annif.backend.nn_ensemble.NNEnsembleBackend.is_trained() - Code Metrics - Inspection of "Implement hyperparameter optimization of ensemble..." - NatLibFi/Annif - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Pull Request — master (#414)

by Osma

created 2020-07-21 12:37 UTC

A

↳ Parent: annif.backend.nn_ensemble.NNEnsembleBackend.default_params()

Complexity

Conditions

Size

Total Lines	3
Code Lines	3

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	3
dl	0
loc	3
rs	10
c	0
b	0
f	0
cc	1
nop	1

"""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, Lambda, Dropout
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 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 = {}
        params.update(super().default_params())
        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)

    def _merge_hits_from_sources(self, hits_from_sources, params):
        score_vector = np.array([hits.as_vector(subjects) * weight
                                 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 = Lambda(lambda x: K.mean(x, axis=2))(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'])

        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)
                doc_scores.append(
                    hits.as_vector(source_project.subjects) * weight)
            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')
        with env.begin(write=True, buffers=True) as txn:
            seq = LMDBSequence(txn, batch_size=32)
            if corpus != 'cached':
                self._corpus_to_vectors(corpus, seq)
            else:
                self.info("Reusing cached training data from previous run.")

            # fit the model
            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']))


1			"""Neural network based ensemble backend that combines results from multiple
2			projects."""
3
4
5			from io import BytesIO
6			import shutil
7			import os.path
8			import numpy as np
9			from scipy.sparse import csr_matrix, csc_matrix
10			import joblib
11			import lmdb
12			from tensorflow.keras.layers import Input, Dense, Add, Flatten, Lambda, Dropout
13			from tensorflow.keras.models import Model, load_model
14			from tensorflow.keras.utils import Sequence
15			import tensorflow.keras.backend as K
16			import annif.corpus
17			import annif.util
18			from annif.exception import NotInitializedException
19			from annif.suggestion import VectorSuggestionResult
20			from . import backend
21			from . import ensemble
22
23
24			def idx_to_key(idx):
25			"""convert an integer index to a binary key for use in LMDB"""
26			return b'%08d' % idx
27
28
29			def key_to_idx(key):
30			"""convert a binary LMDB key to an integer index"""
31			return int(key)
32
33
34			class LMDBSequence(Sequence):
35			"""A sequence of samples stored in a LMDB database."""
36
37			def __init__(self, txn, batch_size):
38			self._txn = txn
39			cursor = txn.cursor()
40			if cursor.last():
41			self._counter = key_to_idx(cursor.key())
42			else: # empty database
43			self._counter = 0
44			self._batch_size = batch_size
45
46			def add_sample(self, inputs, targets):
47			# use zero-padded 8-digit key
48			key = idx_to_key(self._counter)
49			self._counter += 1
50			# convert the sample into a sparse matrix and serialize it as bytes
51			sample = (csc_matrix(inputs), csr_matrix(targets))
52			buf = BytesIO()
53			joblib.dump(sample, buf)
54			buf.seek(0)
55			self._txn.put(key, buf.read())
56
57			def __getitem__(self, idx):
58			"""get a particular batch of samples"""
59			cursor = self._txn.cursor()
60			first_key = idx * self._batch_size
61			cursor.set_key(idx_to_key(first_key))
62			input_arrays = []
63			target_arrays = []
64			for key, value in cursor.iternext():
65			if key_to_idx(key) >= (first_key + self._batch_size):
66			break
67			input_csr, target_csr = joblib.load(BytesIO(value))
68			input_arrays.append(input_csr.toarray())
69			target_arrays.append(target_csr.toarray().flatten())
70			return np.array(input_arrays), np.array(target_arrays)
71
72			def __len__(self):
73			"""return the number of available batches"""
74			return int(np.ceil(self._counter / self._batch_size))
75
76
77			class NNEnsembleBackend(
78			backend.AnnifLearningBackend,
79			ensemble.BaseEnsembleBackend):
80			"""Neural network ensemble backend that combines results from multiple
81			projects"""
82
83			name = "nn_ensemble"
