[SOLVED] CS代考 # models.py

# models.py

from optimizers import *
from nerdata import *

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from utils import *

import random
import time

from collections import Counter
from typing import List

import numpy as np

class ProbabilisticSequenceScorer(object):
Scoring function for sequence models based on conditional probabilities.
Scores are provided for three potentials in the model: initial scores (applied to the first tag),
emissions, and transitions. Note that CRFs typically don’t use potentials of the first type.

Attributes:
tag_indexer: Indexer mapping BIO tags to indices. Useful for dynamic programming
word_indexer: Indexer mapping words to indices in the emission probabilities matrix
init_log_probs: [num_tags]-length array containing initial sequence log probabilities
transition_log_probs: [num_tags, num_tags] matrix containing transition log probabilities (prev, curr)
emission_log_probs: [num_tags, num_words] matrix containing emission log probabilities (tag, word)
def __init__(self, tag_indexer: Indexer, word_indexer: Indexer, init_log_probs: np.ndarray, transition_log_probs: np.ndarray, emission_log_probs: np.ndarray):
self.tag_indexer = tag_indexer
self.word_indexer = word_indexer
self.init_log_probs = init_log_probs
self.transition_log_probs = transition_log_probs
self.emission_log_probs = emission_log_probs

def score_init(self, sentence_tokens: List[Token], tag_idx: int):
return self.init_log_probs[tag_idx]

def score_transition(self, sentence_tokens: List[Token], prev_tag_idx: int, curr_tag_idx: int):
return self.transition_log_probs[prev_tag_idx, curr_tag_idx]

def score_emission(self, sentence_tokens: List[Token], tag_idx: int, word_posn: int):
word = sentence_tokens[word_posn].word
word_idx = self.word_indexer.index_of(word) if self.word_indexer.contains(word) else self.word_indexer.index_of(“UNK”)
return self.emission_log_probs[tag_idx, word_idx]

class HmmNerModel(object):
HMM NER model for predicting tags

Attributes:
tag_indexer: Indexer mapping BIO tags to indices. Useful for dynamic programming
word_indexer: Indexer mapping words to indices in the emission probabilities matrix
init_log_probs: [num_tags]-length array containing initial sequence log probabilities
transition_log_probs: [num_tags, num_tags] matrix containing transition log probabilities (prev, curr)
emission_log_probs: [num_tags, num_words] matrix containing emission log probabilities (tag, word)
def __init__(self, tag_indexer: Indexer, word_indexer: Indexer, init_log_probs, transition_log_probs, emission_log_probs):
self.tag_indexer = tag_indexer
self.word_indexer = word_indexer
self.init_log_probs = init_log_probs
self.transition_log_probs = transition_log_probs
self.emission_log_probs = emission_log_probs

def decode(self, sentence_tokens: List[Token]) -> LabeledSentence:
See BadNerModel for an example implementation
:param sentence_tokens: List of the tokens in the sentence to tag
:return: The LabeledSentence consisting of predictions over the sentence
raise Exception(“IMPLEMENT ME”)

def train_hmm_model(sentences: List[LabeledSentence], silent: bool=False) -> HmmNerModel:
Uses maximum-likelihood estimation to read an HMM off of a corpus of sentences.
Any word that only appears once in the corpus is replaced with UNK. A small amount
of additive smoothing is applied.
:param sentences: training corpus of LabeledSentence objects
:return: trained HmmNerModel
# Index words and tags. We do this in advance so we know how big our
# matrices need to be.
tag_indexer = Indexer()
word_indexer = Indexer()
word_indexer.add_and_get_index(“UNK”)
word_counter = Counter()
for sentence in sentences:
for token in sentence.tokens:
word_counter[token.word] += 1.0
for sentence in sentences:
for token in sentence.tokens:
# If the word occurs fewer than two times, don’t index it — we’ll treat it as UNK
get_word_index(word_indexer, word_counter, token.word)
for tag in sentence.get_bio_tags():
tag_indexer.add_and_get_index(tag)
# Count occurrences of initial tags, transitions, and emissions
# Apply additive smoothing to avoid log(0) / infinities / etc.
init_counts = np.ones((len(tag_indexer)), dtype=float) * 0.001
transition_counts = np.ones((len(tag_indexer),len(tag_indexer)), dtype=float) * 0.001
emission_counts = np.ones((len(tag_indexer),len(word_indexer)), dtype=float) * 0.001
for sentence in sentences:
bio_tags = sentence.get_bio_tags()
for i in range(0, len(sentence)):
tag_idx = tag_indexer.add_and_get_index(bio_tags[i])
word_idx = get_word_index(word_indexer, word_counter, sentence.tokens[i].word)
emission_counts[tag_idx][word_idx] += 1.0
if i == 0:
init_counts[tag_idx] += 1.0
transition_counts[tag_indexer.add_and_get_index(bio_tags[i-1])][tag_idx] += 1.0
# Turn counts into probabilities for initial tags, transitions, and emissions. All
# probabilities are stored as log probabilities
if not silent:
print(repr(init_counts))
init_counts = np.log(init_counts / init_counts.sum())
# transitions are stored as count[prev state][next state], so we sum over the second axis
# and normalize by that to get the right conditional probabilities
transition_counts = np.log(transition_counts / transition_counts.sum(axis=1)[:, np.newaxis])
# similar to transitions
emission_counts = np.log(emission_counts / emission_counts.sum(axis=1)[:, np.newaxis])
if not silent:
print(“Tag indexer: %s” % tag_indexer)
print(“Initial state log probabilities: %s” % init_counts)
print(“Transition log probabilities: %s” % transition_counts)
print(“Emission log probs too big to print…”)
print(“Emission log probs for India: %s” % emission_counts[:,word_indexer.add_and_get_index(“India”)])
print(“Emission log probs for Phil: %s” % emission_counts[:,word_indexer.add_and_get_index(“Phil”)])
print(” note that these distributions don’t normalize because it’s p(word|tag) that normalizes, not p(tag|word)”)
return HmmNerModel(tag_indexer, word_indexer, init_counts, transition_counts, emission_counts)

