annif.backend.nn_ensemble

Complexity

Total Complexity

29

Size/Duplication

Total Lines	217
Duplicated Lines	0 %

Importance

Changes

0

14 Methods

Rating	Name	Duplication	Size	Complexity
A	LMDBSequence.__len__()	0	3	1
A	LMDBSequence.__getitem__()	0	14	3
A	LMDBSequence.add_sample()	0	10	1
A	LMDBSequence.__init__()	0	8	2
A	NNEnsembleBackend._create_model()	0	31	2
A	MeanLayer.call()	0	2	1
A	NNEnsembleBackend.default_params()	0	4	1
A	NNEnsembleBackend._corpus_to_vectors()	0	17	3
A	NNEnsembleBackend.initialize()	0	12	3
A	NNEnsembleBackend._open_lmdb()	0	5	3
A	NNEnsembleBackend._merge_hits_from_sources()	0	9	1
A	NNEnsembleBackend._learn()	0	3	1
A	NNEnsembleBackend._train()	0	4	1
A	NNEnsembleBackend._fit_model()	0	18	4

"""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.util
from annif.exception import NotInitializedException
from annif.suggestion import VectorSuggestionResult
from . import backend
from . import ensemble


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

    def __init__(self, txn, batch_size):
        self._txn = txn
        cursor = txn.cursor()
        if cursor.last():
            self._counter = key_to_idx(cursor.key())
        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 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(
        backend.AnnifLearningBackend,
        ensemble.BaseEnsembleBackend):
    """Neural network ensemble backend that combines results from multiple
    projects"""

    name = "nn_ensemble"

    MODEL_FILE = "nn-model.h5"
    LMDB_FILE = 'nn-train.mdb'
    LMDB_MAP_SIZE = 1024 * 1024 * 1024

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

    # defaults for uninitialized instances
    _model = None

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

    def initialize(self):
        super().initialize()
        if self._model is not None:
            return  # already initialized
        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):
        self.info("creating NN ensemble model")

        inputs = Input(shape=(len(self.project.subjects), len(sources)))

        flat_input = Flatten()(inputs)
        drop_input = Dropout(
            rate=float(
                self.params['dropout_rate']))(flat_input)
        hidden = Dense(int(self.params['nodes']),
                       activation="relu")(drop_input)
        drop_hidden = Dropout(rate=float(self.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])

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

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

    def _train(self, corpus, params):
        sources = annif.util.parse_sources(self.params['sources'])
        self._create_model(sources)
        self._fit_model(corpus, epochs=int(params['epochs']))

    def _corpus_to_vectors(self, corpus, seq):
        # pass corpus through all source projects
        sources = [(self.project.registry.get_project(project_id), weight)
                   for project_id, weight
                   in annif.util.parse_sources(self.params['sources'])]

        for doc in corpus.documents:
            doc_scores = []
            for source_project, weight in sources:
                hits = source_project.suggest(doc.text)
                vector = hits.as_vector(source_project.subjects)
                doc_scores.append(np.sqrt(vector) * weight * len(sources))
            score_vector = np.array(doc_scores,
                                    dtype=np.float32).transpose()
            subjects = annif.corpus.SubjectSet((doc.uris, doc.labels))
            true_vector = subjects.as_vector(self.project.subjects)
            seq.add_sample(score_vector, true_vector)

    def _open_lmdb(self, cached):
        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=self.LMDB_MAP_SIZE, writemap=True)

    def _fit_model(self, corpus, epochs):
        env = self._open_lmdb(corpus == 'cached')
        if corpus != 'cached':
            with env.begin(write=True, buffers=True) as txn:
                seq = LMDBSequence(txn, batch_size=32)
                self._corpus_to_vectors(corpus, seq)
        else:
            self.info("Reusing cached training data from previous run.")

        # fit the model using a read-only view of the LMDB
        with env.begin(buffers=True) as txn:
            seq = LMDBSequence(txn, batch_size=32)
            self._model.fit(seq, verbose=True, 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']))