driftveil 0.2.1

Minimalist, zero-config data drift detection for Python

Driftveil 🌌

Minimalist, zero-config data drift detection for Python. Protect your ML models and data pipelines from silent distribution shifts in 3 lines of code.

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⚡ 30-Second Quickstart

import pandas as pd
from driftveil import DriftVeil

# 1. Define your statistical contract on the reference data
pact = DriftVeil(reference_df)

pact.column("age").is_normal(tolerance=0.1)
pact.column("income").is_lognormal()
pact.column("revenue").mean_stable(tolerance=0.15)
pact.column("category").category_freq_stable(chi2_pvalue=0.05)
pact.pair("clicks", "conversions").correlation_above(0.5)
pact.dataset.row_count_stable(tolerance=0.2)

# 2. Enforce the contract on your production data batch
report = pact.enforce(new_df, raise_on_fail=False)

# 3. Print the summary report
print(report.summary())
# ✓ age            normal distribution   (KS p=0.43)
# ✓ income         lognormal             (KS p=0.61)
# ✗ revenue        mean drift detected   (ref=4200 → 6800, +62%)
# ✓ category       PSI=0.08              (below 0.20 threshold)
# ✓ clicks/conv.   correlation=0.71      (above 0.50)
# ✓ row count      9821 rows             (within ±20%)

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📊 Comparison: Why Driftveil?

Most data quality tools check only the schema (types, nulls, value ranges). Driftveil checks statistical behavior without the heavy configuration overhead.

Feature	Driftveil	Great Expectations	Evidently	Deepchecks
Schema / null checks	Yes	Yes	Partial	Yes
Distribution contracts	Yes	No	Reports only	Reports only
Fluent Python API	Yes	YAML/JSON config	Class-heavy	Class-heavy
Lightweight (≤3 deps)	Yes	Very heavy	Heavy	Heavy
Correlation contracts	Yes	No	No	Partial
Saveable pacts (JSON)	Yes	Yes	No	No

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🛠️ Key Design Principles

1. Pure functions in tests/ Every statistical test is a standalone function (ref, new) -> TestResult. No classes, no side effects. This makes adding custom tests extremely easy.

2. Lazy fluent builder Contracts accumulate lazily — nothing is computed until .enforce() or .save() is called. Defining a pact is fast even with 50+ columns.

3. Statistics-only serialization Saved pacts store reference statistics (mean, std, bin edges, quantiles, value counts) — not the full reference DataFrame. A saved pact is a tiny JSON file you can commit to git and load in CI without needing the original dataset.

4. Pandas-first, Polars via adapter Automatically converts any supported DataFrame type (like Polars) to a Pandas-compatible interface before running tests.

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📖 Full API Capabilities

Column Metrics

# --- Distribution shape ---
pact.column("x").is_normal(tolerance=0.10)
pact.column("x").is_lognormal(tolerance=0.10)
pact.column("x").is_exponential(tolerance=0.15)
pact.column("x").fits_distribution("pareto")       # Fits any scipy.stats distribution

# --- Central tendency and spread ---
pact.column("x").mean_stable(tolerance=0.15)       # Welch t-test
pact.column("x").variance_stable(tolerance=0.20)   # Levene's test
pact.column("x").median_stable(tolerance=0.15)     # Mann-Whitney U test
pact.column("x").quantile_stable(                  # Checks quantiles within tolerance
    [0.25, 0.5, 0.75, 0.95], tolerance=0.10
)

# --- Drift detection ---
pact.column("x").psi_below(0.20)                   # Population Stability Index
pact.column("x").ks_pvalue_above(0.05)             # KS two-sample test
pact.column("x").js_divergence_below(0.10)         # Jensen-Shannon divergence

# --- Structural & Categorical ---
pact.column("x").null_rate_stable(tolerance=0.05)
pact.column("x").outlier_rate_stable(method="iqr", tolerance=0.10)
pact.column("x").stays_in_range(min=0, max=100)
pact.column("category").no_new_categories()
pact.column("category").category_freq_stable(chi2_pvalue=0.05)

Pair Metrics

pact.pair("price", "qty").correlation_above(0.3)
pact.pair("price", "qty").correlation_below(-0.1)
pact.pair("rev", "cost").ratio_stable("rev/cost", tolerance=0.15)
pact.pair("A", "B").mutual_information_stable(tolerance=0.20)

Dataset Metrics

pact.dataset.row_count_stable(tolerance=0.20)
pact.dataset.no_new_columns()
pact.dataset.no_dropped_columns()

Reporting & CI/CD Integration

Python API

report = pact.enforce(new_df, raise_on_fail=False)

# Check status programmatically
if not report.passed:
    print(f"Failed checks count: {len(report.failed)}")

# Enforce and raise error in pipelines
report.assert_passed() # raises ContractViolationError

# Save & Load pacts for CI
pact.save("production.pact.json")
pact2 = DriftVeil.load("production.pact.json", reference_df)

# Export interactive report dashboards (automatically embeds plots inline)
report.to_html("drift_report.html")

# Plot drift metrics
fig = report.plot_drifts()
fig.savefig("drift_summary.png")

# Log directly to active MLflow run
report.to_mlflow()

Command Line Interface (CLI)

Run checks directly on files (CSV, Parquet, or JSON) using saved pact files in bash or scheduler pipelines:

# Exit code is 0 on pass, or 1 on contract failure (when --raise-on-fail is set)
driftveil check new_data.csv --pact production.pact.json --raise-on-fail

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🧪 Installation

Install Driftveil from PyPI (when published):

pip install driftveil

Or install it with plotting and Polars support:

pip install driftveil[all]

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.