body
stringlengths 26
98.2k
| body_hash
int64 -9,222,864,604,528,158,000
9,221,803,474B
| docstring
stringlengths 1
16.8k
| path
stringlengths 5
230
| name
stringlengths 1
96
| repository_name
stringlengths 7
89
| lang
stringclasses 1
value | body_without_docstring
stringlengths 20
98.2k
|
|---|---|---|---|---|---|---|---|
@since('2.0.0')
def setVarianceCol(self, value):
'\n Sets the value of :py:attr:`varianceCol`.\n '
return self._set(varianceCol=value) | -1,104,017,226,281,381,500 | Sets the value of :py:attr:`varianceCol`. | python/pyspark/ml/regression.py | setVarianceCol | AjithShetty2489/spark | python | @since('2.0.0')
def setVarianceCol(self, value):
'\n \n '
return self._set(varianceCol=value) |
@since('3.0.0')
def setVarianceCol(self, value):
'\n Sets the value of :py:attr:`varianceCol`.\n '
return self._set(varianceCol=value) | -6,026,809,695,580,712,000 | Sets the value of :py:attr:`varianceCol`. | python/pyspark/ml/regression.py | setVarianceCol | AjithShetty2489/spark | python | @since('3.0.0')
def setVarianceCol(self, value):
'\n \n '
return self._set(varianceCol=value) |
@property
@since('2.0.0')
def featureImportances(self):
'\n Estimate of the importance of each feature.\n\n This generalizes the idea of "Gini" importance to other losses,\n following the explanation of Gini importance from "Random Forests" documentation\n by Leo Breiman and Adele Cutler, and following the implementation from scikit-learn.\n\n This feature importance is calculated as follows:\n - importance(feature j) = sum (over nodes which split on feature j) of the gain,\n where gain is scaled by the number of instances passing through node\n - Normalize importances for tree to sum to 1.\n\n .. note:: Feature importance for single decision trees can have high variance due to\n correlated predictor variables. Consider using a :py:class:`RandomForestRegressor`\n to determine feature importance instead.\n '
return self._call_java('featureImportances') | -6,933,464,203,652,408,000 | Estimate of the importance of each feature.
This generalizes the idea of "Gini" importance to other losses,
following the explanation of Gini importance from "Random Forests" documentation
by Leo Breiman and Adele Cutler, and following the implementation from scikit-learn.
This feature importance is calculated as follows:
- importance(feature j) = sum (over nodes which split on feature j) of the gain,
where gain is scaled by the number of instances passing through node
- Normalize importances for tree to sum to 1.
.. note:: Feature importance for single decision trees can have high variance due to
correlated predictor variables. Consider using a :py:class:`RandomForestRegressor`
to determine feature importance instead. | python/pyspark/ml/regression.py | featureImportances | AjithShetty2489/spark | python | @property
@since('2.0.0')
def featureImportances(self):
'\n Estimate of the importance of each feature.\n\n This generalizes the idea of "Gini" importance to other losses,\n following the explanation of Gini importance from "Random Forests" documentation\n by Leo Breiman and Adele Cutler, and following the implementation from scikit-learn.\n\n This feature importance is calculated as follows:\n - importance(feature j) = sum (over nodes which split on feature j) of the gain,\n where gain is scaled by the number of instances passing through node\n - Normalize importances for tree to sum to 1.\n\n .. note:: Feature importance for single decision trees can have high variance due to\n correlated predictor variables. Consider using a :py:class:`RandomForestRegressor`\n to determine feature importance instead.\n '
return self._call_java('featureImportances') |
@keyword_only
def __init__(self, featuresCol='features', labelCol='label', predictionCol='prediction', maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity='variance', subsamplingRate=1.0, seed=None, numTrees=20, featureSubsetStrategy='auto', leafCol='', minWeightFractionPerNode=0.0, weightCol=None):
'\n __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance", subsamplingRate=1.0, seed=None, numTrees=20, featureSubsetStrategy="auto", leafCol=", minWeightFractionPerNode=0.0", weightCol=None)\n '
super(RandomForestRegressor, self).__init__()
self._java_obj = self._new_java_obj('org.apache.spark.ml.regression.RandomForestRegressor', self.uid)
self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity='variance', subsamplingRate=1.0, numTrees=20, featureSubsetStrategy='auto', leafCol='', minWeightFractionPerNode=0.0)
kwargs = self._input_kwargs
self.setParams(**kwargs) | 6,518,399,712,782,969,000 | __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance", subsamplingRate=1.0, seed=None, numTrees=20, featureSubsetStrategy="auto", leafCol=", minWeightFractionPerNode=0.0", weightCol=None) | python/pyspark/ml/regression.py | __init__ | AjithShetty2489/spark | python | @keyword_only
def __init__(self, featuresCol='features', labelCol='label', predictionCol='prediction', maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity='variance', subsamplingRate=1.0, seed=None, numTrees=20, featureSubsetStrategy='auto', leafCol=, minWeightFractionPerNode=0.0, weightCol=None):
'\n \n '
super(RandomForestRegressor, self).__init__()
self._java_obj = self._new_java_obj('org.apache.spark.ml.regression.RandomForestRegressor', self.uid)
self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity='variance', subsamplingRate=1.0, numTrees=20, featureSubsetStrategy='auto', leafCol=, minWeightFractionPerNode=0.0)
kwargs = self._input_kwargs
self.setParams(**kwargs) |
@keyword_only
@since('1.4.0')
def setParams(self, featuresCol='features', labelCol='label', predictionCol='prediction', maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity='variance', subsamplingRate=1.0, seed=None, numTrees=20, featureSubsetStrategy='auto', leafCol='', minWeightFractionPerNode=0.0, weightCol=None):
'\n setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance", subsamplingRate=1.0, seed=None, numTrees=20, featureSubsetStrategy="auto", leafCol="", minWeightFractionPerNode=0.0, weightCol=None)\n Sets params for linear regression.\n '
kwargs = self._input_kwargs
return self._set(**kwargs) | -4,650,487,209,188,145,000 | setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance", subsamplingRate=1.0, seed=None, numTrees=20, featureSubsetStrategy="auto", leafCol="", minWeightFractionPerNode=0.0, weightCol=None)
Sets params for linear regression. | python/pyspark/ml/regression.py | setParams | AjithShetty2489/spark | python | @keyword_only
@since('1.4.0')
def setParams(self, featuresCol='features', labelCol='label', predictionCol='prediction', maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity='variance', subsamplingRate=1.0, seed=None, numTrees=20, featureSubsetStrategy='auto', leafCol=, minWeightFractionPerNode=0.0, weightCol=None):
