nlp_classification.ipynb

{"cells":[{"cell_type":"markdown","source":["# **Part A experiments:**\n","1.\t**Experiment variables**: (a) Input: Title, (b) Model: Baseline ML,\n","**Insights:** Very low accuracy (34%),\n","**Next action:** Try a state-of-the-art model\n","\n","2.\tExperiment variables: (a) Input: Title, (b) Model: Transformers,\n","Insights: Better accuracy (52%), but still low,\n","Next action: Experiment with the Input variable\n","\n","3.\tExperiment variables: (a) Input: Title+content, (b) Model: Various ML,\n","Insights: Random Forest performs the best (70%),\n","Next action: Re-try a state-of-the-art model with new Input\n","\n","4.\tExperiment variables: (a) Input: Title+content, (b) Model: Transformers,\n","Insights: Almost the same accuracy (70%), so it is not about the modeling,\n","Next action: Experiment further with Input\n","\n","5.\tExperiment variables: (a) Input: Title+content+url, (b) Model: Random Forest,\n","Insights: High accuracy (87%),\n","Next action: Try only url\n","\n","6.\tExperiment variables: **(a) Input: url, (b) Model: Random Fores**t\n","Insights: Very high accuracy (92.5%),\n","Next action: Re-try a state-of-the-art model for comparison,\n","\n","7.\tExperiment variables: (a) Input: url, (b) Model: Transformer,\n","Insights: Excellent accuracy (95%), but requires exceptionally high resources and time to train"],"metadata":{"id":"fXCIuPRmN3-E"}},{"cell_type":"markdown","source":["# Load data and Preprocessing"],"metadata":{"id":"ADFikFkv8tAI"}},{"cell_type":"code","source":["from google.colab import drive\n","\n","drive.mount('/content/drive')"],"metadata":{"id":"yr_Ef9YeidVk","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1711105198349,"user_tz":-120,"elapsed":20021,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"b3367cee-583a-40da-ced0-18d535c17596"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["cd drive"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jZ-BnF5c5NSy","executionInfo":{"status":"ok","timestamp":1711105198349,"user_tz":-120,"elapsed":5,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"00f96f69-cf9e-4a79-a9b4-86a018bb1508"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive\n"]}]},{"cell_type":"code","source":["cd MyDrive"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4RalcI845Qa6","executionInfo":{"status":"ok","timestamp":1711105198349,"user_tz":-120,"elapsed":4,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"455bff23-5336-414f-bbc3-3c601200c2de"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive\n"]}]},{"cell_type":"code","source":["import pandas as pd\n","\n","file_path = '/content/drive/My Drive/train_data.csv'\n","df = pd.read_csv(file_path)\n","print(df.head())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"z1x9m1Bbp6F_","executionInfo":{"status":"ok","timestamp":1711108049475,"user_tz":-120,"elapsed":810,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"867b5bb0-79d7-420e-9c41-9ff3a70062ef"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["                                                 url  \\\n","0  https://www.platform.gr/life-2/fashion/75668-d...   \n","1  https://www.protothema.gr/greece/article/96354...   \n","2  https://www.vimaorthodoxias.gr/ekklisia-tis-el...   \n","3  http://physics4u.gr/blog/2018/07/13/%ce%bf-%cf...   \n","4  https://www.tilestwra.com/pos-akougete-o-ichos...   \n","\n","                                               title  \\\n","0  Δες εδώ τις ολοκαίνουργιες μάσκες προστασίας α...   \n","1  Φλώρινα: Έκρυβε χασίς, χάπια και άγνωστη ουσία...   \n","2  Άνω κάτω στην Εκκλησία της Ελλάδος για το συλλ...   \n","3                Ο σπειροειδής ραβδωτός Γαλαξίας μας   \n","4  Πώς ακούγεται ο ήχος έναρξης των Windows XP 80...   \n","\n","                                             content                  label  \n","0  Οι συνεισφορές θα διατεθούν στο Κοινοτικό Ταμε...          Style&Fashion  \n","1  Δείτε βίντεο Η στιγμή της ισχυρότατης έκρηξης ...       Sensitive Topics  \n","2  Η απάντηση ήρθε λίγη ώρα αργότερα από τον Μητρ...  Religion&Spirituality  \n","3  Ο Γαλαξίας μας αποτελείται κυρίως από ένα πυρή...                Science  \n","4  Πολλοί άνθρωποι αναγνωρίζουν αμέσως τον ήχο έν...         Viral Articles  \n"]}]},{"cell_type":"code","source":["df.describe()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"8D176ojZqQGp","executionInfo":{"status":"ok","timestamp":1711010203300,"user_tz":-120,"elapsed":485,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"549f267f-e4fa-4d08-b356-02a2b2cd9efb"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                                      url  \\\n","count                                                2498   \n","unique                                               2498   \n","top     https://www.platform.gr/life-2/fashion/75668-d...   \n","freq                                                    1   \n","\n","                                                    title  \\\n","count                                                2498   \n","unique                                               2498   \n","top     Δες εδώ τις ολοκαίνουργιες μάσκες προστασίας α...   \n","freq                                                    1   \n","\n","                                                  content    label  \n","count                                                2498     2498  \n","unique                                               2338       21  \n","top     Δείτε βίντεο Η στιγμή της ισχυρότατης έκρηξης ...  Culture  \n","freq                                                   81      360  "],"text/html":["\n","  <div id=\"df-52dc74ad-ed2a-479c-9522-39dff8dca553\" 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>url</th>\n","      <th>title</th>\n","      <th>content</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>2498</td>\n","      <td>2498</td>\n","      <td>2498</td>\n","      <td>2498</td>\n","    </tr>\n","    <tr>\n","      <th>unique</th>\n","      <td>2498</td>\n","      <td>2498</td>\n","      <td>2338</td>\n","      <td>21</td>\n","    </tr>\n","    <tr>\n","      <th>top</th>\n","      <td>https://www.platform.gr/life-2/fashion/75668-d...</td>\n","      <td>Δες εδώ τις ολοκαίνουργιες μάσκες προστασίας α...</td>\n","      <td>Δείτε βίντεο Η στιγμή της ισχυρότατης έκρηξης ...</td>\n","      <td>Culture</td>\n","    </tr>\n","    <tr>\n","      <th>freq</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>81</td>\n","      <td>360</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-52dc74ad-ed2a-479c-9522-39dff8dca553')\"\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 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\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","    .colab-df-buttons div {\n","      margin-bottom: 4px;\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-52dc74ad-ed2a-479c-9522-39dff8dca553 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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\\u03bf\\u03bb\\u03bf\\u03ba\\u03b1\\u03af\\u03bd\\u03bf\\u03c5\\u03c1\\u03b3\\u03b9\\u03b5\\u03c2 \\u03bc\\u03ac\\u03c3\\u03ba\\u03b5\\u03c2 \\u03c0\\u03c1\\u03bf\\u03c3\\u03c4\\u03b1\\u03c3\\u03af\\u03b1\\u03c2 \\u03b1\\u03c0\\u03cc \\u03c4\\u03bf\\u03bd Covid-19 \\u03c4\\u03bf\\u03c5 \\u03bf\\u03af\\u03ba\\u03bf\\u03c5 Burberry, \\u03bc\\u03b5 iconic print\",\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"content\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2338,\n          \"81\",\n          \"2498\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          21,\n          \"360\",\n          \"2498\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":92}]},{"cell_type":"code","source":["print(df['label'])\n","print(df.shape)\n","print(df['label'].shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dS7ulnRPrX0e","executionInfo":{"status":"ok","timestamp":1711010204686,"user_tz":-120,"elapsed":3,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"75b5becd-22b8-4905-a430-245bb3cc9bdc"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["0               Style&Fashion\n","1            Sensitive Topics\n","2       Religion&Spirituality\n","3                     Science\n","4              Viral Articles\n","                ...          \n","2493                 Politics\n","2494         Sensitive Topics\n","2495                  Culture\n","2496                   Beauty\n","2497                   Sports\n","Name: label, Length: 2498, dtype: object\n","(2498, 4)\n","(2498,)\n"]}]},{"cell_type":"code","source":["print(df['title'][150])\n","print(df['label'][150])\n","print(df['url'][150])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YrXBh2VasC3g","executionInfo":{"status":"ok","timestamp":1711010205144,"user_tz":-120,"elapsed":4,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"61bffbff-f11b-4c7e-ad81-927f7faf5045"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["DxOMark: εξαιρετική η απόδοση του Smartphone for Snapdragon Insiders\n","Tech&Computing\n"]}]},{"cell_type":"code","source":["# Mapping\n","label_mapping = {\n","    'Style&Fashion': 0,\n","    'Sensitive Topics': 1,\n","    'Religion&Spirituality': 2,\n","    'Science': 3,\n","    'Viral Articles': 4,\n","    'Beauty': 5,\n","    'Tech&Computing': 6,\n","    'Healthy Living': 7,\n","    'Sports': 8,\n","    'Culture': 9,\n","    'Business&Finance': 10,\n","    'Education': 11,\n","    'Astrology': 12,\n","    'Family&Relationships': 13,\n","    'Politics': 14,\n","    'Food&Drink': 15,\n","    'Pop Culture': 16,\n","    'Automotive': 17,\n","    'Attractions': 18,\n","    'Home&Garden': 19,\n","    'Travel': 20\n","}\n","df['label'] = df['label'].map(label_mapping)\n","\n","df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"6f-dcgeVayL4","executionInfo":{"status":"ok","timestamp":1711105201387,"user_tz":-120,"elapsed":342,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"028a2b40-08d6-428e-a20c-3e9277c04e4a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                                    url  \\\n","0     https://www.platform.gr/life-2/fashion/75668-d...   \n","1     https://www.protothema.gr/greece/article/96354...   \n","2     https://www.vimaorthodoxias.gr/ekklisia-tis-el...   \n","3     http://physics4u.gr/blog/2018/07/13/%ce%bf-%cf...   \n","4     https://www.tilestwra.com/pos-akougete-o-ichos...   \n","...                                                 ...   \n","2493  https://www.newsbomb.gr/politikh/story/531341/...   \n","2494  https://www.iefimerida.gr/news/272145/mazis-pi...   \n","2495  https://e-tetradio.gr/Article/29210/online-kai...   \n","2496  https://www.tlife.gr/omorfia/mallia/ntoretta-p...   \n","2497  https://www.newsauto.gr/races/wtcc-to-spa-vaft...   \n","\n","                                                  title  \\\n","0     Δες εδώ τις ολοκαίνουργιες μάσκες προστασίας α...   \n","1     Φλώρινα: Έκρυβε χασίς, χάπια και άγνωστη ουσία...   \n","2     Άνω κάτω στην Εκκλησία της Ελλάδος για το συλλ...   \n","3                   Ο σπειροειδής ραβδωτός Γαλαξίας μας   \n","4     Πώς ακούγεται ο ήχος έναρξης των Windows XP 80...   \n","...                                                 ...   \n","2493  Παναγιώταρος: Φονιάς ο Φλωράκης - Ήρωας ο Ντερ...   \n","2494        Mάζης: Πιθανός στόχος του ISIS και η Ελλάδα   \n","2495  Online και δωρεάν οι «Παρόντες» του Γιώργου Αυ...   \n","2496  Η Ντορέττα Παπαδημητρίου με κουμπωτές αφέλειες...   \n","2497       WTCC: To Σπα βάφτηκε στα χρώματα της Citroen   \n","\n","                                                content  label  \n","0     Οι συνεισφορές θα διατεθούν στο Κοινοτικό Ταμε...      0  \n","1     Δείτε βίντεο Η στιγμή της ισχυρότατης έκρηξης ...      1  \n","2     Η απάντηση ήρθε λίγη ώρα αργότερα από τον Μητρ...      2  \n","3     Ο Γαλαξίας μας αποτελείται κυρίως από ένα πυρή...      3  \n","4     Πολλοί άνθρωποι αναγνωρίζουν αμέσως τον ήχο έν...      4  \n","...                                                 ...    ...  \n","2493  Μετά τις καταγγελίες του Ηλία Κασιδιάρη στη Βο...     14  \n","2494  Δείτε βίντεο Η στιγμή της ισχυρότατης έκρηξης ...      1  \n","2495  Είναι η νέα ταινία ντοκιμαντέρ του  Γιώργου Αυ...      9  \n","2496  Το red carpet είναι μια πολύ καλή αφορμή για ν...      5  \n","2497  Η κυριαρχία των Γάλλων συνεχίστηκε στο Βέλγιο,...      8  \n","\n","[2498 rows x 4 columns]"],"text/html":["\n","  <div id=\"df-43c10223-5fbd-46de-bba5-9f05c2af92cb\" 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>url</th>\n","      <th>title</th>\n","      <th>content</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>https://www.platform.gr/life-2/fashion/75668-d...</td>\n","      <td>Δες εδώ τις ολοκαίνουργιες μάσκες προστασίας α...</td>\n","      <td>Οι συνεισφορές θα διατεθούν στο Κοινοτικό Ταμε...</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>https://www.protothema.gr/greece/article/96354...</td>\n","      <td>Φλώρινα: Έκρυβε χασίς, χάπια και άγνωστη ουσία...</td>\n","      <td>Δείτε βίντεο Η στιγμή της ισχυρότατης έκρηξης ...</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>https://www.vimaorthodoxias.gr/ekklisia-tis-el...