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hyperparameters.py
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hyperparameters.py
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from DL_Models import Ensemble
from StandardML_Models.StandardRegressor_1D import StandardRegressor_1D
from StandardML_Models.StandardRegressor_2D import StandardRegressor_2D
from StandardML_Models.StandardClassifier_1D import StandardClassifier_1D
from DL_Models.Ensemble import Ensemble
from config import config
# Use the following formats to add your own models (see hyperparameters.py for examples)
# 'NAME' : [MODEL, {'param1' : value1, 'param2' : value2, ...}]
# the model should be callable with MODEL(param1=value1, param2=value2, ...)
your_models = {
'LR_task': {
},
'Direction_task': {
'amplitude': {
},
'angle': {
}
},
'Position_task': {
}
}
our_ML_models = {
'LR_task' : {
'antisaccade' : {
'max' : {
'KNN' : [StandardClassifier_1D, {'model_name':'KNN', 'leaf_size': 10, 'n_neighbors': 100, 'n_jobs' : -1}],
'GaussianNB' : [StandardClassifier_1D, {'model_name':'GaussianNB', 'var_smoothing': 0.0011513953993264468}],
'LinearSVC' : [StandardClassifier_1D, {'model_name':'LinearSVC', 'C': 0.01, 'tol' : 1e-5, 'max_iter' : 1200}],
'RBF SVC' : [StandardClassifier_1D, {'model_name':'RBF SVC', 'C': 0.1, 'gamma': 1, 'tol' : 1e-5, 'max_iter' : 1200}],
'DecisionTree' : [StandardClassifier_1D, {'model_name':'DecisionTree', 'max_depth': 7}],
'RandomForest' : [StandardClassifier_1D, {'model_name':'RandomForest', 'max_depth': 10, 'n_estimators': 100, 'n_jobs' : -1}],
'GradientBoost' : [StandardClassifier_1D, {'model_name':'GradientBoost', 'learning_rate': 0.1, 'max_depth': 10, 'n_estimators': 50}],
'AdaBoost' : [StandardClassifier_1D, {'model_name':'AdaBoost', 'learning_rate': 0.5, 'n_estimators': 250}],
'XGBoost' : [StandardClassifier_1D, {'model_name':'XGBoost', 'objective' : 'binary:logistic', 'eval_metric' : 'logloss', 'eta': 0.1, 'max_depth': 15, 'n_estimators': 250, 'use_label_encoder' : False}]
},
'min' : {
'KNN' : [StandardClassifier_1D, {'model_name':'KNN', 'leaf_size': 10, 'n_neighbors': 10, 'n_jobs' : -1}],
'GaussianNB' : [StandardClassifier_1D, {'model_name':'GaussianNB', 'var_smoothing': 0.0004941713361323833}],
'LinearSVC' : [StandardClassifier_1D, {'model_name':'LinearSVC', 'C': 0.01, 'tol' : 1e-5, 'max_iter' : 1200}],
'RBF SVC' : [StandardClassifier_1D, {'model_name':'RBF SVC', 'C': 1, 'gamma': 0.01, 'tol' : 1e-5, 'max_iter' : 1200}],
'DecisionTree' : [StandardClassifier_1D, {'model_name':'DecisionTree', 'max_depth': 5}],
'RandomForest' : [StandardClassifier_1D, {'model_name':'RandomForest', 'max_depth': 10, 'n_estimators': 250, 'n_jobs' : -1}],
'GradientBoost' : [StandardClassifier_1D, {'model_name':'GradientBoost', 'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 50}],
'AdaBoost' : [StandardClassifier_1D, {'model_name':'AdaBoost', 'learning_rate': 0.5, 'n_estimators': 100}],
'XGBoost' : [StandardClassifier_1D, {'model_name':'XGBoost', 'objective' : 'binary:logistic', 'eval_metric' : 'logloss', 'eta': 0.1, 'max_depth': 5, 'n_estimators': 250, 'use_label_encoder' : False}]
}
}
},
'Direction_task' : {
'dots' : {
'max' : {
'amplitude' : {
'KNN' : [StandardRegressor_1D, {'model_name':'KNN', 'leaf_size': 10, 'n_neighbors': 100, 'n_jobs' : -1}],
'LinearReg' : [StandardRegressor_1D, {'model_name':'LinearReg', 'n_jobs' : -1}],
'Ridge' : [StandardRegressor_1D, {'model_name':'Ridge', 'alpha': 10000, 'tol' : 1e-5, 'max_iter' : 1200}],
'Lasso' : [StandardRegressor_1D, {'model_name':'Lasso', 'alpha': 1, 'tol' : 1e-5, 'max_iter' : 1200}],
