There are a plenty of use cases were Linear Regression (or other Regression types) can be used, but let’s focus on the first one related to house prices.<\/p>\n
Imagine we are running a real estate company in a particular region of the country, as we are an old company, there is some data record of which were the houses were sold in the past and for how much.<\/p>\n
In this case, each row in our historical data will look like this:<\/p>\n
{\r\n\"id\": 7129300520,\r\n\"date\": \"20141013T000000\",\r\n\"price\": 221900,\r\n\"bedrooms\": 3,\r\n\"bathrooms\": 1,\r\n\"sqft_living\": 1180,\r\n\"sqft_lot\": 5650,\r\n\"floors\": 1,\r\n\"waterfront\": 0,\r\n\"view\": 0,\r\n\"condition\": 3,\r\n\"grade\": 7,\r\n\"sqft_above\": 1180,\r\n\"sqft_basement\": 0,\r\n\"yr_built\": 1955,\r\n\"yr_renovated\": 0,\r\n\"zipcode\": 98178,\r\n\"lat\": 47.5112,\r\n\"long\": -122.257,\r\n\"sqft_living15\": 1340,\r\n\"sqft_lot15\": 5650\r\n}<\/pre>\n <\/p>\n
The problem – How to price a house<\/h3>\n
Now, imagine you just joined the company and you have to sell the following house:<\/p>\n
{\r\n\"id\": 1000001,\r\n\"date\": \"20150422T000000\",\r\n\"bedrooms\": 6,\r\n\"bathrooms\": 3,\r\n\"price\": null,\r\n\"sqft_living\": 2400,\r\n\"sqft_lot\": 9373,\r\n\"floors\": 2,\r\n\"waterfront\": 0,\r\n\"view\": 0,\r\n\"condition\": 3,\r\n\"grade\": 7,\r\n\"sqft_above\": 2400,\r\n\"sqft_basement\": 0,\r\n\"yr_built\": 1991,\r\n\"yr_renovated\": 0,\r\n\"zipcode\": 98002,\r\n\"lat\": 47.3262,\r\n\"long\": -122.214,\r\n\"sqft_living15\": 2060,\r\n\"sqft_lot15\": 7316\r\n}\r\n<\/pre>\nFor how much would you sell it?<\/strong><\/p>\nThe question above would be very challenging if you never sold a similar house in the past. Luckily, you have the right tool for the job: A Linear Regression.<\/p>\n
<\/h3>\nThe Answer – Predicting house prices with Linear Regression<\/h3>\n
Before you go further, you will need to install the following items:<\/p>\n
\n- \u00a0Couchbase Server 5<\/a><\/li>\n
- \u00a0Spark 2.2<\/a><\/li>\n
- \u00a0SBT<\/a> (as we are running using Scala)<\/li>\n<\/ul>\n
\u00a0Loading the Dataset<\/h4>\n
With your Couchbase Server running, go to the administrative portal ( usually at https:\/\/127.0.0.1:8091) and create a new bucket called houses_prices<\/strong><\/p>\n![\"\"](\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2017\/11\/bucket_creation-300x258.png\")
<\/p>\n
Now, let’s clone our tutorial code:<\/p>\n
git clone https:\/\/github.com\/couchbaselabs\/couchbase-spark-mllib-sample.git<\/pre>\nIn the root folder there is a file called house_prices_train_data.zip<\/strong>, it is our dataset which I borrowed from an old machine learning course on Coursera<\/a>. Please unzip it and then run the following command:<\/p>\n.\/cbimport json -c couchbase:\/\/127.0.0.1 -u YOUR_USER -p YOUR_PASSWORD -b houses_prices -d <PATH_TO_UNZIPED_FILE>\/house_prices_train_data -f list -g key::%id% -t 4<\/pre>\nTIP<\/strong>: If you are not familiar with cbimport\u00a0<\/strong>please check this tutorial<\/a><\/p>\nIf your command ran successfully, you should notice that your houses_prices<\/strong> bucket has been populated:<\/p>\n![\"\"](\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2017\/11\/filled_bucket-300x135.png\")
<\/p>\n
Let’s also quickly add a primary index for it:<\/p>\n
CREATE PRIMARY INDEX ON `houses_prices`<\/pre>\n![\"\"](\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2017\/11\/index_creation-300x172.png\")
<\/p>\n
<\/h4>\nTime to Code!<\/h4>\n
Our environment is ready, it is time to code!<\/p>\n
In the LinearRegressionExample<\/a> class we start by creating the Spark context with our bucket credentials:<\/p>\nval spark = SparkSession\r\n .builder()\r\n .appName(\"SparkSQLExample\")\r\n .master(\"local[*]\") \/\/ use the JVM as the master, great for testing\r\n .config(\"spark.couchbase.nodes\", \"127.0.0.1\") \/\/ connect to couchbase on localhost\r\n .config(\"spark.couchbase.bucket.houses_prices\", \"\") \/\/ open the houses_prices bucket with empty password\r\n .config(\"com.couchbase.username\", \"YOUR_USER\")\r\n .config(\"com.couchbase.password\", \"YOUR_PASSWORD\")\r\n .getOrCreate()<\/pre>\nand then we load all the data from the database:<\/p>\n
