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Why Failprint?

Standard metrics (Accuracy, F1) tell you how often you fail. Failprint tells you why.

• Multi-Modal Native: Seamlessly analyze failures in structured DataFrames, raw Text, or Image datasets using a unified API.
• Automated Segmentation: Automatically discovers weak spots (e.g., "Model fails 80% of the time when Income < 50k").
• Lazy & Lightweight: Heavy dependencies (Torch, SpaCy, Transformers) are lazy-loaded. If you only analyze tabular data, you never pay the memory cost of deep learning libraries.
• Robust: Built with graceful degradation. If an optional dependency is missing or incompatible, Failprint adapts instead of crashing.

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Key Features

• Smart Segmentation: Identifies feature ranges or categories where error rates are statistically anomalous.
• Semantic Clustering:
  • NLP: Groups failed texts by semantic meaning using Sentence Transformers.
  • CV: Clusters failed images using ResNet embeddings to find visual patterns.
• Meta-Feature Extraction:
  • Text: Analyzes failures by length, sentiment, subjectivity, and NER entities.
  • Vision: Analyzes failures by brightness, contrast, aspect ratio, and dimensions.
• Counterfactuals: Suggests minimal changes to input data that would flip a failure to a success.
• Actionable Reporting: Outputs detailed Markdown reports with visual insights directly to your workspace.

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Installation

Prerequisites

• Python (3.8 or later)

From PyPI

pip install etsi-failprint

From Source

git clone https://github.com/etsi-failprint/etsi-failprint.git
cd etsi-failprint
pip install -e .

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Quickstart

1. Tabular Data Analysis

Identify which features are driving your model's mistakes.

import pandas as pd
from etsi.failprint import analyze

# Load your data
df = pd.read_csv("loan_predictions.csv")
X = df.drop("target", axis=1)
y_true = df["target"]
y_pred = pd.Series([0, 1, 0, ...]) # Your model's predictions

# Run analysis
report = analyze(
    X, y_true, y_pred,
    cluster=True,       # Cluster similar failures?
    output="markdown"   # Generate 'failprint_report.md'
)

print(report)

Output Insight: "Segment Age < 25 contributes to 40% of all failures."

Multi-Modal Analysis

Failprint isn't just for spreadsheets. It understands unstructured data too.

2. NLP Analysis (Text)

Lazy-loads spacy and sentence-transformers to find semantic and structural failure patterns.

from etsi.failprint import analyze_nlp

texts = [
    "I love this product!", 
    "Terrible service, very slow.", 
    "Product is okay but arrived late."
]
y_true = [1, 0, 0] # Sentiment labels
y_pred = [1, 1, 0] # Model predictions (Error on index 1)

report = analyze_nlp(texts, y_true, y_pred)

Output Insight: "Failures are highly correlated with Sentiment Polarity < -0.5 and Word Count < 5."

3. CV Analysis (Images)

Lazy-loads torch and torchvision to find visual failure clusters (e.g., "Dark images" or "Blurry dogs").

from etsi.failprint import analyze_cv

images = ["img1.jpg", "img2.jpg", "img3.jpg"]
y_true = [0, 1, 0]
y_pred = [0, 0, 0]

analyze_cv(images, y_true, y_pred)

Output Insight: "Cluster 0 (Dark Images) accounts for 60% of false negatives."

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Counterfactuals

Go beyond diagnostics. Ask "What should have happened?" This mode suggests the minimal change required to fix a prediction.

from etsi.failprint import analyze

# Run in counterfactual mode
analyze(
    X, y_true, y_pred,
    output="counterfactuals"
)

Example Output:

Original Input: {'Age': 22, 'Income': 35000, 'Education': 'High School'}
Suggested Change: Education to 'Bachelor's'
Prediction: Success (Counterfactual)

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Contributing

Pull requests are welcome!

Please refer to CONTRIBUTING.md and CODE_OF_CONDUCT.md before submitting a Pull Request.

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Join the Community

Connect with the etsi.ai team and other contributors on our Discord.

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License

This project is distributed under the BSD-2-Clause License. See the LICENSE for details.

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Built with ❤️ by etsi.ai