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On this Page
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Problem Scenario
Machine Learning has been showing promising results in various applications. Healthcare is one of them. Machine Learning can accurately help doctors diagnose patients. In this use case, we are trying to use machine learning algorithms to predict the progression of diabetes of in patients.
Description
In this paper, they collect the baseline measurements: Age, Sex, BMI, BP, and 6 Serum Measurements (S1, S2,...S6) of 422 patients were collected. One year after baselinethat, a measure of diabetes progression is was collected. Our goal is to teach the machine to predict the diabetes progression based on these 10 measurements.
Below screenshot is the preview of this dataset. There are 10 measurements and diabetes progression represented as Y in $Y which is the rightmost columnfield.
The live demo is available at our our Machine Learning Showcase.
Objectives
- Cross Validation: Use the Cross Validator (Regression) Snap from ML Core Snap Pack to perform a 10-fold cross validation with the Linear Regression algorithm. K-Fold Cross Validation is a method of evaluating machine learning algorithms by randomly separating a dataset into training set and test set, the model will be trained on the training set and evaluated on the test setK chunks. Then, K-1 chunks will be used to train the model which will be evaluated on the last chunk. This process repeats K times and the average error and other statistics are computed.
- Model Building: Use Trainer (Regression) Snap from ML Core Snap Pack to build the linear regression model based on the training set of 392 samples; then serialize and store.
- Model EvauationEvaluation: Use Predictor (Regression) Snap from ML Core Snap Pack to apply the model on the test set of containing the remaining 50 samples and compute error.
- Model Hosting: Use Predictor (Regression) Snap from ML Core Snap Pack to host the model and build the API using Ultra Task.
- Test the API : Build the API as a Task then execute the Task to test the APITesting: Use REST Post Snap to send a sample request to the Ultra Task to make sure the API is working as expected.
We are going to build 4 5 pipelines: Cross Validation, Model Building, Model Evaluation, and Model Hosting, and API Testing; and a an Ultra Task to accomplish the above objectives. Each of these pipelines is described in the Pipelines section below.
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In this pipeline, we use the Cross Validator (Regression) Snap to perform a 10-fold cross validation using the Linear Regression a linear regression algorithm. The result shows that the overall mean absolute error is 44.595256.
The File Reader Snap reads the data dataset which is in CSV format. Then, the CSV Parser Snap converts binary data into documents. Since the types of the documents from CSV Parser Snap are String (text)text represented by String data type, we use Type Converter Snap to automatically derive types of columnsdata. In this case, the data will be is converted into either BigInteger or BigDecimal representing numeric values. Then, the Cross Validator (Regression) Snap performs 10-fold cross validation using the Linear Regression linear regression algorithm.
Finally, we use Document to Binary the JSON Formatter Snap and File Writer Snap to save the result. In this case, we save the result on SnapLogic File System (SLFS) which you can preview can be previewed by clicking the document icon next to the File name in the File Writer Snap or download from the Manager page.
The screenshot below shows that the overall mean absolute error is 44.595256. You may try to change the other regression algorithm algorithms in the Cross Validator (Regression) Snap and see which algorithm performs the best on this dataset.
Model Building
In this pipeline, we use the Trainer (Regression) Snap to build the model from the training set using the Linear Regression linear regression algorithm.
The File Reader Snap reads the training set containing 392 samples. Then, the CSV Parser Snap converts binary data into documents. Since the types of the documents from CSV Parser Snap are String (text)text represented by String data type, we use the Type Converter Snap to automatically derive types of columnsdata. Then, the Trainer (Regression) Snap trains the model using the Linear Regression linear regression algorithm. The model consists of two parts: metadata describing the schema (field names and types) of the dataset, and the actual model (serialized). Both metadata and model are serialized. If the Readable option in the Trainer (Regression) Snap is selected, a the readable model will be generated. Finally, the model is saved written as a JSON file in on the SLFS using the JSON Formatter Snap and File Writer SnapsSnap.
Model Evaluation
In this pipeline, the model generated above is tested evaluated against the test set.
The Predictor (Regression) Snap has two input views. The first input view is for the test set. The second input view accepts the model generated in the previous pipeline. In this case, the Predictor (Regression) Snap predicts the progression of diabetes.
The predictions from the Predictor (Regression) Snap are merged with the real diabetes progression (answer) from the Mapper Snap which extracts the $Y column field from the test set. The result of merging is displayed in the screenshot below (lower-right left corner). After that, we use the Aggregate Snap to compute the mean absolute error and mean squared error which is 32.804 and 1793.410 respectively. The result is then saved using CSV Formatter Snap and File Writer Snap.
Model Hosting
This pipeline is scheduled as an Ultra Task to provide a REST API that is accessible by external applications. The core components of this pipeline are File Reader, JSON Parser and Predictor (Regression) Snaps that are the same as in the Model Evaluation pipeline. Instead of taking the data from the test set, the Predictor (Regression) Snap takes the data from API request. The Check Token Filter Snap (Router) is used to authenticate the request by checking the token that can be changed in pipeline parameters. The Extract Params Snap (Mapper) extracts the data required fields from the request. The Body Wrapper Prepare Response Snap (Mapper) maps from prediction to $content that .pred which will be the response body. Finally, CORS Wrapper Snap (Mapper) This Snap also adds headers to allow Cross-Origin Resource Sharing (CORS).
Testing the API
In order to test the API, we must first build it as a Task and execute this Task.
Building API
To build an API from deploy this pipeline . Go to as a REST API, click the calendar icon in the toolbar. You can either use either Triggered Task or Ultra Task.
Triggered Task is good for batch processing since it starts a new pipeline instance for each request. Ultra Task is good to provide REST API to external applications that require low latency. In this case, we use the Ultra Task . You do not need to specify the bearer token here since we use the Router Snap to is preferable. Bearer token is not needed here since the Filter Snap will perform authentication inside the pipeline. You can go to Manager by clicking Show
In order to get the URL, click Show tasks in this project in Manager in Manager in the Create Task window to see task details as shown in the screenshot below. 
Testing
After creating the Ultra Task, you can test it. The screenshot below shows a sample request and response. Based on the following 10 measurements, the pipeline returns 103.88 as predicted diabetes progression. The expected diabetes progression of this patient is 118.
. Click the small triangle next to the task, and then click Details. The task detail shows up with the URL:
API Testing
In this pipeline, a sample request is generated by JSON Generator. The request is sent to the Ultra Task by REST Post Snap. The Mapper Snap is used to extract response which is in $response.entity.
Below is the content of the JSON Generator Snap. It contains $token and $params which will be included in the request body sent by REST Post Snap.
The REST Post Snap gets the URL from the pipeline parameters. Your URL can be found in the Manager page. In some cases, it is required to check Trust all certificates in the REST Post Snap.
The output of the REST Post Snap is shown below. The last Mapper Snap is used to extract $response.entity from the request. In this case, the predicted diabetes progression is 199.95.
Downloads
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