analysis_with_embeddings.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/grosa1/sentiment-analysis-example/blob/main/analysis_with_embeddings.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BrLMhvz1HDhg"
      },
      "source": [
        "# Sentiment Analysis on IMDb dataset - pt.2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iZo3-Ni7bMWz"
      },
      "source": [
        "This notebook reports an experiment with sentence embedding for sentiment analysis. The aim is to build a resource-efficient model that can classify the sentiment of movie reviews (from the IMDb dataset) to achieve the highest accuracy score.\n",
        "In particular, it uses `SentenceTransformers` to extract sentence embeddings used as feature vectors for several machine learning models.\n",
        "The results show that *SVC* is the best-performing model, achieving an accuracy score of 0.90.\n",
        "\n",
        "The remainder of this notebook is structured as follows: Section 0 installs the required dependencies, Section 1 reports the steps to download and load the IMDb dataset, and Section 2 reports the model training and evaluation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1oxAlXeaKMfZ"
      },
      "source": [
        "## 0. Initial setup"
      ]
    },
    {
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          "text": [
            "Collecting sentence-transformers\n",
            "  Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Collecting sentencepiece (from sentence-transformers)\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: huggingface-hub>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (0.19.4)\n",
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            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (2.0.7)\n",
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            "Building wheels for collected packages: sentence-transformers\n",
            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence-transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125923 sha256=87935dcf931e7c3dc2ed0bb1a3d2f0fd1f675f6aec75d0c70975a8525166da37\n",
            "  Stored in directory: /root/.cache/pip/wheels/62/f2/10/1e606fd5f02395388f74e7462910fe851042f97238cbbd902f\n",
            "Successfully built sentence-transformers\n",
            "Installing collected packages: sentencepiece, sentence-transformers\n",
            "Successfully installed sentence-transformers-2.2.2 sentencepiece-0.1.99\n"
          ]
        }
      ],
      "source": [
        "!pip install sentence-transformers==2.2.2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BDj7Ro0DHRxa"
      },
      "source": [
        "## 1. Load dataset"
      ]
    },
    {
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        "id": "SiYihL9xdeAv",
        "outputId": "43298036-7906-4598-974e-9aed48694d80"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# run download_dataset.py before to prepare the dataset\n",
        "df_train = pd.read_csv('train.csv')\n",
        "df_test = pd.read_csv('test.csv')"
      ]
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        "id": "fgsM4hZDcsif",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9dd02e1e-3d96-43b9-acce-d9c804d3cada button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                                    text  score  label\n",
              "0      A nurse travels to a rural psychiatric clinic ...      8      1\n",
              "1      as a 'physically challenged' person (god, how ...      9      1\n",
              "2      This has got to be one of the best episodes of...     10      1\n",
              "3      I was surprised and impressed to find out this...     10      1\n",
              "4      There I was on vacation when my host suggested...      9      1\n",
              "...                                                  ...    ...    ...\n",
              "24995  I was fascinated to read the range of opinions...      2      0\n",
              "24996  I went into this movie with an open mind. I ha...      1      0\n",
              "24997  Worst movie ever seen. Worst acting too. I can...      1      0\n",
              "24998  From reading the back of the box my first thou...      1      0\n",
              "24999  Threadbare horror outing has an innocent teen ...      4      0\n",
              "\n",
              "[24804 rows x 3 columns]"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_train.drop_duplicates()\n",
        "df_test.drop_duplicates()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "150zG7j2HlBs"
      },
      "source": [
        "## 2. Model training and validation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "FjmFHiNq0Ul3"
      },
      "outputs": [],
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "import numpy as np\n",
        "import joblib"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "arJBLTBoHe2L",
        "outputId": "87c361fa-7c73-4e7b-b84f-0672f420d821"
      },
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ]
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          "output_type": "display_data"
        },
        {
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        {
