diagnost 0.1.1

A model diagnostics library for data scientists — performance, calibration, drift, and reporting in a few lines of Python.

diagnost

A model diagnostics library for data scientists.
Performance, calibration, drift detection, and dataset health checks, all in a few lines of Python.

Why diagnost?

Most ML libraries help you build models. diagnost helps you trust them.

After training, the real questions start:

• Is my model actually reliable, or just accurate on average?
• Does it perform equally across different groups?
• Are its confidence scores meaningful?
• Has my data drifted since I trained it?

diagnost answers all of these, cleanly, quickly, and in plain English.

Installation

pip install diagnost

Quickstart

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
import diagnost

X, y = load_iris(return_X_y=True, as_frame=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

model = RandomForestClassifier().fit(X_train, y_train)

report = diagnost.evaluate(model, X_test, y_test, task="classification")
report.summary()

Features

1. Model Evaluation

Evaluate classification, regression, and clustering models with one call.

# Classification
report = diagnost.evaluate(model, X_test, y_test, task="classification")

# Regression
report = diagnost.evaluate(model, X_test, y_test, task="regression")

# Clustering
report = diagnost.evaluate(model, X_test, task="clustering")

Subgroup / fairness analysis — check performance across sensitive groups:

report = diagnost.evaluate(
    model, X_test, y_test,
    task="classification",
    sensitive_features=["gender", "age_group"]
)
report.summary()

2. Model Comparison

Compare multiple models side by side with a winner declared automatically.

from diagnost.compare import compare

report = compare(
    models={"Random Forest": rf, "Logistic Regression": lr},
    X=X_test,
    y=y_test,
    task="classification"
)

df = report.to_dataframe()  # returns a pandas DataFrame

3. Calibration Analysis

Check whether your model's predicted probabilities are actually reliable.

from diagnost.calibration import check_calibration

check_calibration(model, X_test, y_test)

Output includes:

• Expected Calibration Error (ECE) per class
• Plain-English verdict ("Well calibrated", "Poorly calibrated")
• Reliability diagram

4. Drift Detection

Detect whether your input data has shifted since training.

from diagnost.drift import check_drift

check_drift(X_train, X_new)

• Kolmogorov-Smirnov test for numeric features
• Chi-Square test for categorical features
• Per-feature drift verdict with p-values
• Distribution plots for drifted features

5. Dataset Diagnostics

Inspect your dataset before modelling.

results = diagnost.inspect_dataset(df)

Checks for:

• Missing values
• Highly correlated features (r > 0.85)
• Outliers (IQR method)
• Feature distributions (visual)

Saving Reports

report = diagnost.evaluate(model, X_test, y_test, task="classification")
report.save("report.json")  # exports as JSON

Supported Model Types

Task	Supported Frameworks
Classification	scikit-learn, XGBoost, LightGBM, CatBoost
Regression	scikit-learn, XGBoost, LightGBM, CatBoost
Clustering	scikit-learn

Any model with a .predict() method will work.

Requirements

• Python >= 3.9
• numpy, pandas, scipy, matplotlib, scikit-learn

Contributing

Contributions are welcome. To get started:

git clone https://github.com/Eklavya20/diagnost.git
cd diagnost
python -m venv venv
venv\Scripts\activate      # Windows
pip install -e ".[dev]"
pytest tests/ -v

Please open an issue before submitting a large pull request.

License

MIT License — free to use, modify, and distribute.
See LICENSE for details.

Author

Eklavya Jumnani
MSc Data Science, FAU Erlangen-Nürnberg
GitHub · LinkedIn