I made two models for predicting reorder & None. Following are the features I made.

• How often the user reordered items
• Time between orders
• Time of day the user visits
• Whether the user ordered organic, gluten-free, or Asian items in the past
• Features based on order sizes
• How many of the user’s orders contained no previously purchased items

• How often the item is purchased
• Position in the cart
• How many users buy it as "one shot" item
• Stats on the number of items that co-occur with this item
• Stats on the order streak
• Probability of being reordered within N orders
• Distribution of the day of week it is ordered
• Probability it is reordered after the first order
• Statistics around the time between orders

• Number of orders in which the user purchases the item
• Days since the user last purchased the item
• Streak (number of orders in a row the user has purchased the item)
• Position in the cart
• Whether the user already ordered the item today
• Co-occurrence statistics
• Replacement items

• Counts by day of week
• Counts by hour

More detail, please refer to codes.

Regarding F1 maximization, I hadn't read that paper until Faron had published the kernel. But I got high score because of my F1 maximization. Let me explain it. For maximizing F1, I generate y_true according to predicted prob. And check F1 from higher prob. For example, lets say we have ordered item and prob, like {A: 0.3, B:0.5, C:0.4}. Then generate y_true in many times. In my case, generated 9999 times. So now we have many of y_true, like [ [A,B],[B],[B,C],[C],[B],[None].....]. As I mentioned above, next thing we do is to check F1 from [B], [B,C], [B,C,A]. Then we can estimate F1 peak out, and stop calculation, and go next order. You may know, in this method, we don't need to check all pattern, like [A],[A,B],[A,B,C],[B]... I guess some might have figured out this method from my comment of "tips to go farther". However, this method is time consuming as well as depends on seed. So finally I used Faron's kernel. Fortunatelly or not, I got almost same result using Faron's kernel. Please refer to py_model/pyx_get_best_items.pyx

• cd py_feature
• python 901_run_feature.py
• python 902_run_concat.py
• cd ../py_model
• python 999_run.py

Around 300 GB RAM needed(sorry).

Python packages:

• numpy==1.12.1
• pandas==0.19.2
• scipy==0.19.0
• tqdm==4.11.2
• xgboost==0.6