Predict Next Purchase

In this example, we will generate labels on online grocery orders provided by Instacart using Compose. The labels can be used to train a machine learning model to predict whether a customer will buy a specific product within the next month.

If you plan to run this notebook, you can use the following command at the root directory of the repository.

jupyter notebook docs/source/examples/predict-next-purchase/example.ipynb

Load Data

%matplotlib inline
import composeml as cp
import data

The data hosted here will be downloaded automatically into the data module of this notebook unless it already exist. Once the data is in place, we can preview the grocery orders to see how they look.

df = data.load_orders(nrows=1000000)

order_id product_id add_to_cart_order reordered product_name aisle_id department_id department user_id order_time
0 120 33120 13 0 Organic Egg Whites 86 16 dairy eggs 23750 2015-01-11 08:00:00
1 120 31323 7 0 Light Wisconsin String Cheese 21 16 dairy eggs 23750 2015-01-11 08:00:00
2 120 1503 8 0 Low Fat Cottage Cheese 108 16 dairy eggs 23750 2015-01-11 08:00:00
3 120 28156 11 0 Total 0% Nonfat Plain Greek Yogurt 120 16 dairy eggs 23750 2015-01-11 08:00:00
4 120 41273 4 0 Broccoli Florets 123 4 produce 23750 2015-01-11 08:00:00

Generate Labels

Now with the grocery orders loaded, we are ready to generate labels for our prediction problem.

Create Labeling Function

To get started, we define the labeling function that will return whether a customer purchased the product in a given month.

def bought_product(df, product_name):
    purchased = df.product_name.str.contains(product_name).any()
    return purchased

Construct Label Maker

With the labeling function, we create the label maker for our prediction problem. To process one month of orders for each customer, we set the target_entity to the customer ID and the window_size to one month. When window size is set to 1MS, the window size will end on the first day of the next month. Alias definitions are listed here.

lm = cp.LabelMaker(

Search Labels

Next, the label maker will search through the data continously to label whether a customer bought bananas in a given month. This happens when we use and set the product_name to bananas. If you are running this code yourself, feel free to expirement with other products (e.g. limes, avocados, etc.) and different time frames!

lt =

Elapsed: 00:29 | Remaining: 00:00 | Progress: 100%|██████████| user_id: 19477/19477
user_id time bought_product
0 4 2015-01-01 False
1 7 2015-01-01 False
2 10 2015-01-01 False
3 10 2015-02-01 False
4 13 2015-01-01 False

Describe Labels

With the generate label times, we can use LabelTimes.describe to print out the distribution with the settings and transforms that were used to make these labels. This is useful as a reference for understanding how the labels were generated from raw data. Also, the label distribution is helpful for determining if we have imbalanced labels.

Label Distribution
False     13752
True       7044
Total:    20796

gap                            <MonthBegin>
label_type                         discrete
labeling_function            bought_product
minimum_data                     2015-01-01
num_examples_per_instance                -1
target_entity                       user_id
window_size                    <MonthBegin>

No transforms applied

Plot Labels

Additionally, there are plots available for insight to the labels.


This plot shows the label distribution.


Count by Time

This plot shows the label distribution across cutoff times.