'\n setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance", subsamplingRate=1.0, seed=None, numTrees=20, featureSubsetStrategy="auto", leafCol=, minWeightFractionPerNode=0.0, weightCol=None)\n Sets params for linear regression.\n '
kwargs = self._input_kwargs
return self._set(**kwargs) |
def setMaxDepth(self, value):
'\n Sets the value of :py:attr:`maxDepth`.\n '
return self._set(maxDepth=value) | -6,658,512,568,115,957,000 | Sets the value of :py:attr:`maxDepth`. | python/pyspark/ml/regression.py | setMaxDepth | AjithShetty2489/spark | python | def setMaxDepth(self, value):
'\n \n '
return self._set(maxDepth=value) |
def setMaxBins(self, value):
'\n Sets the value of :py:attr:`maxBins`.\n '
return self._set(maxBins=value) | -1,640,362,106,271,631,400 | Sets the value of :py:attr:`maxBins`. | python/pyspark/ml/regression.py | setMaxBins | AjithShetty2489/spark | python | def setMaxBins(self, value):
'\n \n '
return self._set(maxBins=value) |
def setMinInstancesPerNode(self, value):
'\n Sets the value of :py:attr:`minInstancesPerNode`.\n '
return self._set(minInstancesPerNode=value) | -2,656,768,942,740,913,000 | Sets the value of :py:attr:`minInstancesPerNode`. | python/pyspark/ml/regression.py | setMinInstancesPerNode | AjithShetty2489/spark | python | def setMinInstancesPerNode(self, value):
'\n \n '
return self._set(minInstancesPerNode=value) |
def setMinInfoGain(self, value):
'\n Sets the value of :py:attr:`minInfoGain`.\n '
return self._set(minInfoGain=value) | 8,219,744,118,478,087,000 | Sets the value of :py:attr:`minInfoGain`. | python/pyspark/ml/regression.py | setMinInfoGain | AjithShetty2489/spark | python | def setMinInfoGain(self, value):
'\n \n '
return self._set(minInfoGain=value) |
def setMaxMemoryInMB(self, value):
'\n Sets the value of :py:attr:`maxMemoryInMB`.\n '
return self._set(maxMemoryInMB=value) | 6,802,939,914,533,361,000 | Sets the value of :py:attr:`maxMemoryInMB`. | python/pyspark/ml/regression.py | setMaxMemoryInMB | AjithShetty2489/spark | python | def setMaxMemoryInMB(self, value):
'\n \n '
return self._set(maxMemoryInMB=value) |
def setCacheNodeIds(self, value):
'\n Sets the value of :py:attr:`cacheNodeIds`.\n '
return self._set(cacheNodeIds=value) | -5,193,773,612,683,504,000 | Sets the value of :py:attr:`cacheNodeIds`. | python/pyspark/ml/regression.py | setCacheNodeIds | AjithShetty2489/spark | python | def setCacheNodeIds(self, value):
'\n \n '
return self._set(cacheNodeIds=value) |
@since('1.4.0')
def setImpurity(self, value):
'\n Sets the value of :py:attr:`impurity`.\n '
return self._set(impurity=value) | 5,925,454,725,552,672,000 | Sets the value of :py:attr:`impurity`. | python/pyspark/ml/regression.py | setImpurity | AjithShetty2489/spark | python | @since('1.4.0')
def setImpurity(self, value):
'\n \n '
return self._set(impurity=value) |
@since('1.4.0')
def setNumTrees(self, value):
'\n Sets the value of :py:attr:`numTrees`.\n '
return self._set(numTrees=value) | 8,739,145,108,853,067,000 | Sets the value of :py:attr:`numTrees`. | python/pyspark/ml/regression.py | setNumTrees | AjithShetty2489/spark | python | @since('1.4.0')
def setNumTrees(self, value):
'\n \n '
return self._set(numTrees=value) |
@since('1.4.0')
def setSubsamplingRate(self, value):
'\n Sets the value of :py:attr:`subsamplingRate`.\n '
return self._set(subsamplingRate=value) | 7,760,574,118,449,772,000 | Sets the value of :py:attr:`subsamplingRate`. | python/pyspark/ml/regression.py | setSubsamplingRate | AjithShetty2489/spark | python | @since('1.4.0')
def setSubsamplingRate(self, value):
'\n \n '
return self._set(subsamplingRate=value) |
@since('2.4.0')
def setFeatureSubsetStrategy(self, value):
'\n Sets the value of :py:attr:`featureSubsetStrategy`.\n '
return self._set(featureSubsetStrategy=value) | 8,563,096,663,945,954,000 | Sets the value of :py:attr:`featureSubsetStrategy`. | python/pyspark/ml/regression.py | setFeatureSubsetStrategy | AjithShetty2489/spark | python | @since('2.4.0')
def setFeatureSubsetStrategy(self, value):
'\n \n '
return self._set(featureSubsetStrategy=value) |
def setCheckpointInterval(self, value):
'\n Sets the value of :py:attr:`checkpointInterval`.\n '
return self._set(checkpointInterval=value) | 9,195,478,207,231,309,000 | Sets the value of :py:attr:`checkpointInterval`. | python/pyspark/ml/regression.py | setCheckpointInterval | AjithShetty2489/spark | python | def setCheckpointInterval(self, value):
'\n \n '
return self._set(checkpointInterval=value) |
def setSeed(self, value):
'\n Sets the value of :py:attr:`seed`.\n '
return self._set(seed=value) | -88,293,150,966,480,180 | Sets the value of :py:attr:`seed`. | python/pyspark/ml/regression.py | setSeed | AjithShetty2489/spark | python | def setSeed(self, value):
'\n \n '
return self._set(seed=value) |
@since('3.0.0')
def setWeightCol(self, value):
'\n Sets the value of :py:attr:`weightCol`.\n '
return self._set(weightCol=value) | 3,791,292,180,445,544,000 | Sets the value of :py:attr:`weightCol`. | python/pyspark/ml/regression.py | setWeightCol | AjithShetty2489/spark | python | @since('3.0.0')
def setWeightCol(self, value):
'\n \n '
return self._set(weightCol=value) |
@since('3.0.0')
def setMinWeightFractionPerNode(self, value):
'\n Sets the value of :py:attr:`minWeightFractionPerNode`.\n '
return self._set(minWeightFractionPerNode=value) | 5,709,196,588,527,269,000 | Sets the value of :py:attr:`minWeightFractionPerNode`. | python/pyspark/ml/regression.py | setMinWeightFractionPerNode | AjithShetty2489/spark | python | @since('3.0.0')
def setMinWeightFractionPerNode(self, value):
'\n \n '
return self._set(minWeightFractionPerNode=value) |
@property
@since('2.0.0')
def trees(self):
'Trees in this ensemble. Warning: These have null parent Estimators.'
return [DecisionTreeRegressionModel(m) for m in list(self._call_java('trees'))] | 7,781,632,944,006,031,000 | Trees in this ensemble. Warning: These have null parent Estimators. | python/pyspark/ml/regression.py | trees | AjithShetty2489/spark | python | @property
@since('2.0.0')
def trees(self):
return [DecisionTreeRegressionModel(m) for m in list(self._call_java('trees'))] |
@property
@since('2.0.0')
def featureImportances(self):
'\n Estimate of the importance of each feature.\n\n Each feature\'s importance is the average of its importance across all trees in the ensemble\n The importance vector is normalized to sum to 1. This method is suggested by Hastie et al.\n (Hastie, Tibshirani, Friedman. "The Elements of Statistical Learning, 2nd Edition." 2001.)\n and follows the implementation from scikit-learn.\n\n .. seealso:: :py:attr:`DecisionTreeRegressionModel.featureImportances`\n '
return self._call_java('featureImportances') | 253,380,475,036,441,120 | Estimate of the importance of each feature.
Each feature's importance is the average of its importance across all trees in the ensemble
The importance vector is normalized to sum to 1. This method is suggested by Hastie et al.
(Hastie, Tibshirani, Friedman. "The Elements of Statistical Learning, 2nd Edition." 2001.)
and follows the implementation from scikit-learn.
