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"""Definitions for command-line (Click) commands for invoking Annif |
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operations and printing the results to console.""" |
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import collections |
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import os.path |
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import re |
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import sys |
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import json |
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import click |
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import click_log |
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from flask import current_app |
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from flask.cli import FlaskGroup, ScriptInfo |
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import annif |
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import annif.corpus |
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import annif.parallel |
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import annif.project |
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import annif.registry |
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from annif.project import Access |
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from annif.suggestion import SuggestionFilter, ListSuggestionResult |
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from annif.exception import ConfigurationException, NotSupportedException |
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logger = annif.logger |
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click_log.basic_config(logger) |
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cli = FlaskGroup(create_app=annif.create_app, add_version_option=False) |
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cli = click.version_option(message='%(version)s')(cli) |
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def get_project(project_id): |
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""" |
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Helper function to get a project by ID and bail out if it doesn't exist""" |
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try: |
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return annif.registry.get_project(project_id, |
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min_access=Access.private) |
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except ValueError: |
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click.echo( |
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"No projects found with id \'{0}\'.".format(project_id), |
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err=True) |
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sys.exit(1) |
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View Code Duplication |
def open_documents(paths, docs_limit): |
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"""Helper function to open a document corpus from a list of pathnames, |
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each of which is either a TSV file or a directory of TXT files. The |
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corpus will be returned as an instance of DocumentCorpus or |
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LimitingDocumentCorpus.""" |
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def open_doc_path(path): |
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"""open a single path and return it as a DocumentCorpus""" |
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if os.path.isdir(path): |
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return annif.corpus.DocumentDirectory(path, require_subjects=True) |
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return annif.corpus.DocumentFile(path) |
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if len(paths) == 0: |
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logger.warning('Reading empty file') |
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docs = open_doc_path(os.path.devnull) |
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elif len(paths) == 1: |
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docs = open_doc_path(paths[0]) |
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else: |
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corpora = [open_doc_path(path) for path in paths] |
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docs = annif.corpus.CombinedCorpus(corpora) |
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if docs_limit is not None: |
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docs = annif.corpus.LimitingDocumentCorpus(docs, docs_limit) |
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return docs |
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def parse_backend_params(backend_param, project): |
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"""Parse a list of backend parameters given with the --backend-param |
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option into a nested dict structure""" |
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backend_params = collections.defaultdict(dict) |
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for beparam in backend_param: |
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backend, param = beparam.split('.', 1) |
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key, val = param.split('=', 1) |
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validate_backend_params(backend, beparam, project) |
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backend_params[backend][key] = val |
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return backend_params |
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def validate_backend_params(backend, beparam, project): |
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if backend != project.config['backend']: |
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raise ConfigurationException( |
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'The backend {} in CLI option "-b {}" not matching the project' |
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' backend {}.' |
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.format(backend, beparam, project.config['backend'])) |
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BATCH_MAX_LIMIT = 15 |
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def generate_filter_batches(subjects): |
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import annif.eval |
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filter_batches = collections.OrderedDict() |
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for limit in range(1, BATCH_MAX_LIMIT + 1): |
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for threshold in [i * 0.05 for i in range(20)]: |
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hit_filter = SuggestionFilter(subjects, limit, threshold) |
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batch = annif.eval.EvaluationBatch(subjects) |
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filter_batches[(limit, threshold)] = (hit_filter, batch) |
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return filter_batches |
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def set_project_config_file_path(ctx, param, value): |
