annif.backend.nn_ensemble

Complexity

Total Complexity

43

Size/Duplication

Total Lines	332
Duplicated Lines	86.14 %

Importance

Changes

0

18 Methods

Rating	Name	Duplication	Size	Complexity
A	LMDBSequence.__len__()	3	3	1
A	NNEnsembleBackend.get_hp_optimizer()	2	2	1
A	NNEnsembleBackend._create_model()	29	29	2
A	NNEnsembleOptimizer._postprocess()	8	8	1
A	LMDBSequence.__getitem__()	14	14	3
A	MeanLayer.call()	0	2	1
A	NNEnsembleBackend.default_params()	4	4	1
B	NNEnsembleBackend._corpus_to_vectors()	36	36	5
A	NNEnsembleBackend.initialize()	16	16	4
A	LMDBSequence.add_sample()	10	10	1
A	NNEnsembleBackend._open_lmdb()	5	5	3
A	NNEnsembleBackend._merge_hits_from_sources()	9	9	1
B	NNEnsembleOptimizer._prepare()	35	35	5
A	NNEnsembleBackend._learn()	6	6	1
A	NNEnsembleBackend._train()	9	9	1
A	LMDBSequence.__init__()	9	9	2
A	NNEnsembleOptimizer._objective()	26	26	3
A	NNEnsembleBackend._fit_model()	21	21	5

"""Neural network based ensemble backend that combines results from multiple
projects."""


from io import BytesIO
import shutil
import os.path
import numpy as np
from scipy.sparse import csr_matrix, csc_matrix
import joblib
import lmdb
from tensorflow.keras.layers import Input, Dense, Add, Flatten, Dropout, Layer
from tensorflow.keras.models import Model, load_model
from tensorflow.keras.utils import Sequence
import tensorflow.keras.backend as K
import annif.corpus
import annif.parallel
import annif.util
from annif.exception import NotInitializedException, NotSupportedException
from annif.suggestion import VectorSuggestionResult
from . import backend
from . import ensemble
from . import hyperopt


def idx_to_key(idx):
    """convert an integer index to a binary key for use in LMDB"""
    return b'%08d' % idx


def key_to_idx(key):
    """convert a binary LMDB key to an integer index"""
    return int(key)


class LMDBSequence(Sequence):

This code seems to be duplicated in your project.

    """A sequence of samples stored in a LMDB database."""

    def __init__(self, txn, batch_size):
        self._txn = txn
        cursor = txn.cursor()
        if cursor.last():
            # Counter holds the number of samples in the database
            self._counter = key_to_idx(cursor.key()) + 1
        else:  # empty database
            self._counter = 0
        self._batch_size = batch_size

    def add_sample(self, inputs, targets):
        # use zero-padded 8-digit key
        key = idx_to_key(self._counter)
        self._counter += 1
        # convert the sample into a sparse matrix and serialize it as bytes
        sample = (csc_matrix(inputs), csr_matrix(targets))
        buf = BytesIO()
        joblib.dump(sample, buf)
        buf.seek(0)
        self._txn.put(key, buf.read())

    def __getitem__(self, idx):
        """get a particular batch of samples"""
        cursor = self._txn.cursor()
        first_key = idx * self._batch_size
        cursor.set_key(idx_to_key(first_key))
        input_arrays = []
        target_arrays = []
        for key, value in cursor.iternext():
            if key_to_idx(key) >= (first_key + self._batch_size):
                break
            input_csr, target_csr = joblib.load(BytesIO(value))
            input_arrays.append(input_csr.toarray())
            target_arrays.append(target_csr.toarray().flatten())
        return np.array(input_arrays), np.array(target_arrays)

    def __len__(self):
        """return the number of available batches"""
        return int(np.ceil(self._counter / self._batch_size))


class NNEnsembleOptimizer(hyperopt.HyperparameterOptimizer):

This code seems to be duplicated in your project.

