Machine Learning with PySpark and MLlib — Solving a Binary Classification Problem | by Susan Li | Towards Data Science

Apache Spark, once a component of the Hadoop ecosystem, is now becoming the big-data platform of choice for enterprises. It is a powerful open source engine that provides real-time stream processing, interactive processing, graph processing, in-memory processing as well as batch processing with very fast speed, ease of use and standard interface.
In the industry, there is a big demand for a powerful engine that can do all of above. Sooner or later, your company or your clients will be using Spark to develop sophisticated models that would enable you to discover new opportunities or avoid risk. Spark is not hard to learn, if you already known Python and SQL, it is very easy to get started. Let’s give it a try today!
We will use the same data set when we built a Logistic Regression in Python, and it is related to direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict whether the client will subscribe (Yes/No) to a term deposit. The dataset can be downloaded from Kaggle.
from pyspark.sql import SparkSessionspark = SparkSession.builder.appName('ml-bank').getOrCreate()df = spark.read.csv('bank.csv', header = True, inferSchema = True)df.printSchema()
Input variables: age, job, marital, education, default, balance, housing, loan, contact, day, month, duration, campaign, pdays, previous, poutcome.
Output variable: deposit
Have a peek of the first five observations. Pandas data frame is prettier than Spark DataFrame.show().
import pandas as pdpd.DataFrame(df.take(5), columns=df.columns).transpose()
Our classes are perfect balanced.
import pandas as pdpd.DataFrame(df.take(5), columns=df.columns).transpose()
Summary statistics for numeric variables
numeric_features = [t[0] for t in df.dtypes if t[1] == 'int']df.select(numeric_features).describe().toPandas().transpose()
Correlations between independent variables.
numeric_data = df.select(numeric_features).toPandas()axs = pd.scatter_matrix(numeric_data, figsize=(8, 8));n = len(numeric_data.columns)for i in range(n):    v = axs[i, 0]    v.yaxis.label.set_rotation(0)    v.yaxis.label.set_ha('right')    v.set_yticks(())    h = axs[n-1, i]    h.xaxis.label.set_rotation(90)    h.set_xticks(())
It’s obvious that there aren’t highly correlated numeric variables. Therefore, we will keep all of them for the model. However, day and month columns are not really useful, we will remove these two columns.
df = df.select('age', 'job', 'marital', 'education', 'default', 'balance', 'housing', 'loan', 'contact', 'duration', 'campaign', 'pdays', 'previous', 'poutcome', 'deposit')cols = df.columnsdf.printSchema()
The process includes Category Indexing, One-Hot Encoding and VectorAssembler — a feature transformer that merges multiple columns into a vector column.
from pyspark.ml.feature import OneHotEncoderEstimator, StringIndexer, VectorAssemblercategoricalColumns = ['job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'poutcome']stages = []for categoricalCol in categoricalColumns:    stringIndexer = StringIndexer(inputCol = categoricalCol, outputCol = categoricalCol + 'Index')    encoder = OneHotEncoderEstimator(inputCols=[stringIndexer.getOutputCol()], outputCols=[categoricalCol + "classVec"])    stages += [stringIndexer, encoder]label_stringIdx = StringIndexer(inputCol = 'deposit', outputCol = 'label')stages += [label_stringIdx]numericCols = ['age', 'balance', 'duration', 'campaign', 'pdays', 'previous']assemblerInputs = [c + "classVec" for c in categoricalColumns] + numericColsassembler = VectorAssembler(inputCols=assemblerInputs, outputCol="features")stages += [assembler]
The above code are taken from databricks’ official site and it indexes each categorical column using the StringIndexer, then converts the indexed categories into one-hot encoded variables. The resulting output has the binary vectors appended to the end of each row. We use the StringIndexer again to encode our labels to label indices. Next, we use the VectorAssembler to combine all the feature columns into a single vector column.
Pipeline
We use Pipeline to chain multiple Transformers and Estimators together to specify our machine learning workflow. A Pipeline’s stages are specified as an ordered array.
from pyspark.ml import Pipelinepipeline = Pipeline(stages = stages)pipelineModel = pipeline.fit(df)df = pipelineModel.transform(df)selectedCols = ['label', 'features'] + colsdf = df.select(selectedCols)df.printSchema()
pd.DataFrame(df.take(5), columns=df.columns).transpose()
As you can see, we now have features column and label column.
