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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 02_data_augmentation.ipynb\n",
"\n",
"En este notebook realizamos las siguientes tareas:\n",
"\n",
"1. Cargamos y exploramos los datasets `wikidoc` y `medqa`.\n",
"2. Unificamos o combinamos la información mínima necesaria.\n",
"3. Generamos datos sintéticos (data augmentation) para enriquecer el conjunto.\n",
"4. Realizamos pruebas llamando a las herramientas de triaje y asesoramiento.\n",
"\n",
"Al final, podemos convertir este notebook a un script `.py` si lo deseamos.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Importar librerías y configuraciones\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import os\n",
"import random\n",
"from pathlib import Path\n",
"\n",
"# Opcional: configuración para visualizar más columnas\n",
"pd.set_option('display.max_columns', None)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Cargar y explorar datos"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"===== wikidoc.info() =====\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 10000 entries, 0 to 9999\n",
"Data columns (total 3 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 instruction 10000 non-null object\n",
" 1 input 9998 non-null object\n",
" 2 output 10000 non-null object\n",
"dtypes: object(3)\n",
"memory usage: 234.5+ KB\n",
"\n",
"===== wikidoc.head() =====\n"
]
},
{
"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>instruction</th>\n",
" <th>input</th>\n",
" <th>output</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Answer this question truthfully</td>\n",
" <td>Can you provide an overview of the lung's squa...</td>\n",
" <td>Squamous cell carcinoma of the lung may be cla...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Answer this question truthfully</td>\n",
" <td>What does \"Clear: cell\" mean?</td>\n",
" <td>Clear cell tumors are part of the surface epit...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Answer this question truthfully</td>\n",
" <td>Can you provide me with information regarding ...</td>\n",
" <td>Two Japanese scientists commenced research int...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Answer this question truthfully</td>\n",
" <td>What are the historical background and symptom...</td>\n",
" <td>Symptoms of vulvovaginitis caused by Candida s...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Answer this question truthfully</td>\n",
" <td>What does the \"Hypotension: Resident Survival ...</td>\n",
" <td>Hypotension is the term for low blood pressure...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" instruction \\\n",
"0 Answer this question truthfully \n",
"1 Answer this question truthfully \n",
"2 Answer this question truthfully \n",
"3 Answer this question truthfully \n",
"4 Answer this question truthfully \n",
"\n",
" input \\\n",
"0 Can you provide an overview of the lung's squa... \n",
"1 What does \"Clear: cell\" mean? \n",
"2 Can you provide me with information regarding ... \n",
"3 What are the historical background and symptom... \n",
"4 What does the \"Hypotension: Resident Survival ... \n",
"\n",
" output \n",
"0 Squamous cell carcinoma of the lung may be cla... \n",
"1 Clear cell tumors are part of the surface epit... \n",
"2 Two Japanese scientists commenced research int... \n",
"3 Symptoms of vulvovaginitis caused by Candida s... \n",
"4 Hypotension is the term for low blood pressure... "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Ajusta la ruta según tu organización de carpetas\n",
"raw_path = Path(\"../data/raw\")\n",
"wikidoc_file = raw_path / \"medical_meadow_wikidoc.csv\"\n",
"medqa_file = raw_path / \"medqa.csv\"\n",
"\n",
