Making Decisions with Ranker

1 minute read

In this tutorial, we’ll walk through how to use the Ranker class to make decisions, specifically choosing a discount to offer from a list of discounts. We’ll also need to track the decision with the RewardTracker and reward it for the amount of profit if a purchase is made. In this way, the Ranker will learn to rank the most profitable discount first.

Using the Ranker class

First, let’s create an instance of the Ranker class.

from improveai import Ranker

ranker = Ranker(model_url)

Ranking items

To rank items, you can call the rank method of the Ranker class. This method accepts a list of items and an optional context.

discounts = [.10, .15, .20, .25, .30]
ranked_discounts = ranker.rank(discounts)

Selecting the highest-ranking discount

Now that we have the ranked discounts, we can choose the highest-ranking discount.

best_discount = ranked_discounts[0]

Tracking the decision with RewardTracker

Next, we need to track our decision using the RewardTracker class.

Creating a RewardTracker instance

First, create a RewardTracker instance with the required arguments.

from improveai import RewardTracker

tracker = RewardTracker("discounts", track_url)

Tracking the decision

Now we can track our decision using the track method of the RewardTracker class. This method accepts the selected item, a list of candidates, and an optional context.

reward_id = tracker.track(best_discount, discounts)

Rewarding the decision

Once a purchase is made, we can add a reward to the decision using the add_reward method of the RewardTracker class. This method requires a reward value and a reward_id that was obtained when tracking the chosen discount.

reward = profit_amount
tracker.add_reward(reward, reward_id)

Improve AI will automatically balance exploring new discounts with doubling down on the discounts expected to be most profitable. Over time and as more decisions are made, the model will learn to choose the optimal discount.

Conclusion

In this tutorial, we covered how to use the Ranker class to make decisions, such as choosing a discount from a list of discounts. We also learned how to track the decision with the RewardTracker and reward it based on the profit.

Getting Started

Improve AI is available for Python, Swift, and Java. See the Quick-Start Guide to learn more.

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