2.0.model_only_title.ipynb

{"cells":[{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"id":"UoHWdVbYGded","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1651708200209,"user_tz":240,"elapsed":16823,"user":{"displayName":"Dongjun Shin","userId":"07542643315713993435"}},"outputId":"dc99a22a-a610-4ebd-d866-2d3a13140811"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["import os\n","import numpy as np\n","import pandas as pd\n","import tensorflow as tf\n","import sys\n","from sklearn.model_selection import train_test_split"],"metadata":{"id":"NCZyjp6UtQ9F"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df = pd.read_csv('/content/drive/Shareddrives/CS2470/books_datasetv2.csv')\n","df.head"],"metadata":{"id":"Eg85PlmrFqUl","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1651708207873,"user_tz":240,"elapsed":685,"user":{"displayName":"Dongjun Shin","userId":"07542643315713993435"}},"outputId":"edb4f49d-e5aa-4f9a-e47d-1e3dd0e06cf4"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<bound method NDFrame.head of       Unnamed: 0           ISBN  \\\n","0              0  9781419726552   \n","1              1  9780982301821   \n","2              2  9780316407076   \n","3              3  9780718088804   \n","4              4  9780451495426   \n","...          ...            ...   \n","1641        1666  9780451491640   \n","1642        1667  9780008263737   \n","1643        1668  9780525522478   \n","1644        1669  9780062676832   \n","1645        1670  9780062570604   \n","\n","                                                  title  series  \\\n","0     F*ck, That's Delicious: An Annotated Guide to ...       0   \n","1     10-Day Green Smoothie Cleanse: Lose Up to 15 P...       0   \n","2                                           15th Affair       1   \n","3       42 Faith: The Rest of the Jackie Robinson Story       0   \n","4     7 Lessons from Heaven: How Dying Taught Me to ...       0   \n","...                                                 ...     ...   \n","1641                               Archangel's Prophecy       1   \n","1642                  Sunshine at the Comfort Food Café       1   \n","1643                                    Depth of Winter       1   \n","1644                              The Sentence is Death       1   \n","1645                                       Dread Nation       1   \n","\n","                author  rating           genre       publish_company  \\\n","0       Action Bronson    4.17  food-and-drink       Harry N. Abrams   \n","1           J.J. Smith    4.11          health     Adiva Publishing    \n","2      James Patterson    4.09         mystery              Hachette   \n","3             Ed Henry    4.16       biography        Harper Collins   \n","4         Mary C. Neal    4.08     non-fiction      Convergent Books   \n","...                ...     ...             ...                   ...   \n","1641      Nalini Singh    4.24         fantasy  Penguin Random House   \n","1642   Debbie  Johnson    4.27         romance        Harper Collins   \n","1643     Craig Johnson    3.90         mystery  Penguin Random House   \n","1644  Anthony Horowitz    4.01         mystery        Harper Collins   \n","1645   Justina Ireland    4.14     young-adult        Harper Collins   \n","\n","                     format  number_of_pages publish_month  publish_year  \\\n","0                 Hardcover              192     September          2017   \n","1                 Paperback              167      February          2014   \n","2                 Hardcover              351           May          2016   \n","3                 Hardcover              368         April          2017   \n","4                 Paperback              288     September          2017   \n","...                     ...              ...           ...           ...   \n","1641  Mass Market Paperback              372       October          2018   \n","1642              Paperback              400         March          2018   \n","1643              Hardcover              304     September          2018   \n","1644              Hardcover              375           May          2019   \n","1645              Hardcover              455         April          2018   \n","\n","      NYT_bestseller  top5  length_of_title  top_authors  \n","0                1.0     0               57            0  \n","1                1.0     0               63            0  \n","2                1.0     1               11            1  \n","3                1.0     1               47            0  \n","4                1.0     0               68            0  \n","...              ...   ...              ...          ...  \n","1641             0.0     1               20            0  \n","1642             0.0     1               33            0  \n","1643             0.0     1               15            0  \n","1644             0.0     1               21            0  \n","1645             0.0     1               12            0  \n","\n","[1646 rows x 16 columns]>"]},"metadata":{},"execution_count":3}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":687},"executionInfo":{"elapsed":17,"status":"ok","timestamp":1651708207874,"user":{"displayName":"Dongjun Shin","userId":"07542643315713993435"},"user_tz":240},"id":"eAUWxdAJC7e1","outputId":"f15b8429-815c-4d52-e7dc-7af415957b73"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   Unnamed: 0           ISBN  \\\n","0           0  9781419726552   \n","1           1  9780982301821   \n","2           2  9780316407076   \n","3           3  9780718088804   \n","4           4  9780451495426   \n","\n","                                               title  series           author  \\\n","0  F*ck, That's Delicious: An Annotated Guide to ...       0   Action Bronson   \n","1  10-Day Green Smoothie Cleanse: Lose Up to 15 P...       0       J.J. Smith   \n","2                                        15th Affair       1  James Patterson   \n","3    42 Faith: The Rest of the Jackie Robinson Story       0         Ed Henry   \n","4  7 Lessons from Heaven: How Dying Taught Me to ...       0     Mary C. Neal   \n","\n","   rating           genre    publish_company     format  number_of_pages  \\\n","0    4.17  food-and-drink    Harry N. Abrams  Hardcover              192   \n","1    4.11          health  Adiva Publishing   Paperback              167   \n","2    4.09         mystery           Hachette  Hardcover              351   \n","3    4.16       biography     Harper Collins  Hardcover              368   \n","4    4.08     non-fiction   Convergent Books  Paperback              288   \n","\n","  publish_month  publish_year  NYT_bestseller  top5  length_of_title  \\\n","0     September          2017             1.0     0               57   \n","1      February          2014             1.0     0               63   \n","2           May          2016             1.0     1               11   \n","3         April          2017             1.0     1               47   \n","4     September          2017             1.0     0               68   \n","\n","   top_authors                                        title_clean  \n","0            0  f ck  that s delicious  an annotated guide to ...  \n","1            0     day green smoothie cleanse  lose up to    p...  \n","2            1                                          th affair  \n","3            0       faith  the rest of the jackie robinson story  \n","4            0    lessons from heaven  how dying taught me to ...  "],"text/html":["\n","  <div id=\"df-2c364d27-9e5c-4248-b7f6-08700ba249a6\">\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>Unnamed: 0</th>\n","      <th>ISBN</th>\n","      <th>title</th>\n","      <th>series</th>\n","      <th>author</th>\n","      <th>rating</th>\n","      <th>genre</th>\n","      <th>publish_company</th>\n","      <th>format</th>\n","      <th>number_of_pages</th>\n","      <th>publish_month</th>\n","      <th>publish_year</th>\n","      <th>NYT_bestseller</th>\n","      <th>top5</th>\n","      <th>length_of_title</th>\n","      <th>top_authors</th>\n","      <th>title_clean</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>9781419726552</td>\n","      <td>F*ck, That's Delicious: An Annotated Guide to ...</td>\n","      <td>0</td>\n","      <td>Action Bronson</td>\n","      <td>4.17</td>\n","      <td>food-and-drink</td>\n","      <td>Harry N. 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Smith</td>\n","      <td>4.11</td>\n","      <td>health</td>\n","      <td>Adiva Publishing</td>\n","      <td>Paperback</td>\n","      <td>167</td>\n","      <td>February</td>\n","      <td>2014</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>63</td>\n","      <td>0</td>\n","      <td>day green smoothie cleanse  lose up to    p...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>9780316407076</td>\n","      <td>15th Affair</td>\n","      <td>1</td>\n","      <td>James Patterson</td>\n","      <td>4.09</td>\n","      <td>mystery</td>\n","      <td>Hachette</td>\n","      <td>Hardcover</td>\n","      <td>351</td>\n","      <td>May</td>\n","      <td>2016</td>\n","      <td>1.0</td>\n","      <td>1</td>\n","      <td>11</td>\n","      <td>1</td>\n","      <td>th affair</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>9780718088804</td>\n","      <td>42 Faith: The Rest of the Jackie Robinson Story</td>\n","      <td>0</td>\n","      <td>Ed Henry</td>\n","      <td>4.16</td>\n","      <td>biography</td>\n","      <td>Harper Collins</td>\n","      <td>Hardcover</td>\n","      <td>368</td>\n","      <td>April</td>\n","      <td>2017</td>\n","      <td>1.0</td>\n","      <td>1</td>\n","      <td>47</td>\n","      <td>0</td>\n","      <td>faith  the rest of the jackie robinson story</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>9780451495426</td>\n","      <td>7 Lessons from Heaven: How Dying Taught Me to ...