'ElasticNet' : [StandardRegressor_1D, {'model_name':'ElasticNet', 'alpha': 1, 'l1_ratio' : 0.9, 'tol' : 1e-5, 'max_iter' : 1200}],
'RBF SVR' : [StandardRegressor_1D, {'model_name':'RBF SVR', 'C': 100, 'gamma': 0.01, 'tol' : 1e-5, 'max_iter' : 1200}],
'RandomForest' : [StandardRegressor_1D, {'model_name':'RandomForest', 'max_depth': 50, 'n_estimators': 100, 'n_jobs' : -1}],
'GradientBoost' : [StandardRegressor_1D, {'model_name':'GradientBoost', 'learning_rate': 0.05, 'max_depth': 5, 'n_estimators': 100}],
'AdaBoost' : [StandardRegressor_1D, {'model_name':'AdaBoost', 'learning_rate': 0.01, 'n_estimators': 250}],
'XGBoost' : [StandardRegressor_1D, {'model_name':'XGBoost', 'eta': 0.1, 'max_depth': 5, 'n_estimators': 100}]
},
'angle' : {
'KNN' : [StandardRegressor_1D, {'model_name':'KNN', 'leaf_size': 10, 'n_neighbors': 50, 'n_jobs' : -1}],
'LinearReg' : [StandardRegressor_1D, {'model_name':'LinearReg', 'n_jobs' : -1}],
'Ridge' : [StandardRegressor_1D, {'model_name':'Ridge', 'alpha': 10000, 'tol' : 1e-5, 'max_iter' : 1200}],
'Lasso' : [StandardRegressor_1D, {'model_name':'Lasso', 'alpha': 0.01, 'tol' : 1e-5, 'max_iter' : 1200}],
'ElasticNet' : [StandardRegressor_1D, {'model_name':'ElasticNet', 'alpha': 0.1, 'l1_ratio' : 0.3, 'tol' : 1e-5, 'max_iter' : 1200}],
'RBF SVR' : [StandardRegressor_1D, {'model_name':'RBF SVR', 'C': 10, 'gamma': 0.1, 'tol' : 1e-5, 'max_iter' : 1200}],
'RandomForest' : [StandardRegressor_1D, {'model_name':'RandomForest', 'max_depth': 10, 'n_estimators': 10, 'n_jobs' : -1}],
'GradientBoost' : [StandardRegressor_1D, {'model_name':'GradientBoost', 'learning_rate': 0.05, 'max_depth': 5, 'n_estimators': 100}],
'AdaBoost' : [StandardRegressor_1D, {'model_name':'AdaBoost', 'learning_rate': 0.01, 'n_estimators': 50}],
'XGBoost' : [StandardRegressor_1D, {'model_name':'XGBoost', 'eta': 0.1, 'max_depth': 5, 'n_estimators': 250}]
}
},
'min' : {
'amplitude' : {
'KNN' : [StandardRegressor_1D, {'model_name':'KNN', 'leaf_size': 10, 'n_neighbors': 10, 'n_jobs' : -1}],
'LinearReg' : [StandardRegressor_1D, {'model_name':'LinearReg', 'n_jobs' : -1}],
'Ridge' : [StandardRegressor_1D, {'model_name':'Ridge', 'alpha': 1000, 'tol' : 1e-5, 'max_iter' : 1200}],
'Lasso' : [StandardRegressor_1D, {'model_name':'Lasso', 'alpha': 1, 'tol' : 1e-5, 'max_iter' : 1200}],
'ElasticNet' : [StandardRegressor_1D, {'model_name':'ElasticNet', 'alpha': 1, 'l1_ratio' : 0.9, 'tol' : 1e-5, 'max_iter' : 1200}],
'RBF SVR' : [StandardRegressor_1D, {'model_name':'RBF SVR', 'C': 100, 'gamma': 0.01, 'tol' : 1e-5, 'max_iter' : 1200}],
'RandomForest' : [StandardRegressor_1D, {'model_name':'RandomForest', 'max_depth': 50, 'n_estimators': 50, 'n_jobs' : -1}],
'GradientBoost' : [StandardRegressor_1D, {'model_name':'GradientBoost', 'learning_rate': 0.05, 'max_depth': 10, 'n_estimators': 100}],
'AdaBoost' : [StandardRegressor_1D, {'model_name':'AdaBoost', 'learning_rate': 0.1, 'n_estimators': 50}],
'XGBoost' : [StandardRegressor_1D, {'model_name':'XGBoost', 'eta': 0.1, 'max_depth': 5, 'n_estimators': 250}]
},
'angle' : {
'KNN' : [StandardRegressor_1D, {'model_name':'KNN', 'leaf_size': 10, 'n_neighbors': 25, 'n_jobs' : -1}],
'LinearReg' : [StandardRegressor_1D, {'model_name':'LinearReg', 'n_jobs' : -1}],
'Ridge' : [StandardRegressor_1D, {'model_name':'Ridge', 'alpha': 10000, 'tol' : 1e-5, 'max_iter' : 1200}],
'Lasso' : [StandardRegressor_1D, {'model_name':'Lasso', 'alpha': 0.01, 'tol' : 1e-5, 'max_iter' : 1200}],
'ElasticNet' : [StandardRegressor_1D, {'model_name':'ElasticNet', 'alpha': 0.1, 'l1_ratio' : 0.3, 'tol' : 1e-5, 'max_iter' : 1200}],
