Notebook - Join Quality Measure.ipynb

{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"id":"awwMdyArt8nn","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1722252178040,"user_tz":-120,"elapsed":25122,"user":{"displayName":"Marc Maynou Yelamos","userId":"15390060321136749570"}},"outputId":"f4d01848-27a0-4331-c357-c7994a83ee64"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["# Mount Google Drive locally\n","from google.colab import drive\n","drive.mount('/content/drive')\n","\n","# Basics\n","import numpy as np\n","import pandas as pd\n","\n","import matplotlib.pyplot as plt\n","import matplotlib.colors as mcolors\n","\n","# Statistics\n","import math\n","from scipy import stats\n","from scipy.stats import multivariate_normal, truncnorm"]},{"cell_type":"markdown","metadata":{"id":"Zu4_tUm4xEn7"},"source":["# Calculate metrics\n","Code to obtain the four metrics showcased in the paper: containment, Jaccard, multiset Jaccard and the cardinality proportion (K)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KMGSvOd4xNBQ"},"outputs":[],"source":["path_prefix = '/content/drive/My Drive/Data Discovery/data/datasets/' # path where the data is stored\n","\n","def calculateMetrics(row):\n","  print(\"Row index:\", row.name, \" Dataset: \", row[\"ds_name\"], row.name, \" Attribute: \", row[\"att_name\"], row.name, \" Dataset 2: \", row[\"ds_name_2\"], row.name, \" Attribute: \", row[\"att_name_2\"])\n","\n","  ds1 = pd.read_csv(path_prefix + row[\"ds_name\"], header = 0)\n","  col1 = ds1[row[\"att_name\"]].str.lower().str.strip()\n","  ds2 = pd.read_csv(path_prefix + row[\"ds_name_2\"], header = 0)\n","  col2 = ds2[row[\"att_name_2\"]].str.lower().str.strip()\n","\n","  set1 = set(col1)\n","  set2 = set(col2)\n","\n","  intersection = len(set1.intersection(set2))\n","  union = len(set1.union(set2))\n","  lengthes = len(col1) + len(col2) # Careful! The lengthes are of the columns, not the set. That is, we include repeated values\n","\n","  jaccard = intersection / union\n","  containment = intersection / min(len(set1), len(set2))\n","  multisetJaccard = intersection / lengthes\n","  k = min(len(set1),len(set2))/max(len(set1),len(set2))\n","\n","  return pd.Series([containment, jaccard, multisetJaccard, k])"]},{"cell_type":"markdown","metadata":{"id":"f-IplWZ5uHFF"},"source":["# Data loading\n","\n","Load the ground truth. That is, a dataframe with joins (pairs of attributes from different datasets) and the following measurements: containment (C), cardinality proportion (K), Jaccard (J) and multiset Jaccard (MJ) scores. Additionally, each join is assigned a type of relationship (semantic or syntactic)."]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":444},"executionInfo":{"elapsed":1861,"status":"ok","timestamp":1722252179893,"user":{"displayName":"Marc Maynou Yelamos","userId":"15390060321136749570"},"user_tz":-120},"id":"duz5JPKRuAhG","outputId":"a0247171-6554-463a-8c4f-b11c65700a6e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                          ds_name      att_name  \\\n","0     Distributions_data_2016.csv  demographics   \n","1           USA_cars_datasets.csv       country   \n","2               books_updated.csv  languageCode   \n","3                        cars.csv         color   \n","4                        cars.csv  transmission   \n","...                           ...           ...   \n","4313              student-mat.csv       Pstatus   \n","4314                   top10s.csv         title   \n","4315            top_100_books.csv       author2   \n","4316             us_companies.csv   companytype   \n","4317        pte_active_active.csv        drugid   \n","\n","                                        ds_name_2      att_name_2  \\\n","0     Tech_sector_diversity_demographics_2016.csv   raceEthnicity   \n","1                               world_country.csv            Name   \n","2                 countries_metadatacountries.csv     CountryCode   \n","3                                      colors.csv            name   \n","4                             imdb_ijs_movies.csv            name   \n","...                                           ...             ...   \n","4313                         dataset_1_anneal.csv  temper_rolling   \n","4314                                song_data.csv        songname   \n","4315                            books_updated.csv         authors   \n","4316                           worldcitiespop.csv         Country   \n","4317                            pte_bound_cnt.csv            Arg0   \n","\n","      sizeDistinct1  sizeDistinct2  joinSize         C         K         J  \\\n","0                13              6         3  0.230769  0.461538  0.187500   \n","1                 2            239         1  0.500000  0.008368  0.004167   \n","2                25            247         9  0.360000  0.101215  0.034091   \n","3                12            135         9  0.750000  0.088889  0.065217   \n","4                 2          45482         1  0.500000  0.000044  0.000022   \n","...             ...            ...       ...       ...       ...       ...   \n","4313              2              1        51  0.500000  0.500000  0.333333   \n","4314            584          12932         1  0.493151  0.045159  0.021567   \n","4315             83           4636       288  0.409639  0.017903  0.006999   \n","4316              9            234        34  0.111111  0.038462  0.000000   \n","4317            298            340         1  1.000000  0.876471  0.876471   \n","\n","      MultisetJ relationship  \n","0      0.000186    syntactic  \n","1      0.000365     semantic  \n","2      0.000878    syntactic  \n","3      0.000233     semantic  \n","4      0.000011    syntactic  \n","...         ...          ...  \n","4313   0.000773    syntactic  \n","4314   0.014713     semantic  \n","4315   0.003268     semantic  \n","4316   0.000000    syntactic  \n","4317   0.466354    syntactic  \n","\n","[4318 rows x 12 columns]"],"text/html":["\n","  <div id=\"df-ecd8ec77-17af-476c-89f4-36d2e237f4b1\" 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>ds_name</th>\n","      <th>att_name</th>\n","      <th>ds_name_2</th>\n","      <th>att_name_2</th>\n","      <th>sizeDistinct1</th>\n","      <th>sizeDistinct2</th>\n","      <th>joinSize</th>\n","      <th>C</th>\n","      <th>K</th>\n","      <th>J</th>\n","      <th>MultisetJ</th>\n","      <th>relationship</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Distributions_data_2016.csv</td>\n","      <td>demographics</td>\n","      <td>Tech_sector_diversity_demographics_2016.csv</td>\n","      <td>raceEthnicity</td>\n","      <td>13</td>\n","      <td>6</td>\n","      <td>3</td>\n","      <td>0.230769</td>\n","      <td>0.461538</td>\n","      <td>0.187500</td>\n","      <td>0.000186</td>\n","      <td>syntactic</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>USA_cars_datasets.csv</td>\n","      <td>country</td>\n","      <td>world_country.csv</td>\n","      <td>Name</td>\n","      <td>2</td>\n","      <td>239</td>\n","      <td>1</td>\n","      <td>0.500000</td>\n","      <td>0.008368</td>\n","      <td>0.004167</td>\n","      <td>0.000365</td>\n","      <td>semantic</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>books_updated.csv</td>\n","      <td>languageCode</td>\n","      <td>countries_metadatacountries.csv</td>\n","      <td>CountryCode</td>\n","      <td>25</td>\n","      <td>247</td>\n","      <td>9</td>\n","      <td>0.360000</td>\n","      <td>0.101215</td>\n","      <td>0.034091</td>\n","      <td>0.000878</td>\n","      <td>syntactic</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>cars.csv</td>\n","      <td>color</td>\n","      <td>colors.csv</td>\n","      <td>name</td>\n","      <td>12</td>\n","      <td>135</td>\n","      <td>9</td>\n","      <td>0.750000</td>\n","      <td>0.088889</td>\n","      <td>0.065217</td>\n","      <td>0.000233</td>\n","      <td>semantic</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>cars.csv</td>\n","      <td>transmission</td>\n","      <td>imdb_ijs_movies.csv</td>\n","      <td>name</td>\n","      <td>2</td>\n","      <td>45482</td>\n","      <td>1</td>\n","      <td>0.500000</td>\n","      <td>0.000044</td>\n","      <td>0.000022</td>\n","      <td>0.000011</td>\n","      <td>syntactic</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>4313</th>\n","      <td>student-mat.csv</td>\n","      <td>Pstatus</td>\n","      <td>dataset_1_anneal.csv</td>\n","      <td>temper_rolling</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>51</td>\n","      <td>0.500000</td>\n","      <td>0.500000</td>\n","      <td>0.333333</td>\n","      <td>0.000773</td>\n","      <td>syntactic</td>\n","    </tr>\n","    <tr>\n","      <th>4314</th>\n","      <td>top10s.csv</td>\n","      <td>title</td>\n","      <td>song_data.csv</td>\n","      <td>songname</td>\n","      <td>584</td>\n","      <td>12932</td>\n","      <td>1</td>\n","      <td>0.493151</td>\n","      <td>0.045159</td>\n","      <td>0.021567</td>\n","      <td>0.014713</td>\n","      <td>semantic</td>\n","    </tr>\n","    <tr>\n","      <th>4315</th>\n","      <td>top_100_books.csv</td>\n","      <td>author2</td>\n","      <td>books_updated.csv</td>\n","      <td>authors</td>\n","      <td>83</td>\n","      <td>4636</td>\n","      <td>288</td>\n","      <td>0.409639</td>\n","      <td>0.017903</td>\n","      <td>0.006999</td>\n","      <td>0.003268</td>\n","      <td>semantic</td>\n","    </tr>\n","    <tr>\n","      <th>4316</th>\n","      <td>us_companies.csv</td>\n","      <td>companytype</td>\n","      <td>worldcitiespop.csv</td>\n","      <td>Country</td>\n","      <td>9</td>\n","      <td>234</td>\n","      <td>34</td>\n","      <td>0.111111</td>\n","      <td>0.038462</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>syntactic</td>\n","    </tr>\n","    <tr>\n","      <th>4317</th>\n","      <td>pte_active_active.csv</td>\n","      <td>drugid</td>\n","      <td>pte_bound_cnt.csv</td>\n","      <td>Arg0</td>\n","      <td>298</td>\n","      <td>340</td>\n","      <td>1</td>\n","      <td>1.000000</td>\n","      <td>0.876471</td>\n","      <td>0.876471</td>\n","      <td>0.466354</td>\n","      <td>syntactic</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>4318 rows × 12 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ecd8ec77-17af-476c-89f4-36d2e237f4b1')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-ecd8ec77-17af-476c-89f4-36d2e237f4b1 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-ecd8ec77-17af-476c-89f4-36d2e237f4b1');\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","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-d7492086-2421-4d0d-a345-ac9ba50fb61f\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d7492086-2421-4d0d-a345-ac9ba50fb61f')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-d7492086-2421-4d0d-a345-ac9ba50fb61f button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","\n","  <div id=\"id_10fa2c8c-e67f-41f4-bbaa-73d4e2f9ecd0\">\n","    <style>\n","      .colab-df-generate {\n","        background-color: #E8F0FE;\n","        border: none;\n","        border-radius: 50%;\n","        cursor: pointer;\n","        display: none;\n","        fill: #1967D2;\n","        height: 32px;\n","        padding: 0 0 0 0;\n","        width: 32px;\n","      }\n","\n","      .colab-df-generate:hover {\n","        background-color: #E2EBFA;\n","        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","        fill: #174EA6;\n","      }\n","\n","      [theme=dark] .colab-df-generate {\n","        background-color: #3B4455;\n","        fill: #D2E3FC;\n","      }\n","\n","      [theme=dark] .colab-df-generate:hover {\n","        background-color: #434B5C;\n","        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","        fill: #FFFFFF;\n","      }\n","    </style>\n","    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('groundTruth')\"\n","            title=\"Generate code using this dataframe.