Published on

# Feature Selection Using Feature Importance Score - Creating a PySpark Estimator

Authors

In this post I discuss how to create a new pyspark estimator to integrate in an existing machine learning pipeline. This is an extension of my previous post where I discussed how to create a custom cross validation function. Recently, I have been looking at integrating existing code in the pyspark ML pipeline framework. A pipeline is a fantastic concept of abstraction since it allows the analyst to focus on the main tasks that needs to be carried out and allows the entire piece of work to be reusable.

As a fun and useful example, I will show how feature selection using feature importance score can be coded into a pipeline. I find Pyspark's MLlib native feature selection functions relatively limited so this is also part of an effort to extend the feature selection methods. Here, I use the feature importance score as estimated from a model (decision tree / random forest / gradient boosted trees) to extract the variables that are plausibly the most important.

First, let's setup the jupyter notebook and import the relevant functions. I use a local version of spark to illustrate how this works but one can easily use a yarn cluster instead.

from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"

import numpy as np
import pandas as pd
pd.options.display.max_columns = None

import findspark
findspark.init()
from pyspark import SparkContext
from pyspark import SQLContext

sc = SparkContext()
spark = SQLContext(sc)

from pyspark.sql.functions import *
from pyspark.ml.classification import  RandomForestClassifier
from pyspark.ml.feature import StringIndexer, OneHotEncoderEstimator, VectorAssembler, VectorSlicer
from pyspark.ml import Pipeline
from pyspark.ml.evaluation import BinaryClassificationEvaluator
from pyspark.ml.linalg import Vectors
from pyspark.ml.tuning import ParamGridBuilder, TrainValidationSplit


### Bank Marketing Data Set

To show the usefulness of feature selection and to sort of validate the script, I used the Bank Marketing Data Set from UCI Machine Learning Repository as an example throughout this post. This comes from Moro et al., 2014 paper on A Data-Driven Approach to Predict the Success of Bank Telemarketing. As the name of the paper suggests, the goal of this dataset is to predict which bank customers would subscribe to a term deposit product as a result of a phone marketing campaign.

Let us read in the file and take a look at the variables of the dataset.

df = spark.read.option("delimiter", ";").csv("../data/bank-additional/bank-additional-full.csv", header=True, inferSchema = True)

df.dtypes

[('age', 'int'),
('job', 'string'),
('marital', 'string'),
('education', 'string'),
('default', 'string'),
('housing', 'string'),
('loan', 'string'),
('contact', 'string'),
('month', 'string'),
('day_of_week', 'string'),
('duration', 'int'),
('campaign', 'int'),
('pdays', 'int'),
('previous', 'int'),
('poutcome', 'string'),
('emp.var.rate', 'double'),
('cons.price.idx', 'double'),
('cons.conf.idx', 'double'),
('euribor3m', 'double'),
('nr.employed', 'double'),
('y', 'string')]


There are some problematic variable names and we should replace the dot seperator with an underscore.

df = df.toDF(*(c.replace('.', '_') for c in df.columns))

df.limit(5).toPandas()

summary age job marital education default housing loan contact month day_of_week duration
0 count 41188 41188 41188 41188 41188 41188 41188 41188 41188 41188 41188
1 mean 40.02406040594348 None None None None None None None None None 258.2850101971448
2 stddev 10.421249980934057 None None None None None None None None None 259.27924883646455
3 min 17 admin. divorced basic.4y no no no cellular apr fri 0
4 max 98 unknown unknown unknown yes yes yes telephone sep wed 4918

Continued...

campaign pdays previous poutcome emp_var_rate cons_price_idx cons_conf_idx euribor3m nr_employed y
0 41188 41188 41188 41188 41188 41188 41188 41188 41188 41188
1 2.567592502670681 962.4754540157328 0.17296299893172767 None 0.08188550063178392 93.57566436828918 -40.50260027191787 3.6212908128585366 5167.035910944004 None
2 2.770013542902331 186.9109073447414 0.49490107983928927 None 1.57095974051703 0.5788400489541355 4.628197856174595 1.7344474048512557 72.25152766825924 None
3 1 0 0 failure -3.4 92.201 -50.8 0.634 4963.6 no
4 56 999 7 success 1.4 94.767 -26.9 5.045 5228.1 yes

