diff options
Diffstat (limited to 'python/pyspark/ml/tuning.py')
| -rw-r--r-- | python/pyspark/ml/tuning.py | 43 |
1 files changed, 31 insertions, 12 deletions
diff --git a/python/pyspark/ml/tuning.py b/python/pyspark/ml/tuning.py index 86f4dc7368..497841b6c8 100644 --- a/python/pyspark/ml/tuning.py +++ b/python/pyspark/ml/tuning.py @@ -155,7 +155,7 @@ class CrossValidator(Estimator): """ Sets the value of :py:attr:`estimator`. """ - self.paramMap[self.estimator] = value + self._paramMap[self.estimator] = value return self def getEstimator(self): @@ -168,7 +168,7 @@ class CrossValidator(Estimator): """ Sets the value of :py:attr:`estimatorParamMaps`. """ - self.paramMap[self.estimatorParamMaps] = value + self._paramMap[self.estimatorParamMaps] = value return self def getEstimatorParamMaps(self): @@ -181,7 +181,7 @@ class CrossValidator(Estimator): """ Sets the value of :py:attr:`evaluator`. """ - self.paramMap[self.evaluator] = value + self._paramMap[self.evaluator] = value return self def getEvaluator(self): @@ -194,7 +194,7 @@ class CrossValidator(Estimator): """ Sets the value of :py:attr:`numFolds`. """ - self.paramMap[self.numFolds] = value + self._paramMap[self.numFolds] = value return self def getNumFolds(self): @@ -203,13 +203,12 @@ class CrossValidator(Estimator): """ return self.getOrDefault(self.numFolds) - def fit(self, dataset, params={}): - paramMap = self.extractParamMap(params) - est = paramMap[self.estimator] - epm = paramMap[self.estimatorParamMaps] + def _fit(self, dataset): + est = self.getOrDefault(self.estimator) + epm = self.getOrDefault(self.estimatorParamMaps) numModels = len(epm) - eva = paramMap[self.evaluator] - nFolds = paramMap[self.numFolds] + eva = self.getOrDefault(self.evaluator) + nFolds = self.getOrDefault(self.numFolds) h = 1.0 / nFolds randCol = self.uid + "_rand" df = dataset.select("*", rand(0).alias(randCol)) @@ -229,6 +228,15 @@ class CrossValidator(Estimator): bestModel = est.fit(dataset, epm[bestIndex]) return CrossValidatorModel(bestModel) + def copy(self, extra={}): + newCV = Params.copy(self, extra) + if self.isSet(self.estimator): + newCV.setEstimator(self.getEstimator().copy(extra)) + # estimatorParamMaps remain the same + if self.isSet(self.evaluator): + newCV.setEvaluator(self.getEvaluator().copy(extra)) + return newCV + class CrossValidatorModel(Model): """ @@ -240,8 +248,19 @@ class CrossValidatorModel(Model): #: best model from cross validation self.bestModel = bestModel - def transform(self, dataset, params={}): - return self.bestModel.transform(dataset, params) + def _transform(self, dataset): + return self.bestModel.transform(dataset) + + def copy(self, extra={}): + """ + Creates a copy of this instance with a randomly generated uid + and some extra params. This copies the underlying bestModel, + creates a deep copy of the embedded paramMap, and + copies the embedded and extra parameters over. + :param extra: Extra parameters to copy to the new instance + :return: Copy of this instance + """ + return CrossValidatorModel(self.bestModel.copy(extra)) if __name__ == "__main__": |