Source code for mmlspark.lightgbm.LightGBMRegressor

# Copyright (C) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See LICENSE in project root for information.

import sys
from pyspark import SQLContext
from pyspark import SparkContext

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

from mmlspark.lightgbm._LightGBMRegressor import _LightGBMRegressor
from mmlspark.lightgbm._LightGBMRegressor import _LightGBMRegressionModel
from pyspark import SparkContext
from pyspark.ml.common import inherit_doc
from pyspark.ml.wrapper import JavaParams
from mmlspark.core.serialize.java_params_patch import *

[docs]@inherit_doc class LightGBMRegressor(_LightGBMRegressor): def _create_model(self, java_model): model = LightGBMRegressionModel() model._java_obj = java_model model._transfer_params_from_java() return model
[docs]@inherit_doc class LightGBMRegressionModel(_LightGBMRegressionModel):
[docs] def saveNativeModel(self, filename, overwrite=True): """ Save the booster as string format to a local or WASB remote location. """ self._java_obj.saveNativeModel(filename, overwrite)
[docs] @staticmethod def loadNativeModelFromFile(filename, labelColName="label", featuresColName="features", predictionColName="prediction"): """ Load the model from a native LightGBM text file. """ ctx = SparkContext._active_spark_context loader = ctx._jvm.com.microsoft.ml.spark.lightgbm.LightGBMRegressionModel java_model = loader.loadNativeModelFromFile(filename, labelColName, featuresColName, predictionColName) return JavaParams._from_java(java_model)
[docs] @staticmethod def loadNativeModelFromString(model, labelColName="label", featuresColName="features", predictionColName="prediction"): """ Load the model from a native LightGBM model string. """ ctx = SparkContext._active_spark_context loader = ctx._jvm.com.microsoft.ml.spark.lightgbm.LightGBMRegressionModel java_model = loader.loadNativeModelFromString(model, labelColName, featuresColName, predictionColName) return JavaParams._from_java(java_model)
[docs] def getFeatureImportances(self, importance_type="split"): """ Get the feature importances as a list. The importance_type can be "split" or "gain". """ return list(self._java_obj.getFeatureImportances(importance_type))