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Copy file name to clipboardExpand all lines: prebid-server/pbs-modules/greenbids-real-time-data.md
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## Overview
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Greenbids Real Time Data module filters bidders
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SSPs listed in the `imp[].ext.prebid.bidder`
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of the bid request. To perform the filtering the module uses the ML pipeline that outputs the probability
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of bid per SSP for each `imp` for the given bid request.
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Then this probability of bid is compared with the threshold to ensure the necessary level of filtering for a partner publisher.
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Greenbids Real Time Data module filters bidders SSPs listed in the `imp[].ext.prebid.bidder` of the bid request.
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To perform the filtering the module uses AI to predict the bidding probability of bid per SSP for each `imp` for the given bid request.
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This bidding probability is used to choose to send the bid request or not, according to the performance constraints applied by the partner publisher.
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The RTD module uses 2 artefacts that are fetched from the Greenbids Google Cloud Storage bucket
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- ML predictor in `.onnx` format
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- Probability thresholds in `.json` format with the list of thresholds and their corresponding target metrics
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- ML predictor in `.onnx` format: used to predict the bidding probability
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- Probability thresholds in `.json` format: used to convert the bidding probability to a binary choice
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## Configuration
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- `google-cloud-greenbids-project`: Google Cloud project associated with Greenbids
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- `gcs-bucket-name`: Google Cloud Storage (GCS) bucket used to fetch the artefacts ([ONNX](https://onnx.ai/) model and thresholds `.json`) necessary for prediction
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- `cache-expiration-minutes`: The duration (in minutes) after which cached model and thresholds should be considered expired and refreshed
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- `geo-lite-country-path`: Geolocation capabilities to determine the country from an IP address
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- `geo-lite-country-path`: URL to the geo-ip database
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- `onnx-model-cache-key-prefix`: prefix necessary for getting cached ONNX model
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- `thresholds-cache-key-prefix`: prefix necessary for getting cached throttling thresholds
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| Parameter | Scope | Description | Example | Type |
| explorationRate | required | Ratio of traffic without filtering used for training ML model [0-1] (a value of 0.1 will filter 90% of the traffic) | 0.1 | float |
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Here's an example of how a PBS partner publisher setup using both Greenbids RTD Module and Greenbids AnalyticsReporter shoudl look like:
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Here's an example of how a PBS partner publisher setup using both Greenbids RTD Module and Greenbids AnalyticsReporter should look like:
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```json
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"ext": {
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// extension for Greenbids Analytics Reporter
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"greenbids": {
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"pbuid": "PBUID_FROM_GREENBIDS",
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"greenbidsSampling": 1
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"greenbidsSampling": 0.01
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},
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// extension for Greenbids Real Time Data Module
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"greenbids-rtd": {
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## Analytics Tags
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The RTD module also communicates the filtering results with the `GreenbidsAnalyticsReporter` via [AnalyticsTags](https://docs.prebid.org/prebid-server/developers/module-atags.html).
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Here we populate analytics result of analytics tags for each `imp` the with
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Here we populate analytics result of analytics tags for each `imp` the with:
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- `fingerprint`: greenbidsId
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- `isKeptInAuction`map of booleans for each bidder whether we keep them in auction or not for the given imp
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- `isExploration`flag that is necessary to isolate the training data
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- `isKeptInAuction`:map of booleans for each bidder whether we keep them in auction or not for the given imp
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- `isExploration`: flag that is necessary to isolate the training data
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The analytics tag is then parsed by the AnalyticsReporter from `HookExecutionContext`
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and its content added to the analytics payload sent to Greenbids server. The exploration part of traffic is split randomly with the ratio defined for each partner publisher
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