</td>\n","      <td>Άνω κάτω στην Εκκλησία της Ελλάδος για το συλλ...</td>\n","      <td>Η απάντηση ήρθε λίγη ώρα αργότερα από τον Μητρ...</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>http://physics4u.gr/blog/2018/07/13/%ce%bf-%cf...</td>\n","      <td>Ο σπειροειδής ραβδωτός Γαλαξίας μας</td>\n","      <td>Ο Γαλαξίας μας αποτελείται κυρίως από ένα πυρή...</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>https://www.tilestwra.com/pos-akougete-o-ichos...</td>\n","      <td>Πώς ακούγεται ο ήχος έναρξης των Windows XP 80...</td>\n","      <td>Πολλοί άνθρωποι αναγνωρίζουν αμέσως τον ήχο έν...</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>2493</th>\n","      <td>https://www.newsbomb.gr/politikh/story/531341/...</td>\n","      <td>Παναγιώταρος: Φονιάς ο Φλωράκης - Ήρωας ο Ντερ...</td>\n","      <td>Μετά τις καταγγελίες του Ηλία Κασιδιάρη στη Βο...</td>\n","      <td>14</td>\n","    </tr>\n","    <tr>\n","      <th>2494</th>\n","      <td>https://www.iefimerida.gr/news/272145/mazis-pi...</td>\n","      <td>Mάζης: Πιθανός στόχος του ISIS και η Ελλάδα</td>\n","      <td>Δείτε βίντεο Η στιγμή της ισχυρότατης έκρηξης ...</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2495</th>\n","      <td>https://e-tetradio.gr/Article/29210/online-kai...</td>\n","      <td>Online και δωρεάν οι «Παρόντες» του Γιώργου Αυ...</td>\n","      <td>Είναι η νέα ταινία ντοκιμαντέρ του  Γιώργου Αυ...</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>2496</th>\n","      <td>https://www.tlife.gr/omorfia/mallia/ntoretta-p...</td>\n","      <td>Η Ντορέττα Παπαδημητρίου με κουμπωτές αφέλειες...</td>\n","      <td>Το red carpet είναι μια πολύ καλή αφορμή για ν...</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>2497</th>\n","      <td>https://www.newsauto.gr/races/wtcc-to-spa-vaft...</td>\n","      <td>WTCC: To Σπα βάφτηκε στα χρώματα της Citroen</td>\n","      <td>Η κυριαρχία των Γάλλων συνεχίστηκε στο Βέλγιο,...</td>\n","      <td>8</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>2498 rows × 4 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" 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\\u03ac\\u03bd\\u03bf\\u03b9\\u03be\\u03b5 \\u03c0\\u03c5\\u03c1.\\u0389\\u03c4\\u03b1\\u03bd \\u03cc\\u03bc\\u03c9\\u03c2 \\u03b7 \\u03ba\\u03c1\\u03b1\\u03c5\\u03b3\\u03ae \\u03b1\\u03c0\\u03cc\\u03b3\\u03bd\\u03c9\\u03c3\\u03b7\\u03c2 \\u03c4\\u03bf\\u03c5 \\u0392\\u03bf\\u03c1\\u03b5\\u03b9\\u03bf\\u03b7\\u03c0\\u03b5\\u03b9\\u03c1\\u03ce\\u03c4\\u03b7 \\u03ba\\u03b1\\u03b9 \\u03b5\\u03bb\\u03c0\\u03af\\u03b6\\u03c9 \\u03bd\\u03b1 \\u03bc\\u03b7\\u03bd \\u03ad\\u03c6\\u03c5\\u03b3\\u03b5 \\u03ac\\u03b4\\u03b9\\u03ba\\u03b1.\\u0395\\u03bb\\u03c0\\u03af\\u03b6\\u03c9 \\u03bd\\u03b1 \\u03ad\\u03c6\\u03c5\\u03b3\\u03b5 \\u03b3\\u03b9\\u03b1 \\u03bd\\u03b1 \\u03b8\\u03c5\\u03bc\\u03af\\u03c3\\u03b5\\u03b9 \\u03c3\\u03c4\\u03b7 \\u03bc\\u03ac\\u03bd\\u03b1 \\u0395\\u03bb\\u03bb\\u03ac\\u03b4\\u03b1 \\u03c0\\u03c9\\u03c2 \\u03c4\\u03b1 \\u03c0\\u03b1\\u03b9\\u03b4\\u03b9\\u03ac \\u03bc\\u03b1\\u03c2 \\u03c4\\u03b1 \\u03b1\\u03b3\\u03b1\\u03c0\\u03ac\\u03bc\\u03b5 \\u03cc\\u03bb\\u03b1 \\u03c4\\u03bf \\u03af\\u03b4\\u03b9\\u03bf \\u03ba\\u03b1\\u03b9 \\u03b4\\u03b5\\u03bd \\u03ba\\u03ac\\u03bd\\u03bf\\u03c5\\u03bc\\u03b5 \\u03b4\\u03b9\\u03b1\\u03ba\\u03c1\\u03af\\u03c3\\u03b5\\u03b9\\u03c2.\\u0391\\u03bd\\u03b1\\u03c0\\u03b1\\u03cd\\u03c3\\u03bf\\u03c5 \\u03b5\\u03bd \\u03b5\\u03b9\\u03c1\\u03ae\\u03bd\\u03b7 \\u03b8\\u03c5\\u03bc\\u03c9\\u03bc\\u03ad\\u03bd\\u03b5 \\u03b1\\u03b4\\u03b5\\u03c1\\u03c6\\u03ad \\u03bc\\u03bf\\u03c5 \\u039a\\u03c9\\u03bd\\u03c3\\u03c4\\u03b1\\u03bd\\u03c4\\u03af\\u03bd\\u03b5. \\u03a3\\u03b5 \\u03ba\\u03b1\\u03c4\\u03b1\\u03bb\\u03b1\\u03b2\\u03b1\\u03af\\u03bd\\u03c9\\u2026\\u03a7\\u03c1\\u03ae\\u03c3\\u03c4\\u03bf\\u03c2 \\u039c\\u03ac\\u03c3\\u03c4\\u03bf\\u03c1\\u03b1\\u03c2\\u00bb\\u0394\\u03b5\\u03af\\u03c4\\u03b5 \\u03c4\\u03b7\\u03bd \\u03b1\\u03bd\\u03ac\\u03c1\\u03c4\\u03b7\\u03c3\\u03ae \\u03c4\\u03bf\\u03c5:\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 0,\n        \"max\": 20,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          0,\n          17,\n          15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":8}]},{"cell_type":"markdown","source":["# Machine Learning Modeling"],"metadata":{"id":"PTeKVNmV82OO"}},{"cell_type":"markdown","source":["### ML baseline model using title"],"metadata":{"id":"FRfmZ85jx8OB"}},{"cell_type":"markdown","source":["The tile feature seems to be the richest in information..."],"metadata":{"id":"yqdVoKVKbOY6"}},{"cell_type":"code","source":["from sklearn.model_selection import train_test_split\n","from sklearn.feature_extraction.text import TfidfVectorizer\n","from sklearn.naive_bayes import MultinomialNB\n","from sklearn.pipeline import make_pipeline\n","from sklearn.metrics import classification_report, accuracy_score\n","\n","\n","X = df['title']\n","y = df['label']\n","\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","model = make_pipeline(TfidfVectorizer(), MultinomialNB())\n","\n","model.fit(X_train, y_train)\n","\n","y_pred = model.predict(X_test)\n","\n","report = classification_report(y_test, y_pred)\n","\n","acc = accuracy_score(y_test, y_pred)\n","\n","print(f\"Model Type: MultinomialNB\")\n","print(f\"Accuracy: {acc}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OtsFHeXXEG6n","executionInfo":{"status":"ok","timestamp":1711105253695,"user_tz":-120,"elapsed":1487,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"5ff5d2b0-1c89-4856-a8e0-8e7c1c69ae89"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model Type: MultinomialNB\n","Accuracy: 0.346\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n","/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n","/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n"]}]},{"cell_type":"markdown","source":["### Various ML models using title+content\n","\n"],"metadata":{"id":"I_v-PpKJgDpi"}},{"cell_type":"code","source":["import pandas as pd\n","from sklearn.model_selection import train_test_split\n","from sklearn.feature_extraction.text import TfidfVectorizer\n","from sklearn.preprocessing import LabelEncoder\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.svm import SVC\n","from sklearn.metrics import accuracy_score\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.naive_bayes import MultinomialNB\n","from sklearn.ensemble import GradientBoostingClassifier\n","from xgboost import XGBClassifier\n","from sklearn.tree import DecisionTreeClassifier\n","from sklearn.neighbors import KNeighborsClassifier\n","\n","\n","df['text'] = df['title'] + df['content']\n","\n","X_train, X_test, y_train, y_test = train_test_split(df['text'], df['label'], test_size=0.2, random_state=42)\n","\n","tfidf_vectorizer = TfidfVectorizer(max_features=10000, ngram_range=(1,2))\n","X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)\n","X_test_tfidf = tfidf_vectorizer.transform(X_test)\n","\n","# Models\n","models = {\n","    \"Logistic Regression\": LogisticRegression(max_iter=1000),\n","    \"SVM\": SVC(),\n","    \"Random Forest\": RandomForestClassifier(n_estimators=100, random_state=42),\n","    \"Multinomial Naive Bayes\": MultinomialNB(),\n","    \"Gradient Boosting\": GradientBoostingClassifier(n_estimators=100, random_state=42),\n","    \"KNN\": KNeighborsClassifier(n_neighbors=5),\n","    \"Decision Tree\": DecisionTreeClassifier(random_state=42),\n","    \"XGBoost\": XGBClassifier(use_label_encoder=False, eval_metric='mlogloss')\n","}\n","\n","for model_name, model in models.items():\n","    model.fit(X_train_tfidf, y_train)\n","    predictions = model.predict(X_test_tfidf)\n","    accuracy = accuracy_score(y_test, predictions)\n","    print(f\"{model_name} Accuracy: {accuracy}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zNuJ4yN-s-hJ","executionInfo":{"status":"ok","timestamp":1711103951569,"user_tz":-120,"elapsed":430812,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"3ae0aebe-343d-45b7-86d8-1e9b44d65f95"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Logistic Regression Accuracy: 0.684\n","SVM Accuracy: 0.666\n","Random Forest Accuracy: 0.684\n","Multinomial Naive Bayes Accuracy: 0.538\n","Gradient Boosting Accuracy: 0.688\n","KNN Accuracy: 0.644\n","Decision Tree Accuracy: 0.518\n","XGBoost Accuracy: 0.702\n"]}]},{"cell_type":"markdown","source":["Random Forest and XGBoost seem to be the most promising"],"metadata":{"id":"3UCGSVkMzZAw"}},{"cell_type":"code","source":["# Train 5 times the models for stability\n","\n","from xgboost import XGBClassifier\n","from sklearn.metrics import accuracy_score\n","\n","xgb_accuracy_scores = []\n","\n","for i in range(5):\n","    xgb_model = XGBClassifier(use_label_encoder=False, eval_metric='mlogloss', random_state=42 + i)\n","    xgb_model.fit(X_train_tfidf, y_train)\n","    xgb_predictions = xgb_model.predict(X_test_tfidf)\n","    xgb_accuracy = accuracy_score(y_test, xgb_predictions)\n","\n","    xgb_accuracy_scores.append(xgb_accuracy)\n","\n","xgb_accuracy_scores"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yssuw5CcwBff","executionInfo":{"status":"ok","timestamp":1711104768386,"user_tz":-120,"elapsed":568596,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"cbeb16e2-d531-4148-8d5c-3c9ac6a0c803"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[0.702, 0.702, 0.702, 0.702, 0.702]"]},"metadata":{},"execution_count":13}]},{"cell_type":"code","source":["from sklearn.ensemble import RandomForestClassifier\n","from sklearn.metrics import accuracy_score\n","\n","rf_accuracy_scores = []\n","\n","for i in range(5):\n","    rf_model = RandomForestClassifier(n_estimators=100, random_state=42 + i)\n","    rf_model.fit(X_train_tfidf, y_train)\n","    rf_predictions = rf_model.predict(X_test_tfidf)\n","    rf_accuracy = accuracy_score(y_test, rf_predictions)\n","\n","    rf_accuracy_scores.append(rf_accuracy)\n","\n","rf_accuracy_scores"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"x32NBPZg9mSJ","executionInfo":{"status":"ok","timestamp":1711104147235,"user_tz":-120,"elapsed":20194,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"8336c8d3-f5c5-4592-f9b9-50555a827b2e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[0.684, 0.71, 0.704, 0.686, 0.68]"]},"metadata":{},"execution_count":12}]},{"cell_type":"markdown","source":["The Random Forest is a fast and accurate ML model.\n","So it is my final selection from all ML models."],"metadata":{"id":"ojTujFvSU_wz"}},{"cell_type":"markdown","source":["### Random Forest using title, content, url"],"metadata":{"id":"4nCE2xp7-ZLu"}},{"cell_type":"code","source":["# Combine the title, content, url\n","df['text'] = df['title'] + df['content'] + df['url']\n","\n","X_train, X_test, y_train, y_test = train_test_split(df['text'], df['label'], test_size=0.2, random_state=42)\n","\n","tfidf_vectorizer = TfidfVectorizer(max_features=10000, ngram_range=(1,2))\n","X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)\n","X_test_tfidf = tfidf_vectorizer.transform(X_test)"],"metadata":{"id":"DFHcPnvDBBb2"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.ensemble import RandomForestClassifier\n","from sklearn.metrics import accuracy_score\n","\n","rf_accuracy_scores = []\n","\n","for i in range(5):\n","\n","    rf_model = RandomForestClassifier(n_estimators=100, random_state=42 + i)\n","    rf_model.fit(X_train_tfidf, y_train)\n","    rf_predictions = rf_model.predict(X_test_tfidf)\n","    rf_accuracy = accuracy_score(y_test, rf_predictions)\n","\n","    rf_accuracy_scores.append(rf_accuracy)\n","\n","rf_accuracy_scores"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HJ2-nJCY-dyu","executionInfo":{"status":"ok","timestamp":1711105301349,"user_tz":-120,"elapsed":22644,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"70f66f8e-eb78-4ac4-abe4-3d2d1978dce0"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[0.872, 0.87, 0.878, 0.876, 0.862]"]},"metadata":{},"execution_count":12}]},{"cell_type":"markdown","source":["Url is a feature with rich information"],"metadata":{"id":"Pqw-ORzWVv3A"}},{"cell_type":"code","source":["# Combine the url+title or url+content\n","df['text'] = df['title'] + df['url']\n","\n","X_train, X_test, y_train, y_test = train_test_split(df['text'], df['label'], test_size=0.2, random_state=42)\n","\n","tfidf_vectorizer = TfidfVectorizer(max_features=10000, ngram_range=(1,2))\n","X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)\n","X_test_tfidf = tfidf_vectorizer.transform(X_test)"],"metadata":{"id":"8n-VWileByxu"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.ensemble import RandomForestClassifier\n","from sklearn.metrics import accuracy_score\n","\n","rf_accuracy_scores = []\n","\n","for i in range(5):\n","    rf_model = RandomForestClassifier(n_estimators=100, random_state=42 + i)\n","    rf_model.fit(X_train_tfidf, y_train)\n","    rf_predictions = rf_model.predict(X_test_tfidf)\n","    rf_accuracy = accuracy_score(y_test, rf_predictions)\n","\n","    