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How to use command_eval method in play_selenium

Best Python code snippet using play_selenium_python

eval_tools.py

Source:eval_tools.py

1"""2Evaluate system performance.3"""4from __future__ import absolute_import5from __future__ import division6from __future__ import print_function7import collections8import csv9import nltk10import numpy as np11import os12import sys13import random14if sys.version_info > (3, 0):15    from six.moves import xrange16from ..bashlint import data_tools17from ..encoder_decoder import data_utils, graph_utils18from ..eval import token_based, tree_dist19from ..nlp_tools import constants, tokenizer20def manual_eval(prediction_path, dataset, FLAGS, top_k, num_examples=-1, interactive=True, verbose=True):21    """22    Conduct dev/test set evaluation.23    Evaluation metrics:24        1) full command accuracy;25        2) command template accuracy. 26    :param interactive:27        - If set, prompt the user to enter judgement if a prediction does not28            match any of the groundtruths and the correctness of the prediction29            has not been pre-determined;30          Otherwise, all predictions that does not match any of the groundtruths are counted as wrong.31    """32    # Group dataset33    grouped_dataset = data_utils.group_parallel_data(dataset)34    # Load model prediction35    prediction_list = load_predictions(prediction_path, top_k)36    metrics = get_manual_evaluation_metrics(37        grouped_dataset, prediction_list, FLAGS, num_examples=num_examples, interactive=interactive, verbose=verbose)38    return metrics39def gen_manual_evaluation_table(dataset, FLAGS, num_examples=-1, interactive=True):40    """41    Conduct dev/test set evaluation. The results of multiple pre-specified models are tabulated in the same table.42    Evaluation metrics:43        1) full command accuracy;44        2) command template accuracy.45    :param interactive:46        - If set, prompt the user to enter judgement if a prediction does not47            match any of the groundtruths and the correctness of the prediction48            has not been pre-determined;49          Otherwise, all predictions that does not match any of the groundtruths are counted as wrong.50    """51    # Group dataset52    grouped_dataset = data_utils.group_parallel_data(dataset)53    # Load all model predictions54    model_names, model_predictions = load_all_model_predictions(55        grouped_dataset, FLAGS, top_k=3)56    manual_eval_metrics = {}57    for model_id, model_name in enumerate(model_names):58        prediction_list = model_predictions[model_names]59        M = get_manual_evaluation_metrics(60            grouped_dataset, prediction_list, FLAGS, num_examples=num_examples, interactive=interactive, verbose=False)61        manual_eval_metrics[model_name] = [M['acc_f'][0],62                                           M['acc_f'[1]], M['acc_t'][0], M['acc_t'][1]]63    metrics_names = ['Acc_F_1', 'Acc_F_3', 'Acc_T_1', 'Acc_T_3']64    print_eval_table(model_names, metrics_names, manual_eval_metrics)65def get_manual_evaluation_metrics(grouped_dataset, prediction_list, FLAGS, num_examples=-1, interactive=True,66                                  verbose=True):67    if len(grouped_dataset) != len(prediction_list):68        raise ValueError("ground truth and predictions length must be equal: "69                         "{} vs. {}".format(len(grouped_dataset), len(prediction_list)))70    # Get dev set samples (fixed)71    random.seed(100)72    example_ids = list(range(len(grouped_dataset)))73    random.shuffle(example_ids)74    if num_examples > 0:75        sample_ids = example_ids[:num_examples]76    else:77        sample_ids = example_ids78    # Load cached evaluation results79    structure_eval_cache, command_eval_cache = \80        load_cached_evaluations(81            os.path.join(FLAGS.data_dir, 'manual_judgements'), verbose=True)82    eval_bash = FLAGS.dataset.startswith("bash")83    cmd_parser = data_tools.bash_parser if eval_bash \84        else data_tools.paren_parser85    # Interactive manual evaluation86    num_t_top_1_correct = 0.087    num_f_top_1_correct = 0.088    num_t_top_3_correct = 0.089    num_f_top_3_correct = 0.090    for exam_id, example_id in enumerate(sample_ids):91        data_group = grouped_dataset[example_id][1]92        sc_txt = data_group[0].sc_txt.strip()93        sc_key = get_example_nl_key(sc_txt)94        command_gts = [dp.tg_txt for dp in data_group]95        command_gt_asts = [data_tools.bash_parser(gt) for gt in command_gts]96        predictions = prediction_list[example_id]97        top_3_s_correct_marked = False98        top_3_f_correct_marked = False99        for i in xrange(min(3, len(predictions))):100            pred_cmd = predictions[i]101            pred_ast = cmd_parser(pred_cmd)102            pred_temp = data_tools.ast2template(103                pred_ast, loose_constraints=True)104            temp_match = tree_dist.one_match(105                command_gt_asts, pred_ast, ignore_arg_value=True)106            str_match = tree_dist.one_match(107                command_gt_asts, pred_ast, ignore_arg_value=False)108            # Match ground truths & exisitng judgements109            command_example_key = '{}<NL_PREDICTION>{}'.format(110                sc_key, pred_cmd)111            structure_example_key = '{}<NL_PREDICTION>{}'.format(112                sc_key, pred_temp)113            command_eval, structure_eval = '', ''114            if str_match:115                command_eval = 'y'116                structure_eval = 'y'117            elif temp_match:118                structure_eval = 'y'119            if command_eval_cache and command_example_key in command_eval_cache:120                command_eval = command_eval_cache[command_example_key]121            if structure_eval_cache and structure_example_key in structure_eval_cache:122                structure_eval = structure_eval_cache[structure_example_key]123            # Prompt for new judgements124            