★gong-01.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 命名实体识别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "下周三晚上 0 5 TIME\n",
      "第二 6 8 ORDINAL\n"
     ]
    }
   ],
   "source": [
    "import spacy \n",
    "\n",
    "nlp = spacy.load('zh_core_web_sm')\n",
    "text = \"下周三晚上给第二个Keynote定稿\"\n",
    "doc = nlp(text)\n",
    "for ent in doc.ents:\n",
    "    print(ent.text, ent.start_char, ent.end_char, ent.label_)\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分词找一些有用的关键词"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "满意\n",
      "好\n",
      "流畅\n"
     ]
    }
   ],
   "source": [
    "text = '''外形外观：紫色，非常，方方正正，四周圆角\n",
    "屏幕音效：清晰度分辨率都挺满意，听音乐效果也不错\n",
    "拍照效果：非常好\n",
    "运行速度：很流畅\n",
    "待机时间：之前的安卓手机用了近三年，一天3充，现在妥妥的一天一充'''\n",
    "\n",
    "useful_words_list = ['大气', '满意', '好', '流畅']\n",
    "\n",
    "doc = nlp(text)\n",
    "#print(type(doc))\n",
    "\n",
    "for word in doc:\n",
    "    if word.text in useful_words_list:\n",
    "        print(word)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 计算 N-Grams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('外形', '外观'),\n",
       " ('外观', '：'),\n",
       " ('：', '紫色'),\n",
       " ('紫色', '，'),\n",
       " ('，', '非常'),\n",
       " ('非常', '大气'),\n",
       " ('大气', '，'),\n",
       " ('，', '方方正正'),\n",
       " ('方方正正', '，'),\n",
       " ('，', '四周'),\n",
       " ('四周', '圆角'),\n",
       " ('圆角', '\\n'),\n",
       " ('\\n', '屏幕'),\n",
       " ('屏幕', '音效'),\n",
       " ('音效', '：'),\n",
       " ('：', '清晰度'),\n",
       " ('清晰度', '分辨率'),\n",
       " ('分辨率', '都'),\n",
       " ('都', '挺'),\n",
       " ('挺', '满意'),\n",
       " ('满意', '，'),\n",
       " ('，', '听'),\n",
       " ('听', '音乐'),\n",
       " ('音乐', '效果'),\n",
       " ('效果', '也'),\n",
       " ('也', '不错'),\n",
       " ('不错', '\\n'),\n",
       " ('\\n', '拍照'),\n",
       " ('拍照', '效果'),\n",
       " ('效果', '：'),\n",
       " ('：', '非常'),\n",
       " ('非常', '好'),\n",
       " ('好', '\\n'),\n",
       " ('\\n', '运行'),\n",
       " ('运行', '速度'),\n",
       " ('速度', '：'),\n",
       " ('：', '很'),\n",
       " ('很', '流畅'),\n",
       " ('流畅', '\\n'),\n",
       " ('\\n', '待机'),\n",
       " ('待机', '时间'),\n",
       " ('时间', '：'),\n",
       " ('：', '之前'),\n",
       " ('之前', '的'),\n",
       " ('的', '安卓'),\n",
       " ('安卓', '手机'),\n",
       " ('手机', '用'),\n",
       " ('用', '了'),\n",
       " ('了', '近'),\n",
       " ('近', '三年'),\n",
       " ('三年', '，'),\n",
       " ('，', '一'),\n",
       " ('一', '天'),\n",
       " ('天', '3'),\n",
       " ('3', '充'),\n",
       " ('充', '，'),\n",
       " ('，', '现在'),\n",
       " ('现在', '妥妥'),\n",
       " ('妥妥', '的'),\n",
       " ('的', '一'),\n",
       " ('一', '天一'),\n",
       " ('天一', '充')]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text = '''外形外观：紫色，非常大气，方方正正，四周圆角\n",
    "屏幕音效：清晰度分辨率都挺满意，听音乐效果也不错\n",
    "拍照效果：非常好\n",
    "运行速度：很流畅\n",
    "待机时间：之前的安卓手机用了近三年，一天3充，现在妥妥的一天一充'''\n",
    "\n",
    "from nltk.util import ngrams\n",
    "\n",
    "doc = nlp(text)\n",
    "\n",
    "words = []\n",
    "for word in doc:\n",
    "    words.append(word.text)\n",
    "\n",
    "list(ngrams(words, 2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list(ngrams(words, 3))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TF-IDF 演示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "doc_a = 'this document is first document'\n",
    "doc_b = 'this document is the second document'\n",
    "\n",
    "bag_of_words_a = doc_a.split(' ')\n",
    "bag_of_words_b = doc_b.split(' ')\n",
    "\n",
    "unique_words_set = set(bag_of_words_a).union(set(bag_of_words_b))\n",
    "print(unique_words_set)\n",
    "\n",
    "# Now create a dictionary of words and their occurence for each document in the corpus (collection of documents).\n",
    "\n",
    "dict_a = dict.fromkeys(unique_words_set, 0)\n",
    "# print(dict_a) # {'this': 0, 'document': 0, 'second': 0, 'is': 0, 'the': 0}\n",
    "\n",
    "for word in bag_of_words_a:\n",
    "    dict_a[word] += 1\n",
    "\n",
    "print(dict_a)\n",
    "# {'this': 1, 'document': 2, 'second': 1, 'is': 1, 'the': 1}\n",
    "\n",
    "# similarly\n",
    "dict_b = dict.fromkeys(unique_words_set, 0)\n",
    "\n",
    "for word in bag_of_words_b:\n",
    "    dict_b[word] += 1\n",
    "\n",
    "print(dict_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_term_frequency(word_dictionary, bag_of_words):\n",
    "    term_frequency_dictionary = {}\n",
    "    length_of_bag_of_words = len(bag_of_words)\n",
    "\n",
    "    for word, count in word_dictionary.items():\n",
    "        term_frequency_dictionary[word] = count / float(length_of_bag_of_words)\n",
    "\n",
    "    return term_frequency_dictionary\n",
    "\n",
    "# Implementation\n",
    "tf_a = compute_term_frequency(dict_a, bag_of_words_a)\n",
    "print(tf_a)\n",
    "\n",
    "tf_b = compute_term_frequency(dict_b, bag_of_words_b)\n",
    "print(tf_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "def compute_inverse_document_frequency(full_doc_list):\n",
    "    idf_dict = {}\n",
    "    length_of_doc_list = len(full_doc_list)\n",
    "\n",
    "    # Initialize dictionary with words from all documents\n",
    "    idf_dict = dict.fromkeys(full_doc_list[0].keys(), 0)\n",
    "    \n",
    "    # Count the number of documents that contain each word\n",
    "    for doc in full_doc_list:\n",
    "        for word, val in doc.items():\n",
    "            if val > 0:\n",
    "                idf_dict[word] += 1\n",
    "\n",
    "    # Compute IDF for each word\n",
    "    for word, val in idf_dict.items():\n",
    "        idf_dict[word] = math.log((length_of_doc_list+1) / (float(val) + 1))\n",
    "\n",
    "    return idf_dict\n",
    "\n",
    "final_idf_dict = compute_inverse_document_frequency([dict_a, dict_b])\n",
    "\n",
    "import json\n",
    "\n",
    "# Print the final_idf_dict dictionary in a pretty format\n",
    "print(json.dumps(final_idf_dict, indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# doc_a = 'this document is first document'\n",
    "# doc_b = 'this document is the second document'\n",
    "\n",
    "\n",
    "def calculate_tf_id(tf, idf):\n",
    "    tf_idf = {}\n",
    "    for word, val in tf.items():\n",
    "        tf_idf[word] = val * idf[word]\n",
    "    return tf_idf\n",
    "\n",
    "print(calculate_tf_id(tf_a, final_idf_dict))\n",
    "print(calculate_tf_id(tf_b, final_idf_dict))\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 直接使用TF/IDF的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "datafile_path = \"data/fine_food_reviews_with_embeddings_1k.csv\"\n",
    "\n",
    "df = pd.read_csv(datafile_path)\n",
    "df['embedding'] = df.embedding.apply(eval).apply(np.array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of      Unnamed: 0   ProductId          UserId  Score   \n",
       "0             0  B003XPF9BO  A3R7JR3FMEBXQB      5  \\\n",
       "1           297  B003VXHGPK  A21VWSCGW7UUAR      4   \n",
       "2           296  B008JKTTUA  A34XBAIFT02B60      1   \n",
       "3           295  B000LKTTTW  A14MQ40CCU8B13      5   \n",