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n","  </svg>\n","    </button>\n","    <script>\n","      (() => {\n","      const buttonEl =\n","        document.querySelector('#id_10fa2c8c-e67f-41f4-bbaa-73d4e2f9ecd0 button.colab-df-generate');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","      buttonEl.onclick = () => {\n","        google.colab.notebook.generateWithVariable('groundTruth');\n","      }\n","      })();\n","    </script>\n","  </div>\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"groundTruth","summary":"{\n  \"name\": \"groundTruth\",\n  \"rows\": 4318,\n  \"fields\": [\n    {\n      \"column\": \"ds_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 129,\n        \"samples\": [\n          \"pte_five_ring.csv\",\n          \"Books_df.csv\",\n          \"pte_testactive_Neg.csv\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"att_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 225,\n        \"samples\": [\n          \"phos\",\n          \"GeneID1\",\n          \"c_codes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ds_name_2\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 123,\n        \"samples\": [\n          \"pte_drug.csv\",\n          \"World_countries_env_vars.csv\",\n          \"pte_testactive.csv\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"att_name_2\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 203,\n        \"samples\": [\n          \"element\",\n          \"StreetAddress\",\n          \"UnitMeasureCode\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sizeDistinct1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1324,\n        \"min\": 1,\n        \"max\": 24304,\n        \"num_unique_values\": 165,\n        \"samples\": [\n          446,\n          29,\n          12844\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sizeDistinct2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 693454,\n        \"min\": 1,\n        \"max\": 2351883,\n        \"num_unique_values\": 171,\n        \"samples\": [\n          16739,\n          577,\n          12932\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"joinSize\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 620,\n        \"min\": 1,\n        \"max\": 20441,\n        \"num_unique_values\": 306,\n        \"samples\": [\n          181,\n          102,\n          502\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"C\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.30181849153088436,\n        \"min\": 0.100591716,\n        \"max\": 1.0,\n        \"num_unique_values\": 1286,\n        \"samples\": [\n          0.467571145,\n          0.342105263,\n          0.352625052\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"K\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3715399195400264,\n        \"min\": 4.251912191210192e-07,\n        \"max\": 1.0,\n        \"num_unique_values\": 1560,\n        \"samples\": [\n          0.056338028169014,\n          0.0072150072150072,\n          0.0035\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"J\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2831255737668051,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1730,\n        \"samples\": [\n          0.0769230769230769,\n          0.000297044408139,\n          0.1619109734224686\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MultisetJ\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10459159214315024,\n        \"min\": 0.0,\n        \"max\": 0.5,\n        \"num_unique_values\": 2086,\n        \"samples\": [\n          0.0014108964619057,\n          5.142695515283805e-05,\n          0.0020853274989744\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"relationship\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"semantic\",\n          \"syntactic\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":2}],"source":["groundTruth = pd.read_csv('/content/drive/My Drive/Data Discovery/data/ground_truth_sem_or_syn.csv', header = 0)\n","groundTruth"]},{"cell_type":"markdown","metadata":{"id":"tQYWrEcNPQLf"},"source":["# Initial experiments: which set-overlap metric to use?"]},{"cell_type":"markdown","metadata":{"id":"lfJkNkzzPUSN"},"source":["Firstly, we want to assess whether multiset Jaccard (the metric that we want to explore the performance of) is better than traditional set overlap metrics (containment and Jaccard) in separating semantic from syntatic joins."]},{"cell_type":"markdown","metadata":{"id":"Aguvr9JhPSKY"},"source":["## Proportion of semantic-syntactic joins"]},{"cell_type":"markdown","metadata":{"id":"l1_qeTlPPstM"},"source":["We start by ordering the joins in three different \"rankings\", one per each set overlap metric, with higher values of the metrics being placed at the top. Then, we separate each ranking into its four quartiles, and measure the proportion of semantic and syntactic joins in each. Ideally, we should see that for quartiles 3 and 4 (i.e. those representing the the top 50% of joins according to the metrics), they have a higher proportion of semantic joins than buckets 1 and 2.\n","\n","Note that the ground truth contains more syntactic than semantic joins.\n","\n","**Result**: Containment is clearly worse than both Jaccard metrics in developing this separation. Multiset Jaccard seems to make the distribution slightly better than Jaccard, although the evidence is small."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZH00IgrtnJ2r"},"outputs":[],"source":["def proportions(metric, num_partitions):\n","  count_sem = 0\n","  count_syn = 0\n","  groundTruth[metric + '_rank'] = groundTruth[metric].rank(method=\"first\", ascending=False)\n","  groundTruth['partition_' + metric] = pd.qcut(groundTruth[metric], q=num_partitions, labels=False) + 1\n","\n","  # Initialize counts dictionary based on the number of partitions\n","  counts = {str(i): {'sem': 0, 'syn': 0} for i in range(1, num_partitions + 1)}\n","\n","  # Count occurrences\n","  for index, row in groundTruth.iterrows():\n","      if row[\"relationship\"] == \"semantic\":\n","          counts[str(row['partition_' + metric])]['sem'] += 1\n","      else:\n","          counts[str(row['partition_' + metric])]['syn'] += 1\n","\n","  sem_counts = [counts[key][\"sem\"] for key in counts]\n","  syn_counts = [counts[key][\"syn\"] for key in counts]\n","  return sem_counts, syn_counts"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":407},"executionInfo":{"elapsed":3949,"status":"ok","timestamp":1719934463981,"user":{"displayName":"Marc Maynou Yelamos","userId":"15390060321136749570"},"user_tz":-120},"id":"tSE7DGbwELLs","outputId":"91e7add9-f093-4d0c-9fbe-891350126763"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x400 with 3 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["proportions_c = proportions(\"C\", 4)\n","proportions_j = proportions(\"J\", 4)\n","proportions_mj = proportions(\"MultisetJ\", 4)\n","\n","values_sets = [\n","    (proportions_c[0], proportions_c[1], \"Containment\"),\n","    (proportions_j[0], proportions_j[1], \"Jaccard\"),\n","    (proportions_mj[0], proportions_mj[1], \"Multiset Jaccard\")\n","]\n","\n","fig, axs = plt.subplots(1, 3, figsize=(12, 4), sharey=True)\n","\n","categories = ['0%-25%', '25%-50%', '50%-75%', '75%-100%']\n","bar_width = 0.6\n","index = np.arange(len(categories))\n","\n","for i, (values1, values2, title) in enumerate(values_sets):\n","    total_values = np.array(values1) + np.array(values2)\n","    normalized_values1 = np.array(values1) / total_values\n","    normalized_values2 = np.array(values2) / total_values\n","\n","    ax = axs[i]\n","\n","    bars1 = ax.bar(index, normalized_values1, bar_width, label='Semantic joins', color='green')\n","    bars2 = ax.bar(index, normalized_values2, bar_width, label='Syntactic joins', color='red', bottom=normalized_values1)\n","\n","    ax.set_title(title, fontsize=15)\n","    ax.set_xticks(index)\n","    ax.set_xticklabels(categories, size=12)\n","    ax.set_ylim(0, 1.2)\n","\n","    for bar1, bar2, val1, val2 in zip(bars1, bars2, values1, values2):\n","        if val1 == 69:  # Special case\n","            ax.text(bar1.get_x() + bar1.get_width() / 1.9, bar1.get_height(), val1, ha='center', va='bottom', color=\"#1B1212\", size=14)\n","        else:\n","            ax.text(bar1.get_x() + bar1.get_width() / 2., bar1.get_height() - 0.1, val1, ha='center', va='bottom', color=\"#1B1212\", size=14)\n","        ax.text(bar2.get_x() + bar2.get_width() / 2., bar2.get_height() + bar1.get_height() - 0.01, val2, ha='center', va='top', color=\"#1B1212\", size=14)\n","\n","    ax.legend(loc='upper center', bbox_to_anchor=(0.5, 0.98), fancybox=True, shadow=True, ncol=2, fontsize=8)\n","\n","plt.tight_layout()\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"ZkCAgumNRFA-"},"source":["## Precision at k"]},{"cell_type":"markdown","metadata":{"id":"J63xi2JyRHL8"},"source":["The previous experiment takes into consideration all the joins in the ground truth to assess the capacity of the metrics to separate semantic and syntactic joins. On a real scenario, the end user will only consider the top k joins (i.e. those with higher values of join quality). Hence, we will now focus only on the top joins.\n","\n","To do so we will employ the precision at k (P@k) metric, which is similar to the previous procedure: how many of the top k joins are semantic joins? We will set k to 100, which is already far more than what a realistic end user would consider.