Also added unit test for integration between StringIndexerModel and IndexToString
CC: holdenk We realized we should have left in your unit test (to catch the issue with removing the inverse() method), so this adds it back. mengxr
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#8211 from jkbradley/stridx-labels.
in MLlib sometimes we need to set metadata for the new column, thus we will alias the new column with metadata before call `withColumn` and in `withColumn` we alias this clolumn again. Here I overloaded `withColumn` to allow user set metadata, just like what we did for `Column.as`.
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#8159 from cloud-fan/withColumn.
It would be helpful to allow users to pass a pre-computed index to create an indexer, rather than always going through StringIndexer to create the model.
Author: Holden Karau <holden@pigscanfly.ca>
Closes#7267 from holdenk/SPARK-8744-StringIndexerModel-should-have-public-constructor.
This modifies DecisionTreeMetadata construction to treat 1-category features as continuous, so that trees do not fail with such features. It is important for the pipelines API, where VectorIndexer can automatically categorize certain features as categorical.
As stated in the JIRA, this is a temp fix which we can improve upon later by automatically filtering out those features. That will take longer, though, since it will require careful indexing.
Targeted for 1.5 and master
CC: manishamde mengxr yanboliang
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#8187 from jkbradley/tree-1cat.
Some minor clean-ups after SPARK-9661. See my inline comments. MechCoder jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes#8190 from mengxr/SPARK-9661-fix.
What `StringIndexerInverse` does is not strictly associated with `StringIndexer`, and the name is not clearly describing the transformation. Renaming to `IndexToString` might be better.
~~I also changed `invert` to `inverse` without arguments. `inputCol` and `outputCol` could be set after.~~
I also removed `invert`.
jkbradley holdenk
Author: Xiangrui Meng <meng@databricks.com>
Closes#8152 from mengxr/SPARK-9922.
I skimmed through the docs for various instance of Object and replaced them with Java compaible versions of the same.
1. Some methods in LDAModel.
2. runMiniBatchSGD
3. kolmogorovSmirnovTest
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#8126 from MechCoder/java_incop.
To follow the naming rule of ML, change `MultilayerPerceptronClassifierModel` to `MultilayerPerceptronClassificationModel` like `DecisionTreeClassificationModel`, `GBTClassificationModel` and so on.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#8164 from yanboliang/mlp-name.
Copied ML models must have the same parent of original ones
Author: lewuathe <lewuathe@me.com>
Author: Lewuathe <lewuathe@me.com>
Closes#7447 from Lewuathe/SPARK-9073.
This requires some discussion. I'm not sure whether `runs` is a useful parameter. It certainly complicates the implementation. We might want to optimize the k-means implementation with block matrix operations. In this case, having `runs` may not be worth the trade-off. Also it increases the communication cost in a single job, which might cause other issues.
This PR also renames `epsilon` to `tol` to have consistent naming among algorithms. The Python constructor is updated to include all parameters.
jkbradley yu-iskw
Author: Xiangrui Meng <meng@databricks.com>
Closes#8148 from mengxr/SPARK-9918 and squashes the following commits:
149b9e5 [Xiangrui Meng] fix constructor in Python and rename epsilon to tol
3cc15b3 [Xiangrui Meng] fix test and change initStep to initSteps in python
a0a0274 [Xiangrui Meng] remove runs from k-means in the pipeline API
The problem with defining setters in the base class is that it doesn't return the correct type in Java.
ericl
Author: Xiangrui Meng <meng@databricks.com>
Closes#8143 from mengxr/SPARK-9914 and squashes the following commits:
d36c887 [Xiangrui Meng] remove setters from model
a49021b [Xiangrui Meng] define setters explicitly for Java and use setParam group
There exists a chance that the prefixes keep growing to the maximum pattern length. Then the final local processing step becomes unnecessary. feynmanliang
Author: Xiangrui Meng <meng@databricks.com>
Closes#8136 from mengxr/SPARK-9903.
Made ProbabilisticClassifier, Identifiable, VectorUDT public. All are annotated as DeveloperApi.
