Word2vecJavaDP.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "To store all the data and files in a common place we are using Google drive for this project."
      ],
      "metadata": {
        "id": "6U94uZtDZnKi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pbaclLz_bjBY",
        "outputId": "3a7d788f-259a-4529-b7a8-f998b1b08a74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import the stopwords from nltk library. All the necessary library have been imported, genism already has a pre-trained word2vec model that can be imported."
      ],
      "metadata": {
        "id": "CaKQuA08bF1e"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5vqGnihJOtO-",
        "outputId": "3b2ea332-3fe2-4f31-c312-5f3e188325f2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "import gensim\n",
        "from nltk import RegexpTokenizer\n",
        "from nltk.corpus import stopwords\n",
        "from os import listdir\n",
        "from os.path import isfile, join\n",
        "from gensim.models.doc2vec import TaggedDocument\n",
        "import nltk\n",
        "nltk.download('stopwords')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The java files have been modified into .txt files as word2vec only recognise text files. The files have been stored in a folder called Train1 to use it to train the model."
      ],
      "metadata": {
        "id": "7NWHnM0Oap7L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "docLabels = []\n",
        "i=0\n",
        "docLabels = [f for f in listdir(\"/content/drive/MyDrive/Word2vec/Train1\") if \n",
        " f.endswith('.txt')]#create a list data that stores the content of all text files in order of their names in docLabels\n",
        "data = []\n",
        "for doc in docLabels:\n",
        "  i=i+1\n",
        "  data.append(open('/content/drive/MyDrive/Word2vec/Train1/' + doc).read())\n",
        "\n",
        "print(i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EUsaY3qkcHE9",
        "outputId": "b808dae6-5a4f-49f6-ca07-eb6a46233dc4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "140\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = RegexpTokenizer(r'\\w+')\n",
        "stopword_set = set(stopwords.words('english'))\n",
        "def nlp_clean(data):   \n",
        "  new_data = []\n",
        "  for d in data:\n",
        "    new_str = d.lower()\n",
        "    dlist = tokenizer.tokenize(new_str)\n",
        "    dlist = list(set(dlist).difference(stopword_set))\n",
        "    new_data.append(dlist)\n",
        "  return new_data"
      ],
      "metadata": {
        "id": "mo6CWFW5nzne"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class LabeledLineSentence(object):    \n",
        "  def __init__(self, doc_list, labels_list):        \n",
        "    self.labels_list = labels_list\n",
        "    self.doc_list = doc_list   \n",
        "  def __iter__(self):        \n",
        "    for idx, doc in enumerate(self.doc_list):\n",
        "      yield gensim.models.doc2vec.LabeledSentence(doc, [self.labels_list[idx]])"
      ],
      "metadata": {
        "id": "COxe6pECm9jw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = nlp_clean(data)"
      ],
      "metadata": {
        "id": "b99fpwDfpG6-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#iterator returned over all documents\n",
        "it = LabeledLineSentence(data, docLabels)\n",
        "it"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JrYza4-tpK4k",
        "outputId": "262dbf6e-5d9e-4690-f730-5b7237feaa57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<__main__.LabeledLineSentence at 0x7fd4eb2faeb0>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "qBc72HjFalJO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The blow command has been used to train the pre-trained model with new data of Genivi and AndroidAuto which was stored in the Train1 folder."
      ],
      "metadata": {
        "id": "1itQhdj5bhQ3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = gensim.models.Doc2Vec(size=300, min_count=0, alpha=0.025, min_alpha=0.025)\n",
        "model.build_vocab(it)\n",
        "\n",
        "model.train(it,epochs=50,total_examples=140)\n",
        "model.alpha -= 0.002\n",
        "model.min_alpha = model.alpha#saving the created model#\n",
        "model.save('doc2vec.model')\n",
        "#print 'model saved'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D29OJ_OxpOoX",
        "outputId": "19d14e0c-44f3-43b8-ac88-59bb9eef6f05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-ae9e9b1038b7>:7: DeprecationWarning: Call to deprecated `LabeledSentence` (Class will be removed in 4.0.0, use TaggedDocument instead).\n",
            "  yield gensim.models.doc2vec.LabeledSentence(doc, [self.labels_list[idx]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We load the trained model to predict tthe code vector. The link below can be edited to direct to the code you want to extract the embedding."
      ],
      "metadata": {
        "id": "QyvpxqgbaOzx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "d2v_model = gensim.models.doc2vec.Doc2Vec.load('doc2vec.model')\n",
        "docvec = d2v_model.infer_vector('/content/drive/MyDrive/Code2vecJava/code2vec/Test1/Singleton2.java')\n",
        "docvec"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YS44GXZgs9y7",
        "outputId": "d183c50e-5fbb-4df5-cfda-70e419fd4c50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       -1.2145233 , -0.30921605, -0.73161995, -0.1095852 , -0.72100264,\n",
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              "       -0.38399756, -0.13303396,  0.06121431,  1.3002483 , -0.40107566,\n",
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              "        0.05173874, -1.0682836 , -1.451416  , -1.8263096 ,  0.3582217 ,\n",
              "       -1.2959174 ,  0.35086855, -0.16488425,  0.08915466,  0.14961973,\n",
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              "        0.35122216, -0.8297892 , -0.1605545 ,  0.41880345, -0.04513283],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd .."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TJZ45eap9fpD",
        "outputId": "ad9accbb-e1ab-48fb-cf2b-6128ebd1310a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd /content/drive/MyDrive/Word2vec/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_NklcN_29iom",
        "outputId": "831e2d0d-079a-4817-8376-689054e5b237"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Word2vec\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The embeddings have been stored into a csv file."
