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AutoGluon 0.8.3 documentation
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Get Started

  • Install
  • Tabular Quick Start
  • Multimodal Quick Start
  • Time Series Quick Start

Tutorials

  • Tabular
    • Essentials
    • In Depth
    • Feature Engineering
    • Tabular + Text + Images
    • Advanced
      • Multilabel
      • Kaggle
      • GPU
      • Custom Metrics
      • Custom Models
      • Custom Models Advanced
      • Deployment
  • Multimodal
    • Multimodal Prediction
      • AutoMM for Image + Text + Tabular - Quick Start
      • AutoMM for Entity Extraction with Text and Image - Quick Start
      • AutoMM for Text + Tabular - Quick Start
    • Object Detection
      • Object Detection Quick Start
        • AutoMM Detection - Quick Start on a Tiny COCO Format Dataset
        • AutoMM Detection - Quick Start with Foundation Model on Open Vocabulary Detection (OVD)
      • Object Detection Advanced
        • AutoMM Detection - Finetune on COCO Format Dataset with Customized Settings
      • Object Detection Data Preparation
        • Convert Data to COCO Format
        • AutoMM Detection - Prepare Pothole Dataset
        • AutoMM Detection - Prepare Watercolor Dataset
        • AutoMM Detection - Prepare COCO2017 Dataset
        • AutoMM Detection - Prepare Pascal VOC Dataset
        • AutoMM Detection - Convert VOC Format Dataset to COCO Format
    • Image Prediction
      • AutoMM for Image Classification - Quick Start
      • Zero-Shot Image Classification with CLIP
    • Text Prediction
      • AutoMM for Text - Quick Start
      • AutoMM for Named Entity Recognition - Quick Start
      • AutoMM for Named Entity Recognition in Chinese - Quick Start
      • AutoMM for Text - Multilingual Problems
    • Document Data
      • AutoMM for Scanned Document Classification
      • Classifying PDF Documents with AutoMM
    • Matching
      • Image-to-Image Semantic Matching with AutoMM
      • Image-Text Semantic Matching with AutoMM
      • Text-to-Text Semantic Matching with AutoMM
      • Text Semantic Search with AutoMM
      • Image-Text Semantic Matching with AutoMM - Zero-Shot
    • Advanced Topics
      • Hyperparameter Optimization in AutoMM
      • Continuous Training with AutoMM
      • Customize AutoMM
      • Knowledge Distillation in AutoMM
      • Single GPU Billion-scale Model Training via Parameter-Efficient Finetuning
      • Few Shot Learning with FewShotSVMPredictor
      • How to use FocalLoss
      • AutoMM Presets
      • Faster Prediction with TensorRT
  • Time Series
    • Quick Start
    • In Depth
    • Model Zoo
  • Cloud Training and Deployment
    • AutoGluon Cloud
    • AutoGluon Tabular on SageMaker AutoPilot
    • Deploy AutoGluon Models on Serverless Templates
    • Cloud Training and Deployment with Amazon SageMaker

Resources

  • Cheat Sheets
  • Versions
  • What's New
    • Version 0.8.2
    • Version 0.8.1
    • Version 0.8.0
    • Version 0.7.0
    • Version 0.6.2
    • Version 0.6.1
    • Version 0.6.0
    • Version 0.5.2
    • Version 0.5.1
    • Version 0.4.3
    • Version 0.4.2
    • Version 0.4.1
    • Version 0.4.0
  • GitHub
  • Tabular FAQ
  • Multimodal FAQ
  • Time Series FAQ

