Consensus modelling

Rather than picking one descriptor/method combination up front, QSARmil’s beginner pipeline trains many of them and then searches for the best- performing subset to combine into a consensus prediction — averaging predictions across models for regression, or majority-voting for classification. This tends to be more robust than trusting any single model’s choice of descriptor and architecture.

This happens in two steps: LazyMIL trains every descriptor/estimator combination and collects their predictions; qsarcons.consensus.GeneticSearch then searches over which combination of those models to combine.

Step 1 — LazyMIL

LazyMIL trains every combination of the 9 built-in descriptors and the 8 built-in estimators (see Multi-instance learning) on a train/validation split, and predicts on train, validation, and test sets with each one.

from qsarmil.modelling.lazy import LazyMIL

lazy_model = LazyMIL(
    task="continuous",  # or "binary" for classification
    num_conf=10,
    hopt=True,
    output_folder="results",
    verbose=True,
)
result_train, result_val, result_test = lazy_model.run(
    smiles_train, y_train, smiles_val, y_val, smiles_test
)

Each returned DataFrame has one row per molecule and one column per descriptor/estimator combination (named "<descriptor>|<estimator>"), plus a SMILES column and, for train/val, a Y_TRUE column.

Step 2 — GeneticSearch

GeneticSearch treats each trained model as a candidate and searches for the subset that gives the best consensus score, evaluated on the validation set:

from qsarcons.consensus import GeneticSearch

x_val, y_val_true = result_val.iloc[:, 2:], result_val.iloc[:, 1]

cons_search = GeneticSearch(cons_size="auto", n_iter=50)
best_models = cons_search.run(x_val, y_val_true)

x_test = result_test.iloc[:, 1:]
y_test_pred = cons_search.predict(x_test[best_models])

  • cons_size — number of models in the final consensus. "auto" tries several candidate sizes (2, 4, 6, … up to 14 by default) and keeps whichever performs best on the validation set; pass an integer to fix the size instead.

  • n_iter — number of genetic algorithm generations used to search for the best-scoring subset at a given size.

  • .predict() combines the selected models’ predictions by averaging (regression, r2 score used internally) or majority vote (classification, f1_score used internally).

This whole two-step flow (LazyMIL → GeneticSearch) is what MultiConformerEstimator.train_predict() runs end-to-end for the beginner pipeline — see Tutorials.