W2V + LR.ipynb

{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"-FkJTs6kNQdI","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663205463066,"user_tz":-420,"elapsed":22879,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"3cff61c2-8463-4945-8007-feb3312ce1b6"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gC_FjnOTcXHM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663205473411,"user_tz":-420,"elapsed":10349,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"cd0461ef-a9f9-4012-ed29-ce4873251b40"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting gensim==4.1.2\n","  Downloading gensim-4.1.2-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (24.1 MB)\n","\u001b[K     |████████████████████████████████| 24.1 MB 19 kB/s \n","\u001b[?25hRequirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.7/dist-packages (from gensim==4.1.2) (5.2.1)\n","Requirement already satisfied: scipy>=0.18.1 in /usr/local/lib/python3.7/dist-packages (from gensim==4.1.2) (1.7.3)\n","Requirement already satisfied: numpy>=1.17.0 in /usr/local/lib/python3.7/dist-packages (from gensim==4.1.2) (1.21.6)\n","Installing collected packages: gensim\n","  Attempting uninstall: gensim\n","    Found existing installation: gensim 3.6.0\n","    Uninstalling gensim-3.6.0:\n","      Successfully uninstalled gensim-3.6.0\n","Successfully installed gensim-4.1.2\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting underthesea\n","  Downloading underthesea-1.3.4-py3-none-any.whl (7.6 MB)\n","\u001b[K     |████████████████████████████████| 7.6 MB 4.1 MB/s \n","\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from underthesea) (2.23.0)\n","Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from underthesea) (6.0)\n","Collecting python-crfsuite>=0.9.6\n","  Downloading python_crfsuite-0.9.8-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (965 kB)\n","\u001b[K     |████████████████████████████████| 965 kB 42.5 MB/s \n","\u001b[?25hRequirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (from underthesea) (3.7)\n","Collecting unidecode\n","  Downloading Unidecode-1.3.4-py3-none-any.whl (235 kB)\n","\u001b[K     |████████████████████████████████| 235 kB 67.1 MB/s \n","\u001b[?25hRequirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from underthesea) (1.0.2)\n","Collecting underthesea-core==0.0.4_alpha.10\n","  Downloading underthesea_core-0.0.4_alpha.10-cp37-cp37m-manylinux2010_x86_64.whl (581 kB)\n","\u001b[K     |████████████████████████████████| 581 kB 67.6 MB/s \n","\u001b[?25hRequirement already satisfied: Click>=6.0 in /usr/local/lib/python3.7/dist-packages (from underthesea) (7.1.2)\n","Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from underthesea) (1.1.0)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from underthesea) (4.64.1)\n","Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.7/dist-packages (from nltk->underthesea) (2022.6.2)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->underthesea) (1.24.3)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->underthesea) (3.0.4)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->underthesea) (2.10)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->underthesea) (2022.6.15)\n","Requirement already satisfied: numpy>=1.14.6 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->underthesea) (1.21.6)\n","Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->underthesea) (1.7.3)\n","Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->underthesea) (3.1.0)\n","Installing collected packages: unidecode, underthesea-core, python-crfsuite, underthesea\n","\u001b[33m  WARNING: The script unidecode is installed in '/root/.local/bin' which is not on PATH.\n","  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\n","\u001b[33m  WARNING: The script underthesea is installed in '/root/.local/bin' which is not on PATH.