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"""Override the default path or the path given in env by CLI option""" |
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with ctx.ensure_object(ScriptInfo).load_app().app_context(): |
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if value: |
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current_app.config['PROJECTS_FILE'] = value |
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def common_options(f): |
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"""Decorator to add common options for all CLI commands""" |
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f = click.option( |
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'-p', '--projects', help='Set path to projects.cfg', |
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type=click.Path(dir_okay=False, exists=True), |
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callback=set_project_config_file_path, expose_value=False, |
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is_eager=True)(f) |
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return click_log.simple_verbosity_option(logger)(f) |
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def backend_param_option(f): |
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"""Decorator to add an option for CLI commands to override BE parameters""" |
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return click.option( |
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'--backend-param', '-b', multiple=True, |
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help='Override backend parameter of the config file. ' + |
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'Syntax: "-b <backend>.<parameter>=<value>".')(f) |
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View Code Duplication |
@cli.command('list-projects') |
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@common_options |
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@click_log.simple_verbosity_option(logger, default='ERROR') |
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def run_list_projects(): |
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""" |
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List available projects. |
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""" |
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template = "{0: <25}{1: <45}{2: <10}{3: <7}" |
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header = template.format( |
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"Project ID", "Project Name", "Language", "Trained") |
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click.echo(header) |
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click.echo("-" * len(header)) |
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for proj in annif.registry.get_projects( |
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min_access=Access.private).values(): |
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click.echo(template.format( |
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proj.project_id, proj.name, proj.language, str(proj.is_trained))) |
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@cli.command('show-project') |
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@click.argument('project_id') |
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@common_options |
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def run_show_project(project_id): |
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""" |
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Show information about a project. |
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""" |
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proj = get_project(project_id) |
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click.echo(f'Project ID: {proj.project_id}') |
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click.echo(f'Project Name: {proj.name}') |
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click.echo(f'Language: {proj.language}') |
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click.echo(f'Access: {proj.access.name}') |
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click.echo(f'Trained: {proj.is_trained}') |
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click.echo(f'Modification time: {proj.modification_time}') |
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@cli.command('clear') |
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@click.argument('project_id') |
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@common_options |
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def run_clear_project(project_id): |
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""" |
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Initialize the project to its original, untrained state. |
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""" |
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proj = get_project(project_id) |
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proj.remove_model_data() |
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View Code Duplication |
@cli.command('loadvoc') |
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@click.argument('project_id') |
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@click.argument('subjectfile', type=click.Path(exists=True, dir_okay=False)) |
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@common_options |
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def run_loadvoc(project_id, subjectfile): |
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""" |
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Load a vocabulary for a project. |
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""" |
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proj = get_project(project_id) |
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if annif.corpus.SubjectFileSKOS.is_rdf_file(subjectfile): |
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# SKOS/RDF file supported by rdflib |
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subjects = annif.corpus.SubjectFileSKOS(subjectfile, proj.language) |
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else: |
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# probably a TSV file |
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subjects = annif.corpus.SubjectFileTSV(subjectfile) |
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proj.vocab.load_vocabulary(subjects, proj.language) |
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View Code Duplication |
@cli.command('train') |
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@click.argument('project_id') |
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@click.argument('paths', type=click.Path(exists=True), nargs=-1) |
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@click.option('--cached/--no-cached', '-c/-C', default=False, |
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help='Reuse preprocessed training data from previous run') |
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@click.option('--docs-limit', '-d', default=None, |
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type=click.IntRange(0, None), |
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help='Maximum number of documents to use') |
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@click.option('--jobs', |
