armed package

Subpackages

• armed.callbacks package
  • Submodules
  • armed.callbacks.aec_callbacks module
    • compute_image_metrics()
    • make_compute_latents_callback()
    • make_image_metrics_callback()
    • make_recon_figure_callback()
  • armed.callbacks.segmentation module
    • SaveImagesCallback
      • SaveImagesCallback.on_epoch_end()
    • SaveMultiModalImagesCallback
      • SaveMultiModalImagesCallback.on_epoch_end()
  • Module contents
• armed.crossvalidation package
  • Submodules
  • armed.crossvalidation.grouped_cv module
    • StratifiedGroupKFold
    • StratifiedGroupShuffleSplit
      • StratifiedGroupShuffleSplit.split()
  • armed.crossvalidation.splitting module
    • BasicKFoldUtil
      • BasicKFoldUtil.create_folds()
      • BasicKFoldUtil.get_fold()
    • NestedKFoldUtil
      • NestedKFoldUtil.create_folds()
      • NestedKFoldUtil.get_fold()
  • Module contents
• armed.models package
  • Submodules
  • armed.models.autoencoder_classifier module
    • AdversarialClassifier
      • AdversarialClassifier.call()
      • AdversarialClassifier.get_config()
    • AuxClassifier
      • AuxClassifier.call()
      • AuxClassifier.get_config()
    • BaseAutoencoderClassifier
      • BaseAutoencoderClassifier.call()
    • Decoder
      • Decoder.call()
      • Decoder.get_config()
    • DomainAdversarialAEC
      • DomainAdversarialAEC.call()
      • DomainAdversarialAEC.compile()
      • DomainAdversarialAEC.metrics
      • DomainAdversarialAEC.test_step()
      • DomainAdversarialAEC.train_step()
    • DomainEnhancingAutoencoderClassifier
      • DomainEnhancingAutoencoderClassifier.call()
      • DomainEnhancingAutoencoderClassifier.compile()
      • DomainEnhancingAutoencoderClassifier.metrics
      • DomainEnhancingAutoencoderClassifier.test_step()
      • DomainEnhancingAutoencoderClassifier.train_step()
    • Encoder
      • Encoder.call()
      • Encoder.get_config()
    • ImageClassifier
      • ImageClassifier.call()
      • ImageClassifier.get_config()
    • LatentClassifier
      • LatentClassifier.call()
      • LatentClassifier.get_config()
    • MixedEffectAuxClassifier
      • MixedEffectAuxClassifier.call()
      • MixedEffectAuxClassifier.get_config()
    • MixedEffectsAEC
      • MixedEffectsAEC.call()
      • MixedEffectsAEC.compile()
      • MixedEffectsAEC.metrics
      • MixedEffectsAEC.test_step()
      • MixedEffectsAEC.train_step()
    • RandomEffectDecoder
      • RandomEffectDecoder.call()
    • RandomEffectEncoder
      • RandomEffectEncoder.call()
    • TiedConv2DTranspose
      • TiedConv2DTranspose.build()
    • TiedDecoder
    • load_weights_base_aec()
  • armed.models.cnn_classifier module
    • AdversarialClassifier
      • AdversarialClassifier.call()
    • ClusterInputImageClassifier
      • ClusterInputImageClassifier.call()
    • DomainAdversarialImageClassifier
      • DomainAdversarialImageClassifier.call()
      • DomainAdversarialImageClassifier.compile()
      • DomainAdversarialImageClassifier.metrics
      • DomainAdversarialImageClassifier.test_step()
      • DomainAdversarialImageClassifier.train_step()
    • ImageClassifier
      • ImageClassifier.call()
    • MixedEffectsClassifier
      • MixedEffectsClassifier.call()
      • MixedEffectsClassifier.compile()
      • MixedEffectsClassifier.metrics
      • MixedEffectsClassifier.test_step()
      • MixedEffectsClassifier.train_step()
    • RandomEffectsClassifier
      • RandomEffectsClassifier.call()
  • armed.models.lme module
    • LogisticGLM
      • LogisticGLM.fit()
      • LogisticGLM.predict()
    • MixedLogisticGLM
      • MixedLogisticGLM.fit()
      • MixedLogisticGLM.predict()
  • armed.models.metalearning module
    • mldg()
  • armed.models.mlp_classifiers module
    • Adversary
      • Adversary.call()
      • Adversary.get_config()
    • BaseMLP
      • BaseMLP.call()
    • ClusterCovariateMLP
      • ClusterCovariateMLP.call()
    • DomainAdversarialMLP
      • DomainAdversarialMLP.call()
      • DomainAdversarialMLP.compile()
      • DomainAdversarialMLP.metrics
      • DomainAdversarialMLP.test_step()
      • DomainAdversarialMLP.train_step()
    • MLPActivations
      • MLPActivations.call()
      • MLPActivations.get_config()
    • MixedEffectsMLP
      • MixedEffectsMLP.call()
      • MixedEffectsMLP.compile()
      • MixedEffectsMLP.metrics
      • MixedEffectsMLP.test_step()
      • MixedEffectsMLP.train_step()
    • MixedEffectsMLPNonlinearSlope
      • MixedEffectsMLPNonlinearSlope.call()
    • RandomEffectsLinearSlopeIntercept
      • RandomEffectsLinearSlopeIntercept.call()
      • RandomEffectsLinearSlopeIntercept.get_config()
  • armed.models.random_effects module
    • ClusterScaleBiasBlock
      • ClusterScaleBiasBlock.call()
      • ClusterScaleBiasBlock.get_config()
    • GammaRandomEffects
      • GammaRandomEffects.call()
      • GammaRandomEffects.get_config()
    • NamedVariableLayer
      • NamedVariableLayer.get_config()
    • RandomEffects
      • RandomEffects.call()
    • make_deterministic_posterior_fn()
    • make_fixed_prior_fn()
    • make_gamma_prior_fn()
    • make_posterior_fn()
    • make_trainable_prior_fn()
  • Module contents

