Introducción al machine learning#
Universidad Central#
Maestría en analítica de datos#
Métodos estadísticos para analítica de datos.#
Docente: Luis Andrés Campos Maldonado.#
El Machine Learning trata de extraer conocimiento de los datos. Es un campo de investigación que intersecta la estadística, inteligencia artificial y ciencias de la computación, este también conocido como análisis predictivo o aprendizaje estadístico. Las aplicaciones del Machine Learning se han convertido en los últimos años en omnipresentes en la vida cotidiana, desde recomendaciones automáticas de qué películas para ver, qué comida pedir, que productos comprar hasta el reconocimiento de amigos en tus fotos. Al mirar un sitio web complejo como Facebook, Amazon o Netflix, es muy probable que cada parte del sitio contenga múltiples modelos de Machine Learning.
Problemas que el Machine Learning sabe resolver.#
Aprendizaje supervisado:#
En este caso se desea automatizar el proceso de toma de decisiones a partir de ejemplos conocidos. Estos algoritmos de Machine Learning aprenden de los pares input/output conocidos, para al estar implementado crear un output para un input que nunca ha visto antes sin más ayuda.
Consideremos algunos ejemplos:
Clasificación de correos spam: El modelo de
Machine Learningutiliza un algoritmo dotado de una gran cantidad de correos electrónicos (los inputs), junto con información sobre si alguno de estos correos electrónicos es spam (objetivo deseado). Dado un nuevo correo electrónico, el algoritmo producirá una predicción sobre si el nuevo correo electrónico es correo no deseado.Identificar el código postal a partir de dígitos escritos a mano en un sobre: La entrada es un escaneo de la escritura a mano, y la salida deseada es el real código postal. Para crear un conjunto de datos para construir un modelo de machine learning, se necesita recoger muchos sobres. Entonces puede leer los códigos postales usted mismo y almacene los dígitos como sus resultados deseados.
Determinar si un tumor es benigno en base a una imagen médica: La entrada es la imagen y la salida es si el tumor es benigno. Para crear un conjunto de datos para construir un modelo, se necesita una base de datos de imágenes médicas. Asi mismo se necesita la opinión de un experto, por lo que un médico debe observar todas las imágenes y decidir qué tumores son benignos y cuáles no. Incluso podría ser necesario hacer un diagnóstico adicional más allá del contenido de la imagen para determinar si el tumor en la imagen es canceroso o no.
Detección de actividad fraudulenta en transacciones con tarjetas de crédito: La entrada es un registro de la transacción de la tarjeta de crédito y la salida es si es probable que sea fraudulento o no. Suponiendo que usted es la entidad que distribuye las tarjetas de crédito, recopilar un conjunto de datos significa almacenar todas las transacciones y registro si un usuario reporta alguna transacción como fraudulenta.
Algoritmos no supervisados:#
Estos son el otro tipo de algoritmo que cubriremos en este curso. En este aprendizaje, solo se conocen los datos de entrada y no se conoce la salida. Si bien hay muchas aplicaciones exitosas de estos métodos, por lo general son más difíciles de entender y evaluar.
Consideremos algunos ejemplos:
Identificar temas en un conjunto de publicaciones de blog: Si tiene una gran colección de datos de texto, es posible que desee resumirlos y encontrar temas predominantes en él. Es posible que no sepa de antemano cuáles son estos temas, o cuántos temas puede haber. Por lo tanto, no hay salidas conocidas.
Segmentación de clientes en grupos con preferencias similares: Dado un conjunto de registros de clientes, es posible que desee identificar qué clientes son similar, y si hay grupos de clientes con preferencias similares. Para un sitio de compras, estos pueden ser “padres”, “ratones de biblioteca” o “jugadores”. Porque no conoce de antemano cuáles podrían ser estos grupos, o incluso cuántos hay, no tiene salidas conocidas.
Detección de patrones de acceso anormales a un sitio web: Para identificar abusos o errores, a menudo es útil encontrar patrones de acceso que sean diferentes de lo normal. Cada patrón anormal puede ser muy diferente y usted podría no tener ninguna instancia registrada de comportamiento anormal. En este ejemplo, solo se observa el tráfico y no sabe lo que constituye normal y comportamiento anormal.