\n","\n","**Result**: We can observe that multiset Jaccard is clearly better at placing semantic joins in the top spots. In fact, both containment and Jaccard places syntactic joins in the first few positions, which means that the first joins to be given to the user would be false positives. We have a clear argument to prefer multiset Jaccard to the other two."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1YA0BIK_NXHj"},"outputs":[],"source":["def get_precisions(ranking, k_max):\n","  results = []\n","  for k in range(1, k_max + 1, 2):\n","    groundTruthSorted_k = groundTruth.sort_values(by=ranking).head(k)\n","    recall_k = (groundTruthSorted_k['relationship'] == \"semantic\").sum()/k\n","    results.append(recall_k)\n","  return results\n","\n","\n","def precision_at_k_set_overlap_metrics(k_max):\n","  c = get_precisions(\"C_rank\", k_max)\n","  j = get_precisions(\"J_rank\", k_max)\n","  mj = get_precisions(\"MultisetJ_rank\", k_max)\n","\n","  plt.figure(figsize=(6, 3))\n","\n","  x = range(1, k_max + 1, 2)\n","  plt.plot(x, c, label='Containment')\n","  plt.plot(x, j, linestyle='-.', label='Jaccard')\n","  plt.plot(x, mj, linestyle='--', label='Multiset Jaccard')\n","\n","  plt.xlabel('k', fontsize=14)\n","  plt.ylabel('P@k', fontsize=14)\n","\n","  plt.xlim(-0.4, k_max + 1)\n","  plt.ylim(0, 1.03)\n","  plt.tick_params(axis='y', labelsize=12)\n","  plt.tick_params(axis='x', labelsize=12)\n","\n","  plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.2), fancybox=True, shadow=True, ncol=4, fontsize=12)\n","\n","  plt.grid(True)\n","  plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":358},"executionInfo":{"elapsed":1012,"status":"ok","timestamp":1719934472702,"user":{"displayName":"Marc Maynou Yelamos","userId":"15390060321136749570"},"user_tz":-120},"id":"hqxLsZXYOHJK","outputId":"9390aef1-3a49-4742-84e1-0343f13798f1"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 600x300 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["precision_at_k_set_overlap_metrics(100)"]},{"cell_type":"markdown","metadata":{"id":"NtaG0UWMyfSU"},"source":["# Relevance of K\n","\n","We can justify the employment of the cardinality proportion in a preliminary manner by evaluating its capacity to segregate semantic from syntactic joins.\n","\n","**Result**: The plots showcase a clear positive correlation between the value of the cardinality proportion and the amount of semantic joins. That is, higher values of K tend to correspond with semantic joins."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Autvu5DgTIdn"},"outputs":[],"source":["def proportions_joined(metric, num_quantiles, sample_size=1500):\n","    # Sample data for semantic and syntactic relationships\n","    sem_sample = groundTruth[groundTruth[\"relationship\"] == \"semantic\"].sample(n=sample_size, random_state=0)\n","    syn_sample = groundTruth[groundTruth[\"relationship\"] == \"syntactic\"].sample(n=sample_size, random_state=0)\n","    combined_sample = pd.concat([sem_sample, syn_sample])\n","\n","    # Rank the metric values and divide into quantiles\n","    combined_sample[metric + '_rank'] = combined_sample[metric].rank(method=\"first\", ascending=False)\n","    combined_sample['quartile_' + metric] = pd.qcut(combined_sample[metric], q=num_quantiles, labels=False) + 1\n","\n","    # Initialize counts dictionary\n","    quartile_counts = {i: {'sem': 0, 'syn': 0} for i in range(1, num_quantiles + 1)}\n","\n","    # Count occurrences in each quartile\n","    for quartile, relationship in zip(combined_sample['quartile_' + metric], combined_sample['relationship']):\n","        if relationship == \"semantic\":\n","            quartile_counts[quartile]['sem'] += 1\n","        else:\n","            quartile_counts[quartile]['syn'] += 1\n","\n","    # Extract counts for semantic and syntactic relationships\n","    sem_counts = [quartile_counts[q]['sem'] for q in sorted(quartile_counts.keys())]\n","    syn_counts = [quartile_counts[q]['syn'] for q in sorted(quartile_counts.keys())]\n","\n","    print(f\"Semantic counts for metric {metric}: {sem_counts}\")\n","    print(f\"Syntactic counts for metric {metric}: {syn_counts}\")\n","    return sem_counts, syn_counts"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":392},"executionInfo":{"elapsed":466,"status":"ok","timestamp":1719934481006,"user":{"displayName":"Marc Maynou Yelamos","userId":"15390060321136749570"},"user_tz":-120},"id":"ZtUynV7qTMuL","outputId":"10e24834-d765-4c1d-eeb9-116e61e04209"},"outputs":[{"output_type":"stream","name":"stdout","text":["Semantic counts for metric K: [107, 374, 470, 549]\n","Syntactic counts for metric K: [643, 377, 279, 201]\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 600x300 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["values_sets = proportions_joined(\"K\", 4) # [0] -> semantic counts, [1] -> syntactic counts\n","\n","categories = ['0%-25%', '25%-50%', '50%-75%', '75%-100%']\n","\n","# Normalizing the values\n","total_values = np.array(values_sets[0]) + np.array(values_sets[1])\n","semantic_values_normalized = np.array(values_sets[0]) / total_values\n","syntactic_values_normalized = np.array(values_sets[1]) / total_values\n","\n","plt.figure(figsize=(6, 3))\n","index = np.arange(len(categories))\n","\n","# Plotting the first section of bars\n","bars1 = plt.bar(index, semantic_values_normalized, 0.6, label='Semantic joins', color='green')\n","\n","# Plotting the second section of bars on top of the first section\n","bars2 = plt.bar(index, syntactic_values_normalized, 0.6, label='Syntactic joins', color='red', bottom=semantic_values_normalized)\n","\n","plt.ylabel('Proportion', fontsize=13)\n","plt.xlabel('Percentiles of K', fontsize=13)\n","plt.xticks(index, categories, size=12)\n","plt.yticks(size=12)\n","plt.ylim(0, 1.05)\n","plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.2), fancybox=True, shadow=True, ncol=2, fontsize=12)\n","\n","# Adding original values on top of the bars\n","for bar1, bar2, val1, val2 in zip(bars1, bars2, values1, values2):\n","    plt.text(bar1.get_x() + bar1.get_width() / 2., bar1.get_height() - 0.12, val1, ha='center', va='bottom', color=\"#1B1212\", size=14)\n","    plt.text(bar2.get_x() + bar2.get_width() / 2., bar2.get_height() + bar1.get_height() - 0.03, val2, ha='center', va='top', color=\"#1B1212\", size=14)\n","\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":511},"executionInfo":{"elapsed":971,"status":"ok","timestamp":1719934484828,"user":{"displayName":"Marc Maynou Yelamos","userId":"15390060321136749570"},"user_tz":-120},"id":"UeVozIw-TYQ0","outputId":"b0bf969b-d0b0-48f8-90fb-1a7074c06066"},"outputs":[{"output_type":"stream","name":"stdout","text":["Semantic counts for metric K: [107, 374, 470, 549]\n","Syntactic counts for metric K: [643, 377, 279, 201]\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-161-22235835050f>:12: UserWarning: FixedFormatter should only be used together with FixedLocator\n","  ax1.set_xticklabels(categories, rotation=45, ha='right')\n","<ipython-input-161-22235835050f>:19: UserWarning: FixedFormatter should only be used together with FixedLocator\n","  ax2.set_xticklabels(categories, rotation=45, ha='right')\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 800x400 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["values_sets = proportions_joined(\"K\", 4) # [0] -> semantic counts, [1] -> syntactic counts\n","\n","categories = ['0%-25%', '25%-50%', '50%-75%', '75%-100%']\n","\n","fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))\n","\n","# Create the first bar plot\n","ax1.bar(categories, values_sets[0], color='green')\n","ax1.set_title('Semantic joins')\n","ax1.set_xlabel('Percentiles of K')\n","ax1.set_ylabel('Number of joins')\n","ax1.set_xticklabels(categories, rotation=45, ha='right')\n","\n","# Create the second bar plot\n","ax2.bar(categories, values_sets[1], color='red')\n","ax2.set_title('Syntactic joins')\n","ax2.set_xlabel('Percentiles of K')\n","ax2.set_ylabel('Number of joins')\n","ax2.set_xticklabels(categories, rotation=45, ha='right')\n","\n","fig.tight_layout()\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"9akUilj_aufK"},"source":["# Multi-class metric\n","\n","By plotting the scores for MJ and K obtained by the ground truth, we can observe a positive correlation between the values of the metrics and the semantic category of the joins. That is, the higher the values of MJ and K, the more likely for a join to be semantic.\n","\n","Hence, we define a multiclass function that maps the instance of the ground truth onto four (by default, but there might be many levels), quality categories. The idea is that the \"higher\" buckets should contain a higher proportion of semantic joins compared to syntactic joins. It is important to realize that this is not meant to define 100% accurate thresholds (as it is impossible), but rather to capture general trends.\n","\n","We also plot the result of applying such function over our ground truth."]},{"cell_type":"code","execution_count":22,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":400},"executionInfo":{"elapsed":2926,"status":"ok","timestamp":1722252907915,"user":{"displayName":"Marc Maynou Yelamos","userId":"15390060321136749570"},"user_tz":-120},"id":"VID72eVts95r","outputId":"d4d0ec33-d037-457c-e2d1-a4baad9872f8"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 600x400 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["import matplotlib.pyplot as plt\n","import numpy as np\n","import matplotlib.colors as mcolors\n","\n","def pltcolor(lst):\n","    cols = []\n","    for l in lst:\n","        if l == 'semantic':\n","            cols.append(0)\n","        elif l == 'syntactic':\n","            cols.append(1)\n","        else:\n","            cols.append(2)\n","    return cols\n","\n","cols = pltcolor(groundTruth['relationship'])\n","\n","classes = ['Semantic', 'Syntactic']\n","colours = mcolors.ListedColormap(['g', 'b', 'r'])\n","\n","plt.figure(figsize=(6, 4))\n","\n","scatter = plt.scatter(groundTruth[\"MultisetJ\"], groundTruth[\"K\"], c=cols, cmap=colours)\n","plt.xticks(np.linspace(0, 0.5, 6), fontsize=12)\n","plt.yticks(np.linspace(0, 1, 11), fontsize=12)\n","plt.xlabel(r'$\\mathcal{J}$', fontsize=16)\n","plt.ylabel('K', fontsize=16)\n","\n","plt.legend(handles=scatter.legend_elements()[0], labels=classes)\n","plt.show()"]},{"cell_type":"code","execution_count":6,"metadata":{"id":"UblDIzvrCH9r","executionInfo":{"status":"ok","timestamp":1722252276154,"user_tz":-120,"elapsed":251,"user":{"displayName":"Marc Maynou Yelamos","userId":"15390060321136749570"}}},"outputs":[],"source":["def discreteQuality(MJ, K, L):\n","  for i in range(0,L): # range() does not include the last element\n","    if MJ >= 1/2**(i+1) and