rf_accuracy_scores.append(rf_accuracy)\n","\n","rf_accuracy_scores"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tPceyWlcB2Cv","executionInfo":{"status":"ok","timestamp":1711105396845,"user_tz":-120,"elapsed":18185,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"f75d717c-eb63-4267-ebdf-d24dd98c314a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[0.926, 0.926, 0.926, 0.916, 0.924]"]},"metadata":{},"execution_count":14}]},{"cell_type":"code","source":["df['text'] = df['content'] + df['url']\n","\n","X_train, X_test, y_train, y_test = train_test_split(df['text'], df['label'], test_size=0.2, random_state=42)\n","\n","tfidf_vectorizer = TfidfVectorizer(max_features=10000, ngram_range=(1,2))\n","X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)\n","X_test_tfidf = tfidf_vectorizer.transform(X_test)"],"metadata":{"id":"Y73VOUMGCC3O"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.ensemble import RandomForestClassifier\n","from sklearn.metrics import accuracy_score\n","\n","rf_accuracy_scores = []\n","\n","for i in range(5):\n","    rf_model = RandomForestClassifier(n_estimators=100, random_state=42 + i)\n","    rf_model.fit(X_train_tfidf, y_train)\n","    rf_predictions = rf_model.predict(X_test_tfidf)\n","    rf_accuracy = accuracy_score(y_test, rf_predictions)\n","\n","    rf_accuracy_scores.append(rf_accuracy)\n","\n","rf_accuracy_scores"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YkdnHbcoCFlL","executionInfo":{"status":"ok","timestamp":1711105431695,"user_tz":-120,"elapsed":19437,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"a00bf2d3-8cfa-4a3a-8cab-d3a8d9bfa046"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[0.87, 0.87, 0.864, 0.874, 0.864]"]},"metadata":{},"execution_count":16}]},{"cell_type":"code","source":["# Random Forest only using url\n","import pandas as pd\n","from sklearn.model_selection import train_test_split\n","from sklearn.feature_extraction.text import TfidfVectorizer\n","\n","df['text'] = df['url']\n","\n","X_train, X_test, y_train, y_test = train_test_split(df['text'], df['label'], test_size=0.2, random_state=42)\n","\n","tfidf_vectorizer = TfidfVectorizer(max_features=10000, ngram_range=(1,2))\n","X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)\n","X_test_tfidf = tfidf_vectorizer.transform(X_test)"],"metadata":{"id":"JSIIwNofBcr0"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.ensemble import RandomForestClassifier\n","from sklearn.metrics import accuracy_score\n","\n","rf_accuracy_scores = []\n","\n","for i in range(5):\n","    rf_model = RandomForestClassifier(n_estimators=100, random_state=42 + i)\n","    rf_model.fit(X_train_tfidf, y_train)\n","    rf_predictions = rf_model.predict(X_test_tfidf)\n","    rf_accuracy = accuracy_score(y_test, rf_predictions)\n","\n","    rf_accuracy_scores.append(rf_accuracy)\n","\n","rf_accuracy_scores"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gUojVweVBf_y","executionInfo":{"status":"ok","timestamp":1711106376304,"user_tz":-120,"elapsed":7905,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"047235a3-5293-445f-9ea2-4257bc021114"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[0.922, 0.926, 0.922, 0.924, 0.926]"]},"metadata":{},"execution_count":28}]},{"cell_type":"markdown","source":["Random forest with input feature only the URL is the best performant."],"metadata":{"id":"9ay8nF1oV_T5"}},{"cell_type":"markdown","source":["### Random Forest optimization and detailed metrics"],"metadata":{"id":"CgBQKu0qb3Dp"}},{"cell_type":"code","source":["# faster optimization than Grid Search\n","\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.model_selection import RandomizedSearchCV\n","from scipy.stats import randint as sp_randint\n","\n","param_dist = {\n","    'n_estimators': sp_randint(100, 300),\n","    'max_depth': [None, 10, 20, 30],\n","    'min_samples_split': sp_randint(2, 11),\n","    'min_samples_leaf': sp_randint(1, 5),\n","    'max_features': ['auto', 'sqrt']\n","}\n","\n","rf_model = RandomForestClassifier(random_state=42)\n","\n","random_search = RandomizedSearchCV(estimator=rf_model, param_distributions=param_dist, n_iter=10, cv=3, n_jobs=-1, verbose=2, scoring='accuracy', random_state=42)\n","\n","random_search.fit(X_train_tfidf, y_train)\n","\n","optimized_rf_model = random_search.best_estimator_\n","\n","rf_predictions = optimized_rf_model.predict(X_test_tfidf)\n","rf_accuracy = accuracy_score(y_test, rf_predictions)\n","\n","print(f\"Best parameters: {random_search.best_params_}\")\n","print(f\"Best model accuracy: {rf_accuracy}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5Fajmt_RKDhe","executionInfo":{"status":"ok","timestamp":1711106288606,"user_tz":-120,"elapsed":54493,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"a5be4e0a-cc1f-4e93-d3b0-808e11cc4364"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n","  warn(\n"]},{"output_type":"stream","name":"stdout","text":["Best parameters: {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 4, 'n_estimators': 174}\n","Best model accuracy: 0.902\n"]}]},{"cell_type":"markdown","source":["Decide to keep the initial rf model"],"metadata":{"id":"wWjPoEX_4Tp3"}},{"cell_type":"code","source":["rf_model"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":75},"id":"F1XJHegnKDPP","executionInfo":{"status":"ok","timestamp":1711106314526,"user_tz":-120,"elapsed":286,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"27c37332-b214-47bd-a4e4-785174fd7574"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["RandomForestClassifier(random_state=42)"],"text/html":["<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":26}]},{"cell_type":"markdown","source":["Detailed metrics report for the selected model"],"metadata":{"id":"TDpgvK6gWlAv"}},{"cell_type":"code","source":["from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, f1_score, precision_score, recall_score\n","\n","print(\"Classification Report:\")\n","print(classification_report(y_test, rf_predictions))\n","\n","print(\"Confusion Matrix:\")\n","print(confusion_matrix(y_test, rf_predictions))\n","\n","accuracy = accuracy_score(y_test, rf_predictions)\n","print(f\"Accuracy: {accuracy:.4f}\")\n","\n","f1 = f1_score(y_test, rf_predictions, average='weighted')\n","print(f\"F1 Score: {f1:.4f}\")\n","\n","precision = precision_score(y_test, rf_predictions, average='weighted')\n","print(f\"Precision: {precision:.4f}\")\n","\n","recall = recall_score(y_test, rf_predictions, average='weighted')\n","print(f\"Recall: {recall:.4f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sl3zIj8hK35y","executionInfo":{"status":"ok","timestamp":1711106390477,"user_tz":-120,"elapsed":303,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"d23f3d13-c4e0-44d4-83a1-ca0212179596"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.95      0.91      0.93        23\n","           1       0.85      0.93      0.89        57\n","           2       1.00      1.00      1.00        10\n","           3       1.00      0.96      0.98        27\n","           4       0.88      0.88      0.88         8\n","           5       1.00      0.86      0.93        22\n","           6       0.93      0.93      0.93        30\n","           7       0.85      0.81      0.83        36\n","           8       0.97      0.99      0.98        69\n","           9       0.91      0.98      0.95        64\n","          10       0.96      0.96      0.96        55\n","          11       1.00      1.00      1.00         7\n","          12       1.00      0.91      0.95        11\n","          13       0.79      0.65      0.71        17\n","          14       1.00      0.87      0.93        15\n","          15       1.00      1.00      1.00         6\n","          16       0.82      1.00      0.90         9\n","          17       0.86      0.86      0.86         7\n","          18       0.85      0.92      0.88        12\n","          19       0.86      0.75      0.80         8\n","          20       1.00      1.00      1.00         7\n","\n","    accuracy                           0.93       500\n","   macro avg       0.93      0.91      0.92       500\n","weighted avg       0.93      0.93      0.93       500\n","\n","Confusion Matrix:\n","[[21  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  1  0]\n"," [ 0 53  0  0  1  0  0  3  0  0  0  0  0  0  0  0  0  0  0  0  0]\n"," [ 0  0 10  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n"," [ 0  0  0 26  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0]\n"," [ 0  0  0  0  7  0  0  0  0  0  0  0  0  0  0  0  1  0  0  0  0]\n"," [ 1  0  0  0  0 19  1  0  0  0  0  0  0  0  0  0  0  0  1  0  0]\n"," [ 0  0  0  0  0  0 28  0  0  1  1  0  0  0  0  0  0  0  0  0  0]\n"," [ 0  2  0  0  0  0  0 29  0  1  1  0  0  2  0  0  0  0  1  0  0]\n"," [ 0  0  0  0  0  0  0  0 68  0  0  0  0  0  0  0  0  1  0  0  0]\n"," [ 0  1  0  0  0  0  0  0  0 63  0  0  0  0  0  0  0  0  0  0  0]\n"," [ 0  0  0  0  0  0  0  0  1  1 53  0  0  0  0  0  0  0  0  0  0]\n"," [ 0  0  0  0  0  0  0  0  0  0  0  7  0  0  0  0  0  0  0  0  0]\n"," [ 0  0  0  0  0  0  0  0  0  0  0  0 10  0  0  0  1  0  0  0  0]\n"," [ 0  4  0  0  0  0  0  2  0  0  0  0  0 11  0  0  0  0  0  0  0]\n"," [ 0  2  0  0  0  0  0  0  0  0  0  0  0  0 13  0  0  0  0  0  0]\n"," [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  6  0  0  0  0  0]\n"," [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  9  0  0  0  0]\n"," [ 0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  6  0  0  0]\n"," [ 0  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0 11  0  0]\n"," [ 0  0  0  0  0  0  1  0  0  0  0  0  0  1  0  0  0  0  0  6  0]\n"," [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  7]]\n","Accuracy: 0.9260\n","F1 Score: 0.9252\n","Precision: 0.9272\n","Recall: 0.9260\n"]}]},{"cell_type":"markdown","source":["### Random forest model on unseen-test data"],"metadata":{"id":"LgU8QusOSkWp"}},{"cell_type":"code","source":["file_path = '/content/drive/My Drive/unseen_test_data.csv'\n","df_test = pd.read_csv(file_path)\n","df_test"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"-ula3ZvJSvmt","executionInfo":{"status":"ok","timestamp":1711106421026,"user_tz":-120,"elapsed":803,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"c49be2d4-23f5-4ac3-ccbf-9ef5af779d1d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                                   url  \\\n","0          http://www.runningnews.gr/item.php?id=18191   \n","1    https://www.vimaorthodoxias.gr/patriarxeia/ta-...   \n","2    https://www.newsitamea.gr/2015/04/03/prodosia-...   \n","3    https://www.kathimerini.gr/1047549/article/ayt...   \n","4    http://spirossoulis.com/spiti-diakosmisi-kadra...   \n","..                                                 ...   \n","708               https://www.maxmag.gr/taxidia/kotor/   \n","709            https://www.biscotto.gr/birdie-num-num/   \n","710  https://jenny.gr/home-sweet-home/296182/xylina...   \n","711  https://www.newsauto.gr/presentations/apokalip...   \n","712  https://boro.gr/129486/soy-zhthse-na-vgeite-ki...   \n","\n","                                                 title  \\\n","0                Επιμνημόσυνος Δρομικός Αποχαιρετισμός   \n","1           Τα Άγια Θεοφάνεια στα Τίρανα (Φωτογραφίες)   \n","2    Προδοσία: 4 ουσιαστικά βήματα για να την ξεπερ...   \n","3      Η VOLVO CARS: ΑΚΟΜΗ ΕΝΑ ΒΗΜΑ ΣΤΗΝ ΗΛΕΚΤΡΟΚΙΝΗΣΗ   \n","4    Κάδρα: Εκμεταλλευτείτε τα Σωστά και Δημιουργήσ...   \n","..                                                 ...   \n","708   Κότορ: διακοπές στο μαργαριτάρι του Μαυροβουνίου   \n","709                                     Birdie Num Num   \n","710                    Ξύλινα χριστουγεννιάτικα δέντρα   \n","711        Αποκάλυψη για το πολυτελές SUV της Infiniti   \n","712  Σου ζήτησε να βγείτε κι εσύ δεν θες; Δες πώς θ...   \n","\n","                                               content                  label  \n","0    Με σεβασμό και διακριτικότητα συγκεντρώθηκαν π...                 Sports  \n","1    Η εορτινή ατμόσφαιρα των Θεοφανίων και του Αγι...  Religion&Spirituality  \n","2    Όλοι έχουμε προδοθεί. Λίγο ή πολύ, δεν έχει τό...         Healthy Living  \n","3    Δείτε βίντεο Η στιγμή της ισχυρότατης έκρηξης ...             Automotive  \n","4    Ο τρόπος που διακοσμούμε τα σπίτια μας θα πρέπ...            Home&Garden  \n","..                                                 ...                    ...  \n","708  Ήρθε το καλοκαίρι και μαζί με αυτό ήρθε και η ...                 Travel  \n","709  Ένα φρέσκο cocktail bar έκανε την εμφάνισή του...            Attractions  \n","710  Μία εναλλακτική λύση για όσους βαρέθηκαν το κλ...            Home&Garden  \n","711  Δείτε βίντεο Η στιγμή της ισχυρότατης έκρηξης ...             Automotive  \n","712  Είναι από τα πιο άβολα πράγματα που μπορούν να...   Family&Relationships  \n","\n","[713 rows x 4 columns]"],"text/html":["\n","  <div id=\"df-8b93ef1a-efd4-4c97-8958-dd63111f046a\" 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>url</th>\n","      <th>title</th>\n","      <th>content</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>http://www.runningnews.gr/item.php?id=18191</td>\n","      <td>Επιμνημόσυνος Δρομικός Αποχαιρετισμός</td>\n","      <td>Με σεβασμό και διακριτικότητα συγκεντρώθηκαν π...