Supervised Text Classification.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"g5I-XiJ-17wB"},"source":["# Importing libraries \n"," import of all necessary libraries"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qZY9YEFskH_b"},"outputs":[],"source":["import nltk\n","from sklearn.metrics.pairwise import cosine_similarity\n","from sklearn.metrics import classification_report, f1_score\n","\n","import numpy as np\n","import pandas as pd\n","import regex as re\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","!pip install transformers"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"tMgiweucPkFk"},"outputs":[],"source":["from google.colab import drive \n","drive.mount('gdrive')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"collapsed":true,"executionInfo":{"elapsed":422,"status":"ok","timestamp":1653632070042,"user":{"displayName":"Wiem Khlifi","userId":"05865605844600796666"},"user_tz":-60},"id":"sPrZYDcti8RM","outputId":"738c744f-e027-41a1-fe33-e5e629b587db"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["  Categories             Titles  \\\n","0    Animals      animal attack   \n","1    Animals  animal coloration   \n","2    Animals      animal ethics   \n","3    Animals   animal husbandry   \n","4    Animals         animal law   \n","\n","                                               Texts  \\\n","0  animal attacks are violent often fatal attacks...   \n","1  animal coloration is the general appearance of...   \n","2  animal ethics is branch of ethics which examin...   \n","3  animal husbandry is the branch of agriculture ...   \n","4  animal law is combination of statutory and cas...   \n","\n","                                            Keywords  \n","0  violent   bites   aggressive   attack   public...  \n","1  appearance   coloured   camouflage   incidenta...  \n","2  rights   welfare   law   human animal   cognit...  \n","3  agriculture   day to day care   selective bree...  \n","4  statutory   law   rights   violates   policies...  "],"text/html":["\n","  <div id=\"df-e07bc4cc-d765-4ef0-9022-b1dd41af9bea\">\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>Categories</th>\n","      <th>Titles</th>\n","      <th>Texts</th>\n","      <th>Keywords</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Animals</td>\n","      <td>animal attack</td>\n","      <td>animal attacks are violent often fatal attacks...</td>\n","      <td>violent   bites   aggressive   attack   public...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Animals</td>\n","      <td>animal coloration</td>\n","      <td>animal coloration is the general appearance of...</td>\n","      <td>appearance   coloured   camouflage   incidenta...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Animals</td>\n","      <td>animal ethics</td>\n","      <td>animal ethics is branch of ethics which examin...</td>\n","      <td>rights   welfare   law   human animal   cognit...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Animals</td>\n","      <td>animal husbandry</td>\n","      <td>animal husbandry is the branch of agriculture ...</td>\n","      <td>agriculture   day to day care   selective bree...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Animals</td>\n","      <td>animal law</td>\n","      <td>animal law is combination of statutory and cas...</td>\n","      <td>statutory   law   rights   violates   policies...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e07bc4cc-d765-4ef0-9022-b1dd41af9bea')\"\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-e07bc4cc-d765-4ef0-9022-b1dd41af9bea button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-e07bc4cc-d765-4ef0-9022-b1dd41af9bea');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? 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":16}],"source":["df = pd.read_csv(\"gdrive/My Drive/Colab Notebooks/augmented_datafile.csv\")\n","df.drop([\"Unnamed: 0\"],axis=1,inplace=True)\n","\n","old_df = pd.read_csv(\"gdrive/My Drive/Colab Notebooks/old_datafile.csv\")\n","old_df.drop([\"Unnamed: 0\"],axis=1,inplace=True)\n","df.head()"]},{"cell_type":"markdown","metadata":{"id":"5FGAYOyjyweY"},"source":["# Text Augmentation \n","*   Using the text summarization and Keybert techniques to generate additional samples.\n","\n","* At least **10 new samples** are generated for each item in the original data set."]},{"cell_type":"markdown","source":["## Text Summarization"],"metadata":{"id":"LScLw1mHrXXJ"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"zkVuf859fDf1"},"outputs":[],"source":["from transformers import pipeline\n","summarizer = pipeline(\"summarization\") "]},{"cell_type":"code","source":["text =\"\"\"\n","AI has become a catchall term for applications that perform complex tasks that once required human input such as communicating with customers online or playing chess. The term is often used interchangeably with its subfields, which include machine learning and deep learning. There are differences, however. For example, machine learning is focused on building systems that learn or improve their performance based on the data they consume. It’s important to note that although all machine learning is AI, not all AI is machine learning.\n","\n","To get the full value from AI, many companies are making significant investments in data science teams. Data science, an interdisciplinary field that uses scientific and other methods to extract value from data, combines skills from fields such as statistics and computer science with business knowledge to analyze data collected from multiple sources.\n","\"\"\""],"metadata":{"id":"IB1JHSH-KMxG"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":88},"executionInfo":{"elapsed":13186,"status":"ok","timestamp":1653632232671,"user":{"displayName":"Wiem Khlifi","userId":"05865605844600796666"},"user_tz":-60},"id":"rrPn2S4_fMw7","outputId":"3b71abe8-f0db-4eb4-f4fd-a1dde57ca580"},"outputs":[{"output_type":"stream","name":"stdout","text":["The length of the summarized text is 51 \n","\n"]},{"output_type":"execute_result","data":{"text/plain":["' AI has become a catchall term for applications that perform complex tasks that once required human input . The term is often used interchangeably with its subfields, which include machine learning and deep learning . Data science is an interdisciplinary field that uses scientific methods to extract value from data .'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":19}],"source":["#text = \" \".join(df[\"Texts\"][4].split()[:600])\n","result = summarizer(text, min_length=40, max_length=70, do_sample=False)\n","summary = result[0][\"summary_text\"]\n","print(f'The length of the summarized text is {len(summary.split())} \\n')\n","summary"]},{"cell_type":"markdown","metadata":{"id":"sDBVHyZeGobk"},"source":["The summary focuses primarily on the **first part** of the text, so we can see a significant similarity with this one compared to the full text.\n","\n","From my point of view, I think that for each text, **the topic** is mainly presented in the first part.