Calculating Business Value for Binary Classification

This tutorial explains how to use NannyML to calculate business value for binary classification models.

Note

The following example uses timestamps. These are optional but have an impact on the way data is chunked and results are plotted. You can read more about them in the data requirements.

Just The Code

```>>> import nannyml as nml
>>> from IPython.display import display

>>> reference_df, analysis_df, analysis_targets_df = nml.load_synthetic_car_loan_dataset()

>>> analysis_df = analysis_df.merge(analysis_targets_df, left_index=True, right_index=True)

>>> calc = nml.PerformanceCalculator(
...     y_pred_proba='y_pred_proba',
...     y_pred='y_pred',
...     y_true='repaid',
...     timestamp_column_name='timestamp',
...     problem_type='classification_binary',
...     business_value_matrix = [[5, -10],[-50, 50]],
...     chunk_size=5000)

>>> calc.fit(reference_df)

>>> results = calc.calculate(analysis_df)
>>> display(results.filter(period='analysis').to_df())

>>> display(results.filter(period='reference').to_df())

>>> figure = results.plot()
>>> figure.show()
```

Walkthrough

For simplicity this guide is based on a synthetic dataset included in the library, where the monitored model predicts whether a customer will repay a loan to buy a car. Check out Car Loan Dataset to learn more about this dataset.

In order to monitor a model, NannyML needs to learn about it from a reference dataset. Then it can monitor the data that is subject to actual analysis, provided as the analysis dataset. You can read more about this in our section on data periods.

The `analysis_targets` dataframe contains the target results of the analysis period. This is kept separate in the synthetic data because it is not used during performance estimation. But it is required to calculate performance, so the first thing we need to in this case is set up the right data in the right dataframes.

The analysis target values are joined on the analysis frame by their index. Your dataset may already contain the target column, so you may skip this join.

```>>> import nannyml as nml
>>> from IPython.display import display

>>> reference_df, analysis_df, analysis_targets_df = nml.load_synthetic_car_loan_dataset()

>>> analysis_df = analysis_df.merge(analysis_targets_df, left_index=True, right_index=True)

```

id

car_value

salary_range

debt_to_income_ratio

loan_length

repaid_loan_on_prev_car

size_of_downpayment

driver_tenure

repaid

timestamp

y_pred_proba

y_pred

0

0

39811

40K - 60K €

0.63295

19

False

40%

0.212653

1

2018-01-01 00:00:00.000

0.99

1

1

1

12679

40K - 60K €

0.718627

7

True

10%

4.92755

0

2018-01-01 00:08:43.152

0.07

0

2

2

19847

40K - 60K €

0.721724

17

False

0%

0.520817

1

2018-01-01 00:17:26.304

1

1

Next a `PerformanceCalculator` is created with the following parameter specifications:

• y_pred_proba: the name of the column in the reference data that contains the predicted probabilities.

• y_pred: the name of the column in the reference data that contains the predicted classes.

• y_true: the name of the column in the reference data that contains the true classes.

• timestamp_column_name (Optional): the name of the column in the reference data that contains timestamps.

• problem_type: the type of problem being monitored. In this example we will monitor a binary classification problem.

• metrics: a list of metrics to calculate. In this example we will calculate the `business_value` metric.

• business_value_matrix: a 2x2 matrix that specifies the value of each cell in the confusion matrix where the top left cell is the value of a true negative, the top right cell is the value of a false positive, the bottom left cell is the value of a false negative, and the bottom right cell is the value of a true positive.

• normalize_business_value (Optional): how to normalize the business value. The normalization options are:

• None : returns the total value per chunk

• “per_prediction” : returns the total value for the chunk divided by the number of observations in a given chunk.

• chunk_size (Optional): the number of observations in each chunk of data used to calculate performance. For more information about chunking other chunking options check out the chunking tutorial.

```>>> calc = nml.PerformanceCalculator(
...     y_pred_proba='y_pred_proba',
...     y_pred='y_pred',
...     y_true='repaid',
...     timestamp_column_name='timestamp',
...     problem_type='classification_binary',
...     business_value_matrix = [[5, -10],[-50, 50]],
...     chunk_size=5000)
```

Note

When calculating business_value, the `business_value_matrix` parameter is required. The format of the business value matrix must be specified as `[[value_of_TN, value_of_FP], [value_of_FN, value_of_TP]]`. For more information about the business value matrix, check out the Business Value “How it Works” page.

The new `PerformanceCalculator` is fitted using the `fit()` method on the reference data.

```>>> calc.fit(reference_df)
```

The fitted `PerformanceCalculator` can then be used to calculate realized performance metrics on all data which has target values available with the `calculate()` method. NannyML can output a dataframe that contains all the results of the analysis data.

```>>> results = calc.calculate(analysis_df)
>>> display(results.filter(period='analysis').to_df())
```

chunk
key
chunk_index
start_index
end_index
start_date
end_date
period
targets_missing_rate
sampling_error
value
upper_threshold
lower_threshold

0

[0:4999]

0

0

4999

2018-10-30 18:00:00

2018-11-30 00:27:16.848000

analysis

0

0.375491

48.4157

49.51

47.7434

False

1

[5000:9999]

1

5000

9999

2018-11-30 00:36:00

2018-12-30 07:03:16.848000

analysis

0

0.375491

48.5059

49.51

47.7434

False

2

[10000:14999]

