{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "855fdafb-cce9-44ee-bdf9-776a375494ed",
   "metadata": {},
   "source": [
    "\n",
    "# User-Friendly Workflow\n",
    "\n",
    "\n",
    "----\n",
    "\n",
    "\n",
    "In this notebook we present the a __user-friendly approach__ in the well-known ABT-BUY dataset. This is a simple approach, specially developed for novice users in ER."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c43b9af-44cc-4091-8f22-857b15092e76",
   "metadata": {},
   "source": [
    "# Instalation\n",
    "\n",
    "pyJedAI is an open-source library that can be installed from PyPI.\n",
    "\n",
    "For more: [pypi.org/project/pyjedai/](https://pypi.org/project/pyjedai/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74187cf8-e496-4c43-a2ab-ef9a0cc57de0",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install pyjedai -U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b2b0b53e-3fd9-486f-a8aa-dc8be53de12a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: pyjedai\n",
      "Version: 0.0.3\n",
      "Summary: An open-source library that builds powerful end-to-end Entity Resolution workflows.\n",
      "Home-page: \n",
      "Author: \n",
      "Author-email: Konstantinos Nikoletos <nikoletos.kon@gmail.com>, George Papadakis <gpapadis84@gmail.com>\n",
      "License: Apache Software License 2.0\n",
      "Location: c:\\users\\nikol\\appdata\\local\\programs\\python\\python310\\lib\\site-packages\n",
      "Requires: faiss-cpu, gensim, matplotlib, matplotlib-inline, networkx, nltk, numpy, optuna, pandas, pandas-profiling, pandocfilters, PyYAML, rdflib, rdfpandas, regex, scipy, seaborn, sentence-transformers, strsim, strsimpy, tomli, tqdm, transformers\n",
      "Required-by: \n"
     ]
    }
   ],
   "source": [
    "!pip show pyjedai"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31da2f6d-830f-4639-9e67-37100c86d49d",
   "metadata": {},
   "source": [
    "Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4583ae66-fdc5-4e17-a297-83d7b59e79b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d574554b-4712-4a9d-b449-596f91372fda",
   "metadata": {},
   "source": [
    "## Data Reading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "81b21794-778c-451b-b323-77b47022b915",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyjedai.datamodel import Data\n",
    "\n",
    "data = Data(\n",
    "    dataset_1=pd.read_csv(\"./../data/ccer/D2/abt.csv\", sep='|', engine='python', na_filter=False).astype(str),\n",
    "    attributes_1=['id','name','description'],\n",
    "    id_column_name_1='id',\n",
    "    dataset_2=pd.read_csv(\"./../data/ccer/D2/buy.csv\", sep='|', engine='python', na_filter=False).astype(str),\n",
    "    attributes_2=['id','name','description'],\n",
    "    id_column_name_2='id',\n",
    "    ground_truth=pd.read_csv(\"./../data/ccer/D2/gt.csv\", sep='|', engine='python'),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fadab403-9b4c-410f-9112-2264ca299252",
   "metadata": {},
   "source": [
    "## WorkFlow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "94f6212e-3d0d-4b89-b213-d06cfbb553c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyjedai.workflow import WorkFlow, compare_workflows\n",
    "from pyjedai.block_building import StandardBlocking, QGramsBlocking, ExtendedQGramsBlocking, SuffixArraysBlocking, ExtendedSuffixArraysBlocking\n",
    "from pyjedai.block_cleaning import BlockFiltering, BlockPurging\n",
    "from pyjedai.comparison_cleaning import WeightedEdgePruning, WeightedNodePruning, CardinalityEdgePruning, CardinalityNodePruning, BLAST, ReciprocalCardinalityNodePruning, ReciprocalWeightedNodePruning, ComparisonPropagation\n",
    "from pyjedai.matching import EntityMatching\n",
    "from pyjedai.clustering import ConnectedComponentsClustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1a3675c9-a30f-4c20-a91c-affe3e09f14c",
   "metadata": {},
   "outputs": [],
   "source": [
    "w = WorkFlow(\n",
    "    block_building = dict(\n",
    "        method=QGramsBlocking,\n",
    "        params=dict(qgrams=3),\n",
    "        attributes_1=['name'],\n",
    "        attributes_2=['name']\n",
    "    ),\n",
    "    block_cleaning = [\n",
    "        dict(\n",
    "            method=BlockFiltering,\n",
    "            params=dict(ratio=0.8)\n",
    "        ),\n",
    "        dict(\n",
    "            method=BlockPurging, \n",
    "            params=dict(smoothing_factor=1.025)\n",
    "        )\n",
    "    ],\n",
    "    comparison_cleaning = dict(method=CardinalityEdgePruning),\n",
    "        entity_matching = dict(\n",
    "        method=EntityMatching,\n",
    "        metric='sorensen_dice',\n",
    "        similarity_threshold=0.5,\n",
    "        attributes = ['description', 'name']\n",
    "    ),\n",
    "    clustering = dict(method=ConnectedComponentsClustering),\n",
    "    name=\"Worflow-Test\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1f3f7c12-fcae-4175-bb5c-3d4989372107",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***************************************************************************************************************************\n",
      "                                         Μethod:  Q-Grams Blocking\n",
      "***************************************************************************************************************************\n",
      "Method name: Q-Grams Blocking\n",
      "Parameters: \n",
      "\tQ-Gramms: 3\n",
      "Runtime: 0.2410 seconds\n",
      "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "Performance:\n",
      "\tPrecision:      0.08% \n",