CC: mengxr EronWright
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#8004 from jkbradley/ml-api-public-items and squashes the following commits:
7ebefda [Joseph K. Bradley] update per code review
7ff0768 [Joseph K. Bradley] attepting to add mima fix
756d84c [Joseph K. Bradley] VectorUDT annotated as AlphaComponent
ae7767d [Joseph K. Bradley] added another warning
94fd553 [Joseph K. Bradley] Made ProbabilisticClassifier, Identifiable, VectorUDT public APIs
As per the TODO move weightCol to Shared Params.
Author: Holden Karau <holden@pigscanfly.ca>
Closes#8144 from holdenk/SPARK-9909-move-weightCol-toSharedParams.
Reinstated LogisticRegression.threshold Param for binary compatibility. Param thresholds overrides threshold, if set.
CC: mengxr dbtsai feynmanliang
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#8079 from jkbradley/logreg-reinstate-threshold.
From JIRA: Currently, Params.copyValues copies default parameter values to the paramMap of the target instance, rather than the defaultParamMap. It should copy to the defaultParamMap because explicitly setting a parameter can change the semantics.
This issue arose in SPARK-9789, where 2 params "threshold" and "thresholds" for LogisticRegression can have mutually exclusive values. If thresholds is set, then fit() will copy the default value of threshold as well, easily resulting in inconsistent settings for the 2 params.
CC: mengxr
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#8115 from jkbradley/copyvalues-fix.
Went thru the history of changes the file MLUtils.scala and picked up the version that the change went in.
Author: Sudhakar Thota <sudhakarthota@yahoo.com>
Author: Sudhakar Thota <sudhakarthota@sudhakars-mbp-2.usca.ibm.com>
Closes#7436 from sthota2014/SPARK-8925_thotas.
1. Add “asymmetricDocConcentration” and revert docConcentration changes. If the (internal) doc concentration vector is a single value, “getDocConcentration" returns it. If it is a constant vector, getDocConcentration returns the first item, and fails otherwise.
2. Give `LDAModel.gammaShape` a default value in `LDAModel` concrete class constructors.
jkbradley
Author: Feynman Liang <fliang@databricks.com>
Closes#8077 from feynmanliang/SPARK-9788 and squashes the following commits:
6b07bc8 [Feynman Liang] Code review changes
9d6a71e [Feynman Liang] Add asymmetricAlpha alias
bf4e685 [Feynman Liang] Asymmetric docConcentration
4cab972 [Feynman Liang] Default gammaShape
Adds unit test for `equals` on `mllib.linalg.Matrix` class and `equals` to both `SparseMatrix` and `DenseMatrix`. Supports equality testing between `SparseMatrix` and `DenseMatrix`.
mengxr
Author: Feynman Liang <fliang@databricks.com>
Closes#8042 from feynmanliang/SPARK-9750 and squashes the following commits:
bb70d5e [Feynman Liang] Breeze compare for dense matrices as well, in case other is sparse
ab6f3c8 [Feynman Liang] Sparse matrix compare for equals
22782df [Feynman Liang] Add equality based on matrix semantics, not representation
78f9426 [Feynman Liang] Add casts
43d28fa [Feynman Liang] Fix failing test
6416fa0 [Feynman Liang] Add failing sparse matrix equals tests
As a precursor to adding a public constructor add an option to handle unseen values by skipping rather than throwing an exception (default remains throwing an exception),
Author: Holden Karau <holden@pigscanfly.ca>
Closes#7266 from holdenk/SPARK-8764-string-indexer-should-take-option-to-handle-unseen-values and squashes the following commits:
38a4de9 [Holden Karau] fix long line
045bf22 [Holden Karau] Add a second b entry so b gets 0 for sure
81dd312 [Holden Karau] Update the docs for handleInvalid param to be more descriptive
7f37f6e [Holden Karau] remove extra space (scala style)
414e249 [Holden Karau] And switch to using handleInvalid instead of skipInvalid
1e53f9b [Holden Karau] update the param (codegen side)
7a22215 [Holden Karau] fix typo
100a39b [Holden Karau] Merge in master
aa5b093 [Holden Karau] Since we filter we should never go down this code path if getSkipInvalid is true
75ffa69 [Holden Karau] Remove extra newline
d69ef5e [Holden Karau] Add a test
b5734be [Holden Karau] Add support for unseen labels
afecd4e [Holden Karau] Add a param to skip invalid entries.
Implements the transforms which are defined by SQL statement.