if command_eval != 'y':125                if structure_eval == 'y':126                    if not command_eval and interactive:127                        print('#{}. {}'.format(exam_id, sc_txt))128                        for j, gt in enumerate(command_gts):129                            print('- GT{}: {}'.format(j, gt))130                        print('> {}'.format(pred_cmd))131                        command_eval = input(132                            'CORRECT COMMAND? [y/reason] ')133                        add_judgement(FLAGS.data_dir, sc_txt, pred_cmd,134                                      structure_eval, command_eval)135                        print()136                else:137                    if not structure_eval and interactive:138                        print('#{}. {}'.format(exam_id, sc_txt))139                        for j, gt in enumerate(command_gts):140                            print('- GT{}: {}'.format(j, gt))141                        print('> {}'.format(pred_cmd))142                        structure_eval = input(143                            'CORRECT STRUCTURE? [y/reason] ')144                        if structure_eval == 'y':145                            command_eval = input(146                                'CORRECT COMMAND? [y/reason] ')147                        add_judgement(FLAGS.data_dir, sc_txt, pred_cmd,148                                      structure_eval, command_eval)149                        print()150                structure_eval_cache[structure_example_key] = structure_eval151                command_eval_cache[command_example_key] = command_eval152            if structure_eval == 'y':153                if i == 0:154                    num_t_top_1_correct += 1155                if not top_3_s_correct_marked:156                    num_t_top_3_correct += 1157                    top_3_s_correct_marked = True158            if command_eval == 'y':159                if i == 0:160                    num_f_top_1_correct += 1161                if not top_3_f_correct_marked:162                    num_f_top_3_correct += 1163                    top_3_f_correct_marked = True164    metrics = {}165    acc_f_1 = num_f_top_1_correct / len(sample_ids)166    acc_f_3 = num_f_top_3_correct / len(sample_ids)167    acc_t_1 = num_t_top_1_correct / len(sample_ids)168    acc_t_3 = num_t_top_3_correct / len(sample_ids)169    metrics['acc_f'] = [acc_f_1, acc_f_3]170    metrics['acc_t'] = [acc_t_1, acc_t_3]171    if verbose:172        print('{} examples evaluated'.format(len(sample_ids)))173        print('Top 1 Command Acc = {:.3f}'.format(acc_f_1))174        print('Top 3 Command Acc = {:.3f}'.format(acc_f_3))175        print('Top 1 Template Acc = {:.3f}'.format(acc_t_1))176        print('Top 3 Template Acc = {:.3f}'.format(acc_t_3))177    return metrics178def add_judgement(data_dir, nl, command, correct_template='', correct_command=''):179    """180    Append a new judgement181    """182    data_dir = os.path.join(data_dir, 'manual_judgements')183    manual_judgement_path = os.path.join(184        data_dir, 'manual.evaluations.author')185    if not os.path.exists(manual_judgement_path):186        with open(manual_judgement_path, 'w') as o_f:187            o_f.write(188                'description,prediction,template,correct template,correct command\n')189    with open(manual_judgement_path, 'a') as o_f:190        temp = data_tools.cmd2template(command, loose_constraints=True)191        if not correct_template:192            correct_template = 'n'193        if not correct_command:194            correct_command = 'n'195        o_f.write('"{}","{}","{}","{}","{}"\n'.format(196            nl.replace('"', '""'), command.replace('"', '""'),197            temp.replace('"', '""'), correct_template.replace('"', '""'),198            correct_command.replace('"', '""')))199    print('new judgement added to {}'.format(manual_judgement_path))200def automatic_eval(prediction_path, dataset, FLAGS, top_k, num_samples=-1, verbose=False):201    """202    Generate automatic evaluation metrics on dev/test set.203    The following metrics are computed:204        Top 1,3,5,10205            1. Structure accuracy206            2. Full command accuracy207            3. Command keyword overlap208            4. BLEU209    """210    grouped_dataset = data_utils.group_parallel_data(dataset)211    try:212        vocabs = data_utils.load_vocabulary(FLAGS)213    except ValueError:214        vocabs = None215    # Load predictions216    prediction_list = load_predictions(prediction_path, top_k)217    if len(grouped_dataset) != len(prediction_list):218        raise ValueError("ground truth and predictions length must be equal: "219                         "{} vs. {}".format(len(grouped_dataset), len(prediction_list)))220    metrics = get_automatic_evaluation_metrics(grouped_dataset, prediction_list, vocabs, FLAGS,221                                               top_k, num_samples, verbose)222    return metrics223def gen_automatic_evaluation_table(dataset, FLAGS):224    # Group dataset225    grouped_dataset = data_utils.group_parallel_data(dataset)226    vocabs = data_utils.load_vocabulary(FLAGS)227    model_names, model_predictions = load_all_model_predictions(228        grouped_dataset, FLAGS, top_k=3)229    auto_eval_metrics = {}230    for model_id, model_name in enumerate(model_names):231        prediction_list = model_predictions[model_id]232        if prediction_list is not None:233            M = get_automatic_evaluation_metrics(234                grouped_dataset, prediction_list, vocabs, FLAGS, top_k=3)235            auto_eval_metrics[model_name] = [M['bleu'][0],236                                             M['bleu'][1], M['cms'][0], M['cms'][1]]237        else:238            print('Model {} skipped in evaluation'.format(model_name))239    metrics_names = ['BLEU1', 'BLEU3', 'TM1', 'TM3']240    print_eval_table(model_names, metrics_names, auto_eval_metrics)241def get_automatic_evaluation_metrics(grouped_dataset, prediction_list, vocabs, FLAGS, top_k,242                                     