      "\tRecall:       100.00%\n",
      "\tF1-score:       0.17%\n",
      "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "***************************************************************************************************************************\n",
      "                                         Μethod:  Block Filtering\n",
      "***************************************************************************************************************************\n",
      "Method name: Block Filtering\n",
      "Parameters: \n",
      "\tRatio: 0.8\n",
      "Runtime: 0.0990 seconds\n",
      "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "Performance:\n",
      "\tPrecision:      0.12% \n",
      "\tRecall:       100.00%\n",
      "\tF1-score:       0.24%\n",
      "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "***************************************************************************************************************************\n",
      "                                         Μethod:  Block Purging\n",
      "***************************************************************************************************************************\n",
      "Method name: Block Purging\n",
      "Parameters: \n",
      "\tSmoothing factor: 1.025\n",
      "\tMax Comparisons per Block: 22500.0\n",
      "Runtime: 0.0250 seconds\n",
      "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "Performance:\n",
      "\tPrecision:      0.14% \n",
      "\tRecall:        99.91%\n",
      "\tF1-score:       0.28%\n",
      "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "***************************************************************************************************************************\n",
      "                                         Μethod:  Cardinality Edge Pruning\n",
      "***************************************************************************************************************************\n",
      "Method name: Cardinality Edge Pruning\n",
      "Parameters: \n",
      "\tNode centric: False\n",
      "\tWeighting scheme: JS\n",
      "Runtime: 3.0797 seconds\n",
      "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "Performance:\n",
      "\tPrecision:      4.74% \n",
      "\tRecall:        97.30%\n",
      "\tF1-score:       9.04%\n",
      "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "***************************************************************************************************************************\n",
      "                                         Μethod:  Entity Matching\n",
      "***************************************************************************************************************************\n",
      "Method name: Entity Matching\n",
      "Parameters: \n",
      "\tTokenizer: white_space_tokenizer\n",
      "\tMetric: dice\n",
      "\tSimilarity Threshold: 0.5\n",
      "Runtime: 10.4458 seconds\n",
      "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "Performance:\n",
      "\tPrecision:     77.78% \n",
      "\tRecall:         1.95%\n",
      "\tF1-score:       3.81%\n",
      "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "***************************************************************************************************************************\n",
      "                                         Μethod:  Connected Components Clustering\n",
      "***************************************************************************************************************************\n",
      "Method name: Connected Components Clustering\n",
      "Parameters: \n",
      "Runtime: 0.0000 seconds\n",
      "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "Performance:\n",
      "\tPrecision:     90.48% \n",
      "\tRecall:         1.77%\n",
      "\tF1-score:       3.46%\n",
      "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
     ]
    }
   ],
   "source": [
    "w.run(data, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f01b9916-c30f-4409-b634-45cbcdb3270f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>F1</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Runtime (sec)</th>\n",
       "      <th>Params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q-Grams Blocking</td>\n",
       "      <td>0.167526</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.083833</td>\n",
       "      <td>0.240996</td>\n",
       "      <td>{'Q-Gramms': 3}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Block Filtering</td>\n",
       "      <td>0.238795</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.119540</td>\n",
       "      <td>0.098999</td>\n",
       "      <td>{'Ratio': 0.8}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Block Purging</td>\n",
       "      <td>0.277537</td>\n",
       "      <td>99.907063</td>\n",
       "      <td>0.138962</td>\n",
       "      <td>0.025035</td>\n",
       "      <td>{'Smoothing factor': 1.025, 'Max Comparisons p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cardinality Edge Pruning</td>\n",
       "      <td>9.037939</td>\n",
       "      <td>97.304833</td>\n",
       "      <td>4.739058</td>\n",
       "      <td>3.079692</td>\n",
       "      <td>{'Node centric': False, 'Weighting scheme': 'JS'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Entity Matching</td>\n",
       "      <td>3.807797</td>\n",
       "      <td>1.951673</td>\n",
       "      <td>77.777778</td>\n",
       "      <td>10.445795</td>\n",
       "      <td>{'Tokenizer': 'white_space_tokenizer', 'Metric...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Connected Components Clustering</td>\n",