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'-j', |
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default=0, |
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help='Number of parallel jobs (0 means choose automatically)') |
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@backend_param_option |
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@common_options |
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def run_train(project_id, paths, cached, docs_limit, jobs, backend_param): |
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""" |
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Train a project on a collection of documents. |
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""" |
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proj = get_project(project_id) |
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backend_params = parse_backend_params(backend_param, proj) |
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if cached: |
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if len(paths) > 0: |
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raise click.UsageError( |
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"Corpus paths cannot be given when using --cached option.") |
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documents = 'cached' |
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else: |
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documents = open_documents(paths, docs_limit) |
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proj.train(documents, backend_params, jobs) |
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View Code Duplication |
@cli.command('learn') |
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@click.argument('project_id') |
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@click.argument('paths', type=click.Path(exists=True), nargs=-1) |
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@click.option('--docs-limit', '-d', default=None, |
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type=click.IntRange(0, None), |
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help='Maximum number of documents to use') |
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@backend_param_option |
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@common_options |
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def run_learn(project_id, paths, docs_limit, backend_param): |
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""" |
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Further train an existing project on a collection of documents. |
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""" |
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proj = get_project(project_id) |
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backend_params = parse_backend_params(backend_param, proj) |
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documents = open_documents(paths, docs_limit) |
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proj.learn(documents, backend_params) |
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View Code Duplication |
@cli.command('suggest') |
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@click.argument('project_id') |
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@click.option('--limit', '-l', default=10, help='Maximum number of subjects') |
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@click.option('--threshold', '-t', default=0.0, help='Minimum score threshold') |
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@backend_param_option |
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@common_options |
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def run_suggest(project_id, limit, threshold, backend_param): |
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""" |
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Suggest subjects for a single document from standard input. |
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""" |
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project = get_project(project_id) |
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text = sys.stdin.read() |
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backend_params = parse_backend_params(backend_param, project) |
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hit_filter = SuggestionFilter(project.subjects, limit, threshold) |
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hits = hit_filter(project.suggest(text, backend_params)) |
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for hit in hits.as_list(project.subjects): |
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click.echo( |
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"<{}>\t{}\t{}".format( |
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hit.uri, |
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'\t'.join(filter(None, (hit.label, hit.notation))), |
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hit.score)) |
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View Code Duplication |
@cli.command('index') |
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@click.argument('project_id') |
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@click.argument('directory', type=click.Path(exists=True, file_okay=False)) |
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@click.option( |
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'--suffix', |
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'-s', |
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default='.annif', |
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help='File name suffix for result files') |
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@click.option('--force/--no-force', '-f/-F', default=False, |
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help='Force overwriting of existing result files') |
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@click.option('--limit', '-l', default=10, help='Maximum number of subjects') |
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@click.option('--threshold', '-t', default=0.0, help='Minimum score threshold') |
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@backend_param_option |
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@common_options |
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def run_index(project_id, directory, suffix, force, |
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limit, threshold, backend_param): |
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""" |
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Index a directory with documents, suggesting subjects for each document. |
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Write the results in TSV files with the given suffix. |
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""" |
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project = get_project(project_id) |
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backend_params = parse_backend_params(backend_param, project) |
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hit_filter = SuggestionFilter(project.subjects, limit, threshold) |
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for docfilename, dummy_subjectfn in annif.corpus.DocumentDirectory( |
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directory, require_subjects=False): |
