How to use test_results_path method in avocado

Best Python code snippet using avocado_python

eval.py

Source:eval.py

1#2# Based on Sebastian HofstÃ¤tter code published in https://github.com/sebastian-hofstaetter/matchmaker3#45import os6import copy7import time8import glob9from typing import Dict, Tuple, List10os.environ["TOKENIZERS_PARALLELISM"] = "false"1112import numpy as np13import scipy.stats as stats1415from evaluation.utils import *16import time171819def read_bm25(test_results_path):20    with open(test_results_path, 'r') as file:21       lines = file.readlines()22       test_results = {}23       for line in lines:24           splitted_line = line.split(' ')25           query_id = splitted_line[0]26           doc_id = splitted_line[2]27           score = float(splitted_line[4])28           if test_results.get(query_id):29               doc_list = test_results.get(query_id)30               doc_list.append((doc_id, score))31               test_results.update({query_id:doc_list})32           else:33               test_results.update({query_id:[(doc_id, score)]})34    return test_results3536def read_run(test_results_path):37    with open(test_results_path, 'r') as file:38        lines = file.readlines()39        test_results = {}40        for line in lines:41            splitted_line = line.split('\t')42            query_id = splitted_line[0]43            doc_id = splitted_line[1]44            score = float(splitted_line[3])45            if test_results.get(query_id):46                doc_list = test_results.get(query_id)47                doc_list.append((doc_id, score))48                test_results.update({query_id:doc_list})49            else:50                test_results.update({query_id:[(doc_id, score)]})51    return test_results525354def eval_run(test_results_path, qrels_path, binarization_point, qrels_with_irrel=False, with_j=False):55    if 'bm25' in test_results_path:56        test_results = read_bm25(test_results_path)57    else:58        test_results = read_run(test_results_path)5960    metrics = None61    ranked_results = unrolled_to_ranked_result(test_results)6263    if with_j:64        # for j coverage we need all the irrelevant and relevant judgements65        qrels = load_qrels_with_irrel(qrels_path)66        ranked_results_j = {}67        for query_id, value in qrels.items():68            if ranked_results.get(query_id):69                doc_ids_ranked_list = ranked_results.get(query_id)70                ranked_list_per_query_id = []71                for doc_id in doc_ids_ranked_list:72                    if doc_id in list(value.keys()):73                        ranked_list_per_query_id.append(doc_id)74                ranked_results_j.update({query_id: ranked_list_per_query_id})7576        ranked_results = ranked_results_j77        # but for the evaluation of nDCG only the positive judgements should be in the qrels set78        qrels = load_qrels(qrels_path)79    elif qrels_with_irrel:80        qrels = load_qrels_with_irrel(qrels_path)81    else:82        qrels = load_qrels(qrels_path)8384    metrics = calculate_metrics_plain(ranked_results,qrels,binarization_point)8586    if 'dctr' in qrels_path:87        qrels = 'dctr'88    elif 'raw' in qrels_path:89        qrels = 'raw'90    else:91        qrels = 'annotation'9293    metric_file_path = os.path.join('/'.join(test_results_path.split('/')[:-1]), 'test_top200_rerank_head_{}_joption'.format(qrels)+"-metrics.csv")94    save_fullmetrics_oneN(metric_file_path, metrics, -1, -1)95    return metrics969798def get_metrics_for_multiple_qrels_runs(qrels, runs, path, binarization_point):99    metrics = {}100    for qrel in qrels:101        if 'dctr' in qrel:102            qrel_name = 'dctr'103        elif 'raw' in qrel:104            qrel_name = 'raw'105        else:106            qrel_name = 'annotation'107        metrics.update({qrel_name: {}})108        for run in runs:109            test_results_path = os.path.join(path, run)110            run_name = run.split('/')[0]111112            run_metrics = eval_run(test_results_path, qrel, binarization_point)113            metrics.get(qrel_name).update({run_name: run_metrics})114    return metrics115116117def compute_kendalls_tau_between_collections(pairs, measures, metrics, path):118    with open(os.path.join(path, 'kendalltau.txt'), 'w') as f:119        for pair in pairs:120            for measure in measures:121                runs = metrics.get(pair[0])122                measure_numbers = []123                for value in runs.values():124                    measure_numbers.append(value.get(measure))125                runs_per_measure = dict(zip(runs.keys(), measure_numbers))126                runs_per_measure_sorted_1 = dict(127                    sorted(runs_per_measure.items(), key=lambda item: item[1], reverse=True))128129                runs = metrics.get(pair[1])130                measure_numbers = []131                for value in runs.values():132                    measure_numbers.append(value.get(measure))133                runs_per_measure = dict(zip(runs.keys(), measure_numbers))134                runs_per_measure_sorted_2 = dict(135                    sorted(runs_per_measure.items(), key=lambda item: item[1], reverse=True))136137                tau, p_value = stats.kendalltau(list(runs_per_measure_sorted_1.keys()),138                                                list(runs_per_measure_sorted_2.keys()))139                print('pair {} measure {} and kendall tau {} pvalue {}'.format(pair, measure, tau, p_value))140141                f.write('pair {} measure {} and kendall tau {} pvalue {}\n'.format(pair, measure, tau, p_value))142        f.close()143144145def compute_kendalls_tau_between_metrics(pairs, tests, metrics, path):146    with open(os.path.join(path, 'kendalltau_withinstability_ndcg3.txt'), 'w') as f:147        for test in tests:148            for pair in pairs:149                runs = metrics.get(test)150                measure_numbers = []151                for value in runs.values():152                    measure_numbers.append(value.get(pair[0]))153                runs_per_measure = dict(zip(runs.keys(), measure_numbers))154                runs_per_measure_sorted_1 = dict(155                    sorted(runs_per_measure.items(), key=lambda item: item[1], reverse=True))156157                runs = metrics.get(test)158                measure_numbers = []159                for value in runs.values():160                    measure_numbers.append(value.get(pair[1]))161                runs_per_measure = dict(zip(runs.keys(), measure_numbers))162                runs_per_measure_sorted_2 = dict(163                    sorted(runs_per_measure.items(), key=lambda item: item[1], reverse=True))164165                tau, p_value = stats.kendalltau(list(runs_per_measure_sorted_1.keys()),166                                                list(runs_per_measure_sorted_2.keys()))167                print('pair {} test {} and kendall tau {} pvalue {}'.format(pair, test, tau, p_value))168169                f.write('pair {} test {} and kendall tau {} pvalue {}\n'.format(pair, test, tau, p_value))170        f.close()171172if __name__ == "__main__":173    test_results_path = 'path_to/ensemble-output.txt'174    qrels_path_anno = './data/qrels_2class.txt'175    qrels_path_dctr = 'path_to/qrels.dctr.head.test.txt'176    qrels_path_raw = 'path_to/qrels.raw.head.test.txt'177    binarization_point = 1178179    runs = ['bm25/bm25_top1k_head.test.txt',180           'scibert_dot/top1k_head_dctr-output.txt',181           'pubmedbert_dot/top1k_head_dctr-output.txt',182           'colbert_scibert/test_top200_rerank_head_dctr-output.txt',183           'colbert_pubmedbert/test_top200_rerank_head_dctr-output.txt',184           'bert_cat/test_top200_rerank_head_dctr-output.txt',185           '3bert_fix_ensemble_avg/top200_rerank_head_dctr-ensemble-output.txt']186187    path = 'output_path/'188    qrels = [qrels_path_anno, qrels_path_dctr, qrels_path_raw]189190    # evaluate all runs again with the irrelevant ones to find coverage!191    for qrel in qrels:192        for run in runs:193            test_results_path = os.path.join(path, run)194            eval_run(test_results_path, qrel, binarization_point, qrels_with_irrel=True, with_j=True)195196    # evaluate annotated runs with joption197    for run in runs:198        test_results_path = os.path.join(path, run)199        eval_run(test_results_path, qrels_path_anno, binarization_point, qrels_with_irrel=True, with_j=True)200201202    # evaluate multiple runs for kendalls tau203    metrics = get_metrics_for_multiple_qrels_runs(qrels, runs, path, binarization_point)204    measures = ['Recall@100', 'MRR@10', 'nDCG@5', 'nDCG@10', 'nDCG@3']205    pairs = [('annotation', 'dctr'), ('annotation', 'raw'), ('dctr', 'raw')]206207    compute_kendalls_tau_between_collections(pairs, measures, metrics, path)208209    # stability of test collection: kendalls tau between metrics210    measures = ['Recall@100', 'MRR@10', 'nDCG@5', 'nDCG@10', 'nDCG@3']211    pairs = [('nDCG@3', 'Recall@100'), ('nDCG@3', 'MRR@10'), ('nDCG@3', 'nDCG@5')]212    tests = ['annotation', 'raw', 'dctr']213    compute_kendalls_tau_between_metrics(pairs, tests, metrics, path)214
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test_mtom.py

