Managing predictions

Open Predictions module to manage existing predictions and check their details such as logs of every single prediction (except for prediction drafts).

The pane to the left of the list allows you to filter the list of predictions and see your usage limits.

Actions on the prediction list

In the list, you can perform the following actions for one prediction at a time:

Action name Description
Calculate Immediately starts the first calculation of the prediction.
Recalculate Immediately starts a re-calculation of the prediction.
Edit Opens the editing mode of the prediction.
Duplicate Creates a draft copy of the prediction.
Delete Deletes the prediction from the list without removing the events it generated.
Stop Stops the calculation of the prediction.

Prediction statuses

The predictions can have the following statuses:

Status name Description
Calculating The prediction is not ready, the calculation is in progress.
Recalculating The prediction is being recalculated. This may be started manually or according to the configuration of the prediction.
Active The prediction is ready.
Draft The prediction is saved as a draft and not ready to be submitted for calculation.
Issue Prediction generated results, but during the calculation some warnings occurred. Please adhere to the suggested changes.
Error The prediction didn’t pass the calculation stage, try to submit it for calculation once again with the suggested changes or contact the Synerise support team.
Outdated Contact the Synerise support team.

Prediction logs

The Prediction Logs show you the status of an active prediction. You can check the logs to learn when your prediction might be ready, such as when you just activated your prediction or when it is recalculated. There is no specific time given when the prediction will be calculated, but you can see clearly how far in the process your prediction is and track how long it takes to get there. The logs contain details from the last 10 runs (recalculations).

You can find the following information in the logs:

  • the stage at which the prediction is currently,
  • the progress of the process (expressed as a percentage),
  • elapsed time (the duration of the process in a stage),
  • date and time a process in a stage started,
  • log details available for copying (available after the calculation of the prediction)
  • error message for the latest run

Stages

When a prediction is activated it goes through the following stages:

Status Description
Started/Scheduled It is the first stage of processing a prediction:
- Started - the prediction is activated
- Scheduled - the activation of the prediction is scheduled at a future date
Data preparation At this stage, the system organizes and uploads the data for model training. This stage will not be repeated.
Training At this stage, the model is trained based on the provided data. This stage is followed by the calculation of the prediction and will not be repeated.
Predicting The prediction is calculated based on the model that was trained on the provided data. This stage can be repeated when you set the prediction to be recalculated in the configuration of the prediction.

Previewing logs

To access the logs of the prediction:

  1. Go to Image presents the Prediction icon Predictions.
  2. On the list of the predictions, click the prediction you want to see details of.
  3. Select the Logs tab.
    The preview of logs
    The preview of the prediction logs
  4. Next to the run of which you want to see the details, click the Three dot icon icon.
  5. From the dropdown list, select View logs.
    Result: A pop-up appears with the prediction log.
    Click here to see a fragment of a prediction log 

    Model id: XXXXXXXXXXXX
Run id: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
Started at: 2023-03-27T08:19:35.968000


