facet

.. image:: sphinx/source/_images/Gamma_Facet_Logo_RGB_LB.svg

FACET is an open source library for human-explainable AI. It combines sophisticated model inspection and model-based simulation to enable better explanations of your supervised machine learning models.

FACET is composed of the following key components:

+-----------------+-----------------------------------------------------------------------+ | |spacer| | Model Inspection | | | | | |inspect| | FACET introduces a new algorithm to quantify dependencies and | | | interactions between features in ML models. | | | This new tool for human-explainable AI adds a new, global | | | perspective to the observation-level explanations provided by the | | | popular SHAP <https://shap.readthedocs.io/en/stable/>__ approach. | | | To learn more about FACET’s model inspection capabilities, see the | | | getting started example below. | +-----------------+-----------------------------------------------------------------------+ | |spacer| | Model Simulation | | | | | |sim| | FACET’s model simulation algorithms use ML models for | | | virtual experiments to help identify scenarios that optimise | | | predicted outcomes. | | | To quantify the uncertainty in simulations, FACET utilises a range | | | of bootstrapping algorithms including stationary and stratified | | | bootstraps. | | | For an example of FACET’s bootstrap simulations, see the | | | quickstart example below. | +-----------------+-----------------------------------------------------------------------+ | |spacer| | Enhanced Machine Learning Workflow | | | | | |pipe| | FACET offers an efficient and transparent machine learning | | | workflow, enhancing | | | scikit-learn <https://scikit-learn.org/stable/index.html>'s | | | tried and tested pipelining paradigm with new capabilities for model | | | selection, inspection, and simulation. | | | FACET also introduces | | | sklearndf <https://github.com/BCG-X-Official/sklearndf> | | | [documentation <https://bcg-x-official.github.io/sklearndf/index.html>__]| | | an augmented version of scikit-learn with enhanced support for | | | pandas data frames that ensures end-to-end traceability of features.| +-----------------+-----------------------------------------------------------------------+

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FACET supports both PyPI and Anaconda. We recommend to install FACET into a dedicated environment.

Anaconda

.. code-block:: sh

    conda create -n facet
    conda activate facet
    conda install -c bcg_gamma -c conda-forge gamma-facet