"wikidoc = pd.read_csv(wikidoc_file)\n",
"medqa = pd.read_csv(medqa_file)\n",
"\n",
"print(\"===== wikidoc.info() =====\")\n",
"wikidoc.info()\n",
"print(\"\\n===== wikidoc.head() =====\")\n",
"display(wikidoc.head())"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"===== medqa.info() =====\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 10178 entries, 0 to 10177\n",
"Data columns (total 6 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 question 10178 non-null object\n",
" 1 answer 10177 non-null object\n",
" 2 options 10178 non-null object\n",
" 3 meta_info 10178 non-null object\n",
" 4 answer_idx 10178 non-null object\n",
" 5 metamap_phrases 10178 non-null object\n",
"dtypes: object(6)\n",
"memory usage: 477.2+ KB\n",
"\n",
"===== medqa.head() =====\n"
]
},
{
"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>question</th>\n",
" <th>answer</th>\n",
" <th>options</th>\n",
" <th>meta_info</th>\n",
" <th>answer_idx</th>\n",
" <th>metamap_phrases</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>A 23-year-old pregnant woman at 22 weeks gesta...</td>\n",
" <td>Nitrofurantoin</td>\n",
" <td>{'A': 'Ampicillin', 'B': 'Ceftriaxone', 'C': '...</td>\n",
" <td>step2&3</td>\n",
" <td>D</td>\n",
" <td>['23 year old pregnant woman', 'weeks presents...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>A 3-month-old baby died suddenly at night whil...</td>\n",
" <td>Placing the infant in a supine position on a f...</td>\n",
" <td>{'A': 'Placing the infant in a supine position...</td>\n",
" <td>step2&3</td>\n",
" <td>A</td>\n",
" <td>['3 month old baby died', 'night', 'asleep', '...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>A mother brings her 3-week-old infant to the p...</td>\n",
" <td>Abnormal migration of ventral pancreatic bud</td>\n",
" <td>{'A': 'Abnormal migration of ventral pancreati...</td>\n",
" <td>step1</td>\n",
" <td>A</td>\n",
" <td>['mother', 'week old infant', \"pediatrician's ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>A pulmonary autopsy specimen from a 58-year-ol...</td>\n",
" <td>Thromboembolism</td>\n",
" <td>{'A': 'Thromboembolism', 'B': 'Pulmonary ische...</td>\n",
" <td>step1</td>\n",
" <td>A</td>\n",
" <td>['pulmonary autopsy specimen', '58 year old wo...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>A 20-year-old woman presents with menorrhagia ...</td>\n",
" <td>Von Willebrand disease</td>\n",
" <td>{'A': 'Hemophilia A', 'B': 'Lupus anticoagulan...</td>\n",
" <td>step1</td>\n",
" <td>D</td>\n",
" <td>['20 year old woman presents', 'menorrhagia', ...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" question \\\n",
"0 A 23-year-old pregnant woman at 22 weeks gesta... \n",
"1 A 3-month-old baby died suddenly at night whil... \n",
"2 A mother brings her 3-week-old infant to the p... \n",
"3 A pulmonary autopsy specimen from a 58-year-ol... \n",
"4 A 20-year-old woman presents with menorrhagia ... \n",
"\n",
" answer \\\n",
"0 Nitrofurantoin \n",
"1 Placing the infant in a supine position on a f... \n",
"2 Abnormal migration of ventral pancreatic bud \n",
"3 Thromboembolism \n",
"4 Von Willebrand disease \n",
"\n",
" options meta_info answer_idx \\\n",
"0 {'A': 'Ampicillin', 'B': 'Ceftriaxone', 'C': '... step2&3 D \n",
"1 {'A': 'Placing the infant in a supine position... step2&3 A \n",
"2 {'A': 'Abnormal migration of ventral pancreati... step1 A \n",
"3 {'A': 'Thromboembolism', 'B': 'Pulmonary ische... step1 A \n",
"4 {'A': 'Hemophilia A', 'B': 'Lupus anticoagulan... step1 D \n",
"\n",
" metamap_phrases \n",
"0 ['23 year old pregnant woman', 'weeks presents... \n",
"1 ['3 month old baby died', 'night', 'asleep', '... \n",
"2 ['mother', 'week old infant', \"pediatrician's ... \n",