.. seealso:: :py:attr:`DecisionTreeRegressionModel.featureImportances` | python/pyspark/ml/regression.py | featureImportances | AjithShetty2489/spark | python | @property
@since('2.0.0')
def featureImportances(self):
'\n Estimate of the importance of each feature.\n\n Each feature\'s importance is the average of its importance across all trees in the ensemble\n The importance vector is normalized to sum to 1. This method is suggested by Hastie et al.\n (Hastie, Tibshirani, Friedman. "The Elements of Statistical Learning, 2nd Edition." 2001.)\n and follows the implementation from scikit-learn.\n\n .. seealso:: :py:attr:`DecisionTreeRegressionModel.featureImportances`\n '
return self._call_java('featureImportances') |
@since('1.4.0')
def getLossType(self):
'\n Gets the value of lossType or its default value.\n '
return self.getOrDefault(self.lossType) | 6,726,951,567,021,210,000 | Gets the value of lossType or its default value. | python/pyspark/ml/regression.py | getLossType | AjithShetty2489/spark | python | @since('1.4.0')
def getLossType(self):
'\n \n '
return self.getOrDefault(self.lossType) |
@keyword_only
def __init__(self, featuresCol='features', labelCol='label', predictionCol='prediction', maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0, checkpointInterval=10, lossType='squared', maxIter=20, stepSize=0.1, seed=None, impurity='variance', featureSubsetStrategy='all', validationTol=0.01, validationIndicatorCol=None, leafCol='', minWeightFractionPerNode=0.0, weightCol=None):
'\n __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0, checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1, seed=None, impurity="variance", featureSubsetStrategy="all", validationTol=0.01, validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0,\n weightCol=None)\n '
super(GBTRegressor, self).__init__()
self._java_obj = self._new_java_obj('org.apache.spark.ml.regression.GBTRegressor', self.uid)
self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0, checkpointInterval=10, lossType='squared', maxIter=20, stepSize=0.1, impurity='variance', featureSubsetStrategy='all', validationTol=0.01, leafCol='', minWeightFractionPerNode=0.0)
kwargs = self._input_kwargs
self.setParams(**kwargs) | 2,081,694,121,952,453,000 | __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0, checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1, seed=None, impurity="variance", featureSubsetStrategy="all", validationTol=0.01, validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0,
weightCol=None) | python/pyspark/ml/regression.py | __init__ | AjithShetty2489/spark | python | @keyword_only
def __init__(self, featuresCol='features', labelCol='label', predictionCol='prediction', maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0, checkpointInterval=10, lossType='squared', maxIter=20, stepSize=0.1, seed=None, impurity='variance', featureSubsetStrategy='all', validationTol=0.01, validationIndicatorCol=None, leafCol=, minWeightFractionPerNode=0.0, weightCol=None):
'\n __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0, checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1, seed=None, impurity="variance", featureSubsetStrategy="all", validationTol=0.01, validationIndicatorCol=None, leafCol=, minWeightFractionPerNode=0.0,\n weightCol=None)\n '
super(GBTRegressor, self).__init__()
self._java_obj = self._new_java_obj('org.apache.spark.ml.regression.GBTRegressor', self.uid)
self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0, checkpointInterval=10, lossType='squared', maxIter=20, stepSize=0.1, impurity='variance', featureSubsetStrategy='all', validationTol=0.01, leafCol=, minWeightFractionPerNode=0.0)
kwargs = self._input_kwargs
self.setParams(**kwargs) |
@keyword_only
@since('1.4.0')
def setParams(self, featuresCol='features', labelCol='label', predictionCol='prediction', maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0, checkpointInterval=10, lossType='squared', maxIter=20, stepSize=0.1, seed=None, impuriy='variance', featureSubsetStrategy='all', validationTol=0.01, validationIndicatorCol=None, leafCol='', minWeightFractionPerNode=0.0, weightCol=None):
'\n setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0, checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1, seed=None, impurity="variance", featureSubsetStrategy="all", validationTol=0.01, validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0, weightCol=None)\n Sets params for Gradient Boosted Tree Regression.\n '
kwargs = self._input_kwargs
return self._set(**kwargs) | 5,634,973,182,862,086,000 | setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0, checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1, seed=None, impurity="variance", featureSubsetStrategy="all", validationTol=0.01, validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0, weightCol=None)
Sets params for Gradient Boosted Tree Regression. | python/pyspark/ml/regression.py | setParams | AjithShetty2489/spark | python | @keyword_only
@since('1.4.0')
def setParams(self, featuresCol='features', labelCol='label', predictionCol='prediction', maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0, checkpointInterval=10, lossType='squared', maxIter=20, stepSize=0.1, seed=None, impuriy='variance', featureSubsetStrategy='all', validationTol=0.01, validationIndicatorCol=None, leafCol=, minWeightFractionPerNode=0.0, weightCol=None):
'\n setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0, checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1, seed=None, impurity="variance", featureSubsetStrategy="all", validationTol=0.01, validationIndicatorCol=None, leafCol=, minWeightFractionPerNode=0.0, weightCol=None)\n Sets params for Gradient Boosted Tree Regression.\n '
kwargs = self._input_kwargs
return self._set(**kwargs) |
@since('1.4.0')
def setMaxDepth(self, value):
'\n Sets the value of :py:attr:`maxDepth`.\n '
return self._set(maxDepth=value) | 6,900,103,097,222,239,000 | Sets the value of :py:attr:`maxDepth`. | python/pyspark/ml/regression.py | setMaxDepth | AjithShetty2489/spark | python | @since('1.4.0')
def setMaxDepth(self, value):
'\n \n '
return self._set(maxDepth=value) |
@since('1.4.0')
def setMaxBins(self, value):
'\n Sets the value of :py:attr:`maxBins`.\n '
return self._set(maxBins=value) | -7,662,286,719,655,598,000 | Sets the value of :py:attr:`maxBins`. | python/pyspark/ml/regression.py | setMaxBins | AjithShetty2489/spark | python | @since('1.4.0')
def setMaxBins(self, value):
'\n \n '
return self._set(maxBins=value) |
@since('1.4.0')
def setMinInstancesPerNode(self, value):
'\n Sets the value of :py:attr:`minInstancesPerNode`.\n '
return self._set(minInstancesPerNode=value) | -8,071,360,048,086,069,000 | Sets the value of :py:attr:`minInstancesPerNode`. | python/pyspark/ml/regression.py | setMinInstancesPerNode | AjithShetty2489/spark | python | @since('1.4.0')
def setMinInstancesPerNode(self, value):
'\n \n '
return self._set(minInstancesPerNode=value) |
@since('1.4.0')
def setMinInfoGain(self, value):
'\n Sets the value of :py:attr:`minInfoGain`.\n '
return self._set(minInfoGain=value) | -2,189,222,529,958,267,000 | Sets the value of :py:attr:`minInfoGain`. | python/pyspark/ml/regression.py | setMinInfoGain | AjithShetty2489/spark | python | @since('1.4.0')
def setMinInfoGain(self, value):
'\n \n '
return self._set(minInfoGain=value) |
@since('1.4.0')
def setMaxMemoryInMB(self, value):
'\n Sets the value of :py:attr:`maxMemoryInMB`.\n '
return self._set(maxMemoryInMB=value) | -7,743,889,602,156,593,000 | Sets the value of :py:attr:`maxMemoryInMB`. | python/pyspark/ml/regression.py | setMaxMemoryInMB | AjithShetty2489/spark | python | @since('1.4.0')
def setMaxMemoryInMB(self, value):
'\n \n '
return self._set(maxMemoryInMB=value) |
