{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "e9KViDTWeMEE",
      "metadata": {
        "id": "e9KViDTWeMEE"
      },
      "source": [
        "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "kcSuGqjTeOAy",
      "metadata": {
        "id": "kcSuGqjTeOAy"
      },
      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/finance-nlp/03.Word_Sentence_Embeddings.ipynb)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e35e5153",
      "metadata": {
        "id": "e35e5153"
      },
      "source": [
        "# Financial Word and Sentence Embeddings"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "69a14890-bc41-4765-9557-89a969c04d8f",
      "metadata": {
        "id": "69a14890-bc41-4765-9557-89a969c04d8f"
      },
      "source": [
        "# Finance Word and Sentence Embeddings visualization using PCA (Principal Component Analysis)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5f982ae9-0570-4f90-bd30-c9d55d219e5b",
      "metadata": {
        "id": "5f982ae9-0570-4f90-bd30-c9d55d219e5b"
      },
      "source": [
        "Modern NLP models work with a numerical representation of texts and their menaning. For token classification problems (inferring a class for a token, for example Name Entity Recognition) Word Embeddings are required. For sentences, paragraph, document classification - we use Sentence Embeddings.\n",
        "\n",
        "In this notebook, we got token embeddings using Spark NLP Finance Word Embeddings(**bert_embeddings_sec_bert_base**) and using these token embeddings we got sentence embeddings by sparknlp annotator SentenceEmbeddings to get those numerical representations of the semantics of the texts. The result is a 768 embeddings matrix, impossible to process by the human eye.\n",
        "\n",
        "There are many techniques we can use to visualize those embeddings. We are using one of them - Principal Component Analysis, a dimensionality reduction process, carried out by Spark MLLib. Both embeddings have 768 dimensions, so we will reduced this dimensions from **768** to **3** (X, Y, Z) and will use a color for the word / sentence legend."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d07e14ba-b319-4d4a-b38d-5ed93debc5e6",
      "metadata": {
        "id": "d07e14ba-b319-4d4a-b38d-5ed93debc5e6"
      },
      "source": [
        "# Installation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "nRcDPOsDqhkN",
      "metadata": {
        "id": "nRcDPOsDqhkN"
      },
      "outputs": [],
      "source": [
        "! pip install johnsnowlabs"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "YPsbAnNoPt0Z",
      "metadata": {
        "id": "YPsbAnNoPt0Z"
      },
      "source": [
        "## Automatic Installation\n",
        "Using my.johnsnowlabs.com SSO"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "_L-7mLYp3pqr",
      "metadata": {
        "id": "_L-7mLYp3pqr",
        "pycharm": {
          "is_executing": true
        }
      },
      "outputs": [],
      "source": [
        "from johnsnowlabs import nlp, finance\n",
        "\n",
        "# nlp.install(force_browser=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "hsJvn_WWM2GL",
      "metadata": {
        "id": "hsJvn_WWM2GL"
      },
      "source": [
        "## Manual downloading\n",
        "If you are not registered in my.johnsnowlabs.com, you received a license via e-email or you are using Safari, you may need to do a manual update of the license.\n",
        "\n",
        "- Go to my.johnsnowlabs.com\n",
        "- Download your license\n",
        "- Upload it using the following command"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "i57QV3-_P2sQ",
      "metadata": {
        "id": "i57QV3-_P2sQ"
      },
      "outputs": [],
      "source": [
        "from google.colab import files\n",
        "print('Please Upload your John Snow Labs License using the button below')\n",
        "license_keys = files.upload()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "xGgNdFzZP_hQ",
      "metadata": {
        "id": "xGgNdFzZP_hQ"
      },
      "source": [
        "- Install it"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "OfmmPqknP4rR",
      "metadata": {
        "id": "OfmmPqknP4rR"
      },
      "outputs": [],
      "source": [
        "nlp.install()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "DCl5ErZkNNLk",
      "metadata": {
        "id": "DCl5ErZkNNLk"
      },
      "source": [
        "# Starting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "x3jVICoa3pqr",
      "metadata": {
        "id": "x3jVICoa3pqr"
      },
      "outputs": [],
      "source": [
        "spark = nlp.start()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4b7c011d-eac8-4eda-b272-027bab6a8895",
      "metadata": {
        "id": "4b7c011d-eac8-4eda-b272-027bab6a8895"
      },
      "source": [
        "# Get sample text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "XvEU36EbfUZH",
      "metadata": {
        "id": "XvEU36EbfUZH"
      },
      "outputs": [],
      "source": [
        "! pip install -q plotly\n",
        "\n",
        "# Downloading sample datasets.\n",
        "! wget -q https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-workshop/master/finance-nlp/data/finance_pca_samples.csv"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5f411a9b-9744-4f77-bdb9-58702260b5b0",
      "metadata": {
        "id": "5f411a9b-9744-4f77-bdb9-58702260b5b0"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv(\"finance_pca_samples.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3977898f-24e8-43d6-a338-80f3bf8ad2a1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3977898f-24e8-43d6-a338-80f3bf8ad2a1",
        "outputId": "5f8679a4-78ab-4afa-da11-06290fd7abcb",
        "tags": []
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "+--------------------+----------------+\n",
