SPARK-2099. Report progress while task is running.

This is a sketch of a patch that allows the UI to show metrics for tasks that have not yet completed.  It adds a heartbeat every 2 seconds from the executors to the driver, reporting metrics for all of the executor's tasks.

It still needs unit tests, polish, and cluster testing, but I wanted to put it up to get feedback on the approach.

Author: Sandy Ryza <sandy@cloudera.com>

Closes #1056 from sryza/sandy-spark-2099 and squashes the following commits:

93b9fdb [Sandy Ryza] Up heartbeat interval to 10 seconds and other tidying
132aec7 [Sandy Ryza] Heartbeat and HeartbeatResponse are already Serializable as case classes
38dffde [Sandy Ryza] Additional review feedback and restore test that was removed in BlockManagerSuite
51fa396 [Sandy Ryza] Remove hostname race, add better comments about threading, and some stylistic improvements
3084f10 [Sandy Ryza] Make TaskUIData a case class again
3bda974 [Sandy Ryza] Stylistic fixes
0dae734 [Sandy Ryza] SPARK-2099. Report progress while task is running.
[SPARK-2179][SQL] A minor refactoring Java data type APIs (2179 follow-up).

It is a follow-up PR of SPARK-2179 (https://issues.apache.org/jira/browse/SPARK-2179). It makes package names of data type APIs more consistent across languages (Scala: `org.apache.spark.sql`, Java: `org.apache.spark.sql.api.java`, Python: `pyspark.sql`).

Author: Yin Huai <huai@cse.ohio-state.edu>

Closes #1712 from yhuai/javaDataType and squashes the following commits:

62eb705 [Yin Huai] Move package-info.
add4bcb [Yin Huai] Make the package names of data type classes consistent across languages by moving all Java data type classes to package sql.api.java.
[SQL][SPARK-2212]Hash Outer Join

This patch is to support the hash based outer join. Currently, outer join for big relations are resort to `BoradcastNestedLoopJoin`, which is super slow. This PR will create 2 hash tables for both relations in the same partition, which greatly reduce the table scans.

