import tensorflow as tf
from finetune.base import BaseModel
from finetune.encoding.target_encoders import OrdinalRegressionEncoder
from finetune.nn.target_blocks import ordinal_regressor
from finetune.input_pipeline import BasePipeline
from finetune.target_models.comparison_regressor import ComparisonRegressionPipeline
class OrdinalRegressionPipeline(BasePipeline):
def _target_encoder(self):
return OrdinalRegressionEncoder()
class ComparisonOrdinalRegressionPipeline(ComparisonRegressionPipeline):
def _target_encoder(self):
return OrdinalRegressionEncoder()
[docs]class OrdinalRegressor(BaseModel):
"""
Classifies a document into two or more ordered categories.
For a full list of configuration options, see `finetune.config`.
:param config: A config object generated by `finetune.config.get_config` or None (for default config).
:param \**kwargs: key-value pairs of config items to override.
"""
def __init__(self, shared_threshold_weights=True, **kwargs):
super().__init__(**kwargs)
self.config.shared_threshold_weights = shared_threshold_weights
def _get_input_pipeline(self):
return OrdinalRegressionPipeline(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 self._featurize(X)
[docs] def predict(self, X):
"""
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.
"""
return super().predict(X).tolist()
[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.
"""
raise AttributeError("`Regressor` model does not support `predict_proba`.")
[docs] def finetune(self, X, Y=None, batch_size=None):
"""
:param X: list or array of text.
:param Y: floating point targets
: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)
@staticmethod
def _target_model(config, featurizer_state, targets, n_outputs, train=False, reuse=None, **kwargs):
return ordinal_regressor(
hidden=featurizer_state['features'],
targets=targets,
n_targets=n_outputs,
config=config,
train=train,
reuse=reuse,
shared_threshold_weights=config.shared_threshold_weights,
**kwargs
)
def _predict_op(self, logits, **kwargs):
return logits
def _predict_proba_op(self, logits, **kwargs):
return logits
[docs]class ComparisonOrdinalRegressor(OrdinalRegressor):
"""
Compares two documents and classifies into two or more ordered categories.
For a full list of configuration options, see `finetune.config`.
:param config: A config object generated by `finetune.config.get_config` or None (for default config).
:param \**kwargs: key-value pairs of config items to override.
"""
def _get_input_pipeline(self):
return ComparisonOrdinalRegressionPipeline(self.config)
@staticmethod
def _target_model(config, featurizer_state, targets, n_outputs, train=False, reuse=None, **kwargs):
featurizer_state["sequence_features"] = tf.abs(tf.reduce_sum(featurizer_state["sequence_features"], 1))
featurizer_state["features"] = tf.abs(tf.reduce_sum(featurizer_state["features"], 1))
return ordinal_regressor(
hidden=featurizer_state['features'],
targets=targets,
n_targets=n_outputs,
config=config,
shared_threshold_weights=config.shared_threshold_weights,
train=train,
reuse=reuse,
**kwargs
)