/**
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You 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.apache.mahout.cf.taste.hadoop.preparation;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat;
import org.apache.hadoop.util.ToolRunner;
import org.apache.mahout.cf.taste.hadoop.EntityPrefWritable;
import org.apache.mahout.cf.taste.hadoop.ToEntityPrefsMapper;
import org.apache.mahout.cf.taste.hadoop.ToItemPrefsMapper;
import org.apache.mahout.cf.taste.hadoop.item.ItemIDIndexMapper;
import org.apache.mahout.cf.taste.hadoop.item.ItemIDIndexReducer;
import org.apache.mahout.cf.taste.hadoop.item.RecommenderJob;
import org.apache.mahout.cf.taste.hadoop.item.ToUserVectorsReducer;
import org.apache.mahout.common.AbstractJob;
import org.apache.mahout.common.HadoopUtil;
import org.apache.mahout.math.VarIntWritable;
import org.apache.mahout.math.VarLongWritable;
import org.apache.mahout.math.VectorWritable;

import java.util.Map;

public class PreparePreferenceMatrixJob extends AbstractJob {

  public static final String NUM_USERS = "numUsers.bin";
  public static final String ITEMID_INDEX = "itemIDIndex";
  public static final String USER_VECTORS = "userVectors";
  public static final String RATING_MATRIX = "ratingMatrix";

  private static final int DEFAULT_MIN_PREFS_PER_USER = 1;

  public static void main(String[] args) throws Exception {
    ToolRunner.run(new PreparePreferenceMatrixJob(), args);
  }

  @Override
  public int run(String[] args) throws Exception {

    addInputOption();
    addOutputOption();
    addOption("maxPrefsPerUser", "mppu", "max number of preferences to consider per user, " +
            "users with more preferences will be sampled down");
    addOption("minPrefsPerUser", "mp", "ignore users with less preferences than this "
            + "(default: " + DEFAULT_MIN_PREFS_PER_USER + ')', String.valueOf(DEFAULT_MIN_PREFS_PER_USER));
    addOption("booleanData", "b", "Treat input as without pref values", Boolean.FALSE.toString());
    addOption("ratingShift", "rs", "shift ratings by this value", "0.0");

    Map<String, String> parsedArgs = parseArguments(args);
    if (parsedArgs == null) {
      return -1;
    }

    int minPrefsPerUser = Integer.parseInt(parsedArgs.get("--minPrefsPerUser"));
    boolean booleanData = Boolean.valueOf(parsedArgs.get("--booleanData"));
    float ratingShift = Float.parseFloat(parsedArgs.get("--ratingShift"));
    //convert items to an internal index
    Job itemIDIndex = prepareJob(getInputPath(), getOutputPath(ITEMID_INDEX), TextInputFormat.class,
            ItemIDIndexMapper.class, VarIntWritable.class, VarLongWritable.class, ItemIDIndexReducer.class,
            VarIntWritable.class, VarLongWritable.class, SequenceFileOutputFormat.class);
    itemIDIndex.setCombinerClass(ItemIDIndexReducer.class);
    itemIDIndex.waitForCompletion(true);
    //convert user preferences into a vector per user
    Job toUserVectors = prepareJob(getInputPath(), getOutputPath(USER_VECTORS), TextInputFormat.class,
            ToItemPrefsMapper.class, VarLongWritable.class, booleanData ? VarLongWritable.class : EntityPrefWritable.class,
            ToUserVectorsReducer.class, VarLongWritable.class, VectorWritable.class, SequenceFileOutputFormat.class);
    toUserVectors.getConfiguration().setBoolean(RecommenderJob.BOOLEAN_DATA, booleanData);
    toUserVectors.getConfiguration().setInt(ToUserVectorsReducer.MIN_PREFERENCES_PER_USER, minPrefsPerUser);
    toUserVectors.getConfiguration().set(ToEntityPrefsMapper.RATING_SHIFT, String.valueOf(ratingShift));
    toUserVectors.waitForCompletion(true);
    //we need the number of users later
    int numberOfUsers = (int) toUserVectors.getCounters().findCounter(ToUserVectorsReducer.Counters.USERS).getValue();
    HadoopUtil.writeInt(numberOfUsers, getOutputPath(NUM_USERS), getConf());
    //build the rating matrix
    Job toItemVectors = prepareJob(getOutputPath(USER_VECTORS), getOutputPath(RATING_MATRIX),
            ToItemVectorsMapper.class, IntWritable.class, VectorWritable.class, ToItemVectorsReducer.class,
            IntWritable.class, VectorWritable.class);
    toItemVectors.setCombinerClass(ToItemVectorsReducer.class);

    /* configure sampling regarding the uservectors */
    if (parsedArgs.containsKey("--maxPrefsPerUser")) {
      int samplingSize = Integer.parseInt(parsedArgs.get("--maxPrefsPerUser"));
      toItemVectors.getConfiguration().setInt(ToItemVectorsMapper.SAMPLE_SIZE, samplingSize);
    }

    toItemVectors.waitForCompletion(true);

    return 0;
  }
}