K >= 1-(i/(L)): # i + 1 as Mj is bounded in [0, 0.5] (i + 1 is equivalent to 2 * MJ)\n","      return (L-i+1)/L\n","  return 0"]},{"cell_type":"code","execution_count":23,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":400},"executionInfo":{"elapsed":1085,"status":"ok","timestamp":1722252940401,"user":{"displayName":"Marc Maynou Yelamos","userId":"15390060321136749570"},"user_tz":-120},"id":"zRC9G-N6B6F9","outputId":"b2844106-c956-4108-e0c3-139b5d42fead"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 600x400 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["import matplotlib.pyplot as plt\n","import numpy as np\n","import matplotlib.colors as mcolors\n","\n","groundTruth['discreteQuality'] = groundTruth.apply(lambda row: discreteQuality(row['MultisetJ'], row['K'], 4), axis=1)\n","\n","classes = ['Low', 'Medium', 'Good', 'High']\n","colours = mcolors.ListedColormap(['#bac2ff', '#2c2cff', '#be29ec', '#660066'])\n","\n","plt.figure(figsize=(6, 4))\n","plt.xticks(np.linspace(0, 1, 11), fontsize=12)\n","plt.yticks(np.linspace(0, 1, 11), fontsize=12)\n","scatter = plt.scatter(groundTruth['MultisetJ'], groundTruth['K'], c=groundTruth['discreteQuality'], cmap=colours)\n","plt.legend(handles=scatter.legend_elements()[0], labels=classes)\n","\n","plt.xlabel(r'$\\mathcal{J}$', fontsize=16)\n","plt.ylabel('K', fontsize=16)\n","\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"LAVZuk61E8eY"},"source":["# Continuous metric\n","Finally, we transform the multi-class metric to a continuous one, as it would allow us to create rankings. To do so, we fit a multiple Gaussian distribution over the multi-class metric."]},{"cell_type":"code","execution_count":9,"metadata":{"id":"_8q0tiF15Q7o","executionInfo":{"status":"ok","timestamp":1722252351435,"user_tz":-120,"elapsed":285,"user":{"displayName":"Marc Maynou Yelamos","userId":"15390060321136749570"}}},"outputs":[],"source":["def defineModel(mu_MJ, mu_K, sigma_mj, sigma_k):\n","  mu_v = [mu_MJ , mu_K] # Mean vector\n","  \"\"\"\n","  The covariance matrix is defined as:\n","  cov = [[sigma_v[0]**2, 0],\n","        [0, sigma_v[1]**2]]\n","  \"\"\"\n","  sigma_v = [sigma_mj, sigma_k] # Variance vector (we assume an equal sigma for MJ and K)\n","  a1, b1 = (- mu_v[0]) / sigma_v[0], (1 - mu_v[0]) / sigma_v[0] # Defining MJ limits\n","  a2, b2 = (- mu_v[1]) / sigma_v[1], (1 - mu_v[1]) / sigma_v[1] # Defining K limits\n","  MJmodel = truncnorm(a1, b1, scale=sigma_v[0], loc=mu_v[0]) # Creating MJ model\n","  Kmodel = truncnorm(a2, b2, scale=sigma_v[1], loc=mu_v[1]) # Creating K model\n","  return MJmodel,Kmodel\n","\n","def continuousJoinQualityWithAll(MJ,K,MJmodel,Kmodel):\n","  return MJmodel.cdf(MJ)*Kmodel.cdf(K)"]},{"cell_type":"code","execution_count":25,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":10246,"status":"ok","timestamp":1722253076230,"user":{"displayName":"Marc Maynou Yelamos","userId":"15390060321136749570"},"user_tz":-120},"id":"bsQnvcFUF4wZ","outputId":"d276b761-f045-465b-9b18-c57bf2c14478"},"outputs":[{"output_type":"stream","name":"stdout","text":["Best mu for MJ is 0.1111111111111111\n","Best sigma for MJ is 0.1\n","Best wasserstein_distance for MJ is 0.06743474780215303\n"]},{"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":["Best mu for K is 0.4444444444444444\n","Best sigma for K is 0.19\n","Best wasserstein_distance for K is 0.08269476814368401\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["def evaluate_model(mu_values, sigma_values, L, is_MJ_model=True):\n","    best_mu = 1\n","    best_sigma = 1\n","    best_distance = math.inf\n","\n","    for mu in mu_values:\n","        for sigma in sigma_values:\n","            cdf_model = []\n","            discrete_model = []\n","\n","            if is_MJ_model:\n","                MJmodel, Kmodel = defineModel(mu, 0, sigma, 0.5)\n","                X = np.arange(0, 0.5, 0.005)\n","            else:\n","                MJmodel, Kmodel = defineModel(0, mu, 0.5, sigma)\n","                X = np.arange(0, 1, 0.01)\n","\n","            for x in X:\n","                if is_MJ_model:\n","                    cdfVal = continuousJoinQualityWithAll(x, 1, MJmodel, Kmodel)\n","                    discreteVal = discreteQuality(x, 1, L)\n","                else:\n","                    cdfVal = continuousJoinQualityWithAll(1, x, MJmodel, Kmodel)\n","                    discreteVal = discreteQuality(1, x, L)\n","\n","                cdf_model.append(cdfVal)\n","                discrete_model.append(discreteVal)\n","\n","            d = stats.wasserstein_distance(cdf_model, discrete_model)\n","            if d < best_distance:\n","                best_distance = d\n","                best_mu = mu\n","                best_sigma = sigma\n","\n","    return best_mu, best_sigma, best_distance\n","\n","def plot_models(mu, sigma, L, is_MJ_model=True):\n","    if is_MJ_model:\n","        MJmodel, Kmodel = defineModel(mu, 0, sigma, 0.5)\n","        X = np.arange(0, 0.5, 0.005)\n","    else:\n","        MJmodel, Kmodel = defineModel(0, mu, 0.5, sigma)\n","        X = np.arange(0, 1, 0.01)\n","\n","    models = []\n","    cdf_model = []\n","    discrete_model = []\n","\n","    for x in X:\n","        if is_MJ_model:\n","            cdfVal = continuousJoinQualityWithAll(x, 1, MJmodel, Kmodel)\n","            discreteVal = discreteQuality(x, 1, L)\n","        else:\n","            cdfVal = continuousJoinQualityWithAll(1, x, MJmodel, Kmodel)\n","            discreteVal = discreteQuality(1, x, L)\n","\n","        cdf_model.append(cdfVal)\n","        discrete_model.append(discreteVal)\n","        models.append([cdfVal, discreteVal])\n","\n","    plt.plot(X, models)\n","    title = \"Discrete and continuous CDF of $\\mathcal{J}$\" if is_MJ_model else \"Discrete and continuous CDF of K\"\n","    plt.title(title, fontsize=16)\n","    plt.xticks(fontsize=12)\n","    plt.yticks(fontsize=12)\n","    plt.show()\n","\n","L = 4\n","mu_values = np.linspace(0, 1, 10, endpoint=True)\n","sigma_values = np.linspace(0.1, 1, 10, endpoint=False)\n","\n","best_mu_MJ, best_sigma_MJ, best_distance_MJ = evaluate_model(mu_values, sigma_values, L, is_MJ_model=True)\n","print(\"Best mu for MJ is\", best_mu_MJ)\n","print(\"Best sigma for MJ is\", best_sigma_MJ)\n","print(\"Best wasserstein_distance for MJ is\", best_distance_MJ)\n","plot_models(best_mu_MJ, best_sigma_MJ, L, is_MJ_model=True)\n","\n","best_mu_K, best_sigma_K, best_distance_K = evaluate_model(mu_values, sigma_values, L, is_MJ_model=False)\n","print(\"Best mu for K is\", best_mu_K)\n","print(\"Best sigma for K is\", best_sigma_K)\n","print(\"Best wasserstein_distance for K is\", best_distance_K)\n","plot_models(best_mu_K, best_sigma_K, L, is_MJ_model=False)"]},{"cell_type":"markdown","metadata":{"id":"DLiNmaMfSWbI"},"source":["# Strictness\n","\n","Finally, we will provide the metric with some capacity to generalize by defining a strictness modifier. The higher the strictness, the more restrictive it will be when defining the scoring of the joins. That is, a higher strictness value implies that the higher scores of MJ and K are necessary to achieve a high join quality score. We define three levels: relaxed, balanced (which is the one we build the final model with) and strict."]},{"cell_type":"code","execution_count":14,"metadata":{"id":"JV2QB48eTq7r","executionInfo":{"status":"ok","timestamp":1722252576417,"user_tz":-120,"elapsed":278,"user":{"displayName":"Marc Maynou Yelamos","userId":"15390060321136749570"}}},"outputs":[],"source":["def continuousJoinQuality(MJ,K,strictness):\n","  mu_v = [0.44 + 0.25*strictness, 0 + 0.25*strictness]\n","  \"\"\"\n","  The covariance matrix is defined as:\n","  cov = [[sigma_v[0]**2, 0],\n","        [0, sigma_v[1]**2]]\n","  \"\"\"\n","  sigma_v = [0.19, 0.28]\n","  a1, b1 = (- mu_v[0]) / sigma_v[0], (1 - mu_v[0]) / sigma_v[0]\n","  a2, b2 = (- mu_v[1]) / sigma_v[1], (1 - mu_v[1]) / sigma_v[1]\n","  MJmodel = truncnorm(a1, b1, scale=sigma_v[0], loc=mu_v[0])\n","  Kmodel = truncnorm(a2, b2, scale=sigma_v[1], loc=mu_v[1])\n","  return MJmodel.cdf(MJ)*Kmodel.cdf(K)"]},{"cell_type":"code","execution_count":19,"metadata":{"id":"1hQOCrjtiNEE","executionInfo":{"status":"ok","timestamp":1722252691346,"user_tz":-120,"elapsed":35962,"user":{"displayName":"Marc Maynou Yelamos","userId":"15390060321136749570"}}},"outputs":[],"source":["groundTruth['relaxedContinousJoinQuality'] = groundTruth.apply(lambda row: continuousJoinQuality(row['MultisetJ'],row['K'], 0), axis=1)\n","groundTruth['balancedContinousJoinQuality'] = groundTruth.apply(lambda row: continuousJoinQuality(row['MultisetJ'],row['K'], 0.5), axis=1)\n","groundTruth['strictContinousJoinQuality'] = groundTruth.apply(lambda row: continuousJoinQuality(row['MultisetJ'],row['K'], 1), axis=1)"]},{"cell_type":"code","execution_count":20,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":616},"executionInfo":{"elapsed":2319,"status":"ok","timestamp":1722252693647,"user":{"displayName":"Marc Maynou Yelamos","userId":"15390060321136749570"},"user_tz":-120},"id":"gHcMqK0RiGnW","outputId":"a8f8b80f-dd69-466a-adf9-2c839764220b"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 2880x1440 with 4 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["cmap = plt.get_cmap('gnuplot2')\n","colors = cmap(np.linspace(0, 0.9, 256))\n","darker_cmap = mcolors.ListedColormap(colors)\n","\n","custom_ticks = np.linspace(0, 1, 6)\n","x_custom_ticks = np.linspace(0, 0.5, 6)\n","\n","def create_scatter_plot(ax, x_data, y_data, color_data, cmap, custom_ticks, x_custom_ticks):\n","    scatter = ax.scatter(x_data, y_data, c=color_data, cmap=cmap)\n","    cbar = plt.colorbar(scatter, ax=ax)\n","    max_val = color_data.max()\n","    custom_ticks = np.linspace(0, max_val, 6)\n","    cbar.set_ticks(custom_ticks)\n","    cbar.set_ticklabels([f'{tick:.1f}' for tick in custom_ticks])\n","    ax.set_xticks(x_custom_ticks)\n","    ax.set_xticklabels([f'{tick:.1f}' for tick in x_custom_ticks])\n","    ax.set_xlabel(r'$\\mathcal{J}$')\n","    ax.set_ylabel(\"$K$\")\n","\n","fig = plt.figure(figsize=(12, 6), dpi=240)\n","\n","data = [\n","    ('relaxedContinousJoinQuality', 'MultisetJ', 'K'),\n","    # ('balancedContinousJoinQuality', 'MultisetJ', 'K'),\n","    ('strictContinousJoinQuality', 'MultisetJ', 'K')\n","]\n","\n","for i, (color_data_key, x_data_key, y_data_key) in enumerate(data, start=1):\n","    ax = fig.add_subplot(2, 3, i)\n","    create_scatter_plot(\n","        ax,\n","        groundTruth[x_data_key],\n","        groundTruth[y_data_key],\n","        groundTruth[color_data_key],\n","        darker_cmap,\n","        custom_ticks,\n","        x_custom_ticks\n","    )"]},{"cell_type":"markdown","metadata":{"id":"-jmGjC-5jetv"},"source":["# Comparing rankings\n","\n","To evaluate the results of combining Mj and K, we can compare it to the rankings generated only with MJ (showcased at the beginning of the notebook), a slightly modified continuous join quality metric (defined below) and the continuous join quality metrics for Jaccard and Containment.