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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallUse Eurybia to compare a production sample with a trusted baseline, then investigate whether the difference matters to predictions and outcomes. Eurybia is a Python library associated with MAIF that accepts pandas DataFrames, trains a classifier to tell baseline rows from current rows, and summarizes the classifier’s AUC and feature-level evidence in an HTML (or notebook) report. A high drift signal is an investigation trigger—not proof that model quality has fallen.
What Eurybia measures
Eurybia frames data-drift detection as a binary classification problem. Rows from the baseline (for example, the training period) receive one label; rows from the current production window receive another. A classifier is trained to predict which dataset each row came from.
If the classifier performs no better than chance, the two samples are difficult to distinguish under this procedure. Eurybia’s documentation describes AUC = 0.5 as that chance level. Values closer to 1 indicate that the observed feature distributions are more distinguishable. The result is therefore a practical measure of distribution shift for the two samples, not a universal statistical guarantee.
The detector does not, by itself, explain the cause of a shift or establish that business performance has degraded. Those questions require feature investigation, operational context and, when available, labeled outcomes.
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What you need before running a comparison
- A meaningful baseline: a training or reference DataFrame representing the population against which change should be judged.
- A current sample: a production window with compatible columns, data types and semantics.
- Consistent preprocessing: decide whether both DataFrames contain raw inputs or the model-ready representation, and apply the same preparation to both.
- Enough observations: a tiny or highly unusual window can make the classifier unstable, so document the window size and sampling method.
- Optional model context: the deployed model and its encoder can be supplied so report views relate drift to model importance and predictions.
Install and run a first report
The project documents installation through pip. Because the surfaced documentation identifies version 1.4.0 and package compatibility can change, verify the current release and dependencies in your environment before pinning a version.
pip install eurybiaA minimal comparison uses two pandas DataFrames. The variable names below are illustrative; align them with your own schema.
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import pandas as pd
from eurybia import SmartDrift
baseline = pd.read_parquet("training_reference.parquet")
current = pd.read_parquet("production_window.parquet")
analysis = SmartDrift(
df_current=current,
df_baseline=baseline,
)
analysis.compile()
analysis.generate_report()When a deployed estimator and encoder are available, pass them to SmartDrift as supported by the installed release. This adds model context to the investigation; it does not turn a distribution comparison into a proof of model failure.
How to read the Eurybia report
1. Start with classifier performance
Use the drift-classifier AUC as a triage signal. An AUC near 0.5 means the classifier has little ability to separate baseline from current rows in this run. A substantially higher value means the datasets are distinguishable, but it does not specify whether the change is harmful, seasonal or caused by a pipeline defect.
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2. Find the features driving separation
Feature contributions and importance views show which variables help the classifier distinguish the two datasets. Prioritize those variables for checks such as unit changes, missing-value spikes, category additions, sensor recalibration, upstream schema changes and legitimate population changes.
3. Inspect distributions directly
Compare baseline and current distributions for the influential variables rather than relying on AUC alone. Distribution plots can reveal a shifted center, a new category, truncation, an unusual tail or a sudden missingness pattern.
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4. Relate drift to the deployed model
Eurybia documents a view that relates feature drift to importance in the deployed model, plus predicted-value distributions. A heavily drifted variable that the model barely uses may deserve less urgency than a modest shift in a feature with substantial predictive influence.
5. Check time and outcome views
The project describes AUC evolution across periods and model-performance evolution. Use these views to see whether a one-window change persists and whether it coincides with a change in measured task performance. Outcome-based conclusions require labels or another defensible quality metric.
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An operational monitoring workflow
- Define the comparison: record the baseline population, production-window dates, sampling rules and preprocessing version.
- Run SmartDrift: compare the two DataFrames and include the deployed model and encoder when available.
- Compile and publish: generate the HTML report for review or display the visualizations in notebook mode.
- Investigate drivers: inspect AUC, feature contributions, distributions and prediction changes; check data-pipeline logs and upstream releases.
- Validate impact: join predictions to later labels or operational outcomes and measure the task metric that matters for the model.
- Choose an action: document whether the shift is benign, a data-quality incident, a feature change requiring compatibility work, or evidence that retraining or another intervention is justified.
The project describes periodic computation orchestrated by a scheduler. Its tutorial compares a 2006 house-price learning set with later production years, illustrating repeated year-based comparisons. That example demonstrates the workflow, not production-scale effectiveness or a benchmark.
Reference-window choices
Window design changes what “drift” means. Treat it as a deployment decision rather than an undocumented Eurybia default.
| Choice | Strength | Risk or trade-off | Useful when |
|---|---|---|---|
| Fixed training baseline | Stable, interpretable comparison to the population used for learning | Normal long-term evolution can look permanently anomalous | Regulated or versioned models where training data is the contractual reference |
| Rolling reference | Adapts to gradual population change | Can absorb real degradation or a slowly spreading pipeline error | Fast-changing products with explicit controls on reference updates |
| Successive production windows | Shows short-term changes and seasonality | May miss a slow departure from the original training population | High-cadence monitoring used alongside a fixed baseline |
Choose a cadence and window size that match traffic, seasonality and label delay. Eurybia’s cited material does not prescribe a universal size or alert threshold; establish thresholds from your own historical runs and review them when the model or data contract changes.
Data drift is not model-quality degradation
Three situations can produce different responses:
- Benign change: a seasonal or demographic shift that the model handles without a meaningful metric loss.
- Data-pipeline fault: a unit conversion, broken join, encoding change or missingness surge that makes current inputs invalid.
- Harmful concept or relationship change: the relationship between inputs and the target has changed, so predictions become less reliable even if input distributions look familiar.
Only the first two can be diagnosed from input comparisons alone, and neither AUC nor a feature plot establishes the third. Keep a separate performance monitor for metrics such as the task’s error, ranking or calibration measure once labels arrive. Investigate retraining only after confirming the shift, its persistence and its effect on the decision the model supports.
Quick Recap
Common implementation pitfalls
- Incompatible schemas: renamed columns, changed units or different category encodings can create artificial separation. Freeze and test the data contract.
- Leakage from the comparison setup: include only variables available at the same point in the production process; otherwise the classifier may detect collection timing rather than population change.
- Unbalanced or unrepresentative samples: document sampling and consider traffic mix, geography and seasonality before interpreting AUC.
- Overreacting to one window: confirm persistence and inspect pipeline events before rolling back or retraining.
- Ignoring labels: input stability is not a substitute for measuring outcomes when labels become available.
A practical decision checklist
- Can every baseline/current column be traced to the same semantic definition?
- Is the baseline fixed, rolling or both, and why?
- Which features drive classifier separation?
- Are those features important to the deployed model?
- Do distributions show a plausible business or seasonal explanation?
- Did prediction distributions change?
- When labels arrive, did the task metric, calibration or error rate change?
- What owner and rollback or retraining action is assigned if the shift persists?
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