Pip
~~~

macOS and Linux:
^^^^^^^^^^^^^^^^

.. code-block:: sh

    python -m venv facet
    source facet/bin/activate
    pip install gamma-facet

Windows:
^^^^^^^^

.. code-block:: dosbatch

    python -m venv facet
    facet\Scripts\activate.bat
    pip install gamma-facet


Quickstart
----------

The following quickstart guide provides a minimal example workflow to get you
up and running with FACET.
For additional tutorials and the API reference,
see the `FACET documentation <https://bcg-x-official.github.io/facet/docs-version/2-0>`__.

Changes and additions to new versions are summarized in the
`release notes <https://bcg-x-official.github.io/facet/docs-version/2-0/release_notes.html>`__.


Enhanced Machine Learning Workflow

To demonstrate the model inspection capability of FACET, we first create a pipeline to fit a learner. In this simple example we will use the diabetes dataset <https://web.stanford.edu/~hastie/Papers/LARS/diabetes.data>__ which contains age, sex, BMI and blood pressure along with 6 blood serum measurements as features. This dataset was used in this publication <https://statweb.stanford.edu/~tibs/ftp/lars.pdf>. A transformed version of this dataset is also available on scikit-learn here <https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset>.

In this quickstart we will train a Random Forest regressor using 10 repeated 5-fold CV to predict disease progression after one year. With the use of sklearndf we can create a pandas DataFrame compatible workflow. However, FACET provides additional enhancements to keep track of our feature matrix and target vector using a sample object (Sample) and easily compare hyperparameter configurations and even multiple learners with the LearnerSelector.

.. code-block:: Python

# standard imports
import pandas as pd
from sklearn.model_selection import RepeatedKFold, GridSearchCV

# some helpful imports from sklearndf
from sklearndf.pipeline import RegressorPipelineDF
from sklearndf.regression import RandomForestRegressorDF

# relevant FACET imports
from facet.data import Sample
from facet.selection import LearnerSelector, ParameterSpace

# declaring url with data
data_url = 'https://web.stanford.edu/~hastie/Papers/LARS/diabetes.data'

#importing data from url
diabetes_df = pd.read_csv(data_url, delimiter='\t').rename(
    # renaming columns for better readability
    columns={
        'S1': 'TC', # total serum cholesterol
        'S2': 'LDL', # low-density lipoproteins
        'S3': 'HDL', # high-density lipoproteins
        'S4': 'TCH', # total cholesterol/ HDL
        'S5': 'LTG', # lamotrigine level
        'S6': 'GLU', # blood sugar level
        'Y': 'Disease_progression' # measure of progress since 1yr of baseline
    }
)

# create FACET sample object
diabetes_sample = Sample(observations=diabetes_df, target_name="Disease_progression")

# create a (trivial) pipeline for a random forest regressor
rnd_forest_reg = RegressorPipelineDF(
    regressor=RandomForestRegressorDF(n_estimators=200, random_state=42)
)

# define parameter space for models which are "competing" against each other
rnd_forest_ps = ParameterSpace(rnd_forest_reg)
rnd_forest_ps.regressor.min_samples_leaf = [8, 11, 15]
rnd_forest_ps.regressor.max_depth = [4, 5, 6]

# create repeated k-fold CV iterator
rkf_cv = RepeatedKFold(n_splits=5, n_repeats=10, random_state=42)

# rank your candidate models by performance
selector = LearnerSelector(
    searcher_type=GridSearchCV,
    parameter_space=rnd_forest_ps,
    cv=rkf_cv,
    n_jobs=-3,
    scoring="r2"
).fit(diabetes_sample)

# get summary report
selector.summary_report()

.. image:: sphinx/source/_images/ranker_summary.png :width: 600

We can see based on this minimal workflow that a value of 11 for minimum samples in the leaf and 5 for maximum tree depth was the best performing of the three considered values. This approach easily extends to additional hyperparameters for the learner, and for multiple learners.

Model Inspection

FACET implements several model inspection methods for
`scikit-learn <https://scikit-learn.org/stable/index.html>`__ estimators.
FACET enhances model inspection by providing global metrics that complement
the local perspective of SHAP (see
`[arXiv:2107.12436] <https://arxiv.org/abs/2107.12436>`__ for a formal description).

The key global metrics for each pair of features in a model are:

- **Synergy**

  The degree to which the model combines information from one feature with
  another to predict the target. For example, let's assume we are predicting
  cardiovascular health using age and gender and the fitted model includes
  a complex interaction between them. This means these two features are
  synergistic for predicting cardiovascular health. Further, both features
  are important to the model and removing either one would significantly
  impact performance. Let's assume age brings more information to the joint
  contribution than gender. This asymmetric contribution means the synergy for
  (age, gender) is less than the synergy for (gender, age). To think about it another
