ecnet.tasks
ecnet.tasks.select_rfr
select_rfr: reduces input data dimensionality such that specified proportion of total feature importance (derived from random forest regression) is retained in feature subset
Parameters:
Name | Type | Description | Default |
---|---|---|---|
dataset |
QSPRDataset
|
input data |
required |
total_importance |
float
|
total feature importance to retain |
0.95
|
**kwargs |
additional arguments passed to sklearn.ensemble.RandomForestRegressor |
{}
|
Returns:
Type | Description |
---|---|
Tuple[List[int], List[float]]
|
tuple[list[int], list[float]]: (selected feature indices, selected feature importances) |
Source code in ecnet/tasks/feature_selection.py
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ecnet.tasks.tune_batch_size
Tunes the batch size during training; additional **kwargs can include any in: [ # ECNet parameters 'epochs' (default 100), 'valid_size' (default 0.2), 'patience' (default 32), 'lr_decay' (default 0.0), 'hidden_dim' (default 128), 'n_hidden' (default 2), 'dropout': (default 0.0), # Adam optim. alg. arguments 'lr' (default 0.001), 'beta_1' (default 0.9), 'beta_2' (default 0.999), 'eps' (default 1e-8), 'weight_decay' (default 0.0), 'amsgrad' (default False) ]
Parameters:
Name | Type | Description | Default |
---|---|---|---|
n_bees |
int
|
number of employer bees to use in ABC algorithm |
required |
n_iter |
int
|
number of iterations, or "search cycles", for ABC algorithm |
required |
dataset_train |
QSPRDataset
|
dataset used to train evaluation models |
required |
dataset_eval |
QSPRDataset
|
dataset used for evaluation |
required |
n_processes |
int
|
if > 1, uses multiprocessing when evaluating at an iteration |
1
|
**kwargs |
additional arguments |
{}
|
Returns:
Name | Type | Description |
---|---|---|
dict |
dict
|
{'batch_size': int} |
Source code in ecnet/tasks/parameter_tuning.py
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ecnet.tasks.tune_model_architecture
Tunes model architecture parameters (number of hidden layers, neurons per hidden layer, neuron dropout); additional **kwargs can include any in: [ # ECNet parameters 'epochs' (default 100), 'batch_size' (default 32), 'valid_size' (default 0.2), 'patience' (default 32), 'lr_decay' (default 0.0), # Adam optim. alg. arguments 'lr' (default 0.001), 'beta_1' (default 0.9), 'beta_2' (default 0.999), 'eps' (default 1e-8), 'weight_decay' (default 0.0), 'amsgrad' (default False) ]
Parameters:
Name | Type | Description | Default |
---|---|---|---|
n_bees |
int
|
number of employer bees to use in ABC algorithm |
required |
n_iter |
int
|
number of iterations, or "search cycles", for ABC algorithm |
required |
dataset_train |
QSPRDataset
|
dataset used to train evaluation models |
required |
dataset_eval |
QSPRDataset
|
dataset used for evaluation |
required |
n_processes |
int
|
if > 1, uses multiprocessing when evaluating at an iteration |
1
|
**kwargs |
additional arguments |
{}
|
Returns:
Name | Type | Description |
---|---|---|
dict |
dict
|
{'hidden_dim': int, 'n_hidden': int, 'dropout': float} |
Source code in ecnet/tasks/parameter_tuning.py
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ecnet.tasks.tune_training_parameters
Tunes learning rate, learning rate decay; additional **kwargs can include any in: [ # ECNet parameters 'epochs' (default 100), 'batch_size' (default 32), 'valid_size' (default 0.2), 'patience' (default 32), 'hidden_dim' (default 128), 'n_hidden' (default 2), 'dropout': (default 0.0), # Adam optim. alg. arguments 'beta_1' (default 0.9), 'beta_2' (default 0.999), 'eps' (default 1e-8), 'weight_decay' (default 0.0), 'amsgrad' (default False) ]
Parameters:
Name | Type | Description | Default |
---|---|---|---|
n_bees |
int
|
number of employer bees to use in ABC algorithm |
required |
n_iter |
int
|
number of iterations, or "search cycles", for ABC algorithm |
required |
dataset_train |
QSPRDataset
|
dataset used to train evaluation models |
required |
dataset_eval |
QSPRDataset
|
dataset used for evaluation |
required |
n_processes |
int
|
if > 1, uses multiprocessing when evaluating at an iteration |
1
|
**kwargs |
additional arguments |
{}
|
Returns:
Name | Type | Description |
---|---|---|
dict |
dict
|
{'lr': float, 'lr_decay': float} |
Source code in ecnet/tasks/parameter_tuning.py
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