Currently we only support SQL syntax like 'SELECT ... FROM __THIS__'
where '__THIS__' represents the underlying table of the input dataset.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#7465 from yanboliang/spark-8345 and squashes the following commits:
b403fcb [Yanbo Liang] address comments
0d4bb15 [Yanbo Liang] a better transformSchema() implementation
51eb9e7 [Yanbo Liang] Add an SQL node as a feature transformer
Adds method documentations back to `MultivariateOnlineSummarizer`, which were present in 1.4 but disappeared somewhere along the way to 1.5.
jkbradley
Author: Feynman Liang <fliang@databricks.com>
Closes#8045 from feynmanliang/SPARK-9755 and squashes the following commits:
af67fde [Feynman Liang] Add MultivariateOnlineSummarizer docs
Small documentation cleanups, including:
* Adds documentation for `pi` and `theta`
* setParam to `setModelType`
Author: Feynman Liang <fliang@databricks.com>
Closes#8047 from feynmanliang/SPARK-9719 and squashes the following commits:
b372438 [Feynman Liang] Clean up naive bayes doc
These should be made private until there is a public constructor for providing `rootNode: Node` to use these constructors.
jkbradley
Author: Feynman Liang <fliang@databricks.com>
Closes#8046 from feynmanliang/SPARK-9756 and squashes the following commits:
2cbdf08 [Feynman Liang] Make RFRegressionModel aux constructor private
a06f596 [Feynman Liang] Make constructors in ML decision trees private
A minor typo (centriod -> centroid). Readable variable names help every users.
Author: Bertrand Dechoux <BertrandDechoux@users.noreply.github.com>
Closes#8037 from BertrandDechoux/kmeans-typo and squashes the following commits:
47632fe [Bertrand Dechoux] centriod typo
Resubmit of [https://github.com/apache/spark/pull/6906] for adding single-vec predict to GMMs
CC: dkobylarz mengxr
To be merged with master and branch-1.5
Primary author: dkobylarz
Author: Dariusz Kobylarz <darek.kobylarz@gmail.com>
Closes#8039 from jkbradley/gmm-predict-vec and squashes the following commits:
bfbedc4 [Dariusz Kobylarz] [SPARK-8481] [MLlib] GaussianMixtureModel predict accepting single vector
This PR contains the following changes:
* add `featureIndex` to handle vector features (in order to chain isotonic regression easily with output from logistic regression
* make getter/setter names consistent with params
* remove inheritance from Regressor because it is tricky to handle both `DoubleType` and `VectorType`
* simplify test data generation
jkbradley zapletal-martin
Author: Xiangrui Meng <meng@databricks.com>
Closes#7952 from mengxr/SPARK-9493 and squashes the following commits:
8818ac3 [Xiangrui Meng] address comments
05e2216 [Xiangrui Meng] address comments
8d08090 [Xiangrui Meng] add featureIndex to handle vector features make getter/setter names consistent with params remove inheritance from Regressor
After https://github.com/apache/spark/pull/7263 it is pretty straightforward to Python wrappers.
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#7930 from MechCoder/spark-9533 and squashes the following commits:
1bea394 [MechCoder] make getVectors a lazy val
5522756 [MechCoder] [SPARK-9533] [PySpark] [ML] Add missing methods in Word2Vec ML
I have added support for stats in LogisticRegression. The API is similar to that of LinearRegression with LogisticRegressionTrainingSummary and LogisticRegressionSummary
I have some queries and asked them inline.
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#7538 from MechCoder/log_reg_stats and squashes the following commits:
2e9f7c7 [MechCoder] Change defs into lazy vals
d775371 [MechCoder] Clean up class inheritance
9586125 [MechCoder] Add abstraction to handle Multiclass Metrics
40ad8ef [MechCoder] minor
640376a [MechCoder] remove unnecessary dataframe stuff and add docs
80d9954 [MechCoder] Added tests
fbed861 [MechCoder] DataFrame support for metrics
70a0fc4 [MechCoder] [SPARK-9112] [ML] Implement Stats for LogisticRegression
Add VectorSlicer transformer to spark.ml, with features specified as either indices or names. Transfers feature attributes for selected features.
Updated version of [https://github.com/apache/spark/pull/5731]
CC: yinxusen This updates your PR. You'll still be the primary author of this PR.