'RBF SVR' : [StandardRegressor_1D, {'model_name':'RBF SVR', 'C': 10, 'gamma': 0.01, 'tol' : 1e-5, 'max_iter' : 1200}],
'RandomForest' : [StandardRegressor_1D, {'model_name':'RandomForest', 'max_depth': 10, 'n_estimators': 100, 'n_jobs' : -1}],
'GradientBoost' : [StandardRegressor_1D, {'model_name':'GradientBoost', 'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 100}],
'AdaBoost' : [StandardRegressor_1D, {'model_name':'AdaBoost', 'learning_rate': 0.5, 'n_estimators': 50}],
'XGBoost' : [StandardRegressor_1D, {'model_name':'XGBoost', 'eta': 0.05, 'max_depth': 5, 'n_estimators': 250}]
}
}
},
'processing_speed' : {
'max' : {
'amplitude' : {
#TODO
},
'angle' : {
#TODO
}
},
'min' : {
'amplitude' : {
#TODO
},
'angle' : {
#TODO
}
}
}
},
'Position_task' : {
'dots' : {
'max' : {
'KNN' : [StandardRegressor_2D, {'model_name':'KNN', 'leaf_size': 10, 'n_neighbors': 75, 'n_jobs' : -1}],
'LinearReg' : [StandardRegressor_2D, {'model_name':'LinearReg', 'n_jobs' : -1}],
'Ridge' : [StandardRegressor_2D, {'model_name':'Ridge', 'alpha': 10000, 'tol' : 1e-5, 'max_iter' : 1200}],
'Lasso' : [StandardRegressor_2D, {'model_name':'Lasso', 'alpha': 1, 'tol' : 1e-5, 'max_iter' : 1200}],
'ElasticNet' : [StandardRegressor_2D, {'model_name':'ElasticNet', 'alpha': 1, 'l1_ratio' : 0.3, 'tol' : 1e-5, 'max_iter' : 1200}],
'RBF SVR' : [StandardRegressor_2D, {'model_name':'RBF SVR', 'C': 10, 'gamma': 0.01, 'tol' : 1e-5, 'max_iter' : 1200}],
'RandomForest' : [StandardRegressor_2D, {'model_name':'RandomForest', 'max_depth': 500, 'n_estimators': 50, 'n_jobs' : -1}],
'GradientBoost' : [StandardRegressor_2D, {'model_name':'GradientBoost', 'learning_rate': 0.05, 'max_depth': 5, 'n_estimators': 100}],
'AdaBoost' : [StandardRegressor_2D, {'model_name':'AdaBoost', 'learning_rate': 1, 'n_estimators': 10}],
'XGBoost' : [StandardRegressor_2D, {'model_name':'XGBoost', 'eta': 0.1, 'max_depth': 5, 'n_estimators': 100}]
},
'min' : {
'KNN' : [StandardRegressor_2D, {'model_name':'KNN', 'leaf_size': 10, 'n_neighbors': 50, 'n_jobs' : -1}],
'LinearReg' : [StandardRegressor_2D, {'model_name':'LinearReg', 'n_jobs' : -1}],
'Ridge' : [StandardRegressor_2D, {'model_name':'Ridge', 'alpha': 10000, 'tol' : 1e-5, 'max_iter' : 1200}],
'Lasso' : [StandardRegressor_2D, {'model_name':'Lasso', 'alpha': 1, 'tol' : 1e-5, 'max_iter' : 1200}],
'ElasticNet' : [StandardRegressor_2D, {'model_name':'ElasticNet', 'alpha': 1, 'l1_ratio' : 0.6, 'tol' : 1e-5, 'max_iter' : 1200}],
'RBF SVR' : [StandardRegressor_2D, {'model_name':'RBF SVR', 'C': 100, 'gamma': 0.01, 'tol' : 1e-5, 'max_iter' : 1200}],
'RandomForest' : [StandardRegressor_2D, {'model_name':'RandomForest', 'max_depth': 50, 'n_estimators': 250, 'n_jobs' : -1}],
'GradientBoost' : [StandardRegressor_2D, {'model_name':'GradientBoost', 'learning_rate': 0.05, 'max_depth': 10, 'n_estimators': 100}],
'AdaBoost' : [StandardRegressor_2D, {'model_name':'AdaBoost', 'learning_rate': 0.01, 'n_estimators': 50}],
'XGBoost' : [StandardRegressor_2D, {'model_name':'XGBoost', 'eta': 0.1, 'max_depth': 10, 'n_estimators': 100}]
}
}
}
}
our_ML_dummy_models = {
'LR_task' : {
'antisaccade' : {
'max' : {
"Stratified" : [StandardClassifier_1D, {'model_name':'Stratified', 'strategy' : 'stratified'}],
"MostFrequent" : [StandardClassifier_1D, {'model_name':'MostFrequent', 'strategy' : 'most_frequent'}],
"Prior" : [StandardClassifier_1D, {'model_name':'Prior', 'strategy' : 'prior'}],
"Uniform" : [StandardClassifier_1D, {'model_name': 'Uniform', 'strategy' : 'uniform'}]
