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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "b2dd5b02",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ All imports successful!\n",
"📦 Pandas: 2.1.4\n",
"📦 Numpy: 1.26.3\n"
]
}
],
"source": [
"# ═══════════════════════════════════════════════════════════════════\n",
"# 🚀 HRHUB V2.1 - PRODUCTION NOTEBOOK\n",
"# Cell 1: Setup & Imports\n",
"# ═══════════════════════════════════════════════════════════════════\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"# Core\n",
"import pandas as pd\n",
"import numpy as np\n",
"from pathlib import Path\n",
"\n",
"# Embeddings\n",
"from sentence_transformers import SentenceTransformer\n",
"from sklearn.metrics.pairwise import cosine_similarity\n",
"\n",
"# Viz\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import plotly.express as px\n",
"import plotly.graph_objects as go\n",
"from pyvis.network import Network\n",
"\n",
"# Dimensionality reduction\n",
"from sklearn.manifold import TSNE\n",
"\n",
"# Utils\n",
"from tqdm import tqdm\n",
"import pickle\n",
"from typing import List, Dict, Tuple\n",
"import time\n",
"\n",
"# Config\n",
"plt.style.use('seaborn-v0_8-darkgrid')\n",
"sns.set_palette(\"husl\")\n",
"pd.set_option('display.max_columns', None)\n",
"pd.set_option('display.max_rows', 100)\n",
"\n",
"print(\"✅ All imports successful!\")\n",
"print(f\"📦 Pandas: {pd.__version__}\")\n",
"print(f\"📦 Numpy: {np.__version__}\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "b8696a11",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ Paths configured!\n",
"📂 Base path: data\n",
"🤖 Model: sentence-transformers/all-MiniLM-L6-v2\n"
]
}
],
"source": [
"# ═══════════════════════════════════════════════════════════════════\n",
"# Cell 2: Paths & Configuration\n",
"# ═══════════════════════════════════════════════════════════════════\n",
"\n",
"# 🟢 VSCode local - path direto\n",
"BASE_PATH = Path(\"data\")\n",
"\n",
"# Input paths\n",
"DATA_PATHS = {\n",
" 'benefits': BASE_PATH / \"benefits.csv\",\n",
" 'companies': BASE_PATH / \"companies.csv\",\n",
" 'company_industries': BASE_PATH / \"company_industries.csv\",\n",
" 'company_specialties': BASE_PATH / \"company_specialties.csv\",\n",
" 'employee_counts': BASE_PATH / \"employee_counts.csv\",\n",
" 'industries': BASE_PATH / \"industries.csv\",\n",
" 'job_industries': BASE_PATH / \"job_industries.csv\",\n",
" 'job_skills': BASE_PATH / \"job_skills.csv\",\n",
" 'postings': BASE_PATH / \"postings.csv\",\n",
" 'resume_data': BASE_PATH / \"resume_data.csv\",\n",
" 'salaries': BASE_PATH / \"salaries.csv\",\n",
" 'skills': BASE_PATH / \"skills.csv\"\n",
"}\n",
"\n",
"# Output files (salvamos direto com npy/pkl)\n",
"OUTPUT_FILES = {\n",
" 'candidate_embeddings': 'candidate_embeddings.npy',\n",
" 'company_embeddings': 'company_embeddings.npy',\n",
" 'candidate_metadata': 'candidate_metadata.pkl',\n",
" 'company_metadata': 'company_metadata.pkl'\n",
"}\n",
"\n",
"# Model config\n",
"MODEL_NAME = \"sentence-transformers/all-MiniLM-L6-v2\"\n",
"EMBEDDING_DIM = 384\n",
"\n",
"print(\"✅ Paths configured!\")\n",
"print(f\"📂 Base path: {BASE_PATH}\")\n",
"print(f\"🤖 Model: {MODEL_NAME}\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "657220e4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"📥 Loading data...\n",