       "      <td>3.463993</td>\n",
       "      <td>1.765799</td>\n",
       "      <td>90.476190</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                         Algorithm        F1      Recall  Precision  \\\n",
       "0                 Q-Grams Blocking  0.167526  100.000000   0.083833   \n",
       "1                  Block Filtering  0.238795  100.000000   0.119540   \n",
       "2                    Block Purging  0.277537   99.907063   0.138962   \n",
       "3         Cardinality Edge Pruning  9.037939   97.304833   4.739058   \n",
       "4                  Entity Matching  3.807797    1.951673  77.777778   \n",
       "5  Connected Components Clustering  3.463993    1.765799  90.476190   \n",
       "\n",
       "   Runtime (sec)                                             Params  \n",
       "0       0.240996                                    {'Q-Gramms': 3}  \n",
       "1       0.098999                                     {'Ratio': 0.8}  \n",
       "2       0.025035  {'Smoothing factor': 1.025, 'Max Comparisons p...  \n",
       "3       3.079692  {'Node centric': False, 'Weighting scheme': 'JS'}  \n",
       "4      10.445795  {'Tokenizer': 'white_space_tokenizer', 'Metric...  \n",
       "5       0.000000                                                 {}  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.to_df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4dad448b-4871-4064-9ae6-ddab6b6149e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "w.visualize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1a726485-9353-4249-b46d-f3b7e23fd6f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "w.visualize(separate=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7a2035f-14af-4499-8dc9-01dd2abe563a",
   "metadata": {},
   "source": [
    "## Multiple workflows - Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "99758a12-d72b-4c23-9d1f-5a2f453fb7d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "36e22f9672434cffa05bd19b2600402d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Workflow-1:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a5591857c6294678942c6dc96663bcac",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Workflow-2:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w1 = WorkFlow(\n",
    "    block_building = dict(\n",
    "        method=QGramsBlocking, \n",
    "        params=dict(qgrams=4),\n",
    "        attributes_1=['name'],\n",
    "        attributes_2=['name']\n",
    "    ),\n",
    "    block_cleaning = [\n",
    "        dict(\n",
    "            method=BlockFiltering, \n",
    "            params=dict(ratio=0.6)\n",
    "        ),\n",
    "        dict(\n",
    "            method=BlockPurging, \n",
    "            params=dict(smoothing_factor=1.025)\n",
    "        )\n",
    "    ],\n",
    "    comparison_cleaning = dict(method=CardinalityEdgePruning),\n",
    "        entity_matching = dict(\n",
    "        method=EntityMatching, \n",
    "        metric='sorensen_dice',\n",
    "        similarity_threshold=0.5,\n",
    "        attributes = ['description', 'name']\n",
    "    ),\n",
    "    clustering = dict(method=ConnectedComponentsClustering)\n",
    ")\n",
    "w1.run(data, verbose=False, workflow_tqdm_enable=True)\n",
    "w2 = WorkFlow(\n",
    "    block_building = dict(\n",
    "        method=QGramsBlocking, \n",
    "        params=dict(qgrams=4),\n",
    "        attributes_1=['name'],\n",
    "        attributes_2=['name']\n",
    "    ),\n",
    "    block_cleaning = [\n",
    "        dict(\n",
    "            method=BlockFiltering, \n",
    "            params=dict(ratio=0.6)\n",
    "        ),\n",
    "        dict(\n",
    "            method=BlockPurging, \n",
    "            params=dict(smoothing_factor=1.025)\n",
    "        )\n",
    "    ],\n",
    "    comparison_cleaning = dict(method=CardinalityEdgePruning),\n",
    "        entity_matching = dict(\n",
    "        method=EntityMatching, \n",
    "        metric='sorensen_dice',\n",
    "        similarity_threshold=0.6,\n",
    "        attributes = ['description']\n",
    "    ),\n",
    "    clustering = dict(method=ConnectedComponentsClustering)\n",
    ")\n",
    "w2.run(data, verbose=False, workflow_tqdm_enable=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5d3cc423-5ef0-42d3-8607-b34177ecebbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>F1</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Runtime (sec)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Worflow-Test</td>\n",
       "      <td>3.463993</td>\n",
       "      <td>1.765799</td>\n",
       "      <td>90.47619</td>\n",
       "      <td>14.490555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Workflow-1</td>\n",
       "      <td>3.467153</td>\n",
       "      <td>1.765799</td>\n",
       "      <td>95.00000</td>\n",
       "      <td>7.563514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Workflow-2</td>\n",
       "      <td>3.467153</td>\n",
       "      <td>1.765799</td>\n",
       "      <td>95.00000</td>\n",
       "      <td>7.482991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Name        F1    Recall  Precision  Runtime (sec)\n",
       "0  Worflow-Test  3.463993  1.765799   90.47619      14.490555\n",
       "1    Workflow-1  3.467153  1.765799   95.00000       7.563514\n",
       "2    Workflow-2  3.467153  1.765799   95.00000       7.482991"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_workflows([w, w1, w2], with_visualization=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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