Python Texttable._rows方法代码示例

# 需要导入模块: from texttable import Texttable [as 别名]
# 或者: from texttable.Texttable import _rows [as 别名]

#.........这里部分代码省略.........
            continue

        if not os.path.isfile(status_file):
            print 'Skip: {0} is not a file!'.format(status_file)
            continue

        name = os.path.basename(exp_folder)
        if 'hyperparams.json' in os.listdir(os.path.abspath(pjoin(exp_folder, os.path.pardir))):
            name = os.path.basename(os.path.abspath(pjoin(exp_folder, os.path.pardir)))

        names.append(name)

        results_files.append(result_file)
        hyperparams_files.append(hyperparams_file)
        status_files.append(status_file)

    if len([no for no in sort_by if no == 0]) > 0:
        parser.error('Column ID are starting at 1!')

    # Retrieve headers from hyperparams
    headers_hyperparams = set()
    headers_results = set()
    headers_status = set()

    for hyperparams_file, status_file, results_file in zip(hyperparams_files, status_files, results_files):
        hyperparams = load_dict_from_json_file(hyperparams_file)
        results = load_dict_from_json_file(results_file)
        status = load_dict_from_json_file(status_file)
        headers_hyperparams |= set(hyperparams.keys())
        headers_results |= set(results.keys())
        headers_status |= set(status.keys())

    headers_hyperparams = sorted(list(headers_hyperparams))
    headers_status = sorted(list(headers_status))
    # TODO: when generating result.json split 'trainset' scores in two key:
    #       'trainset' and 'trainset_std' (same goes for validset and testset).
    headers_results |= set(["trainset_std", "validset_std", "testset_std"])
    headers_results = sorted(list(headers_results))
    headers = headers_hyperparams + headers_status + ["name"] + headers_results

    # Build results table
    table = Texttable(max_width=0)
    table.set_deco(Texttable.HEADER)
    table.set_precision(8)
    table.set_cols_dtype(['a'] * len(headers))
    table.set_cols_align(['c'] * len(headers))

    # Headers
    table.header([str(i) + "\n" + h for i, h in enumerate(headers, start=1)])

    if args.only_header:
        print table.draw()
        return

    # Results
    for name, hyperparams_file, status_file, results_file in zip(names, hyperparams_files, status_files, results_files):
        hyperparams = load_dict_from_json_file(hyperparams_file)
        results = load_dict_from_json_file(results_file)
        status = load_dict_from_json_file(status_file)

        # Build results table row (hyperparams columns)
        row = []
        for h in headers_hyperparams:
            value = hyperparams.get(h, '')
            row.append(value)

        for h in headers_status:
            value = status.get(h, '')
            row.append(value)

        row.append(name)

        for h in headers_results:
            if h in ["trainset", "validset", "testset"]:
                value = results.get(h, '')[0]
            elif h in ["trainset_std", "validset_std", "testset_std"]:
                value = results.get(h[:-4], '')[1]
            else:
                value = results.get(h, '')
            row.append(value)

        table.add_row(row)

    # Sort
    for col in reversed(sort_by):
        table._rows = sorted(table._rows, key=sort_nicely(abs(col) - 1), reverse=col < 0)

    if args.out is not None:
        import csv

        results = []
        results.append(headers)
        results.extend(table._rows)

        with open(args.out, 'wb') as csvfile:
            w = csv.writer(csvfile)
            w.writerows(results)

    else:
        print table.draw()

# 需要导入模块: from texttable import Texttable [as 别名]
# 或者: from texttable.Texttable import _rows [as 别名]
def main():
    parser = buildArgsParser()
    args = parser.parse_args()

    scoring_files = args.scores
    sort_by = args.sort_by

    if len(scoring_files) == 1 and os.path.isdir(scoring_files[0]):
        scoring_files = [os.path.join(scoring_files[0], f)
                         for f in os.listdir(scoring_files[0])]

    scoring_files = filter(lambda f: f.endswith('.json'), scoring_files)

    if len(scoring_files) == 0:
        parser.error('Need a least one scoring file/folder!')

    if len([no for no in sort_by if no == 0]) > 0:
        parser.error('Column ID are starting at 1.')

    headers_params = set()
    headers_scores = set()
    params = []
    scores = []

    # Retrieves scores
    for scoring_file in scoring_files:
        infos = scoring_file.split('/')[-1][:-5]
        if args.is_split:
            ind = 0 if infos.find('-') < infos.find('_') else 1
            param = dict([tuple(param.split('-'))
                          for param in infos.split('_')[ind:]])
        else:
            param = {"filename": infos}
        # score = pickle.load(open(scoring_file))
        score = json.load(open(scoring_file))

        # Compute the VCCR metric, CSR=1-NC
        # In VCCR, VCWP are considered as IC [Girard et al., NeuroImage, 2014]
        if score['VC'] > 0:
            score['VCCR'] = score['VC'] / (score['VC'] +
                                           score['IC'] +
                                           score['VCWP'])
        else:
            score['VCCR'] = 0

        # Keep only scalar metrics.
        for k in score.keys():
            if k not in METRICS:
                del score[k]

        headers_params |= set(param.keys())
        headers_scores |= set(score.keys())

        scores.append(score)
        params.append(param)

    nbr_cols = len(headers_params) + len(headers_scores)
    if len([no for no in sort_by if abs(no) > nbr_cols]) > 0:
        parser.error('The maximum column ID is {0}.'.format(nbr_cols))

    table = Texttable(max_width=0)
    table.set_deco(Texttable.HEADER)
    table.set_cols_dtype(['a'] * nbr_cols)
    table.set_cols_align(['l'] + ['c'] * (nbr_cols-1))

    # Headers
    headers_params = sorted(headers_params)
    headers_scores = list(headers_scores)
    headers_scores = [headers_scores[headers_scores.index(e)] for e in METRICS if e in headers_scores]

    headers = headers_params + headers_scores
    table.header([str(i) + "\n" + h for i, h in enumerate(headers, start=1)])

    # Data
    for param, score in zip(params, scores):
        data = []
        for header in headers_params:
            data.append(param.get(header, '-'))

        for header in headers_scores:
            data.append(score.get(header, '-'))

        table.add_row(data)

    # Sort
    for col in reversed(sort_by):
        table._rows = sorted(table._rows,
                             key=sort_nicely(abs(col) - 1),
                             reverse=col < 0)

    print(table.draw())