@since('1.4.0')
def setCacheNodeIds(self, value):
'\n Sets the value of :py:attr:`cacheNodeIds`.\n '
return self._set(cacheNodeIds=value) | -7,957,309,380,185,966,000 | Sets the value of :py:attr:`cacheNodeIds`. | python/pyspark/ml/regression.py | setCacheNodeIds | AjithShetty2489/spark | python | @since('1.4.0')
def setCacheNodeIds(self, value):
'\n \n '
return self._set(cacheNodeIds=value) |
@since('1.4.0')
def setImpurity(self, value):
'\n Sets the value of :py:attr:`impurity`.\n '
return self._set(impurity=value) | 5,925,454,725,552,672,000 | Sets the value of :py:attr:`impurity`. | python/pyspark/ml/regression.py | setImpurity | AjithShetty2489/spark | python | @since('1.4.0')
def setImpurity(self, value):
'\n \n '
return self._set(impurity=value) |
@since('1.4.0')
def setLossType(self, value):
'\n Sets the value of :py:attr:`lossType`.\n '
return self._set(lossType=value) | -8,809,049,382,203,914,000 | Sets the value of :py:attr:`lossType`. | python/pyspark/ml/regression.py | setLossType | AjithShetty2489/spark | python | @since('1.4.0')
def setLossType(self, value):
'\n \n '
return self._set(lossType=value) |
@since('1.4.0')
def setSubsamplingRate(self, value):
'\n Sets the value of :py:attr:`subsamplingRate`.\n '
return self._set(subsamplingRate=value) | 7,760,574,118,449,772,000 | Sets the value of :py:attr:`subsamplingRate`. | python/pyspark/ml/regression.py | setSubsamplingRate | AjithShetty2489/spark | python | @since('1.4.0')
def setSubsamplingRate(self, value):
'\n \n '
return self._set(subsamplingRate=value) |
@since('2.4.0')
def setFeatureSubsetStrategy(self, value):
'\n Sets the value of :py:attr:`featureSubsetStrategy`.\n '
return self._set(featureSubsetStrategy=value) | 8,563,096,663,945,954,000 | Sets the value of :py:attr:`featureSubsetStrategy`. | python/pyspark/ml/regression.py | setFeatureSubsetStrategy | AjithShetty2489/spark | python | @since('2.4.0')
def setFeatureSubsetStrategy(self, value):
'\n \n '
return self._set(featureSubsetStrategy=value) |
@since('3.0.0')
def setValidationIndicatorCol(self, value):
'\n Sets the value of :py:attr:`validationIndicatorCol`.\n '
return self._set(validationIndicatorCol=value) | -5,114,315,931,364,224,000 | Sets the value of :py:attr:`validationIndicatorCol`. | python/pyspark/ml/regression.py | setValidationIndicatorCol | AjithShetty2489/spark | python | @since('3.0.0')
def setValidationIndicatorCol(self, value):
'\n \n '
return self._set(validationIndicatorCol=value) |
@since('1.4.0')
def setMaxIter(self, value):
'\n Sets the value of :py:attr:`maxIter`.\n '
return self._set(maxIter=value) | -1,196,247,245,985,027,000 | Sets the value of :py:attr:`maxIter`. | python/pyspark/ml/regression.py | setMaxIter | AjithShetty2489/spark | python | @since('1.4.0')
def setMaxIter(self, value):
'\n \n '
return self._set(maxIter=value) |
@since('1.4.0')
def setCheckpointInterval(self, value):
'\n Sets the value of :py:attr:`checkpointInterval`.\n '
return self._set(checkpointInterval=value) | -7,454,580,376,492,684,000 | Sets the value of :py:attr:`checkpointInterval`. | python/pyspark/ml/regression.py | setCheckpointInterval | AjithShetty2489/spark | python | @since('1.4.0')
def setCheckpointInterval(self, value):
'\n \n '
return self._set(checkpointInterval=value) |
@since('1.4.0')
def setSeed(self, value):
'\n Sets the value of :py:attr:`seed`.\n '
return self._set(seed=value) | 5,160,183,399,222,839,000 | Sets the value of :py:attr:`seed`. | python/pyspark/ml/regression.py | setSeed | AjithShetty2489/spark | python | @since('1.4.0')
def setSeed(self, value):
'\n \n '
return self._set(seed=value) |
@since('1.4.0')
def setStepSize(self, value):
'\n Sets the value of :py:attr:`stepSize`.\n '
return self._set(stepSize=value) | 1,217,394,787,011,605,200 | Sets the value of :py:attr:`stepSize`. | python/pyspark/ml/regression.py | setStepSize | AjithShetty2489/spark | python | @since('1.4.0')
def setStepSize(self, value):
'\n \n '
return self._set(stepSize=value) |
@since('3.0.0')
def setWeightCol(self, value):
'\n Sets the value of :py:attr:`weightCol`.\n '
return self._set(weightCol=value) | 3,791,292,180,445,544,000 | Sets the value of :py:attr:`weightCol`. | python/pyspark/ml/regression.py | setWeightCol | AjithShetty2489/spark | python | @since('3.0.0')
def setWeightCol(self, value):
'\n \n '
return self._set(weightCol=value) |
@since('3.0.0')
def setMinWeightFractionPerNode(self, value):
'\n Sets the value of :py:attr:`minWeightFractionPerNode`.\n '
return self._set(minWeightFractionPerNode=value) | 5,709,196,588,527,269,000 | Sets the value of :py:attr:`minWeightFractionPerNode`. | python/pyspark/ml/regression.py | setMinWeightFractionPerNode | AjithShetty2489/spark | python | @since('3.0.0')
def setMinWeightFractionPerNode(self, value):
'\n \n '
return self._set(minWeightFractionPerNode=value) |
@property
@since('2.0.0')
def featureImportances(self):
'\n Estimate of the importance of each feature.\n\n Each feature\'s importance is the average of its importance across all trees in the ensemble\n The importance vector is normalized to sum to 1. This method is suggested by Hastie et al.\n (Hastie, Tibshirani, Friedman. "The Elements of Statistical Learning, 2nd Edition." 2001.)\n and follows the implementation from scikit-learn.\n\n .. seealso:: :py:attr:`DecisionTreeRegressionModel.featureImportances`\n '
return self._call_java('featureImportances') | 253,380,475,036,441,120 | Estimate of the importance of each feature.
Each feature's importance is the average of its importance across all trees in the ensemble
The importance vector is normalized to sum to 1. This method is suggested by Hastie et al.
(Hastie, Tibshirani, Friedman. "The Elements of Statistical Learning, 2nd Edition." 2001.)
and follows the implementation from scikit-learn.
.. seealso:: :py:attr:`DecisionTreeRegressionModel.featureImportances` | python/pyspark/ml/regression.py | featureImportances | AjithShetty2489/spark | python | @property
@since('2.0.0')
def featureImportances(self):
'\n Estimate of the importance of each feature.\n\n Each feature\'s importance is the average of its importance across all trees in the ensemble\n The importance vector is normalized to sum to 1. This method is suggested by Hastie et al.\n (Hastie, Tibshirani, Friedman. "The Elements of Statistical Learning, 2nd Edition." 2001.)\n and follows the implementation from scikit-learn.\n\n .. seealso:: :py:attr:`DecisionTreeRegressionModel.featureImportances`\n '
return self._call_java('featureImportances') |
@property
@since('2.0.0')
def trees(self):
'Trees in this ensemble. Warning: These have null parent Estimators.'
return [DecisionTreeRegressionModel(m) for m in list(self._call_java('trees'))] | 7,781,632,944,006,031,000 | Trees in this ensemble. Warning: These have null parent Estimators. | python/pyspark/ml/regression.py | trees | AjithShetty2489/spark | python | @property
@since('2.0.0')
def trees(self):
return [DecisionTreeRegressionModel(m) for m in list(self._call_java('trees'))] |
@since('2.4.0')
def evaluateEachIteration(self, dataset, loss):
'\n Method to compute error or loss for every iteration of gradient boosting.\n\n :param dataset:\n Test dataset to evaluate model on, where dataset is an\n instance of :py:class:`pyspark.sql.DataFrame`\n :param loss:\n The loss function used to compute error.\n Supported options: squared, absolute\n '
return self._call_java('evaluateEachIteration', dataset, loss) | 8,534,903,255,737,567,000 | Method to compute error or loss for every iteration of gradient boosting.
:param dataset:
Test dataset to evaluate model on, where dataset is an
instance of :py:class:`pyspark.sql.DataFrame`
:param loss:
The loss function used to compute error.
