Monitoring Realized Performance for Binary Classification

Just The Code

>>> import pandas as pd
>>> import nannyml as nml
>>> from IPython.display import display

>>> reference_df = nml.load_synthetic_binary_classification_dataset()[0]
>>> analysis_df = nml.load_synthetic_binary_classification_dataset()[1]
>>> analysis_target_df = nml.load_synthetic_binary_classification_dataset()[2]
>>> analysis_df = analysis_df.merge(analysis_target_df, on='identifier')

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

>>> calc = nml.PerformanceCalculator(
...     y_pred_proba='y_pred_proba',
...     y_pred='y_pred',
...     y_true='work_home_actual',
...     timestamp_column_name='timestamp',
...     metrics=nml.performance_estimation.confidence_based.results.SUPPORTED_METRIC_VALUES,
...     chunk_size=5000)

>>> calc.fit(reference_df)

>>> results = calc.calculate(analysis_df)

>>> display(results.data.head(3))

>>> for metric in calc.metrics:
...     figure = results.plot(kind='performance', plot_reference=True, metric=metric)
...     figure.show()

Walkthrough

For simplicity this guide is based on a synthetic dataset included in the library, where the monitored model predicts whether an employee will work from home. You can read more about this synthetic 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 the identifier column.

>>> import pandas as pd
>>> import nannyml as nml
>>> from IPython.display import display

>>> reference_df = nml.load_synthetic_binary_classification_dataset()[0]
>>> analysis_df = nml.load_synthetic_binary_classification_dataset()[1]
>>> analysis_target_df = nml.load_synthetic_binary_classification_dataset()[2]
>>> analysis_df = analysis_df.merge(analysis_target_df, on='identifier')

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

	distance_from_office	salary_range	gas_price_per_litre	public_transportation_cost	wfh_prev_workday	workday	tenure	identifier	work_home_actual	timestamp	y_pred_proba	partition	y_pred
0	5.96225	40K - 60K €	2.11948	8.56806	False	Friday	0.212653	0	1	2014-05-09 22:27:20	0.99	reference	1
1	0.535872	40K - 60K €	2.3572	5.42538	True	Tuesday	4.92755	1	0	2014-05-09 22:59:32	0.07	reference	0
2	1.96952	40K - 60K €	2.36685	8.24716	False	Monday	0.520817	2	1	2014-05-09 23:48:25	1	reference	1

Next a PerformanceCalculator is created using a list of metrics to calculate (or just one metric), the data columns required for these metrics, and an optional chunking specification.

The list of metrics specifies which performance metrics of the monitored model will be calculated. The following metrics are currently supported:

• roc_auc - one-vs-the-rest, macro-averaged

• f1 - macro-averaged

• precision - macro-averaged

• recall - macro-averaged

• specificity - macro-averaged

• accuracy

For more information on metrics, check the metrics module.

>>> calc = nml.PerformanceCalculator(
>>>     y_pred_proba='y_pred_proba',
>>>     y_pred='y_pred',
>>>     y_true='work_home_actual',
>>>     timestamp_column_name='timestamp',
>>>     metrics=nml.performance_estimation.confidence_based.results.SUPPORTED_METRIC_VALUES,
>>>     chunk_size=5000)

>>> calc.fit(reference_df)

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

The fitted PerformanceCalculator can then be used to calculate realized performance metrics on all data which has target values available.

NannyML can output a dataframe that contains all the results.

>>> results = calc.calculate(analysis_df)
>>> display(results.data.head(3))

	key	start_index	end_index	start_date	end_date	period	targets_missing_rate	roc_auc	roc_auc_lower_threshold	roc_auc_upper_threshold	roc_auc_alert	f1	f1_lower_threshold	f1_upper_threshold	f1_alert	precision	precision_lower_threshold	precision_upper_threshold	precision_alert	recall	recall_lower_threshold	recall_upper_threshold	recall_alert	specificity	specificity_lower_threshold	specificity_upper_threshold	specificity_alert	accuracy	accuracy_lower_threshold	accuracy_upper_threshold	accuracy_alert
0	[0:4999]	0	4999	2017-08-31 04:20:00	2018-01-02 00:45:44		0	0.970962	0.963317	0.97866	False	0.949549	0.935047	0.961094	False	0.942139	0.924741	0.961131	False	0.957077	0.940831	0.965726	False	0.937034	0.924741	0.960113	False	0.9474	0.935079	0.960601	False
1	[5000:9999]	5000	9999	2018-01-02 01:13:11	2018-05-01 13:10:10		0	0.970248	0.963317	0.97866	False	0.946686	0.935047	0.961094	False	0.943434	0.924741	0.961131	False	0.949959	0.940831	0.965726	False	0.944925	0.924741	0.960113	False	0.9474	0.935079	0.960601	False
2	[10000:14999]	10000	14999	2018-05-01 14:25:25	2018-09-01 15:40:40		0	0.976282	0.963317	0.97866	False	0.950459	0.935047	0.961094	False	0.941438	0.924741	0.961131	False	0.959654	0.940831	0.965726	False	0.943602	0.924741	0.960113	False	0.9514	0.935079	0.960601	False

Apart from chunking and chunk and period-related columns, the results data have a set of columns for each calculated metric. When taking roc_auc as an example:

• targets_missing_rate - The fraction of missing target data.

• <metric> - The value of the metric for a specific chunk.

• <metric>_lower_threshold> and <metric>_upper_threshold> - Lower and upper thresholds for performance metric. Crossing them will raise an alert that there is a significant metric change. The thresholds are calculated based on the realized performance of chunks in the reference period. The thresholds are 3 standard deviations away from the mean performance calculated on reference chunks.

• <metric>_alert - A flag indicating potentially significant performance change. True if realized performance crosses upper or lower threshold.

The results can be plotted for visual inspection.

>>> for metric in calc.metrics:
...     figure = results.plot(kind='performance', plot_reference=True, metric=metric)
...     figure.show()

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 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.