pyanno4rt.learning_model

Learning model module.

The module aims to provide methods and classes for data handling, preprocessing, learning model fitting, inspection & evaluation.

Subpackages

Overview

Classes

DataModelHandler

Data & learning model handling class.

Classes

class pyanno4rt.learning_model.DataModelHandler(model_label, data_path, feature_filter, label_name, label_bounds, time_variable_name, label_viewpoint, tune_splits, oof_splits, fuzzy_matching, write_features)[source]

Data & learning model handling class.

This class implements methods to handle the integration of the base dataset, the feature map generator and the feature calculator.

Parameters:

  • data_path (str) – Path to the data set used for fitting the machine learning model.

  • feature_filter (dict) – Dictionary with a list of feature names and a value from {‘retain’, ‘remove’} as an indicator for retaining/removing the features prior to model fitting.

  • label_bounds (list) – Bounds for the label values to binarize into positive (value lies inside the bounds) and negative class (value lies outside the bounds).

  • label_viewpoint ({'early', 'late', 'long-term', 'longitudinal', 'profile'}) – Time of observation for the presence of tumor control and/or normal tissue complication events.

  • tune_splits (int) – Number of splits for the stratified cross-validation within each hyperparameter optimization step.

  • oof_splits (int) – Number of splits for the stratified cross-validation within the out-of-folds evaluation step.

  • fuzzy_matching (bool) – Indicator for the use of fuzzy string matching to generate the feature map (if False, exact string matching is applied).

  • write_features (bool) – Indicator for writing the iteratively calculated feature vectors into a feature history.

model_label

See ‘Parameters’.

Type:

str

data_path

See ‘Parameters’.

Type:

str

write_features

See ‘Parameters’.

Type:

bool

dataset

The object used to handle the base dataset.

Type:

object of class TabularDataGenerator

feature_map_generator

The object used to map the dataset features to the feature definitions.

Type:

object of class FeatureMapGenerator

feature_calculator

The object used to (re-)calculate the feature values and gradients.

Type:

object of class FeatureCalculator

Overview

Methods

integrate()

Integrate the learning model-related classes.

process_feature_history()

Process the feature history from the feature calculator.

Members

integrate()[source]

Integrate the learning model-related classes.

process_feature_history()[source]

Process the feature history from the feature calculator.