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with open(docfilename, encoding='utf-8-sig') as docfile: |
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text = docfile.read() |
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subjectfilename = re.sub(r'\.txt$', suffix, docfilename) |
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if os.path.exists(subjectfilename) and not force: |
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click.echo( |
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"Not overwriting {} (use --force to override)".format( |
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subjectfilename)) |
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continue |
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with open(subjectfilename, 'w', encoding='utf-8') as subjfile: |
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results = project.suggest(text, backend_params) |
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for hit in hit_filter(results).as_list(project.subjects): |
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line = "<{}>\t{}\t{}".format( |
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hit.uri, |
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'\t'.join(filter(None, (hit.label, hit.notation))), |
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hit.score) |
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click.echo(line, file=subjfile) |
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View Code Duplication |
@cli.command('eval') |
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@click.argument('project_id') |
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@click.argument('paths', type=click.Path(exists=True), nargs=-1) |
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@click.option('--limit', '-l', default=10, help='Maximum number of subjects') |
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@click.option('--threshold', '-t', default=0.0, help='Minimum score threshold') |
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@click.option('--docs-limit', '-d', default=None, |
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type=click.IntRange(0, None), |
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help='Maximum number of documents to use') |
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@click.option( |
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'--metrics-file', |
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'-M', |
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type=click.File( |
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'w', |
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encoding='utf-8', |
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errors='ignore', |
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lazy=True), |
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help="""Specify file in order to write evaluation metrics in JSON format. |
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File directory must exist, existing file will be overwritten.""") |
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@click.option( |
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'--results-file', |
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'-r', |
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type=click.File( |
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'w', |
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encoding='utf-8', |
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errors='ignore', |
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lazy=True), |
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help="""Specify file in order to write non-aggregated results per subject. |
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File directory must exist, existing file will be overwritten.""") |
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@click.option('--jobs', |
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'-j', |
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default=1, |
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help='Number of parallel jobs (0 means all CPUs)') |
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@backend_param_option |
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@common_options |
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def run_eval( |
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project_id, |
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paths, |
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limit, |
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threshold, |
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docs_limit, |
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metrics_file, |
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results_file, |
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jobs, |
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backend_param): |
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""" |
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Analyze documents and evaluate the result. |
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Compare the results of automated indexing against a gold standard. The |
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path may be either a TSV file with short documents or a directory with |
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documents in separate files. |
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""" |
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project = get_project(project_id) |
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backend_params = parse_backend_params(backend_param, project) |
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import annif.eval |
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eval_batch = annif.eval.EvaluationBatch(project.subjects) |
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365
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if results_file: |
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try: |
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print('', end='', file=results_file) |
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click.echo('Writing per subject evaluation results to {!s}'.format( |
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results_file.name)) |
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except Exception as e: |
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raise NotSupportedException( |
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"cannot open results-file for writing: " + str(e)) |
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docs = open_documents(paths, docs_limit) |
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jobs, pool_class = annif.parallel.get_pool(jobs) |
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project.initialize(parallel=True) |
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psmap = annif.parallel.ProjectSuggestMap( |
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project.registry, [project_id], backend_params, limit, threshold) |
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381
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with pool_class(jobs) as pool: |
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for hits, uris, labels in pool.imap_unordered( |
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psmap.suggest, docs.documents): |