val houses = spark.read.couchbase()<\/pre>\nAs Spark uses a lazy approach, the data is not loaded until it is really needed. You can clearly see the beauty of the Couchbase Connector<\/strong> above, we just converted a JSON Document into a Spark Dataframe with zero effort.<\/p>\nIn other databases for example, you would be required to export the data to a CSV file with some specific formats, copy it to your machine, load and do some extra procedures to convert it to a dataframe (not to mention the cases where the file generated is too big).<\/p>\n
In a real world you would need to do some filtering instead of just grabbing all data, again our connector is there for you, as you can even run some N1QL queries with it:<\/p>\n
\/\/loading documents by its type\r\nval airlines = spark.read.couchbase(EqualTo(\"type\", \"airline\"))\r\n\r\n\/\/loading data using N1QL\r\n\/\/ This query groups airports by country and counts them.\r\nval query = N1qlQuery.simple(\"\" +\r\n \"select country, count(*) as count \" +\r\n \"from `travel-sample` \" +\r\n \"where type = 'airport' \" +\r\n \"group by country \" +\r\n \"order by count desc\")\r\n\r\nval schema = StructType(\r\n StructField(\"count\", IntegerType) ::\r\n StructField(\"country\", StringType) :: Nil\r\n)\r\n\r\nval rdd = spark.sparkContext.couchbaseQuery(query).map(\r\n r => Row(r.value.getInt(\"count\"), r.value.getString(\"country\")))\r\nspark.createDataFrame(rdd, schema).show()<\/pre>\nTIP<\/strong>: There are a lot of examples on how to use Couchbase connector here<\/a>.<\/p>\nOur dataframe still looks exactly like what we had in our database:<\/p>\n
houses.show(10)<\/pre>\n![\"\"](\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2017\/11\/dataframe_data-300x59.png\")
<\/p>\n
There are two different types of data here, “scalar numbers<\/em>” such as bathrooms<\/strong>\u00a0and sqft_living<\/strong>\u00a0and “categorical variables<\/em>” such as zipcode<\/strong>\u00a0and yr_renovated<\/strong>. Those categorical variables are not just simple numbers, they have a much deeper meaning as they describe a property, in the zipcode case, for example, it represents the location of the house.<\/p>\nLinear Regression does not like that kind of categorical variables, so if we really want to use zipcode in our Linear Regression, as it seems to be a relevant field to predict the price of a house, we have to convert it to a\u00a0dummy variable<\/strong>, which is fairly simple process:<\/p>\n\n- Distinct all values of the target column.\u00a0Ex:\u00a0<\/strong>SELECT DISTINCT(ZIPCODE) FROM HOUSES_PRICES<\/span><\/li>\n
- Convert each row into a column. Ex:<\/strong> zipcode_98002, zipcode_98188, zipcode_98059<\/li>\n
- Update those new columns with 1s and 0s according to the value of the zipcode content:<\/li>\n<\/ol>\n
Ex:<\/strong><\/p>\n![\"\"](\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2017\/11\/data_before_transformation-300x179.png\")
<\/p>\n
The table above will be transformed to:<\/p>\n
![\"\"](\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2017\/11\/data_after_transformation-300x74.png\")
<\/p>\n
That is what we are doing on the line below:<\/p>\n
val df = transformCategoricalFeatures(houses)<\/pre>\nConverting categorical variables is a very standard procedure and Spark already has some utilities to do this work for you:<\/p>\n
def transformCategoricalFeatures(dataset: Dataset[_]): DataFrame = {\r\n val df1 = encodeFeature(\"zipcode\", \"zipcodeVec\", dataset)\r\n val df2 = encodeFeature(\"yr_renovated\", \"yr_renovatedVec\", df1)\r\n val df3 = encodeFeature(\"condition\", \"conditionVec\", df2)\r\n encodeFeature(\"grade\", \"gradeVec\", df3)\r\n}\r\n\r\ndef encodeFeature(featureName: String, outputName: String, dataset: Dataset[_]): DataFrame = {\r\n val indexer = new StringIndexer()\r\n .setInputCol(featureName)\r\n .setOutputCol(featureName + \"Index\")\r\n .fit(dataset)\r\n\r\n val indexed = indexer.transform(dataset)\r\n\r\n val encoder = new OneHotEncoder()\r\n .setInputCol(featureName + \"Index\")\r\n .setOutputCol(outputName)\r\n\r\n encoder.transform(indexed)\r\n}<\/pre>\nNOTE:<\/strong> The final dataframe will not look exactly like the example shown above as it is already optimized to avoid\u00a0 The Sparse Matrix<\/a> problem.