\n","  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\n","Successfully installed python-crfsuite-0.9.8 underthesea-1.3.4 underthesea-core-0.0.4a10 unidecode-1.3.4\n"]}],"source":["# !pip install transformers==2.1.0\n","!pip install gensim==4.1.2\n","!pip3 install --user underthesea\n","\n","# !pip install pyvi"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"f82YXfOvXH1i"},"outputs":[],"source":["#from pyvi import ViTokenizer, ViPosTagger\n","import regex as re\n","from underthesea import word_tokenize\n","import numpy as np\n","import pandas as pd\n","import gensim\n","import sklearn\n","import tensorflow as tf\n","from sklearn import svm\n","from sklearn.model_selection import train_test_split\n","#from sklearn.decomposition import TruncatedSVD\n","from sklearn.metrics import classification_report\n","from sklearn.feature_extraction.text import TfidfVectorizer\n","from tensorflow.keras.layers import *\n","from keras.layers import *\n","from keras.preprocessing.text import Tokenizer\n","from keras.models import Model\n","from keras.metrics import sparse_categorical_accuracy\n","from tensorflow.keras.optimizers import Adam\n","# Chia tập train/test\n","from sklearn.model_selection import train_test_split\n","from sklearn.preprocessing import LabelEncoder\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jpZ60BlfMwyF","scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663205495654,"user_tz":-420,"elapsed":5312,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"6a1e41ed-f0d7-45f2-f05b-2a6495f8bedc"},"outputs":[{"output_type":"stream","name":"stdout","text":["__label__thể_thao 2611\n","__label__âm_nhạc 2595\n","__label__nhịp_sống 2613\n","__label__công_nghệ 2595\n","__label__thời_sự 2607\n","__label__thế_giới 2602\n","__label__thời_trang 2596\n","__label__du_lịch 2593\n","__label__sống_trẻ 2602\n","__label__giáo_dục 2603\n","__label__kinh_doanh 2597\n","__label__pháp_luật 2592\n","__label__giải_trí 2604\n","__label__phim_ảnh 2596\n","__label__xe_360 2602\n","__label__ẩm_thực 2482\n","__label__xuất_bản 2599\n","__label__sức_khỏe 2589\n"]}],"source":["# Thống kê số lượng data theo nhãn\n","count = {}\n","for line in open('/content/drive/MyDrive/KhanhMD/Data/news_categories.csv'):\n","    key = line.split()[0]\n","    count[key] = count.get(key, 0) + 1\n","\n","for key in count:\n","    print(key, count[key])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ClZ0AZOoMwyL"},"outputs":[],"source":["# Thống kê các word xuất hiện ở tất cả các nhãn\n","total_label = 18\n","vocab = {}\n","label_vocab = {}\n","for line in open('/content/drive/MyDrive/KhanhMD/Data/news_categories.csv'):\n","    words = line.split()\n","    # lưu ý từ đầu tiên là nhãn\n","    label = words[0]\n","    if label not in label_vocab:\n","        label_vocab[label] = {}\n","    for word in words[1:]:\n","        label_vocab[label][word] = label_vocab[label].get(word, 0) + 1\n","        if word not in vocab:\n","            vocab[word] = set()\n","        vocab[word].add(label)\n","\n","# count = {}\n","# for word in vocab:\n","#     if len(vocab[word]) == total_label:\n","#         count[word] = min([label_vocab[x][word] for x in label_vocab])\n","        \n","# sorted_count = sorted(count, key=count.get, reverse=True)\n","# for word in sorted_count[:100]:\n","#     print(word, count[word])\n","text = []\n","label = []\n","\n","for line in open('/content/drive/MyDrive/KhanhMD/Data/news_categories.csv'):\n","    words = line.strip().split()\n","    label.append(words[0])\n","    text.append(' '.join(words[1:]))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HL8l1uWQlFfb"},"outputs":[],"source":["label_encoder = LabelEncoder()\n","label_encoder.fit(label)\n","encode_label = label_encoder.transform(label)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"OC9HK9aLxhH_"},"outputs":[],"source":["import pandas as pd\n","df = pd.DataFrame(text, columns=[\"text\"])\n","df.to_csv('text.csv', index=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_iRnrv9myHhV","colab":{"base_uri":"https://localhost:8080/","height":363},"executionInfo":{"status":"ok","timestamp":1663205536560,"user_tz":-420,"elapsed":3759,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"ab3a41df-0fac-4a73-c0fe-70da8e6e8b42"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                                text\n","0  sanchez không dự trận khai màn nh anh cùng ars...