    """Hyperparameter optimizer for the NN ensemble backend"""

    def _prepare(self, n_jobs=1):
        sources = dict(
            annif.util.parse_sources(self._backend.params['sources']))

        # initialize the source projects before forking, to save memory
        for project_id in sources.keys():
            project = self._backend.project.registry.get_project(project_id)
            project.initialize(parallel=True)

        psmap = annif.parallel.ProjectSuggestMap(
            self._backend.project.registry,
            list(sources.keys()),
            backend_params=None,
            limit=None,
            threshold=0.0)

        jobs, pool_class = annif.parallel.get_pool(n_jobs)

        self._score_vectors = []
        self._gold_subjects = []

        with pool_class(jobs) as pool:
            for hits, uris, labels in pool.imap_unordered(
                    psmap.suggest, self._corpus.documents):
                doc_scores = []
                for project_id, p_hits in hits.items():
                    vector = p_hits.as_vector(self._backend.project.subjects)
                    doc_scores.append(np.sqrt(vector)
                                      * sources[project_id]
                                      * len(sources))
                score_vector = np.array(doc_scores,
                                        dtype=np.float32).transpose()
                subjects = annif.corpus.SubjectSet((uris, labels))
                self._score_vectors.append(score_vector)
                self._gold_subjects.append(subjects)

    def _objective(self, trial):
        sources = annif.util.parse_sources(self._backend.params['sources'])
        params = {
            'nodes': trial.suggest_int('nodes', 50, 200),
            'dropout_rate': trial.suggest_float('dropout_rate', 0.0, 0.5),
            'epochs': trial.suggest_int('epochs', 5, 20),
            'optimizer': 'adam'
        }
        model = self._backend._create_model(sources, params)

        env = self._backend._open_lmdb(True,
                                       self._backend.params['lmdb_map_size'])
        with env.begin(buffers=True) as txn:
            seq = LMDBSequence(txn, batch_size=32)
            model.fit(seq, verbose=0, epochs=params['epochs'])

        batch = annif.eval.EvaluationBatch(self._backend.project.subjects)
        for goldsubj, score_vector in zip(self._gold_subjects,
                                          self._score_vectors):

            results = model.predict(
                np.expand_dims(score_vector, 0))
            output = VectorSuggestionResult(results[0])
            batch.evaluate(output, goldsubj)
        eval_results = batch.results(metrics=[self._metric])
        return eval_results[self._metric]

    def _postprocess(self, study):
        bp = study.best_params
        lines = [
            f"nodes={bp['nodes']}",
            f"dropout_rate={bp['dropout_rate']}",
            f"epochs={bp['epochs']}"
        ]
        return hyperopt.HPRecommendation(lines=lines, score=study.best_value)


class MeanLayer(Layer):
    """Custom Keras layer that calculates mean values along the 2nd axis."""
    def call(self, inputs):
        return K.mean(inputs, axis=2)


class NNEnsembleBackend(

This code seems to be duplicated in your project.

        backend.AnnifLearningBackend,
        ensemble.BaseEnsembleBackend,
        hyperopt.AnnifHyperoptBackend):
    """Neural network ensemble backend that combines results from multiple
    projects"""

    name = "nn_ensemble"

    MODEL_FILE = "nn-model.h5"
    LMDB_FILE = 'nn-train.mdb'

    DEFAULT_PARAMETERS = {
        'nodes': 100,
        'dropout_rate': 0.2,
        'optimizer': 'adam',
        'epochs': 10,
        'learn-epochs': 1,
        'lmdb_map_size': 1024 * 1024 * 1024
    }

    # defaults for uninitialized instances
    _model = None

    def get_hp_optimizer(self, corpus, metric):
        return NNEnsembleOptimizer(self, corpus, metric)

    def default_params(self):
        params = backend.AnnifBackend.DEFAULT_PARAMETERS.copy()
        params.update(self.DEFAULT_PARAMETERS)
        return params

    def initialize(self, parallel=False):
        super().initialize(parallel)
        if self._model is not None:
            return  # already initialized
        if parallel:
            # Don't load TF model just before parallel execution,
            # since it won't work after forking worker processes
            return
        model_filename = os.path.join(self.datadir, self.MODEL_FILE)
        if not os.path.exists(model_filename):
            raise NotInitializedException(
                'model file {} not found'.format(model_filename),
                backend_id=self.backend_id)
        self.debug('loading Keras model from {}'.format(model_filename))
        self._model = load_model(model_filename,
                                 custom_objects={'MeanLayer': MeanLayer})

    def _merge_hits_from_sources(self, hits_from_sources, params):
        score_vector = np.array([np.sqrt(hits.as_vector(subjects))
                                 * weight * len(hits_from_sources)
                                 for hits, weight, subjects
                                 in hits_from_sources],
                                dtype=np.float32)
        results = self._model.predict(
            np.expand_dims(score_vector.transpose(), 0))
        return VectorSuggestionResult(results[0])

