Source code for synapse.ml.lightgbm.mixin

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

from pyspark.ml.linalg import SparseVector, DenseVector
from pyspark.ml.common import inherit_doc
from synapse.ml.core.serialize.java_params_patch import *

[docs]@inherit_doc class LightGBMModelMixin:
[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] 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))
[docs] def getFeatureShaps(self, vector): """ Get the local shap feature importances. """ if isinstance(vector, DenseVector): dense_values = [float(v) for v in vector] return list(self._java_obj.getDenseFeatureShaps(dense_values)) elif isinstance(vector, SparseVector): sparse_indices = [int(i) for i in vector.indices] sparse_values = [float(v) for v in vector.values] return list(self._java_obj.getSparseFeatureShaps(vector.size, sparse_indices, sparse_values)) else: raise TypeError("Vector argument to getFeatureShaps must be a pyspark.linalg sparse or dense vector type")
[docs] def getBoosterBestIteration(self): """Get the best iteration from the booster. Returns: The best iteration, if early stopping was triggered. """ return self._java_obj.getBoosterBestIteration()
[docs] def getBoosterNumTotalIterations(self): """Get the total number of iterations trained. Returns: The total number of iterations trained. """ return self._java_obj.getBoosterNumTotalIterations()
[docs] def getBoosterNumTotalModel(self): """Get the total number of models trained. Returns: The total number of models. """ return self._java_obj.getBoosterNumTotalModel()
[docs] def getBoosterNumFeatures(self): """Get the number of features from the booster. Returns: The number of features. """ return self._java_obj.getBoosterNumFeatures()