Hi,
I'm wondering if there is a concise way to run ML KMeans on a DataFrame if
I have the features in multiple numeric columns.
I.e. as in the Iris dataset:
(a1=5.1, a2=3.5, a3=1.4, a4=0.2, id=u'id_1', label=u'Iris-setosa',
binomial_label=1)
I'd like to use KMeans without recreating the DataSet with the feature
vector added manually as a new column and the original columns hardcoded
repeatedly in the code.
The solution I'd like to improve:
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)
I'm looking for a solution, which is something like:
feature_cols = ["a1", "a2", "a3", "a4"]
prediction_col_name = "prediction"
<dataframe independent code for KMeans>
<New dataframe is created, extended with the `prediction` column.>
Thanks,
Zoltan
