In this guide, we will demonstrate how to use the transforms that are available on LabelTimes. Each transform will return a copy of the label times. This is useful for trying out multiple transforms in different settings without having to recalculate the labels. As a result, we could see which labels give a better performance in less time.
LabelTimes
Let’s start by generating labels on a mock dataset of transactions. Each label is defined as the total spent by a customer given one hour of transactions.
[1]:
import composeml as cp def total_spent(df): return df['amount'].sum() label_maker = cp.LabelMaker( labeling_function=total_spent, target_entity='customer_id', time_index='transaction_time', window_size='1h', ) labels = label_maker.search( cp.demos.load_transactions(), num_examples_per_instance=10, minimum_data='2h', gap='2min', verbose=True, )
Elapsed: 00:00 | Remaining: 00:00 | Progress: 100%|██████████| customer_id: 50/50
To get an idea on how the labels looks, we preview the data frame.
[2]:
labels.head()
LabelTimes.threshold() will create binary labels by testing if label values are above a threshold. In this example, a threshold is applied to determine which customers spent over 100.
LabelTimes.threshold()
[3]:
labels.threshold(100).head()
LabelTimes.apply_lead() will shift the label time earlier. This is useful for training a model to predict in advance. In this example, a one hour lead is applied to the label times.
LabelTimes.apply_lead()
[4]:
labels.apply_lead('1h').head()
LabelTimes.bin() will bin the labels into discrete intervals. There are two types of bins. Bins could either be based on values or quantiles. Additionally, the widths of the bins could either be defined by the user or divided equally. The following examples will go through each type.
LabelTimes.bin()
To use bins based on values, quantiles should be set to False which is the default value.
quantiles
False
To group values into bins of equal width, set bins as a scalar value. In this example, the total spent is grouped into bins of equal width.
[5]:
labels.bin(4, quantiles=False).head()
To group values into bins of custom widths, set bins as an array of values to define edges. In this example, the total spent is grouped into bins of custom widths.
[6]:
inf = float('inf') edges = [-inf, 34, 50, 67, inf] labels.bin(edges, quantiles=False,).head()
To use bins based on quantiles, quantiles should be set to True.
True
To group values into quantile bins of equal width, set bins to the number of quantiles as a scalar value (e.g. 4 for quartiles, 10 for deciles, etc.). In this example, the total spent is grouped into bins based on the quartiles.
[7]:
labels.bin(4, quantiles=True).head()
To verify quartile values, we could check the descriptive statistics.
[8]:
stats = labels.total_spent.describe() stats = stats.round(3).to_string() print(stats)
count 50.000 mean 215.182 std 90.518 min 53.220 25% 196.250 50% 217.940 75% 290.390 max 343.690
To group values into quantile bins of custom widths, set bins as an array of quantiles. In this example, the total spent is grouped into quantile bins of custom widths.
[9]:
quantiles = [0, .34, .5, .67, 1] labels.bin(quantiles, quantiles=True).head()
To assign bins with custom labels, set labels to the array of values. The number of labels need to match the number of bins. In this example, the total spent is grouped into bins with custom labels.
labels
[10]:
values = ['low', 'medium', 'high'] labels.bin(3, labels=values).head()
LabelTimes.describe() will print out the distribution with the settings and transforms that were used to make the 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. In this examlpe, a description of the labels is printed after transforming the labels into discrete values.
LabelTimes.describe()
[11]:
labels.threshold(100).describe()
Label Distribution ------------------ False 8 True 42 Total: 50 Settings -------- gap 2min minimum_data 2h num_examples_per_instance 10 target_column total_spent target_entity customer_id target_type discrete window_size 1h Transforms ---------- 1. threshold - value: 100
LabelTimes.sample() will sample the labels based on a number or fraction. Samples can be reproduced by fixing random_state to an integer.
LabelTimes.sample()
random_state
To sample 10 labels, n is set to 10.
n
[12]:
labels.sample(n=10, random_state=0)
Similarly, to sample 10% of labels, frac is set to 10%.
frac
[13]:
labels.sample(frac=.1, random_state=0)
When working with categorical labels, the number or fraction of labels for each category can be sampled by using a dictionary. Let’s bin the labels into 4 bins to make categorical.
[14]:
categorical = labels.bin(4, labels=['A', 'B', 'C', 'D'])
To sample 2 labels per category, map each category to the number 2.
[15]:
n = {'A': 2, 'B': 2, 'C': 2, 'D': 2} categorical.sample(n=n, random_state=0)
Similarly, to sample 10% of labels per category, map each category to 10%.
[16]:
frac = {'A': .1, 'B': .1, 'C': .1, 'D': .1} categorical.sample(frac=frac, random_state=0)