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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"API key: sk-proj-****************************************************************************************************************************************************-amA_5sA\n",
"Cliente inicializado como <openai.OpenAI object at 0x0000021664BC5ED0>\n"
]
}
],
"source": [
"import os\n",
"import pandas as pd\n",
"from scipy import spatial\n",
"from openai import OpenAI\n",
"from dotenv import load_dotenv\n",
"\n",
"load_dotenv(\"../../../../../../../apis/.env\")\n",
"api_key = os.getenv(\"OPENAI_API_KEY\")\n",
"unmasked_chars = 8\n",
"masked_key = api_key[:unmasked_chars] + '*' * (len(api_key) - unmasked_chars*2) + api_key[-unmasked_chars:]\n",
"print(f\"API key: {masked_key}\")\n",
"client = OpenAI(api_key=api_key)\n",
"print(\"Cliente inicializado como\",client)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Ejemplos b谩sicos de c谩lculo de distancia con embeddings"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>empresa</th>\n",
" <th>puesto</th>\n",
" <th>periodo</th>\n",
" <th>fec_inicio</th>\n",
" <th>fec_final</th>\n",
" <th>duracion</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Aut贸nomo</td>\n",
" <td>Comercial de automoviles</td>\n",
" <td>202401</td>\n",
" <td>2024-01-01</td>\n",
" <td>2024-12-07</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Mercadona</td>\n",
" <td>Vendedor/a de puesto de mercado</td>\n",
" <td>202310-202404</td>\n",
" <td>2023-10-01</td>\n",
" <td>2024-04-01</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>AGRISOLUTIONS</td>\n",
" <td>AUXILIAR DE MANTENIMIENTO INDUSTRIAL</td>\n",
" <td>202001-202401</td>\n",
" <td>2020-01-01</td>\n",
" <td>2024-01-01</td>\n",
" <td>48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>GASTROTEKA ORDIZIA 1990</td>\n",
" <td>Camarero/a de barra</td>\n",
" <td>202303-202309</td>\n",
" <td>2023-03-01</td>\n",
" <td>2023-09-01</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>ZEREGUIN ZERBITZUAK</td>\n",
" <td>limpieza industrial</td>\n",
" <td>202012-202305</td>\n",
" <td>2020-12-01</td>\n",
" <td>2023-05-01</td>\n",
" <td>29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Bellota Herramientas</td>\n",
" <td>Personal de mantenimiento</td>\n",
" <td>202005-202011</td>\n",
" <td>2020-05-01</td>\n",
" <td>2020-11-01</td>\n",
" <td>6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" empresa puesto \\\n",
"0 Aut贸nomo Comercial de automoviles \n",
"1 Mercadona Vendedor/a de puesto de mercado \n",
"2 AGRISOLUTIONS AUXILIAR DE MANTENIMIENTO INDUSTRIAL \n",
"3 GASTROTEKA ORDIZIA 1990 Camarero/a de barra \n",
"4 ZEREGUIN ZERBITZUAK limpieza industrial \n",
"5 Bellota Herramientas Personal de mantenimiento \n",
"\n",
" periodo fec_inicio fec_final duracion \n",
"0 202401 2024-01-01 2024-12-07 11 \n",
"1 202310-202404 2023-10-01 2024-04-01 6 \n",
"2 202001-202401 2020-01-01 2024-01-01 48 \n",
"3 202303-202309 2023-03-01 2023-09-01 6 \n",
"4 202012-202305 2020-12-01 2023-05-01 29 \n",
"5 202005-202011 2020-05-01 2020-11-01 6 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Vendedor/a de puesto de mercado\n"
]
}
],
"source": [
"ejemplos_experiencia = pd.read_pickle(\"../pkl/df_experiencia.pkl\")\n",
"display(ejemplos_experiencia)\n",
"print(ejemplos_experiencia.puesto[1])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Texto: Vendedor/a de puesto de mercado\n",
"Embeddings (1536): [-0.006109286565333605, -0.01615688018500805, 0.02458987757563591, 0.0013343609170988202, -0.04200134426355362, 0.015196849592030048, 0.010587611235678196, 0.03497566282749176, -0.015262306667864323, -0.031200997531414032]...\n"
]
}
],
"source": [
"client = OpenAI()\n",
"puesto_vendedor = ejemplos_experiencia.puesto[1]\n",
"\n",
"response = client.embeddings.create(\n",
" input=puesto_vendedor,\n",
" model=\"text-embedding-3-small\"\n",
")\n",
"emb_puesto_vendedor = response.data[0].embedding\n",
"print(f'Texto: {puesto_vendedor}\\nEmbeddings ({len(emb_puesto_vendedor)}): {emb_puesto_vendedor[:10]}...')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Texto: Camarero/a de barra\n",
"Embeddings (1536): [-0.035160087049007416, -0.0017518880777060986, -0.006896876264363527, -0.040239546447992325, -0.024628372862935066, 0.000213889084989205, 4.456970600585919e-06, 0.047462623566389084, -0.02062072791159153, -0.03217765688896179]...\n"
]
}
],
"source": [
"puesto_camarero = ejemplos_experiencia.puesto[3]\n",
"\n",
"response = client.embeddings.create(\n",
