Welcome to AML’s documentation!

AML is a Python library that contains some of my machine learning code! Check out the Usage section for further information, including how to Install the project.

Note

This project is under active development.

Documentation Links:

Main Documentation

• Documentation
  • Array
    • flatten()
    • make_input_roll()
    • stratification()
    • time_interval_range()
  • Data
    • ECGCorruptor
      • ECGCorruptor.__init__()
      • ECGCorruptor.corrupt_sources
    • GroupAllBatchSampler
      • GroupAllBatchSampler.__init__()
    • GroupBatchSampler
      • GroupBatchSampler.__init__()
    • HelperDataset
      • HelperDataset.__init__()
    • MemoryDataset
      • MemoryDataset.__init__()
    • MyData
      • MyData.__init__()
    • NumpyLoader
      • NumpyLoader.__init__()
      • NumpyLoader.batch_size
      • NumpyLoader.dataset
      • NumpyLoader.drop_last
      • NumpyLoader.num_workers
      • NumpyLoader.pin_memory
      • NumpyLoader.pin_memory_device
      • NumpyLoader.prefetch_factor
      • NumpyLoader.sampler
      • NumpyLoader.timeout
    • PTB_XL
      • PTB_XL.__init__()
      • PTB_XL.aggregate_diagnostic()
      • PTB_XL.download()
      • PTB_XL.single_diagnostic()
    • WrapperDataset
      • WrapperDataset.__init__()
    • sequential_samplers_sampler()
    • split_sampler()
  • Dataframe
    • interpolate_nans()
  • Metrics
    • auc_precision_recall_curve()
    • countna()
    • format_mean_ci()
    • format_mean_iqr_missing()
    • format_mean_std()
    • interquartile_range()
    • make_confusion_matrix()
    • mean_confusion_matrix()
    • npv_score()
    • ppv_score()
    • sensitivity_score()
    • sensitivity_specificity_ppv_npv()
    • specificity_score()
    • std_confusion_matrix()
  • Models
    • AEModel
      • AEModel.__init__()
      • AEModel.fit()
      • AEModel.forward()
      • AEModel.predict()
      • AEModel.predict_step()
      • AEModel.training_step()
      • AEModel.transform()
      • AEModel.validation_step()
    • BaseLightningModule
      • BaseLightningModule.__init__()
      • BaseLightningModule.configure_optimizers()
      • BaseLightningModule.fit()
      • BaseLightningModule.predict()
    • GaussianMixPrior
      • GaussianMixPrior.__init__()
      • GaussianMixPrior.forward()
      • GaussianMixPrior.log_prob()
      • GaussianMixPrior.sample()
      • GaussianMixPrior.training
    • KDAttributeTree
      • KDAttributeTree.__init__()
      • KDAttributeTree.query()
      • KDAttributeTree.query_ball_point()
    • LambdaModule
      • LambdaModule.__init__()
      • LambdaModule.forward()
      • LambdaModule.training
    • MLPModel
      • MLPModel.__init__()
      • MLPModel.fit()
      • MLPModel.forward()
      • MLPModel.predict()
      • MLPModel.predict_proba()
      • MLPModel.predict_step()
      • MLPModel.training_step()
      • MLPModel.validation_step()
    • ResNet1DModel
      • ResNet1DModel.__init__()
      • ResNet1DModel.fit()
      • ResNet1DModel.forward()
      • ResNet1DModel.predict()
      • ResNet1DModel.predict_proba()
      • ResNet1DModel.predict_step()
      • ResNet1DModel.training_step()
      • ResNet1DModel.validation_step()
    • StandardNormalPrior
      • StandardNormalPrior.__init__()
      • StandardNormalPrior.forward()
      • StandardNormalPrior.log_prob()
      • StandardNormalPrior.sample()
      • StandardNormalPrior.training
    • TransformerEncoderModel
      • TransformerEncoderModel.__init__()
      • TransformerEncoderModel.fit()
      • TransformerEncoderModel.forward()
      • TransformerEncoderModel.predict()
      • TransformerEncoderModel.predict_proba()
      • TransformerEncoderModel.predict_step()
      • TransformerEncoderModel.training_step()
      • TransformerEncoderModel.validation_step()
    • VAE
      • VAE.__init__()
      • VAE.forward()
      • VAE.reconstruct()
      • VAE.sample()
      • VAE.training
    • VAEDecoder
      • VAEDecoder.__init__()
      • VAEDecoder.decode()
      • VAEDecoder.forward()
      • VAEDecoder.log_prob()
      • VAEDecoder.sample()
      • VAEDecoder.training
    • VAEEncoder
      • VAEEncoder.__init__()
      • VAEEncoder.encode()
      • VAEEncoder.forward()
      • VAEEncoder.log_prob()
      • VAEEncoder.sample()
      • VAEEncoder.training
  • Model Selection
    • TensorboardLoad
      • TensorboardLoad.__init__()
      • TensorboardLoad.audio()
      • TensorboardLoad.distributions()
      • TensorboardLoad.histograms()
      • TensorboardLoad.images()
      • TensorboardLoad.scalars()
      • TensorboardLoad.tags
      • TensorboardLoad.tensors()
      • TensorboardLoad.type_func_dict
  • Parallel
    • ParallelModelling
      • ParallelModelling.__init__()
    • ProgressParallel
      • ProgressParallel.__init__()
      • ProgressParallel.print_progress()
    • p_apply()
    • p_map()
  • Progress
    • PLTQDMProgressBar
      • PLTQDMProgressBar.__init__()
      • PLTQDMProgressBar.init_predict_tqdm()
      • PLTQDMProgressBar.init_sanity_tqdm()
      • PLTQDMProgressBar.init_test_tqdm()
      • PLTQDMProgressBar.init_train_tqdm()
      • PLTQDMProgressBar.init_validation_tqdm()
      • PLTQDMProgressBar.on_train_epoch_start()
  • Random
    • RandomState
      • RandomState.__init__()
      • RandomState.next()
  • Utils
    • ArgFake
      • ArgFake.__init__()
    • NPEncoder
      • NPEncoder.default()
    • dirtree()
    • module_from_file()