# Using Label Transforms¶

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.

## Generate Labels¶

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,
label_type='continuous',
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()
```

```
[2]:
```

customer_id | time | total_spent | |
---|---|---|---|

0 | 1 | 2014-01-01 02:45:30 | 217.94 |

1 | 1 | 2014-01-01 02:47:30 | 217.94 |

2 | 1 | 2014-01-01 02:49:30 | 217.94 |

3 | 1 | 2014-01-01 02:51:30 | 217.94 |

4 | 1 | 2014-01-01 02:53:30 | 217.94 |

## Threshold on Labels¶

`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.

```
[3]:
```

```
labels.threshold(100).head()
```

```
[3]:
```

customer_id | time | total_spent | |
---|---|---|---|

0 | 1 | 2014-01-01 02:45:30 | True |

1 | 1 | 2014-01-01 02:47:30 | True |

2 | 1 | 2014-01-01 02:49:30 | True |

3 | 1 | 2014-01-01 02:51:30 | True |

4 | 1 | 2014-01-01 02:53:30 | True |

## Lead Labels Times¶

`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.

```
[4]:
```

```
labels.apply_lead('1h').head()
```

```
[4]:
```

customer_id | time | total_spent | |
---|---|---|---|

0 | 1 | 2014-01-01 01:45:30 | 217.94 |

1 | 1 | 2014-01-01 01:47:30 | 217.94 |

2 | 1 | 2014-01-01 01:49:30 | 217.94 |

3 | 1 | 2014-01-01 01:51:30 | 217.94 |

4 | 1 | 2014-01-01 01:53:30 | 217.94 |

## Bin Labels¶

`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.

### Value Based¶

To use bins based on values, `quantiles`

should be set to `False`

which is the default value.

#### Equal Width¶

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()
```

```
[5]:
```

customer_id | time | total_spent | |
---|---|---|---|

0 | 1 | 2014-01-01 02:45:30 | (198.455, 271.072] |

1 | 1 | 2014-01-01 02:47:30 | (198.455, 271.072] |

2 | 1 | 2014-01-01 02:49:30 | (198.455, 271.072] |

3 | 1 | 2014-01-01 02:51:30 | (198.455, 271.072] |

4 | 1 | 2014-01-01 02:53:30 | (198.455, 271.072] |

#### Custom Widths¶

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()
```

```
[6]:
```

customer_id | time | total_spent | |
---|---|---|---|

0 | 1 | 2014-01-01 02:45:30 | (67.0, inf] |

1 | 1 | 2014-01-01 02:47:30 | (67.0, inf] |

2 | 1 | 2014-01-01 02:49:30 | (67.0, inf] |

3 | 1 | 2014-01-01 02:51:30 | (67.0, inf] |

4 | 1 | 2014-01-01 02:53:30 | (67.0, inf] |

### Quantile Based¶

To use bins based on quantiles, `quantiles`

should be set to `True`

.

#### Equal Width¶

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()
```

```
[7]:
```

customer_id | time | total_spent | |
---|---|---|---|

0 | 1 | 2014-01-01 02:45:30 | (196.25, 217.94] |

1 | 1 | 2014-01-01 02:47:30 | (196.25, 217.94] |

2 | 1 | 2014-01-01 02:49:30 | (196.25, 217.94] |

3 | 1 | 2014-01-01 02:51:30 | (196.25, 217.94] |

4 | 1 | 2014-01-01 02:53:30 | (196.25, 217.94] |

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
```

#### Custom Widths¶

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()
```

```
[9]:
```

customer_id | time | total_spent | |
---|---|---|---|

0 | 1 | 2014-01-01 02:45:30 | (196.25, 217.94] |

1 | 1 | 2014-01-01 02:47:30 | (196.25, 217.94] |

2 | 1 | 2014-01-01 02:49:30 | (196.25, 217.94] |

3 | 1 | 2014-01-01 02:51:30 | (196.25, 217.94] |

4 | 1 | 2014-01-01 02:53:30 | (196.25, 217.94] |

### Label Bins¶

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.

```
[10]:
```

```
values = ['low', 'medium', 'high']
labels.bin(3, labels=values).head()
```

```
[10]:
```

customer_id | time | total_spent | |
---|---|---|---|

0 | 1 | 2014-01-01 02:45:30 | medium |

1 | 1 | 2014-01-01 02:47:30 | medium |

2 | 1 | 2014-01-01 02:49:30 | medium |

3 | 1 | 2014-01-01 02:51:30 | medium |

4 | 1 | 2014-01-01 02:53:30 | medium |

## Describe Labels¶

`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.

```
[11]:
```

```
labels.threshold(100).describe()
```

```
Label Distribution
------------------
False 8
True 42
Total: 50
Settings
--------
gap <2 * Minutes>
minimum_data 2h
num_examples_per_instance 10
target_column total_spent
target_entity customer_id
target_type discrete
window_size <Hour>
Transforms
----------
1. threshold
- value: 100
```

## Sample Labels¶

`LabelTimes.sample()`

will sample the labels based on a number or fraction. Samples can be reproduced by fixing `random_state`

to an integer.

To sample 10 labels, `n`

is set to 10.

```
[12]:
```

```
labels.sample(n=10, random_state=0)
```

```
[12]:
```

customer_id | time | total_spent | |
---|---|---|---|

2 | 1 | 2014-01-01 02:49:30 | 217.94 |

4 | 1 | 2014-01-01 02:53:30 | 217.94 |

10 | 2 | 2014-01-01 02:00:00 | 290.39 |

11 | 2 | 2014-01-01 02:02:00 | 290.39 |

22 | 3 | 2014-01-01 03:49:05 | 196.25 |

27 | 3 | 2014-01-01 03:59:05 | 196.25 |

28 | 3 | 2014-01-01 04:01:05 | 196.25 |

31 | 4 | 2014-01-01 02:41:00 | 343.69 |

38 | 4 | 2014-01-01 02:55:00 | 225.18 |

41 | 5 | 2014-01-01 03:48:25 | 53.22 |

Similarly, to sample 10% of labels, `frac`

is set to 10%.

```
[13]:
```

```
labels.sample(frac=.1, random_state=0)
```

```
[13]:
```

customer_id | time | total_spent | |
---|---|---|---|

2 | 1 | 2014-01-01 02:49:30 | 217.94 |

10 | 2 | 2014-01-01 02:00:00 | 290.39 |

11 | 2 | 2014-01-01 02:02:00 | 290.39 |

28 | 3 | 2014-01-01 04:01:05 | 196.25 |

41 | 5 | 2014-01-01 03:48:25 | 53.22 |

### Categorical Labels¶

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)
```

```
[15]:
```

customer_id | time | total_spent | |
---|---|---|---|

6 | 1 | 2014-01-01 02:57:30 | C |

11 | 2 | 2014-01-01 02:02:00 | D |

16 | 2 | 2014-01-01 02:12:00 | D |

26 | 3 | 2014-01-01 03:57:05 | B |

38 | 4 | 2014-01-01 02:55:00 | C |

42 | 5 | 2014-01-01 03:50:25 | A |

46 | 5 | 2014-01-01 03:58:25 | A |

48 | 5 | 2014-01-01 04:02:25 | B |

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)
```

```
[16]:
```

customer_id | time | total_spent | |
---|---|---|---|

6 | 1 | 2014-01-01 02:57:30 | C |

11 | 2 | 2014-01-01 02:02:00 | D |

16 | 2 | 2014-01-01 02:12:00 | D |

26 | 3 | 2014-01-01 03:57:05 | B |

46 | 5 | 2014-01-01 03:58:25 | A |