  way, imagine the prediction is a coordinate you are trying to reach.
  From your starting point, age gets you much closer to this point than
  gender, however, you need both to get there. Synergy reflects the fact
  that gender gets more help from age (higher synergy from the perspective
  of gender) than age does from gender (lower synergy from the perspective of
  age) to reach the prediction. *This leads to an important point: synergy
  is a naturally asymmetric property of the global information two interacting
  features contribute to the model predictions.* Synergy is expressed as a
  percentage ranging from 0% (full autonomy) to 100% (full synergy).

- **Redundancy**

  The degree to which a feature in a model duplicates the information of a
  second feature to predict the target. For example, let's assume we had
  house size and number of bedrooms for predicting house price. These
  features capture similar information as the more bedrooms the larger
  the house and likely a higher price on average. The redundancy for
  (number of bedrooms, house size) will be greater than the redundancy
  for (house size, number of bedrooms). This is because house size
  "knows" more of what number of bedrooms does for predicting house price
  than vice-versa. Hence, there is greater redundancy from the perspective
  of number of bedrooms. Another way to think about it is removing house
  size will be more detrimental to model performance than removing number
  of bedrooms, as house size can better compensate for the absence of
  number of bedrooms. This also implies that house size would be a more
  important feature than number of bedrooms in the model. *The important
  point here is that like synergy, redundancy is a naturally asymmetric
  property of the global information feature pairs have for predicting
  an outcome.* Redundancy is expressed as a percentage ranging from 0%
  (full uniqueness) to 100% (full redundancy).

.. code-block:: Python

    # fit the model inspector
    from facet.inspection import LearnerInspector
    inspector = LearnerInspector(
        pipeline=selector.best_estimator_,
        n_jobs=-3
    ).fit(diabetes_sample)

**Synergy**

.. code-block:: Python

    # visualise synergy as a matrix
    from pytools.viz.matrix import MatrixDrawer
    synergy_matrix = inspector.feature_synergy_matrix()
    MatrixDrawer(style="matplot%").draw(synergy_matrix, title="Synergy Matrix")

.. image:: sphinx/source/_images/synergy_matrix.png
    :width: 600

For any feature pair (A, B), the first feature (A) is the row, and the second
feature (B) the column. For example, looking across the row for `LTG` (Lamotrigine)
there is hardly any synergy with other features in the model (≤ 1%).
However, looking down the column for `LTG` (i.e., from the perspective of other features
relative with `LTG`) we find that many features (the rows) are aided by synergy with
with `LTG` (up to 27% in the case of LDL). We conclude that:

- `LTG` is a strongly autonomous feature, displaying minimal synergy with other
  features for predicting disease progression after one year.
- The contribution of other features to predicting disease progression after one
  year is partly enabled by the presence of `LTG`.

High synergy between pairs of features must be considered carefully when investigating
impact, as the values of both features jointly determine the outcome. It would not make
much sense to consider `LDL` without the context provided by `LTG` given close
to 27% synergy of `LDL` with `LTG` for predicting progression after one year.

**Redundancy**

.. code-block:: Python

    # visualise redundancy as a matrix
    redundancy_matrix = inspector.feature_redundancy_matrix()
    MatrixDrawer(style="matplot%").draw(redundancy_matrix, title="Redundancy Matrix")

.. image:: sphinx/source/_images/redundancy_matrix.png
    :width: 600


For any feature pair (A, B), the first feature (A) is the row, and the second feature
(B) the column. For example, if we look at the feature pair (`LDL`, `TC`) from the
perspective of `LDL` (Low-Density Lipoproteins), then we look-up the row for `LDL`
and the column for `TC` and find 38% redundancy. This means that 38% of the information
in `LDL` to predict disease progression is duplicated in `TC`. This
redundancy is the same when looking "from the perspective" of `TC` for (`TC`, `LDL`),
but need not be symmetrical in all cases (see `LTG` vs. `TCH`).

If we look at `TCH`, it has between 22–32% redundancy each with `LTG` and `HDL`, but
the same does not hold between `LTG` and `HDL` – meaning `TCH` shares different
information with each of the two features.


**Clustering redundancy**

As detailed above redundancy and synergy for a feature pair is from the
"perspective" of one of the features in the pair, and so yields two distinct
values. However, a symmetric version can also be computed that provides not
only a simplified perspective but allows the use of (1 - metric) as a
feature distance. With this distance hierarchical, single linkage clustering
is applied to create a dendrogram visualization. This helps to identify