" input=puesto_camarero,\n",
" model=\"text-embedding-3-small\"\n",
")\n",
"emb_puesto_camarero = response.data[0].embedding\n",
"print(f'Texto: {puesto_camarero}\\nEmbeddings ({len(emb_puesto_camarero)}): {emb_puesto_camarero[:10]}...')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Texto: Cajero supermercado Dia\n",
"Embeddings (1536): [-0.0045319367200136185, -0.04426201060414314, -0.0222327820956707, -0.015300587750971317, 0.008034787140786648, 0.011099428869783878, 0.03736374154686928, 0.07590357959270477, -0.020332932472229004, -0.03946714848279953]...\n"
]
}
],
"source": [
"oferta_cajero = \"Cajero supermercado Dia\"\n",
"\n",
"response = client.embeddings.create(\n",
" input=oferta_cajero,\n",
" model=\"text-embedding-3-small\"\n",
")\n",
"emb_oferta_cajero = response.data[0].embedding\n",
"print(f'Texto: {oferta_cajero}\\nEmbeddings ({len(emb_oferta_cajero)}): {emb_oferta_cajero[:10]}...')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Distancia m铆nima: 0.000\n",
"Distancia entre el puesto de vendedor y la oferta de cajero: 0.557\n",
"Distancia entre el puesto de camarero y la oferta de cajero: 0.587\n"
]
}
],
"source": [
"dist_min = spatial.distance.cosine(emb_oferta_cajero, emb_oferta_cajero)\n",
"print(f\"Distancia m铆nima: {dist_min:.3f}\")\n",
"dist_ven = spatial.distance.cosine(emb_puesto_vendedor, emb_oferta_cajero)\n",
"print(f\"Distancia entre el puesto de vendedor y la oferta de cajero: {dist_ven:.3f}\")\n",
"dist_cam = spatial.distance.cosine(emb_puesto_camarero, emb_oferta_cajero)\n",
"print(f\"Distancia entre el puesto de camarero y la oferta de cajero: {dist_cam:.3f}\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. An谩lisis de c谩lculo de distancias para el CV completo"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>empresa</th>\n",
" <th>puesto</th>\n",
" <th>periodo</th>\n",
" <th>fec_inicio</th>\n",
" <th>fec_final</th>\n",
" <th>duracion</th>\n",
" <th>embeddings</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Aut贸nomo</td>\n",
" <td>Comercial de automoviles</td>\n",
" <td>202401</td>\n",
" <td>2024-01-01</td>\n",
" <td>2024-12-07</td>\n",
" <td>11</td>\n",
" <td>[0.015070287510752678, 0.0029741383623331785, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Mercadona</td>\n",
" <td>Vendedor/a de puesto de mercado</td>\n",
" <td>202310-202404</td>\n",
" <td>2023-10-01</td>\n",
" <td>2024-04-01</td>\n",
" <td>6</td>\n",
" <td>[-0.006109286565333605, -0.01615688018500805, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>AGRISOLUTIONS</td>\n",
" <td>AUXILIAR DE MANTENIMIENTO INDUSTRIAL</td>\n",
" <td>202001-202401</td>\n",
" <td>2020-01-01</td>\n",
" <td>2024-01-01</td>\n",
" <td>48</td>\n",
" <td>[0.00385109125636518, 0.04469580203294754, 0.0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>GASTROTEKA ORDIZIA 1990</td>\n",
" <td>Camarero/a de barra</td>\n",
" <td>202303-202309</td>\n",
" <td>2023-03-01</td>\n",
" <td>2023-09-01</td>\n",
" <td>6</td>\n",
" <td>[-0.035160087049007416, -0.0017518880777060986...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>ZEREGUIN ZERBITZUAK</td>\n",
" <td>limpieza industrial</td>\n",
" <td>202012-202305</td>\n",
" <td>2020-12-01</td>\n",
" <td>2023-05-01</td>\n",
" <td>29</td>\n",
" <td>[0.003700299421325326, 0.0045193759724497795, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Bellota Herramientas</td>\n",
" <td>Personal de mantenimiento</td>\n",
" <td>202005-202011</td>\n",
" <td>2020-05-01</td>\n",
" <td>2020-11-01</td>\n",
" <td>6</td>\n",
" <td>[0.04391268640756607, 0.05462520197033882, 0.0...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" empresa puesto \\\n",
"0 Aut贸nomo Comercial de automoviles \n",
"1 Mercadona Vendedor/a de puesto de mercado \n",
"2 AGRISOLUTIONS AUXILIAR DE MANTENIMIENTO INDUSTRIAL \n",
"3 GASTROTEKA ORDIZIA 1990 Camarero/a de barra \n",
"4 ZEREGUIN ZERBITZUAK limpieza industrial \n",
"5 Bellota Herramientas Personal de mantenimiento \n",
"\n",
" periodo fec_inicio fec_final duracion \\\n",
"0 202401 2024-01-01 2024-12-07 11 \n",
"1 202310-202404 2023-10-01 2024-04-01 6 \n",
"2 202001-202401 2020-01-01 2024-01-01 48 \n",
"3 202303-202309 2023-03-01 2023-09-01 6 \n",
"4 202012-202305 2020-12-01 2023-05-01 29 \n",
"5 202005-202011 2020-05-01 2020-11-01 6 \n",
"\n",
" embeddings \n",
"0 [0.015070287510752678, 0.0029741383623331785, ... \n",
"1 [-0.006109286565333605, -0.01615688018500805, ... \n",
"2 [0.00385109125636518, 0.04469580203294754, 0.0... \n",
"3 [-0.035160087049007416, -0.0017518880777060986... \n",
