Source code for asreview.models.classifiers.nn_2_layer

# Copyright 2019-2022 The ASReview Authors. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

import logging

    import tensorflow as tf
    from tensorflow.keras import regularizers
    from tensorflow.keras.layers import Dense
    from tensorflow.keras.models import Sequential
    from tensorflow.keras.wrappers.scikit_learn import KerasClassifier
except ImportError:
    TF_AVAILABLE = False
    except AttributeError:

import scipy

from asreview.models.classifiers.base import BaseTrainClassifier
from asreview.models.classifiers.lstm_base import _get_optimizer
from asreview.models.classifiers.utils import _set_class_weight

def _check_tensorflow():
    if not TF_AVAILABLE:
        raise ImportError(
            "Install tensorflow package to use"
            " Fully connected neural network (2 hidden layers).")

[docs]class NN2LayerClassifier(BaseTrainClassifier): """Fully connected neural network (2 hidden layers) classifier (``nn-2-layer``). Neural network with two hidden, dense layers of the same size. Recommended feature extraction model is :class:`asreview.models.feature_extraction.Doc2Vec`. .. note:: This model requires ``tensorflow`` to be installed. Use ``pip install tensorflow`` or install all optional ASReview dependencies with ``pip install asreview[all]`` .. warning:: Might crash on some systems with limited memory in combination with :class:`asreview.models.feature_extraction.Tfidf`. Arguments --------- dense_width: int Size of the dense layers. optimizer: str Name of the Keras optimizer. learn_rate: float Learning rate multiplier of the default learning rate. regularization: float Strength of the regularization on the weights and biases. verbose: int Verbosity of the model mirroring the values for Keras. epochs: int Number of epochs to train the neural network. batch_size: int Batch size used for the neural network. shuffle: bool Whether to shuffle the training data prior to training. class_weight: float Class weights for inclusions (1's). """ name = "nn-2-layer" label = "Fully connected neural network (2 hidden layers)" def __init__(self, dense_width=128, optimizer='rmsprop', learn_rate=1.0, regularization=0.01, verbose=0, epochs=35, batch_size=32, shuffle=False, class_weight=30.0): """Initialize the 2-layer neural network model.""" super(NN2LayerClassifier, self).__init__() self.dense_width = int(dense_width) self.optimizer = optimizer self.learn_rate = learn_rate self.regularization = regularization self.verbose = verbose self.epochs = int(epochs) self.batch_size = int(batch_size) self.shuffle = shuffle self.class_weight = class_weight self._model = None self.input_dim = None
[docs] def fit(self, X, y): # check is tensorflow is available _check_tensorflow() if scipy.sparse.issparse(X): X = X.toarray() if self._model is None or X.shape[1] != self.input_dim: self.input_dim = X.shape[1] keras_model = _create_dense_nn_model( self.input_dim, self.dense_width, self.optimizer, self.learn_rate, self.regularization, self.verbose) self._model = KerasClassifier(keras_model, verbose=self.verbose) X, y, batch_size=self.batch_size, epochs=self.epochs, shuffle=self.shuffle, verbose=self.verbose, class_weight=_set_class_weight(self.class_weight))
[docs] def predict_proba(self, X): if scipy.sparse.issparse(X): X = X.toarray() return super(NN2LayerClassifier, self).predict_proba(X)
[docs] def full_hyper_space(self): from hyperopt import hp hyper_choices = { "mdl_optimizer": ["sgd", "rmsprop", "adagrad", "adam", "nadam"] } hyper_space = { "mdl_dense_width": hp.quniform("mdl_dense_width", 2, 100, 1), "mdl_epochs": hp.quniform("mdl_epochs", 20, 60, 1), "mdl_optimizer": hp.choice("mdl_optimizer", hyper_choices["mdl_optimizer"]), "mdl_learn_rate": hp.lognormal("mdl_learn_rate", 0, 1), "mdl_class_weight": hp.lognormal("mdl_class_weight", 3, 1), "mdl_regularization": hp.lognormal("mdl_regularization", -4, 2), } return hyper_space, hyper_choices
def _create_dense_nn_model(vector_size=40, dense_width=128, optimizer='rmsprop', learn_rate_mult=1.0, regularization=0.01, verbose=1): """Return callable lstm model. Returns ------- callable: A function that return the Keras Sklearn model when called. """ # check is tensorflow is available _check_tensorflow() def model_wrapper(): model = Sequential() model.add( Dense( dense_width, input_dim=vector_size, kernel_regularizer=regularizers.l2(regularization), activity_regularizer=regularizers.l1(regularization), activation='relu', )) # add Dense layer with relu activation model.add( Dense( dense_width, kernel_regularizer=regularizers.l2(regularization), activity_regularizer=regularizers.l1(regularization), activation='relu', )) # add Dense layer model.add(Dense(1, activation='sigmoid')) optimizer_fn = _get_optimizer(optimizer, learn_rate_mult) # Compile model model.compile( loss='binary_crossentropy', optimizer=optimizer_fn, metrics=['acc']) if verbose >= 1: model.summary() return model return model_wrapper