It's always nice to take a look at the distribution of the variables

df.describe().toPandas()

summary age job marital education default housing loan contact month day_of_week duration
0 count 41188 41188 41188 41188 41188 41188 41188 41188 41188 41188 41188
1 mean 40.02406040594348 None None None None None None None None None 258.2850101971448
2 stddev 10.421249980934057 None None None None None None None None None 259.27924883646455
3 min 17 admin. divorced basic.4y no no no cellular apr fri 0
4 max 98 unknown unknown unknown yes yes yes telephone sep wed 4918

Continued...

campaign pdays previous poutcome emp_var_rate cons_price_idx cons_conf_idx euribor3m nr_employed y
0 41188 41188 41188 41188 41188 41188 41188 41188 41188 41188
1 2.567592502670681 962.4754540157328 0.17296299893172767 None 0.08188550063178392 93.57566436828918 -40.50260027191787 3.6212908128585366 5167.035910944004 None
2 2.770013542902331 186.9109073447414 0.49490107983928927 None 1.57095974051703 0.5788400489541355 4.628197856174595 1.7344474048512557 72.25152766825924 None
3 1 0 0 failure -3.4 92.201 -50.8 0.634 4963.6 no
4 56 999 7 success 1.4 94.767 -26.9 5.045 5228.1 yes

There are quite a few variables that are encoded as a string in this dataset. Converting strings to a binary indicator variable / dummy variable takes up quite a few degrees of freedom. In machine learning speak it might also lead to the model being overfitted. Let us take a look at what is represented by each variable that is of string type.

for i in df.dtypes:
if i[1]=='string':
df.groupBy(i[0]).count().orderBy('count', ascending=False).toPandas()

job count
1 blue-collar 9254
2 technician 6743
3 services 3969
4 management 2924
5 retired 1720
6 entrepreneur 1456
7 self-employed 1421
8 housemaid 1060
9 unemployed 1014
10 student 875
11 unknown 330
marital count
0 married 24928
1 single 11568
2 divorced 4612
3 unknown 80
education count
0 university.degree 12168
1 high.school 9515
2 basic.9y 6045
3 professional.course 5243
4 basic.4y 4176
5 basic.6y 2292
6 unknown 1731
7 illiterate 18
default count
0 no 32588
1 unknown 8597
2 yes 3
housing count
0 yes 21576
1 no 18622
2 unknown 990
loan count
0 no 33950
1 yes 6248
2 unknown 990
contact count
0 cellular 26144
1 telephone 15044
month count
0 may 13769
1 jul 7174
2 aug 6178
3 jun 5318
4 nov 4101
5 apr 2632
6 oct 718
7 sep 570
8 mar 546
9 dec 182
day_of_week count
0 thu 8623
1 mon 8514
2 wed 8134
3 tue 8090
4 fri 7827
poutcome count
0 nonexistent 35563
1 failure 4252
2 success 1373
y count
0 no 36548
1 yes 4640

The number of categories for each string type is relatively small which makes creating binary indicator variables / one-hot encoding a suitable pre-processing step. Let us take a look at how to do feature selection using the feature importance score the manual way before coding it as an estimator to fit into a Pyspark pipeline.

### Data Preprocessing

Before we run the model on the most relevant features, we would first need to encode the string variables as binary vectors and run a random forest model on the whole feature set to get the feature importance score. Here I just run most of these tasks as part of a pipeline.

# one hot encoding and assembling
encoding_var = [i[0] for i in df.dtypes if (i[1]=='string') & (i[0]!='y')]
num_var = [i[0] for i in df.dtypes if ((i[1]=='int') | (i[1]=='double')) & (i[0]!='y')]

string_indexes = [StringIndexer(inputCol = c, outputCol = 'IDX_' + c, handleInvalid = 'keep') for c in encoding_var]
onehot_indexes = [OneHotEncoderEstimator(inputCols = ['IDX_' + c], outputCols = ['OHE_' + c]) for c in encoding_var]
label_indexes = StringIndexer(inputCol = 'y', outputCol = 'label', handleInvalid = 'keep')
assembler = VectorAssembler(inputCols = num_var + ['OHE_' + c for c in encoding_var], outputCol = "features")
rf = RandomForestClassifier(labelCol="label", featuresCol="features", seed = 8464,
numTrees=10, cacheNodeIds = True, subsamplingRate = 0.7)

pipe = Pipeline(stages = string_indexes + onehot_indexes + [assembler, label_indexes, rf])

mod = pipe.fit(df)

df2 = mod.transform(df)


