This is updated PR apache#16722 to latest master

## What changes were proposed in this pull request?

This patch adds support for sample weights to DecisionTreeRegressor and DecisionTreeClassifier.

Note: This patch does not add support for sample weights to RandomForest. As discussed in the JIRA, we would like to add sample weights into the bagging process. This patch is large enough as is, and there are some additional considerations to be made for random forests. Since the machinery introduced here needs to be present regardless, I have opted to leave random forests for a follow up pr.
## How was this patch tested?

The algorithms are tested to ensure that:
    1. Arbitrary scaling of constant weights has no effect
    2. Outliers with small weights do not affect the learned model
    3. Oversampling and weighting are equivalent

Unit tests are also added to test other smaller components.
## Summary of changes

   - Impurity aggregators now store weighted sufficient statistics. They also store a raw count, however, since this is needed to use minInstancesPerNode.

   - Impurity aggregators now also hold the raw count.

   - This patch maintains the meaning of minInstancesPerNode, in that the parameter still corresponds to raw, unweighted counts. It also adds a new parameter minWeightFractionPerNode which requires that nodes must contain at least minWeightFractionPerNode * weightedNumExamples total weight.

   - This patch modifies findSplitsForContinuousFeatures to use weighted sums. Unit tests are added.

   - TreePoint is modified to hold a sample weight

   - BaggedPoint is modified from:
``` Scala
private[spark] class BaggedPoint[Datum](val datum: Datum, val subsampleWeights: Array[Double]) extends Serializable
```
to
``` Scala
private[spark] class BaggedPoint[Datum](
    val datum: Datum,
    val subsampleCounts: Array[Int],
    val sampleWeight: Double) extends Serializable
```
We do not simply multiply the counts by the weight and store that because we need the raw counts and the weight in order to use both minInstancesPerNode and minWeightPerNode

**Note**: many of the changed files are due simply to using Instance instead of LabeledPoint

Closes apache#21632 from imatiach-msft/ilmat/sample-weights.

Authored-by: Ilya Matiach <[email protected]>
Signed-off-by: Sean Owen <[email protected]>