CC: mengxr
Author: Xusen Yin <yinxusen@gmail.com>
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#7972 from jkbradley/yinxusen-SPARK-5895 and squashes the following commits:
b16e86e [Joseph K. Bradley] fixed scala style
71c65d2 [Joseph K. Bradley] fix import order
86e9739 [Joseph K. Bradley] cleanups per code review
9d8d6f1 [Joseph K. Bradley] style fix
83bc2e9 [Joseph K. Bradley] Updated VectorSlicer
98c6939 [Xusen Yin] fix style error
ecbf2d3 [Xusen Yin] change interfaces and params
f6be302 [Xusen Yin] Merge branch 'master' into SPARK-5895
e4781f2 [Xusen Yin] fix commit error
fd154d7 [Xusen Yin] add test suite of vector slicer
17171f8 [Xusen Yin] fix slicer
9ab9747 [Xusen Yin] add vector slicer
aa5a0bf [Xusen Yin] add vector slicer
mengxr
Author: Feynman Liang <fliang@databricks.com>
Closes#7974 from feynmanliang/SPARK-9657 and squashes the following commits:
7ca533f [Feynman Liang] Fix return type of getMaxPatternLength
mengxr This adds the `BlockMatrix` to PySpark. I have the conversions to `IndexedRowMatrix` and `CoordinateMatrix` ready as well, so once PR #7554 is completed (which relies on PR #7746), this PR can be finished.
Author: Mike Dusenberry <mwdusenb@us.ibm.com>
Closes#7761 from dusenberrymw/SPARK-6486_Add_BlockMatrix_to_PySpark and squashes the following commits:
27195c2 [Mike Dusenberry] Adding one more check to _convert_to_matrix_block_tuple, and a few minor documentation changes.
ae50883 [Mike Dusenberry] Minor update: BlockMatrix should inherit from DistributedMatrix.
b8acc1c [Mike Dusenberry] Moving BlockMatrix to pyspark.mllib.linalg.distributed, updating the logic to match that of the other distributed matrices, adding conversions, and adding documentation.
c014002 [Mike Dusenberry] Using properties for better documentation.
3bda6ab [Mike Dusenberry] Adding documentation.
8fb3095 [Mike Dusenberry] Small cleanup.
e17af2e [Mike Dusenberry] Adding BlockMatrix to PySpark.
This is a major refactoring of the PrefixSpan implementation. It contains the following changes:
1. Expand prefix with one item at a time. The existing implementation generates all subsets for each itemset, which might have scalability issue when the itemset is large.
2. Use a new internal format. `<(12)(31)>` is represented by `[0, 1, 2, 0, 1, 3, 0]` internally. We use `0` because negative numbers are used to indicates partial prefix items, e.g., `_2` is represented by `-2`.
3. Remember the start indices of all partial projections in the projected postfix to help next projection.
4. Reuse the original sequence array for projected postfixes.
5. Use `Prefix` IDs in aggregation rather than its content.
6. Use `ArrayBuilder` for building primitive arrays.
7. Expose `maxLocalProjDBSize`.
8. Tests are not changed except using `0` instead of `-1` as the delimiter.
`Postfix`'s API doc should be a good place to start.
Closes#7594
feynmanliang zhangjiajin
Author: Xiangrui Meng <meng@databricks.com>
Closes#7937 from mengxr/SPARK-9540 and squashes the following commits:
2d0ec31 [Xiangrui Meng] address more comments
48f450c [Xiangrui Meng] address comments from Feynman; fixed a bug in project and added a test
65f90e8 [Xiangrui Meng] naming and documentation
8afc86a [Xiangrui Meng] refactor impl
All compressed sensing applications, and some of the regression use-cases will have better result by turning the feature scaling off. However, if we implement this naively by training the dataset without doing any standardization, the rate of convergency will not be good. This can be implemented by still standardizing the training dataset but we penalize each component differently to get effectively the same objective function but a better numerical problem. As a result, for those columns with high variances, they will be penalized less, and vice versa. Without this, since all the features are standardized, so they will be penalized the same.
In R, there is an option for this.
standardize
Logical flag for x variable standardization, prior to fitting the model sequence. The coefficients are always returned on the original scale. Default is standardize=TRUE. If variables are in the same units already, you might not wish to standardize. See details below for y standardization with family="gaussian".