Supported options: squared, absolute | python/pyspark/ml/regression.py | evaluateEachIteration | AjithShetty2489/spark | python | @since('2.4.0')
def evaluateEachIteration(self, dataset, loss):
'\n Method to compute error or loss for every iteration of gradient boosting.\n\n :param dataset:\n Test dataset to evaluate model on, where dataset is an\n instance of :py:class:`pyspark.sql.DataFrame`\n :param loss:\n The loss function used to compute error.\n Supported options: squared, absolute\n '
return self._call_java('evaluateEachIteration', dataset, loss) |
@since('1.6.0')
def getCensorCol(self):
'\n Gets the value of censorCol or its default value.\n '
return self.getOrDefault(self.censorCol) | -2,230,830,563,015,044,000 | Gets the value of censorCol or its default value. | python/pyspark/ml/regression.py | getCensorCol | AjithShetty2489/spark | python | @since('1.6.0')
def getCensorCol(self):
'\n \n '
return self.getOrDefault(self.censorCol) |
@since('1.6.0')
def getQuantileProbabilities(self):
'\n Gets the value of quantileProbabilities or its default value.\n '
return self.getOrDefault(self.quantileProbabilities) | -207,356,761,093,793,340 | Gets the value of quantileProbabilities or its default value. | python/pyspark/ml/regression.py | getQuantileProbabilities | AjithShetty2489/spark | python | @since('1.6.0')
def getQuantileProbabilities(self):
'\n \n '
return self.getOrDefault(self.quantileProbabilities) |
@since('1.6.0')
def getQuantilesCol(self):
'\n Gets the value of quantilesCol or its default value.\n '
return self.getOrDefault(self.quantilesCol) | -6,848,420,129,026,341,000 | Gets the value of quantilesCol or its default value. | python/pyspark/ml/regression.py | getQuantilesCol | AjithShetty2489/spark | python | @since('1.6.0')
def getQuantilesCol(self):
'\n \n '
return self.getOrDefault(self.quantilesCol) |
@keyword_only
def __init__(self, featuresCol='features', labelCol='label', predictionCol='prediction', fitIntercept=True, maxIter=100, tol=1e-06, censorCol='censor', quantileProbabilities=list([0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99]), quantilesCol=None, aggregationDepth=2):
'\n __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", fitIntercept=True, maxIter=100, tol=1E-6, censorCol="censor", quantileProbabilities=[0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99], quantilesCol=None, aggregationDepth=2)\n '
super(AFTSurvivalRegression, self).__init__()
self._java_obj = self._new_java_obj('org.apache.spark.ml.regression.AFTSurvivalRegression', self.uid)
self._setDefault(censorCol='censor', quantileProbabilities=[0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99], maxIter=100, tol=1e-06)
kwargs = self._input_kwargs
self.setParams(**kwargs) | -6,859,158,537,253,641,000 | __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", fitIntercept=True, maxIter=100, tol=1E-6, censorCol="censor", quantileProbabilities=[0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99], quantilesCol=None, aggregationDepth=2) | python/pyspark/ml/regression.py | __init__ | AjithShetty2489/spark | python | @keyword_only
def __init__(self, featuresCol='features', labelCol='label', predictionCol='prediction', fitIntercept=True, maxIter=100, tol=1e-06, censorCol='censor', quantileProbabilities=list([0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99]), quantilesCol=None, aggregationDepth=2):
'\n \n '
super(AFTSurvivalRegression, self).__init__()
self._java_obj = self._new_java_obj('org.apache.spark.ml.regression.AFTSurvivalRegression', self.uid)
self._setDefault(censorCol='censor', quantileProbabilities=[0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99], maxIter=100, tol=1e-06)
kwargs = self._input_kwargs
self.setParams(**kwargs) |
@keyword_only
@since('1.6.0')
def setParams(self, featuresCol='features', labelCol='label', predictionCol='prediction', fitIntercept=True, maxIter=100, tol=1e-06, censorCol='censor', quantileProbabilities=list([0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99]), quantilesCol=None, aggregationDepth=2):
'\n setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", fitIntercept=True, maxIter=100, tol=1E-6, censorCol="censor", quantileProbabilities=[0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99], quantilesCol=None, aggregationDepth=2):\n '
kwargs = self._input_kwargs
return self._set(**kwargs) | -8,722,679,978,714,797,000 | setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", fitIntercept=True, maxIter=100, tol=1E-6, censorCol="censor", quantileProbabilities=[0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99], quantilesCol=None, aggregationDepth=2): | python/pyspark/ml/regression.py | setParams | AjithShetty2489/spark | python | @keyword_only
@since('1.6.0')
def setParams(self, featuresCol='features', labelCol='label', predictionCol='prediction', fitIntercept=True, maxIter=100, tol=1e-06, censorCol='censor', quantileProbabilities=list([0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99]), quantilesCol=None, aggregationDepth=2):
'\n \n '
kwargs = self._input_kwargs
return self._set(**kwargs) |
@since('1.6.0')
def setCensorCol(self, value):
'\n Sets the value of :py:attr:`censorCol`.\n '
return self._set(censorCol=value) | 541,491,126,207,018,900 | Sets the value of :py:attr:`censorCol`. | python/pyspark/ml/regression.py | setCensorCol | AjithShetty2489/spark | python | @since('1.6.0')
def setCensorCol(self, value):
'\n \n '
return self._set(censorCol=value) |
@since('1.6.0')
def setQuantileProbabilities(self, value):
'\n Sets the value of :py:attr:`quantileProbabilities`.\n '
return self._set(quantileProbabilities=value) | -850,399,242,309,996,200 | Sets the value of :py:attr:`quantileProbabilities`. | python/pyspark/ml/regression.py | setQuantileProbabilities | AjithShetty2489/spark | python | @since('1.6.0')
def setQuantileProbabilities(self, value):
'\n \n '
return self._set(quantileProbabilities=value) |
@since('1.6.0')
def setQuantilesCol(self, value):
'\n Sets the value of :py:attr:`quantilesCol`.\n '
return self._set(quantilesCol=value) | -5,907,029,639,598,257,000 | Sets the value of :py:attr:`quantilesCol`. | python/pyspark/ml/regression.py | setQuantilesCol | AjithShetty2489/spark | python | @since('1.6.0')
def setQuantilesCol(self, value):
'\n \n '
return self._set(quantilesCol=value) |
@since('1.6.0')
def setMaxIter(self, value):
'\n Sets the value of :py:attr:`maxIter`.\n '
return self._set(maxIter=value) | -6,368,118,853,475,780,000 | Sets the value of :py:attr:`maxIter`. | python/pyspark/ml/regression.py | setMaxIter | AjithShetty2489/spark | python | @since('1.6.0')
def setMaxIter(self, value):
'\n \n '
return self._set(maxIter=value) |
@since('1.6.0')
def setTol(self, value):
'\n Sets the value of :py:attr:`tol`.\n '
return self._set(tol=value) | -7,171,505,432,429,106,000 | Sets the value of :py:attr:`tol`. | python/pyspark/ml/regression.py | setTol | AjithShetty2489/spark | python | @since('1.6.0')
def setTol(self, value):
'\n \n '
return self._set(tol=value) |
@since('1.6.0')
def setFitIntercept(self, value):
'\n Sets the value of :py:attr:`fitIntercept`.\n '
return self._set(fitIntercept=value) | 1,145,056,169,683,105,800 | Sets the value of :py:attr:`fitIntercept`. | python/pyspark/ml/regression.py | setFitIntercept | AjithShetty2489/spark | python | @since('1.6.0')
def setFitIntercept(self, value):
'\n \n '
return self._set(fitIntercept=value) |
@since('2.1.0')
def setAggregationDepth(self, value):
'\n Sets the value of :py:attr:`aggregationDepth`.\n '
return self._set(aggregationDepth=value) | 768,663,242,657,319,600 | Sets the value of :py:attr:`aggregationDepth`. | python/pyspark/ml/regression.py | setAggregationDepth | AjithShetty2489/spark | python | @since('2.1.0')
def setAggregationDepth(self, value):
'\n \n '
return self._set(aggregationDepth=value) |
@since('3.0.0')
def setQuantileProbabilities(self, value):
'\n Sets the value of :py:attr:`quantileProbabilities`.\n '