\n","\n","We perform this comparison by:\n","- Total number of correct positions (that is, semantics in the top positions, syntactic in the lower ones).\n","- Number of mistaken spaces: given a join that is not correctly classified, how many positions should it move to reach a \"correct\" position.\n","- Quadratic number of mistaken positions: same as before but squaring the distances, which applies a stronger penalization to further mispositions.\n","- Precision@100, as done in the introduction.\n","\n","**Result**: The continuous join quality defined with Multiset Jaccard, and its subsequent ranking, is the best performing in most of the metrics. It is only slightly surpassed by its alternative formulation in the quadratic number of mistaken positions, and tied in Precision@k. In any case, the improvement over the base Multiset Jaccard ranking (which was already better than Jaccard and containment) is noticeable."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"GocKzI-eys7Z"},"outputs":[],"source":["def continuousJoinQualityC(C,K,strictness):\n","  mu_v = [0.44 + 0.25*strictness, 0 + 0.25*strictness]\n","  \"\"\"\n","  The covariance matrix is defined as:\n","  cov = [[sigma_v[0]**2, 0],\n","        [0, sigma_v[1]**2]]\n","  \"\"\"\n","  sigma_v = [0.19, 0.28]\n","  a1, b1 = (- mu_v[0]) / sigma_v[0], (1 - mu_v[0]) / sigma_v[0]\n","  a2, b2 = (- mu_v[1]) / sigma_v[1], (1 - mu_v[1]) / sigma_v[1]\n","  Cmodel = truncnorm(a1, b1, scale=sigma_v[0], loc=mu_v[0])\n","  Kmodel = truncnorm(a2, b2, scale=sigma_v[1], loc=mu_v[1])\n","  return Cmodel.cdf(C)*Kmodel.cdf(K)\n","\n","def continuousJoinQuality_2(MJ,K,strictness):\n","  mu_v = [0.0 + 0.25*strictness, 0. + 0.25*strictness]\n","  \"\"\"\n","  The covariance matrix is defined as:\n","  cov = [[sigma_v[0]**2, 0],\n","        [0, sigma_v[1]**2]]\n","  \"\"\"\n","  sigma_v = [0.1, 0.37]\n","  a1, b1 = (- mu_v[0]) / sigma_v[0], (1 - mu_v[0]) / sigma_v[0]\n","  a2, b2 = (- mu_v[1]) / sigma_v[1], (1 - mu_v[1]) / sigma_v[1]\n","  MJmodel = truncnorm(a1, b1, scale=sigma_v[0], loc=mu_v[0])\n","  Kmodel = truncnorm(a2, b2, scale=sigma_v[1], loc=mu_v[1])\n","  return MJmodel.cdf(MJ)*Kmodel.cdf(K)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":444},"executionInfo":{"elapsed":42386,"status":"ok","timestamp":1721143617027,"user":{"displayName":"Marc Maynou Yelamos","userId":"15390060321136749570"},"user_tz":-120},"id":"ggnXOAXBx6zt","outputId":"4a2b6014-1bc7-47a9-890d-7ef8f0a1f3a4"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                          ds_name      att_name  \\\n","0     Distributions_data_2016.csv  demographics   \n","1           USA_cars_datasets.csv       country   \n","2               books_updated.csv  languageCode   \n","3                        cars.csv         color   \n","4                        cars.csv  transmission   \n","...                           ...           ...   \n","4313              student-mat.csv       Pstatus   \n","4314                   top10s.csv         title   \n","4315            top_100_books.csv       author2   \n","4316             us_companies.csv   companytype   \n","4317        pte_active_active.csv        drugid   \n","\n","                                        ds_name_2      att_name_2  \\\n","0     Tech_sector_diversity_demographics_2016.csv   raceEthnicity   \n","1                               world_country.csv            Name   \n","2                 countries_metadatacountries.csv     CountryCode   \n","3                                      colors.csv            name   \n","4                             imdb_ijs_movies.csv            name   \n","...                                           ...             ...   \n","4313                         dataset_1_anneal.csv  temper_rolling   \n","4314                                song_data.csv        songname   \n","4315                            books_updated.csv         authors   \n","4316                           worldcitiespop.csv         Country   \n","4317                            pte_bound_cnt.csv            Arg0   \n","\n","      sizeDistinct1  sizeDistinct2  joinSize         C         K         J  \\\n","0                13              6         3  0.230769  0.461538  0.187500   \n","1                 2            239         1  0.500000  0.008368  0.004167   \n","2                25            247         9  0.360000  0.101215  0.034091   \n","3                12            135         9  0.750000  0.088889  0.065217   \n","4                 2          45482         1  0.500000  0.000044  0.000022   \n","...             ...            ...       ...       ...       ...       ...   \n","4313              2              1        51  0.500000  0.500000  0.333333   \n","4314            584          12932         1  0.493151  0.045159  0.021567   \n","4315             83           4636       288  0.409639  0.017903  0.006999   \n","4316              9            234        34  0.111111  0.038462  0.000000   \n","4317            298            340         1  1.000000  0.876471  0.876471   \n","\n","      MultisetJ relationship  MultisetJ_rank      cjqC          cjqJ  \\\n","0      0.000186    syntactic          3218.0  0.063367  3.910159e-02   \n","1      0.000365     semantic          2929.0  0.009820  5.458188e-06   \n","2      0.000878    syntactic          2641.0  0.054708  6.777717e-04   \n","3      0.000233     semantic          3102.0  0.194229  1.423813e-03   \n","4      0.000011    syntactic          3951.0  0.000051  1.465678e-10   \n","...         ...          ...             ...       ...           ...   \n","4313   0.000773    syntactic          2688.0  0.445479  1.657404e-01   \n","4314   0.014713     semantic          1498.0  0.051987  1.741488e-04   \n","4315   0.003268     semantic          1957.0  0.013250  2.007347e-05   \n","4316   0.000000    syntactic          4318.0  0.001449  0.000000e+00   \n","4317   0.466354    syntactic            58.0  0.997589  9.773633e-01   \n","\n","      cjqMultisetJ  cjqMultisetJ_2  \n","0     1.041278e-05    6.340111e-04  \n","1     4.659192e-07    2.594699e-05  \n","2     1.381148e-05    7.626005e-04  \n","3     3.200301e-06    1.771393e-04  \n","4     7.529394e-11    4.204510e-09  \n","...            ...             ...  \n","4313  4.488233e-05    2.769166e-03  \n","4314  1.131892e-04    5.915412e-03  \n","4315  9.130448e-06    5.020441e-04  \n","4316  0.000000e+00    0.000000e+00  \n","4317  4.230553e-01    9.852437e-01  \n","\n","[4318 rows x 17 columns]"],"text/html":["\n","  <div id=\"df-df6d1b97-2fb8-40c7-90ef-e60592665c3b\" 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>ds_name</th>\n","      <th>att_name</th>\n","      <th>ds_name_2</th>\n","      <th>att_name_2</th>\n","      <th>sizeDistinct1</th>\n","      <th>sizeDistinct2</th>\n","      <th>joinSize</th>\n","      <th>C</th>\n","      <th>K</th>\n","      <th>J</th>\n","      <th>MultisetJ</th>\n","      <th>relationship</th>\n","      <th>MultisetJ_rank</th>\n","      <th>cjqC</th>\n","      <th>cjqJ</th>\n","      <th>cjqMultisetJ</th>\n","      <th>cjqMultisetJ_2</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Distributions_data_2016.csv</td>\n","      <td>demographics</td>\n","      <td>Tech_sector_diversity_demographics_2016.csv</td>\n","      <td>raceEthnicity</td>\n","      <td>13</td>\n","      <td>6</td>\n","      <td>3</td>\n","      <td>0.230769</td>\n","      <td>0.461538</td>\n","      <td>0.187500</td>\n","      <td>0.000186</td>\n","      <td>syntactic</td>\n","      <td>3218.0</td>\n","      <td>0.063367</td>\n","      <td>3.910159e-02</td>\n","      <td>1.041278e-05</td>\n","      <td>6.340111e-04</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>USA_cars_datasets.csv</td>\n","      <td>country</td>\n","      <td>world_country.csv</td>\n","      <td>Name</td>\n","      <td>2</td>\n","      <td>239</td>\n","      <td>1</td>\n","      <td>0.500000</td>\n","      <td>0.008368</td>\n","      <td>0.004167</td>\n","      <td>0.000365</td>\n","      <td>semantic</td>\n","      <td>2929.0</td>\n","      <td>0.009820</td>\n","      <td>5.458188e-06</td>\n","      <td>4.659192e-07</td>\n","      <td>2.594699e-05</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>books_updated.csv</td>\n","      <td>languageCode</td>\n","      <td>countries_metadatacountries.csv</td>\n","      <td>CountryCode</td>\n","      <td>25</td>\n","      <td>247</td>\n","      <td>9</td>\n","      <td>0.360000</td>\n","      <td>0.101215</td>\n","      <td>0.034091</td>\n","      <td>0.000878</td>\n","      <td>syntactic</td>\n","      <td>2641.0</td>\n","      <td>0.054708</td>\n","      <td>6.777717e-04</td>\n","      <td>1.381148e-05</td>\n","      <td>7.626005e-04</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>cars.csv</td>\n","      <td>color</td>\n","      <td>colors.csv</td>\n","      <td>name</td>\n","      <td>12</td>\n","      <td>135</td>\n","      <td>9</td>\n","      <td>0.750000</td>\n","      <td>0.088889</td>\n","      <td>0.065217</td>\n","      <td>0.000233</td>\n","      <td>semantic</td>\n","      <td>3102.0</td>\n","      <td>0.194229</td>\n","      <td>1.423813e-03</td>\n","      <td>3.200301e-06</td>\n","      <td>1.771393e-04</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>cars.csv</td>\n","      <td>transmission</td>\n","      <td>imdb_ijs_movies.csv</td>\n","      <td>name</td>\n","      <td>2</td>\n","      <td>45482</td>\n","      <td>1</td>\n","      <td>0.500000</td>\n","      <td>0.000044</td>\n","      <td>0.000022</td>\n","      <td>0.000011</td>\n","      <td>syntactic</td>\n","      <td>3951.0</td>\n","      <td>0.000051</td>\n","      <td>1.465678e-10</td>\n","      <td>7.529394e-11</td>\n","      <td>4.204510e-09</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>4313</th>\n","      <td>student-mat.csv</td>\n","      <td>Pstatus</td>\n","      <td>dataset_1_anneal.csv</td>\n","      <td>temper_rolling</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>51</td>\n","      <td>0.500000</td>\n","      <td>0.500000</td>\n","      <td>0.333333</td>\n","      <td>0.000773</td>\n","      <td>syntactic</td>\n","      <td>2688.0</td>\n","      <td>0.445479</td>\n","      <td>1.657404e-01</td>\n","      <td>4.488233e-05</td>\n","      <td>2.769166e-03</td>\n","    </tr>\n","    <tr>\n","      <th>4314</th>\n","      <td>top10s.csv</td>\n","      <td>title</td>\n","      <td>song_data.csv</td>\n","      <td>songname</td>\n","      <td>584</td>\n","      <td>12932</td>\n","      <td>1</td>\n","      <td>0.493151</td>\n","      <td>0.045159</td>\n","      <td>0.021567</td>\n","      <td>0.014713</td>\n","      <td>semantic</td>\n","      <td>1498.0</td>\n","      <td>0.051987</td>\n","      <td>1.741488e-04</td>\n","      <td>1.131892e-04</td>\n","      <td>5.915412e-03</td>\n","    </tr>\n","    <tr>\n","      <th>4315</th>\n","      <td>top_100_books.csv</td>\n","      <td>author2</td>\n","      <td>books_updated.csv</td>\n","      <td>authors</td>\n","      <td>83</td>\n","      <td>4636</td>\n","      <td>288</td>\n","      <td>0.409639</td>\n","      <td>0.017903</td>\n","      <td>0.006999</td>\n","      <td>0.003268</td>\n","      <td>semantic</td>\n","      <td>1957.0</td>\n","      <td>0.013250</td>\n","      <td>2.007347e-05</td>\n","      <td>9.130448e-06</td>\n","      <td>5.020441e-04</td>\n","    </tr>\n","    <tr>\n","      <th>4316</th>\n","      <td>us_companies.csv</td>\n","      <td>companytype</td>\n","      <td>worldcitiespop.csv</td>\n","      <td>Country</td>\n","      <td>9</td>\n","      <td>234</td>\n","      <td>34</td>\n","      <td>0.111111</td>\n","      <td>0.038462</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>syntactic</td>\n","      <td>4318.0</td>\n","      <td>0.001449</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","    </tr>\n","    <tr>\n","      <th>4317</th>\n","      <td>pte_active_active.csv</td>\n","      <td>drugid</td>\n","      <td>pte_bound_cnt.csv</td>\n","      <td>Arg0</td>\n","      <td>298</td>\n","      <td>340</td>\n","      <td>1</td>\n","      <td>1.000000</td>\n","      <td>0.876471</td>\n","      <td>0.876471</td>\n","      <td>0.466354</td>\n","      <td>syntactic</td>\n","      <td>58.0</td>\n","      <td>0.997589</td>\n","      <td>9.773633e-01</td>\n","      <td>4.230553e-01</td>\n","      <td>9.852437e-01</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>4318 rows × 17 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-df6d1b97-2fb8-40c7-90ef-e60592665c3b')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-df6d1b97-2fb8-40c7-90ef-e60592665c3b 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-df6d1b97-2fb8-40c7-90ef-e60592665c3b');\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","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-97d20477-f136-48b7-870e-835a441cc770\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-97d20477-f136-48b7-870e-835a441cc770')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-97d20477-f136-48b7-870e-835a441cc770 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","\n","  <div id=\"id_e3c151ae-004c-4682-811e-5daca34889bb\">\n","    <style>\n","      .colab-df-generate {\n","        background-color: #E8F0FE;\n","        border: none;\n","        border-radius: 50%;\n","        cursor: pointer;\n","        display: none;\n","        fill: #1967D2;\n","        height: 32px;\n","        padding: 0 0 0 0;\n","        width: 32px;\n","      }\n","\n","      .colab-df-generate:hover {\n","        background-color: #E2EBFA;\n","        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","        fill: #174EA6;\n","      }\n","\n","      [theme=dark] .colab-df-generate {\n","        background-color: #3B4455;\n","        fill: #D2E3FC;\n","      }\n","\n","      [theme=dark] .colab-df-generate:hover {\n","        background-color: #434B5C;\n","        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","        fill: #FFFFFF;\n","      }\n","    </style>\n","    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('groundTruth')\"\n","            title=\"Generate code using this dataframe.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n","  </svg>\n","    </button>\n","    <script>\n","      (() => {\n","      const buttonEl =\n","        document.querySelector('#id_e3c151ae-004c-4682-811e-5daca34889bb button.colab-df-generate');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","      buttonEl.onclick = () => {\n","        google.colab.notebook.generateWithVariable('groundTruth');\n","      }\n","      })();\n","    </script>\n","  </div>\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"groundTruth","summary":"{\n  \"name\": \"groundTruth\",\n  \"rows\": 4318,\n  \"fields\": [\n    {\n      \"column\": \"ds_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 129,\n        \"samples\": [\n          \"pte_five_ring.csv\",\n          \"Books_df.csv\",\n          \"pte_testactive_Neg.csv\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"att_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 225,\n        \"samples\": [\n          \"phos\",\n          \"GeneID1\",\n          \"c_codes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ds_name_2\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 123,\n        \"samples\": [\n          \"pte_drug.csv\",\n          \"World_countries_env_vars.csv\",\n          \"pte_testactive.csv\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"att_name_2\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 203,\n        \"samples\": [\n          \"element\",\n          \"StreetAddress\",\n          \"UnitMeasureCode\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sizeDistinct1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1324,\n        \"min\": 1,\n        \"max\": 24304,\n        \"num_unique_values\": 165,\n        \"samples\": [\n          446,\n          29,\n          12844\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sizeDistinct2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 693454,\n        \"min\": 1,\n        \"max\": 2351883,\n        \"num_unique_values\": 171,\n        \"samples\": [\n          16739,\n          577,\n          12932\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"joinSize\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 620,\n        \"min\": 1,\n        \"max\": 20441,\n        \"num_unique_values\": 306,\n        \"samples\": [\n          181,\n          102,\n          502\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"C\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.30181849153088436,\n        \"min\": 0.100591716,\n        \"max\": 1.0,\n        \"num_unique_values\": 1286,\n        \"samples\": [\n          0.467571145,\n          0.342105263,\n          0.352625052\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"K\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3715399195400264,\n        \"min\": 4.251912191210192e-07,\n        \"max\": 1.0,\n        \"num_unique_values\": 1560,\n        \"samples\": [\n          0.056338028169014,\n          0.0072150072150072,\n          0.0035\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"J\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2831255737668051,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1730,\n        \"samples\": [\n          0.0769230769230769,\n          0.000297044408139,\n          0.1619109734224686\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MultisetJ\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10459159214315024,\n        \"min\": 0.0,\n        \"max\": 0.5,\n        \"num_unique_values\": 2086,\n        \"samples\": [\n          0.0014108964619057,\n          5.142695515283805e-05,\n          0.0020853274989744\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"relationship\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"semantic\",\n          \"syntactic\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MultisetJ_rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1246.6435603919297,\n        \"min\": 1.0,\n        \"max\": 4318.0,\n        \"num_unique_values\": 4318,\n        \"samples\": [\n          3999.0,\n          2561.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cjqC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.36741522334414145,\n        \"min\": 1.571847718588878e-07,\n        \"max\": 1.0,\n        \"num_unique_values\": 2667,\n        \"samples\": [\n          1.9042091363032308e-06,\n          0.02109359588140335\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cjqJ\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.31362255163070896,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2034,\n        \"samples\": [\n          0.004731069379002572,\n          7.987635600364362e-08\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cjqMultisetJ\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07403138487687441,\n        \"min\": 0.0,\n        \"max\": 0.49487187974849317,\n        \"num_unique_values\": 2480,\n        \"samples\": [\n          8.199575798969349e-06,\n          0.466388661240693\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cjqMultisetJ_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2900304529264677,\n        \"min\": 0.0,\n        \"max\": 0.9999917281958985,\n        \"num_unique_values\": 2480,\n        \"samples\": [\n          0.0005714263438351638,\n          0.9975524995966635\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":9}],"source":["groundTruth['cjqC'] = groundTruth.apply(lambda row: continuousJoinQualityC(row['C'],row['K'], 0.25), axis=1)\n","groundTruth['cjqJ'] = groundTruth.apply(lambda row: continuousJoinQualityC(row['J'],row['K'], 0.25), axis=1)\n","groundTruth['cjqMultisetJ'] = groundTruth.apply(lambda row: continuousJoinQuality(row['MultisetJ'],row['K'], 0.25), axis=1)\n","groundTruth['cjqMultisetJ_2'] = groundTruth.apply(lambda row: continuousJoinQuality_2(row['MultisetJ'],row['K'], 0.25), axis=1)\n","\n","groundTruth"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":444},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1721143617028,"user":{"displayName":"Marc Maynou Yelamos","userId":"15390060321136749570"},"user_tz":-120},"id":"KbstoJRsx6vt","outputId":"252a3667-a63b-4cdf-8ec1-9fd480199293"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                          ds_name      att_name  \\\n","0     Distributions_data_2016.csv  demographics   \n","1           USA_cars_datasets.csv       country   \n","2               books_updated.csv  languageCode   \n","3                        cars.csv         color   \n","4                        cars.csv  transmission   \n","...                           ...           ...   \n","4313              student-mat.csv       Pstatus   \n","4314                   top10s.csv         title   \n","4315            top_100_books.csv       author2   \n","4316             us_companies.csv   companytype   \n","4317        pte_active_active.csv        drugid   \n","\n","                                        ds_name_2      att_name_2  \\\n","0     Tech_sector_diversity_demographics_2016.csv   raceEthnicity   \n","1                               world_country.csv            Name   \n","2                 countries_metadatacountries.csv     CountryCode   \n","3                                      colors.csv            name   \n","4                             imdb_ijs_movies.csv            name   \n","...                                           ...             ...   \n","4313                         dataset_1_anneal.csv  temper_rolling   \n","4314                                song_data.csv        songname   \n","4315                            books_updated.csv         authors   \n","4316                           worldcitiespop.csv         Country   \n","4317                            pte_bound_cnt.csv            Arg0   \n","\n","      sizeDistinct1  sizeDistinct2  joinSize         C         K         J  \\\n","0                13              6         3  0.230769  0.461538  0.187500   \n","1                 2            239         1  0.500000  0.008368  0.004167   \n","2                25            247         9  0.360000  0.101215  0.034091   \n","3                12            135         9  0.750000  0.088889  0.065217   \n","4                 2          45482         1  0.500000  0.000044  0.000022   \n","...             ...            ...       ...       ...       ...       ...   \n","4313              2              1        51  0.500000  0.500000  0.333333   \n","4314            584          12932         1  0.493151  0.045159  0.021567   \n","4315             83           4636       288  0.409639  0.017903  0.006999   \n","4316              9            234        34  0.111111  0.038462  0.000000   \n","4317            298            340         1  1.000000  0.876471  0.876471   \n","\n","      ...  relationship MultisetJ_rank      cjqC          cjqJ  cjqMultisetJ  \\\n","0     ...     syntactic         3218.0  0.063367  3.910159e-02  1.041278e-05   \n","1     ...      semantic         2929.0  0.009820  5.458188e-06  4.659192e-07   \n","2     ...     syntactic         2641.0  0.054708  6.777717e-04  1.381148e-05   \n","3     ...      semantic         3102.0  0.194229  1.423813e-03  3.200301e-06   \n","4     ...     syntactic         3951.0  0.000051  1.465678e-10  7.529394e-11   \n","...   ...           ...            ...       ...           ...           ...   \n","4313  ...     syntactic         2688.0  0.445479  1.657404e-01  4.488233e-05   \n","4314  ...      semantic         1498.0  0.051987  1.741488e-04  1.131892e-04   \n","4315  ...      semantic         1957.0  0.013250  2.007347e-05  9.130448e-06   \n","4316  ...     syntactic         4318.0  0.001449  0.000000e+00  0.000000e+00   \n","4317  ...     syntactic           58.0  0.997589  9.773633e-01  4.230553e-01   \n","\n","      cjqMultisetJ_2  cjqMultisetJ_rank  cjqMultisetJ_2_rank  cjqC_rank  \\\n","0       6.340111e-04             2642.0               2631.0     2126.0   \n","1       2.594699e-05             3246.0               3246.0     2948.0   \n","2       7.626005e-04             2582.0               2593.0     2184.0   \n","3       1.771393e-04             2886.0               2902.0     1643.0   \n","4       4.204510e-09             3965.0               3965.0     3951.0   \n","...              ...                ...                  ...        ...   \n","4313    2.769166e-03             2293.0               2288.0     1222.0   \n","4314    5.915412e-03             1980.0               2013.0     2203.0   \n","4315    5.020441e-04             2661.0               2694.0     2781.0   \n","4316    0.000000e+00             4318.0               4318.0     3450.0   \n","4317    9.852437e-01               58.0                162.0      277.0   \n","\n","      cjqJ_rank  \n","0        1469.0  \n","1        3232.0  \n","2        2610.0  \n","3        2487.0  \n","4        3956.0  \n","...         ...  \n","4313     1138.0  \n","4314     2781.0  \n","4315     3039.0  \n","4316     4318.0  \n","4317      254.0  \n","\n","[4318 rows x 21 columns]"],"text/html":["\n","  <div id=\"df-1231ec49-fbcd-4870-a232-c3a6272c9d33\" 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>ds_name</th>\n","      <th>att_name</th>\n","      <th>ds_name_2</th>\n","      <th>att_name_2</th>\n","      <th>sizeDistinct1</th>\n","      <th>sizeDistinct2</th>\n","      <th>joinSize</th>\n","      <th>C</th>\n","      <th>K</th>\n","      <th>J</th>\n","      <th>...</th>\n","      <th>relationship</th>\n","      <th>MultisetJ_rank</th>\n","      <th>cjqC</th>\n","      <th>cjqJ</th>\n","      <th>cjqMultisetJ</th>\n","      <th>cjqMultisetJ_2</th>\n","      <th>cjqMultisetJ_rank</th>\n","      <th>cjqMultisetJ_2_rank</th>\n","      <th>cjqC_rank</th>\n","      <th>cjqJ_rank</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Distributions_data_2016.csv</td>\n","      <td>demographics</td>\n","      <td>Tech_sector_diversity_demographics_2016.csv</td>\n","      <td>raceEthnicity</td>\n","      <td>13</td>\n","      <td>6</td>\n","      <td>3</td>\n","      <td>0.230769</td>\n","      <td>0.461538</td>\n","      <td>0.187500</td>\n","      <td>...</td>\n","      <td>syntactic</td>\n","      <td>3218.0</td>\n","      <td>0.063367</td>\n","      <td>3.910159e-02</td>\n","      <td>1.041278e-05</td>\n","      <td>6.340111e-04</td>\n","      <td>2642.0</td>\n","      <td>2631.0</td>\n","      <td>2126.0</td>\n","      <td>1469.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>USA_cars_datasets.csv</td>\n","      <td>country</td>\n","      <td>world_country.csv</td>\n","      <td>Name</td>\n","      <td>2</td>\n","      <td>239</td>\n","      <td>1</td>\n","      <td>0.500000</td>\n","      <td>0.008368</td>\n","      <td>0.004167</td>\n","      <td>...</td>\n","      <td>semantic</td>\n","      <td>2929.0</td>\n","      <td>0.009820</td>\n","      <td>5.458188e-06</td>\n","      <td>4.659192e-07</td>\n","      <td>2.594699e-05</td>\n","      <td>3246.0</td>\n","      <td>3246.0</td>\n","      <td>2948.0</td>\n","      <td>3232.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>books_updated.csv</td>\n","      <td>languageCode</td>\n","      <td>countries_metadatacountries.csv</td>\n","      <td>CountryCode</td>\n","      <td>25</td>\n","      <td>247</td>\n","      <td>9</td>\n","      <td>0.360000</td>\n","      <td>0.101215</td>\n","      <td>0.034091</td>\n","      <td>...</td>\n","      <td>syntactic</td>\n","      <td>2641.0</td>\n","      <td>0.054708</td>\n","      <td>6.777717e-04</td>\n","      <td>1.381148e-05</td>\n","      <td>7.626005e-04</td>\n","      <td>2582.0</td>\n","      <td>2593.0</td>\n","      <td>2184.0</td>\n","      <td>2610.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>cars.csv</td>\n","      <td>color</td>\n","      <td>colors.csv</td>\n","      <td>name</td>\n","      <td>12</td>\n","      <td>135</td>\n","      <td>9</td>\n","      <td>0.750000</td>\n","      <td>0.088889</td>\n","      <td>0.065217</td>\n","      <td>...</td>\n","      <td>semantic</td>\n","      <td>3102.0</td>\n","      <td>0.194229</td>\n","      <td>1.423813e-03</td>\n","      <td>3.200301e-06</td>\n","      <td>1.771393e-04</td>\n","      <td>2886.0</td>\n","      <td>2902.0</td>\n","      <td>1643.0</td>\n","      <td>2487.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>cars.csv</td>\n","      <td>transmission</td>\n","      <td>imdb_ijs_movies.csv</td>\n","      <td>name</td>\n","      <td>2</td>\n","      <td>45482</td>\n","      <td>1</td>\n","      <td>0.500000</td>\n","      <td>0.000044</td>\n","      <td>0.000022</td>\n","      <td>...</td>\n","      <td>syntactic</td>\n","      <td>3951.0</td>\n","      <td>0.000051</td>\n","      <td>1.465678e-10</td>\n","      <td>7.529394e-11</td>\n","      <td>4.204510e-09</td>\n","      <td>3965.0</td>\n","      <td>3965.0</td>\n","      <td>3951.0</td>\n","      <td>3956.0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>4313</th>\n","      <td>student-mat.csv</td>\n","      <td>Pstatus</td>\n","      <td>dataset_1_anneal.csv</td>\n","      <td>temper_rolling</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>51</td>\n","      <td>0.500000</td>\n","      <td>0.500000</td>\n","      <td>0.333333</td>\n","      <td>...</td>\n","      <td>syntactic</td>\n","      <td>2688.0</td>\n","      <td>0.445479</td>\n","      <td>1.657404e-01</td>\n","      <td>4.488233e-05</td>\n","      <td>2.769166e-03</td>\n","      <td>2293.0</td>\n","      <td>2288.0</td>\n","      <td>1222.0</td>\n","      <td>1138.0</td>\n","    </tr>\n","    <tr>\n","      <th>4314</th>\n","      <td>top10s.csv</td>\n","      <td>title</td>\n","      <td>song_data.csv</td>\n","      <td>songname</td>\n","      <td>584</td>\n","      <td>12932</td>\n","      <td>1</td>\n","      <td>0.493151</td>\n","      <td>0.045159</td>\n","      <td>0.021567</td>\n","      <td>...</td>\n","      <td>semantic</td>\n","      <td>1498.0</td>\n","      <td>0.051987</td>\n","      <td>1.741488e-04</td>\n","      <td>1.131892e-04</td>\n","      <td>5.915412e-03</td>\n","      <td>1980.0</td>\n","      <td>2013.0</td>\n","      <td>2203.0</td>\n","      <td>2781.0</td>\n","    </tr>\n","    <tr>\n","      <th>4315</th>\n","      <td>top_100_books.csv</td>\n","      <td>author2</td>\n","      <td>books_updated.csv</td>\n","      <td>authors</td>\n","      <td>83</td>\n","      <td>4636</td>\n","      <td>288</td>\n","      <td>0.409639</td>\n","      <td>0.017903</td>\n","      <td>0.006999</td>\n","      <td>...</td>\n","      <td>semantic</td>\n","      <td>1957.0</td>\n","      <td>0.013250</td>\n","      <td>2.007347e-05</td>\n","      <td>9.130448e-06</td>\n","      <td>5.020441e-04</td>\n","      <td>2661.0</td>\n","      <td>2694.0</td>\n","      <td>2781.0</td>\n","      <td>3039.0</td>\n","    </tr>\n","    <tr>\n","      <th>4316</th>\n","      <td>us_companies.csv</td>\n","      <td>companytype</td>\n","      <td>worldcitiespop.csv</td>\n","      <td>Country</td>\n","      <td>9</td>\n","      <td>234</td>\n","      <td>34</td>\n","      <td>0.111111</td>\n","      <td>0.038462</td>\n","      <td>0.000000</td>\n","      <td>...</td>\n","      <td>syntactic</td>\n","      <td>4318.0</td>\n","      <td>0.001449</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>4318.0</td>\n","      <td>4318.0</td>\n","      <td>3450.0</td>\n","      <td>4318.0</td>\n","    </tr>\n","    <tr>\n","      <th>4317</th>\n","      <td>pte_active_active.csv</td>\n","      <td>drugid</td>\n","      <td>pte_bound_cnt.csv</td>\n","      <td>Arg0</td>\n","      <td>298</td>\n","      <td>340</td>\n","      <td>1</td>\n","      <td>1.000000</td>\n","      <td>0.876471</td>\n","      <td>0.876471</td>\n","      <td>...