Note that the primary author for this PR is holdenk
Author: Holden Karau <holden@pigscanfly.ca>
Author: DB Tsai <dbt@netflix.com>
Closes#7875 from dbtsai/SPARK-8522 and squashes the following commits:
e856036 [DB Tsai] scala doc
596e96c [DB Tsai] minor
bbff347 [DB Tsai] naming
baa0805 [DB Tsai] touch up
d6234ba [DB Tsai] Merge branch 'master' into SPARK-8522-Disable-Linear_featureScaling-Spark-8601-in-Linear_regression
6b1dc09 [Holden Karau] Merge branch 'master' into SPARK-8522-Disable-Linear_featureScaling-Spark-8601-in-Linear_regression
332f140 [Holden Karau] Merge in master
eebe10a [Holden Karau] Use same comparision operator throughout the test
3f92935 [Holden Karau] merge
b83a41e [Holden Karau] Expand the tests and make them similar to the other PR also providing an option to disable standardization (but for LoR).
0c334a2 [Holden Karau] Remove extra line
99ce053 [Holden Karau] merge in master
e54a8a9 [Holden Karau] Fix long line
e47c574 [Holden Karau] Add support for L2 without standardization.
55d3a66 [Holden Karau] Add standardization param for linear regression
00a1dc5 [Holden Karau] Add the param to the linearregression impl
Update BinaryClassificationEvaluator to use setRawPredictionCol, rather than setScoreCol. Deprecated setScoreCol.
I don't think setScoreCol was actually used anywhere (based on search).
CC: mengxr
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#7921 from jkbradley/binary-eval-rawpred and squashes the following commits:
e5d7dfa [Joseph K. Bradley] Update BinaryClassificationEvaluator to use setRawPredictionCol
This PR adds the RowMatrix, IndexedRowMatrix, and CoordinateMatrix distributed matrices to PySpark. Each distributed matrix class acts as a wrapper around the Scala/Java counterpart by maintaining a reference to the Java object. New distributed matrices can be created using factory methods added to DistributedMatrices, which creates the Java distributed matrix and then wraps it with the corresponding PySpark class. This design allows for simple conversion between the various distributed matrices, and lets us re-use the Scala code. Serialization between Python and Java is implemented using DataFrames as needed for IndexedRowMatrix and CoordinateMatrix for simplicity. Associated documentation and unit-tests have also been added. To facilitate code review, this PR implements access to the rows/entries as RDDs, the number of rows & columns, and conversions between the various distributed matrices (not including BlockMatrix), and does not implement the other linear algebra functions of the matrices, although this will be very simple to add now.
Author: Mike Dusenberry <mwdusenb@us.ibm.com>
Closes#7554 from dusenberrymw/SPARK-6485_Add_CoordinateMatrix_RowMatrix_IndexedMatrix_to_PySpark and squashes the following commits:
bb039cb [Mike Dusenberry] Minor documentation update.
b887c18 [Mike Dusenberry] Updating the matrix conversion logic again to make it even cleaner. Now, we allow the 'rows' parameter in the constructors to be either an RDD or the Java matrix object. If 'rows' is an RDD, we create a Java matrix object, wrap it, and then store that. If 'rows' is a Java matrix object of the correct type, we just wrap and store that directly. This is only for internal usage, and publicly, we still require 'rows' to be an RDD. We no longer store the 'rows' RDD, and instead just compute it from the Java object when needed. The point of this is that when we do matrix conversions, we do the conversion on the Scala/Java side, which returns a Java object, so we should use that directly, but exposing 'java_matrix' parameter in the public API is not ideal. This non-public feature of allowing 'rows' to be a Java matrix object is documented in the '__init__' constructor docstrings, which are not part of the generated public API, and doctests are also included.
7f0dcb6 [Mike Dusenberry] Updating module docstring.
cfc1be5 [Mike Dusenberry] Use 'new SQLContext(matrix.rows.sparkContext)' rather than 'SQLContext.getOrCreate', as the later doesn't guarantee that the SparkContext will be the same as for the matrix.rows data.