API

  • TabularPredictor
    • TabularPredictor.calibrate_decision_threshold
    • TabularPredictor.clone
    • TabularPredictor.clone_for_deployment
    • TabularPredictor.compile_models
    • TabularPredictor.delete_models
    • TabularPredictor.distill
    • TabularPredictor.evaluate
    • TabularPredictor.evaluate_predictions
    • TabularPredictor.feature_importance
    • TabularPredictor.features
    • TabularPredictor.fit
    • TabularPredictor.fit_extra
    • TabularPredictor.fit_pseudolabel
    • TabularPredictor.fit_summary
    • TabularPredictor.fit_weighted_ensemble
    • TabularPredictor.get_model_best
    • TabularPredictor.get_model_failures
    • TabularPredictor.get_model_full_dict
    • TabularPredictor.get_model_names
    • TabularPredictor.get_model_names_persisted
    • TabularPredictor.get_oof_pred
    • TabularPredictor.get_oof_pred_proba
    • TabularPredictor.get_pred_from_proba
    • TabularPredictor.get_size_disk
    • TabularPredictor.get_size_disk_per_file
    • TabularPredictor.info
    • TabularPredictor.leaderboard
    • TabularPredictor.load
    • TabularPredictor.load_data_internal
    • TabularPredictor.load_log
    • TabularPredictor.persist_models
    • TabularPredictor.plot_ensemble_model
    • TabularPredictor.predict
    • TabularPredictor.predict_multi
    • TabularPredictor.predict_proba
    • TabularPredictor.predict_proba_multi
    • TabularPredictor.refit_full
    • TabularPredictor.save
    • TabularPredictor.save_space
    • TabularPredictor.set_decision_threshold
    • TabularPredictor.set_model_best
    • TabularPredictor.transform_features
    • TabularPredictor.transform_labels
    • TabularPredictor.unpersist_models
  • TabularDataset
  • Tabular Models
  • MultiModalPredictor
    • MultiModalPredictor.evaluate
    • MultiModalPredictor.extract_embedding
    • MultiModalPredictor.fit
    • MultiModalPredictor.fit_summary
    • MultiModalPredictor.get_num_gpus
    • MultiModalPredictor.get_predictor_classes
    • MultiModalPredictor.list_supported_models
    • MultiModalPredictor.load
    • MultiModalPredictor.predict
    • MultiModalPredictor.predict_proba
    • MultiModalPredictor.save
    • MultiModalPredictor.set_num_gpus
    • MultiModalPredictor.set_verbosity
    • MultiModalPredictor.update_strategy_by_env
  • TimeSeriesPredictor
    • TimeSeriesPredictor.evaluate
    • TimeSeriesPredictor.fit
    • TimeSeriesPredictor.fit_summary
    • TimeSeriesPredictor.get_model_best
    • TimeSeriesPredictor.get_model_names
    • TimeSeriesPredictor.info
    • TimeSeriesPredictor.leaderboard
    • TimeSeriesPredictor.load
    • TimeSeriesPredictor.predict
    • TimeSeriesPredictor.refit_full
    • TimeSeriesPredictor.save
    • TimeSeriesPredictor.score
  • TimeSeriesDataFrame
    • TimeSeriesDataFrame.convert_frequency
    • TimeSeriesDataFrame.copy
    • TimeSeriesDataFrame.dropna
    • TimeSeriesDataFrame.fill_missing_values
    • TimeSeriesDataFrame.from_data_frame
    • TimeSeriesDataFrame.from_iterable_dataset
    • TimeSeriesDataFrame.from_path
    • TimeSeriesDataFrame.from_pickle
    • TimeSeriesDataFrame.get_model_inputs_for_scoring
    • TimeSeriesDataFrame.get_reindexed_view
    • TimeSeriesDataFrame.num_timesteps_per_item
    • TimeSeriesDataFrame.slice_by_time
    • TimeSeriesDataFrame.slice_by_timestep
    • TimeSeriesDataFrame.split_by_time
    • TimeSeriesDataFrame.to_regular_index
    • TimeSeriesDataFrame.train_test_split
  • Feature Generators
  • FeatureMetadata
    • FeatureMetadata.add_special_types
    • FeatureMetadata.from_df
    • FeatureMetadata.get_feature_type_raw
    • FeatureMetadata.get_feature_types_special
    • FeatureMetadata.get_features
    • FeatureMetadata.get_type_group_map_raw
    • FeatureMetadata.get_type_group_map_special_from_type_map_special
    • FeatureMetadata.get_type_map_special
    • FeatureMetadata.join_metadata
    • FeatureMetadata.join_metadatas
    • FeatureMetadata.keep_features
    • FeatureMetadata.print_feature_metadata_full
    • FeatureMetadata.remove_features
    • FeatureMetadata.rename_features
    • FeatureMetadata.to_dict
  • Search Spaces
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TimeSeriesPredictor.score#

TimeSeriesPredictor.score(data: Union[TimeSeriesDataFrame, DataFrame, str], **kwargs)[source]#

See, evaluate().

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autogluon.timeseries.TimeSeriesDataFrame
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TimeSeriesPredictor.save
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  • TimeSeriesPredictor.score
    • TimeSeriesPredictor.score()