},
'min' : {
"Stratified" : [StandardClassifier_1D, {'model_name':'Stratified', 'strategy' : 'stratified'}],
"MostFrequent" : [StandardClassifier_1D, {'model_name':'MostFrequent', 'strategy' : 'most_frequent'}],
"Prior" : [StandardClassifier_1D, {'model_name':'Prior', 'strategy' : 'prior'}],
"Uniform" : [StandardClassifier_1D, {'model_name': 'Uniform', 'strategy' : 'uniform'}]
}
}
},
'Direction_task' : {
'dots' : {
'max' : {
'amplitude' : {
"Mean" : [StandardRegressor_1D, {'model_name':'Mean', 'strategy' : 'mean'}],
"Median" : [StandardRegressor_1D, {'model_name':'Median', 'strategy' : 'median'}]
},
'angle' : {
"Mean" : [StandardRegressor_1D, {'model_name':'Mean', 'strategy' : 'mean'}],
"Median" : [StandardRegressor_1D, {'model_name':'Median', 'strategy' : 'median'}]
}
},
'min' : {
'amplitude' : {
"Mean" : [StandardRegressor_1D, {'model_name':'Mean', 'strategy' : 'mean'}],
"Median" : [StandardRegressor_1D, {'model_name':'Median', 'strategy' : 'median'}]
},
'angle' : {
"Mean" : [StandardRegressor_1D, {'model_name':'Mean', 'strategy' : 'mean'}],
"Median" : [StandardRegressor_1D, {'model_name':'Median', 'strategy' : 'median'}]
}
}
},
'processing_speed' : {
'max' : {
'amplitude' : {
},
'angle' : {
}
},
'min' : {
'amplitude' : {
},
'angle' : {
}
}
}
},
'Position_task' : {
'dots' : {
'max' : {
"Mean" : [StandardRegressor_2D, {'model_name':'Mean', 'strategy' : 'mean'}],
"Median" : [StandardRegressor_2D, {'model_name':'Median', 'strategy' : 'median'}],
"Constant" : [StandardRegressor_2D, {'model_name':'Constant', 'strategy' : 'constant'}]
},
'min' : {
"Mean" : [StandardRegressor_2D, {'model_name':'Mean', 'strategy' : 'mean'}],
"Median" : [StandardRegressor_2D, {'model_name':'Median', 'strategy' : 'median'}],
"Constant" : [StandardRegressor_2D, {'model_name':'Constant', 'strategy' : 'constant'}]
}
}
}
}
nb_models = 1
batch_size = 64
input_shape = (1, 258) if config['feature_extraction'] else (500, 129)
depth = 12
epochs = 50
verbose = True
our_DL_models = {
'LR_task' : {
'antisaccade' : {
'max' : {
'CNN' : [Ensemble, {'model_name': 'CNN', 'nb_models' : nb_models, 'loss':'bce', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 64, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : True, 'depth' : depth}],
'EEGNet' : [Ensemble, {'model_name' : 'EEGNet', 'nb_models' : nb_models, 'loss':'bce', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'epochs' : epochs, 'F1' : 16, 'F2' : 256, 'verbose' : verbose, 'D' : 4, 'kernel_size' : 256, 'dropout_rate' : 0.5}],
'InceptionTime' : [Ensemble, {'model_name': 'InceptionTime', 'nb_models' : nb_models, 'loss':'bce', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 64, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : True, 'depth' : depth}],
'PyramidalCNN' : [Ensemble, {'model_name': 'PyramidalCNN', 'nb_models' : nb_models, 'loss':'bce', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 16, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : False, 'depth' : 6}],
'Xception' : [Ensemble, {'model_name': 'Xception', 'nb_models' : nb_models, 'loss':'bce', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 40, 'epochs' : epochs, 'nb_filters' : 64, 'verbose' : verbose, 'use_residual' : True, 'depth' : 18}]
},
'min' : {
'CNN' : [Ensemble, {'model_name': 'CNN', 'nb_models' : nb_models, 'loss':'bce', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 64, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : True, 'depth' : depth}],
