Calculating Standard Performance Metrics for Binary Classification

This tutorial explains how to use NannyML to calculate standard performance metrics 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)

>>> display(reference_df.head(3))

>>> calc = nml.PerformanceCalculator(
...     y_pred_proba='y_pred_proba',
...     y_pred='y_pred',
...     y_true='repaid',
...     timestamp_column_name='timestamp',
...     problem_type='classification_binary',
...     metrics=['roc_auc', 'f1', 'precision', 'recall', 'specificity', 'accuracy', 'average_precision'],
...     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()

Advanced configuration

• To learn how Chunk works and to set up custom chunkings check out the chunking tutorial

• To learn how ConstantThreshold works and to set up custom threshold check out the thresholds tutorial

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_df 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 the Realized 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)

>>> display(reference_df.head(3))

	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 using the following:

• 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 following metrics: roc_auc, f1, precision, recall, specificity, accuracy.

• 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.

• thresholds (Optional): the thresholds used to calculate the alert flag. For more information about thresholds, check out the thresholds 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',
...     metrics=['roc_auc', 'f1', 'precision', 'recall', 'specificity', 'accuracy', 'average_precision'],
...     chunk_size=5000)

The 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	roc_auc sampling_error	value	upper_threshold	lower_threshold	alert	f1 sampling_error	value	upper_threshold	lower_threshold	alert	precision sampling_error	value	upper_threshold	lower_threshold	alert	recall sampling_error	value	upper_threshold	lower_threshold	alert	specificity sampling_error	value	upper_threshold	lower_threshold	alert	accuracy sampling_error	value	upper_threshold	lower_threshold	alert	average_precision sampling_error	value	upper_threshold	lower_threshold	alert
0	[0:4999]	0	0	4999	2018-10-30 18:00:00	2018-11-30 00:27:16.848000	analysis	0	0.00181072	0.970962	0.97866	0.963317	False	0.00643972	0.943344	0.95085	0.93466	False	0.00412503	0.95666	0.967054	0.946681	False	0.00513664	0.930394	0.941033	0.9171	False	0.00400118	0.954847	0.967647	0.948756	False	0.0032614	0.9422	0.951421	0.935859	False	0.0043544	0.963617	0.975672	0.948927	False
1	[5000:9999]	1	5000	9999	2018-11-30 00:36:00	2018-12-30 07:03:16.848000	analysis	0	0.00181072	0.970248	0.97866	0.963317	False	0.00643972	0.940178	0.95085	0.93466	False	0.00412503	0.957016	0.967054	0.946681	False	0.00513664	0.923922	0.941033	0.9171	False	0.00400118	0.959874	0.967647	0.948756	False	0.0032614	0.9422	0.951421	0.935859	False	0.0043544	0.960533	0.975672	0.948927	False