<\/p>\nNow, we can select the fields we would like to use and group them in a vector called features<\/strong>, as this linear regression implementation expects a field called\u00a0label<\/strong>, we also have to rename the\u00a0price<\/strong> column :<\/p>\n\/\/just using almost all columns as features, no special feature engineering here\r\nval features = Array(\"sqft_living\", \"bedrooms\",\r\n \"gradeVec\", \"waterfront\",\r\n \"bathrooms\", \"view\",\r\n \"conditionVec\", \"sqft_above\",\r\n \"sqft_basement\",\r\n \"sqft_lot\", \"floors\",\r\n \"yr_built\", \"zipcodeVec\", \"yr_renovatedVec\")\r\n\r\nval assembler = new VectorAssembler()\r\n .setInputCols(features)\r\n .setOutputCol(\"features\")\r\n\r\n\/\/the Linear Regression implementation expect a feature called \"label\"\r\nval renamedDF = assembler.transform(df.withColumnRenamed(\"price\", \"label\"))<\/pre>\nYou can play around with those features removing\/adding them as you wish, later you can try for example remove the “sqft_living<\/em>” feature to see how the algorithm has a much worse performance.<\/p>\nFinally, we will only use houses in which the price is not null to train our machine learning algorithm, as our whole goal is to make our Linear Regression “learn” how to predict the price by a giving set of features.<\/p>\n
val data = renamedDF.select(\"label\", \"features\").filter(\"price is not null\")\r\n<\/pre>\nHere is where the magic happens, first we split our data into training (80%<\/em>) and test (20%<\/em>), but for the purpose of this article let’s ignore the test data, then we create our LinearRegression instance and fit<\/strong> our data into it.<\/p>\n\/\/let's split our data into test and training (a common thing during model selection)\r\nval splits = data.randomSplit(Array(0.8, 0.2), seed = 1L)\r\nval trainingData = splits(0).cache()\r\n\/\/let's ignore the test data for now as we are not doing model selection\r\nval testData = splits(1)\r\n\r\nval lr = new LinearRegression()\r\n.setMaxIter(1000)\r\n.setStandardization(true)\r\n.setRegParam(0.1)\r\n.setElasticNetParam(0.8)\r\n\r\nval lrModel = lr.fit(trainingData)\r\n\r\n<\/pre>\nThe lrModel<\/strong>\u00a0variable is already a trained model capable of predicting house prices!<\/em><\/p>\nBefore we start predicting things, let’s just check some metrics of our trained model:<\/p>\n
println(s\"Coefficients: ${lrModel.coefficients} Intercept: ${lrModel.intercept}\")\r\nval trainingSummary = lrModel.summary\r\nprintln(s\"numIterations: ${trainingSummary.totalIterations}\")\r\nprintln(s\"objectiveHistory: [${trainingSummary.objectiveHistory.mkString(\",\")}]\")\r\ntrainingSummary.residuals.show()\r\nprintln(s\"RMSE: ${trainingSummary.rootMeanSquaredError}\")\r\nprintln(s\"r2: ${trainingSummary.r2}\")\r\n\r\n<\/pre>\nThe one you should care here is called RMSE – Root Mean Squared Error<\/a> which roughly is the\u00a0average deviation of what our model predicts X the actual price sold<\/strong>.<\/p>\nRMSE: 147556.0841305963\r\nr2: 0.8362288980410875<\/pre>\nOn average we miss the actual price by $147556.0841305963<\/em>, which is not bad at all considering we barely did any feature engineering<\/a> or removed any outliers (some houses might have inexplicable high or low prices and it might mess up with your Linear Regression)<\/p>\nThere is only one house with a missing price in this dataset, exactly the one that we pointed in the beginning:<\/p>\n
val missingPriceData = renamedDF.select(\"features\")\r\n .filter(\"price is null\")\r\n\r\nmissingPriceData.show()<\/pre>\nAnd now we can finally predict the expected house price:<\/p>\n
\/\/printing out the predicted values\r\nval predictedValues = lrModel.transform(missingPriceData)\r\npredictedValues.select(\"prediction\").show()\r\n<\/pre>\n <\/p>\n
![\"\"](\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2017\/11\/predicted_price.png\")
<\/p>\n
Awesome, isn’t it?<\/p>\n
For production purpose, you would still need to do a model selection<\/a> first, check other metrics of your regression and save the model instead of training it on the fly, but it’s amazing how much can be done with less than 100 lines of code!<\/p>\nIf you have any questions, feel free to ask me on twitter at @deniswsrosa<\/a>\u00a0 or on our forums<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"The past few years we noticed how machine learning had been proven to be a technology in which companies should invest massively, you can easily find dozens of papers talking about how company X saved tons of money by adding […]<\/p>\n","protected":false},"author":8754,"featured_media":13873,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"ppma_author":[9059],"class_list":["post-4265","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"acf":[],"yoast_head":"\n
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