psiwatch 0.6.0

Dataset drift detection for ML pipelines.

psiwatch

Dataset drift detection for ML pipelines.

Know when your data changes. Before your model breaks.

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What it does

You train a model on last year's data. Six months later it starts making wrong predictions. The reason? Your data changed. Scores dropped. New categories appeared. Distributions shifted.

psiwatch catches this. Point it at your old data and new data — it tells you exactly what drifted, how badly, and gives you a single health score.

psiwatch compare train.csv production.csv

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Install

pip install psiwatch

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CLI Usage

psiwatch compare old.csv new.csv
psiwatch compare old.csv new.csv --output report.html
psiwatch compare old.csv new.csv --output report.json
psiwatch compare old.csv new.csv --columns age,score,city

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Library Usage

import psiwatch

# From CSV files
psiwatch.compare("old.csv", "new.csv")

# Save as HTML
psiwatch.compare("old.csv", "new.csv", output="report.html")

# From Python dicts
psiwatch.compare_data(
    old={"age": [22, 23, 21], "city": ["Chennai", "Delhi", "Mumbai"]},
    new={"age": [28, 30, 29], "city": ["Chennai", "Bangalore", "Hyderabad"]}
)

# From plain lists
psiwatch.compare_columns([22, 23, 21], [28, 30, 29], name="age")

# Raw results
result = psiwatch.analyze("old.csv", "new.csv")
print(result["health_score"])  # 0-100

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Detection Methods

Column Type	Methods
Numeric	Mean shift, Std shift, PSI, Percentile comparison
Categorical	New category detection, Frequency shift, PSI, Chi-square

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Drift Health Score

Score	Status
80 - 100	Stable
50 - 79	Moderate Drift
0 - 49	Significant Drift

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Zero Dependencies

Pure Python only. No numpy, pandas, or scipy. Runs anywhere Python runs including Termux on Android.

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License

MIT © 2026 Tharun · Naeris