(
                 out["confidence_score"].apply(
@@ -101,10 +97,8 @@ class HIFFIRankingEngine:
                 errors="coerce"
             ).fillna(0.0)
 
-        # Compute BVI with the 10,000 sub floor
         out["breakout_velocity_index"] = self.calculate_breakout_velocity(out)
 
-        # Verification & completeness gate
         has_baseline = (
             out["subscriber_count"].notna()
             & out["lifetime_views"].notna()
@@ -117,23 +111,18 @@ class HIFFIRankingEngine:
             & (out["status_flag"].isin(["COMPLETE", "CATALOG_ACTIVE_NO_RECENT_UPLOAD"]))
         )
 
-        # 1. Recent Momentum (35%)
         filled_momentum = out["momentum_30d"].fillna(0.0).clip(lower=-100.0, upper=300.0)
         score_momentum = self._min_max_scale(filled_momentum)
 
-        # 2. View Velocity (30%)
         filled_velocity = out["daily_view_velocity"].fillna(0.0)
         score_velocity = self._log_scale(filled_velocity)
 
-        # 3. Audience Reach (20%)
         log_subs = self._log_scale(out["subscriber_count"].fillna(0.0))
         log_lifetime = self._log_scale(out["lifetime_views"].fillna(0.0))
         score_reach = 0.60 * log_subs + 0.40 * log_lifetime
 
-        # 4. Consistency (15%)
         score_consistency = self._log_scale(out["video_count"].fillna(0.0))
 
-        # Composite HIFFI Score (0 - 100)
         hiffi_raw = (
             0.35 * score_momentum
             + 0.30 * score_velocity
@@ -142,18 +131,16 @@ class HIFFIRankingEngine:
         ) * 100.0
 
         conf_multiplier = 0.8 + 0.2 * out["confidence_score"].clip(0.0, 1.0)
-        out["hiffi_score"] = (hiffi_raw * conf_multiplier).round(2)
+        out["production_hiffi_score"] = (hiffi_raw * conf_multiplier).round(2)
 
-        # Primary HIFFI Rank
         eligible_mask = out["is_rank_eligible"]
-        out["hiffi_rank"] = pd.NA
-        out.loc[eligible_mask, "hiffi_rank"] = (
-            out.loc[eligible_mask, "hiffi_score"]
+        out["production_hiffi_rank"] = pd.NA
+        out.loc[eligible_mask, "production_hiffi_rank"] = (
+            out.loc[eligible_mask, "production_hiffi_score"]
             .rank(ascending=False, method="min")
             .astype(int)
         )
 
-        # Breakout Rank (only for eligible artists that have a valid BVI)
         bvi_eligible_mask = eligible_mask & out["breakout_velocity_index"].notna()
         out["breakout_rank"] = pd.NA
         out.loc[bvi_eligible_mask, "breakout_rank"] = (
@@ -162,6 +149,141 @@ class HIFFIRankingEngine:
             .astype(int)
         )
 
