Tuning W2V + BiLSTM.ipynb

{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"-FkJTs6kNQdI","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663517933858,"user_tz":-420,"elapsed":3625,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"2a31a2f3-1030-4df4-cd45-b27b2bc69c72"},"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"85x8WuAHFmgB"},"outputs":[],"source":["import numpy as np"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gC_FjnOTcXHM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663517939716,"user_tz":-420,"elapsed":5862,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"6455d490-4b6c-4c55-e697-3b9428b8e4f6"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: gensim==4.1.2 in /usr/local/lib/python3.7/dist-packages (4.1.2)\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: 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: numpy>=1.17.0 in /usr/local/lib/python3.7/dist-packages (from gensim==4.1.2) (1.21.6)\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: underthesea in /root/.local/lib/python3.7/site-packages (1.3.4)\n","Requirement already satisfied: python-crfsuite>=0.9.6 in /root/.local/lib/python3.7/site-packages (from underthesea) (0.9.8)\n","Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from underthesea) (6.0)\n","Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (from underthesea) (3.7)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from underthesea) (4.64.1)\n","Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from underthesea) (2.23.0)\n","Requirement already satisfied: underthesea-core==0.0.4_alpha.10 in /root/.local/lib/python3.7/site-packages (from underthesea) (0.0.4a10)\n","Requirement 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: unidecode in /root/.local/lib/python3.7/site-packages (from underthesea) (1.3.4)\n","Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from underthesea) (1.0.2)\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: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->underthesea) (3.0.4)\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: 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: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->underthesea) (3.1.0)\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"]}],"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","source":["!pip install keras-tuner "],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"s22bWgjkQtVS","executionInfo":{"status":"ok","timestamp":1663517942502,"user_tz":-420,"elapsed":2792,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"23f477d4-8685-46ce-bc99-35e4101361fb"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: keras-tuner in /usr/local/lib/python3.7/dist-packages (1.1.3)\n","Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (2.23.0)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (1.21.6)\n","Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (21.3)\n","Requirement already satisfied: ipython in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (7.9.0)\n","Requirement already satisfied: tensorboard in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (2.8.0)\n","Requirement already satisfied: kt-legacy in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (1.0.4)\n","Requirement already satisfied: prompt-toolkit<2.1.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner) (2.0.10)\n","Requirement already satisfied: pickleshare in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner) (0.7.5)\n","Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner) (57.4.0)\n","Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner) (4.4.2)\n","Requirement already satisfied: backcall in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner) (0.2.0)\n","Requirement already satisfied: pygments in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner) (2.6.1)\n","Requirement already satisfied: jedi>=0.10 in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner) (0.18.1)\n","Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner) (5.1.1)\n","Requirement already satisfied: pexpect in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner) (4.8.0)\n","Requirement already satisfied: parso<0.9.0,>=0.8.0 in /usr/local/lib/python3.7/dist-packages (from jedi>=0.10->ipython->keras-tuner) (0.8.3)\n","Requirement 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markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras-tuner) (3.4.1)\n","Requirement already satisfied: protobuf>=3.