ADMET-XSpec
1.0.0

Contents:

  • Overview
    • Three experiments you can run right now
    • Killer features
  • Quick start
    • Installation with conda
      • Run example with conda
        • Optional, recommended: integrate conda with PyCharm
    • Installation with uv
      • Run example with uv
        • Optional, recommended: integrate uv with PyCharm
    • Running classical ML experiments
    • Running deep ML experiments
  • Guides
    • Choosing your “processing plan”
      • Introductory notes
      • Goals
    • Data exploration and visualization
      • Introductory notes
      • Goals
    • Sourcing and setting up data
      • Introductory note
      • Setting up
        • Goals
    • Training and hyperparameter optimization
      • Introductory notes
      • Sections
      • How the raw data is transformed
        • Goals
      • ScikitPredictorBase as the model-training interface & optimizing training
        • Goals
      • Where to find outputted models
        • Goals
      • Where to find outputted models
        • Goals
  • API
    • src.data.data_interface
      • DataInterface
        • DataInterface.check_train_test_split_exists()
        • DataInterface.dump_gin_config()
        • DataInterface.dump_gin_config_to_model_dir()
        • DataInterface.dump_logs()
        • DataInterface.dump_training_logs()
        • DataInterface.get_by_friendly_name()
        • DataInterface.get_clean_smiles_df()
        • DataInterface.get_label_col_in_raw()
        • DataInterface.get_normalized_df()
        • DataInterface.get_smiles_col_in_raw()
        • DataInterface.get_split_friendly_names()
        • DataInterface.get_train_test_friendly_names()
        • DataInterface.load_hyperparams()
        • DataInterface.pickle_model()
        • DataInterface.save_hyperparams()
        • DataInterface.save_metrics()
        • DataInterface.save_model_metadata()
        • DataInterface.save_train_test_split()
        • DataInterface.save_visualization()
        • DataInterface.set_logfile()
        • DataInterface.set_override_cache()
        • DataInterface.set_task_setting()
        • DataInterface.unpickle_model()
        • DataInterface.update_datasets_registry()
        • DataInterface.update_registries()
        • DataInterface.update_splits_registry()
    • src.data.featurizer
      • EcfpFeaturizer
        • EcfpFeaturizer.feature_name
        • EcfpFeaturizer.featurize()
        • EcfpFeaturizer.get_hashable_params_values()
        • EcfpFeaturizer.name
      • FeaturizerBase
        • FeaturizerBase.feature_name
        • FeaturizerBase.featurize()
        • FeaturizerBase.get_cache_key()
        • FeaturizerBase.get_hashable_params_values()
        • FeaturizerBase.name
      • KlekotaRothFeaturizer
        • KlekotaRothFeaturizer.feature_name
        • KlekotaRothFeaturizer.featurize()
        • KlekotaRothFeaturizer.get_hashable_params_values()
        • KlekotaRothFeaturizer.name
      • MaccsFeaturizer
        • MaccsFeaturizer.feature_name
        • MaccsFeaturizer.featurize()
        • MaccsFeaturizer.get_hashable_params_values()
        • MaccsFeaturizer.name
      • Map4Featurizer
        • Map4Featurizer.feature_name
        • Map4Featurizer.featurize()
        • Map4Featurizer.get_hashable_params_values()
        • Map4Featurizer.name
      • PropertyEcfpFeaturizer
        • PropertyEcfpFeaturizer.feature_name
        • PropertyEcfpFeaturizer.featurize()
        • PropertyEcfpFeaturizer.get_hashable_params_values()
        • PropertyEcfpFeaturizer.name
      • PropertyFeaturizer
        • PropertyFeaturizer.feature_name
        • PropertyFeaturizer.featurize()
        • PropertyFeaturizer.get_hashable_params_values()
        • PropertyFeaturizer.name
    • src.processing_pipeline
      • ProcessingPipeline
        • ProcessingPipeline.run()
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