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some first experiments with different architectures and metrics
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svlandeg
parent
9d089c0410
commit
b6d788064a
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@ -4,17 +4,17 @@ from __future__ import unicode_literals
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import os
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import datetime
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from os import listdir
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import numpy as np
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from examples.pipeline.wiki_entity_linking import run_el, training_set_creator, kb_creator
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from spacy._ml import SpacyVectors, create_default_optimizer, zero_init, cosine
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from spacy._ml import SpacyVectors, create_default_optimizer, zero_init
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from thinc.api import chain
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from thinc.api import chain, flatten_add_lengths, with_getitem, clone, with_flatten
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from thinc.v2v import Model, Maxout, Softmax, Affine, ReLu
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from thinc.api import flatten_add_lengths
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from thinc.t2v import Pooling, sum_pool, mean_pool
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from thinc.t2t import ExtractWindow, ParametricAttention
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from thinc.misc import Residual
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from thinc.misc import Residual, LayerNorm as LN
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""" TODO: this code needs to be implemented in pipes.pyx"""
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@ -29,8 +29,8 @@ class EL_Model():
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self.nlp = nlp
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self.kb = kb
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self.entity_encoder = self._simple_encoder(width=300)
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self.article_encoder = self._simple_encoder(width=300)
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self.entity_encoder = self._simple_encoder(in_width=300, out_width=96)
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self.article_encoder = self._simple_encoder(in_width=300, out_width=96)
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def train_model(self, training_dir, entity_descr_output, limit=None, to_print=True):
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instances, pos_entities, neg_entities, doc_by_article = self._get_training_data(training_dir,
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@ -61,13 +61,36 @@ class EL_Model():
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# elif not neg_exs:
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# print("Weird. Couldn't find neg examples for", inst_cluster)
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def _simple_encoder(self, width):
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with Model.define_operators({">>": chain}):
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def _simple_encoder(self, in_width, out_width):
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conv_depth = 1
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cnn_maxout_pieces = 3
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with Model.define_operators({">>": chain, "**": clone}):
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# encoder = SpacyVectors \
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# >> flatten_add_lengths \
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# >> ParametricAttention(in_width)\
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# >> Pooling(mean_pool) \
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# >> Residual(zero_init(Maxout(in_width, in_width))) \
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# >> zero_init(Affine(out_width, in_width, drop_factor=0.0))
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encoder = SpacyVectors \
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>> flatten_add_lengths \
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>> ParametricAttention(width)\
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>> Pooling(sum_pool) \
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>> Residual(zero_init(Maxout(width, width)))
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>> flatten_add_lengths \
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>> with_getitem(0, Affine(in_width, in_width)) \
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>> ParametricAttention(in_width) \
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>> Pooling(sum_pool) \
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>> Residual(ReLu(in_width, in_width)) ** conv_depth \
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>> zero_init(Affine(out_width, in_width, drop_factor=0.0))
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# >> zero_init(Affine(nr_class, width, drop_factor=0.0))
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# >> logistic
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# convolution = Residual(
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# ExtractWindow(nW=1)
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# >> LN(Maxout(width, width * 3, pieces=cnn_maxout_pieces))
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# )
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# embed = SpacyVectors >> LN(Maxout(width, width, pieces=3))
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# encoder = SpacyVectors >> flatten_add_lengths >> convolution ** conv_depth
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# encoder = with_flatten(embed >> convolution ** conv_depth, pad=conv_depth)
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return encoder
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@ -80,25 +103,56 @@ class EL_Model():
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doc_encoding, article_bp = self.article_encoder.begin_update([article_doc], drop=drop)
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true_entity_encoding, true_entity_bp = self.entity_encoder.begin_update([true_entity], drop=drop)
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# true_similarity = cosine(true_entity_encoding, doc_encoding)
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# print("true_similarity", true_similarity)
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# print("encoding dim", len(true_entity_encoding[0]))
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# for false_entity in false_entities:
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# false_entity_encoding, false_entity_bp = self.entity_encoder.begin_update([false_entity], drop=drop)
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# false_similarity = cosine(false_entity_encoding, doc_encoding)
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# print("false_similarity", false_similarity)
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consensus_encoding = self._calculate_consensus(doc_encoding, true_entity_encoding)
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consensus_encoding_t = consensus_encoding.transpose()
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# print("entity/article output dim", len(entity_encoding[0]), len(doc_encoding[0]))
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doc_mse, doc_diffs = self._calculate_similarity(doc_encoding, consensus_encoding)
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mse, diffs = self._calculate_similarity(true_entity_encoding, doc_encoding)
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entity_mses = list()
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true_mse, true_diffs = self._calculate_similarity(true_entity_encoding, consensus_encoding)
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# print("true_mse", true_mse)
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# print("true_diffs", true_diffs)
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entity_mses.append(true_mse)
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# true_exp = np.exp(true_entity_encoding.dot(consensus_encoding_t))
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# print("true_exp", true_exp)
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# false_exp_sum = 0
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for false_entity in false_entities:
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false_entity_encoding, false_entity_bp = self.entity_encoder.begin_update([false_entity], drop=drop)
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false_mse, false_diffs = self._calculate_similarity(false_entity_encoding, consensus_encoding)
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# print("false_mse", false_mse)
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# false_exp = np.exp(false_entity_encoding.dot(consensus_encoding_t))
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# print("false_exp", false_exp)
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# print("false_diffs", false_diffs)
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entity_mses.append(false_mse)
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# if false_mse > true_mse:
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# true_diffs = true_diffs - false_diffs ???
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# false_exp_sum += false_exp
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# prob = true_exp / false_exp_sum
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# print("prob", prob)
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entity_mses = sorted(entity_mses)
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# mse_sum = sum(entity_mses)
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# entity_probs = [1 - x/mse_sum for x in entity_mses]
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# print("entity_mses", entity_mses)
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# print("entity_probs", entity_probs)
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true_index = entity_mses.index(true_mse)
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# print("true index", true_index)
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# print("true prob", entity_probs[true_index])
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print(true_mse)
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# print()
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# TODO: proper backpropagation taking ranking of elements into account ?
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# TODO backpropagation also for negative examples
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true_entity_bp(diffs, sgd=self.sgd_entity)
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article_bp(diffs, sgd=self.sgd_article)
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print(mse)
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true_entity_bp(true_diffs, sgd=self.sgd_entity)
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article_bp(doc_diffs, sgd=self.sgd_article)
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# TODO delete ?
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@ -124,11 +178,19 @@ class EL_Model():
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return mse
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# TODO: expand to more than 2 vectors
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def _calculate_consensus(self, vector1, vector2):
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if len(vector1) != len(vector2):
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raise ValueError("To calculate consenus, both vectors should be of equal length")
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avg = (vector2 + vector1) / 2
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return avg
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def _calculate_similarity(self, vector1, vector2):
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if len(vector1) != len(vector2):
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raise ValueError("To calculate similarity, both vectors should be of equal length")
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diffs = (vector2 - vector1)
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diffs = (vector1 - vector2)
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error_sum = (diffs ** 2).sum()
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mean_square_error = error_sum / len(vector1)
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return float(mean_square_error), diffs
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