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras-tuner) (3.17.3)\n","Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras-tuner) (1.2.0)\n","Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras-tuner) (0.6.1)\n","Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard->keras-tuner) (0.2.8)\n","Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard->keras-tuner) (4.9)\n","Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard->keras-tuner) 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requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->keras-tuner) (3.2.0)\n"]}]},{"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":1663517951849,"user_tz":-420,"elapsed":5333,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"9e065b05-7b8e-4743-8de6-db10fc101a8e"},"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":1663517988160,"user_tz":-420,"elapsed":3522,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"7a69461e-657e-4233-b9f4-fbcd83627c71"},"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-427fef7e-e90f-4dfc-9bc8-4d6be5e3e3ae\">\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-427fef7e-e90f-4dfc-9bc8-4d6be5e3e3ae')\"\n","            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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":15}],"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":1663517998614,"user_tz":-420,"elapsed":15,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"ffbfd8ea-16ef-4310-ba4f-7e7bb53c8a7c"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(46678, 2)"]},"metadata":{},"execution_count":16}],"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":1663518350916,"user_tz":-420,"elapsed":223372,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"af3ce7c8-3617-4f0a-9609-0328b52d043b"},"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":["(227657435, 256614670)"]},"metadata":{},"execution_count":21}],"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":1663518350916,"user_tz":-420,"elapsed":21,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"65a03c8d-f79b-4c7c-a646-cdd87dad3830"},"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":1663518350917,"user_tz":-420,"elapsed":17,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"a808fbdb-4cd4-40d8-e5ce-688e2899254d"},"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=700  # 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":1663518401681,"user_tz":-420,"elapsed":704,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"7ef983b3-45e2-48dc-e8eb-176a15b41cec"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(46678, 700)"]},"metadata":{},"execution_count":28}],"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":1663518401682,"user_tz":-420,"elapsed":6,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"7374e99d-509f-4d25-c008-ba14303893a5"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[4729,   10,  226, ...,    0,    0,    0],\n","       [   1, 3335,   95, ...,   53,  439,   58],\n","       [ 490,   54, 2801, ...,    0,    0,    0],\n","       ...,\n","       [ 354,  328,  376, ...,  120,  260,   55],\n","       [2610,  298,  692, ...,    0,    0,    0],\n","       [  39, 1143, 7507, ...,    0,    0,    0]], dtype=int32)"]},"metadata":{},"execution_count":29}],"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":1663518402613,"user_tz":-420,"elapsed":14,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"472aed58-b641-4963-ca5c-259093a65117"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(93363, 100)"]},"metadata":{},"execution_count":31}],"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":1663518402613,"user_tz":-420,"elapsed":10,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"612e8b70-e702-4c37-af8b-ae1d13fb0f77"},"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":32}],"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":["\n","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(0.20))\n","# model.add(Dense(18,activation='softmax'))"]},{"cell_type":"code","source":["from keras.initializers import Constant\n","from keras.layers import ReLU\n","from keras.layers import Dropout\n","def create_model(hp):\n","\n","  # num_hidden_layers = 1\n","  num_units_BiLSTM = 128\n","  num_units = 64\n","  dropout_rate = 0.2\n","  learning_rate = 0.0005\n","  acti_function = 'relu'\n","\n","  if hp:\n","    # num_hidden_layers = hp.Choice('num_hidden_layers', values=[1,2,3])\n","    \n","    # dropout_rate = hp.Float('dropout_rate', min_value=0.2, max_value=0.5)\n","    num_units = hp.Choice('num_units_BiLSTM', values=[128,64])\n","    num_units = hp.Choice('num_units', values=[64,128])\n","    dropout_rate = hp.Choice('dropout_rate', values=[0.2,0.3])\n","    learning_rate = hp.Float('learning_rate', min_value=0.00005, max_value=0.0005)\n","    acti_function = hp.Choice('acti_function', values=['relu','tanh'])\n","\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(num_units_BiLSTM)))\n","\n","  model.add(Flatten())\n","  model.add(Dense(num_units,activation=acti_function,kernel_regularizer='l2'))\n","  model.add(Dropout(dropout_rate))\n","\n","  model.add(tf.keras.layers.Dense(18, activation='softmax'))\n","  model.compile(\n","      loss='categorical_crossentropy',\n","      optimizer=tf.keras.optimizers.RMSprop(lr=learning_rate),\n","      metrics=['accuracy']\n","  )\n","  return