\n","1  những sao việt hóa_thân thành phó nháy trong m...\n","2  vợ phải quỳ gối mới được nói_chuyện cười zing ...\n","3  lumia 520 sẽ có giá 3 9 triệu đồng tại việt_na...\n","4  hà_nội t t chốt danh_sách dự afc cup thể_thao ...\n","5  vụ xe điên gây tai_nạn ở sài_gòn tài_xế bị tâm...\n","6  lính nga nhảy_dù xuống tảng băng trôi trên bắc...\n","7  ngắm vĩnh_thụy đầy chất chơi thời_trang zing v...\n","8  bố_mẹ dừng tàu lượn siêu_tốc vì lo cho con_gái...\n","9  cụ bà 84 tuổi đặc_trị bệnh chó dại thời_sự zin..."],"text/html":["\n","  <div id=\"df-a3fc25a7-9678-4dfa-b8c0-beb5b2c2c343\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<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>text</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>sanchez không dự trận khai màn nh anh cùng ars...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>những sao việt hóa_thân thành phó nháy trong m...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>vợ phải quỳ gối mới được nói_chuyện cười zing ...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>lumia 520 sẽ có giá 3 9 triệu đồng tại việt_na...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>hà_nội t t chốt danh_sách dự afc cup thể_thao ...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>vụ xe điên gây tai_nạn ở sài_gòn tài_xế bị tâm...</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>lính nga nhảy_dù xuống tảng băng trôi trên bắc...</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>ngắm vĩnh_thụy đầy chất chơi thời_trang zing v...</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>bố_mẹ dừng tàu lượn siêu_tốc vì lo cho con_gái...</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>cụ bà 84 tuổi đặc_trị bệnh chó dại thời_sự zin...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a3fc25a7-9678-4dfa-b8c0-beb5b2c2c343')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 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v...\n","8      1  bố_mẹ dừng tàu lượn siêu_tốc vì lo cho con_gái...\n","9     12  cụ bà 84 tuổi đặc_trị bệnh chó dại thời_sự zin..."],"text/html":["\n","  <div id=\"df-4b6bfd68-69be-4cf6-99f5-42f92270e9c9\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<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>label</th>\n","      <th>text</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>11</td>\n","      <td>sanchez không dự trận khai màn nh anh cùng ars...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>16</td>\n","      <td>những sao việt hóa_thân thành 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":11}],"source":["df = c.copy()\n","df[:10]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qwPG92Ci6cFj","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663205548262,"user_tz":-420,"elapsed":12,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"58a09dc1-4ced-47e4-a94a-bc37b01a3d35"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(46678, 2)"]},"metadata":{},"execution_count":12}],"source":["df.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"31QMWZxATuL2"},"outputs":[],"source":["#tok = Tokenizer()\n","# lst_corpus = []\n","# for string in df['text']:\n","#    lst_words = string.split()\n","#    lst_grams = [\" \".join(lst_words[i:i+1]) for i in range(0, len(lst_words), 1)]\n","#    lst_corpus.append(lst_grams)\n","\n","# print(len(lst_corpus))\n"]},{"cell_type":"code","source":["split_text = [s.split() for s in text]"],"metadata":{"id":"ZC-Dg7o46Z-h"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1vD8KDIpftsA"},"outputs":[],"source":["w2v = gensim.models.Word2Vec(split_text,\n","                                   vector_size=100,\n","                                   window=5,\n","                                   min_count=1)\n","words = set(w2v.wv.index_to_key)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"DeqGmN9iEKnQ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663205891608,"user_tz":-420,"elapsed":218804,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"bfb5e019-2fce-4a25-ed22-c19c22b03496"},"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:gensim.models.word2vec:Effective 'alpha' higher than previous training cycles\n"]},{"output_type":"execute_result","data":{"text/plain":["(227650919, 256614670)"]},"metadata":{},"execution_count":17}],"source":["w2v.train(split_text ,epochs=10,total_examples=len(split_text))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"IIwClV-vFR6J","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663205891609,"user_tz":-420,"elapsed":10,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"36d6fd7d-a7ef-4383-8a92-6c7183023e90"},"outputs":[{"output_type":"stream","name":"stdout","text":["The total number of words are :  233702\n"]}],"source":["vocab=w2v.wv.key_to_index\n","print(\"The total number of words are : \",len(vocab))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mE8KGmHuFws2"},"outputs":[],"source":["vocab=list(vocab.keys())"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kz-ZFxs5F30S","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663205892140,"user_tz":-420,"elapsed":536,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"8fa928fa-08b1-4128-b17a-13aeac4fe171"},"outputs":[{"output_type":"stream","name":"stdout","text":["233702\n"]}],"source":["word_vec_dict={}\n","for word in vocab:\n","  word_vec_dict[word]=w2v.wv.get_vector(word)\n","print(len(word_vec_dict))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cCWOvanPC1ay"},"outputs":[],"source":["# maxi=-1\n","# for i,rev in enumerate(df['text']):\n","#     tokens=rev.split()\n","#     if(len(tokens)>maxi):\n","#         maxi=len(tokens)\n","# print(maxi)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"anYVoP3fC-qR"},"outputs":[],"source":["tok = Tokenizer()\n","tok.fit_on_texts(df['text'])\n","vocab_size = len(tok.word_index) + 1\n","encd_rev = tok.texts_to_sequences(df['text'])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"R9xiq0k9DNTZ"},"outputs":[],"source":["max_rev_len=2000  # max lenght of a review\n","vocab_size = len(tok.word_index) + 1  # total no of words\n","embed_dim=100"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2F2D86cHDSEJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663210309772,"user_tz":-420,"elapsed":1975,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"2f348752-b16f-4790-d257-b427a535a9a9"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(46678, 2000)"]},"metadata":{},"execution_count":343}],"source":["from keras.preprocessing.sequence import pad_sequences\n","pad_rev= pad_sequences(encd_rev, maxlen=max_rev_len, padding='post')\n","pad_rev.shape "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"H-DKX6nvk8Es","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663210309772,"user_tz":-420,"elapsed":4,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"72ed2b41-ea1d-4791-c01f-8a32ce9a06b8"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[4729,   10,  226, ...,    0,    0,    0],\n","       [  12,  254,   58, ...,    0,    0,    0],\n","       [ 490,   54, 2801, ...,    0,    0,    0],\n","       ...,\n","       [ 225,  298,  130, ...,    0,    0,    0],\n","       [2610,  298,  692, ...,    0,    0,    0],\n","       [  39, 1143, 7507, ...,    0,    0,    0]], dtype=int32)"]},"metadata":{},"execution_count":344}],"source":["pad_rev"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Qj9VGNpuDZgj"},"outputs":[],"source":["embed_matrix=np.zeros(shape=(vocab_size,embed_dim))\n","for word,i in tok.word_index.items():\n","  embed_vector=word_vec_dict.get(word)\n","  if embed_vector is not None:  # word is in the vocabulary learned by the w2v model\n","    embed_matrix[i]=embed_vector"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uHP5NYggdU8k","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663210310416,"user_tz":-420,"elapsed":2,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"87dbad1b-79e8-4137-c313-39642c107808"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(93363, 100)"]},"metadata":{},"execution_count":346}],"source":["embed_matrix.