Drift Detection for Multiclass Classification Model Targets

Why Perform Drift Detection for Model Targets

The performance of a machine learning model can be affected if the distribution of targets changes. The target distribution can change both because of data drift but also because of label shift.

A change in the target distribution may mean that business assumptions on which the model is used may need to be revisited.

NannyML uses TargetDistributionCalculator in order to monitor drift in the Target distribution. It can calculate the mean occurrence of positive events for binary classification problems.

It can also calculate the chi squared statistic (from the Chi Squared test) of the available target values for each chunk, for both binary and multiclass classification problems.

Note

The Target Drift detection process can handle missing target values across all data periods.

Just The Code

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

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

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

>>> calc = nml.TargetDistributionCalculator(
...     y_true='y_true',
...     timestamp_column_name='timestamp',
...     problem_type='classification_multiclass'
>>> )

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

>>> target_drift_fig = results.plot(kind='target_drift', plot_reference=True)
>>> target_drift_fig.show()

>>> target_distribution_fig = results.plot(kind='target_distribution', plot_reference=True)
>>> target_distribution_fig.show()

Walkthrough

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.

Let’s start by loading some synthetic data provided by the NannyML package, and setting it up as our reference and analysis dataframes. This synthetic data is for a binary classification model, but multi-class classification can be handled in the same way.

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 detect drift for the targets, 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 = nml.load_synthetic_multiclass_classification_dataset()[0]
>>> analysis_df = nml.load_synthetic_multiclass_classification_dataset()[1]
>>> analysis_target_df = nml.load_synthetic_multiclass_classification_dataset()[2]
>>> analysis_df = analysis_df.merge(analysis_target_df, on='identifier')

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

	acq_channel	app_behavioral_score	requested_credit_limit	app_channel	credit_bureau_score	stated_income	is_customer	period	identifier	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	reference	60000	2020-05-02 02:01:30	0.97	0.03	0	prepaid_card	prepaid_card
1	Partner2	4.38257	500	mobile	418	23000	True	reference	60001	2020-05-02 02:03:33	0.87	0.13	0	prepaid_card	prepaid_card
2	Partner2	-0.787575	400	web	507	24000	False	reference	60002	2020-05-02 02:04:49	0.47	0.35	0.18	prepaid_card	upmarket_card

Now that the data is in place we’ll create a new TargetDistributionCalculator instantiating it with the appropriate parameters. We only need the target (y_true) and timestamp.

>>> calc = nml.TargetDistributionCalculator(
...     y_true='y_true',
...     timestamp_column_name='timestamp',
...     problem_type='classification_multiclass'
>>> )

Afterwards, the fit() method gets called on the reference period, which represent an accepted target distribution which we will compare against the analysis period.

Then the calculate() method is called to calculate the target drift results on the data provided. We use the previously assembled data as an argument.

We can display the results of this calculation in a dataframe.

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

	key	start_index	end_index	start_date	end_date	metric_target_drift	statistical_target_drift	p_value	thresholds	alert	significant
0	[0:5999]	0	5999	2020-09-01 03:10:01	2020-09-13 16:15:10	nan	0.521545	0.770456	0.05	False	False
1	[6000:11999]	6000	11999	2020-09-13 16:15:32	2020-09-25 19:48:42	nan	2.11226	0.3478	0.05	False	False
2	[12000:17999]	12000	17999	2020-09-25 19:50:04	2020-10-08 02:53:47	nan	0.940108	0.624969	0.05	False	False

The results can be also easily plotted by using the plot() method.

>>> target_drift_fig = results.plot(kind='target_drift', plot_reference=True)
>>> target_drift_fig.show()

>>> target_distribution_fig = results.plot(kind='target_distribution', plot_reference=True)
>>> target_distribution_fig.show()

Warning

Since our target data contains non-numerical values and over 3 values, we currently don’t support plotting using the distribution='metric' parameter. NannyML will print out warnings to inform you about this:

UserWarning: the target column contains 3 unique values. NannyML cannot provide a value for 'metric_target_drift' when there are more than 2 unique values. All 'metric_target_drift' values will be set to np.NAN
UserWarning: the target column contains non-numerical values. NannyML cannot provide a value for 'metric_target_drift'.All 'metric_target_drift' values will be set to np.NAN

What Next

The Monitoring Realized Performance functionality of NannyML can can add context to the target drift results showing whether there are associated performance changes.