      ],
      "metadata": {
        "id": "ur7e0vpbfO0v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('word2vecvectors.csv')\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 989
        },
        "id": "wgSIqlAa9nsQ",
        "outputId": "280aed00-33c9-467b-fd9c-6e0118f735ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Singleton     Programs     1     2     3     4     5     6     7     8  \\\n",
              "0           1   Singleton1 -0.10 -0.46 -0.15  0.81 -0.31 -1.18 -2.01  0.13   \n",
              "1           1   Singleton2  0.07 -0.49  0.11  0.86 -0.42 -1.28 -2.03  0.07   \n",
              "2           1   Singleton3 -0.02 -0.62  0.05  1.06 -0.39 -1.57 -2.44  0.23   \n",
              "3           1   Singleton4 -0.02 -0.53  0.06  0.90 -0.45 -1.45 -2.19  0.19   \n",
              "4           1   Singleton5  0.17 -0.54  0.08  1.00 -0.69 -1.52 -2.08  0.10   \n",
              "5           1   Singleton6  0.10 -0.48  0.20  0.82 -0.47 -1.33 -1.97  0.05   \n",
              "6           1   Singleton7  0.03 -0.56  0.06  0.83 -0.48 -1.29 -2.00  0.07   \n",
              "7           1   Singleton8  0.12 -0.41  0.18  0.85 -0.60 -1.31 -1.90 -0.01   \n",
              "8           1   Singleton9  0.11 -0.51  0.06  0.83 -0.49 -1.29 -1.94  0.01   \n",
              "9           1  Singleton10  0.11 -0.37  0.15  0.81 -0.59 -1.22 -1.93 -0.05   \n",
              "10          0   Prototype1  0.26 -0.50  0.14  0.98 -0.76 -1.54 -2.09  0.09   \n",
              "11          0   Prototype2  0.32 -0.35  0.24  0.81 -0.67 -1.18 -1.80 -0.15   \n",
              "12          0   Prototype3  0.08 -0.57  0.18  0.76 -0.59 -1.29 -2.06 -0.06   \n",
              "13          0   Prototype4  0.22 -0.36  0.29  0.85 -0.67 -1.33 -2.00 -0.12   \n",
              "14          0   Prototype5  0.14 -0.47  0.15  0.80 -0.64 -1.36 -2.08 -0.06   \n",
              "15          0   Prototype6  0.15 -0.49  0.18  0.82 -0.62 -1.31 -2.06 -0.08   \n",
              "16          0   Prototype7  0.16 -0.39  0.26  0.80 -0.72 -1.34 -2.08 -0.04   \n",
              "17          0   Prototype8  0.12 -0.50  0.15  0.85 -0.58 -1.42 -2.13 -0.06   \n",
              "18          0   Prototype9  0.23 -0.46  0.18  0.91 -0.68 -1.31 -2.06 -0.06   \n",
              "19          0  Prototype10  0.37 -0.45  0.24  0.98 -0.83 -1.47 -2.05 -0.06   \n",
              "20          2     Builder1  0.26 -0.58  0.03  1.07 -0.75 -1.71 -2.30  0.09   \n",
              "21          2     Builder2  0.23 -0.48  0.10  0.96 -0.60 -1.45 -2.19  0.05   \n",
              "22          2     Builder3  0.11 -0.60  0.04  0.87 -0.56 -1.44 -2.19  0.01   \n",
              "23          2     Builder4  0.20 -0.44  0.15  0.92 -0.68 -1.47 -2.11 -0.06   \n",
              "24          2     Builder5  0.17 -0.54  0.01  0.87 -0.61 -1.46 -2.15 -0.02   \n",
              "25          2     Builder6  0.18 -0.55  0.06  0.90 -0.60 -1.43 -2.15 -0.05   \n",
              "26          2     Builder7  0.19 -0.46  0.09  0.88 -0.64 -1.44 -2.15  0.01   \n",
              "\n",
              "    ...   291   292   293   294   295   296   297   298   299   300  \n",
              "0   ...  0.74  0.95  0.45  0.06  0.98  0.07  0.10  1.05 -0.62 -1.42  \n",
              "1   ...  0.75  0.83  0.22  0.37  1.04  0.04  0.00  1.11 -0.29 -1.47  \n",
              "2   ...  0.79  0.90  0.26  0.41  1.16  0.04  0.13  1.24 -0.37 -1.68  \n",
              "3   ...  0.68  0.90  0.12  0.40  1.26  0.01  0.10  1.18 -0.35 -1.59  \n",
              "4   ...  0.72  0.98 -0.17  0.49  1.33 -0.14  0.05  1.29 -0.25 -1.64  \n",
              "5   ...  0.70  0.83  0.09  0.43  1.09 -0.07  0.01  1.10 -0.23 -1.50  \n",
              "6   ...  0.73  0.83  0.19  0.39  1.18 -0.02  0.10  1.00 -0.33 -1.44  \n",
              "7   ...  0.70  0.95 -0.02  0.42  1.17 -0.04 -0.03  1.23 -0.27 -1.51  \n",
              "8   ...  0.69  0.86  0.11  0.33  1.20  0.03  0.02  1.14 -0.28 -1.34  \n",
              "9   ...  0.78  0.98  0.05  0.39  1.24  0.06 -0.09  1.28 -0.35 -1.64  \n",
              "10  ...  0.79  1.07 -0.17  0.50  1.23 -0.27  0.05  1.24 -0.24 -1.83  \n",
              "11  ...  0.85  0.96  0.03  0.42  1.02 -0.04 -0.12  1.20 -0.24 -1.65  \n",
              "12  ...  0.83  0.93  0.38  0.41  1.17 -0.09  0.03  0.87 -0.35 -1.60  \n",
              "13  ...  0.85  1.07  0.09  0.43  1.07 -0.02 -0.13  1.27 -0.32 -1.75  \n",
              "14  ...  0.75  1.00  0.18  0.36  1.22  0.04 -0.03  1.16 -0.35 -1.54  \n",
              "15  ...  0.80  0.98  0.19  0.40  1.20  0.04 -0.04  1.16 -0.28 -1.50  \n",
              "16  ...  0.75  1.06  0.20  0.40  1.11  0.03 -0.16  1.15 -0.35 -1.67  \n",
              "17  ...  0.77  0.96  0.14  0.38  1.21  0.05 -0.01  1.22 -0.36 -1.56  \n",
              "18  ...  0.88  1.05  0.24  0.42  1.07 -0.03 -0.13  1.12 -0.28 -1.62  \n",
              "19  ...  0.89  1.11 -0.16  0.52  1.25 -0.19 -0.07  1.34 -0.24 -1.95  \n",
              "20  ...  0.85  1.17 -0.22  0.42  1.44 -0.30  0.17  1.38 -0.37 -1.95  \n",
              "21  ...  0.91  1.10  0.07  0.36  1.17 -0.13  0.05  1.27 -0.40 -1.88  \n",
              "22  ...  0.86  1.03  0.22  0.31  1.29 -0.18  0.17  1.04 -0.46 -1.72  \n",
              "23  ...  0.83  1.14 -0.02  0.36  1.28 -0.14  0.03  1.33 -0.40 -1.78  \n",
              "24  ...  0.79  1.08  0.08  0.26  1.37 -0.09  0.11  1.23 -0.43 -1.61  \n",
              "25  ...  0.84  1.07  0.10  0.31  1.35 -0.10  0.10  1.23 -0.37 -1.60  \n",
              "26  ...  0.80  1.11  0.10  0.29  1.25 -0.10  0.02  1.23 -0.43 -1.73  \n",
              "\n",
              "[27 rows x 302 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Singleton</th>\n",
              "      <th>Programs</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
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              "      <th>7</th>\n",
              "      <th>8</th>\n",
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              "      <th>291</th>\n",
              "      <th>292</th>\n",
              "      <th>293</th>\n",
              "      <th>294</th>\n",
              "      <th>295</th>\n",
              "      <th>296</th>\n",
              "      <th>297</th>\n",
              "      <th>298</th>\n",
              "      <th>299</th>\n",
              "      <th>300</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Singleton1</td>\n",
              "      <td>-0.10</td>\n",
              "      <td>-0.46</td>\n",
              "      <td>-0.15</td>\n",
              "      <td>0.81</td>\n",
              "      <td>-0.31</td>\n",
              "      <td>-1.18</td>\n",
              "      <td>-2.01</td>\n",
              "      <td>0.13</td>\n",
              "      <td>...</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.10</td>\n",
              "      <td>1.05</td>\n",
              "      <td>-0.62</td>\n",
              "      <td>-1.42</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Singleton2</td>\n",
              "      <td>0.07</td>\n",
              "      <td>-0.49</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.86</td>\n",
