戒酒的李白
315 lines
8.7 KiB
Plaintext
315 lines
8.7 KiB
Plaintext
{
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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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"### 加载数据集"
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"from utils import load_corpus, stopwords\n",
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"\n",
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"TRAIN_PATH = \"./data/weibo2018/train.txt\"\n",
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"TEST_PATH = \"./data/weibo2018/test.txt\""
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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": 2,
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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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"Building prefix dict from the default dictionary ...\n",
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"Dumping model to file cache /var/folders/rt/khjltk4j6n78x9x3f20hdr6m0000gp/T/jieba.cache\n",
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"Loading model cost 1.013 seconds.\n",
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"Prefix dict has been built successfully.\n"
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]
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}
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],
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"source": [
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"# 分别加载训练集和测试集\n",
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"train_data = load_corpus(TRAIN_PATH)\n",
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"test_data = load_corpus(TEST_PATH)"
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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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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>words</th>\n",
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" <th>label</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>书中 自有 黄金屋 书中 自有 颜如玉 沿着 岁月 的 长河 跋涉 或是 风光旖旎 或是 姹...</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>这是 英超 被 黑 的 最惨 的 一次 二哈 二哈 十几年来 中国 只有 孙继海 董方卓 郑...</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>中国 远洋 海运 集团 副总经理 俞曾 港 月 日 在 上 表示 中央 企业 走 出去 是 ...</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>看 流星花园 其实 也 还好 啦 现在 的 观念 以及 时尚 眼光 都 不一样 了 或许 十...</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>汉武帝 的 罪己 诏 的 真实性 尽管 存在 着 争议 然而 轮台 罪己 诏 作为 中国 历...</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" words label\n",
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"0 书中 自有 黄金屋 书中 自有 颜如玉 沿着 岁月 的 长河 跋涉 或是 风光旖旎 或是 姹... 1\n",
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"1 这是 英超 被 黑 的 最惨 的 一次 二哈 二哈 十几年来 中国 只有 孙继海 董方卓 郑... 0\n",
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"2 中国 远洋 海运 集团 副总经理 俞曾 港 月 日 在 上 表示 中央 企业 走 出去 是 ... 1\n",
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"3 看 流星花园 其实 也 还好 啦 现在 的 观念 以及 时尚 眼光 都 不一样 了 或许 十... 1\n",
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"4 汉武帝 的 罪己 诏 的 真实性 尽管 存在 着 争议 然而 轮台 罪己 诏 作为 中国 历... 1"
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]
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},
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"execution_count": 3,
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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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"import pandas as pd\n",
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"\n",
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"df_train = pd.DataFrame(train_data, columns=[\"words\", \"label\"])\n",
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"df_test = pd.DataFrame(test_data, columns=[\"words\", \"label\"])\n",
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"df_train.head()"
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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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"### 特征编码"
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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": 4,
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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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"/Users/albertdxq/opt/anaconda3/lib/python3.8/site-packages/sklearn/feature_extraction/text.py:383: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['元', '吨', '数', '末'] not in stop_words.\n",
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" warnings.warn('Your stop_words may be inconsistent with '\n"
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]
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}
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],
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"source": [
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"from sklearn.feature_extraction.text import CountVectorizer\n",
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"\n",
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"vectorizer = CountVectorizer(token_pattern='\\[?\\w+\\]?', \n",
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" stop_words=stopwords,\n",
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" max_features=2000)\n",
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"X_train = vectorizer.fit_transform(df_train[\"words\"])\n",
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"y_train = df_train[\"label\"]"
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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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"source": [
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"X_test = vectorizer.transform(df_test[\"words\"])\n",
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"y_test = df_test[\"label\"]"
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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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"### 训练模型&测试"
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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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"import xgboost as xgb\n",
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"\n",
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"param = {\n",
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" 'booster':'gbtree',\n",
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" 'max_depth': 6, \n",
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" 'scale_pos_weight': 0.5,\n",
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" 'colsample_bytree': 0.8,\n",
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" 'objective': 'binary:logistic',\n",
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" 'eval_metric': 'error',\n",
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" 'eta': 0.3,\n",
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" 'nthread': 10,\n",
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"}\n",
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"dmatrix = xgb.DMatrix(X_train, label=y_train)\n",
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"model = xgb.train(param, dmatrix, num_boost_round=200)"
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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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"source": [
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"# 在测试集上用模型预测结果\n",
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"dmatrix = xgb.DMatrix(X_test)\n",
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"y_pred = model.predict(dmatrix)"
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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": 12,
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"metadata": {
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"scrolled": false
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},
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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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" 0 0.75 0.82 0.78 155\n",
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" 1 0.92 0.88 0.90 345\n",
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"\n",
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" accuracy 0.86 500\n",
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" macro avg 0.83 0.85 0.84 500\n",
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"weighted avg 0.86 0.86 0.86 500\n",
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"\n",
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"准确率: 0.86\n",
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"AUC: 0.9040205703599813\n"
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]
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}
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],
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"source": [
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"# 测试集效果检验\n",
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"from sklearn import metrics\n",
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"\n",
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"auc_score = metrics.roc_auc_score(y_test, y_pred) # 先计算AUC\n",
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"y_pred = list(map(lambda x:1 if x > 0.5 else 0, y_pred)) # 二值化\n",
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"print(metrics.classification_report(y_test, y_pred))\n",
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"print(\"准确率:\", metrics.accuracy_score(y_test, y_pred))\n",
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"print(\"AUC:\", auc_score)"
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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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"### 手动输入句子，判断情感倾向（1正/0负）"
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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": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"from utils import processing\n",
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"\n",
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"strs = [\"哈哈哈哈哈笑死我了\", \"我也是有脾气的!\"]\n",
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"words = [processing(s) for s in strs]\n",
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"vec = vectorizer.transform(words)\n",
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"dmatrix = xgb.DMatrix(vec)"
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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": 14,
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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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"array([0.8683682, 0.3285784], dtype=float32)"
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]
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},
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"execution_count": 14,
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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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"output = model.predict(dmatrix)\n",
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"output"
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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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"display_name": "Python 3",
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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": "3.8.5"
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