</td>\n","      <td>0</td>\n","      <td>Mary C. Neal</td>\n","      <td>4.08</td>\n","      <td>non-fiction</td>\n","      <td>Convergent Books</td>\n","      <td>Paperback</td>\n","      <td>288</td>\n","      <td>September</td>\n","      <td>2017</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>68</td>\n","      <td>0</td>\n","      <td>lessons from heaven  how dying taught me to ...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2c364d27-9e5c-4248-b7f6-08700ba249a6')\"\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 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-2c364d27-9e5c-4248-b7f6-08700ba249a6 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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punctuation\n","    df[clean_col] = df[clean_col].apply(lambda x: re.sub('[^a-zA-Z]', ' ', x))\n","\n","   \n","    return df\n","\n","# calling function\n","df = clean_data(df, 'title', 'title_clean')\n","df.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1885,"status":"ok","timestamp":1651708209746,"user":{"displayName":"Dongjun Shin","userId":"07542643315713993435"},"user_tz":240},"id":"r8CSvIQXMwEy","outputId":"7e885c22-d06b-4042-c2a4-f1e558952a4a"},"outputs":[{"output_type":"stream","name":"stdout","text":["[nltk_data] Downloading package punkt to /root/nltk_data...\n","[nltk_data]   Unzipping tokenizers/punkt.zip.\n"]}],"source":["# tokenization\n","import nltk\n","nltk.download('punkt')\n","df[\"title_token\"] = df.loc[:, \"title_clean\"].apply(nltk.word_tokenize)\n","\n","# list to strings\n","#df[\"title_token\"] = [','.join(map(str, l)) for l in df['title_token']]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1651708209746,"user":{"displayName":"Dongjun Shin","userId":"07542643315713993435"},"user_tz":240},"id":"EUfzUVZZM4wB","outputId":"f176229a-7e51-4365-ebce-76274f1545fe"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0       [f, ck, that, s, delicious, an, annotated, gui...\n","1       [day, green, smoothie, cleanse, lose, up, to, ...\n","2                                            [th, affair]\n","3       [faith, the, rest, of, the, jackie, robinson, ...\n","4       [lessons, from, heaven, how, dying, taught, me...\n","                              ...                        \n","1641                             [archangel, s, prophecy]\n","1642              [sunshine, at, the, comfort, food, caf]\n","1643                                  [depth, of, winter]\n","1644                           [the, sentence, is, death]\n","1645                                      [dread, nation]\n","Name: title_token, Length: 1646, dtype: object"]},"metadata":{},"execution_count":6}],"source":["df[\"title_token\"]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"X5qnBkaPNYm5"},"outputs":[],"source":["title = df[\"title_token\"]\n","label = df[\"NYT_bestseller\"]\n","train_x, test_x, train_y, test_y = train_test_split(title, label, test_size=0.3)\n","train_title = [i for i in train_x]\n","train_label = [i for i in train_y]\n","test_title = [i for i in test_x]\n","test_label = [i for i in test_y]"]},{"cell_type":"code","source":["def build_vocab(sentences):\n","\ttokens = []\n","\tfor s in sentences: tokens.extend(s) \n","\tall_words = sorted(list(set(tokens)))  \n","\tvocab =  {word:i for i,word in enumerate(all_words)}\n","\treturn vocab\n","\n","def convert_to_id(vocab, sentences):\n","  id =[]\n","  for j in range(len(sentences)) :\n","    id.append([vocab[i] for i in sentences[j]])\n","  return id"],"metadata":{"id":"yZqyuEaSgUAB"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["vocab_all = build_vocab(title) # dictionary\n","id_all =  convert_to_id(vocab_all, title)"],"metadata":{"id":"caT9du6pmsW2"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["## vocab, id, id with padding\n","\n","train_vocab = build_vocab(train_title)\n","\n","train_id = convert_to_id(train_vocab, train_title)\n","train_id_padded = tf.keras.preprocessing.sequence.pad_sequences(train_id, padding=\"post\")\n","train_id_padded = tf.cast(train_id_padded, dtype = tf.float64)\n","\n","train_label = tf.cast(train_label, dtype = tf.float64)\n","\n","print(train_vocab)\n","print(train_id_padded)\n","\n","test_vocab = build_vocab(test_title)\n","test_id = convert_to_id(test_vocab, test_title)\n","test_id_padded = tf.keras.preprocessing.sequence.pad_sequences(test_id, padding=\"post\")\n","test_id_padded = tf.cast(test_id_padded, dtype = tf.float64)\n","test_label = tf.cast(test_label, dtype = tf.float64)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iy9I94IoEjhy","executionInfo":{"status":"ok","timestamp":1651708209966,"user_tz":240,"elapsed":224,"user":{"displayName":"Dongjun Shin","userId":"07542643315713993435"}},"outputId":"1f09b042-c5d3-41f9-cd98-04eff1163ba7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'a': 