109 lines
4.2 KiB
Python
109 lines
4.2 KiB
Python
from __future__ import annotations
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import math
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import numpy as np
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import pandas as pd
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def _sigmoid(x: float) -> float:
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return 1.0 / (1.0 + math.exp(-x))
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def compute_trend_scores(df: pd.DataFrame) -> pd.DataFrame:
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"""
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在“表结构不确定”的情况下，先基于可用字段做可解释评分：
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- 潜伏期识别（Potential Winners）：新上架/刚出现 + 指标增长快
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- 爆发力：若存在 tiktok/search 等字段则融合，否则使用销量/GMV加速度代理
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- 决策建议：跟卖指数 + 生命周期预警（缺失字段会自动降权）
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"""
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out = df.copy()
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now = pd.Timestamp.now(tz="UTC")
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# 新品/潜伏期：first_seen 越近、同时 units/gmv 相对高 -> potential
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first_seen = pd.to_datetime(out.get("first_seen"), errors="coerce", utc=True)
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age_days = (now - first_seen).dt.total_seconds() / 86400.0
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age_days = age_days.fillna(999.0).clip(lower=0.0)
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units = pd.to_numeric(out.get("units"), errors="coerce").fillna(0.0)
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gmv = pd.to_numeric(out.get("gmv"), errors="coerce").fillna(0.0)
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# 规模归一：log1p 降噪
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units_s = np.log1p(units)
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gmv_s = np.log1p(gmv)
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freshness = 1.0 / (1.0 + (age_days / 7.0)) # 0~1
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scale = (units_s.rank(pct=True) * 0.6 + gmv_s.rank(pct=True) * 0.4).clip(0.0, 1.0)
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out["potential_score"] = (freshness * 0.55 + scale * 0.45).clip(0.0, 1.0)
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# 爆发力：优先融合可选字段
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tiktok_raw = out["tiktok_hot"] if "tiktok_hot" in out.columns else pd.Series(np.nan, index=out.index)
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search_raw = out["search_growth"] if "search_growth" in out.columns else pd.Series(np.nan, index=out.index)
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tiktok = pd.to_numeric(tiktok_raw, errors="coerce")
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search_g = pd.to_numeric(search_raw, errors="coerce")
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if tiktok.notna().any() or search_g.notna().any():
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tiktok_s = tiktok.fillna(tiktok.median() if tiktok.notna().any() else 0.0)
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search_s = search_g.fillna(search_g.median() if search_g.notna().any() else 0.0)
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burst = (
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pd.Series(tiktok_s).rank(pct=True) * 0.6
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+ pd.Series(search_s).rank(pct=True) * 0.4
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).clip(0.0, 1.0)
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else:
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# 无外部热度字段：用规模 + 新鲜度 作为代理
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burst = (scale * 0.65 + freshness * 0.35).clip(0.0, 1.0)
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out["burst_score"] = burst
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# 跟卖指数：竞争（records 越多）负向；利润空间/供应链难度若缺失则降级
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records = pd.to_numeric(out.get("records"), errors="coerce").fillna(0.0)
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competition = records.rank(pct=True).clip(0.0, 1.0) # 越大越卷
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margin_raw = out["margin"] if "margin" in out.columns else pd.Series(np.nan, index=out.index)
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margin = pd.to_numeric(margin_raw, errors="coerce")
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if margin.notna().any():
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margin_s = margin.fillna(margin.median()).rank(pct=True).clip(0.0, 1.0)
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margin_w = 0.35
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else:
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margin_s = pd.Series(0.5, index=out.index)
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margin_w = 0.15
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supply_raw = out["supply_difficulty"] if "supply_difficulty" in out.columns else pd.Series(np.nan, index=out.index)
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supply = pd.to_numeric(supply_raw, errors="coerce")
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if supply.notna().any():
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supply_s = (1.0 - supply.fillna(supply.median()).rank(pct=True)).clip(0.0, 1.0) # 越难越低分
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supply_w = 0.20
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else:
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supply_s = pd.Series(0.5, index=out.index)
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supply_w = 0.10
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# 趋势作为正向
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trend = (out["potential_score"] * 0.5 + out["burst_score"] * 0.5).clip(0.0, 1.0)
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trend_w = 0.45
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comp_w = 0.20
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follow = (
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trend * trend_w
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+ margin_s * margin_w
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+ supply_s * supply_w
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+ (1.0 - competition) * comp_w
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)
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out["follow_score"] = follow.clip(0.0, 1.0)
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# 生命周期预警（简化）：过老 + 规模不增长 + 竞争高 => red-ocean / decline
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lifecycle = []
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for i in out.index:
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a = float(age_days.loc[i])
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comp = float(competition.loc[i])
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tr = float(trend.loc[i])
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if a > 120 and comp > 0.7 and tr < 0.4:
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lifecycle.append("decline_or_red_ocean")
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elif a > 60 and comp > 0.75:
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lifecycle.append("red_ocean")
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elif a < 21 and tr > 0.65:
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lifecycle.append("early_growth")
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else:
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lifecycle.append("normal")
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out["lifecycle"] = lifecycle
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return out
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