num_samples=-1, verbose=False):243    cmd_parser = data_tools.bash_parser244    rev_sc_vocab = vocabs.rev_sc_vocab if vocabs is not None else None245    # Load cached evaluation results246    structure_eval_cache, command_eval_cache = \247        load_cached_evaluations(248            os.path.join(FLAGS.data_dir, 'manual_judgements'))249    # Compute manual evaluation scores on a subset of examples250    if num_samples > 0:251        # Get FIXED dev set samples252        random.seed(100)253        example_ids = list(range(len(grouped_dataset)))254        random.shuffle(example_ids)255        sample_ids = example_ids[:100]256        grouped_dataset = [grouped_dataset[i] for i in sample_ids]257        prediction_list = [prediction_list[i] for i in sample_ids]258    num_eval = 0259    top_k_temp_correct = np.zeros([len(grouped_dataset), top_k])260    top_k_str_correct = np.zeros([len(grouped_dataset), top_k])261    top_k_cms = np.zeros([len(grouped_dataset), top_k])262    top_k_bleu = np.zeros([len(grouped_dataset), top_k])263    command_gt_asts_list, pred_ast_list = [], []264    for data_id in xrange(len(grouped_dataset)):265        _, data_group = grouped_dataset[data_id]266        sc_str = data_group[0].sc_txt.strip()267        sc_key = get_example_nl_key(sc_str)268        if vocabs is not None:269            sc_tokens = [rev_sc_vocab[i] for i in data_group[0].sc_ids]270            if FLAGS.channel == 'char':271                sc_features = ''.join(sc_tokens)272                sc_features = sc_features.replace(constants._SPACE, ' ')273            else:274                sc_features = ' '.join(sc_tokens)275        command_gts = [dp.tg_txt.strip() for dp in data_group]276        command_gt_asts = [cmd_parser(cmd) for cmd in command_gts]277        command_gt_asts_list.append(command_gt_asts)278        template_gts = [data_tools.cmd2template(279            cmd, loose_constraints=True) for cmd in command_gts]280        template_gt_asts = [cmd_parser(temp) for temp in template_gts]281        if verbose:282            print("Example {}".format(data_id))283            print("Original Source: {}".format(sc_str.encode('utf-8')))284            if vocabs is not None:285                print("Source: {}".format(286                    [x.encode('utf-8') for x in sc_features]))287            for j, command_gt in enumerate(command_gts):288                print("GT Target {}: {}".format(289                    j + 1, command_gt.strip().encode('utf-8')))290        num_eval += 1291        predictions = prediction_list[data_id]292        for i in xrange(len(predictions)):293            pred_cmd = predictions[i]294            pred_ast = cmd_parser(pred_cmd)295            if i == 0:296                pred_ast_list.append(pred_ast)297            pred_temp = data_tools.cmd2template(298                pred_cmd, loose_constraints=True)299            # A) Exact match with ground truths & exisitng judgements300            command_example_key = '{}<NL_PREDICTION>{}'.format(301                sc_key, pred_cmd)302            structure_example_key = '{}<NL_PREDICTION>{}'.format(303                sc_key, pred_temp)304            # B) Match ignoring flag orders305            temp_match = tree_dist.one_match(306                template_gt_asts, pred_ast, ignore_arg_value=True)307            str_match = tree_dist.one_match(308                command_gt_asts, pred_ast, ignore_arg_value=False)309            if command_eval_cache and command_example_key in command_eval_cache:310                str_match = normalize_judgement(311                    command_eval_cache[command_example_key]) == 'y'312            if structure_eval_cache and structure_example_key in structure_eval_cache:313                temp_match = normalize_judgement(314                    structure_eval_cache[structure_example_key]) == 'y'315            if temp_match:316                top_k_temp_correct[data_id, i] = 1317            if str_match:318                top_k_str_correct[data_id, i] = 1319            cms = token_based.command_match_score(command_gt_asts, pred_ast)320            # if pred_cmd.strip():321            #     bleu = token_based.sentence_bleu_score(command_gt_asts, pred_ast)322            # else:323            #     bleu = 0324            bleu = nltk.translate.bleu_score.sentence_bleu(325                command_gts, pred_cmd)326            top_k_cms[data_id, i] = cms327            top_k_bleu[data_id, i] = bleu328            if verbose:329                print("Prediction {}: {} ({}, {})".format(330                    i + 1, pred_cmd, cms, bleu))331        if verbose:332            print()333    bleu = token_based.corpus_bleu_score(command_gt_asts_list, pred_ast_list)334    top_temp_acc = [-1 for _ in [1, 3, 5, 10]]335    top_cmd_acc = [-1 for _ in [1, 3, 5, 10]]336    top_cms = [-1 for _ in [1, 3, 5, 10]]337    top_bleu = [-1 for _ in [1, 3, 5, 10]]338    top_temp_acc[0] = top_k_temp_correct[:, 0].mean()339    top_cmd_acc[0] = top_k_str_correct[:, 0].mean()340    top_cms[0] = top_k_cms[:, 0].mean()341    top_bleu[0] = top_k_bleu[:, 0].mean()342    print("{} examples evaluated".format(num_eval))343    print("Top 1 Template Acc = %.3f" % top_temp_acc[0])344    print("Top 1 Command Acc = %.3f" % top_cmd_acc[0])345    print("Average top 1 Template Match Score = %.3f" % top_cms[0])346    print("Average top 1 BLEU Score = %.3f" % top_bleu[0])347    if len(predictions) > 1:348        top_temp_acc[1] = np.max(top_k_temp_correct[:, :3], 1).mean()349        top_cmd_acc[1] = np.max(top_k_str_correct[:, :3], 1).mean()350        top_cms[1] = np.max(top_k_cms[:, :3], 1).mean()351        top_bleu[1] = np.max(top_k_bleu[:, :3], 1).mean()352        print("Top 3 Template Acc = %.3f" % top_temp_acc[1])353        print("Top 3 Command Acc = %.3f" % top_cmd_acc[1])354        print("Average top 3 Template Match Score = %.3f" % top_cms[1])355        print("Average top 3 BLEU Score = %.3f" % top_bleu[1])356    if len(predictions) > 3:357        top_temp_acc[2] = np.max(top_k_temp_correct[:, :5], 