       "4           294  B001D09KAM  A34XBAIFT02B60      1   \n",
       "..          ...         ...             ...    ...   \n",
       "995         623  B0000CFXYA  A3GS4GWPIBV0NT      1   \n",
       "996         624  B0001BH5YM   A1BZ3HMAKK0NC      5   \n",
       "997         625  B0009ET7TC  A2FSDQY5AI6TNX      5   \n",
       "998         619  B007PA32L2  A15FF2P7RPKH6G      5   \n",
       "999         999  B001EQ5GEO  A3VYU0VO6DYV6I      5   \n",
       "\n",
       "                                               Summary   \n",
       "0    where does one  start...and stop... with a tre...  \\\n",
       "1                     Good, but not Wolfgang Puck good   \n",
       "2    Should advertise coconut as an ingredient more...   \n",
       "3                                     Best tomato soup   \n",
       "4    Should advertise coconut as an ingredient more...   \n",
       "..                                                 ...   \n",
       "995                      Strange inflammation response   \n",
       "996                      My favorite and only  MUSTARD   \n",
       "997                           My furbabies LOVE these!   \n",
       "998                          got this for the daughter   \n",
       "999                                I love Maui Coffee!   \n",
       "\n",
       "                                                  Text   \n",
       "0    Wanted to save some to bring to my Chicago fam...  \\\n",
       "1    Honestly, I have to admit that I expected a li...   \n",
       "2    First, these should be called Mac - Coconut ba...   \n",
       "3    I have a hard time finding packaged food of an...   \n",
       "4    First, these should be called Mac - Coconut ba...   \n",
       "..                                                 ...   \n",
       "995  Truthfully wasn't crazy about the taste of the...   \n",
       "996  You've just got to experience this mustard... ...   \n",
       "997  Shake the container and they come running. Eve...   \n",
       "998  all i have heard since she got a kuerig is why...   \n",
       "999  My first experience with Maui Coffee was bring...   \n",
       "\n",
       "                                              combined  n_tokens   \n",
       "0    Title: where does one  start...and stop... wit...        52  \\\n",
       "1    Title: Good, but not Wolfgang Puck good; Conte...       178   \n",
       "2    Title: Should advertise coconut as an ingredie...        78   \n",
       "3    Title: Best tomato soup; Content: I have a har...       111   \n",
       "4    Title: Should advertise coconut as an ingredie...        78   \n",
       "..                                                 ...       ...   \n",
       "995  Title: Strange inflammation response; Content:...       110   \n",
       "996  Title: My favorite and only  MUSTARD; Content:...        80   \n",
       "997  Title: My furbabies LOVE these!; Content: Shak...        47   \n",
       "998  Title: got this for the daughter; Content: all...        50   \n",
       "999  Title: I love Maui Coffee!; Content: My first ...       118   \n",
       "\n",
       "                                             embedding  \n",
       "0    [0.007018072064965963, -0.02731654793024063, 0...  \n",
       "1    [-0.003140551969408989, -0.009995664469897747,...  \n",
       "2    [-0.01757248118519783, -8.266511576948687e-05,...  \n",
       "3    [-0.0013932279543951154, -0.011112828738987446...  \n",
       "4    [-0.01757248118519783, -8.266511576948687e-05,...  \n",
       "..                                                 ...  \n",
       "995  [0.00011091353371739388, -0.00466986745595932,...  \n",
       "996  [-0.020869314670562744, -0.013138455338776112,...  \n",
       "997  [-0.009749102406203747, -0.0068712360225617886...  \n",
       "998  [-0.00521062919870019, 0.0009606690146028996, ...  \n",
       "999  [-0.006057822611182928, -0.015015840530395508,...  \n",
       "\n",
       "[1000 rows x 9 columns]>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "corpus = df['Summary']\n",
    "vectorizer = TfidfVectorizer()\n",
    "tfidf_matrix = vectorizer.fit_transform(corpus)\n",
    "tfidf_df = pd.DataFrame(tfidf_matrix.toarray(), columns=vectorizer.get_feature_names_out())\n",
    "# print(tfidf_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Row 1: [('start', 0.394384915946285), ('stop', 0.394384915946285), ('where', 0.394384915946285), ('does', 0.35649943107170623), ('treat', 0.312176255125172)]\n",
      "Row 2: [('good', 0.5681028016321258), ('puck', 0.49441945926150405), ('wolfgang', 0.49441945926150405), ('but', 0.3153408915839207), ('not', 0.2981914242226147)]\n",
      "Row 3: [('advertise', 0.36559695823357646), ('ingredient', 0.36559695823357646), ('prominently', 0.36559695823357646), ('should', 0.36559695823357646), ('an', 0.3574098136743376)]\n",
      "Row 4: [('tomato', 0.6610768832907321), ('soup', 0.6331508937690398), ('best', 0.4026130898245583), ('12', 0.0), ('13', 0.0)]\n",
      "Row 5: [('advertise', 0.36559695823357646), ('ingredient', 0.36559695823357646), ('prominently', 0.36559695823357646), ('should', 0.36559695823357646), ('an', 0.3574098136743376)]\n"
     ]
    }
   ],
   "source": [
    "def get_top_n_words(row, tfidf_df, top_n=5):\n",
    "    # Get the words and corresponding tf-idf scores for the given row\n",
    "    words_and_scores = [(word, tfidf_df.loc[row, word]) for word in tfidf_df.columns]\n",
    "    \n",
    "    # Sort the words by their tf-idf scores\n",
    "    words_and_scores.sort(key=lambda x: x[1], reverse=True)\n",
    "\n",
    "    # Get the top n words\n",
    "    top_n_words_and_scores = words_and_scores[:top_n]\n",
    "    \n",
    "    return top_n_words_and_scores\n",
    "\n",
    "# Print the highest tf-idf words and their scores for the first 5 rows\n",
    "for i in range(5):\n",
    "    print(f\"Row {i+1}: {get_top_n_words(i, tfidf_df)}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Word2Vec的演示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Word2Vec 的模型文件请自行下载\n",
    "# https://drive.google.com/u/0/uc?id=0B7XkCwpI5KDYNlNUTTlSS21pQmM&export=download\n",
    "\n",
    "from gensim.models import KeyedVectors\n",
    "model = KeyedVectors.load_word2vec_format('data/GoogleNews-vectors-negative300.bin.gz', binary=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = model.most_similar(positive=['woman', 'king'], negative=['man'])\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.25976562e-01  2.97851562e-02  8.60595703e-03  1.39648438e-01\n",
      " -2.56347656e-02 -3.61328125e-02  1.11816406e-01 -1.98242188e-01\n",
      "  5.12695312e-02  3.63281250e-01 -2.42187500e-01 -3.02734375e-01\n",
      " -1.77734375e-01 -2.49023438e-02 -1.67968750e-01 -1.69921875e-01\n",
      "  3.46679688e-02  5.21850586e-03  4.63867188e-02  1.28906250e-01\n",
      "  1.36718750e-01  1.12792969e-01  5.95703125e-02  1.36718750e-01\n",
      "  1.01074219e-01 -1.76757812e-01 -2.51953125e-01  5.98144531e-02\n",
      "  3.41796875e-01 -3.11279297e-02  1.04492188e-01  6.17675781e-02\n",
