psiwatch 0.7.0

Dataset drift detection for ML pipelines.

psiwatch

Python library for dataset drift detection in machine learning pipelines.

Detect covariate drift, distribution shift, and data quality degradation between two datasets — using PSI, Chi-Square, Mean Shift, and Standard Deviation analysis. Zero dependencies. Pure Python.

---

What's New in v0.2.0

pandas DataFrame support

Pass a DataFrame directly — no need to export to CSV first.

import pandas as pd
import psiwatch

train = pd.read_csv("train.csv")
production = pd.read_csv("production.csv")

psiwatch.compare(train, production)
psiwatch.compare(train, production, output="report.html")

Works with compare(), compare_data(), compare_columns(), and analyze(). pandas remains optional — users who don't use it don't need to install it.

---

Column summary stats

Every numeric column now shows min, max, and percentiles (P25, median, P75) alongside PSI and mean/std — so you can see exactly where the distribution shifted, not just that it shifted.

  [!!] credit_score  [numeric]  — HIGH DRIFT
     → Mean shifted by 2.20 std devs (752 → 624)
     → PSI = 11.12 (significant drift)
     ┌ Mean:    752.00 → 624.00
     ├ Std:     48.30 → 61.20
     ├ PSI:     11.1200
     ├ Min:     620.00 → 490.00
     ├ P25:     718.00 → 578.00
     ├ Median:  755.00 → 628.00
     ├ P75:     789.00 → 672.00
     └ Max:     850.00 → 799.00

These stats appear in all output formats — terminal, HTML, JSON, and TXT.

---

Configurable drift thresholds

The default PSI thresholds (0.10 medium, 0.25 high) are industry standards for banking data. Your data may need different sensitivity. Now you can set your own.

Quick shortcut — set the PSI high boundary:

# More sensitive — flag drift earlier
psiwatch.compare("old.csv", "new.csv", psi_threshold=0.10)

# More lenient — only flag large drifts
psiwatch.compare("old.csv", "new.csv", psi_threshold=0.40)

The medium threshold is auto-scaled to 40% of whatever you set, preserving the same ratio as the defaults.

Full control — override any threshold individually:

psiwatch.compare("old.csv", "new.csv", thresholds={
    "psi_medium": 0.05,
    "psi_high": 0.15,
    "mean_shift_medium": 0.1,
    "mean_shift_high": 0.3,
})

CLI flag:

psiwatch compare old.csv new.csv --psi-threshold 0.15

---

The Problem

You train a machine learning model on historical data. Weeks or months later, the model starts making wrong predictions — but nobody knows why.

The reason is data drift. Your production data no longer looks like your training data:

• Customer ages shifted
• New cities or categories appeared
• Salary distributions changed
• Credit scores dropped

Most teams discover this too late — after the model has already failed in production.

---

The Solution

pip install psiwatch
psiwatch compare train.csv production.csv

════════════════════════════════════════════════════════
  PSIWATCH REPORT
════════════════════════════════════════════════════════

  [!!] credit_score  [numeric]  — HIGH DRIFT
     → Mean shifted by 2.20 std devs (752 → 624)
     → PSI = 11.12 (significant drift)
     ┌ Mean:    752.00 → 624.00
     ├ Std:     48.30 → 61.20
     ├ PSI:     11.1200
     ├ Min:     620.00 → 490.00
     ├ P25:     718.00 → 578.00
     ├ Median:  755.00 → 628.00
     ├ P75:     789.00 → 672.00
     └ Max:     850.00 → 799.00

  [!!] loan_type  [categorical]  — HIGH DRIFT
     → New categories found: ['BNPL', 'Crypto']
     → PSI = 4.19

  [OK] customer_id  [categorical]  — STABLE
     → No drift detected

────────────────────────────────────────────────────────
  HIGH: 2   MEDIUM: 0   PASS: 1

  Drift Health Score: 11/100  (Significant Drift)
════════════════════════════════════════════════════════

---

Why psiwatch?