1).mean()358        top_cmd_acc[2] = np.max(top_k_str_correct[:, :5], 1).mean()359        top_cms[2] = np.max(top_k_cms[:, :5], 1).mean()360        top_bleu[2] = np.max(top_k_bleu[:, :5], 1).mean()361        print("Top 5 Template Acc = %.3f" % top_temp_acc[2])362        print("Top 5 Command Acc = %.3f" % top_cmd_acc[2])363        print("Average top 5 Template Match Score = %.3f" % top_cms[2])364        print("Average top 5 BLEU Score = %.3f" % top_bleu[2])365    if len(predictions) > 5:366        top_temp_acc[3] = np.max(top_k_temp_correct[:, :10], 1).mean()367        top_cmd_acc[3] = np.max(top_k_str_correct[:, :10], 1).mean()368        top_cms[3] = np.max(top_k_cms[:, :10], 1).mean()369        top_bleu[3] = np.max(top_k_bleu[:, :10], 1).mean()370        print("Top 10 Template Acc = %.3f" % top_temp_acc[3])371        print("Top 10 Command Acc = %.3f" % top_cmd_acc[3])372        print("Average top 10 Template Match Score = %.3f" % top_cms[3])373        print("Average top 10 BLEU Score = %.3f" % top_bleu[3])374    print('Corpus BLEU = %.3f' % bleu)375    print()376    metrics = {}377    metrics['acc_f'] = top_cmd_acc378    metrics['acc_t'] = top_temp_acc379    metrics['cms'] = top_cms380    metrics['bleu'] = top_bleu381    return metrics382def print_eval_table(model_names, metrics_names, model_metrics):383    def pad_spaces(s, max_len):384        return s + ' ' * (max_len - len(s))385    # print evaluation table386    # pad model names with spaces to create alignment387    max_len = len(max(model_names, key=len))388    max_len_with_offset = (int(max_len / 4) + 1) * 4389    first_row = pad_spaces('Model', max_len_with_offset)390    for metrics_name in metrics_names:391        first_row += '{}    '.format(metrics_name)392    print(first_row.strip())393    print('-' * len(first_row))394    for i, model_name in enumerate(model_names):395        row = pad_spaces(model_name, max_len_with_offset)396        if model_name in model_metrics:397            for metrics in model_metrics[model_name]:398                row += '{:.2f}    '.format(metrics)399        print(row.strip())400    print('-' * len(first_row))401def load_all_model_predictions(grouped_dataset, FLAGS, top_k=1, model_names=('token_seq2seq',402                                                                             'tellina',403                                                                             'token_copynet',404                                                                             'partial_token_seq2seq',405                                                                             'partial_token_copynet',406                                                                             'char_seq2seq',407                                                                             'char_copynet')):408    """409    Load predictions of multiple models (specified with "model_names").410    :return model_predictions: List of model predictions.411    """412    def load_model_predictions():413        model_subdir, decode_sig = graph_utils.get_decode_signature(FLAGS)414        model_dir = os.path.join(FLAGS.model_root_dir, model_subdir)415        prediction_path = os.path.join(416            model_dir, 'predictions.{}.latest'.format(decode_sig))417        prediction_list = load_predictions(prediction_path, top_k)418        if prediction_list is not None and len(grouped_dataset) != len(prediction_list):419            raise ValueError("ground truth list and prediction list length must "420                             "be equal: {} vs. {}".format(len(grouped_dataset),421                                                          len(prediction_list)))422        return prediction_list423    # Load model predictions424    model_predictions = []425    # -- Token426    FLAGS.channel = 'token'427    FLAGS.normalized = False428    FLAGS.fill_argument_slots = False429    FLAGS.use_copy = False430    # --- Seq2Seq431    if 'token_seq2seq' in model_names:432        model_predictions.append(load_model_predictions())433    # --- Tellina434    if 'tellina' in model_names:435        FLAGS.normalized = True436        FLAGS.fill_argument_slots = True437        model_predictions.append(load_model_predictions())438        FLAGS.normalized = False439        FLAGS.fill_argument_slots = False440    # --- CopyNet441    if 'token_copynet' in model_names:442        FLAGS.use_copy = True443        FLAGS.copy_fun = 'copynet'444        model_predictions.append(load_model_predictions())445        FLAGS.use_copy = False446    # -- Parital token447    FLAGS.channel = 'partial.token'448    # --- Seq2Seq449    if 'partial_token_seq2seq' in model_names:450        model_predictions.append(load_model_predictions())451    # --- CopyNet452    if 'partial_token_copynet' in model_names:453        FLAGS.use_copy = True454        FLAGS.copy_fun = 'copynet'455        model_predictions.append(load_model_predictions())456        FLAGS.use_copy = False457    # -- Character458    FLAGS.channel = 'char'459    FLAGS.batch_size = 32460    FLAGS.min_vocab_frequency = 1461    # --- Seq2Seq462    if 'char_seq2seq' in model_names:463        model_predictions.append(load_model_predictions())464    # --= CopyNet465    if 'char_copynet' in model_names:466        FLAGS.use_copy = True467        FLAGS.copy_fun = 'copynet'468        model_predictions.append(load_model_predictions())469        FLAGS.use_copy = False470    return model_names, model_predictions471def load_predictions(prediction_path, top_k, verbose=True):472    """473    Load model predictions (top_k per example) from disk.474    :param prediction_path: path to the decoding output475        We assume the file is of the format:476            1. The i-th line of the file contains predictions for example i in the dataset'477            2. Each line contains top-k predictions separated by "|||".478    :param top_k: Maximum number of predictions to read per example.479    :return: List of top k predictions.480    """481    if os.path.exists(prediction_path):482        with open(prediction_path) as