"❌ benefits: ERROR - [Errno 2] No such file or directory: 'data/benefits.csv'\n",
"❌ companies: ERROR - [Errno 2] No such file or directory: 'data/companies.csv'\n",
"❌ company_industries: ERROR - [Errno 2] No such file or directory: 'data/company_industries.csv'\n",
"❌ company_specialties: ERROR - [Errno 2] No such file or directory: 'data/company_specialties.csv'\n",
"❌ employee_counts: ERROR - [Errno 2] No such file or directory: 'data/employee_counts.csv'\n",
"❌ industries: ERROR - [Errno 2] No such file or directory: 'data/industries.csv'\n",
"❌ job_industries: ERROR - [Errno 2] No such file or directory: 'data/job_industries.csv'\n",
"❌ job_skills: ERROR - [Errno 2] No such file or directory: 'data/job_skills.csv'\n",
"❌ postings: ERROR - [Errno 2] No such file or directory: 'data/postings.csv'\n",
"❌ resume_data: ERROR - [Errno 2] No such file or directory: 'data/resume_data.csv'\n",
"❌ salaries: ERROR - [Errno 2] No such file or directory: 'data/salaries.csv'\n",
"❌ skills: ERROR - [Errno 2] No such file or directory: 'data/skills.csv'\n",
"\n",
"⏱️ Loaded in 0.00s\n",
"\n",
"======================================================================\n",
"🔍 KEY DATASETS PREVIEW\n",
"======================================================================\n",
"\n",
"📋 CANDIDATES (resume_data):\n",
"\n",
"🏢 COMPANIES:\n",
"\n",
"📄 JOB POSTINGS:\n",
"\n",
"✅ Data loaded! Ready to inspect and clean.\n"
]
}
],
"source": [
"# ═══════════════════════════════════════════════════════════════════\n",
"# Cell 3: Load Raw Data\n",
"# ═══════════════════════════════════════════════════════════════════\n",
"\n",
"print(\"📥 Loading data...\")\n",
"start_time = time.time()\n",
"\n",
"# Load all CSVs\n",
"data = {}\n",
"for name, path in DATA_PATHS.items():\n",
" try:\n",
" df = pd.read_csv(path)\n",
" data[name] = df\n",
" print(f\"✅ {name}: {df.shape[0]:,} rows × {df.shape[1]} cols\")\n",
" except Exception as e:\n",
" print(f\"❌ {name}: ERROR - {e}\")\n",
" data[name] = None\n",
"\n",
"load_time = time.time() - start_time\n",
"print(f\"\\n⏱️ Loaded in {load_time:.2f}s\")\n",
"\n",
"# Quick peek at key datasets\n",
"print(\"\\n\" + \"=\"*70)\n",
"print(\"🔍 KEY DATASETS PREVIEW\")\n",
"print(\"=\"*70)\n",
"\n",
"print(\"\\n📋 CANDIDATES (resume_data):\")\n",
"if data['resume_data'] is not None:\n",
" print(f\"Shape: {data['resume_data'].shape}\")\n",
" print(f\"Columns: {list(data['resume_data'].columns)}\")\n",
" print(data['resume_data'].head(2))\n",
"\n",
"print(\"\\n🏢 COMPANIES:\")\n",
"if data['companies'] is not None:\n",
" print(f\"Shape: {data['companies'].shape}\")\n",
" print(f\"Columns: {list(data['companies'].columns)}\")\n",
" print(data['companies'].head(2))\n",
"\n",
"print(\"\\n📄 JOB POSTINGS:\")\n",
"if data['postings'] is not None:\n",
" print(f\"Shape: {data['postings'].shape}\")\n",
" print(f\"Columns: {list(data['postings'].columns)}\")\n",
" print(data['postings'].head(2))\n",
"\n",
"print(\"\\n✅ Data loaded! Ready to inspect and clean.\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "52833afd",
"metadata": {},
"outputs": [],
"source": []
}
],
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"kernelspec": {
"display_name": "venv",
"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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