model"],"metadata":{"id":"bracJozPINQx"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rpKcH9CQHawH","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663525977222,"user_tz":-420,"elapsed":741,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"40888e3b-2a54-44ac-9f5c-a7de89ce7b7e"},"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential_1\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," embedding_1 (Embedding)     (None, 700, 100)          9336300   \n","                                                                 \n"," bidirectional_1 (Bidirectio  (None, 256)              234496    \n"," nal)                                                            \n","                                                                 \n"," flatten_1 (Flatten)         (None, 256)               0         \n","                                                                 \n"," dense_2 (Dense)             (None, 64)                16448     \n","                                                                 \n"," dropout_1 (Dropout)         (None, 64)                0         \n","                                                                 \n"," dense_3 (Dense)             (None, 18)                1170      \n","                                                                 \n","=================================================================\n","Total params: 9,588,414\n","Trainable params: 9,588,414\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}],"source":["create_model(None).summary()"]},{"cell_type":"code","source":["from tensorflow import keras\n","from keras import optimizers\n","import kerastuner "],"metadata":{"id":"dzL1bSg3KvdZ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class CustomTuner(kerastuner.tuners.BayesianOptimization):\n","  def run_trial(self, trial, *args, **kwargs):\n","    kwargs['batch_size'] = trial.hyperparameters.Int('batch_size',64,64)\n","    return super(CustomTuner, self).run_trial(trial, *args, **kwargs)"],"metadata":{"id":"sFeVDaPJK0dZ"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jFxSFaqDHo6c"},"outputs":[],"source":["tuner = CustomTuner(\n","    create_model,\n","    objective='val_loss',\n","    max_trials=20,\n","    directory='logs',\n","    project_name='1_tune',\n","    overwrite=True,\n",")"]},{"cell_type":"code","source":["tuner.search_space_summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"scTbi5DyLE20","executionInfo":{"status":"ok","timestamp":1663525978183,"user_tz":-420,"elapsed":6,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"b91577f3-36ca-4275-d827-6b6cfdbb6a20"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Search space summary\n","Default search space size: 5\n","num_units_BiLSTM (Choice)\n","{'default': 128, 'conditions': [], 'values': [128, 64], 'ordered': True}\n","num_units (Choice)\n","{'default': 64, 'conditions': [], 'values': [64, 128], 'ordered': True}\n","dropout_rate (Choice)\n","{'default': 0.2, 'conditions': [], 'values': [0.2, 0.3], 'ordered': True}\n","learning_rate (Float)\n","{'default': 5e-05, 'conditions': [], 'min_value': 5e-05, 'max_value': 0.0005, 'step': None, 'sampling': None}\n","acti_function (Choice)\n","{'default': 'relu', 'conditions': [], 'values': ['relu', 'tanh'], 'ordered': False}\n"]}]},{"cell_type":"code","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_tunning_model.h5', monitor='val_loss', mode='min', verbose=1, save_best_only=True)"],"metadata":{"id":"PnOkUvx4LFTA"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rAj6DJQ0HrGc"},"outputs":[],"source":["epochs=30\n","#batch_size=64"]},{"cell_type":"code","source":["tuner.search(\n","    X_train, y_train,\n","    validation_data=(X_val,y_val),\n","    epochs=30, verbose=True, callbacks=[es, mc]\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ev1ayj9qLuda","executionInfo":{"status":"ok","timestamp":1663543712433,"user_tz":-420,"elapsed":17734254,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"7bdf8de4-f4c1-45b7-d281-dd414bc6da33"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Trial 20 Complete [00h 13m 17s]\n","val_loss: 0.3608463406562805\n","\n","Best val_loss So Far: 0.34798166155815125\n","Total elapsed time: 04h 55m 35s\n"]}]},{"cell_type":"code","source":["best_hps=tuner.get_best_hyperparameters(num_trials=20)[0]"],"metadata":{"id":"TRQfjTyU-B-q"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# print(f\"\"\"\n","# The hyperparameter search is complete. \n","# Best BiLSTM Units: {best_hps.get('num_units_BiLSTM')},\n","# Best Units: {best_hps.get('num_units')},\n","# Best Dropout: {best_hps.get('dropout_rate')},\n","# Best LR: {best_hps.get('learning_rate')},\n","# \"\"\")"],"metadata":{"id":"FOT2M1uh-C2g"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["best_hp = tuner.get_best_hyperparameters()[0]\n","modelxx = tuner.hypermodel.build(best_hp)"],"metadata":{"id":"6sPpz0ipybnp"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tuner.results_summary(num_trials=20)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NT31CLEGzwef","executionInfo":{"status":"ok","timestamp":1663543713417,"user_tz":-420,"elapsed":25,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"c9375f60-8551-4afc-fe36-e523ad1b1676"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Results summary\n","Results in logs/1_tune\n","Showing 20 best trials\n","<keras_tuner.engine.objective.Objective object at 0x7f8dad491a50>\n","Trial summary\n","Hyperparameters:\n","num_units_BiLSTM: 128\n","num_units: 64\n","dropout_rate: 0.2\n","learning_rate: 0.0005\n","acti_function: relu\n","batch_size: 64\n","Score: 0.34798166155815125\n","Trial summary\n","Hyperparameters:\n","num_units_BiLSTM: 128\n","num_units: 64\n","dropout_rate: 0.3\n","learning_rate: 0.0005\n","acti_function: relu\n","batch_size: 64\n","Score: 0.35054081678390503\n","Trial summary\n","Hyperparameters:\n","num_units_BiLSTM: 128\n","num_units: 64\n","dropout_rate: 0.2\n","learning_rate: 0.0005\n","acti_function: relu\n","batch_size: 64\n","Score: 0.35147398710250854\n","Trial summary\n","Hyperparameters:\n","num_units_BiLSTM: 128\n","num_units: 64\n","dropout_rate: 0.3\n","learning_rate: 0.0005\n","acti_function: relu\n","batch_size: 64\n","Score: 0.3543442487716675\n","Trial summary\n","Hyperparameters:\n","num_units_BiLSTM: 128\n","num_units: 64\n","dropout_rate: 0.3\n","learning_rate: 0.0005\n","acti_function: relu\n","batch_size: 64\n","Score: 0.3549574017524719\n","Trial summary\n","Hyperparameters:\n","num_units_BiLSTM: 128\n","num_units: 64\n","dropout_rate: 0.2\n","learning_rate: 0.0005\n","acti_function: relu\n","batch_size: 64\n","Score: 0.3555867671966553\n","Trial summary\n","Hyperparameters:\n","num_units_BiLSTM: 128\n","num_units: 64\n","dropout_rate: 0.2\n","learning_rate: 0.0005\n","acti_function: relu\n","batch_size: 64\n","Score: 0.3560452461242676\n","Trial summary\n","Hyperparameters:\n","num_units_BiLSTM: 128\n","num_units: 64\n","dropout_rate: 0.2\n","learning_rate: 0.0005\n","acti_function: relu\n","batch_size: 64\n","Score: 0.3598272502422333\n","Trial summary\n","Hyperparameters:\n","num_units_BiLSTM: 128\n","num_units: 64\n","dropout_rate: 0.2\n","learning_rate: 0.0005\n","acti_function: relu\n","batch_size: 64\n","Score: 0.3608463406562805\n","Trial summary\n","Hyperparameters:\n","num_units_BiLSTM: 128\n","num_units: 64\n","dropout_rate: 0.2\n","learning_rate: 0.0005\n","acti_function: relu\n","batch_size: 64\n","Score: 0.3608768582344055\n","Trial summary\n","Hyperparameters:\n","num_units_BiLSTM: 128\n","num_units: 64\n","dropout_rate: 0.3\n","learning_rate: 0.0005\n","acti_function: relu\n","batch_size: 64\n","Score: 0.36092475056648254\n","Trial summary\n","Hyperparameters:\n","num_units_BiLSTM: 128\n","num_units: 64\n","dropout_rate: 0.3\n","learning_rate: 0.0005\n","acti_function: relu\n","batch_size: 64\n","Score: 0.3612986207008362\n","Trial summary\n","Hyperparameters:\n","num_units_BiLSTM: 128\n","num_units: 64\n","dropout_rate: 0.2\n","learning_rate: 0.0005\n","acti_function: relu\n","batch_size: 64\n","Score: 0.36157742142677307\n","Trial summary\n","Hyperparameters:\n","num_units_BiLSTM: 128\n","num_units: 64\n","dropout_rate: 0.3\n","learning_rate: 0.0005\n","acti_function: relu\n","batch_size: 64\n","Score: 0.3656444847583771\n","Trial summary\n","Hyperparameters:\n","num_units_BiLSTM: 64\n","num_units: 128\n","dropout_rate: 0.3\n","learning_rate: 0.0005\n","acti_function: relu\n","batch_size: 64\n","Score: 0.3670240342617035\n","Trial summary\n","Hyperparameters:\n","num_units_BiLSTM: 128\n","num_units: 64\n","dropout_rate: 0.3\n","learning_rate: 0.0005\n","acti_function: tanh\n","batch_size: 64\n","Score: 0.37383779883384705\n","Trial summary\n","Hyperparameters:\n","num_units_BiLSTM: 64\n","num_units: 128\n","dropout_rate: 0.3\n","learning_rate: 0.00038517170427015545\n","acti_function: tanh\n","batch_size: 64\n","Score: 0.37696582078933716\n","Trial summary\n","Hyperparameters:\n","num_units_BiLSTM: 64\n","num_units: 128\n","dropout_rate: 0.2\n","learning_rate: 0.00046490970102064665\n","acti_function: tanh\n","batch_size: 64\n","Score: 0.3824063241481781\n","Trial summary\n","Hyperparameters:\n","num_units_BiLSTM: 64\n","num_units: 128\n","dropout_rate: 0.3\n","learning_rate: 0.0005\n","acti_function: tanh\n","batch_size: 64\n","Score: 0.38517361879348755\n","Trial summary\n","Hyperparameters:\n","num_units_BiLSTM: 64\n","num_units: 128\n","dropout_rate: 0.3\n","learning_rate: 5e-05\n","acti_function: tanh\n","batch_size: 64\n","Score: 0.4933029115200043\n"]}]},{"cell_type":"code","source":["modelxx.