{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 🧠 HRHUB v2.1 - Enhanced with LLM (FREE VERSION)\n", "\n", "## 📘 Project Overview\n", "\n", "**Bilateral HR Matching System with LLM-Powered Intelligence**\n", "\n", "### What's New in v2.1:\n", "- ✅ **FREE LLM**: Using Hugging Face Inference API (no cost)\n", "- ✅ **Job Level Classification**: Zero-shot & few-shot learning\n", "- ✅ **Structured Skills Extraction**: Pydantic schemas\n", "- ✅ **Match Explainability**: LLM-generated reasoning\n", "- ✅ **Flexible Data Loading**: Upload OR Google Drive\n", "\n", "### Tech Stack:\n", "```\n", "Embeddings: sentence-transformers (local, free)\n", "LLM: Hugging Face Inference API (free tier)\n", "Schemas: Pydantic\n", "Platform: Google Colab → VS Code\n", "```\n", "\n", "---\n", "\n", "**Master's Thesis - Aalborg University** \n", "*Business Data Science Program* \n", "*December 2025*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 📊 Step 1: Install Dependencies" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✅ All packages installed!\n" ] } ], "source": [ "# Install required packages\n", "#!pip install -q sentence-transformers huggingface-hub pydantic plotly pyvis nbformat scikit-learn pandas numpy\n", "\n", "print(\"✅ All packages installed!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 📊 Step 2: Import Libraries" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✅ Environment variables loaded from .env\n", "✅ All libraries imported!\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import json\n", "import os\n", "from typing import List, Dict, Optional, Literal\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "# ML & NLP\n", "from sentence_transformers import SentenceTransformer\n", "from sklearn.metrics.pairwise import cosine_similarity\n", "\n", "# LLM Integration (FREE)\n", "from huggingface_hub import InferenceClient\n", "from pydantic import BaseModel, Field\n", "\n", "# Visualization\n", "import plotly.graph_objects as go\n", "from IPython.display import HTML, display\n", "\n", "# Configuration Settings\n", "from dotenv import load_dotenv\n", "\n", "# Carrega variáveis do .env\n", "load_dotenv()\n", "print(\"✅ Environment variables loaded from .env\")\n", "# ============== ATÉ AQUI ⬆️ ==============\n", "\n", "print(\"✅ All libraries imported!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 📊 Step 3: Configuration" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✅ Configuration loaded!\n", "🧠 Embedding model: all-MiniLM-L6-v2\n", "🤖 LLM model: meta-llama/Llama-3.2-3B-Instruct\n", "🔑 HF Token configured: Yes ✅\n", "📂 Data path: ../csv_files/\n" ] } ], "source": [ "class Config:\n", " \"\"\"Centralized configuration for VS Code\"\"\"\n", " \n", " # Paths - VS Code structure\n", " CSV_PATH = '../csv_files/'\n", " PROCESSED_PATH = '../processed/'\n", " RESULTS_PATH = '../results/'\n", " \n", " # Embedding Model\n", " EMBEDDING_MODEL = 'all-MiniLM-L6-v2'\n", " \n", " # LLM Settings (FREE - Hugging Face)\n", " HF_TOKEN = os.getenv('HF_TOKEN', '') # ✅ Pega do .env\n", " LLM_MODEL = 'meta-llama/Llama-3.2-3B-Instruct'\n", " \n", " LLM_MAX_TOKENS = 1000\n", " \n", " # Matching Parameters\n", " TOP_K_MATCHES = 10\n", " SIMILARITY_THRESHOLD = 0.5\n", " RANDOM_SEED = 42\n", "\n", "np.random.seed(Config.RANDOM_SEED)\n", "\n", "print(\"✅ Configuration loaded!\")\n", "print(f\"🧠 Embedding model: {Config.EMBEDDING_MODEL}\")\n", "print(f\"🤖 LLM model: {Config.LLM_MODEL}\")\n", "print(f\"🔑 HF Token configured: {'Yes ✅' if Config.HF_TOKEN else 'No ⚠️'}\")\n", "print(f\"📂 Data path: {Config.CSV_PATH}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 📊 Step 4: Load All Datasets" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "📂 Loading all datasets...\n", "\n", "======================================================================\n", "✅ Candidates: 9,544 rows × 35 columns\n", "✅ Companies (base): 24,473 rows\n", "✅ Company industries: 24,375 rows\n", "✅ Company specialties: 169,387 rows\n", "✅ Employee counts: 35,787 rows\n", "✅ Postings: 123,849 rows × 31 columns\n", "✅ Job skills: 213,768 rows\n", "✅ Job industries: 164,808 rows\n", "\n", "======================================================================\n", "✅ All datasets loaded successfully!\n", "\n" ] } ], "source": [ "print(\"📂 Loading all datasets...\\n\")\n", "print(\"=\" * 70)\n", "\n", "# Load main datasets\n", "candidates = pd.read_csv(f'{Config.CSV_PATH}resume_data.csv')\n", "print(f\"✅ Candidates: {len(candidates):,} rows × {len(candidates.columns)} columns\")\n", "\n", "companies_base = pd.read_csv(f'{Config.CSV_PATH}companies.csv')\n", "print(f\"✅ Companies (base): {len(companies_base):,} rows\")\n", "\n", "company_industries = pd.read_csv(f'{Config.CSV_PATH}company_industries.csv')\n", "print(f\"✅ Company industries: {len(company_industries):,} rows\")\n", "\n", "company_specialties = pd.read_csv(f'{Config.CSV_PATH}company_specialities.csv')\n", "print(f\"✅ Company specialties: {len(company_specialties):,} rows\")\n", "\n", "employee_counts = pd.read_csv(f'{Config.CSV_PATH}employee_counts.csv')\n", "print(f\"✅ Employee counts: {len(employee_counts):,} rows\")\n", "\n", "postings = pd.read_csv(f'{Config.CSV_PATH}postings.csv', on_bad_lines='skip', engine='python')\n", "print(f\"✅ Postings: {len(postings):,} rows × {len(postings.columns)} columns\")\n", "\n", "# Optional datasets\n", "try:\n", " job_skills = pd.read_csv(f'{Config.CSV_PATH}job_skills.csv')\n", " print(f\"✅ Job skills: {len(job_skills):,} rows\")\n", "except:\n", " job_skills = None\n", " print(\"⚠️ Job skills not found (optional)\")\n", "\n", "try:\n", " job_industries = pd.read_csv(f'{Config.CSV_PATH}job_industries.csv')\n", " print(f\"✅ Job industries: {len(job_industries):,} rows\")\n", "except:\n", " job_industries = None\n", " print(\"⚠️ Job industries not found (optional)\")\n", "\n", "print(\"\\n\" + \"=\" * 70)\n", "print(\"✅ All datasets loaded successfully!\\n\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 📊 Step 5: Merge & Enrich Company Data" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🔗 Merging company data...\n", "\n", "✅ Aggregated industries for 24,365 companies\n", "✅ Aggregated specialties for 17,780 companies\n", "\n", "✅ Base company merge complete: 35,787 companies\n", "\n" ] } ], "source": [ "print(\"🔗 Merging company data...\\n\")\n", "\n", "# Aggregate industries\n", "company_industries_agg = company_industries.groupby('company_id')['industry'].apply(\n", " lambda x: ', '.join(map(str, x.tolist()))\n", ").reset_index()\n", "company_industries_agg.columns = ['company_id', 'industries_list']\n", "print(f\"✅ Aggregated industries for {len(company_industries_agg):,} companies\")\n", "\n", "# Aggregate specialties\n", "company_specialties_agg = company_specialties.groupby('company_id')['speciality'].apply(\n", " lambda x: ' | '.join(x.astype(str).tolist())\n", ").reset_index()\n", "company_specialties_agg.columns = ['company_id', 'specialties_list']\n", "print(f\"✅ Aggregated specialties for {len(company_specialties_agg):,} companies\")\n", "\n", "# Merge all company data\n", "companies_merged = companies_base.copy()\n", "companies_merged = companies_merged.merge(company_industries_agg, on='company_id', how='left')\n", "companies_merged = companies_merged.merge(company_specialties_agg, on='company_id', how='left')\n", "companies_merged = companies_merged.merge(employee_counts, on='company_id', how='left')\n", "\n", "print(f\"\\n✅ Base company merge complete: {len(companies_merged):,} companies\\n\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 📊 Step 6: Enrich with Job Postings" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🌉 Enriching companies with job posting data...\n", "\n", "======================================================================\n", "KEY INSIGHT: Postings = 'Requirements Language Bridge'\n", "======================================================================\n", "\n", "✅ Enriched 35,787 companies with posting data\n", "\n" ] } ], "source": [ "print(\"🌉 Enriching companies with job posting data...\\n\")\n", "print(\"=\" * 70)\n", "print(\"KEY INSIGHT: Postings = 'Requirements Language Bridge'\")\n", "print(\"=\" * 70 + \"\\n\")\n", "\n", "postings = postings.fillna('')\n", "postings['company_id'] = postings['company_id'].astype(str)\n", "\n", "# Aggregate postings per company\n", "postings_agg = postings.groupby('company_id').agg({\n", " 'title': lambda x: ' | '.join(x.astype(str).tolist()[:10]),\n", " 'description': lambda x: ' '.join(x.astype(str).tolist()[:5]),\n", " 'skills_desc': lambda x: ' | '.join(x.dropna().astype(str).tolist()),\n", " 'formatted_experience_level': lambda x: ' | '.join(x.dropna().unique().astype(str)),\n", "}).reset_index()\n", "\n", "postings_agg.columns = ['company_id', 'posted_job_titles', 'posted_descriptions', 'required_skills', 'experience_levels']\n", "\n", "companies_merged['company_id'] = companies_merged['company_id'].astype(str)\n", "companies_full = companies_merged.merge(postings_agg, on='company_id', how='left').fillna('')\n", "\n", "print(f\"✅ Enriched {len(companies_full):,} companies with posting data\\n\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ 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company_idnamedescriptioncompany_sizestatecountrycityzip_codeaddressurlindustries_listspecialties_listemployee_countfollower_counttime_recordedposted_job_titlesposted_descriptionsrequired_skillsexperience_levels
01009IBMAt IBM, we do more than work. We create. We cr...7.0NYUSArmonk, New York10504International Business Machines Corp.https://www.linkedin.com/company/ibmIT Services and IT ConsultingCloud | Mobile | Cognitive | Security | Resear...314102162536251712378162
11009IBMAt IBM, we do more than work. We create. We cr...7.0NYUSArmonk, New York10504International Business Machines Corp.https://www.linkedin.com/company/ibmIT Services and IT ConsultingCloud | Mobile | Cognitive | Security | Resear...313142163094641713392385
21009IBMAt IBM, we do more than work. We create. We cr...7.0NYUSArmonk, New York10504International Business Machines Corp.https://www.linkedin.com/company/ibmIT Services and IT ConsultingCloud | Mobile | Cognitive | Security | Resear...313147163099851713402495
31009IBMAt IBM, we do more than work. We create. We cr...7.0NYUSArmonk, New York10504International Business Machines Corp.https://www.linkedin.com/company/ibmIT Services and IT ConsultingCloud | Mobile | Cognitive | Security | Resear...311223163148461713501255
41016GE HealthCareEvery day millions of people feel the impact o...7.00USChicago0-https://www.linkedin.com/company/gehealthcareHospitals and Health CareHealthcare | Biotechnology5687321853681712382540
\n", "
" ], "text/plain": [ " company_id name \\\n", "0 1009 IBM \n", "1 1009 IBM \n", "2 1009 IBM \n", "3 1009 IBM \n", "4 1016 GE HealthCare \n", "\n", " description company_size state \\\n", "0 At IBM, we do more than work. We create. We cr... 7.0 NY \n", "1 At IBM, we do more than work. We create. We cr... 7.0 NY \n", "2 At IBM, we do more than work. We create. We cr... 7.0 NY \n", "3 At IBM, we do more than work. We create. We cr... 7.0 NY \n", "4 Every day millions of people feel the impact o... 7.0 0 \n", "\n", " country city zip_code address \\\n", "0 US Armonk, New York 10504 International Business Machines Corp. \n", "1 US Armonk, New York 10504 International Business Machines Corp. \n", "2 US Armonk, New York 10504 International Business Machines Corp. \n", "3 US Armonk, New York 10504 International Business Machines Corp. \n", "4 US Chicago 0 - \n", "\n", " url \\\n", "0 https://www.linkedin.com/company/ibm \n", "1 https://www.linkedin.com/company/ibm \n", "2 https://www.linkedin.com/company/ibm \n", "3 https://www.linkedin.com/company/ibm \n", "4 https://www.linkedin.com/company/gehealthcare \n", "\n", " industries_list \\\n", "0 IT Services and IT Consulting \n", "1 IT Services and IT Consulting \n", "2 IT Services and IT Consulting \n", "3 IT Services and IT Consulting \n", "4 Hospitals and Health Care \n", "\n", " specialties_list employee_count \\\n", "0 Cloud | Mobile | Cognitive | Security | Resear... 314102 \n", "1 Cloud | Mobile | Cognitive | Security | Resear... 313142 \n", "2 Cloud | Mobile | Cognitive | Security | Resear... 313147 \n", "3 Cloud | Mobile | Cognitive | Security | Resear... 311223 \n", "4 Healthcare | Biotechnology 56873 \n", "\n", " follower_count time_recorded posted_job_titles posted_descriptions \\\n", "0 16253625 1712378162 \n", "1 16309464 1713392385 \n", "2 16309985 1713402495 \n", "3 16314846 1713501255 \n", "4 2185368 1712382540 \n", "\n", " required_skills experience_levels \n", "0 \n", "1 \n", "2 \n", "3 \n", "4 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "companies_full.head()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================================================================================\n", "🔍 DUPLICATE DETECTION REPORT\n", "================================================================================\n", "\n", "┌─ 📊 resume_data.csv (Candidates)\n", "│ Primary Key: Resume_ID\n", "│ Total rows: 9,544\n", "│ Unique rows: 9,544\n", "│ Duplicates: 0\n", "│ Status: ✅ CLEAN\n", "└─\n", "\n", "┌─ 📊 companies.csv (Companies Base)\n", "│ Primary Key: company_id\n", "│ Total rows: 24,473\n", "│ Unique rows: 24,473\n", "│ Duplicates: 0\n", "│ Status: ✅ CLEAN\n", "└─\n", "\n", "┌─ 📊 company_industries.csv\n", "│ Primary Key: company_id + industry\n", "│ Total rows: 24,375\n", "│ Unique rows: 24,375\n", "│ Duplicates: 0\n", "│ Status: ✅ CLEAN\n", "└─\n", "\n", "┌─ 📊 company_specialities.csv\n", "│ Primary Key: company_id + speciality\n", "│ Total rows: 169,387\n", "│ Unique rows: 169,387\n", "│ Duplicates: 0\n", "│ Status: ✅ CLEAN\n", "└─\n", "\n", "┌─ 📊 employee_counts.csv\n", "│ Primary Key: company_id\n", "│ Total rows: 35,787\n", "│ Unique rows: 24,473\n", "│ Duplicates: 11,314\n", "│ Status: 🔴 HAS DUPLICATES\n", "└─\n", "\n", "┌─ 📊 postings.csv (Job Postings)\n", "│ Primary Key: job_id\n", "│ Total rows: 123,849\n", "│ Unique rows: 123,849\n", "│ Duplicates: 0\n", "│ Status: ✅ CLEAN\n", "└─\n", "\n", "┌─ 📊 companies_full (After Enrichment)\n", "│ Primary Key: company_id\n", "│ Total rows: 35,787\n", "│ Unique rows: 24,473\n", "│ Duplicates: 11,314\n", "│ Status: 🔴 HAS DUPLICATES\n", "│\n", "│ Top duplicate company_ids:\n", "│ - 33242739 (Confidential): 13 times\n", "│ - 5235 (LHH): 13 times\n", "│ - 79383535 (Akkodis): 12 times\n", "│ - 1681 (Robert Half): 12 times\n", "│ - 220336 (Hyatt Hotels Corporation): 11 times\n", "└─\n", "\n", "================================================================================\n", "📊 SUMMARY\n", "================================================================================\n", "\n", "✅ Clean datasets: 5/7\n", "🔴 Datasets with duplicates: 2/7\n", "🗑️ Total duplicates found: 22,628 rows\n", "\n", "⚠️ DUPLICATES DETECTED!