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Tabular Classification

The Task

Tabular classification is the task of assigning a class to samples of structured or relational data. The ~flash.tabular.classification.model.TabularClassifier task can be used for classification of samples in more than two classes (multi-class classification).

Example

Let's look at training a model to predict if passenger survival on the Titanic using the classic Kaggle data set. The data is provided in CSV files that look like this:

PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
...

We can create the ~flash.tabular.classification.data.TabularData from our CSV files using the ~flash.tabular.classification.data.TabularData.from_csv method. From the API reference <flash.tabular.classification.data.TabularData.from_csv>, we need to provide:

  • cat_cols- A list of the names of columns that contain categorical data (strings or integers).
  • num_cols- A list of the names of columns that contain numerical continuous data (floats).
  • target- The name of the column we want to predict.
  • train_csv- A CSV file containing the training data converted to a Pandas DataFrame

Next, we create the ~flash.tabular.classification.model.TabularClassifier and finetune on the Titanic data. We then use the trained ~flash.tabular.classification.model.TabularClassifier for inference. Finally, we save the model. Here's the full example:

../../../flash_examples/tabular_classification.py

To learn how to view the available backbones / heads for this task, see backbones_heads.

Flash Zero

The tabular classifier can be used directly from the command line with zero code using flash_zero. You can run the above example with:

flash tabular_classifier

To view configuration options and options for running the tabular classifier with your own data, use:

flash tabular_classifier --help

Serving

The ~flash.tabular.classification.model.TabularClassifier is servable. This means you can call .serve to serve your ~flash.core.model.Task. Here's an example:

../../../flash_examples/serve/tabular_classification/inference_server.py

You can now perform inference from your client like this:

../../../flash_examples/serve/tabular_classification/client.py