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_Gi8_Y-JrGkC","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663210311603,"user_tz":-420,"elapsed":3,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"ba31d156-37da-4e33-fc86-1c36bc203d1b"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<function sklearn.model_selection._split.train_test_split(*arrays, test_size=None, train_size=None, random_state=None, shuffle=True, stratify=None)>"]},"metadata":{},"execution_count":347}],"source":["import keras\n","import keras.utils\n","from keras import utils as np_utils\n","import tensorflow as tf\n","from tensorflow.keras.layers import Dense, LSTM\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.models import Sequential\n","from keras.utils import np_utils\n","sklearn.model_selection.train_test_split"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"sjJiKsExyCjX"},"outputs":[],"source":["# !pip install np_utils\n","from keras.utils.np_utils import to_categorical"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"M_X1MI6Kx02g"},"outputs":[],"source":["# Y=keras.utils.to_categorical(df['label'])\n","# Y.shape\n","from keras import utils as np_utils\n","Y=keras.utils.np_utils.to_categorical(df['label'])  # one hot target as required by NN."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"sgZDxOc4G20t"},"outputs":[],"source":["X_train, X_test, y_train, y_test = train_test_split(pad_rev, Y, test_size=0.1, random_state=42)\n","\n","X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42) # 0.25 x 0.8 = 0.2\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3h7JpbWYHVmj"},"outputs":[],"source":["from keras.initializers import Constant\n","from keras.layers import ReLU\n","from keras.layers import Dropout\n","model=Sequential()\n","model.add(Embedding(input_dim=vocab_size,output_dim=embed_dim,input_length=max_rev_len,embeddings_initializer=Constant(embed_matrix)))\n","# model.add(Bidirectional(LSTM(128)))\n","model.add(Flatten())\n","# model.add(Dense(64,activation='relu',kernel_regularizer='l2'))\n","# model.add(Dropout(0.2))\n","\n","#model.add(Dense(1024,activation='relu'))\n","# model.add(Dense(64,activation='relu',kernel_regularizer='l2'))\n","# model.add(Dropout(0.20))\n","# model.add(Dense(32,activation='relu',kernel_regularizer='l2'))\n","#model.add(Dense(32,activation='relu',kernel_regularizer='l2'))\n","# model.add(Dense(1,activation='relu'))\n","# model.add(Dense(16,activation='relu'))\n","# model.add(Dropout(0.5))\n","model.add(Dense(18,activation='softmax',kernel_regularizer='l1'))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rpKcH9CQHawH","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663210315468,"user_tz":-420,"elapsed":5,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"5d0b39d3-8616-4425-e5e9-f1a998ce8114"},"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential_23\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," embedding_23 (Embedding)    (None, 2000, 100)         9336300   \n","                                                                 \n"," flatten_23 (Flatten)        (None, 200000)            0         \n","                                                                 \n"," dense_23 (Dense)            (None, 18)                3600018   \n","                                                                 \n","=================================================================\n","Total params: 12,936,318\n","Trainable params: 12,936,318\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}],"source":["model.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jFxSFaqDHo6c"},"outputs":[],"source":["from tensorflow import keras\n","from keras import optimizers"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3LNCiHkgHd4U","executionInfo":{"status":"ok","timestamp":1663210316831,"user_tz":-420,"elapsed":3,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"0a05f029-2b60-4ca8-f30d-62ba45652c33"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/rmsprop.py:130: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n","  super(RMSprop, self).