AIModels.AIClasses module

Classes for the AI project

Classes

Field:

Class for fields

EarlyStopping:

Class for early stopping

TimeSeriesDataset:

Class for time series dataset

class AIModels.AIClasses.Field(name, levels, area, mr, dstart='1/1/1940', dtend='12/31/2022')

Bases: object

Class for fields

Parameters:

• name (string) -- Name of the field

• levels (string) -- Level of the field

• area (string) -- Area to be analyzed, possible values are

  • 'TROPIC': Tropics

  • 'GLOBAL': Global

  • 'PACTROPIC': Pacific Tropics

  • 'WORLD': World

  • 'EUROPE': Europe

  • 'NORTH_AMERICA': North America

  • 'NH-ML': Northern Hemisphere Mid-Latitudes

• mr (float) -- Number of EOF retained

• dstart (string) -- Start date for field

• dtend (string) -- End date for field

Variables:

• name (string) -- Name of the field

• levels (string) -- Level of the field

• area (string) -- Area of the field

• mr (float) -- Number of EOF retained

• dstart (string) -- Start date for field

• dtend (string) -- End date for field

class AIModels.AIClasses.EarlyStopping(patience=5, verbose=False, delta=0)

Bases: object

Class for early stopping

Parameters:

• patience (int) -- Number of epochs to wait before stopping

• verbose (boolean) -- If True, print the epoch when stopping

• delta (float) -- Minimum change in loss to be considered an improvement

Variables:

• patience (int) -- Number of epochs to wait before stopping

• verbose (boolean) -- If True, print the epoch when stopping

• delta (float) -- Minimum change in loss to be considered an improvement

• counter (int) -- Number of epochs since last improvement

• best_score (float) -- Best loss score

• early_stop (boolean) -- If True, stop the training

class AIModels.AIClasses.TimeSeriesDataset(datasrc, datatgt, TIN, MIN, T, K, time_features=None)

Bases: Dataset

Class for time series dataset. Includes time feature for transformers

Parameters:

• datasrc (numpy array) -- Source data

• datatgt (numpy array) -- Target data

• TIN (int) -- Input time steps

• MIN (int) -- Input variables size

• T (int) -- Predictions time steps

• K (int) -- Output variables size

• time_features (numpy array (optional)) -- If not None contain Time Features

• shift -- Overlap between source and target, for trasnformers overlap = 0 for LSTM overlap should be TIN-T

Variables:

• datasrc (numpy array) -- Source data

• datatgt (numpy array) -- Target data

• time_features (numpy array) -- Time features

• TIN (int) -- Input time steps

• MIN (int) -- Input variables

• T (int) -- Output time steps

• K (int) -- Output variables