f:483            prediction_list = []484            for line in f:485                predictions = line.split('|||')486                prediction_list.append(predictions[:top_k])487    else:488        if verbose:489            print('Warning: file not found: {}'.format(prediction_path))490        return None491    if verbose:492        print('{} predictions loaded from {}'.format(493            len(prediction_list), prediction_path))494    return prediction_list495def load_cached_correct_translations(data_dir, treat_empty_as_correct=False, verbose=False):496    """497    Load cached correct translations from disk.498    :return: nl -> template translation map, nl -> command translation map499    """500    command_translations = collections.defaultdict(set)501    template_translations = collections.defaultdict(set)502    eval_files = []503    for file_name in os.listdir(data_dir):504        if 'evaluations' in file_name and not file_name.endswith('base'):505            eval_files.append(file_name)506    for file_name in sorted(eval_files):507        manual_judgement_path = os.path.join(data_dir, file_name)508        with open(manual_judgement_path) as f:509            if verbose:510                print('reading cached evaluations from {}'.format(511                    manual_judgement_path))512            reader = csv.DictReader(f)513            current_nl_key = ''514            for row in reader:515                if row['description']:516                    current_nl_key = get_example_nl_key(row['description'])517                pred_cmd = row['prediction']518                if 'template' in row:519                    pred_temp = row['template']520                else:521                    pred_temp = data_tools.cmd2template(522                        pred_cmd, loose_constraints=True)523                structure_eval = row['correct template']524                if treat_empty_as_correct:525                    structure_eval = normalize_judgement(structure_eval)526                command_eval = row['correct command']527                if treat_empty_as_correct:528                    command_eval = normalize_judgement(command_eval)529                if structure_eval == 'y':530                    template_translations[current_nl_key].add(pred_temp)531                if command_eval == 'y':532                    command_translations[current_nl_key].add(pred_cmd)533    print('{} template translations loaded'.format(len(template_translations)))534    print('{} command translations loaded'.format(len(command_translations)))535    return template_translations, command_translations536def load_cached_evaluations(model_dir, verbose=True):537    """538    Load cached evaluation results from disk.539    :param model_dir: Directory where the evaluation result file is stored.540    :param decode_sig: The decoding signature of the model being evaluated.541    :return: dictionaries storing the evaluation results.542    """543    structure_eval_results = {}544    command_eval_results = {}545    eval_files = []546    for file_name in os.listdir(model_dir):547        if 'evaluations' in file_name and not file_name.endswith('base'):548            eval_files.append(file_name)549    for file_name in sorted(eval_files):550        manual_judgement_path = os.path.join(model_dir, file_name)551        ser, cer = load_cached_evaluations_from_file(552            manual_judgement_path, verbose=verbose)553        for key in ser:554            structure_eval_results[key] = ser[key]555        for key in cer:556            command_eval_results[key] = cer[key]557    if verbose:558        print('{} structure evaluation results loaded'.format(559            len(structure_eval_results)))560        print('{} command evaluation results loaded'.format(561            len(command_eval_results)))562    return structure_eval_results, command_eval_results563def load_cached_evaluations_from_file(input_file, treat_empty_as_correct=False, verbose=True):564    structure_eval_results = {}565    command_eval_results = {}566    with open(input_file, encoding='utf-8') as f:567        if verbose:568            print('reading cached evaluations from {}'.format(input_file))569        reader = csv.DictReader(f)570        current_nl_key = ''571        for row in reader:572            if row['description']:573                current_nl_key = get_example_nl_key(row['description'])574            pred_cmd = row['prediction']575            if 'template' in row:576                pred_temp = row['template']577            else:578                pred_temp = data_tools.cmd2template(579                    pred_cmd, loose_constraints=True)580            command_eval = row['correct command']581            if treat_empty_as_correct:582                command_eval = normalize_judgement(command_eval)583            command_example_key = '{}<NL_PREDICTION>{}'.format(584                current_nl_key, pred_cmd)585            if command_eval:586                command_eval_results[command_example_key] = command_eval587            structure_eval = row['correct template']588            if treat_empty_as_correct:589                structure_eval = normalize_judgement(structure_eval)590            structure_example_key = '{}<NL_PREDICTION>{}'.format(591                current_nl_key, pred_temp)592            if structure_eval:593                structure_eval_results[structure_example_key] = structure_eval594    return structure_eval_results, command_eval_results595def get_example_nl_key(nl):596    """597    Get the natural language description in an example with nuances removed.598    """599    tokens, _ = tokenizer.basic_tokenizer(nl)600    return ' '.join(tokens)601def get_example_cm_key(cm):602    """603    TODO: implement command normalization604        1. flag order normalization605        2. flag format normalization (long flag vs. short flag)606        3. remove flags whose effect does not matter607    """608    return cm609def normalize_judgement(x):610    if not x or x.lower() == 'y':611        return 'y'612    else:...

