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CinnaMon is a Python library which allows to monitor data drift on a machine learning system. It provides tools to study data drift between two datasets, especially to detect, explain, and correct data drift.
As a quick example, let's illustrate the use of CinnaMon on the breast cancer data where we voluntarily introduce some data drift.
>>> import pandas as pd
>>> from sklearn import datasets
>>> from sklearn.model_selection import train_test_split
>>> from xgboost import XGBClassifier
# load breast cancer data
>>> dataset = datasets.load_breast_cancer()
>>> X = pd.DataFrame(dataset.data, columns = dataset.feature_names)
>>> y = dataset.target
# split data in train and valid dataset
>>> X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.3, random_state=2021)
# introduce some data drift in valid by filtering with 'worst symmetry' feature
>>> y_valid = y_valid[X_valid['worst symmetry'].values > 0.3]
>>> X_valid = X_valid.loc[X_valid['worst symmetry'].values > 0.3, :].copy()
# fit a XGBClassifier on the training data
>>> clf = XGBClassifier(use_label_encoder=False)
>>> clf.fit(X=X_train, y=y_train, verbose=10)>>> import cinnamon
>>> from cinnamon.drift import ModelDriftExplainer
# initialize a drift explainer with the built XGBClassifier and fit it on train
# and valid data
>>> drift_explainer = ModelDriftExplainer(model=clf)
>>> drift_explainer.fit(X1=X_train, X2=X_valid, y1=y_train, y2=y_valid)# Distribution of logit predictions
>>> cinnamon.plot_prediction_drift(drift_explainer, bins=15)
We can see on this graph that because of the data drift we introduced in validation data the distribution of predictions are different (they do not overlap well). We can also compute the corresponding drift metrics:
# Corresponding metrics
>>> drift_explainer.get_prediction_drift()
[{'mean_difference': -3.643428434667366,
'wasserstein': 3.643428434667366,
'kolmogorov_smirnov': KstestResult(statistic=0.2913775225333014, pvalue=0.00013914094110123454)}]Comparing the distributions of predictions for two datasets is one of the main indicator we use in order to detect data drift. The two other indicators are:
- distribution of the target (see
get_target_drift) - performance metrics (see
get_performance_metrics_drift)
Drift importances can be thought as equivalent of feature importances but in terms of data drift.
# plot drift importances
>>> cinnamon.plot_tree_based_drift_importances(drift_explainer, n=7)
Here the feature worst symmetry is rightly identified as the one which contributes the most to the data drift.
See "notes" below to explore all the functionalities of CinnaMon.
CinnaMon is intended to work with Python 3.7 or above. Installation can be done with pip:
$ pip install cinnamon- CinnaMon documentation
- The two main classes of CinnaMon are
ModelDriftExplainerandAdversarialDriftExplainer - CinnaMon supports both model specific and model agnostic methods for the computation of drift importances. More information here.
- CinnaMon can be used with any model or ML pipeline thanks to model agnostic mode.
See notebooks in the
examples/directory to have an overview of all functionalities. Notably:These two notebooks also go deeper into the topic of how to correct data drift, making use of
AdversarialDriftExplainer- See also the slide presentation of the CinnaMon library. And the video presentation.
Check out the contribution section.
CinnaMon is free and open-source software licensed under the MIT.
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