Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/5137#discussion_r27336036
--- Diff: python/pyspark/mllib/classification.py ---
@@ -112,11 +122,47 @@ class
LogisticRegressionModel(LinearBinaryClassificationModel):
... os.removedirs(path)
... except:
... pass
+ >>> multi_class_data = [
+ ... LabeledPoint(0.0, [0.0, 1.0, 0.0]),
+ ... LabeledPoint(1.0, [1.0, 0.0, 0.0]),
+ ... LabeledPoint(2.0, [0.0, 0.0, 1.0])
+ ... ]
+ >>> mcm =
LogisticRegressionWithLBFGS.train(data=sc.parallelize(multi_class_data),
numClasses=3)
+ >>> mcm.predict([0.0, 0.5, 0.0])
+ 0
+ >>> mcm.predict([0.8, 0.0, 0.0])
+ 1
+ >>> mcm.predict([0.0, 0.0, 0.3])
+ 2
"""
- def __init__(self, weights, intercept):
+ def __init__(self, weights, intercept, numFeatures, numClasses):
super(LogisticRegressionModel, self).__init__(weights, intercept)
+ self._numFeatures = int(numFeatures)
+ self._numClasses = int(numClasses)
self._threshold = 0.5
+ @property
+ def numFeatures(self):
+ return self._numFeatures
+
+ @property
+ def numClasses(self):
+ return self._numClasses
+
+ @property
+ def dataWithBiasSize(self):
+ return self.weights.size / (self.numClasses - 1)
+
+ @property
+ def weightsMatrix(self):
+ if self.numClasses == 2:
+ return None
+ else:
+ if not isinstance(self.weights, DenseVector):
--- End diff --
Please use _convert_to_vector as in LinearModel. That will check types and
support Python types beyond DenseVector.
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