def get_word_index(word_indexer: Indexer, word_counter: Counter, word: str) -> int:
Retrieves a word’s index based on its count. If the word occurs only once, treat it as an “UNK” token
At test time, unknown words will be replaced by UNKs.
:param word_indexer: Indexer mapping words to indices for HMM featurization
:param word_counter: Counter containing word counts of training set
:param word: string word
:return: int of the word index
if word_counter[word] < 1.5:return word_indexer.add_and_get_index(“UNK”)return word_indexer.add_and_get_index(word)################### CRF code followsclass FeatureBasedSequenceScorer(object):Feature-based sequence scoring model. Note that this scorer is instantiated *for every example*: it containsthe feature cache used for that example.def __init__(self, tag_indexer, feature_weights, feat_cache):self.tag_indexer = tag_indexerself.feature_weights = feature_weightsself.feat_cache = feat_cachedef score_init(self, sentence, tag_idx):if isI(self.tag_indexer.get_object(tag_idx)):return -1000def score_transition(self, sentence_tokens, prev_tag_idx, curr_tag_idx):prev_tag = self.tag_indexer.get_object(prev_tag_idx)curr_tag = self.tag_indexer.get_object(curr_tag_idx)if (isO(prev_tag) and isI(curr_tag))or (isB(prev_tag) and isI(curr_tag) and get_tag_label(prev_tag) != get_tag_label(curr_tag)) or (isI(prev_tag) and isI(curr_tag) and get_tag_label(prev_tag) != get_tag_label(curr_tag)):return -1000def score_emission(self, sentence_tokens, tag_idx, word_posn):feats = self.feat_cache[word_posn][tag_idx]return self.feature_weights.score(feats)class CrfNerModel(object):def __init__(self, tag_indexer, feature_indexer, feature_weights):self.tag_indexer = tag_indexerself.feature_indexer = feature_indexerself.feature_weights = feature_weightsdef decode(self, sentence_tokens: List[Token]) -> LabeledSentence:
See BadNerModel for an example implementation
:param sentence_tokens: List of the tokens in the sentence to tag
:return: The LabeledSentence consisting of predictions over the sentence
raise Exception(“IMPLEMENT ME”)

def decode_beam(self, sentence_tokens: List[Token]) -> LabeledSentence:
See BadNerModel for an example implementation
:param sentence_tokens: List of the tokens in the sentence to tag
:return: The LabeledSentence consisting of predictions over the sentence
raise Exception(“IMPLEMENT ME”)