Submodules

armed.metrics module

Custom model metrics.

armed.metrics.balanced_accuracy(y_true, y_pred)

Balanced accuracy metric for multi-class labels. Computes the mean of the accuracy of each class.

Parameters:

• y_true (tf.Tensor) – true labels

• y_pred (tf.Tensor) – probabilistic predictions. Will be thresholded at 0.5.

Returns:

balanced accuracy

Return type:

tf.Tensor

armed.metrics.classification_metrics(y_true: numpy.ndarray, y_pred: numpy.ndarray, youden_point: float | None = None, fixed_sens: float = 0.8, fixed_spec: float = 0.8)

Compute several classification metrics. * AUROC * At Youden point: balanced accuracy, Youden’s index, F1, PPV, NPV,

sensitivity, specificity

• Sensitivity at fixed specificity

• Specificity at fixed sensitivity

Parameters:

• y_true (np.ndarray) – true labels

• y_pred (np.ndarray) – probabilistic predictions

• youden_point (float, optional) – Predetermined Youden point, e.g. based on training data. Defaults to None (computes Youden point based on y_pred).

• fixed_sens (float, optional) – Compute specificity at this sensitivity. Defaults to 0.8.

• fixed_spec (float, optional) – Compute sensitivity at this specificity. Defaults to 0.8.

Returns:

dictionary of metrics, Youden point

Return type:

dict, float

armed.metrics.compute_youden_point(y_true: numpy.ndarray, y_pred: numpy.ndarray)

Compute Youden point (where the Youden index sensitivity + specificity - 1 is maximized)

Parameters:

• y_true (np.ndarray) – true labels

• y_pred (np.ndarray) – probabilistic predictions

Returns:

Youden point, maximum Youden index

Return type:

float, float

armed.metrics.image_metrics(img: numpy.ndarray)

Compute image metrics, including brightness, contrast, sharpness, and SNR

Parameters:

img (np.ndarray) – image

Returns:

metrics

Return type:

dict

armed.metrics.sensitivity_at_specificity(y_true: numpy.ndarray, y_pred: numpy.ndarray, specificity: float = 0.8)

Compute sensitivity at fixed specificity.

Parameters:

• y_true (np.ndarray) – true labels

• y_pred (np.ndarray) – probabilistic predictions

• specificity (float, optional) – fixed specificity. Defaults to 0.8.

Returns:

sensitivity

Return type:

float

armed.metrics.single_sample_dice(y_true: numpy.ndarray, y_pred: numpy.ndarray, threshold: float = 0.5)

Compute Dice score

Parameters:

• y_true (np.ndarray) – true label image

• y_pred (np.ndarray) – probabilistic predictions

• threshold (float, optional) – label threshold. Defaults to 0.5.

Returns:

Dice score

Return type:

float

armed.metrics.specificity_at_sensitivity(y_true: numpy.ndarray, y_pred: numpy.ndarray, sensitivity: float = 0.8)

Compute specificity at fixed sensitivity.

Parameters:

• y_true (np.ndarray) – true labels

• y_pred (np.ndarray) – probabilistic predictions

• sensitivity (float, optional) – fixed sensitivity. Defaults to 0.8.

Returns:

specificity

Return type:

float

armed.misc module

Miscellaneous utilities for expanding paths, getting datestamps, etc.

armed.misc.expand_data_path(path: str, make: bool = False)

Concatenate path to DATADIR in settings.py.

Parameters:

• path (str) – base path

• make (bool, optional) – Make directory if it does not exist. Defaults to False.

Returns:

expanded path

Return type:

str

armed.misc.expand_results_path(path: str, make: bool = False)

Concatenate directory path to RESULTSDIR in settings.py.

Parameters:

• path (str) – base path

• make (bool, optional) – Make directory if it does not exist. Defaults to False.

Returns:

expanded path

Return type:

str

armed.misc.get_datestamp(with_time: bool = False)

Get datestamp with optional timestamp in format YYYY-MM-DD.

Parameters:

• with_time (bool, optional) – Include timestamp in format HH-mm-SS.

• False. (Defaults to) –

Returns:

datestamp

Return type:

str

armed.misc.make_random_onehot(n_rows: int, n_cols: int)

Create a random one-hot encoding matrix

Parameters:

• n_rows (int) – number of rows

• n_cols (int) – number of columns

Returns:

one-hot matrix

Return type:

array

armed.settings module

armed.tfutils module

Utilities for Tensorflow sessions

armed.tfutils.set_gpu(gpu: int, mem_frac: float = 1.0)

Assign GPU to Tensorflow session and limit memory usage

Parameters:

• gpu (int) – GPU index

• mem_frac (float, optional) – memory limit. Defaults to 1.0.

Module contents