Source code for finetune.target_models.ordinal_regressor

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 )