Conocimiento del problema y de los datos.#
La parte más importante del machine learning es comprender los datos con los que estamos trabajando y cómo se relacionan con la tarea que desea realizar. No será efectivo elegir aleatoriamente un algoritmo y arrojarle los datos. Es necesario comprender lo que está sucediendo en el dataset antes de comenzar a construir un modelo. Cada algoritmo es diferente en términos de qué tipo de datos y la configuración del problema para la que funciona mejor.
Mientras se crea una solución de machine learning se debe tener en cuenta:
¿Qué pregunta(s) se está tratando de responder? ¿Creo que los datos recopilados pueden responder esa pregunta?
¿Cuál es la mejor manera de formular mis preguntas como un problema de machine learning?
¿He recopilado suficientes datos para representar el problema que quiero resolver?
¿Qué características de los datos extraje y permitirán las predicciones?
¿Cómo mediré el éxito de mi solución?
¿Cómo interactuará la solución de machine learning con otras partes de mi investigación o producto comercial?
Python y scikit-learn#
Python:#
Como lenguaje de programación de alto nivel, Python, se ha convertido en el lenguaje para muchas aplicaciones de ciencia de datos. Pues mezcla el poder de los lenguajes de programación de propósito general con la facilidad de uso de lenguajes de secuencias de comandos específicos como MATLAB o R. Además, Python tiene módulos para carga de datos, visualización, estadísticas, procesamiento de lenguaje natural, procesamiento de imágenes y más. El machine learning y el análisis de datos son procesos fundamentalmente iterativos, en los que los datos impulsan el análisis.
scikit-learn#
Es un proyecto de código abierto y cualquiera puede obtener fácilmente el código fuente para ver qué sucede detrás de escena. El proyecto scikit-learn se desarrolla y mejora constantemente, y tiene una comunidad de usuarios muy activa. Contiene una serie de algoritmos de machine learning, así como documentación completa sobre cada algoritmo.
scikit-learn es una herramienta muy popular y la librería de Python más destacada para machine learning. Es ampliamente utilizada en la industria y la academia, y una gran cantidad de tutoriales y fragmentos de código están disponibles en línea. scikit-learn funciona bien con otras herramientas científicas de Python.
## Version de python en Colab
!python --version
Python 3.10.12
## Version de pandas
!pip show pandas
Name: pandas
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## Version de numpy
!pip show numpy
Name: numpy
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How to Apply These Terms to Your New Programs
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Name: libquadmath
Files: numpy.libs/libquadmath*.so
Description: dynamically linked to files compiled with gcc
Availability: https://gcc.gnu.org/git/?p=gcc.git;a=tree;f=libquadmath
License: LGPL-2.1-or-later
GCC Quad-Precision Math Library
Copyright (C) 2010-2019 Free Software Foundation, Inc.
Written by Francois-Xavier Coudert <fxcoudert@gcc.gnu.org>
This file is part of the libquadmath library.
Libquadmath is free software; you can redistribute it and/or
modify it under the terms of the GNU Library General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
Libquadmath is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
https://www.gnu.org/licenses/old-licenses/lgpl-2.1.html
Location: /home/andres-campos/github_public/Metodos-Estadisticos/venv/lib/python3.10/site-packages
Requires:
Required-by: catboost, category-encoders, contourpy, factor_analyzer, folium, hdbscan, imageio, imbalanced-learn, langchain, langchain-community, lightgbm, matplotlib, mglearn, numba, pandas, patsy, pingouin, pyarrow, scikit-learn, scikit-optimize, scipy, seaborn, statsmodels, sweetviz, umap-learn, xarray, xgboost
## Version de Scipy
!pip show scipy
Name: scipy
Version: 1.14.0
Summary: Fundamental algorithms for scientific computing in Python
Home-page: https://scipy.org/
Author:
Author-email:
License: Copyright (c) 2001-2002 Enthought, Inc. 2003-2024, SciPy Developers.
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
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notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above
copyright notice, this list of conditions and the following
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with the distribution.
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contributors may be used to endorse or promote products derived
from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
----
This binary distribution of SciPy also bundles the following software:
Name: OpenBLAS
Files: scipy.libs/libopenblas*.so
Description: bundled as a dynamically linked library
Availability: https://github.com/OpenMathLib/OpenBLAS/
License: BSD-3-Clause-Attribution
Copyright (c) 2011-2014, The OpenBLAS Project
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:
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its contributors may be used to endorse or promote products
derived from this software without specific prior written
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE
USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Name: LAPACK
Files: scipy.libs/libopenblas*.so
Description: bundled in OpenBLAS
Availability: https://github.com/OpenMathLib/OpenBLAS/
License: BSD-3-Clause-Attribution
Copyright (c) 1992-2013 The University of Tennessee and The University
of Tennessee Research Foundation. All rights
reserved.
