awasthimaddy/instacart

2nd place solution

Instacart Market Basket Analysis 2nd place solution

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

Features

User feature

• repeat previous ratio
• order span mean
• time for visiting
• Has ordered orgenic, glutenfree, Asian
• hour delta
• order size
• How many None

Item feature

• ratio of ordered time
• ordered time cycle
• co-occur(over order)
• stats of pos_cart
• How many user buy it as "one shot"
• stats of number of items co-occured
• stats of order streak
• 1to1 prob
• prob of reorder within N order_number
• distribution delta of dow
• prob of reorder, after first order
• stats of order_number delta

User x Item feature

• total order
• days since last order
• streak
• stats of pos_cart
• ratio of ordered time
• ordered today
• co-occur
• replacement

datetime feature

• How many come by dow and hour

More detail, please refer to source.

F1 maximization

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

How to run

Pending

Requirements

Pending