              "      <td>-0.42</td>\n",
              "      <td>-1.28</td>\n",
              "      <td>-2.03</td>\n",
              "      <td>0.07</td>\n",
              "      <td>...</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.37</td>\n",
              "      <td>1.04</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.11</td>\n",
              "      <td>-0.29</td>\n",
              "      <td>-1.47</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Singleton3</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>-0.62</td>\n",
              "      <td>0.05</td>\n",
              "      <td>1.06</td>\n",
              "      <td>-0.39</td>\n",
              "      <td>-1.57</td>\n",
              "      <td>-2.44</td>\n",
              "      <td>0.23</td>\n",
              "      <td>...</td>\n",
              "      <td>0.79</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.26</td>\n",
              "      <td>0.41</td>\n",
              "      <td>1.16</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.13</td>\n",
              "      <td>1.24</td>\n",
              "      <td>-0.37</td>\n",
              "      <td>-1.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>Singleton4</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>-0.53</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.90</td>\n",
              "      <td>-0.45</td>\n",
              "      <td>-1.45</td>\n",
              "      <td>-2.19</td>\n",
              "      <td>0.19</td>\n",
              "      <td>...</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.40</td>\n",
              "      <td>1.26</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.10</td>\n",
              "      <td>1.18</td>\n",
              "      <td>-0.35</td>\n",
              "      <td>-1.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>Singleton5</td>\n",
              "      <td>0.17</td>\n",
              "      <td>-0.54</td>\n",
              "      <td>0.08</td>\n",
              "      <td>1.00</td>\n",
              "      <td>-0.69</td>\n",
              "      <td>-1.52</td>\n",
              "      <td>-2.08</td>\n",
              "      <td>0.10</td>\n",
              "      <td>...</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.98</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>0.49</td>\n",
              "      <td>1.33</td>\n",
              "      <td>-0.14</td>\n",
              "      <td>0.05</td>\n",
              "      <td>1.29</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>-1.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>Singleton6</td>\n",
              "      <td>0.10</td>\n",
              "      <td>-0.48</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.82</td>\n",
              "      <td>-0.47</td>\n",
              "      <td>-1.33</td>\n",
              "      <td>-1.97</td>\n",
              "      <td>0.05</td>\n",
              "      <td>...</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.43</td>\n",
              "      <td>1.09</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>0.01</td>\n",
              "      <td>1.10</td>\n",
              "      <td>-0.23</td>\n",
              "      <td>-1.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>Singleton7</td>\n",
              "      <td>0.03</td>\n",
              "      <td>-0.56</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.83</td>\n",
              "      <td>-0.48</td>\n",
              "      <td>-1.29</td>\n",
              "      <td>-2.00</td>\n",
              "      <td>0.07</td>\n",
              "      <td>...</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.18</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.10</td>\n",
              "      <td>1.00</td>\n",
              "      <td>-0.33</td>\n",
              "      <td>-1.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>Singleton8</td>\n",
              "      <td>0.12</td>\n",
              "      <td>-0.41</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.85</td>\n",
              "      <td>-0.60</td>\n",
              "      <td>-1.31</td>\n",
              "      <td>-1.90</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>...</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.95</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.42</td>\n",
              "      <td>1.17</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>1.23</td>\n",
              "      <td>-0.27</td>\n",
              "      <td>-1.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>Singleton9</td>\n",
              "      <td>0.11</td>\n",
              "      <td>-0.51</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.83</td>\n",
              "      <td>-0.49</td>\n",
              "      <td>-1.29</td>\n",
              "      <td>-1.94</td>\n",
              "      <td>0.01</td>\n",
              "      <td>...</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.86</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.33</td>\n",
              "      <td>1.20</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.02</td>\n",
              "      <td>1.14</td>\n",
              "      <td>-0.28</td>\n",
              "      <td>-1.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>Singleton10</td>\n",
              "      <td>0.11</td>\n",
              "      <td>-0.37</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.81</td>\n",
              "      <td>-0.59</td>\n",
              "      <td>-1.22</td>\n",
              "      <td>-1.93</td>\n",
              "      <td>-0.05</td>\n",
              "      <td>...</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.24</td>\n",
              "      <td>0.06</td>\n",
              "      <td>-0.09</td>\n",
              "      <td>1.28</td>\n",
              "      <td>-0.35</td>\n",
              "      <td>-1.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0</td>\n",
              "      <td>Prototype1</td>\n",
              "      <td>0.26</td>\n",
              "      <td>-0.50</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.98</td>\n",
              "      <td>-0.76</td>\n",
              "      <td>-1.54</td>\n",
              "      <td>-2.09</td>\n",
              "      <td>0.09</td>\n",
              "      <td>...</td>\n",
              "      <td>0.79</td>\n",
              "      <td>1.07</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.23</td>\n",
              "      <td>-0.27</td>\n",
              "      <td>0.05</td>\n",
              "      <td>1.24</td>\n",
              "      <td>-0.24</td>\n",
              "      <td>-1.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0</td>\n",
              "      <td>Prototype2</td>\n",
              "      <td>0.32</td>\n",
              "      <td>-0.35</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.81</td>\n",
              "      <td>-0.67</td>\n",
              "      <td>-1.18</td>\n",