0, 'about': 1, 'abraham': 2, 'absolute': 3, 'abundance': 4, 'abyss': 5, 'academy': 6, 'accelerations': 7, 'achieve': 8, 'acker': 9, 'adam': 10, 'addicted': 11, 'addiction': 12, 'administration': 13, 'adult': 14, 'adultolescence': 15, 'adventure': 16, 'adventures': 17, 'adversity': 18, 'afar': 19, 'affair': 20, 'affairs': 21, 'affluence': 22, 'affordable': 23, 'afghanistan': 24, 'african': 25, 'after': 26, 'afterlives': 27, 'again': 28, 'against': 29, 'age': 30, 'agenda': 31, 'agent': 32, 'ageproof': 33, 'akata': 34, 'al': 35, 'alamo': 36, 'alaska': 37, 'albert': 38, 'alchemist': 39, 'alert': 40, 'alexander': 41, 'alice': 42, 'alive': 43, 'all': 44, 'allowed': 45, 'almost': 46, 'alone': 47, 'always': 48, 'am': 49, 'amazing': 50, 'america': 51, 'american': 52, 'amnesia': 53, 'among': 54, 'an': 55, 'anatomy': 56, 'ancient': 57, 'and': 58, 'andy': 59, 'angels': 60, 'anger': 61, 'angler': 62, 'animal': 63, 'animals': 64, 'animators': 65, 'anna': 66, 'annex': 67, 'anomaly': 68, 'anonymous': 69, 'anthology': 70, 'antidote': 71, 'anxiety': 72, 'anything': 73, 'anyway': 74, 'apartment': 75, 'apollo': 76, 'appetite': 77, 'appleseed': 78, 'apply': 79, 'apprentice': 80, 'apricot': 81, 'apricots': 82, 'aquaman': 83, 'archangel': 84, 'archenemies': 85, 'are': 86, 'ariane': 87, 'around': 88, 'arrival': 89, 'art': 90, 'artemis': 91, 'artificial': 92, 'aru': 93, 'as': 94, 'ash': 95, 'ashby': 96, 'ask': 97, 'asked': 98, 'assassin': 99, 'assassination': 100, 'assassinations': 101, 'asthmatic': 102, 'astonishing': 103, 'astrophysical': 104, 'astrophysics': 105, 'asymmetries': 106, 'at': 107, 'ate': 108, 'atlas': 109, 'attack': 110, 'august': 111, 'auschwitz': 112, 'autobiography': 113, 'autoboyography': 114, 'autumn': 115, 'avenue': 116, 'awaken': 117, 'awakening': 118, 'away': 119, 'awkward': 120, 'b': 121, 'babe': 122, 'baby': 123, 'back': 124, 'bad': 125, 'balance': 126, 'ballerina': 127, 'balloon': 128, 'banana': 129, 'band': 130, 'banks': 131, 'bardo': 132, 'barefoot': 133, 'barely': 134, 'barracoon': 135, 'barricades': 136, 'barrowfields': 137, 'base': 138, 'baseball': 139, 'bastard': 140, 'batgirl': 141, 'bathroom': 142, 'batman': 143, 'battery': 144, 'battle': 145, 'baumgartners': 146, 'bawse': 147, 'bay': 148, 'be': 149, 'beach': 150, 'beachcomber': 151, 'bear': 152, 'beast': 153, 'beasts': 154, 'beauties': 155, 'beautiful': 156, 'beauty': 157, 'beauvoir': 158, 'became': 159, 'become': 160, 'becomes': 161, 'becoming': 162, 'bed': 163, 'bedlam': 164, 'before': 165, 'beginning': 166, 'beginnings': 167, 'begins': 168, 'behavior': 169, 'behind': 170, 'being': 171, 'believe': 172, 'believers': 173, 'believing': 174, 'bell': 175, 'belle': 176, 'belles': 177, 'bellewether': 178, 'bellow': 179, 'belly': 180, 'belonging': 181, 'belongings': 182, 'beloved': 183, 'beloveds': 184, 'below': 185, 'ben': 186, 'beneath': 187, 'benefit': 188, 'beren': 189, 'berries': 190, 'best': 191, 'better': 192, 'between': 193, 'beyond': 194, 'bible': 195, 'big': 196, 'bigly': 197, 'billionaire': 198, 'billionaires': 199, 'biography': 200, 'birdcage': 201, 'birds': 202, 'birth': 203, 'bit': 204, 'black': 205, 'blast': 206, 'blerd': 207, 'blinds': 208, 'blitzed': 209, 'block': 210, 'blood': 211, 'blue': 212, 'blues': 213, 'boat': 214, 'bob': 215, 'body': 216, 'boer': 217, 'bohemia': 218, 'bomb': 219, 'bone': 220, 'bones': 221, 'book': 222, 'bookshop': 223, 'bookstore': 224, 'boost': 225, 'border': 226, 'born': 227, 'borne': 228, 'both': 229, 'bow': 230, 'box': 231, 'boy': 232, 'boys': 233, 'brainpower': 234, 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'negro': 1265, 'nelson': 1266, 'nemesis': 1267, 'nerve': 1268, 'nest': 1269, 'never': 1270, 'nevertheless': 1271, 'neverworld': 1272, 'new': 1273, 'next': 1274, 'nicks': 1275, 'night': 1276, 'nighthawk': 1277, 'nightingale': 1278, 'nightwalker': 1279, 'nightwing': 1280, 'nikan': 1281, 'nine': 1282, 'ninth': 1283, 'no': 1284, 'noah': 1285, 'nobel': 1286, 'noir': 1287, 'non': 1288, 'nonsense': 1289, 'nonstop': 1290, 'normal': 1291, 'norse': 1292, 'not': 1293, 'note': 1294, 'notebook': 1295, 'notes': 1296, 'noteworthy': 1297, 'nothing': 1298, 'noumenon': 1299, 'novel': 1300, 'now': 1301, 'nowhere': 1302, 'nudge': 1303, 'number': 1304, 'oathbringer': 1305, 'obama': 1306, 'obsession': 1307, 'obsessive': 1308, 'occasions': 1309, 'occult': 1310, 'occupied': 1311, 'ocean': 1312, 'odds': 1313, 'of': 1314, 'off': 1315, 'okay': 1316, 'old': 1317, 'oleomargarine': 1318, 'oliver': 1319, 'olivia': 1320, 'on': 1321, 'once': 1322, 'one': 1323, 'ones': 1324, 'only': 1325, 'opera': 1326, 'operation': 1327, 'opportunity': 1328, 'optimist': 1329, 'option': 1330, 'or': 1331, 'oracle': 1332, 'origin': 1333, 'original': 1334, 'originals': 1335, 'orlando': 1336, 'orphan': 1337, 'osage': 1338, 'osmond': 1339, 'other': 1340, 'others': 1341, 'otherworld': 1342, 