Copyright (c) 2000-2013 The University of California Berkeley. All
rights reserved.
Copyright (c) 2006-2013 The University of Colorado Denver. All rights
reserved.
$COPYRIGHT$
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met:
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The copyright holders provide no reassurances that the source code
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Name: GCC runtime library
Files: scipy.libs/libgfortran*.so
Description: dynamically linked to files compiled with gcc
Availability: https://gcc.gnu.org/git/?p=gcc.git;a=tree;f=libgfortran
License: GPL-3.0-with-GCC-exception
Copyright (C) 2002-2017 Free Software Foundation, Inc.
Libgfortran is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 3, or (at your option)
any later version.
Libgfortran is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
Under Section 7 of GPL version 3, you are granted additional
permissions described in the GCC Runtime Library Exception, version
3.1, as published by the Free Software Foundation.
You should have received a copy of the GNU General Public License and
a copy of the GCC Runtime Library Exception along with this program;
see the files COPYING3 and COPYING.RUNTIME respectively. If not, see
<http://www.gnu.org/licenses/>.
----
Full text of license texts referred to above follows (that they are
listed below does not necessarily imply the conditions apply to the
present binary release):
----
GCC RUNTIME LIBRARY EXCEPTION
Version 3.1, 31 March 2009
Copyright (C) 2009 Free Software Foundation, Inc. <http://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies of this
license document, but changing it is not allowed.
This GCC Runtime Library Exception ("Exception") is an additional
permission under section 7 of the GNU General Public License, version
3 ("GPLv3"). It applies to a given file (the "Runtime Library") that
bears a notice placed by the copyright holder of the file stating that
the file is governed by GPLv3 along with this Exception.
When you use GCC to compile a program, GCC may combine portions of
certain GCC header files and runtime libraries with the compiled
program. The purpose of this Exception is to allow compilation of
non-GPL (including proprietary) programs to use, in this way, the
header files and runtime libraries covered by this Exception.
0. Definitions.
A file is an "Independent Module" if it either requires the Runtime
Library for execution after a Compilation Process, or makes use of an
interface provided by the Runtime Library, but is not otherwise based
on the Runtime Library.
"GCC" means a version of the GNU Compiler Collection, with or without
modifications, governed by version 3 (or a specified later version) of
the GNU General Public License (GPL) with the option of using any
subsequent versions published by the FSF.
"GPL-compatible Software" is software whose conditions of propagation,
modification and use would permit combination with GCC in accord with
the license of GCC.
"Target Code" refers to output from any compiler for a real or virtual
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The "Compilation Process" transforms code entirely represented in
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use of source code generators and preprocessors need not be considered
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understood as starting with the output of the generators or
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A Compilation Process is "Eligible" if it is done using GCC, alone or
with other GPL-compatible software, or if it is done without using any
work based on GCC. For example, using non-GPL-compatible Software to
optimize any GCC intermediate representations would not qualify as an
Eligible Compilation Process.
1. Grant of Additional Permission.
You have permission to propagate a work of Target Code formed by
combining the Runtime Library with Independent Modules, even if such
propagation would otherwise violate the terms of GPLv3, provided that
all Target Code was generated by Eligible Compilation Processes. You
may then convey such a combination under terms of your choice,
consistent with the licensing of the Independent Modules.
2. No Weakening of GCC Copyleft.
The availability of this Exception does not imply any general
presumption that third-party software is unaffected by the copyleft
requirements of the license of GCC.
----
GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
Preamble
The GNU General Public License is a free, copyleft license for
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The licenses for most software and other practical works are designed
to take away your freedom to share and change the works. By contrast,
the GNU General Public License is intended to guarantee your freedom to
share and change all versions of a program--to make sure it remains free
software for all its users. We, the Free Software Foundation, use the
GNU General Public License for most of our software; it applies also to
any other work released this way by its authors. You can apply it to
your programs, too.
When we speak of free software, we are referring to freedom, not
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To protect your rights, we need to prevent others from denying you
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For example, if you distribute copies of such a program, whether
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Developers that use the GNU GPL protect your rights with two steps:
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Some devices are designed to deny users access to install or run
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TERMS AND CONDITIONS
0. Definitions.
"This License" refers to version 3 of the GNU General Public License.