"4 [0.003700299421325326, 0.0045193759724497795, ... \n",
"5 [0.04391268640756607, 0.05462520197033882, 0.0... "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ejemplos_experiencia['embeddings'] = ejemplos_experiencia['puesto'].apply(lambda puesto: client.embeddings.create(input=puesto, model=\"text-embedding-3-small\").data[0].embedding)\n",
"display(ejemplos_experiencia)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculamos la distancia entre la oferta \"Cajero supermercado Dia\" y cada uno de los puestos. Podemos observar que el modelo de embeddings de OpenAI es razonablemente bueno encontrando las relaciones sem谩nticas entre textos como los del ejemplo. La experiencia que claramente tiene m谩s relaci贸n es la que obtiene una distancia m谩s baja. Para valorar la adecuaci贸n de los curr铆culos a una oferta dada podr铆amos, obviamente, usar m谩s datos tanto del CV como de la oferta, pero este ejemplo a peque帽a escala demuestra la utilidad de los embeddings para discriminar puestos de trabajo relacionados entre s铆:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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" vertical-align: middle;\n",
" }\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>empresa</th>\n",
" <th>puesto</th>\n",
" <th>periodo</th>\n",
" <th>fec_inicio</th>\n",
" <th>fec_final</th>\n",
" <th>duracion</th>\n",
" <th>distancia_oferta_cajero</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Mercadona</td>\n",
" <td>Vendedor/a de puesto de mercado</td>\n",
" <td>202310-202404</td>\n",
" <td>2023-10-01</td>\n",
" <td>2024-04-01</td>\n",
" <td>6</td>\n",
" <td>0.556915</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>GASTROTEKA ORDIZIA 1990</td>\n",
" <td>Camarero/a de barra</td>\n",
" <td>202303-202309</td>\n",
" <td>2023-03-01</td>\n",
" <td>2023-09-01</td>\n",
" <td>6</td>\n",
" <td>0.587302</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>AGRISOLUTIONS</td>\n",
" <td>AUXILIAR DE MANTENIMIENTO INDUSTRIAL</td>\n",
" <td>202001-202401</td>\n",
" <td>2020-01-01</td>\n",
" <td>2024-01-01</td>\n",
" <td>48</td>\n",
" <td>0.617411</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Aut贸nomo</td>\n",
" <td>Comercial de automoviles</td>\n",
" <td>202401</td>\n",
" <td>2024-01-01</td>\n",
" <td>2024-12-07</td>\n",
" <td>11</td>\n",
" <td>0.628034</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Bellota Herramientas</td>\n",
" <td>Personal de mantenimiento</td>\n",
" <td>202005-202011</td>\n",
" <td>2020-05-01</td>\n",
" <td>2020-11-01</td>\n",
" <td>6</td>\n",
" <td>0.647794</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>ZEREGUIN ZERBITZUAK</td>\n",
" <td>limpieza industrial</td>\n",
" <td>202012-202305</td>\n",
" <td>2020-12-01</td>\n",
" <td>2023-05-01</td>\n",
" <td>29</td>\n",
" <td>0.701754</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" empresa puesto \\\n",
"1 Mercadona Vendedor/a de puesto de mercado \n",
"3 GASTROTEKA ORDIZIA 1990 Camarero/a de barra \n",
"2 AGRISOLUTIONS AUXILIAR DE MANTENIMIENTO INDUSTRIAL \n",
"0 Aut贸nomo Comercial de automoviles \n",
"5 Bellota Herramientas Personal de mantenimiento \n",
"4 ZEREGUIN ZERBITZUAK limpieza industrial \n",
"\n",
" periodo fec_inicio fec_final duracion distancia_oferta_cajero \n",
"1 202310-202404 2023-10-01 2024-04-01 6 0.556915 \n",
"3 202303-202309 2023-03-01 2023-09-01 6 0.587302 \n",
"2 202001-202401 2020-01-01 2024-01-01 48 0.617411 \n",
"0 202401 2024-01-01 2024-12-07 11 0.628034 \n",
"5 202005-202011 2020-05-01 2020-11-01 6 0.647794 \n",
"4 202012-202305 2020-12-01 2023-05-01 29 0.701754 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"oferta_cajero = \"Cajero supermercado Dia\"\n",
"response = client.embeddings.create(\n",
" input=oferta_cajero,\n",
" model=\"text-embedding-3-small\"\n",
")\n",
"emb_oferta_cajero = response.data[0].embedding\n",
"\n",
"ejemplos_experiencia['distancia_oferta_cajero'] = ejemplos_experiencia['embeddings'].apply(lambda emb: spatial.distance.cosine(emb, emb_oferta_cajero))\n",
"ejemplos_experiencia.drop(columns=['embeddings'], inplace=True)\n",
"ejemplos_experiencia_sorted = ejemplos_experiencia.sort_values(by='distancia_oferta_cajero', ascending=True).copy()\n",
