hoops_ai.ml.EXPERIMENTAL
Quick Overview
Classes
GraphClassification([num_classes, ...])GraphClassification is a user-friendly wrapper around the UVNet model.
GraphNodeClassification([num_classes, ...])GraphNodeClassification is a user-friendly wrapper around the BrepSeg model.
- class hoops_ai.ml.EXPERIMENTAL.GraphClassification(num_classes=10, result_dir=None, log_file='cnn_graph_training_errors.log', generate_stream_cache_for_visu=False)
Bases:
FlowModelGraphClassification is a user-friendly wrapper around the UVNet model. It provides default hyperparameters and an interface for users to interact without directly accessing the UVnet class.
- Parameters:
- convert_encoded_data_to_graph(storage, graph_handler, filename)
Converts encoded data from storage into a graph representation, which serves as input for the ML model.
- encode_cad_data(cad_file, cad_loader, storage)
Opens the CAD file and encodes its data into a format suitable for machine learning. Stores the encoded data using the provided storage handler.
- Parameters:
cad_file (str)
cad_loader (CADLoader)
storage (DataStorage)
- Return type:
- encode_label_data(label_storage, storage)
Uses the LabelStorage object to retrieve the labeling information for a given input Stores the label data for the specific machine learning Task
return the str key when the label data is found in the storage object and the size of the label data
- Parameters:
label_storage (LabelStorage)
storage (DataStorage)
- Return type:
- load_model_input_from_files(graph_file, data_id, label_file=None)
Loads a single graph from a file to be used as input for the machine learning model.
- metrics()
Publish/push the ml metrics after traiing the model
- Return type:
- predict_and_postprocess(batch)
Post-processes and formats the raw model output into a structured prediction. Returns a numpy array with top 3 predictions and their probability percentages as integers.
- Returns:
Array with shape (batch_size, 2, 3), where: - First dimension: batch items - Second dimension [0]: class indices (int) - Second dimension [1]: probability percentages (int)
- Return type:
- class hoops_ai.ml.EXPERIMENTAL.GraphNodeClassification(num_classes=25, n_layers_encode=8, dim_node=256, d_model=512, n_heads=32, dropout=0.3, attention_dropout=0.3, act_dropout=0.3, learning_rate=0.002, optimizer_betas=(0.99, 0.999), scheduler_factor=0.5, scheduler_patience=5, scheduler_threshold=0.0001, scheduler_min_lr=1e-06, scheduler_cooldown=2, max_warmup_steps=5000, log_file='training_errors.log', result_dir=None, generate_stream_cache_for_visu=False, **kwargs)
Bases:
FlowModelGraphNodeClassification is a user-friendly wrapper around the BrepSeg model. It provides default hyperparameters and an interface for users to interact without directly accessing the BrepSeg class.
- Parameters:
num_classes (int, optional) – Number of classes for classification. Default: 10
n_layers_encode (int, optional) – Number of encoding layers in the BrepEncoder. Default: 6
dim_node (int, optional) – Dimension of node embeddings. Default: 256
d_model (int, optional) – Dimension of the model in BrepEncoder. Default: 512
n_heads (int, optional) – Number of attention heads in BrepEncoder. Default: 8
dropout (float, optional) – Dropout rate for the classifier. Default: 0.3
attention_dropout (float, optional) – Dropout rate for attention layers. Default: 0.1
act_dropout (float, optional) – Dropout rate for activation layers. Default: 0.1
learning_rate (float, optional) – Initial learning rate for the optimizer. Default: 0.002
optimizer_betas (Tuple[float, float], optional) – Betas for the AdamW optimizer. Default: (0.99, 0.999)
scheduler_factor (float, optional) – Factor for ReduceLROnPlateau scheduler. Default: 0.5
scheduler_patience (int, optional) – Patience for ReduceLROnPlateau scheduler. Default: 5
scheduler_threshold (float, optional) – Threshold for ReduceLROnPlateau scheduler. Default: 1e-4
scheduler_min_lr (float, optional) – Minimum learning rate for scheduler. Default: 1e-6
scheduler_cooldown (int, optional) – Cooldown period for scheduler. Default: 2
max_warmup_steps (int, optional) – Number of warmup steps for learning rate scaling. Default: 5000
log_file (str, optional) – Path to the log file. Default: ‘training_errors.log’
**kwargs (Any) – Additional keyword arguments for BrepSeg.
result_dir (str)
generate_stream_cache_for_visu (bool)
**kwargs
- convert_encoded_data_to_graph(storage, graph_handler, filename)
Converts encoded data from storage into a graph representation, which serves as input for the ML model.
- encode_cad_data(cad_file, cad_loader, storage)
Opens the CAD file and encodes its data into a format suitable for machine learning. Stores the encoded data using the provided storage handler.
- Parameters:
cad_file (str)
cad_loader (CADLoader)
storage (DataStorage)
- Return type:
- encode_label_data(label_storage, storage)
Uses the LabelStorage object to retrieve the labeling information for a given input Stores the label data for the specific machine learning Task
return the str key when the label data is found in the storage object and the size of the label data
- Parameters:
label_storage (LabelStorage)
storage (DataStorage)
- Return type:
- load_model_input_from_files(graph_file, data_id, label_file=None)
Loads a single graph from a file to be used as input for the machine learning model.
- metrics()
Publish/push the ml metrics after training the model
- Return type:
- predict_and_postprocess(batch)
Post-processes and formats the raw model output into a structured prediction. Returns the predictions and their associated probabilities.
- Return type:
