from typing import List, Union
from datasets import DatasetDict
from DashAI.back.dataloaders.classes.dashai_dataset import DashAIDataset
from DashAI.back.tasks.classification_task import ClassificationTask
from DashAI.back.types.categorical import Categorical
from DashAI.back.types.value_types import Float, Integer
[docs]
class TabularClassificationTask(ClassificationTask):
"""Base class for tabular classification tasks.
Here you can change the methods provided by class Task.
"""
DESCRIPTION: str = """
Tabular classification in machine learning involves predicting categorical
labels for structured data organized in tabular form (rows and columns).
Models are trained to learn patterns and relationships in the data, enabling
accurate classification of new instances."""
DISPLAY_NAME: str = "Tabular Classification"
metadata: dict = {
"inputs_types": [Float, Integer, Categorical],
"outputs_types": [Categorical],
"inputs_cardinality": "n",
"outputs_cardinality": 1,
}
def prepare_for_task(
self,
dataset: Union[DatasetDict, DashAIDataset],
input_columns: List[str],
output_columns: List[str],
) -> DashAIDataset:
"""Convert the dataset to DashAIDataset and check the columns types
A copy of the dataset is created.
Parameters
----------
dataset : Union[DatasetDict, DashAIDataset]
Dataset to be changed
Returns
-------
DashAIDataset
Dataset with the new types
"""
dashai_dataset = super().prepare_for_task(
dataset, input_columns, output_columns
)
return dashai_dataset