'our': 1343, 'ourselves': 1344, 'out': 1345, 'outcasts': 1346, 'outsider': 1347, 'over': 1348, 'overboard': 1349, 'overstory': 1350, 'owl': 1351, 'packing': 1352, 'pages': 1353, 'pain': 1354, 'painted': 1355, 'paisley': 1356, 'panda': 1357, 'paper': 1358, 'paradise': 1359, 'paradoxes': 1360, 'paris': 1361, 'parker': 1362, 'part': 1363, 'parties': 1364, 'party': 1365, 'passing': 1366, 'passionate': 1367, 'passions': 1368, 'past': 1369, 'patrick': 1370, 'patriotism': 1371, 'patrol': 1372, 'patterns': 1373, 'paul': 1374, 'peaceouthaterz': 1375, 'peaks': 1376, 'pearl': 1377, 'pencil': 1378, 'people': 1379, 'peoples': 1380, 'perfect': 1381, 'perfectly': 1382, 'perfects': 1383, 'performance': 1384, 'perplexed': 1385, 'personal': 1386, 'personality': 1387, 'personalize': 1388, 'persuasion': 1389, 'perversions': 1390, 'petticoats': 1391, 'petty': 1392, 'phasma': 1393, 'phenomenon': 1394, 'phoebe': 1395, 'pickle': 1396, 'picture': 1397, 'pie': 1398, 'piece': 1399, 'pieces': 1400, 'pilgrims': 1401, 'pine': 1402, 'piracy': 1403, 'pirate': 1404, 'pisces': 1405, 'pit': 1406, 'pitch': 1407, 'place': 1408, 'plain': 1409, 'plan': 1410, 'played': 1411, 'player': 1412, 'playing': 1413, 'please': 1414, 'plus': 1415, 'podcast': 1416, 'poems': 1417, 'poets': 1418, 'point': 1419, 'political': 1420, 'politics': 1421, 'ponty': 1422, 'poppy': 1423, 'populist': 1424, 'portraits': 1425, 'possibilities': 1426, 'potential': 1427, 'potter': 1428, 'pottymouth': 1429, 'pounds': 1430, 'power': 1431, 'powers': 1432, 'prairie': 1433, 'predictions': 1434, 'present': 1435, 'presidency': 1436, 'president': 1437, 'pressure': 1438, 'prevent': 1439, 'prey': 1440, 'price': 1441, 'priceless': 1442, 'prime': 1443, 'prince': 1444, 'princess': 1445, 'principle': 1446, 'prison': 1447, 'prisoner': 1448, 'privilege': 1449, 'problems': 1450, 'producer': 1451, 'profiles': 1452, 'promise': 1453, 'propelled': 1454, 'prophecy': 1455, 'protocol': 1456, 'proud': 1457, 'proven': 1458, 'proving': 1459, 'prussian': 1460, 'psychedelics': 1461, 'psychology': 1462, 'pucked': 1463, 'puddin': 1464, 'puddles': 1465, 'pulse': 1466, 'punch': 1467, 'pure': 1468, 'purloining': 1469, 'put': 1470, 'qb': 1471, 'qualityland': 1472, 'quarters': 1473, 'queen': 1474, 'queens': 1475, 'quest': 1476, 'questions': 1477, 'quick': 1478, 'ra': 1479, 'rabbit': 1480, 'race': 1481, 'rad': 1482, 'radical': 1483, 'rage': 1484, 'rain': 1485, 'rainbirds': 1486, 'rainy': 1487, 'ransom': 1488, 'rape': 1489, 'rapid': 1490, 'rather': 1491, 'rationalist': 1492, 'raw': 1493, 're': 1494, 'read': 1495, 'reading': 1496, 'reagan': 1497, 'real': 1498, 'really': 1499, 'reaper': 1500, 'reasons': 1501, 'rebel': 1502, 'rebels': 1503, 'reboot': 1504, 'reborn': 1505, 'rebuild': 1506, 'rebuilt': 1507, 'recipes': 1508, 'reckless': 1509, 'reckoning': 1510, 'record': 1511, 'red': 1512, 'redder': 1513, 'rediscovering': 1514, 'ree': 1515, 'reflection': 1516, 'reflections': 1517, 'refresh': 1518, 'refugees': 1519, 'reich': 1520, 'rejection': 1521, 'relaxation': 1522, 'release': 1523, 'reliance': 1524, 'relief': 1525, 'religion': 1526, 'remains': 1527, 'remarkable': 1528, 'renegades': 1529, 'report': 1530, 'republic': 1531, 'rescue': 1532, 'reserved': 1533, 'reservoir': 1534, 'reset': 1535, 'resilience': 1536, 'resistance': 1537, 'resort': 1538, 'rest': 1539, 'restaurant': 1540, 'restore': 1541, 'resurrection': 1542, 'rethinking': 1543, 'return': 1544, 'reveal': 1545, 'revere': 1546, 'reverse': 1547, 'revolution': 1548, 'rhett': 1549, 'rhyming': 1550, 'rich': 1551, 'right': 1552, 'rights': 1553, 'ring': 1554, 'rise': 1555, 'riser': 1556, 'rising': 1557, 'risk': 1558, 'risky': 1559, 'rivals': 1560, 'road': 1561, 'roanoke': 1562, 'robert': 1563, 'rock': 1564, 'rogue': 1565, 'roman': 1566, 'romances': 1567, 'romanov': 1568, 'romantic': 1569, 'rome': 1570, 'room': 1571, 'roomies': 1572, 'rose': 1573, 'rosselli': 1574, 'round': 1575, 'row': 1576, 'royally': 1577, 'royals': 1578, 'rue': 1579, 'ruin': 1580, 'ruined': 1581, 'rules': 1582, 'run': 1583, 'runaways': 1584, 'running': 1585, 'rush': 1586, 'rust': 1587, 'ruth': 1588, 'ryder': 1589, 's': 1590, 'saboteur': 1591, 'sacagawea': 1592, 'sacred': 1593, 'sacrifice': 1594, 'sadie': 1595, 'safe': 1596, 'saga': 1597, 'sailor': 1598, 'saints': 1599, 'salt': 1600, 'sam': 1601, 'sane': 1602, 'sartre': 1603, 'saudi': 1604, 'sauvage': 1605, 'save': 1606, 'saved': 1607, 'saviors': 1608, 'sawkill': 1609, 'say': 1610, 'scandal': 1611, 'scars': 1612, 'school': 1613, 'science': 1614, 'scientific': 1615, 'scientifically': 1616, 'scissors': 1617, 'scribe': 1618, 'scythe': 1619, 'sea': 1620, 'search': 1621, 'season': 1622, 'seat': 1623, 'second': 1624, 'secret': 1625, 'secrets': 1626, 'seduction': 1627, 'see': 1628, 'seeing': 1629, 'segreta': 1630, 'selected': 1631, 'self': 1632, 'sellout': 1633, 'senate': 1634, 'sense': 1635, 'senses': 1636, 'sentence': 1637, 'separation': 1638, 'sequel': 1639, 