Randomly split data into train and test sets, and set seed for reproducibility.
train, test = df.randomSplit([0.7, 0.3], seed = 2018)print("Training Dataset Count: " + str(train.count()))print("Test Dataset Count: " + str(test.count()))
Training Dataset Count: 7764Test Dataset Count: 3398
from pyspark.ml.classification import LogisticRegressionlr = LogisticRegression(featuresCol = 'features', labelCol = 'label', maxIter=10)lrModel = lr.fit(train)
We can obtain the coefficients by using LogisticRegressionModel’s attributes.
import matplotlib.pyplot as pltimport numpy as npbeta = np.sort(lrModel.coefficients)plt.plot(beta)plt.ylabel('Beta Coefficients')plt.show()
Summarize the model over the training set, we can also obtain the receiver-operating characteristic and areaUnderROC.
trainingSummary = lrModel.summaryroc = trainingSummary.roc.toPandas()plt.plot(roc['FPR'],roc['TPR'])plt.ylabel('False Positive Rate')plt.xlabel('True Positive Rate')plt.title('ROC Curve')plt.show()print('Training set areaUnderROC: ' + str(trainingSummary.areaUnderROC))
Precision and recall.
pr = trainingSummary.pr.toPandas()plt.plot(pr['recall'],pr['precision'])plt.ylabel('Precision')plt.xlabel('Recall')plt.show()
Make predictions on the test set.
predictions = lrModel.transform(test)predictions.select('age', 'job', 'label', 'rawPrediction', 'prediction', 'probability').show(10)
Evaluate our Logistic Regression model.
from pyspark.ml.evaluation import BinaryClassificationEvaluatorevaluator = BinaryClassificationEvaluator()print('Test Area Under ROC', evaluator.evaluate(predictions))
Test Area Under ROC 0.8858324614449619
Decision trees are widely used since they are easy to interpret, handle categorical features, extend to the multi-class classification, do not require feature scaling, and are able to capture non-linearities and feature interactions.
from pyspark.ml.classification import DecisionTreeClassifierdt = DecisionTreeClassifier(featuresCol = 'features', labelCol = 'label', maxDepth = 3)dtModel = dt.fit(train)predictions = dtModel.transform(test)predictions.select('age', 'job', 'label', 'rawPrediction', 'prediction', 'probability').show(10)
Evaluate our Decision Tree model.
evaluator = BinaryClassificationEvaluator()print("Test Area Under ROC: " + str(evaluator.evaluate(predictions, {evaluator.metricName: "areaUnderROC"})))
Test Area Under ROC: 0.7807240050065357
One simple decision tree performed poorly because it is too weak given the range of different features. The prediction accuracy of decision trees can be improved by Ensemble methods, such as Random Forest and Gradient-Boosted Tree.
from pyspark.ml.classification import RandomForestClassifierrf = RandomForestClassifier(featuresCol = 'features', labelCol = 'label')rfModel = rf.fit(train)predictions = rfModel.transform(test)predictions.select('age', 'job', 'label', 'rawPrediction', 'prediction', 'probability').show(10)
Evaluate our Random Forest Classifier.
evaluator = BinaryClassificationEvaluator()print("Test Area Under ROC: " + str(evaluator.evaluate(predictions, {evaluator.metricName: "areaUnderROC"})))
Test Area Under ROC: 0.8846453518867426
from pyspark.ml.classification import GBTClassifiergbt = GBTClassifier(maxIter=10)gbtModel = gbt.fit(train)predictions = gbtModel.transform(test)predictions.select('age', 'job', 'label', 'rawPrediction', 'prediction', 'probability').show(10)
Evaluate our Gradient-Boosted Tree Classifier.
evaluator = BinaryClassificationEvaluator()print("Test Area Under ROC: " + str(evaluator.evaluate(predictions, {evaluator.metricName: "areaUnderROC"})))
Test Area Under ROC: 0.8940728473145346
Gradient-Boosted Tree achieved the best results, we will try tuning this model with the ParamGridBuilder and the CrossValidator. Before that we can use explainParams() to print a list of all params and their definitions to understand what params available for tuning.
print(gbt.explainParams())
from pyspark.ml.tuning import ParamGridBuilder, CrossValidatorparamGrid = (ParamGridBuilder()             .addGrid(gbt.maxDepth, [2, 4, 6])             .addGrid(gbt.maxBins, [20, 60])             .addGrid(gbt.maxIter, [10, 20])             .build())cv = CrossValidator(estimator=gbt, estimatorParamMaps=paramGrid, evaluator=evaluator, numFolds=5)# Run cross validations.  This can take about 6 minutes since it is training over 20 trees!cvModel = cv.fit(train)predictions = cvModel.transform(test)evaluator.evaluate(predictions)
0.8981050997838095
To sum it up, we have learned how to build a binary classification application using PySpark and MLlib Pipelines API. We tried four algorithms and gradient boosting performed best on our data set.
Source code can be found on Github. I look forward to hearing feedback or questions.
Reference: Apache Spark 2.1.0