Here is the testing code that I used:
```
package org.apache.spark.sql.hive

import org.apache.spark.SparkContext
import org.apache.spark.SparkConf
import org.apache.spark.sql._

case class Record(key: String, value: String)

object JoinTablePrepare extends App {
  import TestHive2._

  val rdd = sparkContext.parallelize((1 to 3000000).map(i => Record(s"${i % 828193}", s"val_$i")))

  runSqlHive("SHOW TABLES")
  runSqlHive("DROP TABLE if exists a")
  runSqlHive("DROP TABLE if exists b")
  runSqlHive("DROP TABLE if exists result")
  rdd.registerAsTable("records")

  runSqlHive("""CREATE TABLE a (key STRING, value STRING)
                 | ROW FORMAT SERDE
                 | 'org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe'
                 | STORED AS RCFILE
               """.stripMargin)
  runSqlHive("""CREATE TABLE b (key STRING, value STRING)
                 | ROW FORMAT SERDE
                 | 'org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe'
                 | STORED AS RCFILE
               """.stripMargin)
  runSqlHive("""CREATE TABLE result (key STRING, value STRING)
                 | ROW FORMAT SERDE
                 | 'org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe'
                 | STORED AS RCFILE
               """.stripMargin)

  hql(s"""from records
             | insert into table a
             | select key, value
           """.stripMargin)
  hql(s"""from records
             | insert into table b select key + 100000, value
           """.stripMargin)
}

object JoinTablePerformanceTest extends App {
  import TestHive2._

  hql("SHOW TABLES")
  hql("set spark.sql.shuffle.partitions=20")

  val leftOuterJoin = "insert overwrite table result select a.key, b.value from a left outer join b on a.key=b.key"
  val rightOuterJoin = "insert overwrite table result select a.key, b.value from a right outer join b on a.key=b.key"
  val fullOuterJoin = "insert overwrite table result select a.key, b.value from a full outer join b on a.key=b.key"

  val results = ("LeftOuterJoin", benchmark(leftOuterJoin)) :: ("LeftOuterJoin", benchmark(leftOuterJoin)) ::
                ("RightOuterJoin", benchmark(rightOuterJoin)) :: ("RightOuterJoin", benchmark(rightOuterJoin)) ::
                ("FullOuterJoin", benchmark(fullOuterJoin)) :: ("FullOuterJoin", benchmark(fullOuterJoin)) :: Nil
  val explains = hql(s"explain $leftOuterJoin").collect ++ hql(s"explain $rightOuterJoin").collect ++ hql(s"explain $fullOuterJoin").collect
  println(explains.mkString(",\n"))
  results.foreach { case (prompt, result) => {
      println(s"$prompt: took ${result._1} ms (${result._2} records)")
    }
  }

  def benchmark(cmd: String) = {
    val begin = System.currentTimeMillis()
    val result = hql(cmd)
    val end = System.currentTimeMillis()
    val count = hql("select count(1) from result").collect.mkString("")
    ((end - begin), count)
  }
}
```
And the result as shown below:
```
[Physical execution plan:],
[InsertIntoHiveTable (MetastoreRelation default, result, None), Map(), true],
[ Project [key#95,value#98]],
[  HashOuterJoin [key#95], [key#97], LeftOuter, None],
[   Exchange (HashPartitioning [key#95], 20)],
[    HiveTableScan [key#95], (MetastoreRelation default, a, None), None],
[   Exchange (HashPartitioning [key#97], 20)],
[    HiveTableScan [key#97,value#98], (MetastoreRelation default, b, None), None],
[Physical execution plan:],
[InsertIntoHiveTable (MetastoreRelation default, result, None), Map(), true],
[ Project [key#102,value#105]],
[  HashOuterJoin [key#102], [key#104], RightOuter, None],
[   Exchange (HashPartitioning [key#102], 20)],
[    HiveTableScan [key#102], (MetastoreRelation default, a, None), None],
[   Exchange (HashPartitioning [key#104], 20)],
[    HiveTableScan [key#104,value#105], (MetastoreRelation default, b, None), None],
[Physical execution plan:],
[InsertIntoHiveTable (MetastoreRelation default, result, None), Map(), true],
[ Project [key#109,value#112]],
[  HashOuterJoin [key#109], [key#111], FullOuter, None],
[   Exchange (HashPartitioning [key#109], 20)],
[    HiveTableScan [key#109], (MetastoreRelation default, a, None), None],
[   Exchange (HashPartitioning [key#111], 20)],
[    HiveTableScan [key#111,value#112], (MetastoreRelation default, b, None), None]
LeftOuterJoin: took 16072 ms ([3000000] records)
LeftOuterJoin: took 14394 ms ([3000000] records)
RightOuterJoin: took 14802 ms ([3000000] records)
RightOuterJoin: took 14747 ms ([3000000] records)
FullOuterJoin: took 17715 ms ([6000000] records)
FullOuterJoin: took 17629 ms ([6000000] records)
```

Without this PR, the benchmark will run seems never end.

Author: Cheng Hao <hao.cheng@intel.com>

Closes #1147 from chenghao-intel/hash_based_outer_join and squashes the following commits:

65c599e [Cheng Hao] Fix issues with the community comments
72b1394 [Cheng Hao] Fix bug of stale value in joinedRow
55baef7 [Cheng Hao] Add HashOuterJoin
[SPARK-2729] [SQL] Forgot to match Timestamp type in ColumnBuilder

just a match forgot, found after SPARK-2710 , TimestampType can be used by a SchemaRDD generated from JDBC ResultSet

Author: chutium <teng.qiu@gmail.com>

Closes #1636 from chutium/SPARK-2729 and squashes the following commits:

71af77a [chutium] [SPARK-2729] [SQL] added Timestamp in NullableColumnAccessorSuite
39cf9f8 [chutium] [SPARK-2729] add Timestamp Type into ColumnBuilder TestSuite, ref. #1636
ab6ff97 [chutium] [SPARK-2729] Forgot to match Timestamp type in ColumnBuilder
[SPARK-2767] [SQL] SparkSQL CLI doens't output error message if query failed.