"display(ejemplos_experiencia_sorted)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Guardamos el pickle para continuar usando este ejemplo en el siguiente bloque:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"ejemplos_experiencia_sorted.to_pickle(\"../pkl/df_ejemplos_con_distancia.pkl\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Algoritmo de c谩lculo de puntuaci贸n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Experimentando con m煤ltiples ficheros de datos, podr铆amos llegar a refinar una f贸rmula de c谩lculo de \"puntuaci贸n\" que se adapte a nuestro caso de uso, en funci贸n de las distancias simples calculadas con embeddings y los datos de cada experiencia (tiempo de permanencia en el puesto, antig眉edad de la experiencia, etc.). Con suficientes datos, podr铆amos incluso entrenar nuestra propia red neuronal con embeddings para determinar la predictibilidad de un cierto cambio de puesto. Por ejemplo, parece relativamente asequible, con suficientes datos de curr铆culos incluyendo fechas, conseguir \"predecir\" que un CV cuyas 煤ltimas dos experiencias sean \"Vendedor de Planta\" y \"Analista de Pricing\" sea m谩s apropiado para un puesto con t铆tulo \"Jefe de Compras\", que un CV con 煤ltima experiencia \"Jefe de Compras\" a un puesto con t铆tulo \"Vendedor de Planta\". Ese tipo de relaciones sem谩nticas y causales espec铆ficas a una industria o a un 谩mbito muy espec铆fico es muy dif铆cil de obtener con un modelo de lenguaje preentrenado, pero a d铆a de hoy tenemos las herramientas que nos facilitan \"refinar\" (finetuning) cualquiera de esos grandes modelos sin un coste muy elevado, utilizando los datos que se adapten a nuestro espec铆fico caso de uso. \n",
"\n",
"<br>Para esta prueba de concepto, no disponemos de una amplia base de datos de curr铆culos, por lo que definiremos un **sistema de puntuaci贸n simplificado basado exclusivamente en las distancias de embeddings, en la cantidad de experiencias previas y en la duraci贸n de las mismas**. No tendremos en cuenta factores muy importantes como la inferencia de causalidad y secuencialidad, as铆 como detalles de los curr铆culos y de la oferta de trabajo m谩s all谩 de los t铆tulos. \n",
"\n",
"<br>En cualquier caso, debe tenerse en cuenta que un sistema de an谩lisis algor铆tmico sobre datos de CVs ha de usarse con suma cautela, debido al alto riesgo de obtener \"falsos negativos\" (https://es.wikipedia.org/wiki/Falso_positivo_y_falso_negativo): descartar un candidato potencialmente bueno, sin llegar a ver m谩s datos que los de un fichero de texto. En este caso de uso, el riesgo de \"falso positivo\" (no descartar a un candidato no apropiado), no es tan cr铆tico, dado que la revisi贸n de datos de CVs es s贸lo una fase muy preliminar de un proceso de selecci贸n. En otras palabras, **el impacto en el negocio del \"falso positivo\" es hacer una entrevista de m谩s, mientras que el impacto de un \"falso negativo\" es perder un buen candidato.**\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>empresa</th>\n",
" <th>puesto</th>\n",
" <th>periodo</th>\n",
" <th>fec_inicio</th>\n",
" <th>fec_final</th>\n",
" <th>duracion</th>\n",
" <th>distancia</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Mercadona</td>\n",
" <td>Vendedor/a de puesto de mercado</td>\n",
" <td>202310-202404</td>\n",
" <td>2023-10-01</td>\n",
" <td>2024-04-01</td>\n",
" <td>6</td>\n",
" <td>0.556915</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>GASTROTEKA ORDIZIA 1990</td>\n",
" <td>Camarero/a de barra</td>\n",
" <td>202303-202309</td>\n",
" <td>2023-03-01</td>\n",
" <td>2023-09-01</td>\n",
" <td>6</td>\n",
" <td>0.587302</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>AGRISOLUTIONS</td>\n",
" <td>AUXILIAR DE MANTENIMIENTO INDUSTRIAL</td>\n",
" <td>202001-202401</td>\n",
" <td>2020-01-01</td>\n",
" <td>2024-01-01</td>\n",
" <td>48</td>\n",
" <td>0.617411</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Aut贸nomo</td>\n",
" <td>Comercial de automoviles</td>\n",
" <td>202401</td>\n",
" <td>2024-01-01</td>\n",
" <td>2024-12-07</td>\n",
" <td>11</td>\n",
" <td>0.628034</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Bellota Herramientas</td>\n",
" <td>Personal de mantenimiento</td>\n",
" <td>202005-202011</td>\n",
" <td>2020-05-01</td>\n",
" <td>2020-11-01</td>\n",
" <td>6</td>\n",
" <td>0.647790</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>ZEREGUIN ZERBITZUAK</td>\n",
" <td>limpieza industrial</td>\n",
" <td>202012-202305</td>\n",
" <td>2020-12-01</td>\n",
" <td>2023-05-01</td>\n",
" <td>29</td>\n",
" <td>0.701754</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" empresa puesto \\\n",
"1 Mercadona Vendedor/a de puesto de mercado \n",
"3 GASTROTEKA ORDIZIA 1990 Camarero/a de barra \n",
"2 AGRISOLUTIONS AUXILIAR DE MANTENIMIENTO INDUSTRIAL \n",
