Handling data (corneto.data)#

Simple datasets (Data and Sample)#

corneto.data.Data([dict])

A dataset container that maps sample IDs to Sample objects.

corneto.data.Sample([features])

Represents a sample as a collection of features.

Modules#

corneto.data.util

Utility functions for data generation.

Data handling utilities for CORNETO#

This module provides data handling capabilities to define input data for CORNETO’s methods and algorithms. It provides:

Classes#

  • Data: Main data container that maps sample IDs to Sample objects

  • Sample: Container for sample features and their associated metadata

Key Features#

  • Rich metadata support for data features

  • Flexible data import/export methods

  • Conversion between different data formats

  • Filtering and subsetting capabilities

  • Data manipulation and transformation utilities

Examples

Basic usage with Data and Sample classes:

>>> from corneto.data import Data, Sample
>>> # Create a dataset and add samples with features
>>> dataset = Data()
>>> dataset.add_sample("patient1", {"age": 45, "treatment": {"value": "drugA", "dose": "high"}})
>>> print(dataset)
Dataset(num_samples=1)

>>> # Convert to dictionary format
>>> data_dict = dataset.to_dict()
>>> print(data_dict["patient1"]["treatment"])
{'value': 'drugA', 'dose': 'high'}

Utilities#

The package also provides utility functions for generating random data:

>>> from corneto.data.util import generate_random_signalling_network
>>> # Generate a random signaling network
>>> network = generate_random_signalling_network(n=10, m=3, p_inhibitory=0.3)
>>> print(f"Generated network with {len(network)} edges")