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xMzSReQlzYsY","executionInfo":{"status":"ok","timestamp":1663543713418,"user_tz":-420,"elapsed":23,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"0f6d56c2-2c8e-45fa-920e-dcc3bf2c77e3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential_1\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," embedding_1 (Embedding)     (None, 700, 100)          9336300   \n","                                                                 \n"," bidirectional_1 (Bidirectio  (None, 256)              234496    \n"," nal)                                                            \n","                                                                 \n"," flatten_1 (Flatten)         (None, 256)               0         \n","                                                                 \n"," dense_2 (Dense)             (None, 64)                16448     \n","                                                                 \n"," dropout_1 (Dropout)         (None, 64)                0         \n","                                                                 \n"," dense_3 (Dense)             (None, 18)                1170      \n","                                                                 \n","=================================================================\n","Total params: 9,588,414\n","Trainable params: 9,588,414\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["# model = tuner.get_best_models()[0]\n","# model.summary()"],"metadata":{"id":"z6m-Z7bOMX2h"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["modelx = keras.models.load_model('best_tunning_model.h5')"],"metadata":{"id":"tN9c8rnbSo3-"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["modelxx.summary(expand_nested=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"f1pThlbi9SYB","executionInfo":{"status":"ok","timestamp":1663543713419,"user_tz":-420,"elapsed":13,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"8b58d0e6-aad7-401c-af2b-9177be5947dd"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential_1\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," embedding_1 (Embedding)     (None, 700, 100)          9336300   \n","                                                                 \n"," bidirectional_1 (Bidirectio  (None, 256)              234496    \n"," nal)                                                            \n","                                                                 \n"," flatten_1 (Flatten)         (None, 256)               0         \n","                                                                 \n"," dense_2 (Dense)             (None, 64)                16448     \n","                                                                 \n"," dropout_1 (Dropout)         (None, 64)                0         \n","                                                                 \n"," dense_3 (Dense)             (None, 18)                1170      \n","                                                                 \n","=================================================================\n","Total params: 9,588,414\n","Trainable params: 9,588,414\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["y_pred_one_hot_encoded = (modelxx.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":1663543717809,"user_tz":-420,"elapsed":21,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"9426a73b-50e0-46f6-9575-52f92bef6cda"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.00      0.00      0.00       268\n","           1       0.00      0.00      0.00       267\n","           2       0.00      0.00      0.00       270\n","           3       0.00      0.00      0.00       228\n","           4       0.00      0.00      0.00       270\n","           5       0.00      0.00      0.00       252\n","           6       0.00      0.00      0.00       250\n","           7       0.00      0.00      0.00       270\n","           8       0.00      0.00      0.00       249\n","           9       0.00      0.00      0.00       252\n","          10       0.00      0.00      0.00       258\n","          11       0.00      0.00      0.00       264\n","          12       0.00      0.00      0.00       241\n","          13       0.00      0.00      0.00       277\n","          14       0.00      0.00      0.00       262\n","          15       0.00      0.00      0.00       247\n","          16       0.00      0.00      0.00       290\n","          17       0.00      0.00      0.00       253\n","\n","   micro avg       0.00      0.00      0.00      4668\n","   macro avg       0.00      0.00      0.00      4668\n","weighted avg       0.00      0.00      0.00      4668\n"," samples avg       0.00      0.00      0.00      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 labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n","/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 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n","/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":1663543721736,"user_tz":-420,"elapsed":3945,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"44283b4d-f65b-478b-fa10-c96062cda452"},"outputs":[{"output_type":"stream","name":"stdout","text":["73/73 [==============================] - 3s 30ms/step - loss: 3.9293 - accuracy: 0.0476\n"]},{"output_type":"execute_result","data":{"text/plain":["[3.9293012619018555, 0.04755784198641777]"]},"metadata":{},"execution_count":101}],"source":["modelxx.evaluate(X_test, y_test, batch_size=64)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"aETHc2oXMj8o"},"outputs":[],"source":["\n","def loaddicchar():\n","    dic = {}\n","    char1252 = 'à|á|ả|ã|ạ|ầ|ấ|ẩ|ẫ|ậ|ằ|ắ|ẳ|ẵ|ặ|è|é|ẻ|ẽ|ẹ|ề|ế|ể|ễ|ệ|ì|í|ỉ|ĩ|ị|ò|ó|ỏ|õ|ọ|ồ|ố|ổ|ỗ|ộ|ờ|ớ|ở|ỡ|ợ|ù|ú|ủ|ũ|ụ|ừ|ứ|ử|ữ|ự|ỳ|ý|ỷ|ỹ|ỵ|À|Á|Ả|Ã|Ạ|Ầ|Ấ|Ẩ|Ẫ|Ậ|Ằ|Ắ|Ẳ|Ẵ|Ặ|È|É|Ẻ|Ẽ|Ẹ|Ề|Ế|Ể|Ễ|Ệ|Ì|Í|Ỉ|Ĩ|Ị|Ò|Ó|Ỏ|Õ|Ọ|Ồ|Ố|Ổ|Ỗ|Ộ|Ờ|Ớ|Ở|Ỡ|Ợ|Ù|Ú|Ủ|Ũ|Ụ|Ừ|Ứ|Ử|Ữ|Ự|Ỳ|Ý|Ỷ|Ỹ|Ỵ'.split(\n","        '|')\n","    charutf8 = \"à|á|ả|ã|ạ|ầ|ấ|ẩ|ẫ|ậ|ằ|ắ|ẳ|ẵ|ặ|è|é|ẻ|ẽ|ẹ|ề|ế|ể|ễ|ệ|ì|í|ỉ|ĩ|ị|ò|ó|ỏ|õ|ọ|ồ|ố|ổ|ỗ|ộ|ờ|ớ|ở|ỡ|ợ|ù|ú|ủ|ũ|ụ|ừ|ứ|ử|ữ|ự|ỳ|ý|ỷ|ỹ|ỵ|À|Á|Ả|Ã|Ạ|Ầ|Ấ|Ẩ|Ẫ|Ậ|Ằ|Ắ|Ẳ|Ẵ|Ặ|È|É|Ẻ|Ẽ|Ẹ|Ề|Ế|Ể|Ễ|Ệ|Ì|Í|Ỉ|Ĩ|Ị|Ò|Ó|Ỏ|Õ|Ọ|Ồ|Ố|Ổ|Ỗ|Ộ|Ờ|Ớ|Ở|Ỡ|Ợ|Ù|Ú|Ủ|Ũ|Ụ|Ừ|Ứ|Ử|Ữ|Ự|Ỳ|Ý|Ỷ|Ỹ|Ỵ\".split(\n","        '|')\n","    for i in range(len(char1252)):\n","        dic[char1252[i]] = charutf8[i]\n","    return dic\n","dicchar = loaddicchar()\n","\n","# Hàm chuyển Unicode dựng sẵn về Unicde tổ hợp (phổ biến hơn)\n","def convert_unicode(txt):\n","    return re.sub(\n","        r'à|á|ả|ã|ạ|ầ|ấ|ẩ|ẫ|ậ|ằ|ắ|ẳ|ẵ|ặ|è|é|ẻ|ẽ|ẹ|ề|ế|ể|ễ|ệ|ì|í|ỉ|ĩ|ị|ò|ó|ỏ|õ|ọ|ồ|ố|ổ|ỗ|ộ|ờ|ớ|ở|ỡ|ợ|ù|ú|ủ|ũ|ụ|ừ|ứ|ử|ữ|ự|ỳ|ý|ỷ|ỹ|ỵ|À|Á|Ả|Ã|Ạ|Ầ|Ấ|Ẩ|Ẫ|Ậ|Ằ|Ắ|Ẳ|Ẵ|Ặ|È|É|Ẻ|Ẽ|Ẹ|Ề|Ế|Ể|Ễ|Ệ|Ì|Í|Ỉ|Ĩ|Ị|Ò|Ó|Ỏ|Õ|Ọ|Ồ|Ố|Ổ|Ỗ|Ộ|Ờ|Ớ|Ở|Ỡ|Ợ|Ù|Ú|Ủ|Ũ|Ụ|Ừ|Ứ|Ử|Ữ|Ự|Ỳ|Ý|Ỷ|Ỹ|Ỵ',\n","        lambda x: dicchar[x.group()], txt)\n","\n","def chuan_hoa_dau_cau_tieng_viet(sentence):\n","    \"\"\"\n","        Chuyển câu tiếng việt về chuẩn gõ dấu kiểu cũ.\n","        :param sentence:\n","        :return:\n","        \"\"\"\n","    sentence = sentence.lower()\n","    words = sentence.split()\n","    for index, word in enumerate(words):\n","        cw = re.sub(r'(^\\p{P}*)([p{L}.]*\\p{L}+)(\\p{P}*$)', r'\\1/\\2/\\3', word).split('/')\n","        words[index] = ''.join(cw)\n","    return ' '.join(words)\n","\n","def remove_html(txt):\n","    return re.sub(r'<[^>]*>', '', txt)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9f3ofvuMMaB4"},"outputs":[],"source":["def text_preprocess(document):\n","    # xóa html code\n","    document = remove_html(document)\n","    # chuẩn hóa unicode\n","    document = convert_unicode(document)\n","    # tách từ\n","    document = word_tokenize(document, format=\"text\")\n","    # đưa về lower\n","    document = document.lower()\n","    # xóa khoảng trắng thừa\n","    document = re.sub(r'\\s+', ' ', document).strip()\n","    return document"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iyMJHPCAQ_A6"},"outputs":[],"source":["lst_stopword = []\n","with open('/content/drive/MyDrive/KhanhMD/Data/vietnamese-stopwords.txt','r') as f:\n","    for line in f:\n","        for word in line.split():\n","           lst_stopword.append(word)  \n","    \n","def remove_stopwords(line):\n","    words = []\n","    for word in line.strip().split():\n","        if word not in lst_stopword:\n","            words.append(word)\n","    return ' '.join(words)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oZxCW1ESRK8G","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663543722911,"user_tz":-420,"elapsed":55,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"aadba9f0-0bb8-466d-bc21-49bd78d398ed"},"outputs":[{"output_type":"stream","name":"stdout","text":["['hãng hàng_không vietjet nữ tỷ_phú nguyễn_thị_phương_thảo kế_hoạch huy_động nửa tỷ usd mở_rộng hoạt_động du_lịch sôi_động trở_lại hãng hàng_không quốc_tế khó_khăn ._ctcp hàng_không vietjet ( vjc ) nữ chủ_tịch nguyễn_thị_phương_thảo công_bố nghị_quyết hội_đồng_quản_trị phương_án phát_hành cổ_phiếu riêng_lẻ 2022 mở_rộng đầu_tư , mua thuê máy_bay , động_cơ , trang_thiết_bị , bổ_sung lưu_động , ... thị_trường hàng_không sôi_động trở_lại . , vietjet chào riêng_lẻ 34,8 triệu cổ_phiếu 100 đầu_tư chứng_khoán chuyên_nghiệp giá_bán 135.000 / cp thu 4.700 tỷ 2022 - 2023 ủy_ban chứng_khoán nhà_nước chấp_thuận ._tiền huy_động giải_ngân quý iv / 2022 quý i / 2023 , 2.300 tỷ bổ_sung khoản lưu_động ; 1.100 tỷ thuê , mua máy_bay ; 1.200 tỷ thuê , mua động_cơ , trang_thiết_bị , ... nghị_quyết hđqt vietjet . , đại_hội cổ_đông vietjet 2022 thông_qua kế_hoạch phát_hành trái_phiếu chuyển_đổi tổng giá_trị mệnh_giá tối_đa 6.960 tỷ , tương_đương 300 triệu usd kỳ_hạn 3-5 ._tiền thu sử_dụng đầu_tư , thuê , mua động_cơ bổ_sung khoản , lưu_động hoạt_động kinh_doanh ._cũng đhcđ 2022 , cổ_đông vietjet thông_qua phương_án phát_hành 108,3 triệu cổ_phiếu cổ_tức cổ_đông hiện_hữu ( tỷ_lệ 10 : 2 ) điều_lệ . thực_hiện lợi_nhuận thuế phân_phối 2021 . như_vậy , tổng_cộng 2 phương_án , vietjet huy_động 11,4 nghìn tỷ ( tương_đương 500 triệu usd ) . điều_lệ hai phương_án phương_án cổ_tức cổ_phiếu ._bà nguyễn_thị_phương_thảo ._ngành hàng_không ghi_nhận kết_quả kinh_doanh lợi_nhuận