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        {
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            ]
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        },
        {
          "data": {
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            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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      ],
      "source": [
        "# Load a pre-trained model for SentenceTransformer\n",
        "encoder = SentenceTransformer('all-mpnet-base-v2',  device='cuda')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "unPuK5BOHsMu"
      },
      "source": [
        "### 2.1 Feature extraction"
      ]
    },
    {
      "cell_type": "code",
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        "colab": {
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      "outputs": [
        {
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              "version_major": 2,
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      ],
      "source": [
        "embeddings_train = encoder.encode(df_train[\"text\"], convert_to_tensor=False, show_progress_bar=True)"
      ]
    },
    {
      "cell_type": "code",
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        "id": "kp25WpD8eWlQ",
        "outputId": "4093f9d5-f553-4345-b3ad-ad9c096937a6"
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      "outputs": [
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bbe2914095134ec39585b398bf4c0d7b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Batches:   0%|          | 0/782 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "embeddings_test = encoder.encode(df_test[\"text\"], convert_to_tensor=False, show_progress_bar=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_EwE3AH741Hg",
        "outputId": "e5073f70-ebc6-4d9c-f95b-0e1dd6c20eda"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['embeddings_test.joblib']"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "joblib.dump(embeddings_train, \"embeddings_train.joblib\")\n",
        "joblib.dump(embeddings_test, \"embeddings_test.joblib\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "KWgjlVXQfAdb"
      },
      "outputs": [],
      "source": [
        "y_train = df_train['label']\n",
        "y_test = df_test['label']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SnHgnyWsIM-4"
      },
      "source": [
        "### 2.2 Prediction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "nR62uuXcDPcz"
      },
      "outputs": [],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, classification_report, roc_auc_score\n",
        "import time"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "CMhss_woIXEd"
      },
      "outputs": [],
      "source": [
        "models = [RandomForestClassifier(random_state=1), LogisticRegression(random_state=1), SVC(random_state=1), KNeighborsClassifier(), AdaBoostClassifier(random_state=1), GradientBoostingClassifier(random_state=1)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4nsi087MJKJV",
        "outputId": "406cdb82-3d70-45b7-d9ae-1823406f84bf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "==> model: RandomForestClassifier - training time (s): 99\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.83      0.84     12500\n",
            "           1       0.83      0.86      0.85     12500\n",
            "\n",
            "    accuracy                           0.85     25000\n",
            "   macro avg       0.85      0.85      0.85     25000\n",
            "weighted avg       0.85      0.85      0.85     25000\n",
            "\n",
            "\n",
            "==> model: LogisticRegression - training time (s): 2\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.90      0.89      0.89     12500\n",
            "           1       0.89      0.90      0.89     12500\n",
            "\n",
            "    accuracy                           0.89     25000\n",
            "   macro avg       0.89      0.89      0.89     25000\n",
            "weighted avg       0.89      0.89      0.89     25000\n",
            "\n",
            "\n",
            "==> model: SVC - training time (s): 434\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.90      0.90     12500\n",
            "           1       0.90      0.89      0.90     12500\n",
            "\n",
            "    accuracy                           0.90     25000\n",
            "   macro avg       0.90      0.90      0.90     25000\n",
            "weighted avg       0.90      0.90      0.90     25000\n",
            "\n",
            "\n",
            "==> model: KNeighborsClassifier - training time (s): 38\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.75      0.87      0.80     12500\n",
            "           1       0.84      0.70      0.77     12500\n",
            "\n",
            "    accuracy                           0.79     25000\n",
            "   macro avg       0.79      0.78      0.78     25000\n",
            "weighted avg       0.79      0.79      0.78     25000\n",
            "\n",
            "\n",
            "==> model: AdaBoostClassifier - training time (s): 175\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.82      0.83     12500\n",