'EEGNet' : [Ensemble, {'model_name' : 'EEGNet', 'nb_models' : nb_models, 'loss':'bce', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'epochs' : epochs, 'F1' : 16, 'F2' : 256, 'verbose' : verbose, 'D' : 4, 'kernel_size' : 256, 'dropout_rate' : 0.5}],
'InceptionTime' : [Ensemble, {'model_name': 'InceptionTime', 'nb_models' : nb_models, 'loss':'bce', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 64, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : True, 'depth' : depth}],
'PyramidalCNN' : [Ensemble, {'model_name': 'PyramidalCNN', 'nb_models' : nb_models, 'loss':'bce', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 16, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : False, 'depth' : 6}],
'Xception' : [Ensemble, {'model_name': 'Xception', 'nb_models' : nb_models, 'loss':'bce', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 40, 'epochs' : epochs, 'nb_filters' : 64, 'verbose' : verbose, 'use_residual' : True, 'depth' : 18}]
}
}
},
'Direction_task' : {
'dots' : {
'max' : {
'amplitude' : {
'CNN' : [Ensemble, {'model_name': 'CNN', 'nb_models' : nb_models, 'loss':'mse', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 64, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : True, 'depth' : depth}],
'EEGNet' : [Ensemble, {'model_name' : 'EEGNet', 'nb_models' : nb_models, 'loss':'mse', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'epochs' : epochs, 'F1' : 16, 'F2' : 256, 'verbose' : verbose, 'D' : 4, 'kernel_size' : 256, 'dropout_rate' : 0.5}],
'InceptionTime' : [Ensemble, {'model_name': 'InceptionTime', 'nb_models' : nb_models, 'loss':'mse', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 64, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : True, 'depth' : depth}],
'PyramidalCNN' : [Ensemble, {'model_name': 'PyramidalCNN', 'nb_models' : nb_models, 'loss':'mse', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 16, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : False, 'depth' : 6}],
'Xception' : [Ensemble, {'model_name': 'Xception', 'nb_models' : nb_models, 'loss':'mse', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 40, 'epochs' : epochs, 'nb_filters' : 64, 'verbose' : verbose, 'use_residual' : True, 'depth' : 18}]
},
'angle' : {
'CNN' : [Ensemble, {'model_name': 'CNN', 'nb_models' : nb_models, 'loss':'angle-loss', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 64, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : True, 'depth' : depth}],
'EEGNet' : [Ensemble, {'model_name' : 'EEGNet', 'nb_models' : nb_models, 'loss':'angle-loss', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'epochs' : epochs, 'F1' : 16, 'F2' : 256, 'verbose' : verbose, 'D' : 4, 'kernel_size' : 256, 'dropout_rate' : 0.5}],
'InceptionTime' : [Ensemble, {'model_name': 'InceptionTime', 'nb_models' : nb_models, 'loss':'angle-loss', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 64, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : True, 'depth' : depth}],
'PyramidalCNN' : [Ensemble, {'model_name': 'PyramidalCNN', 'nb_models' : nb_models, 'loss':'angle-loss', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 16, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : False, 'depth' : 6}],
'Xception' : [Ensemble, {'model_name': 'Xception', 'nb_models' : nb_models, 'loss':'angle-loss', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 40, 'epochs' : epochs, 'nb_filters' : 64, 'verbose' : verbose, 'use_residual' : True, 'depth' : 18}]
}
},
'min' : {
'amplitude' : {
'CNN' : [Ensemble, {'model_name': 'CNN', 'nb_models' : nb_models, 'loss':'mse', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 64, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : True, 'depth' : depth}],
