Topic_Analysis_with_BERT.ipynb

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        "<a href=\"https://colab.research.google.com/github/madhavi1102/natural_language_processing/blob/main/Topic_Analysis_with_BERT.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q8uxCSNqoXh4"
      },
      "source": [
        "# Topic Modeling with BERT\n",
        "\n",
        "**NOTE**: This is significantly faster if you choose a **gpu** as runtime. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QUPt2VfX6PlJ",
        "outputId": "395daaa4-eb1a-4807-97d3-cb305972660d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!pip install umap-learn\n",
        "!pip install -U sentence-transformers\n",
        "!pip install hdbscan"
      ],
      "execution_count": 15,
      "outputs": [
        {
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      "cell_type": "code",
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import umap\n",
        "import hdbscan\n",
        "from sentence_transformers import SentenceTransformer\n",
        "from sklearn.datasets import fetch_20newsgroups\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from tqdm import tqdm\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "def c_tf_idf(documents, m, ngram_range=(1, 1)):\n",
        "    \"\"\" Calculate a class-based TF-IDF where m is the number of total documents. \"\"\"\n",
        "    count = CountVectorizer(ngram_range=ngram_range, stop_words=\"english\").fit(documents)\n",
        "    t = count.transform(documents).toarray()\n",
        "    w = t.sum(axis=1)\n",
        "    tf = np.divide(t.T, w)\n",
        "    sum_t = t.sum(axis=0)\n",
        "    idf = np.log(np.divide(m, sum_t)).reshape(-1, 1)\n",
        "    tf_idf = np.multiply(tf, idf)\n",
        "\n",
        "    return tf_idf, count\n",
        "\n",
        "def extract_top_n_words_per_topic(tf_idf, count, docs_per_topic, n=20):\n",
        "    words = count.get_feature_names()\n",
        "    labels = list(docs_per_topic.Topic)\n",
        "    tf_idf_transposed = tf_idf.T\n",
        "    indices = tf_idf_transposed.argsort()[:, -n:]\n",
        "    top_n_words = {label: [(words[j], tf_idf_transposed[i][j]) for j in indices[i]][::-1] for i, label in enumerate(labels)}\n",
        "    return top_n_words\n",
        "\n",
        "def extract_topic_sizes(df):\n",
        "    topic_sizes = (df.groupby(['Topic'])\n",
        "                     .Doc\n",
        "                     .count()\n",
        "                     .reset_index()\n",
        "                     .rename({\"Topic\": \"Topic\", \"Doc\": \"Size\"}, axis='columns')\n",
        "                     .sort_values(\"Size\", ascending=False))\n",
        "    return topic_sizes"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MVLh9q2aRJd3"
      },
      "source": [
        "## Load data\n",
        "For this example, let's use the famous 20Newsgroups dataset which contains roughly 18000 newsgroups posts on 20 topics."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i47mA1qY6O04",
        "outputId": "289df1f3-95b2-43b3-a928-a76f4064b498",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "np.random.seed(0)\n",
        "data = fetch_20newsgroups(subset='all')['data']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading 20news dataset. This may take a few minutes.\n",
            "Downloading dataset from https://ndownloader.figshare.com/files/5975967 (14 MB)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "## Create embeddings\n",
        "The very first step is converting the documents to numerical data. There are many methods that can be applied, but since we are modeling topics with **BERT** that is what we are going to be using.\n",
        "\n",
        "There are many pre-trained models that you can use for a large amount of languages [here](https://www.sbert.net/docs/pretrained_models.html). Simply plug-in the name instead of *distilbert-base-nli-mean-tokens*. "
      ]
    },
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      "source": [
        "%%time\n",
        "model = SentenceTransformer('distilbert-base-nli-mean-tokens')\n",
        "embeddings = model.encode(data, show_progress_bar=True)"
      ],
      "execution_count": null,
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          "text": [
            "100%|██████████| 245M/245M [00:17<00:00, 14.4MB/s]\n"
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          "text": [
            "\n",
            "CPU times: user 3min 57s, sys: 10 s, total: 4min 7s\n",
            "Wall time: 4min 31s\n"
          ],
          "name": "stdout"
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rO1GX8TvRXCM"
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      "source": [
        "## Reduce dimensionality\n",
        "We use **UMAP** to reduce the dimensionality of the embeddings created above. It is important that we keep a little bit of dimensionality as that allows the reduced embeddings to contain more structure to improve clustering at a later stage. \n",
        "\n",
