from typing import List
from datasets import DatasetDict, Value
from DashAI.back.dataloaders.classes.dashai_dataset import (
DashAIDataset,
to_dashai_dataset,
)
from DashAI.back.tasks.base_task import BaseTask
[docs]
class RegressionTask(BaseTask):
"""Base class for regression tasks.
Here you can change the methods provided by class Task.
"""
DESCRIPTION: str = """
Regression in machine learning involves predicting continuous values for
structured data organized in tabular form (rows and columns).
Models are trained to learn patterns and relationships in the data,
enabling accurate prediction of new instances."""
DISPLAY_NAME: str = "Regression"
metadata: dict = {
"inputs_types": [Value],
"outputs_types": [Value],
"inputs_cardinality": "n",
"outputs_cardinality": 1,
}
def prepare_for_task(
self, datasetdict: DatasetDict, outputs_columns: List[str]
) -> DashAIDataset:
"""Change the column types to suit the regression task.
A copy of the dataset is created.
Parameters
----------
datasetdict : DatasetDict
Dataset to be changed
Returns
-------
DashAIDataset
Dataset with the new types
"""
return to_dashai_dataset(datasetdict)
def process_predictions(self, dataset, predictions, output_column):
"""Process the predictions
Parameters
----------
dataset : DashAIDataset
Dataset used for training
predictions : np.ndarray
Predictions from the model
output_column : str
Output column
Returns
-------
Processed predictions
"""
return predictions
def num_labels(self, dataset: DashAIDataset, output_column: str) -> int | None:
"""Get the number of unique labels in the output column.
Parameters
----------
dataset : DashAIDataset
Dataset used for training
output_column : str
Output column
Returns
-------
int | None
Number of unique labels or None if not applicable
"""
return None