687e345 [Mike Dusenberry] Improving conversion performance. This adds an optional 'java_matrix' parameter to the constructors, and pulls the conversion logic out into a '_create_from_java' function. Now, if the constructors are given a valid Java distributed matrix object as 'java_matrix', they will store those internally, rather than create a new one on the Scala/Java side.
3e50b6e [Mike Dusenberry] Moving the distributed matrices to pyspark.mllib.linalg.distributed.
308f197 [Mike Dusenberry] Using properties for better documentation.
1633f86 [Mike Dusenberry] Minor documentation cleanup.
f0c13a7 [Mike Dusenberry] CoordinateMatrix should inherit from DistributedMatrix.
ffdd724 [Mike Dusenberry] Updating doctests to make documentation cleaner.
3fd4016 [Mike Dusenberry] Updating docstrings.
27cd5f6 [Mike Dusenberry] Simplifying input conversions in the constructors for each distributed matrix.
a409cf5 [Mike Dusenberry] Updating doctests to be less verbose by using lists instead of DenseVectors explicitly.
d19b0ba [Mike Dusenberry] Updating code and documentation to note that a vector-like object (numpy array, list, etc.) can be used in place of explicit Vector object, and adding conversions when necessary to RowMatrix construction.
4bd756d [Mike Dusenberry] Adding param documentation to IndexedRow and MatrixEntry.
c6bded5 [Mike Dusenberry] Move conversion logic from tuples to IndexedRow or MatrixEntry types from within the IndexedRowMatrix and CoordinateMatrix constructors to separate _convert_to_indexed_row and _convert_to_matrix_entry functions.
329638b [Mike Dusenberry] Moving the Experimental tag to the top of each docstring.
0be6826 [Mike Dusenberry] Simplifying doctests by removing duplicated rows/entries RDDs within the various tests.
c0900df [Mike Dusenberry] Adding the colons that were accidentally not inserted.
4ad6819 [Mike Dusenberry] Documenting the and parameters.
3b854b9 [Mike Dusenberry] Minor updates to documentation.
10046e8 [Mike Dusenberry] Updating documentation to use class constructors instead of the removed DistributedMatrices factory methods.
119018d [Mike Dusenberry] Adding static methods to each of the distributed matrix classes to consolidate conversion logic.
4d7af86 [Mike Dusenberry] Adding type checks to the constructors. Although it is slightly verbose, it is better for the user to have a good error message than a cryptic stacktrace.
93b6a3d [Mike Dusenberry] Pulling the DistributedMatrices Python class out of this pull request.
f6f3c68 [Mike Dusenberry] Pulling the DistributedMatrices Scala class out of this pull request.
6a3ecb7 [Mike Dusenberry] Updating pattern matching.
08f287b [Mike Dusenberry] Slight reformatting of the documentation.
a245dc0 [Mike Dusenberry] Updating Python doctests for compatability between Python 2 & 3. Since Python 3 removed the idea of a separate 'long' type, all values that would have been outputted as a 'long' (ex: '4L') will now be treated as an 'int' and outputed as one (ex: '4'). The doctests now explicitly convert to ints so that both Python 2 and 3 will have the same output. This is fine since the values are all small, and thus can be easily represented as ints.
4d3a37e [Mike Dusenberry] Reformatting a few long Python doctest lines.
7e3ca16 [Mike Dusenberry] Fixing long lines.
f721ead [Mike Dusenberry] Updating documentation for each of the distributed matrices.
ab0e8b6 [Mike Dusenberry] Updating unit test to be more useful.
dda2f89 [Mike Dusenberry] Added wrappers for the conversions between the various distributed matrices. Added logic to be able to access the rows/entries of the distributed matrices, which requires serialization through DataFrames for IndexedRowMatrix and CoordinateMatrix types. Added unit tests.
0cd7166 [Mike Dusenberry] Implemented the CoordinateMatrix API in PySpark, following the idea of the IndexedRowMatrix API, including using DataFrames for serialization.
3c369cb [Mike Dusenberry] Updating the architecture a bit to make conversions between the various distributed matrix types easier. The different distributed matrix classes are now only wrappers around the Java objects, and take the Java object as an argument during construction. This way, we can call for example on an , which returns a reference to a Java RowMatrix object, and then construct a PySpark RowMatrix object wrapped around the Java object. This is analogous to the behavior of PySpark RDDs and DataFrames. We now delegate creation of the various distributed matrices from scratch in PySpark to the factory methods on .