\n", "================================================================================\n" ] } ], "source": [ "## 🔍 Data Quality Check - Duplicate Detection\n", "\n", "\"\"\"\n", "Checking for duplicates in all datasets based on primary keys.\n", "This cell only REPORTS duplicates, does not modify data.\n", "\"\"\"\n", "\n", "print(\"=\" * 80)\n", "print(\"🔍 DUPLICATE DETECTION REPORT\")\n", "print(\"=\" * 80)\n", "print()\n", "\n", "# Define primary keys for each dataset\n", "duplicate_report = []\n", "\n", "# 1. Candidates\n", "print(\"┌─ 📊 resume_data.csv (Candidates)\")\n", "print(f\"│ Primary Key: Resume_ID\")\n", "cand_total = len(candidates)\n", "cand_unique = candidates['Resume_ID'].nunique() if 'Resume_ID' in candidates.columns else len(candidates)\n", "cand_dups = cand_total - cand_unique\n", "print(f\"│ Total rows: {cand_total:,}\")\n", "print(f\"│ Unique rows: {cand_unique:,}\")\n", "print(f\"│ Duplicates: {cand_dups:,}\")\n", "print(f\"│ Status: {'✅ CLEAN' if cand_dups == 0 else '🔴 HAS DUPLICATES'}\")\n", "print(\"└─\\n\")\n", "duplicate_report.append(('Candidates', cand_total, cand_unique, cand_dups))\n", "\n", "# 2. Companies Base\n", "print(\"┌─ 📊 companies.csv (Companies Base)\")\n", "print(f\"│ Primary Key: company_id\")\n", "comp_total = len(companies_base)\n", "comp_unique = companies_base['company_id'].nunique()\n", "comp_dups = comp_total - comp_unique\n", "print(f\"│ Total rows: {comp_total:,}\")\n", "print(f\"│ Unique rows: {comp_unique:,}\")\n", "print(f\"│ Duplicates: {comp_dups:,}\")\n", "print(f\"│ Status: {'✅ CLEAN' if comp_dups == 0 else '🔴 HAS DUPLICATES'}\")\n", "if comp_dups > 0:\n", " dup_ids = companies_base[companies_base.duplicated('company_id', keep=False)]['company_id'].value_counts().head(3)\n", " print(f\"│ Top duplicates:\")\n", " for cid, count in dup_ids.items():\n", " print(f\"│ - company_id={cid}: {count} times\")\n", "print(\"└─\\n\")\n", "duplicate_report.append(('Companies Base', comp_total, comp_unique, comp_dups))\n", "\n", "# 3. Company Industries\n", "print(\"┌─ 📊 company_industries.csv\")\n", "print(f\"│ Primary Key: company_id + industry\")\n", "ci_total = len(company_industries)\n", "ci_unique = len(company_industries.drop_duplicates(subset=['company_id', 'industry']))\n", "ci_dups = ci_total - ci_unique\n", "print(f\"│ Total rows: {ci_total:,}\")\n", "print(f\"│ Unique rows: {ci_unique:,}\")\n", "print(f\"│ Duplicates: {ci_dups:,}\")\n", "print(f\"│ Status: {'✅ CLEAN' if ci_dups == 0 else '🔴 HAS DUPLICATES'}\")\n", "print(\"└─\\n\")\n", "duplicate_report.append(('Company Industries', ci_total, ci_unique, ci_dups))\n", "\n", "# 4. Company Specialties\n", "print(\"┌─ 📊 company_specialities.csv\")\n", "print(f\"│ Primary Key: company_id + speciality\")\n", "cs_total = len(company_specialties)\n", "cs_unique = len(company_specialties.drop_duplicates(subset=['company_id', 'speciality']))\n", "cs_dups = cs_total - cs_unique\n", "print(f\"│ Total rows: {cs_total:,}\")\n", "print(f\"│ Unique rows: {cs_unique:,}\")\n", "print(f\"│ Duplicates: {cs_dups:,}\")\n", "print(f\"│ Status: {'✅ CLEAN' if cs_dups == 0 else '🔴 HAS DUPLICATES'}\")\n", "print(\"└─\\n\")\n", "duplicate_report.append(('Company Specialties', cs_total, cs_unique, cs_dups))\n", "\n", "# 5. Employee Counts\n", "print(\"┌─ 📊 employee_counts.csv\")\n", "print(f\"│ Primary Key: company_id\")\n", "ec_total = len(employee_counts)\n", "ec_unique = employee_counts['company_id'].nunique()\n", "ec_dups = ec_total - ec_unique\n", "print(f\"│ Total rows: {ec_total:,}\")\n", "print(f\"│ Unique rows: {ec_unique:,}\")\n", "print(f\"│ Duplicates: {ec_dups:,}\")\n", "print(f\"│ Status: {'✅ CLEAN' if ec_dups == 0 else '🔴 HAS DUPLICATES'}\")\n", "print(\"└─\\n\")\n", "duplicate_report.append(('Employee Counts', ec_total, ec_unique, ec_dups))\n", "\n", "# 6. Postings\n", "print(\"┌─ 📊 postings.csv (Job Postings)\")\n", "print(f\"│ Primary Key: job_id\")\n", "if 'job_id' in postings.columns:\n", " post_total = len(postings)\n", " post_unique = postings['job_id'].nunique()\n", " post_dups = post_total - post_unique\n", "else:\n", " post_total = len(postings)\n", " post_unique = len(postings.drop_duplicates())\n", " post_dups = post_total - post_unique\n", "print(f\"│ Total rows: {post_total:,}\")\n", "print(f\"│ Unique rows: {post_unique:,}\")\n", "print(f\"│ Duplicates: {post_dups:,}\")\n", "print(f\"│ Status: {'✅ CLEAN' if post_dups == 0 else '🔴 HAS DUPLICATES'}\")\n", "print(\"└─\\n\")\n", "duplicate_report.append(('Postings', post_total, post_unique, post_dups))\n", "\n", "# 7. Companies Full (After Merge)\n", "print(\"┌─ 📊 companies_full (After Enrichment)\")\n", "print(f\"│ Primary Key: company_id\")\n", "cf_total = len(companies_full)\n", "cf_unique = companies_full['company_id'].nunique()\n", "cf_dups = cf_total - cf_unique\n", "print(f\"│ Total rows: {cf_total:,}\")\n", "print(f\"│ Unique rows: {cf_unique:,}\")\n", "print(f\"│ Duplicates: {cf_dups:,}\")\n", "print(f\"│ Status: {'✅ CLEAN' if cf_dups == 0 else '🔴 HAS DUPLICATES'}\")\n", "if cf_dups > 0:\n", " dup_ids = companies_full[companies_full.duplicated('company_id', keep=False)]['company_id'].value_counts().head(5)\n", " print(f\"│\")\n", " print(f\"│ Top duplicate company_ids:\")\n", " for cid, count in dup_ids.items():\n", " comp_name = companies_full[companies_full['company_id'] == cid]['name'].iloc[0]\n", " print(f\"│ - {cid} ({comp_name}): {count} times\")\n", "print(\"└─\\n\")\n", "duplicate_report.append(('Companies Full', cf_total, cf_unique, cf_dups))\n", "\n", "# Summary\n", "print(\"=\" * 80)\n", "print(\"📊 SUMMARY\")\n", "print(\"=\" * 80)\n", "print()\n", "\n", "total_dups = sum(r[3] for r in duplicate_report)\n", "clean_datasets = sum(1 for r in duplicate_report if r[3] == 0)\n", "dirty_datasets = len(duplicate_report) - clean_datasets\n", "\n", "print(f\"✅ Clean datasets: {clean_datasets}/{len(duplicate_report)}\")\n", "print(f\"🔴 Datasets with duplicates: {dirty_datasets}/{len(duplicate_report)}\")\n", "print(f\"🗑️ Total duplicates found: {total_dups:,} rows\")\n", "print()\n", "\n", "if dirty_datasets > 0:\n", " print(\"⚠️ DUPLICATES DETECTED!\")\n", "else:\n", " print(\"✅ All datasets are clean! No duplicates found.\")\n", "\n", "print(\"=\" * 80)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🧹 CLEANING DUPLICATES...\n", "\n", "================================================================================\n", "✅ companies_base: Already clean\n", "\n", "✅ company_industries: Already clean\n", "\n", "✅ company_specialties: Already clean\n", "\n", "✅ employee_counts:\n", " Removed 11,314 duplicates\n", " 35,787 → 24,473 rows\n", "\n", "✅ postings: Already clean\n", "\n", "✅ companies_full:\n", " Removed 11,314 duplicates\n", " 35,787 → 24,473 rows\n", "\n", "================================================================================\n", "✅ DATA CLEANING COMPLETE!\n", "================================================================================\n", "\n", "📊 Total duplicates removed: 22,628 rows\n", "\n", "Cleaned datasets:\n", " - employee_counts: 35,787 → 24,473\n", " - companies_full: 35,787 → 24,473\n" ] } ], "source": [ "\"\"\"\n", "## 🧹 Data Cleaning - Remove Duplicates\n", "\n", "Based on the report above, removing duplicates from datasets.\n", "\"\"\"\n", "\n", "print(\"🧹 CLEANING DUPLICATES...\\n\")\n", "print(\"=\" * 80)\n", "\n", "# Store original counts\n", "original_counts = {}\n", "\n", "# 1. Clean Companies Base (if needed)\n", "if len(companies_base) != companies_base['company_id'].nunique():\n", " original_counts['companies_base'] = len(companies_base)\n", " companies_base = companies_base.drop_duplicates(subset=['company_id'], keep='first')\n", " removed = original_counts['companies_base'] - len(companies_base)\n", " print(f\"✅ companies_base:\")\n", " print(f\" Removed {removed:,} duplicates\")\n", " print(f\" {original_counts['companies_base']:,} → {len(companies_base):,} rows\\n\")\n", "else:\n", " print(f\"✅ companies_base: Already clean\\n\")\n", "\n", "# 2. Clean Company Industries (if needed)\n", "if len(company_industries) != len(company_industries.drop_duplicates(subset=['company_id', 'industry'])):\n", " original_counts['company_industries'] = len(company_industries)\n", " company_industries = company_industries.drop_duplicates(subset=['company_id', 'industry'], keep='first')\n", " removed = original_counts['company_industries'] - len(company_industries)\n", " print(f\"✅ company_industries:\")\n", " print(f\" Removed {removed:,} duplicates\")\n", " print(f\" {original_counts['company_industries']:,} → {len(company_industries):,} rows\\n\")\n", "else:\n", " print(f\"✅ company_industries: Already clean\\n\")\n", "\n", "# 3. Clean Company Specialties (if needed)\n", "if len(company_specialties) != len(company_specialties.drop_duplicates(subset=['company_id', 'speciality'])):\n", " original_counts['company_specialties'] = len(company_specialties)\n", " company_specialties = company_specialties.drop_duplicates(subset=['company_id', 'speciality'], keep='first')\n", " removed = original_counts['company_specialties'] - len(company_specialties)\n", " print(f\"✅ company_specialties:\")\n", " print(f\" Removed {removed:,} duplicates\")\n", " print(f\" {original_counts['company_specialties']:,} → {len(company_specialties):,} rows\\n\")\n", "else:\n", " print(f\"✅ company_specialties: Already clean\\n\")\n", "\n", "# 4. Clean Employee Counts (if needed)\n", "if len(employee_counts) != employee_counts['company_id'].nunique():\n", " original_counts['employee_counts'] = len(employee_counts)\n", " employee_counts = employee_counts.drop_duplicates(subset=['company_id'], keep='first')\n", " removed = original_counts['employee_counts'] - len(employee_counts)\n", " print(f\"✅ employee_counts:\")\n", " print(f\" Removed {removed:,} duplicates\")\n", " print(f\" {original_counts['employee_counts']:,} → {len(employee_counts):,} rows\\n\")\n", "else:\n", " print(f\"✅ employee_counts: Already clean\\n\")\n", "\n", "# 5. Clean Postings (if needed)\n", "if 'job_id' in postings.columns:\n", " if len(postings) != postings['job_id'].nunique():\n", " original_counts['postings'] = len(postings)\n", " postings = postings.drop_duplicates(subset=['job_id'], keep='first')\n", " removed = original_counts['postings'] - len(postings)\n", " print(f\"✅ postings:\")\n", " print(f\" Removed {removed:,} duplicates\")\n", " print(f\" {original_counts['postings']:,} → {len(postings):,} rows\\n\")\n", " else:\n", " print(f\"✅ postings: Already clean\\n\")\n", "\n", "# 6. Clean Companies Full (if needed)\n", "if len(companies_full) != companies_full['company_id'].nunique():\n", " original_counts['companies_full'] = len(companies_full)\n", " companies_full = companies_full.drop_duplicates(subset=['company_id'], keep='first')\n", " removed = original_counts['companies_full'] - len(companies_full)\n", " print(f\"✅ companies_full:\")\n", " print(f\" Removed {removed:,} duplicates\")\n", " print(f\" {original_counts['companies_full']:,} → {len(companies_full):,} rows\\n\")\n", "else:\n", " print(f\"✅ companies_full: Already clean\\n\")\n", "\n", "print(\"=\" * 80)\n", "print(\"✅ DATA CLEANING COMPLETE!\")\n", "print(\"=\" * 80)\n", "print()\n", "\n", "# Summary\n", "if original_counts:\n", " total_removed = sum(original_counts[k] - globals()[k].shape[0] if k in globals() else 0 \n", " for k in original_counts.keys())\n", " print(f\"📊 Total duplicates removed: {total_removed:,} rows\")\n", " print()\n", " print(\"Cleaned datasets:\")\n", " for dataset, original in original_counts.items():\n", " current = len(globals()[dataset]) if dataset in globals() else 0\n", " print(f\" - {dataset}: {original:,} → {current:,}\")\n", "else:\n", " print(\"✅ No duplicates found - all datasets were already clean!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 📊 Step 7: Load Embedding Model & Pre-computed Vectors" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🧠 Loading embedding model...\n", "\n", "✅ Model loaded: all-MiniLM-L6-v2\n", "📐 Embedding dimension: ℝ^384\n", "\n", "📂 Loading pre-computed embeddings...\n", "✅ Loaded from ../processed/\n", "📊 Candidate vectors: (9544, 384)\n", "📊 Company vectors: (35787, 384)\n", "\n" ] } ], "source": [ "print(\"🧠 Loading embedding model...\\n\")\n", "model = SentenceTransformer(Config.EMBEDDING_MODEL)\n", "embedding_dim = model.get_sentence_embedding_dimension()\n", "print(f\"✅ Model loaded: {Config.EMBEDDING_MODEL}\")\n", "print(f\"📐 Embedding dimension: ℝ^{embedding_dim}\\n\")\n", "\n", "print(\"📂 Loading pre-computed embeddings...\")\n", "\n", "try:\n", " # Try to load from processed folder\n", " cand_vectors = np.load(f'{Config.PROCESSED_PATH}candidate_embeddings.npy')\n", " comp_vectors = np.load(f'{Config.PROCESSED_PATH}company_embeddings.npy')\n", " \n", " print(f\"✅ Loaded from {Config.PROCESSED_PATH}\")\n", " print(f\"📊 Candidate vectors: {cand_vectors.shape}\")\n", " print(f\"📊 Company vectors: {comp_vectors.shape}\\n\")\n", " \n", "except FileNotFoundError:\n", " print(\"⚠️ Pre-computed embeddings not found!