tiếp_tục duy_trì , bớt lỗ . tuy_nhiên , doanh_nghiệp đối_mặt khó_khăn nhiên_liệu đường quốc_tế hồi_phục hoàn_toàn ._trong quý ii / 2022 , ctcp hàng_không vietjet ( vjc ) nữ tỷ_phú nguyễn_thị_phương_thảo ghi_nhận lợi_nhuận thuế 181 tỷ , nâng lũy kế 6 426 tỷ ._doanh_thu hợp_nhất quý ii / 2022 vietjet 11,6 nghìn tỷ , lũy kế 16,1 nghìn tỷ , gấp 2019 thời_kỳ đại_dịch covid-19 . kết_quả kinh_doanh quý ii 6 2022 vietjet ấn_tượng phản_ánh hồi_phục nhu_cầu đi_lại đại_dịch . vietjet , doanh_nghiệp tần_suất khai_thác chặng thực_hiện 52,5 nghìn chuyến vận_chuyển 9 triệu lượt hành_khách nửa 2022 , tương_ứng gấp rưỡi gấp đôi . con_số giai_đoạn dịch 2019 ._nghị_quyết đhcd_2022 viet jet . mặc_dù ngành hàng_không đón tín_hiệu hồi_phục hậu_đại_dịch khó_khăn đối_với doanh_nghiệp ngành ._giá nhiên_liệu thời_gian đường quốc_tế hồi_phục hoàn_toàn ._tổng_công_ty hàng_không việt_nam - vietnam_airlines ( hvn ) ghi_nhận quý ii / 2022 tiếp_tục thua_lỗ 2.600 tỷ doanh_thu gấp 3 ._đây quý lỗ 10 liên_tiếp doanh_nghiệp ._thị_trường hàng_không nội_địa phục_hồi nhanh_chóng . tuy_nhiên , nhiên_liệu hãng hàng_không quốc_gia ghi_nhận tình_trạng kinh_doanh . lãi_suất xu_hướng ảnh_hưởng , chi_phí tài_chính doanh_nghiệp . mặc_dù , thua_lỗ vietnam_airlines nặng_nề khoản lợi_nhuận âm 4.500 tỷ quý ii / 2021 ._tính 6 , hãng lỗ 5.250 tỷ , 3.400 tỷ . tuy_nhiên , vietnam_airlines tình_thế ngàn cân_treo sợi tóc chờ giải_cứu ._hãng hàng_không quốc_gia nguy_cơ hủy niêm_yết bắt_buộc khả_năng hoạt_động liên_tục doanh_nghiệp phụ_thuộc hỗ_trợ tài_chính chính_phủ , gia_hạn thanh_toán khoản vay ngân_hàng … công_ty tnhh deloitte việt_nam , khả_năng hoạt_động liên_tục vietnam_airlines_hiện phụ_thuộc hỗ_trợ tài_chính chính_phủ , gia_hạn thanh_toán khoản vay ngân_hàng , khoản nhà_cung_cấp , thuê . deloitte , quý ii / 2022 , khoản nợ ngắn_hạn hãng hàng_không quốc_gia việt_nam tài_sản ngắn_hạn . cụ_thể , quý ii / 2022 , vietnam_airlines ghi_nhận chủ_sở_hữu_âm 4.900 tỷ ._nợ ngắn_hạn 52,7 nghìn tỷ , tài_sản ngắn_hạn , 16,2 nghìn tỷ ._các khoản quá_hạn vietnam_airlines 10,5 nghìn tỷ ._sở_giao_dịch chứng_khoán tp hcm ( hose ) lưu_ý khả_năng hủy niêm_yết bắt_buộc cổ_phiếu hvn vietnam_airlines ( hvn ) ._hvn vi_phạm 3 yếu_tố quy_định hủy niêm_yết bắt_buộc ._vietnam_airlines ghi_nhận lỗ lũy kế 28,9 nghìn tỷ ( tương_đương 1,2 tỷ usd ) .']\n"]},{"output_type":"execute_result","data":{"text/plain":["[[554,\n","  56,\n","  10,\n","  3804,\n","  163,\n","  374,\n","  652,\n","  249,\n","  160,\n","  247,\n","  805,\n","  329,\n","  742,\n","  793,\n","  67,\n","  1075,\n","  374,\n","  509,\n","  520,\n","  685,\n","  369,\n","  67,\n","  220,\n","  259,\n","  1261,\n","  67,\n","  182,\n","  45,\n","  554,\n","  56,\n","  10,\n","  77,\n","  199,\n","  304,\n","  676,\n","  4565,\n","  56,\n","  10,\n","  3804,\n","  15748,\n","  163,\n","  180,\n","  750,\n","  249,\n","  160,\n","  247,\n","  805,\n","  17,\n","  360,\n","  697,\n","  306,\n","  134,\n","  41,\n","  599,\n","  348,\n","  247,\n","  325,\n","  139,\n","  113,\n","  415,\n","  1162,\n","  438,\n","  1405,\n","  7373,\n","  520,\n","  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khi du lịch sôi động trở lại và nhiều hãng hàng không trong nước và quốc tế còn đang khó khăn. CTCP Hàng không Vietjet (VJC) của nữ chủ tịch Nguyễn Thị Phương Thảo vừa công bố nghị quyết của Hội đồng quản trị về phương án phát hành cổ phiếu riêng lẻ 2022 nhằm mở rộng đầu tư, mua thuê máy bay, động cơ, trang thiết bị, bổ sung vốn lưu động,... khi thị trường hàng không sôi động trở lại. Theo đó, VietJet sẽ chào bán riêng lẻ 34,8 triệu cổ phiếu cho 100 nhà đầu tư chứng khoán chuyên nghiệp với giá bán là 135.000 đồng/cp để thu về gần 4.700 tỷ đồng trong năm 2022-2023 sau khi được Ủy ban Chứng khoán Nhà nước chấp thuận. Tiền huy động sẽ được giải ngân ngay trong quý IV/2022 cho tới quý I/2023, trong đó hơn 2.300 tỷ bổ sung thanh khoản và vốn lưu động; hơn 1.100 tỷ đồng cho thuê, mua máy bay; hơn 1.200 tỷ đồng cho thuê, mua động cơ, trang thiết bị,... Nghị quyết HĐQT VietJet. Trước đó, đại hội cổ đông VietJet 2022 đã thông qua kế hoạch phát hành trái phiếu chuyển đổi với tổng giá trị mệnh giá tối đa 6.960 tỷ đồng, hoặc tương đương 300 triệu USD với kỳ hạn 3-5 năm. Tiền thu về cũng sẽ được sử dụng để đầu tư, thuê, mua động cơ và bổ sung nguồn thanh khoản, vốn lưu động hoạt động kinh doanh. Cũng tại ĐHCĐ 2022, cổ đông VietJet đã thông qua phương án phát hành hơn 108,3 triệu cổ phiếu để trả cổ tức cho cổ đông hiện hữu (theo tỷ lệ 10:2) nhằm tăng vốn điều lệ. Nguồn vốn thực hiện lấy từ lợi nhuận sau thuế chưa phân phối đến cuối 2021. Như vậy, tổng cộng với 2 phương án đầu, VietJet sẽ huy động thêm khoảng 11,4 nghìn tỷ đồng (tương đương gần 500 triệu USD). Vốn điều lệ cũng tăng mạnh nhờ hai phương án này và phương án trả cổ tức bằng cổ phiếu. Bà Nguyễn Thị Phương Thảo. Ngành hàng không gần đây ghi nhận kết quả kinh doanh sáng dần với lợi nhuận tiếp tục được duy trì, hoặc đã bớt