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eval_batch.evaluate(hits[project_id], |
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annif.corpus.SubjectSet((uris, labels))) |
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387
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template = "{0:<30}\t{1}" |
388
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metrics = eval_batch.results(results_file=results_file) |
389
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for metric, score in metrics.items(): |
390
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click.echo(template.format(metric + ":", score)) |
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if metrics_file: |
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print(metrics) |
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json.dump(metrics, metrics_file, indent=2) |
394
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395
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View Code Duplication |
@cli.command('optimize') |
|
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|
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@click.argument('project_id') |
398
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@click.argument('paths', type=click.Path(exists=True), nargs=-1) |
399
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@click.option('--docs-limit', '-d', default=None, |
400
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type=click.IntRange(0, None), |
401
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help='Maximum number of documents to use') |
402
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@backend_param_option |
403
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@common_options |
404
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def run_optimize(project_id, paths, docs_limit, backend_param): |
405
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""" |
406
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Analyze documents, testing multiple limits and thresholds. |
407
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|
408
|
|
|
Evaluate the analysis results for a directory with documents against a |
409
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gold standard given in subject files. Test different limit/threshold |
410
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|
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values and report the precision, recall and F-measure of each combination |
411
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of settings. |
412
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""" |
413
|
|
|
project = get_project(project_id) |
414
|
|
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backend_params = parse_backend_params(backend_param, project) |
415
|
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|
416
|
|
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filter_batches = generate_filter_batches(project.subjects) |
417
|
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|
418
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|
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ndocs = 0 |
419
|
|
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docs = open_documents(paths, docs_limit) |
420
|
|
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for doc in docs.documents: |
421
|
|
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raw_hits = project.suggest(doc.text, backend_params) |
422
|
|
|
hits = raw_hits.filter(project.subjects, limit=BATCH_MAX_LIMIT) |
423
|
|
|
assert isinstance(hits, ListSuggestionResult), \ |
424
|
|
|
"Optimize should only be done with ListSuggestionResult " + \ |
425
|
|
|
"as it would be very slow with VectorSuggestionResult." |
426
|
|
|
gold_subjects = annif.corpus.SubjectSet((doc.uris, doc.labels)) |
427
|
|
|
for hit_filter, batch in filter_batches.values(): |
428
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|
|
batch.evaluate(hit_filter(hits), gold_subjects) |
429
|
|
|
ndocs += 1 |
430
|
|
|
|
431
|
|
|
click.echo("\t".join(('Limit', 'Thresh.', 'Prec.', 'Rec.', 'F1'))) |
432
|
|
|
|
433
|
|
|
best_scores = collections.defaultdict(float) |
434
|
|
|
best_params = {} |
435
|
|
|
|
436
|
|
|
template = "{:d}\t{:.02f}\t{:.04f}\t{:.04f}\t{:.04f}" |
437
|
|
|
# Store the batches in a list that gets consumed along the way |
438
|
|
|
# This way GC will have a chance to reclaim the memory |
439
|
|
|
filter_batches = list(filter_batches.items()) |
440
|
|
|
while filter_batches: |
441
|
|
|
params, filter_batch = filter_batches.pop(0) |
442
|
|
|
metrics = ['Precision (doc avg)', |
443
|
|
|
'Recall (doc avg)', |
444
|
|
|
'F1 score (doc avg)'] |
445
|
|
|
results = filter_batch[1].results(metrics=metrics) |
446
|
|
|
for metric, score in results.items(): |
447
|
|
|
if score >= best_scores[metric]: |
448
|
|
|
best_scores[metric] = score |
449
|
|
|
best_params[metric] = params |
450
|
|
|
click.echo( |
451
|
|
|
template.format( |
452
|
|
|
params[0], |
453
|
|
|
params[1], |
454
|
|
|
results['Precision (doc avg)'], |
455
|
|
|
results['Recall (doc avg)'], |
456
|
|
|
results['F1 score (doc avg)'])) |
457
|
|
|
|
458
|
|
|
click.echo() |
459
|
|
|
template2 = "Best {:>19}: {:.04f}\tLimit: {:d}\tThreshold: {:.02f}" |
460
|
|
|
for metric in metrics: |
461
|
|
|
click.echo( |
462
|
|
|
template2.format( |
463
|
|
|
metric, |
464
|
|
|
best_scores[metric], |
465
|
|
|
best_params[metric][0], |
466
|
|
|
best_params[metric][1])) |
467
|
|
|
click.echo("Documents evaluated:\t{}".format(ndocs)) |
468
|
|
|
|
469
|
|
|
|
470
|
|
View Code Duplication |
@cli.command('hyperopt') |
|
|
|
|
471
|
|
|
@click.argument('project_id') |
472
|
|
|
@click.argument('paths', type=click.Path(exists=True), nargs=-1) |
473
|
|
|
@click.option('--docs-limit', '-d', default=None, |
474
|
|
|
type=click.IntRange(0, None), |
475
|
|
|
help='Maximum number of documents to use') |
476
|
|
|
@click.option('--trials', '-T', default=10, help='Number of trials') |
477
|
|
|
@click.option('--jobs', |
478
|
|
|
'-j', |
479
|
|
|
default=1, |
480
|
|
|
help='Number of parallel runs (0 means all CPUs)') |
481
|
|
|
@click.option('--metric', '-m', default='NDCG', |
482
|
|
|
help='Metric to optimize (default: NDCG)') |
483
|
|
|
@click.option( |
484
|
|
|
'--results-file', |
485
|
|
|
'-r', |
486
|
|
|
type=click.File( |
487
|
|
|
'w', |
488
|
|
|
encoding='utf-8', |
489
|
|
|
errors='ignore', |
490
|
|
|
lazy=True), |
491
|
|
|
help="""Specify file path to write trial results as CSV. |
492
|
|
|
File directory must exist, existing file will be overwritten.""") |
493
|
|
|
@common_options |
494
|
|
|
def run_hyperopt(project_id, paths, docs_limit, trials, jobs, metric, |
495
|
|
|
results_file): |
496
|
|
|
""" |
497
|
|
|
Optimize the hyperparameters of a project using a validation corpus. |
498
|
|
|
""" |
499
|
|
|
proj = get_project(project_id) |
500
|
|
|
documents = open_documents(paths, docs_limit) |
501
|
|
|
click.echo(f"Looking for optimal hyperparameters using {trials} trials") |
502
|
|
|
rec = proj.hyperopt(documents, trials, jobs, metric, results_file) |
503
|
|
|
click.echo(f"Got best {metric} score {rec.score:.4f} with:") |
504
|
|
|
click.echo("---") |
505
|
|
|
for line in rec.lines: |
506
|
|
|
click.echo(line) |
507
|
|
|
click.echo("---") |
508
|
|
|
|
509
|
|
|
|
510
|
|
|
if __name__ == '__main__': |
511
|
|
|
cli() |
512
|
|
|
|