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":615,"status":"ok","timestamp":1650214114909,"user":{"displayName":"Wiem Khlifi","userId":"05865605844600796666"},"user_tz":-60},"id":"juKYMpkItKAl","outputId":"052f1918-e436-411a-caa7-60cafbe643d3"},"outputs":[{"name":"stdout","output_type":"stream","text":[" Similarity between the summary and the full text 0.6378721594810486\n"," Similarity between the summary and the first part of the text 0.8806988596916199\n"]}],"source":["vectors = sbert_model.encode([summary,text,text[:1000]], convert_to_numpy=True)  \n","sim_vector = cosine_similarity(vectors,vectors)\n","print(f' Similarity between the summary and the full text {sim_vector[0][1]}')\n","print(f' Similarity between the summary and the first part of the text {sim_vector[0][2]}')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7650367,"status":"ok","timestamp":1651149955996,"user":{"displayName":"Wiem Khlifi","userId":"05865605844600796666"},"user_tz":-60},"id":"FkRniHx1-aqf","outputId":"2213b519-00ff-4ada-c56c-c849bb1d22df"},"outputs":[{"name":"stderr","output_type":"stream","text":["No model was supplied, defaulted to sshleifer/distilbart-cnn-12-6 (https://huggingface.co/sshleifer/distilbart-cnn-12-6)\n"]}],"source":["# this function takes more than 2hours for each text with 600 tokens (note: tested with more than 800 tokens but it didn't work \n","#so we have to change the max length of the model which is 1024 tokens (based on it's vocablary))\n","\n","def text_summarization(texts=df[\"Texts\"]):\n","  \"\"\"\n","  Input: The List of all texts in our dataframe\n","  Output: The list of summarized texts\n","  \"\"\"\n","  summarized_texts = []\n","  #summarizer = pipeline(\"summarization\")\n","  texts = texts.tolist()\n","  # summarizes each text in texts list\n","  summarized_texts = [summarizer( \" \".join(text.split()[:600]) , min_length=60, max_length=70, do_sample=False)[0][\"summary_text\"]  for text in texts]    \n","    \n","    \n","  return summarized_texts\n","\n","# Creat a new columns in df for the summaries\n","df[\"Summarized Texts\"] = text_summarization()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"T8eilDAh-agR"},"outputs":[],"source":["# delete the extra white space\n","df[\"Summarized Texts\"] = df[\"Summarized Texts\"].apply(lambda x : x.replace(\" .\",\"\")) # eliminate the point at the end of each summary\n","df.to_csv(\"gdrive/My Drive/Colab Notebooks/new_datafile.csv\")"]},{"cell_type":"markdown","source":["## Data Augmentation"],"metadata":{"id":"-Sj4D9lUrM1Y"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"IGJ4uG6JzFNm"},"outputs":[],"source":["!pip install textattack\n","!pip install tensorflow_text"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"r2X5MOXhy0g5"},"outputs":[],"source":["#import contractions\n","#from autocorrect import Speller\n","from tqdm import tqdm\n","from textattack.augmentation import EasyDataAugmenter"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Qt1QlDhY2zjj"},"outputs":[],"source":["def augmented_corpus(df=df):\n","  \"\"\"\n","  Input: a dataframe that contains the texts and the keywords that we want to augment\n","  Ouput: lists of the augmented texts and keywords with the titles and categories \n","  \"\"\"\n","  categories,titles,texts,keywords = [],[],[],[] \n","  n=0\n","  m=0\n"," # initialize the augmentation model that can generate up to 15 samples\n","  aug = EasyDataAugmenter(pct_words_to_swap=0.25, \n","                        transformations_per_example=15)\n","  # Clean the list of keywords (which has been considered as a type of str with the brackets ect...)\n","  df[\"Keywords\"] = df[\"Keywords\"].apply(lambda x: re.sub('[^A-Za-z]',\" \",str(x)).strip())\n","\n","  for i in tqdm(range(len(df))):\n","    \n","      aug_text,aug_keys = [], []\n","      aug_text = aug.augment(df[\"Summarized Texts\"][i])\n","      #Extract new keywords then do the augmentation\n","      key = kw_model_bert.extract_keywords(df[\"Texts\"][i][:1000], keyphrase_ngram_range=(1, 2), nr_candidates=15, top_n=10,stop_words='english')\n","      keys = [item[0] for item in key]\n","      new_keys = \" \".join(keys)\n","      aug_keys = aug.augment(new_keys)\n","      \n","      n = len(aug_text)\n","      m=len(aug_keys)\n","      print(\" The lengths n=\",n,\"and m=\",m)\n","\n","      texts.extend(aug_text)\n","      keywords.extend(aug_keys)\n","      \n","\n","      if n>m : # if the list of texts larger than the lis of keywords then we should add n-m items to keywords\n","        k = n-m\n","        add_items = aug.augment(aug_keys[m-1]) # generate more items to add to keywords list\n","        keywords.extend(add_items[:k])\n","        categories.extend([df[\"Categories\"][i]]*n) # if n=0 (in case of passing), there is no list to be added\n","        titles.extend([df['Titles'][i]]*n)\n","\n","      elif n<m:\n","        k= m-n\n","        add_items = aug.augment(aug_text[n-1]) # generate more items to add to text list\n","        texts.extend(add_items[:k])\n","        categories.extend([df[\"Categories\"][i]]*m) # if n=0 (in case of passing), there is no list to be added\n","        titles.extend([df['Titles'][i]]*m)\n","        \n","      else:\n","        categories.extend([df[\"Categories\"][i]]*n) # if n=0 (in case of passing), there is no list to be added\n","        titles.extend([df['Titles'][i]]*n)\n","\n","    \n","\n","  return categories,titles,texts,keywords\n","\n","categories,titles,texts,keywords = augmented_corpus()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1651183962803,"user":{"displayName":"IEEE ENSI CIS Chapter","userId":"10635189582009435434"},"user_tz":-60},"id":"8uzL-ivsTBRG","outputId":"17937c42-f885-4e6f-f458-c6c7eedb9245"},"outputs":[{"name":"stdout","output_type":"stream","text":["7008 7008 7008 7008\n"]}],"source":["print(len(categories),len(titles),len(texts),len(keywords))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":245,"status":"ok","timestamp":1651184336762,"user":{"displayName":"IEEE ENSI CIS Chapter","userId":"10635189582009435434"},"user_tz":-60},"id":"cnMdVQPveafJ","outputId":"77b53da4-fa3f-49e7-a3b0-73b5199a555d"},"outputs":[{"data":{"text/plain":["7592"]},"execution_count":33,"metadata":{},"output_type":"execute_result"}],"source":["old_df = df[['Categories','Titles','Texts','Keywords']]\n","new_df1 = pd.DataFrame({\"Categories\":categories,\"Titles\":titles,\"Texts\":texts,\"Keywords\":keywords})\n","new_df = pd.concat([[old_df,new_df1]])\n","len(new_df)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CnXTTZcWeynV"},"outputs":[],"source":["new_df.to_csv(\"gdrive/My Drive/Colab Notebooks/augmented_datafile.csv\")"]},{"cell_type":"markdown","metadata":{"id":"HBCXCFSXVWpd"},"source":["# Supervised Text Classification "]},{"cell_type":"markdown","source":["\n","\n","1.   Classification using LSTM after data augmentation because it's not pre-trained.\n","2.   Classification using bert before and after the data augmentation.\n","3.   Construction of a model combining the model that classifies from texts and the other that uses keywords.