'EEGNet' : [Ensemble, {'model_name' : 'EEGNet', 'nb_models' : nb_models, 'loss':'mse', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'epochs' : epochs, 'F1' : 16, 'F2' : 256, 'verbose' : verbose, 'D' : 4, 'kernel_size' : 256, 'dropout_rate' : 0.5}],
'InceptionTime' : [Ensemble, {'model_name': 'InceptionTime', 'nb_models' : nb_models, 'loss':'mse', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 64, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : True, 'depth' : depth}],
'PyramidalCNN' : [Ensemble, {'model_name': 'PyramidalCNN', 'nb_models' : nb_models, 'loss':'mse', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 16, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : False, 'depth' : 6}],
'Xception' : [Ensemble, {'model_name': 'Xception', 'nb_models' : nb_models, 'loss':'mse', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 40, 'epochs' : epochs, 'nb_filters' : 64, 'verbose' : verbose, 'use_residual' : True, 'depth' : 18}]
},
'angle' : {
'CNN' : [Ensemble, {'model_name': 'CNN', 'nb_models' : nb_models, 'loss':'angle-loss', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 64, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : True, 'depth' : depth}],
'EEGNet' : [Ensemble, {'model_name' : 'EEGNet', 'nb_models' : nb_models, 'loss':'angle-loss', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'epochs' : epochs, 'F1' : 16, 'F2' : 256, 'verbose' : verbose, 'D' : 4, 'kernel_size' : 256, 'dropout_rate' : 0.5}],
'InceptionTime' : [Ensemble, {'model_name': 'InceptionTime', 'nb_models' : nb_models, 'loss':'angle-loss', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 64, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : True, 'depth' : depth}],
'PyramidalCNN' : [Ensemble, {'model_name': 'PyramidalCNN', 'nb_models' : nb_models, 'loss':'angle-loss', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 16, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : False, 'depth' : 6}],
'Xception' : [Ensemble, {'model_name': 'Xception', 'nb_models' : nb_models, 'loss':'angle-loss', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 40, 'epochs' : epochs, 'nb_filters' : 64, 'verbose' : verbose, 'use_residual' : True, 'depth' : 18}]
}
}
},
'processing_speed' : {
'max' : {
'amplitude' : {
'CNN' : [Ensemble, {'model_name': 'CNN', 'nb_models' : nb_models, 'loss':'mse', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 64, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : True, 'depth' : depth}],
'EEGNet' : [Ensemble, {'model_name' : 'EEGNet', 'nb_models' : nb_models, 'loss':'mse', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'epochs' : epochs, 'F1' : 16, 'F2' : 256, 'verbose' : verbose, 'D' : 4, 'kernel_size' : 256, 'dropout_rate' : 0.5}],
'InceptionTime' : [Ensemble, {'model_name': 'InceptionTime', 'nb_models' : nb_models, 'loss':'mse', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 64, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : True, 'depth' : depth}],
'PyramidalCNN' : [Ensemble, {'model_name': 'PyramidalCNN', 'nb_models' : nb_models, 'loss':'mse', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 16, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : False, 'depth' : 6}],
'Xception' : [Ensemble, {'model_name': 'Xception', 'nb_models' : nb_models, 'loss':'mse', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 40, 'epochs' : epochs, 'nb_filters' : 64, 'verbose' : verbose, 'use_residual' : True, 'depth' : 18}]
},
'angle' : {