4bdd09b [Mike Dusenberry] Implemented the IndexedRowMatrix API in PySpark, following the idea of the RowMatrix API. Note that for the IndexedRowMatrix, we use DataFrames to serialize the data between Python and Scala/Java, so we accept PySpark RDDs, then convert to a DataFrame, then convert back to RDDs on the Scala/Java side before constructing the IndexedRowMatrix.
23bf1ec [Mike Dusenberry] Updating documentation to add PySpark RowMatrix. Inserting newline above doctest so that it renders properly in API docs.
b194623 [Mike Dusenberry] Updating design to have a PySpark RowMatrix simply create and keep a reference to a wrapper over a Java RowMatrix. Updating DistributedMatrices factory methods to accept numRows and numCols with default values. Updating PySpark DistributedMatrices factory method to simply create a PySpark RowMatrix. Adding additional doctests for numRows and numCols parameters.
bc2d220 [Mike Dusenberry] Adding unit tests for RowMatrix methods.
d7e316f [Mike Dusenberry] Implemented the RowMatrix API in PySpark by doing the following: Added a DistributedMatrices class to contain factory methods for creating the various distributed matrices. Added a factory method for creating a RowMatrix from an RDD of Vectors. Added a createRowMatrix function to the PythonMLlibAPI to interface with the factory method. Added DistributedMatrix, DistributedMatrices, and RowMatrix classes to the pyspark.mllib.linalg api.
Small cleanups to recent LDA additions and docs.
CC: feynmanliang
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#7916 from jkbradley/lda-cleanups and squashes the following commits:
f7021d9 [Joseph K. Bradley] broadcasting large matrices for LDA in local model and online learning
97947aa [Joseph K. Bradley] a few more cleanups
5b03f88 [Joseph K. Bradley] reverted split of lda log likelihood
c566915 [Joseph K. Bradley] small edit to make review easier
63f6c7d [Joseph K. Bradley] clarified log likelihood for lda models
This PR replaces the old "threshold" with a generalized "thresholds" Param. We keep getThreshold,setThreshold for backwards compatibility for binary classification.
Note that the primary author of this PR is holdenk
Author: Holden Karau <holden@pigscanfly.ca>
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#7909 from jkbradley/holdenk-SPARK-8069-add-cutoff-aka-threshold-to-random-forest and squashes the following commits:
3952977 [Joseph K. Bradley] fixed pyspark doc test
85febc8 [Joseph K. Bradley] made python unit tests a little more robust
7eb1d86 [Joseph K. Bradley] small cleanups
6cc2ed8 [Joseph K. Bradley] Fixed remaining merge issues.
0255e44 [Joseph K. Bradley] Many cleanups for thresholds, some more tests
7565a60 [Holden Karau] fix pep8 style checks, add a getThreshold method similar to our LogisticRegression.scala one for API compat
be87f26 [Holden Karau] Convert threshold to thresholds in the python code, add specialized support for Array[Double] to shared parems codegen, etc.
6747dad [Holden Karau] Override raw2prediction for ProbabilisticClassifier, fix some tests
25df168 [Holden Karau] Fix handling of thresholds in LogisticRegression
c02d6c0 [Holden Karau] No default for thresholds
5e43628 [Holden Karau] CR feedback and fixed the renamed test
f3fbbd1 [Holden Karau] revert the changes to random forest :(
51f581c [Holden Karau] Add explicit types to public methods, fix long line
f7032eb [Holden Karau] Fix a java test bug, remove some unecessary changes
adf15b4 [Holden Karau] rename the classifier suite test to ProbabilisticClassifierSuite now that we only have it in Probabilistic
398078a [Holden Karau] move the thresholding around a bunch based on the design doc
4893bdc [Holden Karau] Use numtrees of 3 since previous result was tied (one tree for each) and the switch from different max methods picked a different element (since they were equal I think this is ok)
638854c [Holden Karau] Add a scala RandomForestClassifierSuite test based on corresponding python test
e09919c [Holden Karau] Fix return type, I need more coffee....
