def sklearn2pmml(pipeline, pmml, user_classpath = [], with_repr = False, debug = False, java_encoding = "UTF-8"):
	"""Converts a fitted Scikit-Learn pipeline to PMML.

	Parameters:
	----------
	pipeline: PMMLPipeline
		The pipeline.

	pmml: string
		The path to where the PMML document should be stored.

	user_classpath: list of strings, optional
		The paths to JAR files that provide custom Transformer, Selector and/or Estimator converter classes.
		The JPMML-SkLearn classpath is constructed by appending user JAR files to package JAR files.

	with_repr: boolean, optional
		If true, insert the string representation of pipeline into the PMML document.

	debug: boolean, optional
		If true, print information about the conversion process.

	java_encoding: string, optional
		The character encoding to use for decoding Java output and error byte streams.

	"""
	if debug:
		java_version = _java_version(java_encoding)
		if java_version is None:
			java_version = ("java", "N/A")
		print("python: {0}".format(platform.python_version()))
		print("sklearn: {0}".format(sklearn.__version__))
		print("sklearn.externals.joblib: {0}".format(joblib.__version__))
		print("pandas: {0}".format(pandas.__version__))
		print("sklearn_pandas: {0}".format(sklearn_pandas.__version__))
		print("sklearn2pmml: {0}".format(__version__))
		print("{0}: {1}".format(java_version[0], java_version[1]))
	if not isinstance(pipeline, PMMLPipeline):
		raise TypeError("The pipeline object is not an instance of " + PMMLPipeline.__name__ + ". Use the 'sklearn2pmml.make_pmml_pipeline(obj)' utility function to translate a regular Scikit-Learn estimator or pipeline to a PMML pipeline")
	estimator = pipeline._final_estimator
	cmd = ["java", "-cp", os.pathsep.join(_classpath(user_classpath)), "org.jpmml.sklearn.Main"]
	dumps = []
	try:
		if with_repr:
			pipeline.repr_ = repr(pipeline)
		# if isinstance(estimator, H2OEstimator):
		if hasattr(estimator, "download_mojo"):
			estimator_mojo = estimator.download_mojo()
			dumps.append(estimator_mojo)
			estimator._mojo_path = estimator_mojo
		pipeline_pkl = _dump(pipeline, "pipeline")
		cmd.extend(["--pkl-pipeline-input", pipeline_pkl])
		dumps.append(pipeline_pkl)
		cmd.extend(["--pmml-output", pmml])
		if debug:
			print("Executing command:\n{0}".format(" ".join(cmd)))
		try:
			process = Popen(cmd, stdout = PIPE, stderr = PIPE, bufsize = 1)
		except OSError:
			raise RuntimeError("Java is not installed, or the Java executable is not on system path")
		output, error = process.communicate()
		retcode = process.poll()
		if debug or retcode:
			if(len(output) > 0):
				print("Standard output:\n{0}".format(_decode(output, java_encoding)))
			else:
				print("Standard output is empty")
			if(len(error) > 0):
				print("Standard error:\n{0}".format(_decode(error, java_encoding)))
			else:
				print("Standard error is empty")
		if retcode:
			raise RuntimeError("The JPMML-SkLearn conversion application has failed. The Java executable should have printed more information about the failure into its standard output and/or standard error streams")
	finally:
		if debug:
			print("Preserved joblib dump file(s): {0}".format(" ".join(dumps)))
		else:
			for dump in dumps:
				os.remove(dump)
def sklearn2pmml(pipeline, pmml, user_classpath = [], with_repr = False, debug = False, java_encoding = "UTF-8"):
	"""Converts a fitted Scikit-Learn pipeline to PMML.

	Parameters:
	----------
	pipeline: PMMLPipeline
		The pipeline.

	pmml: string
		The path to where the PMML document should be stored.

	user_classpath: list of strings, optional
		The paths to JAR files that provide custom Transformer, Selector and/or Estimator converter classes.
		The JPMML-SkLearn classpath is constructed by appending user JAR files to package JAR files.

	with_repr: boolean, optional
		If true, insert the string representation of pipeline into the PMML document.

	debug: boolean, optional
		If true, print information about the conversion process.

	java_encoding: string, optional
		The character encoding to use for decoding Java output and error byte streams.