\n","\n"],"metadata":{"id":"u57_ZostryBe"}},{"cell_type":"markdown","metadata":{"id":"EjSxK7VDIx7L"},"source":["## Building the models"]},{"cell_type":"markdown","metadata":{"id":"l3h7HUfEDtSu"},"source":["### Data Preparation"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uPqlgpv8Yuiv"},"outputs":[],"source":["from sklearn.model_selection import train_test_split\n","from sklearn.preprocessing import LabelEncoder\n","import tensorflow as tf\n","import tensorflow_hub as hub\n","!pip install  tensorflow_text\n","import tensorflow_text as text\n","\n","from keras.preprocessing.text import Tokenizer\n","from keras.preprocessing.sequence import pad_sequences\n","from keras.models import Sequential\n","from keras.layers import Embedding, Dense, LSTM,Dropout"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"tKT-vA1pD-xT"},"outputs":[],"source":["# Encode the categories labels to numerical arguments\n","lb = LabelEncoder()\n","lb.fit(df['Categories'])\n","old_df['Target'] = lb.transform(old_df['Categories'])\n","df['Target'] = lb.transform(df['Categories'])\n","num_classes =  len(lb.classes_)\n","\n","#lb.inverse_transform(df['Target']) gives the label assigned to each number"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gGjt1IPIX0nr"},"outputs":[],"source":["# Process the list of keywords (which has been considered as a type of str)\n","df[\"Keywords\"] = df[\"Keywords\"].apply(lambda x: re.sub('[^A-Za-z]',\" \",str(x)).strip())\n","old_df[\"Keywords\"] = old_df[\"Keywords\"].apply(lambda x: re.sub('[^A-Za-z]',\" \",str(x)).strip())\n","\n","\n","# Splitting the data\n","Y = tf.keras.utils.to_categorical(df[\"Target\"].values, num_classes=num_classes)\n","old_Y = tf.keras.utils.to_categorical(old_df[\"Target\"].values, num_classes=num_classes)\n","\n","\n","def split_data(df,data,Y):\n","  return train_test_split(df[data], Y, test_size=0.2, random_state=110, stratify = Y)\n","\n","X_train_old, X_test_old, Y_train_old, Y_test_old = split_data(old_df,'Texts',old_Y)\n","X_train, X_test, Y_train, Y_test = split_data(df,'Texts',Y)\n","\n","x_train_old, x_test_old, y_train_old, y_test_old = split_data(old_df,'Keywords',old_Y)\n","x_train, x_test, y_train, y_test = split_data(df,'Keywords',Y)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ygRc8vi52OoX"},"outputs":[],"source":["x_test.shape,y_test.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BM3qg1lBOYgD"},"outputs":[],"source":["# Observe the data if it's inbalanced of not\n","def plot_categories(df=df):\n","  plt.style.use('ggplot')\n","  num_classes = len(lb.classes_)\n","  colors = plt.cm.Dark2(np.linspace(0, 1, num_classes))\n","  iter_color = iter(colors)\n","\n","  df[\"Categories\"].value_counts().plot.barh(title=\"Reviews for each topic (n, %)\", ylabel=\"Topics\", color=colors, figsize=(7,7))\n","\n","  for i, v in enumerate(df[\"Categories\"].value_counts()):\n","    c = next(iter_color)\n","    plt.text(v, i,\n","            \" \"+str(v)+\", \"+str(round(v*100/df.shape[0],2))+\"%\", \n","            color=c, \n","            va='center', \n","            fontweight='bold')\n","    \n","plot_categories()    "]},{"cell_type":"code","source":["plot_categories(old_df)"],"metadata":{"id":"e4qXo6wUCJn9"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Y9iHSYrsiQLq"},"outputs":[],"source":["# Function aims to plot the accuracy of the model and it's evaluation on the test set\n","def plot_graphs(model, metric,title):\n","  plt.plot(model.history.history[metric],marker='o', markersize=4) # plots the metric used on train set\n","  plt.plot(model.history.history['val_'+metric], '',marker='x', markersize=5) # plots the metric used on test set\n","  plt.xlabel(\"Epochs\")\n","  plt.ylabel(metric)\n","  plt.title(title,fontsize=10)\n","  plt.legend([metric, 'val_'+metric],loc='lower right',fontsize=10)\n","  plt.show()"]},{"cell_type":"markdown","metadata":{"id":"rCt0syHFD8jc"},"source":["### Building the models"]},{"cell_type":"markdown","source":["#### LSTM model\n"],"metadata":{"id":"awIyzp-PSYPy"}},{"cell_type":"code","source":["def LSTM_model(MAX_NB_WORDS, EMBEDDING_DIM,X):\n","  \n","  model = Sequential()\n","  model.add(Embedding(MAX_NB_WORDS, EMBEDDING_DIM,  input_length=X.shape[1]))\n","  model.add(Dropout(0.2,name='dropout'))\n","  model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2))\n","  model.add(Dense(30, activation='softmax'))\n","  \n","  METRICS = [\n","      tf.keras.metrics.CategoricalAccuracy(name='accuracy'),\n","      tf.keras.metrics.Precision(name='Precision'),\n","      tf.keras.metrics.Recall(name='Recall')]\n","\n","\n","  model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=2e-5),\n","              loss=tf.keras.losses.CategoricalCrossentropy() ,\n","              metrics=METRICS)\n","  return model"],"metadata":{"id":"MVjyVI4HdHD0"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["#### Bert models"],"metadata":{"id":"PERsclEQSZaC"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"IifoCbusQ69P"},"outputs":[],"source":["\n","def build_model():\n","  # Bert layers\n","  bert_preprocess = hub.KerasLayer(\"https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3\", name=\"preprocessing\")\n","  bert_encoder = hub.KerasLayer(\"https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/4\",trainable=True,name=\"Bert_encoder\")\n","\n","  text_input = tf.keras.layers.Input(shape=(), dtype=tf.string, name='input text')\n","  preprocessed_text = bert_preprocess(text_input)\n","  outputs = bert_encoder(preprocessed_text)\n","\n","  # Neural network layers\n","  L = tf.keras.layers.Dropout(0.1, name=\"dropout\")(outputs['pooled_output'])\n","  L = tf.keras.layers.Dense(num_classes, activation='softmax', name=\"output\")(L)\n","\n","  # Use inputs and outputs to construct a final model\n","  model = tf.keras.Model(inputs=[text_input], outputs = [L])\n","\n","  METRICS = [\n","      tf.keras.metrics.CategoricalAccuracy(name='accuracy'),\n","      tf.keras.metrics.Precision(name='Precision'),\n","      tf.keras.metrics.Recall(name='Recall')]\n","\n","\n","  model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=2e-5),\n","              loss=tf.keras.losses.CategoricalCrossentropy() ,\n","              metrics=METRICS)\n","\n","  return model\n","\n","earlystop_callback = tf.keras.callbacks.EarlyStopping(monitor = \"val_loss\", \n","                                                      patience = 3,\n","                                                      restore_best_weights = True)\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4A-VSj2xF5b7"},"outputs":[],"source":["def build_model_combination():\n","  # Bert layers\n","  bert_preprocess = hub.KerasLayer(\"https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3\", name=\"preprocessing\")\n","  bert_encoder = hub.KerasLayer(\"https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/4\",trainable=True,name=\"Bert_encoder\")\n","\n","  # Neural network layers for texts classification\n","  text_input = tf.keras.layers.Input(shape=(), dtype=tf.string, name='input_text')\n","  preprocessed_text = bert_preprocess(text_input)\n","  outputs_texts = bert_encoder(preprocessed_text)\n","  T = tf.keras.layers.Dropout(0.2, name=\"dropout_texts\")(outputs_texts['pooled_output'])\n","  T = tf.keras.layers.Dense(num_classes, activation='softmax', name=\"output_texts\")(T)\n","\n","\n","  # Neural network layers for keywords classification\n","  keywords_input = tf.keras.layers.Input(shape=(), dtype=tf.string, name='input_keywords')\n","  preprocessed_keys = bert_preprocess(keywords_input)\n","  outputs_keys = bert_encoder(preprocessed_keys)\n","  K = tf.keras.layers.Dropout(0.2, name=\"dropout_keywords\")(outputs_keys['pooled_output'])\n","  K = tf.keras.layers.Dense(num_classes, activation='softmax', name=\"output_keywords\")(K)\n","\n","  # Use inputs and outputs to construct a final model\n","  Average = tf.keras.layers.Average(name=\"Average_Layer\")([T, K])\n","  model = tf.keras.Model(inputs=[text_input,keywords_input], outputs = [Average])\n","\n","  METRICS = [\n","      tf.keras.metrics.CategoricalAccuracy(name='accuracy'),\n","      tf.keras.metrics.Precision(name='Precision'),\n","      tf.keras.metrics.Recall(name='Recall')]\n","\n","\n","  model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=2e-5),\n","              loss=tf.keras.losses.CategoricalCrossentropy() ,\n","              metrics=METRICS)\n","\n","  return model\n","\n","earlystop_callback = tf.keras.callbacks.EarlyStopping(monitor = \"val_loss\", \n","                                                      patience = 3,\n","                                                      restore_best_weights = True)"]},{"cell_type":"markdown","source":["## Testing LSTM Model on texts"],"metadata":{"id":"PofJPtHHR1uL"}},{"cell_type":"code","source":["# The maximum number of words to be used. (most frequent)\n","MAX_NB_WORDS = 50000\n","# Max number of words in each complaint.\n","MAX_SEQUENCE_LENGTH = 500\n","# This is fixed.\n","EMBEDDING_DIM = 200\n","\n","tokenizer = Tokenizer(num_words=MAX_NB_WORDS, filters='!\"#$%&()*+,-./:;<=>?@[\\]^_`{|}~', lower=True)\n","tokenizer.fit_on_texts(df['Texts'].values)\n","word_index = tokenizer.word_index\n","print('Found %s unique tokens.' % len(word_index))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zDfcfNcrYgll","executionInfo":{"status":"ok","timestamp":1651686026638,"user_tz":-60,"elapsed":1078,"user":{"displayName":"wiem khlifi","userId":"15071756733142784699"}},"outputId":"d0355ae2-b9e1-411f-c015-64dc0c979a63"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Found 62715 unique tokens.\n"]}]},{"cell_type":"code","source":["X = tokenizer.texts_to_sequences(df['Texts'].values)\n","X = pad_sequences(X, maxlen=MAX_SEQUENCE_LENGTH)\n","print('Shape of data tensor:', X.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sq2Gy38Hcjvh","executionInfo":{"status":"ok","timestamp":1651686027565,"user_tz":-60,"elapsed":929,"user":{"displayName":"wiem khlifi","userId":"15071756733142784699"}},"outputId":"b98bf04c-ec22-42a8-dda7-8e6a86d49ca5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Shape of data tensor: (7592, 500)\n"]}]},{"cell_type":"code","source":["#Y = pd.get_dummies(df['Categories']).values\n","Y = tf.keras.utils.to_categorical(df[\"Target\"].values, num_classes=num_classes)\n","print('Shape of label tensor:', Y.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JBjAuGx9a7SM","executionInfo":{"status":"ok","timestamp":1651686027566,"user_tz":-60,"elapsed":4,"user":{"displayName":"wiem khlifi","userId":"15071756733142784699"}},"outputId":"015a29f4-5b70-4734-e44e-76063b78b5b2"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Shape of label tensor: (7592, 30)\n"]}]},{"cell_type":"code","source":["X_train_old, X_test_old, Y_train_old, Y_test_old = train_test_split(X,Y, test_size = 0.10, random_state = 42)\n","print(X_train_old.shape,Y_train_old.shape)\n","print(X_test_old.shape,Y_test_old.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vUIjE6fPanyX","executionInfo":{"status":"ok","timestamp":1651686028211,"user_tz":-60,"elapsed":647,"user":{"displayName":"wiem khlifi","userId":"15071756733142784699"}},"outputId":"fe676a2d-23c1-489f-a878-3a09d9363b88"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(6832, 500) (6832, 30)\n","(760, 500) (760, 30)\n"]}]},{"cell_type":"code","source":["text_old_model = LSTM_model(MAX_NB_WORDS, EMBEDDING_DIM,X)\n","\n","text_old_model.fit(X_train_old, Y_train_old,epochs=10,\n","           validation_data=(X_test_old,Y_test_old))"],"metadata":{"id":"C0UkayGiT0ik"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["plt.title('Accuracy')\n","plt.plot(text_old_model.history.history['accuracy'], label='train accuracy')\n","plt.plot(text_old_model.history.history['val_accuracy'], label='test accuracy')\n","plt.legend()\n","plt.show();"],"metadata":{"id":"Ssyy5dLXfaAl"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"2gkuslEBGrRA"},"source":["##Classification without data augmentation"]},{"cell_type":"markdown","metadata":{"id":"FNtn9sMu17ga"},"source":["### Classification using Texts "]},{"cell_type":"code","source":["#text_old_model = build_model()\n","#tf.keras.utils.plot_model(model=text_old_model, show_shapes=True, dpi=100)"],"metadata":{"id":"FboNyIZ6Luh5"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":458732,"status":"ok","timestamp":1651396297695,"user":{"displayName":"Wiem Khlifi","userId":"05865605844600796666"},"user_tz":-60},"id":"GdEXDgqd2FJH","outputId":"db642c84-348e-40f5-f44a-4c8bd71449d8"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/10\n","15/15 [==============================] - 56s 2s/step - loss: 3.5719 - accuracy: 0.0514 - Precision: 0.0000e+00 - Recall: 0.0000e+00 - val_loss: 3.2754 - val_accuracy: 0.0940 - val_Precision: 0.0000e+00 - val_Recall: 0.0000e+00\n","Epoch 2/10\n","15/15 [==============================] - 37s 3s/step - loss: 3.1263 - accuracy: 0.1049 - Precision: 0.0000e+00 - Recall: 0.0000e+00 - val_loss: 2.9844 - val_accuracy: 0.1538 - val_Precision: 0.0000e+00 - val_Recall: 0.0000e+00\n","Epoch 3/10\n","15/15 [==============================] - 37s 2s/step - loss: 2.8185 - accuracy: 0.2548 - Precision: 0.0000e+00 - Recall: 0.0000e+00 - val_loss: 2.6558 - val_accuracy: 0.3248 - val_Precision: 0.0000e+00 - val_Recall: 0.0000e+00\n","Epoch 4/10\n","15/15 [==============================] - 34s 2s/step - loss: 2.3354 - accuracy: 0.4582 - Precision: 0.0000e+00 - Recall: 0.0000e+00 - val_loss: 2.2013 - val_accuracy: 0.5043 - val_Precision: 0.0000e+00 - val_Recall: 0.0000e+00\n","Epoch 5/10\n","15/15 [==============================] - 34s 2s/step - loss: 1.8687 - accuracy: 0.6788 - Precision: 1.0000 - Recall: 0.0086 - val_loss: 1.8120 - val_accuracy: 0.7009 - val_Precision: 1.0000 - val_Recall: 0.0256\n","Epoch 6/10\n","15/15 [==============================] - 30s 2s/step - loss: 1.4191 - accuracy: 0.8308 - Precision: 1.0000 - Recall: 0.1135 - val_loss: 1.4469 - val_accuracy: 0.7863 - val_Precision: 1.0000 - val_Recall: 0.1111\n","Epoch 7/10\n","15/15 [==============================] - 29s 2s/step - loss: 1.0827 - accuracy: 0.8801 - Precision: 0.9792 - Recall: 0.3019 - val_loss: 1.1665 - val_accuracy: 0.8205 - val_Precision: 0.9714 - val_Recall: 0.2906\n","Epoch 8/10\n","15/15 [==============================] - 