Author: Cheng Hao <hao.cheng@intel.com>

Closes #1686 from chenghao-intel/spark_sql_cli and squashes the following commits:

eb664cc [Cheng Hao] Output detailed failure message in console
93b0382 [Cheng Hao] Fix Bug of no output in cli if exception thrown internally
[SQL] Documentation: Explain cacheTable command

add the `cacheTable` specification

Author: CrazyJvm <crazyjvm@gmail.com>

Closes #1681 from CrazyJvm/sql-programming-guide-cache and squashes the following commits:

0a231e0 [CrazyJvm] grammar fixes
a04020e [CrazyJvm] modify title to Cached tables
18b6594 [CrazyJvm] fix format
2cbbf58 [CrazyJvm] add cacheTable guide
[SPARK-695] In DAGScheduler's getPreferredLocs, track set of visited partitions.

getPreferredLocs traverses a dependency graph of partitions using depth first search.  Given a complex dependency graph, the old implementation may explore a set of paths in the graph that is exponential in the number of nodes.  By maintaining a set of visited nodes the new implementation avoids revisiting nodes, preventing exponential blowup.

Some comment and whitespace cleanups are also included.

Author: Aaron Staple <aaron.staple@gmail.com>

Closes #1362 from staple/SPARK-695 and squashes the following commits:

ecea0f3 [Aaron Staple] address review comments
751c661 [Aaron Staple] [SPARK-695] Add a unit test.
5adf326 [Aaron Staple] Replace getPreferredLocsInternal's HashMap argument with a simpler HashSet.
58e37d0 [Aaron Staple] Replace comment documenting NarrowDependency.
6751ced [Aaron Staple] Revert "Remove unused variable."
04c7097 [Aaron Staple] Fix indentation.
0030884 [Aaron Staple] Remove unused variable.
33f67c6 [Aaron Staple] Clarify comment.
4e42b46 [Aaron Staple] Remove apparently incorrect comment describing NarrowDependency.
65c2d3d [Aaron Staple] [SPARK-695] In DAGScheduler's getPreferredLocs, track set of visited partitions.
[SPARK-2490] Change recursive visiting on RDD dependencies to iterative approach

When performing some transformations on RDDs after many iterations, the dependencies of RDDs could be very long. It can easily cause StackOverflowError when recursively visiting these dependencies in Spark core. For example:

    var rdd = sc.makeRDD(Array(1))
    for (i <- 1 to 1000) {
      rdd = rdd.coalesce(1).cache()
      rdd.collect()
    }

This PR changes recursive visiting on rdd's dependencies to iterative approach to avoid StackOverflowError.

In addition to the recursive visiting, since the Java serializer has a known [bug](http://bugs.java.com/bugdatabase/view_bug.do?bug_id=4152790) that causes StackOverflowError too when serializing/deserializing a large graph of objects. So applying this PR only solves part of the problem. Using KryoSerializer to replace Java serializer might be helpful. However, since KryoSerializer is not supported for `spark.closure.serializer` now, I can not test if KryoSerializer can solve Java serializer's problem completely.

Author: Liang-Chi Hsieh <viirya@gmail.com>

Closes #1418 from viirya/remove_recursive_visit and squashes the following commits:

6b2c615 [Liang-Chi Hsieh] change function name; comply with code style.
5f072a7 [Liang-Chi Hsieh] add comments to explain Stack usage.
8742dbb [Liang-Chi Hsieh] comply with code style.
900538b [Liang-Chi Hsieh] change recursive visiting on rdd's dependencies to iterative approach to avoid stackoverflowerror.
SPARK-1612: Fix potential resource leaks

JIRA: https://issues.apache.org/jira/browse/SPARK-1612

Move the "close" statements into a "finally" block.

Author: zsxwing <zsxwing@gmail.com>

Closes #535 from zsxwing/SPARK-1612 and squashes the following commits:

ae52f50 [zsxwing] Update to follow the code style
549ba13 [zsxwing] SPARK-1612: Fix potential resource leaks
[SPARK-2379] Fix the bug that streaming's receiver may fall into a dead loop

Author: joyyoj <sunshch@gmail.com>

Closes #1694 from joyyoj/SPARK-2379 and squashes the following commits:

d73790d [joyyoj] SPARK-2379 Fix the bug that streaming's receiver may fall into a dead loop
22e7821 [joyyoj] Merge remote-tracking branch 'apache/master'
3f4a602 [joyyoj] Merge remote-tracking branch 'remotes/apache/master'
f4660c5 [joyyoj] [SPARK-1998] SparkFlumeEvent with body bigger than 1020 bytes are not read properly
SPARK-2791: Fix committing, reverting and state tracking in shuffle file consolidation

All changes from this PR are by mridulm and are drawn from his work in #1609. This patch is intended to fix all major issues related to shuffle file consolidation that mridulm found, while minimizing changes to the code, with the hope that it may be more easily merged into 1.1.