	"""
	if debug:
		java_version = _java_version(java_encoding)
		if java_version is None:
			java_version = ("java", "N/A")
		print("python: {0}".format(platform.python_version()))
		print("sklearn: {0}".format(sklearn.__version__))
		print("sklearn.externals.joblib: {0}".format(joblib.__version__))
		print("pandas: {0}".format(pandas.__version__))
		print("sklearn_pandas: {0}".format(sklearn_pandas.__version__))
		print("sklearn2pmml: {0}".format(__version__))
		print("{0}: {1}".format(java_version[0], java_version[1]))
	if not isinstance(pipeline, PMMLPipeline):
		raise TypeError("The pipeline object is not an instance of " + PMMLPipeline.__name__ + ". Use the 'sklearn2pmml.make_pmml_pipeline(obj)' utility function to translate a regular Scikit-Learn estimator or pipeline to a PMML pipeline")
	estimator = pipeline._final_estimator
	cmd = ["java", "-cp", os.pathsep.join(_classpath(user_classpath)), "org.jpmml.sklearn.Main"]
	dumps = []
	try:
		if with_repr:
			pipeline.repr_ = repr(pipeline)
		# if isinstance(estimator, H2OEstimator):
		if hasattr(estimator, "download_mojo"):
			estimator_mojo = estimator.download_mojo()
			dumps.append(estimator_mojo)
			estimator._mojo_path = estimator_mojo
		pipeline_pkl = _dump(pipeline, "pipeline")
		cmd.extend(["--pkl-pipeline-input", pipeline_pkl])
		dumps.append(pipeline_pkl)
		cmd.extend(["--pmml-output", pmml])
		if debug:
			print("Executing command:\n{0}".format(" ".join(cmd)))
		try:
			process = Popen(cmd, stdout = PIPE, stderr = PIPE, bufsize = 1)
		except OSError:
			raise RuntimeError("Java is not installed, or the Java executable is not on system path")
		output, error = process.communicate()
		retcode = process.poll()
		if debug or retcode:
			if(len(output) > 0):
				print("Standard output:\n{0}".format(_decode(output, java_encoding)))
			else:
				print("Standard output is empty")
			if(len(error) > 0):
				print("Standard error:\n{0}".format(_decode(error, java_encoding)))
			else:
				print("Standard error is empty")
		if retcode:
			raise RuntimeError("The JPMML-SkLearn conversion application has failed. The Java executable should have printed more information about the failure into its standard output and/or standard error streams")
	finally:
		if debug:
			print("Preserved joblib dump file(s): {0}".format(" ".join(dumps)))
		else:
			for dump in dumps:
				os.remove(dump)
def sklearn2pmml ( pipeline , pmml , user_classpath = [ ] , with_repr = False , debug = False , java_encoding = "UTF-8" ) : if debug : java_version = _java_version ( java_encoding ) if java_version is None : java_version = ( "java" , "N/A" ) print ( "python: {0}" . format ( platform . python_version ( ) ) ) print ( "sklearn: {0}" . format ( sklearn . __version__ ) ) print ( "sklearn.externals.joblib: {0}" . format ( joblib . __version__ ) ) print ( "pandas: {0}" . format ( pandas . __version__ ) ) print ( "sklearn_pandas: {0}" . format ( sklearn_pandas . __version__ ) ) print ( "sklearn2pmml: {0}" . format ( __version__ ) ) print ( "{0}: {1}" . format ( java_version [ 0 ] , java_version [ 1 ] ) ) if not isinstance ( pipeline , PMMLPipeline ) : raise TypeError ( "The pipeline object is not an instance of " + PMMLPipeline . __name__ + ". Use the 'sklearn2pmml.make_pmml_pipeline(obj)' utility function to translate a regular Scikit-Learn estimator or pipeline to a PMML pipeline" ) estimator = pipeline . _final_estimator cmd = [ "java" , "-cp" , os . pathsep . join ( _classpath ( user_classpath ) ) , "org.jpmml.sklearn.Main" ] dumps = [ ] try : if with_repr : pipeline . repr_ = repr ( pipeline ) # if isinstance(estimator, H2OEstimator): if hasattr ( estimator , "download_mojo" ) : estimator_mojo = estimator . download_mojo ( ) dumps . append ( estimator_mojo ) estimator . _mojo_path = estimator_mojo pipeline_pkl = _dump ( pipeline , "pipeline" ) cmd . extend ( [ "--pkl-pipeline-input" , pipeline_pkl ] ) dumps . append ( pipeline_pkl ) cmd . extend ( [ "--pmml-output" , pmml ] ) if debug : print ( "Executing command:\n{0}" . format ( " " . join ( cmd ) ) ) try : process = Popen ( cmd , stdout = PIPE , stderr = PIPE , bufsize = 1 ) except OSError : raise RuntimeError ( "Java is not installed, or the Java executable is not on system path" ) output , error = process . communicate ( ) retcode = process . poll ( ) if debug or retcode : if ( len ( output ) > 0 ) : print ( "Standard output:\n{0}" . format ( _decode ( output , java_encoding ) ) ) else : print ( "Standard output is empty" ) if ( len ( error ) > 0 ) : print ( "Standard error:\n{0}" . format ( _decode ( error , java_encoding ) ) ) else : print ( "Standard error is empty" ) if retcode : raise RuntimeError ( "The JPMML-SkLearn conversion application has failed. The Java executable should have printed more information about the failure into its standard output and/or standard error streams" ) finally : if debug : print ( "Preserved joblib dump file(s): {0}" . format ( " " . join ( dumps ) ) ) else : for dump in dumps : os . remove ( dump )
Converts a fitted Scikit-Learn pipeline to PMML.

	Parameters:
	----------
	pipeline: PMMLPipeline
		The pipeline.

	pmml: string
		The path to where the PMML document should be stored.

	user_classpath: list of strings, optional
		The paths to JAR files that provide custom Transformer, Selector and/or Estimator converter classes.
		The JPMML-SkLearn classpath is constructed by appending user JAR files to package JAR files.

	with_repr: boolean, optional
		If true, insert the string representation of pipeline into the PMML document.

	debug: boolean, optional
		If true, print information about the conversion process.

	java_encoding: string, optional
		The character encoding to use for decoding Java output and error byte streams.
Converts a fitted Scikit - Learn pipeline to PMML .