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CRF based sklearn_crfsuite.
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‎charpter24_CRF/crf.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### CRF"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"基于sklearn_crfsuite NER系统搭建，本例来自于sklearn_crfsuite官方tutorial"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 导入相关库\n",
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"import nltk\n",
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"import sklearn\n",
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"import scipy.stats\n",
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"from sklearn.metrics import make_scorer\n",
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"from sklearn.model_selection import cross_val_score\n",
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"from sklearn.model_selection import RandomizedSearchCV\n",
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"\n",
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"import sklearn_crfsuite\n",
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"from sklearn_crfsuite import scorers\n",
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"from sklearn_crfsuite import metrics"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"[nltk_data] Downloading package conll2002 to\n",
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"[nltk_data] C:\\Users\\92070\\AppData\\Roaming\\nltk_data...\n",
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"[nltk_data] Unzipping corpora\\conll2002.zip.\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# 基于NLTK下载示例数据集\n",
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"nltk.download('conll2002')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 设置训练和测试样本\n",
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"train_sents = list(nltk.corpus.conll2002.iob_sents('esp.train'))\n",
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"test_sents = list(nltk.corpus.conll2002.iob_sents('esp.testb'))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[('Melbourne', 'NP', 'B-LOC'),\n",
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" ('(', 'Fpa', 'O'),\n",
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" ('Australia', 'NP', 'B-LOC'),\n",
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" (')', 'Fpt', 'O'),\n",
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" (',', 'Fc', 'O'),\n",
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" ('25', 'Z', 'O'),\n",
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" ('may', 'NC', 'O'),\n",
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" ('(', 'Fpa', 'O'),\n",
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" ('EFE', 'NC', 'B-ORG'),\n",
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" (')', 'Fpt', 'O'),\n",
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" ('.', 'Fp', 'O')]"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"train_sents[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 单词转化为数值特征\n",
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"def word2features(sent, i):\n",
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" word = sent[i][0]\n",
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" postag = sent[i][1]\n",
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"\n",
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" features = {\n",
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" 'bias': 1.0,\n",
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" 'word.lower()': word.lower(),\n",
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" 'word[-3:]': word[-3:],\n",
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" 'word[-2:]': word[-2:],\n",
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" 'word.isupper()': word.isupper(),\n",
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" 'word.istitle()': word.istitle(),\n",
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" 'word.isdigit()': word.isdigit(),\n",
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" 'postag': postag,\n",
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" 'postag[:2]': postag[:2],\n",
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" }\n",
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" if i > 0:\n",
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" word1 = sent[i-1][0]\n",
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" postag1 = sent[i-1][1]\n",
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" features.update({\n",
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" '-1:word.lower()': word1.lower(),\n",
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" '-1:word.istitle()': word1.istitle(),\n",
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" '-1:word.isupper()': word1.isupper(),\n",
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" '-1:postag': postag1,\n",
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" '-1:postag[:2]': postag1[:2],\n",
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" })\n",
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" else:\n",
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" features['BOS'] = True\n",
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"\n",
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" if i < len(sent)-1:\n",
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" word1 = sent[i+1][0]\n",
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" postag1 = sent[i+1][1]\n",
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" features.update({\n",
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" '+1:word.lower()': word1.lower(),\n",
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" '+1:word.istitle()': word1.istitle(),\n",
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" '+1:word.isupper()': word1.isupper(),\n",
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" '+1:postag': postag1,\n",
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" '+1:postag[:2]': postag1[:2],\n",
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" })\n",
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" else:\n",
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" features['EOS'] = True\n",
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"\n",
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" return features\n",
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"\n",
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"\n",
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"def sent2features(sent):\n",
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" return [word2features(sent, i) for i in range(len(sent))]\n",
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"\n",
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"def sent2labels(sent):\n",
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" return [label for token, postag, label in sent]\n",
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"\n",
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"def sent2tokens(sent):\n",
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" return [token for token, postag, label in sent]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'bias': 1.0,\n",
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" 'word.lower()': 'melbourne',\n",
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" 'word[-3:]': 'rne',\n",
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" 'word[-2:]': 'ne',\n",
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" 'word.isupper()': False,\n",
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" 'word.istitle()': True,\n",
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" 'word.isdigit()': False,\n",
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" 'postag': 'NP',\n",
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" 'postag[:2]': 'NP',\n",
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" 'BOS': True,\n",
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" '+1:word.lower()': '(',\n",
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" '+1:word.istitle()': False,\n",
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" '+1:word.isupper()': False,\n",
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" '+1:postag': 'Fpa',\n",
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" '+1:postag[:2]': 'Fp'}"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"sent2features(train_sents[0])[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 构造训练集和测试集\n",
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"X_train = [sent2features(s) for s in train_sents]\n",
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"y_train = [sent2labels(s) for s in train_sents]\n",
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"\n",
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"X_test = [sent2features(s) for s in test_sents]\n",
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"y_test = [sent2labels(s) for s in test_sents]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"8323 1517\n"
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]
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}
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],
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"source": [
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"print(len(X_train), len(X_test))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.7964686316443963"
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]
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},
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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# 创建CRF模型实例\n",
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"crf = sklearn_crfsuite.CRF(\n",
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" algorithm='lbfgs',\n",
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" c1=0.1,\n",
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" c2=0.1,\n",
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" max_iterations=100,\n",
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" all_possible_transitions=True\n",
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")\n",
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"# 模型训练\n",
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"crf.fit(X_train, y_train)\n",
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"# 类别标签\n",
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"labels = list(crf.classes_)\n",
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"labels.remove('O')\n",
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"# 模型预测\n",
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"y_pred = crf.predict(X_test)\n",
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"# 计算F1得分\n",
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"metrics.flat_f1_score(y_test, y_pred,\n",
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" average='weighted', labels=labels)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" precision recall f1-score support\n",
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"\n",
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" B-LOC 0.810 0.784 0.797 1084\n",
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" I-LOC 0.690 0.637 0.662 325\n",
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" B-MISC 0.731 0.569 0.640 339\n",
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" I-MISC 0.699 0.589 0.639 557\n",
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" B-ORG 0.807 0.832 0.820 1400\n",
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" I-ORG 0.852 0.786 0.818 1104\n",
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" B-PER 0.850 0.884 0.867 735\n",
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" I-PER 0.893 0.943 0.917 634\n",
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"\n",
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" micro avg 0.813 0.787 0.799 6178\n",
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" macro avg 0.791 0.753 0.770 6178\n",
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"weighted avg 0.809 0.787 0.796 6178\n",
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"\n"
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]
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}
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],
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"source": [
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"# 打印B和I组的模型结果\n",
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"sorted_labels = sorted(\n",
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" labels,\n",
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" key=lambda name: (name[1:], name[0])\n",
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")\n",
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"print(metrics.flat_classification_report(\n",
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" y_test, y_pred, labels=sorted_labels, digits=3\n",
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"))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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},
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"toc": {
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"base_numbering": 1,
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"nav_menu": {},
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"number_sections": true,
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"sideBar": true,
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"skip_h1_title": false,
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"title_cell": "Table of Contents",
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"title_sidebar": "Contents",
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"toc_cell": false,
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"toc_position": {},
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"toc_section_display": true,
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"toc_window_display": false
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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