            "|                text|           label|\n",
            "+--------------------+----------------+\n",
            "|I called Huntingt...|        Accounts|\n",
            "|I opened an citi ...|        Accounts|\n",
            "|I have been a lon...|    Credit Cards|\n",
            "|My credit limit w...|    Credit Cards|\n",
            "|I am filing this ...|Credit Reporting|\n",
            "|The Credit Bureau...|Credit Reporting|\n",
            "|I noticed an arti...| Debt Collection|\n",
            "|A bank account wa...| Debt Collection|\n",
            "|I was contacted v...|           Loans|\n",
            "|My husband recent...|           Loans|\n",
            "|I wire transfered...| Money Transfers|\n",
            "|PayPal holds fund...| Money Transfers|\n",
            "|We have requested...|        Mortgage|\n",
            "|I filled out a co...|        Mortgage|\n",
            "+--------------------+----------------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Create spark dataframe\n",
        "sdf = spark.createDataFrame(df)\n",
        "sdf.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5de27fdb-3fc1-4d58-8505-4f02238635b8",
      "metadata": {
        "id": "5de27fdb-3fc1-4d58-8505-4f02238635b8"
      },
      "source": [
        "# Pipeline with Spark NLP and Spark MLLIB"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "YwQjfHcM_a4q",
      "metadata": {
        "id": "YwQjfHcM_a4q"
      },
      "outputs": [],
      "source": [
        "# We defined a generic pipeline for word and sentence embeddings\n",
        "\n",
        "def generic_pipeline():\n",
        "  document_assembler = nlp.DocumentAssembler()\\\n",
        "      .setInputCol(\"text\")\\\n",
        "      .setOutputCol(\"document\")\n",
        "\n",
        "  tokenizer = nlp.Tokenizer()\\\n",
        "      .setInputCols(\"document\")\\\n",
        "      .setOutputCol(\"token\")\n",
        "\n",
        "  word_embeddings = nlp.BertEmbeddings.pretrained(\"bert_embeddings_sec_bert_base\",\"en\")\\\n",
        "      .setInputCols([\"document\", \"token\"])\\\n",
        "      .setOutputCol(\"word_embeddings\")\n",
        "\n",
        "  pipeline = nlp.Pipeline(stages = [\n",
        "      document_assembler,\n",
        "      tokenizer,\n",
        "      word_embeddings\n",
        "  ])\n",
        "\n",
        "  return pipeline\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d751fd45-9810-4b43-b893-0d373e8d4870",
      "metadata": {
        "id": "d751fd45-9810-4b43-b893-0d373e8d4870"
      },
      "source": [
        "## Sentence Embeddings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b990cfd8-576f-4057-a548-0434d39897bd",
      "metadata": {
        "id": "b990cfd8-576f-4057-a548-0434d39897bd",
        "tags": []
      },
      "outputs": [],
      "source": [
        "embeddings_sentence = nlp.SentenceEmbeddings()\\\n",
        "    .setInputCols([\"document\", \"word_embeddings\"])\\\n",
        "    .setOutputCol(\"sentence_embeddings\")\\\n",
        "    .setPoolingStrategy(\"AVERAGE\")\n",
        "# We used sparknlp SentenceEmbeddings anootator to get each sentence embeddings from token embeddings"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fde3b08f-6187-43fd-8d7b-3fe6da054c18",
      "metadata": {
        "id": "fde3b08f-6187-43fd-8d7b-3fe6da054c18"
      },
      "source": [
        "# Custom transform to retrieve the numerical embeddings from Spark NLP and pass it to Spark MLLib"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "Qe4s0BQxkMPU",
      "metadata": {
        "id": "Qe4s0BQxkMPU"
      },
      "outputs": [],
      "source": [
        "from pyspark.sql import DataFrame\n",
        "import pyspark.sql.functions as F\n",
        "import pyspark.sql.types as T\n",
        "import pyspark.sql as SQL\n",
        "from pyspark import keyword_only"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "30cfff3f-487b-4808-8b32-f54fc443777b",
      "metadata": {
        "id": "30cfff3f-487b-4808-8b32-f54fc443777b"
      },
      "outputs": [],
      "source": [
        "# This class extracts the embeddings from the Spark NLP Annotation object\n",
        "# from pyspark import ml as ML\n",
        "class EmbeddingsUDF(\n",
        "    nlp.Transformer, nlp.ML.param.shared.HasInputCol, nlp.ML.param.shared.HasOutputCol,\n",
        "    nlp.ML.util.DefaultParamsReadable, nlp.ML.util.DefaultParamsWritable\n",
        "):\n",
        "    @keyword_only\n",
        "    def __init__(self):\n",
        "        super(EmbeddingsUDF, self).__init__()\n",
        "\n",
        "        def _sum(r):\n",
        "            result = 0.0\n",
        "            for e in r:\n",
        "                result += e\n",
        "            return result\n",
        "\n",
        "        self.udfs = {\n",
        "            'convertToVectorUDF': F.udf(lambda vs: nlp.ML.linalg.Vectors.dense(vs), nlp.ML.linalg.VectorUDT()),\n",
        "            'sumUDF': F.udf(lambda r: _sum(r), T.FloatType())\n",
        "        }\n",
        "\n",
        "    def _transform(self, dataset):\n",
        "\n",
        "        results = dataset.select(\n",
        "            \"*\", F.explode(\"sentence_embeddings.embeddings\").alias(\"embeddings\")\n",
        "        )\n",
        "        results = results.withColumn(\n",
        "            \"features\",\n",
        "            self.udfs['convertToVectorUDF'](F.col(\"embeddings\"))\n",
        "        )\n",
        "        results = results.withColumn(\n",
        "            \"emb_sum\",\n",
        "            self.udfs['sumUDF'](F.col(\"embeddings\"))\n",
        "        )\n",
        "        # Remove those with embeddings all zeroes (so we can calculate cosine distance)\n",