2	[10000:14999]	2	10000	14999	2018-12-30 07:12:00	2019-01-29 13:39:16.848000	analysis	0	0.00181072	0.976282	0.97866	0.963317	False	0.00643972	0.948201	0.95085	0.93466	False	0.00412503	0.958368	0.967054	0.946681	False	0.00513664	0.938246	0.941033	0.9171	False	0.00400118	0.961494	0.967647	0.948756	False	0.0032614	0.9502	0.951421	0.935859	False	0.0043544	0.966286	0.975672	0.948927	False
3	[15000:19999]	3	15000	19999	2019-01-29 13:48:00	2019-02-28 20:15:16.848000	analysis	0	0.00181072	0.967721	0.97866	0.963317	False	0.00643972	0.942142	0.95085	0.93466	False	0.00412503	0.959866	0.967054	0.946681	False	0.00513664	0.92506	0.941033	0.9171	False	0.00400118	0.961875	0.967647	0.948756	False	0.0032614	0.9436	0.951421	0.935859	False	0.0043544	0.960979	0.975672	0.948927	False
4	[20000:24999]	4	20000	24999	2019-02-28 20:24:00	2019-03-31 02:51:16.848000	analysis	0	0.00181072	0.969886	0.97866	0.963317	False	0.00643972	0.940488	0.95085	0.93466	False	0.00412503	0.953764	0.967054	0.946681	False	0.00513664	0.927577	0.941033	0.9171	False	0.00400118	0.954564	0.967647	0.948756	False	0.0032614	0.941	0.951421	0.935859	False	0.0043544	0.962524	0.975672	0.948927	False
5	[25000:29999]	5	25000	29999	2019-03-31 03:00:00	2019-04-30 09:27:16.848000	analysis	0	0.00181072	0.96005	0.97866	0.963317	True	0.00643972	0.913685	0.95085	0.93466	True	0.00412503	0.922449	0.967054	0.946681	True	0.00513664	0.905086	0.941033	0.9171	True	0.00400118	0.924091	0.967647	0.948756	True	0.0032614	0.9146	0.951421	0.935859	True	0.0043544	0.952855	0.975672	0.948927	False
6	[30000:34999]	6	30000	34999	2019-04-30 09:36:00	2019-05-30 16:03:16.848000	analysis	0	0.00181072	0.95853	0.97866	0.963317	True	0.00643972	0.914954	0.95085	0.93466	True	0.00412503	0.931473	0.967054	0.946681	True	0.00513664	0.89901	0.941033	0.9171	True	0.00400118	0.932525	0.967647	0.948756	True	0.0032614	0.9156	0.951421	0.935859	True	0.0043544	0.953275	0.975672	0.948927	False
7	[35000:39999]	7	35000	39999	2019-05-30 16:12:00	2019-06-29 22:39:16.848000	analysis	0	0.00181072	0.959041	0.97866	0.963317	True	0.00643972	0.913395	0.95085	0.93466	True	0.00412503	0.925379	0.967054	0.946681	True	0.00513664	0.901718	0.941033	0.9171	True	0.00400118	0.927113	0.967647	0.948756	True	0.0032614	0.9144	0.951421	0.935859	True	0.0043544	0.951779	0.975672	0.948927	False
8	[40000:44999]	8	40000	44999	2019-06-29 22:48:00	2019-07-30 05:15:16.848000	analysis	0	0.00181072	0.963094	0.97866	0.963317	True	0.00643972	0.920589	0.95085	0.93466	True	0.00412503	0.935524	0.967054	0.946681	True	0.00513664	0.906124	0.941033	0.9171	True	0.00400118	0.938443	0.967647	0.948756	True	0.0032614	0.9224	0.951421	0.935859	True	0.0043544	0.954327	0.975672	0.948927	False
9	[45000:49999]	9	45000	49999	2019-07-30 05:24:00	2019-08-29 11:51:16.848000	analysis	0	0.00181072	0.957556	0.97866	0.963317	True	0.00643972	0.913498	0.95085	0.93466	True	0.00412503	0.921304	0.967054	0.946681	True	0.00513664	0.905823	0.941033	0.9171	True	0.00400118	0.918886	0.967647	0.948756	True	0.0032614	0.9122	0.951421	0.935859	True	0.0043544	0.95196	0.975672	0.948927	False

