GitHub user davies opened a pull request:
https://github.com/apache/spark/pull/3320
[SPARK-4434] [MLlib] add python api for random forest
```
class WeightedEnsembleModel
| A model trained by RandomForest
|
| numWeakHypotheses(self)
| Get number of trees in forest.
|
| predict(self, x)
| Predict values for a single data point or an RDD of points
using the model trained.
|
| toDebugString(self)
| Full model
|
| totalNumNodes(self)
| Get total number of nodes, summed over all trees in the forest.
|
class RandomForest
| trainClassifier(cls, data, numClassesForClassification,
categoricalFeaturesInfo, numTrees, featureSubsetStrategy='auto',
impurity='gini', maxDepth=4, maxBins=32, seed=None):
| Method to train a decision tree model for binary or multiclass
classification.
|
| :param data: Training dataset: RDD of LabeledPoint.
| Labels should take values {0, 1, ...,
numClasses-1}.
| :param numClassesForClassification: number of classes for
classification.
| :param categoricalFeaturesInfo: Map storing arity of
categorical features.
| E.g., an entry (n -> k) indicates
that feature n is categorical
| with k categories indexed from 0:
{0, 1, ..., k-1}.
| :param numTrees: Number of trees in the random forest.
| :param featureSubsetStrategy: Number of features to consider
for splits at each node.
| Supported: "auto" (default), "all",
"sqrt", "log2", "onethird".
| If "auto" is set, this parameter is
set based on numTrees:
| if numTrees == 1, set to "all";
| if numTrees > 1 (forest) set to
"sqrt" for classification and
| to "onethird" for regression.
| :param impurity: Criterion used for information gain
calculation.
| Supported values: "gini" (recommended) or
"entropy".
| :param maxDepth: Maximum depth of the tree. E.g., depth 0 means
1 leaf node; depth 1 means
| 1 internal node + 2 leaf nodes. (default: 4)
| :param maxBins: maximum number of bins used for splitting
features (default: 100)
| :param seed: Random seed for bootstrapping and choosing
feature subsets.
| :return: WeightedEnsembleModel that can be used for prediction
|
| trainRegressor(cls, data, categoricalFeaturesInfo, numTrees,
featureSubsetStrategy='auto', impurity='variance', maxDepth=4, maxBins=32,
seed=None):
| Method to train a decision tree model for regression.
|
| :param data: Training dataset: RDD of LabeledPoint.
| Labels are real numbers.
| :param categoricalFeaturesInfo: Map storing arity of
categorical features.
| E.g., an entry (n -> k) indicates
that feature n is categorical
| with k categories indexed from 0:
{0, 1, ..., k-1}.
| :param numTrees: Number of trees in the random forest.
| :param featureSubsetStrategy: Number of features to consider
for splits at each node.
| Supported: "auto" (default), "all",
"sqrt", "log2", "onethird".
| If "auto" is set, this parameter is
set based on numTrees:
| if numTrees == 1, set to "all";
| if numTrees > 1 (forest) set to
"sqrt" for classification and
| to "onethird" for regression.
| :param impurity: Criterion used for information gain
calculation.
| Supported values: "variance".
| :param maxDepth: Maximum depth of the tree. E.g., depth 0 means
1 leaf node; depth 1 means
| 1 internal node + 2 leaf nodes.(default: 4)
| :param maxBins: maximum number of bins used for splitting
features (default: 100)
| :param seed: Random seed for bootstrapping and choosing
feature subsets.
| :return: WeightedEnsembleModel that can be used for prediction
|
```
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/davies/spark forest
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/3320.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #3320
----
commit 565d47627953bd5e420b81d48a9a80afe4e6f66b
Author: Davies Liu <[email protected]>
Date: 2014-11-17T19:18:07Z
add python api for random forest
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