return self._set(quantileProbabilities=value) | 3,744,453,599,272,685,000 | Sets the value of :py:attr:`quantileProbabilities`. | python/pyspark/ml/regression.py | setQuantileProbabilities | AjithShetty2489/spark | python | @since('3.0.0')
def setQuantileProbabilities(self, value):
'\n \n '
return self._set(quantileProbabilities=value) |
@since('3.0.0')
def setQuantilesCol(self, value):
'\n Sets the value of :py:attr:`quantilesCol`.\n '
return self._set(quantilesCol=value) | -8,202,487,490,823,432,000 | Sets the value of :py:attr:`quantilesCol`. | python/pyspark/ml/regression.py | setQuantilesCol | AjithShetty2489/spark | python | @since('3.0.0')
def setQuantilesCol(self, value):
'\n \n '
return self._set(quantilesCol=value) |
@property
@since('2.0.0')
def coefficients(self):
'\n Model coefficients.\n '
return self._call_java('coefficients') | 6,857,518,054,360,473,000 | Model coefficients. | python/pyspark/ml/regression.py | coefficients | AjithShetty2489/spark | python | @property
@since('2.0.0')
def coefficients(self):
'\n \n '
return self._call_java('coefficients') |
@property
@since('1.6.0')
def intercept(self):
'\n Model intercept.\n '
return self._call_java('intercept') | -3,560,865,619,730,612,700 | Model intercept. | python/pyspark/ml/regression.py | intercept | AjithShetty2489/spark | python | @property
@since('1.6.0')
def intercept(self):
'\n \n '
return self._call_java('intercept') |
@property
@since('1.6.0')
def scale(self):
'\n Model scale parameter.\n '
return self._call_java('scale') | -7,138,564,796,128,718,000 | Model scale parameter. | python/pyspark/ml/regression.py | scale | AjithShetty2489/spark | python | @property
@since('1.6.0')
def scale(self):
'\n \n '
return self._call_java('scale') |
@since('2.0.0')
def predictQuantiles(self, features):
'\n Predicted Quantiles\n '
return self._call_java('predictQuantiles', features) | -2,929,868,469,692,212,000 | Predicted Quantiles | python/pyspark/ml/regression.py | predictQuantiles | AjithShetty2489/spark | python | @since('2.0.0')
def predictQuantiles(self, features):
'\n \n '
return self._call_java('predictQuantiles', features) |
@since('2.0.0')
def getFamily(self):
'\n Gets the value of family or its default value.\n '
return self.getOrDefault(self.family) | 3,807,383,793,635,875,000 | Gets the value of family or its default value. | python/pyspark/ml/regression.py | getFamily | AjithShetty2489/spark | python | @since('2.0.0')
def getFamily(self):
'\n \n '
return self.getOrDefault(self.family) |
@since('2.0.0')
def getLinkPredictionCol(self):
'\n Gets the value of linkPredictionCol or its default value.\n '
return self.getOrDefault(self.linkPredictionCol) | 4,153,613,732,417,093,600 | Gets the value of linkPredictionCol or its default value. | python/pyspark/ml/regression.py | getLinkPredictionCol | AjithShetty2489/spark | python | @since('2.0.0')
def getLinkPredictionCol(self):
'\n \n '
return self.getOrDefault(self.linkPredictionCol) |
@since('2.0.0')
def getLink(self):
'\n Gets the value of link or its default value.\n '
return self.getOrDefault(self.link) | 3,719,389,862,898,408,400 | Gets the value of link or its default value. | python/pyspark/ml/regression.py | getLink | AjithShetty2489/spark | python | @since('2.0.0')
def getLink(self):
'\n \n '
return self.getOrDefault(self.link) |
@since('2.2.0')
def getVariancePower(self):
'\n Gets the value of variancePower or its default value.\n '
return self.getOrDefault(self.variancePower) | 5,905,842,859,415,928,000 | Gets the value of variancePower or its default value. | python/pyspark/ml/regression.py | getVariancePower | AjithShetty2489/spark | python | @since('2.2.0')
def getVariancePower(self):
'\n \n '
return self.getOrDefault(self.variancePower) |
@since('2.2.0')
def getLinkPower(self):
'\n Gets the value of linkPower or its default value.\n '
return self.getOrDefault(self.linkPower) | 917,688,027,843,338,800 | Gets the value of linkPower or its default value. | python/pyspark/ml/regression.py | getLinkPower | AjithShetty2489/spark | python | @since('2.2.0')
def getLinkPower(self):
'\n \n '
return self.getOrDefault(self.linkPower) |
@since('2.3.0')
def getOffsetCol(self):
'\n Gets the value of offsetCol or its default value.\n '
return self.getOrDefault(self.offsetCol) | -5,945,135,978,674,455,000 | Gets the value of offsetCol or its default value. | python/pyspark/ml/regression.py | getOffsetCol | AjithShetty2489/spark | python | @since('2.3.0')
def getOffsetCol(self):
'\n \n '
return self.getOrDefault(self.offsetCol) |
@keyword_only
def __init__(self, labelCol='label', featuresCol='features', predictionCol='prediction', family='gaussian', link=None, fitIntercept=True, maxIter=25, tol=1e-06, regParam=0.0, weightCol=None, solver='irls', linkPredictionCol=None, variancePower=0.0, linkPower=None, offsetCol=None, aggregationDepth=2):
'\n __init__(self, labelCol="label", featuresCol="features", predictionCol="prediction", family="gaussian", link=None, fitIntercept=True, maxIter=25, tol=1e-6, regParam=0.0, weightCol=None, solver="irls", linkPredictionCol=None, variancePower=0.0, linkPower=None, offsetCol=None, aggregationDepth=2)\n '
super(GeneralizedLinearRegression, self).__init__()
self._java_obj = self._new_java_obj('org.apache.spark.ml.regression.GeneralizedLinearRegression', self.uid)
self._setDefault(family='gaussian', maxIter=25, tol=1e-06, regParam=0.0, solver='irls', variancePower=0.0, aggregationDepth=2)
kwargs = self._input_kwargs
self.setParams(**kwargs) | -2,628,348,636,192,205,300 | __init__(self, labelCol="label", featuresCol="features", predictionCol="prediction", family="gaussian", link=None, fitIntercept=True, maxIter=25, tol=1e-6, regParam=0.0, weightCol=None, solver="irls", linkPredictionCol=None, variancePower=0.0, linkPower=None, offsetCol=None, aggregationDepth=2) | python/pyspark/ml/regression.py | __init__ | AjithShetty2489/spark | python | @keyword_only
def __init__(self, labelCol='label', featuresCol='features', predictionCol='prediction', family='gaussian', link=None, fitIntercept=True, maxIter=25, tol=1e-06, regParam=0.0, weightCol=None, solver='irls', linkPredictionCol=None, variancePower=0.0, linkPower=None, offsetCol=None, aggregationDepth=2):
'\n \n '
super(GeneralizedLinearRegression, self).__init__()
self._java_obj = self._new_java_obj('org.apache.spark.ml.regression.GeneralizedLinearRegression', self.uid)
self._setDefault(family='gaussian', maxIter=25, tol=1e-06, regParam=0.0, solver='irls', variancePower=0.0, aggregationDepth=2)
kwargs = self._input_kwargs
self.setParams(**kwargs) |
@keyword_only
@since('2.0.0')
def setParams(self, labelCol='label', featuresCol='features', predictionCol='prediction', family='gaussian', link=None, fitIntercept=True, maxIter=25, tol=1e-06, regParam=0.0, weightCol=None, solver='irls', linkPredictionCol=None, variancePower=0.0, linkPower=None, offsetCol=None, aggregationDepth=2):
'\n setParams(self, labelCol="label", featuresCol="features", predictionCol="prediction", family="gaussian", link=None, fitIntercept=True, maxIter=25, tol=1e-6, regParam=0.0, weightCol=None, solver="irls", linkPredictionCol=None, variancePower=0.0, linkPower=None, offsetCol=None, aggregationDepth=2)\n Sets params for generalized linear regression.\n '
kwargs = self._input_kwargs
return self._set(**kwargs) | 8,200,085,354,386,206,000 | setParams(self, labelCol="label", featuresCol="features", predictionCol="prediction", family="gaussian", link=None, fitIntercept=True, maxIter=25, tol=1e-6, regParam=0.0, weightCol=None, solver="irls", linkPredictionCol=None, variancePower=0.0, linkPower=None, offsetCol=None, aggregationDepth=2)
Sets params for generalized linear regression. | python/pyspark/ml/regression.py | setParams | AjithShetty2489/spark | python | @keyword_only
@since('2.0.0')
def setParams(self, labelCol='label', featuresCol='features', predictionCol='prediction', family='gaussian', link=None, fitIntercept=True, maxIter=25, tol=1e-06, regParam=0.0, weightCol=None, solver='irls', linkPredictionCol=None, variancePower=0.0, linkPower=None, offsetCol=None, aggregationDepth=2):