33s 2s/step - loss: 0.8074 - accuracy: 0.9186 - Precision: 0.9882 - Recall: 0.5375 - val_loss: 0.9756 - val_accuracy: 0.8547 - val_Precision: 0.9661 - val_Recall: 0.4872\n","Epoch 9/10\n","15/15 [==============================] - 36s 2s/step - loss: 0.6078 - accuracy: 0.9465 - Precision: 0.9910 - Recall: 0.7088 - val_loss: 0.8350 - val_accuracy: 0.8889 - val_Precision: 0.9452 - val_Recall: 0.5897\n","Epoch 10/10\n","15/15 [==============================] - 30s 2s/step - loss: 0.4734 - accuracy: 0.9679 - Precision: 0.9896 - Recall: 0.8158 - val_loss: 0.7117 - val_accuracy: 0.8803 - val_Precision: 0.9398 - val_Recall: 0.6667\n"]},{"data":{"text/plain":["<keras.callbacks.History at 0x7f3ba22d7390>"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["text_old_model = build_model()\n","\n","text_old_model.fit(X_train_old, Y_train_old, \n","           epochs=10,\n","           validation_data=(X_test_old,Y_test_old))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":296},"executionInfo":{"elapsed":16,"status":"ok","timestamp":1651396297696,"user":{"displayName":"Wiem Khlifi","userId":"05865605844600796666"},"user_tz":-60},"id":"fmaFVMfa14lR","outputId":"f39c6fb4-2d94-4f5b-ebed-0d5b11a5cd4f"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plot_graphs(text_old_model, 'accuracy','Text Classification Accuracy without data augmentation')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FIuja_VyUqdw"},"outputs":[],"source":["#text_old_model.save('gdrive/My Drive/Colab Notebooks/text_old_model.h5')\n","text_old_model = tf.keras.models.load_model(('gdrive/My Drive/Colab Notebooks/text_old_model.h5'),custom_objects={'KerasLayer': hub.KerasLayer})"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"RsagPjNZQaP2"},"outputs":[],"source":["# Calculating the F1_score\n","y_pred = np.argmax(text_old_model(X_test_old),axis=1)\n","y_true = np.argmax(Y_test_old,axis=1)\n","print(classification_report(y_true, y_pred, target_names=lb.classes_))\n","#print(\"The F1_score is: \",f1_score(y_true, y_pred, average='weighted') ) # average = 'weighted' used in imbalanced data "]},{"cell_type":"markdown","metadata":{"id":"rCmuT1b980ef"},"source":["###Classification using keywords"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":310240,"status":"ok","timestamp":1651400116122,"user":{"displayName":"Wiem Khlifi","userId":"05865605844600796666"},"user_tz":-60},"id":"gR1soZOq8yVQ","outputId":"9d8cbecf-b96d-4284-b173-f714e050f80f"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/10\n","15/15 [==============================] - 46s 2s/step - loss: 3.5432 - accuracy: 0.0471 - Precision: 0.0000e+00 - Recall: 0.0000e+00 - val_loss: 3.3489 - val_accuracy: 0.0684 - val_Precision: 0.0000e+00 - val_Recall: 0.0000e+00\n","Epoch 2/10\n","15/15 [==============================] - 26s 2s/step - loss: 3.1923 - accuracy: 0.1006 - Precision: 0.0000e+00 - Recall: 0.0000e+00 - val_loss: 3.1081 - val_accuracy: 0.1795 - val_Precision: 0.0000e+00 - val_Recall: 0.0000e+00\n","Epoch 3/10\n","15/15 [==============================] - 26s 2s/step - loss: 2.8983 - accuracy: 0.2313 - Precision: 0.0000e+00 - Recall: 0.0000e+00 - val_loss: 2.7459 - val_accuracy: 0.3162 - val_Precision: 0.0000e+00 - val_Recall: 0.0000e+00\n","Epoch 4/10\n","15/15 [==============================] - 26s 2s/step - loss: 2.5295 - accuracy: 0.3919 - Precision: 1.0000 - Recall: 0.0021 - val_loss: 2.4489 - val_accuracy: 0.4615 - val_Precision: 1.0000 - val_Recall: 0.0085\n","Epoch 5/10\n","15/15 [==============================] - 26s 2s/step - loss: 2.1414 - accuracy: 0.5418 - Precision: 0.9000 - Recall: 0.0193 - val_loss: 2.1416 - val_accuracy: 0.5726 - val_Precision: 1.0000 - val_Recall: 0.0171\n","Epoch 6/10\n","15/15 [==============================] - 26s 2s/step - loss: 1.7253 - accuracy: 0.6852 - Precision: 0.9677 - Recall: 0.0642 - val_loss: 1.8993 - val_accuracy: 0.5812 - val_Precision: 1.0000 - val_Recall: 0.0598\n","Epoch 7/10\n","15/15 [==============================] - 26s 2s/step - loss: 1.4013 - accuracy: 0.7816 - Precision: 0.9589 - Recall: 0.1499 - val_loss: 1.7423 - val_accuracy: 0.6068 - val_Precision: 0.7500 - val_Recall: 0.1026\n","Epoch 8/10\n","15/15 [==============================] - 26s 2s/step - loss: 1.0916 - accuracy: 0.8287 - Precision: 0.9880 - Recall: 0.3533 - val_loss: 1.6003 - val_accuracy: 0.6239 - val_Precision: 0.8710 - val_Recall: 0.2308\n","Epoch 9/10\n","15/15 [==============================] - 26s 2s/step - loss: 0.9185 - accuracy: 0.8522 - Precision: 0.9957 - Recall: 0.4968 - val_loss: 1.5380 - val_accuracy: 0.5983 - val_Precision: 0.8261 - val_Recall: 0.3248\n","Epoch 10/10\n","15/15 [==============================] - 26s 2s/step - loss: 0.7661 - accuracy: 0.8801 - Precision: 0.9605 - Recall: 0.6253 - val_loss: 1.4763 - val_accuracy: 0.6154 - val_Precision: 0.8148 - val_Recall: 0.3761\n"]},{"data":{"text/plain":["<keras.callbacks.History at 0x7f3b90fa4190>"]},"execution_count":18,"metadata":{},"output_type":"execute_result"}],"source":["keywords_old_model = build_model()\n","keywords_old_model.fit(x_train_old, y_train_old, \n","           epochs=10,\n","           validation_data=(x_test_old,y_test_old))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":296},"executionInfo":{"elapsed":578,"status":"ok","timestamp":1651400116696,"user":{"displayName":"Wiem Khlifi","userId":"05865605844600796666"},"user_tz":-60},"id":"cK1oKMkWAbO-","outputId":"5f0d5b7f-636c-4fb5-d277-13d5d4f276be"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plot_graphs(keywords_old_model, 'accuracy','Keywords Classification Accuracy without data augmentation') "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HjWB1da73xsS"},"outputs":[],"source":["#keywords_old_model.save('gdrive/My Drive/Colab Notebooks/keywords_old_model.h5')\n","keywords_old_model = tf.keras.models.load_model('gdrive/My Drive/Colab Notebooks/keywords_old_model.h5',custom_objects={'KerasLayer': hub.KerasLayer})"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ri_s447fWUti"},"outputs":[],"source":["# Calculating the F1_score\n","y_pred = np.argmax(keywords_old_model(X_test_old),axis=1)\n","y_true = np.argmax(Y_test_old,axis=1)\n","print(classification_report(y_true, y_pred, target_names=lb.classes_))\n","#print(\"The F1_score is: \",f1_score(y_true, y_pred, average='weighted') ) # average = 'weighted' used in imbalanced data "]},{"cell_type":"markdown","metadata":{"id":"euzrKJTw9Buw"},"source":["###Final Model with combinaision"]},{"cell_type":"code","source":["final_old_model = build_model_combination()\n","\n","tf.keras.utils.plot_model(model=final_old_model, show_shapes=True, dpi=76)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":133},"id":"4UZb1AvxJjVZ","executionInfo":{"status":"ok","timestamp":1651585718958,"user_tz":-60,"elapsed":25874,"user":{"displayName":"Wiem Khlifi","userId":"05865605844600796666"}},"outputId":"35e7d5df-f013-491d-8623-20f8a6a6adbc"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<IPython.core.display.Image object>"],"image/png":"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\n"},"metadata":{},"execution_count":22}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":329109,"status":"ok","timestamp":1651454324438,"user":{"displayName":"IEEE