spend-optimization-last-touch-attribution.py

def last_touch_attribution(df):
    
    # count the number of events for each campaign in df
    def count_by_campaign(df):                         
        counters = np.zeros(n_campaigns)
        for campaign_one_hot in df['campaigns'].values:
            campaign_id = np.argmax(campaign_one_hot)
            counters[campaign_id] = counters[campaign_id] + 1
        return counters
        
    # count the number of impressions for each campaign
    campaign_impressions = count_by_campaign(df)       
    
    # count the number of times the campaign is the last touch before the conversion
    dfc = df[df['conversion'] == 1]
    idx = dfc.groupby(['jid'])['timestamp_norm'].transform(max) == dfc['timestamp_norm']
    campaign_conversions = count_by_campaign(dfc[idx])
        
    return campaign_conversions / campaign_impressions

lta = last_touch_attribution(df6)