"Copyright" also means copyright-like laws that apply to other kinds of
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"The Program" refers to any copyrightable work licensed under this
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An interactive user interface displays "Appropriate Legal Notices"
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1. Source Code.
The "source code" for a work means the preferred form of the work
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"Major Component", in this context, means a major essential component
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The Corresponding Source need not include anything that users
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The Corresponding Source for a work in source code form is that
same work.
2. Basic Permissions.
All rights granted under this License are granted for the term of
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You may make, run and propagate covered works that you do not
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parts of the aggregate.
6. Conveying Non-Source Forms.
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customarily used for software interchange.
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(including a physical distribution medium), accompanied by a
written offer, valid for at least three years and valid for as
long as you offer spare parts or customer support for that product
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copy of the Corresponding Source for all the software in the
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d) Convey the object code by offering access from a designated
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If you convey an object code work under this section in, or with, or
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if neither you nor any third party retains the ability to install
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The requirement to provide Installation Information does not include a
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Corresponding Source conveyed, and Installation Information provided,
in accord with this section must be in a format that is publicly
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unpacking, reading or copying.
7. Additional Terms.
"Additional permissions" are terms that supplement the terms of this
License by making exceptions from one or more of its conditions.
Additional permissions that are applicable to the entire Program shall
be treated as though they were included in this License, to the extent
that they are valid under applicable law. If additional permissions
apply only to part of the Program, that part may be used separately
under those permissions, but the entire Program remains governed by
this License without regard to the additional permissions.
When you convey a copy of a covered work, you may at your option
remove any additional permissions from that copy, or from any part of
it. (Additional permissions may be written to require their own
removal in certain cases when you modify the work.) You may place
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for which you have or can give appropriate copyright permission.
Notwithstanding any other provision of this License, for material you
add to a covered work, you may (if authorized by the copyright holders of
that material) supplement the terms of this License with terms:
a) Disclaiming warranty or limiting liability differently from the
terms of sections 15 and 16 of this License; or
b) Requiring preservation of specified reasonable legal notices or
author attributions in that material or in the Appropriate Legal
Notices displayed by works containing it; or
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All other non-permissive additional terms are considered "further
restrictions" within the meaning of section 10. If the Program as you
received it, or any part of it, contains a notice stating that it is
governed by this License along with a term that is a further
restriction, you may remove that term. If a license document contains
a further restriction but permits relicensing or conveying under this
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of that license document, provided that the further restriction does
not survive such relicensing or conveying.
If you add terms to a covered work in accord with this section, you
must place, in the relevant source files, a statement of the
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where to find the applicable terms.
Additional terms, permissive or non-permissive, may be stated in the
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the above requirements apply either way.
8. Termination.
You may not propagate or modify a covered work except as expressly
provided under this License. Any attempt otherwise to propagate or
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this License (including any patent licenses granted under the third
paragraph of section 11).
However, if you cease all violation of this License, then your
license from a particular copyright holder is reinstated (a)
provisionally, unless and until the copyright holder explicitly and
finally terminates your license, and (b) permanently, if the copyright
holder fails to notify you of the violation by some reasonable means
prior to 60 days after the cessation.
Moreover, your license from a particular copyright holder is
reinstated permanently if the copyright holder notifies you of the
violation by some reasonable means, this is the first time you have
received notice of violation of this License (for any work) from that
copyright holder, and you cure the violation prior to 30 days after
your receipt of the notice.
Termination of your rights under this section does not terminate the
licenses of parties who have received copies or rights from you under
this License. If your rights have been terminated and not permanently
reinstated, you do not qualify to receive new licenses for the same
material under section 10.
9. Acceptance Not Required for Having Copies.
You are not required to accept this License in order to receive or
run a copy of the Program. Ancillary propagation of a covered work
occurring solely as a consequence of using peer-to-peer transmission
to receive a copy likewise does not require acceptance. However,
nothing other than this License grants you permission to propagate or
modify any covered work. These actions infringe copyright if you do
not accept this License. Therefore, by modifying or propagating a
covered work, you indicate your acceptance of this License to do so.
10. Automatic Licensing of Downstream Recipients.
Each time you convey a covered work, the recipient automatically
receives a license from the original licensors, to run, modify and
propagate that work, subject to this License. You are not responsible
for enforcing compliance by third parties with this License.