      "  1.24511719e-01  4.00390625e-01 -3.22265625e-01  8.39843750e-02\n",
      "  3.90625000e-02  5.85937500e-03  7.03125000e-02  1.72851562e-01\n",
      "  1.38671875e-01 -2.31445312e-01  2.83203125e-01  1.42578125e-01\n",
      "  3.41796875e-01 -2.39257812e-02 -1.09863281e-01  3.32031250e-02\n",
      " -5.46875000e-02  1.53198242e-02 -1.62109375e-01  1.58203125e-01\n",
      " -2.59765625e-01  2.01416016e-02 -1.63085938e-01  1.35803223e-03\n",
      " -1.44531250e-01 -5.68847656e-02  4.29687500e-02 -2.46582031e-02\n",
      "  1.85546875e-01  4.47265625e-01  9.58251953e-03  1.31835938e-01\n",
      "  9.86328125e-02 -1.85546875e-01 -1.00097656e-01 -1.33789062e-01\n",
      " -1.25000000e-01  2.83203125e-01  1.23046875e-01  5.32226562e-02\n",
      " -1.77734375e-01  8.59375000e-02 -2.18505859e-02  2.05078125e-02\n",
      " -1.39648438e-01  2.51464844e-02  1.38671875e-01 -1.05468750e-01\n",
      "  1.38671875e-01  8.88671875e-02 -7.51953125e-02 -2.13623047e-02\n",
      "  1.72851562e-01  4.63867188e-02 -2.65625000e-01  8.91113281e-03\n",
      "  1.49414062e-01  3.78417969e-02  2.38281250e-01 -1.24511719e-01\n",
      " -2.17773438e-01 -1.81640625e-01  2.97851562e-02  5.71289062e-02\n",
      " -2.89306641e-02  1.24511719e-02  9.66796875e-02 -2.31445312e-01\n",
      "  5.81054688e-02  6.68945312e-02  7.08007812e-02 -3.08593750e-01\n",
      " -2.14843750e-01  1.45507812e-01 -4.27734375e-01 -9.39941406e-03\n",
      "  1.54296875e-01 -7.66601562e-02  2.89062500e-01  2.77343750e-01\n",
      " -4.86373901e-04 -1.36718750e-01  3.24218750e-01 -2.46093750e-01\n",
      " -3.03649902e-03 -2.11914062e-01  1.25000000e-01  2.69531250e-01\n",
      "  2.04101562e-01  8.25195312e-02 -2.01171875e-01 -1.60156250e-01\n",
      " -3.78417969e-02 -1.20117188e-01  1.15234375e-01 -4.10156250e-02\n",
      " -3.95507812e-02 -8.98437500e-02  6.34765625e-03  2.03125000e-01\n",
      "  1.86523438e-01  2.73437500e-01  6.29882812e-02  1.41601562e-01\n",
      " -9.81445312e-02  1.38671875e-01  1.82617188e-01  1.73828125e-01\n",
      "  1.73828125e-01 -2.37304688e-01  1.78710938e-01  6.34765625e-02\n",
      "  2.36328125e-01 -2.08984375e-01  8.74023438e-02 -1.66015625e-01\n",
      " -7.91015625e-02  2.43164062e-01 -8.88671875e-02  1.26953125e-01\n",
      " -2.16796875e-01 -1.73828125e-01 -3.59375000e-01 -8.25195312e-02\n",
      " -6.49414062e-02  5.07812500e-02  1.35742188e-01 -7.47070312e-02\n",
      " -1.64062500e-01  1.15356445e-02  4.45312500e-01 -2.15820312e-01\n",
      " -1.11328125e-01 -1.92382812e-01  1.70898438e-01 -1.25000000e-01\n",
      "  2.65502930e-03  1.92382812e-01 -1.74804688e-01  1.39648438e-01\n",
      "  2.92968750e-01  1.13281250e-01  5.95703125e-02 -6.39648438e-02\n",
      "  9.96093750e-02 -2.72216797e-02  1.96533203e-02  4.27246094e-02\n",
      " -2.46093750e-01  6.39648438e-02 -2.25585938e-01 -1.68945312e-01\n",
      "  2.89916992e-03  8.20312500e-02  3.41796875e-01  4.32128906e-02\n",
      "  1.32812500e-01  1.42578125e-01  7.61718750e-02  5.98144531e-02\n",
      " -1.19140625e-01  2.74658203e-03 -6.29882812e-02 -2.72216797e-02\n",
      " -4.82177734e-03 -8.20312500e-02 -2.49023438e-02 -4.00390625e-01\n",
      " -1.06933594e-01  4.24804688e-02  7.76367188e-02 -1.16699219e-01\n",
      "  7.37304688e-02 -9.22851562e-02  1.07910156e-01  1.58203125e-01\n",
      "  4.24804688e-02  1.26953125e-01  3.61328125e-02  2.67578125e-01\n",
      " -1.01074219e-01 -3.02734375e-01 -5.76171875e-02  5.05371094e-02\n",
      "  5.26428223e-04 -2.07031250e-01 -1.38671875e-01 -8.97216797e-03\n",
      " -2.78320312e-02 -1.41601562e-01  2.07031250e-01 -1.58203125e-01\n",
      "  1.27929688e-01  1.49414062e-01 -2.24609375e-02 -8.44726562e-02\n",
      "  1.22558594e-01  2.15820312e-01 -2.13867188e-01 -3.12500000e-01\n",
      " -3.73046875e-01  4.08935547e-03  1.07421875e-01  1.06933594e-01\n",
      "  7.32421875e-02  8.97216797e-03 -3.88183594e-02 -1.29882812e-01\n",
      "  1.49414062e-01 -2.14843750e-01 -1.83868408e-03  9.91210938e-02\n",
      "  1.57226562e-01 -1.14257812e-01 -2.05078125e-01  9.91210938e-02\n",
      "  3.69140625e-01 -1.97265625e-01  3.54003906e-02  1.09375000e-01\n",
      "  1.31835938e-01  1.66992188e-01  2.35351562e-01  1.04980469e-01\n",
      " -4.96093750e-01 -1.64062500e-01 -1.56250000e-01 -5.22460938e-02\n",
      "  1.03027344e-01  2.43164062e-01 -1.88476562e-01  5.07812500e-02\n",
      " -9.37500000e-02 -6.68945312e-02  2.27050781e-02  7.61718750e-02\n",
      "  2.89062500e-01  3.10546875e-01 -5.37109375e-02  2.28515625e-01\n",
      "  2.51464844e-02  6.78710938e-02 -1.21093750e-01 -2.15820312e-01\n",
      " -2.73437500e-01 -3.07617188e-02 -3.37890625e-01  1.53320312e-01\n",
      "  2.33398438e-01 -2.08007812e-01  3.73046875e-01  8.20312500e-02\n",
      "  2.51953125e-01 -7.61718750e-02 -4.66308594e-02 -2.23388672e-02\n",
      "  2.99072266e-02 -5.93261719e-02 -4.66918945e-03 -2.44140625e-01\n",
      " -2.09960938e-01 -2.87109375e-01 -4.54101562e-02 -1.77734375e-01\n",
      " -2.79296875e-01 -8.59375000e-02  9.13085938e-02  2.51953125e-01]\n"
     ]
    }
   ],
   "source": [
    "vector = model['king']\n",
    "print(vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('Paris', 0.7190686464309692), ('French', 0.5460342764854431), ('Avignon', 0.5356248021125793), ('Hopital_Europeen_Georges_Pompidou', 0.5236213803291321), ('Strasbourg', 0.5220195651054382), ('Brussels', 0.5087736248970032), ('Pantheon_Sorbonne', 0.5009362697601318), ('Reims', 0.5001292824745178), ('Poitiers', 0.4956184923648834), ('Rheims', 0.4893844723701477)]\n"
     ]
    }
   ],
   "source": [
    "# 词向量的加减法\n",
    "# Rome - Italy + France = ?\n",
    "result = model.most_similar(positive=['Rome', 'France'], negative=['Italy'])\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('cats', 0.8099379539489746), ('dog', 0.760945737361908), ('kitten', 0.7464985251426697), ('feline', 0.7326234579086304), ('beagle', 0.7150582671165466), ('puppy', 0.7075453400611877), ('pup', 0.6934291124343872), ('pet', 0.6891531348228455), ('felines', 0.6755931973457336), ('chihuahua', 0.6709762215614319)]\n"
     ]
    }
   ],
   "source": [
    "result = model.most_similar(\"cat\")\n",
    "print(result)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA降维，展示不同词之间的二维关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "def visualize_words(words):\n",
    "    # Get the vectors for the words\n",
    "    vectors = [model[word] for word in words]\n",
    "\n",
    "    # Create a PCA object and fit_transform the vectors\n",
    "    pca = PCA(n_components=2)\n",
    "    pca_result = pca.fit_transform(vectors)\n",
    "\n",
    "    # Create a color map\n",
    "    colors = [\"b\", \"g\", \"r\", \"c\"]\n",
    "\n",
    "    # Plot each word in 2D space using PCA results\n",
    "    for i, word in enumerate(words):\n",
    "        plt.scatter(pca_result[i, 0], pca_result[i, 1], c=colors[i], label=word)\n",
    "        plt.annotate(word, xy=(pca_result[i, 0], pca_result[i, 1]))\n",
    "\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# List of words to visualize\n",