"0 Aut贸nomo Comercial de automoviles \n",
"5 Bellota Herramientas Personal de mantenimiento \n",
"4 ZEREGUIN ZERBITZUAK limpieza industrial \n",
"\n",
" periodo fec_inicio fec_final duracion distancia \n",
"1 202310-202404 2023-10-01 2024-04-01 6 0.556915 \n",
"3 202303-202309 2023-03-01 2023-09-01 6 0.587302 \n",
"2 202001-202401 2020-01-01 2024-01-01 48 0.617411 \n",
"0 202401 2024-01-01 2024-12-07 11 0.628034 \n",
"5 202005-202011 2020-05-01 2020-11-01 6 0.647790 \n",
"4 202012-202305 2020-12-01 2023-05-01 29 0.701754 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ejemplos_experiencia_sorted = pd.read_pickle(\"../pkl/df_ejemplos_con_distancia.pkl\")\n",
"ejemplos_experiencia_sorted.rename(columns={'distancia_oferta_cajero':'distancia'}, inplace=True)\n",
"display(ejemplos_experiencia_sorted)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Algoritmo de puntuaci贸n:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"def calcula_puntuacion(df, req_experience, positions_cap=4, min_dist_threshold=0.6, max_dist_threshold=0.7):\n",
" \"\"\"\n",
" Calcula la puntuaci贸n de un CV a partir de su tabla de distancias (con respecto a un puesto dado) y duraciones. \n",
"\n",
" Params:\n",
" df (pandas.DataFrame): datos de un CV incluyendo diferentes experiencias incluyendo duracies y distancia previamente calculadas sobre los embeddings de un puesto de trabajo\n",
" req_experience (float): experiencia requerida en meses para el puesto de trabajo (valor de referencia para calcular una puntuaci贸n entre 0 y 100 en base a diferentes experiencias)\n",
" positions_cap (int, optional): Maximum number of positions to consider for scoring. Defaults to 4.\n",
" min_dist_threshold (float, optional): Distancia entre embeddings a partir de la cual el puesto del CV se considera \"equivalente\" al de la oferta.\n",
" max_dist_threshold (float, optional): Distancia entre embeddings a partir de la cual el puesto del CV no punt煤a.\n",
" \n",
" Returns:\n",
" pandas.DataFrame: DataFrame original a帽adiendo una columna con las puntuaciones individuales contribuidas por cada puesto.\n",
" float: Puntuaci贸n total entre 0 y 100.\n",
" \"\"\"\n",
" # A efectos de puntuaci贸n, computamos para cada puesto como m谩ximo el n煤mero total de meses de experiencia requeridos\n",
" df['duration_capped'] = df['duracion'].apply(lambda x: min(x, req_experience))\n",
" # Normalizamos la distancia entre 0 y 1, siendo 0 la distancia m铆nima y 1 la m谩xima\n",
" df['adjusted_distance'] = df['distancia'].apply(\n",
" lambda x: 0 if x <= min_dist_threshold else (\n",
" 1 if x >= max_dist_threshold else (x - min_dist_threshold) / (max_dist_threshold - min_dist_threshold)\n",
" )\n",
" )\n",
" # Cada puesto punt煤a en base a su duraci贸n y a la inversa de la distancia (a menor distancia, mayor puntuaci贸n)\n",
" df['position_score'] = ((1 - df['adjusted_distance']) * (df['duration_capped']/req_experience) * 100)\n",
" # Descartamos puestos con distancia superior al umbral definido (asignamos puntuaci贸n 0), y ordenamos por puntuaci贸n\n",
" df.loc[df['distancia'] >= max_dist_threshold, 'position_score'] = 0\n",
" df = df.sort_values(by='position_score', ascending=False)\n",
" # Nos quedamos con los positions_cap puestos con mayor puntuaci贸n\n",
" df.iloc[positions_cap:, df.columns.get_loc('position_score')] = 0\n",
" # Totalizamos (no deber铆a superar 100 nunca, pero ponemos un l铆mite para asegurar)\n",
" total_score = min(df['position_score'].sum(), 100)\n",
" return df, total_score"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Para entender mejor el algoritmo, podemos probar el curr铆culo de ejemplo para el que hab铆amos calculado las distancias con el puesto \"Cajero supermercado Dia\". En su experiencia anterior, ve铆amos que el puesto m谩s cercano es el de \"Vendedor/a de puesto de mercado\", pero s贸lo tiene 6 meses de experiencia. Si prob谩ramos con una experiencia requerida muy alta, como 48 meses, este CV dar铆a una puntuaci贸n muy baja. Si, en cambio, el requisito de experiencia es m谩s bajo, el CV obtendr谩 una puntuaci贸n alta gracias a este puesto. Adem谩s, los puestos que tienen menor relaci贸n sem谩ntica con la oferta, pero m谩s meses de experiencia, puntuar谩n m谩s en funci贸n del ajuste de los par谩metros de umbral m铆nimo y m谩ximo de distancia. \n",
"\n",
