From fd98a7f54111c70ed0c7f2d1a2fca3b9133e3c41 Mon Sep 17 00:00:00 2001 From: "CORE-FOLDCC\\Core" <1813547935@qq.com> Date: Fri, 5 Jun 2026 21:44:35 +0800 Subject: [PATCH] Add TapADN smart preload attribution --- .../Configs/IAAAdDebugSampleConfig.asset | 13 +- .../Runtime/TapadnIAAAdDebugSampleGui.cs | 8 + Assets/Tapadn_Adapter/Runtime/Resources.meta | 8 + .../TapadnSmartLoadPolicy_Default.json | 40 + .../TapadnSmartLoadPolicy_Default.json.meta | 7 + .../Runtime/Scripts/TapadnAdController.cs | 1 + .../Runtime/Scripts/TapadnAwardVideoPlayer.cs | 17 +- .../Scripts/TapadnControllerOptions.cs | 22 +- .../Scripts/TapadnInteractionPlayer.cs | 16 + .../Scripts/TapadnSmartLoadOrchestrator.cs | 981 ++++ .../TapadnSmartLoadOrchestrator.cs.meta | 11 + .../Runtime/Scripts/TapadnSplashPlayer.cs | 16 + GLOBAL_DESIGN.md | 40 +- README.md | 79 +- TapADN自动加载策略分析报告.md | 76 +- .../TapADN_智能预加载_敏感度验收报告.md | 160 + .../output/heatmap_immediate_r_0.20.png | Bin 0 -> 58155 bytes .../output/heatmap_immediate_r_0.35.png | Bin 0 -> 59297 bytes .../output/heatmap_immediate_r_0.60.png | Bin 0 -> 59745 bytes .../output/heatmap_preload_success_r_0.20.png | Bin 0 -> 57739 bytes .../output/heatmap_preload_success_r_0.35.png | Bin 0 -> 57812 bytes .../output/heatmap_preload_success_r_0.60.png | Bin 0 -> 57854 bytes .../output/heatmap_wait_ms_r_0.20.png | Bin 0 -> 58766 bytes .../output/heatmap_wait_ms_r_0.35.png | Bin 0 -> 58815 bytes .../output/heatmap_wait_ms_r_0.60.png | Bin 0 -> 58737 bytes .../output/heatmap_waste_ratio_r_0.20.png | Bin 0 -> 58302 bytes .../output/heatmap_waste_ratio_r_0.35.png | Bin 0 -> 57629 bytes .../output/heatmap_waste_ratio_r_0.60.png | Bin 0 -> 59334 bytes .../output/line_immediate_vs_retention.png | Bin 0 -> 81046 bytes .../line_mode_immediate_vs_retention.png | Bin 0 -> 49598 bytes .../output/line_mode_wait_vs_retention.png | Bin 0 -> 48397 bytes .../line_network_immediate_vs_retention.png | Bin 0 -> 86541 bytes .../output/line_network_wait_vs_retention.png | Bin 0 -> 95320 bytes .../line_threshold_0.55_retention_0.20.png | Bin 0 -> 78248 bytes .../line_threshold_0.55_retention_0.25.png | Bin 0 -> 79201 bytes .../line_threshold_0.55_retention_0.30.png | Bin 0 -> 83700 bytes .../line_threshold_0.55_retention_0.35.png | Bin 0 -> 83508 bytes .../line_threshold_0.55_retention_0.40.png | Bin 0 -> 83638 bytes .../line_threshold_0.55_retention_0.50.png | Bin 0 -> 83135 bytes .../line_threshold_0.55_retention_0.60.png | Bin 0 -> 83623 bytes .../output/line_wait_vs_retention.png | Bin 0 -> 99995 bytes .../output/smartload_mode_comparison.csv | 8 + .../smartload_network_mode_comparison.csv | 29 + .../output/smartload_retention_rank.csv | 736 +++ .../output/smartload_robot_cohort_summary.txt | 8 + .../output/smartload_robot_cohorts.csv | 5001 +++++++++++++++++ .../output/smartload_sensitivity_summary.csv | 743 +++ .../smartload_sensitivity_simulation.py | 1439 +++++ 48 files changed, 9441 insertions(+), 18 deletions(-) create mode 100644 Assets/Tapadn_Adapter/Runtime/Resources.meta create mode 100644 Assets/Tapadn_Adapter/Runtime/Resources/TapadnSmartLoadPolicy_Default.json create mode 100644 Assets/Tapadn_Adapter/Runtime/Resources/TapadnSmartLoadPolicy_Default.json.meta create mode 100644 Assets/Tapadn_Adapter/Runtime/Scripts/TapadnSmartLoadOrchestrator.cs create mode 100644 Assets/Tapadn_Adapter/Runtime/Scripts/TapadnSmartLoadOrchestrator.cs.meta create mode 100644 Tools/SmartLoadSensitivity/output/TapADN_智能预加载_敏感度验收报告.md create mode 100644 Tools/SmartLoadSensitivity/output/heatmap_immediate_r_0.20.png create mode 100644 Tools/SmartLoadSensitivity/output/heatmap_immediate_r_0.35.png create mode 100644 Tools/SmartLoadSensitivity/output/heatmap_immediate_r_0.60.png create mode 100644 Tools/SmartLoadSensitivity/output/heatmap_preload_success_r_0.20.png create mode 100644 Tools/SmartLoadSensitivity/output/heatmap_preload_success_r_0.35.png create mode 100644 Tools/SmartLoadSensitivity/output/heatmap_preload_success_r_0.60.png create mode 100644 Tools/SmartLoadSensitivity/output/heatmap_wait_ms_r_0.20.png create mode 100644 Tools/SmartLoadSensitivity/output/heatmap_wait_ms_r_0.35.png create mode 100644 Tools/SmartLoadSensitivity/output/heatmap_wait_ms_r_0.60.png create mode 100644 Tools/SmartLoadSensitivity/output/heatmap_waste_ratio_r_0.20.png create mode 100644 Tools/SmartLoadSensitivity/output/heatmap_waste_ratio_r_0.35.png create mode 100644 Tools/SmartLoadSensitivity/output/heatmap_waste_ratio_r_0.60.png create mode 100644 Tools/SmartLoadSensitivity/output/line_immediate_vs_retention.png create mode 100644 Tools/SmartLoadSensitivity/output/line_mode_immediate_vs_retention.png create mode 100644 Tools/SmartLoadSensitivity/output/line_mode_wait_vs_retention.png create mode 100644 Tools/SmartLoadSensitivity/output/line_network_immediate_vs_retention.png create mode 100644 Tools/SmartLoadSensitivity/output/line_network_wait_vs_retention.png create mode 100644 Tools/SmartLoadSensitivity/output/line_threshold_0.55_retention_0.20.png create mode 100644 Tools/SmartLoadSensitivity/output/line_threshold_0.55_retention_0.25.png create mode 100644 Tools/SmartLoadSensitivity/output/line_threshold_0.55_retention_0.30.png create mode 100644 Tools/SmartLoadSensitivity/output/line_threshold_0.55_retention_0.35.png create mode 100644 Tools/SmartLoadSensitivity/output/line_threshold_0.55_retention_0.40.png create mode 100644 Tools/SmartLoadSensitivity/output/line_threshold_0.55_retention_0.50.png create mode 100644 Tools/SmartLoadSensitivity/output/line_threshold_0.55_retention_0.60.png create mode 100644 Tools/SmartLoadSensitivity/output/line_wait_vs_retention.png create mode 100644 Tools/SmartLoadSensitivity/output/smartload_mode_comparison.csv create mode 100644 Tools/SmartLoadSensitivity/output/smartload_network_mode_comparison.csv create mode 100644 Tools/SmartLoadSensitivity/output/smartload_retention_rank.csv create mode 100644 Tools/SmartLoadSensitivity/output/smartload_robot_cohort_summary.txt create mode 100644 Tools/SmartLoadSensitivity/output/smartload_robot_cohorts.csv create mode 100644 Tools/SmartLoadSensitivity/output/smartload_sensitivity_summary.csv create mode 100644 Tools/SmartLoadSensitivity/smartload_sensitivity_simulation.py diff --git a/Assets/Samples~/IAAAdDebugSample/Configs/IAAAdDebugSampleConfig.asset b/Assets/Samples~/IAAAdDebugSample/Configs/IAAAdDebugSampleConfig.asset index 66608cb..72c6060 100644 --- a/Assets/Samples~/IAAAdDebugSample/Configs/IAAAdDebugSampleConfig.asset +++ b/Assets/Samples~/IAAAdDebugSample/Configs/IAAAdDebugSampleConfig.asset @@ -33,7 +33,7 @@ MonoBehaviour: - key: tapadn.sub_channel value: debug - key: tapadn.rewarded_auto_load - value: true + value: false - key: tapadn.rewarded_prewarm_on_init value: false - key: tapadn.rewarded_max_load_attempts @@ -47,7 +47,7 @@ MonoBehaviour: - key: tapadn.reward_amount value: 1 - key: tapadn.interstitial_auto_load - value: true + value: false - key: tapadn.interstitial_prewarm_on_init value: false - key: tapadn.interstitial_max_load_attempts @@ -57,7 +57,7 @@ MonoBehaviour: - key: tapadn.interstitial_show_timeout_ms value: 20000 - key: tapadn.splash_auto_load - value: true + value: false - key: tapadn.splash_prewarm_on_init value: false - key: tapadn.splash_max_load_attempts @@ -66,3 +66,10 @@ MonoBehaviour: value: 500 - key: tapadn.splash_show_timeout_ms value: 20000 + - key: tapadn.smart_preload_enabled + value: true + - key: tapadn.smart_preload_config_asset_path + value: TapadnSmartLoadPolicy_Default + - key: tapadn.smart_preload_config_json + value: >- + {"GlobalDefault":{"AdType":-1,"Scene":"__default__","BaseProbability":0.08,"PreloadThreshold":0.75,"CooldownSeconds":120,"MinSamplesForConfidence":8,"DecayHalfLifeHours":72},"ScenePolicies":[{"AdType":1,"Scene":"splash_debug","BaseProbability":0.25,"PreloadThreshold":0.7,"CooldownSeconds":120,"MinSamplesForConfidence":6,"DecayHalfLifeHours":48},{"AdType":0,"Scene":"reward_debug","BaseProbability":0.6,"PreloadThreshold":0.5,"CooldownSeconds":60,"MinSamplesForConfidence":4,"DecayHalfLifeHours":48},{"AdType":2,"Scene":"interstitial_debug","BaseProbability":0.45,"PreloadThreshold":0.65,"CooldownSeconds":90,"MinSamplesForConfidence":5,"DecayHalfLifeHours":48}]} diff --git a/Assets/Samples~/IAAAdDebugSample/Runtime/TapadnIAAAdDebugSampleGui.cs b/Assets/Samples~/IAAAdDebugSample/Runtime/TapadnIAAAdDebugSampleGui.cs index dfe3f77..4920d82 100644 --- a/Assets/Samples~/IAAAdDebugSample/Runtime/TapadnIAAAdDebugSampleGui.cs +++ b/Assets/Samples~/IAAAdDebugSample/Runtime/TapadnIAAAdDebugSampleGui.cs @@ -183,6 +183,8 @@ public sealed class TapadnIAAAdDebugSampleGui : MonoBehaviour GUILayout.Label( $"Channel/SubChannel/Debug: {DisplayValue(options.Channel)}/{DisplayValue(options.SubChannel)}/{options.Debug}", _textStyle); + GUILayout.Label($"Smart Preload: enabled={options.SmartPreloadEnabled}, asset={DisplayValue(options.SmartPreloadConfigAssetPath)}", _textStyle); + GUILayout.Label($"Smart Policy Snapshot: {TapadnSmartLoadOrchestrator.GetDebugStateDump()}", _textStyle); } if (showVerboseState && GUILayout.Button("Refresh Status Snapshot", _buttonStyle, GUILayout.Height(64f))) @@ -191,6 +193,12 @@ public sealed class TapadnIAAAdDebugSampleGui : MonoBehaviour AppendLog("Manual status snapshot refreshed."); } + if (GUILayout.Button("Reset SmartLoad Learning State", _buttonStyle, GUILayout.Height(64f))) + { + TapadnSmartLoadOrchestrator.ResetLearningState(); + AppendLog("TapADN SmartLoad learning state reset."); + } + GUILayout.Space(10f); } diff --git a/Assets/Tapadn_Adapter/Runtime/Resources.meta b/Assets/Tapadn_Adapter/Runtime/Resources.meta new file mode 100644 index 0000000..eb52a0b --- /dev/null +++ b/Assets/Tapadn_Adapter/Runtime/Resources.meta @@ -0,0 +1,8 @@ +fileFormatVersion: 2 +guid: 0f4f3adf5f7b4b2a8e4f3ff8f3a58c8e +folderAsset: yes +DefaultImporter: + externalObjects: {} + userData: + assetBundleName: + assetBundleVariant: diff --git a/Assets/Tapadn_Adapter/Runtime/Resources/TapadnSmartLoadPolicy_Default.json b/Assets/Tapadn_Adapter/Runtime/Resources/TapadnSmartLoadPolicy_Default.json new file mode 100644 index 0000000..a9fd90d --- /dev/null +++ b/Assets/Tapadn_Adapter/Runtime/Resources/TapadnSmartLoadPolicy_Default.json @@ -0,0 +1,40 @@ +{ + "GlobalDefault": { + "AdType": -1, + "Scene": "__default__", + "BaseProbability": 0.08, + "PreloadThreshold": 0.75, + "CooldownSeconds": 120, + "MinSamplesForConfidence": 8, + "DecayHalfLifeHours": 72 + }, + "ScenePolicies": [ + { + "AdType": 0, + "Scene": "reward_debug", + "BaseProbability": 0.6, + "PreloadThreshold": 0.55, + "CooldownSeconds": 90, + "MinSamplesForConfidence": 6, + "DecayHalfLifeHours": 48 + }, + { + "AdType": 2, + "Scene": "interstitial_debug", + "BaseProbability": 0.45, + "PreloadThreshold": 0.65, + "CooldownSeconds": 90, + "MinSamplesForConfidence": 5, + "DecayHalfLifeHours": 48 + }, + { + "AdType": 1, + "Scene": "splash_debug", + "BaseProbability": 0.25, + "PreloadThreshold": 0.7, + "CooldownSeconds": 120, + "MinSamplesForConfidence": 6, + "DecayHalfLifeHours": 48 + } + ] +} diff --git a/Assets/Tapadn_Adapter/Runtime/Resources/TapadnSmartLoadPolicy_Default.json.meta b/Assets/Tapadn_Adapter/Runtime/Resources/TapadnSmartLoadPolicy_Default.json.meta new file mode 100644 index 0000000..e39aef5 --- /dev/null +++ b/Assets/Tapadn_Adapter/Runtime/Resources/TapadnSmartLoadPolicy_Default.json.meta @@ -0,0 +1,7 @@ +fileFormatVersion: 2 +guid: b586678bfef849f2a293548b54f7790a +TextScriptImporter: + externalObjects: {} + userData: + assetBundleName: + assetBundleVariant: diff --git a/Assets/Tapadn_Adapter/Runtime/Scripts/TapadnAdController.cs b/Assets/Tapadn_Adapter/Runtime/Scripts/TapadnAdController.cs index 2ff8d1c..f7aa563 100644 --- a/Assets/Tapadn_Adapter/Runtime/Scripts/TapadnAdController.cs +++ b/Assets/Tapadn_Adapter/Runtime/Scripts/TapadnAdController.cs @@ -19,6 +19,7 @@ public sealed class TapadnAdController : IAdController _adConfig = adConfig; _options = TapadnControllerOptions.Resolve(adConfig, args); CurrentOptions = _options; + TapadnSmartLoadOrchestrator.Initialize(_options); var sdkConfig = _options.BuildSdkConfig(); DirichletSdk.Init( diff --git a/Assets/Tapadn_Adapter/Runtime/Scripts/TapadnAwardVideoPlayer.cs b/Assets/Tapadn_Adapter/Runtime/Scripts/TapadnAwardVideoPlayer.cs index 4bb80c5..20183aa 100644 --- a/Assets/Tapadn_Adapter/Runtime/Scripts/TapadnAwardVideoPlayer.cs +++ b/Assets/Tapadn_Adapter/Runtime/Scripts/TapadnAwardVideoPlayer.cs @@ -70,10 +70,12 @@ public sealed class TapadnAwardVideoPlayer : ADPlayer, IDirichletRewardVideoAuto } curState = 1; + TapadnSmartLoadOrchestrator.OnLoadStarted(AD_Type.AwardVideo, AdScene); _adNative.LoadRewardVideoAd( TapadnAdRequestFactory.BuildRewarded(Key, TapadnAdController.CurrentOptions), ad => { + TapadnSmartLoadOrchestrator.OnLoadResult(AD_Type.AwardVideo, AdScene, true); _loadedAd?.Destroy(); _loadedAd = ad; _loadedAd.Shown += OnManualShown; @@ -86,6 +88,7 @@ public sealed class TapadnAwardVideoPlayer : ADPlayer, IDirichletRewardVideoAuto }, error => { + TapadnSmartLoadOrchestrator.OnLoadResult(AD_Type.AwardVideo, AdScene, false); curState = 0; Debug.LogError($"[TapADN] Rewarded load failed. code={error.Code}, message={error.Message}"); }); @@ -127,12 +130,14 @@ public sealed class TapadnAwardVideoPlayer : ADPlayer, IDirichletRewardVideoAuto _rewardCloseSettleHandler?.Kill(); _rewardCloseSettleHandler = null; curState = 0; + TapadnSmartLoadOrchestrator.OnShowError(AD_Type.AwardVideo, AdScene); Debug.LogError($"[TapADN] Rewarded show failed. code={error?.Code}, message={error?.Message}"); adListener.OnShowError(); } public void OnAdShow() { + TapadnSmartLoadOrchestrator.OnShowStart(AD_Type.AwardVideo, AdScene); NotifyShowStarted(); } @@ -168,6 +173,16 @@ public sealed class TapadnAwardVideoPlayer : ADPlayer, IDirichletRewardVideoAuto { } + public override void OnPlayRequestStarted() + { + TapadnSmartLoadOrchestrator.OnPlayRequestStarted(AD_Type.AwardVideo, AdScene, !UseAutoLoad() && IsReadly()); + } + + public override void EnterAdScenario(string scenario) + { + TapadnSmartLoadOrchestrator.OnEnterAdScenario(AD_Type.AwardVideo, scenario, Key); + } + private bool UseAutoLoad() { return TapadnAdController.CurrentOptions?.RewardedAutoLoad ?? true; @@ -175,7 +190,7 @@ public sealed class TapadnAwardVideoPlayer : ADPlayer, IDirichletRewardVideoAuto private void OnManualShown() { - NotifyShowStarted(); + OnAdShow(); } private void OnManualClicked() diff --git a/Assets/Tapadn_Adapter/Runtime/Scripts/TapadnControllerOptions.cs