This patch is **not** intended as a replacement for #1609, which provides many additional benefits, including fixes to ExternalAppendOnlyMap, improvements to DiskBlockObjectWriter's API, and several new unit tests.

If it is feasible to merge #1609 for the 1.1 deadline, that is a preferable option.

Author: Aaron Davidson <aaron@databricks.com>

Closes #1678 from aarondav/consol and squashes the following commits:

53b3f6d [Aaron Davidson] Correct behavior when writing unopened file
701d045 [Aaron Davidson] Rebase with sort-based shuffle
9160149 [Aaron Davidson] SPARK-2532: Minimal shuffle consolidation fixes
[SPARK-2786][mllib] Python correlations

Author: Doris Xin <doris.s.xin@gmail.com>

Closes #1713 from dorx/pythonCorrelation and squashes the following commits:

5f1e60c [Doris Xin] reviewer comments.
46ff6eb [Doris Xin] reviewer comments.
ad44085 [Doris Xin] style fix
e69d446 [Doris Xin] fixed missed conflicts.
eb5bf56 [Doris Xin] merge master
cc9f725 [Doris Xin] units passed.
9141a63 [Doris Xin] WIP2
d199f1f [Doris Xin] Moved correlation names into a public object
cd163d6 [Doris Xin] WIP
[SPARK-2796] [mllib] DecisionTree bug fix: ordered categorical features

Bug: In DecisionTree, the method sequentialBinSearchForOrderedCategoricalFeatureInClassification() indexed bins from 0 to (math.pow(2, featureCategories.toInt - 1) - 1). This upper bound is the bound for unordered categorical features, not ordered ones. The upper bound should be the arity (i.e., max value) of the feature.

Added new test to DecisionTreeSuite to catch this: "regression stump with categorical variables of arity 2"

Bug fix: Modified upper bound discussed above.

Also: Small improvements to coding style in DecisionTree.

CC mengxr manishamde

Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>

Closes #1720 from jkbradley/decisiontree-bugfix2 and squashes the following commits:

225822f [Joseph K. Bradley] Bug: In DecisionTree, the method sequentialBinSearchForOrderedCategoricalFeatureInClassification() indexed bins from 0 to (math.pow(2, featureCategories.toInt - 1) - 1). This upper bound is the bound for unordered categorical features, not ordered ones. The upper bound should be the arity (i.e., max value) of the feature.
[SPARK-2010] [PySpark] [SQL] support nested structure in SchemaRDD

Convert Row in JavaSchemaRDD into Array[Any] and unpickle them as tuple in Python, then convert them into namedtuple, so use can access fields just like attributes.

This will let nested structure can be accessed as object, also it will reduce the size of serialized data and better performance.

root
 |-- field1: integer (nullable = true)
 |-- field2: string (nullable = true)
 |-- field3: struct (nullable = true)
 |    |-- field4: integer (nullable = true)
 |    |-- field5: array (nullable = true)
 |    |    |-- element: integer (containsNull = false)
 |-- field6: array (nullable = true)
 |    |-- element: struct (containsNull = false)
 |    |    |-- field7: string (nullable = true)

Then we can access them by row.field3.field5[0]  or row.field6[5].field7

It also will infer the schema in Python, convert Row/dict/namedtuple/objects into tuple before serialization, then call applySchema in JVM. During inferSchema(), the top level of dict in row will be StructType, but any nested dictionary will be MapType.

You can use pyspark.sql.Row to convert unnamed structure into Row object, make the RDD can be inferable. Such as:

ctx.inferSchema(rdd.map(lambda x: Row(a=x[0], b=x[1]))

Or you could use Row to create a class just like namedtuple, for example:

Person = Row("name", "age")
ctx.inferSchema(rdd.map(lambda x: Person(*x)))

Also, you can call applySchema to apply an schema to a RDD of tuple/list and turn it into a SchemaRDD. The `schema` should be StructType, see the API docs for details.

schema = StructType([StructField("name, StringType, True),
                                    StructType("age", IntegerType, True)])
ctx.applySchema(rdd, schema)

PS: In order to use namedtuple to inferSchema, you should make namedtuple picklable.