"<br>El ajuste fino de los par谩metros de umbral m铆nimo y m谩ximo de distancia de embeddings hace que las experiencias con t铆tulo m谩s diferente al de la oferta tengan m谩s o menos peso en la puntuaci贸n. Estos no son par谩metros intuitivos y s贸lo se pueden ajustar en base a la experiencia: en la aplicaci贸n de usuario final, se etiquetar谩n como \"par谩metros avanzados\" y la recomendaci贸n ser铆a encontrar unos valores por defecto \"贸ptimos\" en funci贸n de la experiencia de m煤ltiples casos de uso. Para este ejemplo, hemos elegido 0.55 y 0.63, dado que sirven para ilustrar muy bien el siguiente ejemplo, si probamos diferentes valores para req_experience (el par谩metro positions_cap podemos dejarlo en 4 y no impacta mucho en la puntuaci贸n). Estos par谩metros se pueden ajustar en funci贸n del t铆tulo de la oferta, quedando fijos para comparar diferentes curr铆culos. **El rango 贸ptimo para los par谩metros min_dist_threshold y max_dist_threshold depende funcamentalmente de la longitud del texto de la oferta de trabajo a introducir**. En un entorno real, en el que se eval煤en diferentes ofertas, se podr铆an determinar unos valores \"recomendados\" de umbrales, pero para este sencillo ejercicio, l贸gicamente, no disponemos de datos suficientes para realizar ese ajuste fino. "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Puntuaci贸n: 90.4/100\n"
]
},
{
"data": {
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" <th>puesto</th>\n",
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" <th>position_score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Mercadona</td>\n",
" <td>Vendedor/a de puesto de mercado</td>\n",
" <td>202310-202404</td>\n",
" <td>2023-10-01</td>\n",
" <td>2024-04-01</td>\n",
" <td>6</td>\n",
" <td>0.556915</td>\n",
" <td>6</td>\n",
" <td>0.086437</td>\n",
" <td>45.678127</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>GASTROTEKA ORDIZIA 1990</td>\n",
" <td>Camarero/a de barra</td>\n",
" <td>202303-202309</td>\n",
" <td>2023-03-01</td>\n",
" <td>2023-09-01</td>\n",
" <td>6</td>\n",
" <td>0.587302</td>\n",
" <td>6</td>\n",
" <td>0.466269</td>\n",
" <td>26.686531</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>AGRISOLUTIONS</td>\n",
" <td>AUXILIAR DE MANTENIMIENTO INDUSTRIAL</td>\n",
" <td>202001-202401</td>\n",
" <td>2020-01-01</td>\n",
" <td>2024-01-01</td>\n",
" <td>48</td>\n",
" <td>0.617411</td>\n",
" <td>12</td>\n",
" <td>0.842632</td>\n",
" <td>15.736790</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Aut贸nomo</td>\n",
" <td>Comercial de automoviles</td>\n",
" <td>202401</td>\n",
" <td>2024-01-01</td>\n",
" <td>2024-12-07</td>\n",
" <td>11</td>\n",
" <td>0.628034</td>\n",
" <td>11</td>\n",
" <td>0.975419</td>\n",
" <td>2.253279</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Bellota Herramientas</td>\n",
" <td>Personal de mantenimiento</td>\n",
" <td>202005-202011</td>\n",
" <td>2020-05-01</td>\n",
" <td>2020-11-01</td>\n",
" <td>6</td>\n",
" <td>0.647790</td>\n",
" <td>6</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>ZEREGUIN ZERBITZUAK</td>\n",
" <td>limpieza industrial</td>\n",
" <td>202012-202305</td>\n",
" <td>2020-12-01</td>\n",
" <td>2023-05-01</td>\n",
" <td>29</td>\n",
" <td>0.701754</td>\n",
" <td>12</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" empresa puesto \\\n",
"1 Mercadona Vendedor/a de puesto de mercado \n",
"3 GASTROTEKA ORDIZIA 1990 Camarero/a de barra \n",
"2 AGRISOLUTIONS AUXILIAR DE MANTENIMIENTO INDUSTRIAL \n",
"0 Aut贸nomo Comercial de automoviles \n",
"5 Bellota Herramientas Personal de mantenimiento \n",
"4 ZEREGUIN ZERBITZUAK limpieza industrial \n",
"\n",
" periodo fec_inicio fec_final duracion distancia \\\n",
"1 202310-202404 2023-10-01 2024-04-01 6 0.556915 \n",
"3 202303-202309 2023-03-01 2023-09-01 6 0.587302 \n",
"2 202001-202401 2020-01-01 2024-01-01 48 0.617411 \n",
"0 202401 2024-01-01 2024-12-07 11 0.628034 \n",
"5 202005-202011 2020-05-01 2020-11-01 6 0.647790 \n",
"4 202012-202305 2020-12-01 2023-05-01 29 0.701754 \n",
"\n",
" duration_capped adjusted_distance position_score \n",
"1 6 0.086437 45.678127 \n",
"3 6 0.466269 26.686531 \n",
"2 12 0.842632 15.736790 \n",
"0 11 0.975419 2.253279 \n",
"5 6 1.000000 0.000000 \n",
"4 12 1.000000 0.000000 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Ejemplo de uso con el curr铆culo del notebook anterior\n",
"args = [12, 4, 0.55, 0.63] # Argumentos req_experience, positions_cap, min_distance, max_distance\n",
"scored_df, total_score = calcula_puntuacion(ejemplos_experiencia_sorted, *args)\n",
"print(f\"Puntuaci贸n: {total_score:.1f}/100\")\n",