The feature importance score that is returned comes in the form of a sparse vector. This is not very human readable and we would need to map this to the actual variable names for some insights. I wrote a little function to return the variable names sorted by importance score as a pandas data frame. This was inspired by the following post on stackoverflow

mod.stages[-1].featureImportances

SparseVector(63, {0: 0.0257, 1: 0.1596, 2: 0.0037, 3: 0.2212, 4: 0.0305, 5: 0.0389, 6: 0.0762, 7: 0.0423, 8: 0.1869, 9: 0.063, 10: 0.0002, 12: 0.0003, 13: 0.0002, 14: 0.0003, 15: 0.0005, 16: 0.0002, 18: 0.0006, 19: 0.0003, 20: 0.0002, 21: 0.0, 22: 0.001, 23: 0.0003, 24: 0.0005, 26: 0.0005, 27: 0.0007, 28: 0.0008, 29: 0.0003, 30: 0.0, 31: 0.0001, 34: 0.0002, 35: 0.0021, 37: 0.0001, 38: 0.0003, 39: 0.0003, 40: 0.0003, 41: 0.0001, 42: 0.0002, 43: 0.0284, 44: 0.0167, 45: 0.0038, 46: 0.0007, 47: 0.0008, 48: 0.0132, 49: 0.0003, 50: 0.0014, 51: 0.0159, 52: 0.0114, 53: 0.0103, 54: 0.0036, 55: 0.0002, 56: 0.0021, 57: 0.0002, 58: 0.0006, 59: 0.0005, 60: 0.0158, 61: 0.0038, 62: 0.0121})

def ExtractFeatureImp(featureImp, dataset, featuresCol):
list_extract = []
varlist = pd.DataFrame(list_extract)
varlist['score'] = varlist['idx'].apply(lambda x: featureImp[x])
return(varlist.sort_values('score', ascending = False))

ExtractFeatureImp(mod.stages[-1].featureImportances, df2, "features").head(10)

idx name score
3 3 pdays 0.221203
8 8 euribor3m 0.186892
1 1 duration 0.159579
6 6 cons_price_idx 0.076177
9 9 nr_employed 0.063016
7 7 cons_conf_idx 0.042298
5 5 emp_var_rate 0.038875
4 4 previous 0.030470
43 43 OHE_contact_cellular 0.028401
0 0 age 0.025732

Now that we have the most important faatures in a nicely formatted list, we can extract the top 10 features and create a new input vector column with only these variables. Pyspark has a VectorSlicer function that does exactly that. A new model can then be trained just on these 10 variables.

varlist = ExtractFeatureImp(mod.stages[-1].featureImportances, df2, "features")

varidx = [x for x in varlist['idx'][0:10]]

varidx

[3, 8, 1, 6, 9, 7, 5, 4, 43, 0]

slicer = VectorSlicer(inputCol="features", outputCol="features2", indices=varidx)
df3 = slicer.transform(df2)

df3 = df3.drop('rawPrediction', 'probability', 'prediction')
rf2 = RandomForestClassifier(labelCol="label", featuresCol="features2", seed = 8464,
numTrees=10, cacheNodeIds = True, subsamplingRate = 0.7)
mod2 = rf2.fit(df3)
df4 = mod2.transform(df3)


### Building the estimator function

Now let us learn to build a new pipeline object that makes the above task easy!

First a bit of theory as taken from the ML pipeline documentation:

DataFrame: This ML API uses DataFrame from Spark SQL as an ML dataset, which can hold a variety of data types. E.g., a DataFrame could have different columns storing text, feature vectors, true labels, and predictions.

Transformer: A Transformer is an algorithm which can transform one DataFrame into another DataFrame. E.g., an ML model is a Transformer which transforms a DataFrame with features into a DataFrame with predictions.

Estimator: An Estimator is an algorithm which can be fit on a DataFrame to produce a Transformer. E.g., a learning algorithm is an Estimator which trains on a DataFrame and produces a model.

