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add pyspark-iforest implementation

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# PySpark-IForest
Pyspark-iforest is a pyspark wrapper for spark-iforest.
## Install
Step 1. Package spark-iforest jar and deploy it into spark lib
```bash
cd spark-iforest/
mvn clean package -DskipTests
cp target/spark-iforest-<version>.jar $SPARK_HOME/jars/
```
Step 2. Package pyspark-iforest and install it via pip
```bash
cd spark-iforest/python
python setup.py sdist
pip install dist/pyspark-iforest-<version>.tar.gz
```
## Usage
Parameters are the same with spark-iforest,
more details can be found in https://github.com/titicaca/spark-iforest#usage
Examples can be found in pyspark_iforest/example/
from pyspark.sql import SparkSession
from pyspark.ml.linalg import Vectors
import os
import tempfile
if __name__ == "__main__":
if "SPARK_HOME" in os.environ.keys():
print("SPARK_HOME: ", os.environ['SPARK_HOME'])
else:
raise ValueError("Environment variable SPARK_HOME needs to be specified,"
" and make sure spark-iforest.jar is added into your lib path ($SPARK_HOME/jars")
spark = SparkSession \
.builder.master("local[*]") \
.appName("IForestExample") \
.getOrCreate()
data = [(Vectors.dense([0.0, 0.0]),), (Vectors.dense([7.0, 9.0]),),
(Vectors.dense([9.0, 8.0]),), (Vectors.dense([8.0, 9.0]),)]
df = spark.createDataFrame(data, ["features"])
from pyspark_iforest.ml.iforest import *
iforest = IForest(contamination=0.3, maxDepth=2)
model = iforest.fit(df)
model.hasSummary
summary = model.summary
summary.numAnomalies
transformed = model.transform(df)
rows = transformed.collect()
temp_path = tempfile.mkdtemp()
iforest_path = temp_path + "/iforest"
iforest.save(iforest_path)
loaded_iforest = IForest.load(iforest_path)
model_path = temp_path + "/iforest_model"
model.save(model_path)
loaded_model = IForestModel.load(model_path)
loaded_model.hasSummary
loaded_model.transform(df).show()
from pyspark import since, keyword_only
from pyspark.ml.util import *
from pyspark.ml.wrapper import JavaEstimator, JavaModel, JavaParams, JavaWrapper
from pyspark.ml.param.shared import *
from pyspark.ml.common import inherit_doc
from pyspark_iforest.ml.util import *
__all__ = ['IForestSummary', 'IForest', 'IForestModel']
class IForestSummary(JavaWrapper):
"""
.. note:: Experimental
Clustering results for IForest model.
.. versionadded:: 2.1.0
"""
@property
@since("2.1.0")
def predictionCol(self):
"""
Name for column of predicted clusters in `predictions`.
"""
return self._call_java("predictionCol")
@property
@since("2.1.0")
def predictions(self):
"""
DataFrame produced by the model's `transform` method.
"""
return self._call_java("predictions")
@property
@since("2.1.0")
def featuresCol(self):
"""
Name for column of features in `predictions`.
"""
return self._call_java("featuresCol")
@property
@since("2.1.0")
def anomalyScoreCol(self):
"""
Name for column of anomalyScore.
"""
return self._call_java("anomalyScoreCol")
@property
@since("2.1.0")
def anomalies(self):
"""
DataFrame of predicted anomalies for each training data point.
"""
return self._call_java("anomalies")
@property
@since("2.1.0")
def anomalyScores(self):
"""
DataFrame of predicted anomalyScores for each training data point.
"""
return self._call_java("anomalyScores")
@property
@since("2.1.0")
def numAnomalies(self):
"""
Number of anomalies.
"""
return self._call_java("numAnomalies")
class CustomizedJavaMLReader(JavaMLReader):
@classmethod
def _java_loader_class(cls, clazz):
"""
Returns the full class name of the Java ML instance. The default
implementation replaces "pyspark" by "org.apache.spark" in
the Python full class name.
"""
java_package = clazz.__module__.replace("pyspark_iforest", "org.apache.spark")
if clazz.__name__ in ("Pipeline", "PipelineModel"):
# Remove the last package name "pipeline" for Pipeline and PipelineModel.
java_package = ".".join(java_package.split(".")[0:-1])
return java_package + "." + clazz.__name__
def load(self, path):
"""Load the ML instance from the input path."""
if not isinstance(path, basestring):
raise TypeError("path should be a basestring, got type %s" % type(path))
java_obj = self._jread.load(path)
if not hasattr(self._clazz, "_from_java"):
raise NotImplementedError("This Java ML type cannot be loaded into Python currently: %r"
% self._clazz)
return customized_from_java(java_obj)
@inherit_doc
class CustomizedJavaMLReadable(MLReadable):
"""
(Private) Mixin for instances that provide JavaMLReader.
"""
@classmethod
def read(cls):
"""Returns an MLReader instance for this class."""
return CustomizedJavaMLReader(cls)
class IForestModel(JavaModel, JavaMLWritable, CustomizedJavaMLReadable):
"""
Model fitted by IForest.
.. versionadded:: 2.1.0
"""
@property
@since("2.1.0")
def hasSummary(self):
"""
Indicates whether a training summary exists for this model instance.
"""
return self._call_java("hasSummary")
@property
@since("2.1.0")
def summary(self):
"""
Gets summary of the model trained on the training set.
