main_heuristics.py

import csv
import math
import pickle
import random
import numpy as np
from Heuristics.heuristics_guess import not_greedy_heuristic_guess
from Heuristics.heuristics_guess import choose_order_given_projections
from find_filename import find_dataset_filename
from test_models import compute_metrics

nvar = 3
testing_method = 'Normal'
test_dataset_filename = find_dataset_filename('Test',
                                              testing_method)
with open(test_dataset_filename, 'rb') as test_dataset_file:
    testing_dataset = pickle.load(test_dataset_file)
output_file = "heuristics_output_acc_time.csv"

# Testing in heuristics that make all the choice at once
first_heuristic = 1
for heuristic in ['gmods', 'brown', 'random', 'virtual best']:
    reps = 100
    sum_metrics = dict()
    for i in range(reps):
        if heuristic == 'virtual best':
            chosen_indices = [np.argmin(timings) for timings in testing_dataset['timings']]
        elif heuristic == 'random':
            chosen_indices = [random.randint(0, 5) for timings in testing_dataset['timings']]
        else:
            chosen_indices = [not_greedy_heuristic_guess(projection[0][0], heuristic)
                              for projection in testing_dataset['projections']]
        metrics = compute_metrics(chosen_indices,
                                  testing_dataset['labels'],
                                  testing_dataset['timings'],
                                  testing_dataset['cells'])
        if len(sum_metrics) == 0:
            sum_metrics = metrics
        else:
            sum_metrics = {key: metrics[key] + sum_metrics[key] for key in metrics}
    aveg_metrics = {key: sum_metrics[key]/reps for key in sum_metrics}
    augmented_metrics = {key: aveg_metrics[key] if key in ['Accuracy', 'Markup'] else math.factorial(nvar)*aveg_metrics[key] for key in sum_metrics}
    
    print(heuristic, augmented_metrics)
    if first_heuristic == 1:
        first_heuristic = 0
        keys = list(augmented_metrics.keys())
        with open(output_file, 'a') as f:
            f.write('Choosing the whole ordering in the beggining \n')
            f.write(', '.join(['Model'] + keys) + '\n')
    with open(output_file, 'a', newline='') as f:
        writer = csv.writer(f)
        writer.writerow([heuristic] + [augmented_metrics[key] for key in keys])

# Testing on greedy heuristics
for heuristic in ['brown', 'gmods', 'random', 'virtual best']:
    reps = 100
    sum_metrics = dict()
    for i in range(reps):
        if heuristic == 'virtual best':
            chosen_indices = [np.argmin(timings) for timings in testing_dataset['timings']]
        elif heuristic == 'random':
            chosen_indices = [random.randint(0, 5) for timings in testing_dataset['timings']]
        else:
            chosen_indices = [choose_order_given_projections(projection, heuristic)
                              for projection in testing_dataset['projections']]
        metrics = compute_metrics(chosen_indices,
                                  testing_dataset['labels'],
                                  testing_dataset['timings'],
                                  testing_dataset['cells'])
        if len(sum_metrics) == 0:
            sum_metrics = metrics
        else:
            sum_metrics = {key: metrics[key] + sum_metrics[key] for key in metrics}
    aveg_metrics = {key: sum_metrics[key]/reps for key in sum_metrics}
    augmented_metrics = {key: aveg_metrics[key] if key in ['Accuracy', 'Markup'] else math.factorial(nvar)*aveg_metrics[key] for key in sum_metrics}
        
    print(heuristic, augmented_metrics)
    if first_heuristic == 1:
        first_heuristic = 0
        keys = list(augmented_metrics.keys())
        with open(output_file, 'a') as f:
            f.write('Now choosing greedily \n')
            f.write(', '.join(['Model'] + keys) + '\n')
    with open(output_file, 'a', newline='') as f:
        writer = csv.writer(f)
        writer.writerow([heuristic] + [augmented_metrics[key] for key in keys])
# print(sum(min(timings) for timings in testing_dataset['timings']))