SSRMolecularPredictor¶
Regularization for generalization across graph sizes.
from torch_molecule import SSRMolecularPredictor
Constructor¶
- class SSRMolecularPredictor(coarse_ratios=[0.8, 0.9], cmd_coeff=0.1, fine_grained=True, n_moments=5, coarse_pool='mean', num_task=1, task_type='regression', num_layer=5, hidden_size=300, gnn_type='gin-virtual', drop_ratio=0.5, norm_layer='batch_norm', graph_pooling='sum', augmented_feature=None, batch_size=128, epochs=500, learning_rate=0.001, weight_decay=0.0, grad_clip_value=None, patience=50, use_lr_scheduler=False, scheduler_factor=0.5, scheduler_patience=5, loss_criterion=None, evaluate_criterion=None, evaluate_higher_better=None, verbose='none', device=None, model_name='SSRMolecularPredictor')[source]¶
This predictor implements a SizeShiftReg model with the GNN.
References
Paper: SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks. https://arxiv.org/abs/2207.07888
Reference Code: https://github.com/DavideBuffelli/SizeShiftReg/tree/main
- Parameters:
coarse_ratios (List[float], default=[0.8, 0.9]) – List of ratios for graph coarsening. Each ratio determines the percentage of nodes to keep in the coarsened graph.
cmd_coeff (float, default=0.1) – Weight for CMD (Central Moment Discrepancy) loss. Controls the strength of the size-shift regularization.
fine_grained (bool, default=True) – Whether to use fine-grained CMD. When True, matches distributions at a more detailed level.
n_moments (int, default=5) – Number of moments to match in the CMD calculation. Higher values capture more complex distribution characteristics.
coarse_pool (str, default='mean') – Pooling method for coarsened graphs.
num_task (int, default=1) – Number of prediction tasks.
task_type (str, default="regression") – Type of prediction task, either “regression” or “classification”.
num_layer (int, default=5) – Number of GNN layers.
hidden_size (int, default=300) – Dimension of hidden node features.
gnn_type (str, default="gin-virtual") – Type of GNN architecture to use. One of [“gin-virtual”, “gcn-virtual”, “gin”, “gcn”].
drop_ratio (float, default=0.5) – Dropout probability.
norm_layer (str, default="batch_norm") – Type of normalization layer to use. One of [“batch_norm”, “layer_norm”, “instance_norm”, “graph_norm”, “size_norm”, “pair_norm”].
graph_pooling (str, default="sum") – Method for aggregating node features to graph-level representations. One of [“sum”, “mean”, “max”].
augmented_feature (list or None, default=None) – Additional molecular fingerprints to use as features. It will be concatenated with the graph representation after pooling. Examples like [“morgan”, “maccs”] or None.
batch_size (int, default=128) – Number of samples per batch for training.
epochs (int, default=500) – Maximum number of training epochs.
loss_criterion (callable, optional) – Loss function for training.
evaluate_criterion (str or callable, optional) – Metric for model evaluation.
evaluate_higher_better (bool, optional) – Whether higher values of the evaluation metric are better.
learning_rate (float, default=0.001) – Learning rate for optimizer.
grad_clip_value (float, optional) – Maximum norm of gradients for gradient clipping.
weight_decay (float, default=0.0) – L2 regularization strength.
patience (int, default=50) – Number of epochs to wait for improvement before early stopping.
use_lr_scheduler (bool, default=False) – Whether to use learning rate scheduler.
scheduler_factor (float, default=0.5) – Factor by which to reduce learning rate when plateau is reached.
scheduler_patience (int, default=5) – Number of epochs with no improvement after which learning rate will be reduced.
verbose (str, default="none") – Whether to display progress info. Options are: “none”, “progress_bar”, “print_statement”. If any other, “none” is automatically chosen.
device (torch.device or str, optional) – Device to use for computations.
model_name (str, default="SSRMolecularPredictor") – Name of the model.
