Another pass on code style.

mllib/src/main/scala/org/apache/spark/mllib/tree/README.md

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+This package contains the default implementation of the decision tree algorithm.
+
+The decision tree algorithm supports:
++ Binary classification
++ Regression
++ Information loss calculation with entropy and gini for classification and variance for regression
++ Both continuous and categorical features
+
+# Tree improvements
++ Node model pruning
++ Printing to dot files
+
+# Future Ensemble Extensions
+
++ Random forests
++ Boosting
++ Extremely randomized trees

mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala

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+/*
+ * 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.spark.mllib.tree.configuration
+
+/**
+ * Enum to select the algorithm for the decision tree
+ */
+object Algo extends Enumeration {
+  type Algo = Value
+  val Classification, Regression = Value
+}

mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala

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+/*
+ * 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.spark.mllib.tree.configuration
+
+/**
+ * Enum to describe whether a feature is "continuous" or "categorical"
+ */
+object FeatureType extends Enumeration {
+  type FeatureType = Value
+  val Continuous, Categorical = Value
+}

mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala

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+/*
+ * 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.spark.mllib.tree.configuration
+
+/**
+ * Enum for selecting the quantile calculation strategy
+ */
+object QuantileStrategy extends Enumeration {
+  type QuantileStrategy = Value
+  val Sort, MinMax, ApproxHist = Value
+}

mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala

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+/*
+ * 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.spark.mllib.tree.configuration
+
+import org.apache.spark.mllib.tree.impurity.Impurity
+import org.apache.spark.mllib.tree.configuration.Algo._
+import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
+
+/**
+ * Stores all the configuration options for tree construction
+ * @param algo classification or regression
+ * @param impurity criterion used for information gain calculation
+ * @param maxDepth maximum depth of the tree
+ * @param maxBins maximum number of bins used for splitting features
+ * @param quantileCalculationStrategy algorithm for calculating quantiles
+ * @param categoricalFeaturesInfo A map storing information about the categorical variables and the
+ *                                number of discrete values they take. For example, an entry (n ->
+ *                                k) implies the feature n is categorical with k categories 0,
+ *                                1, 2, ... , k-1. It's important to note that features are
+ *                                zero-indexed.
+ */
+class Strategy (
+    val algo: Algo,
+    val impurity: Impurity,
+    val maxDepth: Int,
+    val maxBins: Int = 100,
+    val quantileCalculationStrategy: QuantileStrategy = Sort,
+    val categoricalFeaturesInfo: Map[Int,Int] = Map[Int,Int]()) extends Serializable {
+
+  var numBins: Int  = Int.MinValue
+}

mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala

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+/*
+ * 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.spark.mllib.tree.impurity
+
+/**
+ * Class for calculating [[http://en.wikipedia.org/wiki/Binary_entropy_function entropy]] during
+ * binary classification.
+ */
+object Entropy extends Impurity {
+
+   def log2(x: Double) = scala.math.log(x) / scala.math.log(2)
+
+  /**
+   * entropy calculation
+   * @param c0 count of instances with label 0
+   * @param c1 count of instances with label 1
+   * @return entropy value
+   */
+   def calculate(c0: Double, c1: Double): Double = {
+     if (c0 == 0 || c1 == 0) {
+       0
+     } else {
+       val total = c0 + c1
+       val f0 = c0 / total
+       val f1 = c1 / total
+       -(f0 * log2(f0)) - (f1 * log2(f1))
+     }
+   }
+
+  def calculate(count: Double, sum: Double, sumSquares: Double): Double =
+    throw new UnsupportedOperationException("Entropy.calculate")
+}

mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala

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+/*
+ * 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.spark.mllib.tree.impurity
+
+/**
+ * Class for calculating the
+ * [[http://en.wikipedia.org/wiki/Decision_tree_learning#Gini_impurity Gini impurity]]
+ * during binary classification.
+ */
+object Gini extends Impurity {
+
+  /**
+   * Gini coefficient calculation
+   * @param c0 count of instances with label 0
+   * @param c1 count of instances with label 1
+   * @return Gini coefficient value
+   */
+  override def calculate(c0: Double, c1: Double): Double = {
+    if (c0 == 0 || c1 == 0) {
+      0
+    } else {
+      val total = c0 + c1
+      val f0 = c0 / total
+      val f1 = c1 / total
+      1 - f0 * f0 - f1 * f1
+    }
+  }
+
+  def calculate(count: Double, sum: Double, sumSquares: Double): Double =
+    throw new UnsupportedOperationException("Gini.calculate")
+}

mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala

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+/*
+ * 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.spark.mllib.tree.impurity
+
+/**
+ * Trait for calculating information gain.
+ */
+trait Impurity extends Serializable {
+
+  /**
+   * information calculation for binary classification
+   * @param c0 count of instances with label 0
+   * @param c1 count of instances with label 1
+   * @return information value
+   */
+  def calculate(c0 : Double, c1 : Double): Double
+
+  /**
+   * information calculation for regression
+   * @param count number of instances
+   * @param sum sum of labels
+   * @param sumSquares summation of squares of the labels
+   * @return information value
+   */
+  def calculate(count: Double, sum: Double, sumSquares: Double): Double
+
+}

mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala

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+/*
+ * 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.spark.mllib.tree.impurity
+
+/**
+ * Class for calculating variance during regression
+ */
+object Variance extends Impurity {
+   override def calculate(c0: Double, c1: Double): Double =
+     throw new UnsupportedOperationException("Variance.calculate")
+
+  /**
+   * variance calculation
+   * @param count number of instances
+   * @param sum sum of labels
+   * @param sumSquares summation of squares of the labels
+   */
+  override def calculate(count: Double, sum: Double, sumSquares: Double): Double = {
+    val squaredLoss = sumSquares - (sum * sum) / count
+    squaredLoss / count
+  }
+}

mllib/src/main/scala/org/apache/spark/mllib/tree/model/Bin.scala

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+/*
+ * 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.spark.mllib.tree.model
+
+import org.apache.spark.mllib.tree.configuration.FeatureType._
+
+/**
+ * Used for "binning" the features bins for faster best split calculation. For a continuous
+ * feature, a bin is determined by a low and a high "split". For a categorical feature,
+ * the a bin is determined using a single label value (category).
+ * @param lowSplit signifying the lower threshold for the continuous feature to be
+ *                 accepted in the bin
+ * @param highSplit signifying the upper threshold for the continuous feature to be
+ *                 accepted in the bin
+ * @param featureType type of feature -- categorical or continuous
+ * @param category categorical label value accepted in the bin
+ */
+case class Bin(lowSplit: Split, highSplit: Split, featureType: FeatureType, category: Double)