\")\n", " print(\" Embeddings will need to be generated (takes ~5-10 minutes)\")\n", " print(\" This is normal if running for the first time.\\n\")\n", " \n", " # You can add embedding generation code here if needed\n", " # For now, we'll skip to keep notebook clean\n", " cand_vectors = None\n", " comp_vectors = None" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 📊 Step 8: Core Matching Function" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✅ Matching function ready\n" ] } ], "source": [ "def find_top_matches(candidate_idx: int, top_k: int = 10) -> List[tuple]:\n", " \"\"\"\n", " Find top K company matches for a candidate using cosine similarity.\n", " \n", " Args:\n", " candidate_idx: Index of candidate\n", " top_k: Number of top matches to return\n", " \n", " Returns:\n", " List of (company_index, similarity_score) tuples\n", " \"\"\"\n", " if cand_vectors is None or comp_vectors is None:\n", " raise ValueError(\"Embeddings not loaded! Please run Step 8 first.\")\n", " \n", " cand_vec = cand_vectors[candidate_idx].reshape(1, -1)\n", " similarities = cosine_similarity(cand_vec, comp_vectors)[0]\n", " top_indices = np.argsort(similarities)[::-1][:top_k]\n", " \n", " return [(int(idx), float(similarities[idx])) for idx in top_indices]\n", "\n", "print(\"✅ Matching function ready\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 📊 Step 9: Initialize FREE LLM (Hugging Face)\n", "\n", "### Get your FREE token: https://huggingface.co/settings/tokens" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✅ Hugging Face client initialized (FREE)\n", "🤖 Model: meta-llama/Llama-3.2-3B-Instruct\n", "💰 Cost: $0.00 (completely free!)\n", "\n", "✅ LLM helper functions ready\n" ] } ], "source": [ "# Initialize Hugging Face Inference Client (FREE)\n", "if Config.HF_TOKEN:\n", " try:\n", " hf_client = InferenceClient(token=Config.HF_TOKEN)\n", " print(\"✅ Hugging Face client initialized (FREE)\")\n", " print(f\"🤖 Model: {Config.LLM_MODEL}\")\n", " print(\"💰 Cost: $0.00 (completely free!)\\n\")\n", " LLM_AVAILABLE = True\n", " except Exception as e:\n", " print(f\"⚠️ Failed to initialize HF client: {e}\")\n", " LLM_AVAILABLE = False\n", "else:\n", " print(\"⚠️ No Hugging Face token configured\")\n", " print(\" LLM features will be disabled\")\n", " print(\"\\n📝 To enable:\")\n", " print(\" 1. Go to: https://huggingface.co/settings/tokens\")\n", " print(\" 2. Create a token (free)\")\n", " print(\" 3. Set: Config.HF_TOKEN = 'your-token-here'\\n\")\n", " LLM_AVAILABLE = False\n", " hf_client = None\n", "\n", "def call_llm(prompt: str, max_tokens: int = 1000) -> str:\n", " \"\"\"\n", " Generic LLM call using Hugging Face Inference API (FREE).\n", " \"\"\"\n", " if not LLM_AVAILABLE:\n", " return \"[LLM not available - check .env file for HF_TOKEN]\"\n", " \n", " try:\n", " response = hf_client.chat_completion( # ✅ chat_completion\n", " messages=[{\"role\": \"user\", \"content\": prompt}],\n", " model=Config.LLM_MODEL,\n", " max_tokens=max_tokens,\n", " temperature=0.7\n", " )\n", " return response.choices[0].message.content # ✅ Extrai conteúdo\n", " except Exception as e:\n", " return f\"[Error: {str(e)}]\"\n", "\n", "print(\"✅ LLM helper functions ready\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 📊 Step 10: Pydantic Schemas for Structured Output" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✅ Pydantic schemas defined\n" ] } ], "source": [ "class JobLevelClassification(BaseModel):\n", " \"\"\"Job level classification result\"\"\"\n", " level: Literal['Entry', 'Mid', 'Senior', 'Executive']\n", " confidence: float = Field(ge=0.0, le=1.0)\n", " reasoning: str\n", "\n", "class SkillsTaxonomy(BaseModel):\n", " \"\"\"Structured skills extraction\"\"\"\n", " technical_skills: List[str] = Field(default_factory=list)\n", " soft_skills: List[str] = Field(default_factory=list)\n", " certifications: List[str] = Field(default_factory=list)\n", " languages: List[str] = Field(default_factory=list)\n", "\n", "class MatchExplanation(BaseModel):\n", " \"\"\"Match reasoning\"\"\"\n", " overall_score: float = Field(ge=0.0, le=1.0)\n", " match_strengths: List[str]\n", " skill_gaps: List[str]\n", " recommendation: str\n", " fit_summary: str = Field(max_length=200)\n", "\n", "print(\"✅ Pydantic schemas defined\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 📊 Step 11: Job Level Classification (Zero-Shot)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🧪 Testing zero-shot classification...\n", "\n", "📊 Classification Result:\n", "{\n", " \"level\": \"Mid\",\n", " \"confidence\": 0.9,\n", " \"reasoning\": \"The job posting mentions the candidate will be working closely with the sales team and executive team, indicating a level of independence, but no mention of technical leadership or C-level responsibilities.\"\n", "}\n" ] } ], "source": [ "def classify_job_level_zero_shot(job_description: str) -> Dict:\n", " \"\"\"\n", " Zero-shot job level classification.\n", " \n", " Returns classification as: Entry, Mid, Senior, or Executive\n", " \"\"\"\n", " \n", " prompt = f\"\"\"Classify this job posting into ONE seniority level.\n", "\n", "Levels:\n", "- Entry: 0-2 years experience, junior roles\n", "- Mid: 3-5 years experience, independent work\n", "- Senior: 6-10 years experience, technical leadership\n", "- Executive: 10+ years, strategic leadership, C-level\n", "\n", "Job Posting:\n", "{job_description[:500]}\n", "\n", "Return ONLY valid JSON:\n", "{{\n", " \"level\": \"Entry|Mid|Senior|Executive\",\n", " \"confidence\": 0.85,\n", " \"reasoning\": \"Brief explanation\"\n", "}}\n", "\"\"\"\n", " \n", " response = call_llm(prompt)\n", " \n", " try:\n", " # Extract JSON\n", " json_str = response.strip()\n", " if '```json' in json_str:\n", " json_str = json_str.split('```json')[1].split('```')[0].strip()\n", " elif '```' in json_str:\n", " json_str = json_str.split('```')[1].split('```')[0].strip()\n", " \n", " # Find JSON in response\n", " if '{' in json_str and '}' in json_str:\n", " start = json_str.index('{')\n", " end = json_str.rindex('}') + 1\n", " json_str = json_str[start:end]\n", " \n", " result = json.loads(json_str)\n", " return result\n", " except:\n", " return {\n", " \"level\": \"Unknown\",\n", " \"confidence\": 0.0,\n", " \"reasoning\": \"Failed to parse response\"\n", " }\n", "\n", "# Test if LLM available and data loaded\n", "if LLM_AVAILABLE and len(postings) > 0:\n", " print(\"🧪 Testing zero-shot classification...\\n\")\n", " sample = postings.iloc[0]['description']\n", " result = classify_job_level_zero_shot(sample)\n", " \n", " print(\"📊 Classification Result:\")\n", " print(json.dumps(result, indent=2))\n", "else:\n", " print(\"⚠️ Skipped - LLM not available or no data\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 📊 Step 12: Few-Shot Learning" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🧪 Comparing Zero-Shot vs Few-Shot...\n", "\n", "📊 Comparison:\n", "Zero-shot: Mid (confidence: 0.85)\n", "Few-shot: Mid (confidence: 0.80)\n" ] } ], "source": [ "def classify_job_level_few_shot(job_description: str) -> Dict:\n", " \"\"\"\n", " Few-shot classification with examples.\n", " \"\"\"\n", " \n", " prompt = f\"\"\"Classify this job posting using examples.\n", "\n", "EXAMPLES:\n", "\n", "Example 1 (Entry):\n", "\"Recent graduate wanted. Python basics. Mentorship provided.\"\n", "→ Entry level (learning focus, 0-2 years)\n", "\n", "Example 2 (Senior):\n", "\"5+ years backend. Lead team of 3. System architecture.\"\n", "→ Senior level (technical leadership, 6-10 years)\n", "\n", "Example 3 (Executive):\n", "\"CTO position. 15+ years. Define technical strategy.\"\n", "→ Executive level (C-level, strategic)\n", "\n", "NOW CLASSIFY:\n", "{job_description[:500]}\n", "\n", "Return JSON:\n", "{{\n", " \"level\": \"Entry|Mid|Senior|Executive\",\n", " \"confidence\": 0.0-1.0,\n", " \"reasoning\": \"Explain\"\n", "}}\n", "\"\"\"\n", " \n", " response = call_llm(prompt)\n", " \n", " try:\n", " json_str = response.strip()\n", " if '```json' in json_str:\n", " json_str = json_str.split('```json')[1].split('```')[0].strip()\n", " \n", " if '{' in json_str and '}' in json_str:\n", " start = json_str.index('{')\n", " end = json_str.rindex('}') + 1\n", " json_str = json_str[start:end]\n", " \n", " result = json.loads(json_str)\n", " return result\n", " except:\n", " return {\"level\": \"Unknown\", \"confidence\": 0.0, \"reasoning\": \"Parse error\"}\n", "\n", "# Compare zero-shot vs few-shot\n", "if LLM_AVAILABLE and len(postings) > 0:\n", " print(\"🧪 Comparing Zero-Shot vs Few-Shot...\\n\")\n", " sample = postings.iloc[0]['description']\n", " \n", " zero = classify_job_level_zero_shot(sample)\n", " few = classify_job_level_few_shot(sample)\n", " \n", " print(\"📊 Comparison:\")\n", " print(f\"Zero-shot: {zero['level']} (confidence: {zero['confidence']:.2f})\")\n", " print(f\"Few-shot: {few['level']} (confidence: {few['confidence']:.2f})\")\n", "else:\n", " print(\"⚠️ Skipped\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 📊 Step 13: Structured Skills Extraction" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🔍 Testing skills extraction...\n", "\n", "📊 Extracted Skills:\n", "{\n", " \"technical_skills\": [],\n", " \"soft_skills\": [\n", " \"Communication\",\n", " \"Leadership\"\n", " ],\n", " \"certifications\": [\n", " \"AWS Certified\"\n", " ],\n", " \"languages\": [\n", " \"English\",\n", " \"Danish\"\n", " ]\n", "}\n" ] } ], "source": [ "def extract_skills_taxonomy(job_description: str) -> Dict:\n", " \"\"\"\n", " Extract structured skills using LLM + Pydantic validation.\n", " \"\"\"\n", " \n", " prompt = f\"\"\"Extract skills from this job posting.\n", "\n", "Job Posting:\n", "{job_description[:800]}\n", "\n", "Return ONLY valid JSON:\n", "{{\n", " \"technical_skills\": [\"Python\", \"Docker\", \"AWS\"],\n", " \"soft_skills\": [\"Communication\", \"Leadership\"],\n", " \"certifications\": [\"AWS Certified\"],\n", " \"languages\": [\"English\", \"Danish\"]\n", "}}\n", "\"\"\"\n", " \n", " response = call_llm(prompt, max_tokens=800)\n", " \n", " try:\n", " json_str = response.strip()\n", " if '```json' in json_str:\n", " json_str = json_str.split('```json')[1].split('```')[0].strip()\n", " \n", " if '{' in json_str and '}' in json_str:\n", " start = json_str.index('{')\n", " end = json_str.rindex('}') + 1\n", " json_str = json_str[start:end]\n", " \n", " data = json.loads(json_str)\n", " # Validate with Pydantic\n", " validated = SkillsTaxonomy(**data)\n", " return validated.model_dump()\n", " except:\n", " return {\n", " \"technical_skills\": [],\n", " \"soft_skills\": [],\n", " \"certifications\": [],\n", " \"languages\": []\n", " }\n", "\n", "# Test extraction\n", "if LLM_AVAILABLE and len(postings) > 0:\n", " print(\"🔍 Testing skills extraction...\\n\")\n", " sample = postings.iloc[0]['description']\n", " skills = extract_skills_taxonomy(sample)\n", " \n", " print(\"📊 Extracted Skills:\")\n", " print(json.dumps(skills, indent=2))\n", "else:\n", " print(\"⚠️ Skipped\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 📊 Step 14: Match Explainability" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "💡 Testing match explainability...\n", "\n", "📊 Match Explanation:\n", "{\n", " \"overall_score\": 0.7028058171272278,\n", " \"match_strengths\": [\n", " \"Unable to generate\"\n", " ],\n", " \"skill_gaps\": [],\n", " \"recommendation\": \"Review manually\",\n", " \"fit_summary\": \"Match score: 0.70\"\n", "}\n" ] } ], "source": [ "def explain_match(candidate_idx: int, company_idx: int, similarity_score: float) -> Dict:\n", " \"\"\"\n", " Generate LLM explanation for why candidate matches company.\n", " \"\"\"\n", " \n", " cand = candidates.iloc[candidate_idx]\n", " comp = companies_full.iloc[company_idx]\n", " \n", " cand_skills = str(cand.get('skills', 'N/A'))[:300]\n", " cand_exp = str(cand.get('positions', 'N/A'))[:300]\n", " comp_req = str(comp.get('required_skills', 'N/A'))[:300]\n", " comp_name = comp.get('name', 'Unknown')\n", " \n", " prompt = f\"\"\"Explain why this candidate matches this company.\n", "\n", "Candidate:\n", "Skills: {cand_skills}\n", "Experience: {cand_exp}\n", "\n", "Company: {comp_name}\n", "Requirements: {comp_req}\n", "\n", "Similarity Score: {similarity_score:.2f}\n", "\n", "Return JSON:\n", "{{\n", " \"overall_score\": {similarity_score},\n", " \"match_strengths\": [\"Top 3-5 matching factors\"],\n", " \"skill_gaps\": [\"Missing skills\"],\n", " \"recommendation\": \"What candidate should do\",\n", " \"fit_summary\": \"One sentence summary\"\n", "}}\n", "\"\"\"\n", " \n", " response = call_llm(prompt, max_tokens=1000)\n", " \n", " try:\n", " json_str = response.strip()\n", " if '```json' in json_str:\n", " json_str = json_str.split('```json')[1].split('```')[0].strip()\n", " \n", " if '{' in json_str and '}' in json_str:\n", " start = json_str.index('{')\n", " end = json_str.rindex('}') + 1\n", " json_str = json_str[start:end]\n", " \n", " data = json.loads(json_str)\n", " return data\n", " except:\n", " return {\n", " \"overall_score\": similarity_score,\n", " \"match_strengths\": [\"Unable to generate\"],\n", " \"skill_gaps\": [],\n", " \"recommendation\": \"Review manually\",\n", " \"fit_summary\": f\"Match score: {similarity_score:.2f}\"\n", " }\n", "\n", "# Test explainability\n", "if LLM_AVAILABLE and cand_vectors is not None and len(candidates) > 0:\n", " print(\"💡 Testing match explainability...\\n\")\n", " matches = find_top_matches(0, top_k=1)\n", " if matches:\n", " comp_idx, score = matches[0]\n", " explanation = explain_match(0, comp_idx, score)\n", " \n", " print(\"📊 Match Explanation:\")\n", " print(json.dumps(explanation, indent=2))\n", "else:\n", " print(\"⚠️ Skipped - requirements not met\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 📊 Step 16: Detailed Match Visualization" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🔍 DETAILED MATCH ANALYSIS\n", "====================================================================================================\n", "\n", "🎯 CANDIDATE #0\n", "Resume ID: N/A\n", "Category: N/A\n", "Skills: ['Big Data', 'Hadoop', 'Hive', 'Python', 'Mapreduce', 'Spark', 'Java', 'Machine Learning', 'Cloud', 'Hdfs', 'YARN', 'Core Java', 'Data Science', 'C++'...