Most drift detection tools like evidently, alibi-detect, or scipy are heavy — they require numpy, pandas, scikit-learn, or scipy as dependencies and are designed for data scientists running Jupyter notebooks.

psiwatch is different:

Feature	psiwatch	evidently	alibi-detect
Dependencies	Zero*	Heavy	Heavy
Install size	~15KB	~50MB+	~100MB+
Works on Termux	Yes	No	No
CLI tool	Yes	No	No
Pure Python	Yes	No	No
HTML reports	Yes	Yes	No
DataFrame support	Yes	Yes	Yes

*pandas is optional. If you pass a DataFrame, psiwatch uses it. If you don't, psiwatch never imports it.

---

Install

pip install psiwatch

Works on Windows, Mac, Linux, VPS, Google Colab, Jupyter, and Termux on Android.

---

Quickstart

git clone https://github.com/tharunstryker/psiwatch
cd psiwatch
pip install -e .
psiwatch compare samples/train.csv samples/new.csv

---

CLI

# Compare two CSV files
psiwatch compare old.csv new.csv

# Save as HTML report
psiwatch compare old.csv new.csv --output report.html

# Save as JSON for pipelines
psiwatch compare old.csv new.csv --output report.json

# Save as plain text
psiwatch compare old.csv new.csv --output report.txt

# Compare specific columns only
psiwatch compare old.csv new.csv --columns age,score,city

# Set a custom PSI threshold
psiwatch compare old.csv new.csv --psi-threshold 0.15

---

Python Library

import psiwatch

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

# Compare pandas DataFrames directly
import pandas as pd
psiwatch.compare(pd.read_csv("old.csv"), pd.read_csv("new.csv"))

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

# Compare specific columns
psiwatch.compare("old.csv", "new.csv", columns=["age", "score"])

# Custom PSI threshold
psiwatch.compare("old.csv", "new.csv", psi_threshold=0.15)

# Fine-grained threshold control
psiwatch.compare("old.csv", "new.csv", thresholds={
    "psi_medium": 0.05,
    "psi_high": 0.15,
    "mean_shift_high": 0.3,
})

# From Python dicts — no files needed
psiwatch.compare_data(
    old={"age": [22, 23, 21], "city": ["Chennai", "Delhi", "Mumbai"]},
    new={"age": [28, 30, 29], "city": ["Chennai", "Bangalore", "Hyderabad"]}
)

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

# Get raw results programmatically
result = psiwatch.analyze("old.csv", "new.csv")
print(result["health_score"])         # 0-100
for col, data in result["columns"].items():
    print(col, data["severity"])      # HIGH / MEDIUM / PASS
    print(col, data["metrics"])       # PSI, mean, std, min, p25, median, p75, max

---

Input Modes

Mode	Example
CSV file path	psiwatch.compare("old.csv", "new.csv")
pandas DataFrame	psiwatch.compare(old_df, new_df)
Python dict	psiwatch.compare_data(old_dict, new_dict)
Python list	psiwatch.compare_columns([1,2,3], [4,5,6])

Your files stay on your machine. pandas is never required — only used if you pass a DataFrame.

---

Output Formats

Format	Use case
Terminal (default)	Quick checks during development
HTML --output report.html	Sharing with team, presentations
JSON --output report.json	CI/CD pipelines, automation
TXT --output report.txt	Server logs, plain text reports

---

Detection Methods

Numeric columns — age, score, salary, credit score...

Method	What it detects
Mean Shift	Average moved significantly between datasets
Std Deviation Shift	Spread of values changed
PSI (Population Stability Index)	Overall distribution shape changed
Min / Max / Percentiles	Where exactly in the distribution the shift happened

Categorical columns — city, grade, status, loan type...

Method	What it detects
New Category Detection	Values appeared that never existed in training data
Frequency Distribution Shift	Category proportions changed significantly
PSI	Overall distribution changed
Chi-Square Statistic	Frequency mismatch is statistically significant

---

PSI Reference

PSI (Population Stability Index) is the industry standard metric for production data drift monitoring.

