Monitoring Realized Performance for Multiclass Classification

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_target_df = nml.load_synthetic_multiclass_classification_dataset()

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

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

>>> calc = nml.PerformanceCalculator(
...     y_pred_proba={
...         'prepaid_card': 'y_pred_proba_prepaid_card',
...         'highstreet_card': 'y_pred_proba_highstreet_card',
...         'upmarket_card': 'y_pred_proba_upmarket_card'
...     },
...     y_pred='y_pred',
...     y_true='y_true',
...     timestamp_column_name='timestamp',
...     problem_type='classification_multiclass',
...     metrics=['roc_auc', 'f1', 'precision', 'recall', 'specificity', 'accuracy'],
...     chunk_size=6000)

>>> 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

• Set up custom chunking [tutorial] [API reference]

• Set up custom thresholds [tutorial] [API reference]

Walkthrough

For simplicity the guide is based on a synthetic dataset where the monitored model predicts which type of credit card product new customers should be assigned to. You can 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 the identifier column.

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

>>> reference_df, analysis_df, analysis_target_df = nml.load_synthetic_multiclass_classification_dataset()

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

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

	acq_channel	app_behavioral_score	requested_credit_limit	app_channel	credit_bureau_score	stated_income	is_customer	timestamp	y_pred_proba_prepaid_card	y_pred_proba_highstreet_card	y_pred_proba_upmarket_card	y_pred	y_true
0	Partner3	1.80823	350	web	309	15000	True	2020-05-02 02:01:30	0.97	0.03	0	prepaid_card	prepaid_card
1	Partner2	4.38257	500	mobile	418	23000	True	2020-05-02 02:03:33	0.87	0.13	0	prepaid_card	prepaid_card
2	Partner2	-0.787575	400	web	507	24000	False	2020-05-02 02:04:49	0.47	0.35	0.18	prepaid_card	upmarket_card

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={
...         'prepaid_card': 'y_pred_proba_prepaid_card',
...         'highstreet_card': 'y_pred_proba_highstreet_card',
...         'upmarket_card': 'y_pred_proba_upmarket_card'
...     },
...     y_pred='y_pred',
...     y_true='y_true',
...     timestamp_column_name='timestamp',
...     problem_type='classification_multiclass',
...     metrics=['roc_auc', 'f1', 'precision', 'recall', 'specificity', 'accuracy'],
...     chunk_size=6000)