lỗ. Tuy nhiên, doanh nghiệp vẫn đối mặt với khó khăn do giá nhiên liệu cao và đường quốc tế chưa hồi phục hoàn toàn. Trong quý II/2022, CTCP Hàng không Vietjet (VJC) của nữ tỷ phú Nguyễn Thị Phương Thảo ghi nhận lợi nhuận sau thuế đạt hơn 181 tỷ đồng, nâng lũy kế 6 tháng đầu năm lên gần 426 tỷ đồng. Doanh thu hợp nhất quý II/2022 của VietJet đạt gần 11,6 nghìn tỷ đồng, lũy kế đạt hơn 16,1 nghìn tỷ đồng, tăng mạnh gấp vài lần so với cùng kỳ năm trước và đã cao hơn so với cùng kỳ năm 2019 thời kỳ trước đại dịch Covid-19. Kết quả kinh doanh quý II cũng như 6 tháng đầu năm 2022 của VietJet là khá ấn tượng và nó phản ánh sự hồi phục về nhu cầu đi lại tăng cao sau đại dịch. Theo VietJet, doanh nghiệp đã tăng tần suất khai thác trên nhiều chặng bay thực hiện 52,5 nghìn chuyến bay và vận chuyển 9 triệu lượt hành khách trong nửa đầu  năm 2022, tăng tương ứng gấp rưỡi và gần gấp đôi so với cùng kỳ năm trước. Con số này cũng đã cao hơn so với giai đoạn trước dịch 2019. Nghị quyết ĐHCD 2022 của Viet Jet. Mặc dù ngành hàng không đón những tín hiệu hồi phục hậu đại dịch nhưng khó khăn đối với các doanh nghiệp trong ngành còn nhiều. Giá giá nhiên liệu tăng mạnh và ở mức cao trong thời gian gần đây và đường quốc tế chưa hồi phục hoàn toàn. Tổng công ty Hàng không Việt Nam - Vietnam Airlines (HVN) vừa ghi nhận quý II/2022 tiếp tục thua lỗ gần 2.600 tỷ đồng dù doanh thu tăng gấp gần 3 so với cùng kỳ. Đây là quý lỗ thứ 10 liên tiếp của doanh nghiệp này. Thị trường hàng không nội địa phục hồi nhanh chóng. Tuy nhiên, việc giá nhiên liệu tăng nhanh và mạnh khiến hãng hàng không quốc gia ghi nhận tình trạng kinh doanh dưới giá vốn. Lãi suất có xu hướng tăng cũng ảnh hưởng, khiến chi phí tài chính của doanh nghiệp tăng lên. Mặc dù vậy, mức thua lỗ của Vietnam Airlines không còn nặng nề như khoản lợi nhuận âm hơn 4.500 tỷ đồng trong quý II/2021. Tính trong 6 tháng đầu năm, hãng lỗ hơn 5.250 tỷ đồng, ít hơn gần 3.400 tỷ đồng so với cùng kỳ năm trước. Tuy nhiên, Vietnam Airlines đang ở tình thế ngàn cân treo sợi tóc và chờ thêm một lần giải cứu. Hãng hàng không quốc gia nguy cơ bị hủy niêm yết bắt buộc và khả năng hoạt động liên tục của doanh nghiệp sẽ phụ thuộc sự hỗ trợ tài chính của Chính phủ, việc được gia hạn thanh toán các khoản vay từ ngân hàng… Theo Công ty TNHH Deloitte Việt Nam, khả năng hoạt động liên tục của Vietnam Airlines hiện phụ thuộc sự hỗ trợ tài chính của Chính phủ, việc được gia hạn thanh toán các khoản vay từ ngân hàng, các khoản phải trả cho nhà cung cấp, bên cho thuê. Theo Deloitte, tới cuối quý II/2022, khoản nợ ngắn hạn của hãng hàng không quốc gia Việt Nam đã vượt quá tài sản ngắn hạn. Cụ thể, tới cuối quý II/2022, Vietnam Airlines ghi nhận vốn chủ sở hữu âm gần 4.900 tỷ đồng. Nợ ngắn hạn ở mức rất lớn ở mức gần 52,7 nghìn tỷ đồng, vượt xa so với tài sản ngắn hạn, vốn chỉ ở mức 16,2 nghìn tỷ đồng. Các khoản phải trả quá hạn của Vietnam Airlines là gần 10,5 nghìn tỷ đồng. Sở Giao dịch Chứng khoán TP HCM (HoSE) cũng vừa đưa ra lưu ý khả năng hủy niêm yết bắt buộc cổ phiếu HVN của Vietnam Airlines (HVN). HVN đã vi phạm cả 3 yếu tố mà theo quy định sẽ bị hủy niêm yết bắt buộc. Vietnam Airlines ghi nhận lỗ lũy kế lên tới trên 28,9 nghìn tỷ đồng (tương đương 1,2 tỷ USD).'\n","\n","document = text_preprocess(document)\n","document = remove_stopwords(document)\n","document = [document]\n","print(document)\n","#tok.fit_on_texts(df['text'])\n","encd = tok.texts_to_sequences(document)\n","encd"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TSguwwLF6lrG"},"outputs":[],"source":["pad= pad_sequences(encd, maxlen=max_rev_len, padding='post')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"me4k0i4Nm0IV","colab":{"base_uri":"https://localhost:8080/","height":182},"executionInfo":{"status":"error","timestamp":1663543722913,"user_tz":-420,"elapsed":54,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"4e62b0ee-80a2-47d3-dd28-8d6def551ffc"},"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-107-e7aba5483dc4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpredict_x\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodelx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpad\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mpredict_x\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'modelx' is not defined"]}],"source":["predict_x = modelx.predict(pad)\n","predict_x"]},{"cell_type":"code","source":["predict_onehot = (modelx.predict(pad)> 0.5).astype(\"float\")\n","predict_onehot"],"metadata":{"id":"zbBE3N765mPA"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pNVOJp0li9hV"},"outputs":[],"source":["make_predict = predict_onehot.argmax(axis=1)\n","label_encoder.inverse_transform(make_predict)"]},{"cell_type":"code","source":["for i in range(18):\n","  print(label_encoder.inverse_transform(np.array([i])))"],"metadata":{"id":"FsI1eiim2XKs"},"execution_count":null,"outputs":[]}],"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}