diff options
Diffstat (limited to 'python/pyspark/ml/classification.py')
| -rw-r--r-- | python/pyspark/ml/classification.py | 14 |
1 files changed, 7 insertions, 7 deletions
diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py index 5c11aa71b4..a1c3f72984 100644 --- a/python/pyspark/ml/classification.py +++ b/python/pyspark/ml/classification.py @@ -53,7 +53,7 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti Currently, this class only supports binary classification. >>> from pyspark.sql import Row - >>> from pyspark.mllib.linalg import Vectors + >>> from pyspark.ml.linalg import Vectors >>> df = sc.parallelize([ ... Row(label=1.0, weight=2.0, features=Vectors.dense(1.0)), ... Row(label=0.0, weight=2.0, features=Vectors.sparse(1, [], []))]).toDF() @@ -496,7 +496,7 @@ class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred It supports both binary and multiclass labels, as well as both continuous and categorical features. - >>> from pyspark.mllib.linalg import Vectors + >>> from pyspark.ml.linalg import Vectors >>> from pyspark.ml.feature import StringIndexer >>> df = sqlContext.createDataFrame([ ... (1.0, Vectors.dense(1.0)), @@ -625,7 +625,7 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred >>> import numpy >>> from numpy import allclose - >>> from pyspark.mllib.linalg import Vectors + >>> from pyspark.ml.linalg import Vectors >>> from pyspark.ml.feature import StringIndexer >>> df = sqlContext.createDataFrame([ ... (1.0, Vectors.dense(1.0)), @@ -752,7 +752,7 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol `SPARK-4240 <https://issues.apache.org/jira/browse/SPARK-4240>`_ >>> from numpy import allclose - >>> from pyspark.mllib.linalg import Vectors + >>> from pyspark.ml.linalg import Vectors >>> from pyspark.ml.feature import StringIndexer >>> df = sqlContext.createDataFrame([ ... (1.0, Vectors.dense(1.0)), @@ -884,7 +884,7 @@ class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, H The input feature values must be nonnegative. >>> from pyspark.sql import Row - >>> from pyspark.mllib.linalg import Vectors + >>> from pyspark.ml.linalg import Vectors >>> df = sqlContext.createDataFrame([ ... Row(label=0.0, features=Vectors.dense([0.0, 0.0])), ... Row(label=0.0, features=Vectors.dense([0.0, 1.0])), @@ -1028,7 +1028,7 @@ class MultilayerPerceptronClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, Number of inputs has to be equal to the size of feature vectors. Number of outputs has to be equal to the total number of labels. - >>> from pyspark.mllib.linalg import Vectors + >>> from pyspark.ml.linalg import Vectors >>> df = sqlContext.createDataFrame([ ... (0.0, Vectors.dense([0.0, 0.0])), ... (1.0, Vectors.dense([0.0, 1.0])), @@ -1193,7 +1193,7 @@ class OneVsRest(Estimator, OneVsRestParams, MLReadable, MLWritable): is picked to label the example. >>> from pyspark.sql import Row - >>> from pyspark.mllib.linalg import Vectors + >>> from pyspark.ml.linalg import Vectors >>> df = sc.parallelize([ ... Row(label=0.0, features=Vectors.dense(1.0, 0.8)), ... Row(label=1.0, features=Vectors.sparse(2, [], [])), |