</td>\n","      <td>syntactic</td>\n","      <td>58.0</td>\n","      <td>0.997589</td>\n","      <td>9.773633e-01</td>\n","      <td>4.230553e-01</td>\n","      <td>9.852437e-01</td>\n","      <td>58.0</td>\n","      <td>162.0</td>\n","      <td>277.0</td>\n","      <td>254.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>4318 rows × 21 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1231ec49-fbcd-4870-a232-c3a6272c9d33')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-1231ec49-fbcd-4870-a232-c3a6272c9d33 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-1231ec49-fbcd-4870-a232-c3a6272c9d33');\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","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-56917cfe-f60a-4818-bc89-212009fb44b2\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-56917cfe-f60a-4818-bc89-212009fb44b2')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-56917cfe-f60a-4818-bc89-212009fb44b2 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","\n","  <div id=\"id_3c794643-e5be-4e48-a68b-4eff6cb97872\">\n","    <style>\n","      .colab-df-generate {\n","        background-color: #E8F0FE;\n","        border: none;\n","        border-radius: 50%;\n","        cursor: pointer;\n","        display: none;\n","        fill: #1967D2;\n","        height: 32px;\n","        padding: 0 0 0 0;\n","        width: 32px;\n","      }\n","\n","      .colab-df-generate:hover {\n","        background-color: #E2EBFA;\n","        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","        fill: #174EA6;\n","      }\n","\n","      [theme=dark] .colab-df-generate {\n","        background-color: #3B4455;\n","        fill: #D2E3FC;\n","      }\n","\n","      [theme=dark] .colab-df-generate:hover {\n","        background-color: #434B5C;\n","        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","        fill: #FFFFFF;\n","      }\n","    </style>\n","    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('groundTruth')\"\n","            title=\"Generate code using this dataframe.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n","  </svg>\n","    </button>\n","    <script>\n","      (() => {\n","      const buttonEl =\n","        document.querySelector('#id_3c794643-e5be-4e48-a68b-4eff6cb97872 button.colab-df-generate');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","      buttonEl.onclick = () => {\n","        google.colab.notebook.generateWithVariable('groundTruth');\n","      }\n","      })();\n","    </script>\n","  </div>\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"groundTruth"}},"metadata":{},"execution_count":10}],"source":["groundTruth['MultisetJ_rank'] = groundTruth['MultisetJ'].rank(method=\"first\", ascending=False)\n","groundTruth['cjqMultisetJ_rank'] = groundTruth['cjqMultisetJ'].rank(method=\"first\", ascending=False)\n","groundTruth['cjqMultisetJ_2_rank'] = groundTruth['cjqMultisetJ_2'].rank(method=\"first\", ascending=False)\n","groundTruth['cjqC_rank'] = groundTruth['cjqC'].rank(method=\"first\", ascending=False)\n","groundTruth['cjqJ_rank'] = groundTruth['cjqJ'].rank(method=\"first\", ascending=False)\n","\n","groundTruth"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uESZCcTvfXnQ"},"outputs":[],"source":["def count_correct_rankings(ranking):\n","  count_correct_semantic = 0\n","  count_correct_syntactic = 0\n","  for index, row in groundTruth.iterrows():\n","    if (row['relationship'] == \"semantic\" and row[ranking] < 1696):\n","      count_correct_semantic = count_correct_semantic + 1\n","    elif (row['relationship'] == \"syntactic\" and row[ranking] > 1696):\n","      count_correct_syntactic = count_correct_syntactic + 1\n","  print(ranking, \"-------\", \"Correct semantic:\", count_correct_semantic, \"Correct syntactic:\", count_correct_syntactic)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1515,"status":"ok","timestamp":1719934729137,"user":{"displayName":"Marc Maynou Yelamos","userId":"15390060321136749570"},"user_tz":-120},"id":"uqLtwL9iFGBD","outputId":"821f9908-5a5e-4075-b98a-4a4b2cd0b8d8"},"outputs":[{"output_type":"stream","name":"stdout","text":["MultisetJ_rank ------- Correct semantic: 1203 Correct syntactic: 2129\n","cjqMultisetJ_rank ------- Correct semantic: 1207 Correct syntactic: 2133\n","cjqMultisetJ_2_rank ------- Correct semantic: 1196 Correct syntactic: 2123\n","cjqC_rank ------- Correct semantic: 1069 Correct syntactic: 1995\n","cjqJ_rank ------- Correct semantic: 1051 Correct syntactic: 1977\n"]}],"source":["count_correct_rankings(\"MultisetJ_rank\")\n","count_correct_rankings(\"cjqMultisetJ_rank\")\n","count_correct_rankings(\"cjqMultisetJ_2_rank\")\n","count_correct_rankings(\"cjqC_rank\")\n","count_correct_rankings(\"cjqJ_rank\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cJVdEHiUiYEX"},"outputs":[],"source":["def count_mistaken_positions(ranking):\n","  count_mistaken_positions_semantic = 0\n","  count_mistaken_positions_syntactic = 0\n","  for index, row in groundTruth.iterrows():\n","    if (row['relationship'] == \"semantic\" and row[ranking] > 1696):\n","      count_mistaken_positions_semantic = count_mistaken_positions_semantic + (row[ranking] - 1696)\n","    elif (row['relationship'] == \"syntactic\" and row[ranking] < 1696):\n","      count_mistaken_positions_syntactic = count_mistaken_positions_syntactic + (-row[ranking] + 1696)\n","  print(f\"{ranking} ------- Mistaken positions semantic: {count_mistaken_positions_semantic:,} Mistaken positions syntactic: {count_mistaken_positions_syntactic:,}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1581,"status":"ok","timestamp":1719934730717,"user":{"displayName":"Marc Maynou Yelamos","userId":"15390060321136749570"},"user_tz":-120},"id":"12wFiYZbJ9ku","outputId":"bf5393a7-5046-4c1a-8cdb-c8cf4c1bd3b7"},"outputs":[{"output_type":"stream","name":"stdout","text":["MultisetJ_rank ------- Mistaken positions semantic: 460,557.0 Mistaken positions syntactic: 355,964.0\n","cjqMultisetJ_rank ------- Mistaken positions semantic: 414,594.0 Mistaken positions syntactic: 356,484.0\n","cjqMultisetJ_2_rank ------- Mistaken positions semantic: 413,590.0 Mistaken positions syntactic: 357,101.0\n","cjqC_rank ------- Mistaken positions semantic: 525,836.0 Mistaken positions syntactic: 504,939.0\n","cjqJ_rank ------- Mistaken positions semantic: 504,016.0 Mistaken positions syntactic: 499,430.0\n"]}],"source":["count_mistaken_positions(\"MultisetJ_rank\")\n","count_mistaken_positions(\"cjqMultisetJ_rank\")\n","count_mistaken_positions(\"cjqMultisetJ_2_rank\")\n","count_mistaken_positions(\"cjqC_rank\")\n","count_mistaken_positions(\"cjqJ_rank\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lipI-rGTCa72"},"outputs":[],"source":["def count_mistaken_positions_quadratic(ranking):\n","  count_mistaken_positions_semantic = 0\n","  count_mistaken_positions_syntactic = 0\n","  for index, row in groundTruth.iterrows():\n","    if (row['relationship'] == \"semantic\" and row[ranking] > 1696):\n","      count_mistaken_positions_semantic = count_mistaken_positions_semantic + ((row[ranking] - 1696) ** 2)\n","    elif (row['relationship'] == \"syntactic\" and row[ranking] < 1696):\n","      count_mistaken_positions_syntactic = count_mistaken_positions_syntactic + ((-row[ranking] + 1696) ** 2)\n","  print(f\"{ranking} ------- Mistaken quadratic positions semantic: {count_mistaken_positions_semantic:,} Mistaken quadratic positions syntactic: {count_mistaken_positions_syntactic:,}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1801,"status":"ok","timestamp":1719934732517,"user":{"displayName":"Marc Maynou Yelamos","userId":"15390060321136749570"},"user_tz":-120},"id":"uk-efzBWCfZU","outputId":"154bdda8-b659-42a7-995d-35992ef14b19"},"outputs":[{"output_type":"stream","name":"stdout","text":["MultisetJ_rank ------- Mistaken quadratic positions semantic: 663,680,537.0 Mistaken quadratic positions syntactic: 379,189,732.0\n","cjqMultisetJ_rank ------- Mistaken quadratic positions semantic: 562,732,812.0 Mistaken quadratic positions syntactic: 379,635,866.0\n","cjqMultisetJ_2_rank ------- Mistaken quadratic positions semantic: 561,168,018.0 Mistaken quadratic positions syntactic: 377,576,521.0\n","cjqC_rank ------- Mistaken quadratic positions semantic: 652,927,148.0 Mistaken quadratic positions syntactic: 583,376,315.0\n","cjqJ_rank ------- Mistaken quadratic positions semantic: 610,433,968.0 Mistaken quadratic positions syntactic: 556,253,358.0\n"]}],"source":["count_mistaken_positions_quadratic(\"MultisetJ_rank\")\n","count_mistaken_positions_quadratic(\"cjqMultisetJ_rank\")\n","count_mistaken_positions_quadratic(\"cjqMultisetJ_2_rank\")\n","count_mistaken_positions_quadratic(\"cjqC_rank\")\n","count_mistaken_positions_quadratic(\"cjqJ_rank\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XBcycHuGdMS_"},"outputs":[],"source":["def get_precisions(ranking, k_max):\n","  results = []\n","  for k in range(1, k_max + 1, 2):\n","    groundTruthSorted_k = groundTruth.sort_values(by=ranking).head(k)\n","    recall_k = (groundTruthSorted_k['relationship'] == \"semantic\").sum()/k\n","    results.append(recall_k)\n","  return results\n","\n","\n","def precision_at_k_continous_metrics(k_max):\n","  cjqC = get_precisions(\"cjqC_rank\", k_max)\n","  cjqJ = get_precisions(\"cjqJ_rank\", k_max)\n","  cjqMJ = get_precisions(\"cjqMultisetJ_rank\", k_max)\n","  cjqMJ_2 = get_precisions(\"cjqMultisetJ_2_rank\", k_max)\n","  mj = get_precisions(\"MultisetJ_rank\", k_max)\n","\n","  plt.figure(figsize=(6, 3))\n","\n","  x = range(1, k_max + 1, 2)\n","  plt.plot(x, cjqC, label='CJQ Containment')\n","  plt.plot(x, cjqJ, linestyle='-.', label='CJQ Jaccard')\n","  plt.plot(x, cjqMJ_2, linestyle='--', label='CJQ Multiset Jaccard')\n","  plt.plot(x, cjqMJ_2, linestyle=':', label='CJQ Multiset Jaccard 2')\n","  plt.plot(x, mj, linestyle='solid', label='Multiset Jaccard')\n","\n","  plt.xlabel('k', fontsize=14)\n","  plt.ylabel('P@k', fontsize=14)\n","\n","  plt.xlim(-0.4, k_max + 1)\n","  plt.ylim(0, 1.03)\n","  plt.tick_params(axis='y', labelsize=12)\n","  plt.tick_params(axis='x', labelsize=12)\n","\n","  plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.2), fancybox=True, shadow=True, ncol=5, fontsize=10)\n","\n","  plt.grid(True)\n","  plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":359},"executionInfo":{"elapsed":2114,"status":"ok","timestamp":1721143769694,"user":{"displayName":"Marc Maynou Yelamos","userId":"15390060321136749570"},"user_tz":-120},"id":"i7T18Xp1lsbo","outputId":"24abbe88-0ab9-4ac9-a197-da9ec66d848e"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 600x300 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["precision_at_k_continous_metrics(100)"]}],"metadata":{"colab":{"collapsed_sections":["Zu4_tUm4xEn7"],"toc_visible":true,"provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}