ENSI CIS Chapter","userId":"10635189582009435434"},"user_tz":-60},"id":"-0L2Uewb9GrP","outputId":"6c634c12-1546-47ce-9eb7-cf803a8a0a91"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/10\n","47/47 [==============================] - 49s 643ms/step - loss: 3.4829 - accuracy: 0.0278 - Precision: 0.0000e+00 - Recall: 0.0000e+00 - val_loss: 3.3446 - val_accuracy: 0.0684 - val_Precision: 0.0000e+00 - val_Recall: 0.0000e+00\n","Epoch 2/10\n","47/47 [==============================] - 29s 620ms/step - loss: 3.0651 - accuracy: 0.2034 - Precision: 0.0000e+00 - Recall: 0.0000e+00 - val_loss: 2.6281 - val_accuracy: 0.3846 - val_Precision: 0.0000e+00 - val_Recall: 0.0000e+00\n","Epoch 3/10\n","47/47 [==============================] - 29s 616ms/step - loss: 2.2233 - accuracy: 0.5910 - Precision: 0.0000e+00 - Recall: 0.0000e+00 - val_loss: 1.8296 - val_accuracy: 0.7436 - val_Precision: 0.0000e+00 - val_Recall: 0.0000e+00\n","Epoch 4/10\n","47/47 [==============================] - 29s 614ms/step - loss: 1.4879 - accuracy: 0.8287 - Precision: 1.0000 - Recall: 0.0236 - val_loss: 1.3766 - val_accuracy: 0.8205 - val_Precision: 0.8333 - val_Recall: 0.0427\n","Epoch 5/10\n","47/47 [==============================] - 29s 616ms/step - loss: 1.0227 - accuracy: 0.8887 - Precision: 0.9886 - Recall: 0.1863 - val_loss: 1.1386 - val_accuracy: 0.8291 - val_Precision: 0.8333 - val_Recall: 0.1282\n","Epoch 6/10\n","47/47 [==============================] - 29s 616ms/step - loss: 0.7977 - accuracy: 0.9465 - Precision: 0.9885 - Recall: 0.3683 - val_loss: 1.0302 - val_accuracy: 0.8462 - val_Precision: 0.9032 - val_Recall: 0.2393\n","Epoch 7/10\n","47/47 [==============================] - 29s 616ms/step - loss: 0.6538 - accuracy: 0.9550 - Precision: 0.9961 - Recall: 0.5439 - val_loss: 0.9274 - val_accuracy: 0.8803 - val_Precision: 0.9216 - val_Recall: 0.4017\n","Epoch 8/10\n","47/47 [==============================] - 29s 615ms/step - loss: 0.5565 - accuracy: 0.9743 - Precision: 0.9966 - Recall: 0.6210 - val_loss: 0.8741 - val_accuracy: 0.8718 - val_Precision: 0.9194 - val_Recall: 0.4872\n","Epoch 9/10\n","47/47 [==============================] - 29s 613ms/step - loss: 0.4897 - accuracy: 0.9679 - Precision: 0.9968 - Recall: 0.6724 - val_loss: 0.8224 - val_accuracy: 0.8462 - val_Precision: 0.9254 - val_Recall: 0.5299\n","Epoch 10/10\n","47/47 [==============================] - 29s 614ms/step - loss: 0.4188 - accuracy: 0.9786 - Precision: 1.0000 - Recall: 0.7602 - val_loss: 0.7717 - val_accuracy: 0.8547 - val_Precision: 0.9054 - val_Recall: 0.5726\n"]},{"data":{"text/plain":["<keras.callbacks.History at 0x7fa6a03158d0>"]},"execution_count":24,"metadata":{},"output_type":"execute_result"}],"source":["final_old_model = build_model_combination()\n","\n","# The training took more than 1hour\n","final_old_model.fit([X_train_old,x_train_old], Y_train_old, \n","           epochs=10,\n","           batch_size=10,\n","           validation_data=([X_test_old,x_test_old],Y_test_old))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":296},"executionInfo":{"elapsed":18,"status":"ok","timestamp":1651437072108,"user":{"displayName":"wiem khlifi","userId":"15071756733142784699"},"user_tz":-60},"id":"3TQtkV-gAnU8","outputId":"5dc00ad1-e42d-4f81-e90b-3d9f92cd3492"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plot_graphs(final_old_model,'accuracy','Combination Model Accuracy without data augmentation')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6Byy4cuu4a4c"},"outputs":[],"source":["#final_old_model.save('gdrive/My Drive/Colab Notebooks/final_old_model.h5')\n","final_old_model = tf.keras.models.load_model('gdrive/My Drive/Colab Notebooks/final_old_model.h5',custom_objects={'KerasLayer': hub.KerasLayer})"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"s-gL4gSSZ82L"},"outputs":[],"source":["# Calculating the F1_score\n","y_pred = np.argmax(final_old_model([X_test_old,x_test_old]),axis=1)\n","y_true = np.argmax(Y_test_old,axis=1)\n","print(classification_report(y_true, y_pred, target_names=lb.classes_))\n","#print(\"The F1_score is: \",f1_score(y_true, y_pred, average='weighted') ) # average = 'weighted' used in imbalanced data "]},{"cell_type":"markdown","metadata":{"id":"-0wA166CSF20"},"source":["## Classification using data augmentation"]},{"cell_type":"markdown","metadata":{"id":"7qJJsSQ42Aa1"},"source":["### Classification using Texts \n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1865629,"status":"ok","timestamp":1651456248213,"user":{"displayName":"IEEE ENSI CIS Chapter","userId":"10635189582009435434"},"user_tz":-60},"id":"uMRO_hZ5-Ei1","outputId":"8f9be9b1-1b67-47a4-cbec-cf3c17721f8e"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/10\n","190/190 [==============================] - 194s 955ms/step - loss: 1.6202 - accuracy: 0.6340 - Precision: 0.9724 - Recall: 0.3540 - val_loss: 0.2400 - val_accuracy: 0.9618 - val_Precision: 0.9758 - val_Recall: 0.9282\n","Epoch 2/10\n","190/190 [==============================] - 180s 949ms/step - loss: 0.1637 - accuracy: 0.9676 - Precision: 0.9762 - Recall: 0.9541 - val_loss: 0.0635 - val_accuracy: 0.9868 - val_Precision: 0.9875 - val_Recall: 0.9855\n","Epoch 3/10\n","190/190 [==============================] - 180s 950ms/step - loss: 0.0644 - accuracy: 0.9835 - Precision: 0.9848 - Recall: 0.9821 - val_loss: 0.0395 - val_accuracy: 0.9875 - val_Precision: 0.9875 - val_Recall: 0.9875\n","Epoch 4/10\n","190/190 [==============================] - 180s 949ms/step - loss: 0.0463 - accuracy: 0.9849 - Precision: 0.9852 - Recall: 0.9845 - val_loss: 0.0410 - val_accuracy: 0.9842 - val_Precision: 0.9842 - val_Recall: 0.9835\n","Epoch 5/10\n","190/190 [==============================] - 180s 949ms/step - loss: 0.0364 - accuracy: 0.9870 - Precision: 0.9872 - Recall: 0.9868 - val_loss: 0.0370 - val_accuracy: 0.9855 - val_Precision: 0.9855 - val_Recall: 0.9855\n","Epoch 6/10\n","190/190 [==============================] - 180s 949ms/step - loss: 0.0294 - accuracy: 0.9886 - Precision: 0.9888 - Recall: 0.9885 - val_loss: 0.0364 - val_accuracy: 0.9868 - val_Precision: 0.9868 - val_Recall: 0.9868\n","Epoch 7/10\n","190/190 [==============================] - 180s 949ms/step - loss: 0.0282 - accuracy: 0.9886 - Precision: 0.9888 - Recall: 0.9885 - val_loss: 0.0370 - val_accuracy: 0.9862 - val_Precision: 0.9868 - val_Recall: 0.9862\n","Epoch 8/10\n","190/190 [==============================] - 180s 949ms/step - loss: 0.0281 - accuracy: 0.9885 - Precision: 0.9886 - Recall: 0.9881 - val_loss: 0.0350 - val_accuracy: 0.9875 - val_Precision: 0.9875 - val_Recall: 0.9875\n","Epoch 9/10\n","190/190 [==============================] - 180s 948ms/step - loss: 0.0219 - accuracy: 0.9901 - Precision: 0.9901 - Recall: 0.9898 - val_loss: 0.0425 - val_accuracy: 0.9862 - val_Precision: 0.9862 - val_Recall: 0.9862\n","Epoch 10/10\n","190/190 [==============================] - 180s 948ms/step - loss: 0.0224 - accuracy: 0.9903 - Precision: 0.9904 - Recall: 0.9903 - val_loss: 0.0425 - val_accuracy: 0.9862 - val_Precision: 0.9862 - val_Recall: 0.9862\n"]},{"data":{"text/plain":["<keras.callbacks.History at 0x7fa43c2ed4d0>"]},"execution_count":26,"metadata":{},"output_type":"execute_result"}],"source":["text_model = build_model()\n","\n","text_model.fit(X_train, Y_train, \n","           epochs=10,\n","           validation_data=(X_test,Y_test))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":296},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1651399805887,"user":{"displayName":"Wiem Khlifi","userId":"05865605844600796666"},"user_tz":-60},"id":"z17a6267AW6w","outputId":"de766e47-da8d-45e7-8a09-78aa9b3c28d3"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plot_graphs(text_model, 'accuracy','Text Classification Accuracy with data augmentation')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5JKIaVGdtFVO"},"outputs":[],"source":["#text_model.save('gdrive/My Drive/Colab Notebooks/text_model.h5') \n","text_model = tf.keras.models.load_model('gdrive/My Drive/Colab Notebooks/text_model.h5',custom_objects={'KerasLayer': hub.KerasLayer})"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_lEaXhSAFIBi"},"outputs":[],"source":["# Calculating the F1_score\n","y_pred = np.argmax(text_model.predict(X_test),axis=1)\n","y_true = np.argmax(Y_test,axis=1)\n","print(classification_report(y_true, y_pred, target_names=lb.classes_))\n","#print(\"The F1_score is: \",f1_score(y_true, y_pred, average='weighted') ) # average = 'weighted' used in imbalanced data "]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1858,"status":"ok","timestamp":1651194448382,"user":{"displayName":"wiem khlifi","userId":"15071756733142784699"},"user_tz":-60},"id":"nVCp77BIH6Mc","outputId":"2f2f1523-aeaa-4e89-af46-e6ba6f6d98b3"},"outputs":[{"data":{"text/plain":["array(['Marketing'], dtype=object)"]},"execution_count":39,"metadata":{},"output_type":"execute_result"}],"source":["txt = text_cleaning(\"Marketing is the activity, set of institutions, and processes for creating, communicating, delivering, and exchanging offerings that have value for customers, clients, partners, and society at large.\")\n","\n","lb.inverse_transform(np.argmax(text_model.predict([txt])).reshape(1)) # Get the class name"]},{"cell_type":"markdown","metadata":{"id":"R-Rkjya189Qb"},"source":["###Classification using keywords"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3460626,"status":"ok","timestamp":1651404462405,"user":{"displayName":"Wiem Khlifi","userId":"05865605844600796666"},"user_tz":-60},"id":"zXc3lgrIIAt0","outputId":"ba384f5b-d672-4dd9-efb3-c5c4ee805fd9"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/10\n","190/190 [==============================] - 365s 2s/step - loss: 1.5852 - accuracy: 0.6532 - Precision: 0.9679 - Recall: 0.3425 - val_loss: 0.3034 - val_accuracy: 0.9375 - val_Precision: 0.9610 - val_Recall: 0.9092\n","Epoch 2/10\n","190/190 [==============================] - 344s 2s/step - loss: 0.2190 - accuracy: 0.9564 - Precision: 0.9705 - Recall: 0.9359 - val_loss: 0.1294 - val_accuracy: 0.9671 - val_Precision: 0.9728 - val_Recall: 0.9638\n","Epoch 3/10\n","190/190 [==============================] - 343s 2s/step - loss: 0.1000 - accuracy: 0.9768 - Precision: 0.9814 - Recall: 0.9717 - val_loss: 0.1134 - val_accuracy: 0.9658 - val_Precision: 0.9741 - val_Recall: 0.9651\n","Epoch 4/10\n","190/190 [==============================] - 344s 2s/step - loss: 0.0667 - accuracy: 0.9789 - Precision: 0.9818 - Recall: 0.9776 - val_loss: 0.1191 - val_accuracy: 0.9691 - val_Precision: 0.9741 - val_Recall: 0.9658\n","Epoch 5/10\n","190/190 [==============================] - 344s 2s/step - loss: 0.0465 - accuracy: 0.9827 - Precision: 0.9838 - Recall: 0.9807 - val_loss: 0.1155 - val_accuracy: 0.9691 - val_Precision: 0.9716 - val_Recall: 0.9671\n","Epoch 6/10\n","190/190 [==============================] - 344s 2s/step - loss: 0.0410 - accuracy: 0.9824 - Precision: 0.9829 - Recall: 0.9816 - val_loss: 0.1100 - val_accuracy: 0.9697 - val_Precision: 0.9735 - val_Recall: 0.9671\n","Epoch 7/10\n","190/190 [==============================] - 343s 2s/step - loss: 0.0333 - accuracy: 0.9842 - Precision: 0.9845 - Recall: 0.9839 - val_loss: 0.1161 - val_accuracy: 0.9677 - val_Precision: 0.9715 - val_Recall: 0.9664\n","Epoch 8/10\n","190/190 [==============================] - 343s 2s/step - loss: 0.0290 - accuracy: 0.9868 - Precision: 0.9868 - Recall: 0.9862 - val_loss: 0.1217 - val_accuracy: 0.9658 - val_Precision: 0.9702 - val_Recall: 0.9638\n","Epoch 9/10\n","190/190 [==============================] - 343s 2s/step - loss: 0.0299 - accuracy: 0.9847 - Precision: 0.9852 - Recall: 0.9844 - val_loss: 0.1233 - val_accuracy: 0.9658 - val_Precision: 0.9696 - val_Recall: 0.9658\n","Epoch 10/10\n","190/190 [==============================] - 343s 2s/step - loss: 0.0281 - accuracy: 0.9870 - Precision: 0.9872 - Recall: 0.9868 - val_loss: 0.1296 - val_accuracy: 0.9651 - val_Precision: 0.9670 - val_Recall: 0.9645\n"]},{"data":{"text/plain":["<keras.callbacks.History at 0x7f7b9f7ec5d0>"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["keywords_model = build_model()\n","\n","keywords_model.fit(x_train, y_train, \n","           epochs=10,\n","           validation_data=(x_test,y_test))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":296},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1651404462406,"user":{"displayName":"Wiem Khlifi","userId":"05865605844600796666"},"user_tz":-60},"id":"AjcDvss6AfNq","outputId":"0d9588d5-0528-4dec-e171-d032e78511a5"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plot_graphs(keywords_model, 'accuracy','Keywords Classification Accuracy with data augmentation') "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Lt_I6rvrFFB6"},"outputs":[],"source":["#keywords_model.save('gdrive/My Drive/Colab Notebooks/keywords_model.h5')\n","keywords_model = tf.keras.models.load_model(('gdrive/My Drive/Colab Notebooks/keywords_model.h5'),custom_objects={'KerasLayer': hub.KerasLayer}) "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LzpsxeD6WlnS"},"outputs":[],"source":["# Calculating the F1_score\n","y_pred = np.argmax(keywords_model.predict(x_test),axis=1)\n","y_true = np.argmax(y_test,axis=1)\n","print(classification_report(y_true, y_pred, target_names=lb.classes_))\n","#print(\"The F1_score is: \",f1_score(y_true, y_pred, average='weighted') ) # average = 'weighted' used in imbalanced data "]},{"cell_type":"markdown","metadata":{"id":"5LxUbo1j9Dr3"},"source":["###Final Model with combinaision"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7556704,"status":"ok","timestamp":1651228498222,"user":{"displayName":"Wiem Khlifi","userId":"05865605844600796666"},"user_tz":-60},"id":"CGM9_UC4G63O","outputId":"fb5dfa5d-cdb9-4ec4-a436-adcb876089c3"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/10\n","608/608 [==============================] - 798s 1s/step - loss: 1.1656 - accuracy: 0.7766 - Precision: 0.9744 - Recall: 0.4380 - val_loss: 0.2041 - val_accuracy: 0.9598 - val_Precision: 0.9778 - val_Recall: 0.8999\n","Epoch 2/10\n","608/608 [==============================] - 744s 1s/step - loss: 0.1354 - accuracy: 