groups of low distance, features which activate "in tandem" to predict the
outcome. Such information can then be used to either reduce clusters of
highly redundant features to a subset or highlight clusters of highly
synergistic features that should always be considered together.

Let's look at the example for redundancy.

.. code-block:: Python

    # visualise redundancy using a dendrogram
    from pytools.viz.dendrogram import DendrogramDrawer
    redundancy = inspector.feature_redundancy_linkage()
    DendrogramDrawer().draw(data=redundancy, title="Redundancy Dendrogram")

.. image:: sphinx/source/_images/redundancy_dendrogram.png
    :width: 600

Based on the dendrogram we can see that the feature pairs (`LDL`, `TC`)
and (`HDL`, `TCH`) each represent a cluster in the dendrogram and that `LTG` and `BMI`
have the highest importance. As potential next actions we could explore the impact of
removing `TCH`, and one of `TC` or `LDL` to further simplify the model and obtain a
reduced set of independent features.

Please see the
`API reference <https://bcg-x-official.github.io/facet/apidoc/facet.html>`__
for more detail.


Model Simulation

Taking the BMI feature as an example of an important and highly independent feature, we do the following for the simulation:

• We use FACET's ContinuousRangePartitioner to split the range of observed values of BMI into intervals of equal size. Each partition is represented by the central value of that partition.
• For each partition, the simulator creates an artificial copy of the original sample assuming the variable to be simulated has the same value across all observations – which is the value representing the partition. Using the best estimator acquired from the selector, the simulator now re-predicts all targets using the models trained for full sample and determines the uplift of the target variable resulting from this.
• The FACET SimulationDrawer allows us to visualise the result; both in a matplotlib and a plain-text style.

.. code-block:: Python

# FACET imports
from facet.validation import BootstrapCV
from facet.simulation import UnivariateUpliftSimulator
from facet.data.partition import ContinuousRangePartitioner
from facet.simulation.viz import SimulationDrawer

# create bootstrap CV iterator
bscv = BootstrapCV(n_splits=1000, random_state=42)

SIM_FEAT = "BMI"
simulator = UnivariateUpliftSimulator(
    model=selector.best_estimator_,
    sample=diabetes_sample,
    n_jobs=-3
)

# split the simulation range into equal sized partitions
partitioner = ContinuousRangePartitioner()

# run the simulation
simulation = simulator.simulate_feature(feature_name=SIM_FEAT, partitioner=partitioner)

# visualise results
SimulationDrawer().draw(data=simulation, title=SIM_FEAT)

.. image:: sphinx/source/_images/simulation_output.png

We would conclude from the figure that higher values of BMI are associated with an increase in disease progression after one year, and that for a BMI of 28 and above, there is a significant increase in disease progression after one year of at least 26 points.

FACET is stable and is being supported long-term.

Contributions to FACET are welcome and appreciated. For any bug reports or feature requests/enhancements please use the appropriate GitHub form <https://github.com/BCG-X-Official/facet/issues>_, and if you wish to do so, please open a PR addressing the issue.

We do ask that for any major changes please discuss these with us first via an issue or using our team email: [email protected].

For further information on contributing please see our contribution guide <https://bcg-x-official.github.io/facet/contribution_guide.html>__.

FACET is licensed under Apache 2.0 as described in the LICENSE <https://github.com/BCG-X-Official/facet/blob/develop/LICENSE>_ file.

FACET is built on top of two popular packages for Machine Learning:

• The scikit-learn <https://scikit-learn.org/stable/index.html>__ learners and pipelining make up implementation of the underlying algorithms. Moreover, we tried to design the FACET API to align with the scikit-learn API.
• The SHAP <https://shap.readthedocs.io/en/latest/>__ implementation is used to estimate the shapley vectors which FACET then decomposes into synergy, redundancy, and independence vectors.

If you would like to know more about the team behind FACET please see the about us <https://bcg-x-official.github.io/facet/about_us.html>__ page.

We are always on the lookout for passionate and talented data scientists to join the BCG GAMMA team. If you would like to know more you can find out about BCG GAMMA <https://www.bcg.com/en-gb/beyond-consulting/bcg-gamma/default>, or have a look at career opportunities <https://www.bcg.com/en-gb/beyond-consulting/bcg-gamma/careers>.

.. |pipe| image:: sphinx/source/_images/icons/pipe_icon.png :width: 100px :class: facet_icon

.. |inspect| image:: sphinx/source/_images/icons/inspect_icon.png :width: 100px :class: facet_icon

.. |sim| image:: sphinx/source/_images/icons/sim_icon.png :width: 100px :class: facet_icon

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