def train_crf_model(sentences: List[LabeledSentence], silent: bool=False) -> CrfNerModel:
Trains a CRF NER model on the given corpus of sentences.
:param sentences: The training data
:param silent: True to suppress output, false to print certain debugging outputs
:return: The CrfNerModel, which is primarily a wrapper around the tag + feature indexers as well as weights
tag_indexer = Indexer()
for sentence in sentences:
for tag in sentence.get_bio_tags():
tag_indexer.add_and_get_index(tag)
if not silent:
print(“Extracting features”)
feature_indexer = Indexer()
# 4-d list indexed by sentence index, word index, tag index, feature index
feature_cache = [[[[] for k in range(0, len(tag_indexer))] for j in range(0, len(sentences[i]))] for i in range(0, len(sentences))]
for sentence_idx in range(0, len(sentences)):
if sentence_idx % 100 == 0 and not silent:
print(“Ex %i/%i” % (sentence_idx, len(sentences)))
for word_idx in range(0, len(sentences[sentence_idx])):
for tag_idx in range(0, len(tag_indexer)):
feature_cache[sentence_idx][word_idx][tag_idx] = extract_emission_features(sentences[sentence_idx].tokens, word_idx, tag_indexer.get_object(tag_idx), feature_indexer, add_to_indexer=True)
if not silent:
print(“Training”)
weight_vector = UnregularizedAdagradTrainer(np.zeros((len(feature_indexer))), eta=1.0)
num_epochs = 3
random.seed(0)
for epoch in range(0, num_epochs):
epoch_start = time.time()
if not silent:
print(“Epoch %i” % epoch)
sent_indices = [i for i in range(0, len(sentences))]
random.shuffle(sent_indices)
total_obj = 0.0
for counter, i in enumerate(sent_indices):
if counter % 100 == 0 and not silent:
print(“Ex %i/%i” % (counter, len(sentences)))
scorer = FeatureBasedSequenceScorer(tag_indexer, weight_vector, feature_cache[i])
(gold_log_prob, gradient) = compute_gradient(sentences[i], tag_indexer, scorer, feature_indexer)
total_obj += gold_log_prob
weight_vector.apply_gradient_update(gradient, 1)
if not silent:
print(“Objective for epoch: %.2f in time %.2f” % (total_obj, time.time() – epoch_start))
return CrfNerModel(tag_indexer, feature_indexer, weight_vector)

def extract_emission_features(sentence_tokens: List[Token], word_index: int, tag: str, feature_indexer: Indexer, add_to_indexer: bool):
Extracts emission features for tagging the word at word_index with tag.
:param sentence_tokens: sentence to extract over
:param word_index: word index to consider
:param tag: the tag that we’re featurizing for
:param feature_indexer: Indexer over features
:param add_to_indexer: boolean variable indicating whether we should be expanding the indexer or not. This should
be True at train time (since we want to learn weights for all features) and False at test time (to avoid creating
any features we don’t have weights for).
:return: an ndarray
feats = []
curr_word = sentence_tokens[word_index].word
# Lexical and POS features on this word, the previous, and the next (Word-1, Word0, Word1)
for idx_offset in range(-1, 2):
if word_index + idx_offset < 0:active_word = “ ”
elif word_index + idx_offset >= len(sentence_tokens):
active_word = “”
active_word = sentence_tokens[word_index + idx_offset].word
if word_index + idx_offset < 0:active_pos = “ ”
elif word_index + idx_offset >= len(sentence_tokens):
active_pos = “”
active_pos = sentence_tokens[word_index + idx_offset].pos
maybe_add_feature(feats, feature_indexer, add_to_indexer, tag + “:Word” + repr(idx_offset) + “=” + active_word)
maybe_add_feature(feats, feature_indexer, add_to_indexer, tag + “:Pos” + repr(idx_offset) + “=” + active_pos)
# Character n-grams of the current word
max_ngram_size = 3
for ngram_size in range(1, max_ngram_size+1):
start_ngram = curr_word[0:min(ngram_size, len(curr_word))]
maybe_add_feature(feats, feature_indexer, add_to_indexer, tag + “:StartNgram=” + start_ngram)
end_ngram = curr_word[max(0, len(curr_word) – ngram_size):]
maybe_add_feature(feats, feature_indexer, add_to_indexer, tag + “:EndNgram=” + end_ngram)
# Look at a few word shape features
maybe_add_feature(feats, feature_indexer, add_to_indexer, tag + “:IsCap=” + repr(curr_word[0].isupper()))
# Compute word shape
new_word = []
for i in range(0, len(curr_word)):
if curr_word[i].isupper():
new_word += “X”
elif curr_word[i].islower():
new_word += “x”
elif curr_word[i].isdigit():
new_word += “0”
new_word += “?”
maybe_add_feature(feats, feature_indexer, add_to_indexer, tag + “:WordShape=” + repr(new_word))
return np.asarray(feats, dtype=int)

def compute_gradient(sentence: LabeledSentence, tag_indexer: Indexer, scorer: FeatureBasedSequenceScorer, feature_indexer: Indexer) -> (float, Counter):
Computes the gradient of the given example (sentence). The bulk of this code will be computing marginals via
forward-backward: you should first compute these marginals, then accumulate the gradient based on the log
probabilities.
:param sentence: The LabeledSentence of the current example
:param tag_indexer: The Indexer of the tags
:param scorer: FeatureBasedSequenceScorer is a scoring model that wraps the weight vector and which also contains a
feat_cache field that will be useful when computing the gradient.
:param feature_indexer: The Indexer of the features
:return: A tuple of two items. The first is the log probability of the correct sequence, which corresponds to the
training objective. This value is only needed for printing, so technically you do not *need* to return it, but it
will probably be useful to compute for debugging purposes.
The second value is a Counter containing the gradient — this is a sparse map from indices (features)
to weights (gradient values).
raise Exception(“IMPLEMENT ME”)

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