'CNN' : [Ensemble, {'model_name': 'CNN', 'nb_models' : nb_models, 'loss':'angle-loss', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 64, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : True, 'depth' : depth}],
'EEGNet' : [Ensemble, {'model_name' : 'EEGNet', 'nb_models' : nb_models, 'loss':'angle-loss', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'epochs' : epochs, 'F1' : 16, 'F2' : 256, 'verbose' : verbose, 'D' : 4, 'kernel_size' : 256, 'dropout_rate' : 0.5}],
'InceptionTime' : [Ensemble, {'model_name': 'InceptionTime', 'nb_models' : nb_models, 'loss':'angle-loss', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 64, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : True, 'depth' : depth}],
'PyramidalCNN' : [Ensemble, {'model_name': 'PyramidalCNN', 'nb_models' : nb_models, 'loss':'angle-loss', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 16, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : False, 'depth' : 6}],
'Xception' : [Ensemble, {'model_name': 'Xception', 'nb_models' : nb_models, 'loss':'angle-loss', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 40, 'epochs' : epochs, 'nb_filters' : 64, 'verbose' : verbose, 'use_residual' : True, 'depth' : 18}]
}
},
'min' : {
'amplitude' : {
'CNN' : [Ensemble, {'model_name': 'CNN', 'nb_models' : nb_models, 'loss':'mse', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 64, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : True, 'depth' : depth}],
'EEGNet' : [Ensemble, {'model_name' : 'EEGNet', 'nb_models' : nb_models, 'loss':'mse', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'epochs' : epochs, 'F1' : 16, 'F2' : 256, 'verbose' : verbose, 'D' : 4, 'kernel_size' : 256, 'dropout_rate' : 0.5}],
'InceptionTime' : [Ensemble, {'model_name': 'InceptionTime', 'nb_models' : nb_models, 'loss':'mse', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 64, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : True, 'depth' : depth}],
'PyramidalCNN' : [Ensemble, {'model_name': 'PyramidalCNN', 'nb_models' : nb_models, 'loss':'mse', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 16, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : False, 'depth' : 6}],
'Xception' : [Ensemble, {'model_name': 'Xception', 'nb_models' : nb_models, 'loss':'mse', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 40, 'epochs' : epochs, 'nb_filters' : 64, 'verbose' : verbose, 'use_residual' : True, 'depth' : 18}]
},
'angle' : {
'CNN' : [Ensemble, {'model_name': 'CNN', 'nb_models' : nb_models, 'loss':'angle-loss', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 64, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : True, 'depth' : depth}],
'EEGNet' : [Ensemble, {'model_name' : 'EEGNet', 'nb_models' : nb_models, 'loss':'angle-loss', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'epochs' : epochs, 'F1' : 16, 'F2' : 256, 'verbose' : verbose, 'D' : 4, 'kernel_size' : 256, 'dropout_rate' : 0.5}],
'InceptionTime' : [Ensemble, {'model_name': 'InceptionTime', 'nb_models' : nb_models, 'loss':'angle-loss', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 64, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : True, 'depth' : depth}],
'PyramidalCNN' : [Ensemble, {'model_name': 'PyramidalCNN', 'nb_models' : nb_models, 'loss':'angle-loss', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 16, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : False, 'depth' : 6}],
'Xception' : [Ensemble, {'model_name': 'Xception', 'nb_models' : nb_models, 'loss':'angle-loss', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 1,
