SUMMARY
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SUMMARY
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Analysis package for stimulus, analysis_default is to be refactored, see git issues.
Modules:
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analysis_default–Default analysis module for stimulus package.
Feedback
Analysis package for stimulus, analysis_default is to be refactored, see git issues.
Modules:
-
analysis_default–Default analysis module for stimulus package.
Feedback
Command line interface package for the stimulus library.
Modules:
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analysis_default–Analysis default module for running model analysis and performance evaluation.
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check_model–CLI module for checking model configuration and running initial tests.
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predict–CLI module for model prediction on datasets.
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shuffle_csv–CLI module for shuffling CSV data files.
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split_csv–CLI module for splitting CSV data files.
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split_yaml–CLI module for splitting YAML configuration files.
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transform_csv–CLI module for transforming CSV data files.
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tuning–CLI module for tuning model hyperparameters using Ray Tune.
Feedback
Command line interface package for the stimulus library.
Modules:
-
analysis_default–Analysis default module for running model analysis and performance evaluation.
-
check_model–CLI module for checking model configuration and running initial tests.
-
predict–CLI module for model prediction on datasets.
-
shuffle_csv–CLI module for shuffling CSV data files.
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split_csv–CLI module for splitting CSV data files.
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split_yaml–CLI module for splitting YAML configuration files.
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transform_csv–CLI module for transforming CSV data files.
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tuning–CLI module for tuning model hyperparameters using Ray Tune.
Feedback
Feedback
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Data handling and processing module.
This module provides functionality for loading, transforming, and managing data in various formats like CSV. It includes classes and utilities for:
- Loading and processing CSV data files
- Applying data transformations and augmentations
- Splitting data into train/validation/test sets
- Converting data into PyTorch datasets
Modules:
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csv–This module provides classes for handling CSV data files in the STIMULUS format.
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encoding– -
experiments–Loaders serve as interfaces between the CSV master class and custom methods.
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handlertorch–This file provides the class API for handling the data in pytorch using the Dataset and Dataloader classes.
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splitters–This package provides splitter classes for splitting data into train, validation, and test sets.
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transform–
Feedback
Data handling and processing module.
This module provides functionality for loading, transforming, and managing data in various formats like CSV. It includes classes and utilities for:
- Loading and processing CSV data files
- Applying data transformations and augmentations
- Splitting data into train/validation/test sets
- Converting data into PyTorch datasets
Modules:
-
csv–This module provides classes for handling CSV data files in the STIMULUS format.
-
encoding– -
experiments–Loaders serve as interfaces between the CSV master class and custom methods.
-
handlertorch–This file provides the class API for handling the data in pytorch using the Dataset and Dataloader classes.
-
splitters–This package provides splitter classes for splitting data into train, validation, and test sets.
-
transform–
Feedback
This package provides splitter classes for splitting data into train, validation, and test sets.
Modules:
-
splitters–This file contains the splitter classes for splitting data accordingly.
Feedback
This package provides splitter classes for splitting data into train, validation, and test sets.
Modules:
-
splitters–This file contains the splitter classes for splitting data accordingly.
Feedback
Modules:
-
data_transformation_generators–This file contains noise generators classes for generating various types of noise.
Feedback
Modules:
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data_transformation_generators–This file contains noise generators classes for generating various types of noise.
Feedback
Feedback
Feedback
Modules:
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predict–A module for making predictions with PyTorch models using DataLoaders.
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raytune_learner–Ray Tune wrapper and trainable model classes for hyperparameter optimization.
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raytune_parser–
Feedback
Modules:
-
predict–A module for making predictions with PyTorch models using DataLoaders.
-
raytune_learner–Ray Tune wrapper and trainable model classes for hyperparameter optimization.
-
raytune_parser–