"display(scored_df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Ejemplos de puntuaci贸n:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Para entender mejor el sistema de puntuaci贸n, podemos evaluar diferentes ejemplos en los que el requisito de experiencia sea 100 meses y establezcamos un l铆mite de 4 posiciones a considerar. Los l铆mites de distancia de embeddings no son relevantes en este caso, aunque los elegimos en funci贸n de los experimentos realizados anteriormente. Utilizamos los umbrales 0.6 y 0.7 para ilustrar un posible rango razonable de distancias de embeddings para una descripci贸n corta como la utilizada. **El rango 贸ptimo para estos par谩metros depende funcamentalmente de la longitud del texto de la oferta de trabajo a introducir**."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"args = [100, 4, 0.6, 0.7] # req_experience, positions_cap, min_distance, max_distance"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"4 experiencias en puesto muy similar al ofertado, sumando 99 meses:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total Score: 99.00\n"
]
},
{
"data": {
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" <th>duracion</th>\n",
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" <th>2</th>\n",
" <td>25</td>\n",
" <td>0.6</td>\n",
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" <td>0</td>\n",
" <td>25.0</td>\n",
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" <td>24</td>\n",
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" <td>23</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" duracion distancia duration_capped adjusted_distance position_score\n",
"0 25 0.6 25 0 25.0\n",
"1 25 0.6 25 0 25.0\n",
"2 25 0.6 25 0 25.0\n",
"3 24 0.6 24 0 24.0\n",
"4 23 0.6 23 0 0.0"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data = [\n",
" {'duracion': 25, 'distancia': 0.6},\n",
" {'duracion': 25, 'distancia': 0.6},\n",
" {'duracion': 25, 'distancia': 0.6},\n",
" {'duracion': 24, 'distancia': 0.6},\n",
" {'duracion': 23, 'distancia': 0.6} # Esta 煤ltima posici贸n no cuenta, al poner un l铆mite de 4 y ser la de menor puntuaci贸n\n",
"]\n",
"\n",
"df_very_high_score = pd.DataFrame(data)\n",
"scored_df, total_score = calcula_puntuacion(df_very_high_score, *args)\n",
"print(f\"Total Score: {total_score:.2f}\")\n",
"display(scored_df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"4 experiencias en puestos menos similares al ofertado, sumando 100 meses:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total Score: 90.00\n"
]
},
{
"data": {
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" <th></th>\n",
" <th>duracion</th>\n",
" <th>distancia</th>\n",
" <th>duration_capped</th>\n",
" <th>adjusted_distance</th>\n",
" <th>position_score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>25</td>\n",
" <td>0.61</td>\n",
" <td>25</td>\n",
" <td>0.1</td>\n",
" <td>22.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>25</td>\n",
" <td>0.61</td>\n",
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" <td>22.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>25</td>\n",
" <td>0.61</td>\n",
" <td>25</td>\n",
" <td>0.1</td>\n",
" <td>22.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>25</td>\n",
" <td>0.61</td>\n",
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" <td>0.1</td>\n",
" <td>22.5</td>\n",
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" <th>4</th>\n",
" <td>25</td>\n",
" <td>0.62</td>\n",
" <td>25</td>\n",
" <td>0.2</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" duracion distancia duration_capped adjusted_distance position_score\n",
"0 25 0.61 25 0.1 22.5\n",
"1 25 0.61 25 0.1 22.5\n",
"2 25 0.61 25 0.1 22.5\n",
"3 25 0.61 25 0.1 22.5\n",
"4 25 0.62 25 0.2 0.0"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data = [\n",
" {'duracion': 25, 'distancia': 0.61},\n",
" {'duracion': 25, 'distancia': 0.61},\n",
" {'duracion': 25, 'distancia': 0.61},\n",
" {'duracion': 25, 'distancia': 0.61},\n",
" {'duracion': 25, 'distancia': 0.62} # Esta 煤ltima posici贸n no cuenta, al poner un l铆mite de 4 y ser la de menor puntuaci贸n\n",
"]\n",
"\n",
"df_high_score = pd.DataFrame(data)\n",
"scored_df, total_score = calcula_puntuacion(df_high_score, *args)\n",
"print(f\"Total Score: {total_score:.2f}\")\n",
"display(scored_df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Una experiencia de 100 meses en un puesto de \"distancia intermedia\" al ofertado:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total Score: 50.00\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" }\n",
"\n",
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" text-align: right;\n",
" }\n",
