Estimating Performance for Binary Classification

This tutorial explains how to use NannyML to estimate the performance of binary classification models in the absence of target data. To find out how CBPE estimates performance, read the explanation of Confidence-based Performance Estimation.

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]

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

>>> estimator = nml.CBPE(
...     y_pred_proba='y_pred_proba',
...     y_pred='y_pred',
...     y_true='work_home_actual',
...     timestamp_column_name='timestamp',
...     metrics=['roc_auc', 'f1'],
...     chunk_size=5000
>>> )

>>> estimator.fit(reference_df)
>>>
>>> results = estimator.estimate(analysis_df)

>>> display(results.data.head(3))
>>>
>>> for metric in estimator.metrics:
>>>     fig1 = results.plot(kind='performance', metric=metric)
>>>     fig1.show()

>>> for metric in estimator.metrics:
>>>     fig2 = results.plot(kind='performance', plot_reference=True, metric=metric)
>>>     fig2.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.

>>> 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]

>>> 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 we create the Confidence-based Performance Estimation (CBPE) estimator with a list of metrics, and an optional chunking specification.

The list of metrics specifies which performance metrics of the monitored model will be estimated. 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 about chunking you can check the setting up page and advanced guide.

>>> estimator = nml.CBPE(
...     y_pred_proba='y_pred_proba',
...     y_pred='y_pred',
...     y_true='work_home_actual',
...     timestamp_column_name='timestamp',
...     metrics=['roc_auc', 'f1'],
...     chunk_size=5000)

>>> estimator.fit(reference_df)

The CBPE estimator is then fitted using the fit() method on the reference data.

The fitted cbpe can be used to estimate performance on other data, for which performance cannot be calculated. Typically, this would be used on the latest production data where target is missing. In our example this is the analysis_df data.

NannyML can then output a dataframe that contains all the results. Let’s have a look at the results for analysis period only.

>>> results = estimator.estimate(analysis_df)
>>> display(results.data.head(3))

	key	start_index	end_index	start_date	end_date	realized_roc_auc	estimated_roc_auc	upper_confidence_roc_auc	lower_confidence_roc_auc	upper_threshold_roc_auc	lower_threshold_roc_auc	alert_roc_auc	realized_f1	estimated_f1	upper_confidence_f1	lower_confidence_f1	upper_threshold_f1	lower_threshold_f1	alert_f1
0	[0:4999]	0	4999	2017-08-31 04:20:00	2018-01-02 00:45:44	nan	0.968631	0.968988	0.968273	0.963317	0.97866	False	nan	0.948555	0.949506	0.947604	0.935047	0.961094	False
1	[5000:9999]	5000	9999	2018-01-02 01:13:11	2018-05-01 13:10:10	nan	0.969044	0.969401	0.968686	0.963317	0.97866	False	nan	0.946578	0.947529	0.945627	0.935047	0.961094	False
2	[10000:14999]	10000	14999	2018-05-01 14:25:25	2018-09-01 15:40:40	nan	0.969444	0.969801	0.969086	0.963317	0.97866	False	nan	0.948807	0.949758	0.947856	0.935047	0.961094	False

Apart from chunk-related data, the results data have the following columns for each metric that was estimated:

• realized_<metric> - when target values are available for a chunk, the realized performance metric will also be calculated and included within the results.

• estimated_<metric> - the estimate of a metric for a specific chunk,

• upper_confidence_<metric> and lower_confidence_<metric> - these equal to estimated value +/- 1 standard deviation of performance estimated on reference data (hence calculated during fit phase).

• upper_threshold_<metric> and lower_threshold_<metric> - 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_<metric> - flag indicating potentially significant performance change. True if estimated performance crosses upper or lower threshold.

These results can be also plotted. Our plot contains several key elements.

• The purple dashed step plot shows the estimated performance in each chunk of the analysis period. Thick squared point marker indicates the middle of this period.

• The solid, low-saturated purple line behind indicates the confidence band.

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

• If the estimated performance crosses the upper or lower threshold an alert is raised which is indicated with a red, low-saturated background in the whole width of the relevant chunk. This is additionally indicated by a red point marker in the middle of the chunk.

Description of tabular results above explains how the confidence bands and thresholds are calculated. Additional information is shown in the hover (these are interactive plots, though only static views are included here).

>>> for metric in estimator.metrics:
...     fig1 = results.plot(kind='performance', metric=metric)
...     fig1.show()

To get a better context let’s also plot estimation of performance on analysis data together with calculated performance on the reference period (where the target was available).

• The right-hand side of the plot shows the estimated performance for the analysis period, as before.

• The purple dashed vertical line splits the reference and analysis periods.

• On the left-hand side of the line, the actual model performance (not estimation!) is plotted with a solid light blue line. This facilitates comparison of the estimation against the reference period, and sets expectations on the variability of the performance.

>>> for metric in estimator.metrics:
...     fig2 = results.plot(kind='performance', plot_reference=True, metric=metric)
...     fig2.show()

Insights

After reviewing the performance estimation results, we should be able to see any indications of performance change that NannyML has detected based upon the model’s inputs and outputs alone.

What’s next

The Data Drift functionality can help us to understand whether data drift is causing the performance problem. When the target values become available they can be compared with the estimated results.

You can learn more about the Confidence Based Performance Estimation and its limitations in the How it Works page.