              "      <td>-1.80</td>\n",
              "      <td>-0.15</td>\n",
              "      <td>...</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.42</td>\n",
              "      <td>1.02</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>-0.12</td>\n",
              "      <td>1.20</td>\n",
              "      <td>-0.24</td>\n",
              "      <td>-1.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0</td>\n",
              "      <td>Prototype3</td>\n",
              "      <td>0.08</td>\n",
              "      <td>-0.57</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.76</td>\n",
              "      <td>-0.59</td>\n",
              "      <td>-1.29</td>\n",
              "      <td>-2.06</td>\n",
              "      <td>-0.06</td>\n",
              "      <td>...</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0.41</td>\n",
              "      <td>1.17</td>\n",
              "      <td>-0.09</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.87</td>\n",
              "      <td>-0.35</td>\n",
              "      <td>-1.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0</td>\n",
              "      <td>Prototype4</td>\n",
              "      <td>0.22</td>\n",
              "      <td>-0.36</td>\n",
              "      <td>0.29</td>\n",
              "      <td>0.85</td>\n",
              "      <td>-0.67</td>\n",
              "      <td>-1.33</td>\n",
              "      <td>-2.00</td>\n",
              "      <td>-0.12</td>\n",
              "      <td>...</td>\n",
              "      <td>0.85</td>\n",
              "      <td>1.07</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.43</td>\n",
              "      <td>1.07</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>-0.13</td>\n",
              "      <td>1.27</td>\n",
              "      <td>-0.32</td>\n",
              "      <td>-1.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0</td>\n",
              "      <td>Prototype5</td>\n",
              "      <td>0.14</td>\n",
              "      <td>-0.47</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.80</td>\n",
              "      <td>-0.64</td>\n",
              "      <td>-1.36</td>\n",
              "      <td>-2.08</td>\n",
              "      <td>-0.06</td>\n",
              "      <td>...</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.36</td>\n",
              "      <td>1.22</td>\n",
              "      <td>0.04</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>1.16</td>\n",
              "      <td>-0.35</td>\n",
              "      <td>-1.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0</td>\n",
              "      <td>Prototype6</td>\n",
              "      <td>0.15</td>\n",
              "      <td>-0.49</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.82</td>\n",
              "      <td>-0.62</td>\n",
              "      <td>-1.31</td>\n",
              "      <td>-2.06</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>...</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.40</td>\n",
              "      <td>1.20</td>\n",
              "      <td>0.04</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>1.16</td>\n",
              "      <td>-0.28</td>\n",
              "      <td>-1.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0</td>\n",
              "      <td>Prototype7</td>\n",
              "      <td>0.16</td>\n",
              "      <td>-0.39</td>\n",
              "      <td>0.26</td>\n",
              "      <td>0.80</td>\n",
              "      <td>-0.72</td>\n",
              "      <td>-1.34</td>\n",
              "      <td>-2.08</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>...</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.06</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.40</td>\n",
              "      <td>1.11</td>\n",
              "      <td>0.03</td>\n",
              "      <td>-0.16</td>\n",
              "      <td>1.15</td>\n",
              "      <td>-0.35</td>\n",
              "      <td>-1.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0</td>\n",
              "      <td>Prototype8</td>\n",
              "      <td>0.12</td>\n",
              "      <td>-0.50</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.85</td>\n",
              "      <td>-0.58</td>\n",
              "      <td>-1.42</td>\n",
              "      <td>-2.13</td>\n",
              "      <td>-0.06</td>\n",
              "      <td>...</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.38</td>\n",
              "      <td>1.21</td>\n",
              "      <td>0.05</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>1.22</td>\n",
              "      <td>-0.36</td>\n",
              "      <td>-1.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0</td>\n",
              "      <td>Prototype9</td>\n",
              "      <td>0.23</td>\n",
              "      <td>-0.46</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.91</td>\n",
              "      <td>-0.68</td>\n",
              "      <td>-1.31</td>\n",
              "      <td>-2.06</td>\n",
              "      <td>-0.06</td>\n",
              "      <td>...</td>\n",
              "      <td>0.88</td>\n",
              "      <td>1.05</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.42</td>\n",
              "      <td>1.07</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>-0.13</td>\n",
              "      <td>1.12</td>\n",
              "      <td>-0.28</td>\n",
              "      <td>-1.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0</td>\n",
              "      <td>Prototype10</td>\n",
              "      <td>0.37</td>\n",
              "      <td>-0.45</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.98</td>\n",
              "      <td>-0.83</td>\n",
              "      <td>-1.47</td>\n",
              "      <td>-2.05</td>\n",
              "      <td>-0.06</td>\n",
              "      <td>...</td>\n",
              "      <td>0.89</td>\n",
              "      <td>1.11</td>\n",
              "      <td>-0.16</td>\n",
              "      <td>0.52</td>\n",
              "      <td>1.25</td>\n",
              "      <td>-0.19</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>1.34</td>\n",
              "      <td>-0.24</td>\n",
              "      <td>-1.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2</td>\n",
              "      <td>Builder1</td>\n",
              "      <td>0.26</td>\n",
              "      <td>-0.58</td>\n",
              "      <td>0.03</td>\n",
              "      <td>1.07</td>\n",
              "      <td>-0.75</td>\n",
              "      <td>-1.71</td>\n",
              "      <td>-2.30</td>\n",
              "      <td>0.09</td>\n",
              "      <td>...</td>\n",
              "      <td>0.85</td>\n",
              "      <td>1.17</td>\n",
              "      <td>-0.22</td>\n",
              "      <td>0.42</td>\n",
              "      <td>1.44</td>\n",
              "      <td>-0.30</td>\n",