'\n setParams(self, labelCol="label", featuresCol="features", predictionCol="prediction", family="gaussian", link=None, fitIntercept=True, maxIter=25, tol=1e-6, regParam=0.0, weightCol=None, solver="irls", linkPredictionCol=None, variancePower=0.0, linkPower=None, offsetCol=None, aggregationDepth=2)\n Sets params for generalized linear regression.\n '
kwargs = self._input_kwargs
return self._set(**kwargs) |
@since('2.0.0')
def setFamily(self, value):
'\n Sets the value of :py:attr:`family`.\n '
return self._set(family=value) | 1,164,448,383,522,171,100 | Sets the value of :py:attr:`family`. | python/pyspark/ml/regression.py | setFamily | AjithShetty2489/spark | python | @since('2.0.0')
def setFamily(self, value):
'\n \n '
return self._set(family=value) |
@since('2.0.0')
def setLinkPredictionCol(self, value):
'\n Sets the value of :py:attr:`linkPredictionCol`.\n '
return self._set(linkPredictionCol=value) | 6,628,946,941,373,349,000 | Sets the value of :py:attr:`linkPredictionCol`. | python/pyspark/ml/regression.py | setLinkPredictionCol | AjithShetty2489/spark | python | @since('2.0.0')
def setLinkPredictionCol(self, value):
'\n \n '
return self._set(linkPredictionCol=value) |
@since('2.0.0')
def setLink(self, value):
'\n Sets the value of :py:attr:`link`.\n '
return self._set(link=value) | -7,532,497,425,619,843,000 | Sets the value of :py:attr:`link`. | python/pyspark/ml/regression.py | setLink | AjithShetty2489/spark | python | @since('2.0.0')
def setLink(self, value):
'\n \n '
return self._set(link=value) |
@since('2.2.0')
def setVariancePower(self, value):
'\n Sets the value of :py:attr:`variancePower`.\n '
return self._set(variancePower=value) | 3,701,548,667,630,760,000 | Sets the value of :py:attr:`variancePower`. | python/pyspark/ml/regression.py | setVariancePower | AjithShetty2489/spark | python | @since('2.2.0')
def setVariancePower(self, value):
'\n \n '
return self._set(variancePower=value) |
@since('2.2.0')
def setLinkPower(self, value):
'\n Sets the value of :py:attr:`linkPower`.\n '
return self._set(linkPower=value) | 7,172,790,407,754,197,000 | Sets the value of :py:attr:`linkPower`. | python/pyspark/ml/regression.py | setLinkPower | AjithShetty2489/spark | python | @since('2.2.0')
def setLinkPower(self, value):
'\n \n '
return self._set(linkPower=value) |
@since('2.3.0')
def setOffsetCol(self, value):
'\n Sets the value of :py:attr:`offsetCol`.\n '
return self._set(offsetCol=value) | -7,094,106,673,869,634,000 | Sets the value of :py:attr:`offsetCol`. | python/pyspark/ml/regression.py | setOffsetCol | AjithShetty2489/spark | python | @since('2.3.0')
def setOffsetCol(self, value):
'\n \n '
return self._set(offsetCol=value) |
@since('2.0.0')
def setMaxIter(self, value):
'\n Sets the value of :py:attr:`maxIter`.\n '
return self._set(maxIter=value) | -8,587,339,129,955,141,000 | Sets the value of :py:attr:`maxIter`. | python/pyspark/ml/regression.py | setMaxIter | AjithShetty2489/spark | python | @since('2.0.0')
def setMaxIter(self, value):
'\n \n '
return self._set(maxIter=value) |
@since('2.0.0')
def setRegParam(self, value):
'\n Sets the value of :py:attr:`regParam`.\n '
return self._set(regParam=value) | 1,711,442,500,235,189,200 | Sets the value of :py:attr:`regParam`. | python/pyspark/ml/regression.py | setRegParam | AjithShetty2489/spark | python | @since('2.0.0')
def setRegParam(self, value):
'\n \n '
return self._set(regParam=value) |
@since('2.0.0')
def setTol(self, value):
'\n Sets the value of :py:attr:`tol`.\n '
return self._set(tol=value) | 5,756,669,959,330,179,000 | Sets the value of :py:attr:`tol`. | python/pyspark/ml/regression.py | setTol | AjithShetty2489/spark | python | @since('2.0.0')
def setTol(self, value):
'\n \n '
return self._set(tol=value) |
@since('2.2.0')
def setFitIntercept(self, value):
'\n Sets the value of :py:attr:`fitIntercept`.\n '
return self._set(fitIntercept=value) | 2,783,097,627,851,963,000 | Sets the value of :py:attr:`fitIntercept`. | python/pyspark/ml/regression.py | setFitIntercept | AjithShetty2489/spark | python | @since('2.2.0')
def setFitIntercept(self, value):
'\n \n '
return self._set(fitIntercept=value) |
@since('2.0.0')
def setWeightCol(self, value):
'\n Sets the value of :py:attr:`weightCol`.\n '
return self._set(weightCol=value) | -1,547,864,048,159,526,000 | Sets the value of :py:attr:`weightCol`. | python/pyspark/ml/regression.py | setWeightCol | AjithShetty2489/spark | python | @since('2.0.0')
def setWeightCol(self, value):
'\n \n '
return self._set(weightCol=value) |
@since('2.0.0')
def setSolver(self, value):
'\n Sets the value of :py:attr:`solver`.\n '
return self._set(solver=value) | -2,492,674,661,642,492,000 | Sets the value of :py:attr:`solver`. | python/pyspark/ml/regression.py | setSolver | AjithShetty2489/spark | python | @since('2.0.0')
def setSolver(self, value):
'\n \n '
return self._set(solver=value) |
@since('3.0.0')
def setAggregationDepth(self, value):
'\n Sets the value of :py:attr:`aggregationDepth`.\n '
return self._set(aggregationDepth=value) | 4,437,374,844,223,364,600 | Sets the value of :py:attr:`aggregationDepth`. | python/pyspark/ml/regression.py | setAggregationDepth | AjithShetty2489/spark | python | @since('3.0.0')
def setAggregationDepth(self, value):
'\n \n '
return self._set(aggregationDepth=value) |
@since('3.0.0')
def setLinkPredictionCol(self, value):
'\n Sets the value of :py:attr:`linkPredictionCol`.\n '
return self._set(linkPredictionCol=value) | -914,956,450,454,516,500 | Sets the value of :py:attr:`linkPredictionCol`. | python/pyspark/ml/regression.py | setLinkPredictionCol | AjithShetty2489/spark | python | @since('3.0.0')
def setLinkPredictionCol(self, value):
'\n \n '
return self._set(linkPredictionCol=value) |
@property
@since('2.0.0')
def coefficients(self):
'\n Model coefficients.\n '
return self._call_java('coefficients') | 6,857,518,054,360,473,000 | Model coefficients. | python/pyspark/ml/regression.py | coefficients | AjithShetty2489/spark | python | @property
@since('2.0.0')
def coefficients(self):
'\n \n '
return self._call_java('coefficients') |
@property
@since('2.0.0')
def intercept(self):
'\n Model intercept.\n '
return self._call_java('intercept') | -5,173,635,402,826,854,000 | Model intercept. | python/pyspark/ml/regression.py | intercept | AjithShetty2489/spark | python | @property
@since('2.0.0')
def intercept(self):
'\n \n '
return self._call_java('intercept') |
@property
@since('2.0.0')
def summary(self):
'\n Gets summary (e.g. residuals, deviance, pValues) of model on\n training set. An exception is thrown if\n `trainingSummary is None`.\n '
if self.hasSummary:
return GeneralizedLinearRegressionTrainingSummary(super(GeneralizedLinearRegressionModel, self).summary)
else:
raise RuntimeError(('No training summary available for this %s' % self.__class__.__name__)) | 580,146,496,962,842,500 | Gets summary (e.g. residuals, deviance, pValues) of model on
training set. An exception is thrown if
`trainingSummary is None`. | python/pyspark/ml/regression.py | summary | AjithShetty2489/spark | python | @property
@since('2.0.0')
def summary(self):
'\n Gets summary (e.g. residuals, deviance, pValues) of model on\n training set. An exception is thrown if\n `trainingSummary is None`.\n '
if self.hasSummary:
return GeneralizedLinearRegressionTrainingSummary(super(GeneralizedLinearRegressionModel, self).summary)
else:
raise RuntimeError(('No training summary available for this %s' % self.__class__.__name__)) |
@since('2.0.0')
def evaluate(self, dataset):
'\n Evaluates the model on a test dataset.\n\n :param dataset:\n Test dataset to evaluate model on, where dataset is an\n instance of :py:class:`pyspark.sql.DataFrame`\n '
if (not isinstance(dataset, DataFrame)):
raise ValueError(('dataset must be a DataFrame but got %s.' % type(dataset)))
java_glr_summary = self._call_java('evaluate', dataset)
return GeneralizedLinearRegressionSummary(java_glr_summary) | -7,544,132,382,661,572,000 | Evaluates the model on a test dataset.
