Understanding the results from Spark's KMeans clustering object

[shubham <sh...@gmail.com>]

Hello Everyone,

I am performing clustering on a dataset using PySpark. To find the number of
clusters I performed clustering over a range of values (2,20) and found the
wsse (within-cluster sum of squares) values for each value of k. This where
I found something unusual. According to my understanding when you increase
the number of clusters, the wsse decreases monotonically. But results I got
say otherwise. I 'm displaying wsse for first few clusters only

Results from spark

For k = 002 WSSE is 255318.793358
For k = 003 WSSE is 209788.479560
For k = 004 WSSE is 208498.351074
For k = 005 WSSE is 142573.272672
For k = 006 WSSE is 154419.027612
For k = 007 WSSE is 115092.404604
For k = 008 WSSE is 104753.205635
For k = 009 WSSE is 98000.985547
For k = 010 WSSE is 95134.137071
If you look at the wsse value of for k=5 and k=6, you'll see the wsse has
increased. I turned to sklearn to see if I get similar results. The codes I
used for spark and sklearn are in the appendix section towards the end of
the post. I have tried to use same values for the parameters in spark and
sklearn KMeans model. The following are the results from sklearn and they
are as I expected them to be - monotonically decreasing.

Results from sklearn

For k = 002 WSSE is 245090.224247
For k = 003 WSSE is 201329.888159
For k = 004 WSSE is 166889.044195
For k = 005 WSSE is 142576.895154
For k = 006 WSSE is 123882.070776
For k = 007 WSSE is 112496.692455
For k = 008 WSSE is 102806.001664
For k = 009 WSSE is 95279.837212
For k = 010 WSSE is 89303.574467
I am not sure as to why I the wsse values increase in Spark. I tried using
different datasets and found similar behavior there as well. Is there
someplace I am going wrong? Any clues would be great.

APPENDIX
The dataset is located here.

Read the data and set declare variables

# get data
import pandas as pd
url =
"https://raw.githubusercontent.com/vectosaurus/bb_lite/master/3.0%20data/adult_comp_cont.csv"

df_pandas = pd.read_csv(url)
df_spark = sqlContext(df_pandas)
target_col = 'high_income'
numeric_cols = [i for i in df_pandas.columns if i !=target_col]

k_min = 2 # 2 in inclusive
k_max = 21 # 2i is exlusive. will fit till 20

max_iter = 1000
seed = 42    
This is the code I am using for getting the sklearn results:

from sklearn.cluster import KMeans as KMeans_SKL
from sklearn.preprocessing import StandardScaler as StandardScaler_SKL

ss = StandardScaler_SKL(with_std=True, with_mean=True)
ss.fit(df_pandas.loc[:, numeric_cols])
df_pandas_scaled = pd.DataFrame(ss.transform(df_pandas.loc[:,
numeric_cols]))

wsse_collect = []

for i in range(k_min, k_max):
    km = KMeans_SKL(random_state=seed, max_iter=max_iter, n_clusters=i)
    _ = km.fit(df_pandas_scaled)
    wsse = km.inertia_
    print('For k = {i:03d} WSSE is {wsse:10f}'.format(i=i, wsse=wsse))
    wsse_collect.append(wsse)
This is the code I am using for getting the spark results

from pyspark.ml.feature import StandardScaler, VectorAssembler
from pyspark.ml.clustering import KMeans

standard_scaler_inpt_features = 'ss_features'
kmeans_input_features = 'features'
kmeans_prediction_features = 'prediction'


assembler = VectorAssembler(inputCols=numeric_cols,
outputCol=standard_scaler_inpt_features)
assembled_df = assembler.transform(df_spark)

scaler = StandardScaler(inputCol=standard_scaler_inpt_features,
outputCol=kmeans_input_features, withStd=True, withMean=True)
scaler_model = scaler.fit(assembled_df)
scaled_data = scaler_model.transform(assembled_df)

wsse_collect_spark = []

for i in range(k_min, k_max):
    km = KMeans(featuresCol=kmeans_input_features,
predictionCol=kmeans_prediction_col,
                        k=i, maxIter=max_iter, seed=seed)
    km_fit = km.fit(scaled_data)
    wsse_spark = km_fit.computeCost(scaled_data)
    wsse_collect_spark .append(wsse_spark)
    print('For k = {i:03d} WSSE is {wsse:10f}'.format(i=i, wsse=wsse_spark))




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