'serenity': 1640, 'sermon': 1641, 'service': 1642, 'seven': 1643, 'seventeen': 1644, 'severy': 1645, 'sex': 1646, 'seymour': 1647, 'shack': 1648, 'shadow': 1649, 'shadowhunter': 1650, 'shadows': 1651, 'shah': 1652, 'shakespeare': 1653, 'shape': 1654, 'shaped': 1655, 'sharp': 1656, 'shattered': 1657, 'shaw': 1658, 'she': 1659, 'shelter': 1660, 'shepherd': 1661, 'shining': 1662, 'ship': 1663, 'shoot': 1664, 'shooting': 1665, 'shopping': 1666, 'shore': 1667, 'shores': 1668, 'short': 1669, 'show': 1670, 'side': 1671, 'siena': 1672, 'silence': 1673, 'silenced': 1674, 'silent': 1675, 'silk': 1676, 'silver': 1677, 'simone': 1678, 'simple': 1679, 'simpler': 1680, 'since': 1681, 'sins': 1682, 'sisters': 1683, 'site': 1684, 'six': 1685, 'sixth': 1686, 'skaar': 1687, 'skin': 1688, 'skip': 1689, 'sky': 1690, 'slaves': 1691, 'sleeping': 1692, 'small': 1693, 'smear': 1694, 'smoothie': 1695, 'snow': 1696, 'snowman': 1697, 'so': 1698, 'soar': 1699, 'soldier': 1700, 'solo': 1701, 'solutions': 1702, 'someone': 1703, 'something': 1704, 'sometimes': 1705, 'son': 1706, 'song': 1707, 'songs': 1708, 'soonish': 1709, 'sorry': 1710, 'soul': 1711, 'soulful': 1712, 'sound': 1713, 'sour': 1714, 'source': 1715, 'south': 1716, 'southernmost': 1717, 'space': 1718, 'spaceborn': 1719, 'spaceman': 1720, 'spark': 1721, 'sparsholt': 1722, 'speak': 1723, 'speaking': 1724, 'spell': 1725, 'spies': 1726, 'spinster': 1727, 'spiral': 1728, 'spiritual': 1729, 'springs': 1730, 'spy': 1731, 'st': 1732, 'stacks': 1733, 'stairs': 1734, 'stallions': 1735, 'stand': 1736, 'star': 1737, 'stardust': 1738, 'stare': 1739, 'starlight': 1740, 'stars': 1741, 'state': 1742, 'states': 1743, 'stevie': 1744, 'sticks': 1745, 'still': 1746, 'sting': 1747, 'stolen': 1748, 'stone': 1749, 'stones': 1750, 'stoopid': 1751, 'stop': 1752, 'stopped': 1753, 'store': 1754, 'storie': 1755, 'storied': 1756, 'stories': 1757, 'storm': 1758, 'stormcaster': 1759, 'story': 1760, 'storyteller': 1761, 'stra': 1762, 'stranded': 1763, 'strange': 1764, 'stranger': 1765, 'strangers': 1766, 'strategy': 1767, 'street': 1768, 'stress': 1769, 'strong': 1770, 'stronger': 1771, 'struggle': 1772, 'struggles': 1773, 'student': 1774, 'stuff': 1775, 'style': 1776, 'stylish': 1777, 'success': 1778, 'sugar': 1779, 'summer': 1780, 'sun': 1781, 'sunburn': 1782, 'sundown': 1783, 'sunshine': 1784, 'superficial': 1785, 'sure': 1786, 'survival': 1787, 'surviving': 1788, 'survivors': 1789, 'swedish': 1790, 'sweet': 1791, 'swimming': 1792, 'swing': 1793, 'swings': 1794, 'swords': 1795, 'syria': 1796, 't': 1797, 'tail': 1798, 'tak': 1799, 'take': 1800, 'tale': 1801, 'tales': 1802, 'talk': 1803, 'talking': 1804, 'tap': 1805, 'target': 1806, 'targeted': 1807, 'taste': 1808, 'tate': 1809, 'tattooist': 1810, 'taught': 1811, 'tea': 1812, 'teacher': 1813, 'teaches': 1814, 'teacup': 1815, 'team': 1816, 'tears': 1817, 'technologies': 1818, 'teen': 1819, 'teeth': 1820, 'tell': 1821, 'tells': 1822, 'temple': 1823, 'ten': 1824, 'tendencies': 1825, 'tense': 1826, 'terror': 1827, 'terrorist': 1828, 'texas': 1829, 'th': 1830, 'than': 1831, 'thank': 1832, 'that': 1833, 'the': 1834, 'theater': 1835, 'their': 1836, 'them': 1837, 'there': 1838, 'these': 1839, 'they': 1840, 'thick': 1841, 'thief': 1842, 'thien': 1843, 'thieves': 1844, 'thing': 1845, 'things': 1846, 'think': 1847, 'third': 1848, 'thirst': 1849, 'thirteen': 1850, 'this': 1851, 'thorns': 1852, 'thorpe': 1853, 'those': 1854, 'thoughts': 1855, 'thousand': 1856, 'thrawn': 1857, 'three': 1858, 'thrilling': 1859, 'thriving': 1860, 'through': 1861, 'thunderhead': 1862, 'tide': 1863, 'tides': 1864, 'tidings': 1865, 'tiger': 1866, 'till': 1867, 'time': 1868, 'times': 1869, 'timing': 1870, 'tin': 1871, 'tinfoil': 1872, 'titan': 1873, 'titans': 1874, 'to': 1875, 'tombland': 1876, 'tomfoolery': 1877, 'tomorrows': 1878, 'too': 1879, 'tools': 1880, 'touch': 1881, 'touched': 1882, 'tour': 1883, 'toward': 1884, 'tower': 1885, 'town': 1886, 'trace': 1887, 'tragedy': 1888, 'tragic': 1889, 'tragicomic': 1890, 'trail': 1891, 'trailblazers': 1892, 'train': 1893, 'traitor': 1894, 'trajectory': 1895, 'transcendence': 1896, 'transform': 1897, 'transformation': 1898, 'trash': 1899, 'trauma': 1900, 'travel': 1901, 'traveler': 1902, 'traveller': 1903, 'tre': 1904, 'treasure': 1905, 'tree': 1906, 'trees': 1907, 'trembled': 1908, 'trespassing': 1909, 'trial': 1910, 'triangle': 1911, 'tribute': 1912, 'tricky': 1913, 'trilogy': 1914, 'trouble': 1915, 'troublemaker': 1916, 'trucker': 1917, 'true': 1918, 'truly': 1919, 'trump': 1920, 'trust': 1921, 'truth': 1922, 'truths': 1923, 'try': 1924, 'tudor': 1925, 'turbulent': 1926, 'turf': 1927, 'turning': 1928, 'turns': 1929, 'turtles': 1930, 'tv': 1931, 'twelfth': 1932, 'twentieth': 1933, 'twenty': 1934, 'twin': 1935, 'twist': 1936, 'twisted': 1937, 'two': 1938, 'tyler': 1939, 'tyranny': 1940, 'u': 1941, 'ugly': 1942, 'unbelievable': 1943, 'unbreakable': 1944, 