基于决策树的中文地址要素解析

import os
import sys
import codecs
import numpy as np
from sklearn.metrics import classification_report
from sklearn.preprocessing import LabelEncoder
from sklearn.feature_extraction import DictVectorizer
from sklearn.tree import DecisionTreeClassifier
import pickle

# 定义函数，读取数据文件
def read_data_file(data_file):
    with codecs.open(data_file, 'r', encoding='utf-8') as f:
        lines = f.readlines()
    data = []
    labels = []
    for line in lines:
        if line.strip():
            parts = line.strip().split('
')
            if len(parts) < 2:
                continue
            data.append(parts[1])
            labels.append(parts[0])
    return data, labels

# 定义函数，读取训练数据文件
def read_train_file(train_file):
    with codecs.open(train_file, 'r', encoding='utf-8') as f:
        lines = f.readlines()
    data = []
    labels = []
    sent_data = []
    sent_labels = []
    for line in lines:
        if line.strip():
            parts = line.strip().split('	')
            if len(parts) < 2:
                continue
            sent_data.append(parts[1])
            sent_labels.append(parts[0])
        else:
            data.append(sent_data)
            labels.append(sent_labels)
            sent_data = []
            sent_labels = []
    if sent_data:
        data.append(sent_data)
        labels.append(sent_labels)
    return data, labels

# 定义函数，将数据转换为特征向量
def data2features(data):
    features = []
    for sent in data:
        for i in range(len(sent)):
            feature = {}
            feature['word'] = sent[i]
            if i == 0:
                feature['prev_word'] = '<s>'
            else:
                feature['prev_word'] = sent[i-1]
            if i == len(sent)-1:
                feature['next_word'] = '</s>'
            else:
                feature['next_word'] = sent[i+1]
            features.append(feature)
    return features

# 定义函数，训练模型
def train(train_file, model_file):
    # 读取训练数据
    data, labels = read_train_file(train_file)

    # 将数据转换为特征向量
    features = data2features(data)

    # 将标签编码为数字
    label_encoder = LabelEncoder()
    label_encoder.fit(np.concatenate(labels))
    labels_encoded = [label_encoder.transform(l) for l in labels]

    # 将特征向量转换为稀疏矩阵
    dict_vectorizer = DictVectorizer()
    features_sparse = dict_vectorizer.fit_transform(features)

    # 训练决策树模型
    clf = DecisionTreeClassifier()
    clf.fit(features_sparse, np.concatenate(labels_encoded))

    # 保存模型
    with open(model_file, 'wb') as f:
        model = (label_encoder, dict_vectorizer, clf)
        pickle.dump(model, f)

# 定义函数，使用模型进行预测
def predict(data_file, model_file, result_file):
    # 读取数据
    data, _ = read_data_file(data_file)

    # 读取模型
    with open(model_file, 'rb') as f:
        label_encoder, dict_vectorizer, clf = pickle.load(f)

    # 将数据转换为特征向量
    features = data2features(data)

    # 将特征向量转换为稀疏矩阵
    features_sparse = dict_vectorizer.transform(features)

    # 使用模型进行预测
    labels_encoded = clf.predict(features_sparse)
    labels = label_encoder.inverse_transform(labels_encoded)

    # 将预测结果输出到文件
    with codecs.open(result_file, 'w', encoding='utf-8') as f:
        for i in range(len(data)):
            f.write('{}
{}
{}
'.format(i+1, data[i], labels[i]))

# 定义函数，评估模型
def evaluate(data_file, model_file):
    # 读取数据
    data, labels = read_data_file(data_file)

    # 读取模型
    with open(model_file, 'rb') as f:
        label_encoder, dict_vectorizer, clf = pickle.load(f)

    # 将数据转换为特征向量
    features = data2features(data)

    # 将特征向量转换为稀疏矩阵
    features_sparse = dict_vectorizer.transform(features)

    # 使用模型进行预测
    labels_encoded = clf.predict(features_sparse)
    labels_predicted = label_encoder.inverse_transform(labels_encoded)

    # 输出评估结果
    print(classification_report(labels, labels_predicted))

# 定义函数，解析地址要素
def parse_address(data_file, model_file, result_file):
    # 读取数据
    data, _ = read_data_file(data_file)

    # 读取模型
    with open(model_file, 'rb') as f:
        label_encoder, dict_vectorizer, clf = pickle.load(f)

    # 将数据转换为特征向量
    features = data2features(data)

    # 将特征向量转换为稀疏矩阵
    features_sparse = dict_vectorizer.transform(features)

    # 使用模型进行预测
    labels_encoded = clf.predict(features_sparse)
    labels = label_encoder.inverse_transform(labels_encoded)

    # 将预测结果解析为地址要素
    results = []
    for i in range(len(data)):
        if labels[i] == 'O':
            results.append((data[i], 'O'))
        else:
            tag, type = labels[i].split('-')
            if tag == 'B':
                result = [data[i], type]
            elif tag == 'I':
                if result:
                    result[0] += data[i]
                else:
                    result = [data[i], type]
            elif tag == 'E':
                if result:
                    result[0] += data[i]
                    results.append((result[0], result[1]))
                    result = None
                else:
                    results.append((data[i], type))
            else:
                result = None
    if result:
        results.append((result[0], result[1]))

    # 将解析结果输出到文件
    with codecs.open(result_file, 'w', encoding='utf-8') as f:
        for i in range(len(data)):
            f.write('{}
{}
{}
'.format(i+1, data[i], labels[i]))

    # 将解析结果输出到控制台
    for result in results:
        print(result)


# 解析地址要素
parse_address('3.txt', 'model.pkl', '对对对队_addr_parsing_runid.txt')