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	roc_auc sampling_error	value	upper_threshold	lower_threshold	alert	f1 sampling_error	value	upper_threshold	lower_threshold	alert	precision sampling_error	value	upper_threshold	lower_threshold	alert	recall sampling_error	value	upper_threshold	lower_threshold	alert	specificity sampling_error	value	upper_threshold	lower_threshold	alert	accuracy sampling_error	value	upper_threshold	lower_threshold	alert	average_precision sampling_error	value	upper_threshold	lower_threshold	alert
0	[0:4999]	0	0	4999	2018-01-01 00:00:00	2018-01-31 06:27:16.848000	reference	0	0.00181072	0.976253	0.97866	0.963317	False	0.00643972	0.944707	0.95085	0.93466	False	0.00412503	0.960301	0.967054	0.946681	False	0.00513664	0.929612	0.941033	0.9171	False	0.00400118	0.962421	0.967647	0.948756	False	0.0032614	0.9462	0.951421	0.935859	False	0.0043544	0.967584	0.975672	0.948927	False
1	[5000:9999]	1	5000	9999	2018-01-31 06:36:00	2018-03-02 13:03:16.848000	reference	0	0.00181072	0.969045	0.97866	0.963317	False	0.00643972	0.937564	0.95085	0.93466	False	0.00412503	0.95268	0.967054	0.946681	False	0.00513664	0.922921	0.941033	0.9171	False	0.00400118	0.955424	0.967647	0.948756	False	0.0032614	0.9394	0.951421	0.935859	False	0.0043544	0.963523	0.975672	0.948927	False
2	[10000:14999]	2	10000	14999	2018-03-02 13:12:00	2018-04-01 19:39:16.848000	reference	0	0.00181072	0.971742	0.97866	0.963317	False	0.00643972	0.945973	0.95085	0.93466	False	0.00412503	0.959658	0.967054	0.946681	False	0.00513664	0.932673	0.941033	0.9171	False	0.00400118	0.96	0.967647	0.948756	False	0.0032614	0.9462	0.951421	0.935859	False	0.0043544	0.960445	0.975672	0.948927	False
3	[15000:19999]	3	15000	19999	2018-04-01 19:48:00	2018-05-02 02:15:16.848000	reference	0	0.00181072	0.971642	0.97866	0.963317	False	0.00643972	0.943212	0.95085	0.93466	False	0.00412503	0.95942	0.967054	0.946681	False	0.00513664	0.927542	0.941033	0.9171	False	0.00400118	0.960831	0.967647	0.948756	False	0.0032614	0.9442	0.951421	0.935859	False	0.0043544	0.965588	0.975672	0.948927	False
4	[20000:24999]	4	20000	24999	2018-05-02 02:24:00	2018-06-01 08:51:16.848000	reference	0	0.00181072	0.969085	0.97866	0.963317	False	0.00643972	0.937989	0.95085	0.93466	False	0.00412503	0.955047	0.967054	0.946681	False	0.00513664	0.92153	0.941033	0.9171	False	0.00400118	0.955357	0.967647	0.948756	False	0.0032614	0.9382	0.951421	0.935859	False	0.0043544	0.964502	0.975672	0.948927	False
5	[25000:29999]	5	25000	29999	2018-06-01 09:00:00	2018-07-01 15:27:16.848000	reference	0	0.00181072	0.967364	0.97866	0.963317	False	0.00643972	0.942581	0.95085	0.93466	False	0.00412503	0.952975	0.967054	0.946681	False	0.00513664	0.93241	0.941033	0.9171	False	0.00400118	0.955582	0.967647	0.948756	False	0.0032614	0.9442	0.951421	0.935859	False	0.0043544	0.956521	0.975672	0.948927	False
6	[30000:34999]	6	30000	34999	2018-07-01 15:36:00	2018-07-31 22:03:16.848000	reference	0	0.00181072	0.968692	0.97866	0.963317	False	0.00643972	0.94414	0.95085	0.93466	False	0.00412503	0.957561	0.967054	0.946681	False	0.00513664	0.93109	0.941033	0.9171	False	0.00400118	0.958866	0.967647	0.948756	False	0.0032614	0.945	0.951421	0.935859	False	0.0043544	0.954467	0.975672	0.948927	False
7	[35000:39999]	7	35000	39999	2018-07-31 22:12:00	2018-08-31 04:39:16.848000	reference	0	0.00181072	0.970205	0.97866	0.963317	False	0.00643972	0.944612	0.95085	0.93466	False	0.00412503	0.955972	0.967054	0.946681	False	0.00513664	0.933519	0.941033	0.9171	False	0.00400118	0.956592	0.967647	0.948756	False	0.0032614	0.945	0.951421	0.935859	False	0.0043544	0.959816	0.975672	0.948927	False
8	[40000:44999]	8	40000	44999	2018-08-31 04:48:00	2018-09-30 11:15:16.848000	reference	0	0.00181072	0.974096	0.97866	0.963317	False	0.00643972	0.944523	0.95085	0.93466	False	0.00412503	0.962582	0.967054	0.946681	False	0.00513664	0.927129	0.941033	0.9171	False	0.00400118	0.963232	0.967647	0.948756	False	0.0032614	0.945	0.951421	0.935859	False	0.0043544	0.969247	0.975672	0.948927	False
9	[45000:49999]	9	45000	49999	2018-09-30 11:24:00	2018-10-30 17:51:16.848000	reference	0	0.00181072	0.971757	0.97866	0.963317	False	0.00643972	0.942249	0.95085	0.93466	False	0.00412503	0.952478	0.967054	0.946681	False	0.00513664	0.932237	0.941033	0.9171	False	0.00400118	0.953711	0.967647	0.948756	False	0.0032614	0.943	0.951421	0.935859	False	0.0043544	0.961304	0.975672	0.948927	False

Apart from chunk-related data, 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 blue step plot shows the performance in each chunk of the provided data. Thick squared point markers indicate the middle of these chunks.

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

• The red horizontal dashed lines show upper and lower thresholds that indicate the range of expected performance values.

• 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. We can also plot the realized performance and compare it with the estimated results.

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.