fix: fixed suggesting algorythm
This commit is contained in:
@@ -1,4 +1,5 @@
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import contextlib
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from decimal import ROUND_HALF_UP, Decimal
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from typing import Any, Self
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from uuid import UUID
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@@ -10,7 +11,6 @@ from django.core.validators import (
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MinValueValidator,
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)
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from django.db import models
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from decimal import Decimal, ROUND_HALF_UP
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from apps.advertiser.models import Advertiser
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from apps.campaign.validators import (
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@@ -22,6 +22,7 @@ from apps.campaign.validators import (
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)
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from apps.client.models import Client
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from apps.core.models import BaseModel
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from apps.mlscore.models import Mlscore
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from config.errors import ConflictError, ForbiddenError
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@@ -110,7 +111,7 @@ class Campaign(BaseModel):
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raise ValidationError(err)
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except Campaign.DoesNotExist:
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if self.start_date < current_date:
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raise ValidationError(err)
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raise ValidationError(err) from None
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@property
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def ad_id(self) -> UUID:
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@@ -277,101 +278,114 @@ class Campaign(BaseModel):
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| models.Q(age_from__isnull=True)
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) & (models.Q(age_to__gte=client.age) | models.Q(age_to__isnull=True))
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queryset = (
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return (
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cls.objects.filter(
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date_filter,
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location_filter,
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gender_filter,
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age_filter,
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)
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.prefetch_related("clicks", "impressions")
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.select_related("advertiser")
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.prefetch_related("clicks", "impressions", "advertiser__mlscores")
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)
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return queryset
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@classmethod
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def suggest(cls, client: Client) -> None | Self:
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available_campaigns = Campaign.get_available_campaigns(client)
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if not available_campaigns or available_campaigns == []:
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def suggest(cls, client: Client) -> Self:
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base_campaigns = cls.get_available_campaigns(client)
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if not base_campaigns or base_campaigns == []:
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return None
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campaign_ids = [c.id for c in available_campaigns]
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impressions_counts = (
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CampaignImpression.objects.filter(campaign_id__in=campaign_ids)
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.values("campaign_id")
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.annotate(total=models.Count("id"))
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)
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impressions_dict = {
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item["campaign_id"]: item["total"] for item in impressions_counts
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}
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clicks_counts = (
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CampaignClick.objects.filter(campaign_id__in=campaign_ids)
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.values("campaign_id")
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.annotate(total=models.Count("id"))
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)
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clicks_dict = {
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item["campaign_id"]: item["total"] for item in clicks_counts
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}
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existing_impressions = CampaignImpression.objects.filter(
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client=client, campaign_id__in=campaign_ids
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).values_list("campaign_id", flat=True)
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existing_impressions_set = set(existing_impressions)
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advertisers = {c.advertiser_id for c in available_campaigns}
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ml_scores = models.Mlscore.objects.filter(
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client=client, advertiser_id__in=advertisers
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advertiser_ids = list({c.advertiser_id for c in base_campaigns})
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ml_scores = Mlscore.objects.filter(
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client=client, advertiser_id__in=advertiser_ids
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).values("advertiser_id", "score")
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ml_score_dict = {ms["advertiser_id"]: ms["score"] for ms in ml_scores}
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max_ml = max(ml_score_dict.values(), default=0)
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ml_dict = {m["advertiser_id"]: m["score"] for m in ml_scores}
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valid_campaigns = []
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for campaign in available_campaigns:
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current_impressions = impressions_dict.get(campaign.id, 0)
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if current_impressions >= campaign.impressions_limit:
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continue
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if campaign.id in existing_impressions_set:
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continue
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valid_campaigns.append(campaign)
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campaigns = list(
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base_campaigns.annotate(
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impressions_count=models.Count("impressions"),
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clicks_count=models.Count("clicks"),
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)
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)
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campaign_ids = [c.id for c in campaigns]
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client_impressions = set(
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CampaignImpression.objects.filter(
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client=client, campaign_id__in=campaign_ids
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).values_list("campaign_id", flat=True)
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)
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client_clicks = set(
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CampaignClick.objects.filter(
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client=client, campaign_id__in=campaign_ids
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).values_list("campaign_id", flat=True)
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)
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prioritized = []
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for campaign in valid_campaigns:
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ml_score = ml_score_dict.get(campaign.advertiser_id, 0)
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ml_values = []
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profit_values = []
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remaining_impressions = (
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campaign.impressions_limit
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- impressions_dict.get(campaign.id, 0)
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)
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weight_imp = (
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remaining_impressions / campaign.impressions_limit
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if campaign.impressions_limit > 0
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else 1.0
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)
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for campaign in campaigns:
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if campaign.impressions_count >= campaign.impressions_limit:
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continue
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current_clicks = clicks_dict.get(campaign.id, 0)
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remaining_clicks = campaign.clicks_limit - current_clicks
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if remaining_clicks <= 0:
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cpc_contribution = 0.0
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ml_score = ml_dict.get(campaign.advertiser_id, 0)
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ml_values.append(ml_score)
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has_impression = campaign.id in client_impressions
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has_click = campaign.id in client_clicks
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if has_impression:
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profit = campaign.cost_per_click if not has_click else 0
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else:
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click_availability = (
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(remaining_clicks / campaign.clicks_limit)
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if campaign.clicks_limit > 0
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else 1.0
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)
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prob_click = (ml_score / max_ml) if max_ml != 0 else 0.0
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cpc_contribution = (
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campaign.cost_per_click * prob_click * click_availability
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)
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profit = campaign.cost_per_impression + campaign.cost_per_click
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print(profit)
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if profit <= 0:
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continue
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expected_profit = campaign.cost_per_impression + cpc_contribution
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priority = ml_score * expected_profit * weight_imp
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prioritized.append((campaign, priority))
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profit_values.append(profit)
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prioritized.sort(key=lambda x: -x[1])
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return prioritized[0]
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remaining_imp = (
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campaign.impressions_limit - campaign.impressions_count
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)
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capacity_ratio = (
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remaining_imp / campaign.impressions_limit
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if campaign.impressions_limit > 0
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else 1
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)
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prioritized.append(
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(
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campaign,
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{
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"profit": profit,
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"ml": ml_score,
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"capacity": 1 - capacity_ratio,
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},
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)
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)
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max_ml = max(ml_values) if ml_values else 1
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max_profit = max(profit_values) if profit_values else 1
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min_profit = min(profit_values) if profit_values else 0
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profit_range = (
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max_profit - min_profit if max_profit != min_profit else 1
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)
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print(prioritized, max_ml, max_profit, min_profit, profit_range)
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final_list = []
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for campaign, metrics in prioritized:
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norm_profit = (metrics["profit"] - min_profit) / profit_range
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norm_ml = metrics["ml"] / max_ml if max_ml > 0 else 0
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priority = (
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0.5 * norm_profit + 0.25 * norm_ml + 0.15 * metrics["capacity"]
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)
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final_list.append((campaign, priority))
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final_list.sort(key=lambda x: -x[1])
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return final_list[0][0] if len(final_list) >= 1 else None
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class CampaignImpression(BaseModel):
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