        "        results = results.where(F.col(\"emb_sum\")!=0.0)\n",
        "\n",
        "        return results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5f82e139-1cc3-423d-9d53-c0ccfec835c2",
      "metadata": {
        "id": "5f82e139-1cc3-423d-9d53-c0ccfec835c2"
      },
      "outputs": [],
      "source": [
        "embeddings_for_pca = EmbeddingsUDF()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5213dfcf-61bc-4db2-a8c7-797a318a13f4",
      "metadata": {
        "id": "5213dfcf-61bc-4db2-a8c7-797a318a13f4"
      },
      "outputs": [],
      "source": [
        "DIMENSIONS  = 3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "285ba04a-dbe2-4f53-9aa0-fc66ba783e14",
      "metadata": {
        "id": "285ba04a-dbe2-4f53-9aa0-fc66ba783e14"
      },
      "outputs": [],
      "source": [
        "pca = nlp.ML.feature.PCA(k=DIMENSIONS, inputCol=\"features\", outputCol=\"pca_features\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "82f95669-0f8e-491b-b6d8-33f590aa06cf",
      "metadata": {
        "id": "82f95669-0f8e-491b-b6d8-33f590aa06cf"
      },
      "source": [
        "### Full Spark NLP + Spark MLLib pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c279de78-563f-474c-8d3e-3c72243bb04b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c279de78-563f-474c-8d3e-3c72243bb04b",
        "outputId": "d66eddb3-ba45-42c3-9ade-0f2047c5884c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "bert_embeddings_sec_bert_base download started this may take some time.\n",
            "Approximate size to download 390.4 MB\n",
            "[OK!]\n"
          ]
        }
      ],
      "source": [
        "# We did all process in one pipeline\n",
        "pipeline = nlp.Pipeline().setStages([generic_pipeline(), embeddings_sentence, embeddings_for_pca, pca])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0d46c615-d2b6-408a-99c3-bac0f824dcf4",
      "metadata": {
        "id": "0d46c615-d2b6-408a-99c3-bac0f824dcf4"
      },
      "outputs": [],
      "source": [
        "model = pipeline.fit(sdf)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4d52ebe0-4257-4354-817f-5b8f2a363b8e",
      "metadata": {
        "id": "4d52ebe0-4257-4354-817f-5b8f2a363b8e"
      },
      "outputs": [],
      "source": [
        "result = model.transform(sdf)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7b852cad-e1e7-48bc-94b0-9da7188a9727",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7b852cad-e1e7-48bc-94b0-9da7188a9727",
        "outputId": "4c71cda6-2527-4a36-e6e3-676905a8dc7c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "+--------------------------------------------------------------+----------------+\n",
            "|pca_features                                                  |label           |\n",
            "+--------------------------------------------------------------+----------------+\n",
            "|[3.39576448119276,-1.060361129782475,-1.568794006399417]      |Accounts        |\n",
            "|[2.3660850756971623,0.8591941003552866,-0.8066168807669747]   |Accounts        |\n",
            "|[0.6867735108170906,1.4823947144210112,0.006591220237646302]  |Credit Cards    |\n",
            "|[-0.28834125177427167,1.0031549697755784,-0.7963810505318434] |Credit Cards    |\n",
            "|[-0.5037809008469382,-1.3771583372345915,0.4449701036930799]  |Credit Reporting|\n",
            "|[1.039756950301059,-1.7194174825036457,1.8539366217014026]    |Credit Reporting|\n",
            "|[2.7731701148109815,1.1680247656394984,1.3949448202984454]    |Debt Collection |\n",
            "|[-0.45951034017887454,0.833969250052939,0.5051728405912744]   |Debt Collection |\n",
            "|[0.2703079726928541,1.1069420631113542,-0.4247559623637921]   |Loans           |\n",
            "|[0.8662523064864315,1.1435249671794807,0.8703562689970329]    |Loans           |\n",
            "|[-0.7580966795506656,0.6312432474265479,0.6829074622939197]   |Money Transfers |\n",
            "|[0.38557719496563764,-1.4420990245260328,-0.19825482305628117]|Money Transfers |\n",
            "|[2.45690397730987,0.33025601313067965,1.2981705024775965]     |Mortgage        |\n",
            "|[2.3553279838082126,0.8329467950039564,1.390405767602749]     |Mortgage        |\n",
            "+--------------------------------------------------------------+----------------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "result.select('pca_features', 'label').show(truncate=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "adbf7811-b04c-4f63-88e4-10e51341ce0f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "adbf7811-b04c-4f63-88e4-10e51341ce0f",
        "outputId": "757e141e-3b40-4033-c351-c30597e762ad"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-62ab510f-3264-4bff-8b08-4c50aee52e72\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pca_features</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>[3.39576448119276, -1.060361129782475, -1.5687...</td>\n",
              "      <td>Accounts</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>[2.3660850756971623, 0.8591941003552866, -0.80...</td>\n",
              "      <td>Accounts</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>[0.6867735108170906, 1.4823947144210112, 0.006...</td>\n",
              "      <td>Credit Cards</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>[-0.28834125177427167, 1.0031549697755784, -0....</td>\n",
              "      <td>Credit Cards</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>[-0.5037809008469382, -1.3771583372345915, 0.4...</td>\n",
              "      <td>Credit Reporting</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>[1.039756950301059, -1.7194174825036457, 1.853...</td>\n",
