Source code for soweego.linker.train

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

"""Train supervised linking algorithms."""

__author__ = 'Marco Fossati'
__email__ = ''
__version__ = '1.0'
__license__ = 'GPL-3.0'
__copyright__ = 'Copyleft 2018, Hjfocs'

import logging
import os
import sys
from typing import Dict, Tuple

import click
import joblib
import pandas as pd
from keras import backend as K
from recordlinkage.base import BaseClassifier
from sklearn.model_selection import GridSearchCV

from soweego.commons import constants, keys, target_database, utils
from soweego.linker import blocking, workflow

LOGGER = logging.getLogger(__name__)

# Let the user pass extra kwargs to the classifier
# This is for development purposes only, and is not explicitly documented
    context_settings={'ignore_unknown_options': True, 'allow_extra_args': True}
@click.argument('classifier', type=click.Choice(constants.CLASSIFIERS))
    'catalog', type=click.Choice(target_database.supported_targets())
    'entity', type=click.Choice(target_database.supported_entities())
    help='Run grid search for hyperparameters tuning.',
    help="Number of folds for hyperparameters tuning. Use with '--tune'. "
    "Default: 5.",
    help=f'Input/output directory, default: {constants.SHARED_FOLDER}.',
def cli(ctx, classifier, catalog, entity, tune, k_folds, dir_io):
    """Train a supervised linker.

    Build the training set relevant to the given catalog and entity,
    then train a model with the given classification algorithm.
    kwargs = utils.handle_extra_cli_args(ctx.args)
    if kwargs is None:

    actual_classifier = constants.CLASSIFIERS[classifier]

    model = execute(
        actual_classifier, catalog, entity, tune, k_folds, dir_io, **kwargs

    outfile = os.path.join(
        constants.LINKER_MODEL.format(catalog, entity, actual_classifier),
    os.makedirs(os.path.dirname(outfile), exist_ok=True)
    joblib.dump(model, outfile)"%s model dumped to '%s'", classifier, outfile)

    # Free memory in case of neural networks or
    # ensembles which use them:
    # can be done only after the model dump
    if actual_classifier in (
        K.clear_session()  # Clear the TensorFlow graph'Training completed')

[docs]def execute( classifier: str, catalog: str, entity: str, tune: bool, k: int, dir_io: str, **kwargs, ) -> BaseClassifier: """Train a supervised linker. 1. Build the training set relevant to the given catalog and entity 2. train a model with the given classifier :param classifier: ``{'naive_bayes', 'linear_support_vector_machines', 'support_vector_machines', 'single_layer_perceptron', 'multi_layer_perceptron'}``. A supported classifier :param catalog: ``{'discogs', 'imdb', 'musicbrainz'}``. A supported catalog :param entity: ``{'actor', 'band', 'director', 'musician', 'producer', 'writer', 'audiovisual_work', 'musical_work'}``. A supported entity :param tune: whether to run grid search for hyperparameters tuning or not :param k: number of folds for hyperparameters tuning. It is used only when `tune=True` :param dir_io: input/output directory where working files will be read/written :param kwargs: extra keyword arguments that will be passed to the model initialization :return: the trained model """ feature_vectors, positive_samples_index = build_training_set( catalog, entity, dir_io ) if tune: best_params = _grid_search( k, feature_vectors, positive_samples_index, classifier, **kwargs ) # TODO find a way to avoid retraining: # pass `_grid_search.best_estimator_` to recordlinkage classifiers. # See `refit` param in # return _train( classifier, feature_vectors, positive_samples_index, **best_params ) return _train(classifier, feature_vectors, positive_samples_index, **kwargs)
[docs]def build_training_set( catalog: str, entity: str, dir_io: str ) -> Tuple[pd.DataFrame, pd.MultiIndex]: """Build a training set. :param catalog: ``{'discogs', 'imdb', 'musicbrainz'}``. A supported catalog :param entity: ``{'actor', 'band', 'director', 'musician', 'producer', 'writer', 'audiovisual_work', 'musical_work'}``. A supported entity :param dir_io: input/output directory where working files will be read/written :return: the feature vectors and positive samples pair. Features are computed by comparing *(QID, catalog ID)* pairs. Positive samples are catalog IDs available in Wikidata """ goal = 'training' # Wikidata side wd_reader = workflow.build_wikidata(goal, catalog, entity, dir_io) wd_generator = workflow.preprocess_wikidata(goal, wd_reader) positive_samples, feature_vectors = None, None for i, wd_chunk in enumerate(wd_generator, 1): # Positive samples come from Wikidata if positive_samples is None: positive_samples = wd_chunk[keys.TID] else: # We concatenate the current chunk # and reset `positive_samples` at each iteration, # instead of appending each chunk to a list, # then concatenate it at the end of the loop. # Reason: keeping multiple yet small pandas objects # is less memory-efficient positive_samples = pd.concat([positive_samples, wd_chunk[keys.TID]]) # All samples come from queries to the target DB # and include negative ones all_samples = blocking.find_samples( goal, catalog, wd_chunk[keys.NAME_TOKENS], i, target_database.get_main_entity(catalog, entity), dir_io, ) # Build target chunk from all samples target_reader = workflow.build_target( goal, catalog, entity, set(all_samples.get_level_values(keys.TID)) ) # Preprocess target chunk target_chunk = workflow.preprocess_target(goal, target_reader) features_path = os.path.join( dir_io, constants.FEATURES.format(catalog, entity, goal, i) ) # Extract features from all samples chunk_fv = workflow.extract_features( all_samples, wd_chunk, target_chunk, features_path ) if feature_vectors is None: feature_vectors = chunk_fv else: feature_vectors = pd.concat([feature_vectors, chunk_fv], sort=False) # Final positive samples index positive_samples_index = pd.MultiIndex.from_tuples( zip(positive_samples.index, positive_samples), names=[keys.QID, keys.TID], )'Built positive samples index from Wikidata') feature_vectors = feature_vectors.fillna(constants.FEATURE_MISSING_VALUE) return feature_vectors, positive_samples_index
def _grid_search( k: int, feature_vectors: pd.DataFrame, positive_samples_index: pd.MultiIndex, classifier: str, **kwargs, ) -> Dict: k_fold, target = utils.prepare_stratified_k_fold( k, feature_vectors, positive_samples_index ) model = utils.init_model(classifier, feature_vectors.shape[1], **kwargs) grid_search = GridSearchCV( model.kernel, constants.PARAMETER_GRIDS[classifier], scoring='f1', n_jobs=-1, cv=k_fold, ), target) return grid_search.best_params_ def _train(classifier, feature_vectors, positive_samples_index, **kwargs): model = utils.init_model(classifier, feature_vectors.shape[1], **kwargs)'Training a %s ...', classifier), positive_samples_index)'Training done') return model