import warnings
import tensorflow as tf
from finetune.base import BaseModel
from finetune.target_encoders import MultilabelClassificationEncoder
from finetune.network_modules import multi_classifier
from finetune.input_pipeline import BasePipeline
class MultilabelClassificationPipeline(BasePipeline):
def _target_encoder(self):
return MultilabelClassificationEncoder()
[docs]class MultiLabelClassifier(BaseModel):
"""
Classifies a single document into upto N of N categories.
:param config: A :py:class:`finetune.config.Settings` object or None (for default config).
:param \**kwargs: key-value pairs of config items to override.
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.threshold_placeholder = None
def _get_input_pipeline(self):
return MultilabelClassificationPipeline(self.config)
[docs] def featurize(self, X):
"""
Embeds inputs in learned feature space. Can be called before or after calling :meth:`finetune`.
:param X: list or array of text to embed.
:returns: np.array of features of shape (n_examples, embedding_size).
"""
return super().featurize(X)
[docs] def predict(self, X, threshold=None):
"""
Produces a list of most likely class labels as determined by the fine-tuned model.
:param X: list or array of text to embed.
:returns: list of class labels.
"""
self.config._threshold = threshold or self.config.multi_label_threshold
return self._predict(X)
[docs] def predict_proba(self, X):
"""
Produces a probability distribution over classes for each example in X.
:param X: list or array of text to embed.
:returns: list of dictionaries. Each dictionary maps from a class label to its assigned class probability.
"""
return super().predict_proba(X)
[docs] def finetune(self, X, Y=None, batch_size=None):
"""
:param X: list or array of text.
:param Y: A list of lists containing labels for the corresponding X
:param batch_size: integer number of examples per batch. When N_GPUS > 1, this number
corresponds to the number of training examples provided to each GPU.
"""
return super().finetune(X, Y=Y, batch_size=batch_size)
def _target_model(self, featurizer_state, targets, n_outputs, train=False, reuse=None, **kwargs):
return multi_classifier(
hidden=featurizer_state['features'],
targets=targets,
n_targets=n_outputs,
config=self.config,
train=train,
reuse=reuse,
**kwargs
)
def _predict_op(self, logits, **kwargs):
threshold = kwargs.get("threshold", self.config.multi_label_threshold)
return tf.cast(tf.nn.sigmoid(logits) > threshold, tf.int32)
def _predict_proba_op(self, logits, **kwargs):
return tf.nn.sigmoid(logits)