Example¶
import numpy as np
import torch
from torch_molecule import SSRMolecularPredictor
from torch_molecule.utils.search import ParameterType, ParameterSpec
import os
EPOCHS = 5
def train_ssr_predictor():
smiles_list = [
'CNC[C@H]1OCc2cnnn2CCCC(=O)N([C@H](C)CO)C[C@@H]1C',
'CNC[C@@H]1OCc2cnnn2CCCC(=O)N([C@H](C)CO)C[C@H]1C',
'C[C@H]1CN([C@@H](C)CO)C(=O)CCCn2cc(nn2)CO[C@@H]1CN(C)C(=O)CCC(F)(F)F',
'CC1=CC=C(C=C1)C2=CC(=NN2C3=CC=C(C=C3)S(=O)(=O)N)C(F)(F)F'
]
properties = np.array([0, 0, 1, 1]) # Binary classification
unlabeled_smiles = [
'CC1=CC=C(C=C1)C2=CC(=NN2C3=CC=C(C=C3)S(=O)(=O)N)C(F)(F)F',
'CNC[C@H]1OCc2cnnn2CCCC(=O)N([C@H](C)CO)C[C@@H]1C'
]
print("\n=== rpgnn model initialization ===")
model = SSRMolecularPredictor(
num_task=1,
task_type="regression",
num_layer=3,
coarse_ratios=[0.8, 0.9],
cmd_coeff=0.1,
fine_grained=True,
n_moments=5,
hidden_size=300,
batch_size=128,
epochs=EPOCHS,
verbose="print_statement"
)
print("SSR model initialized successfully")
# 2. Basic fitting test
print("\n=== Testing SSR model fitting ===")
model.fit(smiles_list[:3], properties[:3])
print("SSR model fitting completed")
# 3. Prediction test
print("\n=== Testing SSR model prediction ===")
predictions = model.predict(smiles_list[3:])
print(f"Prediction shape: {predictions['prediction'].shape}")
print(f"Prediction for new molecule: {predictions['prediction']}")
# 4. Auto-fitting test with search space parameters
print("\n=== Testing SSR model auto-fitting ===")
# Test auto-fitting
search_parameters = {
'num_layer': ParameterSpec(
param_type=ParameterType.INTEGER,
value_range=(2, 4)
),
'hidden_size': ParameterSpec(
param_type=ParameterType.INTEGER,
value_range=(64, 256)
),
'learning_rate': ParameterSpec(
param_type=ParameterType.LOG_FLOAT,
value_range=(1e-4, 1e-2)
)
}
model_auto = SSRMolecularPredictor(
num_task=1,
task_type="regression",
epochs=3,
verbose="print_statement"
)
model_auto.autofit(
smiles_list,
properties,
X_unlbl=unlabeled_smiles,
search_parameters=search_parameters,
n_trials=2
)
# Test save/load
save_path = "test_ssr_model.pt"
model.save(save_path)
new_model = SSRMolecularPredictor(
num_task=1,
task_type="regression"
)
new_model.load(save_path)
if os.path.exists(save_path):
os.remove(save_path)
if __name__ == "__main__":
train_ssr_predictor()
Methods¶
Fit the model to the training data with optional validation set. |
|
Automatically find the best hyperparameters using Optuna optimization. |
|
Make predictions using the fitted model. |
For shared checkpoint and parameter operations, see Base Modules.
fit¶
- SSRMolecularPredictor.fit(X_train, y_train, X_val=None, y_val=None, X_unlbl=None)¶
Fit the model to the training data with optional validation set.
- Parameters:
X_train (List[str]) – Training set input molecular structures as SMILES strings
y_train (Union[List, np.ndarray]) – Training set target values for property prediction
X_val (List[str], optional) – Validation set input molecular structures as SMILES strings. If None, training data will be used for validation
y_val (Union[List, np.ndarray], optional) – Validation set target values. Required if X_val is provided
X_unlbl (List[str], optional) – Unlabeled set input molecular structures as SMILES strings.
- Returns:
self – Fitted estimator
- Return type:
autofit¶
- SSRMolecularPredictor.autofit(X_train, y_train, X_val=None, y_val=None, X_unlbl=None, search_parameters=None, n_trials=10)¶
Automatically find the best hyperparameters using Optuna optimization.
- Parameters:
X_train (List[str])
y_train (List | ndarray | None)
X_val (List[str] | None)
y_val (List | ndarray | None)
X_unlbl (List[str] | None)
search_parameters (Dict[str, ParameterSpec] | None)
n_trials (int)
- Return type:
predict¶
- SSRMolecularPredictor.predict(X)¶
Make predictions using the fitted model.
- Parameters:
X (List[str]) – List of SMILES strings to make predictions for
- Returns:
- Dictionary containing:
’prediction’: Model predictions (shape: [n_samples, n_tasks])
- Return type:
Dict[str, np.ndarray]