ads2_2022/code/python/src/algorithms/rucksack/display.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# IMPORTS
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

from src.thirdparty.code import *;
from src.thirdparty.maths import *;
from src.thirdparty.types import *;

from src.core.utils import *;
from src.setup import config;
from models.generated.config import *;
from src.models.stacks import *;
from src.models.rucksack import *;

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# EXPORTS
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

__all__ = [
    'display_order',
    'display_rucksack',
    'display_branch_and_bound',
    'display_sum',
];

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# METHOD display order
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

def display_order(
    order: List[int],
    costs: np.ndarray,
    values: np.ndarray,
    items: np.ndarray,
    one_based: bool = False,
) -> str:
    table = pd.DataFrame({
        'items':  items,
        'order':  iperm(order),
        'values': values,
        'costs':  costs,
        'margin': [f'{value/cost:.6f}'  for cost, value in zip(costs, values)],
    }) \
    .reset_index(drop=True);
    if one_based:
        table['order'] += 1;
    # benutze pandas-Dataframe + tabulate, um schöner darzustellen:
    repr = tabulate(
        table,
        headers=['item', 'greedy order', 'value', 'cost', 'value/cost'],
        showindex=False,
        colalign=('left', 'center', 'center', 'center', 'right'),
        tablefmt='rst'
    );
    return repr;

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# METHOD display rucksack
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

def display_rucksack(
    items: np.ndarray,
    costs: np.ndarray,
    values: np.ndarray,
    choice: List[Fraction],
) -> str:
    show_options = config.OPTIONS.rucksack.show;
    render = lambda r: f'{r:g}';
    choice = np.asarray(choice);
    rucksack = np.where(choice > 0);
    if not(EnumRucksackShow.all_weights in show_options):
        items = items[rucksack];
        costs = costs[rucksack];
        values = values[rucksack];
        choice = choice[rucksack];
    table = pd.DataFrame({
        'items':  items.tolist() + ['----', '∑'],
        'nr':  list(map(str, choice))
            + ['----', f'\x1b[92;1m{float(sum(choice)):g}\x1b[0m'],
        'costs':  list(map(render, costs))
            + ['----', f'\x1b[92;1m{sum(choice*costs):g}\x1b[0m'],
        'values': list(map(render, values))
            + ['----', f'\x1b[92;1m{sum(choice*values):g}\x1b[0m'],
    });
    repr = tabulate(
        table,
        headers=['item', 'nr', 'cost', 'value'],
        showindex=False,
        colalign=('left', 'center', 'center', 'center'),
        tablefmt='rst'
    );
    return repr;

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# METHOD display result of branch and bound
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

def display_branch_and_bound(values: np.ndarray, steps: List[Step]) -> str:
    show_options = config.OPTIONS.rucksack.show;
    show_all_sums = (EnumRucksackShow.all_sums in show_options);

    rows = [];
    used_choices = [];
    index_soln = max([-1] + [ i for i, step in enumerate(steps) if step.move == EnumBranchAndBoundMove.BOUND ]);
    for i, step in enumerate(steps):
        if show_all_sums or step.choice not in used_choices:
            # Füge Summen-Ausdrücke für Greedy-Alg hinzu:
            used_choices.append(step.choice);
            expr = display_sum(choice=step.choice, values=values, as_maximum=False, order=step.order, indexes=step.indexes);
        else:
            expr = '';
        bound_str = f'{step.bound:+g}';
        solution_str = f'{step.solution or ""}';
        move_str = ('' if step.move == EnumBranchAndBoundMove.NONE else step.move.value);
        if i == index_soln:
            bound_str = f'* \x1b[92;1m{bound_str}\x1b[0m';
        rows.append({
            'bound': f'{bound_str}',
            'bound_subtree': f'{step.bound_subtree:g}',
            'bound_subtree_sum': expr,
            'stack': step.stack_str,
            'solution': f'\x1b[2m{solution_str}\x1b[0m',
            'move': f'\x1b[2m{move_str}\x1b[0m',
        });

    table = pd.DataFrame(rows).reset_index(drop=True);
    # benutze pandas-Dataframe + tabulate, um schöner darzustellen:
    repr = tabulate(
        table,
        headers=['bound', 'g(TOP(S))', '', 'S — stack', '\x1b[2msoln\x1b[0m', '\x1b[2mmove\x1b[0m'],
        showindex=False,
        colalign=('right', 'right', 'left', 'right', 'center', 'left'),
        tablefmt='simple'
    );
    return repr;

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# METHOD display sum
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

def display_sum(
    choice: List[Fraction],
    values: np.ndarray,
    order: Optional[List[int]] = None,
    indexes: List[int] = [],
    as_maximum: bool = True,
) -> str:
    show_options = config.OPTIONS.rucksack.show;
    show_all_weights = (EnumRucksackShow.all_weights in show_options);

    def render(x: Tuple[bool, Fraction, float]):
        b, u, value = x;
        if u == 0:
            expr = f'\x1b[94;2m{value:g}\x1b[0m' if b else f'\x1b[2m{value:g}\x1b[0m';
        else:
            expr = f'\x1b[94m{value:g}\x1b[0m' if b else f'\x1b[0m{value:g}\x1b[0m';
        if not show_all_weights and u == 1:
            return expr;
        return f'\x1b[2;4m{u}\x1b[0m\x1b[2m·\x1b[0m{expr}';

    parts = [ (i in indexes, u, x) for i, (u, x) in enumerate(zip(choice, values)) ];
    if not (order is None):
        parts = [ parts[j] for j in order ];
    if not show_all_weights:
        parts = list(filter(lambda x: x[1] > 0, parts));

    expr = '\x1b[2m + \x1b[0m'.join(map(render, parts));

    if as_maximum:
        return f'\x1b[2m=\x1b[0m {expr}';
    return f'\x1b[2m= -(\x1b[0m{expr}\x1b[2m)\x1b[0m';