              "      <td>0.17</td>\n",
              "      <td>1.38</td>\n",
              "      <td>-0.37</td>\n",
              "      <td>-1.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2</td>\n",
              "      <td>Builder2</td>\n",
              "      <td>0.23</td>\n",
              "      <td>-0.48</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.96</td>\n",
              "      <td>-0.60</td>\n",
              "      <td>-1.45</td>\n",
              "      <td>-2.19</td>\n",
              "      <td>0.05</td>\n",
              "      <td>...</td>\n",
              "      <td>0.91</td>\n",
              "      <td>1.10</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.36</td>\n",
              "      <td>1.17</td>\n",
              "      <td>-0.13</td>\n",
              "      <td>0.05</td>\n",
              "      <td>1.27</td>\n",
              "      <td>-0.40</td>\n",
              "      <td>-1.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2</td>\n",
              "      <td>Builder3</td>\n",
              "      <td>0.11</td>\n",
              "      <td>-0.60</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.87</td>\n",
              "      <td>-0.56</td>\n",
              "      <td>-1.44</td>\n",
              "      <td>-2.19</td>\n",
              "      <td>0.01</td>\n",
              "      <td>...</td>\n",
              "      <td>0.86</td>\n",
              "      <td>1.03</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.31</td>\n",
              "      <td>1.29</td>\n",
              "      <td>-0.18</td>\n",
              "      <td>0.17</td>\n",
              "      <td>1.04</td>\n",
              "      <td>-0.46</td>\n",
              "      <td>-1.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2</td>\n",
              "      <td>Builder4</td>\n",
              "      <td>0.20</td>\n",
              "      <td>-0.44</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.92</td>\n",
              "      <td>-0.68</td>\n",
              "      <td>-1.47</td>\n",
              "      <td>-2.11</td>\n",
              "      <td>-0.06</td>\n",
              "      <td>...</td>\n",
              "      <td>0.83</td>\n",
              "      <td>1.14</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.36</td>\n",
              "      <td>1.28</td>\n",
              "      <td>-0.14</td>\n",
              "      <td>0.03</td>\n",
              "      <td>1.33</td>\n",
              "      <td>-0.40</td>\n",
              "      <td>-1.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2</td>\n",
              "      <td>Builder5</td>\n",
              "      <td>0.17</td>\n",
              "      <td>-0.54</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.87</td>\n",
              "      <td>-0.61</td>\n",
              "      <td>-1.46</td>\n",
              "      <td>-2.15</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>...</td>\n",
              "      <td>0.79</td>\n",
              "      <td>1.08</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.37</td>\n",
              "      <td>-0.09</td>\n",
              "      <td>0.11</td>\n",
              "      <td>1.23</td>\n",
              "      <td>-0.43</td>\n",
              "      <td>-1.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>2</td>\n",
              "      <td>Builder6</td>\n",
              "      <td>0.18</td>\n",
              "      <td>-0.55</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.90</td>\n",
              "      <td>-0.60</td>\n",
              "      <td>-1.43</td>\n",
              "      <td>-2.15</td>\n",
              "      <td>-0.05</td>\n",
              "      <td>...</td>\n",
              "      <td>0.84</td>\n",
              "      <td>1.07</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.31</td>\n",
              "      <td>1.35</td>\n",
              "      <td>-0.10</td>\n",
              "      <td>0.10</td>\n",
              "      <td>1.23</td>\n",
              "      <td>-0.37</td>\n",
              "      <td>-1.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>2</td>\n",
              "      <td>Builder7</td>\n",
              "      <td>0.19</td>\n",
              "      <td>-0.46</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.88</td>\n",
              "      <td>-0.64</td>\n",
              "      <td>-1.44</td>\n",
              "      <td>-2.15</td>\n",
              "      <td>0.01</td>\n",
              "      <td>...</td>\n",
              "      <td>0.80</td>\n",
              "      <td>1.11</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.29</td>\n",
              "      <td>1.25</td>\n",
              "      <td>-0.10</td>\n",
              "      <td>0.02</td>\n",
              "      <td>1.23</td>\n",
              "      <td>-0.43</td>\n",
              "      <td>-1.73</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>27 rows × 302 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a78bd4ae-b628-481c-8855-43e21587e3f5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
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              "            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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dff=df\n",
        "x=df.drop(columns=[\"Programs\"])\n",
        "x\n",
        "\n",
        "arr=x.to_numpy()\n",
        "arr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tPVz8hyy91jH",
        "outputId": "b71024c0-fdc0-4d94-f570-0cd5db05d714"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.  , -0.1 , -0.46, ...,  1.05, -0.62, -1.42],\n",
              "       [ 1.  ,  0.07, -0.49, ...,  1.11, -0.29, -1.47],\n",
              "       [ 1.  , -0.02, -0.62, ...,  1.24, -0.37, -1.68],\n",
              "       ...,\n",
              "       [ 2.  ,  0.17, -0.54, ...,  1.23, -0.43, -1.61],\n",
              "       [ 2.  ,  0.18, -0.55, ...,  1.23, -0.37, -1.6 ],\n",
              "       [ 2.  ,  0.19, -0.46, ...,  1.23, -0.43, -1.73]])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "TSNE has been used for dimensity reduction and plotting"
      ],
      "metadata": {
        "id": "7GBSm8SafXN3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.manifold import TSNE\n",
        "\n",
        "# Convert to a list of lists of floats\n",
        "\n",
        "\n",
        "# Create a t-SNE model and transform the data\n",
        "tsne = TSNE(n_components=2, \n",
        "            perplexity=15, \n",
        "            random_state=42, \n",
        "            init='random', \n",
        "            learning_rate=200)\n",
        "\n",
        "vis_dims = tsne.fit_transform(arr)\n",
        "vis_dims.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_zm_2BYN95cV",
        "outputId": "cbee681d-cd77-400f-f38c-9ebf1929ebe1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(27, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import matplotlib\n",
        "import numpy as np\n",
        "\n",