              "      <td>Credit Reporting</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>[2.7731701148109815, 1.1680247656394984, 1.394...</td>\n",
              "      <td>Debt Collection</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>[-0.45951034017887454, 0.833969250052939, 0.50...</td>\n",
              "      <td>Debt Collection</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>[0.2703079726928541, 1.1069420631113542, -0.42...</td>\n",
              "      <td>Loans</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>[0.8662523064864315, 1.1435249671794807, 0.870...</td>\n",
              "      <td>Loans</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>[-0.7580966795506656, 0.6312432474265479, 0.68...</td>\n",
              "      <td>Money Transfers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>[0.38557719496563764, -1.4420990245260328, -0....</td>\n",
              "      <td>Money Transfers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>[2.45690397730987, 0.33025601313067965, 1.2981...</td>\n",
              "      <td>Mortgage</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>[2.3553279838082126, 0.8329467950039564, 1.390...</td>\n",
              "      <td>Mortgage</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-62ab510f-3264-4bff-8b08-4c50aee52e72');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                         pca_features             label\n",
              "0   [3.39576448119276, -1.060361129782475, -1.5687...          Accounts\n",
              "1   [2.3660850756971623, 0.8591941003552866, -0.80...          Accounts\n",
              "2   [0.6867735108170906, 1.4823947144210112, 0.006...      Credit Cards\n",
              "3   [-0.28834125177427167, 1.0031549697755784, -0....      Credit Cards\n",
              "4   [-0.5037809008469382, -1.3771583372345915, 0.4...  Credit Reporting\n",
              "5   [1.039756950301059, -1.7194174825036457, 1.853...  Credit Reporting\n",
              "6   [2.7731701148109815, 1.1680247656394984, 1.394...   Debt Collection\n",
              "7   [-0.45951034017887454, 0.833969250052939, 0.50...   Debt Collection\n",
              "8   [0.2703079726928541, 1.1069420631113542, -0.42...             Loans\n",
              "9   [0.8662523064864315, 1.1435249671794807, 0.870...             Loans\n",
              "10  [-0.7580966795506656, 0.6312432474265479, 0.68...   Money Transfers\n",
              "11  [0.38557719496563764, -1.4420990245260328, -0....   Money Transfers\n",
              "12  [2.45690397730987, 0.33025601313067965, 1.2981...          Mortgage\n",
              "13  [2.3553279838082126, 0.8329467950039564, 1.390...          Mortgage"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = result.select('pca_features', 'label').toPandas()\n",
        "\n",
        "df\n",
        "# As you see, dimension values are inside a list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5c3707de-8ff4-49b7-89c2-85e9616bc337",
      "metadata": {
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          "height": 488
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        "outputId": "8f3f817f-297b-42f9-c914-13f4d0889180"
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>z</th>\n",
              "      <th>label</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3.395764</td>\n",
              "      <td>-1.060361</td>\n",
              "      <td>-1.568794</td>\n",
              "      <td>Accounts</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.366085</td>\n",
              "      <td>0.859194</td>\n",
              "      <td>-0.806617</td>\n",
              "      <td>Accounts</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.686774</td>\n",
              "      <td>1.482395</td>\n",
              "      <td>0.006591</td>\n",
              "      <td>Credit Cards</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.288341</td>\n",
              "      <td>1.003155</td>\n",
              "      <td>-0.796381</td>\n",
              "      <td>Credit Cards</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.503781</td>\n",
              "      <td>-1.377158</td>\n",
              "      <td>0.444970</td>\n",
              "      <td>Credit Reporting</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.039757</td>\n",
              "      <td>-1.719417</td>\n",
              "      <td>1.853937</td>\n",
              "      <td>Credit Reporting</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2.773170</td>\n",
              "      <td>1.168025</td>\n",
              "      <td>1.394945</td>\n",
              "      <td>Debt Collection</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>-0.459510</td>\n",
              "      <td>0.833969</td>\n",
              "      <td>0.505173</td>\n",
              "      <td>Debt Collection</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.270308</td>\n",
              "      <td>1.106942</td>\n",
              "      <td>-0.424756</td>\n",
              "      <td>Loans</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.866252</td>\n",
              "      <td>1.143525</td>\n",
              "      <td>0.870356</td>\n",
              "      <td>Loans</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>-0.758097</td>\n",
              "      <td>0.631243</td>\n",
              "      <td>0.682907</td>\n",
              "      <td>Money Transfers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.385577</td>\n",
              "      <td>-1.442099</td>\n",
              "      <td>-0.198255</td>\n",
              "      <td>Money Transfers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2.456904</td>\n",
              "      <td>0.330256</td>\n",
              "      <td>1.298171</td>\n",