-        return out.sort_values(
-            by=["is_rank_eligible", "hiffi_score"], ascending=[False, False]
-        ).reset_index(drop=True)
+        return out
+
+    def calculate_hiffi_xp(self, df: pd.DataFrame) -> pd.DataFrame:
+        """
+        Calculates HIFFI-XP composite multi-platform score and rank.
+        Enforces proportional weight redistribution when signals are missing (NaN).
+        Weights: YouTube (0.50), Spotify Monthly (0.25), Spotify Velocity (0.05), Instagram (0.20).
+        """
+        out = df.copy()
+
+        base_weights = {
+            "yt": 0.50,
+            "sp_monthly": 0.25,
+            "sp_velocity": 0.05,
+            "ig": 0.20
+        }
+
+        def log_scale_vector(series: pd.Series) -> pd.Series:
+            valid = series.dropna()
+            if valid.empty:
+                return pd.Series(np.nan, index=series.index)
+            log_val = np.log1p(np.maximum(0.0, valid.astype(float)))
+            min_v, max_v = log_val.min(), log_val.max()
+            if np.isclose(max_v, min_v):
+                return pd.Series(1.0, index=valid.index)
+            scaled = (log_val - min_v) / (max_v - min_v + 1e-9)
+            return scaled.reindex(series.index)
+
+        def linear_scale_vector(series: pd.Series) -> pd.Series:
+            valid = series.dropna()
+            if valid.empty:
+                return pd.Series(np.nan, index=series.index)
+            min_v, max_v = valid.min(), valid.max()
+            if np.isclose(max_v, min_v):
+                return pd.Series(0.5, index=valid.index)
+            scaled = (valid - min_v) / (max_v - min_v + 1e-9)
+            return scaled.reindex(series.index)
+
+        s_yt = log_scale_vector(out["production_hiffi_score"]) if "production_hiffi_score" in out.columns else pd.Series(np.nan, index=out.index)
+        s_sp_m = log_scale_vector(out["spotify_monthly_listeners"]) if "spotify_monthly_listeners" in out.columns else pd.Series(np.nan, index=out.index)
+        s_sp_v = linear_scale_vector(out["spotify_daily_change"]) if "spotify_daily_change" in out.columns else pd.Series(np.nan, index=out.index)
+        s_ig = log_scale_vector(out["instagram_followers"]) if "instagram_followers" in out.columns else pd.Series(np.nan, index=out.index)
+
+        signals = pd.DataFrame({
+            "yt": s_yt,
+            "sp_monthly": s_sp_m,
+            "sp_velocity": s_sp_v,
+            "ig": s_ig
+        }, index=out.index)
+
+        # Proportional Dynamic Weight Redistribution Matrix
+        weight_mask = signals.notna()
+        allocated_weights = weight_mask.multiply(pd.Series(base_weights), axis=1)
+        weight_sum = allocated_weights.sum(axis=1)
+
+        weight_sum = weight_sum.replace(0.0, np.nan)
+        normalized_weights = allocated_weights.divide(weight_sum, axis=0)
+
+        component_scores = (signals.fillna(0.0) * normalized_weights).sum(axis=1) * 100.0
+        out["hiffi_xp_score"] = component_scores.round(2)
+        out.loc[weight_sum.isna(), "hiffi_xp_score"] = np.nan
+
+        # Deterministic tie-breaking for raw mathematical ranking
+        sort_cols = ["hiffi_xp_score", "production_hiffi_score", "spotify_monthly_listeners"]
+        present_sort = [c for c in sort_cols if c in out.columns]
+        
+        valid_mask = out["hiffi_xp_score"].notna()
+        sorted_indices = (
+            out[valid_mask]
+            .sort_values(by=present_sort, ascending=[False] * len(present_sort))
+            .index
+        )
+
+        out["hiffi_xp_rank"] = pd.NA
+        out.loc[sorted_indices, "hiffi_xp_rank"] = range(1, len(sorted_indices) + 1)
+        out.loc[valid_mask, "hiffi_xp_rank"] = out.loc[valid_mask, "hiffi_xp_rank"].astype(int)
+
+        if "production_hiffi_rank" in out.columns:
+            out["rank_delta"] = pd.to_numeric(out["production_hiffi_rank"], errors="coerce") - pd.to_numeric(out["hiffi_xp_rank"], errors="coerce")
+        else:
+            out["rank_delta"] = np.nan
+
+        return out
+
+    def apply_publication_eligibility_gates(self, df: pd.DataFrame) -> pd.DataFrame:
+        """
+        Constructs a publication ranking layer separate from raw mathematical scores:
+        - Top 10 requires all 3 platform signals (Tier 3).
+        - Top 50 requires at least 2 platform signals (Tier >= 2).
+        - Preserves hiffi_xp_score and hiffi_xp_rank unchanged.
+        - Produces consecutive, unique ranks in 'hiffi_xp_published_rank' (1 to N).
+        """
+        out = df.copy()
+
+        # 1. Determine verified platform presence tier
+        has_yt = out["production_hiffi_score"].notna()
+        has_sp = out["spotify_monthly_listeners"].notna()
+        has_ig = out["instagram_followers"].notna()
+        out["platform_evidence_tier"] = (
+            has_yt.astype(int) + has_sp.astype(int) + has_ig.astype(int)
+        )
+
+        # 2. Sort the entire pool deterministically by raw composite score
+        sort_cols = ["hiffi_xp_score", "production_hiffi_score", "spotify_monthly_listeners"]
+        present_sort_cols = [c for c in sort_cols if c in out.columns]
+        pool = out.sort_values(
+            by=present_sort_cols,
+            ascending=[False] * len(present_sort_cols)
+        ).copy()
+
+        assigned_indices = []
+
+        # Pass 1: Select Top 10 from Tier 3 candidates
+        tier3_candidates = pool[pool["platform_evidence_tier"] == 3]
+        top_10_indices = tier3_candidates.head(10).index.tolist()
+        assigned_indices.extend(top_10_indices)
+
+        # Pass 2: Select Positions 11 to 50 from remaining Tier >= 2 candidates
+        remaining_pool = pool.drop(index=assigned_indices)
+        tier2_plus_candidates = remaining_pool[remaining_pool["platform_evidence_tier"] >= 2]
+        top_50_fill_indices = tier2_plus_candidates.head(40).index.tolist()
+        assigned_indices.extend(top_50_fill_indices)
+
+        # Pass 3: Fill Positions 51+ with all remaining artists by score order
+        final_remaining = pool.drop(index=assigned_indices)
+        assigned_indices.extend(final_remaining.index.tolist())
+
+        # 3. Assign consecutive published ranks without touching raw scores
+        out["hiffi_xp_published_rank"] = pd.NA
+        rank_series = pd.Series(
+            range(1, len(assigned_indices) + 1),
+            index=assigned_indices,
+            dtype="Int64"
+        )
+        out.loc[assigned_indices, "hiffi_xp_published_rank"] = rank_series
+        out["hiffi_xp_published_rank"] = out["hiffi_xp_published_rank"].astype(int)
+
+        return out