b/Assets/Tapadn_Adapter/Runtime/Scripts/TapadnControllerOptions.cs index 49915a4..0534b44 100644 --- a/Assets/Tapadn_Adapter/Runtime/Scripts/TapadnControllerOptions.cs +++ b/Assets/Tapadn_Adapter/Runtime/Scripts/TapadnControllerOptions.cs @@ -39,6 +39,10 @@ public sealed class TapadnControllerOptions public const string SplashMaxLoadAttemptsKey = "tapadn.splash_max_load_attempts"; public const string SplashLoadRetryDelayMsKey = "tapadn.splash_load_retry_delay_ms"; public const string SplashShowTimeoutMsKey = "tapadn.splash_show_timeout_ms"; + public const string SmartPreloadEnabledKey = "tapadn.smart_preload_enabled"; + public const string SmartPreloadConfigJsonKey = "tapadn.smart_preload_config_json"; + public const string SmartPreloadConfigAssetPathKey = "tapadn.smart_preload_config_asset_path"; + public const string SmartPreloadRemoteConfigJsonKey = "tapadn.smart_preload_remote_json"; public const string ExpressWidthKey = "tapadn.express_width"; public const string ExpressHeightKey = "tapadn.express_height"; @@ -55,23 +59,27 @@ public sealed class TapadnControllerOptions public string ATags { get; set; } public bool AllowIDFAAccess { get; set; } = true; public bool RequestPermissionOnInit { get; set; } - public bool RewardedAutoLoad { get; set; } = true; + public bool RewardedAutoLoad { get; set; } = false; public bool RewardedPrewarmOnInit { get; set; } public int RewardedMaxLoadAttempts { get; set; } = 1; public int RewardedLoadRetryDelayMs { get; set; } = 500; public int RewardedShowTimeoutMs { get; set; } = 20000; public string RewardName { get; set; } = "reward"; public int RewardAmount { get; set; } = 1; - public bool InterstitialAutoLoad { get; set; } = true; + public bool InterstitialAutoLoad { get; set; } = false; public bool InterstitialPrewarmOnInit { get; set; } public int InterstitialMaxLoadAttempts { get; set; } = 1; public int InterstitialLoadRetryDelayMs { get; set; } = 500; public int InterstitialShowTimeoutMs { get; set; } = 20000; - public bool SplashAutoLoad { get; set; } = true; + public bool SplashAutoLoad { get; set; } = false; public bool SplashPrewarmOnInit { get; set; } public int SplashMaxLoadAttempts { get; set; } = 1; public int SplashLoadRetryDelayMs { get; set; } = 500; public int SplashShowTimeoutMs { get; set; } = 20000; + public bool SmartPreloadEnabled { get; set; } = false; + public string SmartPreloadConfigAssetPath { get; set; } = "TapadnSmartLoadPolicy_Default"; + public string SmartPreloadConfigJson { get; set; } + public string SmartPreloadRemoteConfigJson { get; set; } public int? ExpressWidth { get; set; } public int? ExpressHeight { get; set; } @@ -209,6 +217,10 @@ public sealed class TapadnControllerOptions SplashMaxLoadAttempts = incoming.SplashMaxLoadAttempts; SplashLoadRetryDelayMs = incoming.SplashLoadRetryDelayMs; SplashShowTimeoutMs = incoming.SplashShowTimeoutMs; + SmartPreloadEnabled = incoming.SmartPreloadEnabled; + SmartPreloadConfigAssetPath = incoming.SmartPreloadConfigAssetPath ?? SmartPreloadConfigAssetPath; + SmartPreloadConfigJson = incoming.SmartPreloadConfigJson; + SmartPreloadRemoteConfigJson = incoming.SmartPreloadRemoteConfigJson; ExpressWidth = incoming.ExpressWidth ?? ExpressWidth; ExpressHeight = incoming.ExpressHeight ?? ExpressHeight; } @@ -266,6 +278,10 @@ public sealed class TapadnControllerOptions SplashMaxLoadAttempts = GetInt(map, SplashMaxLoadAttemptsKey) ?? SplashMaxLoadAttempts; SplashLoadRetryDelayMs = GetInt(map, SplashLoadRetryDelayMsKey) ?? SplashLoadRetryDelayMs; SplashShowTimeoutMs = GetInt(map, SplashShowTimeoutMsKey) ?? SplashShowTimeoutMs; + SmartPreloadEnabled = GetBool(map, SmartPreloadEnabledKey) ?? SmartPreloadEnabled; + SmartPreloadConfigAssetPath = GetString(map, SmartPreloadConfigAssetPathKey) ?? SmartPreloadConfigAssetPath; + SmartPreloadConfigJson = GetString(map, SmartPreloadConfigJsonKey) ?? SmartPreloadConfigJson; + SmartPreloadRemoteConfigJson = GetString(map, SmartPreloadRemoteConfigJsonKey) ?? SmartPreloadRemoteConfigJson; ExpressWidth = GetInt(map, ExpressWidthKey) ?? ExpressWidth; ExpressHeight = GetInt(map, ExpressHeightKey) ?? ExpressHeight; } diff --git a/Assets/Tapadn_Adapter/Runtime/Scripts/TapadnInteractionPlayer.cs b/Assets/Tapadn_Adapter/Runtime/Scripts/TapadnInteractionPlayer.cs index 43bec6a..1d516c0 100644 --- a/Assets/Tapadn_Adapter/Runtime/Scripts/TapadnInteractionPlayer.cs +++ b/Assets/Tapadn_Adapter/Runtime/Scripts/TapadnInteractionPlayer.cs @@ -56,10 +56,12 @@ public sealed class TapadnInteractionPlayer : ADPlayer, IDirichletInterstitialAu } curState = 1; + TapadnSmartLoadOrchestrator.OnLoadStarted(AD_Type.Interaction, AdScene); _adNative.LoadInterstitialAd( TapadnAdRequestFactory.BuildInterstitial(Key, TapadnAdController.CurrentOptions), ad => { + TapadnSmartLoadOrchestrator.OnLoadResult(AD_Type.Interaction, AdScene, true); _loadedAd?.Destroy(); _loadedAd = ad; _loadedAd.Shown += OnManualShown; @@ -71,6 +73,7 @@ public sealed class TapadnInteractionPlayer : ADPlayer, IDirichletInterstitialAu }, error => { + TapadnSmartLoadOrchestrator.OnLoadResult(AD_Type.Interaction, AdScene, false); curState = 0; Debug.LogError($"[TapADN] Interstitial load failed. code={error.Code}, message={error.Message}"); }); @@ -103,6 +106,7 @@ public sealed class TapadnInteractionPlayer : ADPlayer, IDirichletInterstitialAu } _showSettled = true; + TapadnSmartLoadOrchestrator.OnShowError(AD_Type.Interaction, AdScene); curState = 0; Debug.LogError($"[TapADN] Interstitial show failed. code={error?.Code}, message={error?.Message}"); adListener.OnShowError(); @@ -110,6 +114,7 @@ public sealed class TapadnInteractionPlayer : ADPlayer, IDirichletInterstitialAu public void OnAdShow() { + TapadnSmartLoadOrchestrator.OnShowStart(AD_Type.Interaction, AdScene); NotifyShowStarted(); } @@ -129,6 +134,16 @@ public sealed class TapadnInteractionPlayer : ADPlayer, IDirichletInterstitialAu { } + public override void OnPlayRequestStarted() + { + TapadnSmartLoadOrchestrator.OnPlayRequestStarted(AD_Type.Interaction, AdScene, !UseAutoLoad() && IsReadly()); + } + + public override void EnterAdScenario(string scenario) + { + TapadnSmartLoadOrchestrator.OnEnterAdScenario(AD_Type.Interaction, scenario, Key); + } + private bool UseAutoLoad() { return TapadnAdController.CurrentOptions?.InterstitialAutoLoad ?? true; @@ -136,6 +151,7 @@ public sealed class TapadnInteractionPlayer : ADPlayer, IDirichletInterstitialAu private void OnManualShown() { + TapadnSmartLoadOrchestrator.OnShowStart(AD_Type.Interaction, AdScene); NotifyShowStarted(); } diff --git a/Assets/Tapadn_Adapter/Runtime/Scripts/TapadnSmartLoadOrchestrator.cs b/Assets/Tapadn_Adapter/Runtime/Scripts/TapadnSmartLoadOrchestrator.cs new file mode 100644 index 0000000..1e27c6d --- /dev/null +++ b/Assets/Tapadn_Adapter/Runtime/Scripts/TapadnSmartLoadOrchestrator.cs @@ -0,0 +1,981 @@ +using System; +using System.Collections.Generic; +using System.Text; +using Runtime.ADAggregator; +using UnityEngine; + +// 智能预加载实验：基于“场景出现次数/该场景实际播放请求次数”进行置信决策， +// 并允许通过策略配置进行服务端默认值覆盖。 +public static class TapadnSmartLoadOrchestrator +{ + private const string StatsPrefsKey = "TapadnSmartLoadStats.v1"; + private const string DefaultScene = "__default__"; + private const string PolicyDefaultScene = "__default__"; + + private static TapadnSmartLoadConfig _runtimeConfig; + private static Dictionary _states = new Dictionary(StringComparer.Ordinal); + private static Dictionary _policies = new Dictionary(StringComparer.Ordinal); + private static Dictionary _cacheStates = new Dictionary(); + private static bool _initialized; + private static bool _enabled; + + public static bool IsEnabled => _enabled; + + public static string GetDebugStateDump() + { + if (!_initialized) + { + return "{}"; + } + + var lines = new List(); + foreach (var entry in _states.Values) + { + if (entry == null) + { + continue; + } + + var adType = SafeToAdType(entry.AdType); + if (!adType.HasValue) + { + continue; + } + + var policy = ResolvePolicy(adType.Value, entry.Scenario); + var prob = EstimateShowProbability(adType.Value, entry.Scenario); + lines.Add( + $"[{entry.AdType}]{entry.Scenario} e={entry.EnterCount} pReq={entry.PlayRequestCount} preReq={entry.PreloadRequestCount} preSuc={entry.PreloadSuccessCount} preFail={entry.PreloadFailureCount} showReq={entry.ShowRequestCount} showOk={entry.ShowStartCount} showFail={entry.ShowFailureCount} imm={entry.ImmediateHitCount} smartHit={entry.SmartCacheHitCount} smartUsed={entry.SmartPreloadConsumedCount} score={prob:F3} policy={policy?.PreloadThreshold:F2}"); + } + + return $"enabled={_enabled},states={lines.Count},policies={_policies.Count},cache={GetCacheDebugDump()},snapshot={string.Join(" ; ", lines)}"; + } + + public static string ExportSnapshotCsv() + { + if (!_initialized) + { + return GetSnapshotCsvHeader(); + } + + var sb = new StringBuilder(); + sb.AppendLine(GetSnapshotCsvHeader()); + foreach (var entry in _states.Values) + { + if (entry == null) + { + continue; + } + + sb.AppendLine(string.Join(",", + entry.AdType, + EncodeCsv(NormalizeScenario(entry.Scenario)), + entry.EnterCount, + entry.PlayRequestCount, + entry.PreloadRequestCount, + entry.PreloadSuccessCount, + entry.PreloadFailureCount, + entry.ShowRequestCount, + entry.ShowStartCount, + entry.ShowFailureCount, + entry.ImmediateHitCount, + entry.SmartCacheHitCount, + entry.SmartCacheCrossSceneHitCount, + entry.UnattributedCacheHitCount, + entry.SmartPreloadConsumedCount, + entry.SmartPreloadConsumedSameSceneCount, + entry.SmartPreloadConsumedOtherSceneCount, + entry.SmartPreloadShowFailureCount, + entry.SmartPreloadExpiredCount, + EncodeCsv(NormalizeScenario(entry.LastSmartPreloadConsumedByScene)), + entry.LastSmartPreloadConsumedUnix, + entry.LastPreloadUnix, + entry.LastUpdatedUnix, + EstimateShowProbability((AD_Type)entry.AdType, entry.Scenario))); + } + + return sb.ToString(); + } + + public static void ResetLearningState() + { + if (!_initialized) + { + return; + } + + _states.Clear(); + _cacheStates.Clear(); + PlayerPrefs.DeleteKey(StatsPrefsKey); + PlayerPrefs.Save(); + } + + public static void Initialize(TapadnControllerOptions options) + { + _runtimeConfig = BuildConfig(options); + _enabled = options?.SmartPreloadEnabled ?? false; + _initialized = true; + _states = new Dictionary(StringComparer.Ordinal); + _cacheStates = new Dictionary(); + LoadStates(); + EnsureDefaultPolicies(_runtimeConfig); + } + + public static void OnEnterAdScenario(AD_Type adType, string scenario, string _slotId) + { + if (!_initialized || !_enabled) + { + return; + } + + var normalizedScenario = NormalizeScenario(scenario); + var record = GetOrCreateState(adType, normalizedScenario); + record.EnterCount = Math.Max(0, record.EnterCount) + 1; + record.LastUpdatedUnix = GetNowUnixSeconds(); + record.ShowRequestCount = Math.Max(0, record.ShowRequestCount); + SaveStates(); + + TryPreload(adType, normalizedScenario); + } + + public static void OnPlayRequestStarted(AD_Type adType, string scenario, bool cacheReadyAtRequest) + { + if (!_initialized || !_enabled) + { + return; + } + + var normalizedScenario = NormalizeScenario(scenario); + var record = GetOrCreateState(adType, normalizedScenario); + record.PlayRequestCount = Math.Max(0, record.PlayRequestCount) + 1; + record.ShowRequestCount = Math.Max(0, record.ShowRequestCount) + 1; + record.LastUpdatedUnix = GetNowUnixSeconds(); + if (cacheReadyAtRequest) + { + MarkImmediateHit(adType, normalizedScenario); + } + else + { + MarkCacheExpiredIfStale(adType); + } + + SaveStates(); + } + + public static void OnLoadRequested(AD_Type adType, string scenario) + { + if (!_initialized || !_enabled) + { + return; + } + + PrepareSmartLoadRequest(adType, scenario); + } + + public static void OnLoadStarted(AD_Type adType, string scenario) + { + if (!_initialized || !_enabled) + { + return; + } + + BeginLoadRequest(adType, scenario); + SaveStates(); + } + + public static void OnLoadResult(AD_Type adType, string scenario, bool success) + { + if (!_initialized || !_enabled) + { + return; + } + + CompleteLoadRequest(adType, scenario, success); + SaveStates(); + } + + public static void OnShowStart(AD_Type adType, string scenario) + { + if (!_initialized || !_enabled) + { + return; + } + + var record = GetOrCreateState(adType, NormalizeScenario(scenario)); + record.ShowStartCount = Math.Max(0, record.ShowStartCount) + 1; + record.LastUpdatedUnix = GetNowUnixSeconds(); + MarkCacheConsumed(adType, scenario); + SaveStates(); + } + + public static void OnShowError(AD_Type adType, string scenario) + { + if (!_initialized || !_enabled) + { + return; + } + + var record = GetOrCreateState(adType, NormalizeScenario(scenario)); + record.ShowFailureCount = Math.Max(0, record.ShowFailureCount) + 1; + record.LastUpdatedUnix = GetNowUnixSeconds(); + MarkCacheShowFailed(adType, scenario); + SaveStates(); + } + + private static void TryPreload(AD_Type adType, string scenario) + { + if (!_initialized || !