anyml 0.2.2

Simple AutoML wrapper for tabular data. One-liner API for classification and regression.

anyml

AutoML in one line — classify and regress instantly

Simple AutoML for tabular data. One-liner API for classification and regression with automatic preprocessing, model selection, and evaluation.

Runs completely offline. All processing uses local scikit-learn and XGBoost models. No cloud APIs or internet connection required.

Built by Viet-Anh Nguyen | nrl.ai

Installation

pip install anyml

With XGBoost support:

pip install "anyml[xgboost]"

With all optional dependencies:

pip install "anyml[full]"

Quick Start

Classification

import pandas as pd
import anyml

df = pd.read_csv("data.csv")

# Auto-preprocessing pipeline:
#   1. Detects column types (numeric, categorical, datetime)
#   2. Fills missing values (median for numeric, mode for categorical)
#   3. One-hot encodes categoricals, scales numerics
# Trains multiple models (LogisticRegression, RandomForest, XGBoost if installed)
# Selects best by stratified 5-fold cross-validation
# Returns AutoResult with .model, .score, .predict(), .explain()
result = anyml.classify(df, target="label")

print(result.score)         # Best cross-validation accuracy
print(result.model_name)    # e.g. "random_forest"
print(result.report())      # Full classification report

Regression

result = anyml.regress(df, target="price")

print(result.score)         # Best cross-validation RMSE (negative)
print(result.report())      # RMSE, MAE, R2

Predict on New Data

predictions = result.predict(new_df)

Feature Importance

result.explain()
# Returns a DataFrame with feature names and importance scores

Compare Models

scores = anyml.compare(df, target="label")
# {'logistic_regression': 0.92, 'random_forest': 0.95, ...}

Choose Specific Models

result = anyml.classify(df, target="label", models=["xgboost", "random_forest"])

Standalone Preprocessing

processed_df = anyml.preprocess(df)
# or with target separation:
X, y = anyml.preprocess(df, target="label")

Save and Load

result.save("model.joblib")
loaded = anyml.load_model("model.joblib")
loaded.predict(new_df)

How It Works

Automatic Preprocessing

anyml automatically detects column types and applies appropriate transformations:

Data Type	Handling
Numeric	Median imputation + StandardScaler
Categorical (low cardinality)	Mode imputation + OneHotEncoding
Categorical (high cardinality)	Mode imputation + OneHotEncoding
Datetime	Extract year, month, day, day-of-week
Missing values	Automatic imputation (median for numeric, mode for categorical)

Model Selection

anyml tries multiple models and selects the best one via cross-validation:

Classification:

• Logistic Regression
• Random Forest
• XGBoost (optional)
• LightGBM (optional)

Regression:

• Linear Regression
• Random Forest
• XGBoost (optional)
• LightGBM (optional)

Evaluation Metrics

Classification: Accuracy, F1 (weighted), F1 (macro), full classification report

Regression: RMSE, MAE, R2

Comparison with Other AutoML Tools

Feature	anyml	auto-sklearn	TPOT	H2O
One-liner API	Yes	No	No	No
No config needed	Yes	Partial	Partial	No
Lightweight	Yes	No	No	No
Preprocessing included	Yes	Yes	Yes	Yes
Explainability	Yes	No	No	Partial
Pure Python	Yes	Yes	Yes	No (Java)

anyml is designed for simplicity. If you need extensive hyperparameter tuning or neural architecture search, consider auto-sklearn or TPOT. If you want to go from data to predictions in one line, anyml is for you.

API Reference

anyml.classify(df, target, models=None, cv=5, scoring=None)

Auto-classify a tabular dataset. Returns an AutoResult.

anyml.regress(df, target, models=None, cv=5, scoring=None)

Auto-regress a tabular dataset. Returns an AutoResult.

anyml.compare(df, target, task=None, models=None, cv=5, scoring=None)

Compare multiple models. Returns a dict of {model_name: cv_score}.

anyml.preprocess(df, target=None)

Standalone preprocessing. Returns processed DataFrame (or (X, y) tuple if target given).

AutoResult

Attribute / Method	Description
.model	Fitted sklearn estimator
.score	Best mean CV score
.model_name	Name of the winning model
.metrics	Evaluation metrics dict
.feature_importances	Feature importance dict
.all_scores	CV scores for all models tried
.predict(df)	Predict on new data
.explain()	Feature importance DataFrame
.report()	Human-readable report string
.save(path)	Save to disk with joblib

Local-First / Edge AI

This package is designed to work completely offline. All model training and inference uses local libraries (scikit-learn, XGBoost, LightGBM). No internet connection or cloud APIs are required.

Development

git clone https://github.com/vietanhdev/anyml.git
cd anyml
pip install -e ".[dev]"
pytest tests/ -v

License

MIT License. See LICENSE for details.