        "You can play around with the **number of components** (dimensionality to reduce to) and the **number of neighbors** (the nearby points to look at). "
      ]
    },
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      "cell_type": "code",
      "metadata": {
        "id": "V_rBb73b7AKd",
        "outputId": "41e7a62a-bc8f-425d-b8de-d4aaab0d70c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "%%time\n",
        "umap_embeddings = umap.UMAP(n_neighbors=15,\n",
        "                            n_components=5,\n",
        "                            min_dist=0.0,\n",
        "                            metric='cosine',\n",
        "                            random_state=42).fit_transform(embeddings)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 45.9 s, sys: 1.63 s, total: 47.5 s\n",
            "Wall time: 46.6 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p_STjPe9RxSO"
      },
      "source": [
        "## Cluster documents\n",
        "Since **UMAP** keeps some of the original high-embedded structure, it makes sense to use **HDBSCAN** to find highly-densed clusters. The metric is euclidean since it does not suffer from high-dimensionality and the **minimum cluster size** allows you to decrease the number of topics found and increase the topic sizes. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MGqxxsTt7AGV",
        "outputId": "1e9ed717-fd39-4d1b-f50f-c4d8e0d6c441",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "%%time\n",
        "cluster = hdbscan.HDBSCAN(min_cluster_size=30,\n",
        "                          metric='euclidean',\n",
        "                          cluster_selection_method='eom', \n",
        "                          prediction_data=True).fit(umap_embeddings)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 846 ms, sys: 313 ms, total: 1.16 s\n",
            "Wall time: 3.5 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KYj2_bbQaaqt"
      },
      "source": [
        "## Visualize Clusters\n",
        "We can visualize the resulting cluster by embedding the data into **2d-space** using **UMAP** and using matplotlib to color the clusters. Some clusters are difficult to spot as there may be > 50 topics generated. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZhmwTYI_oCpQ",
        "outputId": "b95a4236-876e-4da2-b73e-037ce6a3ea99",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        }
      },
      "source": [
        "# Prepare data\n",
        "umap_data = umap.UMAP(n_neighbors=15, n_components=2, min_dist=0.0, metric='cosine').fit_transform(embeddings)\n",
        "result = pd.DataFrame(umap_data, columns=['x', 'y'])\n",
        "result['labels'] = cluster.labels_\n",
        "\n",
        "# Visualize clusters\n",
        "fig, ax = plt.subplots(figsize=(20, 10))\n",
        "outliers = result.loc[result.labels == -1, :]\n",
        "clustered = result.loc[result.labels != -1, :]\n",
        "plt.scatter(outliers.x, outliers.y, color='#BDBDBD', s=0.05)\n",
        "plt.scatter(clustered.x, clustered.y, c=clustered.labels, s=0.05, cmap='hsv_r')\n",
        "plt.colorbar()\n",
        "# plt.savefig(\"result1.png\", dpi = 300)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.colorbar.Colorbar at 0x7f08d9299128>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OPD_OftOR-pi"
      },
      "source": [
        "## Prepare results\n",
        "For easier selection, we put the results in a pandas dataframe. Then, *docs_per_label* is created in which all documents within a single cluster are joined. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dp2TdT5z6iWy"
      },
      "source": [
        "docs_df = pd.DataFrame(data, columns=[\"Doc\"])\n",
        "docs_df['Topic'] = cluster.labels_\n",
        "docs_df['Doc_ID'] = range(len(docs_df))\n",
        "docs_per_topic = docs_df.groupby(['Topic'], as_index = False).agg({'Doc': ' '.join})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EtzXjdDlSIPH"
      },
      "source": [
        "## Calculate word importance per topic\n",
        "Calculate the importance of words  in a topic compared to all other topics by considering all documents in a topic to be a single document instead before applying **TF-IDF**. Then, we simply extract the words with the highest values in each cluster as a representative of a topic. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-lZKtdwv818W"
      },
      "source": [
        "tf_idf, count = c_tf_idf(docs_per_topic.Doc.values, m = len(data))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xpQOamfNu5-W",
        "outputId": "e666f69a-3a32-49a0-9bbc-5c55ad6a5b9f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "source": [
        "top_n_words = extract_top_n_words_per_topic(tf_idf, count, docs_per_topic, n=20)\n",
        "topic_sizes = extract_topic_sizes(docs_df); topic_sizes.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Topic</th>\n",
              "      <th>Size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1</td>\n",
              "      <td>8377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>7</td>\n",
              "      <td>1761</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>43</td>\n",
