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@@ -154,7 +154,7 @@ def main():
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return BinarySolution.random(p_length, validator)
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- class SVMEvaluator(Evaluator):
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+ class RandomForestEvaluator(Evaluator):
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# define evaluate function here (need of data information)
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def compute(self, solution):
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@@ -172,9 +172,9 @@ def main():
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y_train_filters = self._data['y_train']
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x_test_filters = self._data['x_test'].iloc[:, indices]
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- model = _get_best_model(x_train_filters, y_train_filters)
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- #model = RandomForestClassifier(n_estimators=10)
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- #model = model.fit(x_train_filters, y_train_filters)
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+ # model = _get_best_model(x_train_filters, y_train_filters)
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+ model = RandomForestClassifier(n_estimators=500, class_weight='balanced', bootstrap=True, max_samples=0.75, n_jobs=-1)
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+ model = model.fit(x_train_filters, y_train_filters)
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y_test_model = model.predict(x_test_filters)
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test_roc_auc = roc_auc_score(self._data['y_test'], y_test_model)
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@@ -183,7 +183,7 @@ def main():
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diff = end - start
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- #print("Real evaluation took: {}, score found: {}".format(divmod(diff.days * 86400 + diff.seconds, 60), test_roc_auc))
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+ print("Real evaluation took: {}, score found: {}".format(divmod(diff.days * 86400 + diff.seconds, 60), test_roc_auc))
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return test_roc_auc
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@@ -216,7 +216,7 @@ def main():
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# custom ILS for surrogate use
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algo = ILSPopSurrogate(initalizer=init,
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- evaluator=SVMEvaluator(data={'x_train': x_train, 'y_train': y_train, 'x_test': x_test, 'y_test': y_test}), # same evaluator by default, as we will use the surrogate function
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+ evaluator=RandomForestEvaluator(data={'x_train': x_train, 'y_train': y_train, 'x_test': x_test, 'y_test': y_test}), # same evaluator by default, as we will use the surrogate function
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operators=operators,
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policy=policy,
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validator=validator,
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