        "colors = [\"red\", \"darkgreen\",\"grey\"]\n",
        "x = [x for x,y in vis_dims]\n",
        "y = [y for x,y in vis_dims]\n",
        "rndperm = np.random.permutation(df.shape[0])\n",
        "color_indices = df['Programs']\n",
        "colormap = matplotlib.colors.ListedColormap(colors)\n",
        "plt.scatter(x, y,c=df[\"Singleton\"], cmap=colormap, alpha=0.3)\n",
        "for score in [0,1]:\n",
        "  color = colors[score]\n",
        "plt.title(\"Code framents embeddings\")\n",
        "\n",
        "# zip joins x and y coordinates in pairs\n",
        "iIndex = 0\n",
        "for x,y in vis_dims:\n",
        "\n",
        "    label = df.iloc[iIndex].Programs\n",
        "    iIndex +=1\n",
        "\n",
        "    plt.annotate(label, # this is the text\n",
        "                 (x,y), # these are the coordinates to position the label\n",
        "                 textcoords=\"offset points\", # how to position the text\n",
        "                 xytext=(0,-10), # distance from text to points (x,y)\n",
        "                 ha='center') # horizontal align"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "JS10v7Kn98xm",
        "outputId": "12754644-9383-47bd-e58b-53e47c5d861b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The reduced dimention has been stored in a dataframe along with their classes Prototype, singelton or builder."
      ],
      "metadata": {
        "id": "pVIf0VGqfgLL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "d_array=pd.DataFrame(vis_dims, columns=['x','y'])\n",
        "d_array['class'] =df['Singleton']\n",
        "d_array"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 896
        },
        "id": "09M9zQzEIT1s",
        "outputId": "300a6dba-40a1-46dd-8c3e-b9ceafa6afc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             x           y  class\n",
              "0    22.079842  229.654770      1\n",
              "1   -40.886993  153.506409      1\n",
              "2  -115.291321  237.698410      1\n",
              "3  -135.640396  175.805084      1\n",
              "4  -184.895294   83.728233      1\n",
              "5   -25.379967   85.123169      1\n",
              "6    22.874655  127.779892      1\n",
              "7   -57.906639   29.290556      1\n",
              "8   -90.547890  104.021683      1\n",
              "9   -12.643028  -21.055870      1\n",
              "10    7.532873 -135.079544      0\n",
              "11  159.895386 -132.741287      0\n",
              "12  189.775955   85.936409      0\n",
              "13  221.370300 -100.434464      0\n",
              "14  151.922058   14.754801      0\n",
              "15  109.545845   56.122917      0\n",
              "16  226.676880  -15.843916      0\n",
              "17   89.557854   -8.698396      0\n",
              "18  162.528366  -52.843426      0\n",
              "19   70.761818 -143.614090      0\n",
              "20 -255.616058   15.715817      2\n",
              "21 -105.929550 -130.964310      2\n",
              "22 -241.481064  -94.465004      2\n",
              "23 -169.860855 -124.623108      2\n",
              "24 -184.225250  -48.536850      2\n",
              "25 -144.579041   -8.200784      2\n",
              "26 -106.221657  -65.580116      2"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-83e392e6-4c44-4dc9-a505-bcdf726df49e\">\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>x</th>\n",
              "      <th>y</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>22.079842</td>\n",
              "      <td>229.654770</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-40.886993</td>\n",
              "      <td>153.506409</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-115.291321</td>\n",
              "      <td>237.698410</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-135.640396</td>\n",
              "      <td>175.805084</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-184.895294</td>\n",
              "      <td>83.728233</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-25.379967</td>\n",
              "      <td>85.123169</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>22.874655</td>\n",
              "      <td>127.779892</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>-57.906639</td>\n",
              "      <td>29.290556</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>-90.547890</td>\n",
              "      <td>104.021683</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>-12.643028</td>\n",
              "      <td>-21.055870</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>7.532873</td>\n",
              "      <td>-135.079544</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>159.895386</td>\n",
              "      <td>-132.741287</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>189.775955</td>\n",
              "      <td>85.936409</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>221.370300</td>\n",
              "      <td>-100.434464</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>151.922058</td>\n",
              "      <td>14.754801</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>109.545845</td>\n",
              "      <td>56.122917</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>226.676880</td>\n",
              "      <td>-15.843916</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>89.557854</td>\n",
              "      <td>-8.698396</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>162.528366</td>\n",
              "      <td>-52.843426</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>70.761818</td>\n",
              "      <td>-143.614090</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>-255.616058</td>\n",
              "      <td>15.715817</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>-105.929550</td>\n",
              "      <td>-130.964310</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>-241.481064</td>\n",
              "      <td>-94.465004</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>-169.860855</td>\n",
              "      <td>-124.623108</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>-184.225250</td>\n",
              "      <td>-48.536850</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>-144.579041</td>\n",
              "      <td>-8.200784</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>-106.221657</td>\n",
              "      <td>-65.580116</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-83e392e6-4c44-4dc9-a505-bcdf726df49e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  <style>\n",
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              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-83e392e6-4c44-4dc9-a505-bcdf726df49e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-83e392e6-4c44-4dc9-a505-bcdf726df49e');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This chunk of code has been used to perform clustering. Code snippets below are similar but have been used to show the clusters in different forms."
