Trait-Driven Model Imports๏
fusilli
offers various models for data fusion available in fusilli.fusionmodels
.
Each model is adaptable to different degrees, and you can modify them as described in Modify the Fusion Models.
Choosing a Model๏
You have several ways to select a model:
Direct Import: Import a specific model from
fusilli.fusionmodels
at the beginning of your script.Use Model Chooser Function: Utilize
fusilli.utils.model_chooser.import_chosen_fusion_models()
to filter models based on specific criteria.
Model Chooser Function Usage๏
The fusilli.utils.model_chooser.import_chosen_fusion_models()
function takes a dictionary of criteria. The keys represent criteria names, and the values are the desired criteria.
For instance, to fetch models capable of fusing tabular and image data, use:
from fusilli.utils.model_chooser import import_chosen_fusion_models
criteria = {
"modality_type": ["tab_img"],
}
models = import_chosen_fusion_models(criteria)
This function returns a list of models that fulfill the specified criteria. Access these models by indexing the list (e.g., models[0]
, models[1]
, etc.). It will also display a list of all models meeting that description.
Examples of Criteria๏
Attention-Based Fusion for Tabular and Image Data:
criteria = {
"modality_type": ["tab_img"],
"fusion_type": ["attention"],
}
Operation- or Attention-Based Fusion for Tabular and Image Data; also Uni-Modal Benchmark Models:
criteria = {
"modality_type": ["tab_img", "tabular1", "img"],
"fusion_type": ["operation", "attention"],
}
Subspace-Based Fusion for Any Modality Type:
criteria = {
"fusion_type": ["subspace"],
"modality_type": "all",
}
Specific Models by Name: Tabular1Unimodal, Tabular2Unimodal, and ConcatTabularData:
criteria = {
"class_name": ["Tabular1Unimodal", "Tabular2Unimodal", "ConcatTabularData"],
}