              "      <td>Mortgage</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2.355328</td>\n",
              "      <td>0.832947</td>\n",
              "      <td>1.390406</td>\n",
              "      <td>Mortgage</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-31d1d24a-99bb-4917-88ff-2b613915a604');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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            ],
            "text/plain": [
              "           x         y         z             label\n",
              "0   3.395764 -1.060361 -1.568794          Accounts\n",
              "1   2.366085  0.859194 -0.806617          Accounts\n",
              "2   0.686774  1.482395  0.006591      Credit Cards\n",
              "3  -0.288341  1.003155 -0.796381      Credit Cards\n",
              "4  -0.503781 -1.377158  0.444970  Credit Reporting\n",
              "5   1.039757 -1.719417  1.853937  Credit Reporting\n",
              "6   2.773170  1.168025  1.394945   Debt Collection\n",
              "7  -0.459510  0.833969  0.505173   Debt Collection\n",
              "8   0.270308  1.106942 -0.424756             Loans\n",
              "9   0.866252  1.143525  0.870356             Loans\n",
              "10 -0.758097  0.631243  0.682907   Money Transfers\n",
              "11  0.385577 -1.442099 -0.198255   Money Transfers\n",
              "12  2.456904  0.330256  1.298171          Mortgage\n",
              "13  2.355328  0.832947  1.390406          Mortgage"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# We extract the dimension values out off the list\n",
        "\n",
        "df[\"x\"] = df[\"pca_features\"].apply(lambda x: x[0])\n",
        "\n",
        "df[\"y\"] = df[\"pca_features\"].apply(lambda x: x[1])\n",
        "\n",
        "df[\"z\"] = df[\"pca_features\"].apply(lambda x: x[2])\n",
        "\n",
        "df = df[[\"x\", \"y\", \"z\", \"label\"]]\n",
        "\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7281d091-9356-41a7-83e7-6b5ee541a09c",
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          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "7281d091-9356-41a7-83e7-6b5ee541a09c",
        "outputId": "3972e1b8-6144-46aa-85fc-2afc12dad49b",
        "tags": []
      },
      "outputs": [
        {
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import plotly.express as px\n",
        "\n",
        "fig = px.scatter_3d(df, x = 'x', y = 'y', z = 'z', color = 'label', width=800, height=600)\n",
        "\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "602bc911-284b-4e0e-8882-99ac444fbb51",
      "metadata": {
        "id": "602bc911-284b-4e0e-8882-99ac444fbb51"
      },
      "source": [
        "### Word Embeddings"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "11bfbc63-cb97-4189-8d6c-6629bbc898e8",
      "metadata": {
        "id": "11bfbc63-cb97-4189-8d6c-6629bbc898e8"
      },
      "source": [
        "We can also visualize the semantics of words, instead of full texts, by using Word Embeddings. We will add a Tokenizer and a WordEmbeddings model to get those embeddings, and them apply PCA as before. Firstly we splitted the pipeline in two to get all token embeddings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "28f0cced-e804-4415-aa57-6ae05587adf2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "28f0cced-e804-4415-aa57-6ae05587adf2",
        "outputId": "bbfd73e0-624c-407d-cb4d-8c1f46a1fe8f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "bert_embeddings_sec_bert_base download started this may take some time.\n",
            "Approximate size to download 390.4 MB\n",
            "[OK!]\n"
          ]
        }
      ],
      "source": [
        "model = generic_pipeline().fit(sdf)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "31090faa-0bbe-4e94-8384-e38a338e1989",
      "metadata": {
        "id": "31090faa-0bbe-4e94-8384-e38a338e1989"
      },
      "outputs": [],
      "source": [
        "result = model.transform(sdf)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1815b79d-81c5-45ec-ac80-418e31f9df8e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1815b79d-81c5-45ec-ac80-418e31f9df8e",
        "outputId": "4f50264e-2aad-4fc5-eb4e-175b3b9d4f8a",
        "tags": []
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "+----------+--------+--------------------------------------------------------------------------------+\n",
            "|     token|   label|                                                                      embeddings|\n",
            "+----------+--------+--------------------------------------------------------------------------------+\n",
            "|         I|Accounts|[-0.29679197, 0.80952483, 0.026026089, 0.08434192, 0.7434629, -0.02694758, -0...|\n",
            "|    called|Accounts|[0.28905854, -0.29229686, -0.42990392, -0.3833449, 0.026178285, -0.12728442, ...|\n",
            "|Huntington|Accounts|[0.20684586, -0.010130149, -0.259025, -0.37558293, 0.45792142, 0.3114912, -0....|\n",
            "|      Bank|Accounts|[-0.034710683, 0.46047488, -0.6221113, -0.011169381, 0.2938512, 0.31341088, -...|\n",
            "|        to|Accounts|[-0.40457863, -0.3768647, -0.08015404, -0.58909655, -0.33856544, -0.39321256,...|\n",
            "|     close|Accounts|[0.35089388, 0.9568475, 0.86328286, -0.4334402, 0.11386797, -0.48837784, -0.8...|\n",
            "|        my|Accounts|[-0.36591864, 0.2655603, -0.32495034, -0.5081896, -0.39623818, -0.63347244, -...|\n",
            "|   account|Accounts|[0.004639961, 0.5340125, 0.77567977, 0.23316649, -0.4303767, -0.2937901, -0.5...|\n",
            "|         ,|Accounts|[0.17874305, -0.026907753, 0.19498396, -0.7929611, -0.26044437, -0.3964327, -...|\n",
            "|       and|Accounts|[0.5011346, 0.6637548, 0.15587743, -0.79522926, -0.8198417, -0.24028614, -0.6...|\n",
