Creando rutinas de preprocesamiento declarativas

Introducción

Las programación declaritiva es una tendencia en ingeniería de software donde, a diferencía de la programación imperativa, permite describir las instrucciones que se desean realizar para que luego, un proceso, convierta esas instrucciones en código que puede ser ejecutado.

Kedro es una framework de código abierto de Python diseñado para crear código de ciencia de datos declarativo, reproducible, mantenible y modular.

Kedro aplica prácticas de ingeniería de software para ayudar a los usuarios a crear pipelines de ciencia de datos y data engineering listos para producción.

Instalación

Necesitaremos instalar las librerias:

[1]:

%pip install kedro kedro-viz --quiet

   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 198.6/198.6 kB 13.2 MB/s eta 0:00:00
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 98.2/98.2 kB 7.8 MB/s eta 0:00:00
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 236.5/236.5 kB 11.1 MB/s eta 0:00:00
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   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 66.4/66.4 kB 4.9 MB/s eta 0:00:00
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Sobre el conjunto de datos del censo UCI

El conjunto de datos del censo de la UCI es un conjunto de datos en el que cada registro representa a una persona. Cada registro contiene 14 columnas que describen a una una sola persona, de la base de datos del censo de Estados Unidos de 1994. Esto incluye información como la edad, el estado civil y el nivel educativo. La tarea es determinar si una persona tiene un ingreso alto (definido como ganar más de $50 mil al año). Esta tarea, dado el tipo de datos que utiliza, se usa a menudo en el estudio de equidad, en parte debido a los atributos comprensibles del conjunto de datos, incluidos algunos que contienen tipos sensibles como la edad y el género, y en parte también porque comprende una tarea claramente del mundo real.

Descargamos el conjunto de datos

[4]:

!wget https://santiagxf.blob.core.windows.net/public/datasets/uci_census.zip \
    --quiet --no-clobber
!mkdir -p datasets/uci_census
!unzip -qq uci_census.zip -d datasets/uci_census

Lo importamos

[5]:

import pandas as pd
import numpy as np

train = pd.read_csv('datasets/uci_census/data/adult-train.csv')
test = pd.read_csv('datasets/uci_census/data/adult-test.csv')

[6]:

train

[6]:

	income	age	workclass	fnlwgt	education	education-num	marital-status	occupation	relationship	race	gender	capital-gain	capital-loss	hours-per-week	native-country
0	<=50K	39	State-gov	77516	Bachelors	13	Never-married	Adm-clerical	Not-in-family	White	Male	2174	0	40	United-States
1	<=50K	50	Self-emp-not-inc	83311	Bachelors	13	Married-civ-spouse	Exec-managerial	Husband	White	Male	0	0	13	United-States
2	<=50K	38	Private	215646	HS-grad	9	Divorced	Handlers-cleaners	Not-in-family	White	Male	0	0	40	United-States
3	<=50K	53	Private	234721	11th	7	Married-civ-spouse	Handlers-cleaners	Husband	Black	Male	0	0	40	United-States
4	<=50K	28	Private	338409	Bachelors	13	Married-civ-spouse	Prof-specialty	Wife	Black	Female	0	0	40	Cuba
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
32556	<=50K	27	Private	257302	Assoc-acdm	12	Married-civ-spouse	Tech-support	Wife	White	Female	0	0	38	United-States
32557	>50K	40	Private	154374	HS-grad	9	Married-civ-spouse	Machine-op-inspct	Husband	White	Male	0	0	40	United-States
32558	<=50K	58	Private	151910	HS-grad	9	Widowed	Adm-clerical	Unmarried	White	Female	0	0	40	United-States
32559	<=50K	22	Private	201490	HS-grad	9	Never-married	Adm-clerical	Own-child	White	Male	0	0	20	United-States
32560	>50K	52	Self-emp-inc	287927	HS-grad	9	Married-civ-spouse	Exec-managerial	Wife	White	Female	15024	0	40	United-States

32561 rows × 15 columns

Proyectos

Kedro utiliza el concepto de proyectos para organizar los elementos necesarios en un dado problema.

[2]:

!kedro new --starter=spaceflights-pandas

[06/29/25 16:47:10] INFO     Using                               ]8;id=669892;file:///usr/local/lib/python3.11/dist-packages/kedro/framework/project/__init__.py\__init__.py]8;;\:]8;id=391138;file:///usr/local/lib/python3.11/dist-packages/kedro/framework/project/__init__.py#272\272]8;;\
                             '/usr/local/lib/python3.11/dist-pac
                             kages/kedro/framework/project/rich_
                             logging.yml' as logging
                             configuration.

Project Name
============
Please enter a human readable name for your new project.
Spaces, hyphens, and underscores are allowed.
 [Spaceflights Pandas]: spaceflights

Congratulations!
Your project 'spaceflights' has been created in the directory
/content/spaceflights

[06/29/25 16:47:22] INFO     Kedro is sending anonymous usage data ]8;id=475426;file:///usr/local/lib/python3.11/dist-packages/kedro_telemetry/plugin.py\plugin.py]8;;\:]8;id=492610;file:///usr/local/lib/python3.11/dist-packages/kedro_telemetry/plugin.py#233\233]8;;\
                             with the sole purpose of improving
                             the product. No personal data or IP
                             addresses are stored on our side. If
                             you want to opt out, set the
                             `KEDRO_DISABLE_TELEMETRY` or
                             `DO_NOT_TRACK` environment variables,
                             or create a `.telemetry` file in the
                             current working directory with the
                             contents `consent: false`. Read more
                             at
                             https://docs.kedro.org/en/stable/conf
                             iguration/telemetry.html

Instalar dependencias del proyecto

Los proyectos de Kedro tienen un archivo requirements.txt para especificar sus dependencias y habilitar proyectos compartibles al garantizar la coherencia en los paquetes y versiones de Python.