8d92cac [Holden Karau] Use ClassifierParams as the head
3456ed3 [Holden Karau] Add explicit return types even though just test
a0f3b0c [Holden Karau] scala style fixes
6f14314 [Holden Karau] Since hasthreshold/hasthresholds is in root classifier now
ffc8dab [Holden Karau] Update the sharedParams
0420290 [Holden Karau] Allow us to override the get methods selectively
978e77a [Holden Karau] Move HasThreshold into classifier params and start defining the overloaded getThreshold/getThresholds functions
1433e52 [Holden Karau] Revert "try and hide threshold but chainges the API so no dice there"
1f09a2e [Holden Karau] try and hide threshold but chainges the API so no dice there
efb9084 [Holden Karau] move setThresholds only to where its used
6b34809 [Holden Karau] Add a test with thresholding for the RFCS
74f54c3 [Holden Karau] Fix creation of vote array
1986fa8 [Holden Karau] Setting the thresholds only makes sense if the underlying class hasn't overridden predict, so lets push it down.
2f44b18 [Holden Karau] Add a global default of null for thresholds param
f338cfc [Holden Karau] Wait that wasn't a good idea, Revert "Some progress towards unifying threshold and thresholds"
634b06f [Holden Karau] Some progress towards unifying threshold and thresholds
85c9e01 [Holden Karau] Test passes again... little fnur
099c0f3 [Holden Karau] Move thresholds around some more (set on model not trainer)
0f46836 [Holden Karau] Start adding a classifiersuite
f70eb5e [Holden Karau] Fix test compile issues
a7d59c8 [Holden Karau] Move thresholding into Classifier trait
5d999d2 [Holden Karau] Some more progress, start adding a test (maybe try and see if we can find a better thing to use for the base of the test)
1fed644 [Holden Karau] Use thresholds to scale scores in random forest classifcation
31d6bf2 [Holden Karau] Start threading the threshold info through
0ef228c [Holden Karau] Add hasthresholds
Enable most javac lint warnings; fix a lot of build warnings. In a few cases, touch up surrounding code in the process.
I'll explain several of the changes inline in comments.
Author: Sean Owen <sowen@cloudera.com>
Closes#7862 from srowen/SPARK-9534 and squashes the following commits:
ea51618 [Sean Owen] Enable most javac lint warnings; fix a lot of build warnings. In a few cases, touch up surrounding code in the process.
Add missing methods
1. getVectors
2. findSynonyms
to W2Vec scala and python API
mengxr
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#7263 from MechCoder/missing_methods_w2vec and squashes the following commits:
149d5ca [MechCoder] minor doc
69d91b7 [MechCoder] [SPARK-8874] [ML] Add missing methods in Word2Vec
Add Python API for RFormula. Similar to other feature transformers in Python. This is just a thin wrapper over the Scala implementation. ericl MechCoder
Author: Xiangrui Meng <meng@databricks.com>
Closes#7879 from mengxr/SPARK-9544 and squashes the following commits:
3d5ff03 [Xiangrui Meng] add an doctest for . and -
5e969a5 [Xiangrui Meng] fix pydoc
1cd41f8 [Xiangrui Meng] organize imports
3c18b10 [Xiangrui Meng] add Python API for RFormula
Added featureImportance to RandomForestClassifier and Regressor.
This follows the scikit-learn implementation here: [a95203b249/sklearn/tree/_tree.pyx (L3341)]
CC: yanboliang Would you mind taking a look? Thanks!
Author: Joseph K. Bradley <joseph@databricks.com>
Author: Feynman Liang <fliang@databricks.com>
Closes#7838 from jkbradley/dt-feature-importance and squashes the following commits:
72a167a [Joseph K. Bradley] fixed unit test
86cea5f [Joseph K. Bradley] Modified RF featuresImportances to return Vector instead of Map
5aa74f0 [Joseph K. Bradley] finally fixed unit test for real
33df5db [Joseph K. Bradley] fix unit test
42a2d3b [Joseph K. Bradley] fix unit test
fe94e72 [Joseph K. Bradley] modified feature importance unit tests
cc693ee [Feynman Liang] Add classifier tests
79a6f87 [Feynman Liang] Compare dense vectors in test
21d01fc [Feynman Liang] Added failing SKLearn test
ac0b254 [Joseph K. Bradley] Added featureImportance to RandomForestClassifier/Regressor. Need to add unit tests
