annif/backend/nn_ensemble.py
"""Neural network based ensemble backend that combines results from multiple
projects."""
from __future__ import annotations
import importlib
import json
import os.path
import shutil
import zipfile
from io import BytesIO
from typing import TYPE_CHECKING, Any
import joblib
import keras.backend as K
import lmdb
import numpy as np
from keras.layers import Add, Dense, Dropout, Flatten, Input, Layer
from keras.models import Model
from keras.saving import load_model
from keras.utils import Sequence
from scipy.sparse import csc_matrix, csr_matrix
import annif.corpus
import annif.parallel
import annif.util
from annif.exception import (
NotInitializedException,
NotSupportedException,
OperationFailedException,
)
from annif.suggestion import SuggestionBatch, vector_to_suggestions
from . import backend, ensemble
if TYPE_CHECKING:
from tensorflow.python.framework.ops import EagerTensor
from annif.corpus.document import DocumentCorpus
logger = annif.logger
def idx_to_key(idx: int) -> bytes:
"""convert an integer index to a binary key for use in LMDB"""
return b"%08d" % idx
def key_to_idx(key: memoryview | bytes) -> int:
"""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():
# 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: np.ndarray, targets: np.ndarray) -> None:
# 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: int) -> tuple[np.ndarray, np.ndarray]:
"""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) -> int:
"""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: EagerTensor) -> EagerTensor:
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.keras"
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 initialize(self, parallel: bool = False) -> None:
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))
try:
self._model = load_model(
model_filename, custom_objects={"MeanLayer": MeanLayer}
)
except Exception as err:
metadata = self.get_model_metadata(model_filename)
keras_version = importlib.metadata.version("keras")
message = (
f"loading Keras model from {model_filename}; "
f"model metadata: {metadata}; "
f"you have Keras version {keras_version}. "
f'Original error message: "{err}"'
)
raise OperationFailedException(message, backend_id=self.backend_id)
def _merge_source_batches(
self,
batch_by_source: dict[str, SuggestionBatch],
sources: list[tuple[str, float]],
params: dict[str, Any],
) -> SuggestionBatch:
src_weight = dict(sources)
score_vectors = np.array(
[
[
np.sqrt(suggestions.as_vector())
* src_weight[project_id]
* len(batch_by_source)
for suggestions in batch
]
for project_id, batch in batch_by_source.items()
],
dtype=np.float32,
).transpose(1, 2, 0)
prediction = self._model(score_vectors).numpy()
return SuggestionBatch.from_sequence(
[
vector_to_suggestions(row, limit=int(params["limit"]))
for row in prediction
],
self.project.subjects,
)
def _create_model(self, sources: list[tuple[str, float]]) -> None:
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: DocumentCorpus,
params: dict[str, Any],
jobs: int = 0,
) -> None:
sources = annif.util.parse_sources(self.params["sources"])
self._create_model(sources)
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: DocumentCorpus,
seq: LMDBSequence,
n_jobs: int,
) -> None:
# 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, subject_set in pool.imap_unordered(
psmap.suggest, corpus.documents
):
doc_scores = []
for project_id, p_hits in hits.items():
vector = p_hits.as_vector()
doc_scores.append(
np.sqrt(vector) * sources[project_id] * len(sources)
)
score_vector = np.array(doc_scores, dtype=np.float32).transpose()
true_vector = subject_set.as_vector(len(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: DocumentCorpus,
epochs: int,
lmdb_map_size: int,
n_jobs: int = 1,
) -> None:
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=True, epochs=epochs)
annif.util.atomic_save(self._model, self.datadir, self.MODEL_FILE)
def _learn(
self,
corpus: DocumentCorpus,
params: dict[str, Any],
) -> None:
self.initialize()
self._fit_model(
corpus, int(params["learn-epochs"]), int(params["lmdb_map_size"])
)
def get_model_metadata(self, model_filename: str) -> dict | None:
"""Read metadata from Keras model files."""
try:
with zipfile.ZipFile(model_filename, "r") as zip:
with zip.open("metadata.json") as metadata_file:
metadata_str = metadata_file.read().decode("utf-8")
metadata = json.loads(metadata_str)
return metadata
except Exception:
self.warning(f"Failed to read metadata from {model_filename}")
return None