            "|      they|Accounts|[-0.2188998, 0.17353022, -0.3897713, -0.4219988, -0.66089946, -0.6682683, -0....|\n",
            "|   refused|Accounts|[-0.71534324, 0.4092898, -0.58240926, 0.2768947, -0.7440806, -0.016842518, -0...|\n",
            "|        to|Accounts|[-0.062417023, -0.30230471, 0.17689183, -0.36983997, 0.22308639, -0.20912732,...|\n",
            "|     close|Accounts|[0.49901608, 0.93363476, 0.89050376, -0.20053658, 0.47381917, -0.24397722, -0...|\n",
            "|        my|Accounts|[-0.11864859, 0.068643466, -0.47048938, -0.33866596, -0.1448204, -0.59992373,...|\n",
            "|   account|Accounts|[-0.045060933, 0.55244875, 0.9458424, 0.3263075, -0.26439214, -0.14597315, -0...|\n",
            "|      over|Accounts|[0.19230467, -0.47188944, 0.33582675, 0.008950032, 0.3479425, 0.107840315, -0...|\n",
            "|       the|Accounts|[-0.44176129, -0.17911726, -0.9623183, 0.09716578, 0.19224198, 0.1584882, 0.5...|\n",
            "|     phone|Accounts|[-0.44973916, -0.9114662, -0.06911273, -0.18094938, 0.10837507, -0.8229777, -...|\n",
            "|         .|Accounts|[0.0502675, 0.32013232, 0.22356117, -0.6540274, 0.48769465, -0.81690645, -0.6...|\n",
            "+----------+--------+--------------------------------------------------------------------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "result_df = result.select(\"label\", F.explode(F.arrays_zip(result.token.result, result.word_embeddings.embeddings)).alias(\"cols\"))\\\n",
        "      .select(F.expr(\"cols['0']\").alias(\"token\"),\n",
        "              \"label\",\n",
        "              F.expr(\"cols['1']\").alias(\"embeddings\"))\n",
        "\n",
        "result_df.show(truncate = 80)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6186b5d6-fa75-42db-b5f6-febbe0f6c957",
      "metadata": {
        "id": "6186b5d6-fa75-42db-b5f6-febbe0f6c957"
      },
      "outputs": [],
      "source": [
        "# Here we defined inheritance class from that defined previously EmbeddingsUDF class\n",
        "class WordEmbeddingsUDF(EmbeddingsUDF):    \n",
        "    def _transform(self, dataset):\n",
        "        \n",
        "        results = dataset.select('token', 'label', 'embeddings') # We changed this line because our embedding cloumn is already exploded\n",
        "\n",
        "        results = results.withColumn(\n",
        "            \"features\",\n",
        "            self.udfs['convertToVectorUDF'](F.col(\"embeddings\"))\n",
        "        )\n",
        "        results = results.withColumn(\n",
        "            \"emb_sum\",\n",
        "            self.udfs['sumUDF'](F.col(\"embeddings\"))\n",
        "        )\n",
        "        # Remove those with embeddings all zeroes (so we can calculate cosine distance)\n",
        "        results = results.where(F.col(\"emb_sum\")!=0.0)\n",
        "\n",
        "        return results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e416012e-ba80-4ca8-98de-aa675660ecb1",
      "metadata": {
        "id": "e416012e-ba80-4ca8-98de-aa675660ecb1"
      },
      "outputs": [],
      "source": [
        "embeddings_for_pca = WordEmbeddingsUDF()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0cca3d1f-0261-42de-96f8-f68f7fe6210a",
      "metadata": {
        "id": "0cca3d1f-0261-42de-96f8-f68f7fe6210a"
      },
      "outputs": [],
      "source": [
        "DIMENSIONS  = 3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2f9e30a2-cc23-463f-87d1-4846c7a321e7",
      "metadata": {
        "id": "2f9e30a2-cc23-463f-87d1-4846c7a321e7"
      },
      "outputs": [],
      "source": [
        "pca = nlp.ML.feature.PCA(k=DIMENSIONS, inputCol=\"features\", outputCol=\"pca_features\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4ec00af2-8e16-4473-8b43-4f7cff630a3d",
      "metadata": {
        "id": "4ec00af2-8e16-4473-8b43-4f7cff630a3d"
      },
      "source": [
        "### Full Spark NLP + Spark MLLib pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "69b8590b-2bf4-45b9-924d-d7438b87f08a",
      "metadata": {
        "id": "69b8590b-2bf4-45b9-924d-d7438b87f08a"
      },
      "outputs": [],
      "source": [
        "# We run the second part of the pipeline. Here 768 dimensions is reduced to 3 dimensions\n",
        "\n",
        "pipeline = nlp.Pipeline().setStages([embeddings_for_pca, pca])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8207c543-9fb0-4187-a359-61abad891934",
      "metadata": {
        "id": "8207c543-9fb0-4187-a359-61abad891934",
        "tags": []
      },
      "outputs": [],
      "source": [
        "model = pipeline.fit(result_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cf674445-20d2-4ee9-8409-6a3782794aae",
      "metadata": {
        "id": "cf674445-20d2-4ee9-8409-6a3782794aae"
      },
      "outputs": [],
      "source": [
        "result = model.transform(result_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9093ca0e-e46e-40e8-8958-c47702fc4a43",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9093ca0e-e46e-40e8-8958-c47702fc4a43",
        "outputId": "a697bed3-95af-4997-ba5b-b5c5d894af25",
        "tags": []
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "+----------+--------+------------------------------------------------------------+\n",
            "|     token|   label|                                                pca_features|\n",
            "+----------+--------+------------------------------------------------------------+\n",
            "|         I|Accounts|  [9.850468172808704,0.02182025684995559,1.7128883074588641]|\n",
            "|    called|Accounts|   [0.5703260311955864,0.346658149631252,-2.867726751670609]|\n",
            "|Huntington|Accounts|  [8.635450770647445,0.8802312004740499,-0.8417105564124523]|\n",