      ],
      "metadata": {
        "id": "cM89Np3Yfkeu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from copy import deepcopy\n",
        "# Getting the values and plotting it\n",
        "f1 = d_array['x'].values\n",
        "f2 = d_array['y'].values\n",
        "X = np.array(list(zip(f1, f2)))\n",
        "#plt.figure(8,5)\n",
        "plt.scatter(f1, f2, c='black', s=7)\n",
        "plt.show()\n",
        "def dist(a, b, ax=1):\n",
        "    return np.linalg.norm(a - b, axis=ax)\n",
        "\n",
        "k = 3\n",
        "# X coordinates of random centroids\n",
        "C_x = np.random.randint(0, np.max(X)-20, size=k)\n",
        "# Y coordinates of random centroids\n",
        "C_y = np.random.randint(0, np.max(X)-20, size=k)\n",
        "C = np.array(list(zip(C_x, C_y)), dtype=np.float32)\n",
        "print(C)\n",
        "\n",
        "# To store the value of centroids when it updates\n",
        "C_old = np.zeros(C.shape)\n",
        "# Cluster Lables(0, 1, 2)\n",
        "clusters = np.zeros(len(X))\n",
        "# Error func. - Distance between new centroids and old centroids\n",
        "error = dist(C, C_old, None)\n",
        "# Loop will run till the error becomes zero\n",
        "while error != 0:\n",
        "    # Assigning each value to its closest cluster\n",
        "    for i in range(len(X)):\n",
        "        distances = dist(X[i], C)\n",
        "        cluster = np.argmin(distances)\n",
        "        clusters[i] = cluster\n",
        "    # Storing the old centroid values\n",
        "    C_old = deepcopy(C)\n",
        "    # Finding the new centroids by taking the average value\n",
        "    for i in range(k):\n",
        "        points = [X[j] for j in range(len(X)) if clusters[j] == i]\n",
        "        C[i] = np.mean(points, axis=0)\n",
        "    error = dist(C, C_old, None)\n",
        "\n",
        "colors = ['r', 'g', 'b', 'y', 'c', 'm']\n",
        "fig, ax = plt.subplots()\n",
        "for i in range(k):\n",
        "        points = np.array([X[j] for j in range(len(X)) if clusters[j] == i])\n",
        "        ax.scatter(points[:, 0], points[:, 1], s=20, c=colors[i])\n",
        "ax.scatter(C[:, 0], C[:, 1], s=100, c='#050505')\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 914
        },
        "id": "dHEo6t6JIkEs",
        "outputId": "5076b0e4-7ae3-4059-cc62-b143b067e698"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[151.  64.]\n",
            " [ 58.  61.]\n",
            " [163.  37.]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f5a30268b80>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.spatial import ConvexHull\n",
        "\n",
        "# Getting the values and plotting it\n",
        "f1 = d_array['x'].values\n",
        "f2 = d_array['y'].values\n",
        "X = np.array(list(zip(f1, f2)))\n",
        "plt.scatter(f1, f2, c='black', s=7)\n",
        "\n",
        "k = 3\n",
        "# X coordinates of random centroids\n",
        "C_x = np.random.randint(0, np.max(X)-20, size=k)\n",
        "# Y coordinates of random centroids\n",
        "C_y = np.random.randint(0, np.max(X)-20, size=k)\n",
        "C = np.array(list(zip(C_x, C_y)), dtype=np.float32)\n",
        "\n",
        "# To store the value of centroids when it updates\n",
        "C_old = np.zeros(C.shape)\n",
        "# Cluster Lables(0, 1, 2)\n",
        "clusters = np.zeros(len(X))\n",
        "# Error func. - Distance between new centroids and old centroids\n",
        "error = dist(C, C_old, None)\n",
        "# Loop will run till the error becomes zero\n",
        "while error != 0:\n",
        "    # Assigning each value to its closest cluster\n",
        "    for i in range(len(X)):\n",
        "        distances = dist(X[i], C)\n",
        "        cluster = np.argmin(distances)\n",
        "        clusters[i] = cluster\n",
        "    # Storing the old centroid values\n",
        "    C_old = deepcopy(C)\n",
        "    # Finding the new centroids by taking the average value\n",
        "    for i in range(k):\n",
        "        points = [X[j] for j in range(len(X)) if clusters[j] == i]\n",
        "        C[i] = np.mean(points, axis=0)\n",
        "    error = dist(C, C_old, None)\n",
        "\n",
        "colors = ['r', 'g', 'b', 'y', 'c', 'm']\n",
        "fig, ax = plt.subplots()\n",
        "for i in range(k):\n",
        "    points = np.array([X[j] for j in range(len(X)) if clusters[j] == i])\n",
        "    hull = ConvexHull(points)\n",
        "    ax.scatter(points[:, 0], points[:, 1], s=30, c=colors[i])\n",
        "    for simplex in hull.simplices:\n",
        "        ax.plot(points[simplex, 0], points[simplex, 1], colors[i])\n",
        "\n",
        "ax.scatter(C[:, 0], C[:, 1], s=100, c='#050505')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 843
        },
        "id": "sm1dHH9ghJmh",
        "outputId": "7fdb45d4-7d85-43f2-9688-0ea3739307a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "for i in range(k):\n",
        "    points = np.array([X[j] for j in range(len(X)) if clusters[j] == i])\n",
        "    ax.scatter(points[:, 0], points[:, 1], s=20, c=colors[i])\n",
        "    for j in range(len(points)):\n",