An "entity transaction" is a transaction transferring control of an
organization, or substantially all assets of one, or subdividing an
organization, or merging organizations. If propagation of a covered
work results from an entity transaction, each party to that
transaction who receives a copy of the work also receives whatever
licenses to the work the party's predecessor in interest had or could
give under the previous paragraph, plus a right to possession of the
Corresponding Source of the work from the predecessor in interest, if
the predecessor has it or can get it with reasonable efforts.
You may not impose any further restrictions on the exercise of the
rights granted or affirmed under this License. For example, you may
not impose a license fee, royalty, or other charge for exercise of
rights granted under this License, and you may not initiate litigation
(including a cross-claim or counterclaim in a lawsuit) alleging that
any patent claim is infringed by making, using, selling, offering for
sale, or importing the Program or any portion of it.
11. Patents.
A "contributor" is a copyright holder who authorizes use under this
License of the Program or a work on which the Program is based. The
work thus licensed is called the contributor's "contributor version".
A contributor's "essential patent claims" are all patent claims
owned or controlled by the contributor, whether already acquired or
hereafter acquired, that would be infringed by some manner, permitted
by this License, of making, using, or selling its contributor version,
but do not include claims that would be infringed only as a
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patent sublicenses in a manner consistent with the requirements of
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Each contributor grants you a non-exclusive, worldwide, royalty-free
patent license under the contributor's essential patent claims, to
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In the following three paragraphs, a "patent license" is any express
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(such as an express permission to practice a patent or covenant not to
sue for patent infringement). To "grant" such a patent license to a
party means to make such an agreement or commitment not to enforce a
patent against the party.
If you convey a covered work, knowingly relying on a patent license,
and the Corresponding Source of the work is not available for anyone
to copy, free of charge and under the terms of this License, through a
publicly available network server or other readily accessible means,
then you must either (1) cause the Corresponding Source to be so
available, or (2) arrange to deprive yourself of the benefit of the
patent license for this particular work, or (3) arrange, in a manner
consistent with the requirements of this License, to extend the patent
license to downstream recipients. "Knowingly relying" means you have
actual knowledge that, but for the patent license, your conveying the
covered work in a country, or your recipient's use of the covered work
in a country, would infringe one or more identifiable patents in that
country that you have reason to believe are valid.
If, pursuant to or in connection with a single transaction or
arrangement, you convey, or propagate by procuring conveyance of, a
covered work, and grant a patent license to some of the parties
receiving the covered work authorizing them to use, propagate, modify
or convey a specific copy of the covered work, then the patent license
you grant is automatically extended to all recipients of the covered
work and works based on it.
A patent license is "discriminatory" if it does not include within
the scope of its coverage, prohibits the exercise of, or is
conditioned on the non-exercise of one or more of the rights that are
specifically granted under this License. You may not convey a covered
work if you are a party to an arrangement with a third party that is
in the business of distributing software, under which you make payment
to the third party based on the extent of your activity of conveying
the work, and under which the third party grants, to any of the
parties who would receive the covered work from you, a discriminatory
patent license (a) in connection with copies of the covered work
conveyed by you (or copies made from those copies), or (b) primarily
for and in connection with specific products or compilations that
contain the covered work, unless you entered into that arrangement,
or that patent license was granted, prior to 28 March 2007.
Nothing in this License shall be construed as excluding or limiting
any implied license or other defenses to infringement that may
otherwise be available to you under applicable patent law.
12. No Surrender of Others' Freedom.
If conditions are imposed on you (whether by court order, agreement or
otherwise) that contradict the conditions of this License, they do not
excuse you from the conditions of this License. If you cannot convey a
covered work so as to satisfy simultaneously your obligations under this
License and any other pertinent obligations, then as a consequence you may
not convey it at all. For example, if you agree to terms that obligate you
to collect a royalty for further conveying from those to whom you convey
the Program, the only way you could satisfy both those terms and this
License would be to refrain entirely from conveying the Program.
13. Use with the GNU Affero General Public License.
Notwithstanding any other provision of this License, you have
permission to link or combine any covered work with a work licensed
under version 3 of the GNU Affero General Public License into a single
combined work, and to convey the resulting work. The terms of this
License will continue to apply to the part which is the covered work,
but the special requirements of the GNU Affero General Public License,
section 13, concerning interaction through a network will apply to the
combination as such.