    "words = [\"queen\", \"king\", \"girl\", \"boy\"]\n",
    "visualize_words(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# List of words to visualize\n",
    "words = [\"Italy\", \"France\", \"Rome\", \"Paris\"]\n",
    "visualize_words(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DA7VixYoC969Zs0YZGRmKj4+/rOOUl4yMjCL3nzp1SsHBwaV2vLNnzyqX/3YE8H/t3XucjnX+x/H3PWNmzJlxmEMYgxzDrtOsw1ARM6gVbSlb7IpfoZKotCGr2PTQQWlTbZHFRodVm7I5xGCoVaLULHJISEszwwxjzFy/P9q57u91zQyjZcbF6/l4zMP3vq7v/b2+p+twf1z3dQMAcB4Q3AIAVJguXbros88+cwSa1q1bpxYtWigtLU0bNmxwBEDWrVsnn8+nzp07S5JOnTqlKVOmqGHDhgoJCVH9+vX10EMPKT8/37Gd+vXrq2/fvlq2bJnatWun0NBQzZ49W5K0b98+9evXT+Hh4apdu7buvffeEu8vTWhoqPr3768VK1bo0KFDJdYvWLBAkZGRuu666yRJWVlZGj16tOrWrauQkBA1atRIjz/+eIkAT1FRkZ555hm1bNlSVatWVa1atZSamqp//etfkn76ul5ubq7mzp0rn88nn8/n+OraZ599prS0NEVFRSkiIkLdu3fXhg0bHNuYM2eOfD6fVq9erREjRqh27dqqU6dOqe0szmtZlmbNmmVvs9g333yj3/zmN4qJiVFYWJh+9atf6b333nOUUfwMs7/97W96+OGHddlllyksLEw5OTln7GdJ2rJli3w+n9555x172aZNm+Tz+dSmTRtH3rS0NCUnJzuWPf/882rRooVCQkKUkJCgkSNHKisry5Hnyiuv1BVXXKEtW7aoW7duCgsLU6NGjfTGG29IklavXq3k5GSFhoaqSZMmWr58ueP9e/bs0YgRI9SkSROFhoaqRo0a+s1vfqPdu3eX2p/r1q3TmDFjVKtWLYWHh+v666/XDz/8UK7+AAAApavsrwrytcQLB8EtAECF6dKliwoKCrRx40Z72bp169SpUyd16tRJ2dnZ+uKLLxzrmjZtan/N7/bbb9fEiRPVpk0bPfXUU+rWrZumTZumgQMHlthWZmambr75Zl1zzTV65pln9Itf/ELHjx9X9+7dtWzZMo0aNUp/+MMflJ6ervvvv79c9R80aJBOnTqlRYsWOZYfOXJEy5Yt0/XXX6/Q0FDl5eWpW7du+utf/6rbbrtNM2fOVOfOnTV+/HiNGTPG8d6hQ4faQbDHH39cDz74oKpWrWoHqObNm6eQkBClpKRo3rx5mjdvnv7v//5PkvTll18qJSVFn3/+ue6//35NmDBBu3bt0pVXXuno42IjRozQtm3bNHHiRD344IOltrFr166aN2+eJOmaa66xtyn99JD5Tp06admyZRoxYoQee+wxnThxQtddd12pX9ecMmWK3nvvPY0dO1ZTp05VcHBwufr5iiuuULVq1bRmzRp7WXp6ugICAvT555/bQbKioiKtX79eXbt2tfM98sgjGjlypBISEjRjxgwNGDBAs2fPVs+ePVVQUODYzo8//qi+ffsqOTlZ06dPV0hIiAYOHKjXX39dAwcOVO/evfWnP/1Jubm5uuGGGxzPW/vkk0+0fv16DRw4UDNnztQdd9yhFStW6Morr1ReXl6JNt111136/PPPNWnSJN1555169913NWrUqHL1BwAAAM7AAgCggnz55ZeWJGvKlCmWZVlWQUGBFR4ebs2dO9eyLMuKjY21Zs2aZVmWZeXk5FiBgYHWsGHDLMuyrM2bN1uSrNtvv91R5tixYy1J1sqVK+1liYmJliTrgw8+cOR9+umnLUnWokWL7GW5ublWo0aNLEnWqlWrTlv/U6dOWfHx8VbHjh0dy1944QVLkrVs2TLLsixrypQpVnh4uPXvf//bke/BBx+0AgMDrb1791qWZVkrV660JFl33313iW0VFRXZ6fDwcGvw4MEl8vTr188KDg62du7caS/bv3+/FRkZaXXt2tVe9uqrr1qSrC5dulinTp06bRuLSbJGjhzpWDZ69GhLkpWenm4vO3r0qJWUlGTVr1/fKiwstCzLslatWmVJsho0aGDl5eWVa3tuffr0sTp06GC/7t+/v9W/f38rMDDQev/99y3LsqxPP/3UkmQtWbLEsizLOnTokBUcHGz17NnTrotlWdZzzz1nSbJeeeUVe1m3bt0sSdaCBQvsZV9//bUlyQoICLA2bNhgL1+2bJklyXr11VftZaW1KyMjw5Jkvfbaa/ay4r7v0aOHY0zvvfdeKzAw0MrKyvo53QMAwCUtOzvbkmQFBWVbwcHWRf8XFPRTe7Ozsyu76y9Y3LkFAKgwzZo1U40aNexnaX3++efKzc21fw2xU6dO9kPlMzIyVFhYaD9va+nSpZJU4s6n++67T5JKfDUuKSlJvXr1cixbunSp4uPjdcMNN9jLwsLCNHz48HLVPzAwUAMHDlRGRobj62cLFixQbGysunfvLklavHixUlJSVL16df3nP/+x/3r06KHCwkL7jqQ333xTPp9PkyZNKrEt86uApSksLNQ///lP9evXTw0aNLCXx8fH65ZbbtHatWtLfA1w2LBhCgwMLFdbS7N06VJ16NDB8Qy0iIgIDR8+XLt379a2bdsc+QcPHqzQ0NCfta2UlBR9+umn9rPB1q5dq969e+sXv/iF0tPTJf10N5fP57Prs3z5cp08eVKjR49WQID/EmfYsGGKiooqMUciIiIcd/01adJE1apVU7NmzRxfdSxOf/PNN/Yys10FBQU6fPiwGjVqpGrVqunTTz8t0Z7hw4c7xjQlJUWFhYXas2fP2XcOAACQVPlfFeRriRcOglsAgArj8/nUqVMn+9la69atU+3atdWoUSNJzuBW8b/FgYs9e/YoICDAzlssLi5O1apVKxEkSEpKKrH9PXv2qFGjRiUCR02aNCl3G4ofGF/8YPl9+/YpPT1dAwcOtANH27dv1wcffKBatWo5/nr06CFJ9jO7du7cqYSEBMXExJR7+8V++OEH5eXllVr3Zs2aqaioSN9++61jeWl9cjb27NlT5vaK15+r7aWkpOjUqVPKyMhQZmamDh06pJSUFHXt2tUR3GrevLndf8Xbd9cxODhYDRo0KFG/OnXqlJgL0dHRqlu3boll0k9fYyx2/PhxTZw40X6mWs2aNVWrVi1lZWUpOzu7RHvq1avneF29evUSZQIAAODnqVLZFQAAXFq6dOmid999V1u3brWft1WsU6dOGjdunL777jutXbtWCQkJjruSpDPf0VTs594xdCZt27ZV06ZNtXDhQj300ENauHChLMty/EpiUVGRrrnmmjKf5dW4cePzUrczOV99cj62165dO1WtWlVr1qxRvXr1VLt2bTVu3FgpKSl6/vnnlZ+fr/T0dF1//fU/extl3cVW1nLLsuz0XXfdpVdffVWjR49Wx44dFR0dLZ/Pp4EDB5b6q5DlKRMAAAA/D8EtAECFKr4Ta+3atVq3bp1Gjx5tr2vbtq1CQkL00UcfaePGjerdu7e9LjExUUVFRdq+fbt9p5D000POs7KylJiYeMZtJyYm6osvvpBlWY4gWWZm5lm1YdCgQZowYYK2bNmiBQsW6PLLL1f79u3t9Q0bNtSxY8fsO7XK0rBhQy1btkxHjhw57d1bpQX0atWqpbCwsFLr/vXXXysgIKDEHUj/q8TExDK3V7z+XAkODlaHDh2Unp6uevXqKSUlRdJPd3Tl5+dr/vz5+v777x0Pky/efmZmpiMoevLkSe3ateuM43E23njjDQ0ePFgzZsywl504caLErzICAIDzp6hIKuf/e3oa/xd2ZnwtEQBQoYrvyJk/f76+++47x51bISEhatOmjWbNmqXc3FzHs52KA11PP/20o7wnn3xSktSnT58zbrt3797av3+/3njjDXtZXl6eXnzxxbNqQ/FdWhMnTtTmzZsdd21J0o033qiMjAwtW7asxHuzsrJ06tQpSdKAAQNkWZYmT55cIp95R094eHiJoElgYKB69uypJUuWOJ7/9f3332vBggXq0qWLoqKizqpdZ9K7d299/PHHysjIsJfl5ubqxRdfVP369dW8efNzur2UlBRt3LhRq1atsoNbNWvWVLNmzfT444/beYr16NFDwcHBmjlzpqP//vKXvyg7O7tcc6S8AgMDS9x19eyzz6qwsPCcbQMAAADlw51bAIAKFRwcrPbt2ys9PV0hISFq27atY32nTp3su2HM4Fbr1q01ePBgvfjii8rKylK3bt308ccfa+7cuerXr5+uuuqqM2572LBheu6553Tbbbdp06ZNio+P17x58xQWFnZWbUhKSlKnTp20ZMkSSSoR3Bo3bpzeeecd9e3bV0OGDFHbtm2Vm5urrVu36o033tDu3btVs2ZNXXXVVbr11ls1c+ZMbd++XampqSoqKlJ6erquuuoqjRo1StJPd7QtX75cTz75pBISEpSUlKTk5GQ9+uij+vDDD9WlSxeNGDFCVapU0ezZs5Wfn6/p06efVZvK48EHH9TChQuVlpamu+++WzExMZo7d6527dqlN9980/EQ93MhJSVFjz32mL799ltHEKtr166aPXu26tevrzp16tjLa9WqpfHjx2vy5MlKTU3Vddddp8zMTD3//PNq3769fvvb356zuvXt21fz5s1TdHS0mjdvroyMDC1fvlw1atQ4Z9sAAABA+RDcAgBUuC5duig9Pd3+GqKpc+fOmjFjhiIjI9W6dWvHupdfflkNGjTQnDlz9PbbbysuLk7jx48v9dcGSxMWFqYVK1borrvu0rPPPquwsDANGjRIaWlpSk1NPas2DBo0SOvXr1eHDh1KPOQ+LCxMq1ev1tSpU7V48WK99tprioqKUuPGjTV58mT7AeWS9Oqrr6pVq1b6y1/+onHjxik6Olrt2rVz3NH25JNPavjw4Xr44Yd1/PhxDR48WMnJyWrRooXS09M1fvx4TZs2TUVFRUpOTtZf//pXx6/9nSuxsbFav369HnjgAT377LM6ceKEWrVqpXffffec3hVVrFOnTgoMDFRYWJhjLqSkpGj27NmOgFexRx55RLVq1dJzzz2ne++9VzExMRo+fLimTp2qoKCgc1a3Z555RoGBgZo/f75OnDihzp07a/ny5SV+oRMAAADnn8/iSaYAAAAAAMAjcnJy/vufhdmSzu1jGC5MOZKilZ2dfc4fO3Gx4JlbAAAAAAAA8Cy+lggAAAAAADwop7IrUEEulXb+fAS3AAAAAACAZwQHBysuLk4HD9at7KpUmLi4OAUHB1d2NS5YPHMLAAAAAAB4yokTJ3Ty5MnKrkaFCQ4OVtWqVSu7GhcsglsAAAAAAADwLB4oDwAAAAAAAM8662duFRUVaf/+/YqMjJTP5zsfdQIAAAAAAOVkWZaOHj2qhIQEBQRwDwsuPWcd3Nq/f7/q1r10HtoGAAAAAIAXfPvtt6pTp05lVwOocGcd3IqMjJQkfdu3r6KCgpT9zBzH+uxsf7qoyJ+uv/avjny7u/zWThcW+pc3zN/m3OCWLf50gwZ28rvLOjiy/eEP/vTs2f70zp3O4prXPep/sXKlnXyvyq8d+VJT/enAdWuchbRtq1KFhTleFhb572zr1s2/fO1r3zjyvbzS3y6jibp61gBn+UZf/G3Kdjs98Mc/O/Ndf72dnPNBnJ2+8kpntoICf/ryjNf8L156yZlxjb/93+33t+myZx90ZFt/3Z/sdKeWRj+//LKzvN//3p9+8007eajv7x3ZQkL86ejdn/tfNGrkLM8sv4NzXsgMxO7d60+HhjqyZTf4pZ02/6MjN9dZXESEkf7j/f4X//d/zozDhvnTPXr40w884Mz3j3/40+ZJ6MgRZ768PH/666/96cOHHdmOTphupyNnPOJf8eOPjnwvNHvGTt9RY7F/Rffuzu2OHetPjx/vT5sTVZKm+7er/Hx/+hvnXN/7qH+e1fvyff/yFmmOfOYwHuzs3A/W3+efM52y/WVo6VJnnf7kn49PvRhup+vXd2br2NGfjqvyH/8Lc2wk6Zf+OaKjxvyOjnbme/ddf/rOO+3kR59Xd2RbvdqfntTB346VIc6+uHqhfy5tusO/b7b94QNHviX5/oPWr8M+tNNzvrvGkW9I70P+Fzt2+NMbNzryqWVLO7k+zD+H3T/QYk6FH35wrmtyeL3/xdNP+9O/d+7ratfOTr65uqadHtCv0JFt34FAO13nn6/4V9So4SyveXN/evlyf9rcjyRl336fnX7QOJy5j5U3JX3sf2HOgw+cY6Deve3ka/P9db3pJme2kJXGvP373+3kNw/MduRr8KUxl1atspOvXPGkI9/vX+9lpz/70zJ/VZc84sj3t6b+14H+6uk3f77ake/9cf5zY9oPrznWral/m53u+uMS/wrXMebIdUPsdEzePv8K45gvSUduvcdOp6f7l/+69W5HPrVp408bJ/lv2v7Gkc18imjDbf7+W1J0rSPfr6sb5/V69fxp93/erVtnJ7dW72qn9+1zZqtiXE2Zz7Xts3iIM6OxrSVt/+ivz5XZznzffedPGyeezw7Xc2R75x1/OjnZWURq4XulblePPebI93KPv9npoUP9y30H9jsLXLjQnzZPlCtWOPP17etPG/vm9l84x8o8Fp844U9H/uA8b3y403+Qufxy//Lt2x3ZdNll/vTBg6VWQZLUOtsY+927/emvvnLki35+mp3OPmSc106dchZonvP+e40sSf/6PMiRzWyvuf9Vv+c2R74Pb/Xvc9f8c5xzW/37+9PmcW7iRGe+ESP86W3+6+q/5l7vyPbb+f7zzfaZ/uOSOf0k6crGxlzY70+vyWvnyPfZZ/70PYf9dcq+74+OfOa1lHEprmuudh7zHScVc/598okjW8ZdC+z0r37lX+4aUjX/0H/to9hYf7pnT2fGnBw7WVi3vp0OzDvqzDd1qj993XXOdU2a2MmCyBg7HfS68/OQY2K0bm0nP9sR6chmXvqa/efb9qUj33fVWtjpXbv8y12XvWo75y7/C7Of5893ZjT6fVa2/7Pbbc5p69gNas57yk6/1/heR74+sf/yvzCve7e5Pv9lZvqTV/uvpdztqFfruP+FcS26tf8kR76WVY0Dxi23ONbt/Jt/PjWM9F8jffZdbUe+rVv96duuNk4CLVo48pmfS4739R/3Zs50ZjPH0ezPyKWvOzMaH9iO9PaPQcyWj5z52rdXztGjqtukif15HbjUnHVwq/iriFFBQYoKCpIVFeVYbwa0zHSU62gUGel/n3mdEBUU4cjnOIqF+z+g5kQ6txtkXEOYVYpwFRcVZXyV0ghGhVWJcuXzpwON7ZZYaTpNcMu8kIlyHXBCQ/3lmZuKCnJeGJkXk+Z7oo67fjHBKN/M5z7OmRfgjvExKys52ptz1N+mKDP6JCk83KiT2c/uX3Qw+8/Y7nHXmJpvizIH0t3/Zkb3WJmNNte5xsqcx+Y1u/uOXrO4CLP97olm9qGZz113sx5mGeaVvpvZXtcY+IzyI811roiEY/6YdXBPEnMOlncMynq/nPu9ud1I19ib33h27weOeVZg1N0ddTHqWLWqf+xdQ+9oclQV4+rMfQVltt88uLnHvozxNutdIptRqfCqrmOR0a6ICKPtuc6GhAWW3rfmWEtSVKRxIWjuE+4xNNaFh/nLcHez2X/HjzvXRZ0wyjfH8TSDEBZmHkecH3Qij/n3K8cxy12eOSZmu8xxk3O/N9vlHvoox4HZ6E/3do11oaGBpS2WJIWElT5v3ftBVBn5SoypEVlxzBHX8cF8nxmMiXId8x1j4OoMx/6Xb9TPdcwqMPf1QGOSuOaZmc9srvs86TgonObY4bjuMM/xRa4+M8fUcRBwDZaRz+xb99Cb/enoW/d53BgT51x3/baP+T+FRv0i8ss+jpSYjoVlnF9cdXKcD4zifcdcH+TNsTNPjlVcl5Jl7Jtm/7m3Ze5/kSecY2/2kzlUp9vtzeEtcR14ylhp1tW1v0hmv5wmuGXOfccx333+86dPN0cc88J9wC3rWOTOZzbaPB+494Myjh0lLnsjjblgnht8p7luM/rT/VmhjOqVOOY7+tYs/HTXBaf7DGCWYY69+3hjRMkLjQIDq7gex2LOmdNcfxYYZQS5TzBljGlEhLNOZbXL52qk+fnoNJe9zrlV1ocoydFPVfNL72fJtRsY/WzOZ+k0x153/xllnO7YGxVl1N0YjxLHm6pGP7nOeY7zpnGN5C7DHLoS5yiTUckgx7WoM5tjfzE2FemeI8Z+6ji3nubzKY8OwqWKL+MCAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLMIbgEAAAAAAMCzCG4BAAAAAADAswhuAQAAAAAAwLOqnO0bLMuSJOUUFPz0b06OY/3Ro/50UZE/nXP8uCuf/32FhUa+/GPODZrvy80t9f2S9N/q/LdO/vQxV3E5OUYF8/L8ySo5rnz+dKCx3RIrTadOOV4WFvn8abONZidJOn7cX565qRyzUZKjQ8335Jw44cxnlH/8eHhpiyW5+szsZ7OykqO9R4/625STn+/Ilptr1MnsZ3f9zP4ztuse05Mn/WmfOZDu/jfLd4+V2WhznTk55ZzHAUbI113cf6f/T0WY7XdPNLMPzXzuuhtz0FGGudz92myvawyOGuVb5jqzM+WaP2bZp5sk5R0Dc7uuOWyOsbld99ib/ezeDxzzzKy7q41mHU+c8I+Hu2vNJodXMV64jlmO9psTo4rrMFrGeOfmBpadzahUbqHrWGS069ixMtouKS/fWCf/OnOsJdfxx2yHez811uVa/jLchyWzuBLHW7N8842nGYS8vBD/+3MKXdn8feg4ZrnLMytitsvVRnO/N6ePe+gd7TDnvnu7xrrjxwNLWyxJCilj3rr3g7Lmd4kxNc49jjniOj6Y7ws0pmOO65ifl2eU4eqMMve/05zjg/LKPh+Y+czi3OdJx0GhvMcO8xxf5OqzEGNMzW25B8sY+2PBpddVch4GzLlU4jxujImjn93bde9M9mJnPnOIS0zHwjLOL646Oc4HRvE+9xiYY2eeKF3XPipj33TX3dyWWbTl2q7ZT0dLv4T7b/n+tLnLVq3qzOfYn826uvYXyewXY527vSXeV1yHIMdrs+7m/hfoGg/HvHCf18o6FrnzlXE9Ud5jR4nL3jLOG7l5zvLMcTSPP+75bV6COfZ71zHf0Wlm4ae7LjjdZwCzDHPs3XPdeF1oFBiY58pnjv1prj8L5J8LQe4TTBljeuyY5chmtsvsP5+rkUerlD6Orste59wq60OU5OinEydK72fJ2RUhRj/nueaIY/8r63rkp43ZSXNulviIUrX0fbjE8eaU0U+uQhznTd9RY3moI585dCXOUSZzn3NcizqzmecNsz8t9xwxxsdxbi3l82lxvSzLOX+AS4XPOsvZv2/fPtWtW/d81QcAAAAAAPwM3377rerUqVPZ1QAq3FkHt4qKipSZmanmzZvr22+/VVRU1PmqG3DWcnJyVLduXeYmLjjMTVyomJu4UDE3cSFjfuJCY1mWjh49qoSEBAWYd9gCl4iz/lpiQECALrvsMklSVFQUB3NckJibuFAxN3GhYm7iQsXcxIWM+YkLSXR0dGVXAag0hHQBAAAAAADgWQS3AAAAAAAA4Fk/K7gVEhKiSZMmKSQk5MyZgQrE3MSFirmJCxVzExcq5iYuZMxPALiwnPUD5QEAAAAAAIALBV9LBAAAAAAAgGcR3AIAAAAAAIBnEdwCAAAAAACAZxHcAgAAAAAAgGf9rODWrFmzVL9+fVWtWlXJycn6+OOPz3W9gNN65JFH5PP5HH9Nmza11584cUIjR45UjRo1FBERoQEDBuj777+vxBrjYrVmzRpde+21SkhIkM/n09///nfHesuyNHHiRMXHxys0NFQ9evTQ9u3bHXmOHDmiQYMGKSoqStWqVdPQoUN17NixCmwFLkZnmptDhgwpcRxNTU115GFu4lybNm2a2rdvr8jISNWuXVv9+vVTZmamI095zuF79+5Vnz59FBYWptq1a2vcuHE6depURTYFF5nyzM0rr7yyxHHzjjvucORhbgJA5Tjr4Nbrr7+uMWPGaNKkSfr000/VunVr9erVS4cOHTof9QPK1KJFCx04cMD+W7t2rb3u3nvv1bvvvqvFixdr9erV2r9/v/r371+JtcXFKjc3V61bt9asWbNKXT99+nTNnDlTL7zwgjZu3Kjw8HD16tVLJ06csPMMGjRIX375pT788EP94x//0Jo1azR8+PCKagIuUmeam5KUmprqOI4uXLjQsZ65iXNt9erVGjlypDZs2KAPP/xQBQUF6tmzp3Jzc+08ZzqHFxYWqk+fPjp58qTWr1+vuXPnas6cOZo4cWJlNAkXifLMTUkaNmyY47g5ffp0ex1zEwAqkXWWOnToYI0cOdJ+XVhYaCUkJFjTpk0726KAn23SpElW69atS12XlZVlBQUFWYsXL7aXffXVV5YkKyMjo4JqiEuRJOvtt9+2XxcVFVlxcXHWE088YS/LysqyQkJCrIULF1qWZVnbtm2zJFmffPKJnef999+3fD6f9d1331VY3XFxc89Ny7KswYMHW7/+9a/LfA9zExXh0KFDliRr9erVlmWV7xy+dOlSKyAgwDp48KCd589//rMVFRVl5efnV2wDcNFyz03Lsqxu3bpZ99xzT5nvYW4CQOU5qzu3Tp48qU2bNqlHjx72soCAAPXo0UMZGRnnLOAGlMf27duVkJCgBg0aaNCgQdq7d68kadOmTSooKHDM06ZNm6pevXrMU1SoXbt26eDBg465GB0dreTkZHsuZmRkqFq1amrXrp2dp0ePHgoICNDGjRsrvM64tHz00UeqXbu2mjRpojvvvFOHDx+21zE3URGys7MlSTExMZLKdw7PyMhQy5YtFRsba+fp1auXcnJy9OWXX1Zg7XExc8/NYvPnz1fNmjV1xRVXaPz48crLy7PXMTcBoPJUOZvM//nPf1RYWOg4YEtSbGysvv7663NaMeB0kpOTNWfOHDVp0kQHDhzQ5MmTlZKSoi+++EIHDx5UcHCwqlWr5nhPbGysDh48WDkVxiWpeL6VdswsXnfw4EHVrl3bsb5KlSqKiYlhvuK8Sk1NVf/+/ZWUlKSdO3fqoYceUlpamjIyMhQYGMjcxHlXVFSk0aNHq3PnzrriiiskqVzn8IMHD5Z6XC1eB/yvSpubknTLLbcoMTFRCQkJ2rJlix544AFlZmbqrbfeksTcBIDKdFbBLeBCkZaWZqdbtWql5ORkJSYmatGiRQoNDa3EmgGANwwcONBOt2zZUq1atVLDhg310UcfqXv37pVYM1wqRo4cqS+++MLxzEzgQlDW3DSfOdiyZUvFx8ere/fu2rlzpxo2bFjR1QQAGM7qa4k1a9ZUYGBgiV+s+f777xUXF3dOKwacjWrVqqlx48basWOH4uLidPLkSWVlZTnyME9R0Yrn2+mOmXFxcSV+kOPUqVM6cuQI8xUVqkGDBqpZs6Z27NghibmJ82vUqFH6xz/+oVWrVqlOnTr28vKcw+Pi4ko9rhavA/4XZc3N0iQnJ0uS47jJ3ASAynFWwa3g4GC1bdtWK1assJcVFRVpxYoV6tix4zmvHFBex44d086dOxUfH6+2bdsqKCjIMU8zMzO1d+9e5ikqVFJSkuLi4hxzMScnRxs3brTnYseOHZWVlaVNmzbZeVauXKmioiL7ohmoCPv27dPhw4cVHx8vibmJ88OyLI0aNUpvv/22Vq5cqaSkJMf68pzDO3bsqK1btzqCrx9++KGioqLUvHnzimkILjpnmpul2bx5syQ5jpvMTQCoHGf9tcQxY8Zo8ODBateunTp06KCnn35aubm5+t3vfnc+6geUauzYsbr22muVmJio/fv3a9KkSQoMDNTNN9+s6OhoDR06VGPGjFFMTIyioqJ01113qWPHjvrVr35V2VXHRebYsWP2/9hKPz1EfvPmzYqJiVG9evU0evRoPfroo7r88suVlJSkCRMmKCEhQf369ZMkNWvWTKmpqRo2bJheeOEFFRQUaNSoURo4cKASEhIqqVW4GJxubsbExGjy5MkaMGCA4uLitHPnTt1///1q1KiRevXqJYm5ifNj5MiRWrBggZYsWaLIyEj7OUTR0dEKDQ0t1zm8Z8+eat68uW699VZNnz5dBw8e1MMPP6yRI0cqJCSkMpsHDzvT3Ny5c6cWLFig3r17q0aNGtqyZYvuvfdede3aVa1atZLE3ASASvVzfmLx2WefterVq2cFBwdbHTp0sDZs2HBuf8MROIObbrrJio+Pt4KDg63LLrvMuummm6wdO3bY648fP26NGDHCql69uhUWFmZdf/311oEDByqxxrhYrVq1ypJU4m/w4MGWZVlWUVGRNWHCBCs2NtYKCQmxunfvbmVmZjrKOHz4sHXzzTdbERERVlRUlPW73/3OOnr0aCW0BheT083NvLw8q2fPnlatWrWsoKAgKzEx0Ro2bJjj5+sti7mJc6+0OSnJevXVV+085TmH796920pLS7NCQ0OtmjVrWvfdd59VUFBQwa3BxeRMc3Pv3r1W165drZiYGCskJMRq1KiRNW7cOCs7O9tRDnMTACqHz7IsqyKDaQAAAAAAAMC5clbP3AIAAAAAAAAuJAS3AAAAAAAA4FkEtwAAAAAAAOBZBLcAAAAAAADgWQS3AAAAAAAA4FkEtwAAAAAAAOBZBLcAAAAAAADgWQS3AAAAAAAA4FkEtwAAuED4fD79/e9/r+xqnNZHH30kn8+nrKysyq4KAAAAIIngFgAA592QIUPk8/nk8/kUFBSk2NhYXXPNNXrllVdUVFRk5ztw4IDS0tIqsaZn1qlTJx04cEDR0dGVXRUAAABAEsEtAAAqRGpqqg4cOKDdu3fr/fff11VXXaV77rlHffv21alTpyRJcXFxCgkJqeSanl5wcLDi4uLk8/kquyoAAACAJIJbAABUiJCQEMXFxemyyy5TmzZt9NBDD2nJkiV6//33NWfOHEnOryXu3r1bPp9PixYtUkpKikJDQ9W+fXv9+9//1ieffKJ27dopIiJCaWlp+uGHHxzbevnll9WsWTNVrVpVTZs21fPPP2+vKy73rbfe0lVXXaWwsDC1bt1aGRkZdp49e/bo2muvVfXq1RUeHq4WLVpo6dKlkkr/WuKbb76pFi1aKCQkRPXr19eMGTMc9alfv76mTp2q3//+94qMjFS9evX04osv2utPnjypUaNGKT4+XlWrVlViYqKmTZt2LrodAAAAlwCCWwAAVJKrr75arVu31ltvvVVmnkmTJunhhx/Wp59+qipVquiWW27R/fffr2eeeUbp6enasWOHJk6caOefP3++Jk6cqMcee0xfffWVpk6dqgkTJmju3LmOcv/whz9o7Nix2rx5sxo3bqybb77ZvoNs5MiRys/P15o1a7R161Y9/vjjioiIKLV+mzZt0o033qiBAwdq69ateuSRRzRhwgQ7YFdsxowZateunT777DONGDFCd955pzIzMyVJM2fO1DvvvKNFixYpMzNT8+fPV/369X9GjwIAAOBSVKWyKwAAwKWsadOm2rJlS5nrx44dq169ekmS7rnnHt18881asWKFOnfuLEkaOnSoI5A0adIkzZgxQ/3795ckJSUladu2bZo9e7YGDx7sKLdPnz6SpMmTJ6tFixbasWOHmjZ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      "text/plain": [
       "<Figure size 1200x1200 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def colorize_words(words, vectors):\n",
    "    # 创建色带图的颜色映射\n",
    "    cmap = plt.get_cmap('bwr')\n",
    "\n",
    "    # 创建一个子图的网格，每行一个子图\n",