Author: Davies Liu <davies.liu@gmail.com>

Closes #1598 from davies/nested and squashes the following commits:

f1d15b6 [Davies Liu] verify schema with the first few rows
8852aaf [Davies Liu] check type of schema
abe9e6e [Davies Liu] address comments
61b2292 [Davies Liu] add @deprecated to pythonToJavaMap
1e5b801 [Davies Liu] improve cache of classes
51aa135 [Davies Liu] use Row to infer schema
e9c0d5c [Davies Liu] remove string typed schema
353a3f2 [Davies Liu] fix code style
63de8f8 [Davies Liu] fix typo
c79ca67 [Davies Liu] fix serialization of nested data
6b258b5 [Davies Liu] fix pep8
9d8447c [Davies Liu] apply schema provided by string of names
f5df97f [Davies Liu] refactor, address comments
9d9af55 [Davies Liu] use arrry to applySchema and infer schema in Python
84679b3 [Davies Liu] Merge branch 'master' of github.com:apache/spark into nested
0eaaf56 [Davies Liu] fix doc tests
b3559b4 [Davies Liu] use generated Row instead of namedtuple
c4ddc30 [Davies Liu] fix conflict between name of fields and variables
7f6f251 [Davies Liu] address all comments
d69d397 [Davies Liu] refactor
2cc2d45 [Davies Liu] refactor
182fb46 [Davies Liu] refactor
bc6e9e1 [Davies Liu] switch to new Schema API
547bf3e [Davies Liu] Merge branch 'master' into nested
a435b5a [Davies Liu] add docs and code refactor
2c8debc [Davies Liu] Merge branch 'master' into nested
644665a [Davies Liu] use tuple and namedtuple for schemardd
[SPARK-2212][SQL] Hash Outer Join (follow-up bug fix).

We need to carefully set the ouputPartitioning of the HashOuterJoin Operator. Otherwise, we may not correctly handle nulls.

Author: Yin Huai <huai@cse.ohio-state.edu>

Closes #1721 from yhuai/SPARK-2212-BugFix and squashes the following commits:

ed5eef7 [Yin Huai] Correctly choosing outputPartitioning for the HashOuterJoin operator.
[SPARK-2116] Load spark-defaults.conf from SPARK_CONF_DIR if set

If SPARK_CONF_DIR environment variable is set, search it for spark-defaults.conf.

Author: Albert Chu <chu11@llnl.gov>

Closes #1059 from chu11/SPARK-2116 and squashes the following commits:

9f3ac94 [Albert Chu] SPARK-2116: If SPARK_CONF_DIR environment variable is set, search it for spark-defaults.conf.
[SPARK-2800]: Exclude scalastyle-output.xml Apache RAT checks

Author: GuoQiang Li <witgo@qq.com>

Closes #1729 from witgo/SPARK-2800 and squashes the following commits:

13ca966 [GuoQiang Li] Add scalastyle-output.xml  to .rat-excludes file
[SPARK-2764] Simplify daemon.py process structure

Curently, daemon.py forks a pool of numProcessors subprocesses, and those processes fork themselves again to create the actual Python worker processes that handle data.

I think that this extra layer of indirection is unnecessary and adds a lot of complexity.  This commit attempts to remove this middle layer of subprocesses by launching the workers directly from daemon.py.

See https://github.com/mesos/spark/pull/563 for the original PR that added daemon.py, where I raise some issues with the current design.

Author: Josh Rosen <joshrosen@apache.org>

Closes #1680 from JoshRosen/pyspark-daemon and squashes the following commits:

5abbcb9 [Josh Rosen] Replace magic number: 4 -> EINTR
5495dff [Josh Rosen] Throw IllegalStateException if worker launch fails.
b79254d [Josh Rosen] Detect failed fork() calls; improve error logging.
282c2c4 [Josh Rosen] Remove daemon.py exit logging, since it caused problems:
8554536 [Josh Rosen] Fix daemon’s shutdown(); log shutdown reason.
4e0fab8 [Josh Rosen] Remove shared-memory exit_flag; don't die on worker death.
e9892b4 [Josh Rosen] [WIP] [SPARK-2764] Simplify daemon.py process structure.
Streaming mllib [SPARK-2438][MLLIB]

This PR implements a streaming linear regression analysis, in which a linear regression model is trained online as new data arrive. The design is based on discussions with tdas and mengxr, in which we determined how to add this functionality in a general way, with minimal changes to existing libraries.