[1]:

!cat spaceflights/requirements.txt

ipython>=8.10
jupyterlab>=3.0
notebook
kedro[jupyter]~=0.19.14
kedro-datasets[pandas-csvdataset, pandas-exceldataset, pandas-parquetdataset, plotly-plotlydataset, plotly-jsondataset, matplotlib-matplotlibwriter]>=3.0
kedro-viz>=6.7.0
scikit-learn~=1.5.1
seaborn~=0.12.1

Para instalar todas las dependencias específicas del proyecto, ejecute lo siguiente desde el directorio raíz del proyecto:

[5]:

%pip install -r spaceflights/requirements.txt

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Conjuntos de datos del proyecto

El proyecto de ejemplo que tenemos de vuelos espaciales utiliza tres conjuntos de datos ficticios de empresas que transportan clientes a la Luna y de regreso. Los datos vienen en dos formatos diferentes: .csv y .xlsx:

companies.csv: Contiene datos sobre las empresas de viajes espaciales, como su ubicación, número de flotas y clasificación
reviews.csv: es un conjunto de reseñas de clientes para categorías como comodidad y precio
shuttles.xlsx: es un conjunto de atributos para las naves espaciales de toda la flota, como su tipo de motor y capacidad de pasajeros

Esta información se encuentra en el directorio data/01_raw, que simula su origine de datos.

Registro de conjuntos de datos

Herramientas como querdro pueden mantener un catalog de datos con información de los origines de datos disponibles para el procesamiento. La siguiente información sobre un conjunto de datos debe registrarse antes de que Kedro pueda cargarlo:

Ubicación del archivo (ruta)
Parámetros para el conjunto de datos dado
Tipo de datos
Version

Kedro utiliza archivos declarativos en formato YAML, un formato ampliamente utilizado en la industria para estas tareas.

catalog.yml

companies:
  type: pandas.CSVDataset
  filepath: data/01_raw/companies.csv

reviews:
  type: pandas.CSVDataset
  filepath: data/01_raw/reviews.csv

shuttles:
  type: pandas.ExcelDataset
  filepath: data/01_raw/shuttles.xlsx
  load_args:
    engine: openpyxl

Para mostrar como Kedro puede configurar nuestro ambiente automáticamente, utilizaremos una función para cargar el contexto del proyecto en el que estamos trabajando en Colaboratory. Esto se logra con la función reload_kedro.

En ambientes productivos, usted no necesitaría ejecutar el siguiente código ya está preconfigurado. En este ejemplo, haremos que Kedro cargue el contexto del projecto dentro de Colaboratory.

[6]:

from kedro.ipython import reload_kedro

reload_kedro(path='spaceflights')

[06/29/25 17:04:48] INFO     Kedro is sending anonymous usage data plugin.py:233
                             with the sole purpose of improving
                             the product. No personal data or IP
                             addresses are stored on our side. If
                             you want to opt out, set the
                             `KEDRO_DISABLE_TELEMETRY` or
                             `DO_NOT_TRACK` environment variables,
                             or create a `.telemetry` file in the
                             current working directory with the
                             contents `consent: false`. Read more
                             at
                             https://docs.kedro.org/en/stable/conf
                             iguration/telemetry.html
                    INFO     Kedro project spaceflights          __init__.py:146
                    INFO     Defined global variable 'context',  __init__.py:147
                             'session', 'catalog' and
                             'pipelines'
[06/29/25 17:04:49] INFO     Registered line magic 'run_viz'     __init__.py:153

El siguiente comando crea una variable (companies), que es de tipo pandas.DataFrame y carga el conjunto de datos (también llamado companies según la configuración en el archivo catalog.yml) desde la ruta del archivo subyacente data/01_raw/companies.csv.

[9]:

companies = catalog.load("companies")

[06/29/25 17:05:55] INFO     Loading data from companies     data_catalog.py:403
                             (CSVDataset)...

[10]:

companies.head()

[10]:

	id	company_rating	company_location	total_fleet_count	iata_approved
0	3888	100%	Isle of Man	1.0	f
1	46728	100%	NaN	1.0	f
2	34618	38%	Isle of Man	1.0	f
3	28619	100%	Bosnia and Herzegovina	1.0	f
4	8240	NaN	Chile	1.0	t

Podemos también ver todos los conjuntos que tenemos disponibles:

[15]:

catalog.list()

[15]:


[
    'companies',
    'reviews',
    'shuttles',
    'preprocessed_companies',
    'preprocessed_shuttles',
    'model_input_table',
    'regressor',
    'shuttle_passenger_capacity_plot_exp',
    'shuttle_passenger_capacity_plot_go',
    'dummy_confusion_matrix',
    'parameters',
    'params:model_options',
    'params:model_options.test_size',
    'params:model_options.random_state',
    'params:model_options.features'
]

Note que el proyecto tiene más conjuntos de datos de los que configuramos en un principio. Puede ignorar los otros conjuntos de datos.

Crear un pipeline de procesamiento de datos

El proceso de procesamiento de datos prepara los datos para la construcción del modelo combinando los conjuntos de datos para crear una tabla con los predictores del modelo.

En este ejemplo, el proceso de procesamiento de datos se compone de lo siguiente:

[ ]:

##