2023-03-27T08:22:46.153391639Z 2023-03-27 08:22:46  [gsmodels.training] Running training for model_id=XXXXXXXXXXXX
2023-03-27T08:22:46.188222886Z 2023-03-27 08:22:46  [gsmodels.model_configuration] Downloading feature groups metadata
2023-03-27T08:22:46.501382138Z 2023-03-27 08:22:46  [gsmodels.training] Getting data for feature selection
2023-03-27T08:23:01.598561180Z 2023-03-27 08:23:01  [gsmodels.training] Running feature selection
2023-03-27T08:23:03.792744644Z 2023-03-27 08:23:03  [gsmodels.training] Selected 2 out of 197 features (1 out of 4 feature groups).
2023-03-27T08:23:03.792761653Z 2023-03-27 08:23:03  [gsmodels.training] Selected feature groups: {'transaction_charge_lifetime'}
2023-03-27T08:23:03.812890938Z 2023-03-27 08:23:03  [gsmodels.training] Getting data for training
2023-03-27T08:23:27.799897482Z 2023-03-27 08:23:27  [gsmodels.training] Loaded 339321 rows.
2023-03-27T08:23:27.802709891Z 2023-03-27 08:23:27  [gsmodels.training] Getting target statistics
2023-03-27T08:23:27.814395343Z 2023-03-27 08:23:27  [gsmodels.training] Classes distribution: '0': 316093, '1': 23228, total rows: 339321
2023-03-27T08:23:28.188081785Z 2023-03-27 08:23:28  [gsmodels.training] Training
2023-03-27T08:23:30.156435752Z 2023-03-27 08:23:30  [gsmodels.training] Training finished
2023-03-27T08:23:30.156466673Z 2023-03-27 08:23:30  [gsmodels.training] Getting metrics
2023-03-27T08:23:31.433284183Z 2023-03-27 08:23:31  [gsmodels.training] Training metrics
2023-03-27T08:23:31.433404908Z 2023-03-27 08:23:31  [gsmodels.training] {
2023-03-27T08:23:31.433423697Z 2023-03-27 08:23:31  [gsmodels.training]  "accuracy": 0.9968466344453613,
2023-03-27T08:23:31.433440324Z 2023-03-27 08:23:31  [gsmodels.training]  "f1": 0.9773389103616243,
2023-03-27T08:23:31.433446411Z 2023-03-27 08:23:31  [gsmodels.training]  "precision": 0.9618070029178825,
2023-03-27T08:23:31.433482319Z 2023-03-27 08:23:31  [gsmodels.training]  "recall": 0.9933806909912819,
2023-03-27T08:23:31.433486067Z 2023-03-27 08:23:31  [gsmodels.training]  "roc_auc": 0.995881139038252,
2023-03-27T08:23:31.433518355Z 2023-03-27 08:23:31  [gsmodels.training]  "balanced_accuracy": 0.9904820141799053,
2023-03-27T08:23:31.433523310Z 2023-03-27 08:23:31  [gsmodels.training]  "ndcg": 0.9953841716713426,
2023-03-27T08:23:31.433526307Z 2023-03-27 08:23:31  [gsmodels.training]  "avg_precision": 0.9619352874525635
2023-03-27T08:23:31.433565399Z 2023-03-27 08:23:31  [gsmodels.training] }
2023-03-27T08:23:31.666928161Z 2023-03-27 08:23:31  [gsmodels.training] Validation metrics
2023-03-27T08:23:31.666965694Z 2023-03-27 08:23:31  [gsmodels.training] {
2023-03-27T08:23:31.666970312Z 2023-03-27 08:23:31  [gsmodels.training]  "accuracy": 0.9968466344453613,
2023-03-27T08:23:31.666982606Z 2023-03-27 08:23:31  [gsmodels.training]  "f1": 0.9773640786968479,
2023-03-27T08:23:31.667016070Z 2023-03-27 08:23:31  [gsmodels.training]  "precision": 0.9608985024958403,
2023-03-27T08:23:31.667054754Z 2023-03-27 08:23:31  [gsmodels.training]  "recall": 0.9944037882049075,
2023-03-27T08:23:31.667088336Z 2023-03-27 08:23:31  [gsmodels.training]  "roc_auc": 0.9960857895235649,
2023-03-27T08:23:31.667100999Z 2023-03-27 08:23:31  [gsmodels.training]  "balanced_accuracy": 0.9914299516393723,
2023-03-27T08:23:31.667104683Z 2023-03-27 08:23:31  [gsmodels.training]  "ndcg": 0.9948570584075384,
2023-03-27T08:23:31.667137914Z 2023-03-27 08:23:31  [gsmodels.training]  "avg_precision": 0.9646890438948853
2023-03-27T08:23:31.667151243Z 2023-03-27 08:23:31  [gsmodels.training] }
2023-03-27T08:23:32.056626092Z 2023-03-27 08:23:32  [gsmodels.training] Test metrics
2023-03-27T08:23:32.056702368Z 2023-03-27 08:23:32  [gsmodels.training] {
2023-03-27T08:23:32.056724111Z 2023-03-27 08:23:32  [gsmodels.training]  "accuracy": 0.9969940765626382,
2023-03-27T08:23:32.056729217Z 2023-03-27 08:23:32  [gsmodels.training]  "f1": 0.9783806697753286,
2023-03-27T08:23:32.056737940Z 2023-03-27 08:23:32  [gsmodels.training]  "precision": 0.9636743215031315,
2023-03-27T08:23:32.056794806Z 2023-03-27 08:23:32  [gsmodels.training]  "recall": 0.993542832544124,
2023-03-27T08:23:32.056799284Z 2023-03-27 08:23:32  [gsmodels.training]  "roc_auc": 0.9954989600303854,
2023-03-27T08:23:32.056839005Z 2023-03-27 08:23:32  [gsmodels.training]  "balanced_accuracy": 0.9907905389661424,
2023-03-27T08:23:32.056858283Z 2023-03-27 08:23:32  [gsmodels.training]  "ndcg": 0.9953921574572213,
2023-03-27T08:23:32.056861879Z 2023-03-27 08:23:32  [gsmodels.training]  "avg_precision": 0.9682815577902703
2023-03-27T08:23:32.056887169Z 2023-03-27 08:23:32  [gsmodels.training] }
2023-03-27T08:23:32.056961044Z 2023-03-27 08:23:32  [gsmodels.training] Getting feature importance
2023-03-27T08:23:32.599428102Z 2023-03-27 08:23:32  [gsmodels.training] Saving model
2023-03-27T08:23:32.610470225Z 2023-03-27 08:23:32  [gsmodels.training] Uploading model
2023-03-27T08:23:33.010795571Z 2023-03-27 08:23:33  [gsmodels.training] Publishing training results

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