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tessl/pypi-autogluon

AutoGluon automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications.

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pypipkg:pypi/autogluon@1.4.x

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AutoGluon

AutoGluon is a comprehensive automated machine learning (AutoML) library that enables developers to build high-accuracy ML models with minimal code across multiple data modalities including tabular, text, image, and time series data. The library provides a unified interface through various predictor classes that automatically handle feature engineering, model selection, hyperparameter optimization, and ensemble creation.

Package Information

  • Package Name: autogluon
  • Language: Python
  • Installation: pip install autogluon

Core Imports

import autogluon

Common imports for specific domains:

from autogluon.tabular import TabularPredictor, InterpretableTabularPredictor, TabularDataset
from autogluon.multimodal import MultiModalPredictor
from autogluon.timeseries import TimeSeriesPredictor, TimeSeriesDataFrame

Core utilities and data structures:

from autogluon.core import constants, metrics
from autogluon.common import TabularDataset, FeatureMetadata
from autogluon.features import *  # Feature generators
from autogluon.eda import AnalysisState

Basic Usage

# Tabular ML (most common use case)
predictor = TabularPredictor(label='target_column')
predictor.fit('train.csv', presets='best_quality')

# Make predictions
predictions = predictor.predict('test.csv')
probabilities = predictor.predict_proba('test.csv')

# Evaluate performance
performance = predictor.evaluate('test.csv')
leaderboard = predictor.leaderboard()

# Multimodal data (text + image + tabular)
mm_predictor = MultiModalPredictor(label='label', problem_type='multiclass')
mm_predictor.fit(train_data)  # pandas DataFrame with text, image paths, and numerical columns

# Time series forecasting
ts_data = TimeSeriesDataFrame.from_data_frame(df, id_column='item_id', timestamp_column='timestamp')
ts_predictor = TimeSeriesPredictor(prediction_length=24, freq='H')
ts_predictor.fit(ts_data)

# Generate forecasts
forecasts = ts_predictor.predict(ts_data)

Architecture

AutoGluon uses a modular architecture centered around specialized predictor classes:

  • TabularPredictor: Handles structured/tabular data with automatic feature engineering, model selection from 10+ algorithms (RandomForest, XGBoost, LightGBM, CatBoost, Neural Networks), and intelligent ensembling
  • MultiModalPredictor: Processes heterogeneous data combining text, images, and tabular features using foundation models (BERT, ResNet, Vision Transformers) with automatic modality-specific preprocessing
  • TimeSeriesPredictor: Performs probabilistic forecasting with both statistical models (ARIMA, ETS) and deep learning models (DeepAR, Transformers), supporting multiple quantile levels
  • Core Infrastructure: Shared utilities for metrics, hyperparameter optimization, model training, and evaluation across all predictor types

This design enables easy switching between domains while maintaining consistent APIs and leveraging state-of-the-art models for each data type.

Capabilities

Tabular Machine Learning

Automated machine learning for structured/tabular data supporting classification and regression tasks. Handles feature engineering, model selection, hyperparameter tuning, and ensembling with minimal configuration.

class TabularPredictor:
    def __init__(self, label: str, problem_type: str = None, path: str = None, **kwargs): ...
    def fit(self, train_data, presets: str = None, time_limit: int = None, **kwargs): ...
    def predict(self, data): ...
    def predict_proba(self, data): ...
    def evaluate(self, data, **kwargs): ...
    def leaderboard(self, data=None, **kwargs): ...

Tabular ML

Multimodal Machine Learning

Automated machine learning for heterogeneous data combining text, images, and tabular features. Supports classification, regression, object detection, named entity recognition, and semantic matching tasks.

class MultiModalPredictor:
    def __init__(self, label: str = None, problem_type: str = None, presets: str = None, **kwargs): ...
    def fit(self, train_data, **kwargs): ...
    def predict(self, data, **kwargs): ...
    def predict_proba(self, data, **kwargs): ...
    def evaluate(self, data, **kwargs): ...
    def extract_embedding(self, data, **kwargs): ...

Multimodal ML

Time Series Forecasting

Probabilistic forecasting for univariate and multivariate time series data. Supports both statistical and deep learning models with automatic model selection and quantile predictions.

class TimeSeriesPredictor:
    def __init__(self, target: str = "target", prediction_length: int = 1, freq: str = None, **kwargs): ...
    def fit(self, train_data, **kwargs): ...
    def predict(self, data, **kwargs): ...
    def evaluate(self, data, **kwargs): ...
    def leaderboard(self, data=None, **kwargs): ...

Time Series

Feature Engineering

Comprehensive feature generation and transformation capabilities for automated feature engineering across different data types.

class AutoMLPipelineFeatureGenerator:
    def __init__(self, **kwargs): ...
    def fit_transform(self, X: DataFrame, y: Series = None, **kwargs): ...
    def transform(self, X: DataFrame, **kwargs): ...

Feature Engineering

Core Utilities

Shared utilities for metrics, constants, and data structures used across all AutoGluon predictors.

class TabularDataset:
    def __init__(self, df: DataFrame): ...
    @classmethod
    def load(cls, file_path: str): ...

class Scorer:
    def __init__(self, name: str, score_func: callable, **kwargs): ...

Core Utilities

Types

# Problem type constants
BINARY = "binary"
MULTICLASS = "multiclass" 
REGRESSION = "regression"
QUANTILE = "quantile"

# Common data structures
TabularDataset = pandas.DataFrame  # Enhanced DataFrame with AutoGluon utilities
TimeSeriesDataFrame = pandas.DataFrame  # Time series specific DataFrame structure

# Predictor types
TabularPredictor = Type[TabularPredictor]
MultiModalPredictor = Type[MultiModalPredictor]
TimeSeriesPredictor = Type[TimeSeriesPredictor]