"</style>\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>duracion</th>\n",
" <th>distancia</th>\n",
" <th>duration_capped</th>\n",
" <th>adjusted_distance</th>\n",
" <th>position_score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>100</td>\n",
" <td>0.65</td>\n",
" <td>100</td>\n",
" <td>0.5</td>\n",
" <td>50.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>25</td>\n",
" <td>0.70</td>\n",
" <td>25</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>25</td>\n",
" <td>0.70</td>\n",
" <td>25</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>25</td>\n",
" <td>0.70</td>\n",
" <td>25</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>23</td>\n",
" <td>0.70</td>\n",
" <td>23</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" duracion distancia duration_capped adjusted_distance position_score\n",
"0 100 0.65 100 0.5 50.0\n",
"1 25 0.70 25 1.0 0.0\n",
"2 25 0.70 25 1.0 0.0\n",
"3 25 0.70 25 1.0 0.0\n",
"4 23 0.70 23 1.0 0.0"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data = [\n",
" {'duracion': 100, 'distancia': 0.65},\n",
" {'duracion': 25, 'distancia': 0.7}, # Descartado por distancia\n",
" {'duracion': 25, 'distancia': 0.7}, # Descartado por distancia\n",
" {'duracion': 25, 'distancia': 0.7}, # Descartado por distancia\n",
" {'duracion': 23, 'distancia': 0.7} # Descartado por distancia\n",
"]\n",
"\n",
"df_mid_score = pd.DataFrame(data)\n",
"scored_df, total_score = calcula_puntuacion(df_mid_score, *args)\n",
"print(f\"Total Score: {total_score:.2f}\")\n",
"display(scored_df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"50 meses en un puesto muy similar y 50 meses en un puesto de \"distancia intermedia\":"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total Score: 75.00\n"
]
},
{
"data": {
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"</style>\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>duracion</th>\n",
" <th>distancia</th>\n",
" <th>duration_capped</th>\n",
" <th>adjusted_distance</th>\n",
" <th>position_score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>50</td>\n",
" <td>0.60</td>\n",
" <td>50</td>\n",
" <td>0.0</td>\n",
" <td>50.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>50</td>\n",
" <td>0.65</td>\n",
" <td>50</td>\n",
" <td>0.5</td>\n",
" <td>25.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>25</td>\n",
" <td>0.70</td>\n",
" <td>25</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>25</td>\n",
" <td>0.70</td>\n",
" <td>25</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>25</td>\n",
" <td>0.70</td>\n",
" <td>25</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" duracion distancia duration_capped adjusted_distance position_score\n",
"0 50 0.60 50 0.0 50.0\n",
"1 50 0.65 50 0.5 25.0\n",
"2 25 0.70 25 1.0 0.0\n",
"3 25 0.70 25 1.0 0.0\n",
"4 25 0.70 25 1.0 0.0"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data = [\n",
" {'duracion': 50, 'distancia': 0.6},\n",
" {'duracion': 50, 'distancia': 0.65},\n",
" {'duracion': 25, 'distancia': 0.7}, # Descartado por distancia\n",
" {'duracion': 25, 'distancia': 0.7}, # Descartado por distancia\n",
" {'duracion': 25, 'distancia': 0.7}, # Descartado por distancia\n",
"]\n",
"\n",
"df_mid_high_score = pd.DataFrame(data)\n",
"scored_df, total_score = calcula_puntuacion(df_mid_high_score, *args)\n",
"print(f\"Total Score: {total_score:.2f}\")\n",
"display(scored_df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Llamada al modelo para generar el fichero JSON final de salida"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"El 煤ltimo paso, una vez extra铆dos los datos y calculadas las puntuaciones, ser谩 llamar al modelo para que genere un fichero JSON de salida con la siguiente informaci贸n:\n",
"\n",
"- Puntuaci贸n total.\n",
"- Listado de experiencias relevantes.\n",
"- Descripci贸n de la experiencia.\n",
"\n",
"Los dos primeros elementos se calculan mediante la inferencia de reconocimiento de entidades nombradas del notebook 01, y los c谩lculos con embeddings de este notebook. Para obetener la salida estructurada completa, haremos una nueva llamada a un modelo gpt en la que le pasaremos la puntuaci贸n y la tabla de datos completa, para que elabore un texto explicativo y coherente con los datos calculados. En el siguiente notebook, ejecutaremos el proceso completo para el CV de ejemplo con el que hemos estado trabajando."
]
}
],
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"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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|