PSI	Status	Recommended Action
< 0.10	Stable	Model is fine
0.10 - 0.25	Moderate Drift	Monitor closely, investigate
> 0.25	Significant Drift	Retrain your model

These are the defaults. Override them with psi_threshold or thresholds if your domain requires different sensitivity.

---

Threshold Reference

All thresholds can be overridden via the thresholds dict:

Key	Default	What it controls
psi_medium	0.10	PSI value that triggers MEDIUM severity
psi_high	0.25	PSI value that triggers HIGH severity
mean_shift_medium	0.20	Mean shift (in std devs) for MEDIUM
mean_shift_high	0.50	Mean shift (in std devs) for HIGH
std_shift_medium	0.20	Std dev shift ratio for MEDIUM
std_shift_high	0.50	Std dev shift ratio for HIGH
category_share_shift	0.15	Category frequency change for MEDIUM
chi_square_medium	0.50	Chi-square value for MEDIUM

---

Drift Health Score

Every report includes a single score — a quick read on how healthy your dataset is overall.

Score	Status	What it means
80 - 100	Stable	Data is stable, model is likely fine
50 - 79	Moderate Drift	Some columns changed — investigate
0 - 49	Significant Drift	Major shifts — consider retraining

---

Real World Example — Banking Data

psiwatch compare bank_2023.csv bank_2026.csv

What psiwatch caught:

• Credit scores dropped from 752 to 624 — riskier customers
• Salaries dropped from 63k to 45k — lower income applicants
• Loan amounts jumped from 500k to 800k — borrowing more despite earning less
• New loan types appeared — BNPL and Crypto (never in training data)
• New statuses appeared — Defaulted and Frozen
• Branches completely changed — 5 old cities gone, 5 new cities added

Health Score: 11/100 — a model trained on 2023 data would be completely blind to all of this in 2026.

---

Project Structure

psiwatch/
├── src/psiwatch/
│   ├── __init__.py      ← public API (compare, compare_data, analyze)
│   ├── loader.py        ← CSV, dict, list, DataFrame input modes
│   ├── analyzer.py      ← PSI, mean/std, chi-square, percentiles, thresholds
│   ├── reporter.py      ← terminal, HTML, JSON, TXT output
│   └── cli.py           ← psiwatch compare command
├── samples/
│   ├── train.csv        ← example baseline dataset
│   └── new.csv          ← example drifted dataset
├── tests/
│   └── test_analyzer.py
├── pyproject.toml
└── README.md

---

Run Tests

python tests/test_analyzer.py

---

Zero Dependencies

psiwatch uses only Python's standard library:

• csv — file reading
• math — statistical calculations
• json — JSON output
• os — file operations
• argparse — CLI interface

pandas is optional. It is only imported when you pass a DataFrame. No pip conflicts. No install failures. If Python runs, psiwatch runs.

---

Changelog

v0.2.0 — Phase 1 Upgrades

• DataFrame support — pass a pd.DataFrame directly to any input parameter
• Column summary stats — min, P25, median, P75, max shown for every numeric column in all output formats
• Configurable thresholds — psi_threshold shortcut and full thresholds dict available on all public functions and the CLI

v0.1.0 — Initial Release

• CSV file comparison via CLI and Python API
• PSI, mean shift, std shift, chi-square, frequency analysis
• Terminal, HTML, JSON, TXT output formats
• Dict and list input modes
• Drift health score

---

Related Tools

If you need heavier drift detection with statistical testing frameworks:

• evidently — full ML monitoring platform
• alibi-detect — advanced drift detection with deep learning support
• scipy.stats — statistical tests

Use psiwatch when you need something lightweight, fast, and dependency-free.

---

License

MIT © 2026 Tharun · Naeris

Built entirely on Android using Termux. No laptop. No PC. No IDE.