/**
 * 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.horn.bsp;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.lang.reflect.Constructor;
import java.lang.reflect.InvocationTargetException;
import java.util.Map;

import org.apache.commons.lang.SerializationUtils;
import org.apache.hadoop.fs.FSDataInputStream;
import org.apache.hadoop.fs.FSDataOutputStream;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.io.WritableUtils;
import org.apache.hama.HamaConfiguration;
import org.apache.hama.ml.util.DefaultFeatureTransformer;
import org.apache.hama.ml.util.FeatureTransformer;

import com.google.common.base.Preconditions;
import com.google.common.io.Closeables;

/**
 * NeuralNetwork defines the general operations for all the derivative models.
 * Typically, all derivative models such as Linear Regression, Logistic
 * Regression, and Multilayer Perceptron consist of neurons and the weights
 * between neurons.
 * 
 */
abstract class NeuralNetwork implements Writable {
  protected HamaConfiguration conf;
  protected FileSystem fs;

  private static final double DEFAULT_LEARNING_RATE = 0.5;

  protected double learningRate;
  protected boolean learningRateDecay = false;

  // the name of the model
  protected String modelType;
  // the path to store the model
  protected String modelPath;

  protected FeatureTransformer featureTransformer;

  public NeuralNetwork() {
    this.learningRate = DEFAULT_LEARNING_RATE;
    this.modelType = this.getClass().getSimpleName();
    this.featureTransformer = new DefaultFeatureTransformer();
  }

  public NeuralNetwork(String modelPath) {
  }

  public NeuralNetwork(HamaConfiguration conf, String modelPath) {
    try {
      this.conf = conf;
      this.fs = FileSystem.get(conf);
      this.modelPath = modelPath;

      this.readFromModel();
    } catch (IOException e) {
      e.printStackTrace();
    }

  }

  /**
   * Set the degree of aggression during model training, a large learning rate
   * can increase the training speed, but it also decrease the chance of model
   * converge. Recommend in range (0, 0.3).
   * 
   * @param learningRate
   */
  public void setLearningRate(double learningRate) {
    Preconditions.checkArgument(learningRate > 0,
        "Learning rate must be larger than 0.");
    this.learningRate = learningRate;
  }

  public double getLearningRate() {
    return this.learningRate;
  }

  public void isLearningRateDecay(boolean decay) {
    this.learningRateDecay = decay;
  }

  public String getModelType() {
    return this.modelType;
  }

  /**
   * Train the model with the path of given training data and parameters.
   * 
   * @param dataInputPath The path of the training data.
   * @param trainingParams The parameters for training.
   * @throws IOException
   */
  public void train(HamaConfiguration hamaConf, Path dataInputPath, Map<String, String> trainingParams) {
    Preconditions.checkArgument(this.modelPath != null,
        "Please set the model path before training.");
    // train with BSP job
    try {
      trainInternal(hamaConf, dataInputPath, trainingParams);
      // write the trained model back to model path
      this.readFromModel();
    } catch (IOException e) {
      e.printStackTrace();
    } catch (InterruptedException e) {
      e.printStackTrace();
    } catch (ClassNotFoundException e) {
      e.printStackTrace();
    }
  }

  /**
   * Train the model with the path of given training data and parameters.
   * 
   * @param dataInputPath
   * @param trainingParams
   */
  protected abstract void trainInternal(HamaConfiguration hamaConf,
      Path dataInputPath, Map<String, String> trainingParams)
      throws IOException, InterruptedException, ClassNotFoundException;

  /**
   * Read the model meta-data from the specified location.
   * 
   * @throws IOException
   */
  protected void readFromModel() throws IOException {
    Preconditions.checkArgument(this.modelPath != null,
        "Model path has not been set.");
    FSDataInputStream is = new FSDataInputStream(fs.open(new Path(modelPath)));
    this.readFields(is);
    Closeables.close(is, false);
  }

  /**
   * Write the model data to specified location.
   * 
   * @throws IOException
   */
  public void writeModelToFile() throws IOException {
    Preconditions.checkArgument(this.modelPath != null,
        "Model path has not been set.");
    
    FSDataOutputStream is = fs.create(new Path(this.modelPath), true);
    this.write(is);

    Closeables.close(is, false);
  }

  /**
   * Set the model path.
   * 
   * @param modelPath
   */
  public void setModelPath(String modelPath) {
    this.modelPath = modelPath;
  }

  /**
   * Get the model path.
   * 
   * @return the path to store the model.
   */
  public String getModelPath() {
    return this.modelPath;
  }

  @SuppressWarnings({ "rawtypes", "unchecked" })
  @Override
  public void readFields(DataInput input) throws IOException {
    // read model type
    this.modelType = WritableUtils.readString(input);
    // read learning rate
    this.learningRate = input.readDouble();
    // read model path
    this.modelPath = WritableUtils.readString(input);

    if (this.modelPath.equals("null")) {
      this.modelPath = null;
    }

    // read feature transformer
    int bytesLen = input.readInt();
    byte[] featureTransformerBytes = new byte[bytesLen];
    for (int i = 0; i < featureTransformerBytes.length; ++i) {
      featureTransformerBytes[i] = input.readByte();
    }

    Class<? extends FeatureTransformer> featureTransformerCls = (Class<? extends FeatureTransformer>) SerializationUtils
        .deserialize(featureTransformerBytes);

    Constructor[] constructors = featureTransformerCls
        .getDeclaredConstructors();
    Constructor constructor = constructors[0];

    try {
      this.featureTransformer = (FeatureTransformer) constructor
          .newInstance(new Object[] {});
    } catch (InstantiationException e) {
      e.printStackTrace();
    } catch (IllegalAccessException e) {
      e.printStackTrace();
    } catch (IllegalArgumentException e) {
      e.printStackTrace();
    } catch (InvocationTargetException e) {
      e.printStackTrace();
    }
  }

  @Override
  public void write(DataOutput output) throws IOException {
    // write model type
    WritableUtils.writeString(output, modelType);
    // write learning rate
    output.writeDouble(learningRate);
    // write model path
    if (this.modelPath != null) {
      WritableUtils.writeString(output, modelPath);
    } else {
      WritableUtils.writeString(output, "null");
    }

    // serialize the class
    Class<? extends FeatureTransformer> featureTransformerCls = this.featureTransformer
        .getClass();
    byte[] featureTransformerBytes = SerializationUtils
        .serialize(featureTransformerCls);
    output.writeInt(featureTransformerBytes.length);
    output.write(featureTransformerBytes);
  }

  public void setFeatureTransformer(FeatureTransformer featureTransformer) {
    this.featureTransformer = featureTransformer;
  }

  public FeatureTransformer getFeatureTransformer() {
    return this.featureTransformer;
  }

}