Source code for cacp.info

import pathlib
import typing
from pathlib import Path

import pandas as pd
import river.datasets.base

from cacp.dataset import ClassificationDatasetBase
from cacp.util import to_latex


def dataset_info(datasets: typing.Iterable[typing.Union[ClassificationDatasetBase, river.datasets.base.Dataset]],
                 result_dir: Path):
    """
    Produces results files with list of all datasets used in experiment alog with their attributes.

    :param datasets: dataset collection
    :param result_dir: results directory

    """
    records = []
    for dataset_idx, dataset in enumerate(datasets):
        name = dataset.__class__.__name__.lower()
        if hasattr(dataset, "name"):
            name = dataset.name

        i = 0
        x = {}
        labels = set()
        for i, (x, y) in enumerate(dataset):
            labels.add(y)

        row = {
            'Dataset': name,
            'Instances': i + 1,
            'Features': len(x),
            'Classes': len(labels),
        }
        records.append(row)

    df = pd.DataFrame(records)
    df.index += 1
    info_dir = result_dir.joinpath('info')
    info_dir.mkdir(exist_ok=True, parents=True)
    df.to_csv(info_dir.joinpath('datasets.csv'), index=True)
    f = info_dir.joinpath('datasets.tex').open('w')
    tex = to_latex(
        df,
        caption='Datasets used to perform experiments',
        label='tab:datasets'
    )
    f.write(tex)


def classifier_info(classifiers: typing.Iterable[typing.Tuple[str, typing.Callable]], result_dir: pathlib.Path):
    """
    Produces results files with list of all classifiers used in experiment along with their attributes.

    :param classifiers: classifiers collection
    :param result_dir: results directory

    """
    records = []
    for cn, c in classifiers:
        cz = c(2, 2).__class__
        library = cz.__module__.split('.')[0]
        name = cz.__name__
        classifier_type = 'CUSTOM'
        if library == 'sklearn':
            classifier_type = 'BATCH'
        elif library == 'skmultiflow':
            classifier_type = 'INCREMENTAL'
        elif library == 'river':
            classifier_type = 'INCREMENTAL'

        row = {
            'Name': cn,
            'Class Name': name,
            'Library': library,
            'Type': classifier_type
        }
        records.append(row)

    df = pd.DataFrame(records)
    df = df.drop_duplicates()
    df.sort_values(['Type', 'Name'], inplace=True)
    df = df.reset_index(drop=True)
    df.index += 1
    info_dir = result_dir.joinpath('info')
    info_dir.mkdir(exist_ok=True, parents=True)
    df.to_csv(info_dir.joinpath('classifiers.csv'), index=True)
    f = info_dir.joinpath('classifiers.tex').open('w')
    tex = to_latex(
        df,
        caption='Classifiers used to perform experiments',
        label='tab:algorithm'
    )
    f.write(tex)