Source code for pyspark.ml.util

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import sys
import uuid
import warnings

if sys.version > '3':
    basestring = str
    unicode = str

from pyspark import SparkContext, since
from pyspark.ml.common import inherit_doc


def _jvm():
    """
    Returns the JVM view associated with SparkContext. Must be called
    after SparkContext is initialized.
    """
    jvm = SparkContext._jvm
    if jvm:
        return jvm
    else:
        raise AttributeError("Cannot load _jvm from SparkContext. Is SparkContext initialized?")


[docs]class Identifiable(object): """ Object with a unique ID. """ def __init__(self): #: A unique id for the object. self.uid = self._randomUID() def __repr__(self): return self.uid @classmethod def _randomUID(cls): """ Generate a unique unicode id for the object. The default implementation concatenates the class name, "_", and 12 random hex chars. """ return unicode(cls.__name__ + "_" + uuid.uuid4().hex[12:])
@inherit_doc
[docs]class MLWriter(object): """ Utility class that can save ML instances. .. versionadded:: 2.0.0 """
[docs] def save(self, path): """Save the ML instance to the input path.""" raise NotImplementedError("MLWriter is not yet implemented for type: %s" % type(self))
[docs] def overwrite(self): """Overwrites if the output path already exists.""" raise NotImplementedError("MLWriter is not yet implemented for type: %s" % type(self))
[docs] def context(self, sqlContext): """ Sets the SQL context to use for saving. .. note:: Deprecated in 2.1 and will be removed in 3.0, use session instead. """ raise NotImplementedError("MLWriter is not yet implemented for type: %s" % type(self))
[docs] def session(self, sparkSession): """Sets the Spark Session to use for saving.""" raise NotImplementedError("MLWriter is not yet implemented for type: %s" % type(self))
@inherit_doc
[docs]class JavaMLWriter(MLWriter): """ (Private) Specialization of :py:class:`MLWriter` for :py:class:`JavaParams` types """ def __init__(self, instance): super(JavaMLWriter, self).__init__() _java_obj = instance._to_java() self._jwrite = _java_obj.write()
[docs] def save(self, path): """Save the ML instance to the input path.""" if not isinstance(path, basestring): raise TypeError("path should be a basestring, got type %s" % type(path)) self._jwrite.save(path)
[docs] def overwrite(self): """Overwrites if the output path already exists.""" self._jwrite.overwrite() return self
[docs] def context(self, sqlContext): """ Sets the SQL context to use for saving. .. note:: Deprecated in 2.1 and will be removed in 3.0, use session instead. """ warnings.warn("Deprecated in 2.1 and will be removed in 3.0, use session instead.") self._jwrite.context(sqlContext._ssql_ctx) return self
[docs] def session(self, sparkSession): """Sets the Spark Session to use for saving.""" self._jwrite.session(sparkSession._jsparkSession) return self
@inherit_doc
[docs]class MLWritable(object): """ Mixin for ML instances that provide :py:class:`MLWriter`. .. versionadded:: 2.0.0 """
[docs] def write(self): """Returns an MLWriter instance for this ML instance.""" raise NotImplementedError("MLWritable is not yet implemented for type: %r" % type(self))
[docs] def save(self, path): """Save this ML instance to the given path, a shortcut of `write().save(path)`.""" self.write().save(path)
@inherit_doc
[docs]class JavaMLWritable(MLWritable): """ (Private) Mixin for ML instances that provide :py:class:`JavaMLWriter`. """
[docs] def write(self): """Returns an MLWriter instance for this ML instance.""" return JavaMLWriter(self)
@inherit_doc
[docs]class MLReader(object): """ Utility class that can load ML instances. .. versionadded:: 2.0.0 """
[docs] def load(self, path): """Load the ML instance from the input path.""" raise NotImplementedError("MLReader is not yet implemented for type: %s" % type(self))
[docs] def context(self, sqlContext): """ Sets the SQL context to use for loading. .. note:: Deprecated in 2.1 and will be removed in 3.0, use session instead. """ raise NotImplementedError("MLReader is not yet implemented for type: %s" % type(self))
[docs] def session(self, sparkSession): """Sets the Spark Session to use for loading.""" raise NotImplementedError("MLReader is not yet implemented for type: %s" % type(self))
@inherit_doc
[docs]class JavaMLReader(MLReader): """ (Private) Specialization of :py:class:`MLReader` for :py:class:`JavaParams` types """ def __init__(self, clazz): self._clazz = clazz self._jread = self._load_java_obj(clazz).read()
[docs] 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 self._clazz._from_java(java_obj)
[docs] def context(self, sqlContext): """ Sets the SQL context to use for loading. .. note:: Deprecated in 2.1 and will be removed in 3.0, use session instead. """ warnings.warn("Deprecated in 2.1 and will be removed in 3.0, use session instead.") self._jread.context(sqlContext._ssql_ctx) return self
[docs] def session(self, sparkSession): """Sets the Spark Session to use for loading.""" self._jread.session(sparkSession._jsparkSession) return self
@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", "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__ @classmethod def _load_java_obj(cls, clazz): """Load the peer Java object of the ML instance.""" java_class = cls._java_loader_class(clazz) java_obj = _jvm() for name in java_class.split("."): java_obj = getattr(java_obj, name) return java_obj
@inherit_doc
[docs]class MLReadable(object): """ Mixin for instances that provide :py:class:`MLReader`. .. versionadded:: 2.0.0 """ @classmethod
[docs] def read(cls): """Returns an MLReader instance for this class.""" raise NotImplementedError("MLReadable.read() not implemented for type: %r" % cls)
@classmethod
[docs] def load(cls, path): """Reads an ML instance from the input path, a shortcut of `read().load(path)`.""" return cls.read().load(path)
@inherit_doc
[docs]class JavaMLReadable(MLReadable): """ (Private) Mixin for instances that provide JavaMLReader. """ @classmethod
[docs] def read(cls): """Returns an MLReader instance for this class.""" return JavaMLReader(cls)
@inherit_doc
[docs]class JavaPredictionModel(): """ (Private) Java Model for prediction tasks (regression and classification). To be mixed in with class:`pyspark.ml.JavaModel` """ @property @since("2.1.0")
[docs] def numFeatures(self): """ Returns the number of features the model was trained on. If unknown, returns -1 """ return self._call_java("numFeatures")