        "        if i == 0 and j < 10:\n",
        "            label = 'Singleton{}'.format(j+1)\n",
        "        elif i == 1 and j < 10:\n",
        "            label = 'Prototype{}'.format(j+1)\n",
        "        elif i == 2 and j < 7:\n",
        "            label = 'Builder{}'.format(j+1)\n",
        "        else:\n",
        "            label = None\n",
        "        if label:\n",
        "            ax.annotate(label, (points[j][0], points[j][1]))\n",
        "ax.scatter(C[:, 0], C[:, 1], s=100, c='#050505')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "eP1MOSefo64z",
        "outputId": "4b6493a8-9664-4c38-f400-eb05c526f5f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f5a2a737130>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from copy import deepcopy\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Getting the values and plotting it\n",
        "f1 = d_array['x'].values\n",
        "f2 = d_array['y'].values\n",
        "X = np.array(list(zip(f1, f2)))\n",
        "\n",
        "plt.scatter(f1, f2, c='black', s=7)\n",
        "\n",
        "def dist(a, b, ax=1):\n",
        "    return np.linalg.norm(a - b, axis=ax)\n",
        "\n",
        "k = 3\n",
        "# X coordinates of random centroids\n",
        "C_x = np.random.randint(0, np.max(X)-20, size=k)\n",
        "# Y coordinates of random centroids\n",
        "C_y = np.random.randint(0, np.max(X)-20, size=k)\n",
        "C = np.array(list(zip(C_x, C_y)), dtype=np.float32)\n",
        "\n",
        "# To store the value of centroids when it updates\n",
        "C_old = np.zeros(C.shape)\n",
        "# Cluster Lables(0, 1, 2)\n",
        "clusters = np.zeros(len(X))\n",
        "# Error func. - Distance between new centroids and old centroids\n",
        "error = dist(C, C_old, None)\n",
        "# Loop will run till the error becomes zero\n",
        "while error != 0:\n",
        "    # Assigning each value to its closest cluster\n",
        "    for i in range(len(X)):\n",
        "        distances = dist(X[i], C)\n",
        "        cluster = np.argmin(distances)\n",
        "        clusters[i] = cluster\n",
        "    # Storing the old centroid values\n",
        "    C_old = deepcopy(C)\n",
        "    # Finding the new centroids by taking the average value\n",
        "    for i in range(k):\n",
        "        points = [X[j] for j in range(len(X)) if clusters[j] == i]\n",
        "        C[i] = np.mean(points, axis=0)\n",
        "    error = dist(C, C_old, None)\n",
        "\n",
        "colors = ['r', 'g', 'b', 'y', 'c', 'm']\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "for i in range(k):\n",
        "    points = np.array([X[j] for j in range(len(X)) if clusters[j] == i])\n",
        "    circle_radius = np.max(dist(points, C[i]))\n",
        "    circle = plt.Circle((C[i][0], C[i][1]), circle_radius, color=colors[i], alpha=0.1)\n",
        "    ax.add_artist(circle)\n",
        "    ax.scatter(points[:, 0], points[:, 1], s=20, c=colors[i])\n",
        "\n",
        "ax.scatter(C[:, 0], C[:, 1], s=100, c='#050505')\n",
        "ax.set_aspect('equal', adjustable='box')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "extqNVtNpPa0",
        "outputId": "8013db5d-938a-4bd5-89fd-ace5708a2f0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 843
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "PCA dimensity reduction"
      ],
      "metadata": {
        "id": "G3CKfAj_fnlS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "\n",
        "pcaComp = PCA(n_components=2,\n",
        "              random_state=42)\n",
        "\n",
        "vis_dims = pcaComp.fit_transform(arr)\n",
        "vis_dims.shape"
      ],
      "metadata": {
        "id": "pIry4mbW--AP",
        "outputId": "b079a6c2-2d79-4a01-e041-5af7b886ce70",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(27, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import matplotlib\n",
        "import numpy as np\n",
        "\n",
        "colors = [\"red\", \"darkgreen\",\"blue\"]\n",
        "x = [x for x,y in vis_dims]\n",
        "y = [y for x,y in vis_dims]\n",
        "\n",
        "\n",
        "colormap = matplotlib.colors.ListedColormap(colors)\n",
        "plt.scatter(x, y,c=df[\"Singleton\"], cmap=colormap, alpha=0.3)\n",
        "for score in [0,1]:\n",
        "    color = colors[score]\n",
        "plt.title(\"Code fragments embeddings\")\n",
        "plt.rcParams['figure.figsize'] = (5,5)\n",
        "\n",
        "# zip joins x and y coordinates in pairs\n",
        "iIndex = 0\n",
        "for x,y in vis_dims:\n",
        "  label = df.iloc[iIndex].Programs\n",
        "  iIndex +=1\n",
        "\n",
        "  plt.annotate(label, # this is the text\n",
        "                (x,y), # these are the coordinates to position the label\n",
        "                textcoords=\"offset points\", # how to position the text\n",
        "                xytext=(0,-10), # distance from text to points (x,y)\n",
        "                ha='center') "
      ],
      "metadata": {
        "id": "bLkE_F34_CcN",
        "outputId": "c82064a2-3daf-47c1-f0c0-2918ae727980",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}