              "      <td>1090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>12</td>\n",
              "      <td>966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>41</td>\n",
              "      <td>705</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>52</td>\n",
              "      <td>516</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>50</td>\n",
              "      <td>507</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>49</td>\n",
              "      <td>500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>35</td>\n",
              "      <td>373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>37</td>\n",
              "      <td>282</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Topic  Size\n",
              "0      -1  8377\n",
              "8       7  1761\n",
              "44     43  1090\n",
              "13     12   966\n",
              "42     41   705\n",
              "53     52   516\n",
              "51     50   507\n",
              "50     49   500\n",
              "36     35   373\n",
              "38     37   282"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uVA0zHMAsH-J",
        "outputId": "4cf4bef5-c93c-4cac-afed-b2cfdf389057",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "source": [
        "top_n_words[7][:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('game', 0.010457064574205876),\n",
              " ('team', 0.009330623698817741),\n",
              " ('hockey', 0.008341477022610098),\n",
              " ('games', 0.006831457696895118),\n",
              " ('players', 0.006753830927421891),\n",
              " ('play', 0.006293209317999615),\n",
              " ('season', 0.006227752030983029),\n",
              " ('baseball', 0.0060984850344868195),\n",
              " ('year', 0.005789161738305711),\n",
              " ('nhl', 0.005736180378958607)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "inIQPln8vCQj"
      },
      "source": [
        "## Topic Reduction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b8MBLqisBezg",
        "outputId": "94e27844-179e-48d7-8da3-99f6ac63ec20",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        }
      },
      "source": [
        "for i in tqdm(range(20)):\n",
        "  # Calculate cosine similarity\n",
        "  similarities = cosine_similarity(tf_idf.T)\n",
        "  np.fill_diagonal(similarities, 0)\n",
        "\n",
        "  # Extract label to merge into and from where\n",
        "  topic_sizes = docs_df.groupby(['Topic']).count().sort_values(\"Doc\", ascending=False).reset_index()\n",
        "  topic_to_merge = topic_sizes.iloc[-1].Topic\n",
        "  topic_to_merge_into = np.argmax(similarities[topic_to_merge + 1]) - 1\n",
        "  \n",
        "  # Adjust topics\n",
        "  docs_df.loc[docs_df.Topic == topic_to_merge, \"Topic\"] = topic_to_merge_into\n",
        "  old_topics = docs_df.sort_values(\"Topic\").Topic.unique()\n",
        "  map_topics = {old_topic: index - 1 for index, old_topic in enumerate(old_topics)}\n",
        "  docs_df.Topic = docs_df.Topic.map(map_topics)\n",
        "  docs_per_topic = docs_df.groupby(['Topic'], as_index = False).agg({'Doc': ' '.join})\n",
        "\n",
        "  # Calculate new topic words\n",
        "  m = len(data)\n",
        "  tf_idf, count = c_tf_idf(docs_per_topic.Doc.values, m)\n",
        "  top_n_words = extract_top_n_words_per_topic(tf_idf, count, docs_per_topic, n=20)\n",
        "\n",
        "topic_sizes = extract_topic_sizes(docs_df); topic_sizes.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 20/20 [02:37<00:00,  7.89s/it]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Topic</th>\n",
              "      <th>Size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1</td>\n",
              "      <td>8377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>1793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>26</td>\n",
              "      <td>1341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5</td>\n",
              "      <td>1001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>25</td>\n",
              "      <td>752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>28</td>\n",
              "      <td>631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>31</td>\n",
              "      <td>516</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>29</td>\n",
              "      <td>507</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>20</td>\n",
              "      <td>413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>21</td>\n",
              "      <td>320</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Topic  Size\n",
              "0      -1  8377\n",
              "4       3  1793\n",
              "27     26  1341\n",
              "6       5  1001\n",
              "26     25   752\n",
              "29     28   631\n",
              "32     31   516\n",
              "30     29   507\n",
              "21     20   413\n",
              "22     21   320"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    }
  ]
}