Source:test_mtom.py

1"""2CEASIOMpy: Conceptual Aircraft Design Software3Developed by CFS ENGINEERING, 1015 Lausanne, Switzerland4Test functions for 'ceasiompy/WeightConventional/func/mtom.py'5Python version: >=3.76| Author : Aidan Jungo7| Creation: 2022-05-308"""9# =================================================================================================10#   IMPORTS11# =================================================================================================12from pathlib import Path13import shutil14from pytest import approx15from ceasiompy.WeightConventional.func.mtom import estimate_mtom16from ceasiompy.utils.commonnames import MTOM_FIGURE_NAME17MODULE_DIR = Path(__file__).parent18TEST_RESULTS_PATH = Path(MODULE_DIR, "ToolOutput")19# =================================================================================================20#   CLASSES21# =================================================================================================22# =================================================================================================23#   FUNCTIONS24# =================================================================================================25def test_estimate_mtom():26    """Test function 'estimate_mtom'"""27    if TEST_RESULTS_PATH.exists():28        shutil.rmtree(TEST_RESULTS_PATH)29    TEST_RESULTS_PATH.mkdir()30    # estimate_mtom(fuselage_length, fuselage_width, wing_area, wing_span)31    # These tests are made from random value just to check if the function return the same kind of32    # result after it will be refactored.33    assert estimate_mtom(50, 5, 750, 60, TEST_RESULTS_PATH) == approx(223398, rel=1e-2)34    assert estimate_mtom(55, 5, 750, 60, TEST_RESULTS_PATH) == approx(231389, rel=1e-2)35    assert estimate_mtom(50, 5, 550, 60, TEST_RESULTS_PATH) == approx(176957, rel=1e-2)36    assert estimate_mtom(50, 5, 550, 50, TEST_RESULTS_PATH) == approx(173153, rel=1e-2)37    assert estimate_mtom(20, 2.5, 250, 28, TEST_RESULTS_PATH) == approx(23255, rel=1e-2)38    assert estimate_mtom(12, 2, 65, 15, TEST_RESULTS_PATH) == approx(18900, rel=1e-2)39    assert estimate_mtom(12, 2, 62, 15, TEST_RESULTS_PATH) == approx(17707, rel=1e-2)40    # Seems wrong, maybe out of range41    # assert estimate_mtom(10, 1.8, 50, 11, TEST_RESULTS_PATH) == approx(17707, rel=1e-2)42    # --> Obtained: 33795.11843    assert Path(TEST_RESULTS_PATH, MTOM_FIGURE_NAME).exists()44# =================================================================================================45#    MAIN46# =================================================================================================47if __name__ == "__main__":48    print("Running Test WeightConventional")49    print("To run test use the following command:")...

test_oem.py

Source:test_oem.py

1"""2CEASIOMpy: Conceptual Aircraft Design Software3Developed by CFS ENGINEERING, 1015 Lausanne, Switzerland4Test functions for 'ceasiompy/WeightConventional/func/mtom.py'5Python version: >=3.76| Author : Aidan Jungo7| Creation: 2022-05-308"""9# =================================================================================================10#   IMPORTS11# =================================================================================================12import shutil13from pathlib import Path14from ceasiompy.WeightConventional.func.oem import estimate_oem15from pytest import approx16MODULE_DIR = Path(__file__).parent17TEST_RESULTS_PATH = Path(MODULE_DIR, "ToolOutput")18# =================================================================================================19#   CLASSES20# =================================================================================================21# =================================================================================================22#   FUNCTIONS23# =================================================================================================24def test_estimate_oem():25    """Test function 'estimate_oem'"""26    if TEST_RESULTS_PATH.exists():27        shutil.rmtree(TEST_RESULTS_PATH)28    TEST_RESULTS_PATH.mkdir()29    # estimate_oem(mtom, fuse_length, wing_span, turboprop)30    assert estimate_oem(350_000, 58, 61, False) == approx(171_452, rel=1e-2)31    assert estimate_oem(250_000, 48, 51, False) == approx(125_807, rel=1e-2)32    assert estimate_oem(150_000, 38, 44, False) == approx(78_633, rel=1e-2)33    assert estimate_oem(150_000, 38, 44, True) == approx(76_690, rel=1e-2)34    assert estimate_oem(50_000, 24, 34, False) == approx(26_431, rel=1e-2)35    assert estimate_oem(50_000, 24, 34, True) == approx(28_897, rel=1e-2)36# =================================================================================================37#    MAIN38# =================================================================================================39if __name__ == "__main__":40    print("Running Test WeightConventional")41    print("To run test use the following command:")...

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