inter_annotator_agreement.py

Source:inter_annotator_agreement.py

1"""2Compute the inter-annotator agreement.3"""4from __future__ import absolute_import5from __future__ import division6from __future__ import print_function7import collections8import csv9import os10import sys11from bashlint import data_tools12from eval.eval_tools import load_cached_evaluations_from_file13from eval.eval_tools import get_example_nl_key, get_example_cm_key14from eval.eval_tools import normalize_judgement15def iaa(a1, a2):16    assert(len(a1) == len(a2))17    num_agree = 018    for i in range(len(a1)):19        if a1[i].lower() == a2[i].lower():20            num_agree += 121    return float(num_agree) / len(a1)22def read_annotations(input_file):23    command_judgements, template_judgements = [], []24    with open(input_file) as f:25        reader = csv.DictReader(f)26        for row in reader:27            command_eval = normalize_judgement(row['correct command'].strip())28            template_eval = normalize_judgement(row['correct template'].strip())29            command_judgements.append(command_eval)30            template_judgements.append(template_eval)31    return command_judgements, template_judgements32def inter_annotator_agreement(input_files1, input_files2):33    command_judgements1, template_judgements1 = [], []34    command_judgements2, template_judgements2 = [], []35    for input_file in input_files1:36        cj, tj = read_annotations(input_file)37        command_judgements1.extend(cj)38        template_judgements1.extend(tj)39    for input_file in input_files2:40        cj, tj = read_annotations(input_file)41        command_judgements2.extend(cj)42        template_judgements2.extend(tj)43    print('IAA-F: {}'.format(iaa(command_judgements1, command_judgements2)))44    print('IAA-T: {}'.format(iaa(template_judgements1, template_judgements2)))45def combine_annotations_multi_files():46    """47    Combine multiple annotations files and discard the annotations that has a conflict.48    """49    input_dir = sys.argv[1]50    template_evals = {}51    command_evals = {}52    discarded_keys = set({})53    for in_csv in os.listdir(input_dir):54        in_csv_path = os.path.join(input_dir, in_csv)55        with open(in_csv_path) as f:56            reader = csv.DictReader(f)57            current_description = ''58            for row in reader:59                template_eval = normalize_judgement(row['correct template'])60                command_eval = normalize_judgement(row['correct command'])61                description = get_example_nl_key(row['description'])62                if description.strip():63                    current_description = description64                else:65                    description = current_description66                prediction = row['prediction']67                example_key = '{}<NL_PREDICTION>{}'.format(description, prediction)68                if example_key in template_evals and template_evals[example_key] != template_eval:69                    discarded_keys.add(example_key)70                    continue71                if example_key in command_evals and command_evals[example_key] != command_eval:72                    discarded_keys.add(example_key)73                    continue74                template_evals[example_key] = template_eval75                command_evals[example_key] = command_eval76            print('{} read ({} manually annotated examples, {} discarded)'.format(in_csv_path, len(template_evals), len(discarded_keys)))77    # Write to new file78    assert(len(template_evals) == len(command_evals))79    with open('manual_annotations.additional', 'w') as o_f:80        o_f.write('description,prediction,template,correct template,correct comand\n')81        for key in sorted(template_evals.keys()):82            if key in discarded_keys:83                continue84            description, prediction = key.split('<NL_PREDICTION>')85            template_eval = template_evals[example_key]86            command_eval = command_evals[example_key]87            pred_tree = data_tools.bash_parser(prediction)88            pred_temp = data_tools.ast2template(pred_tree, loose_constraints=True)89            o_f.write('"{}","{}","{}",{},{}\n'.format(90                description.replace('"', '""'),91                prediction.replace('"', '""'),92                pred_temp.replace('"', '""'),93                template_eval,94                command_eval95            ))96    97def combine_annotations_multi_annotators():98    """99    Combine the annotations input by three annotators.100    :param input_file1: main annotation file 1.101    :param input_file2: main annotation file 2 (should contain the same number of102        lines as input_file1).103    :param input_file3: supplementary annotation file which contains annotations104        of lines in input_file1 and input_file2 that contain a disagreement.105    :param output_file: file that contains the combined annotations.106    """107    input_file1 = sys.argv[1]108    input_file2 = sys.argv[2]109    input_file3 = sys.argv[3]110    output_file = sys.argv[4]111    o_f = open(output_file, 'w')112    o_f.write('description,prediction,template,correct template,correct command,'113              'correct template A,correct command A,'114              'correct template B,correct command B,'115              'correct template C,correct command C\n')116    sup_structure_eval, sup_command_eval = load_cached_evaluations_from_file(117        input_file3, treat_empty_as_correct=True)118    with open(input_file1) as f1:119        with open(input_file2) as f2:120            reader1 = csv.DictReader(f1)121            reader2 = csv.DictReader(f2)122            current_desp = ''123            for row1, row2 in zip(reader1, reader2):124                row1_template_eval = normalize_judgement(row1['correct template'].strip())125                row1_command_eval = normalize_judgement(row1['correct command'].strip())126                row2_template_eval = normalize_judgement(row2['correct template'].strip())127                row2_command_eval = normalize_judgement(row2['correct command'].strip())128                if row1['description']:129                    current_desp = row1['description'].strip()130                sc_key = get_example_nl_key(current_desp)131                