    "    fig, axs = plt.subplots(len(words), 1, figsize=(12, 2*len(words)))\n",
    "\n",
    "    for ax, word, vector in zip(axs, words, vectors):\n",
    "        # 获取词向量并调整为二维\n",
    "        matrix = np.array(vector).reshape(1, -1)\n",
    "\n",
    "        # 在子图上显示色带图\n",
    "        img = ax.imshow(matrix, cmap=cmap, aspect='auto')\n",
    "\n",
    "        # 隐藏y轴\n",
    "        ax.set_yticks([])\n",
    "\n",
    "        # 为子图添加标题\n",
    "        ax.set_title(f\"Word Vector for '{word}'\")\n",
    "        ax.set_xlabel('Dimensions')\n",
    "\n",
    "    plt.tight_layout()\n",
    "\n",
    "    # 调整子图和colorbar的位置\n",
    "    fig.subplots_adjust(right=0.85)  # leave some space for the colorbar\n",
    "    cbar_ax = fig.add_axes([0.9, 0.15, 0.05, 0.7])  # position of the colorbar\n",
    "    fig.colorbar(img, cax=cbar_ax, label='Value')\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# 词列表\n",
    "words = [\"king\", \"queen\", \"boy\", \"girl\", \"man\", \"woman\"]\n",
    "vectors = [model[word] for word in words]\n",
    "colorize_words(words, vectors)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# 词列表\n",
    "words = [\"king\", \"queen\"]\n",
    "vectors = [model[word] for word in words]\n",
    "\n",
    "\n",
    "words.append(\"king-boy+girl\")\n",
    "vector = model[\"king\"] - model[\"boy\"] + model[\"girl\"]\n",
    "vectors.append(vector)\n",
    "colorize_words(words, vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cosine similarity between 'queen' and 'king - boy + girl': 0.7506669759750366\n"
     ]
    }
   ],
   "source": [
    "similarity = model.cosine_similarities(model['queen'], [vector])\n",
    "print(f\"Cosine similarity between 'queen' and 'king - boy + girl': {similarity[0]}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cosine similarity between 'queen' and 'king': 0.6510956287384033\n",
      "Cosine similarity between 'queen' and 'girl': 0.3500562012195587\n",
      "Cosine similarity between 'queen' and 'boy': 0.20767609775066376\n",
      "Cosine similarity between 'queen' and 'paris': 0.243021622300148\n"
     ]
    }
   ],
   "source": [
    "similarity = model.cosine_similarities(model['queen'], [model['king']])\n",
    "print(f\"Cosine similarity between 'queen' and 'king': {similarity[0]}\")\n",
    "\n",
    "similarity = model.cosine_similarities(model['queen'], [model['girl']])\n",
    "print(f\"Cosine similarity between 'queen' and 'girl': {similarity[0]}\")\n",
    "\n",
    "\n",
    "similarity = model.cosine_similarities(model['queen'], [model['boy']])\n",
    "print(f\"Cosine similarity between 'queen' and 'boy': {similarity[0]}\")\n",
    "\n",
    "\n",
    "similarity = model.cosine_similarities(model['queen'], [model['paris']])\n",
    "print(f\"Cosine similarity between 'queen' and 'paris': {similarity[0]}\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 直接通过 Word2Vec 做情感分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "datafile_path = \"data/fine_food_reviews_with_embeddings_1k.csv\"\n",
    "\n",
    "df = pd.read_csv(datafile_path)\n",
    "df['embedding'] = df.embedding.apply(eval).apply(np.array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>Score</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "      <th>combined</th>\n",
       "      <th>n_tokens</th>\n",
       "      <th>embedding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>B003XPF9BO</td>\n",
       "      <td>A3R7JR3FMEBXQB</td>\n",
       "      <td>5</td>\n",
       "      <td>where does one  start...and stop... with a tre...</td>\n",
       "      <td>Wanted to save some to bring to my Chicago fam...</td>\n",
       "      <td>Title: where does one  start...and stop... wit...</td>\n",
       "      <td>52</td>\n",
       "      <td>[0.007018072064965963, -0.02731654793024063, 0...</td>\n",
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       "      <td>Title: Good, but not Wolfgang Puck good; Conte...</td>\n",
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       "      <td>B008JKTTUA</td>\n",
       "      <td>A34XBAIFT02B60</td>\n",
       "      <td>1</td>\n",
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       "      <td>Title: Should advertise coconut as an ingredie...</td>\n",
       "      <td>78</td>\n",
       "      <td>[-0.01757248118519783, -8.266511576948687e-05,...</td>\n",
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       "      <th>3</th>\n",
       "      <td>295</td>\n",
       "      <td>B000LKTTTW</td>\n",
       "      <td>A14MQ40CCU8B13</td>\n",
       "      <td>5</td>\n",
       "      <td>Best tomato soup</td>\n",
       "      <td>I have a hard time finding packaged food of an...</td>\n",
       "      <td>Title: Best tomato soup; Content: I have a har...</td>\n",
       "      <td>111</td>\n",
       "      <td>[-0.0013932279543951154, -0.011112828738987446...</td>\n",
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       "      <td>A34XBAIFT02B60</td>\n",
       "      <td>1</td>\n",
       "      <td>Should advertise coconut as an ingredient more...</td>\n",
       "      <td>First, these should be called Mac - Coconut ba...</td>\n",
       "      <td>Title: Should advertise coconut as an ingredie...</td>\n",
       "      <td>78</td>\n",
       "      <td>[-0.01757248118519783, -8.266511576948687e-05,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0   ProductId          UserId  Score   \n",
       "0           0  B003XPF9BO  A3R7JR3FMEBXQB      5  \\\n",
       "1         297  B003VXHGPK  A21VWSCGW7UUAR      4   \n",
       "2         296  B008JKTTUA  A34XBAIFT02B60      1   \n",
       "3         295  B000LKTTTW  A14MQ40CCU8B13      5   \n",
       "4         294  B001D09KAM  A34XBAIFT02B60      1   \n",
       "\n",
       "                                             Summary   \n",
       "0  where does one  start...and stop... with a tre...  \\\n",
       "1                   Good, but not Wolfgang Puck good   \n",
       "2  Should advertise coconut as an ingredient more...   \n",
       "3                                   Best tomato soup   \n",
       "4  Should advertise coconut as an ingredient more...   \n",
       "\n",
       "                                                Text   \n",
       "0  Wanted to save some to bring to my Chicago fam...  \\\n",
       "1  Honestly, I have to admit that I expected a li...   \n",
       "2  First, these should be called Mac - Coconut ba...   \n",
       "3  I have a hard time finding packaged food of an...   \n",
       "4  First, these should be called Mac - Coconut ba...   \n",
       "\n",
       "                                            combined  n_tokens   \n",
       "0  Title: where does one  start...and stop... wit...        52  \\\n",
       "1  Title: Good, but not Wolfgang Puck good; Conte...       178   \n",
       "2  Title: Should advertise coconut as an ingredie...        78   \n",
       "3  Title: Best tomato soup; Content: I have a har...       111   \n",
       "4  Title: Should advertise coconut as an ingredie...        78   \n",
       "\n",