84
85			MODEL_FILE = "nn-model.h5"
86			LMDB_FILE = 'nn-train.mdb'
87			LMDB_MAP_SIZE = 1024 * 1024 * 1024
88
89			DEFAULT_PARAMETERS = {
90			'nodes': 100,
91			'dropout_rate': 0.2,
92			'optimizer': 'adam',
93			'epochs': 10,
94			'learn-epochs': 1,
95			}
96
97			# defaults for uninitialized instances
98			_model = None
99
100			def default_params(self):
101			params = {}
102			params.update(super().default_params())
103			params.update(self.DEFAULT_PARAMETERS)
104			return params
105
106			def initialize(self):
107			super().initialize()
108			if self._model is not None:
109			return # already initialized
110			model_filename = os.path.join(self.datadir, self.MODEL_FILE)
111			if not os.path.exists(model_filename):
112			raise NotInitializedException(
113			'model file {} not found'.format(model_filename),
114			backend_id=self.backend_id)
115			self.debug('loading Keras model from {}'.format(model_filename))
116			self._model = load_model(model_filename)
117
118			def _merge_hits_from_sources(self, hits_from_sources, params):
119			score_vector = np.array([hits.as_vector(subjects) * weight
120			for hits, weight, subjects
121			in hits_from_sources],
122			dtype=np.float32)
123			results = self._model.predict(
124			np.expand_dims(score_vector.transpose(), 0))
125			return VectorSuggestionResult(results[0])
126
127			def _create_model(self, sources):
128			self.info("creating NN ensemble model")
129
130			inputs = Input(shape=(len(self.project.subjects), len(sources)))
131
132			flat_input = Flatten()(inputs)
133			drop_input = Dropout(
134			rate=float(
135			self.params['dropout_rate']))(flat_input)
136			hidden = Dense(int(self.params['nodes']),
137			activation="relu")(drop_input)
138			drop_hidden = Dropout(rate=float(self.params['dropout_rate']))(hidden)
139			delta = Dense(len(self.project.subjects),
140			kernel_initializer='zeros',
141			bias_initializer='zeros')(drop_hidden)
142
143			mean = Lambda(lambda x: K.mean(x, axis=2))(inputs)
144
145			predictions = Add()([mean, delta])
146
147			self._model = Model(inputs=inputs, outputs=predictions)
148			self._model.compile(optimizer=self.params['optimizer'],
149			loss='binary_crossentropy',
150			metrics=['top_k_categorical_accuracy'])
151
152			summary = []
153			self._model.summary(print_fn=summary.append)
154			self.debug("Created model: \n" + "\n".join(summary))
155
156			def _train(self, corpus, params):
157			sources = annif.util.parse_sources(self.params['sources'])
158			self._create_model(sources)
159			self._fit_model(corpus, epochs=int(params['epochs']))
160
161			def _corpus_to_vectors(self, corpus, seq):
162			# pass corpus through all source projects
163			sources = [(self.project.registry.get_project(project_id), weight)
164			for project_id, weight
165			in annif.util.parse_sources(self.params['sources'])]
166
167			for doc in corpus.documents:
168			doc_scores = []
169			for source_project, weight in sources:
170			hits = source_project.suggest(doc.text)
171			doc_scores.append(
172			hits.as_vector(source_project.subjects) * weight)
173			score_vector = np.array(doc_scores,
174			dtype=np.float32).transpose()
175			subjects = annif.corpus.SubjectSet((doc.uris, doc.labels))
176			true_vector = subjects.as_vector(self.project.subjects)
177			seq.add_sample(score_vector, true_vector)
178
179			def _open_lmdb(self, cached):
180			lmdb_path = os.path.join(self.datadir, self.LMDB_FILE)
181			if not cached and os.path.exists(lmdb_path):
182			shutil.rmtree(lmdb_path)
183			return lmdb.open(lmdb_path, map_size=self.LMDB_MAP_SIZE, writemap=True)
184
185			def _fit_model(self, corpus, epochs):
186			env = self._open_lmdb(corpus == 'cached')
187			with env.begin(write=True, buffers=True) as txn:
188			seq = LMDBSequence(txn, batch_size=32)
189			if corpus != 'cached':
190			self._corpus_to_vectors(corpus, seq)
191			else:
192			self.info("Reusing cached training data from previous run.")
193
194			# fit the model
195			self._model.fit(seq, verbose=True, epochs=epochs)
196
197			annif.util.atomic_save(
198			self._model,
199			self.datadir,
200			self.MODEL_FILE)
201
202			def _learn(self, corpus, params):
203			self.initialize()
204			self._fit_model(corpus, int(params['learn-epochs']))
205

NatLibFi / Annif

Pull Request — master (#414)

A

Complexity

Size

Duplication

Importance

Duplication Side-by-Side

Filter issues like