AI 创作日记 | 从经验主义到机器直觉，DeepSeek如何重写选品规则手册

def causal_inference(data):
    """回答灵魂拷问：如果没有李佳琦推荐，这个产品还能火吗？"""
    cf_model = CounterfactualRL()
    return cf_model.simulate(
        treatment=data['KOL_impact'],
        outcome=data['sales'],
        confounders=data['price','seasonality']
    )

import snscrape.modules.twitter as sntwitter
import instaloader
from TikTokApi import TikTokApi

class SocialMiner:
    def __init__(self):
        self.tiktok = TikTokApi()
        self.ig_loader = instaloader.Instaloader()
        
    def mine_twitter(self, keyword, limit=1000):
        """推特趋势探测器"""
        tweets = []
        for i, tweet in enumerate(
            sntwitter.TwitterSearchScraper(f'{keyword} since:2023-01-01').get_items()
        ):
            if i >= limit:
                break
            tweets.append({
                'content': tweet.content,
                'hashtags': tweet.hashtags,
                'engagement': tweet.likeCount + tweet.retweetCount
            })
        return tweets
    
    def track_instagram(self, hashtag):
        """IG美学特征提取器"""
        posts = []
        for post in self.ig_loader.get_hashtag_posts(hashtag):
            posts.append({
                'image_url': post.url,
                'color_palette': self._extract_color(post),
                'composition_score': self._calc_composition(post)
            })
        return posts
    
    def _extract_color(self, post):
        """使用CV提取主色系"""
        # 实现细节省略
        return ['#FF6B6B', '#4ECDC4']

import torch
from torch.nn import Transformer

class TemporalFolding(torch.nn.Module):
    """
    时空折叠预测模型
    输入：多维度时间序列 [batch_size, seq_len, features]
    输出：预测窗口的概率分布
    """
    def __init__(self, d_model=512, nhead=8):
        super().__init__()
        self.encoder = TransformerEncoder(d_model, nhead)
        self.decoder = TransformerDecoder(d_model, nhead)
        self.temporal_unfolder = TemporalUnfolder()
        
    def forward(self, src):
        memory = self.encoder(src)
        output = self.decoder(memory)
        return self.temporal_unfolder(output)

class TemporalUnfolder(torch.nn.Module):
    """将潜在表示映射到多时间尺度预测"""
    def __init__(self):
        super().__init__()
        self.scales = [7, 14, 30]  # 预测7天、14天、30天的趋势
        
    def forward(self, x):
        return [self._project(x, s) for s in self.scales]

class DataFlywheel:
    """自我强化的数据生态系统"""
    def __init__(self):
        self.miner = SocialMiner()
        self.predictor = TemporalFolding()
        self.validator = A/BValidator()
        
    def daily_cycle(self):
        while True:
            new_data = self.miner.mine()
            self.predictor.update(new_data)
            predictions = self.predictor.run()
            results = self.validator.test(predictions)
            self.miner.adjust_params(results)

# 模型监控报警器
import warnings
from datetime import datetime

class ModelGuardian:
    def __init__(self, model):
        self.model = model
        self.performance_log = []
        
    def check_anomaly(self, current_metrics):
        """检测模型性能异动"""
        if len(self.performance_log) < 10:
            return False
            
        avg = sum(self.performance_log[-10:])/10
        if current_metrics < avg * 0.7:
            self._trigger_alert()
            return True
            
    def _trigger_alert(self):
        warnings.warn(f"""
        !!!模型性能暴跌警告!!!
        时间：{datetime.now()}
        建议操作：
        1. 检查数据管道完整性
        2. 验证社交平台API变更
        3. 回滚到上一稳定版本
        """)

# 爆款预测全流程示例
import deepseek

# 1. 社交数据实时摄取
stream = SocialStream(
    sources=['weibo', 'xiaohongshu', 'douyin'],
    filters=AgeFilter(18-25)
)

# 2. 特征工厂
feature_pipeline = Pipeline([
    TextAlchemy(),       # 文本炼金术
    GraphEmbedding(),    # 关系图谱嵌入
    TemporalEncoder()    # 时间序列编码
])

# 3. 混合预测模型
model = EnsembleModel(
    base_models=[XGBoost(), TemporalFusionTransformer()],
    meta_model=NeuralGreedyAggregator()
)

# 4. 动态学习系统
active_learner = UncertaintySampler(
    update_interval='3h',
    retention_policy=SoftDeletePolicy()
)

# 启动预测引擎
forecast_engine = DeepSeekEngine(
    stream, feature_pipeline, model, active_learner
)
forecast_engine.run()