       "                                           embedding  \n",
       "0  [0.007018072064965963, -0.02731654793024063, 0...  \n",
       "1  [-0.003140551969408989, -0.009995664469897747,...  \n",
       "2  [-0.01757248118519783, -8.266511576948687e-05,...  \n",
       "3  [-0.0013932279543951154, -0.011112828738987446...  \n",
       "4  [-0.01757248118519783, -8.266511576948687e-05,...  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import KeyedVectors\n",
    "model = KeyedVectors.load_word2vec_format('data/GoogleNews-vectors-negative300.bin.gz', binary=True)\n",
    "\n",
    "def get_word2vec(line):\n",
    "    vec = np.zeros(300)\n",
    "    count = 0\n",
    "    for word in line.split():\n",
    "        if word in model:\n",
    "            vec += model[word]\n",
    "            count += 1\n",
    "    vec /= count\n",
    "    return vec\n",
    "\n",
    "df[\"word2vec\"] = df.combined.apply(get_word2vec)\n",
    "df = df[df.Score != 3]\n",
    "df[\"sentiment\"] = df.Score.replace({1: \"negative\", 2: \"negative\", 4: \"positive\", 5: \"positive\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>Score</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "      <th>combined</th>\n",
       "      <th>n_tokens</th>\n",
       "      <th>embedding</th>\n",
       "      <th>word2vec</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>B003XPF9BO</td>\n",
       "      <td>A3R7JR3FMEBXQB</td>\n",
       "      <td>5</td>\n",
       "      <td>where does one  start...and stop... with a tre...</td>\n",
       "      <td>Wanted to save some to bring to my Chicago fam...</td>\n",
       "      <td>Title: where does one  start...and stop... wit...</td>\n",
       "      <td>52</td>\n",
       "      <td>[0.007018072064965963, -0.02731654793024063, 0...</td>\n",
       "      <td>[0.0011102084455818966, 0.018486368245091932, ...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>297</td>\n",
       "      <td>B003VXHGPK</td>\n",
       "      <td>A21VWSCGW7UUAR</td>\n",
       "      <td>4</td>\n",
       "      <td>Good, but not Wolfgang Puck good</td>\n",
       "      <td>Honestly, I have to admit that I expected a li...</td>\n",
       "      <td>Title: Good, but not Wolfgang Puck good; Conte...</td>\n",
       "      <td>178</td>\n",
       "      <td>[-0.003140551969408989, -0.009995664469897747,...</td>\n",
       "      <td>[0.02858405277646821, 0.010423528737035292, 0....</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>296</td>\n",
       "      <td>B008JKTTUA</td>\n",
       "      <td>A34XBAIFT02B60</td>\n",
       "      <td>1</td>\n",
       "      <td>Should advertise coconut as an ingredient more...</td>\n",
       "      <td>First, these should be called Mac - Coconut ba...</td>\n",
       "      <td>Title: Should advertise coconut as an ingredie...</td>\n",
       "      <td>78</td>\n",
       "      <td>[-0.01757248118519783, -8.266511576948687e-05,...</td>\n",
       "      <td>[0.05599321637834821, -0.0036221458798363095, ...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>295</td>\n",
       "      <td>B000LKTTTW</td>\n",
       "      <td>A14MQ40CCU8B13</td>\n",
       "      <td>5</td>\n",
       "      <td>Best tomato soup</td>\n",
       "      <td>I have a hard time finding packaged food of an...</td>\n",
       "      <td>Title: Best tomato soup; Content: I have a har...</td>\n",
       "      <td>111</td>\n",
       "      <td>[-0.0013932279543951154, -0.011112828738987446...</td>\n",
       "      <td>[-0.018390753330328524, 0.028358630644969452, ...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>294</td>\n",
       "      <td>B001D09KAM</td>\n",
       "      <td>A34XBAIFT02B60</td>\n",
       "      <td>1</td>\n",
       "      <td>Should advertise coconut as an ingredient more...</td>\n",
       "      <td>First, these should be called Mac - Coconut ba...</td>\n",
       "      <td>Title: Should advertise coconut as an ingredie...</td>\n",
       "      <td>78</td>\n",
       "      <td>[-0.01757248118519783, -8.266511576948687e-05,...</td>\n",
       "      <td>[0.05599321637834821, -0.0036221458798363095, ...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0   ProductId          UserId  Score   \n",
       "0           0  B003XPF9BO  A3R7JR3FMEBXQB      5  \\\n",
       "1         297  B003VXHGPK  A21VWSCGW7UUAR      4   \n",
       "2         296  B008JKTTUA  A34XBAIFT02B60      1   \n",
       "3         295  B000LKTTTW  A14MQ40CCU8B13      5   \n",
       "4         294  B001D09KAM  A34XBAIFT02B60      1   \n",
       "\n",
       "                                             Summary   \n",
       "0  where does one  start...and stop... with a tre...  \\\n",
       "1                   Good, but not Wolfgang Puck good   \n",
       "2  Should advertise coconut as an ingredient more...   \n",
       "3                                   Best tomato soup   \n",
       "4  Should advertise coconut as an ingredient more...   \n",
       "\n",
       "                                                Text   \n",
       "0  Wanted to save some to bring to my Chicago fam...  \\\n",
       "1  Honestly, I have to admit that I expected a li...   \n",
       "2  First, these should be called Mac - Coconut ba...   \n",
       "3  I have a hard time finding packaged food of an...   \n",
       "4  First, these should be called Mac - Coconut ba...   \n",
       "\n",
       "                                            combined  n_tokens   \n",
       "0  Title: where does one  start...and stop... wit...        52  \\\n",
       "1  Title: Good, but not Wolfgang Puck good; Conte...       178   \n",
       "2  Title: Should advertise coconut as an ingredie...        78   \n",
       "3  Title: Best tomato soup; Content: I have a har...       111   \n",
       "4  Title: Should advertise coconut as an ingredie...        78   \n",
       "\n",
       "                                           embedding   \n",
       "0  [0.007018072064965963, -0.02731654793024063, 0...  \\\n",
       "1  [-0.003140551969408989, -0.009995664469897747,...   \n",
       "2  [-0.01757248118519783, -8.266511576948687e-05,...   \n",
       "3  [-0.0013932279543951154, -0.011112828738987446...   \n",
       "4  [-0.01757248118519783, -8.266511576948687e-05,...   \n",
       "\n",
       "                                            word2vec sentiment  \n",
       "0  [0.0011102084455818966, 0.018486368245091932, ...  positive  \n",
       "1  [0.02858405277646821, 0.010423528737035292, 0....  positive  \n",
       "2  [0.05599321637834821, -0.0036221458798363095, ...  negative  \n",
       "3  [-0.018390753330328524, 0.028358630644969452, ...  positive  \n",
       "4  [0.05599321637834821, -0.0036221458798363095, ...  negative  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.00      0.00      0.00        33\n",
      "    positive       0.82      1.00      0.90       152\n",
      "\n",
      "    accuracy                           0.82       185\n",
      "   macro avg       0.41      0.50      0.45       185\n",