2

10000

14999

2018-12-30 07:12:00

2019-01-29 13:39:16.848000

analysis

0

0.375491

49.3556

49.51

47.7434

False

3

[15000:19999]

3

15000

19999

2019-01-29 13:48:00

2019-02-28 20:15:16.848000

analysis

0

0.375491

48.6694

49.51

47.7434

False

4

[20000:24999]

4

20000

24999

2019-02-28 20:24:00

2019-03-31 02:51:16.848000

analysis

0

0.375491

48.3096

49.51

47.7434

False

5

[25000:29999]

5

25000

29999

2019-03-31 03:00:00

2019-04-30 09:27:16.848000

analysis

0

0.375491

45.2352

49.51

47.7434

True

6

[30000:34999]

6

30000

34999

2019-04-30 09:36:00

2019-05-30 16:03:16.848000

analysis

0

0.375491

45.4163

49.51

47.7434

True

7

[35000:39999]

7

35000

39999

2019-05-30 16:12:00

2019-06-29 22:39:16.848000

analysis

0

0.375491

45.2397

49.51

47.7434

True

8

[40000:44999]

8

40000

44999

2019-06-29 22:48:00

2019-07-30 05:15:16.848000

analysis

0

0.375491

46.195

49.51

47.7434

True

9

[45000:49999]

9

45000

49999

2019-07-30 05:24:00

2019-08-29 11:51:16.848000

analysis

0

0.375491

44.9421

49.51

47.7434

True

The results from the reference data are also available.

```>>> display(results.filter(period='reference').to_df())
```

chunk
key
chunk_index
start_index
end_index
start_date
end_date
period
targets_missing_rate
sampling_error
value
upper_threshold
lower_threshold

0

[0:4999]

0

0

4999

2018-01-01 00:00:00

2018-01-31 06:27:16.848000

reference

0

0.375491

48.9472

49.51

47.7434

False

1

[5000:9999]

1

5000

9999

2018-01-31 06:36:00

2018-03-02 13:03:16.848000

reference

0

0.375491

48.16

49.51

47.7434

False

2

[10000:14999]

2

10000

14999

2018-03-02 13:12:00

2018-04-01 19:39:16.848000

reference

0

0.375491

48.907

49.51

47.7434

False

3

[15000:19999]

3

15000

19999

2018-04-01 19:48:00

2018-05-02 02:15:16.848000

reference

0

0.375491

48.7142

49.51

47.7434

False

4

[20000:24999]

4

20000

24999

2018-05-02 02:24:00

2018-06-01 08:51:16.848000

reference

0

0.375491

48.0157

49.51

47.7434

False

5

[25000:29999]

5

25000

29999

2018-06-01 09:00:00

2018-07-01 15:27:16.848000

reference

0

0.375491

48.6629

49.51

47.7434

False

6

[30000:34999]

6

30000

34999

2018-07-01 15:36:00

2018-07-31 22:03:16.848000

reference

0

0.375491

48.777

49.51

47.7434

False

7

[35000:39999]

7

35000

39999

2018-07-31 22:12:00

2018-08-31 04:39:16.848000

reference

0

0.375491

48.7521

49.51

47.7434

False

8

[40000:44999]

8

40000

44999

2018-08-31 04:48:00

2018-09-30 11:15:16.848000

reference

0

0.375491

48.8141

49.51

47.7434

False

9

[45000:49999]

9

45000

49999

2018-09-30 11:24:00

2018-10-30 17:51:16.848000

reference

0

0.375491

48.5164

49.51

47.7434

False

Apart from chunk and period-related columns, the results data have a set of columns for each calculated metric.

• targets_missing_rate - The fraction of missing target data.

• value - the realized metric value for a specific chunk.

• sampling_error - the estimate of the Sampling Error.

• upper_threshold and lower_threshold - crossing these thresholds will raise an alert on significant performance change. The thresholds are calculated based on the actual performance of the monitored model on chunks in the reference partition. The thresholds are 3 standard deviations away from the mean performance calculated on chunks. They are calculated during fit phase.

• alert - flag indicating potentially significant performance change. `True` if estimated performance crosses upper or lower threshold.

The results can be plotted for visual inspection. Our plot contains several key elements.

• The purple step plot shows the performance in each chunk of the analysis period. Thick squared point markers indicate the middle of these chunks.

• The blue step plot shows the performance in each chunk of the reference period. Thick squared point markers indicate the middle of these chunks.

• The gray vertical line splits the reference and analysis periods.

• The red horizontal dashed lines show upper and lower thresholds for alerting purposes.

• The red diamond-shaped point markers in the middle of a chunk indicate that an alert has been raised. Alerts are caused by the performance crossing the upper or lower threshold.

```>>> figure = results.plot()
>>> figure.show()
```

Additional information such as the chunk index range and chunk date range (if timestamps were provided) is shown in the hover for each chunk (these are interactive plots, though only static views are included here).

Insights

After reviewing the performance calculation results, we should be able to clearly see how the model is performing against the targets, according to whatever metrics we wish to track.

What’s Next

If we decide further investigation is needed, the Data Drift functionality can help us to see what feature changes may be contributing to any performance changes.

It is also wise to check whether the model’s performance is satisfactory according to business requirements. This is an ad-hoc investigation that is not covered by NannyML.