0.9766 - Precision: 0.9861 - Recall: 0.9356 - val_loss: 0.0835 - val_accuracy: 0.9789 - val_Precision: 0.9925 - val_Recall: 0.9598\n","Epoch 3/10\n","608/608 [==============================] - 757s 1s/step - loss: 0.0610 - accuracy: 0.9844 - Precision: 0.9926 - Recall: 0.9699 - val_loss: 0.0497 - val_accuracy: 0.9776 - val_Precision: 0.9952 - val_Recall: 0.9598\n","Epoch 4/10\n","608/608 [==============================] - 748s 1s/step - loss: 0.0356 - accuracy: 0.9867 - Precision: 0.9948 - Recall: 0.9725 - val_loss: 0.0477 - val_accuracy: 0.9803 - val_Precision: 0.9899 - val_Recall: 0.9638\n","Epoch 5/10\n","608/608 [==============================] - 743s 1s/step - loss: 0.0489 - accuracy: 0.9816 - Precision: 0.9929 - Recall: 0.9667 - val_loss: 0.0449 - val_accuracy: 0.9822 - val_Precision: 0.9946 - val_Recall: 0.9625\n","Epoch 6/10\n","608/608 [==============================] - 741s 1s/step - loss: 0.0297 - accuracy: 0.9845 - Precision: 0.9960 - Recall: 0.9733 - val_loss: 0.0411 - val_accuracy: 0.9842 - val_Precision: 0.9986 - val_Recall: 0.9612\n","Epoch 7/10\n","608/608 [==============================] - 743s 1s/step - loss: 0.0414 - accuracy: 0.9821 - Precision: 0.9928 - Recall: 0.9713 - val_loss: 0.0455 - val_accuracy: 0.9842 - val_Precision: 0.9919 - val_Recall: 0.9724\n","Epoch 8/10\n","608/608 [==============================] - 742s 1s/step - loss: 0.0309 - accuracy: 0.9840 - Precision: 0.9898 - Recall: 0.9776 - val_loss: 0.0409 - val_accuracy: 0.9816 - val_Precision: 0.9866 - val_Recall: 0.9710\n","Epoch 9/10\n","608/608 [==============================] - 742s 1s/step - loss: 0.0235 - accuracy: 0.9867 - Precision: 0.9912 - Recall: 0.9801 - val_loss: 0.0497 - val_accuracy: 0.9822 - val_Precision: 0.9880 - val_Recall: 0.9743\n","Epoch 10/10\n","608/608 [==============================] - 746s 1s/step - loss: 0.0266 - accuracy: 0.9862 - Precision: 0.9899 - Recall: 0.9809 - val_loss: 0.0474 - val_accuracy: 0.9809 - val_Precision: 0.9866 - val_Recall: 0.9677\n"]},{"data":{"text/plain":["<keras.callbacks.History at 0x7f7164d4a710>"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["# The training took more than 2hours\n","final_model = build_model_combination()\n","\n","final_model.fit([X_train,x_train], Y_train, \n","           epochs=10,\n","           batch_size=10,\n","           validation_data=([X_test,x_test],Y_test))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"executionInfo":{"elapsed":993,"status":"ok","timestamp":1651229041756,"user":{"displayName":"Wiem Khlifi","userId":"05865605844600796666"},"user_tz":-60},"id":"4DXJeqS3Ic4d","outputId":"6f6bd1fc-7348-4ad6-f4bd-9492cee9a9e7"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plot_graphs(final_model,'accuracy','Combination Model Accuracy with data augmentation')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"aU2eAAP7dV2-"},"outputs":[],"source":["#final_model.save('gdrive/My Drive/Colab Notebooks/final_model.h5')\n","final_model = tf.keras.models.load_model('gdrive/My Drive/Colab Notebooks/final_model.h5',custom_objects={'KerasLayer': hub.KerasLayer})"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xKhMKeQaara4"},"outputs":[],"source":["# Calculating the F1_score\n","y_pred = np.argmax(final_model.predict([X_test,x_test]),axis=1)\n","y_true = np.argmax(Y_test,axis=1)\n","print(classification_report(y_true, y_pred, target_names=lb.classes_))\n","#print(\"The F1_score is: \",f1_score(y_true, y_pred, average='weighted') ) # average = 'weighted' used in imbalanced data "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4CDSbHE1y9SS"},"outputs":[],"source":["txt=\"\"\"AI has become a catchall term for applications that perform complex tasks that once required human input such as communicating with customers online or playing chess. The term is often used interchangeably with its subfields, which include machine learning and deep learning. There are differences, however. For example, machine learning is focused on building systems that learn or improve their performance based on the data they consume. It’s important to note that although all machine learning is AI, not all AI is machine learning.\n","\n","To get the full value from AI, many companies are making significant investments in data science teams. Data science, an interdisciplinary field that uses scientific and other methods to extract value from data, combines skills from fields such as statistics and computer science with business knowledge to analyze data collected from multiple sources.\"\"\""]},{"cell_type":"code","source":["def text_cleaning(text):\n","    \"\"\"\n","    text: text is string type to be cleaned.\n","    Function aims to clean the text from punctuation or special character, remove the stop words and lemmatizing the words.\n","   \"\"\"\n","\n","    \n","    text = text.lower()     \n","    text = re.sub(\"[^a-zA-Z]\",\" \",text)\n","    words = word_tokenize(text)\n","    words = [word for word in words if len(word)>=2 and not in set(stopwords.words('english'))] \n","\n","    return \" \".join(words)"],"metadata":{"id":"acC_0oCJFmBN"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"O8LyeFkgdNk5"},"outputs":[],"source":["key_txt = kw_model_bert.extract_keywords(txt, keyphrase_ngram_range=(1, 2), nr_candidates=15, top_n=10,stop_words='english')\n","k = str(list(dict(key_txt).keys()))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nwIEfwFH_tDD"},"outputs":[],"source":["prediction = final_model.predict([tf.convert_to_tensor([txt]) ,tf.convert_to_tensor([k]) ])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1651708390747,"user":{"displayName":"Wiem Khlifi","userId":"05865605844600796666"},"user_tz":-60},"id":"NMKgKq2Zzynv","outputId":"6e401cec-d4e4-4dea-d5c8-049d467fbd80"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["False"]},"metadata":{},"execution_count":51}],"source":["np.any(prediction>0.6) # Tester si un des valeurs de classification est superieur à 0.5 (30 valeurs<=>30classes) "]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"executionInfo":{"elapsed":16,"status":"ok","timestamp":1651708042758,"user":{"displayName":"Wiem Khlifi","userId":"05865605844600796666"},"user_tz":-60},"id":"DcHbauyTzmle","outputId":"7b06af72-4870-486f-aba9-d4faf4b65b42"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'School'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":40}],"source":["result = lb.inverse_transform(np.argmax(prediction).reshape(1)) # Get the class name\n","result[0] # That's why we need a threshold to detect if the topic belongs to our database or it's a new one"]}],"metadata":{"colab":{"collapsed_sections":["g5I-XiJ-17wB","5FGAYOyjyweY","LScLw1mHrXXJ","-Sj4D9lUrM1Y","EjSxK7VDIx7L","l3h7HUfEDtSu","rCt0syHFD8jc","awIyzp-PSYPy","PERsclEQSZaC","PofJPtHHR1uL","2gkuslEBGrRA","FNtn9sMu17ga","rCmuT1b980ef","euzrKJTw9Buw","7qJJsSQ42Aa1","R-Rkjya189Qb"],"name":"Supervised Text Classification.ipynb","provenance":[]},"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":"3.8.8"},"toc-autonumbering":false,"toc-showcode":false,"toc-showmarkdowntxt":false,"accelerator":"GPU"},"nbformat":4,"nbformat_minor":0}