'uncomfortable': 1945, 'uncovering': 1946, 'undefeated': 1947, 'undercover': 1948, 'understanding': 1949, 'underworld': 1950, 'unearthed': 1951, 'unexpected': 1952, 'unicorn': 1953, 'united': 1954, 'unites': 1955, 'universe': 1956, 'unlimited': 1957, 'unpacking': 1958, 'unrequited': 1959, 'unshakeable': 1960, 'unsheltered': 1961, 'unthinkable': 1962, 'until': 1963, 'untold': 1964, 'unwanted': 1965, 'up': 1966, 'uppers': 1967, 'ups': 1968, 'upside': 1969, 'us': 1970, 'usa': 1971, 'uterus': 1972, 'utmost': 1973, 'vacations': 1974, 'vader': 1975, 'valiant': 1976, 'valley': 1977, 'vanishes': 1978, 'vanishing': 1979, 'variations': 1980, 'varlet': 1981, 'vengeance': 1982, 'vere': 1983, 'very': 1984, 'vice': 1985, 'vicious': 1986, 'vietnam': 1987, 'villains': 1988, 'violence': 1989, 'violent': 1990, 'viper': 1991, 'virgil': 1992, 'virtue': 1993, 'visionaries': 1994, 'vista': 1995, 'visual': 1996, 'vita': 1997, 'voices': 1998, 'vol': 1999, 'voltage': 2000, 'volume': 2001, 'von': 2002, 'vote': 2003, 'vox': 2004, 'voyeur': 2005, 'w': 2006, 'wait': 2007, 'waiting': 2008, 'wake': 2009, 'waking': 2010, 'walk': 2011, 'wall': 2012, 'wallflower': 2013, 'walls': 2014, 'wander': 2015, 'wanderers': 2016, 'want': 2017, 'wanted': 2018, 'war': 2019, 'warbringer': 2020, 'warlight': 2021, 'warrior': 2022, 'warriors': 2023, 'wars': 2024, 'was': 2025, 'wash': 2026, 'washington': 2027, 'watch': 2028, 'watcher': 2029, 'water': 2030, 'way': 2031, 'wayfarer': 2032, 'we': 2033, 'wealth': 2034, 'weather': 2035, 'weave': 2036, 'wed': 2037, 'wedding': 2038, 'wedlocked': 2039, 'week': 2040, 'weeks': 2041, 'weight': 2042, 'welcome': 2043, 'well': 2044, 'were': 2045, 'wesley': 2046, 'west': 2047, 'westaway': 2048, 'western': 2049, 'whaleship': 2050, 'what': 2051, 'when': 2052, 'where': 2053, 'whiskey': 2054, 'white': 2055, 'who': 2056, 'why': 2057, 'wicked': 2058, 'widow': 2059, 'widows': 2060, 'wiener': 2061, 'wife': 2062, 'wild': 2063, 'wildcard': 2064, 'wilder': 2065, 'wilderness': 2066, 'wildfire': 2067, 'will': 2068, 'william': 2069, 'win': 2070, 'windfall': 2071, 'windwitch': 2072, 'winning': 2073, 'winston': 2074, 'winter': 2075, 'wintersong': 2076, 'winthrop': 2077, 'wired': 2078, 'wish': 2079, 'wishes': 2080, 'wishing': 2081, 'witch': 2082, 'with': 2083, 'without': 2084, 'wives': 2085, 'wizardry': 2086, 'wolf': 2087, 'wolfsbane': 2088, 'wolves': 2089, 'woman': 2090, 'women': 2091, 'wonder': 2092, 'wonderful': 2093, 'wood': 2094, 'woods': 2095, 'word': 2096, 'wordly': 2097, 'words': 2098, 'work': 2099, 'world': 2100, 'worlds': 2101, 'worse': 2102, 'worst': 2103, 'worth': 2104, 'would': 2105, 'wrecked': 2106, 'wright': 2107, 'write': 2108, 'writer': 2109, 'writing': 2110, 'writings': 2111, 'written': 2112, 'wrong': 2113, 'wtf': 2114, 'wundersmith': 2115, 'wyld': 2116, 'wyoming': 2117, 'y': 2118, 'year': 2119, 'years': 2120, 'yesterday': 2121, 'yesterdays': 2122, 'yet': 2123, 'yips': 2124, 'york': 2125, 'you': 2126, 'young': 2127, 'your': 2128, 'yours': 2129, 'yourself': 2130, 'youse': 2131, 'z': 2132, 'zone': 2133, 'zoo': 2134, 'zookeeper': 2135}\n","tf.Tensor(\n","[[1068. 1505.    0. ...    0.    0.    0.]\n"," [1472.    0.    0. ...    0.    0.    0.]\n"," [1475. 1314.  763. ...    0.    0.    0.]\n"," ...\n"," [1834.   39.    0. ...    0.    0.    0.]\n"," [ 770.  725.    0. ...    0.    0.    0.]\n"," [2052. 1834. 1177. ...    0.    0.    0.]], shape=(1152, 29), dtype=float64)\n"]}]},{"cell_type":"code","source":["from keras.models import Sequential\n","from keras.layers import Dense, Conv1D, Flatten, MaxPool1D, GRU, Dropout\n","from keras.layers.embeddings import Embedding\n","from keras.preprocessing import sequence\n","from collections import Counter\n","from sklearn.model_selection import train_test_split\n"],"metadata":{"id":"hyA2XqAso68Y"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tf.random.set_seed(1001)\n","batch_size = 100 \n","embedding_size = 128\t\t\n","hidden_size = 64\n","vocab_size = len(train_id_padded)\n","\n","# expand model / incorporte other types of variables / compare diff. strategies \n","\n","model = Sequential()\n","embedding_layer = Embedding(input_dim = 5000, mask_zero=True, output_dim = hidden_size)\n","model.add(embedding_layer)\n","model.add(Conv1D(filters=256, kernel_size=4, padding='same', activation='relu'))\n","model.add(MaxPool1D(pool_size=2, strides=1, padding='same'))\n","model.add(Conv1D(filters=128, kernel_size=4, padding='same', activation='relu'))\n","model.add(MaxPool1D(pool_size=4))\n","model.add(Conv1D(filters=64, kernel_size=2, padding='same', activation='relu'))\n","model.add(MaxPool1D(pool_size=4))\n","model.add(GRU(hidden_size))\n","model.add(Dropout(0.4))\n","model.add(Flatten())\n","model.add(Dense(1,activation='sigmoid'))\n","model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['acc'])\n","model.fit(train_id_padded, train_label, epochs=5, batch_size=batch_size)\n","model.summary()\n","\n"],"metadata":{"id":"VnF0vkKGB4xO","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1651708223722,"user_tz":240,"elapsed":13758,"user":{"displayName":"Dongjun