__Summary of additions:__

_StreamingLinearAlgorithm_
- An abstract class for fitting generalized linear models online to streaming data, including training on (and updating) a model, and making predictions.

_StreamingLinearRegressionWithSGD_
- Class and companion object for running streaming linear regression

_StreamingLinearRegressionTestSuite_
- Unit tests

_StreamingLinearRegression_
- Example use case: fitting a model online to data from one stream, and making predictions on other data

__Notes__
- If this looks good, I can use the StreamingLinearAlgorithm class to easily implement other analyses that follow the same logic (Ridge, Lasso, Logistic, SVM).

Author: Jeremy Freeman <the.freeman.lab@gmail.com>
Author: freeman <the.freeman.lab@gmail.com>

Closes #1361 from freeman-lab/streaming-mllib and squashes the following commits:

775ea29 [Jeremy Freeman] Throw error if user doesn't initialize weights
4086fee [Jeremy Freeman] Fixed current weight formatting
8b95b27 [Jeremy Freeman] Restored broadcasting
29f27ec [Jeremy Freeman] Formatting
8711c41 [Jeremy Freeman] Used return to avoid indentation
777b596 [Jeremy Freeman] Restored treeAggregate
74cf440 [Jeremy Freeman] Removed static methods
d28cf9a [Jeremy Freeman] Added usage notes
c3326e7 [Jeremy Freeman] Improved documentation
9541a41 [Jeremy Freeman] Merge remote-tracking branch 'upstream/master' into streaming-mllib
66eba5e [Jeremy Freeman] Fixed line lengths
2fe0720 [Jeremy Freeman] Minor cleanup
7d51378 [Jeremy Freeman] Moved streaming loader to MLUtils
b9b69f6 [Jeremy Freeman] Added setter methods
c3f8b5a [Jeremy Freeman] Modified logging
00aafdc [Jeremy Freeman] Add modifiers
14b801e [Jeremy Freeman] Name changes
c7d38a3 [Jeremy Freeman] Move check for empty data to GradientDescent
4b0a5d3 [Jeremy Freeman] Cleaned up tests
74188d6 [Jeremy Freeman] Eliminate dependency on commons
50dd237 [Jeremy Freeman] Removed experimental tag
6bfe1e6 [Jeremy Freeman] Fixed imports
a2a63ad [freeman] Makes convergence test more robust
86220bc [freeman] Streaming linear regression unit tests
fb4683a [freeman] Minor changes for scalastyle consistency
fd31e03 [freeman] Changed logging behavior
453974e [freeman] Fixed indentation
c4b1143 [freeman] Streaming linear regression
604f4d7 [freeman] Expanded private class to include mllib
d99aa85 [freeman] Helper methods for streaming MLlib apps
0898add [freeman] Added dependency on streaming
[SPARK-2550][MLLIB][APACHE SPARK] Support regularization and intercept in pyspark's linear methods.

Related to issue: [SPARK-2550](https://issues.apache.org/jira/browse/SPARK-2550?jql=project%20%3D%20SPARK%20AND%20resolution%20%3D%20Unresolved%20AND%20priority%20%3D%20Major%20ORDER%20BY%20key%20DESC).

Author: Michael Giannakopoulos <miccagiann@gmail.com>

Closes #1624 from miccagiann/new-branch and squashes the following commits:

c02e5f5 [Michael Giannakopoulos] Merge cleanly with upstream/master.
8dcb888 [Michael Giannakopoulos] Putting the if/else if statements in brackets.
fed8eaa [Michael Giannakopoulos] Adding a space in the message related to the IllegalArgumentException.
44e6ff0 [Michael Giannakopoulos] Adding a blank line before python class LinearRegressionWithSGD.
8eba9c5 [Michael Giannakopoulos] Change function signatures. Exception is thrown from the scala component and not from the python one.
638be47 [Michael Giannakopoulos] Modified code to comply with code standards.
ec50ee9 [Michael Giannakopoulos] Shorten the if-elif-else statement in regression.py file
b962744 [Michael Giannakopoulos] Replaced the enum classes, with strings-keywords for defining the values of 'regType' parameter.
78853ec [Michael Giannakopoulos] Providing intercept and regualizer functionallity for linear methods in only one function.
3ac8874 [Michael Giannakopoulos] Added support for regularizer and intercection parameters for linear regression method.