</td>\n","      <td>Sports</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>https://www.vimaorthodoxias.gr/patriarxeia/ta-...</td>\n","      <td>Τα Άγια Θεοφάνεια στα Τίρανα (Φωτογραφίες)</td>\n","      <td>Η εορτινή ατμόσφαιρα των Θεοφανίων και του Αγι...</td>\n","      <td>Religion&amp;Spirituality</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>https://www.newsitamea.gr/2015/04/03/prodosia-...</td>\n","      <td>Προδοσία: 4 ουσιαστικά βήματα για να την ξεπερ...</td>\n","      <td>Όλοι έχουμε προδοθεί. Λίγο ή πολύ, δεν έχει τό...</td>\n","      <td>Healthy Living</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>https://www.kathimerini.gr/1047549/article/ayt...</td>\n","      <td>Η VOLVO CARS: ΑΚΟΜΗ ΕΝΑ ΒΗΜΑ ΣΤΗΝ ΗΛΕΚΤΡΟΚΙΝΗΣΗ</td>\n","      <td>Δείτε βίντεο Η στιγμή της ισχυρότατης έκρηξης ...</td>\n","      <td>Automotive</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>http://spirossoulis.com/spiti-diakosmisi-kadra...</td>\n","      <td>Κάδρα: Εκμεταλλευτείτε τα Σωστά και Δημιουργήσ...</td>\n","      <td>Ο τρόπος που διακοσμούμε τα σπίτια μας θα πρέπ...</td>\n","      <td>Home&amp;Garden</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>708</th>\n","      <td>https://www.maxmag.gr/taxidia/kotor/</td>\n","      <td>Κότορ: διακοπές στο μαργαριτάρι του Μαυροβουνίου</td>\n","      <td>Ήρθε το καλοκαίρι και μαζί με αυτό ήρθε και η ...</td>\n","      <td>Travel</td>\n","    </tr>\n","    <tr>\n","      <th>709</th>\n","      <td>https://www.biscotto.gr/birdie-num-num/</td>\n","      <td>Birdie Num Num</td>\n","      <td>Ένα φρέσκο cocktail bar έκανε την εμφάνισή του...</td>\n","      <td>Attractions</td>\n","    </tr>\n","    <tr>\n","      <th>710</th>\n","      <td>https://jenny.gr/home-sweet-home/296182/xylina...</td>\n","      <td>Ξύλινα χριστουγεννιάτικα δέντρα</td>\n","      <td>Μία εναλλακτική λύση για όσους βαρέθηκαν το κλ...</td>\n","      <td>Home&amp;Garden</td>\n","    </tr>\n","    <tr>\n","      <th>711</th>\n","      <td>https://www.newsauto.gr/presentations/apokalip...</td>\n","      <td>Αποκάλυψη για το πολυτελές SUV της Infiniti</td>\n","      <td>Δείτε βίντεο Η στιγμή της ισχυρότατης έκρηξης ...</td>\n","      <td>Automotive</td>\n","    </tr>\n","    <tr>\n","      <th>712</th>\n","      <td>https://boro.gr/129486/soy-zhthse-na-vgeite-ki...</td>\n","      <td>Σου ζήτησε να βγείτε κι εσύ δεν θες; Δες πώς θ...</td>\n","      <td>Είναι από τα πιο άβολα πράγματα που μπορούν να...</td>\n","      <td>Family&amp;Relationships</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>713 rows × 4 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8b93ef1a-efd4-4c97-8958-dd63111f046a')\"\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 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\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","    .colab-df-buttons div {\n","      margin-bottom: 4px;\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-8b93ef1a-efd4-4c97-8958-dd63111f046a button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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\\u03ba\\u03b1\\u03c1\\u03b9\\u03ad\\u03c1\\u03b1 \\u03c4\\u03b7\\u03c2 \\u03c3\\u03c5\\u03bd\\u03ad\\u03c0\\u03b5\\u03c3\\u03b5 \\u03bc\\u03b5 \\u03bc\\u03b9\\u03b1 \\u03ba\\u03b1\\u03ba\\u03ae \\u03c0\\u03c1\\u03bf\\u03c3\\u03c9\\u03c0\\u03b9\\u03ba\\u03ae \\u03c3\\u03c4\\u03b9\\u03b3\\u03bc\\u03ae:  \\u03c4\\u03bf \\u03b4\\u03b9\\u03b1\\u03b6\\u03cd\\u03b3\\u03b9\\u03cc \\u03c4\\u03b7\\u03c2 \\u03b1\\u03c0\\u03cc \\u03c4\\u03bf\\u03bd Tom Cruise\\u00a0\\u03c4\\u03bf 2001.\\u00a0 \\u0397 \\u0391\\u03c5\\u03c3\\u03c4\\u03c1\\u03b1\\u03bb\\u03ad\\u03b6\\u03b1\\u00a0\\u03b7\\u03b8\\u03bf\\u03c0\\u03bf\\u03b9\\u03cc\\u03c2 \\u03b5\\u03be\\u03ae\\u03b3\\u03b7\\u03c3\\u03b5 \\u03c3\\u03c4\\u03bf\\u00a0Sydney Morning Herald \\u03cc\\u03c4\\u03b9 \\u03cc\\u03bb\\u03b7 \\u03b1\\u03c5\\u03c4\\u03ae \\u03b7 \\u03b1\\u03bd\\u03b1\\u03b3\\u03bd\\u03c9\\u03c1\\u03b9\\u03c3\\u03b9\\u03bc\\u03cc\\u03c4\\u03b7\\u03c4\\u03b1\\u00a0\\u03ba\\u03b1\\u03b9 \\u03b7 \\u03b4\\u03cc\\u03be\\u03b1 \\u03c0\\u03bf\\u03c5 \\u03ac\\u03c1\\u03c7\\u03b9\\u03c3\\u03b5 \\u03bd\\u03b1 \\u03b3\\u03bd\\u03c9\\u03c1\\u03af\\u03b6\\u03b5\\u03b9 \\u03bc\\u03b5\\u03c4\\u03ac \\u03c4\\u03b9\\u03c2 \\u03c0\\u03c1\\u03ce\\u03c4\\u03b5\\u03c2 \\u03c4\\u03b7\\u03c2 \\u03b5\\u03c0\\u03b9\\u03c4\\u03c5\\u03c7\\u03af\\u03b5\\u03c2 \\\"\\u03ae\\u03c4\\u03b1\\u03bd \\u03bc\\u03b9\\u03b1 \\u03c0\\u03c1\\u03cc\\u03ba\\u03bb\\u03b7\\u03c3\\u03b7\\\" \\u03c3\\u03c4\\u03b1 \\u03c7\\u03c1\\u03cc\\u03bd\\u03b9\\u03b1 \\u03c0\\u03bf\\u03c5 \\u03b1\\u03ba\\u03bf\\u03bb\\u03bf\\u03cd\\u03b8\\u03b7\\u03c3\\u03b1\\u03bd \\u03c4\\u03bf\\u03bd \\u03c7\\u03c9\\u03c1\\u03b9\\u03c3\\u03bc\\u03cc \\u03c4\\u03b7\\u03c2 \\u03b3\\u03b9\\u03b1\\u03c4\\u03af  \\\"\\u03c9\\u03c2 \\u03bc\\u03cc\\u03bd\\u03b7 \\u03c0\\u03b9\\u03b1 \\u03b4\\u03b5\\u03bd \\u03b5\\u03af\\u03c7\\u03b1 \\u03ba\\u03ac\\u03c0\\u03bf\\u03b9\\u03bf\\u03bd \\u03c9\\u03c2 \\u03ba\\u03b1\\u03c4\\u03b1\\u03c6\\u03cd\\u03b3\\u03b9\\u03bf\\\" .\\u00a0 \\u0397 Kidman\\u00a0 \\u03b6\\u03ae\\u03c4\\u03b7\\u03c3\\u03b5 \\u03b1\\u03c0\\u03cc \\u03c4\\u03b7\\u03bd \\u03b1\\u03b4\\u03b5\\u03bb\\u03c6\\u03ae \\u03c4\\u03b7\\u03c2\\u00a0Antonia  \\u03bd\\u03b1 \\u03ad\\u03c1\\u03b8\\u03b5\\u03b9 \\u03b3\\u03b9\\u03b1 \\u03b2\\u03bf\\u03ae\\u03b8\\u03b5\\u03b9\\u03b1: \\\"\\u0398\\u03c5\\u03bc\\u03ac\\u03bc\\u03b1\\u03b9 \\u03c0\\u03bf\\u03c5 \\u03c4\\u03b1\\u03be\\u03af\\u03b4\\u03b5\\u03c8\\u03b5 \\u03bc\\u03ad\\u03c7\\u03c1\\u03b9 \\u03c4\\u03b9\\u03c2 \\u039a\\u03ac\\u03bd\\u03bd\\u03b5\\u03c2, \\u03c4\\u03bf 2001, \\u03b3\\u03b9\\u03b1\\u03c4\\u03af \\u03bc\\u03bf\\u03c5 \\u03ae\\u03c4\\u03b1\\u03bd \\u03c4\\u03c1\\u03bf\\u03bc\\u03b1\\u03ba\\u03c4\\u03b9\\u03ba\\u03cc \\u03bd\\u03b1 \\u03b2\\u03b1\\u03b4\\u03af\\u03b6\\u03c9 \\u03c3\\u03c4\\u03bf \\u03ba\\u03cc\\u03ba\\u03ba\\u03b9\\u03bd\\u03bf \\u03c7\\u03b1\\u03bb\\u03af \\u03ba\\u03ac\\u03c4\\u03c9 \\u03b1\\u03c0\\u03cc \\u03c4\\u03bf\\u03bd \\u03b5\\u03be\\u03bf\\u03bd\\u03c5\\u03c7\\u03b9\\u03c3\\u03c4\\u03b9\\u03ba\\u03cc \\u03ad\\u03bb\\u03b5\\u03b3\\u03c7\\u03bf \\u03c4\\u03c9\\u03bd \\u03bc\\u03af\\u03bd\\u03c4\\u03b9\\u03b1, \\u03bd\\u03b1 \\u03b1\\u03b9\\u03c3\\u03b8\\u03ac\\u03bd\\u03bf\\u03bc\\u03b1\\u03b9 \\u03b1\\u03bd\\u03b1\\u03c3\\u03c6\\u03b1\\u03bb\\u03ae\\u03c2 \\u03ba\\u03b1\\u03b9 \\u03ba\\u03b1\\u03b8\\u03cc\\u03bb\\u03bf\\u03c5 \\u03c3\\u03af\\u03b3\\u03bf\\u03c5\\u03c1\\u03b7 \\u03b3\\u03b9\\u03b1 \\u03c4\\u03bf \\u03c0\\u03bf\\u03cd \\u03bd\\u03b1 \\u03b2\\u03c1\\u03c9 \\u03b4\\u03b9\\u03b1\\u03c6\\u03c5\\u03b3\\u03ae\\u00a0\\u03ae \\u03c0\\u03ce\\u03c2 \\u03bd\\u03b1 \\u03b5\\u03c0\\u03b9\\u03b2\\u03b9\\u03ce\\u03c3\\u03c9\\\". \\u00a0 \\r\\n             \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 21,\n        \"samples\": [\n          \"Sports\",\n          \"Attractions\",\n          \"Culture\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":30}]},{"cell_type":"code","source":["df_test['text'] = df_test['url']\n","\n","# Mapping\n","label_mapping = {\n","    'Style&Fashion': 0,\n","    'Sensitive Topics': 1,\n","    'Religion&Spirituality': 2,\n","    'Science': 3,\n","    'Viral Articles': 4,\n","    'Beauty': 5,\n","    'Tech&Computing': 6,\n","    'Healthy Living': 7,\n","    'Sports': 8,\n","    'Culture': 9,\n","    'Business&Finance': 10,\n","    'Education': 11,\n","    'Astrology': 12,\n","    'Family&Relationships': 13,\n","    'Politics': 14,\n","    'Food&Drink': 15,\n","    'Pop Culture': 16,\n","    'Automotive': 17,\n","    'Attractions': 18,\n","    'Home&Garden': 19,\n","    'Travel': 20\n","}\n","df_test['label'] = df_test['label'].map(label_mapping)"],"metadata":{"id":"f5ZqGhKgKnXN"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["X_test_new = tfidf_vectorizer.transform(df_test['text'])\n","\n","# Make predictions with the best performing Random Forest model\n","predictions_new = rf_model.predict(X_test_new)\n","\n","from sklearn.metrics import classification_report, accuracy_score\n","\n","accuracy_new = accuracy_score(df_test['label'], predictions_new)\n","\n","report_new = classification_report(df_test['label'], predictions_new)\n","\n","print(f\"Accuracy on unseen dataset: {accuracy_new}\")\n","print(\"Classification report on unseen dataset:\")\n","print(report_new)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PhXjJTTdLnGU","executionInfo":{"status":"ok","timestamp":1711106496514,"user_tz":-120,"elapsed":264,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"13671f66-efac-412c-d2d9-5e60b4b7230a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy on unseen dataset: 0.8190743338008415\n","Classification report on unseen dataset:\n","              precision    recall  f1-score   support\n","\n","           0       0.86      0.67      0.75        27\n","           1       0.35      0.89      0.50        27\n","           2       1.00      1.00      1.00        31\n","           3       0.86      0.67      0.75        27\n","           4       0.97      0.68      0.80        56\n","           5       0.85      0.85      0.85        27\n","           6       0.82      0.85      0.84        27\n","           7       0.65      0.74      0.69        27\n","           8       0.82      1.00      0.90        27\n","           9       0.47      0.96      0.63        27\n","          10       0.96      0.96      0.96        27\n","          11       1.00      1.00      1.00        27\n","          12       1.00      0.89      0.94        36\n","          13       0.86      0.67      0.75        27\n","          14       1.00      0.90      0.95        40\n","          15       0.94      0.75      0.83        40\n","          16       0.88      0.86      0.87        35\n","          17       1.00      0.75      0.86        48\n","          18       0.86      0.71      0.78        52\n","          19       0.93      0.79      0.85        47\n","          20       0.90      0.87      0.89        31\n","\n","    accuracy                           0.82       713\n","   macro avg       0.86      0.83      0.83       713\n","weighted avg       0.87      0.82      0.83       713\n","\n"]}]},{"cell_type":"markdown","source":["### Save the model and vectorizer to deploy in flask"],"metadata":{"id":"5w2X36fFborg"}},{"cell_type":"code","source":["rf_model\n","tfidf_vectorizer"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":75},"id":"j19RfArUbuwf","executionInfo":{"status":"ok","timestamp":1711017241579,"user_tz":-120,"elapsed":451,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"7a4af9dc-b44f-42a6-e359-3a14f6e0ecb7"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["TfidfVectorizer(max_features=10000, ngram_range=(1, 2))"],"text/html":["<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TfidfVectorizer(max_features=10000, ngram_range=(1, 2))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer(max_features=10000, ngram_range=(1, 2))</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":14}]},{"cell_type":"code","source":["import joblib\n","\n","joblib.dump(rf_model, 'rf_model.pkl')\n","joblib.dump(tfidf_vectorizer, 'tfidf_vectorizer.pkl')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_eaEL5N7buth","executionInfo":{"status":"ok","timestamp":1711017308915,"user_tz":-120,"elapsed":2308,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"7e793eee-8edd-47bb-e9a0-a0ffefc6f54d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['tfidf_vectorizer.pkl']"]},"metadata":{},"execution_count":17}]},{"cell_type":"markdown","source":["#### Flask app"],"metadata":{"id":"Zn67p59IsWs_"}},{"cell_type":"code","source":["from flask import Flask, request, jsonify\n","import joblib\n","\n","\n","app = Flask(__name__)\n","\n","\n","model = joblib.load(\"rf_model.pkl\")\n","vectorizer = joblib.load(\"tfidf_vectorizer.pkl\")\n","\n","label_dict = {\n","    0: 'Style&Fashion',\n","    1: 'Sensitive Topics',\n","    2: 'Religion&Spirituality',\n","    3: 'Science',\n","    4: 'Viral Articles',\n","    5: 'Beauty',\n","    6: 'Tech&Computing',\n","    7: 'Healthy Living',\n","    8: 'Sports',\n","    9: 'Culture',\n","    10: 'Business&Finance',\n","    11: 'Education',\n","    12: 'Astrology',\n","    13: 'Family&Relationships',\n","    14: 'Politics',\n","    15: 'Food&Drink',\n","    16: 'Pop Culture',\n","    17: 'Automotive',\n","    18: 'Attractions',\n","    19: 'Home&Garden',\n","    20: 'Travel'\n","}\n","\n","@app.route('/predict/rf', methods=['POST'])\n","def predict():\n","    data = request.get_json()\n","\n","    if not data or 'url' not in data:\n","        return jsonify({'error': 'Missing url in request'}), 400\n","\n","    try:\n","        vectorized_text = vectorizer.transform([data['url']])\n","        prediction = model.predict(vectorized_text)\n","\n","        predicted_label_name = label_dict.get(prediction[0])\n","        return jsonify({'prediction': predicted_label_name})\n","    except