An exception is thrown if no summary exists.
"""
if self.hasSummary:
return IForestSummary(self._call_java("summary"))
else:
raise RuntimeError("No training summary available for this %s" %
self.__class__.__name__)
@inherit_doc
class IForest(JavaEstimator, HasFeaturesCol, HasPredictionCol, HasSeed, JavaMLWritable, CustomizedJavaMLReadable):
"""
Isolation Forest for detecting anomalies
>>> from pyspark.ml.linalg import Vectors
>>>
>>> data = [(Vectors.dense([0.0, 0.0]),), (Vectors.dense([7.0, 9.0]),),
... (Vectors.dense([9.0, 8.0]),), (Vectors.dense([8.0, 9.0]),)]
>>>
...
... df = spark.createDataFrame(data, ["features"])
>>>
>>>
>>> from pyspark_iforest.ml.iforest import *
>>>
>>> iforest = IForest(contamination=0.3, maxDepth=2)
>>> model = iforest.fit(df)
>>>
>>> model.hasSummary
True
>>>
>>> summary = model.summary
>>>
>>> summary.numAnomalies
1
>>>
>>> transformed = model.transform(df)
>>>
>>> rows = transformed.collect()
>>>
>>> import tempfile
>>> temp_path = tempfile.mkdtemp()
>>>
>>> iforest_path = temp_path + "/iforest"
>>>
>>> iforest.save(iforest_path)
>>>
>>> loaded_iforest = IForest.load(iforest_path)
>>>
>>> model_path = temp_path + "/iforest_model"
>>>
>>> model.save(model_path)
>>>
>>> loaded_model = IForestModel.load(model_path)
>>>
>>> loaded_model.hasSummary
False
>>>
>>> loaded_model.transform(df).show()
+---------+-------------------+----------+
| features| anomalyScore|prediction|
+---------+-------------------+----------+
|[0.0,0.0]| 0.652628934546283| 1.0|
|[7.0,9.0]| 0.3806804982830844| 0.0|
|[9.0,8.0]|0.40116303198069875| 0.0|
|[8.0,9.0]| 0.366693565357915| 0.0|
+---------+-------------------+----------+
.. versionadded:: 2.1.0
"""
numTrees = Param(Params._dummy(), "numTrees", "The number of trees to create. Must be > 1.",
typeConverter=TypeConverters.toInt)
maxSamples = Param(Params._dummy(), "maxSamples",
"The number of samples to draw from data to train each tree (>0)",
typeConverter=TypeConverters.toFloat)
maxFeatures = Param(Params._dummy(), "maxFeatures",
"The number of features to draw from data to train each tree (>0)",
typeConverter=TypeConverters.toFloat)
maxDepth = Param(Params._dummy(), "maxDepth",
"The height limit used in constructing a tree (>0)",
typeConverter=TypeConverters.toInt)
contamination = Param(Params._dummy(), "contamination",
"The proportion of outliers in the data set (0< contamination < 1)",
typeConverter=TypeConverters.toFloat)
bootstrap = Param(Params._dummy(), "bootstrap",
"If true, the training data sampled with replacement (boolean)",
typeConverter=TypeConverters.toBoolean)
@keyword_only
def __init__(self, featuresCol="features", predictionCol="prediction", anomalyScore="anomalyScore",
numTrees=100, maxSamples=1.0, maxFeatures=1.0, maxDepth=10, contamination=0.1,
bootstrap=False):
super(IForest, self).__init__()
self._java_obj = self._new_java_obj("org.apache.spark.ml.iforest.IForest", self.uid)
self._setDefault(numTrees=100, maxSamples=1.0, maxFeatures=1.0, maxDepth=10, contamination=0.1,
bootstrap=False)
kwargs = self._input_kwargs
self.setParams(**kwargs)
def _create_model(self, java_model):
return IForestModel(java_model)
@keyword_only
@since("2.1.0")
def setParams(self, featuresCol="features", predictionCol="prediction", anomalyScore="anomalyScore",
numTrees=100, maxSamples=1.0, maxFeatures=1.0, maxDepth=10, contamination=0.1,
bootstrap=False):
"""
Sets params for KMeans.
"""
kwargs = self._input_kwargs
return self._set(**kwargs)
@since("2.1.0")
def setNumTrees(self, value):
"""
Sets the value of :py:attr:`numTrees`.
"""
return self._set(numTrees=value)
@since("2.1.0")
def getNumTrees(self):
"""
Gets the value of `numTrees`
"""
return self.getOrDefault(self.numTrees)
@since("2.1.0")
def setMaxSamples(self, value):
"""
Sets the value of :py:attr:`maxSamples`.
"""
return self._set(maxSamples=value)
@since("2.1.0")
def getMaxSamples(self):
"""
Gets the value of `maxSamples`
"""
return self.getOrDefault(self.maxSamples)
@since("2.1.0")
def setMaxFeatures(self, value):
"""
Sets the value of :py:attr:`maxFeatures`.
"""
return self._set(maxFeatures=value)
@since("2.1.0")
def getMaxFeatures(self):
"""
Gets the value of `maxFeatures`
"""
return self.getOrDefault(self.MaxSamples)
@since("2.1.0")
def setMaxDepth(self, value):
"""
Sets the value of :py:attr:`maxDepth`.
"""
return self._set(maxDepth=value)
@since("2.1.0")
def getMaxDepth(self):
"""
Gets the value of `maxDepth`