_enabled) + { + return; + } + + if (!ADManager.Instance.CheckNetwork()) + { + return; + } + + var policy = ResolvePolicy(adType, scenario); + if (policy == null) + { + return; + } + + if (!NeedPreload(adType, scenario, policy)) + { + return; + } + + var now = GetNowUnixSeconds(); + var state = GetOrCreateState(adType, scenario); + if (now - state.LastPreloadUnix < policy.CooldownSeconds) + { + return; + } + + if (ADManager.Instance.IsRealy(adType)) + { + return; + } + + MarkCacheExpiredIfStale(adType); + OnLoadRequested(adType, scenario); + ADManager.Instance.LoadAD(adType); + var smartLoadStarted = HasPendingSmartLoadForScene(adType, scenario); + ClearPreparedLoadIfNotStarted(adType); + if (!smartLoadStarted) + { + return; + } + + state.LastPreloadUnix = now; + state.LastUpdatedUnix = now; + SaveStates(); + Debug.Log($"[TapADN SmartLoad] Preload triggered: type={adType}, scenario={scenario}, score={EstimateShowProbability(adType, scenario):F3}"); + } + + private static bool NeedPreload(AD_Type adType, string scenario, TapadnSmartLoadPolicyItem policy) + { + var score = EstimateShowProbability(adType, scenario); + return score >= policy.PreloadThreshold; + } + + private static float EstimateShowProbability(AD_Type adType, string scenario) + { + var policy = ResolvePolicy(adType, scenario); + if (policy == null) + { + return 0f; + } + + var state = GetOrCreateState(adType, scenario); + if (state.EnterCount <= 0) + { + return policy.BaseProbability; + } + + var observedRate = Mathf.Clamp01(state.PlayRequestCount / (float)state.EnterCount); + var confidence = Mathf.Clamp01(state.EnterCount / (float)Mathf.Max(1, policy.MinSamplesForConfidence)); + var ageHours = Mathf.Max(0f, (GetNowUnixSeconds() - state.LastUpdatedUnix) / 3600f); + var decay = Mathf.Pow(0.5f, ageHours / Mathf.Max(1f, policy.DecayHalfLifeHours)); + var trust = Mathf.Clamp01(confidence * decay); + + return Mathf.Lerp(policy.BaseProbability, observedRate, trust); + } + + private static void PrepareSmartLoadRequest(AD_Type adType, string scenario) + { + var state = GetOrCreateCacheState(adType); + if (state.PendingSmartLoad) + { + return; + } + + state.PreparedSmartLoad = true; + state.PreparedOriginScene = NormalizeScenario(scenario); + state.PreparedOriginScore = EstimateShowProbability(adType, scenario); + state.PreparedRequestUnix = GetNowUnixSeconds(); + } + + private static void BeginLoadRequest(AD_Type adType, string scenario) + { + var state = GetOrCreateCacheState(adType); + var now = GetNowUnixSeconds(); + if (state.PreparedSmartLoad) + { + var originScene = NormalizeScenario(state.PreparedOriginScene); + var record = GetOrCreateState(adType, originScene); + record.PreloadRequestCount = Math.Max(0, record.PreloadRequestCount) + 1; + record.LastUpdatedUnix = now; + state.PendingSmartLoad = true; + state.PendingOriginScene = originScene; + state.PendingOriginScore = state.PreparedOriginScore; + state.PendingRequestUnix = now; + } + else + { + state.PendingSmartLoad = false; + state.PendingOriginScene = NormalizeScenario(scenario); + state.PendingOriginScore = EstimateShowProbability(adType, scenario); + state.PendingRequestUnix = now; + } + + state.HasReadyCache = false; + state.PreparedSmartLoad = false; + state.PreparedOriginScene = null; + state.PreparedOriginScore = 0f; + state.PreparedRequestUnix = 0; + } + + private static void ClearPreparedLoadIfNotStarted(AD_Type adType) + { + var state = GetOrCreateCacheState(adType); + if (state.PendingSmartLoad) + { + return; + } + + state.PreparedSmartLoad = false; + state.PreparedOriginScene = null; + state.PreparedOriginScore = 0f; + state.PreparedRequestUnix = 0; + } + + private static bool HasPendingSmartLoadForScene(AD_Type adType, string scenario) + { + var state = GetOrCreateCacheState(adType); + return state.PendingSmartLoad && + string.Equals(NormalizeScenario(state.PendingOriginScene), NormalizeScenario(scenario), StringComparison.Ordinal); + } + + private static void CompleteLoadRequest(AD_Type adType, string scenario, bool success) + { + var cacheState = GetOrCreateCacheState(adType); + var normalizedScenario = NormalizeScenario(scenario); + var originScene = cacheState.PendingSmartLoad ? NormalizeScenario(cacheState.PendingOriginScene) : normalizedScenario; + var originRecord = GetOrCreateState(adType, originScene); + + if (cacheState.PendingSmartLoad) + { + if (success) + { + originRecord.PreloadSuccessCount = Math.Max(0, originRecord.PreloadSuccessCount) + 1; + } + else + { + originRecord.PreloadFailureCount = Math.Max(0, originRecord.PreloadFailureCount) + 1; + } + } + + originRecord.LastUpdatedUnix = GetNowUnixSeconds(); + if (success) + { + cacheState.HasReadyCache = true; + cacheState.ReadyFromSmart = cacheState.PendingSmartLoad; + cacheState.ReadyOriginScene = originScene; + cacheState.ReadyOriginScore = cacheState.PendingSmartLoad ? cacheState.PendingOriginScore : EstimateShowProbability(adType, originScene); + cacheState.ReadyLoadedUnix = GetNowUnixSeconds(); + cacheState.ReadyRequestUnix = cacheState.PendingRequestUnix; + cacheState.Consumed = false; + } + else if (cacheState.PendingSmartLoad) + { + ClearReadyCache(cacheState); + } + + cacheState.PendingSmartLoad = false; + cacheState.PendingOriginScene = null; + cacheState.PendingOriginScore = 0f; + cacheState.PendingRequestUnix = 0; + } + + private static void MarkImmediateHit(AD_Type adType, string scenario) + { + var consumedScene = NormalizeScenario(scenario); + var currentSceneRecord = GetOrCreateState(adType, consumedScene); + currentSceneRecord.ImmediateHitCount = Math.Max(0, currentSceneRecord.ImmediateHitCount) + 1; + currentSceneRecord.LastUpdatedUnix = GetNowUnixSeconds(); + + var cacheState = GetOrCreateCacheState(adType); + if (!cacheState.HasReadyCache) + { + currentSceneRecord.UnattributedCacheHitCount = Math.Max(0, currentSceneRecord.UnattributedCacheHitCount) + 1; + } + } + + private static void MarkCacheConsumed(AD_Type adType, string scenario) + { + var cacheState = GetOrCreateCacheState(adType); + if (!cacheState.HasReadyCache || cacheState.Consumed) + { + return; + } + + var consumedScene = NormalizeScenario(scenario); + var originScene = NormalizeScenario(cacheState.ReadyOriginScene); + var currentSceneRecord = GetOrCreateState(adType, consumedScene); + var originSceneRecord = GetOrCreateState(adType, originScene); + + if (cacheState.ReadyFromSmart) + { + currentSceneRecord.SmartCacheHitCount = Math.Max(0, currentSceneRecord.SmartCacheHitCount) + 1; + originSceneRecord.SmartPreloadConsumedCount = Math.Max(0, originSceneRecord.SmartPreloadConsumedCount) + 1; + originSceneRecord.LastSmartPreloadConsumedByScene = consumedScene; + originSceneRecord.LastSmartPreloadConsumedUnix = GetNowUnixSeconds(); + + if (string.Equals(originScene, consumedScene, StringComparison.Ordinal)) + { + originSceneRecord.SmartPreloadConsumedSameSceneCount = Math.Max(0, originSceneRecord.SmartPreloadConsumedSameSceneCount) + 1; + } + else + { + currentSceneRecord.SmartCacheCrossSceneHitCount = Math.Max(0, currentSceneRecord.SmartCacheCrossSceneHitCount) + 1; + originSceneRecord.SmartPreloadConsumedOtherSceneCount = Math.Max(0, originSceneRecord.SmartPreloadConsumedOtherSceneCount) + 1; + } + } + currentSceneRecord.LastUpdatedUnix = GetNowUnixSeconds(); + originSceneRecord.LastUpdatedUnix = GetNowUnixSeconds(); + cacheState.Consumed = true; + ClearReadyCache(cacheState); + } + + private static void MarkCacheShowFailed(AD_Type adType, string scenario) + { + var cacheState = GetOrCreateCacheState(adType); + if (!cacheState.HasReadyCache) + { + return; + } + + var originScene = NormalizeScenario(cacheState.ReadyOriginScene); + if (cacheState.ReadyFromSmart) + { + var originSceneRecord = GetOrCreateState(adType, originScene); + originSceneRecord.SmartPreloadShowFailureCount = Math.Max(0, originSceneRecord.SmartPreloadShowFailureCount) + 1; + originSceneRecord.LastSmartPreloadConsumedByScene = NormalizeScenario(scenario); + originSceneRecord.LastSmartPreloadConsumedUnix = GetNowUnixSeconds(); + originSceneRecord.LastUpdatedUnix = GetNowUnixSeconds(); + } + + ClearReadyCache(cacheState); + } + + private static void MarkCacheExpiredIfStale(AD_Type adType) + { + var cacheState = GetOrCreateCacheState(adType); + if (!cacheState.HasReadyCache) + { + return; + } + + if (ADManager.Instance.IsRealy(adType)) + { + return; + } + + if (cacheState.ReadyFromSmart) + { + var originSceneRecord = GetOrCreateState(adType, NormalizeScenario(cacheState.ReadyOriginScene)); + originSceneRecord.SmartPreloadExpiredCount = Math.Max(0, originSceneRecord.SmartPreloadExpiredCount) + 1; + originSceneRecord.LastUpdatedUnix = GetNowUnixSeconds(); + } + + ClearReadyCache(cacheState); + } + + private static TapadnSmartLoadCacheState GetOrCreateCacheState(AD_Type adType) + { + var key = (int)adType; + if (_cacheStates.TryGetValue(key, out var state) && state != null) + { + return state; + } + + state = new TapadnSmartLoadCacheState(); + _cacheStates[key] = state; + return state; + } + + private static void ClearReadyCache(TapadnSmartLoadCacheState state) + { + if (state == null) + { + return; + } + + state.HasReadyCache = false; + state.ReadyFromSmart = false; + state.ReadyOriginScene = null; + state.ReadyOriginScore = 0f; + state.ReadyLoadedUnix = 0; + state.ReadyRequestUnix = 0; + state.Consumed = false; + } + + private static string GetCacheDebugDump() + { + if (_cacheStates == null || _cacheStates.Count == 0) + { + return "empty"; + } + + var lines = new List(); + foreach (var entry in _cacheStates) + { + var state = entry.Value; + if (state == null) + { + continue; + } + + lines.Add($"[{entry.Key}]prepared={state.PreparedSmartLoad}:{NormalizeScenario(state.PreparedOriginScene)} pending={state.PendingSmartLoad}:{NormalizeScenario(state.PendingOriginScene)} ready={state.HasReadyCache}:{state.ReadyFromSmart}:{NormalizeScenario(state.ReadyOriginScene)} score={state.ReadyOriginScore:F3}"); + } + + return string.Join(" | ", lines); + } + + private static string GetSnapshotCsvHeader() + { + return "ad_type,scenario,enter_count,play_request_count,preload_request,preload_success,preload_fail,show_request,show_start,show_fail,immediate_hit,smart_cache_hit,smart_cache_cross_scene_hit,unattributed_cache_hit,smart_preload_consumed,smart_preload_consumed_same_scene,smart_preload_consumed_other_scene,smart_preload_show_fail,smart_preload_expired,last_smart_preload_consumed_by_scene,last_smart_preload_consumed_unix,last_preload_unix,last_updated_unix,last_score"; + } + + private static TapadnSmartLoadPolicyItem ResolvePolicy(AD_Type adType, string scenario) + { + if (_runtimeConfig == null) + { + _runtimeConfig = BuildDefaultConfig(); + } + + var normalizedScenario = NormalizeScenario(scenario); + var exactKey = ComposeKey(adType, normalizedScenario); + if (_policies.TryGetValue(exactKey, out var exactPolicy)) + { + return exactPolicy; + } + + var defaultScenarioKey = ComposeKey(adType, PolicyDefaultScene); + if (_policies.TryGetValue(defaultScenarioKey, out var defaultPolicy)) + { + return defaultPolicy; + } + + return _runtimeConfig?.GlobalDefault != null ? _runtimeConfig.GlobalDefault : BuildDefaultConfig().GlobalDefault; + } + + private static void EnsureDefaultPolicies(TapadnSmartLoadConfig config) + { + _policies.Clear(); + if (config == null || config.ScenePolicies == null) + { + config = BuildDefaultConfig(); + } + + if (config.GlobalDefault == null) + { + config.GlobalDefault = BuildDefaultPolicy(); + } + + foreach (var policy in config.ScenePolicies) + { + if (policy == null || string.IsNullOrWhiteSpace(policy.Scene)) + { + continue; + } + + if (policy.AdType < 0 || policy.AdType > 2) + { + continue; + } + + var key = ComposeKey((AD_Type)policy.AdType, NormalizeScenario(policy.Scene)); + _policies[key] = new TapadnSmartLoadPolicyItem + { + AdType = policy.AdType, + Scene = NormalizeScenario(policy.Scene), + BaseProbability = Mathf.Clamp01(policy.BaseProbability), + PreloadThreshold = Mathf.Clamp(policy.PreloadThreshold, 0f, 1f), + CooldownSeconds = Math.Max(0, policy.CooldownSeconds), + MinSamplesForConfidence = Math.Max(1, policy.MinSamplesForConfidence), + DecayHalfLifeHours = Math.Max(1f, policy.DecayHalfLifeHours), + }; + } + + foreach (AD_Type adType in Enum.GetValues(typeof(AD_Type))) + { + var key = ComposeKey(adType, PolicyDefaultScene); + if (!_policies.ContainsKey(key)) + { + _policies[key] = ClonePolicy(config.GlobalDefault, adType); + } + } + } + + private static TapadnSmartLoadPolicyItem ClonePolicy(TapadnSmartLoadPolicyItem source, AD_Type adType) + { + if (source == null) + { + source = BuildDefaultPolicy(); + } + + return new TapadnSmartLoadPolicyItem + { + AdType = (int)adType, + Scene = PolicyDefaultScene, + BaseProbability = source.BaseProbability, + PreloadThreshold = source.PreloadThreshold, + CooldownSeconds = source.CooldownSeconds, + MinSamplesForConfidence = source.MinSamplesForConfidence, + DecayHalfLifeHours = source.DecayHalfLifeHours + }; + } + + private static TapadnSmartLoadConfig BuildConfig(TapadnControllerOptions options) + { + var config = BuildDefaultConfig(); + MergePolicyJson(config, ReadPolicyAssetJson(options)); + MergePolicyJson(config, options?.SmartPreloadConfigJson); + MergePolicyJson(config, options?.SmartPreloadRemoteConfigJson); + return config; + } + + private static string ReadPolicyAssetJson(TapadnControllerOptions options) + { + if (options == null || string.IsNullOrWhiteSpace(options.SmartPreloadConfigAssetPath)) + { + return null; + } + + try + { + var asset = Resources.Load(options.SmartPreloadConfigAssetPath); + return asset != null ? asset.text : null; + } + catch (Exception exception) + { + Debug.LogWarning($"[TapADN SmartLoad] Load policy asset failed: {exception.Message}"); + return null; + } + } + + private static TapadnSmartLoadConfig BuildDefaultConfig() + { + return new TapadnSmartLoadConfig + { + GlobalDefault = BuildDefaultPolicy(), + ScenePolicies = new List(), + }; + } + + private static TapadnSmartLoadPolicyItem BuildDefaultPolicy() + { + return new TapadnSmartLoadPolicyItem + { + AdType = -1, + Scene = PolicyDefaultScene, + BaseProbability = 0.08f, + PreloadThreshold = 0.75f, + CooldownSeconds = 120, + MinSamplesForConfidence = 8, + DecayHalfLifeHours = 72f + }; + } + + private static void MergePolicyJson(TapadnSmartLoadConfig target, string json) + { + if (string.IsNullOrWhiteSpace(json)) + { + return; + } + + try + { + var parsed = JsonUtility.FromJson(json); + if (parsed == null) + { + return; + } + + if (parsed.GlobalDefault != null) + { + target.GlobalDefault = NormalizePolicy(parsed.GlobalDefault); + } + + if (parsed.ScenePolicies == null || parsed.ScenePolicies.Count == 0) + { + return; + } + + if (target.ScenePolicies == null) + { + target.ScenePolicies = new List(); + } + + foreach (var policy in parsed.ScenePolicies) + { + if (policy == null) + { + continue; + } + + if (policy.AdType < 0 || policy.AdType > 2) + { + continue; + } + + if (string.IsNullOrWhiteSpace(policy.Scene)) + { + continue; + } + + target.ScenePolicies.Add(policy); + } + } + catch (Exception exception) + { + Debug.LogWarning($"[TapADN SmartLoad] Merge policy JSON failed: {exception.Message}"); + } + } + + private static string EncodeCsv(string value) + { + if (string.IsNullOrEmpty(value)) + { + return "0"; + } + + if (value.Contains(",") || value.Contains("\"") || value.Contains("\n")) + { + return $"\"{value.Replace("\"", "\"\"")}\""; + } + + return value; + } + + private static TapadnSmartLoadPolicyItem NormalizePolicy(TapadnSmartLoadPolicyItem policy) + { + return new TapadnSmartLoadPolicyItem + { + AdType = policy.AdType, + Scene = NormalizeScenario(policy.Scene), + BaseProbability = Mathf.Clamp01(policy.BaseProbability), + PreloadThreshold = Mathf.Clamp(policy.PreloadThreshold, 0f, 1f), + CooldownSeconds = Math.Max(0, policy.CooldownSeconds), + MinSamplesForConfidence = Math.Max(1, policy.MinSamplesForConfidence), + DecayHalfLifeHours = Math.Max(1f, policy.DecayHalfLifeHours), + }; + } + + private static TapadnSmartLoadSceneState GetOrCreateState(AD_Type adType, string scenario) + { + var key = ComposeKey(adType, NormalizeScenario(scenario)); + if (_states.TryGetValue(key, out var state)) + { + return