Experiment working with smoothing and disc-algo
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@@ -142,6 +142,7 @@ namespace platform {
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auto states = dataset.getStates(); // Get the states of the features Once they are discretized
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double best_fold_score = 0.0;
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int best_idx_combination = -1;
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bayesnet::Smoothing_t smoothing = bayesnet::Smoothing_t::NONE;
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json best_fold_hyper;
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for (int idx_combination = 0; idx_combination < combinations.size(); ++idx_combination) {
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auto hyperparam_line = combinations[idx_combination];
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@@ -167,7 +168,7 @@ namespace platform {
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hyperparameters.check(valid, dataset_name);
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clf->setHyperparameters(hyperparameters.get(dataset_name));
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// Train model
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clf->fit(X_nested_train, y_nested_train, features, className, states);
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clf->fit(X_nested_train, y_nested_train, features, className, states, smoothing);
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// Test model
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score += clf->score(X_nested_test, y_nested_test);
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}
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@@ -186,7 +187,7 @@ namespace platform {
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auto valid = clf->getValidHyperparameters();
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hyperparameters.check(valid, dataset_name);
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clf->setHyperparameters(best_fold_hyper);
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clf->fit(X_train, y_train, features, className, states);
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clf->fit(X_train, y_train, features, className, states, smoothing);
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best_fold_score = clf->score(X_test, y_test);
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// Return the result
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result->idx_dataset = task["idx_dataset"].get<int>();
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@@ -194,8 +194,7 @@ namespace platform {
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//
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// Train model
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//
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clf->setSmoothing(smooth_type);
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clf->fit(X_train, y_train, features, className, states);
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clf->fit(X_train, y_train, features, className, states, smooth_type);
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if (!quiet)
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showProgress(nfold + 1, getColor(clf->getStatus()), "b");
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auto clf_notes = clf->getNotes();
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