\n", "\n", "🔗 TOP 5 MATCHES:\n", "\n", "#1. TeachTown (Score: 0.7028)\n", " Industries: E-Learning Providers...\n", "#3. Wolverine Power Systems (Score: 0.7026)\n", " Industries: Renewable Energy Semiconductor Manufacturing...\n", "#5. Mariner (Score: 0.7010)\n", " Industries: Financial Services...\n", "\n", "====================================================================================================\n" ] }, { "data": { "text/plain": [ "[(9418, 0.7028058171272278),\n", " (30989, 0.7026211023330688),\n", " (9417, 0.7025721669197083),\n", " (30990, 0.7019376754760742),\n", " (9416, 0.7010321021080017)]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# ============================================================================\n", "# 🔍 DETAILED MATCH EXAMPLE\n", "# ============================================================================\n", "\n", "def show_detailed_match_example(candidate_idx=0, top_k=5):\n", " print(\"🔍 DETAILED MATCH ANALYSIS\")\n", " print(\"=\" * 100)\n", " \n", " if candidate_idx >= len(candidates):\n", " print(f\"❌ ERROR: Candidate {candidate_idx} out of range\")\n", " return None\n", " \n", " cand = candidates.iloc[candidate_idx]\n", " \n", " print(f\"\\n🎯 CANDIDATE #{candidate_idx}\")\n", " print(f\"Resume ID: {cand.get('Resume_ID', 'N/A')}\")\n", " print(f\"Category: {cand.get('Category', 'N/A')}\")\n", " print(f\"Skills: {str(cand.get('skills', 'N/A'))[:150]}...\\n\")\n", " \n", " matches = find_top_matches(candidate_idx, top_k=top_k)\n", " \n", " print(f\"🔗 TOP {len(matches)} MATCHES:\\n\")\n", " \n", " for rank, (comp_idx, score) in enumerate(matches, 1):\n", " if comp_idx >= len(companies_full):\n", " continue\n", " \n", " company = companies_full.iloc[comp_idx]\n", " print(f\"#{rank}. {company.get('name', 'N/A')} (Score: {score:.4f})\")\n", " print(f\" Industries: {str(company.get('industries_list', 'N/A'))[:60]}...\")\n", " \n", " print(\"\\n\" + \"=\" * 100)\n", " return matches\n", "\n", "# Test\n", "show_detailed_match_example(candidate_idx=0, top_k=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 📊 Step 17: Bridging Concept Analysis" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🌉 THE BRIDGING CONCEPT\n", "==========================================================================================\n", "\n", "📊 DATA REALITY:\n", " Total companies: 24,473\n", " WITH postings: 0 (0.0%)\n", " WITHOUT postings: 24,473\n", "\n", "🎯 THE PROBLEM:\n", " Companies: 'We are in TECH INDUSTRY'\n", " Candidates: 'I know PYTHON, AWS'\n", " → Different languages! 🚫\n", "\n", "🌉 THE SOLUTION (BRIDGING):\n", " 1. Extract from postings: 'Need PYTHON developers'\n", " 2. Enrich company profile with skills\n", " 3. Now both speak SKILLS LANGUAGE! ✅\n", "\n", "==========================================================================================\n" ] }, { "data": { "text/plain": [ "(Empty DataFrame\n", " Columns: [company_id, name, description, company_size, state, country, city, zip_code, address, url, industries_list, specialties_list, employee_count, follower_count, time_recorded, posted_job_titles, posted_descriptions, required_skills, experience_levels]\n", " Index: [],\n", " company_id name \\\n", " 0 1009 IBM \n", " 4 1016 GE HealthCare \n", " 14 1025 Hewlett Packard Enterprise \n", " 18 1028 Oracle \n", " 23 1033 Accenture \n", " ... ... ... \n", " 35782 103463217 JRC Services \n", " 35783 103466352 Centent Consulting LLC \n", " 35784 103467540 Kings and Queens Productions, LLC \n", " 35785 103468936 WebUnite \n", " 35786 103472979 BlackVe \n", " \n", " description company_size \\\n", " 0 At IBM, we do more than work. We create. We cr... 7.0 \n", " 4 Every day millions of people feel the impact o... 7.0 \n", " 14 Official LinkedIn of Hewlett Packard Enterpris... 7.0 \n", " 18 We’re a cloud technology company that provides... 7.0 \n", " 23 Accenture is a leading global professional ser... 7.0 \n", " ... ... ... \n", " 35782 2.0 \n", " 35783 Centent Consulting LLC is a reputable human re... \n", " 35784 We are a small but mighty collection of thinke... \n", " 35785 Our mission at WebUnite is to offer experience... \n", " 35786 1.0 \n", " \n", " state country city zip_code \\\n", " 0 NY US Armonk, New York 10504 \n", " 4 0 US Chicago 0 \n", " 14 Texas US Houston 77389 \n", " 18 Texas US Austin 78741 \n", " 23 0 IE Dublin 2 0 \n", " ... ... ... ... ... \n", " 35782 0 0 0 0 \n", " 35783 0 0 0 0 \n", " 35784 0 0 0 0 \n", " 35785 Pennsylvania US Southampton 18966 \n", " 35786 0 0 0 0 \n", " \n", " address \\\n", " 0 International Business Machines Corp. \n", " 4 - \n", " 14 1701 E Mossy Oaks Rd Spring \n", " 18 2300 Oracle Way \n", " 23 Grand Canal Harbour \n", " ... ... \n", " 35782 0 \n", " 35783 0 \n", " 35784 0 \n", " 35785 720 2nd Street Pike \n", " 35786 0 \n", " \n", " url \\\n", " 0 https://www.linkedin.com/company/ibm \n", " 4 https://www.linkedin.com/company/gehealthcare \n", " 14 https://www.linkedin.com/company/hewlett-packa... \n", " 18 https://www.linkedin.com/company/oracle \n", " 23 https://www.linkedin.com/company/accenture \n", " ... ... \n", " 35782 https://www.linkedin.com/company/jrcservices \n", " 35783 https://www.linkedin.com/company/centent-consu... \n", " 35784 https://www.linkedin.com/company/kings-and-que... \n", " 35785 https://www.linkedin.com/company/webunite \n", " 35786 https://www.linkedin.com/company/blackve \n", " \n", " industries_list \\\n", " 0 IT Services and IT Consulting \n", " 4 Hospitals and Health Care \n", " 14 IT Services and IT Consulting \n", " 18 IT Services and IT Consulting \n", " 23 Business Consulting and Services \n", " ... ... \n", " 35782 Facilities Services \n", " 35783 Business Consulting and Services \n", " 35784 Broadcast Media Production and Distribution \n", " 35785 Business Consulting and Services \n", " 35786 Defense and Space Manufacturing \n", " \n", " specialties_list employee_count \\\n", " 0 Cloud | Mobile | Cognitive | Security | Resear... 314102 \n", " 4 Healthcare | Biotechnology 56873 \n", " 14 79528 \n", " 18 enterprise | software | applications | databas... 192099 \n", " 23 Management Consulting | Systems Integration an... 574664 \n", " ... ... ... \n", " 35782 0 \n", " 35783 0 \n", " 35784 0 \n", " 35785 0 \n", " 35786 0 \n", " \n", " follower_count time_recorded posted_job_titles posted_descriptions \\\n", " 0 16253625 1712378162 \n", " 4 2185368 1712382540 \n", " 14 3586194 1712870106 \n", " 18 9465968 1712642952 \n", " 23 11864908 1712641699 \n", " ... ... ... ... ... \n", " 35782 21 1713552037 \n", " 35783 0 1713550651 \n", " 35784 12 1713554225 \n", " 35785 1 1713535939 \n", " 35786 0 1713539379 \n", " \n", " required_skills experience_levels \n", " 0 \n", " 4 \n", " 14 \n", " 18 \n", " 23 \n", " ... ... ... \n", " 35782 \n", " 35783 \n", " 35784 \n", " 35785 \n", " 35786 \n", " \n", " [24473 rows x 19 columns])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# ============================================================================\n", "# 🌉 BRIDGING CONCEPT ANALYSIS\n", "# ============================================================================\n", "\n", "def show_bridging_concept_analysis():\n", " print(\"🌉 THE BRIDGING CONCEPT\")\n", " print(\"=\" * 90)\n", " \n", " companies_with = companies_full[companies_full['required_skills'] != '']\n", " companies_without = companies_full[companies_full['required_skills'] == '']\n", " \n", " print(f\"\\n📊 DATA REALITY:\")\n", " print(f\" Total companies: {len(companies_full):,}\")\n", " print(f\" WITH postings: {len(companies_with):,} ({len(companies_with)/len(companies_full)*100:.1f}%)\")\n", " print(f\" WITHOUT postings: {len(companies_without):,}\\n\")\n", " \n", " print(\"🎯 THE PROBLEM:\")\n", " print(\" Companies: 'We are in TECH INDUSTRY'\")\n", " print(\" Candidates: 'I know PYTHON, AWS'\")\n", " print(\" → Different languages! 🚫\\n\")\n", " \n", " print(\"🌉 THE SOLUTION (BRIDGING):\")\n", " print(\" 1. Extract from postings: 'Need PYTHON developers'\")\n", " print(\" 2. Enrich company profile with skills\")\n", " print(\" 3. Now both speak SKILLS LANGUAGE! ✅\\n\")\n", " \n", " print(\"=\" * 90)\n", " return companies_with, companies_without\n", "\n", "# Test\n", "show_bridging_concept_analysis()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 📊 Step 18: Export Results to CSV" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "💾 Exporting 50 candidates (top 5 each)...\n", "\n", " Processing 1/50...\n", "\n", "✅ Exported 129 matches\n", "📄 File: ../results/hrhub_matches.csv\n", "\n" ] } ], "source": [ "# ============================================================================\n", "# 💾 EXPORT MATCHES TO CSV\n", "# ============================================================================\n", "\n", "def export_matches_to_csv(num_candidates=100, top_k=10):\n", " print(f\"💾 Exporting {num_candidates} candidates (top {top_k} each)...\\n\")\n", " \n", " results = []\n", " \n", " for i in range(min(num_candidates, len(candidates))):\n", " if i % 50 == 0:\n", " print(f\" Processing {i+1}/{num_candidates}...\")\n", " \n", " matches = find_top_matches(i, top_k=top_k)\n", " cand = candidates.iloc[i]\n", " \n", " for rank, (comp_idx, score) in enumerate(matches, 1):\n", " if comp_idx >= len(companies_full):\n", " continue\n", " \n", " company = companies_full.iloc[comp_idx]\n", " \n", " results.append({\n", " 'candidate_id': i,\n", " 'candidate_category': cand.get('Category', 'N/A'),\n", " 'company_id': company.get('company_id', 'N/A'),\n", " 'company_name': company.get('name', 'N/A'),\n", " 'match_rank': rank,\n", " 'similarity_score': round(float(score), 4)\n", " })\n", " \n", " results_df = pd.DataFrame(results)\n", " output_file = f'{Config.RESULTS_PATH}hrhub_matches.csv'\n", " results_df.to_csv(output_file, index=False)\n", " \n", " print(f\"\\n✅ Exported {len(results_df):,} matches\")\n", " print(f\"📄 File: {output_file}\\n\")\n", " \n", " return results_df\n", "\n", "# Export sample\n", "matches_df = export_matches_to_csv(num_candidates=50, top_k=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 📊 Interactive Visualization 1: t-SNE Vector Space\n", "\n", "Project embeddings from ℝ³⁸⁴ → ℝ² to visualize candidates and companies" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🎨 VECTOR SPACE VISUALIZATION\n", "\n", "======================================================================\n", "📊 Visualizing:\n", " • 500 candidates\n", " • 2000 companies\n", " • From ℝ^384 → ℝ² (t-SNE)\n", "\n", "🔄 Running t-SNE (2-3 minutes)...\n", "\n", "✅ t-SNE complete!\n" ] } ], "source": [ "# ============================================================================\n", "# 🎨 T-SNE VECTOR SPACE VISUALIZATION\n", "# ============================================================================\n", "\n", "from sklearn.manifold import TSNE\n", "\n", "print(\"🎨 VECTOR SPACE VISUALIZATION\\n\")\n", "print(\"=\" * 70)\n", "\n", "# Sample for visualization\n", "n_cand_viz = min(500, len(candidates))\n", "n_comp_viz = min(2000, len(companies_full))\n", "\n", "print(f\"📊 Visualizing:\")\n", "print(f\" • {n_cand_viz} candidates\")\n", "print(f\" • {n_comp_viz} companies\")\n", "print(f\" • From ℝ^384 → ℝ² (t-SNE)\\n\")\n", "\n", "# Sample vectors\n", "cand_sample = cand_vectors[:n_cand_viz]\n", "comp_sample = comp_vectors[:n_comp_viz]\n", "all_vectors = np.vstack([cand_sample, comp_sample])\n", "\n", "print(\"🔄 Running t-SNE (2-3 minutes)...\")\n", "tsne = TSNE(\n", " n_components=2,\n", " perplexity=30,\n", " random_state=42,\n", " n_iter=1000\n", ")\n", "\n", "vectors_2d = tsne.fit_transform(all_vectors)\n", "cand_2d = vectors_2d[:n_cand_viz]\n", "comp_2d = vectors_2d[n_cand_viz:]\n", "\n", "print(\"\\n✅ t-SNE complete!\")" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "%{text}", "marker": { "color": "#ff6b6b", "opacity": 0.6, "size": 6 }, "mode": "markers", "name": "Companies", "text": [ "Company: IBM", "Company: GE HealthCare", "Company: Hewlett Packard Enterprise", "Company: Oracle", "Company: Accenture", "Company: Microsoft", "Company: Deloitte", "Company: Siemens", "Company: PwC", "Company: AT&T", "Company: Intel Corporation", "Company: Ericsson", "Company: Cisco", "Company: Motorola Mobility (a Lenovo Co", "Company: JPMorgan Chase & Co.", "Company: Nokia", "Company: EY", "Company: KPMG US", "Company: NXP Semiconductors", "Company: Philips", "Company: Verizon", "Company: SAP", "Company: Procter & Gamble", "Company: Bank of America", "Company: Elite Technology", "Company: BT Group", "Company: Pfizer", "Company: Johnson & Johnson", "Company: UBS", "Company: US Army Corps of Engineers", "Company: Wells Fargo", "Company: Unilever", "Company: Sony", "Company: Sony Electronics", "Company: Sony Pictures Entertainment", "Company: Atos", "Company: Deutsche Bank", "Company: DWS Group", "Company: Chubb", "Company: Shell", "Company: American Express", "Company: Unisys", "Company: Infosys", "Company: Yahoo", "Company: The Walt Disney Company", "Company: Fidelity Investments", "Company: Wipro", "Company: LinkedIn", "Company: Air Force Research Laboratory", "Company: Honeywell", "Company: Tata Consultancy Services", "Company: National Security Agency", "Company: National Computer Systems", "Company: McKinsey & Company", "Company: Xerox", "Company: Fujitsu Network Communications", "Company: Goldman Sachs", "Company: Boeing", "Company: bp", "Company: T-Mobile", "Company: Nestlé", "Company: GSK", "Company: Thomson Reuters", "Company: Booz Allen Hamilton", "Company: Novartis", "Company: Northrop Grumman", "Company: CGI", "Company: Capital One", "Company: Barclays", "Company: PepsiCo", "Company: Google", "Company: Electronic Arts (EA)", "Company: SUSE", "Company: ADP", "Company: CDK Global", "Company: Teradata", "Company: SLB", "Company: General Motors", "Company: Ally", "Company: Adobe", "Company: eBay", "Company: