I’m Harun-Ur-Rashid. I'm a self-taught Data Scientist.
Spark is the name of the engine to realize cluster computing while PySpark is the Python's library to use Spark.
PySpark is a great language for performing exploratory data analysis at scale, building machine learning pipelines, and creating ETLs for a data platform. If you’re already familiar with Python and libraries such as Pandas, then PySpark is a great language to learn in order to create more scalable analyses and pipelines.
The goal of this post is to show how to build a machine learning model using PySpark.
PySpark installing process is very easy as like others python's packages.(eg.Pandas,Numpy,scikit-learn).
One important thing is, firstly ensure java has installed in your machine. then you can run PySpark on your jupyter notebook.
We will use the same data set when we built machine learning models in Python, and it is related to diabetes diseases of a National Institute of Diabetes and Digestive and Kidney Diseases. The classification goal is to predict whether the patient has diabetes (Yes/No). The dataset can be downloaded from Kaggle.
from pyspark.sql import SparkSession spark = SparkSession.builder.appName('ml-diabetes').getOrCreate() df = spark.read.csv('diabetes.csv', header = True, inferSchema = True) df.printSchema()
The datasets consists of several medical predictor variables and one target variable, Outcome. Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin level, age, and so on.
Input variables: Glucose,BloodPressure,BMI,Age,Pregnancies,Insulin,SkinThikness,DiabetesPedigreeFunction.
Output variables: Outcome.
Have a peek of the first five observations. Pandas data frame is prettier than Spark DataFrame.show().
import pandas as pd pd.DataFrame(df.take(5), columns=df.columns).transpose()
In PySpark you can show the data with Pandas' DataFrame using
Checking the classes are perfectly balanced!!
numeric_features = [t for t in df.dtypes if t == 'int'] df.select(numeric_features).describe().toPandas().transpose()
Correlations between independent variables
from pandas.plotting import scatter_matrix numeric_data = df.select(numeric_features).toPandas() axs = scatter_matrix(numeric_data, figsize=(8, 8)); # Rotate axis labels and remove axis ticks 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(())
Data preparation and feature engineering
In this part, we will remove unnecessary columns and fill the missing values. Finally, selecting features for machine learning models. These features will be divided into two parts train and test.
Lets starting mission 👨🚀
Missing Data Handling:
from pyspark.sql.functions import isnull, when, count, col df.select([count(when(isnull(c), c)).alias(c) for c in df.columns]).show()
Wow!! 👌 That's great in this datasets haven't any missing values.😀
Unnecessary columns dropping
dataset = dataset.drop('SkinThickness') dataset = dataset.drop('Insulin') dataset = dataset.drop('DiabetesPedigreeFunction') dataset = dataset.drop('Pregnancies') dataset.show()
Features Convert into Vector
VectorAssembler — a feature transformer that merges multiple columns into a vector column.
# Assemble all the features with VectorAssembler required_features = ['Glucose', 'BloodPressure', 'BMI', 'Age' ] from pyspark.ml.feature import VectorAssembler assembler = VectorAssembler(inputCols=required_features, outputCol='features') transformed_data = assembler.transform(dataset) transformed_data.show()
Done!!✌️ Now features converted into a vector. 🧮
Train and Test Split
Randomly split data into train and test sets, and set seed for reproducibility.
# Split the data (training_data, test_data) = transformed_data.randomSplit([0.8,0.2], seed =2020) print("Training Dataset Count: " + str(training_data.count())) print("Test Dataset Count: " + str(test_data.count()))
Training Dataset Count: 620
Test Dataset Count: 148
Machine learning Model Building
Random forest is a supervised learning algorithm which is used for both classification as well as regression. But however, it is mainly used for classification problems. As we know that a forest is made up of trees and more trees means more robust forest. Similarly, random forest algorithm creates decision trees on data samples and then gets the prediction from each of them and finally selects the best solution by means of voting. It is an ensemble method which is better than a single decision tree because it reduces the over-fitting by averaging the result.
from pyspark.ml.classification import RandomForestClassifier rf = RandomForestClassifier(labelCol='Outcome', featuresCol='features', maxDepth=5) model = rf.fit(training_data) rf_predictions = model.transform(test_data)
Evaluate our Random Forest Classifier model.
from pyspark.ml.evaluation import MulticlassClassificationEvaluator multi_evaluator = MulticlassClassificationEvaluator(labelCol = 'Outcome', metricName = 'accuracy') print('Random Forest classifier Accuracy:', multi_evaluator.evaluate(rf_predictions))
Random Forest classifier Accuracy: 0.7945205479452054 (79.5%)
Decision trees are widely used since they are easy to interpret, handle categorical features, extend to the multiclass classification setting, do not require feature scaling, and are able to capture non-linearities and feature interactions.
from pyspark.ml.classification import DecisionTreeClassifier dt = DecisionTreeClassifier(featuresCol = 'features', labelCol = 'Outcome', maxDepth = 3) dtModel = dt.fit(training_data) dt_predictions = dtModel.transform(test_data) dt_predictions.select('Glucose', 'BloodPressure', 'BMI', 'Age', 'Outcome').show(10)
Evaluate our Decision Tree model.
from pyspark.ml.evaluation import MulticlassClassificationEvaluator multi_evaluator = MulticlassClassificationEvaluator(labelCol = 'Outcome', metricName = 'accuracy') print('Decision Tree Accuracy:', multi_evaluator.evaluate(dt_predictions))
Decision Tree Accuracy: 0.7876712328767124 (79.7%)
Logistic regression is the appropriate regression analysis to conduct when the dependent variable is dichotomous (binary). Like all regression analyses, the logistic regression is a predictive analysis. Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables. Logistic Regression is used when the dependent variable(target) is categorical.
from pyspark.ml.classification import LogisticRegression lr = LogisticRegression(featuresCol = 'features', labelCol = 'Outcome', maxIter=10) lrModel = lr.fit(training_data) lr_predictions = lrModel.transform(test_data)
Evaluate our Logistic Regression model.
from pyspark.ml.evaluation import MulticlassClassificationEvaluator multi_evaluator = MulticlassClassificationEvaluator(labelCol = 'Outcome', metricName = 'accuracy') print('Logistic Regression Accuracy:', multi_evaluator.evaluate(lr_predictions))
Logistic Regression Accuracy: 0.7876712328767124 (79.7%)
Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.
from pyspark.ml.classification import GBTClassifier gb = GBTClassifier(labelCol = 'Outcome', featuresCol = 'features') gbModel = gb.fit(training_data) gb_predictions = gbModel.transform(test_data)
Evaluate our Gradient-Boosted Tree Classifier.
from pyspark.ml.evaluation import MulticlassClassificationEvaluator multi_evaluator = MulticlassClassificationEvaluator(labelCol = 'Outcome', metricName = 'accuracy') print('Gradient-boosted Trees Accuracy:', multi_evaluator.evaluate(gb_predictions))
Gradient-boosted Trees Accuracy: 0.8013698630136986(80.13%)
PySpark is a great language for data scientists to learn because it enables scalable analysis and ML pipelines. If you’re already familiar with Python and Pandas, then much of your knowledge can be applied to Spark. To sum it up, we have learned how to build a machine learning application using PySpark. We tried three 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.
Machine learning models sparking when PySpark gave the accelerator gear like the need for speed gaming cars.
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