Shin","userId":"07542643315713993435"}},"outputId":"040ad864-9efe-48df-da04-6122963de8e6"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/5\n","12/12 [==============================] - 5s 118ms/step - loss: 0.6575 - acc: 0.6632\n","Epoch 2/5\n","12/12 [==============================] - 1s 114ms/step - loss: 0.6377 - acc: 0.6693\n","Epoch 3/5\n","12/12 [==============================] - 1s 114ms/step - loss: 0.6245 - acc: 0.6693\n","Epoch 4/5\n","12/12 [==============================] - 1s 113ms/step - loss: 0.6041 - acc: 0.6762\n","Epoch 5/5\n","12/12 [==============================] - 1s 114ms/step - loss: 0.5018 - acc: 0.7179\n","Model: \"sequential\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," embedding (Embedding)       (None, None, 64)          320000    \n","                                                                 \n"," conv1d (Conv1D)             (None, None, 256)         65792     \n","                                                                 \n"," max_pooling1d (MaxPooling1D  (None, None, 256)        0         \n"," )                                                               \n","                                                                 \n"," conv1d_1 (Conv1D)           (None, None, 128)         131200    \n","                                                                 \n"," max_pooling1d_1 (MaxPooling  (None, None, 128)        0         \n"," 1D)                                                             \n","                                                                 \n"," conv1d_2 (Conv1D)           (None, None, 64)          16448     \n","                                                                 \n"," max_pooling1d_2 (MaxPooling  (None, None, 64)         0         \n"," 1D)                                                             \n","                                                                 \n"," gru (GRU)                   (None, 64)                24960     \n","                                                                 \n"," dropout (Dropout)           (None, 64)                0         \n","                                                                 \n"," flatten (Flatten)           (None, 64)                0         \n","                                                                 \n"," dense (Dense)               (None, 1)                 65        \n","                                                                 \n","=================================================================\n","Total params: 558,465\n","Trainable params: 558,465\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["model(train_id_padded)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uXZTwNMYkt33","executionInfo":{"status":"ok","timestamp":1651709786145,"user_tz":240,"elapsed":716,"user":{"displayName":"Dongjun Shin","userId":"07542643315713993435"}},"outputId":"75f563c7-945f-411c-fc86-5c37eb048436"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<tf.Tensor: shape=(1152, 1), dtype=float32, numpy=\n","array([[0.07647231],\n","       [0.24304086],\n","       [0.1360879 ],\n","       ...,\n","       [0.46613434],\n","       [0.4720856 ],\n","       [0.018374  ]], dtype=float32)>"]},"metadata":{},"execution_count":15}]},{"cell_type":"code","source":["# y_score = model.predict(test_id_padded)\n","# y_score = [[1 if i == max(sc) else 0 for i in sc] for sc in y_score]\n","\n","# n_right = 0\n","# for i in range(len(y_score)):\n","#     if all(y_score[i][j] == test_label[i] for j in range(len(y_score[i]))):\n","#         n_right += 1\n","\n","# print(\"Accuracy in test: %.2f%%\" % ((n_right/float(len(test_y)) * 100)))"],"metadata":{"id":"irDrVvcCM9Xw"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["y_score = model.predict(test_id_padded)\n","y_score = [[1 if i >= 0.5 else 0 for i in sc] for sc in y_score] # threshold 0.5 okay?\n","\n","n_right = 0\n","for i in range(len(y_score)):\n","    if all(y_score[i][j] == test_label[i] for j in range(len(y_score[i]))):\n","        n_right += 1\n","\n","print(\"Accuracy in test: %.2f%%\" % ((n_right/float(len(test_y)) * 100)))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HVwkjww1mDg_","executionInfo":{"status":"ok","timestamp":1651708224534,"user_tz":240,"elapsed":830,"user":{"displayName":"Dongjun Shin","userId":"07542643315713993435"}},"outputId":"e63f001a-bbb6-4411-ae53-8a5185907748"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy in test: 65.79%\n"]}]},{"cell_type":"code","source":[""],"metadata":{"id":"_TnS7SeNkPSM"},"execution_count":null,"outputs":[]}],"metadata":{"colab":{"collapsed_sections":[],"name":"2.0.model_only_title.ipynb","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}