test: add test_multiplatform_ranking test suite
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"""
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test_multiplatform_ranking.py
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Simulates the expanded multi-platform HIFFI formula on the pilot cohort.
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Merges YouTube metrics from enriched_artists.csv with Spotify and Instagram signals.
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"""
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import pandas as pd
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import numpy as np
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from collectors.browser_collector import MultiPlatformSignalCollector, sync_playwright
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PILOT_ARTISTS = [
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{
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"name": "Kendrick Lamar",
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"spotify_url": "https://open.spotify.com/artist/2YZyLoL8N0Wb9xBt1NhZWg",
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"ig_url": "https://www.instagram.com/kendricklamar/",
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},
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{
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"name": "Drake",
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"spotify_url": "https://open.spotify.com/artist/3TVXtAsR1Inumwj472S9r4",
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"ig_url": "https://www.instagram.com/champagnepapi/",
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},
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{
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"name": "Central Cee",
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"spotify_url": "https://open.spotify.com/artist/5H4ikTs84ffUwPVEMOzPt0",
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"ig_url": "https://www.instagram.com/centralcee/",
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},
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{
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"name": "GloRilla",
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"spotify_url": "https://open.spotify.com/artist/2qoQgPAilUQnvEBEnfZQI8",
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"ig_url": "https://www.instagram.com/glorillapimp/",
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},
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{
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"name": "Sexyy Red",
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"spotify_url": "https://open.spotify.com/artist/1pPmIToKXyG75V4XZgqpvf",
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"ig_url": "https://www.instagram.com/sexyyred/",
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},
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]
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def min_max_normalize(series: pd.Series) -> pd.Series:
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"""Scales numeric values to a 0-100 range."""
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min_v = series.min()
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max_v = series.max()
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if max_v == min_v:
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return pd.Series(50.0, index=series.index)
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return ((series - min_v) / (max_v - min_v)) * 100.0
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def main():
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print("=" * 80)
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print("STEP 1: COLLECTING LIVE SPOTIFY & INSTAGRAM SIGNALS (PILOT)")
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print("=" * 80)
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collector = MultiPlatformSignalCollector(headless=True)
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collected_rows = []
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with sync_playwright() as p:
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browser = p.chromium.launch(headless=True)
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context = browser.new_context(
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viewport={"width": 1920, "height": 1080},
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user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/124.0.0.0 Safari/537.36",
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locale="en-US",
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)
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page = context.new_page()
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for a in PILOT_ARTISTS:
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data = collector.collect_artist(a, page)
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collected_rows.append(data)
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sp_display = (
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f"{data['spotify_monthly_listeners']:,}"
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if data["spotify_monthly_listeners"] is not None
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else "None"
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)
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ig_display = (
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f"{data['instagram_followers']:,}"
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if data["instagram_followers"] is not None
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else "None"
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)
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print(f"Collected: {data['name']} -> Spotify: {sp_display}, IG: {ig_display}")
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browser.close()
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new_signals_df = pd.DataFrame(collected_rows)
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print("\n" + "=" * 80)
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print("STEP 2: MERGING WITH EXISTING YOUTUBE METRICS FROM enriched_artists.csv")
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print("=" * 80)
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df_existing = pd.read_csv("enriched_artists.csv")
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df_existing["name_clean"] = df_existing["name"].str.strip().str.lower()
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new_signals_df["name_clean"] = new_signals_df["name"].str.strip().str.lower()
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merged_df = pd.merge(
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new_signals_df,
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df_existing,
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on="name_clean",
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how="inner",
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suffixes=("", "_yt"),
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)
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if merged_df.empty:
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matched_indices = []
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for name in new_signals_df["name"]:
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match = df_existing[df_existing["name"].str.contains(name, case=False, na=False)]
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if not match.empty:
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matched_indices.append(match.index[0])
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merged_df = df_existing.loc[matched_indices].copy()
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merged_df = merged_df.merge(new_signals_df, left_on="name", right_on="name")
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eval_cols = [
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"name",
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"subscriber_count",
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"recent_avg_views",
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"spotify_monthly_listeners",
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"instagram_followers",
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"hiffi_score",
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]
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pilot_view = merged_df[[c for c in eval_cols if c in merged_df.columns]].copy()
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# Fill uncollected values with 0
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pilot_view["spotify_monthly_listeners"] = pilot_view["spotify_monthly_listeners"].fillna(0)
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pilot_view["instagram_followers"] = pilot_view["instagram_followers"].fillna(0)
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print("\n" + "=" * 80)
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print("STEP 3: COMPUTING NORMALIZED COMPOSITE MULTI-PLATFORM RANKING")
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print("=" * 80)
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# 1. Normalize individual signal channels (0 - 100)
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pilot_view["norm_yt_views"] = min_max_normalize(pilot_view["recent_avg_views"])
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pilot_view["norm_spotify"] = min_max_normalize(pilot_view["spotify_monthly_listeners"])
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pilot_view["norm_instagram"] = min_max_normalize(pilot_view["instagram_followers"])
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# 2. Formula weighting:
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# 40% Spotify Streaming (Core Audio Demand)
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# 35% YouTube Engagement (Video / Catalog Depth)
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# 25% Instagram Reach (Social Following / Cultural Reach)
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pilot_view["multi_platform_score"] = (
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(0.40 * pilot_view["norm_spotify"])
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+ (0.35 * pilot_view["norm_yt_views"])
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+ (0.25 * pilot_view["norm_instagram"])
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).round(2)
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# 3. Compute Ranks
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pilot_view["new_multi_rank"] = (
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pilot_view["multi_platform_score"]
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.rank(ascending=False, method="min")
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.astype(int)
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)
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if "hiffi_score" in pilot_view.columns:
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pilot_view["original_yt_rank"] = (
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pilot_view["hiffi_score"].rank(ascending=False, method="min").astype(int)
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)
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display_cols = [
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"name",
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"original_yt_rank",
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"new_multi_rank",
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"multi_platform_score",
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"spotify_monthly_listeners",
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"instagram_followers",
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]
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print("\nComparison Table (Original YouTube Rank vs. Multi-Platform Rank):")
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print(
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pilot_view[[c for c in display_cols if c in pilot_view.columns]]
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.sort_values("new_multi_rank")
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.to_string(index=False)
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)
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print("=" * 80)
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if __name__ == "__main__":
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main()
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