      "weighted avg       0.68      0.82      0.74       185\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alan/mactalk-ai-course/.venv/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/home/alan/mactalk-ai-course/.venv/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/home/alan/mactalk-ai-course/.venv/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    list(df.word2vec.values), df.sentiment, test_size=0.2, random_state=42\n",
    ")\n",
    "\n",
    "clf = LogisticRegression()\n",
    "clf.fit(X_train, y_train)\n",
    "preds = clf.predict(X_test)\n",
    "probas = clf.predict_proba(X_test)\n",
    "\n",
    "report = classification_report(y_test, preds)\n",
    "print(report)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对比Fasttext效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型文件下载地址 https://fasttext.cc/docs/en/crawl-vectors.html\n",
    "# 或者 链接: https://pan.baidu.com/s/1Cl0eFNLOkQqquf9ls0trEw 提取码: jvr4\n",
    "\n",
    "import gensim\n",
    "import numpy as np\n",
    "# Load the FastText pre-trained model\n",
    "model = gensim.models.fasttext.load_facebook_model('data/cc.en.300.bin')\n",
    "\n",
    "def get_fasttext(line):\n",
    "    vec = np.zeros(300) # Initialize an empty 300-dimensional vector\n",
    "    for word in line.split():\n",
    "        vec += model.wv[word]\n",
    "    vec /= len(line.split()) # Take the average over all words in the line\n",
    "    return vec\n",
    "\n",
    "df[\"fasttext\"] = df.combined.apply(get_fasttext)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    list(df.fasttext.values), df.sentiment, test_size=0.2, random_state=42\n",
    ")\n",
    "\n",
    "clf = LogisticRegression()\n",
    "clf.fit(X_train, y_train)\n",
    "preds = clf.predict(X_test)\n",
    "probas = clf.predict_proba(X_test)\n",
    "\n",
    "report = classification_report(y_test, preds)\n",
    "print(report)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对比OpenAI的Embedding的效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       1.00      0.12      0.22        33\n",
      "    positive       0.84      1.00      0.91       152\n",
      "\n",
      "    accuracy                           0.84       185\n",
      "   macro avg       0.92      0.56      0.56       185\n",
      "weighted avg       0.87      0.84      0.79       185\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    list(df.embedding.values), df.sentiment, test_size=0.2, random_state=42\n",
    ")\n",
    "\n",
    "clf = LogisticRegression()\n",
    "clf.fit(X_train, y_train)\n",
    "preds = clf.predict(X_test)\n",
    "probas = clf.predict_proba(X_test)\n",
    "\n",
    "report = classification_report(y_test, preds)\n",
    "print(report)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用Gensim的Doc2Vec训练一个Doc Vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim\n",
    "from gensim.models.doc2vec import TaggedDocument\n",
    "\n",
    "# Prepare the data for Doc2Vec\n",
    "documents = [TaggedDocument(doc, [i]) for i, doc in enumerate(df['combined'].apply(str.split))]\n",
    "\n",
    "# Train a Doc2Vec model\n",
    "model = gensim.models.Doc2Vec(documents, vector_size=300, min_count=2, epochs=40)\n",
    "\n",
    "def get_doc2vec(line):\n",
    "    # Tokenize the line, convert to lowercase, remove punctuation and stop words\n",
    "    words = gensim.utils.simple_preprocess(line)\n",
    "    # Infer the vector for the line\n",
    "    vec = model.infer_vector(words)\n",
    "    return vec\n",
    "\n",
    "# Add the Doc2Vec vectors to the dataframe\n",
    "df[\"doc2vec\"] = df.combined.apply(get_doc2vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.79      0.33      0.47        33\n",
      "    positive       0.87      0.98      0.92       152\n",
      "\n",
      "    accuracy                           0.86       185\n",
      "   macro avg       0.83      0.66      0.70       185\n",
      "weighted avg       0.86      0.86      0.84       185\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    list(df.doc2vec.values), df.sentiment, test_size=0.2, random_state=42\n",
    ")\n",
    "\n",
    "clf = LogisticRegression()\n",
    "clf.fit(X_train, y_train)\n",
    "preds = clf.predict(X_test)\n",
    "probas = clf.predict_proba(X_test)\n",
    "\n",
    "report = classification_report(y_test, preds)\n",
    "print(report)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Word2Vec的平均，也一样对数据进行预处理，看看会怎么样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import KeyedVectors\n",
    "model = KeyedVectors.load_word2vec_format('data/GoogleNews-vectors-negative300.bin.gz', binary=True)\n",
    "\n",
    "def get_word2vec(line):\n",
    "    vec = np.zeros(300)\n",
    "    count = 0\n",
    "    words = gensim.utils.simple_preprocess(line)\n",
    "\n",
    "    for word in words:\n",
    "        if word in model:\n",
    "            vec += model[word]\n",
    "            count += 1\n",
    "    vec /= count\n",
    "    return vec\n",
    "\n",
    "df[\"word2vec\"] = df.combined.apply(get_word2vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    list(df.word2vec.values), df.sentiment, test_size=0.2, random_state=42\n",
    ")\n",
    "\n",
    "clf = LogisticRegression()\n",
    "clf.fit(X_train, y_train)\n",
    "preds = clf.predict(X_test)\n",
    "probas = clf.predict_proba(X_test)\n",
    "\n",
    "report = classification_report(y_test, preds)\n",
    "print(report)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Position Encoding的可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://github.com/jalammar/jalammar.github.io/blob/master/notebookes/transformer/transformer_positional_encoding_graph.ipynb\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# Code from https://www.tensorflow.org/tutorials/text/transformer\n",
    "def get_angles(pos, i, d_model):\n",
    "  angle_rates = 1 / np.power(10000, (2 * (i//2)) / np.float32(d_model))\n",
    "  return pos * angle_rates\n",
    "\n",
    "def positional_encoding(position, d_model):\n",
    "  angle_rads = get_angles(np.arange(position)[:, np.newaxis],\n",
    "                          np.arange(d_model)[np.newaxis, :],\n",
    "                          d_model)\n",
    "  \n",
    "  # apply sin to even indices in the array; 2i\n",
    "  angle_rads[:, 0::2] = np.sin(angle_rads[:, 0::2])\n",
    "  \n",
    "  # apply cos to odd indices in the array; 2i+1\n",
    "  angle_rads[:, 1::2] = np.cos(angle_rads[:, 1::2])\n",
    "    \n",
    "  pos_encoding = angle_rads[np.newaxis, ...]\n",
    "    \n",
    "  return pos_encoding\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokens = 10\n",
    "dimensions = 64\n",
    "\n",
    "pos_encoding = positional_encoding(tokens, dimensions)\n",
    "print (pos_encoding.shape)\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.pcolormesh(pos_encoding[0], cmap='viridis')\n",
    "plt.xlabel('Embedding Dimensions')\n",
    "plt.xlim((0, dimensions))\n",
    "plt.ylim((tokens,0))\n",
    "plt.ylabel('Token Position')\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print (pos_encoding)\n"
   ]
  }
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