Source code for finetune.target_models.classifier

import tensorflow as tf
import numpy as np
from imblearn.over_sampling import RandomOverSampler
from sklearn.utils import shuffle

from finetune.base import BaseModel
from finetune.encoding.target_encoders import OneHotLabelEncoder
from finetune.nn.target_blocks import classifier
from finetune.input_pipeline import BasePipeline

class ClassificationPipeline(BasePipeline):

    def resampling(self, Xs, Y):
        if self.config.oversample:
            idxs, Ys = shuffle(*RandomOverSampler().fit_sample([[i] for i in range(len(Xs))], Y))
            return [Xs[i[0]] for i in idxs], Ys
        return Xs, Y

    def _target_encoder(self):
        return OneHotLabelEncoder()

[docs]class Classifier(BaseModel): """ Classifies a single document into 1 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 _get_input_pipeline(self): return ClassificationPipeline(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): """ 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)
[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: integer or string-valued class labels. :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)
@classmethod def get_eval_fn(cls): return lambda labels, targets: np.mean(np.asarray(labels) == np.asarray(targets)) @staticmethod def _target_model(config, featurizer_state, targets, n_outputs, train=False, reuse=None, **kwargs): return classifier( hidden=featurizer_state['features'], targets=targets, n_targets=n_outputs, config=config, train=train, reuse=reuse, **kwargs ) def _predict_op(self, logits, **kwargs): return tf.argmax(logits, -1) def _predict_proba_op(self, logits, **kwargs): return tf.nn.softmax(logits, -1)