Exception as e:\n","        return jsonify({'error': str(e)}), 500\n","\n","\n","if __name__ == '__main__':\n","    app.run(debug=False, port=9999)"],"metadata":{"id":"zzs0hbSXbvcd"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### Subtask 1: Examine the sensitive topics (FN to zero)"],"metadata":{"id":"dbXrJzn0T71h"}},{"cell_type":"code","source":["import seaborn as sns\n","import matplotlib.pyplot as plt\n","from sklearn.metrics import confusion_matrix\n","\n","label_names = [\n","    'Style&Fashion', 'Sensitive Topics', 'Religion&Spirituality', 'Science', 'Viral Articles',\n","    'Beauty', 'Tech&Computing', 'Healthy Living', 'Sports', 'Culture',\n","    'Business&Finance', 'Education', 'Astrology', 'Family&Relationships', 'Politics',\n","    'Food&Drink', 'Pop Culture', 'Automotive', 'Attractions', 'Home&Garden', 'Travel'\n","]\n","\n","cm = confusion_matrix(df_test['label'], predictions_new)\n","\n","plt.figure(figsize=(14, 12))\n","sns.set(font_scale=0.9)\n","sns.heatmap(cm, annot=True, fmt='g', cmap='Blues', xticklabels=label_names, yticklabels=label_names, cbar=True)\n","\n","plt.xticks(rotation=90, ha='right')\n","plt.yticks(rotation=0)\n","plt.xlabel('Predicted labels')\n","plt.ylabel('True labels')\n","plt.title('Confusion Matrix')\n","\n","plt.tight_layout()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"qmpiQDgvLnBf","executionInfo":{"status":"ok","timestamp":1711106624268,"user_tz":-120,"elapsed":4641,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"46d40326-23ef-4de3-d8e4-3e2059bb145e"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1400x1200 with 2 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\n"},"metadata":{}}]},{"cell_type":"code","source":["# Label encoding for 'Sensitive Topics' is 1\n","sensitive_topic_index = 1\n","\n","# Sum all the values in the 'Sensitive Topics' row except for the true positive at cm[1, 1]\n","FN_sensitive = sum(cm[sensitive_topic_index, :]) - cm[sensitive_topic_index, sensitive_topic_index]\n","\n","FN_sensitive"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Pn0pxKxSRDd6","executionInfo":{"status":"ok","timestamp":1711106641451,"user_tz":-120,"elapsed":283,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"8f96124f-cd8b-4e99-e8b0-d7caafe1b49b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["3"]},"metadata":{},"execution_count":35}]},{"cell_type":"code","source":["TP_sensitive = cm[sensitive_topic_index, sensitive_topic_index]\n","\n","FP_sensitive = sum(cm[:, sensitive_topic_index]) - TP_sensitive\n","\n","FP_sensitive"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jKjaImrbYdDN","executionInfo":{"status":"ok","timestamp":1711106765038,"user_tz":-120,"elapsed":256,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"38ad590c-5222-4898-e47b-4e6b452474b3"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["45"]},"metadata":{},"execution_count":37}]},{"cell_type":"markdown","source":["Possible solutions to make False Negatives of sensitive topic to be sero:\n","1. Adjust Class Weights: Assign a higher class weight to \"Sensitive Topics\" during model training.\n","\n","2. Threshold Tuning: Adjust the decision threshold for the \"Sensitive Topics\" class after you receive the probabilities from the model. Lowering the threshold means that you are willing to accept more false positives for reducing false negatives.\n","\n","3. Resampling Techniques: Use resampling techniques such as oversampling the \"Sensitive Topics\" class or undersampling the other classes.\n","\n","4. Custom Loss Function: Design a loss function that heavily penalizes misclassifying sensitive topics as non-sensitive.\n","\n","Lets try solution 2."],"metadata":{"id":"CactnRZIWtiv"}},{"cell_type":"code","source":["import numpy as np\n","\n","probabilities = rf_model.predict_proba(X_test_new)\n","\n","sensitive_probabilities = probabilities[:, 1]\n","\n","# Define a new threshold that aims to minimize false negatives for 'Sensitive Topics'\n","# You can adjust this threshold based on your requirements\n","new_threshold = 0.14\n","\n","# Apply threshold to determine new predictions\n","new_predictions = np.where(sensitive_probabilities >= new_threshold, 1, predictions_new)\n","\n","# Calculate how many 'Sensitive Topics' predictions are now different\n","num_changed_predictions = np.sum((predictions_new != new_predictions) & (new_predictions == 1))\n","\n","print(f\"Number of articles now predicted as 'Sensitive Topics' instead of non-sensitive: {num_changed_predictions}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_pb91fmtR-kg","executionInfo":{"status":"ok","timestamp":1711106908793,"user_tz":-120,"elapsed":286,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"5be5788c-471f-4fec-a57f-86f3d3859b82"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of articles now predicted as 'Sensitive Topics' instead of non-sensitive: 79\n"]}]},{"cell_type":"code","source":["from sklearn.metrics import classification_report\n","\n","# Compute a classification report which includes the recall for each class\n","report = classification_report(df_test['label'], new_predictions, target_names=label_names, output_dict=True)\n","\n","# The recall for 'Sensitive Topics'\n","sensitive_recall = report['Sensitive Topics']['recall']\n","print(classification_report(df_test['label'], new_predictions, target_names=label_names))\n","\n","weighted_recall = report['weighted avg']['recall']\n","\n","print(f\"Recall for 'Sensitive Topics': {sensitive_recall}\")\n","print(f\"Weighted average recall: {weighted_recall}\")\n","\n","# False negatives for 'Sensitive Topics' would be in the 'support' of the class minus the 'true positive'\n","# 'support' is the number of actual occurrences of the class in the specified dataset\n","sensitive_support = report['Sensitive Topics']['support']\n","true_positives_sensitive = sensitive_support * sensitive_recall\n","false_negatives_sensitive = sensitive_support - true_positives_sensitive\n","\n","print(f\"False Negatives for 'Sensitive Topics': {false_negatives_sensitive}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"u8FNqw_nR-h6","executionInfo":{"status":"ok","timestamp":1711106915335,"user_tz":-120,"elapsed":256,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"176681f0-c838-453a-9897-71f881da5e7a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["                       precision    recall  f1-score   support\n","\n","        Style&Fashion       0.84      0.59      0.70        27\n","     Sensitive Topics       0.18      1.00      0.31        27\n","Religion&Spirituality       1.00      1.00      1.00        31\n","              Science       1.00      0.59      0.74        27\n","       Viral Articles       1.00      0.57      0.73        56\n","               Beauty       0.85      0.85      0.85        27\n","       Tech&Computing       0.82      0.85      0.84        27\n","       Healthy Living       0.75      0.56      0.64        27\n","               Sports       0.80      0.89      0.84        27\n","              Culture       0.49      0.89      0.63        27\n","     Business&Finance       0.96      0.85      0.90        27\n","            Education       1.00      1.00      1.00        27\n","            Astrology       1.00      0.89      0.94        36\n"," Family&Relationships       0.94      0.56      0.70        27\n","             Politics       1.00      0.60      0.75        40\n","           Food&Drink       0.93      0.70      0.80        40\n","          Pop Culture       0.88      0.86      0.87        35\n","           Automotive       1.00      0.65      0.78        48\n","          Attractions       0.88      0.69      0.77        52\n","          Home&Garden       0.91      0.62      0.73        47\n","               Travel       0.86      0.61      0.72        31\n","\n","             accuracy                           0.74       713\n","            macro avg       0.86      0.75      0.77       713\n","         weighted avg       0.88      0.74      0.78       713\n","\n","Recall for 'Sensitive Topics': 1.0\n","Weighted average recall: 0.7363253856942497\n","False Negatives for 'Sensitive Topics': 0.0\n"]}]},{"cell_type":"code","source":["from sklearn.metrics import accuracy_score\n","\n","# Calculate the accuracy with the new predictions\n","new_accuracy = accuracy_score(df_test['label'], new_predictions)\n","\n","print(f\"New accuracy after adjusting the threshold: {new_accuracy}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OTWLKXgrM7Vk","executionInfo":{"status":"ok","timestamp":1711106972644,"user_tz":-120,"elapsed":250,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"050b89c2-435c-485c-c7c5-99a0b163f26e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["New accuracy after adjusting the threshold: 0.7363253856942497\n"]}]},{"cell_type":"markdown","source":["### Subtask 2: Further labeling (Clustering)"],"metadata":{"id":"MwmhKm37Y75J"}},{"cell_type":"code","source":["import pandas as pd\n","from sklearn.feature_extraction.text import TfidfVectorizer\n","from sklearn.preprocessing import normalize\n","from sklearn.cluster import KMeans\n","from sklearn.metrics import silhouette_score\n","import numpy as np\n","\n","data = df\n","\n","# Filter dataset for a specific category\n","# Tried all categories, most promising based on silhouette score: sensitive topics, culture\n","# selected_category = 'sensitive topics'\n","selected_category = 'Culture'\n","filtered_data = data[data['label'] == selected_category]\n","\n","tfidf_vectorizer = TfidfVectorizer(stop_words='english', max_features=1000)\n","tfidf_matrix = tfidf_vectorizer.fit_transform(filtered_data['content'])\n","# tfidf_matrix = tfidf_vectorizer.fit_transform(filtered_data['url'])\n","\n","tfidf_norm = normalize(tfidf_matrix)\n","\n","n_clusters_options = range(2, 11)\n","silhouette_scores = []\n","\n","for n_clusters in n_clusters_options:\n","    kmeans = KMeans(n_clusters=n_clusters, random_state=42)\n","    cluster_labels = kmeans.fit_predict(tfidf_norm)\n","    silhouette_avg = silhouette_score(tfidf_norm, cluster_labels)\n","    silhouette_scores.append(silhouette_avg)\n","\n","silhouette_scores"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uWrhTp7YTGtN","executionInfo":{"status":"ok","timestamp":1711108059561,"user_tz":-120,"elapsed":2205,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"707568e4-e845-47df-b441-5e43a8ea1a1c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n","  warnings.warn(\n"]},{"output_type":"execute_result","data":{"text/plain":["[0.3536323037438268,\n"," 0.2699933756232643,\n"," 0.22651396323607004,\n"," 0.22114328523111848,\n"," 0.2223969452483416,\n"," 0.23048753630687105,\n"," 0.2113055300804917,\n"," 0.2232408923864419,\n"," 0.226967094037534]"]},"metadata":{},"execution_count":43}]},{"cell_type":"code","source":["# Choosed the optimal number of clusters based on silhouette score\n","optimal_clusters = 2\n","kmeans_optimal = KMeans(n_clusters=optimal_clusters, random_state=42)\n","cluster_labels_optimal = kmeans_optimal.fit_predict(tfidf_norm)\n","\n","# Map the cluster labels back to the dataset\n","filtered_data['cluster'] = cluster_labels_optimal\n","\n","# Function to get top terms per cluster\n","def get_top_terms_per_cluster(n_terms, cluster_labels, tfidf_matrix, feature_names):\n","    top_terms = {}\n","    for cluster_num in range(optimal_clusters):\n","        indices = np.where(cluster_labels == cluster_num)[0]\n","        cluster_sum = np.sum(tfidf_matrix[indices], axis=0)\n","        top_terms[cluster_num] = [feature_names[i] for i in cluster_sum.argsort()[::-1][:n_terms]]\n","    return top_terms\n","\n","# Get the feature names from the TF-IDF vectorizer\n","feature_names = tfidf_vectorizer.get_feature_names_out()\n","\n","# Get top terms for each cluster\n","top_terms_per_cluster = get_top_terms_per_cluster(10, cluster_labels_optimal, tfidf_norm, feature_names)\n","\n","print(top_terms_per_cluster)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8PWpEXPK1na6","executionInfo":{"status":"ok","timestamp":1711108079906,"user_tz":-120,"elapsed":370,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"9e5d2984-2457-4c61-f1be-303324ed72ee"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{0: [array([['δειτε', 'λινκ', '2024', 'περισσοτερα', 'whatsapp', 'παρακατω',\n","        'google', 'ήθελα', 'yellowstone', 'jedi', 'κυριακη', 'παρασκευη',\n","        'σαββατο', 'σκηνοθεσια', 'edition', 'δθπ', 'καραμπάχ',\n","        'αρμένιοι', 'facebook', 'books', 'πω', 'διαδικασία', 'discogs',\n","        'verlag', 'καθένας', 'πάρα', 'wars', 'αττικής', 'εμένα',\n","        'γεμάτη', 'θέλω', 'artist', 'είχα', 'δημοσιεύθηκε', 'πιστεύω',\n","        'σύμπαν', 'παρθενώνα', 'σύλλογος', 'προσωπικά', 'κάνω', 'στόμα',\n","        'αφήγηση', 'τίποτα', 'τριλογίας', 'inside', 'έχεις', 'πράγμα',\n","        'καταφέρνει', 'καμία', 'κείμενα', 'κω', 'πλοκή', 'collection',\n","        'είμαστε', 'απλώς', 'ουσιαστικά', 'εντελώς', 'παναγιωτάτος',\n","        'αζερμπαϊτζάν', 'επανάσταση', 'connery', 'τύχη', 'αρμενία',\n","        