            "|      Bank|Accounts| [9.391061503515894,0.45066516018168057,-1.2157436459087525]|\n",
            "|        to|Accounts|  [-2.093784358504493,-1.1261933945050695,4.473374538741789]|\n",
            "|     close|Accounts| [-2.897764751048121,-0.1633032944974737,2.6316552582800594]|\n",
            "|        my|Accounts|    [3.542237747747922,-2.721495573008954,2.847896218683586]|\n",
            "|   account|Accounts|[-1.2533257167247633,0.006480340909400874,1.9023215773218...|\n",
            "|         ,|Accounts|  [-1.371343619695057,0.16043397738672746,2.236148062116737]|\n",
            "|       and|Accounts| [0.2574783722223581,-0.39882523377542006,4.898577649457495]|\n",
            "|      they|Accounts|  [2.649181792582909,-2.0965602813943836,3.0047699978661027]|\n",
            "|   refused|Accounts|   [-1.447842544994814,-3.120728385057716,1.623718089120733]|\n",
            "|        to|Accounts|   [-3.476136836992586,-0.955126757467589,5.927975835938944]|\n",
            "|     close|Accounts|  [-3.22033000871663,-0.09380183797818464,2.502218213385302]|\n",
            "|        my|Accounts|  [3.0773967959997126,-2.732351171853666,3.4638980333557523]|\n",
            "|   account|Accounts|[-1.7590956965611935,0.049200510343874765,2.0855752458205...|\n",
            "|      over|Accounts|  [-0.7852937839017823,-1.0837250583596254,2.77524528481479]|\n",
            "|       the|Accounts| [-1.9152538017789913,-0.5845586090479645,5.449708419677918]|\n",
            "|     phone|Accounts|  [-3.7960455527563335,-2.527243794119921,1.563631667500876]|\n",
            "|         .|Accounts|[-0.04212375650031434,-0.5960727710945473,0.4870793244043...|\n",
            "+----------+--------+------------------------------------------------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "result.select(\"token\", \"label\", \"pca_features\").show(truncate = 60)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2299c9bc-8e8e-4c49-ad29-133df31da543",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "2299c9bc-8e8e-4c49-ad29-133df31da543",
        "outputId": "16b3d720-7c81-4010-8e17-447d492f8330"
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        {
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              "      <th>token</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>I</td>\n",
              "      <td>Accounts</td>\n",
              "      <td>[9.850468172808704, 0.02182025684995559, 1.712...</td>\n",
              "    </tr>\n",
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              "      <th>1</th>\n",
              "      <td>called</td>\n",
              "      <td>Accounts</td>\n",
              "      <td>[0.5703260311955864, 0.346658149631252, -2.867...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Huntington</td>\n",
              "      <td>Accounts</td>\n",
              "      <td>[8.635450770647445, 0.8802312004740499, -0.841...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Bank</td>\n",
              "      <td>Accounts</td>\n",
              "      <td>[9.391061503515894, 0.45066516018168057, -1.21...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>to</td>\n",
              "      <td>Accounts</td>\n",
              "      <td>[-2.093784358504493, -1.1261933945050695, 4.47...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>1364</th>\n",
              "      <td>the</td>\n",
              "      <td>Mortgage</td>\n",
              "      <td>[0.20783178705004846, 1.2121685298369587, 2.34...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1365</th>\n",
              "      <td>company</td>\n",
              "      <td>Mortgage</td>\n",
              "      <td>[0.9758784877952482, 1.1525640123640015, 1.548...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1366</th>\n",
              "      <td>never</td>\n",
              "      <td>Mortgage</td>\n",
              "      <td>[-0.009449827591906173, -1.360506257943843, -0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1367</th>\n",
              "      <td>responds</td>\n",
              "      <td>Mortgage</td>\n",
              "      <td>[-1.3105360623344586, -0.3952000653886483, -1....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1368</th>\n",
              "      <td>.</td>\n",
              "      <td>Mortgage</td>\n",
              "      <td>[1.732371824614684, -14.254692680656397, -4.51...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1369 rows × 3 columns</p>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            "text/plain": [
              "           token     label                                       pca_features\n",
              "0              I  Accounts  [9.850468172808704, 0.02182025684995559, 1.712...\n",
              "1         called  Accounts  [0.5703260311955864, 0.346658149631252, -2.867...\n",
              "2     Huntington  Accounts  [8.635450770647445, 0.8802312004740499, -0.841...\n",
              "3           Bank  Accounts  [9.391061503515894, 0.45066516018168057, -1.21...\n",
              "4             to  Accounts  [-2.093784358504493, -1.1261933945050695, 4.47...\n",
              "...          ...       ...                                                ...\n",
              "1364         the  Mortgage  [0.20783178705004846, 1.2121685298369587, 2.34...\n",
              "1365     company  Mortgage  [0.9758784877952482, 1.1525640123640015, 1.548...\n",
              "1366       never  Mortgage  [-0.009449827591906173, -1.360506257943843, -0...\n",
              "1367    responds  Mortgage  [-1.3105360623344586, -0.3952000653886483, -1....\n",
              "1368           .  Mortgage  [1.732371824614684, -14.254692680656397, -4.51...\n",
              "\n",
