我想知道如果我有多个数字列中的功能,是否有简洁的方法在pyspark中的DataFrame上运行ML(例如KMeans)。
即。与Iris
数据集中一样:
(a1=5.1, a2=3.5, a3=1.4, a4=0.2, id=u'id_1', label=u'Iris-setosa', binomial_label=1)
我想在不重新创建DataSet的情况下使用KMeans,手动添加功能向量作为新列,并在代码中重复硬编码原始列。
我想改进的解决方案:
from pyspark.mllib.linalg import Vectors
from pyspark.sql.types import Row
from pyspark.ml.clustering import KMeans, KMeansModel
iris = sqlContext.read.parquet("/opt/data/iris.parquet")
iris.first()
# Row(a1=5.1, a2=3.5, a3=1.4, a4=0.2, id=u'id_1', label=u'Iris-setosa', binomial_label=1)
df = iris.map(lambda r: Row(
id = r.id,
a1 = r.a1,
a2 = r.a2,
a3 = r.a3,
a4 = r.a4,
label = r.label,
binomial_label=r.binomial_label,
features = Vectors.dense(r.a1, r.a2, r.a3, r.a4))
).toDF()
kmeans_estimator = KMeans()\
.setFeaturesCol("features")\
.setPredictionCol("prediction")\
kmeans_transformer = kmeans_estimator.fit(df)
predicted_df = kmeans_transformer.transform(df).drop("features")
predicted_df.first()
# Row(a1=5.1, a2=3.5, a3=1.4, a4=0.2, binomial_label=1, id=u'id_1', label=u'Iris-setosa', prediction=1)
我正在寻找一个解决方案,例如:
feature_cols = ["a1", "a2", "a3", "a4"]
prediction_col_name = "prediction"
<dataframe independent code for KMeans>
<New dataframe is created, extended with the `prediction` column.>
答案 0 :(得分:26)
您可以使用VectorAssembler
:
from pyspark.ml.feature import VectorAssembler
ignore = ['id', 'label', 'binomial_label']
assembler = VectorAssembler(
inputCols=[x for x in df.columns if x not in ignore],
outputCol='features')
assembler.transform(df)
可以使用ML Pipeline与k-means结合使用:
from pyspark.ml import Pipeline
pipeline = Pipeline(stages=[assembler, kmeans_estimator])
model = pipeline.fit(df)