    def _create_model(self, sources, params):
        inputs = Input(shape=(len(self.project.subjects), len(sources)))

        flat_input = Flatten()(inputs)
        drop_input = Dropout(
            rate=float(params['dropout_rate']))(flat_input)
        hidden = Dense(int(params['nodes']),
                       activation="relu")(drop_input)
        drop_hidden = Dropout(rate=float(params['dropout_rate']))(hidden)
        delta = Dense(len(self.project.subjects),
                      kernel_initializer='zeros',
                      bias_initializer='zeros')(drop_hidden)

        mean = MeanLayer()(inputs)

        predictions = Add()([mean, delta])

        model = Model(inputs=inputs, outputs=predictions)
        model.compile(optimizer=params['optimizer'],
                      loss='binary_crossentropy',
                      metrics=['top_k_categorical_accuracy'])
        if 'lr' in params:
            model.optimizer.learning_rate.assign(
                float(params['lr']))

        summary = []
        model.summary(print_fn=summary.append)
        self.debug("Created model: \n" + "\n".join(summary))
        return model

    def _train(self, corpus, params, jobs=0):
        sources = annif.util.parse_sources(params['sources'])
        self.info("creating NN ensemble model")
        self._model = self._create_model(sources, params)
        self._fit_model(
            corpus,
            epochs=int(params['epochs']),
            lmdb_map_size=int(params['lmdb_map_size']),
            n_jobs=jobs)

    def _corpus_to_vectors(self, corpus, seq, n_jobs):
        # pass corpus through all source projects
        sources = dict(
            annif.util.parse_sources(self.params['sources']))

        # initialize the source projects before forking, to save memory
        self.info(
            f"Initializing source projects: {', '.join(sources.keys())}")
        for project_id in sources.keys():
            project = self.project.registry.get_project(project_id)
            project.initialize(parallel=True)

        psmap = annif.parallel.ProjectSuggestMap(
            self.project.registry,
            list(sources.keys()),
            backend_params=None,
            limit=None,
            threshold=0.0)

        jobs, pool_class = annif.parallel.get_pool(n_jobs)

        self.info("Processing training documents...")
        with pool_class(jobs) as pool:
            for hits, uris, labels in pool.imap_unordered(
                    psmap.suggest, corpus.documents):
                doc_scores = []
                for project_id, p_hits in hits.items():
                    vector = p_hits.as_vector(self.project.subjects)
                    doc_scores.append(np.sqrt(vector)
                                      * sources[project_id]
                                      * len(sources))
                score_vector = np.array(doc_scores,
                                        dtype=np.float32).transpose()
                subjects = annif.corpus.SubjectSet((uris, labels))
                true_vector = subjects.as_vector(self.project.subjects)
                seq.add_sample(score_vector, true_vector)

    def _open_lmdb(self, cached, lmdb_map_size):
        lmdb_path = os.path.join(self.datadir, self.LMDB_FILE)
        if not cached and os.path.exists(lmdb_path):
            shutil.rmtree(lmdb_path)
        return lmdb.open(lmdb_path, map_size=lmdb_map_size, writemap=True)

    def _fit_model(self, corpus, epochs, lmdb_map_size, n_jobs=1):
        env = self._open_lmdb(corpus == 'cached', lmdb_map_size)
        if corpus != 'cached':
            if corpus.is_empty():
                raise NotSupportedException(
                    'Cannot train nn_ensemble project with no documents')
            with env.begin(write=True, buffers=True) as txn:
                seq = LMDBSequence(txn, batch_size=32)
                self._corpus_to_vectors(corpus, seq, n_jobs)
        else:
            self.info("Reusing cached training data from previous run.")
        # fit the model using a read-only view of the LMDB
        self.info("Training neural network model...")
        with env.begin(buffers=True) as txn:
            seq = LMDBSequence(txn, batch_size=32)
            self._model.fit(seq, verbose=1, epochs=epochs)

        annif.util.atomic_save(
            self._model,
            self.datadir,
            self.MODEL_FILE)

    def _learn(self, corpus, params):
        self.initialize()
        self._fit_model(
            corpus,
            int(params['learn-epochs']),
            int(params['lmdb_map_size']))