'kernel_size': 40, 'epochs' : epochs, 'nb_filters' : 64, 'verbose' : verbose, 'use_residual' : True, 'depth' : 18}]
}
}
}
},
'Position_task' : {
'dots' : {
'max' : {
'CNN' : [Ensemble, {'model_name': 'CNN', 'nb_models' : nb_models, 'loss':'mse', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 2,
'kernel_size': 64, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : True, 'depth' : depth}],
'EEGNet' : [Ensemble, {'model_name' : 'EEGNet', 'nb_models' : nb_models, 'loss':'mse', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 2,
'epochs' : epochs, 'F1' : 16, 'F2' : 256, 'verbose' : verbose, 'D' : 4, 'kernel_size' : 256, 'dropout_rate' : 0.5}],
'InceptionTime' : [Ensemble, {'model_name': 'InceptionTime', 'nb_models' : nb_models, 'loss':'mse', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 2,
'kernel_size': 64, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : True, 'depth' : depth}],
'PyramidalCNN' : [Ensemble, {'model_name': 'PyramidalCNN', 'nb_models' : nb_models, 'loss':'mse', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 2,
'kernel_size': 16, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : False, 'depth' : 6}],
'Xception' : [Ensemble, {'model_name': 'Xception', 'nb_models' : nb_models, 'loss':'mse', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 2,
'kernel_size': 40, 'epochs' : epochs, 'nb_filters' : 64, 'verbose' : verbose, 'use_residual' : True, 'depth' : 18}]
},
'min' : {
'CNN' : [Ensemble, {'model_name': 'CNN', 'nb_models' : nb_models, 'loss':'mse', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 2,
'kernel_size': 64, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : True, 'depth' : depth}],
'EEGNet' : [Ensemble, {'model_name' : 'EEGNet', 'nb_models' : nb_models, 'loss':'mse', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 2,
'epochs' : epochs, 'F1' : 16, 'F2' : 256, 'verbose' : verbose, 'D' : 4, 'kernel_size' : 256, 'dropout_rate' : 0.5}],
'InceptionTime' : [Ensemble, {'model_name': 'InceptionTime', 'nb_models' : nb_models, 'loss':'mse', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 2,
'kernel_size': 64, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : True, 'depth' : depth}],
'PyramidalCNN' : [Ensemble, {'model_name': 'PyramidalCNN', 'nb_models' : nb_models, 'loss':'mse', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 2,
'kernel_size': 16, 'epochs' : epochs, 'nb_filters' : 16, 'verbose' : verbose, 'use_residual' : False, 'depth' : 6}],
'Xception' : [Ensemble, {'model_name': 'Xception', 'nb_models' : nb_models, 'loss':'mse', 'batch_size': batch_size, 'input_shape': input_shape, 'output_shape' : 2,
'kernel_size': 40, 'epochs' : epochs, 'nb_filters' : 64, 'verbose' : verbose, 'use_residual' : True, 'depth' : 18}]
}
}
}
}
# merge two dict, new_dict overrides base_dict in case of incompatibility
def merge_models(base_dict, new_dict):
result = dict()
keys = base_dict.keys() | new_dict.keys()
for k in keys:
if k in base_dict and k in new_dict:
if type(base_dict[k]) == dict and type(new_dict[k]) == dict:
result[k] = merge_models(base_dict[k], new_dict[k])
else:
# overriding
result[k] = new_dict[k]
elif k in base_dict:
result[k] = base_dict[k]
else:
result[k] = new_dict[k]
return result
all_models = dict()
if config['include_ML_models']:
all_models = merge_models(all_models, our_ML_models)
if config['include_DL_models']:
all_models = merge_models(all_models, our_DL_models)
if config['include_dummy_models']:
all_models = merge_models(all_models, our_ML_dummy_models)
if config['include_your_models']:
all_models = merge_models(all_models, your_models)