:param dataset:
Test dataset to evaluate model on, where dataset is an
instance of :py:class:`pyspark.sql.DataFrame` | python/pyspark/ml/regression.py | evaluate | AjithShetty2489/spark | python | @since('2.0.0')
def evaluate(self, dataset):
'\n Evaluates the model on a test dataset.\n\n :param dataset:\n Test dataset to evaluate model on, where dataset is an\n instance of :py:class:`pyspark.sql.DataFrame`\n '
if (not isinstance(dataset, DataFrame)):
raise ValueError(('dataset must be a DataFrame but got %s.' % type(dataset)))
java_glr_summary = self._call_java('evaluate', dataset)
return GeneralizedLinearRegressionSummary(java_glr_summary) |
@property
@since('2.0.0')
def predictions(self):
"\n Predictions output by the model's `transform` method.\n "
return self._call_java('predictions') | -9,200,266,731,621,595,000 | Predictions output by the model's `transform` method. | python/pyspark/ml/regression.py | predictions | AjithShetty2489/spark | python | @property
@since('2.0.0')
def predictions(self):
"\n \n "
return self._call_java('predictions') |
@property
@since('2.0.0')
def predictionCol(self):
"\n Field in :py:attr:`predictions` which gives the predicted value of each instance.\n This is set to a new column name if the original model's `predictionCol` is not set.\n "
return self._call_java('predictionCol') | -6,291,787,637,265,208,000 | Field in :py:attr:`predictions` which gives the predicted value of each instance.
This is set to a new column name if the original model's `predictionCol` is not set. | python/pyspark/ml/regression.py | predictionCol | AjithShetty2489/spark | python | @property
@since('2.0.0')
def predictionCol(self):
"\n Field in :py:attr:`predictions` which gives the predicted value of each instance.\n This is set to a new column name if the original model's `predictionCol` is not set.\n "
return self._call_java('predictionCol') |
@property
@since('2.2.0')
def numInstances(self):
'\n Number of instances in DataFrame predictions.\n '
return self._call_java('numInstances') | 447,667,802,768,058,500 | Number of instances in DataFrame predictions. | python/pyspark/ml/regression.py | numInstances | AjithShetty2489/spark | python | @property
@since('2.2.0')
def numInstances(self):
'\n \n '
return self._call_java('numInstances') |
@property
@since('2.0.0')
def rank(self):
'\n The numeric rank of the fitted linear model.\n '
return self._call_java('rank') | -3,806,673,583,552,185,000 | The numeric rank of the fitted linear model. | python/pyspark/ml/regression.py | rank | AjithShetty2489/spark | python | @property
@since('2.0.0')
def rank(self):
'\n \n '
return self._call_java('rank') |
@property
@since('2.0.0')
def degreesOfFreedom(self):
'\n Degrees of freedom.\n '
return self._call_java('degreesOfFreedom') | -4,423,156,539,142,590,000 | Degrees of freedom. | python/pyspark/ml/regression.py | degreesOfFreedom | AjithShetty2489/spark | python | @property
@since('2.0.0')
def degreesOfFreedom(self):
'\n \n '
return self._call_java('degreesOfFreedom') |
@property
@since('2.0.0')
def residualDegreeOfFreedom(self):
'\n The residual degrees of freedom.\n '
return self._call_java('residualDegreeOfFreedom') | 3,038,285,719,347,285,500 | The residual degrees of freedom. | python/pyspark/ml/regression.py | residualDegreeOfFreedom | AjithShetty2489/spark | python | @property
@since('2.0.0')
def residualDegreeOfFreedom(self):
'\n \n '
return self._call_java('residualDegreeOfFreedom') |
@property
@since('2.0.0')
def residualDegreeOfFreedomNull(self):
'\n The residual degrees of freedom for the null model.\n '
return self._call_java('residualDegreeOfFreedomNull') | 7,911,363,567,214,588,000 | The residual degrees of freedom for the null model. | python/pyspark/ml/regression.py | residualDegreeOfFreedomNull | AjithShetty2489/spark | python | @property
@since('2.0.0')
def residualDegreeOfFreedomNull(self):
'\n \n '
return self._call_java('residualDegreeOfFreedomNull') |
@since('2.0.0')
def residuals(self, residualsType='deviance'):
'\n Get the residuals of the fitted model by type.\n\n :param residualsType: The type of residuals which should be returned.\n Supported options: deviance (default), pearson, working, and response.\n '
return self._call_java('residuals', residualsType) | 4,521,332,300,123,337,000 | Get the residuals of the fitted model by type.
:param residualsType: The type of residuals which should be returned.
Supported options: deviance (default), pearson, working, and response. | python/pyspark/ml/regression.py | residuals | AjithShetty2489/spark | python | @since('2.0.0')
def residuals(self, residualsType='deviance'):
'\n Get the residuals of the fitted model by type.\n\n :param residualsType: The type of residuals which should be returned.\n Supported options: deviance (default), pearson, working, and response.\n '
return self._call_java('residuals', residualsType) |
@property
@since('2.0.0')
def nullDeviance(self):
'\n The deviance for the null model.\n '
return self._call_java('nullDeviance') | -8,684,909,638,740,993,000 | The deviance for the null model. | python/pyspark/ml/regression.py | nullDeviance | AjithShetty2489/spark | python | @property
@since('2.0.0')
def nullDeviance(self):
'\n \n '
return self._call_java('nullDeviance') |
@property
@since('2.0.0')
def deviance(self):
'\n The deviance for the fitted model.\n '
return self._call_java('deviance') | -3,850,408,545,137,996,000 | The deviance for the fitted model. | python/pyspark/ml/regression.py | deviance | AjithShetty2489/spark | python | @property
@since('2.0.0')
def deviance(self):
'\n \n '
return self._call_java('deviance') |
@property
@since('2.0.0')
def dispersion(self):
'\n The dispersion of the fitted model.\n It is taken as 1.0 for the "binomial" and "poisson" families, and otherwise\n estimated by the residual Pearson\'s Chi-Squared statistic (which is defined as\n sum of the squares of the Pearson residuals) divided by the residual degrees of freedom.\n '
return self._call_java('dispersion') | 6,445,401,432,828,434,000 | The dispersion of the fitted model.
It is taken as 1.0 for the "binomial" and "poisson" families, and otherwise
estimated by the residual Pearson's Chi-Squared statistic (which is defined as
sum of the squares of the Pearson residuals) divided by the residual degrees of freedom. | python/pyspark/ml/regression.py | dispersion | AjithShetty2489/spark | python | @property
@since('2.0.0')
def dispersion(self):
'\n The dispersion of the fitted model.\n It is taken as 1.0 for the "binomial" and "poisson" families, and otherwise\n estimated by the residual Pearson\'s Chi-Squared statistic (which is defined as\n sum of the squares of the Pearson residuals) divided by the residual degrees of freedom.\n '
return self._call_java('dispersion') |
@property
@since('2.0.0')
def aic(self):
'\n Akaike\'s "An Information Criterion"(AIC) for the fitted model.\n '
return self._call_java('aic') | 8,133,851,070,450,337,000 | Akaike's "An Information Criterion"(AIC) for the fitted model. | python/pyspark/ml/regression.py | aic | AjithShetty2489/spark | python | @property
@since('2.0.0')
def aic(self):
'\n Akaike\'s "An Information Criterion"(AIC) for the fitted model.\n '
return self._call_java('aic') |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.