__init__(name, **kwargs)\n"]}],"source":["model.compile(optimizer=keras.optimizers.RMSprop(lr=0.0001),loss='categorical_crossentropy',metrics=['accuracy'])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rAj6DJQ0HrGc"},"outputs":[],"source":["epochs=30\n","batch_size=64"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"GOvYg3Pi0CtY"},"outputs":[],"source":["from keras.callbacks import EarlyStopping\n","from keras.callbacks import ModelCheckpoint\n","es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=5)\n","mc = ModelCheckpoint('best_model.h5', monitor='val_loss', mode='min', verbose=1, save_best_only=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1LIq8H34qX60","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663210525930,"user_tz":-420,"elapsed":206467,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"5008dab3-a31c-46e2-9dd0-36c80cffdee5"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/30\n","521/526 [============================>.] - ETA: 0s - loss: 14.2599 - accuracy: 0.7148\n","Epoch 1: val_loss improved from inf to 5.32533, saving model to best_model.h5\n","526/526 [==============================] - 14s 25ms/step - loss: 14.1920 - accuracy: 0.7156 - val_loss: 5.3253 - val_accuracy: 0.7680\n","Epoch 2/30\n","526/526 [==============================] - ETA: 0s - loss: 5.4619 - accuracy: 0.8299\n","Epoch 2: val_loss did not improve from 5.32533\n","526/526 [==============================] - 6s 11ms/step - loss: 5.4619 - accuracy: 0.8299 - val_loss: 6.1807 - val_accuracy: 0.7483\n","Epoch 3/30\n","525/526 [============================>.] - ETA: 0s - loss: 5.2362 - accuracy: 0.8496\n","Epoch 3: val_loss did not improve from 5.32533\n","526/526 [==============================] - 6s 11ms/step - loss: 5.2362 - accuracy: 0.8496 - val_loss: 5.5093 - val_accuracy: 0.7246\n","Epoch 4/30\n","522/526 [============================>.] - ETA: 0s - loss: 5.0922 - accuracy: 0.8585\n","Epoch 4: val_loss did not improve from 5.32533\n","526/526 [==============================] - 6s 11ms/step - loss: 5.0913 - accuracy: 0.8586 - val_loss: 5.6209 - val_accuracy: 0.7949\n","Epoch 5/30\n","522/526 [============================>.] - ETA: 0s - loss: 5.0024 - accuracy: 0.8675\n","Epoch 5: val_loss improved from 5.32533 to 5.15191, saving model to best_model.h5\n","526/526 [==============================] - 12s 22ms/step - loss: 5.0012 - accuracy: 0.8675 - val_loss: 5.1519 - val_accuracy: 0.8193\n","Epoch 6/30\n","523/526 [============================>.] - ETA: 0s - loss: 4.9313 - accuracy: 0.8704\n","Epoch 6: val_loss improved from 5.15191 to 5.07689, saving model to best_model.h5\n","526/526 [==============================] - 13s 24ms/step - loss: 4.9326 - accuracy: 0.8703 - val_loss: 5.0769 - val_accuracy: 0.8492\n","Epoch 7/30\n","523/526 [============================>.] - ETA: 0s - loss: 4.8683 - accuracy: 0.8749\n","Epoch 7: val_loss did not improve from 5.07689\n","526/526 [==============================] - 6s 11ms/step - loss: 4.8696 - accuracy: 0.8747 - val_loss: 7.2811 - val_accuracy: 0.7424\n","Epoch 8/30\n","524/526 [============================>.] - ETA: 0s - loss: 4.8410 - accuracy: 0.8792\n","Epoch 8: val_loss improved from 5.07689 to 5.02774, saving model to best_model.h5\n","526/526 [==============================] - 12s 22ms/step - loss: 4.8411 - accuracy: 0.8792 - val_loss: 5.0277 - val_accuracy: 0.8375\n","Epoch 9/30\n","521/526 [============================>.] - ETA: 0s - loss: 4.7876 - accuracy: 0.8799\n","Epoch 9: val_loss did not improve from 5.02774\n","526/526 [==============================] - 6s 11ms/step - loss: 4.7856 - accuracy: 0.8799 - val_loss: 5.6907 - val_accuracy: 0.8286\n","Epoch 10/30\n","523/526 [============================>.] - ETA: 0s - loss: 4.7483 - accuracy: 0.8841\n","Epoch 10: val_loss did not improve from 5.02774\n","526/526 [==============================] - 6s 11ms/step - loss: 4.7484 - accuracy: 0.8838 - val_loss: 5.2469 - val_accuracy: 0.8040\n","Epoch 11/30\n","524/526 [============================>.] - ETA: 0s - loss: 4.7127 - accuracy: 0.8862\n","Epoch 11: val_loss improved from 5.02774 to 4.86972, saving model to best_model.h5\n","526/526 [==============================] - 13s 24ms/step - loss: 4.7126 - accuracy: 0.8862 - val_loss: 4.8697 - val_accuracy: 0.8043\n","Epoch 12/30\n","525/526 [============================>.] - ETA: 0s - loss: 4.6666 - accuracy: 0.8873\n","Epoch 12: val_loss improved from 4.86972 to 4.84944, saving model to best_model.h5\n","526/526 [==============================] - 12s 22ms/step - loss: 4.6666 - accuracy: 0.8873 - val_loss: 4.8494 - val_accuracy: 0.8672\n","Epoch 13/30\n","526/526 [==============================] - ETA: 0s - loss: 4.6269 - accuracy: 0.8918\n","Epoch 13: val_loss improved from 4.84944 to 4.84438, saving model to best_model.h5\n","526/526 [==============================] - 13s 24ms/step - loss: 4.6269 - accuracy: 0.8918 - val_loss: 4.8444 - val_accuracy: 0.8618\n","Epoch 14/30\n","522/526 [============================>.] - ETA: 0s - loss: 4.5980 - accuracy: 0.8925\n","Epoch 14: val_loss improved from 4.84438 to 4.76966, saving model to best_model.h5\n","526/526 [==============================] - 12s 22ms/step - loss: 4.5979 - accuracy: 0.8924 - val_loss: 4.7697 - val_accuracy: 0.8302\n","Epoch 15/30\n","525/526 [============================>.] - ETA: 0s - loss: 4.5739 - accuracy: 0.8945\n","Epoch 15: val_loss improved from 4.76966 to 4.27561, saving model to best_model.h5\n","526/526 [==============================] - 12s 22ms/step - loss: 4.5738 - accuracy: 0.8945 - val_loss: 4.2756 - val_accuracy: 0.8813\n","Epoch 16/30\n","525/526 [============================>.] - ETA: 0s - loss: 4.5269 - accuracy: 0.8971\n","Epoch 16: val_loss did not improve from 4.27561\n","526/526 [==============================] - 6s 11ms/step - loss: 4.5269 - accuracy: 0.8972 - val_loss: 4.7953 - val_accuracy: 0.8674\n","Epoch 17/30\n","526/526 [==============================] - ETA: 0s - loss: 4.4996 - accuracy: 0.9001\n","Epoch 17: val_loss did not improve from 4.27561\n","526/526 [==============================] - 6s 11ms/step - loss: 4.4996 - accuracy: 0.9001 - val_loss: 4.9131 - val_accuracy: 0.8687\n","Epoch 18/30\n","526/526 [==============================] - ETA: 0s - loss: 4.4787 - accuracy: 0.9006\n","Epoch 18: val_loss did not improve from 4.27561\n","526/526 [==============================] - 6s 11ms/step - loss: 4.4787 - accuracy: 0.9006 - val_loss: 5.1197 - val_accuracy: 0.8697\n","Epoch 19/30\n","524/526 [============================>.] - ETA: 0s - loss: 4.4483 - accuracy: 0.9011\n","Epoch 19: val_loss did not improve from 4.27561\n","526/526 [==============================] - 6s 11ms/step - loss: 4.4482 - accuracy: 0.9010 - val_loss: 4.3379 - val_accuracy: 0.8591\n","Epoch 20/30\n","524/526 [============================>.] - ETA: 0s - loss: 4.4181 - accuracy: 0.9045\n","Epoch 20: val_loss improved from 4.27561 to 3.97341, saving model to best_model.h5\n","526/526 [==============================] - 13s 24ms/step - loss: 4.4174 - accuracy: 0.9046 - val_loss: 3.9734 - val_accuracy: 0.8854\n","Epoch 21/30\n","523/526 [============================>.] - ETA: 0s - loss: 4.3897 - accuracy: 0.9073\n","Epoch 21: val_loss did not improve from 3.97341\n","526/526 [==============================] - 6s 11ms/step - loss: 4.3898 - accuracy: 0.9073 - val_loss: 4.3558 - val_accuracy: 0.8874\n","Epoch 22/30\n","525/526 [============================>.] - ETA: 0s - loss: 4.3616 - accuracy: 0.9086\n","Epoch 22: val_loss did not improve from 3.97341\n","526/526 [==============================] - 6s 11ms/step - loss: 4.3616 - accuracy: 0.9086 - val_loss: 4.4166 - val_accuracy: 0.8806\n","Epoch 23/30\n","524/526 [============================>.] - ETA: 0s - loss: 4.3325 - accuracy: 0.9093\n","Epoch 23: val_loss did not improve from 3.97341\n","526/526 [==============================] - 6s 11ms/step - loss: 4.3333 - accuracy: 0.9092 - val_loss: 4.8221 - val_accuracy: 0.8816\n","Epoch 24/30\n","522/526 [============================>.] - ETA: 0s - loss: 4.3122 - accuracy: 0.9107\n","Epoch 24: val_loss did not improve from 3.97341\n","526/526 [==============================] - 6s 11ms/step - loss: 4.3123 - accuracy: 0.9106 - val_loss: 4.7352 - val_accuracy: 0.8885\n","Epoch 25/30\n","522/526 [============================>.] - ETA: 0s - loss: 4.2842 - accuracy: 0.9128\n","Epoch 25: val_loss did not improve from 3.97341\n","526/526 [==============================] - 6s 11ms/step - loss: 4.2842 - accuracy: 0.9129 - val_loss: 5.7033 - val_accuracy: 0.8360\n","Epoch 25: early stopping\n"]},{"output_type":"execute_result","data":{"text/plain":["<keras.callbacks.History at 0x7fb2d271c8d0>"]},"metadata":{},"execution_count":357}],"source":["model.fit(X_train,y_train,epochs=epochs,batch_size=batch_size,validation_data=(X_val,y_val),callbacks=[es, mc])"]},{"cell_type":"code","source":["modelx = keras.models.load_model('best_model.h5')"],"metadata":{"id":"tN9c8rnbSo3-"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["y_pred_one_hot_encoded = (modelx.predict(X_test)> 0.5).astype(\"float\")\n","#y_pred_test = np.array(tf.argmax(y_pred_one_hot_encoded, axis=1))\n"],"metadata":{"id":"g6XLvBJVxKYK"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(classification_report(y_test, y_pred_one_hot_encoded))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"POoNxzfx02F7","executionInfo":{"status":"ok","timestamp":1663210544722,"user_tz":-420,"elapsed":5,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"05e8b49d-8876-463b-afa9-8f9c6a9cca98"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.98      0.76      0.86       268\n","           1       1.00      0.72      0.83       267\n","           2       0.98      0.56      0.71       270\n","           3       0.97      0.73      0.83       228\n","           4       1.00      0.46      0.63       270\n","           5       0.98      0.62      0.76       252\n","           6       0.98      0.86      0.91       250\n","           7       0.99      0.77      0.87       270\n","           8       1.00      0.60      0.75       249\n","           9       0.93      0.86      0.89       252\n","          10       0.96      0.63      0.76       258\n","          11       1.00      0.69      0.82       264\n","          12       0.99      0.75      0.86       241\n","          13       0.99      0.63      0.77       277\n","          14       0.99      0.81      0.89       262\n","          15       1.00      0.55      0.71       247\n","          16       0.99      0.80      0.89       290\n","          17       0.98      0.72      0.83       253\n","\n","   micro avg       0.98      0.69      0.81      4668\n","   macro avg       0.98      0.69      0.81      4668\n","weighted avg       0.98      0.69      0.81      4668\n"," samples avg       0.69      0.69      0.69      4668\n","\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EAgjG6hD_QSg","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663210160089,"user_tz":-420,"elapsed":889,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"f92bfdc3-f738-4ab1-c7c8-3af62e105567"},"outputs":[{"output_type":"stream","name":"stdout","text":["73/73 [==============================] - 0s 3ms/step - loss: 4.1418 - accuracy: 0.8464\n"]},{"output_type":"execute_result","data":{"text/plain":["[4.1417717933654785, 0.846401035785675]"]},"metadata":{},"execution_count":322}],"source":["modelx.evaluate(X_test, y_test, batch_size=64)"]}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":[],"machine_shape":"hm","provenance":[{"file_id":"1XBy3TPMb3IuEVkCxtliYpWUgdo13EH1e","timestamp":1661843950562},{"file_id":"1yasZMzwARi7_MLSZBhiwcK-cz-0OSUUU","timestamp":1661308164140},{"file_id":"1M7C_zjI7-q22B-9ymuYvGy-QHmzwBELS","timestamp":1660274269126},{"file_id":"1nc8hybaV8wPECAkoW9Fiay_diQFlCBgm","timestamp":1660270521743},{"file_id":"1i1XkgHsjFhlwfnEtteSvJCZ61q_IXH_8","timestamp":1659406730650}]},"gpuClass":"standard","kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.3"}},"nbformat":4,"nbformat_minor":0}