            "           1       0.83      0.84      0.83     12500\n",
            "\n",
            "    accuracy                           0.83     25000\n",
            "   macro avg       0.83      0.83      0.83     25000\n",
            "weighted avg       0.83      0.83      0.83     25000\n",
            "\n",
            "\n",
            "==> model: GradientBoostingClassifier - training time (s): 896\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.85      0.86     12500\n",
            "           1       0.85      0.87      0.86     12500\n",
            "\n",
            "    accuracy                           0.86     25000\n",
            "   macro avg       0.86      0.86      0.86     25000\n",
            "weighted avg       0.86      0.86      0.86     25000\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "res = list()\n",
        "preds = list()\n",
        "\n",
        "for m in models:\n",
        "    t_start = time.time()\n",
        "    model = m.fit(embeddings_train, y_train)\n",
        "    y_pred = model.predict(embeddings_test)\n",
        "    print(\"==> model:\", m.__class__.__name__, \"- training time (s):\", int(time.time() - t_start))\n",
        "    print(classification_report(y_test, y_pred))\n",
        "    print()\n",
        "\n",
        "    preds.append(y_pred)\n",
        "    res.append({\n",
        "        \"model\": model.__class__.__name__,\n",
        "        \"features\": \"tfidf\",\n",
        "        \"accuracy\": accuracy_score(y_test, y_pred),\n",
        "        \"precision\": precision_score(y_test, y_pred),\n",
        "        \"recall\": recall_score(y_test, y_pred),\n",
        "        \"f1\": f1_score(y_test, y_pred),\n",
        "        \"roc_auc\": roc_auc_score(y_test, y_pred)\n",
        "    })"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_E4PzQyCJMNh",
        "outputId": "af6060ef-80ee-4aef-81a0-a0f0e71e5105"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['preds_embeddings.pkl']"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "joblib.dump(res, 'res_embeddings.pkl')\n",
        "joblib.dump(preds, 'preds_embeddings.pkl')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bavWw5wbJjOO"
      },
      "source": [
        "### 2.3 Results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "ItzvQ2oUJMVm",
        "outputId": "5449add8-e843-434f-a377-d41bc6d93a78"
      },
      "outputs": [
        {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>features</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
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              "      <th>roc_auc</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SVC</td>\n",
              "      <td>tfidf</td>\n",
              "      <td>0.89700</td>\n",
              "      <td>0.899782</td>\n",
              "      <td>0.89352</td>\n",
              "      <td>0.896640</td>\n",
              "      <td>0.89700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LogisticRegression</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>GradientBoostingClassifier</td>\n",
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              "      <td>0.85836</td>\n",
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              "      <td>0.860001</td>\n",
              "      <td>0.85836</td>\n",
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              "      <td>RandomForestClassifier</td>\n",
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              "      <td>0.86496</td>\n",
              "      <td>0.848466</td>\n",
              "      <td>0.84552</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AdaBoostClassifier</td>\n",
              "      <td>tfidf</td>\n",
              "      <td>0.82972</td>\n",
              "      <td>0.825476</td>\n",
              "      <td>0.83624</td>\n",
              "      <td>0.830823</td>\n",
              "      <td>0.82972</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>KNeighborsClassifier</td>\n",
              "      <td>tfidf</td>\n",
              "      <td>0.78500</td>\n",
              "      <td>0.839706</td>\n",
              "      <td>0.70448</td>\n",
              "      <td>0.766172</td>\n",
              "      <td>0.78500</td>\n",
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            ],
            "text/plain": [
              "                        model features  accuracy  precision   recall  \\\n",
              "2                         SVC    tfidf   0.89700   0.899782  0.89352   \n",
              "1          LogisticRegression    tfidf   0.89328   0.888003  0.90008   \n",
              "5  GradientBoostingClassifier    tfidf   0.85836   0.850152  0.87008   \n",
              "0      RandomForestClassifier    tfidf   0.84552   0.832589  0.86496   \n",
              "4          AdaBoostClassifier    tfidf   0.82972   0.825476  0.83624   \n",
              "3        KNeighborsClassifier    tfidf   0.78500   0.839706  0.70448   \n",
              "\n",
              "         f1  roc_auc  \n",
              "2  0.896640  0.89700  \n",
              "1  0.894001  0.89328  \n",
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