              "[1369 rows x 3 columns]"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = result.select('token', 'label', 'pca_features').toPandas()\n",
        "\n",
        "df"
      ]
    },
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      "execution_count": null,
      "id": "20443a2f-9e6e-4cd2-885e-b42720bcb840",
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        {
          "data": {
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              "      <th></th>\n",
              "      <th>token</th>\n",
              "      <th>label</th>\n",
              "      <th>x</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>I</td>\n",
              "      <td>Accounts</td>\n",
              "      <td>9.850468</td>\n",
              "      <td>0.021820</td>\n",
              "      <td>1.712888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>called</td>\n",
              "      <td>Accounts</td>\n",
              "      <td>0.570326</td>\n",
              "      <td>0.346658</td>\n",
              "      <td>-2.867727</td>\n",
              "    </tr>\n",
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              "      <th>2</th>\n",
              "      <td>Huntington</td>\n",
              "      <td>Accounts</td>\n",
              "      <td>8.635451</td>\n",
              "      <td>0.880231</td>\n",
              "      <td>-0.841711</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Bank</td>\n",
              "      <td>Accounts</td>\n",
              "      <td>9.391062</td>\n",
              "      <td>0.450665</td>\n",
              "      <td>-1.215744</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>to</td>\n",
              "      <td>Accounts</td>\n",
              "      <td>-2.093784</td>\n",
              "      <td>-1.126193</td>\n",
              "      <td>4.473375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1364</th>\n",
              "      <td>the</td>\n",
              "      <td>Mortgage</td>\n",
              "      <td>0.207832</td>\n",
              "      <td>1.212169</td>\n",
              "      <td>2.345686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1365</th>\n",
              "      <td>company</td>\n",
              "      <td>Mortgage</td>\n",
              "      <td>0.975878</td>\n",
              "      <td>1.152564</td>\n",
              "      <td>1.548878</td>\n",
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              "    <tr>\n",
              "      <th>1366</th>\n",
              "      <td>never</td>\n",
              "      <td>Mortgage</td>\n",
              "      <td>-0.009450</td>\n",
              "      <td>-1.360506</td>\n",
              "      <td>-0.080957</td>\n",
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              "    <tr>\n",
              "      <th>1367</th>\n",
              "      <td>responds</td>\n",
              "      <td>Mortgage</td>\n",
              "      <td>-1.310536</td>\n",
              "      <td>-0.395200</td>\n",
              "      <td>-1.634091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1368</th>\n",
              "      <td>.</td>\n",
              "      <td>Mortgage</td>\n",
              "      <td>1.732372</td>\n",
              "      <td>-14.254693</td>\n",
              "      <td>-4.517188</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1369 rows × 5 columns</p>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-a57600ec-91ad-42e8-97f1-d7a95d658926');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "           token     label         x          y         z\n",
              "0              I  Accounts  9.850468   0.021820  1.712888\n",
              "1         called  Accounts  0.570326   0.346658 -2.867727\n",
              "2     Huntington  Accounts  8.635451   0.880231 -0.841711\n",
              "3           Bank  Accounts  9.391062   0.450665 -1.215744\n",
              "4             to  Accounts -2.093784  -1.126193  4.473375\n",
              "...          ...       ...       ...        ...       ...\n",
              "1364         the  Mortgage  0.207832   1.212169  2.345686\n",
              "1365     company  Mortgage  0.975878   1.152564  1.548878\n",
              "1366       never  Mortgage -0.009450  -1.360506 -0.080957\n",
              "1367    responds  Mortgage -1.310536  -0.395200 -1.634091\n",
              "1368           .  Mortgage  1.732372 -14.254693 -4.517188\n",
              "\n",
              "[1369 rows x 5 columns]"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[\"x\"] = df[\"pca_features\"].apply(lambda x: x[0])\n",
        "\n",
        "df[\"y\"] = df[\"pca_features\"].apply(lambda x: x[1])\n",
        "\n",
        "df[\"z\"] = df[\"pca_features\"].apply(lambda x: x[2])\n",
        "\n",
        "df = df[[\"token\", \"label\", \"x\", \"y\", \"z\"]]\n",
        "\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e96d9799-e838-4cdc-903e-dbd5cdc37aa9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817
        },
        "id": "e96d9799-e838-4cdc-903e-dbd5cdc37aa9",
        "outputId": "fc9faccb-52b9-4c50-914a-a83f2177fc66",
        "tags": []
      },
      "outputs": [
        {
          "data": {
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              "</body>\n",
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          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import plotly.express as px\n",
        "\n",
        "fig = px.scatter_3d(df, x = 'x', y = 'y', z = 'z', color = \"label\", width=1000, height = 800, hover_data = [\"token\", \"label\"])\n",
        "\n",
        "fig.show()"
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  ],
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    "gpuClass": "standard",
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