# Getting Started¶

In this example, we will generate labels on a mock dataset of transactions. For each customer, we want to label whether the total purchase amount over the next hour of transactions will exceed $300. Additionally, we want to predict one hour in advance. [1]:  %matplotlib inline import composeml as cp  ## Load Data¶ With the package installed, we load in the data. To get an idea on how the transactions looks, we preview the data frame. [2]:  df = cp.demos.load_transactions() df[df.columns[:7]].head()  [2]:  transaction_id session_id transaction_time product_id amount customer_id device 0 298 1 2014-01-01 00:00:00 5 127.64 2 desktop 1 10 1 2014-01-01 00:09:45 5 57.39 2 desktop 2 495 1 2014-01-01 00:14:05 5 69.45 2 desktop 3 460 10 2014-01-01 02:33:50 5 123.19 2 tablet 4 302 10 2014-01-01 02:37:05 5 64.47 2 tablet ## Create Labeling Function¶ To get started, we define the labeling function that will return the total purchase amount given a hour of transactions. [3]:  def total_spent(df): total = df['amount'].sum() return total  ## Construct Label Maker¶ With the labeling function, we create the LabelMaker for our prediction problem. To process one hour of transactions for each customer, we set the target_entity to the customer ID and the window_size to one hour. [4]:  label_maker = cp.LabelMaker( target_entity="customer_id", time_index="transaction_time", labeling_function=total_spent, window_size="1h", )  ## Generate Labels¶ Next, we automatically search and extract the labels by using LabelMaker.search(). [5]:  labels = label_maker.search( df.sort_values('transaction_time'), num_examples_per_instance=-1, gap=1, verbose=True, ) labels.head()  Elapsed: 00:00 | Remaining: 00:00 | Progress: 100%|██████████| customer_id: 5/5  [5]:  customer_id cutoff_time total_spent id 0 1 2014-01-01 00:45:30 914.73 1 1 2014-01-01 00:46:35 806.62 2 1 2014-01-01 00:47:40 694.09 3 1 2014-01-01 00:52:00 687.80 4 1 2014-01-01 00:53:05 656.43 ## Transform Labels¶ With the generated LabelTimes, we will apply specific transforms for our prediction problem. ### Apply Threshold on Labels¶ To make the labels binary, LabelTimes.threshold() is applied for amounts exceeding$300.

[6]:

labels = labels.threshold(300)

labels.head()

[6]:

customer_id cutoff_time total_spent
id
0 1 2014-01-01 00:45:30 True
1 1 2014-01-01 00:46:35 True
2 1 2014-01-01 00:47:40 True
3 1 2014-01-01 00:52:00 True
4 1 2014-01-01 00:53:05 True

### Lead Label Times¶

Additionally, the label times are shifted one hour earlier for predicting in advance by using LabelTimes.apply_lead().

[7]:

labels = labels.apply_lead('1h')

labels.head()

[7]:

customer_id cutoff_time total_spent
id
0 1 2013-12-31 23:45:30 True
1 1 2013-12-31 23:46:35 True
2 1 2013-12-31 23:47:40 True
3 1 2013-12-31 23:52:00 True
4 1 2013-12-31 23:53:05 True

## Describe Labels¶

After transforming the labels, 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.

[8]:

labels.describe()

Label Distribution
------------------
False      56
True       44
Total:    100

Settings
--------
gap                                    1
label_type                      discrete
labeling_function            total_spent
minimum_data                        None
num_examples_per_instance             -1
target_entity                customer_id
window_size                       <Hour>

Transforms
----------
1. threshold
- value:    300

2. apply_lead
- value:    1h



## Plot Labels¶

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

### Distribution¶

This plot shows the label distribution.

[9]:

labels.plot.distribution();


### Count by Time¶

This plot shows the label distribution across cutoff times.

[10]:

labels.plot.count_by_time();