pred_cmd = row1['prediction'].strip()132                if not pred_cmd:133                    row1_template_eval, row1_command_eval = 'n', 'n'134                    row2_template_eval, row2_command_eval = 'n', 'n'135                pred_temp = data_tools.cmd2template(pred_cmd, loose_constraints=True)136                structure_example_key = '{}<NL_PREDICTION>{}'.format(sc_key, pred_temp)137                command_example_key = '{}<NL_PREDICTION>{}'.format(sc_key, pred_cmd)138                row3_template_eval, row3_command_eval = None, None139                if structure_example_key in sup_structure_eval:140                    row3_template_eval = sup_structure_eval[structure_example_key]141                if command_example_key in sup_command_eval:142                    row3_command_eval = sup_command_eval[command_example_key]143                if row1_template_eval != row2_template_eval or row1_command_eval != row2_command_eval:144                    if row1_template_eval != row2_template_eval:145                        if row3_template_eval is None:146                            print(structure_example_key)147                        assert(row3_template_eval is not None)148                        template_eval = row3_template_eval149                    else:150                        template_eval = row1_template_eval151                    if row1_command_eval != row2_command_eval:152                        # if row3_command_eval is None:153                        #     print(command_example_key)154                        assert(row3_command_eval is not None)155                        command_eval = row3_command_eval156                    else:157                        command_eval = row1_command_eval158                else:159                    template_eval = row1_template_eval160                    command_eval = row1_command_eval161                if row3_template_eval is None:162                    row3_template_eval = ''163                if row3_command_eval is None:164                    row3_command_eval = ''165                o_f.write('"{}","{}","{}",{},{},{},{},{},{},{},{}\n'.format(166                    current_desp.replace('"', '""'), pred_cmd.replace('"', '""'), pred_temp.replace('"', '""'),167                    template_eval, command_eval,168                    row1_template_eval, row1_command_eval,169                    row2_template_eval, row2_command_eval,170                    row3_template_eval, row3_command_eval))171    o_f.close()172def print_error_analysis_sheet():173    input_file1 = sys.argv[1]174    input_file2 = sys.argv[2]175    input_file3 = sys.argv[3]176    output_file = sys.argv[4]177    o_f = open(output_file, 'w')178    o_f.write('description,model,prediction,correct template,correct command,'179              'correct template A,correct command A,'180              'correct template B,correct command B,'181              'correct template C,correct command C\n')182    sup_structure_eval, sup_command_eval = load_cached_evaluations_from_file(183        input_file3, treat_empty_as_correct=True)184    # for key in sup_structure_eval:185    #     print(key)186    # print('------------------')187    with open(input_file1) as f1:188        with open(input_file2) as f2:189            reader1 = csv.DictReader(f1)190            reader2 = csv.DictReader(f2)191            current_desp = ''192            for row_id, (row1, row2) in enumerate(zip(reader1, reader2)):193                if row1['description']:194                    current_desp = row1['description'].strip()195                model_name = row2['model']196                if not model_name in ['partial.token-copynet', 'tellina']:197                    continue198                if row_id % 3 != 0:199                    continue200                row1_template_eval = normalize_judgement(row1['correct template'].strip())201                row1_command_eval = normalize_judgement(row1['correct command'].strip())202                row2_template_eval = normalize_judgement(row2['correct template'].strip())203                row2_command_eval = normalize_judgement(row2['correct command'].strip())204                sc_key = get_example_nl_key(current_desp)205                pred_cmd = row1['prediction'].strip()206                if not pred_cmd:207                    row1_template_eval, row1_command_eval = 'n', 'n'208                    row2_template_eval, row2_command_eval = 'n', 'n'209                pred_temp = data_tools.cmd2template(pred_cmd, loose_constraints=True)210                structure_example_key = '{}<NL_PREDICTION>{}'.format(sc_key, pred_temp)211                command_example_key = '{}<NL_PREDICTION>{}'.format(sc_key, pred_cmd)212                row3_template_eval, row3_command_eval = None, None213                if structure_example_key in sup_structure_eval:214                    row3_template_eval = sup_structure_eval[structure_example_key]215                if command_example_key in sup_command_eval:216                    row3_command_eval = sup_command_eval[command_example_key]217                if row1_template_eval != row2_template_eval or row1_command_eval != row2_command_eval:218                    if row1_template_eval != row2_template_eval:219                        if row3_template_eval is None:220                            print(pred_cmd, structure_example_key)221                        assert (row3_template_eval is not None)222                        template_eval = row3_template_eval223                    else:224                        template_eval = row1_template_eval225                    if row1_command_eval != row2_command_eval:226                        # if row3_command_eval is None:227                        #     print(command_example_key)228                        assert (row3_command_eval is not None)229                        command_eval = row3_command_eval230                    else:231                        command_eval = row1_command_eval232                else:233                    template_eval = row1_template_eval234                    command_eval = row1_command_eval235                if row3_template_eval is None:236                    row3_template_eval = ''237                if row3_command_eval is None:238                    row3_command_eval = ''239                