etsi-failprint 0.1.2

Automated failure pattern detection and root-cause analysis for ML workflows

failprint

failprint is an MLOps-first diagnostic tool that performs automatic root cause analysis on your ML model's failure patterns.

It segments, clusters, and correlates failed predictions with input data features — surfacing which features are contributing to failure, which data segments fail the most, and how drift or imbalance may be related to model degradation.

Installation

pip install etsi-failprint

Quick Start

import pandas as pd
from etsi.failprint import analyze

# Sample inputs
X = pd.DataFrame({
    "feature1": [1, 2, 2, 3, 3, 3, 4],
    "feature2": [10, 15, 14, 13, 12, 13, 20],
    "category": ["A", "B", "B", "B", "C", "C", "A"]
})
y_true = pd.Series([1, 1, 1, 0, 0, 1, 0])
y_pred = pd.Series([1, 1, 0, 0, 0, 1, 1])

# Analyze misclassifications
report = analyze(X, y_true, y_pred, output="markdown", cluster=True)
print(report)

What It Does

• Segments failures by input feature values (numerical/categorical)
• Highlights overrepresented values in failure cases
• Clusters similar failure samples for pattern recognition
• Writes log files and markdown reports for audit or CI/CD
• Compatible with MLOps tools (like MLflow, DVC, Airflow, Watchdog)