.. nbimport::
:path: ./example_notebooks/Tutorial - Drift - Univariate.ipynb
:cells: 1 3 4 10 12 14 16
Advanced configuration
- Set up custom chunking [:ref:`tutorial <chunking>`] [API reference]
- Set up custom thresholds [:ref:`tutorial <thresholds>`] [API reference]
NannyML's univariate approach for data drift looks at each variable individually and compares the :ref:`chunks<chunking>` created from the analysis :ref:`data period<data-drift-periods>` with the reference period. You can read more about periods and other data requirements in our section on :ref:`data periods<data-drift-periods>`.
The comparison results in a single number, a drift metric, representing the amount of drift between the reference and analysis chunks. NannyML calculates them for every chunk, allowing you to track them over time.
NannyML offers both statistical tests as well as distance measures to detect drift. They are being referred to as methods. Some methods are only applicable to continuous data, others to categorical data and some might be used on both. NannyML lets you choose which methods are to be used on these two types of data.
We begin by loading some synthetic data provided in the NannyML package. This is data for a binary classification model, but other model types operate in the same way.
.. nbimport::
:path: ./example_notebooks/Tutorial - Drift - Univariate.ipynb
:cells: 1
.. nbtable::
:path: ./example_notebooks/Tutorial - Drift - Univariate.ipynb
:cell: 2
The :class:`~nannyml.drift.univariate.calculator.UnivariateDriftCalculator` class implements the functionality needed for univariate drift detection. We need to instantiate it with appropriate parameters:
- The names of the columns to be evaluated.
- A list of methods to use on continuous columns. You can chose from :ref:`kolmogorov_smirnov<univ_cont_method_ks>`, :ref:`jensen_shannon<univariate-drift-detection-cont-jensen-shannon>`, :ref:`wasserstein<univariate-drift-detection-cont-wasserstein>` and :ref:`hellinger<univariate-drift-detection-cont-hellinger>`.
- A list of methods to use on categorical columns. You can chose from :ref:`chi2<univ_cat_method_chi2>`, :ref:`jensen_shannon<univ_cat_method_js>`, :ref:`l_infinity<univ_cat_method_l8>` and :ref:`hellinger<univ_cat_method_hellinger>`.
- Optionally, the name of the column containing the observation timestamps.
- Optionally, a chunking approach or a predifined chunker. If neither is provided, the default chunker creating 10 chunks will be used.
.. nbimport::
:path: ./example_notebooks/Tutorial - Drift - Univariate.ipynb
:cells: 3
Next, the :meth:`~nannyml.drift.univariate.calculator.UnivariateDriftCalculator.fit` method needs to be called on the reference data, which provides the baseline that the analysis data will be compared with. Then the :meth:`~nannyml.drift.univariate.calculator.UnivariateDriftCalculator.calculate` method will calculate the drift results on the data provided to it.
The results can be filtered to only include a certain data period, method or column by using the filter method.
You can evaluate the result data by converting the results into a DataFrame,
by calling the :meth:`~nannyml.drift.univariate.result.Result.to_df` method.
By default this will return a DataFrame with a multi-level index. The first level represents the column, the second level
is the method that was used and the third level are the values, thresholds and alerts for that method.
.. nbimport::
:path: ./example_notebooks/Tutorial - Drift - Univariate.ipynb
:cells: 4
.. nbtable::
:path: ./example_notebooks/Tutorial - Drift - Univariate.ipynb
:cell: 5
You can also disable the multi-level index behavior and return a flat structure by setting multilevel=False.
Both the column name and the method have now been included within the column names.
.. nbimport::
:path: ./example_notebooks/Tutorial - Drift - Univariate.ipynb
:cells: 6
.. nbtable::
:path: ./example_notebooks/Tutorial - Drift - Univariate.ipynb
:cell: 7
The drift results from the reference data are accessible though the filter() method of the drift calculator results:
.. nbimport::
:path: ./example_notebooks/Tutorial - Drift - Univariate.ipynb
:cells: 8
.. nbtable::
:path: ./example_notebooks/Tutorial - Drift - Univariate.ipynb
:cell: 9
The next step is visualizing the results. NannyML can plot both the drift as well as distribution for a given column.
We'll first plot the jensen_shannon method results for each continuous column which are shown below.
.. nbimport::
:path: ./example_notebooks/Tutorial - Drift - Univariate.ipynb
:cells: 10
Note that among the columns shown y_pred_proba is included.
The drift calculator operates on any column. This not only limits it to model features, but allows it to work
on model scores and predictions as well. This also applies to
categorical columns. The plot below shows the chi2 results for each categorical column
and that also includes the y_pred column.
.. nbimport::
:path: ./example_notebooks/Tutorial - Drift - Univariate.ipynb
:cells: 12
NannyML also shows details about the distributions of continuous and categorical variables.
For continuous variables NannyML plots the estimated probability distribution of the variable for each chunk in a plot called joyplot. The chunks where drift was detected are highlighted.
We can create joyplots for the model's continuous variables with the code below.
.. nbimport::
:path: ./example_notebooks/Tutorial - Drift - Univariate.ipynb
:cells: 14
For categorical variables NannyML plots stacked bar charts to show the variable's distribution for each chunk. If a variable has more than 5 categories, the top 4 are displayed and the rest are grouped together to make the plots easier to view. The chunks where drift was detected are highlighted.
We can create stacked bar charts for the model's categorical variables with the code below.
.. nbimport::
:path: ./example_notebooks/Tutorial - Drift - Univariate.ipynb
:cells: 16
After reviewing the above results we have a good understanding of what has changed in our model's population.
The :ref:`Performance Estimation<performance-estimation>` functionality of NannyML can help provide estimates of the impact of the observed changes to Model Performance. The :ref:`ranking<tutorial-ranking>` functionality can help rank drifted features in order to suggest which ones to prioritize for further investigation if needed. This would be an ad-hoc investigating that is not covered by NannyML.