PayPal", "Company: Ford Motor Company", "Company: Merck", "Company: SAS", "Company: Avaya", "Company: AMD", "Company: MIT Lincoln Laboratory", "Company: Raytheon", "Company: BNP Paribas", "Company: Mondelēz International", "Company: Eastman Kodak Company", "Company: Carestream", "Company: UPS", "Company: Agilent Technologies", "Company: The Home Depot", "Company: Amdocs", "Company: Mars", "Company: Kaiser Permanente", "Company: Amazon", "Company: BMC Software", "Company: Roche", "Company: AstraZeneca", "Company: Abbott", "Company: SAIC", "Company: Dignity Health", "Company: Owens & Minor", "Company: Stanford Children's Health | L", "Company: Boston Scientific", "Company: Sanofi", "Company: Harvard Medical School", "Company: Harvard University", "Company: Harvard Law School", "Company: Dana-Farber Cancer Institute", "Company: Boston Children's Hospital", "Company: Beth Israel Deaconess Medical ", "Company: L'Oréal", "Company: Eli Lilly and Company", "Company: Intuit", "Company: FedEx Ground", "Company: FedEx Services", "Company: Ogilvy", "Company: Gap Inc.", "Company: Banana Republic", "Company: Cognizant", "Company: Robert Half", "Company: ExxonMobil", "Company: Societe Generale", "Company: The Coca-Cola Company", "Company: Comcast", "Company: Nielsen", "Company: HCLTech", "Company: AIG", "Company: BBC", "Company: State Street", "Company: Bristol Myers Squibb", "Company: Boston Consulting Group (BCG)", "Company: SLAC National Accelerator Labo", "Company: Stanford University School of ", "Company: Stanford University", "Company: ManpowerGroup", "Company: RBC", "Company: TotalEnergies", "Company: NBC News", "Company: NBCUniversal", "Company: CNBC", "Company: Allstate", "Company: Medtronic", "Company: Prudential Financial", "Company: Charles Schwab", "Company: 3M", "Company: Capco Energy Solutions", "Company: Marsh", "Company: Autodesk", "Company: BAE Systems, Inc.", "Company: Nickelodeon", "Company: Bayer", "Company: McKesson", "Company: General Dynamics Information T", "Company: General Dynamics Land Systems", "Company: General Dynamics Mission Syste", "Company: Philip Morris International", "Company: McCann Worldgroup", "Company: MRM", "Company: UM Worldwide", "Company: The Adecco Group", "Company: PTC", "Company: Thales", "Company: Sogeti", "Company: Rabobank", "Company: Mavenir", "Company: NASA - National Aeronautics an", "Company: Qualcomm", "Company: Applied Materials", "Company: Western Union", "Company: Nike", "Company: Spectrum Enterprise", "Company: Coldwell Banker Realty", "Company: Aon", "Company: CNN", "Company: TE Connectivity", "Company: Amgen", "Company: Gartner", "Company: Volvo Group", "Company: Volvo Penta", "Company: Volvo Buses", "Company: Mack Trucks", "Company: Volvo Construction Equipment", "Company: NetApp", "Company: 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"Company: Marriott International", "Company: CBRE", "Company: Randstad", "Company: Schneider Electric", "Company: Nationwide", "Company: Baxter International Inc.", "Company: United Airlines", "Company: State Farm", "Company: Dun & Bradstreet", "Company: Mercer", "Company: Pratt & Whitney", "Company: Carrier HVAC", "Company: Grant Thornton LLP (US)", "Company: Alstom", "Company: Northwestern Mutual", "Company: Hilton", "Company: Oliver Wyman", "Company: Synopsys Inc", "Company: Zurich North America", "Company: Digitas North America", "Company: The Hartford", "Company: UCLA Health", "Company: Children's Hospital Los Angele", "Company: UCLA", "Company: Wolters Kluwer", "Company: Cigna Healthcare", "Company: Bloomberg", "Company: Diageo", "Company: Rockwell Automation", "Company: Michigan Medicine", "Company: University of Michigan", "Company: U.S. Bank", "Company: Experian", "Company: iHeartMedia", "Company: Clear Channel Outdoor", "Company: Whirlpool Corporation", "Company: Dow", "Company: Ingram Micro", "Company: Crédit Agricole CIB", "Company: University of Washington", "Company: Momentum Worldwide", "Company: Eaton", "Company: Tetra Pak", "Company: Panasonic Automotive North Ame", "Company: Panasonic North America", "Company: Panasonic Avionics Corporation", "Company: Caterpillar Inc.", "Company: Columbia University Irving Med", "Company: Columbia University", "Company: BASF", "Company: American Airlines", "Company: Citrix", "Company: Walmart", "Company: University of Illinois Chicago", "Company: University of Illinois Urbana-", "Company: Caltrans", "Company: County of San Diego", "Company: CalPERS", "Company: California Department of Justi", "Company: Valeo", "Company: McDonald's", "Company: Cargill", "Company: John Hancock", "Company: Manulife", "Company: Liberty Mutual Insurance", "Company: OpenText", "Company: KLA", "Company: BOMBARDIER", "Company: RR Donnelley", "Company: Acxiom", "Company: IKEA", "Company: Colgate-Palmolive", "Company: Expedia Group", "Company: Emerson", "Company: TD", "Company: Andersen Corporation", "Company: Federal Reserve Board", "Company: Federal Reserve Bank of San Fr", "Company: Federal Reserve Bank of Boston", "Company: Sage", "Company: Publicis", "Company: General Mills", "Company: BlackBerry", "Company: Mary Kay Global", "Company: University of California, Sant", "Company: University of California, Davi", "Company: UC Davis Health", "Company: Commonwealth Bank", "Company: BDO USA", "Company: Visteon Corporation", "Company: Seagate Technology", "Company: Canon Business Process Service", "Company: ITT Inc.", "Company: Aerotek", "Company: Brigham and Women's Hospital", "Company: Massachusetts General Hospital", "Company: Newton-Wellesley Hospital", "Company: NYC Department of Education", "Company: Albertsons Companies", "Company: Shaw's Supermarkets", "Company: Acme Markets", "Company: The Save Mart Companies", "Company: Teradyne", "Company: S&P Global", "Company: Teacher Retirement System of T", "Company: 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"Company: Office Depot", "Company: Lionbridge", "Company: Edwards Vacuum", "Company: FIS", "Company: The HEINEKEN Company", "Company: Hyatt Regency", "Company: Levi Strauss & Co.", "Company: Scotiabank", "Company: Freddie Mac", "Company: Stop & Shop", "Company: Software Engineering Institute", "Company: NYU Stern School of Business", "Company: The University of Texas at Aus", "Company: Penn Medicine, University of P", "Company: University of Pennsylvania", "Company: The Ohio State University Wexn", "Company: The Ohio State University", "Company: Ohio Department of Education a", "Company: Ingersoll Rand", "Company: JLL", "Company: University of Minnesota", "Company: Salesforce", "Company: Mallinckrodt Pharmaceuticals", "Company: Northwestern University", "Company: Mattel, Inc.", "Company: AkzoNobel", "Company: Agfa", "Company: Boehringer Ingelheim", "Company: Farmers Insurance", "Company: International Paper", "Company: CNA Insurance", "Company: KeyBank", "Company: Aegon", "Company: 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International Finance Co", "Company: Apex Systems", "Company: Smith+Nephew", "Company: Hallmark Cards", "Company: Atlas Copco", "Company: North Carolina State Universit", "Company: DB Schenker", "Company: SEI", "Company: JTI (Japan Tobacco Internation", "Company: Konica Minolta Business Soluti", "Company: U.S. Department of Commerce", "Company: F5", "Company: Jabil", "Company: EF Education First", "Company: PPG", "Company: Skadden, Arps, Slate, Meagher ", "Company: Harvard Business School", "Company: UNICEF", "Company: Pulte Mortgage", "Company: PulteGroup", "Company: University of Utah Health", "Company: Blue Shield of California", "Company: Parsons Corporation", "Company: Specialty Equipment Market Ass", "Company: Kroger", "Company: Metso", "Company: White & Case LLP", "Company: Internal Revenue Service", "Company: Otis Elevator Co.", "Company: Latham & Watkins", "Company: Hasbro", "Company: Reebok", "Company: Porter Novelli", "Company: Bankers Trust", "Company: AMERICAN EAGLE OUTFITTERS INC.", "Company: EPAM Systems", "Company: Temple Health – Temple Univers", "Company: Phoenix Technologies", "Company: Lawrence Livermore National La", "Company: Cartier", "Company: CNO Financial Group", "Company: Bankers Life", "Company: University of Rochester Medica", "Company: University of Rochester", "Company: News Corp", "Company: Persistent Systems", "Company: Federal Aviation Administratio", "Company: USAID", "Company: Cintas", "Company: KONE", "Company: Alfa Laval", "Company: Sophos", "Company: Ketchum", "Company: Intelsat", "Company: CHEP", "Company: Dana Incorporated", "Company: Southern Company", "Company: Jones Day", "Company: Ticketmaster", "Company: aramco", "Company: Lam Research", "Company: Acer", "Company: Navistar Inc", "Company: Constellation", "Company: The TJX Companies, Inc.", "Company: Nasdaq", "Company: Anritsu", "Company: Virtusa", "Company: IGT", "Company: Vestcom", "Company: Jefferies", "Company: Trimble Inc.", "Company: Morningstar", "Company: Los Angeles Times", "Company: Sandvik", "Company: Sandvik Coromant", "Company: Bausch + Lomb", "Company: Ascensus", "Company: GRUNDFOS", "Company: L.E.K. Consulting", "Company: Teleperformance", "Company: Nalco Water, An Ecolab Company", "Company: Colliers", "Company: Rackspace Technology", "Company: LHH", "Company: ZS", "Company: HCLTech - SAP Practice", "Company: DSV - Global Transport and Log", "Company: McMaster-Carr", "Company: SRI", "Company: Northeastern University", "Company: Alcon", "Company: Rent.", "Company: LPL Financial", "Company: Luxoft", "Company: Esri", "Company: Owens Corning", "Company: Tenet Healthcare", "Company: MFS Investment Management", "Company: ALTEN", "Company: Los Alamos National Laboratory", "Company: H&M", "Company: Blizzard Entertainment", "Company: Sysco", "Company: Softtek", "Company: Connection", "Company: GSA", "Company: G4S", "Company: UC Santa Barbara", "Company: VELUX", "Company: Sandia National Laboratories", "Company: The Hanover Insurance Group", "Company: Abercrombie & Fitch Co.", "Company: University of Missouri Health ", "Company: University of Missouri-Columbi", "Company: Hess Corporation", "Company: ManTech", "Company: Ashland", "Company: Capco", "Company: GALLO", "Company: Ferrero", "Company: Takeda", "Company: Mercatus Center at George Maso", "Company: George Mason University", "Company: CME Group", "Company: Consilio LLC", "Company: Crowe", "Company: Morrison Foerster", "Company: FTI Consulting", "Company: Concurrent Technologies Corpor", "Company: Southern Methodist University", "Company: KPIT", "Company: Westfield Insurance", "Company: Premera Blue Cross", "Company: ADM", "Company: The Standard", "Company: Plexus Corp.", "Company: Allied Telesis", "Company: Group Lotus", "Company: Nintendo", "Company: Forbes", "Company: USANA Health Sciences", "Company: Mercury Systems", "Company: Capella University", "Company: Greenberg Traurig, LLP", "Company: Cirrus Logic", "Company: Coach", "Company: Herbalife", "Company: McClatchy", "Company: Varian", "Company: Memorial Sloan Kettering Cance", "Company: Huntsman Corporation", "Company: Cleveland Clinic", "Company: Dominion Energy", "Company: LyondellBasell", "Company: Kohler Co.", "Company: Cooper University Health Care", "Company: CJ", "Company: Netia", "Company: U.S. Department of Labor", "Company: ACCO Brands", "Company: Santander Bank, N.A.", "Company: Sasol", "Company: University of Denver", "Company: Ball Corporation", "Company: Kirkland & Ellis", "Company: Morgan, Lewis & Bockius LLP", "Company: ICF", "Company: Kohl's", "Company: CPS, Inc.", "Company: Huron", "Company: Penske Logistics", "Company: Penske Truck Leasing", "Company: SPX Cooling Tech, LLC", "Company: Viasat", "Company: Turner Construction Company", "Company: Univision", "Company: Louis Vuitton", "Company: Kerry", "Company: Cobham Satcom", "Company: Gensler", "Company: Moss Adams", "Company: RTI International", "Company: Tommy Hilfiger", "Company: Hogan Lovells", "Company: Fairfax County Public Schools", "Company: ICON plc", "Company: Orrick, Herrington & Sutcliffe", "Company: Omron Automation", "Company: Arcadis", "Company: Johns Manville", "Company: Tennessee Valley Authority", "Company: Valassis Marketing Solutions", "Company: Federal Highway Administration", "Company: U.S. Department of Transportat", "Company: IFF", "Company: Smithsonian Enterprises", "Company: MKS Instruments", "Company: Motion Recruitment", "Company: TDS Telecommunications LLC", "Company: Cubic Corporation", "Company: National Grid", "Company: O'Melveny & Myers LLP", "Company: University of Nebraska Foundat", "Company: University of Nebraska-Lincoln", "Company: Flowserve Corporation", "Company: Quadrangle", "Company: Autoliv", "Company: Boston Public Schools", "Company: Armstrong World Industries", "Company: Chr. Hansen", "Company: FM Global", "Company: Fresenius Medical Care North A", "Company: Union Pacific Railroad", "Company: W. L. Gore & Associates", "Company: University of Kentucky", "Company: Cooley LLP", "Company: Michaels Stores", "Company: Yoh, A Day & Zimmermann Compan", "Company: Mayer Brown", "Company: Choice Hotels International", "Company: Rensselaer Polytechnic Institu", "Company: Advantage Technical", "Company: CBIZ", "Company: Lands'​ End", "Company: AppleOne Employment Services", "Company: UNSW", "Company: NYU Langone Health", "Company: Atlanticus", "Company: NetSuite", "Company: Agilysys", "Company: County of Santa Clara", "Company: Icahn School of Medicine at Mo", "Company: Match", "Company: 7-Eleven", "Company: Zions Bancorporation", "Company: Schindler Group", "Company: Schindler Elevator Corporation", "Company: Brunswick Corporation", "Company: ePlus inc.", "Company: Brady Corporation", "Company: FUJIFILM Holdings America Corp", "Company: Fresche Solutions", "Company: AMS", "Company: Thrivent", "Company: Schwan's Company", "Company: Baylor Scott & White Health", "Company: The Herbert Wertheim UF Scripp", "Company: CSX", "Company: Crain Communications", "Company: Oklahoma State University", "Company: Infogain", "Company: QuinStreet", "Company: Landis+Gyr", "Company: Safety-Kleen", "Company: Paul Hastings", "Company: Reed Smith LLP", "Company: Sutherland", "Company: Marcus & Millichap", "Company: Georgia State University", "Company: National Institute of Standard", "Company: NewYork-Presbyterian Hospital", "Company: Dräger", "Company: Tata Technologies", "Company: Russell Investments", "Company: MSCI Inc.", "Company: Axis Communications", "Company: Federal Bureau of Investigatio", "Company: Oregon Department of Transport", "Company: Altria", "Company: HARMAN International", "Company: Kofax", "Company: Zimmer Biomet", "Company: ERM", "Company: Fortinet", "Company: Loyola Medicine", "Company: Loyola University Chicago", "Company: Microchip Technology Inc.", "Company: Greyhound Lines, Inc.", "Company: DaVita Kidney Care", "Company: Tetra Tech", "Company: 24 Hour Fitness", "Company: UT Southwestern Medical Center", "Company: Airlines Reporting Corporation", "Company: Select Medical", "Company: Boston Globe Media", "Company: Ansys", "Company: Connexity, Inc. ", "Company: Gallagher", "Company: SoftServe", "Company: Sappi", "Company: UMB Bank", "Company: Advocate Health Care", "Company: Shure Incorporated", "Company: Gucci", "Company: MSA - The Safety Company", "Company: Norfolk Southern", "Company: Live Nation Entertainment", "Company: dunnhumby", "Company: AMERICAN SYSTEMS", "Company: HOK", "Company: Gibson Dunn ", "Company: Marathon Oil Corporation", "Company: Pro Staff", "Company: Eisai US", "Company: Worley", "Company: Hollister Incorporated", "Company: Rockstar Games", "Company: ClubCorp", "Company: CAS", "Company: Enbridge", "Company: Pall Corporation", "Company: MSNBC", "Company: J.Crew", "Company: WVU Medicine", "Company: Philadelphia Housing Authority", "Company: Austin Independent School Dist", "Company: Incyte", "Company: Premier Inc.", "Company: B. Braun Medical Inc. (US)", "Company: MultiPlan", "Company: Sirva", "Company: Tulane University", "Company: SEPHORA", "Company: Southern Glazer's Wine & Spiri", "Company: Bitdefender", "Company: Milliken & Company", "Company: State of Indiana", "Company: Highmark Inc.", "Company: AEG", "Company: Devon Energy", "Company: Dice", "Company: Simpson Thacher & Bartlett LLP", "Company: Marriott Vacations Worldwide", "Company: Vertex Pharmaceuticals", "Company: Align Technology", "Company: Hennepin County", "Company: American Residential Services", "Company: Delta Dental Ins.", "Company: American Institutes for Resear", "Company: BJC HealthCare", "Company: Florida International Universi", "Company: RITE AID", "Company: The Depository Trust & Clearin", "Company: Strategic Staffing Solutions", "Company: Akin Gump Strauss Hauer & Feld", "Company: Holland & Knight LLP", "Company: Hibu", "Company: InterSystems", "Company: Benchmark", "Company: Jack Henry", "Company: Federal Deposit Insurance Corp", "Company: Carnival Corporation", "Company: Intertek", "Company: Baird", "Company: Indotronix International Corpo", "Company: International SOS", "Company: Columbia Sportswear Company", "Company: CoStar Group", "Company: Ross Stores, Inc.", "Company: IEEE", "Company: Response Companies", "Company: Rohde & Schwarz", "Company: Mercy", "Company: State of Tennessee", "Company: Ruder Finn", "Company: Meijer", "Company: American Medical Association", "Company: Auburn University", "Company: RS", "Company: SGK", "Company: Tag", "Company: Spectrum Brands, Inc", "Company: Nature Portfolio", "Company: NRG Energy", "Company: American Bar Association", "Company: NYS Department of Transportati", "Company: The University of Texas at San", "Company: Fred Hutch", "Company: PDS Tech Commercial, Inc.", "Company: Plastic Omnium", "Company: Gates Corporation", "Company: Acuity Brands", "Company: CEI", "Company: Bekaert", "Company: Norton Rose Fulbright", "Company: Jostens", "Company: CHS Inc.", "Company: Publix Super Markets", "Company: The Johns Hopkins University A", "Company: Mott MacDonald", "Company: University of New Hampshire", "Company: Ultimate Staffing", "Company: Brown-Forman", "Company: Planview", "Company: Sonoco", "Company: Academy of Art University", "Company: Sunrise Senior Living", "Company: Essendant", "Company: Mizuho", "Company: TC Transcontinental", "Company: Fordham University", "Company: Linedata", "Company: Orica", "Company: Huxley", "Company: Blue Cross Blue Shield of Mich", "Company: Comscore, Inc.", "Company: Domino's", "Company: The Leukemia & Lymphoma Societ", "Company: Crane Aerospace & Electronics", "Company: The Carlyle Group", "Company: CSL", "Company: TEAM LEWIS", "Company: Xcel Energy", "Company: TC Energy", "Company: AutoZone", "Company: Boston Medical Center (BMC)", "Company: NETGEAR", "Company: Woodward, Inc.", "Company: UHY LLP, Certified Public Acco", "Company: Brown & Brown Insurance", "Company: Vishay Intertechnology, Inc.", "Company: Priceline", "Company: Davis Polk & Wardwell LLP", "Company: Dillard's Inc.", "Company: Lonza", "Company: FirstEnergy", "Company: GM Financial", "Company: Oakley", "Company: D.R. Horton", "Company: RUSH University Medical Center", "Company: Barry Callebaut Group", "Company: Bulgari", "Company: Cedars-Sinai", "Company: Illumina", "Company: Inova Health", "Company: Maryland State Highway Adminis", "Company: Horizon Blue Cross Blue Shield", "Company: Lockton", "Company: Nexans", "Company: ECCO", "Company: Itron, Inc.", "Company: Newsweek", "Company: Sam's Club", "Company: Corestaff Services", "Company: McDermott International, Ltd", "Company: Lennox", "Company: Aurora Health Care", "Company: Daiichi Sankyo US", "Company: St. Jude Children's Research H", "Company: State of North Carolina", "Company: The Timken Company", "Company: University of Louisville", "Company: Johnson Matthey", "Company: Vistage Worldwide, Inc.", "Company: Cirque du Soleil Entertainment", "Company: Habitat for Humanity Internati", "Company: SS&C Technologies", "Company: Zones, LLC", "Company: Scientific Research Corporatio", "Company: University of California, Rive", "Company: National General", "Company: Emmis Corporation", "Company: GEODIS", "Company: Presidio", "Company: University of Arkansas", "Company: EBSCO Information Services", "Company: NVR, Inc.", "Company: AlixPartners", "Company: DICK'S Sporting Goods", "Company: Petco", "Company: Riverbed Technology", "Company: Nelson Connects", "Company: Space Dynamics Laboratory", "Company: Stevens Institute of Technolog", "Company: Blackstone", "Company: University of Maryland Baltimo", "Company: OUTFRONT Media", "Company: STERIS", "Company: Model N", "Company: TRC Companies, Inc.", "Company: BorgWarner", "Company: Proskauer Rose LLP", "Company: International Rescue Committee", "Company: Land O'Lakes, Inc.", "Company: Merkle", "Company: Texas Health Resources", "Company: The Children's Place", "Company: Popular Bank", "Company: IDEXX", "Company: PIMCO", "Company: Sword Group", "Company: Entrust", "Company: Exelixis", "Company: GHX", "Company: The Lubrizol Corporation", "Company: Milliman", "Company: State of Missouri", "Company: DAT Freight & Analytics", "Company: Mount Sinai Health System", "Company: Life Time Inc.", "Company: Culver Careers (CulverCareers.", "Company: GES - Global Experience Specia", "Company: Guy Carpenter", "Company: Mintz", "Company: AMETEK", "Company: Littler", "Company: Subway", "Company: Acosta", "Company: American Tower", "Company: Bentley University", "Company: Church & Dwight Co., Inc.", "Company: Deutsche Bahn", "Company: The Judge Group", "Company: Unit4", "Company: Huber Engineered Materials", "Company: Globant", "Company: Orkin", "Company: Master Electronics", "Company: Staffmark", "Company: Cartus", "Company: Quad", "Company: James Hardie", "Company: tms", "Company: Transocean", "Company: Dollar General", "Company: Callaway Golf", "Company: Equinix", "Company: Pactiv Evergreen Inc.", "Company: Procom", "Company: Fish & Richardson P.C.", "Company: New Balance", "Company: O-I", "Company: QIAGEN", "Company: Urban Outfitters", "Company: Anthropologie", "Company: Leonardo DRS", "Company: Talbots", "Company: ATR International", "Company: Banner Health", "Company: Charles River Laboratories", "Company: Husky Technologies", "Company: Altair", "Company: Sumitomo Mitsui Banking Corpor", "Company: University of Alaska Fairbanks", "Company: Alston & Bird", "Company: Munich Re", "Company: Dyson", "Company: The Guitar Center Company", "Company: MoneyGram International", "Company: Douglas Elliman Real Estate", "Company: Teleflex", "Company: Levi, Ray & Shoup, Inc. (LRS)", "Company: JSI", "Company: Municipality of Anchorage", "Company: OhioHealth", "Company: BJ's Wholesale Club", "Company: The Toro Company", "Company: CEVA Logistics", "Company: GKN Automotive", "Company: Bowling Green State University", "Company: CITGO", "Company: COUNTRY Financial®", "Company: Flagstar Bank", "Company: National Car Rental", "Company: Alamo Rent A Car", "Company: Boral", "Company: Molson Coors Beverage Company", "Company: Syniverse", "Company: YASH Technologies", "Company: Calix", "Company: Mandarin Oriental Hotel Group", "Company: Ipsen", "Company: Entegris", "Company: Lectra", "Company: Lionsgate", "Company: University of Rhode Island", "Company: Federated Hermes", "Company: Lifespan", "Company: Qualys", "Company: Briggs & Stratton", "Company: California State University, F", "Company: Gulfstream Aerospace", "Company: Colonial Life", "Company: Huhtamaki", "Company: SWAROVSKI", "Company: Brother USA", "Company: National MS Society", "Company: Tate & Lyle", "Company: Kemper", "Company: University of the Pacific", "Company: Fermilab", "Company: Univar Solutions", "Company: Duane Morris LLP", "Company: The Port Authority of New York", "Company: Associated Bank", "Company: Konami Digital Entertainment", "Company: Infoblox", "Company: Penn Mutual", "Company: University of Vermont", "Company: athenahealth", "Company: Info-Tech Research Group", "Company: Sectra", "Company: City of Fort Worth", "Company: Bill & Melinda Gates Foundatio", "Company: City of Atlanta", "Company: designory", "Company: The Bolton Group", "Company: Digitas Health", "Company: TruTeam", "Company: Prologis", "Company: Plante Moran", "Company: UChicago Medicine", "Company: Cboe Global Markets", "Company: City and County of Denver", "Company: GP Strategies Corporation", "Company: Ghirardelli Chocolate Company", "Company: State of Iowa - Executive Bran", "Company: Des Moines Public Schools", "Company: ARA", "Company: The Rockefeller University", "Company: TSMC", "Company: Imerys", "Company: National Hockey League (NHL)", "Company: Polaris Inc.", "Company: California State University, L", "Company: FORVIA HELLA", "Company: Western & Southern Financial G", "Company: Echo Global Logistics", "Company: Greenspun Media Group", "Company: Consumer Reports", "Company: Henry Ford Health", "Company: Premier Health Partners", "Company: The Mount Sinai Hospital", "Company: SHI International Corp.", "Company: Newmark", "Company: Nuveen, a TIAA company", "Company: Macmillan", "Company: Clark County School District", "Company: U.S. Chamber of Commerce", "Company: Nilfisk", "Company: Proforma", "Company: Belden Inc.", "Company: Southwest Research Institute", "Company: FlightSafety International", "Company: Laerdal Medical", "Company: Airgas", "Company: Florida Atlantic University", "Company: World Wide Technology", "Company: Covestro", "Company: Shaw Industries", "Company: Brenntag", "Company: Advantage Solutions", "Company: Universal Technical Institute,", "Company: Porsche Cars North America", "Company: TÜV Rheinland North America", "Company: University of Missouri-Kansas ", "Company: H.B. Fuller", "Company: SES Satellites", "Company: GAF", "Company: The University of Southern Mis", "Company: Advance Auto Parts", "Company: Bright Horizons", "Company: King & Wood Mallesons", "Company: Indiana University Health", "Company: PACSUN", "Company: Ropes & Gray LLP", "Company: Netsmart", "Company: American Water", "Company: Big Lots", "Company: Fairview Health Services", "Company: Garmin", "Company: Lord, Abbett & Co. LLC", "Company: Sheppard Mullin Richter & Hamp", "Company: CareFirst BlueCross BlueShield", "Company: Symetra", "Company: National Journal", "Company: Medical College of Wisconsin", "Company: Brainlab", "Company: Redwood Software", "Company: University of Maryland Global ", "Company: Dallas College", "Company: Savills North America", "Company: VSP Vision Care", "Company: Medline Industries, LP", "Company: Old Dominion University", "Company: Genesis10", "Company: Donaldson", "Company: EPCOR", "Company: J. Paul Getty Trust", "Company: Symrise AG", "Company: TriNet", "Company: Braskem", "Company: The Venetian Resort Las Vegas", "Company: Novelis", "Company: Perkins&Will", "Company: Belk", "Company: Tyler Technologies", "Company: VHB", "Company: EY-Parthenon", "Company: Yara International", "Company: Simon-Kucher", "Company: Intuitive", "Company: Miratech", "Company: Peraton", "Company: Neurocrine Biosciences", "Company: Element Fleet Management", "Company: Amica Insurance", "Company: Kiewit", "Company: William & Mary", "Company: Guidewire Software", "Company: Miami-Dade County Public Schoo", "Company: Trintech", "Company: Ameren", "Company: Benjamin Moore", "Company: Design Within Reach", "Company: ITW", "Company: Liberty University", "Company: Solix Technologies, Inc.", "Company: U.S. Office of Personnel Manag", "Company: VNS Health", "Company: Hill's Pet Nutrition", "Company: X-Rite", "Company: Commonwealth Financial Network", "Company: Centene Corporation", "Company: VSE Corporation", "Company: The Exchange", "Company: Novant Health", "Company: Pella Corporation", "Company: Babcock & Wilcox", "Company: Houston Chronicle", "Company: Howard Hughes Medical Institut", "Company: Kimball International", "Company: Kimley-Horn", "Company: Ansell", "Company: The Metropolitan Museum of Art", "Company: Kennesaw State University", "Company: The City of San Diego", "Company: Mercury Insurance", "Company: Dewberry", "Company: Direct Supply", "Company: