Chunking

Why do we need chunks?

NannyML monitors ML models in production by doing data drift detection and performance estimation or monitoring. This functionality relies on aggregate metrics that are evaluated on samples of production data. These samples are called chunks. All the results generated are calculated and presented per chunk i.e. a chunk is a single data point on the monitoring results. You can refer to Data Drift guide or Performance Estimation guide to review example results.

Walkthrough on creating chunks

To allow for flexibility there are many ways to create chunks. The examples below will show how different kinds of chunks can be created. The examples will be run based on the performance estimation flow on the synthetic binary classification dataset provided by NannyML. First we set up this dataset.

>>> import pandas as pd
>>> import nannyml as nml
>>> from IPython.display import display
>>> reference = nml.load_synthetic_binary_classification_dataset()[0]
>>> analysis = nml.load_synthetic_binary_classification_dataset()[1]

Time-based chunking

Time-based chunking creates chunks based on time intervals. One chunk can contain all the observations from a single hour, day, week, month etc. In most cases, such chunks will vary in the number of observations they contain. Specify the chunk_period argument to get appropriate split. The example below chunks data quarterly.

>>> cbpe = nml.CBPE(
>>>    y_pred_proba='y_pred_proba',
>>>    y_pred='y_pred',
>>>    y_true='work_home_actual',
>>>    timestamp_column_name='timestamp',
>>>    metrics=['roc_auc'],
>>>    chunk_period="Q")
>>> cbpe.fit(reference)
>>> est_perf = cbpe.estimate(analysis)
>>> est_perf.data.iloc[:3,:5]

key

start_index

end_index

start_date

end_date

0

2017Q3

0

1261

2017-08-31 00:00:00

2017-09-30 23:59:59

1

2017Q4

1262

4951

2017-10-01 00:00:00

2017-12-31 23:59:59

2

2018Q1

4952

8702

2018-01-01 00:00:00

2018-03-31 23:59:59

Note

Notice that each calendar quarter was taken into account, even if it was not fully covered with records. This means some chunks contain fewer observations (usually the last and the first). See the first row above - Q3 is July-September, but the first record in the data is from the last day of August. The first chunk has ~1200 of observations while the 2nd and 3rd contain above 3000. This can cause some chunks to be less relibaly estimated or calculated.

Possible time offsets are listed in the table below:

Alias

Description

S

second

T, min

minute

H

hour

D

day

W

week

M

month

Q

quarter

A, y

year

Size-based chunking

Chunks can be of fixed size, i.e. each chunk contains the same number of observations. Set this up by specifying the chunk_size parameter.

>>> cbpe = nml.CBPE(
>>>    y_pred_proba='y_pred_proba',
>>>    y_pred='y_pred',
>>>    y_true='work_home_actual',
>>>    timestamp_column_name='timestamp',
>>>    metrics=['roc_auc'],
>>>    chunk_size=3500)
>>> cbpe.fit(reference_data=reference)
>>> est_perf = cbpe.estimate(analysis)
>>> est_perf.data.iloc[:3,:5]

key

start_index

end_index

start_date

end_date

0

[0:3499]

0

3499

2017-08-31 00:00:00

2017-11-26 23:59:59

1

[3500:6999]

3500

6999

2017-11-26 00:00:00

2018-02-18 23:59:59

2

[7000:10499]

7000

10499

2018-02-18 00:00:00

2018-05-14 23:59:59

Note

If the number of observations is not divisible by the chunk size required, the number of rows equal to the remainder of a division will be dropped. This ensures that each chunk has the same size, but in worst case scenario it results in dropping chunk_size-1 rows. Notice that the last index in the last chunk is 48999 while the last index in the raw data is 49999:

>>> est_perf.data.iloc[-2:,:5]

key

start_index

end_index

start_date

end_date

12

[42000:45499]

42000

45499

2020-06-18 00:00:00

2020-09-13 23:59:59

13

[45500:48999]

45500

48999

2020-09-13 00:00:00

2020-12-08 23:59:59

>>> analysis.index.max()
49999

Number-based chunking

The total number of chunks can be set by the chunk_number parameter:

>>> cbpe = nml.CBPE(
>>>    y_pred_proba='y_pred_proba',
>>>    y_pred='y_pred',
>>>    y_true='work_home_actual',
>>>    timestamp_column_name='timestamp',
>>>    metrics=['roc_auc'],
>>>    chunk_number=9)
>>> cbpe.fit(reference_data=reference)
>>> est_perf = cbpe.estimate(analysis)
>>> len(est_perf.data)
9

Note

Chunks created this way will be equal in size. If the number of observations is not divisible by the chunk_number then the number of observations equal to the residual of the division will be dropped.

>>> est_perf.data.iloc[-2:,:5]

key

start_index

end_index

start_date

end_date

7

[38885:44439]

38885

44439

2020-04-03 00:00:00

2020-08-18 23:59:59

8

[44440:49994]

44440

49994

2020-08-18 00:00:00

2021-01-01 23:59:59

>>> analysis.index.max()
49999

Note

The same splitting rule is always applied to the dataset used for fitting (reference) and the dataset of interest (in the presented case - analysis). Unless these two datasets are of the same size, the chunk sizes can be considerably different. E.g. if the reference dataset has 10 000 observations and the analysis dataset has 80 000, and chunking is number-based, chunks in reference will be much smaller than in analysis. Additionally, if the data drift or performance estimation is calculated on combined reference and analysis the results presented for reference will be calculated on different chunks than they were fitted.

Automatic chunking

The default chunking method is count-based, with the desired count set to 10. This is used if a chunking method isn’t specified.

>>> cbpe = nml.CBPE(
>>>    y_pred_proba='y_pred_proba',
>>>    y_pred='y_pred',
>>>    y_true='work_home_actual',
>>>    timestamp_column_name='timestamp',
>>>    metrics=['roc_auc'])
>>> cbpe.fit(reference_data=reference)
>>> est_perf = cbpe.estimate(pd.concat([reference, analysis]))
>>> len(est_perf.data)
10

Chunks on plots with results

Finally, once the chunking method is selected, the full performance estimation can be run.

Each point on the plot represents a single chunk, with the y-axis showing the performance. They are aligned on the x axis with the date at the end of the chunk, not the date in the middle of the chunk. Plots are interactive - hovering over the point will display the precise information about the period, to help prevent any confusion.

>>> cbpe = nml.CBPE(model_metadata=metadata, chunk_size=5_000).fit(reference_data=reference)
>>> est_perf = cbpe.estimate(analysis)
>>> est_perf.plot(kind='performance').show()
../_images/guide-chunking_your_data-pe_plot.svg