'αρκετές', 'ομάδες', 'μήπως', 'μόνος', 'καθηγητής', 'ηθοποιών',\n","        'red', 'μπορώ', 'ας', 'ουίτμαν', 'καταστροφή', 'δεις', 'γέλιο',\n","        'john', 'έστω', 'sport', 'hd', 'ροκ', 'σιγά', 'μερικά', 'στόχος',\n","        'world', 'photo', 'θεατρικού', 'λιγότερο', 'μπορείς', 'ήρθε',\n","        'τόπο', 'άνθρωπο', 'δράσεις', 'ομάδας', 'μικρούς', 'μεγαλύτερη',\n","        'μπορούμε', 'γιορτή', 'ευθύνη', 'έδωσε', 'παρ', 'αθλητισμού',\n","        'συγγραφείς', 'συναισθήματα', 'υπήρχε', 'λέξεις', 'barton',\n","        'προγράμματα', 'συγκεκριμένη', 'πολέμου', 'μαθητές', 'ξεκινά',\n","        'εκπαίδευσης', 'μεγαλύτερο', 'σόου', 'απλά', 'αμέσως', 'μήκους',\n","        'φίλους', 'περί', 'τρόπος', 'ευρύ', 'οκτώβριο', 'καλύτερα',\n","        'επίπεδο', 'kathryn', 'κεφάλι', 'new', 'man', 'προγράμματος',\n","        'ηλικίες', 'λίγα', 'σύγχρονο', 'ιστορικό', 'πει', 'published',\n","        'σύνθεση', 'μυαλό', 'τέτοια', 'μιλάει', 'έτους', 'νίκου',\n","        'φαντασίας', 'ελλάςhappy', 'ζωγραφικής', 'έξι', 'περιοχής',\n","        'μικρές', 'αμερική', 'κάποιος', 'τελευταίες', 'σημασία', 'van',\n","        'μπορείτε', 'τρόμου', 'παράδοση', 'μεγάλης', 'σύντομα',\n","        'κριτικές', 'περάσει', 'εξουσία', 'δύναμη', 'εικόνας', 'φάση',\n","        'συμμετείχε', 'γνωστή', 'ανθρώπινη', 'μικρό', 'καλλιτέχνη',\n","        'κοινού', 'πού', 'φωνές', 'δύσκολο', 'βρέθηκαν', 'οπότε',\n","        'εφορεία', 'οποίου', 'έρχονται', 'πάντως', 'πολιτική', 'λόγια',\n","        'cosmote', 'είπε', 'σχετικά', 'έπειτα', 'μάτια', 'δράσης',\n","        'πρόσωπο', 'ακούγεται', 'κρίση', 'ακαδημία', 'έπρεπε', 'άποψη',\n","        'πια', 'συνήθως', 'γερμανία', 'αξίζει', '300', 'booker',\n","        'παρουσιαστεί', '2004', 'μητέρα', 'μπει', 'τέτοιο', 'μάρκες',\n","        'χριστούγεννα', 'μεταφορά', 'νιώθει', 'birthday', 'πατρίδα',\n","        'συγκεκριμένο', 'τουλάχιστον', 'κριστόφ', 'κοινωνική', 'στόχο',\n","        'περιβάλλον', 'ύφος', 'εξαιρετική', 'εξαιρετικά', 'david',\n","        'ιδέα', 'γνώμη', 'μεγάλους', 'διαφορετική', 'σημαντικό',\n","        'πόλεμο', 'επισκέπτες', 'όλων', 'πατέρα', 'πλευρά', 'καιρό',\n","        'θάνατο', 'βασικό', 'κινηματογράφο', 'δρόμο', 'φωτογραφίες',\n","        'κερδίσει', 'χιλιάδες', 'σύγχρονης', 'μνήμη', 'προσπαθεί',\n","        'θεατρικό', 'παιχνίδια', 'αληθινή', 'έκπληξη', 'περιπέτειες',\n","        'σκηνής', 'ανάγκη', 'ελληνικού', 'αυτοί', '2021', 'self',\n","        'μέλλον', 'μα', '28', 'προκειμένου', 'καλύτερο', 'ηθοποιοί',\n","        'μικρός', 'σώμα', 'δει', '07', 'love', 'έρευνα', 'εκθέσεις',\n","        'προσπάθεια', 'έχω', 'πρώτα', 'προβλήματα', 'παράδειγμα', '1971',\n","        'αυτού', 'νέους', 'ερωτήματα', 'πρωτότυπη', 'καλό', '2012',\n","        'δημήτρη', 'κάποιες', 'χρόνων', 'πορεία', 'τσώρτσιλ',\n","        'συντελεστές', 'περίπου', 'θεωρείται', 'όσα', 'πανεπιστημίου',\n","        'γαλλία', 'μερικές', 'πραγματικά', 'ελπίδα', 'ζει', '2013',\n","        'άλλωστε', 'κάποιο', 'https', 'μήνα', 'μάχη', 'βήματα', 'εαυτό',\n","        'αναφέρει', 'athens', 'ναι', 'δίπλα', 'γιάννη', 'σπίτι',\n","        'αλέξανδρος', 'συμμετέχουν', 'δούμε', 'κόρη', 'σημαίνει',\n","        'σχολείο', 'γιώργου', 'παρουσιάσει', 'τρία', 'ποίηση', 'χρόνος',\n","        'χέρια', 'γίνονται', 'γυναίκες', 'αναλυτικά', 'κοινωνικές',\n","        'διαγωνισμό', 'προσφέρει', 'βοήθεια', 'παρουσιάζεται', 'μικρή',\n","        'παιδί', 'υπήρξε', 'σίγουρα', 'ντεμπούτο', 'προβολή', 'βαθιά',\n","        'ερτ', 'χώρες', 'διηγήματα', 'εκείνος', 'ευρώπης', 'άρχισε',\n","        'μεγάλες', 'μιχάλης', 'παραμένει', 'λειτουργίας', 'διάθεση',\n","        'κέρδισε', 'γεγονότα', 'αυτούς', 'σκοπό', 'σημεία', 'αίσθηση',\n","        'ανά', 'θρίλερ', 'del', 'τελευταίο', 'λονδίνο', 'διαβάστε',\n","        'οποίοι', 'δεκαετία', 'swish', 'εβδομάδα', 'ερμηνεία',\n","        'δημοτικό', 'ανθρώπους', 'ήρωα', 'άλλος', 'κοστούμια', 'μεγάλου',\n","        'δήλωσε', 'ήθελε', 'χαρακτηριστικά', 'επιτυχημένη', 'ελεύθερη',\n","        'θεατές', 'αναφορές', 'εικόνα', 'διαθέτει', 'πάρει', 'αποτελούν',\n","        '2009', 'κοινωνία', 'γνωστό', 'υπόθεση', 'βιβλίων', 'πόσο',\n","        'άλλων', 'καλή', 'οθόνη', 'αυτής', 'πάλι', 'ειδικά', 'χρονιά',\n","        'ιδιαίτερη', 'συνάντηση', 'ανθρώπου', 'δίσκο', 'μάλλον',\n","        'records', 'δημιουργία', 'σεπτεμβρίου', 'σχέσεις', 'προσωπική',\n","        'james', 'γιος', 'περιγραφή', 'απέναντι', 'πρόεδρος', 'σκηνικά',\n","        'χαρακτήρες', 'αγώνα', 'la', 'άλλους', 'αλλαγές', 'οικογένεια',\n","        'κάποια', 'χθες', 'μέτρα', 'όσους', 'photography', 'μοιάζει',\n","        'σύνολο', 'συνολικά', 'περίπτωση', 'διαφορετικές', 'καλλιτέχνης',\n","        'εμείς', 'στιγμές', 'δράση', 'τηλεόραση', 'βραβεία', 'ηπα',\n","        'οικογένειας', 'μέρες', 'τέσσερα', 'νέων', 'ιουνίου', 'ήρωες',\n","        'προσωπικό', 'δείχνει', 'ελευθερία', 'πρωταγωνιστές', 'μεγάλα',\n","        'νόμπελ', 'ευρώπη', 'επιλογή', 'κυκλοφορεί', 'οργάνωση', 'album',\n","        'michael', 'λονδίνου', 'καρδιά', 'θεσσαλονίκης', 'ηθοποιούς',\n","        'live', 'βασίζεται', 'έφυγε', 'λειτουργεί', 'διανομή', 'φως',\n","        'διευθυντής', 'παραστάσεων', 'open', 'χρήστος', 'video',\n","        'σύγχρονη', 'εκ', 'αρκετά', 'εμπειρία', 'δώσει', 'διεύθυνση',\n","        'ρόλους', 'είδους', 'συγκεκριμένα', 'βρει', 'ακριβώς', 'γρήγορα',\n","        'γλώσσα', 'δευτέρα', 'χώρου', 'σταύρος', 'χώρους', 'εθνικό',\n","        'πέντε', 'θέματα', 'έγραψε', 'τεράστια', 'νέες', 'μαΐου', 'χάρη',\n","        'γράψει', 'συχνά', 'χαρακτήρα', 'λογοτεχνία', 'αίθουσα', 'βάση',\n","        'μοναδικό', 'χιούμορ', 'λόγος', 'κατάφερε', 'τέλη', 'ιδιαίτερα',\n","        'φέρει', 'επειδή', 'γεννήθηκε', 'alpha', 'κρασί', 'φορές',\n","        '2007', 'πλαίσιο', 'περισσότερα', 'jazz', 'μελέτη', 'χώρας',\n","        'πειραιά', 'παγκόσμια', 'γράφει', 'αφορμή', 'κινηματογράφου',\n","        '90', 'βιβλίου', 'άλλα', 'πέρα', 'δικό', 'έκδοση', 'επόμενη',\n","        'κατάσταση', '2015', 'παραμύθι', 'γι', 'ενδιαφέρον', 'τιμή',\n","        'τζον', 'γεγονός', 'ταυτόχρονα', 'παρουσίαση', 'λίστα',\n","        'ελληνικό', 'συνεχίζει', 'καλλιτεχνών', 'σειρές', '2018',\n","        'παρακάτω', 'κόσμος', 'μέρος', 'σχολή', 'κομμάτια', 'λένα',\n","        'ξεκίνησε', 'λόγο', 'μπροστά', 'ευρήματα', 'πολλοί', 'έχοντας',\n","        'πραγματοποιηθεί', '100', 'ανθρώπων', 'φιλμ', 'άρθρο', '2014',\n","        'ανήκει', 'ηλικία', 'εικόνες', 'γενική', '11', 'αφορά', 'άραγε',\n","        'άνθρωπος', 'παρελθόν', 'αργότερα', 'ζαν', 'τμήμα', 'ποιος',\n","        'θέλει', 'όλη', 'χώρος', 'ελλάδος', 'star', 'εκείνη', 'είδος',\n","        'νέου', 'δική', 'έργων', 'βέβαια', 'ταινιών', 'μουσικά',\n","        'κάνουν', 'έρωτα', 'συγγραφέα', 'ιουλίου', 'έλληνες', 'αντώνης',\n","        'κανείς', 'μήνες', 'μουσείου', 'κώστας', 'πρόσφατα', 'ελλάδας',\n","        'rock', 'μη', 'φυσικά', 'ευκαιρία', 'πέμπτη', 'παραγωγής',\n","        'καλλιτέχνες', 'αρχαιοτήτων', 'αρχές', 'τέχνη', 'καλοκαίρι',\n","        'ούτε', 'σειράς', 'πρόσωπα', 'βράδυ', 'παίζει', 'υόρκη',\n","        'αθηνών', 'πραγματικότητα', 'πράγματα', 'καλλιτεχνική', 'μέρα',\n","        'πηγή', 'κάτω', 'σημείο', 'ελένη', 'μπορούν', 'περιλαμβάνει',\n","        'άλλοτε', 'κυκλοφορία', 'αφού', 'παρουσία', 'σκηνές', '29',\n","        'μέλη', 'δέκα', 'ντοκιμαντέρ', 'στούντιο', 'αίθουσες', 'ματιά',\n","        'λόγω', 'χώρα', 'περισσότερες', 'άνθρωποι', 'δείτε', 'κομμάτι',\n","        'παίζουν', 'επί', 'μετάφραση', 'κοντά', 'ημέρα', 'άλλες',\n","        'τελικά', 'αυτόν', 'συνέχεια', 'αγάπη', 'λοιπόν', 'θέμα',\n","        'όνομα', 'ακολουθεί', 'θεατρική', 'αυτός', 'οκτωβρίου', 'χρόνου',\n","        'μέσω', 'έρχεται', 'δουλειά', 'είμαι', 'επίσημη', 'δεύτερη',\n","        'αλήθεια', 'οποίες', 'www', 'καλά', 'εν', '50', '24', 'δολάρια',\n","        'έκθεσης', 'δίνει', 'εγώ', 'βρίσκονται', 'ελλήνων', '2020',\n","        'κυρίως', 'περίοδο', '210', 'περισσότερο', 'απριλίου',\n","        'κυκλοφόρησε', 'τετάρτη', '14', 'όλοι', 'είσοδος', 'μορφή',\n","        'επιμέλεια', 'υλικό', 'αθήνας', 'ιανουαρίου', 'απ', 'ταξίδι',\n","        'βίντεο', '23', 'κόσμου', 'αρχή', 'μουσικό', 'συλλογή',\n","        'δεύτερο', 'δυο', 'σκαϊ', 'αυτές', 'ανάμεσα', 'φαίνεται',\n","        'μπορούσε', '16', 'μην', 'δηλαδή', 'εκατ', 'υπό', 'γίνει',\n","        'βιβλία', 'επιστρέφει', '25', 'μυθιστόρημα', 'ελληνικής',\n","        'σινεμά', 'μαρτίου', 'σάββατο', 'art', 'ετών', 'παιχνίδι',\n","        'γυναίκα', 'com', 'ίδια', 'παράστασης', 'όλους', 'σκηνοθέτης',\n","        'μουσικής', 'συμμετοχή', 'παιδιά', 'υπάρχουν', 'ηθοποιός',\n","        'αυτήν', 'φέτος', '13', 'άλλη', 'δεκεμβρίου', 'ώστε', 'λέει',\n","        'αποτέλεσμα', 'ήδη', 'νέας', 'πολλές', 'σκηνοθέτη', 'γίνεται',\n","        'ίδιος', 'παραγωγή', 'πρέπει', 'εκατομμύρια', 'κείμενο',\n","        'παρουσιάζει', 'άλλο', 'συνεργασία', 'έκανε', 'παρά', 'κατερίνα',\n","        'ιστορίες', '26', 'εκτός', 'ίσως', 'είτε', 'επιτυχία', '22',\n","        'πίσω', 'αθήνα', 'λίγες', 'εποχής', 'λίγο', '000', 'έχουμε',\n","        'δε', 'βρίσκεται', 'σενάριο', 'ιστορίας', 'πάντα', 'παρασκευή',\n","        'ίδιο', 'μεγάλο', 'στοιχεία', 'έγινε', 'κέντρο', 'μάλιστα',\n","        'single', 'gr', 'πώς', 'σχεδόν', 'σου', 'κυκλοφορήσει', 'στιγμή',\n","        'τελευταία', 'βασίλης', 'γύρω', 'χρόνο', 'λογοτεχνίας', 'όλο',\n","        'οποίος', 'ημέρες', 'εποχή', 'παράλληλα', 'τώρα', 'ωστόσο',\n","        'ελληνική', '20', 'ποτέ', 'μαρία', 'πολιτισμού', 'music', 'τότε',\n","        'συγγραφέας', 'τραγούδια', 'πρόγραμμα', 'νίκος', 'ώρες', '2016',\n","        'έργου', 'netflix', 'πολλά', 'κυριακή', 'αιώνα', 'σαν', 'θέση',\n","        'πρεμιέρα', 'τρεις', 'ομάδα', 'σκηνή', 'ξανά', 'ζωής',\n","        'πρόκειται', 'πόλη', 'μεταξύ', 'άλμπουμ', 'αποτελεί', 'τρίτη',\n","        'σχέση', 'τρόπο', '17', 'όλες', 'περιοχή', 'γιατί', 'πόλης',\n","        'υπάρχει', 'μόλις', 'διάρκεια', 'προς', '2019', 'σεζόν', 'σας',\n","        'νοεμβρίου', 'μουσείο', 'όσκαρ', 'φεβρουαρίου', 'παραστάσεις',\n","        'δημήτρης', 'εκεί', 'αντ1', 'πλέον', 'ώρα', 'τέχνης', 'βιβλίο',\n","        'τίτλο', 'χώρο', '19', '2017', 'ακόμα', 'ακόμη', 'θεάτρου',\n","        'είχαν', 'μπορεί', 'ευρώ', 'ρόλο', 'πριν', 'πληροφορίες',\n","        'εκπομπή', '15', '18', 'ελλάδα', 'τέλος', 'βραβείο', 'μιας',\n","        'γιώργος', '21', 'γιάννης', 'πρώτο', 'φεστιβάλ', 'όχι', 'χωρίς',\n","        'πάνω', 'φωτογραφία', 'κάτι', 'εδώ', 'έτσι', 'νέο', 'όπου',\n","        'επίσης', 'φορά', '12', 'όσο', 'κόσμο', 'αυτά', 'ταινίες',\n","        'σκηνοθεσία', 'τόσο', 'μεγάλη', 'ταινίας', 'κοινό', 'τραγούδι',\n","        'έργα', 'όλα', 'εκδόσεις', 'σύμφωνα', 'μουσική', 'σειρά', 'έναν',\n","        'οποίο', '10', 'σήμερα', 'μία', 'στους', '30', 'έκθεση',\n","        'παράσταση', 'τι', 'μόνο', 'έως', 'κατά', 'ενός', 'όταν',\n","        'κάνει', 'πολύ', 'μαζί', 'καθώς', 'νέα', 'είχε', 'θέατρο', 'αν',\n","        'ιστορία', 'μου', 'πρώτη', 'έργο', 'μέχρι', 'ζωή', 'οποία',\n","        'έχουν', 'όμως', 'ένας', 'χρόνια', 'κάθε', 'αυτή', 'πως',\n","        'ταινία', 'μετά', 'μέσα', 'δύο', 'ενώ', 'αυτό', 'πιο', 'όπως',\n","        'κι', '00', 'ως', 'μας', 'ήταν', 'στα', 'αλλά', 'έχει', 'ότι',\n","        'στον', 'στη', 'στις', 'δεν', 'ένα', 'μια', 'οι', 'τις', 'στο',\n","        'στην', 'τη', 'των', 'θα', 'είναι', 'τους', 'τα', 'σε', 'τον',\n","        'για', 'που', 'από', 'με', 'να', 'την', 'της', 'το', 'του',\n","        'και']], dtype=object)], 1: [array([['00', 'οποίες', 'οποίο', 'οποίοι', 'οποίος', 'οποίου', 'οπότε',\n","        'οργάνωση', 'ουίτμαν', 'ουσιαστικά', 'ούτε', 'πάλι', 'πάντα',\n","        'πάντως', 'πάνω', 'πάρα', 'πάρει', 'πέμπτη', 'πέντε', 'πέρα',\n","        'πίσω', 'παίζει', 'παίζουν', 'παγκόσμια', 'παιδί', 'παιδιά',\n","        'παιχνίδι', 'παιχνίδια', 'οποία', 'παναγιωτάτος', 'ομάδες',\n","        'ομάδα', 'μόνο', 'μόνος', 'νέα', 'νέας', 'νέες', 'νέο', 'νέου',\n","        'νέους', 'νέων', 'νίκος', 'νίκου', 'να', 'ναι', 'νιώθει',\n","        'νοεμβρίου', 'ντεμπούτο', 'ντοκιμαντέρ', 'νόμπελ', 'ξανά',\n","        'ξεκίνησε', 'ξεκινά', 'οθόνη', 'οι', 'οικογένεια', 'οικογένειας',\n","        'οκτωβρίου', 'οκτώβριο', 'ομάδας', 'μόλις', 'πανεπιστημίου',\n","        'παρά', 'περιβάλλον', 'περιγραφή', 'περιλαμβάνει', 'περιοχή',\n","        'περιοχής', 'περιπέτειες', 'περισσότερα', 'περισσότερες',\n","        'περισσότερο', 'πηγή', 'πια', 'πιο', 'πιστεύω', 'πλέον',\n","        'πλαίσιο', 'πλευρά', 'πληροφορίες', 'πλοκή', 'ποίηση', 'ποιος',\n","        'πολέμου', 'πολιτική', 'πολιτισμού', 'πολλά', 'πολλές', 'πολλοί',\n","        'πολύ', 'περίπτωση', 'παρ', 'περίπου', 'περί', 'παράδειγμα',\n","        'παράδοση', 'παράλληλα', 'παράσταση', 'παράστασης', 'παραγωγή',\n","        'παραγωγής', 'παρακάτω', 'παραμένει', 'παραμύθι', 'παρασκευή',\n","        'παρασκευη', 'παραστάσεις', 'παραστάσεων', 'παρελθόν',\n","        'παρθενώνα', 'παρουσία', 'παρουσίαση', 'παρουσιάζει',\n","        'παρουσιάζεται', 'παρουσιάσει', 'παρουσιαστεί', 'πατέρα',\n","        'πατρίδα', 'πει', 'πειραιά', 'περάσει', 'περίοδο', 'μυθιστόρημα',\n","        'μυαλό', 'μπροστά', 'λίγα', 'λίγες', 'λίγο', 'λίστα',\n","        'λειτουργίας', 'λειτουργεί', 'λιγότερο', 'λογοτεχνία',\n","        'λογοτεχνίας', 