Extending PHOTONAI Graph

Extending PHOTONAI Graph is equally easy as extending PHOTONAI itself.

For new Transformers see adding Transformers.

For new Estimators see adding Estimators

PHOTONAI Graph specific base classes

PHOTONAI Graph offers a few base classes for simple integration.

To extend PHOTONAI Graph simply inherit from the desired base class and register your custom model to PHOTONAI.

Base class for all graph constructors. Implements methods shared by different constructors.

Parameters:

Name	Type	Description	Default
`one_hot_nodes`	`int`	whether to return a one hot node encoding as feature or not	`0`
`use_abs`	`int`	whether to convert all matrix values to absolute values before applying other transformations	`0`
`fisher_transform`	`int`	whether to perform a fisher transform of every matrix	`0`
`discard_original_connectivity`	`bool`	If true the second index of the last dimension will be the original connectivity. Otherwise the original connectivity will be dropped from the matrix.	`False`
`use_abs_fisher`	`int`	Changes the values to absolute values. Is applied after fisher transform and before z-score transformation	`0`
`zscore`	`int`	performs a zscore transformation of the data. Applied after fisher transform and np_abs eval_final_perfomance is set to True	`0`
`use_abs_zscore`	`int`	whether to use the absolute values of the z-score transformation or allow for negative values. Applied after fisher transform, use_abs and zscore	`0`
`adjacency_axis`	`int`	position of the adjacency matrix, default being zero	`0`
`logs`	`str`	Path to the log data	`None`

Abstract base class for classification algorithms

Parameters:

Name	Type	Description	Default
`nn_epochs`	`int`	Number of epochs to fit the model	`200`
`learning_rate`	`float`	Learning rate for model training	`0.001`
`batch_size`	`int`	Batch size for model training	`32`
`adjacency_axis`	`int`	Axis which contains the adjacency	`0`
`feature_axis`	`int`	Axis which contains the features	`1`
`add_self_loops`	`bool`	If this value is true, a self loop is added to each node of each graph	`True`
`allow_zero_in_degree`	`bool`	If true the dgl model allows zero-in-degree Graphs	`False`
`validation_score`	`bool`	It true the input data is split into train and test (90%/10%). The testset is then used to get validation results during training	`False`
`verbose`	`bool`	If true verbose output is generated	`False`
`logs`	`str`	Default logging directory	`None`

Abstract base class for regression algorithms

Parameters:

Name	Type	Description	Default
`nn_epochs`	`int`	Number of epochs to fit the model	`200`
`learning_rate`	`float`	Learning rate for model training	`0.001`
`batch_size`	`int`	Batch size for model training	`32`
`adjacency_axis`	`int`	Axis which contains the adjacency	`0`
`feature_axis`	`int`	Axis which contains the features	`1`
`add_self_loops`	`bool`	If this value is true, a self loop is added to each node of each graph	`True`
`allow_zero_in_degree`	`bool`	If true the dgl model allows zero-in-degree Graphs	`False`
`validation_score`	`bool`	If true the input data is split into train and test (90%/10%). The testset is then used to get validation results during training	`False`
`verbose`	`bool`	If true verbose output is generated	`False`
`logs`	`str`	Default logging directory	`None`