"3 ['pulmonary autopsy specimen', '58 year old wo... \n",
"4 ['20 year old woman presents', 'menorrhagia', ... "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"print(\"\\n===== medqa.info() =====\")\n",
"medqa.info()\n",
"print(\"\\n===== medqa.head() =====\")\n",
"display(medqa.head())\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (2686254339.py, line 3)",
"output_type": "error",
"traceback": [
"\u001b[1;36m Cell \u001b[1;32mIn[5], line 3\u001b[1;36m\u001b[0m\n\u001b[1;33m Aquí podemos hacer una unificación mínima o simplemente quedarnos con columnas que nos interesen.\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
]
}
],
"source": [
"## 3. Unificar o manipular datos\n",
"\n",
"Aquí podemos hacer una unificación mínima o simplemente quedarnos con columnas que nos interesen.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Copias de los dataframes\n",
"df_wikidoc = wikidoc.copy()\n",
"df_wikidoc.rename(columns={\"instruction\": \"consulta\", \"output\": \"respuesta\"}, inplace=True)\n",
"\n",
"# Un ejemplo: concatenar 'instruction' e 'input'\n",
"df_wikidoc[\"consulta\"] = df_wikidoc[\"consulta\"].fillna(\"\") + \": \" + df_wikidoc[\"input\"].fillna(\"\")\n",
"df_wikidoc = df_wikidoc[[\"consulta\", \"respuesta\"]]\n",
"\n",
"df_medqa = medqa.copy()\n",
"df_medqa.rename(columns={\"question\": \"consulta\", \"answer\": \"respuesta\"}, inplace=True)\n",
"\n",
"# Unificación mínima\n",
"df_unificado = pd.concat(\n",
" [df_wikidoc, df_medqa[[\"consulta\", \"respuesta\"]]],\n",
" ignore_index=True\n",
")\n",
"\n",
"# Eliminamos filas con NaN en consulta/respuesta\n",
"df_unificado.dropna(subset=[\"consulta\", \"respuesta\"], inplace=True)\n",
"\n",
"print(\"Tamaño de df_unificado:\", df_unificado.shape)\n",
"df_unificado.head()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Generación de Datos Sintéticos\n",
"\n",
"Definimos una función que toma ejemplos del dataframe unificado y genera variaciones.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def generar_datos_sinteticos(df, n=10):\n",
" \"\"\"\n",
" Genera un conjunto de datos sintéticos basados en el dataframe unificado.\n",
" - Toma filas aleatorias de 'consulta' y 'respuesta'.\n",
" - Modifica ligeramente el texto para simular nuevas entradas.\n",
" \n",
" Retorna un DataFrame con las columnas 'consulta_sintetica' y 'respuesta_sintetica'.\n",
" \"\"\"\n",
" sampled_rows = df.sample(n, replace=True)\n",
"\n",
" consultas_sinteticas = []\n",
" respuestas_sinteticas = []\n",
"\n",
" modificaciones = [\n",
" \"(Por favor, detalle más los síntomas)\",\n",
" \"(Caso leve, pero podría complicarse)\",\n",
" \"(Consulta para un familiar)\",\n",
" \"(Síntomas descritos hace 2 días)\",\n",
" \"(Sugerir revisión médica)\"\n",
" ]\n",
"\n",
" for _, row in sampled_rows.iterrows():\n",
" consulta_original = row[\"consulta\"]\n",
" respuesta_original = row[\"respuesta\"]\n",
"\n",
" # Elegimos una modificación aleatoria\n",
" extra = random.choice(modificaciones)\n",
" nueva_consulta = f\"{consulta_original} {extra}\"\n",
"\n",
" nueva_respuesta = (\n",
" respuesta_original\n",
" + \"\\nNota: Esta es una versión sintética para pruebas.\"\n",
" )\n",
"\n",
" consultas_sinteticas.append(nueva_consulta)\n",
" respuestas_sinteticas.append(nueva_respuesta)\n",
"\n",
" df_sintetico = pd.DataFrame({\n",
" \"consulta_sintetica\": consultas_sinteticas,\n",
" \"respuesta_sintetica\": respuestas_sinteticas\n",
" })\n",
"\n",
" return df_sintetico\n",
"\n",
"# Generamos un ejemplo de datos sintéticos\n",
"df_sintetico = generar_datos_sinteticos(df_unificado, n=15)\n",
"print(\"Tamaño de df_sintetico:\", df_sintetico.shape)\n",
"df_sintetico.head(10)\n"
]
}
],
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|