>>> 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 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
0	[0:5999]	0	0	5999	2020-09-01 03:10:01	2020-09-13 16:15:10	analysis	0	0.00214318	0.907595	0.913516	0.900902	False	0.00565227	0.751103	0.764944	0.741254	False	0.00556588	0.75127	0.764978	0.741261	False	0.00556473	0.751033	0.765026	0.741261	False	0.0030024	0.87555	0.882586	0.870566	False	0.00556637	0.751167	0.765102	0.741231	False
1	[6000:11999]	1	6000	11999	2020-09-13 16:15:32	2020-09-25 19:48:42	analysis	0	0.00214318	0.910534	0.913516	0.900902	False	0.00565227	0.763045	0.764944	0.741254	False	0.00556588	0.763125	0.764978	0.741261	False	0.00556473	0.763148	0.765026	0.741261	False	0.0030024	0.881508	0.882586	0.870566	False	0.00556637	0.763	0.765102	0.741231	False
2	[12000:17999]	2	12000	17999	2020-09-25 19:50:04	2020-10-08 02:53:47	analysis	0	0.00214318	0.909414	0.913516	0.900902	False	0.00565227	0.758487	0.764944	0.741254	False	0.00556588	0.758503	0.764978	0.741261	False	0.00556473	0.758484	0.765026	0.741261	False	0.0030024	0.879367	0.882586	0.870566	False	0.00556637	0.758667	0.765102	0.741231	False
3	[18000:23999]	3	18000	23999	2020-10-08 02:57:34	2020-10-20 15:48:19	analysis	0	0.00214318	0.911577	0.913516	0.900902	False	0.00565227	0.758944	0.764944	0.741254	False	0.00556588	0.758973	0.764978	0.741261	False	0.00556473	0.758986	0.765026	0.741261	False	0.0030024	0.87963	0.882586	0.870566	False	0.00556637	0.759167	0.765102	0.741231	False
4	[24000:29999]	4	24000	29999	2020-10-20 15:49:06	2020-11-01 22:04:40	analysis	0	0.00214318	0.907533	0.913516	0.900902	False	0.00565227	0.757964	0.764944	0.741254	False	0.00556588	0.75795	0.764978	0.741261	False	0.00556473	0.757979	0.765026	0.741261	False	0.0030024	0.878986	0.882586	0.870566	False	0.00556637	0.758	0.765102	0.741231	False
5	[30000:35999]	5	30000	35999	2020-11-01 22:04:59	2020-11-14 03:55:33	analysis	0	0.00214318	0.748588	0.913516	0.900902	True	0.00565227	0.557112	0.764944	0.741254	True	0.00556588	0.559696	0.764978	0.741261	True	0.00556473	0.557829	0.765026	0.741261	True	0.0030024	0.779905	0.882586	0.870566	True	0.00556637	0.560833	0.765102	0.741231	True
6	[36000:41999]	6	36000	41999	2020-11-14 03:55:49	2020-11-26 09:19:06	analysis	0	0.00214318	0.751137	0.913516	0.900902	True	0.00565227	0.559148	0.764944	0.741254	True	0.00556588	0.562915	0.764978	0.741261	True	0.00556473	0.56017	0.765026	0.741261	True	0.0030024	0.780676	0.882586	0.870566	True	0.00556637	0.562333	0.765102	0.741231	True
7	[42000:47999]	7	42000	47999	2020-11-26 09:19:22	2020-12-08 14:33:56	analysis	0	0.00214318	0.756399	0.913516	0.900902	True	0.00565227	0.565055	0.764944	0.741254	True	0.00556588	0.569069	0.764978	0.741261	True	0.00556473	0.565943	0.765026	0.741261	True	0.0030024	0.784223	0.882586	0.870566	True	0.00556637	0.569833	0.765102	0.741231	True
8	[48000:53999]	8	48000	53999	2020-12-08 14:34:25	2020-12-20 18:30:30	analysis	0	0.00214318	0.758561	0.913516	0.900902	True	0.00565227	0.563897	0.764944	0.741254	True	0.00556588	0.566673	0.764978	0.741261	True	0.00556473	0.564723	0.765026	0.741261	True	0.0030024	0.783422	0.882586	0.870566	True	0.00556637	0.567833	0.765102	0.741231	True
9	[54000:59999]	9	54000	59999	2020-12-20 18:31:09	2021-01-01 22:57:55	analysis	0	0.00214318	0.753937	0.913516	0.900902	True	0.00565227	0.561644	0.764944	0.741254	True	0.00556588	0.565129	0.764978	0.741261	True	0.00556473	0.562772	0.765026	0.741261	True	0.0030024	0.782429	0.882586	0.870566	True	0.00556637	0.566	0.765102	0.741231	True