o_f.write('"{}","{}","{}",{},{},{},{},{},{},{},{}\n'.format(240                    current_desp.replace('"', '""'), model_name, pred_cmd.replace('"', '""'),241                    template_eval, command_eval,242                    row1_template_eval, row1_command_eval,243                    row2_template_eval, row2_command_eval,244                    row3_template_eval, row3_command_eval))245    o_f.close()246def compute_error_overlap():247    input_file = sys.argv[1]248    template_judgements = []249    command_judgements = []250    with open(input_file) as f:251        reader = csv.DictReader(f)252        for row in reader:253            template_eval = row['correct template']254            command_eval = row['correct command']255            if row['model'] == 'tellina':256                template_judgements.append([template_eval])257                command_judgements.append([command_eval])258            else:259                template_judgements[-1].append(template_eval)260                command_judgements[-1].append(command_eval)261    temp_error_hist = [0, 0, 0, 0]262    for t1, t2 in template_judgements:263        if t1 == 'y' and t2 == 'y':264            temp_error_hist[0] += 1265        elif t1 == 'y' and t2 == 'n':266            temp_error_hist[1] += 1267        elif t1 == 'n' and t2 == 'y':268            temp_error_hist[2] += 1269        elif t1 == 'n' and t2 == 'n':270            temp_error_hist[3] += 1271    print('Template Judgements:')272    print('\ty y: {}'.format(temp_error_hist[0]))273    print('\ty n: {}'.format(temp_error_hist[1]))274    print('\tn y: {}'.format(temp_error_hist[2]))275    print('\tn n: {}'.format(temp_error_hist[3]))276    command_error_hist = [0, 0, 0, 0]277    for c1, c2 in command_judgements:278        if c1 == 'y' and c2 == 'y':279            command_error_hist[0] += 1280        elif c1 == 'y' and c2 == 'n':281            command_error_hist[1] += 1282        elif c1 == 'n' and c2 == 'y':283            command_error_hist[2] += 1284        elif c1 == 'n' and c2 == 'n':285            command_error_hist[3] += 1286    print('Command Judgements"')287    print('\ty y: {}'.format(command_error_hist[0]))288    print('\ty n: {}'.format(command_error_hist[1]))289    print('\tn y: {}'.format(command_error_hist[2]))290    print('\tn n: {}'.format(command_error_hist[3]))291def compute_error_category():292    input_file = sys.argv[1]293    tellina_error_hist = collections.defaultdict(int)294    pc_error_hist = collections.defaultdict(int)295    with open(input_file) as f:296        reader = csv.DictReader(f)297        for row in reader:298            error_cat = row['error category']299            if error_cat:300                if row['model'] == 'tellina':301                    tellina_error_hist[error_cat] += 1302                elif row['model'] == 'partial.token-copynet':303                    pc_error_hist[error_cat] += 1304                else:305                    raise ValueError306    print('Tellina errors:')307    for ec, freq in sorted(tellina_error_hist.items(), key=lambda x:x[1], reverse=True):308        print(ec, freq)309    print()310    print('Sub-token CopyNet errors:')311    for ec, freq in sorted(pc_error_hist.items(), key=lambda x:x[1], reverse=True):312        print(ec, freq)313def export_annotation_differences(input_file1, input_file2, output_file, command_header):314    o_f = open(output_file, 'w')315    o_f.write('description,{},correct template A,correct command A,correct template B,correct command B\n'.format(316        command_header))317    with open(input_file1) as f1:318        with open(input_file2) as f2:319            reader1 = csv.DictReader(f1)320            reader2 = csv.DictReader(f2)321            current_desp = ''322            desp_written = False323            for row1, row2 in zip(reader1, reader2):324                if row1['description']:325                    current_desp = row1['description']326                    desp_written = False327                if not row1[command_header]:328                    continue329                row1_template_eval = normalize_judgement(row1['correct template'].strip())330                row1_command_eval = normalize_judgement(row1['correct command'].strip())331                row2_template_eval = normalize_judgement(row2['correct template'].strip())332                row2_command_eval = normalize_judgement(row2['correct command'].strip())333                if (row1_template_eval != row2_template_eval) or \334                        (row1_command_eval != row2_command_eval):335                    if not desp_written:336                        o_f.write('"{}","{}",{},{},{},{}\n'.format(337                            current_desp.replace('"', '""'), row1[command_header].replace('"', '""'),338                            row1_template_eval, row1_command_eval, row2_template_eval, row2_command_eval))339                        desp_written = True340                    else:341                        o_f.write(',"{}",{},{},{},{}\n'.format(row1[command_header].replace('"', '""'),342                            row1_template_eval, row1_command_eval, row2_template_eval, row2_command_eval))343    o_f.close()344def main():345    # print_error_analysis_sheet()346    # combine_annotations_multi_annotators()347    # input_files1 = ['unreleased_files/manual.evaluations.test.stc.annotator.1.csv', 'unreleased_files/manual.evaluations.test.tellina.annotator.1.csv']348    # input_files2 = ['unreleased_files/manual.evaluations.test.stc.annotator.2.csv', 'unreleased_files/manual.evaluations.test.tellina.annotator.2.csv']349    # input_files1 = ['unreleased_files/NL-Cmd Judgement (Hamid) - pc.csv', 'unreleased_files/NL-Cmd Judgement (Hamid) - tellina.csv']350    # input_files2 = ['unreleased_files/NL-Cmd Judgement (Shridhar) - pc.csv', 'unreleased_files/NL-Cmd Judgement (Shridhar) - tellina.csv']351    # input_files1 = ['unreleased_files/manual.evaluations.dev.samples.annotator.1.csv']352    # input_files2 = ['unreleased_files/manual.evaluations.dev.samples.annotator.2.csv']353    # inter_annotator_agreement(input_files1, input_files2)354    # compute_error_overlap()355    # compute_error_category()356    combine_annotations_multi_files()    357if __name__ == '__main__':...

Automation Testing Tutorials

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