Getting Started

Data classification is the process of organizing and categorizing data based on specific criteria or attributes. It helps make data more structured, accessible, and understandable.

Let’s see an example

Suppose you need to categorize a set of texts that contain mixed references to cats, actors, dogs, and other things that don’t matter.

You will need a CSV file containing a few examples. In this file, each example corresponds to a row that provides at least two fields:

• prototype: a text that exemplifies a typical element in the category specified in the second field
• class: a short text that represent a category name

These examples are used by EZClassifier to create a personalized model. You can add to the model as many prototypes and categories as you like, as long as there is at least one example for each category. EZClassifier works with any language, even when used simultaneously in the same text or the same data file and/or examples.

Once you created your model, you use it to classify your data stream. EZClassifier will add two additional fields to your data:

• class: that is the predicted category for the described row
• similarity: a number from 0 to 1 that represents the confidence of EZClassifier about its classification (1=maxumum confidence , 0=no confidence).

• model examples
• input data
• classified data

Try it out!

HW & SW Prerequisites

To run EZClassifier, you need any computer with Java 17+ installed. You can download the Java 17+ for your architecture (Linux, macOS, Windows) from the the official Java site.

Install EZClassifier

Next, download the latest version of the EZC JAR file to a directory of your choice.

Lastly, you’ll need an API Key to enable the services (see prices here). Proof of Concept (POC) and free plans are available upon request.

Create shortcut for launching the java command and set the TC_API_KEY environment with your api key:

For example, with bash, open a terminal and type:

export TC_API_KEY=HERE-IS-YOUR-API-KEY
alias ezc='java -jar ezc.jar' 

For example, in windows open a terminal windows (CMD) and type:

set TC_API_KEY=HERE-IS-YOUR-API-KEY
doskey ezc=java -jar "%USERPROFILE%\Downloads\ezc.jar" $*

Test that the system is working:

# be sure it reports a java version > 17 
ezc --version

Step 1: download example files

Here you can download some sample data:

• model source file: some examples about cat, dog, and actor descriptions
• data to be classified: some strings to classify

Step 2: create a model

Create a new model from your examples:

ezc model train --name=mymodel --header --input=examples.csv

Be sure to use as –input argument the path (relative or absolute) of the downloaded model source file

To list all the available models you can use ezc model ls

Step 3: classify your data using the created model:

ezc classify --name=mymodel --header --input=input-data.csv --output=result.csv --threshold=0.84

The result.csv file in your current directory will contain your classified data. Note that data with a confidence less than 0.84 are assigned to the “OTHER” category

cat input-data.csv | ezc classify --name=mymodel > result.csv

Remove your model with ezc model rm --name=mymodel.