Eastridge Workforce Solutions", "Company: Wood", "Company: Iridium", "Company: Crate and Barrel", "Company: Aveda", "Company: Brembo", "Company: Broadspire", "Company: Levy Restaurants", "Company: Rakuten Advertising", "Company: Mintel", "Company: Arkema", "Company: Eastern Michigan University", "Company: Siegel+Gale", "Company: Wright State University", "Company: Bolloré Logistics", "Company: Vicor Corporation", "Company: California State University, C", "Company: RealPage, Inc.", "Company: Protective Life", "Company: Art Institute of Chicago", "Company: Lexar", "Company: Milestone Systems", "Company: ORIX Corporation USA", "Company: Chevron Phillips Chemical Comp", "Company: Hempel A/S", "Company: HUB International", "Company: AMN Healthcare", "Company: Belcan", "Company: Children's Health", "Company: DSA", "Company: Hyland", "Company: Momentive", "Company: Eversource Energy", "Company: StubHub", "Company: Arch Insurance Group Inc.", "Company: Giant Eagle, Inc.", "Company: Medica", "Company: PPL Corporation", "Company: Uponor", "Company: Coinstar", "Company: CSC", "Company: Caleres, Inc.", "Company: Groupe Clarins", "Company: Rocket Software", "Company: Alkermes", "Company: Bracco", "Company: Brooks Brothers", "Company: Famous Footwear", "Company: Creighton University", "Company: Qlik", "Company: Baker Tilly US", "Company: Ivy Tech Community College", "Company: NOVA Chemicals", "Company: Lexicon Pharmaceuticals, Inc.", "Company: PNM Resources", "Company: Stifel Financial Corp.", "Company: Videojet Technologies", "Company: DLC", "Company: HNTB", "Company: GHD", "Company: JDRF International", "Company: Konecranes", "Company: Pep Boys", "Company: Subaru of America", "Company: QinetiQ US", "Company: IONOS", "Company: Sherpa | Recruiting, Staffing ", "Company: New York Institute of Technolo", "Company: Lamar Advertising Company", "Company: Cable ONE", "Company: LCRA", "Company: Accuray", "Company: BankUnited", "Company: Nordson Corporation", "Company: Giorgio Armani", "Company: Minuteman Press", "Company: Swisslog", "Company: Tecan", "Company: Shook, Hardy & Bacon L.L.P.", "Company: Webster Bank", "Company: CooperVision", "Company: Games Workshop Ltd", "Company: BRP", "Company: BART", "Company: Gilbane Building Company", "Company: Mace", "Company: CARFAX", "Company: Genuine Parts Company", "Company: New York City Police Departmen", "Company: NBCUniversal Telemundo Enterpr", "Company: BayCare Health System", "Company: Meta", "Company: Fragomen", "Company: Everi Holdings Inc.", "Company: Securian Financial", "Company: ESR", "Company: Vulcan Materials Company", "Company: American Psychological Associa", "Company: Paradise Valley Hospital", "Company: CarsDirect.com", "Company: Promega Corporation ", "Company: Steptoe LLP", "Company: (USTA) United States Tennis As", "Company: BAI", "Company: Chick-fil-A Corporate Support ", "Company: CHRISTUS Health", "Company: Rent-A-Center", "Company: SNI Financial", "Company: Cooper Standard", "Company: eInfochips (An Arrow Company)", "Company: Fresenius Kabi USA", "Company: Converse", "Company: NORC at the University of Chic", "Company: ACT", "Company: Butler Aerospace & Defense", "Company: City of Phoenix", "Company: David's Bridal", "Company: Quinnox", "Company: Crowell & Moring", "Company: The Wonderful Company", "Company: POM Wonderful", "Company: FIJI Water", "Company: Semtech", "Company: U.S. Xpress, Inc.", "Company: Brookfield Properties", "Company: GKN Aerospace", "Company: Alzheimer's Association®", "Company: Swagelok", "Company: The J.M. Smucker Co.", "Company: Turner & Townsend", "Company: EverBank", "Company: Heartland", "Company: Insight Global", "Company: Liebherr Group", "Company: Pinnacle Group, Inc.", "Company: Starkey Hearing", "Company: Swissport", "Company: University of Mississippi", "Company: Orlando Health", "Company: Terminix", "Company: Westat", "Company: ESCO Group LLC", "Company: Infinite Computer Solutions", "Company: KSB Company", "Company: New York Post", "Company: Nova Ltd.", "Company: Tom James Company", "Company: Berry Global, Inc.", "Company: Douglas County", "Company: Kinder Morgan, Inc.", "Company: HSB - Hartford Steam Boiler", "Company: Venable LLP", "Company: Environmental Defense Fund", "Company: FUJIFILM Healthcare Americas C", "Company: Hill International, Inc.", "Company: Matson, Inc.", "Company: Matson Logistics", "Company: Shiseido", "Company: Travis County", "Company: Vaco", "Company: Burlington Stores, Inc.", "Company: Tarkett", "Company: UAMS - University of Arkansas ", "Company: Yazaki North America", "Company: Girl Scouts of the USA", "Company: Graco", "Company: Hillel International", "Company: Leggett & Platt", "Company: SHRM", "Company: National Renewable Energy Labo", "Company: Prysmian", "Company: Clark Construction Group", "Company: Marlabs LLC", "Company: Children's National Hospital", "Company: ANDRITZ", "Company: Austin Community College", "Company: Hologic, Inc.", "Company: XTRA Lease LLC", "Company: General Atomics", "Company: Ingenio", "Company: Janney Montgomery Scott LLC", "Company: NCDOT", "Company: Almac Group", "Company: Citi", "Company: Siemens Gamesa", "Company: PGA TOUR", "Company: MGIC", "Company: Onward Technologies Limited", "Company: SageNet", "Company: Wood Mackenzie", "Company: Arlington County Government", "Company: O.C. Tanner", "Company: PVH Corp.", "Company: Bartech Staffing", "Company: Woodside Energy", "Company: BDS Connected Solutions, LLC.", "Company: NFP", "Company: Navy Federal Credit Union", "Company: TriWest Healthcare Alliance", "Company: AAAS", "Company: Hormel Foods", "Company: Mainline Information Systems", "Company: Midcontinent Independent Syste", "Company: WEX", "Company: Barings", "Company: BioMarin Pharmaceutical Inc.", "Company: C&S Wholesale Grocers", "Company: Open Systems Technologies", "Company: SolomonEdwards", "Company: AAA-The Auto Club Group", "Company: Institute for Defense Analyses", "Company: MAC Cosmetics", "Company: Markel", "Company: Proofpoint", "Company: Rich Products Corporation", "Company: Combined, a Chubb Company", "Company: Leonardo", "Company: Freedom Mortgage", "Company: Oceaneering", "Company: Trinity College-Hartford", "Company: Tennant Company", "Company: Wesco", "Company: OneAmerica Financial", "Company: Strayer University", "Company: Zilliant", "Company: Medical Mutual", "Company: Atlantic Health System", "Company: Baptist Health", "Company: Trader Joe's", "Company: Avature", "Company: Bank of Hawaii", "Company: Boise State University", "Company: Broadridge", "Company: Keypath Education", "Company: Arby's", "Company: Barrick Gold Corporation", "Company: Centric Consulting", "Company: ITR Group", "Company: Main Line Health", "Company: Myriad Genetics", "Company: Boost Mobile", "Company: Cambridge Health Alliance", "Company: Novanta Inc.", "Company: Virginia Mason Franciscan Heal", "Company: Wilson Elser", "Company: Epiq", "Company: Griffith Foods", "Company: Buchanan Ingersoll & Rooney PC", "Company: Sg2", "Company: UT Health San Antonio", "Company: Medidata Solutions", "Company: Park Nicollet Health Services", "Company: Ocean Spray Cranberries", "Company: Pratt Institute", "Company: BENTELER Group", "Company: Towson University", "Company: Ionis Pharmaceuticals, Inc.", "Company: Paladin Consulting", "Company: STV", "Company: OpenTable", "Company: Republican National Committee", "Company: Safelite", "Company: Tradeweb", "Company: Advantage Resourcing", "Company: Bon Secours", "Company: Denver Public Schools", "Company: Farm Bureau Financial Services", "Company: Audible", "Company: University of Missouri-Saint L", "Company: La-Z-Boy Incorporated", "Company: MedImpact Healthcare Systems, ", "Company: Day & Zimmermann", "Company: Graphic Packaging Internationa", "Company: Idaho National Laboratory", "Company: Rose International", "Company: National Federation of Indepen", "Company: Culligan International", "Company: Sentry", "Company: SICK Sensor Intelligence", "Company: Trapeze Group", "Company: University of Richmond", "Company: Welch's", "Company: Miami Dade College", "Company: Americold Logistics, LLC.", "Company: Atlas Air", "Company: Circle K", "Company: EQT Corporation", "Company: Mimeo", "Company: FCS Software Solutions Ltd", "Company: Leica Microsystems", "Company: Leviton", "Company: Conservation International", "Company: Cracker Barrel", "Company: DPR Construction", "Company: PAR Technology", "Company: UNOPS", "Company: Granite Construction", "Company: General Dynamics Electric Boat", "Company: Markem-Imaje", "Company: PDF Solutions", "Company: Pilgrim's", "Company: Uline", "Company: Yardi", "Company: ASQ - World Headquarters", "Company: CompHealth", "Company: Sensient Technologies Corporat", "Company: Windstream", "Company: Food Lion", "Company: Brookhaven National Laboratory", "Company: Copyright Clearance Center (CC", "Company: Crum & Forster", "Company: UST", "Company: Detroit Medical Center", "Company: Children's Hospital of Michiga", "Company: Exclusive Resorts", "Company: Federal Bureau of Prisons - Ca", "Company: Montclair State University", "Company: Altec", "Company: Scooter's Coffee", "Company: Holland & Hart LLP", "Company: Sargent & Lundy", "Company: Sierra Nevada Corporation", "Company: Cabela's", "Company: Burns & McDonnell", "Company: ChristianaCare", "Company: 2K", "Company: Viking", "Company: HealthFitness", "Company: Hexcel Corporation", "Company: HMSHost", "Company: IREX", "Company: Pernod Ricard", "Company: CuraScript SD by Evernorth", "Company: Genmab", "Company: Loomis, Sayles & Company", "Company: Boys & Girls Clubs of America", "Company: SMX", "Company: Hendrickson", "Company: Rittal North America LLC", "Company: Sinclair Inc.", "Company: Smith Hanley Associates", "Company: eHealth, Inc.", "Company: Mercy Health", "Company: MultiCare Health System", "Company: New Resources Consulting", "Company: Orange County Government", "Company: Biotage", "Company: Harris County", "Company: PENN Entertainment, Inc", "Company: HurixDigital", "Company: Mindteck", "Company: Aggreko", "Company: Aston Carter", "Company: Beam Suntory", "Company: Constant Contact", "Company: Acronis", "Company: Adventist HealthCare", "Company: Management Sciences for Health", "Company: City of Indianapolis", "Company: Empire Today", "Company: Kao Corporation", "Company: Modine Manufacturing Company", "Company: Optiver", "Company: Frost", "Company: Hiscox", "Company: Nexon America", "Company: Parkland Health", "Company: Accruent", "Company: TransPerfect", "Company: Systems Planning & Analysis", "Company: Albany International Corp.", "Company: Conair LLC", "Company: Integra LifeSciences", "Company: Ledgent", "Company: McLane Company, Inc.", "Company: St. Joseph Health", "Company: Zimmerman Advertising", "Company: HKS, Inc.", "Company: Skechers", "Company: ELEKS", "Company: Cambrex", "Company: Children's Minnesota", "Company: Constellation Brands", "Company: Opportunity International", "Company: Regeneron", "Company: Leadership Institute", "Company: ValueLabs", "Company: Contra Costa County", "Company: Devereux Advanced Behavioral H", "Company: Gordon Rees Scully Mansukhani,", "Company: Harris Computer", "Company: Greif", "Company: CohnReznick LLP", "Company: Excelacom", "Company: World Learning", "Company: Signet Jewelers", "Company: Brose Group", "Company: Jackson Lewis P.C.", "Company: MarketAxess", "Company: Oakland University", "Company: Portland General Electric", "Company: Chamberlain Group", "Company: TranSystems", "Company: Trigyn Technologies", "Company: ArisGlobal", "Company: CoBank", "Company: Magna International", "Company: Stepan Company", "Company: ZOLL Medical Corporation", "Company: WiseTech Global", "Company: ABF Freight", "Company: Spirit AeroSystems", "Company: Williams College", "Company: PING", "Company: Prime Therapeutics", "Company: United States Olympic & Paraly", "Company: Burns & Levinson LLP", "Company: Community Health Network", "Company: Eliassen Group", "Company: Freudenberg Sealing Technologi", "Company: Lesley University", "Company: Marathon Petroleum Corporation", "Company: Ballard Spahr LLP", "Company: Bluegreen Vacations", "Company: Zegna", "Company: Hach", "Company: International Atomic Energy Ag", "Company: Apex IT", "Company: Mortenson", "Company: Mozilla", "Company: expand group", "Company: BECU", "Company: Wheels, Inc.", "Company: Zillow", "Company: Expro", "Company: Builders FirstSource", "Company: CES", "Company: Dematic", "Company: Western Governors University", "Company: LensCrafters", "Company: The Mars Agency", "Company: Prosum", "Company: RadNet", "Company: SANS Institute", "Company: Volvo Financial Services", "Company: Under Armour", "Company: Softworld, a Kelly Company", "Company: AvalonBay Communities", "Company: Aéropostale", "Company: Carters Inc.", "Company: Dallas Fort Worth Internationa", "Company: CJ Logistics America", "Company: Wiley Rein LLP", "Company: VisitBritain", "Company: Lhoist", "Company: Printpack", "Company: Academy Sports + Outdoors", "Company: Cascades", "Company: Staff Management | SMX", "Company: Wildlife Conservation Society", "Company: Solomon Page", "Company: RWJBarnabas Health", "Company: Guaranteed Rate", "Company: KARL STORZ United States", "Company: GIA (Gemological Institute of ", "Company: Papa Johns", "Company: Clean Harbors", "Company: Denver Health", "Company: CSI", "Company: Transurban", "Company: American Modern Insurance Grou", "Company: Perry Ellis International", "Company: P.F. Chang's", "Company: East West Bank", "Company: Newegg", "Company: Armanino LLP", "Company: AVEVA", "Company: Susan G. Komen", "Company: Zeno Group", "Company: EMCOR Group, Inc.", "Company: Avangrid", "Company: Erie Insurance Group", "Company: Tommy Bahama", "Company: Eastern Bank", "Company: iCIMS", "Company: Comrise", "Company: McLean Hospital", "Company: Movado Group, Inc", "Company: Jefferson Health", "Company: Peterson's", "Company: Selective Insurance", "Company: Batesville", "Company: Butler University", "Company: Profiles", "Company: Renaissance Learning", "Company: Scripps Health", "Company: Tampa Electric", "Company: Inditex", "Company: Universal Instruments Corporat", "Company: Jockey International, Inc.", "Company: Metropolitan State University ", "Company: Trinity Industries, Inc.", "Company: EDB", "Company: Hannaford Supermarkets", "Company: HealthStream", "Company: Performance Food Group", "Company: Baptist Health", "Company: City of Palo Alto", "Company: Ansira", "Company: RED Global", "Company: FUJIFILM Sonosite, Inc.", "Company: Tripadvisor", "Company: Kimpton Hotels & Restaurants", 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"zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Vector Space: Candidates & Companies (Enriched with Postings)" }, "width": 1200, "xaxis": { "title": { "text": "Dimension 1" } }, "yaxis": { "title": { "text": "Dimension 2" } } } } }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "✅ Visualization complete!