Source code for deeppavlov.models.classifiers.cos_sim_classifier

# Copyright 2017 Neural Networks and Deep Learning lab, MIPT
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, softwaredata
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

from logging import getLogger
from typing import List, Tuple, Union

import numpy as np
from scipy.sparse import vstack, csr_matrix
from scipy.sparse.linalg import norm as sparse_norm

from deeppavlov.core.common.file import load_pickle
from deeppavlov.core.common.file import save_pickle
from deeppavlov.core.common.registry import register
from deeppavlov.core.models.estimator import Estimator
from deeppavlov.core.models.serializable import Serializable

logger = getLogger(__name__)

[docs]@register("cos_sim_classifier") class CosineSimilarityClassifier(Estimator, Serializable): """ Classifier based on cosine similarity between vectorized sentences Parameters: save_path: path to save the model load_path: path to load the model """ def __init__(self, top_n: int = 1, save_path: str = None, load_path: str = None, **kwargs) -> None: super().__init__(save_path=save_path, load_path=load_path, **kwargs) self.top_n = top_n self.x_train_features = self.y_train = None if kwargs['mode'] != 'train': self.load()
[docs] def __call__(self, q_vects: Union[csr_matrix, List]) -> Tuple[List[str], List[int]]: """Found most similar answer for input vectorized question Parameters: q_vects: vectorized questions Returns: Tuple of Answer and Score """ if isinstance(q_vects[0], csr_matrix): q_norm = sparse_norm(q_vects) if q_norm == 0.0: cos_similarities = np.zeros((q_vects.shape[0], self.x_train_features.shape[0])) else: norm = q_norm * sparse_norm(self.x_train_features, axis=1) cos_similarities = np.array( cos_similarities = cos_similarities / norm elif isinstance(q_vects[0], np.ndarray): q_vects = np.array(q_vects) self.x_train_features = np.array(self.x_train_features) norm = np.linalg.norm(q_vects) * np.linalg.norm(self.x_train_features, axis=1) cos_similarities = / norm elif q_vects[0] is None: cos_similarities = np.zeros(len(self.x_train_features)) else: raise NotImplementedError('Not implemented this type of vectors') # get cosine similarity for each class y_labels = np.unique(self.y_train) labels_scores = np.zeros((len(cos_similarities), len(y_labels))) for i, label in enumerate(y_labels): labels_scores[:, i] = np.max([cos_similarities[:, i] for i, value in enumerate(self.y_train) if value == label], axis=0) labels_scores_sum = labels_scores.sum(axis=1, keepdims=True) labels_scores = np.divide(labels_scores, labels_scores_sum, out=np.zeros_like(labels_scores), where=(labels_scores_sum != 0)) answer_ids = np.argsort(labels_scores)[:, -self.top_n:] # generate top_n answers and scores answers = [] scores = [] for i in range(len(answer_ids)): answers.extend([y_labels[id] for id in answer_ids[i, ::-1]]) scores.extend([np.round(labels_scores[i, id], 2) for id in answer_ids[i, ::-1]]) return answers, scores
[docs] def fit(self, x_train_vects: Tuple[Union[csr_matrix, List]], y_train: Tuple[str]) -> None: """Train classifier Parameters: x_train_vects: vectorized question for train dataset y_train: answers for train dataset Returns: None """ if isinstance(x_train_vects, tuple): if len(x_train_vects) != 0: if isinstance(x_train_vects[0], csr_matrix): self.x_train_features = vstack(list(x_train_vects)) elif isinstance(x_train_vects[0], np.ndarray): self.x_train_features = np.vstack(list(x_train_vects)) else: raise NotImplementedError('Not implemented this type of vectors') else: raise ValueError("Train vectors can't be empty") else: self.x_train_features = x_train_vects self.y_train = list(y_train)
[docs] def save(self) -> None: """Save classifier parameters""""Saving faq_model to {}".format(self.save_path)) save_pickle((self.x_train_features, self.y_train), self.save_path)
[docs] def load(self) -> None: """Load classifier parameters""""Loading faq_model from {}".format(self.load_path)) self.x_train_features, self.y_train = load_pickle(self.load_path)