state; + } + + state = new TapadnSmartLoadSceneState + { + AdType = (int)adType, + Scenario = NormalizeScenario(scenario), + EnterCount = 0, + PlayRequestCount = 0, + LastUpdatedUnix = GetNowUnixSeconds() + }; + _states[key] = state; + return state; + } + + private static void LoadStates() + { + try + { + var raw = PlayerPrefs.GetString(StatsPrefsKey, string.Empty); + if (string.IsNullOrWhiteSpace(raw)) + { + return; + } + + var data = JsonUtility.FromJson(raw); + if (data == null || data.Entries == null) + { + return; + } + + foreach (var entry in data.Entries) + { + if (entry == null || entry.AdType < 0) + { + continue; + } + + var adType = SafeToAdType(entry.AdType); + var scenario = NormalizeScenario(entry.Scenario); + if (adType == null) + { + continue; + } + + var key = ComposeKey(adType.Value, scenario); + _states[key] = entry; + } + } + catch (Exception exception) + { + Debug.LogWarning($"[TapADN SmartLoad] Load state failed: {exception.Message}"); + } + } + + private static AD_Type? SafeToAdType(int value) + { + if (value < 0 || value > 2) + { + return null; + } + + return (AD_Type)value; + } + + private static void SaveStates() + { + var data = new TapadnSmartLoadStateData(); + data.Entries = new List(); + + foreach (var entry in _states.Values) + { + data.Entries.Add(entry); + } + + var raw = JsonUtility.ToJson(data); + if (string.IsNullOrWhiteSpace(raw)) + { + return; + } + + PlayerPrefs.SetString(StatsPrefsKey, raw); + PlayerPrefs.Save(); + } + + private static string ComposeKey(AD_Type adType, string scenario) + { + return ((int)adType) + "|" + NormalizeScenario(scenario); + } + + private static string NormalizeScenario(string scenario) + { + if (string.IsNullOrWhiteSpace(scenario)) + { + return DefaultScene; + } + + return scenario.Trim(); + } + + private static long GetNowUnixSeconds() + { + return (long)(DateTime.UtcNow - new DateTime(1970, 1, 1, 0, 0, 0, DateTimeKind.Utc)).TotalSeconds; + } + + [Serializable] + private sealed class TapadnSmartLoadConfig + { + public TapadnSmartLoadPolicyItem GlobalDefault; + public List ScenePolicies; + } + + [Serializable] + private sealed class TapadnSmartLoadPolicyItem + { + public int AdType; + public string Scene; + public float BaseProbability; + public float PreloadThreshold; + public int CooldownSeconds; + public int MinSamplesForConfidence; + public float DecayHalfLifeHours; + } + + [Serializable] + private sealed class TapadnSmartLoadPolicyItemDto + { + public int AdType; + public string Scene; + public float BaseProbability = 0.08f; + public float PreloadThreshold = 0.75f; + public int CooldownSeconds = 120; + public int MinSamplesForConfidence = 8; + public float DecayHalfLifeHours = 72f; + } + + [Serializable] + private sealed class TapadnSmartLoadStateData + { + public int Version = 1; + public List Entries; + } + + private sealed class TapadnSmartLoadCacheState + { + public bool PreparedSmartLoad; + public string PreparedOriginScene; + public float PreparedOriginScore; + public long PreparedRequestUnix; + public bool PendingSmartLoad; + public string PendingOriginScene; + public float PendingOriginScore; + public long PendingRequestUnix; + public bool HasReadyCache; + public bool ReadyFromSmart; + public string ReadyOriginScene; + public float ReadyOriginScore; + public long ReadyLoadedUnix; + public long ReadyRequestUnix; + public bool Consumed; + } + + [Serializable] + private sealed class TapadnSmartLoadSceneState + { + public int AdType; + public string Scenario; + public int EnterCount; + public int PlayRequestCount; + public int PreloadRequestCount; + public int PreloadSuccessCount; + public int PreloadFailureCount; + public int ShowRequestCount; + public int ShowStartCount; + public int ShowFailureCount; + public int ImmediateHitCount; + public int SmartCacheHitCount; + public int SmartCacheCrossSceneHitCount; + public int UnattributedCacheHitCount; + public int SmartPreloadConsumedCount; + public int SmartPreloadConsumedSameSceneCount; + public int SmartPreloadConsumedOtherSceneCount; + public int SmartPreloadShowFailureCount; + public int SmartPreloadExpiredCount; + public string LastSmartPreloadConsumedByScene; + public long LastSmartPreloadConsumedUnix; + public long LastUpdatedUnix; + public long LastPreloadUnix; + } +} diff --git a/Assets/Tapadn_Adapter/Runtime/Scripts/TapadnSmartLoadOrchestrator.cs.meta b/Assets/Tapadn_Adapter/Runtime/Scripts/TapadnSmartLoadOrchestrator.cs.meta new file mode 100644 index 0000000..aabb9db --- /dev/null +++ b/Assets/Tapadn_Adapter/Runtime/Scripts/TapadnSmartLoadOrchestrator.cs.meta @@ -0,0 +1,11 @@ +fileFormatVersion: 2 +guid: c383420c72bb4c1f90c0e88f0e250b5d +MonoImporter: + externalObjects: {} + serializedVersion: 2 + defaultReferences: [] + executionOrder: 0 + icon: {instanceID: 0} + userData: + assetBundleName: + assetBundleVariant: diff --git a/Assets/Tapadn_Adapter/Runtime/Scripts/TapadnSplashPlayer.cs b/Assets/Tapadn_Adapter/Runtime/Scripts/TapadnSplashPlayer.cs index 85c3b24..55e03c8 100644 --- a/Assets/Tapadn_Adapter/Runtime/Scripts/TapadnSplashPlayer.cs +++ b/Assets/Tapadn_Adapter/Runtime/Scripts/TapadnSplashPlayer.cs @@ -56,10 +56,12 @@ public sealed class TapadnSplashPlayer : ADPlayer, IDirichletSplashAutoAdListene } curState = 1; + TapadnSmartLoadOrchestrator.OnLoadStarted(AD_Type.Splash, AdScene); _adNative.LoadSplashAd( TapadnAdRequestFactory.BuildSplash(Key, TapadnAdController.CurrentOptions), ad => { + TapadnSmartLoadOrchestrator.OnLoadResult(AD_Type.Splash, AdScene, true); _loadedAd?.Destroy(); _loadedAd = ad; _loadedAd.Shown += OnManualShown; @@ -71,6 +73,7 @@ public sealed class TapadnSplashPlayer : ADPlayer, IDirichletSplashAutoAdListene }, error => { + TapadnSmartLoadOrchestrator.OnLoadResult(AD_Type.Splash, AdScene, false); curState = 0; Debug.LogError($"[TapADN] Splash load failed. code={error.Code}, message={error.Message}"); }); @@ -103,6 +106,7 @@ public sealed class TapadnSplashPlayer : ADPlayer, IDirichletSplashAutoAdListene } _showSettled = true; + TapadnSmartLoadOrchestrator.OnShowError(AD_Type.Splash, AdScene); curState = 0; Debug.LogError($"[TapADN] Splash show failed. code={error?.Code}, message={error?.Message}"); adListener.OnShowError(); @@ -110,6 +114,7 @@ public sealed class TapadnSplashPlayer : ADPlayer, IDirichletSplashAutoAdListene public void OnAdShow() { + TapadnSmartLoadOrchestrator.OnShowStart(AD_Type.Splash, AdScene); NotifyShowStarted(); } @@ -129,6 +134,16 @@ public sealed class TapadnSplashPlayer : ADPlayer, IDirichletSplashAutoAdListene { } + public override void OnPlayRequestStarted() + { + TapadnSmartLoadOrchestrator.OnPlayRequestStarted(AD_Type.Splash, AdScene, !UseAutoLoad() && IsReadly()); + } + + public override void EnterAdScenario(string scenario) + { + TapadnSmartLoadOrchestrator.OnEnterAdScenario(AD_Type.Splash, scenario, Key); + } + private bool UseAutoLoad() { return TapadnAdController.CurrentOptions?.SplashAutoLoad ?? true; @@ -136,6 +151,7 @@ public sealed class TapadnSplashPlayer : ADPlayer, IDirichletSplashAutoAdListene private void OnManualShown() { + TapadnSmartLoadOrchestrator.OnShowStart(AD_Type.Splash, AdScene); NotifyShowStarted(); } diff --git a/GLOBAL_DESIGN.md b/GLOBAL_DESIGN.md index d2a4cf7..cc10ac3 100644 --- a/GLOBAL_DESIGN.md +++ b/GLOBAL_DESIGN.md @@ -35,12 +35,12 @@ ## 广告播放器 -默认方案：使用 TapADN auto-ad 接口。 +默认方案：优先手动 load/show，避免把 `ADManager.AsyncAdPlayer` 的时序语义与 TapADN auto 语义混用。 原因： -* 官方 Unity 文档推荐 Android auto-ad，接口把加载和展示合并，SDK 自己管理缓存。 -* `ADManager.AsyncAdPlayer` 已有遮罩、超时和回调收口；auto-ad 可以让业务侧首次播放少一个 preload 时序。 +* TapADN auto-ad 是官方 Android 场景下的“加载 + 展示合并”调用，不等价于完整场景分发与实时 readiness 能力。 +* `ADManager.AsyncAdPlayer` 先做 `IsReadly` 决策，未 ready 则调用 `LoadAD`，ready 后再 `ShowAD`，所以手动模式下生命周期和失败收口更稳定。 备选方案：手动 load/show。 @@ -105,3 +105,37 @@ 该路径以 `Packages/manifest.json` 所在目录为基准，符合 Unity 本地 UPM package 规则；`Packages/CC-Framework.Commercialization` 只是本机验证副本，已被 `.gitignore` 排除，不进入发布包。 如果 batchmode 出现 `Failed to resolve packages: The "path" argument must be of type string. Received undefined`，不把它直接归因为 manifest 路径错误，也不使用 `-noUpm` 绕过。处理顺序是保存 Unity Editor log 和 `%LOCALAPPDATA%\Unity\Editor\upm.log`，确认没有并发 Unity/UPM 进程，再用官方 `Unity.exe -batchmode -quit -projectPath -logFile ` 做一次只解析 package 的验证；若仍复现，则用 Unity Hub/已打开 Editor 作为 GUI 对照入口继续看 Console 编译错误。 + +## 策略验收与可视化 + +模块内置智能预加载策略评估脚本已接入，默认次留验收目标为 `35%`，用于验证 `PreloadThreshold`、`CooldownSeconds` 在不同留存下的策略敏感度。 + +验收输出位于： + +* `Tools/SmartLoadSensitivity/output/TapADN_智能预加载_敏感度验收报告.md` +* `Tools/SmartLoadSensitivity/output/smartload_sensitivity_summary.csv` +* `Tools/SmartLoadSensitivity/output/smartload_retention_rank.csv` +* `Tools/SmartLoadSensitivity/output/*.png` + +推荐复现命令： + +```bash +python Tools/SmartLoadSensitivity/smartload_sensitivity_simulation.py --users 5000 --out-dir Tools/SmartLoadSensitivity/output +``` + +核心对比维度（含 35%次留）： + +* `threshold` 的下调会提升 `Immediate`，但会抬高 `Waste`。 +* `cooldown` 拉长可压低 waste、降低重复预加载频次，但也会把 show 等待时延上抬。 +* 默认策略建议先用 `threshold=0.20~0.40` 做高即时性验证，再通过 waste 上限（如 8%~12%）回退到 `0.35~0.55` 区间。 + +### 全局缓存归因 + +`CC-Framework.Commercialization` 当前是一类广告一个全局 `ADPlayer`，场景只是播放与策略标签，不是缓存隔离单位。因此 TapADN 智能预加载按两层口径统计： + +* 策略触发层：哪个场景触发了真实 SDK load，记录 `preload_request/preload_success/preload_fail`。 +* 播放消费层：哪个场景实际发起 `AsyncPlayAD` 并展示，记录 `immediate_hit/show_start/smart_cache_hit`。 + +如果 A 场景触发的全局缓存最终在 B 场景展示，A 会记录 `smart_preload_consumed_other_scene`，B 会记录 `smart_cache_cross_scene_hit`。这不会改变玩家实际体验，只用于避免把“全局缓存命中”误读成“同场景完全自消费”。 + +验收产物建议每周回放：用最近 7 天留存分布与 fill 成功率替换脚本默认参数，再做一次敏感度重算并对比排名变化。 diff --git a/README.md b/README.md index 0cfaedd..bcf5d60 100644 --- a/README.md +++ b/README.md @@ -83,10 +83,56 @@ ADManager.Instance.Init(callback, userId, adConfig, new TapadnAdController()); * `tapadn.splash_max_load_attempts` * `tapadn.splash_load_retry_delay_ms` * `tapadn.splash_show_timeout_ms` +* `tapadn.smart_preload_enabled`（开启智能预加载策略） +* `tapadn.smart_preload_config_json`（智能预加载静态配置 JSON） +* `tapadn.smart_preload_config_asset_path`（预加载策略资源表路径，默认 `TapadnSmartLoadPolicy_Default`） +* `tapadn.smart_preload_remote_json`（远端覆盖配置 JSON，按场景覆盖静态配置） * `tapadn.express_width` * `tapadn.express_height` -默认激励、插屏、开屏都使用 TapADN Android auto-ad 接口。若遇到渠道缓存策略差异，可将对应 `*_auto_load` 设为 `false`，切换为手动 load/show。 +默认激励、插屏、开屏都使用手动 load/show；如无特殊策略验证需求，不建议开启 auto-ad。若要做 auto-ad AB 测试，再将对应 `*_auto_load` 设为 `true`。 + +### 智能预加载（实验） + +默认会按“场景进入次数 + 场景播放请求次数”维护一个小样本统计： + +* 进入场景时记录 `EnterCount` +* `AsyncPlayAD` 执行前调用的 `OnPlayRequestStarted` 记录 `PlayRequestCount` +* 按置信加权算法推算场景播放概率，达到阈值后触发 `ADManager.LoadAD` 预加载 +* 预加载归因按“全局广告类型缓存”记录：触发场景记录 `preload_request/success/fail`，实际播放场景记录 `show/immediate/smart_cache_hit` +* 当 A 场景触发的全局缓存在 B 场景展示时，A 会记录 `smart_preload_consumed_other_scene`，B 会记录 `smart_cache_cross_scene_hit` + +配置 JSON 示例（可通过 `CommonKeyValues` 下发）： + +```json +{ + "GlobalDefault": { + "AdType": -1, + "Scene": "__default__", + "BaseProbability": 0.08, + "PreloadThreshold": 0.75, + "CooldownSeconds": 120, + "MinSamplesForConfidence": 8, + "DecayHalfLifeHours": 72 + }, + "ScenePolicies": [ + { + "AdType": 0, + "Scene": "reward_debug", + "BaseProbability": 0.6, + "PreloadThreshold": 0.5, + "CooldownSeconds": 60, + "MinSamplesForConfidence": 4, + "DecayHalfLifeHours": 48 + } + ] +} +``` + +说明： +* `AdType` 使用 `AD_Type` 枚举值：`0=AwardVideo`, `1=Splash`, `2=Interaction` +* `Scene` 用于和 `ADManager.EnterAdScenario` 的场景名对齐；场景未命中时回退到对应广告位的默认场景配置。 +* `ExportSnapshotCsv()` 会额外导出 `immediate_hit`、`smart_cache_hit`、`smart_preload_consumed`、`smart_preload_expired` 等列，用于区分“哪个场景触发预加载”和“哪个场景最终消费缓存”。 ## Android 构建 @@ -100,3 +146,34 @@ ADManager.Instance.Init(callback, userId, adConfig, new TapadnAdController()); * `android.useAndroidX=true` 与 `android.enableJetifier=true`。 包内不默认暴露可视化编辑面板；调试样例通过 `Samples~` 作为可选导入内容。 + +## 智能预加载敏感度验收（默认次留 35%） + +本模块包含一套本地仿真脚本，用于模拟 IAA 场景下不同 `PreloadThreshold` 与 `CooldownSeconds` 的收益差异，输出完整 CSV 与变化曲线。 + +默认次留基线设置为 `35%`（你可以改保留率列表），默认模型参数位于 `Assets/Tapadn_Adapter/Runtime/Resources/TapadnSmartLoadPolicy_Default.json`。 + +```bash +python Tools/SmartLoadSensitivity/smartload_sensitivity_simulation.py --users 5000 --out-dir Tools/SmartLoadSensitivity/output +``` + +输出文件（可复现）： + +* `Tools/SmartLoadSensitivity/output/smartload_sensitivity_summary.csv` +* `Tools/SmartLoadSensitivity/output/smartload_retention_rank.csv` +* `Tools/SmartLoadSensitivity/output/TapADN_智能预加载_敏感度验收报告.md` +* `Tools/SmartLoadSensitivity/output/*.png`（热力图和曲线） + +重点图示（`TapADN_智能预加载_敏感度验收报告.md` 中已自动嵌入）： + +* `heatmap_immediate_r_0.35.png`（次留 35% 下的即时命中率） +* `heatmap_wait_ms_r_0.35.png`（次留 35% 下的平均等待时延） +* `heatmap_waste_ratio_r_0.35.png`（次留 35% 下的 waste 率） +* `line_immediate_vs_retention.png`（留存与即时命中率关系） +* `line_wait_vs_retention.png`（留存与等待时延关系） + +你也可以直接查看部分曲线对比： + +![Immediate vs retention](Tools/SmartLoadSensitivity/output/line_immediate_vs_retention.png) +![Wait vs retention](Tools/SmartLoadSensitivity/output/line_wait_vs_retention.png) +![Heatmap immediate 35%](Tools/SmartLoadSensitivity/output/heatmap_immediate_r_0.35.png) diff --git a/TapADN自动加载策略分析报告.md b/TapADN自动加载策略分析报告.md index 6a6346c..5350927 100644 --- a/TapADN自动加载策略分析报告.md +++ b/TapADN自动加载策略分析报告.md @@ -6,7 +6,10 @@ 但不能把 TapADN 的 auto 与 TopOn 的全自动加载视为同一种能力。TopOn 的 auto 是一套更完整的广告位请求维护方案，包含广告位注册、真实 ready 查询、场景进入统计、缓存状态统计和自动续载；TapADN 当前 Unity wrapper 暴露的是“加载 + 展示合并”的 auto show API，没有看到类似 `entryAutoAdScenarioWithPlacementID` 的场景入口，也没有看到 auto ready 查询 API。 -因此，生产默认策略建议调整为“激励视频可优先 auto，但插屏和开屏建议默认手动或由项目配置显式开启 auto”。如果为了兼容当前模块和快速调试继续保留 auto 默认值，也应该在正式项目配置里显式写清楚每个广告类型的选择，避免项目层误以为 `IsReadly()` 表示真实填充可展示。 +因此，生产默认策略建议改为“默认全部手动”，并保留每类广告的开关用于分场景 AB。原因是： + +* 当前 `AsyncAdPlayer` 已经承载了 ready/load/show 的生命周期统一，手动模式更能把回调和失败收口稳定在框架层。 +* 上层仍会调用 `ADManager.EnterAdScenario(...)`，这更适合我们把策略判断放到“场景上”，而不是让 SDK 的 auto 接口替代场景决策。 ## 问题定义 @@ -20,9 +23,9 @@ 当前模块默认配置位于 `Assets/Tapadn_Adapter/Runtime/Scripts/TapadnControllerOptions.cs`： -* `RewardedAutoLoad = true` -* `InterstitialAutoLoad = true` -* `SplashAutoLoad = true` + * `RewardedAutoLoad = false` + * `InterstitialAutoLoad = false` + * `SplashAutoLoad = false` * `RewardedPrewarmOnInit`、`InterstitialPrewarmOnInit`、`SplashPrewarmOnInit` 默认为 `false` 三个播放器的 auto 分支行为如下： @@ -77,8 +80,8 @@ TapADN / Dirichlet Unity 文档对 auto 的描述主要是“加载和展示合 建议在生产项目里按如下方式显式配置： ```text -tapadn.rewarded_auto_load=true -tapadn.rewarded_prewarm_on_init=true 或在进入高概率广告场景前主动 LoadAD +tapadn.rewarded_auto_load=false +tapadn.rewarded_prewarm_on_init=false （建议改用入口驱动的手动 preload） tapadn.interstitial_auto_load=false tapadn.interstitial_prewarm_on_init=false @@ -87,7 +90,7 @@ tapadn.splash_auto_load=false tapadn.splash_prewarm_on_init=false ``` -如果项目追求快速接入、广告位较少、可以接受展示时等待，则可以短期保持当前三类 auto 默认开启。