'λοιπόν', 'λονδίνο', 'λονδίνου', 'λόγια', 'λόγο',\n","        'λόγος', 'λόγω', 'μάλιστα', 'μάλλον', 'μάρκες', 'μάτια', 'μάχη',\n","        'μέλη', 'μέλλον', 'μέρα', 'μέρες', 'μέρος', 'μέσα', 'λέξεις',\n","        'μέσω', 'λένα', 'κώστας', 'ώρες', 'κινηματογράφου', 'κοινού',\n","        'κοινωνία', 'κοινωνικές', 'κοινωνική', 'κοινό', 'κομμάτι',\n","        'κομμάτια', 'κοντά', 'κοστούμια', 'κρίση', 'κρασί', 'κριστόφ',\n","        'κριτικές', 'κυκλοφορήσει', 'κυκλοφορία', 'κυκλοφορεί',\n","        'κυκλοφόρησε', 'κυρίως', 'κυριακή', 'κυριακη', 'κω', 'κόρη',\n","        'κόσμο', 'κόσμος', 'κόσμου', 'λέει', 'μέτρα', 'μέχρι', 'μήκους',\n","        'μιας', 'μικρές', 'μικρή', 'μικρούς', 'μικρό', 'μικρός',\n","        'μιλάει', 'μιχάλης', 'μνήμη', 'μοιάζει', 'μοναδικό', 'μορφή',\n","        'μου', 'μουσείο', 'μουσείου', 'μουσικά', 'μουσική', 'μουσικής',\n","        'μουσικό', 'μπει', 'μπορεί', 'μπορείς', 'μπορείτε', 'μπορούμε',\n","        'μπορούν', 'μπορούσε', 'μπορώ', 'μια', 'μητέρα', 'μην', 'μη',\n","        'μήνα', 'μήνες', 'μήπως', 'μία', 'μα', 'μαΐου', 'μαζί',\n","        'μαθητές', 'μαρία', 'μας', 'ματιά', 'με', 'μεγάλα', 'πορεία',\n","        'μεγάλες', 'μεγάλης', 'μεγάλο', 'μεγάλου', 'μεγάλους',\n","        'μεγαλύτερη', 'μεγαλύτερο', 'μελέτη', 'μερικά', 'μερικές',\n","        'μετά', 'μετάφραση', 'μεταξύ', 'μεταφορά', 'μεγάλη', 'κι',\n","        'ποτέ', 'πού', 'του', 'τουλάχιστον', 'τους', 'τρία', 'τρίτη',\n","        'τραγούδι', 'τραγούδια', 'τρεις', 'τριλογίας', 'τρόμου', 'τρόπο',\n","        'τρόπος', 'τσώρτσιλ', 'των', 'τόπο', 'τόσο', 'τότε', 'τύχη',\n","        'τώρα', 'υλικό', 'υπάρχει', 'υπάρχουν', 'υπήρξε', 'υπήρχε',\n","        'υπό', 'υπόθεση', 'υόρκη', 'τον', 'φάση', 'το', 'τις', 'τέσσερα',\n","        'τέτοια', 'τέτοιο', 'τέχνη', 'τέχνης', 'τίποτα', 'τίτλο', 'τα',\n","        'ταινία', 'ταινίας', 'ταινίες', 'ταινιών', 'ταξίδι',\n","        'ταυτόχρονα', 'τελευταία', 'τελευταίες', 'τελευταίο', 'τελικά',\n","        'τεράστια', 'τετάρτη', 'τζον', 'τη', 'τηλεόραση', 'την', 'της',\n","        'τι', 'τιμή', 'τμήμα', 'τέλος', 'φέρει', 'φίλους', 'χώρες',\n","        'χώρο', 'χώρος', 'χώρου', 'χώρους', 'ως', 'ωστόσο', 'όλα',\n","        'όλες', 'όλη', 'όλο', 'όλοι', 'όλους', 'όλων', 'όμως', 'όνομα',\n","        'όπου', 'όπως', 'όσα', 'όσκαρ', 'όσο', 'όσους', 'όταν', 'ότι',\n","        'όχι', 'ύφος', 'ώρα', 'χώρας', 'φέτος', 'χώρα', 'χρόνων',\n","        'φαίνεται', 'φαντασίας', 'φεβρουαρίου', 'φεστιβάλ', 'φιλμ',\n","        'φορά', 'φορές', 'φυσικά', 'φωνές', 'φως', 'φωτογραφία',\n","        'φωτογραφίες', 'χάρη', 'χέρια', 'χαρακτήρα', 'χαρακτήρες',\n","        'χαρακτηριστικά', 'χθες', 'χιλιάδες', 'χιούμορ', 'χρήστος',\n","        'χριστούγεννα', 'χρονιά', 'χρόνια', 'χρόνο', 'χρόνος', 'χρόνου',\n","        'χωρίς', 'τέλη', 'σώμα', 'σύντομα', 'πω', 'πως', 'πόλεμο',\n","        'πόλη', 'πόλης', 'πόσο', 'πώς', 'ροκ', 'ρόλο', 'ρόλους',\n","        'σάββατο', 'σήμερα', 'σίγουρα', 'σαββατο', 'σαν', 'σας', 'σε',\n","        'σεζόν', 'σειρά', 'σειράς', 'σειρές', 'σενάριο', 'σεπτεμβρίου',\n","        'σημαίνει', 'σημαντικό', 'σημασία', 'σημεία', 'πρώτο', 'σημείο',\n","        'πρώτη', 'πρόσωπο', 'πράγμα', 'πράγματα', 'πρέπει', 'πραγματικά',\n","        'πραγματικότητα', 'πραγματοποιηθεί', 'πρεμιέρα', 'πριν',\n","        'προβλήματα', 'προβολή', 'προγράμματα', 'προγράμματος',\n","        'προκειμένου', 'προς', 'προσπάθεια', 'προσπαθεί', 'προσφέρει',\n","        'προσωπικά', 'προσωπική', 'προσωπικό', 'πρωταγωνιστές',\n","        'πρωτότυπη', 'πρόγραμμα', 'πρόεδρος', 'πρόκειται', 'πρόσφατα',\n","        'πρόσωπα', 'πρώτα', 'σιγά', 'σινεμά', 'σκαϊ', 'συμμετέχουν',\n","        'συμμετείχε', 'συμμετοχή', 'συνάντηση', 'συνέχεια', 'συνήθως',\n","        'συναισθήματα', 'συνεργασία', 'συνεχίζει', 'συνολικά',\n","        'συντελεστές', 'συχνά', 'σχέσεις', 'σχέση', 'σχεδόν', 'σχετικά',\n","        'σχολή', 'σχολείο', 'σόου', 'σύγχρονη', 'σύγχρονης', 'σύγχρονο',\n","        'σύλλογος', 'σύμπαν', 'σύμφωνα', 'σύνθεση', 'σύνολο', 'συλλογή',\n","        'συγκεκριμένο', 'συγκεκριμένη', 'συγκεκριμένα', 'σκηνές',\n","        'σκηνή', 'σκηνής', 'σκηνικά', 'σκηνοθέτη', 'σκηνοθέτης',\n","        'σκηνοθεσία', 'σκηνοθεσια', 'σκοπό', 'σου', 'σπίτι', 'στα',\n","        'σταύρος', 'που', 'στη', 'στιγμές', 'στιγμή', 'στις', 'στοιχεία',\n","        'στον', 'στους', 'στούντιο', 'στόμα', 'στόχο', 'στόχος',\n","        'συγγραφέα', 'συγγραφέας', 'συγγραφείς', 'στην', 'κεφάλι',\n","        'κινηματογράφο', 'κείμενο', 'αίθουσα', 'αίθουσες', 'αίσθηση',\n","        'αγάπη', 'αγώνα', 'αζερμπαϊτζάν', 'αθήνα', 'αθήνας', 'αθηνών',\n","        'αθλητισμού', 'αιώνα', 'ακαδημία', 'ακολουθεί', 'ακούγεται',\n","        'ακριβώς', 'ακόμα', 'ακόμη', 'αλέξανδρος', 'αλήθεια', 'αληθινή',\n","        'αλλά', 'αλλαγές', 'αμέσως', 'αμερική', 'αν', 'ανά', 'ανάγκη',\n","        'ίσως', 'ανάμεσα', 'ίδιος', 'ίδια', 'έπρεπε', 'έργα', 'έργο',\n","        'έργου', 'έργων', 'έρευνα', 'έρχεται', 'έρχονται', 'έρωτα',\n","        'έστω', 'έτους', 'έτσι', 'έφυγε', 'έχει', 'έχεις', 'έχοντας',\n","        'έχουμε', 'έχουν', 'έχω', 'έως', 'ήδη', 'ήθελα', 'ήθελε', 'ήρθε',\n","        'ήρωα', 'ήρωες', 'ήταν', 'ίδιο', 'έπειτα', 'ανήκει', 'αναφέρει',\n","        'αυτής', 'αυτοί', 'αυτού', 'αυτούς', 'αυτό', 'αυτόν', 'αυτός',\n","        'αφήγηση', 'αφορά', 'αφορμή', 'αφού', 'βάση', 'βέβαια', 'βήματα',\n","        'βίντεο', 'βαθιά', 'βασίζεται', 'βασίλης', 'βασικό', 'βιβλία',\n","        'βιβλίο', 'βιβλίου', 'βιβλίων', 'βοήθεια', 'βράδυ', 'βρέθηκαν',\n","        'κερδίσει', 'αυτήν', 'αναλυτικά', 'αυτή', 'αυτά', 'αναφορές',\n","        'ανθρώπινη', 'ανθρώπου', 'ανθρώπους', 'ανθρώπων', 'αντ1',\n","        'αντώνης', 'αξίζει', 'απ', 'απέναντι', 'απλά', 'απλώς',\n","        'αποτέλεσμα', 'αποτελεί', 'αποτελούν', 'απριλίου', 'από',\n","        'αργότερα', 'αρκετά', 'αρκετές', 'αρμένιοι', 'αρμενία', 'αρχές',\n","        'αρχή', 'αρχαιοτήτων', 'ας', 'αττικής', 'αυτές', 'βρίσκονται',\n","        'έξι', 'έναν', '24', '25', '26', '28', '29', '30', '300', '50',\n","        '90', 'album', 'alpha', 'art', 'artist', 'athens', 'barton',\n","        'birthday', 'booker', 'books', 'collection', 'com', 'connery',\n","        'cosmote', 'david', 'del', 'discogs', 'edition', 'gr', '23',\n","        'hd', '22', '21', '000', '07', '10', '100', '12', '13', '14',\n","        '15', '16', '17', '18', '19', '1971', '20', '2004', '2007',\n","        '2009', '2012', '2013', '2014', '2015', '2016', '2017', '2018',\n","        '2019', '2020', '2021', '210', 'ένας', 'https', 'james', 'άλλα',\n","        'άλλες', 'άλλη', 'άλλο', 'άλλος', 'άλλοτε', 'άλλους', 'άλλων',\n","        'άλλωστε', 'άλμπουμ', 'άνθρωπο', 'άνθρωποι', 'άνθρωπος', 'άποψη',\n","        'άραγε', 'άρθρο', 'άρχισε', 'έγινε', 'έγραψε', 'έδωσε', 'έκανε',\n","        'έκδοση', 'έκθεση', 'έκθεσης', 'έκπληξη', 'έλληνες', 'ένα',\n","        'yellowstone', 'inside', 'www', 'wars', 'jazz', 'jedi', 'john',\n","        'kathryn', 'la', 'live', 'love', 'man', 'michael', 'music',\n","        'netflix', 'new', 'open', 'photo', 'photography', 'published',\n","        'records', 'red', 'rock', 'self', 'single', 'sport', 'star',\n","        'swish', 'van', 'verlag', 'video', 'world', 'βραβεία',\n","        'βρίσκεται', 'βρει', 'ερωτήματα', 'ετών', 'ευθύνη', 'ευκαιρία',\n","        'ευρήματα', 'ευρύ', 'ευρώ', 'ευρώπη', 'ευρώπης', 'εφορεία',\n","        'ζαν', 'ζει', 'ζωή', 'ζωής', 'ζωγραφικής', 'ηθοποιοί',\n","        'ηθοποιούς', 'ηθοποιός', 'ηθοποιών', 'ηλικία', 'ηλικίες',\n","        'ημέρα', 'ημέρες', 'ηπα', 'θάνατο', 'θέατρο', 'θέλει', 'ερτ',\n","        'θέλω', 'ερμηνεία', 'εποχής', 'ελληνικού', 'βραβείο', 'ελπίδα',\n","        'εμένα', 'εμείς', 'εμπειρία', 'εν', 'ενδιαφέρον', 'εντελώς',\n","        'ενός', 'ενώ', 'εξαιρετικά', 'εξαιρετική', 'εξουσία', 'επί',\n","        'επίπεδο', 'επίσημη', 'επίσης', 'επανάσταση', 'επειδή',\n","        'επιλογή', 'επιμέλεια', 'επισκέπτες', 'επιστρέφει', 'επιτυχία',\n","        'επιτυχημένη', 'εποχή', 'επόμενη', 'ελληνικής', 'θέμα', 'θέση',\n","        'καθένας', 'καθηγητής', 'καθώς', 'και', 'καιρό', 'καλά', 'καλή',\n","        'καλλιτέχνες', 'καλλιτέχνη', 'καλλιτέχνης', 'καλλιτεχνική',\n","        'καλλιτεχνών', 'καλοκαίρι', 'καλό', 'καλύτερα', 'καλύτερο',\n","        'καμία', 'κανείς', 'καραμπάχ', 'καρδιά', 'κατά', 'κατάσταση',\n","        'κατάφερε', 'καταστροφή', 'καταφέρνει', 'κατερίνα', 'κείμενα',\n","        'κέρδισε', 'θέματα', 'κέντρο', 'κάτι', 'θα', 'θεάτρου', 'θεατές',\n","        'θεατρική', 'θεατρικού', 'θεατρικό', 'θεσσαλονίκης', 'θεωρείται',\n","        'θρίλερ', 'ιανουαρίου', 'ιδέα', 'ιδιαίτερα', 'ιδιαίτερη',\n","        'ιουλίου', 'ιουνίου', 'ιστορία', 'ιστορίας', 'ιστορίες',\n","        'ιστορικό', 'κάθε', 'κάνει', 'κάνουν', 'κάνω', 'κάποια',\n","        'κάποιες', 'κάποιο', 'κάποιος', 'κάτω', 'ελληνική', 'ελληνικό',\n","        'ελλάςhappy', 'δήλωσε', 'δίνει', 'δίπλα', 'δίσκο', 'δε', 'δείτε',\n","        'δείχνει', 'δει', 'δεις', 'δεκαετία', 'δεκεμβρίου', 'δεν',\n","        'δέκα', 'δεύτερη', 'δηλαδή', 'δημήτρη', 'δημήτρης', 'δημιουργία',\n","        'δημοσιεύθηκε', 'δημοτικό', 'δθπ', 'διάθεση', 'διάρκεια',\n","        'ελλήνων', 'διαγωνισμό', 'διαδικασία', 'δεύτερο', 'γύρω',\n","        'γυναίκες', 'γυναίκα', 'γέλιο', 'γίνει', 'γίνεται', 'γίνονται',\n","        'γαλλία', 'γεγονός', 'γεγονότα', 'γεμάτη', 'γενική', 'γεννήθηκε',\n","        'γερμανία', 'γι', 'γιάννη', 'γιάννης', 'για', 'γιατί', 'γιορτή',\n","        'γιος', 'γιώργος', 'γιώργου', 'γλώσσα', 'γνωστή', 'γνωστό',\n","        'γνώμη', 'γράφει', 'γράψει', 'γρήγορα', 'διαθέτει', 'διανομή',\n","        'διαβάστε', 'διαφορετική', 'είχα', 'είχαν', 'είχε', 'εαυτό',\n","        'εβδομάδα', 'εγώ', 'εδώ', 'εθνικό', 'ειδικά', 'εικόνα',\n","        'εικόνας', 'εικόνες', 'εκ', 'εκατ', 'εκατομμύρια', 'εκδόσεις',\n","        'εκεί', 'εκείνη', 'εκείνος', 'εκθέσεις', 'εκπαίδευσης',\n","        'εκπομπή', 'εκτός', 'ελένη', 'ελευθερία', 'ελεύθερη', 'ελλάδα',\n","        'ελλάδας', 'ελλάδος', 'διαφορετικές', 'είσοδος', 'είτε', 'ώστε',\n","        'δούμε', 'δράσεις', 'δράση', 'δράσης', 'δρόμο', 'είπε', 'δυο',\n","        'δική', 'δικό', 'δύναμη', 'δουλειά', 'δύο', 'διηγήματα', 'δώσει',\n","        'είδος', 'είδους', 'διεύθυνση', 'διευθυντής', 'είμαι', 'είμαστε',\n","        'είναι', 'δύσκολο', 'δολάρια', 'στο', '11', 'μαρτίου', 'δευτέρα',\n","        'facebook', 'google', 'whatsapp', 'δειτε', 'λινκ', '2024',\n","        'περισσοτερα', 'παρακατω']], dtype=object)]}\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n","  warnings.warn(\n","<ipython-input-44-cb1d505ac00d>:7: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  filtered_data['cluster'] = cluster_labels_optimal\n"]}]},{"cell_type":"code","source":["# Manual Interpretation and Label Suggestion\n","new_labels = {\n","    0: \"Cultural and Artistic Expressions\",\n","    1: \"General Interest and Lifestyle\"\n","}"],"metadata":{"id":"Kxuo3x1c0PZR"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Fine-Tuning Transformers HuggingFace (3 tests for text classification)\n","\n","#### 1. model: distilbert-base-multilingual-cased, input: title,\n","####yielded accuracy: 52%\n","#### 2. model: distilbert-base-multilingual-cased, input: title+content,\n","####yielded accuracy: 70%\n","#### 3. model: distilbert-base-multilingual-cased, input: url,\n","####yielded accuracy: 95.4%"],"metadata":{"id":"BCN4JwT_0QyC"}},{"cell_type":"markdown","source":["................................................................................................................................................................"],"metadata":{"id":"6aVVjUI4fDPB"}},{"cell_type":"markdown","source":["Selected Distillbert because combines accuracy and speed for text classification.\n","\n","Initially tested distilbert-base-uncased, but the performance was bad (15%).\n","This was as expected since the model is not trained on greek context.\n","\n","So the the selected Transformer model is distilbert-base-multilingual-cased.