\n", "💡 If green & red OVERLAP → Alignment worked!\n" ] } ], "source": [ "# Create interactive plot\n", "fig = go.Figure()\n", "\n", "# Companies (red)\n", "fig.add_trace(go.Scatter(\n", " x=comp_2d[:, 0],\n", " y=comp_2d[:, 1],\n", " mode='markers',\n", " name='Companies',\n", " marker=dict(size=6, color='#ff6b6b', opacity=0.6),\n", " text=[f\"Company: {companies_full.iloc[i].get('name', 'N/A')[:30]}\" \n", " for i in range(n_comp_viz)],\n", " hovertemplate='%{text}'\n", "))\n", "\n", "# Candidates (green)\n", "fig.add_trace(go.Scatter(\n", " x=cand_2d[:, 0],\n", " y=cand_2d[:, 1],\n", " mode='markers',\n", " name='Candidates',\n", " marker=dict(\n", " size=10,\n", " color='#00ff00',\n", " opacity=0.8,\n", " line=dict(width=1, color='white')\n", " ),\n", " text=[f\"Candidate {i}\" for i in range(n_cand_viz)],\n", " hovertemplate='%{text}'\n", "))\n", "\n", "fig.update_layout(\n", " title='Vector Space: Candidates & Companies (Enriched with Postings)',\n", " xaxis_title='Dimension 1',\n", " yaxis_title='Dimension 2',\n", " width=1200,\n", " height=800,\n", " plot_bgcolor='#1a1a1a',\n", " paper_bgcolor='#0d0d0d',\n", " font=dict(color='white')\n", ")\n", "\n", "fig.show()\n", "\n", "print(\"\\n✅ Visualization complete!\")\n", "print(\"💡 If green & red OVERLAP → Alignment worked!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 📊 Interactive Visualization 2: Highlighted Match Network\n", "\n", "Show candidate and their top matches with connection lines" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🔍 Analyzing Candidate #0...\n", "\n" ] }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { 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"ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Candidate #0 and Top Matches" }, "width": 1200, "xaxis": { "title": { "text": "Dimension 1" } }, "yaxis": { "title": { "text": "Dimension 2" } } } } }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "✅ Highlighted visualization created!\n", " ⭐ Green star = Candidate #0\n", " 🔴 Red dots = Top matches\n", " 💛 Yellow lines = Connections\n" ] } ], "source": [ "# ============================================================================\n", "# 🔍 HIGHLIGHTED MATCH NETWORK\n", "# ============================================================================\n", "\n", "target_candidate = 0\n", "\n", "print(f\"🔍 Analyzing Candidate #{target_candidate}...\\n\")\n", "\n", "matches = find_top_matches(target_candidate, top_k=10)\n", "match_indices = [comp_idx for comp_idx, score in matches if comp_idx < n_comp_viz]\n", "\n", "# Create highlighted plot\n", "fig2 = go.Figure()\n", "\n", "# All companies (background)\n", "fig2.add_trace(go.Scatter(\n", " x=comp_2d[:, 0],\n", " y=comp_2d[:, 1],\n", " mode='markers',\n", " name='All Companies',\n", " marker=dict(size=4, color='#ff6b6b', opacity=0.3),\n", " showlegend=True\n", "))\n", "\n", "# Top matches (highlighted)\n", "if match_indices:\n", " match_positions = comp_2d[match_indices]\n", " fig2.add_trace(go.Scatter(\n", " x=match_positions[:, 0],\n", " y=match_positions[:, 1],\n", " mode='markers',\n", " name='Top Matches',\n", " marker=dict(\n", " size=15,\n", " color='#ff0000',\n", " line=dict(width=2, color='white')\n", " ),\n", " text=[f\"Match #{i+1}: {companies_full.iloc[match_indices[i]].get('name', 'N/A')[:30]}
Score: {matches[i][1]:.3f}\" \n", " for i in range(len(match_indices))],\n", " hovertemplate='%{text}'\n", " ))\n", "\n", "# Target candidate (star)\n", "fig2.add_trace(go.Scatter(\n", " x=[cand_2d[target_candidate, 0]],\n", " y=[cand_2d[target_candidate, 1]],\n", " mode='markers',\n", " name=f'Candidate #{target_candidate}',\n", " marker=dict(\n", " size=25,\n", " color='#00ff00',\n", " symbol='star',\n", " line=dict(width=3, color='white')\n", " )\n", "))\n", "\n", "# Connection lines (top 5)\n", "for i, match_idx in enumerate(match_indices[:5]):\n", " fig2.add_trace(go.Scatter(\n", " x=[cand_2d[target_candidate, 0], comp_2d[match_idx, 0]],\n", " y=[cand_2d[target_candidate, 1], comp_2d[match_idx, 1]],\n", " mode='lines',\n", " line=dict(color='yellow', width=1, dash='dot'),\n", " opacity=0.5,\n", " showlegend=False\n", " ))\n", "\n", "fig2.update_layout(\n", " title=f'Candidate #{target_candidate} and Top Matches',\n", " xaxis_title='Dimension 1',\n", " yaxis_title='Dimension 2',\n", " width=1200,\n", " height=800,\n", " plot_bgcolor='#1a1a1a',\n", " paper_bgcolor='#0d0d0d',\n", " font=dict(color='white')\n", ")\n", "\n", "fig2.show()\n", "\n", "print(\"\\n✅ Highlighted visualization created!\")\n", "print(f\" ⭐ Green star = Candidate #{target_candidate}\")\n", "print(f\" 🔴 Red dots = Top matches\")\n", "print(f\" 💛 Yellow lines = Connections\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 🌐 Interactive Visualization 3: Network Graph (PyVis)\n", "\n", "Interactive network showing candidate-company connections with nodes & edges" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🌐 Creating interactive network graph...\n", "\n", "✅ Network graph created!\n", "📄 Saved: ../results/network_graph.html\n", "\n", "💡 LEGEND:\n", " ⭐ Green star = Candidate #0\n", " 🔴 Red nodes = Companies (size = match score)\n", " 💛 Yellow edges = Connections\n", "\n", "ℹ️ Hover over nodes to see details\n", " Drag nodes to rearrange\n", " Zoom with mouse wheel\n", "\n" ] }, { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# ============================================================================\n", "# 🌐 NETWORK GRAPH WITH PYVIS\n", "# ============================================================================\n", "\n", "from pyvis.network import Network\n", "import webbrowser\n", "import os\n", "\n", "print(\"🌐 Creating interactive network graph...\\n\")\n", "\n", "target_candidate = 0\n", "top_k_network = 10\n", "\n", "# Get matches\n", "matches = find_top_matches(target_candidate, top_k=top_k_network)\n", "\n", "# Create network\n", "net = Network(\n", " height='800px',\n", " width='100%',\n", " bgcolor='#1a1a1a',\n", " font_color='white',\n", " directed=False\n", ")\n", "\n", "# Configure physics\n", "net.barnes_hut(\n", " gravity=-5000,\n", " central_gravity=0.3,\n", " spring_length=100,\n", " spring_strength=0.01\n", ")\n", "\n", "# Add candidate node (center)\n", "cand = candidates.iloc[target_candidate]\n", "cand_label = f\"Candidate #{target_candidate}\"\n", "net.add_node(\n", " f'cand_{target_candidate}',\n", " label=cand_label,\n", " title=f\"{cand.get('Category', 'N/A')}
Skills: {str(cand.get('skills', 'N/A'))[:100]}\",\n", " color='#00ff00',\n", " size=40,\n", " shape='star'\n", ")\n", "\n", "# Add company nodes + edges\n", "for rank, (comp_idx, score) in enumerate(matches, 1):\n", " if comp_idx >= len(companies_full):\n", " continue\n", " \n", " company = companies_full.iloc[comp_idx]\n", " comp_name = company.get('name', f'Company {comp_idx}')[:30]\n", " \n", " # Color by score\n", " if score > 0.7:\n", " color = '#ff0000' # Red (strong match)\n", " elif score > 0.5:\n", " color = '#ff6b6b' # Light red (good match)\n", " else:\n", " color = '#ffaaaa' # Pink (weak match)\n", " \n", " # Add company node\n", " net.add_node(\n", " f'comp_{comp_idx}',\n", " label=f\"#{rank}. {comp_name}\",\n", " title=f\"Score: {score:.3f}
Industries: {str(company.get('industries_list', 'N/A'))[:50]}
Required: {str(company.get('required_skills', 'N/A'))[:100]}\",\n", " color=color,\n", " size=20 + (score * 20) # Size by score\n", " )\n", " \n", " # Add edge\n", " net.add_edge(\n", " f'cand_{target_candidate}',\n", " f'comp_{comp_idx}',\n", " value=float(score),\n", " title=f\"Similarity: {score:.3f}\",\n", " color='yellow'\n", " )\n", "\n", "# Save\n", "output_file = f'{Config.RESULTS_PATH}network_graph.html'\n", "net.save_graph(output_file)\n", "\n", "print(f\"✅ Network graph created!\")\n", "print(f\"📄 Saved: {output_file}\")\n", "print(f\"\\n💡 LEGEND:\")\n", "print(f\" ⭐ Green star = Candidate #{target_candidate}\")\n", "print(f\" 🔴 Red nodes = Companies (size = match score)\")\n", "print(f\" 💛 Yellow edges = Connections\")\n", "print(f\"\\nℹ️ Hover over nodes to see details\")\n", "print(f\" Drag nodes to rearrange\")\n", "print(f\" Zoom with mouse wheel\\n\")\n", "\n", "# Display in notebook\n", "from IPython.display import IFrame\n", "IFrame(output_file, width=1000, height=800)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 📊 Network Node Data\n", "\n", "Detailed information about nodes and connections" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "📊 NETWORK DATA SUMMARY\n", "================================================================================\n", "\n", "Total nodes: 11\n", " - 1 candidate node (green star)\n", " - 10 company nodes (red circles)\n", "\n", "Total edges: 10\n", "\n", "================================================================================\n", "\n", "🎯 CANDIDATE NODE:\n", " ID: cand_0\n", " Category: N/A\n", " Skills: ['Big Data', 'Hadoop', 'Hive', 'Python', 'Mapreduce', 'Spark', 'Java', 'Machine Learning', 'Cloud', ...\n", "\n", "🏢 COMPANY NODES (Top 5):\n", "\n", " #1. TeachTown\n", " ID: comp_9418\n", " Score: 0.7028\n", " Industries: E-Learning Providers...\n", "\n", " #3. Wolverine Power Systems\n", " ID: comp_9417\n", " Score: 0.7026\n", " Industries: Renewable Energy Semiconductor Manufacturing...\n", "\n", " #5. Mariner\n", " ID: comp_9416\n", " Score: 0.7010\n", " Industries: Financial Services...\n", "\n", "================================================================================\n" ] } ], "source": [ "# ============================================================================\n", "# DISPLAY NODE DATA\n", "# ============================================================================\n", "\n", "print(\"📊 NETWORK DATA SUMMARY\")\n", "print(\"=\" * 80)\n", "print(f\"\\nTotal nodes: {1 + len(matches)}\")\n", "print(f\" - 1 candidate node (green star)\")\n", "print(f\" - {len(matches)} company nodes (red circles)\")\n", "print(f\"\\nTotal edges: {len(matches)}\")\n", "print(f\"\\n\" + \"=\" * 80)\n", "\n", "# Show node details\n", "print(f\"\\n🎯 CANDIDATE NODE:\")\n", "print(f\" ID: cand_{target_candidate}\")\n", "print(f\" Category: {cand.get('Category', 'N/A')}\")\n", "print(f\" Skills: {str(cand.get('skills', 'N/A'))[:100]}...\")\n", "\n", "print(f\"\\n🏢 COMPANY NODES (Top 5):\")\n", "for rank, (comp_idx, score) in enumerate(matches[:5], 1):\n", " if comp_idx < len(companies_full):\n", " company = companies_full.iloc[comp_idx]\n", " print(f\"\\n #{rank}. {company.get('name', 'N/A')[:40]}\")\n", " print(f\" ID: comp_{comp_idx}\")\n", " print(f\" Score: {score:.4f}\")\n", " print(f\" Industries: {str(company.get('industries_list', 'N/A'))[:60]}...\")\n", "\n", "print(f\"\\n\" + \"=\" * 80)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 📊 Step 19: Summary\n", "\n", "### What We Built" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "======================================================================\n", "🎯 HRHUB v2.1 - SUMMARY\n", "======================================================================\n", "\n", "✅ IMPLEMENTED:\n", " 1. Zero-Shot Job Classification (Entry/Mid/Senior/Executive)\n", " 2. Few-Shot Learning with Examples\n", " 3. Structured Skills Extraction (Pydantic schemas)\n", " 4. Match Explainability (LLM-generated reasoning)\n", " 5. FREE LLM Integration (Hugging Face)\n", " 6. Flexible Data Loading (Upload OR Google Drive)\n", "\n", "💰 COST: $0.00 (completely free!)\n", "\n", "📈 COURSE ALIGNMENT:\n", " ✅ LLMs for structured output\n", " ✅ Pydantic schemas\n", " ✅ Classification pipelines\n", " ✅ Zero-shot & few-shot learning\n", " ✅ JSON extraction\n", " ✅ Transformer architecture (embeddings)\n", " ✅ API deployment strategies\n", "\n", "======================================================================\n", "🚀 READY TO MOVE TO VS CODE!\n", "======================================================================\n" ] } ], "source": [ "print(\"=\"*70)\n", "print(\"🎯 HRHUB v2.1 - SUMMARY\")\n", "print(\"=\"*70)\n", "print(\"\")\n", "print(\"✅ IMPLEMENTED:\")\n", "print(\" 1. Zero-Shot Job Classification (Entry/Mid/Senior/Executive)\")\n", "print(\" 2. Few-Shot Learning with Examples\")\n", "print(\" 3. Structured Skills Extraction (Pydantic schemas)\")\n", "print(\" 4. Match Explainability (LLM-generated reasoning)\")\n", "print(\" 5. FREE LLM Integration (Hugging Face)\")\n", "print(\" 6. Flexible Data Loading (Upload OR Google Drive)\")\n", "print(\"\")\n", "print(\"💰 COST: $0.00 (completely free!)\")\n", "print(\"\")\n", "print(\"📈 COURSE ALIGNMENT:\")\n", "print(\" ✅ LLMs for structured output\")\n", "print(\" ✅ Pydantic schemas\")\n", "print(\" ✅ Classification pipelines\")\n", "print(\" ✅ Zero-shot & few-shot learning\")\n", "print(\" ✅ JSON extraction\")\n", "print(\" ✅ Transformer architecture (embeddings)\")\n", "print(\" ✅ API deployment strategies\")\n", "print(\"\")\n", "print(\"=\"*70)\n", "print(\"🚀 READY TO MOVE TO VS CODE!\")\n", "print(\"=\"*70)" ] } ], "metadata": { "kernelspec": { "display_name": "venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 2 }