但正式灰度前至少应完成以下验证： +如果你想走“上层智能策略”也可以让默认逻辑保持上面的配置，再在高概率收益场景按策略临时开 auto 进行实验。必须完成以下验证： * auto 模式下点击激励按钮到 `OnAdShow` 的延迟分布。 * auto 模式下 show fail、load fail、close、reward verify 是否都被 `ADManager` 收口。 @@ -95,6 +98,42 @@ tapadn.splash_prewarm_on_init=false * 开屏失败或无填充时是否能按产品要求进入主场景。 * 每个业务广告场景是否有自己的埋点、频控和远程配置。 +## 内部智能策略建议（结合场景声明） + +上层仍然在 `ADManager.EnterAdScenario(adType, scenario)` 里会声明场景，这个入口可以成为 TapADN 的控制平面。建议用一个本地策略层（或接入现有 remote config）来替代“固定 auto 默认”。核心规则建议: + +* 入场闸门：新手 / 首次会话内降低开屏和插屏频率，设置较长冷却。 +* 价值分层：`resume_continue`、`revive`、`double_reward`、`level_complete`、`session_end` 使用不同触发阈值。 +* 预算分发：按场景给定每小时/每日展示配额，按最近 N 次 fill 成功率和关闭率动态降权。 +* 失败避障：对连续 fail 场景计入惩罚分，自动降级到手动模式或延后下一次尝试。 +* 回落路径：场景声明后可先触发预加载（手动），再在 `AsyncPlayAD` 时做 ready 判断，避免 show 时阻塞。 + +### 实战落地推荐（先做这 4 件） + +为了先上线再迭代，这版实验建议按下面节奏推进（先保守后扩展）： + +1. 先做“是否预加载”二分类决策 + - 输入：场景进入次数 / 播放请求次数 / 最近一次更新距今时长 + - 输出：`preload_score` 与 `cooldown` + - 初始阈值：`score >= threshold && cooldown 过期` + +2. 再加“场景独立配置表” + - 默认配置放在 `TapadnSmartLoadPolicy_Default.json`（本地资源表） + - 支持服务端覆盖配置（`tapadn.smart_preload_remote_json`） + - 场景命中优先、无命中回退到该位 default + +3. 建立在线反馈回路（每 1~3 天可更新） + - `enter_count` / `play_request_count` + `fill 成功率` + - `show_success - close 率` 作为质量约束（用于上调/下调阈值） + +4. 降噪与退化 + - 连续低效场景自动降 `threshold`、拉长 `cooldown` + - 连续高效场景适当提高预加载优先级 + +这样比“固定 always auto”更安全：能先守住体验（手动 show 生命周期），再逐步把预加载放到高价值场景。 + +这样我们保持框架语义：`AsyncAdPlayer` 负责生命周期和回调收口，场景策略只负责“该不该、何时、给谁”。 + ## 当前实现评价 当前 TapADN auto 接入本身是合规且完整的 API 级接入，但默认策略偏激进。它适合样例工程、快速调试和 Android 单平台验证；对于正式 IAA 游戏商业化，建议把默认认知改成： @@ -119,3 +158,26 @@ tapadn.splash_prewarm_on_init=false * [Google AdMob recommended interstitial implementations](https://support.google.com/admob/answer/6201350?hl=en) * [Google AdMob frequency capping](https://support.google.com/admob/answer/6244508?hl=en) * [Unity LevelPlay placement best practices](https://docs.unity.com/grow/levelplay/platform/best-practices/placement) + +## 仿真验收输出（新增） + +为便于快速比较 `threshold / cooldown` 对用户体验与 waste 的影响，本模块附带一套敏感度脚本产物，默认次留目标为 35%： + +- 报告：`Tools/SmartLoadSensitivity/output/TapADN_智能预加载_敏感度验收报告.md` +- 明细 CSV：`Tools/SmartLoadSensitivity/output/smartload_sensitivity_summary.csv` +- 排名 CSV：`Tools/SmartLoadSensitivity/output/smartload_retention_rank.csv` +- 图表：`Tools/SmartLoadSensitivity/output/*.png` + +常用可视化入口（打开仓库根目录）： + +- [热力图（35%次留）](Tools/SmartLoadSensitivity/output/heatmap_immediate_r_0.35.png) +- [平均时延（35%次留）](Tools/SmartLoadSensitivity/output/heatmap_wait_ms_r_0.35.png) +- [Waste 热力图（35%次留）](Tools/SmartLoadSensitivity/output/heatmap_waste_ratio_r_0.35.png) +- [留存变化线（即时性）](Tools/SmartLoadSensitivity/output/line_immediate_vs_retention.png) +- [留存变化线（时延）](Tools/SmartLoadSensitivity/output/line_wait_vs_retention.png) + +脚本运行命令： + +```bash +python Tools/SmartLoadSensitivity/smartload_sensitivity_simulation.py --users 5000 --out-dir Tools/SmartLoadSensitivity/output +``` diff --git a/Tools/SmartLoadSensitivity/output/TapADN_智能预加载_敏感度验收报告.md b/Tools/SmartLoadSensitivity/output/TapADN_智能预加载_敏感度验收报告.md new file mode 100644 index 0000000..fb4037d --- /dev/null +++ b/Tools/SmartLoadSensitivity/output/TapADN_智能预加载_敏感度验收报告.md @@ -0,0 +1,160 @@ +# TapADN 智能预加载策略验收报告（含随机偏好机器人 + 网络环境） + +## 仿真前提 +- 模型定位：IAA 场景中 3 类广告位（激励/开屏/插屏）统一参与策略决策。 +- 次留默认验收基线：35% +- 随机机器人数量：5000 +- 场景进入->展示请求->预加载决策->展示时延为核心链路。 +- 每次样本运行前先生成一批“偏好机器人”，再在其上分别运行：纯手动（无 smart）与智能预加载两种模式。 +- 预加载触发后在 cooldown 内有效一次，不命中将视作普通 load。 +- `Immediate`：请求时已命中可直接播放的占比（值越高越好） +- `Waste`：预加载后在 cooldown 内未被消费即失效的比例（值越低越好） +- 网络环境：Wi-Fi / 4G / 3G / 2G 按机器人偏好加权采样，逐回合独立变化。 + +## 机器人与网络设置 +- 机器人类型：随机采样以下偏好族（reward_heavy、interstitial_focus、splash_driven、balanced、churn_sensitive、network_bound）。 +- 每类机器人具有独立的场景进入偏好、请求偏好、fill 成功倍率、加载时延倍率和留存倍率。 +- 网络环境以会话粒度采样，低网速会同步影响网络请求率、fill 成功率和加载耗时。 + +## 参数扫描范围 +- Threshold: `0.20~0.90` 步长 0.05 +- Cooldown: `30,60,90,120,180,240,300` +- 次留：`20%,25%,30%,35%,40%,50%,60%` + +## 基线对照 +- 次留 35% 基线（纯手动，无 smart）即时命中率：0.00% +- 次留 35% 基线平均时延：1615 ms（p95 2913） +- 次留 35% 建议起点（当前模型）：`threshold=0.25, cooldown=30s` + - 对比基线即时命中提升：`38.09%` + - 对比基线时延变化：`-565 ms` +- 平衡候选（Waste<=12%）： + - threshold=0.25, cd=60s，Immediate 25.12%，Waste 4.88% + +## 最优/最差（次留=35%） +| 排名 | Threshold | Cooldown | 即时命中率 | 平均等待 | P95等待 | 浪费率 | 播放成功率 | +|---|---:|---:|---:|---:|---:|---:|---:| +| 1 | 0.25 | 30 | 38.09% | 1050 | 2787 | 14.03% | 81.35% | +| 2 | 0.20 | 30 | 36.65% | 1073 | 2790 | 13.96% | 81.35% | +| 3 | 0.25 | 60 | 25.12% | 1234 | 2774 | 4.88% | 77.96% | +| 4 | 0.20 | 60 | 24.81% | 1242 | 2830 | 6.75% | 77.45% | +| 5 | 0.30 | 30 | 24.65% | 1285 | 2894 | 7.73% | 77.79% | +| 6 | 0.30 | 60 | 24.56% | 1239 | 2812 | 5.76% | 77.61% | +| 7 | 0.35 | 30 | 23.05% | 1318 | 2895 | 5.44% | 77.58% | +| 8 | 0.50 | 30 | 22.96% | 1320 | 2893 | 4.99% | 77.54% | + +### 最差 8 组 +| 排名 | Threshold | Cooldown | 即时命中率 | 平均等待 | P95等待 | 浪费率 | 播放成功率 | +|---|---:|---:|---:|---:|---:|---:|---:| +| 1 | 0.90 | 300 | 0.00% | 1629 | 2924 | 0.00% | 70.55% | +| 2 | 0.90 | 240 | 0.00% | 1630 | 2899 | 0.00% | 70.09% | +| 3 | 0.90 | 180 | 0.00% | 1622 | 2907 | 0.00% | 70.60% | +| 4 | 0.90 | 120 | 0.00% | 1633 | 2943 | 0.00% | 70.51% | +| 5 | 0.90 | 90 | 0.00% | 1637 | 2963 | 0.00% | 70.84% | +| 6 | 0.90 | 60 | 0.00% | 1628 | 2863 | 0.00% | 70.72% | +| 7 | 0.90 | 30 | 0.00% | 1627 | 2933 | 0.00% | 69.52% | +| 8 | 0.85 | 300 | 0.00% | 1623 | 2910 | 0.00% | 70.69% | + +## 与纯手动模式对比（次留35%） +- 纯手动：Immediate `0.00%`，AvgWait `1615`ms，Waste `0.00%`。 +- smart 最优：阈值 `0.25`，cd `30`，Immediate `38.09%`，AvgWait `1050`ms，Waste `14.03%`。 +- 增益：`38.09%`。 + +### 网络分层对比（次留35%） +- WIFI：手动即时 `0.00%` / 等待 `1248ms`；智能（`threshold=0.25, cd=30s`）即时 `40.13%` / 等待 `767ms`；提升 `40.13%`。 +- 4G：手动即时 `0.00%` / 等待 `1601ms`；智能（`threshold=0.25, cd=30s`）即时 `39.66%` / 等待 `1003ms`；提升 `39.66%`。 +- 3G：手动即时 `0.00%` / 等待 `2165ms`；智能（`threshold=0.20, cd=30s`）即时 `32.64%` / 等待 `1498ms`；提升 `32.64%`。 +- 2G：手动即时 `0.00%` / 等待 `3044ms`；智能（`threshold=0.25, cd=30s`）即时 `32.06%` / 等待 `2116ms`；提升 `32.06%`。 +## 次留敏感度（35%基线） +- 次留 20%：手动基线时延 1618ms，智能最佳 `threshold=0.20, cd=30s`，最佳 Immediate 38.69%，Waste 13.14%；最差 `threshold=0.90, cd=300s`，Immediate 0.00%，Waste 0.00%。 +- 次留 25%：手动基线时延 1641ms，智能最佳 `threshold=0.20, cd=30s`，最佳 Immediate 37.52%，Waste 14.16%；最差 `threshold=0.90, cd=300s`，Immediate 0.00%，Waste 0.00%。 +- 次留 30%：手动基线时延 1629ms，智能最佳 `threshold=0.20, cd=30s`，最佳 Immediate 37.42%，Waste 13.33%；最差 `threshold=0.90, cd=300s`，Immediate 0.00%，Waste 0.00%。 +- 次留 35%：手动基线时延 1615ms，智能最佳 `threshold=0.25, cd=30s`，最佳 Immediate 38.09%，Waste 14.03%；最差 `threshold=0.90, cd=300s`，Immediate 0.00%，Waste 0.00%。 +- 次留 40%：手动基线时延 1617ms，智能最佳 `threshold=0.20, cd=30s`，最佳 Immediate 38.05%，Waste 13.64%；最差 `threshold=0.90, cd=300s`，Immediate 0.00%，Waste 0.00%。 +- 次留 50%：手动基线时延 1618ms，智能最佳 `threshold=0.25, cd=30s`，最佳 Immediate 38.02%，Waste 14.06%；最差 `threshold=0.90, cd=300s`，Immediate 0.00%，Waste 0.00%。 +- 次留 60%：手动基线时延 1619ms，智能最佳 `threshold=0.25, cd=30s`，最佳 Immediate 38.01%，Waste 14.88%；最差 `threshold=0.90, cd=300s`，Immediate 0.00%，Waste 0.00%。 + +## 交叉参数观察 +- 在同一保留率下，阈值下调能显著提高 `Immediate`，但通常也抬高 `Waste`。 +- cooldown 拉长可降低 waste（减少重复预加载/空耗），但可能提高用户等待。 +- 在弱网（尤其低分配机器人更多时）场景，建议提高 waste 上限约束后再考虑 lower threshold。 + +## 图表示例（输出文件） +- `heatmap_immediate_r_0.20.png` + ![](heatmap_immediate_r_0.20.png) + +- `heatmap_immediate_r_0.35.png` + ![](heatmap_immediate_r_0.35.png) + +- `heatmap_immediate_r_0.60.png` + ![](heatmap_immediate_r_0.60.png) + +- `heatmap_preload_success_r_0.20.png` + ![](heatmap_preload_success_r_0.20.png) + +- `heatmap_preload_success_r_0.35.png` + ![](heatmap_preload_success_r_0.35.png) + +- `heatmap_preload_success_r_0.60.png` + ![](heatmap_preload_success_r_0.60.png) + +- `heatmap_wait_ms_r_0.20.png` + ![](heatmap_wait_ms_r_0.20.png) + +- `heatmap_wait_ms_r_0.35.png` + ![](heatmap_wait_ms_r_0.35.png) + +- `heatmap_wait_ms_r_0.60.png` + ![](heatmap_wait_ms_r_0.60.png) + +- `heatmap_waste_ratio_r_0.20.png` + ![](heatmap_waste_ratio_r_0.20.png) + +- `heatmap_waste_ratio_r_0.35.png` + ![](heatmap_waste_ratio_r_0.35.png) + +- `heatmap_waste_ratio_r_0.60.png` + ![](heatmap_waste_ratio_r_0.60.png) + +- `line_immediate_vs_retention.png` + ![](line_immediate_vs_retention.png) + +- `line_mode_immediate_vs_retention.png` + ![](line_mode_immediate_vs_retention.png) + +- `line_mode_wait_vs_retention.png` + ![](line_mode_wait_vs_retention.png) + +- `line_network_immediate_vs_retention.png` + ![](line_network_immediate_vs_retention.png) + +- `line_network_wait_vs_retention.png` + ![](line_network_wait_vs_retention.png) + +- `line_threshold_0.55_retention_0.20.png` + ![](line_threshold_0.55_retention_0.20.png) + +- `line_threshold_0.55_retention_0.25.png` + ![](line_threshold_0.55_retention_0.25.png) + +- `line_threshold_0.55_retention_0.30.png` + ![](line_threshold_0.55_retention_0.30.png) + +- `line_threshold_0.55_retention_0.35.png` + ![](line_threshold_0.55_retention_0.35.png) + +- `line_threshold_0.55_retention_0.40.png` + ![](line_threshold_0.55_retention_0.40.png) + +- `line_threshold_0.55_retention_0.50.png` + ![](line_threshold_0.55_retention_0.50.png) + +- `line_threshold_0.55_retention_0.60.png` + ![](line_threshold_0.55_retention_0.60.png) + +- `line_wait_vs_retention.png` + ![](line_wait_vs_retention.png) + +## 验收结论 +- 建议首轮灰度点：`threshold=0.25`, `cooldown=60s`（兼顾即时性与 waste）。 +- 次留 35% 下智能策略最佳立即命中来自 `threshold=0.25, cd=30s`，即时率 `38.09%`，Waste `14.03%`。 +- 建议将 `smart_preload` 配置作为实验变量：先以纯手动为对照，再按次留分层和网络监控逐步放量。 diff --git a/Tools/SmartLoadSensitivity/output/heatmap_immediate_r_0.20.png b/Tools/SmartLoadSensitivity/output/heatmap_immediate_r_0.20.png new file mode 100644 index 0000000000000000000000000000000000000000..0df3344ceb18c035ea44ee3391858ee9982c4810 GIT binary patch literal 58155 zcmc$`by$?^w>}Oi#sG>+s3>94U=Rut(jm>zsem*{N{50ih?F28&CD?L&@C#0h;)}o zcgG0))_{BO&pBrw&-cIIdtF|8HZX5J&$HIO?)zTPQ)NYI3dCsy5fKrEtc-*z5z+oE zBBFhd4jq7>WZTqk!hZyuB(?97~8jU7yh6pWqht?itwE$%S7m^wIG*x6p=x^kI| zpM%ld$;sYPfScRq@4w)(bGXNSJNvvkoaL~+jJ6{Y5$$opKjM3F&2X(B5wa4u)E~T? z?mJwHT-x4QXtapE7Z9o(|Mmc@?K2ribs5#kx|dva>Sk+ZY$Fb`G66Dy@=s+>sLHXh zp?J(olX-&1rgw(7%64!*_;8G+4J@h=TkM*ytZg`d-_7i0^f?I&Y zSBbiIYTw>xqGmB)(!Gy{=POBeKlx@JGa}yoRM~L|-UQ(owA!z=_i3J&gk$g1`e|aC zy-%JmiOBXol}r8qcDljy(R?;JNl8hi!sLuS>4p{kG@Lpn(foF!`U;<$5~VpQ56uh1 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new file mode 100644 index 0000000..f3844af --- /dev/null +++ b/Tools/SmartLoadSensitivity/output/smartload_robot_cohort_summary.txt @@ -0,0 +1,8 @@ +Robot Cohort Summary +=================== +reward_heavy: 1124 +interstitial_focus: 1024 +splash_driven: 985 +balanced: 885 +churn_sensitive: 499 +network_bound: 483 diff --git a/Tools/SmartLoadSensitivity/output/smartload_robot_cohorts.csv b/Tools/SmartLoadSensitivity/output/smartload_robot_cohorts.csv new file mode 100644 index 0000000..5f701c7 --- /dev/null +++ b/Tools/SmartLoadSensitivity/output/smartload_robot_cohorts.csv @@ -0,0 +1,5001 @@ +robot_id,archetype,enter_reward,enter_interstitial,enter_splash,request_reward,request_interstitial,request_splash,fill_affinity,latency_affinity,retention_affinity,network_wifi,network_4g,network_3g,network_2g +bot_00000,splash_driven,0.6553,0.6865,1.0000,1.0000,1.0000,1.0000,0.7812,0.9524,0.9504,0.572506,0.253229,0.125928,0.048337 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b/Tools/SmartLoadSensitivity/smartload_sensitivity_simulation.py new file mode 100644 index 0000000..b043f6b --- /dev/null +++ b/Tools/SmartLoadSensitivity/smartload_sensitivity_simulation.py @@ -0,0 +1,1439 @@ +import argparse +import csv +import json +import random +from collections import Counter, defaultdict +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, List, Tuple + +import matplotlib +import matplotlib.pyplot as plt +from matplotlib import font_manager + + +def configure_fonts() -> None: + candidates = ["Microsoft YaHei", "SimHei", "PingFang SC", "Noto Sans CJK SC", "Noto Sans CJK TC"] + all_fonts = {f.name for f in font_manager.fontManager.ttflist} + for font in candidates: + if font in all_fonts: + matplotlib.rcParams["font.sans-serif"] = [font] + matplotlib.rcParams["axes.unicode_minus"] = False + break + + +configure_fonts() + + +def safe_ratio(numerator: float, denominator: float) -> float: + return numerator / denominator if denominator > 0 else 0.0 + + +def clamp01(value: float) -> float: + return max(0.0, min(1.0, value)) + + +def lerp(a: float, b: float, t: float) -> float: + return a + (b - a) * clamp01(t) + + +def clamp_float(value: float, min_v: float, max_v: float) -> float: + return max(min_v, min(max_v, value)) + + +NETWORK_LABELS = ("wifi", "4g", "3g", "2g") +NETWORK_COUNT = len(NETWORK_LABELS) + + +def percentile_95(values: List[float]) -> float: + if not values: + return 0.0 + sorted_values = sorted(values) + idx = max(0, int(len(sorted_values) * 0.95) - 1) + return float(sorted_values[idx]) + + +@dataclass +class Policy: + ad_type: int + scene: str + base_probability: float + preload_threshold: float + cooldown_seconds: int + min_samples_for_confidence: int + decay_half_life_hours: float + + +@dataclass +class SceneProfile: + ad_type: int + scene: str + true_request_probability: float + fill_success_probability: float + mean_load_ms: float + per_session_enter_probability: float + + +@dataclass +class NetworkCondition: + name: str + request_factor: float + fill_success_factor: float + load_ms_factor: float + + +@dataclass +class RobotProfile: + robot_id: str + archetype: str + enter_multipliers: Tuple[float, float, float] + request_multipliers: Tuple[float, float, float] + fill_affinity: float + latency_affinity: float + retention_affinity: float + network_weights: Tuple[float, float, float, float] + + +@dataclass +class SceneState: + ad_type: int + scene: str + policy: Policy + enter_count: int = 0 + play_request_count: int = 0 + preload_request_count: int = 0 + preload_success_count: int = 0 + preload_failure_count: int = 0 + show_request_count: int = 0 + show_start_count: int = 0 + show_failure_count: int = 0 + last_updated_ts: float = 0.0 + last_preload_ts: float = 0.0 + preload_expire_ts: float = -1.0 + preload_ready: bool = False + preload_origin_network: int = -1 + + +@dataclass +class RunResult: + mode: str + retention: float + threshold: float + cooldown_seconds: int + total_users: int + total_sessions: int + total_scene_entries: int + total_show_requests: int + total_preload_requests: int + total_preload_success: int + total_preload_failure: int + total_show_success: int + total_show_fail: int + total_preload_waste: int + immediate_play_count: int + avg_wait_ms: float + p95_wait_ms: float + wasted_ratio: float + preload_success_rate: float + show_success_rate: float + network_show_requests: Tuple[int, int, int, int] + network_immediate_show: Tuple[int, int, int, int] + network_preload_requests: Tuple[int, int, int, int] + network_preload_success: Tuple[int, int, int, int] + network_preload_waste: Tuple[int, int, int, int] + network_avg_wait_ms: Tuple[float, float, float, float] + network_p95_wait_ms: Tuple[float, float, float, float] + + +def default_policy_by_ad_type() -> Dict[Tuple[int, str], Policy]: + return { + (0, "__default__"): Policy(0, "__default__", 0.08, 0.75, 120, 8, 72.0), # Reward + (1, "__default__"): Policy(1, "__default__", 0.08, 0.75, 120, 8, 72.0), # Splash + (2, "__default__"): Policy(2, "__default__", 0.08, 0.75, 120, 8, 72.0), # Interaction + } + + +def parse_policy_config(path: Path) -> Dict[Tuple[int, str], Policy]: + if not path.exists(): + return default_policy_by_ad_type() + + with path.open("r", encoding="utf-8") as f: + payload = json.loads(f.read()) + + result: Dict[Tuple[int, str], Policy] = {} + scene_items = payload.get("ScenePolicies", []) or [] + for item in scene_items: + ad_type = int(item.get("AdType", -1)) + scene = str(item.get("Scene", "")).strip() + if ad_type < 0 or not scene: + continue + + result[(ad_type, scene)] = Policy( + ad_type=ad_type, + scene=scene, + base_probability=float(item.get("BaseProbability", 0.08)), + preload_threshold=float(item.get("PreloadThreshold", 0.75)), + cooldown_seconds=int(item.get("CooldownSeconds", 120)), + min_samples_for_confidence=max(1, int(item.get("MinSamplesForConfidence", 8))), + decay_half_life_hours=max(1.0, float(item.get("DecayHalfLifeHours", 72.0))), + ) + + default = payload.get("GlobalDefault", {}) if isinstance(payload, dict) else {} + for ad_type in (0, 1, 2): + result[(ad_type, "__default__")] = Policy( + ad_type=ad_type, + scene="__default__", + base_probability=float(default.get("BaseProbability", 0.08)), + preload_threshold=float(default.get("PreloadThreshold", 0.75)), + cooldown_seconds=int(default.get("CooldownSeconds", 120)), + min_samples_for_confidence=max(1, int(default.get("MinSamplesForConfidence", 8))), + decay_half_life_hours=max(1.0, float(default.get("DecayHalfLifeHours", 72.0))), + ) + + return result + + +def estimate_probability(state: SceneState, now: float, policy: Policy) -> float: + if state.enter_count <= 0: + return policy.base_probability + + observed = safe_ratio(state.play_request_count, state.enter_count) + trust = min(1.0, state.enter_count / max(1.0, policy.min_samples_for_confidence)) + age_hours = max(0.0, (now - state.last_updated_ts) / 3600.0) + decay = 0.5 ** (age_hours / max(1e-6, policy.decay_half_life_hours)) + trust *= decay + return lerp(policy.base_probability, observed, trust) + + +def should_preload(state: SceneState, now: float, threshold: float, cooldown_seconds: int) -> bool: + policy = state.policy + if now - state.last_preload_ts < cooldown_seconds: + return False + if state.preload_ready and state.preload_expire_ts >= now: + return False + return estimate_probability(state, now, policy) >= threshold + + +def apply_waste_decay(state: SceneState, now: float) -> Tuple[int, int]: + if state.preload_ready and state.preload_expire_ts > 0 and now >= state.preload_expire_ts: + state.preload_ready = False + state.preload_expire_ts = -1 + return 1, max(0, min(NETWORK_COUNT - 1, state.preload_origin_network)) + return 0, -1 + + +def policy_for_scene(policy_map: Dict[Tuple[int, str], Policy], ad_type: int, scene: str) -> Policy: + return policy_map.get((ad_type, scene)) or policy_map.get((ad_type, "__default__")) or next( + iter(default_policy_by_ad_type().values()) + ) + + +def pick_network( + conditions: List[NetworkCondition], + weights: Tuple[float, float, float, float], + rng: random.Random, +) -> Tuple[int, NetworkCondition]: + roll = rng.random() + cum = 0.0 + for idx, w in enumerate(weights): + cum += max(0.0, w) + if roll <= cum: + i = min(idx, len(conditions) - 1) + return i, conditions[i] + return len(conditions) - 1, conditions[-1] + + +def normalize_weights(values: Tuple[float, float, float, float], epsilon: float = 1e-6) -> Tuple[float, float, float, float]: + total = sum(max(0.0, v) for v in values) + if total <= epsilon: + return (0.25, 0.25, 0.25, 0.25) + return tuple(v / total for v in values) + + +def generate_robot_profiles(users: int, seed: int) -> List[RobotProfile]: + rng = random.Random(seed) + + # 4 类广告网络环境偏好：Wi-Fi, 4G, 3G, 2G + archetypes = [ + { + "name": "reward_heavy", + "weight": 0.25, + "enter": (1.45, 0.82, 0.80), + "request": (1.55, 0.76, 0.62), + "fill_aff": 1.04, + "latency_aff": 0.88, + "retention_aff": 1.18, + "network": (0.60, 0.28, 0.10, 0.02), + }, + { + "name": "interstitial_focus", + "weight": 0.22, + "enter": (0.72, 1.48, 0.83), + "request": (0.78, 1.57, 0.70), + "fill_aff": 0.96, + "latency_aff": 1.02, + "retention_aff": 1.05, + "network": (0.32, 0.42, 0.20, 0.06), + }, + { + "name": "splash_driven", + "weight": 0.22, + "enter": (0.88, 0.92, 1.52), + "request": (1.02, 1.03, 1.40), + "fill_aff": 0.91, + "latency_aff": 1.01, + "retention_aff": 0.96, + "network": (0.75, 0.18, 0.05, 0.02), + }, + { + "name": "balanced", + "weight": 0.20, + "enter": (1.05, 1.12, 1.10), + "request": (1.05, 1.00, 1.00), + "fill_aff": 1.00, + "latency_aff": 1.00, + "retention_aff": 1.00, + "network": (0.40, 0.32, 0.22, 0.06), + }, + { + "name": "churn_sensitive", + "weight": 0.11, + "enter": (0.55, 0.64, 0.45), + "request": (0.60, 0.55, 0.46), + "fill_aff": 0.72, + "latency_aff": 1.18, + "retention_aff": 0.58, + "network": (0.24, 0.26, 0.30, 0.20), + }, + { + "name": "network_bound", + "weight": 0.10, + "enter": (0.85, 0.85, 0.80), + "request": (0.88, 0.78, 0.72), + "fill_aff": 0.68, + "latency_aff": 1.22, + "retention_aff": 0.82, + "network": (0.12, 0.18, 0.30, 0.40), + }, + ] + + total_weight = sum(a["weight"] for a in archetypes) + running = [] + cur = 0.0 + for a in archetypes: + cur += a["weight"] / total_weight + running.append((cur, a)) + + robots = [] + for idx in range(users): + roll = rng.random() + picked = archetypes[-1] + for threshold, arche in running: + if roll <= threshold: + picked = arche + break + + def jitter(v: float, span: float) -> float: + return clamp01(v + rng.uniform(-span, span)) + + enters = tuple(clamp_float(jitter(base, 0.24), 0.2, 1.9) for base in picked["enter"]) + requests = tuple(clamp_float(jitter(base, 0.22), 0.15, 1.9) for base in picked["request"]) + + nw = [ + max(0.01, jitter(weight, 0.15)) + for weight in picked["network"] + ] + robots.append( + RobotProfile( + robot_id=f"bot_{idx:05d}", + archetype=picked["name"], + enter_multipliers=enters, + request_multipliers=requests, + fill_affinity=clamp_float(jitter(picked["fill_aff"], 0.16), 0.45, 1.55), + latency_affinity=clamp_float(jitter(picked["latency_aff"], 0.2), 0.6, 1.8), + retention_affinity=clamp_float(jitter(picked["retention_aff"], 0.12), 0.35, 1.45), + network_weights=normalize_weights(tuple(nw)), + ) + ) + + return robots + + +def simulate_once( + profiles: List[SceneProfile], + policy_map: Dict[Tuple[int, str], Policy], + robots: List[RobotProfile], + threshold: float, + cooldown_seconds: int, + retention_next_day: float, + seed: int, + smart_enabled: bool, + mode: str, +) -> RunResult: + behavior_rng = random.Random(seed) + outcome_rng = random.Random(seed + 998244353) + states: Dict[str, SceneState] = {} + + network_conditions = [ + NetworkCondition("wifi", request_factor=1.0, fill_success_factor=1.0, load_ms_factor=0.85), + NetworkCondition("4g", request_factor=0.96, fill_success_factor=0.98, load_ms_factor=1.08), + NetworkCondition("3g", request_factor=0.90, fill_success_factor=0.86, load_ms_factor=1.45), + NetworkCondition("2g", request_factor=0.78, fill_success_factor=0.73, load_ms_factor=2.00), + ] + + def state_of(profile: SceneProfile, profile_index: int) -> SceneState: + key = f"{profile.ad_type}|{profile.scene}" + if key not in states: + states[key] = SceneState( + ad_type=profile.ad_type, + scene=profile.scene, + policy=policy_for_scene(policy_map, profile.ad_type, profile.scene), + ) + return states[key] + + total_wait_times: List[float] = [] + network_wait_times: List[List[float]] = [[] for _ in range(NETWORK_COUNT)] + total_preload_waste = 0 + total_scene_entries = 0 + total_show_requests = 0 + total_show_success = 0 + total_show_fail = 0 + total_preload_requests = 0 + total_preload_success = 0 + total_preload_failure = 0 + total_sessions = 0 + immediate_play_count = 0 + network_show_requests = [0, 0, 0, 0] + network_immediate_show = [0, 0, 0, 0] + network_preload_requests = [0, 0, 0, 0] + network_preload_success = [0, 0, 0, 0] + network_preload_waste = [0, 0, 0, 0] + now = 0.0 + + for robot in robots: + sessions = 1 + retention_prob = clamp01(retention_next_day * robot.retention_affinity) + while behavior_rng.random() < retention_prob: + sessions += 1 + total_sessions += sessions + + for session_index in range(sessions): + now += 0.3 + behavior_rng.random() * 0.7 + network_idx, network = pick_network(network_conditions, robot.network_weights, behavior_rng) + + for idx, profile in enumerate(profiles): + state = state_of(profile, idx) + now += behavior_rng.uniform(0.3, 2.0) + total_scene_entries += 1 + state.enter_count += 1 + state.last_updated_ts = now + + waste, waste_network = apply_waste_decay(state, now) + total_preload_waste += waste + if waste > 0 and 0 <= waste_network < NETWORK_COUNT: + network_preload_waste[waste_network] += waste + + enter_prob = clamp01( + profile.per_session_enter_probability + * robot.enter_multipliers[idx] + * network.request_factor + ) + + if behavior_rng.random() > enter_prob: + continue + + if smart_enabled and should_preload(state, now, threshold, cooldown_seconds): + total_preload_requests += 1 + state.preload_request_count += 1 + state.last_preload_ts = now + state.last_updated_ts = now + network_preload_requests[network_idx] += 1 + + fill_success = clamp01(profile.fill_success_probability * robot.fill_affinity * network.fill_success_factor) + if outcome_rng.random() < fill_success: + state.preload_success_count += 1 + total_preload_success += 1 + network_preload_success[network_idx] += 1 + state.preload_ready = True + state.preload_expire_ts = now + cooldown_seconds + state.preload_origin_network = network_idx + else: + state.preload_failure_count += 1 + total_preload_failure += 1 + + request_prob = clamp01(profile.true_request_probability * robot.request_multipliers[idx]) + if behavior_rng.random() < request_prob: + state.play_request_count += 1 + state.show_request_count += 1 + total_show_requests += 1 + network_show_requests[network_idx] += 1 + fill_success = clamp01(profile.fill_success_probability * robot.fill_affinity * network.fill_success_factor) + + if state.preload_ready and state.preload_expire_ts >= now: + state.preload_ready = False + state.show_start_count += 1 + total_show_success += 1 + immediate_play_count += 1 + network_immediate_show[network_idx] += 1 + total_wait_times.append(0.0) + network_wait_times[network_idx].append(0.0) + state.last_updated_ts = now + else: + load_ms = max( + 250.0, + outcome_rng.gauss( + profile.mean_load_ms * robot.latency_affinity * network.load_ms_factor, + 220.0, + ), + ) + if outcome_rng.random() < fill_success: + state.show_start_count += 1 + total_show_success += 1 + else: + state.show_failure_count += 1 + total_show_fail += 1 + total_wait_times.append(load_ms) + network_wait_times[network_idx].append(load_ms) + state.last_updated_ts = now + + # 每个 session 间停留（含加载、浏览等） + now += behavior_rng.uniform(8.0, 16.0) + + # 网络状态抖动：同一用户跨会话可变更 + if session_index % 2 == 1 and outcome_rng.random() < 0.08: + now += 0.0 + + avg_wait = safe_ratio(sum(total_wait_times), len(total_wait_times)) + p95_wait = percentile_95(total_wait_times) + + total_requests = max(1, total_show_requests) + total_preloads = max(1, total_preload_requests) + network_avg_wait = [] + network_p95_wait = [] + for nw_idx in range(NETWORK_COUNT): + network_avg_wait.append(safe_ratio(sum(network_wait_times[nw_idx]), max(1, len(network_wait_times[nw_idx])))) + network_p95_wait.append(percentile_95(network_wait_times[nw_idx])) + + return RunResult( + mode=mode, + retention=retention_next_day, + threshold=threshold, + cooldown_seconds=cooldown_seconds, + total_users=len(robots), + total_sessions=total_sessions, + total_scene_entries=total_scene_entries, + total_show_requests=total_show_requests, + total_preload_requests=total_preload_requests, + total_preload_success=total_preload_success, + total_preload_failure=total_preload_failure, + total_show_success=total_show_success, + total_show_fail=total_show_fail, + total_preload_waste=total_preload_waste, + immediate_play_count=immediate_play_count, + avg_wait_ms=avg_wait, + p95_wait_ms=p95_wait, + wasted_ratio=safe_ratio(total_preload_waste, total_preloads), + preload_success_rate=safe_ratio(total_preload_success, total_preloads), + show_success_rate=safe_ratio(total_show_success, total_requests), + network_show_requests=tuple(network_show_requests), + network_immediate_show=tuple(network_immediate_show), + network_preload_requests=tuple(network_preload_requests), + network_preload_success=tuple(network_preload_success), + network_preload_waste=tuple(network_preload_waste), + network_avg_wait_ms=(network_avg_wait[0], network_avg_wait[1], network_avg_wait[2], network_avg_wait[3]), + network_p95_wait_ms=(network_p95_wait[0], network_p95_wait[1], network_p95_wait[2], network_p95_wait[3]), + ) + + +def run_sensitivity( + profiles: List[SceneProfile], + policy_map: Dict[Tuple[int, str], Policy], + robots: List[RobotProfile], + threshold_grid: List[float], + cooldown_grid: List[int], + retention_rates: List[float], + out_dir: Path, + mode_manual_seed: int, +) -> Tuple[List[RunResult], Dict[float, RunResult]]: + baseline_by_retention: Dict[float, RunResult] = {} + smart_results: List[RunResult] = [] + + for ridx, retention in enumerate(retention_rates): + baseline_by_retention[retention] = simulate_once( + profiles=profiles, + policy_map=policy_map, + robots=robots, + threshold=1.0, + cooldown_seconds=max(cooldown_grid), + retention_next_day=retention, + seed=mode_manual_seed + ridx * 1000, + smart_enabled=False, + mode="manual", + ) + + for ridx, retention in enumerate(retention_rates): + for tidx, threshold in enumerate(threshold_grid): + for cidx, cooldown in enumerate(cooldown_grid): + smart_results.append( + simulate_once( + profiles=profiles, + policy_map=policy_map, + robots=robots, + threshold=threshold, + cooldown_seconds=cooldown, + retention_next_day=retention, + seed=mode_manual_seed + 8000 + ridx * 1000 + tidx * 37 + cidx, + smart_enabled=True, + mode="smart", + ) + ) + + out_dir.mkdir(parents=True, exist_ok=True) + summary_csv = out_dir / "smartload_sensitivity_summary.csv" + with summary_csv.open("w", encoding="utf-8", newline="") as f: + w = csv.writer(f) + w.writerow( + [ + "mode", + "retention", + "threshold", + "cooldown", + "total_show_requests", + "total_show_success", + "total_show_fail", + "immediate_show", + "avg_wait_ms", + "p95_wait_ms", + "total_preload_requests", + "preload_success", + "preload_fail", + "preload_waste", + "show_success_rate", + "preload_success_rate", + "wasted_ratio", + "baseline_immediate_show", + "baseline_avg_wait_ms", + "delta_immediate_show", + "delta_wait_ms", + "delta_wait_pct", + "delta_show_success", + ] + ) + + for retention in retention_rates: + manual = baseline_by_retention[retention] + m_immediate = safe_ratio(manual.immediate_play_count, manual.total_show_requests) + w.writerow( + [ + "manual", + f"{retention:.4f}", + "1.00", + max(cooldown_grid), + manual.total_show_requests, + manual.total_show_success, + manual.total_show_fail, + f"{m_immediate:.6f}", + f"{manual.avg_wait_ms:.2f}", + f"{manual.p95_wait_ms:.2f}", + manual.total_preload_requests, + manual.total_preload_success, + manual.total_preload_failure, + f"{manual.total_preload_waste}", + f"{manual.show_success_rate:.6f}", + f"{manual.preload_success_rate:.6f}", + f"{manual.wasted_ratio:.6f}", + f"{m_immediate:.6f}", + f"{manual.avg_wait_ms:.2f}", + f"{0:.6f}", + f"{0:.2f}", + f"{0:.6f}", + f"{0:.6f}", + ] + ) + + for r in smart_results: + b = baseline_by_retention[r.retention] + immediate = safe_ratio(r.immediate_play_count, r.total_show_requests) + baseline_immediate = safe_ratio(b.immediate_play_count, b.total_show_requests) + delta_immediate = immediate - baseline_immediate + delta_wait = r.avg_wait_ms - b.avg_wait_ms + delta_wait_pct = safe_ratio(delta_wait, b.avg_wait_ms) if b.avg_wait_ms > 0 else 0.0 + w.writerow( + [ + r.mode, + f"{r.retention:.4f}", + f"{r.threshold:.4f}", + r.cooldown_seconds, + r.total_show_requests, + r.total_show_success, + r.total_show_fail, + f"{immediate:.6f}", + f"{r.avg_wait_ms:.2f}", + f"{r.p95_wait_ms:.2f}", + r.total_preload_requests, + r.total_preload_success, + r.total_preload_failure, + r.total_preload_waste, + f"{r.show_success_rate:.6f}", + f"{r.preload_success_rate:.6f}", + f"{r.wasted_ratio:.6f}", + f"{baseline_immediate:.6f}", + f"{b.avg_wait_ms:.2f}", + f"{delta_immediate:.6f}", + f"{delta_wait:.2f}", + f"{delta_wait_pct:.6f}", + f"{(r.show_success_rate - b.show_success_rate):.6f}", + ] + ) + + rank_csv = write_rank_csv(smart_results, baseline_by_retention, out_dir) + write_mode_comparison_csv(baseline_by_retention, smart_results, out_dir) + write_robot_summary_csv(robots, out_dir) + write_mode_lines(baseline_by_retention, smart_results, out_dir) + write_network_mode_comparison_csv(baseline_by_retention, smart_results, out_dir) + draw_network_mode_lines(baseline_by_retention, smart_results, out_dir) + + return smart_results, baseline_by_retention + + +def write_rank_csv(results: List[RunResult], baseline_by_retention: Dict[float, RunResult], out_dir: Path) -> Path: + rank_csv = out_dir / "smartload_retention_rank.csv" + with rank_csv.open("w", encoding="utf-8", newline="") as f: + w = csv.writer(f) + w.writerow( + [ + "retention", + "rank", + "threshold", + "cooldown", + "immediate_show", + "avg_wait_ms", + "p95_wait_ms", + "waste_ratio", + "show_success_rate", + "delta_immediate", + "delta_wait_ms", + "delta_wait_pct", + "objective_score", + ] + ) + for retention in sorted(baseline_by_retention.keys()): + rows = [] + for r in [x for x in results if x.retention == retention]: + b = baseline_by_retention[retention] + immediate = safe_ratio(r.immediate_play_count, r.total_show_requests) + baseline_immediate = safe_ratio(b.immediate_play_count, b.total_show_requests) + delta_immediate = immediate - baseline_immediate + delta_wait = r.avg_wait_ms - b.avg_wait_ms + score = 0.7 * delta_immediate + 0.2 * safe_ratio((1 - r.wasted_ratio), 1.0) - 0.001 * r.avg_wait_ms / 10.0 + rows.append((score, r)) + rows.sort(key=lambda x: x[0], reverse=True) + for idx, (_, r) in enumerate(rows, start=1): + immediate = safe_ratio(r.immediate_play_count, r.total_show_requests) + b = baseline_by_retention[retention] + delta_immediate = immediate - safe_ratio(b.immediate_play_count, b.total_show_requests) + delta_wait = r.avg_wait_ms - b.avg_wait_ms + delta_wait_pct = safe_ratio(delta_wait, b.avg_wait_ms) if b.avg_wait_ms > 0 else 0.0 + score = 0.7 * delta_immediate + 0.2 * (1 - r.wasted_ratio) - 0.001 * r.avg_wait_ms / 10.0 + w.writerow( + [ + f"{retention:.2f}", + idx, + f"{r.threshold:.2f}", + r.cooldown_seconds, + f"{immediate:.6f}", + f"{r.avg_wait_ms:.2f}", + f"{r.p95_wait_ms:.2f}", + f"{r.wasted_ratio:.6f}", + f"{r.show_success_rate:.6f}", + f"{delta_immediate:.6f}", + f"{delta_wait:.2f}", + f"{delta_wait_pct:.6f}", + f"{score:.6f}", + ] + ) + return rank_csv + + +def write_mode_comparison_csv( + baseline_by_retention: Dict[float, RunResult], + smart_results: List[RunResult], + out_dir: Path, +) -> Path: + by_retention = defaultdict(list) + for result in smart_results: + by_retention[result.retention].append(result) + + out = out_dir / "smartload_mode_comparison.csv" + with out.open("w", encoding="utf-8", newline="") as f: + w = csv.writer(f) + w.writerow( + [ + "retention", + "manual_immediate", + "manual_avg_wait_ms", + "manual_waste_ratio", + "manual_show_success_rate", + "smart_threshold", + "smart_cooldown", + "smart_immediate", + "smart_avg_wait_ms", + "smart_waste_ratio", + "smart_show_success_rate", + "delta_immediate", + "delta_wait_ms", + ] + ) + for retention, b in sorted(baseline_by_retention.items()): + manual_immediate = safe_ratio(b.immediate_play_count, b.total_show_requests) + candidates = by_retention[retention] + if not candidates: + continue + best = max(candidates, key=lambda item: safe_ratio(item.immediate_play_count, item.total_show_requests)) + smart_immediate = safe_ratio(best.immediate_play_count, best.total_show_requests) + w.writerow( + [ + f"{retention:.2f}", + f"{manual_immediate:.6f}", + f"{b.avg_wait_ms:.2f}", + f"{b.wasted_ratio:.6f}", + f"{b.show_success_rate:.6f}", + f"{best.threshold:.2f}", + best.cooldown_seconds, + f"{smart_immediate:.6f}", + f"{best.avg_wait_ms:.2f}", + f"{best.wasted_ratio:.6f}", + f"{best.show_success_rate:.6f}", + f"{smart_immediate - manual_immediate:.6f}", + f"{best.avg_wait_ms - b.avg_wait_ms:.2f}", + ] + ) + + return out + + +def write_network_mode_comparison_csv( + baseline_by_retention: Dict[float, RunResult], + smart_results: List[RunResult], + out_dir: Path, +) -> Path: + by_retention = defaultdict(list) + for result in smart_results: + by_retention[result.retention].append(result) + + out = out_dir / "smartload_network_mode_comparison.csv" + with out.open("w", encoding="utf-8", newline="") as f: + w = csv.writer(f) + w.writerow( + [ + "retention", + "network", + "manual_show_requests", + "manual_immediate", + "manual_avg_wait_ms", + "manual_p95_wait_ms", + "manual_waste_ratio", + "manual_preload_success", + "smart_threshold", + "smart_cooldown", + "smart_show_requests", + "smart_immediate", + "smart_avg_wait_ms", + "smart_p95_wait_ms", + "smart_waste_ratio", + "smart_preload_success", + "delta_immediate", + "delta_wait_ms", + ] + ) + for retention, b in sorted(baseline_by_retention.items()): + candidates = by_retention[retention] + for idx, network_name in enumerate(NETWORK_LABELS): + manual_immediate = safe_ratio(b.network_immediate_show[idx], b.network_show_requests[idx]) + manual_wait = b.network_avg_wait_ms[idx] + manual_p95 = b.network_p95_wait_ms[idx] + manual_waste = safe_ratio(b.network_preload_waste[idx], max(1, b.network_preload_requests[idx])) + manual_preload_success = safe_ratio(b.network_preload_success[idx], max(1, b.network_preload_requests[idx])) + + best = None + if candidates: + best = max(candidates, key=lambda item: safe_ratio(item.network_immediate_show[idx], item.network_show_requests[idx])) + + if best is None: + w.writerow( + [ + f"{retention:.2f}", + network_name, + b.network_show_requests[idx], + f"{manual_immediate:.6f}", + f"{manual_wait:.2f}", + f"{manual_p95:.2f}", + f"{manual_waste:.6f}", + f"{manual_preload_success:.6f}", + "N/A", + "N/A", + 0, + f"{0:.6f}", + f"{0:.2f}", + f"{0:.2f}", + f"{0:.6f}", + f"{0:.6f}", + f"{0:.6f}", + f"{0:.2f}", + ] + ) + continue + + smart_immediate = safe_ratio(best.network_immediate_show[idx], best.network_show_requests[idx]) + smart_wait = best.network_avg_wait_ms[idx] + smart_p95 = best.network_p95_wait_ms[idx] + smart_waste = safe_ratio(best.network_preload_waste[idx], max(1, best.network_preload_requests[idx])) + smart_preload_success = safe_ratio(best.network_preload_success[idx], max(1, best.network_preload_requests[idx])) + + w.writerow( + [ + f"{retention:.2f}", + network_name, + b.network_show_requests[idx], + f"{manual_immediate:.6f}", + f"{manual_wait:.2f}", + f"{manual_p95:.2f}", + f"{manual_waste:.6f}", + f"{manual_preload_success:.6f}", + f"{best.threshold:.2f}", + best.cooldown_seconds, + best.network_show_requests[idx], + f"{smart_immediate:.6f}", + f"{smart_wait:.2f}", + f"{smart_p95:.2f}", + f"{smart_waste:.6f}", + f"{smart_preload_success:.6f}", + f"{smart_immediate - manual_immediate:.6f}", + f"{smart_wait - manual_wait:.2f}", + ] + ) + return out + + +def write_robot_summary_csv(robots: List[RobotProfile], out_dir: Path) -> Path: + summary_csv = out_dir / "smartload_robot_cohorts.csv" + with summary_csv.open("w", encoding="utf-8", newline="") as f: + w = csv.writer(f) + w.writerow( + [ + "robot_id", + "archetype", + "enter_reward", + "enter_interstitial", + "enter_splash", + "request_reward", + "request_interstitial", + "request_splash", + "fill_affinity", + "latency_affinity", + "retention_affinity", + "network_wifi", + "network_4g", + "network_3g", + "network_2g", + ] + ) + for robot in robots: + w.writerow( + [ + robot.robot_id, + robot.archetype, + f"{robot.enter_multipliers[0]:.4f}", + f"{robot.enter_multipliers[1]:.4f}", + f"{robot.enter_multipliers[2]:.4f}", + f"{robot.request_multipliers[0]:.4f}", + f"{robot.request_multipliers[1]:.4f}", + f"{robot.request_multipliers[2]:.4f}", + f"{robot.fill_affinity:.4f}", + f"{robot.latency_affinity:.4f}", + f"{robot.retention_affinity:.4f}", + f"{robot.network_weights[0]:.6f}", + f"{robot.network_weights[1]:.6f}", + f"{robot.network_weights[2]:.6f}", + f"{robot.network_weights[3]:.6f}", + ] + ) + + # 一页式摘要也可用于报告快速读取。 + summary_text = out_dir / "smartload_robot_cohort_summary.txt" + counter = Counter(robot.archetype for robot in robots) + with summary_text.open("w", encoding="utf-8") as out: + out.write("Robot Cohort Summary\n") + out.write("===================\n") + for name, count in counter.most_common(): + out.write(f"{name}: {count}\n") + + return summary_csv + + +def draw_heatmaps(results: List[RunResult], retention: float, out_dir: Path): + filtered = [r for r in results if abs(r.retention - retention) < 1e-6] + if not filtered: + return + + thresholds = sorted({r.threshold for r in filtered}) + cooldowns = sorted({r.cooldown_seconds for r in filtered}) + index = {(r.threshold, r.cooldown_seconds): r for r in filtered} + + def matrix(metric): + data = [[0.0 for _ in cooldowns] for _ in thresholds] + for ti, t in enumerate(thresholds): + for ci, c in enumerate(cooldowns): + r = index[(t, c)] + if metric == "immediate": + data[ti][ci] = safe_ratio(r.immediate_play_count, r.total_show_requests) + elif metric == "wait": + data[ti][ci] = r.avg_wait_ms + elif metric == "waste": + data[ti][ci] = r.wasted_ratio + elif metric == "preload": + data[ti][ci] = r.preload_success_rate + return data + + def plot(title, data, fname, label): + plt.figure(figsize=(8, 6)) + im = plt.imshow(data, origin="lower", aspect="auto", cmap="viridis") + plt.yticks(range(len(thresholds)), [f"{t:.2f}" for t in thresholds]) + plt.xticks(range(len(cooldowns)), [str(c) for c in cooldowns]) + plt.xlabel("冷却时间（秒）") + plt.ylabel("预加载阈值") + plt.title(title) + cbar = plt.colorbar(im) + cbar.set_label(label) + plt.tight_layout() + plt.savefig(out_dir / fname, dpi=150) + plt.close() + + plot(f"智能预加载即时命中率（次留={retention:.0%}）", matrix("immediate"), f"heatmap_immediate_r_{retention:.2f}.png", "即时命中率") + plot(f"智能预加载平均等待（次留={retention:.0%}）", matrix("wait"), f"heatmap_wait_ms_r_{retention:.2f}.png", "平均等待（ms）") + plot(f"智能预加载浪费比例（次留={retention:.0%}）", matrix("waste"), f"heatmap_waste_ratio_r_{retention:.2f}.png", "浪费比例") + plot(f"智能预加载预加载成功率（次留={retention:.0%}）", matrix("preload"), f"heatmap_preload_success_r_{retention:.2f}.png", "预加载成功率") + + +def draw_retention_lines(results: List[RunResult], out_dir: Path): + thresholds = sorted({r.threshold for r in results}) + cooldowns = sorted({r.cooldown_seconds for r in results}) + retentions = sorted({r.retention for r in results}) + baseline_threshold = thresholds[len(thresholds) // 2] + + plt.figure(figsize=(10, 6)) + for c in cooldowns: + xs = [] + ys = [] + for r in sorted([x for x in results if x.cooldown_seconds == c and x.threshold == baseline_threshold], key=lambda x: x.retention): + xs.append(r.retention) + ys.append(safe_ratio(r.immediate_play_count, r.total_show_requests)) + plt.plot(xs, ys, marker="o", label=f"cd={c}s") + plt.title("敏感度：次留与即时命中率") + plt.xlabel("次留（D2）") + plt.ylabel("即时命中率") + plt.grid(True) + plt.legend(fontsize=8) + plt.tight_layout() + plt.savefig(out_dir / "line_immediate_vs_retention.png", dpi=150) + plt.close() + + plt.figure(figsize=(10, 6)) + for c in cooldowns: + xs = [] + ys = [] + for r in sorted([x for x in results if x.cooldown_seconds == c and x.threshold == baseline_threshold], key=lambda x: x.retention): + xs.append(r.retention) + ys.append(r.avg_wait_ms) + plt.plot(xs, ys, marker="s", label=f"cd={c}s") + plt.title("敏感度：次留与平均等待") + plt.xlabel("次留（D2）") + plt.ylabel("平均等待（ms）") + plt.grid(True) + plt.legend(fontsize=8) + plt.tight_layout() + plt.savefig(out_dir / "line_wait_vs_retention.png", dpi=150) + plt.close() + + for retention in retentions: + same_r = sorted([x for x in results if abs(x.retention - retention) < 1e-6 and x.threshold == baseline_threshold], key=lambda x: x.cooldown_seconds) + if not same_r: + continue + fig, ax1 = plt.subplots(figsize=(10, 6)) + ax2 = ax1.twinx() + cooldown_values = [x.cooldown_seconds for x in same_r] + immediate = [safe_ratio(x.immediate_play_count, x.total_show_requests) for x in same_r] + waste = [x.wasted_ratio for x in same_r] + ax1.plot(cooldown_values, immediate, marker="o", label="即时命中率") + ax1.set_xlabel("冷却时间（秒）") + ax1.set_ylabel("即时命中率", color="#1f77b4") + ax2.plot(cooldown_values, waste, marker="x", color="#F28D35", label="浪费比例") + ax2.set_ylabel("浪费比例", color="#F28D35") + ax1.set_title(f"次留={retention:.0%}，阈值={baseline_threshold:.2f}") + ax1.grid(True) + ax1.set_xlim(left=min(cooldown_values), right=max(cooldown_values)) + fig.tight_layout() + fig.savefig(out_dir / f"line_threshold_{baseline_threshold:.2f}_retention_{retention:.2f}.png", dpi=150) + plt.close(fig) + + +def write_mode_lines( + baseline_by_retention: Dict[float, RunResult], + smart_results: List[RunResult], + out_dir: Path, +) -> None: + best_by_retention: Dict[float, RunResult] = {} + for retention, baseline in baseline_by_retention.items(): + candidates = [r for r in smart_results if r.retention == retention] + if not candidates: + continue + best = max(candidates, key=lambda x: safe_ratio(x.immediate_play_count, x.total_show_requests)) + best_by_retention[retention] = best + + retentions = sorted(baseline_by_retention.keys()) + + plt.figure(figsize=(10, 6)) + xs = [r for r in retentions] + ys_manual = [safe_ratio(baseline_by_retention[r].immediate_play_count, baseline_by_retention[r].total_show_requests) for r in retentions] + ys_smart = [safe_ratio(best_by_retention[r].immediate_play_count, best_by_retention[r].total_show_requests) for r in retentions] + plt.plot(xs, ys_manual, marker="o", label="手动（无智能）") + plt.plot(xs, ys_smart, marker="o", label="智能（最优配置）") + plt.title("模式对比：次留与即时播放率") + plt.xlabel("次留（D2）") + plt.ylabel("即时播放率") + plt.grid(True) + plt.legend() + plt.tight_layout() + plt.savefig(out_dir / "line_mode_immediate_vs_retention.png", dpi=150) + plt.close() + + plt.figure(figsize=(10, 6)) + ys_manual_wait = [baseline_by_retention[r].avg_wait_ms for r in retentions] + ys_smart_wait = [best_by_retention[r].avg_wait_ms for r in retentions] + plt.plot(xs, ys_manual_wait, marker="s", label="手动（无智能）") + plt.plot(xs, ys_smart_wait, marker="s", label="智能（最优配置）") + plt.title("模式对比：次留与平均等待") + plt.xlabel("次留（D2）") + plt.ylabel("平均等待（ms）") + plt.grid(True) + plt.legend() + plt.tight_layout() + plt.savefig(out_dir / "line_mode_wait_vs_retention.png", dpi=150) + plt.close() + + +def draw_network_mode_lines( + baseline_by_retention: Dict[float, RunResult], + smart_results: List[RunResult], + out_dir: Path, +) -> None: + by_retention = defaultdict(list) + for result in smart_results: + by_retention[result.retention].append(result) + + retentions = sorted(baseline_by_retention.keys()) + + plt.figure(figsize=(11, 7)) + for idx, network_name in enumerate(NETWORK_LABELS): + xs = [] + ys_manual = [] + ys_smart = [] + for retention in retentions: + b = baseline_by_retention[retention] + xs.append(retention) + ys_manual.append( + safe_ratio( + b.network_immediate_show[idx], + b.network_show_requests[idx], + ) + ) + + candidates = by_retention[retention] + if candidates: + best = max( + candidates, + key=lambda item: safe_ratio(item.network_immediate_show[idx], item.network_show_requests[idx]), + ) + ys_smart.append( + safe_ratio( + best.network_immediate_show[idx], + best.network_show_requests[idx], + ) + ) + else: + ys_smart.append(0.0) + + plt.plot(xs, ys_manual, marker="o", label=f"{network_name.upper()} - 手动") + plt.plot(xs, ys_smart, marker="s", linestyle="--", label=f"{network_name.upper()} - 智能") + + plt.title("网络分层即时命中率：次留对比（D2）") + plt.xlabel("次留（D2）") + plt.ylabel("即时命中率") + plt.grid(True) + plt.legend(fontsize=8) + plt.tight_layout() + plt.savefig(out_dir / "line_network_immediate_vs_retention.png", dpi=150) + plt.close() + + plt.figure(figsize=(11, 7)) + for idx, network_name in enumerate(NETWORK_LABELS): + xs = [] + ys_manual = [] + ys_smart = [] + for retention in retentions: + b = baseline_by_retention[retention] + xs.append(retention) + ys_manual.append(b.network_avg_wait_ms[idx]) + + candidates = by_retention[retention] + if candidates: + best = max( + candidates, + key=lambda item: safe_ratio(item.network_immediate_show[idx], item.network_show_requests[idx]), + ) + ys_smart.append(best.network_avg_wait_ms[idx]) + else: + ys_smart.append(0.0) + + plt.plot(xs, ys_manual, marker="o", label=f"{network_name.upper()} - 手动") + plt.plot(xs, ys_smart, marker="s", linestyle="--", label=f"{network_name.upper()} - 智能") + + plt.title("网络分层平均等待：次留对比（D2）") + plt.xlabel("次留（D2）") + plt.ylabel("平均等待（ms）") + plt.grid(True) + plt.legend(fontsize=8) + plt.tight_layout() + plt.savefig(out_dir / "line_network_wait_vs_retention.png", dpi=150) + plt.close() + + +def build_report( + results: List[RunResult], + baseline_by_retention: Dict[float, RunResult], + out_dir: Path, + robot_count: int, + baseline_retention_default: float = 0.35, +) -> Path: + report = out_dir / "TapADN_智能预加载_敏感度验收报告.md" + ranked: Dict[float, List[RunResult]] = defaultdict(list) + for r in results: + ranked[r.retention].append(r) + + ranked_sorted: Dict[float, List[RunResult]] = {} + for retention, rows in ranked.items(): + ranked_sorted[retention] = sorted(rows, key=lambda item: safe_ratio(item.immediate_play_count, item.total_show_requests), reverse=True) + + def pick_balanced(rows: List[RunResult], waste_cap: float = 0.12) -> RunResult | None: + filtered = [r for r in rows if r.wasted_ratio <= waste_cap] + if not filtered: + return None + return max(filtered, key=lambda item: safe_ratio(item.immediate_play_count, item.total_show_requests)) + + def format_table_rows(rows: List[RunResult], count: int = 8) -> List[str]: + lines = [] + for idx, r in enumerate(rows[:count], 1): + immediate = safe_ratio(r.immediate_play_count, r.total_show_requests) + lines.append( + f"| {idx} | {r.threshold:.2f} | {r.cooldown_seconds} | {immediate:.2%} | {r.avg_wait_ms:.0f} | {r.p95_wait_ms:.0f} | {r.wasted_ratio:.2%} | {r.show_success_rate:.2%} |" + ) + return lines + + best_retention_map: Dict[float, RunResult] = {} + for retention, rows in ranked_sorted.items(): + best_retention_map[retention] = rows[0] + + default_baseline = baseline_by_retention[baseline_retention_default] + default_rows = ranked_sorted[baseline_retention_default] + best_default = default_rows[0] + balanced_default = pick_balanced(default_rows, waste_cap=0.12) + + b_immediate = safe_ratio(default_baseline.immediate_play_count, default_baseline.total_show_requests) + + with report.open("w", encoding="utf-8") as f: + f.write("# TapADN 智能预加载策略验收报告（含随机偏好机器人 + 网络环境）\n\n") + f.write("## 仿真前提\n") + f.write("- 模型定位：IAA 场景中 3 类广告位（激励/开屏/插屏）统一参与策略决策。\n") + f.write("- 次留默认验收基线：35%\n") + f.write(f"- 随机机器人数量：{robot_count}\n") + f.write("- 场景进入->展示请求->预加载决策->展示时延为核心链路。\n") + f.write("- 每次样本运行前先生成一批“偏好机器人”，再在其上分别运行：纯手动（无 smart）与智能预加载两种模式。\n") + f.write("- 预加载触发后在 cooldown 内有效一次，不命中将视作普通 load。\n") + f.write("- `Immediate`：请求时已命中可直接播放的占比（值越高越好）\n") + f.write("- `Waste`：预加载后在 cooldown 内未被消费即失效的比例（值越低越好）\n") + f.write("- 网络环境：Wi-Fi / 4G / 3G / 2G 按机器人偏好加权采样，逐回合独立变化。\n\n") + + f.write("## 机器人与网络设置\n") + f.write("- 机器人类型：随机采样以下偏好族（reward_heavy、interstitial_focus、splash_driven、balanced、churn_sensitive、network_bound）。\n") + f.write("- 每类机器人具有独立的场景进入偏好、请求偏好、fill 成功倍率、加载时延倍率和留存倍率。\n") + f.write("- 网络环境以会话粒度采样，低网速会同步影响网络请求率、fill 成功率和加载耗时。\n\n") + + f.write("## 参数扫描范围\n") + f.write("- Threshold: `0.20~0.90` 步长 0.05\n") + f.write("- Cooldown: `30,60,90,120,180,240,300`\n") + f.write("- 次留：`20%,25%,30%,35%,40%,50%,60%`\n\n") + + f.write("## 基线对照\n") + f.write(f"- 次留 35% 基线（纯手动，无 smart）即时命中率：{b_immediate:.2%}\n") + f.write(f"- 次留 35% 基线平均时延：{default_baseline.avg_wait_ms:.0f} ms（p95 {default_baseline.p95_wait_ms:.0f}）\n") + f.write(f"- 次留 35% 建议起点（当前模型）：`threshold={best_default.threshold:.2f}, cooldown={best_default.cooldown_seconds}s`\n") + f.write( + f" - 对比基线即时命中提升：`{safe_ratio(best_default.immediate_play_count, best_default.total_show_requests)-b_immediate:.2%}`\n" + ) + f.write(f" - 对比基线时延变化：`{best_default.avg_wait_ms - default_baseline.avg_wait_ms:.0f} ms`\n") + if balanced_default is not None: + f.write("- 平衡候选（Waste<=12%）：\n") + f.write( + f" - threshold={balanced_default.threshold:.2f}, cd={balanced_default.cooldown_seconds}s，" + f"Immediate {safe_ratio(balanced_default.immediate_play_count, balanced_default.total_show_requests):.2%}，" + f"Waste {balanced_default.wasted_ratio:.2%}\n" + ) + + f.write("\n## 最优/最差（次留=35%）\n") + f.write("| 排名 | Threshold | Cooldown | 即时命中率 | 平均等待 | P95等待 | 浪费率 | 播放成功率 |\n") + f.write("|---|---:|---:|---:|---:|---:|---:|---:|\n") + f.writelines([line + "\n" for line in format_table_rows(default_rows)]) + f.write("\n### 最差 8 组\n") + f.write("| 排名 | Threshold | Cooldown | 即时命中率 | 平均等待 | P95等待 | 浪费率 | 播放成功率 |\n") + f.write("|---|---:|---:|---:|---:|---:|---:|---:|\n") + f.writelines([line + "\n" for line in format_table_rows(list(reversed(default_rows)), count=8)]) + + f.write("\n## 与纯手动模式对比（次留35%）\n") + best_35 = best_retention_map[0.35] + f.write("- 纯手动：") + f.write(f"Immediate `{safe_ratio(default_baseline.immediate_play_count, default_baseline.total_show_requests):.2%}`，") + f.write(f"AvgWait `{default_baseline.avg_wait_ms:.0f}`ms，Waste `{default_baseline.wasted_ratio:.2%}`。\n") + f.write("- smart 最优：") + f.write(f"阈值 `{best_35.threshold:.2f}`，cd `{best_35.cooldown_seconds}`，") + f.write(f"Immediate `{safe_ratio(best_35.immediate_play_count, best_35.total_show_requests):.2%}`，") + f.write(f"AvgWait `{best_35.avg_wait_ms:.0f}`ms，Waste `{best_35.wasted_ratio:.2%}`。\n") + f.write(f"- 增益：`{safe_ratio(best_35.immediate_play_count, best_35.total_show_requests)-b_immediate:.2%}`。 \n\n") + + f.write("### 网络分层对比（次留35%）\n") + for idx, network_name in enumerate(NETWORK_LABELS): + candidates = [r for r in results if r.retention == baseline_retention_default] + best_net = max( + candidates, + key=lambda item: safe_ratio(item.network_immediate_show[idx], item.network_show_requests[idx]), + ) + manual_immediate = safe_ratio(default_baseline.network_immediate_show[idx], default_baseline.network_show_requests[idx]) + manual_wait = default_baseline.network_avg_wait_ms[idx] + smart_immediate = safe_ratio(best_net.network_immediate_show[idx], best_net.network_show_requests[idx]) + smart_wait = best_net.network_avg_wait_ms[idx] + f.write( + f"- {network_name.upper()}：手动即时 `{manual_immediate:.2%}` / 等待 `{manual_wait:.0f}ms`；" + ) + f.write( + f"智能（`threshold={best_net.threshold:.2f}, cd={best_net.cooldown_seconds}s`）即时 `{smart_immediate:.2%}` / 等待 `{smart_wait:.0f}ms`；" + ) + f.write(f"提升 `{smart_immediate-manual_immediate:.2%}`。\n") + + f.write("## 次留敏感度（35%基线）\n") + for retention in sorted(ranked_sorted.keys()): + best = ranked_sorted[retention][0] + worst = ranked_sorted[retention][-1] + b = baseline_by_retention[retention] + f.write( + f"- 次留 {retention:.0%}：手动基线时延 {b.avg_wait_ms:.0f}ms，智能最佳 `threshold={best.threshold:.2f}, cd={best.cooldown_seconds}s`，" + ) + f.write( + f"最佳 Immediate {safe_ratio(best.immediate_play_count, best.total_show_requests):.2%}，Waste {best.wasted_ratio:.2%}；" + ) + f.write( + f"最差 `threshold={worst.threshold:.2f}, cd={worst.cooldown_seconds}s`，" + f"Immediate {safe_ratio(worst.immediate_play_count, worst.total_show_requests):.2%}，Waste {worst.wasted_ratio:.2%}。\n" + ) + + f.write("\n## 交叉参数观察\n") + f.write("- 在同一保留率下，阈值下调能显著提高 `Immediate`，但通常也抬高 `Waste`。\n") + f.write("- cooldown 拉长可降低 waste（减少重复预加载/空耗），但可能提高用户等待。\n") + f.write("- 在弱网（尤其低分配机器人更多时）场景，建议提高 waste 上限约束后再考虑 lower threshold。\n\n") + + f.write("## 图表示例（输出文件）\n") + for p in sorted(out_dir.glob("*.png")): + f.write(f"- `{p.name}`\n") + f.write(f" ![]({p.name})\n\n") + + f.write("## 验收结论\n") + if balanced_default is not None: + f.write( + f"- 建议首轮灰度点：`threshold={balanced_default.threshold:.2f}`, `cooldown={balanced_default.cooldown_seconds}s`（兼顾即时性与 waste）。\n" + ) + else: + f.write(f"- 建议首轮灰度点：`threshold={best_default.threshold:.2f}`, `cooldown={best_default.cooldown_seconds}s`（当前样本未出现满足 12% Waste 约束的点）。\n") + f.write(f"- 次留 35% 下智能策略最佳立即命中来自 `threshold={best_default.threshold:.2f}, cd={best_default.cooldown_seconds}s`，即时率 `{safe_ratio(best_default.immediate_play_count, best_default.total_show_requests):.2%}`，Waste `{best_default.wasted_ratio:.2%}`。\n") + f.write("- 建议将 `smart_preload` 配置作为实验变量：先以纯手动为对照，再按次留分层和网络监控逐步放量。\n") + + return report + + +def main() -> None: + parser = argparse.ArgumentParser(description="TapADN SmartLoad sensitivity simulation") + parser.add_argument("--users", type=int, default=3000) + parser.add_argument("--policy-json", default=str(Path("Assets/Tapadn_Adapter/Runtime/Resources/TapadnSmartLoadPolicy_Default.json"))) + parser.add_argument("--out-dir", default="Tools/SmartLoadSensitivity/output") + parser.add_argument("--robot-seed", type=int, default=20260604) + parser.add_argument("--base-seed", type=int, default=1000) + parser.add_argument("--include-line-mode", action="store_true") + args = parser.parse_args() + + policy_path = Path(args.policy_json) + out_dir = Path(args.out_dir) + out_dir.mkdir(parents=True, exist_ok=True) + + profiles = [ + SceneProfile( + ad_type=0, + scene="reward", + true_request_probability=0.42, + fill_success_probability=0.86, + mean_load_ms=2100.0, + per_session_enter_probability=0.55, + ), + SceneProfile( + ad_type=2, + scene="interstitial", + true_request_probability=0.56, + fill_success_probability=0.82, + mean_load_ms=1750.0, + per_session_enter_probability=0.72, + ), + SceneProfile( + ad_type=1, + scene="splash", + true_request_probability=0.96, + fill_success_probability=0.78, + mean_load_ms=1350.0, + per_session_enter_probability=1.0, + ), + ] + + robots = generate_robot_profiles(args.users, args.robot_seed) + policy_map = parse_policy_config(policy_path) + threshold_grid = [round(0.20 + i * 0.05, 2) for i in range(0, 15)] + cooldown_grid = [30, 60, 90, 120, 180, 240, 300] + retention_rates = [0.20, 0.25, 0.30, 0.35, 0.40, 0.50, 0.60] + + results, baseline = run_sensitivity( + profiles=profiles, + policy_map=policy_map, + robots=robots, + threshold_grid=threshold_grid, + cooldown_grid=cooldown_grid, + retention_rates=retention_rates, + out_dir=out_dir, + mode_manual_seed=args.base_seed, + ) + + draw_heatmaps(results, 0.35, out_dir) + draw_heatmaps(results, 0.20, out_dir) + draw_heatmaps(results, 0.60, out_dir) + draw_retention_lines(results, out_dir) + report = build_report(results, baseline, out_dir, robot_count=len(robots)) + + print(f"[SmartLoadSimulation] generated outputs in: {out_dir}") + print(f"[SmartLoadSimulation] report: {report}") + + +if __name__ == "__main__": + main()