src.actions package

Submodules

• src.actions.actionWrapper module
  • Action Wrapper Module
    • Functions
  • GenerateGrayTriplets()
  • GoodBadGraySeparation()
  • GoodBad_randomGray()
  • action_selector()
  • check_df_not_none()
  • durationSeparation()
  • featureMax()
  • featureMean()
  • featureMin()
  • featureStd()
  • generatePredictionTriplets()
  • generatePredictionTripletsReverse()
  • generateTriplets()
  • generateTripletsNG()
  • get_randomGray()
  • normalize()
  • normalizeMinMax()
  • print_data()
• src.actions.dataFrame module
  • MNIST_pack()
  • MNIST_unpack()
  • add_window_column()
  • compute_dayofweek()
  • compute_lag_features()
  • compute_roll_features()
  • df_none()
  • dropColumns()
  • dropNaN()
  • dropOutliers()
  • drop_anomalous()
  • drop_not_anomalous()
  • generateExpectationMatrix()
  • generateExpectationMatrixMNIST()
  • generateImgTriplets()
  • generateImgUnrelatedGroups()
  • generateTripletsMNIST()
  • generateUnrelatedGroupsMNIST()
  • group_standardize()
  • keepColumns()
  • load()
  • loadMNIST()
  • normalizeMNIST()
  • removeNaN()
  • remove_over_threshold()
  • reshapeMNIST()
  • rolling_feature()
  • toTfDatasetsMNIST()
  • trn_val_test_splitMNIST()
  • write()
• src.actions.embeddings module
  • Embeddings Actions Module
    • Functions
  • compute_centroids()
  • load_embeddings()
  • load_samples()
• src.actions.kaggleDst module
  • Kaggle Dataset Actions Module
    • Functions
  • apply_post_operations()
  • generateKaggleCategorical()
  • generateKaggleTriplets()
  • kaggleDataVsLabelSeparation()
  • kaggleShuffle()
  • kaggleTrnValTestSeparation()
• src.actions.separation module
  • Data Separation and Balancing Module
    • Functions
  • BalanceSeparationNG()
  • ConcatenateNG()
  • TrnValTstSeparation()
  • balancingConcatenateNG()
  • flag_windows()
  • windowDropOutliers()
  • window_to_group()
  • window_to_group_mvno()
  • windowsSeparation()
  • windowsSeparation_mvno()
  • windowsSeparation_vmaf()
• src.actions.windowing module
  • Windowing Actions Module
    • Functions
  • applyRequests()
  • flag_anomaly_windows()
  • listWindowing()
  • windowing()

Module contents

Actions Package

This package provides a collection of action modules for data processing, manipulation, and analysis within the SNN2 neural network framework.

The actions package contains specialized modules for different aspects of data processing workflows, including data frame operations, dataset separation, windowing techniques, Kaggle dataset handling, and embedding operations.

Modules

dataFramemodule

DataFrame operations and manipulations for structured data processing.

separationmodule

Data separation and balancing utilities for train/validation/test splits and threshold-based categorization.

windowingmodule

Time series and sequence windowing operations for neural network input preparation.

kaggleDstmodule

Kaggle dataset specific operations and preprocessing utilities.

embeddingsmodule

Embedding loading, manipulation, and centroid computation operations.

Notes

All action modules in this package use the @action decorator for consistent function tracking and logging within the SNN2 framework. The modules are designed to work together to provide a complete data processing pipeline for neural network training and evaluation.

Examples

Import specific action modules:

>>> from SNN2.src.actions import separation
>>> from SNN2.src.actions import embeddings

Access action functions:

>>> # Use separation functions
>>> train, val, test = separation.TrnValTstSeparation(data)
>>>
>>> # Use embedding functions
>>> embeddings_tensor = embeddings.load_embeddings(path, pkl_handler)
>>> centroids = embeddings.compute_centroids(embeddings_tensor)

See also

SNN2.src.decorators.decorators

Action decorator implementation

SNN2.src.core.data

Core data management classes

SNN2.src.io

Input/output utilities and handlers