Pipeline: A Pipeline chains multiple Transformers and Estimators together to specify an ML workflow.

The important thing to remember is that the pipeline object has two components. The first is the estimator which returns a model and the second is the model/transformer which returns a dataframe.

We begin by coding up the estimator object. The cross-validation function in the previous post provides a thorough walk-through on creating the estimator object and params needed. In this case, I wanted the function to select either the top n features or based on a certain cut-off so these parameters are included as arguments to the function. An estimator (either decision tree / random forest / gradient boosted trees) is also required as an input.

def __init__(self, estimator = None, selectorType = "numTopFeatures",
numTopFeatures = 20, threshold = 0.01, outputCol = "features")


Given a dataset we can write a fit function that extracts the feature importance scores

mod = est.fit(dataset)
dataset2 = mod.transform(dataset)
varlist = ExtractFeatureImp(mod.featureImportances, dataset2, est.getFeaturesCol())


Some conditional statements to select the correct indexes that corresponds to the feature we want to extract. This gives us the output of the model - a list of features we want to extract.

if (selectorType == "numTopFeatures"):
varidx = [x for x in varlist['idx'][0:nfeatures]]
elif (selectorType == "threshold"):
varidx = [x for x in varlist[varlist['score'] > threshold]['idx']]


Now for the second part of the problem - we want to take this list of features and create a transform function that returns the dataset with a new column containing our most relevant features. Sounds familiar? This is exactly what the VectorSlicer transformer does. So there is no need to re-invent the wheel and we can just reurn a VectorSlicer with the correct indices to slice.

return VectorSlicer(inputCol = est.getFeaturesCol(),
outputCol = outputCol,
indices = varidx)


That concludes our new feature selection estimator! The full code can be obtained here.

### Putting the new function to the test

Let's try out the new function. I saved it as a file called FeatureImportanceSelector.py. Notice there is a new pipeline object called fis (featureImpSelector). This takes in the first random forest model and uses the feature importance score from it to extract the top 10 variables.

from FeatureImportanceSelector import ExtractFeatureImp, FeatureImpSelector

# one hot encoding and assembling
encoding_var = [i[0] for i in df.dtypes if (i[1]=='string') & (i[0]!='y')]
num_var = [i[0] for i in df.dtypes if ((i[1]=='int') | (i[1]=='double')) & (i[0]!='y')]

string_indexes = [StringIndexer(inputCol = c, outputCol = 'IDX_' + c, handleInvalid = 'keep') for c in encoding_var]
onehot_indexes = [OneHotEncoderEstimator(inputCols = ['IDX_' + c], outputCols = ['OHE_' + c]) for c in encoding_var]
label_indexes = StringIndexer(inputCol = 'y', outputCol = 'label', handleInvalid = 'keep')
assembler = VectorAssembler(inputCols = num_var + ['OHE_' + c for c in encoding_var], outputCol = "features")

rf = RandomForestClassifier(labelCol="label", featuresCol="features", seed = 8464,
numTrees=10, cacheNodeIds = True, subsamplingRate = 0.7)
fis = FeatureImpSelector(estimator = rf, selectorType = "numTopFeatures",
numTopFeatures = 10, outputCol = "features_subset")
rf2 = RandomForestClassifier(labelCol="label", featuresCol="features_subset", seed = 8464,
numTrees=10, cacheNodeIds = True, subsamplingRate = 0.7)

pipe = Pipeline(stages = string_indexes + onehot_indexes + [assembler, label_indexes, fis, rf2])

pipeline_mod = pipe.fit(df)

df2 = pipeline_mod.transform(df)

ExtractFeatureImp(mod.stages[-1].featureImportances, df2, "features_subset")

idx name score
3 3 cons_price_idx 0.221203
9 8 OHE_contact_cellular 0.186892
1 1 euribor3m 0.159579
6 6 emp_var_rate 0.076177
8 9 age 0.063016
7 7 previous 0.042298
5 5 cons_conf_idx 0.038875
4 4 nr_employed 0.030470
0 0 pdays 0.025732
2 2 duration 0.003744

10 features as intended and not suprisingly, it matches the top 10 features as generated by our previous non-pipeline method.

Hope you found the tutorial useful and maybe it will inspire you to create more useful extensions for pyspark.