There 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
0	[0:5999]	0	0	5999	2020-05-02 02:01:30	2020-05-14 12:25:35	reference	0	0.00214318	0.90476	0.913516	0.900902	False	0.00565227	0.750532	0.764944	0.741254	False	0.00556588	0.7505	0.764978	0.741261	False	0.00556473	0.750576	0.765026	0.741261	False	0.0030024	0.875226	0.882586	0.870566	False	0.00556637	0.7505	0.765102	0.741231	False
1	[6000:11999]	1	6000	11999	2020-05-14 12:29:25	2020-05-26 18:27:42	reference	0	0.00214318	0.905917	0.913516	0.900902	False	0.00565227	0.751148	0.764944	0.741254	False	0.00556588	0.751142	0.764978	0.741261	False	0.00556473	0.751157	0.765026	0.741261	False	0.0030024	0.875424	0.882586	0.870566	False	0.00556637	0.751	0.765102	0.741231	False
2	[12000:17999]	2	12000	17999	2020-05-26 18:31:06	2020-06-07 19:55:45	reference	0	0.00214318	0.909329	0.913516	0.900902	False	0.00565227	0.75714	0.764944	0.741254	False	0.00556588	0.75728	0.764978	0.741261	False	0.00556473	0.757174	0.765026	0.741261	False	0.0030024	0.878628	0.882586	0.870566	False	0.00556637	0.757167	0.765102	0.741231	False
3	[18000:23999]	3	18000	23999	2020-06-07 19:58:39	2020-06-19 19:42:20	reference	0	0.00214318	0.906731	0.913516	0.900902	False	0.00565227	0.750274	0.764944	0.741254	False	0.00556588	0.750415	0.764978	0.741261	False	0.00556473	0.750241	0.765026	0.741261	False	0.0030024	0.875163	0.882586	0.870566	False	0.00556637	0.750333	0.765102	0.741231	False
4	[24000:29999]	4	24000	29999	2020-06-19 19:44:14	2020-07-02 01:58:05	reference	0	0.00214318	0.910577	0.913516	0.900902	False	0.00565227	0.759144	0.764944	0.741254	False	0.00556588	0.759175	0.764978	0.741261	False	0.00556473	0.759197	0.765026	0.741261	False	0.0030024	0.879686	0.882586	0.870566	False	0.00556637	0.759333	0.765102	0.741231	False
5	[30000:35999]	5	30000	35999	2020-07-02 02:06:56	2020-07-14 08:14:04	reference	0	0.00214318	0.904577	0.913516	0.900902	False	0.00565227	0.74863	0.764944	0.741254	False	0.00556588	0.74866	0.764978	0.741261	False	0.00556473	0.748657	0.765026	0.741261	False	0.0030024	0.874329	0.882586	0.870566	False	0.00556637	0.748667	0.765102	0.741231	False
6	[36000:41999]	6	36000	41999	2020-07-14 08:14:08	2020-07-26 12:55:42	reference	0	0.00214318	0.906673	0.913516	0.900902	False	0.00565227	0.752763	0.764944	0.741254	False	0.00556588	0.752684	0.764978	0.741261	False	0.00556473	0.752944	0.765026	0.741261	False	0.0030024	0.876407	0.882586	0.870566	False	0.00556637	0.752833	0.765102	0.741231	False
7	[42000:47999]	7	42000	47999	2020-07-26 12:57:37	2020-08-07 16:32:15	reference	0	0.00214318	0.908703	0.913516	0.900902	False	0.00565227	0.755883	0.764944	0.741254	False	0.00556588	0.75582	0.764978	0.741261	False	0.00556473	0.756006	0.765026	0.741261	False	0.0030024	0.877891	0.882586	0.870566	False	0.00556637	0.755833	0.765102	0.741231	False
8	[48000:53999]	8	48000	53999	2020-08-07 16:33:44	2020-08-20 00:06:08	reference	0	0.00214318	0.905072	0.913516	0.900902	False	0.00565227	0.74742	0.764944	0.741254	False	0.00556588	0.747441	0.764978	0.741261	False	0.00556473	0.747436	0.765026	0.741261	False	0.0030024	0.873832	0.882586	0.870566	False	0.00556637	0.747667	0.765102	0.741231	False
9	[54000:59999]	9	54000	59999	2020-08-20 00:07:58	2020-09-01 03:03:23	reference	0	0.00214318	0.909749	0.913516	0.900902	False	0.00565227	0.758055	0.764944	0.741254	False	0.00556588	0.758077	0.764978	0.741261	False	0.00556473	0.758052	0.765026	0.741261	False	0.0030024	0.879177	0.882586	0.870566	False	0.00556637	0.758333	0.765102	0.741231	False

Apart from chunking and chunk and period-related columns, the results data have the 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:

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