Models subpackage melusine.models

TODO : ADD DESCRIPTION OF THE SUBPACKAGE

List of submodules

NeuralArchitectures melusine.models.neural_architectures

melusine.models.neural_architectures.bert_model(ntargets=18, seq_max=100, nb_meta=134, loss='categorical_crossentropy', activation='softmax', bert_model='jplu/tf-camembert-base')[source]

Pre-defined architecture of a pre-trained Bert model.

Parameters
ntargetsint, optional

Dimension of model output. Default value, 18.

seq_maxint, optional

Maximum input length. Default value, 100.

nb_metaint, optional

Dimension of meta data input. Default value, 252.

lossstr, optional

Loss function for training. Default value, ‘categorical_crossentropy’.

activationstr, optional

Activation function. Default value, ‘softmax’.

bert_modelstr, optional

Model name from HuggingFace library or path to local model Only Camembert and Flaubert supported Default value, ‘camembert-base’

Returns
Model instance
melusine.models.neural_architectures.cnn_model(embedding_matrix_init, ntargets, seq_max, nb_meta, loss='categorical_crossentropy', activation='softmax')[source]

Pre-defined architecture of a CNN model.

Parameters
embedding_matrix_initnp.array,

Pretrained embedding matrix.

ntargetsint, optional

Dimension of model output. Default value, 18.

seq_maxint, optional

Maximum input length. Default value, 100.

nb_metaint, optional

Dimension of meta data input. Default value, 252.

lossstr, optional

Loss function for training. Default value, ‘categorical_crossentropy’.

activationstr, optional

Activation function. Default value, ‘softmax’.

Returns
Model instance
melusine.models.neural_architectures.rnn_model(embedding_matrix_init, ntargets=18, seq_max=100, nb_meta=252, loss='categorical_crossentropy', activation='softmax')[source]

Pre-defined architecture of a RNN model.

Parameters
embedding_matrix_initnp.array,

Pretrained embedding matrix.

ntargetsint, optional

Dimension of model output. Default value, 18.

seq_maxint, optional

Maximum input length. Default value, 100.

nb_metaint, optional

Dimension of meta data input. Default value, 252.

lossstr, optional

Loss function for training. Default value, ‘categorical_crossentropy’.

activationstr, optional

Activation function. Default value, ‘softmax’.

Returns
Model instance
melusine.models.neural_architectures.transformers_model(embedding_matrix_init, ntargets=18, seq_max=100, nb_meta=134, loss='categorical_crossentropy', activation='softmax')[source]

Pre-defined architecture of a Transformer model.

Parameters
embedding_matrix_initnp.array,

Pretrained embedding matrix.

ntargetsint, optional

Dimension of model output. Default value, 18.

seq_maxint, optional

Maximum input length. Default value, 100.

nb_metaint, optional

Dimension of meta data input. Default value, 252.

lossstr, optional

Loss function for training. Default value, ‘categorical_crossentropy’.

activationstr, optional

Activation function. Default value, ‘softmax’.

Returns
Model instance

Train melusine.models.train

class melusine.models.train.NeuralModel[source]

Bases: sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin

Generic class for neural models.

It is compatible with scikit-learn API (i.e. contains fit, transform methods).

Parameters
neural_architecture_functionfunction,

Function which returns a Model instance from Keras. Implemented model functions are: cnn_model, rnn_model, transformers_model, bert_model

pretrained_embeddingnp.array,

Pretrained embedding matrix.

text_input_columnstr,

Input text column to consider for the model.

meta_input_listlist, optional

List of the names of the columns containing the metadata. If empty list or None the model is used without metadata Default value, [‘extension’, ‘dayofweek’, ‘hour’, ‘min’].

vocab_sizeint, optional

Size of vocabulary for neurol network model. Default value, 25000.

seq_sizeint, optional

Maximum size of input for neural model. Default value, 100.

lossstr, optional

Loss function for training. Default value, ‘categorical_crossentropy’.

activationstr, optional

Activation function. Default value, ‘softmax’.

batch_sizeint, optional

Size of batches for the training of the neural network model. Default value, 4096.

n_epochsint, optional

Number of epochs for the training of the neural network model. Default value, 15.

bert_tokenizerstr, optional

Tokenizer name from HuggingFace library or path to local tokenizer Only Camembert and Flaubert supported Default value, ‘camembert-base’

bert_modelstr, optional

Model name from HuggingFace library or path to local model

Only Camembert and Flaubert supported Default value, ‘camembert-base’

Examples

>>> from melusine.models.train import NeuralModel
>>> from melusine.models.neural_architectures import cnn_model
>>> from melusine.nlp_tools.embedding import Embedding
>>> pretrained_embedding = Embedding.load()
>>> list_meta = ['extension', 'dayofweek', 'hour']
>>> nn_model = NeuralModel(cnn_model, pretrained_embedding, list_meta)  #noqa
>>> nn_model.fit(X_train, y_train)  #noqa
>>> y_res = nn_model.predict(X_test)  #noqa
Attributes
architecture_function, pretrained_embedding, text_input_column,
meta_input_list, vocab_size, seq_size, loss, batch_size, n_epochs,
modelModel instance from Keras,
tokenizerTokenizer instance from Melusine,
embedding_matrixnp.array,

Embedding matrix used as input for the neural network model.

fit(X_train, y_train, tensorboard_log_dir=None, validation_data=None, **kwargs)[source]

Fit the neural network model on X and y. If meta_input list is empty list or None the model is used without metadata.

Compatible with scikit-learn API.

Parameters
X_trainpd.DataFrame
y_trainpd.Series
tensorboard_log_dirstr

If not None, will be used as path to write logs for tensorboard Tensordboard callback parameters can be changed in config file

validation_data: tuple

Tuple of validation data Data on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. This could be a tuple (x_val, y_val). validation_data will override validation_split. Default value, None.

Returns
selfobject

Returns the instance

load_nn_model(filepath)[source]

Save model from json and load weights from .h5.

predict(X, **kwargs)[source]

Returns the class predicted.

Parameters
Xpd.DataFrame
Returns
int
predict_proba(X, **kwargs)[source]

Returns the probabilities associated to each classes. If meta_input list is empty list or None the model is used without metadata.

Parameters
Xpd.DataFrame
Returns
np.array
save_nn_model(filepath)[source]

Save model to pickle, json and save weights to .h5.

tokens_to_indices(tokens)[source]

Input : list of tokens [“ma”, “carte_verte”, …] Output : list of indices [46, 359, …]