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Provides common utility functions

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/*
 * Copyright (c) CQSE GmbH
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.conqat.lib.commons.datamining;

import java.util.HashMap;
import java.util.HashSet;
import java.util.Map;
import java.util.Set;

import org.conqat.lib.commons.collections.IdentityHashSet;

/**
 * Mines association rules from a set of shopping baskets. Uses Apriori algorithm. See
 * http://en.wikipedia.org/wiki/Apriori_algorithm.
 * 
 * @param 
 *            the item type; must support hashing.
 */
public class AssociationRuleMiner {

	/** Threshold for confidence */
	private final float confidenceThreshold;

	/** Miner for frequent item sets */
	private final FrequentItemSetMiner itemSetMiner;

	/**
	 * Constructor.
	 * 
	 * @param supportThreshold
	 *            the support threshold [0..1], i.e. the fraction of the baskets in which a frequent
	 *            item set must be present in order to be considered.
	 * @param confidenceThreshold
	 *            the minimal confidence of the mined rules [0..1].
	 */
	public AssociationRuleMiner(float supportThreshold, float confidenceThreshold) {
		this.confidenceThreshold = confidenceThreshold;
		itemSetMiner = new FrequentItemSetMiner<>(supportThreshold);
	}

	/** Mines frequent item sets from the given shopping baskets. */
	public Set> mineAssociationRules(Set> baskets) {
		Set> result = new IdentityHashSet<>();

		Set> frequentItemSets = itemSetMiner.mineFrequentItemSets(baskets);

		Map, Double> supportMap = new HashMap<>();

		for (FrequentItemSet frequentItemset : frequentItemSets) {
			supportMap.put(frequentItemset.getItems(), frequentItemset.getSupport());
		}

		for (FrequentItemSet frequentItemSet : frequentItemSets) {
			Set items = frequentItemSet.getItems();
			if (items.size() > 1) {
				for (T item : items) {
					Set reducedItemSet = new HashSet<>(items);
					reducedItemSet.remove(item);

					double confidence = frequentItemSet.getSupport() / supportMap.get(reducedItemSet);
					if (confidence >= confidenceThreshold) {
						result.add(new AssociationRule<>(reducedItemSet, item, confidence));
					}
				}
			}
		}
		return result;
	}
}