\n"],"metadata":{"id":"S2Wla7IUc9Wv"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"j3GzkvcQgiSN","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1711105178334,"user_tz":-120,"elapsed":11151,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"a843e30f-2c78-4929-bf44-e6c045823e6f"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.38.2)\n","Collecting datasets\n","  Downloading datasets-2.18.0-py3-none-any.whl (510 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.5/510.5 kB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hCollecting evaluate\n","  Downloading evaluate-0.4.1-py3-none-any.whl (84 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 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requests->transformers) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.6)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.2.2)\n","Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.4)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n","Installing collected packages: xxhash, dill, responses, multiprocess, datasets, evaluate\n","Successfully installed datasets-2.18.0 dill-0.3.8 evaluate-0.4.1 multiprocess-0.70.16 responses-0.18.0 xxhash-3.4.1\n"]}],"source":["! pip install transformers datasets evaluate"]},{"cell_type":"code","source":["from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer\n","from datasets import Dataset, DatasetDict, load_metric\n","import pandas as pd\n","from sklearn.model_selection import train_test_split\n","\n","\n","# Assuming 'data' is your DataFrame and is already loaded\n","# df['text'] = df['title']\n","# df['text'] = df['title'] + df['content']\n","df['text'] = df['url']\n","data = df[['text', 'label']]\n","\n","train_df, test_df = train_test_split(data, test_size=0.2, random_state=42)\n","\n","train_df = train_df.reset_index(drop=True)\n","test_df = test_df.reset_index(drop=True)\n","\n","# Convert DataFrame to Hugging Face Dataset\n","train_dataset = Dataset.from_pandas(train_df)\n","test_dataset = Dataset.from_pandas(test_df)\n","\n","datasets = DatasetDict({'train': train_dataset, 'test': test_dataset})"],"metadata":{"id":"Z2l3skId1aoB"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(datasets)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GuD0a6Wg2ayp","executionInfo":{"status":"ok","timestamp":1711100726005,"user_tz":-120,"elapsed":3,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"e37277e2-842f-4afa-a9d2-0361e89258a1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["DatasetDict({\n","    train: Dataset({\n","        features: ['text', 'label'],\n","        num_rows: 1998\n","    })\n","    test: Dataset({\n","        features: ['text', 'label'],\n","        num_rows: 500\n","    })\n","})\n"]}]},{"cell_type":"code","source":["datasets['train']"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QWKV6cMd7dG5","executionInfo":{"status":"ok","timestamp":1711100727230,"user_tz":-120,"elapsed":7,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"e7e1e6e8-358b-4d44-e67d-076a750ceb25"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Dataset({\n","    features: ['text', 'label'],\n","    num_rows: 1998\n","})"]},"metadata":{},"execution_count":29}]},{"cell_type":"code","source":["datasets['train'][0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HeV3ptfm7dVX","executionInfo":{"status":"ok","timestamp":1711100728830,"user_tz":-120,"elapsed":4,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"1cb4ce45-e581-4d50-a8b2-7843022c6f51"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'text': 'https://www.tanea.gr/2020/11/13/politics/syriza-kata-mitsotaki-na-parei-piso-ton-ptoxeytiko/',\n"," 'label': 14}"]},"metadata":{},"execution_count":30}]},{"cell_type":"code","source":["from transformers import AutoTokenizer\n","\n","tokenizer = AutoTokenizer.from_pretrained(\"distilbert/distilbert-base-multilingual-cased\")\n","# tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")"],"metadata":{"id":"Lz2Di0nb8a-b"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def preprocess_function(examples):\n","    # return tokenizer(examples[\"text\"], truncation=True)\n","    return tokenizer(examples[\"text\"], truncation=True, padding=\"max_length\", max_length=512)"],"metadata":{"id":"FnYk4fBa9CAV"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tokenized_datasets = datasets.map(preprocess_function, batched=True)\n","tokenized_datasets"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":255,"referenced_widgets":["da9e107328264af7992063195687ac56","e0d51af755a44ac28f39116bde7f4ffa","10d4f500205c4cecab74df118dae4cea","2a840f1a1a3d42e9a7e4dcf93e6ae50a","790c0c933af84569950d3c4baad47ec6","81ed54eb6dfb42f4935c9de6cc491f43","f828a0710d1f4a7eaf387e283ed2a0b4","ce260b70bb5e471eac405ff45d261b99","8b4057d5bd7b447d904965b13055cb91","ed9cbd9f48fd4b5e802571937ac34992","0c1f7d02a5ea4d47867b2163b678768a","4007a728c2e04b82bed018baecb10971","df3ccffdf0264b6b92deed106f20681f","fa9e1ec70a874ee1accc1ee5f359df00","61d1c01894834fcea0a6b07cf911ef41","e56667e0d72d4d47be0a54923f90ae79","b838634da1ad4e6f98a2a288c3ae1821","25e173d32d394b61a2b1c0310cecb525","7bc02bbe351840809d69e649960dfdbe","4fb2bbd8889f4a08bb2ee4f5ae709df9","f158f2f2bc6f4b75aac742215a1392b5","9610f5b8991d40e1bc80e7e96279b7a9"]},"id":"52VVJG7t9B4S","executionInfo":{"status":"ok","timestamp":1711100734759,"user_tz":-120,"elapsed":1255,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"f64379b8-7cce-4cc7-e005-adaef7584fca"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/1998 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"da9e107328264af7992063195687ac56"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/500 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4007a728c2e04b82bed018baecb10971"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['text', 'label', 'input_ids', 'attention_mask'],\n","        num_rows: 1998\n","    })\n","    test: Dataset({\n","        features: ['text', 'label', 'input_ids', 'attention_mask'],\n","        num_rows: 500\n","    })\n","})"]},"metadata":{},"execution_count":33}]},{"cell_type":"code","source":["from transformers import DataCollatorWithPadding\n","\n","data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")"],"metadata":{"id":"iBWfRQcu9KIc"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import evaluate\n","\n","accuracy = evaluate.load(\"accuracy\")"],"metadata":{"id":"CVHRIjuF9KCa"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","\n","\n","def compute_metrics(eval_pred):\n","    predictions, labels = eval_pred\n","    predictions = np.argmax(predictions, axis=1)\n","    return accuracy.compute(predictions=predictions, references=labels)"],"metadata":{"id":"2THXRmTZ9Xq3"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["label_list = data['label'].unique().tolist()\n","label2id = {\n","    'Style&Fashion': 0,\n","    'Sensitive Topics': 1,\n","    'Religion&Spirituality': 2,\n","    'Science': 3,\n","    'Viral Articles': 4,\n","    'Beauty': 5,\n","    'Tech&Computing': 6,\n","    'Healthy Living': 7,\n","    'Sports': 8,\n","    'Culture': 9,\n","    'Business&Finance': 10,\n","    'Education': 11,\n","    'Astrology': 12,\n","    'Family&Relationships': 13,\n","    'Politics': 14,\n","    'Food&Drink': 15,\n","    'Pop Culture': 16,\n","    'Automotive': 17,\n","    'Attractions': 18,\n","    'Home&Garden': 19,\n","    'Travel': 20\n","}\n","id2label = id2label = {\n","    0: 'Style&Fashion',\n","    1: 'Sensitive Topics',\n","    2: 'Religion&Spirituality',\n","    3: 'Science',\n","    4: 'Viral Articles',\n","    5: 'Beauty',\n","    6: 'Tech&Computing',\n","    7: 'Healthy Living',\n","    8: 'Sports',\n","    9: 'Culture',\n","    10: 'Business&Finance',\n","    11: 'Education',\n","    12: 'Astrology',\n","    13: 'Family&Relationships',\n","    14: 'Politics',\n","    15: 'Food&Drink',\n","    16: 'Pop Culture',\n","    17: 'Automotive',\n","    18: 'Attractions',\n","    19: 'Home&Garden',\n","    20: 'Travel'\n","}\n","\n","print(label2id)\n","print(id2label)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Z8z3oa4L9e66","executionInfo":{"status":"ok","timestamp":1711100738241,"user_tz":-120,"elapsed":7,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"e80ba54e-19fd-47df-ebce-76e53cb9f99c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'Style&Fashion': 0, 'Sensitive Topics': 1, 'Religion&Spirituality': 2, 'Science': 3, 'Viral Articles': 4, 'Beauty': 5, 'Tech&Computing': 6, 'Healthy Living': 7, 'Sports': 8, 'Culture': 9, 'Business&Finance': 10, 'Education': 11, 'Astrology': 12, 'Family&Relationships': 13, 'Politics': 14, 'Food&Drink': 15, 'Pop Culture': 16, 'Automotive': 17, 'Attractions': 18, 'Home&Garden': 19, 'Travel': 20}\n","{0: 'Style&Fashion', 1: 'Sensitive Topics', 2: 'Religion&Spirituality', 3: 'Science', 4: 'Viral Articles', 5: 'Beauty', 6: 'Tech&Computing', 7: 'Healthy Living', 8: 'Sports', 9: 'Culture', 10: 'Business&Finance', 11: 'Education', 12: 'Astrology', 13: 'Family&Relationships', 14: 'Politics', 15: 'Food&Drink', 16: 'Pop Culture', 17: 'Automotive', 18: 'Attractions', 19: 'Home&Garden', 20: 'Travel'}\n"]}]},{"cell_type":"code","source":["from transformers import create_optimizer\n","import tensorflow as tf\n","\n","batch_size = 16\n","num_epochs = 5\n","batches_per_epoch = len(tokenized_datasets[\"train\"]) // batch_size\n","total_train_steps = int(batches_per_epoch * num_epochs)\n","optimizer, schedule = create_optimizer(init_lr=2e-5, num_warmup_steps=0, num_train_steps=total_train_steps)"],"metadata":{"id":"6m0e8XX-9eLT"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from transformers import TFAutoModelForSequenceClassification\n","\n","model = TFAutoModelForSequenceClassification.from_pretrained(\n","    \"distilbert/distilbert-base-multilingual-cased\", num_labels=len(label_list), id2label=id2label, label2id=label2id\n","    # \"distilbert-base-uncased\", num_labels=len(label_list), id2label=id2label, label2id=label2id\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"k_00jgT59eIT","executionInfo":{"status":"ok","timestamp":1711100744658,"user_tz":-120,"elapsed":1543,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"93391f50-c950-43cc-acb3-1ec41afaf364"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFDistilBertForSequenceClassification: ['vocab_transform.weight', 'vocab_transform.bias', 'vocab_layer_norm.weight', 'vocab_projector.bias', 'vocab_layer_norm.bias']\n","- This IS expected if you are initializing TFDistilBertForSequenceClassification from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing TFDistilBertForSequenceClassification from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\n","Some weights or buffers of the TF 2.0 model TFDistilBertForSequenceClassification were not initialized from the PyTorch model and are newly initialized: ['pre_classifier.weight', 'pre_classifier.bias', 'classifier.weight', 'classifier.bias']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]}]},{"cell_type":"code","source":["tf_train_set = model.prepare_tf_dataset(\n","    tokenized_datasets[\"train\"],\n","    shuffle=True,\n","    batch_size=16,\n","    collate_fn=data_collator,\n",")\n","\n","tf_validation_set = model.prepare_tf_dataset(\n","    tokenized_datasets[\"test\"],\n","    shuffle=False,\n","    batch_size=16,\n","    collate_fn=data_collator,\n",")"],"metadata":{"id":"RVcnR29e9XoD"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import tensorflow as tf\n","\n","model.compile(optimizer=optimizer)"],"metadata":{"id":"Em8QM5OB3tch"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from transformers.keras_callbacks import KerasMetricCallback\n","\n","metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set)\n","\n","callbacks = [metric_callback]"],"metadata":{"id":"FaSs9Ooi3tSY"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Training is done utilizing nvidia T4"],"metadata":{"id":"CEnU4CaAhTCX"}},{"cell_type":"code","source":["model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=4, callbacks=callbacks)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SEVdr4Jdc-Vb","outputId":"9873b872-cc66-4f09-e25a-e8dff67a5c62","executionInfo":{"status":"ok","timestamp":1711101397173,"user_tz":-120,"elapsed":643835,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/4\n","124/124 [==============================] - 177s 1s/step - loss: 2.5509 - val_loss: 1.8726 - accuracy: 0.5480\n","Epoch 2/4\n","124/124 [==============================] - 130s 1s/step - loss: 1.3725 - val_loss: 0.8978 - accuracy: 0.8480\n","Epoch 3/4\n","124/124 [==============================] - 130s 1s/step - loss: 0.7521 - val_loss: 0.5767 - accuracy: 0.9400\n","Epoch 4/4\n","124/124 [==============================] - 131s 1s/step - loss: 0.5149 - val_loss: 0.4572 - accuracy: 0.9540\n"]},{"output_type":"execute_result","data":{"text/plain":["<tf_keras.src.callbacks.History at 0x7e188823a8f0>"]},"metadata":{},"execution_count":43}]},{"cell_type":"code","source":["model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tomV6z1-lorq","executionInfo":{"status":"ok","timestamp":1711101431717,"user_tz":-120,"elapsed":589,"user":{"displayName":"Paraskevopoulos Dimitrios","userId":"03814999480949425187"}},"outputId":"d3e9309c-95c6-4fa1-e7d3-f86537615281"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"tf_distil_bert_for_sequence_classification_1\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," distilbert (TFDistilBertMa  multiple                  134734080 \n"," inLayer)                                                        \n","                                                                 \n"," pre_classifier (Dense)      multiple                  590592    \n","                                                                 \n"," classifier (Dense)          multiple                  16149     \n","                                                                 \n"," dropout_39 (Dropout)        multiple                  0         \n","                                                                 \n","=================================================================\n","Total params: 135340821 (516.28 MB)\n","Trainable params: 135340821 (516.28 MB)\n","Non-trainable params: 0 (0.00 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