diff options
Diffstat (limited to 'python/pyspark/mllib/classification.py')
-rw-r--r-- | python/pyspark/mllib/classification.py | 17 |
1 files changed, 9 insertions, 8 deletions
diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py index eda0b60f8b..a70c664a71 100644 --- a/python/pyspark/mllib/classification.py +++ b/python/pyspark/mllib/classification.py @@ -86,7 +86,7 @@ class LogisticRegressionModel(LinearClassificationModel): ... LabeledPoint(0.0, [0.0, 1.0]), ... LabeledPoint(1.0, [1.0, 0.0]), ... ] - >>> lrm = LogisticRegressionWithSGD.train(sc.parallelize(data)) + >>> lrm = LogisticRegressionWithSGD.train(sc.parallelize(data), iterations=10) >>> lrm.predict([1.0, 0.0]) 1 >>> lrm.predict([0.0, 1.0]) @@ -95,7 +95,7 @@ class LogisticRegressionModel(LinearClassificationModel): [1, 0] >>> lrm.clearThreshold() >>> lrm.predict([0.0, 1.0]) - 0.123... + 0.279... >>> sparse_data = [ ... LabeledPoint(0.0, SparseVector(2, {0: 0.0})), @@ -103,7 +103,7 @@ class LogisticRegressionModel(LinearClassificationModel): ... LabeledPoint(0.0, SparseVector(2, {0: 1.0})), ... LabeledPoint(1.0, SparseVector(2, {1: 2.0})) ... ] - >>> lrm = LogisticRegressionWithSGD.train(sc.parallelize(sparse_data)) + >>> lrm = LogisticRegressionWithSGD.train(sc.parallelize(sparse_data), iterations=10) >>> lrm.predict(array([0.0, 1.0])) 1 >>> lrm.predict(array([1.0, 0.0])) @@ -129,7 +129,8 @@ class LogisticRegressionModel(LinearClassificationModel): ... 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) + >>> data = sc.parallelize(multi_class_data) + >>> mcm = LogisticRegressionWithLBFGS.train(data, iterations=10, numClasses=3) >>> mcm.predict([0.0, 0.5, 0.0]) 0 >>> mcm.predict([0.8, 0.0, 0.0]) @@ -298,7 +299,7 @@ class LogisticRegressionWithLBFGS(object): ... LabeledPoint(0.0, [0.0, 1.0]), ... LabeledPoint(1.0, [1.0, 0.0]), ... ] - >>> lrm = LogisticRegressionWithLBFGS.train(sc.parallelize(data)) + >>> lrm = LogisticRegressionWithLBFGS.train(sc.parallelize(data), iterations=10) >>> lrm.predict([1.0, 0.0]) 1 >>> lrm.predict([0.0, 1.0]) @@ -330,14 +331,14 @@ class SVMModel(LinearClassificationModel): ... LabeledPoint(1.0, [2.0]), ... LabeledPoint(1.0, [3.0]) ... ] - >>> svm = SVMWithSGD.train(sc.parallelize(data)) + >>> svm = SVMWithSGD.train(sc.parallelize(data), iterations=10) >>> svm.predict([1.0]) 1 >>> svm.predict(sc.parallelize([[1.0]])).collect() [1] >>> svm.clearThreshold() >>> svm.predict(array([1.0])) - 1.25... + 1.44... >>> sparse_data = [ ... LabeledPoint(0.0, SparseVector(2, {0: -1.0})), @@ -345,7 +346,7 @@ class SVMModel(LinearClassificationModel): ... LabeledPoint(0.0, SparseVector(2, {0: 0.0})), ... LabeledPoint(1.0, SparseVector(2, {1: 2.0})) ... ] - >>> svm = SVMWithSGD.train(sc.parallelize(sparse_data)) + >>> svm = SVMWithSGD.train(sc.parallelize(sparse_data), iterations=10) >>> svm.predict(SparseVector(2, {1: 1.0})) 1 >>> svm.predict(SparseVector(2, {0: -1.0})) |