14. Revised Versions of this License.
The Free Software Foundation may publish revised and/or new versions of
the GNU General Public License from time to time. Such new versions will
be similar in spirit to the present version, but may differ in detail to
address new problems or concerns.
Each version is given a distinguishing version number. If the
Program specifies that a certain numbered version of the GNU General
Public License "or any later version" applies to it, you have the
option of following the terms and conditions either of that numbered
version or of any later version published by the Free Software
Foundation. If the Program does not specify a version number of the
GNU General Public License, you may choose any version ever published
by the Free Software Foundation.
If the Program specifies that a proxy can decide which future
versions of the GNU General Public License can be used, that proxy's
public statement of acceptance of a version permanently authorizes you
to choose that version for the Program.
Later license versions may give you additional or different
permissions. However, no additional obligations are imposed on any
author or copyright holder as a result of your choosing to follow a
later version.
15. Disclaimer of Warranty.
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
16. Limitation of Liability.
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGES.
17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
<program> Copyright (C) <year> <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<http://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<http://www.gnu.org/philosophy/why-not-lgpl.html>.
Name: libquadmath
Files: scipy.libs/libquadmath*.so
Description: dynamically linked to files compiled with gcc
Availability: https://gcc.gnu.org/git/?p=gcc.git;a=tree;f=libquadmath
License: LGPL-2.1-or-later
GCC Quad-Precision Math Library
Copyright (C) 2010-2019 Free Software Foundation, Inc.
Written by Francois-Xavier Coudert <fxcoudert@gcc.gnu.org>
This file is part of the libquadmath library.
Libquadmath is free software; you can redistribute it and/or
modify it under the terms of the GNU Library General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
Libquadmath is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
https://www.gnu.org/licenses/old-licenses/lgpl-2.1.html
Location: /home/andres-campos/github_public/Metodos-Estadisticos/venv/lib/python3.10/site-packages
Requires: numpy
Required-by: catboost, category-encoders, factor_analyzer, hdbscan, imbalanced-learn, lightgbm, pingouin, pynndescent, scikit-learn, scikit-optimize, statsmodels, sweetviz, umap-learn, xgboost
## Version scikit-learn
!pip show scikit-learn
Name: scikit-learn
Version: 1.5.1
Summary: A set of python modules for machine learning and data mining
Home-page: https://scikit-learn.org
Author:
Author-email:
License: new BSD
Location: /home/andres-campos/github_public/Metodos-Estadisticos/venv/lib/python3.10/site-packages
Requires: joblib, numpy, scipy, threadpoolctl
Required-by: category-encoders, factor_analyzer, hdbscan, imbalanced-learn, mglearn, pingouin, prince, pynndescent, scikit-optimize, umap-learn
#!pip list
import numpy as np
x = np.array([[1, 2, 3], [4, 5, 6]])
print("x:\n{}".format(x))
x:
[[1 2 3]
[4 5 6]]
from scipy import sparse
# Create a 2D NumPy array with a diagonal of ones, and zeros everywhere else
eye = np.eye(4)
print("NumPy array:\n{}".format(eye))
NumPy array:
[[1. 0. 0. 0.]
[0. 1. 0. 0.]
[0. 0. 1. 0.]
[0. 0. 0. 1.]]
# Convert the NumPy array to a SciPy sparse matrix in CSR format
# Only the nonzero entries are stored
sparse_matrix = sparse.csr_matrix(eye)
print("\nSciPy sparse CSR matrix:\n{}".format(sparse_matrix))
SciPy sparse CSR matrix:
<Compressed Sparse Row sparse matrix of dtype 'float64'
with 4 stored elements and shape (4, 4)>
Coords Values
(0, 0) 1.0
(1, 1) 1.0
(2, 2) 1.0
(3, 3) 1.0
data = np.ones(4)
row_indices = np.arange(4)
col_indices = np.arange(4)
eye_coo = sparse.coo_matrix((data, (row_indices, col_indices)))
print("COO representation:\n{}".format(eye_coo))
COO representation:
<COOrdinate sparse matrix of dtype 'float64'
with 4 stored elements and shape (4, 4)>
Coords Values
(0, 0) 1.0
(1, 1) 1.0
(2, 2) 1.0
(3, 3) 1.0
import matplotlib.pyplot as plt
# Genera una secuencia de números de -10 a 10 con 100 valores.
x = np.linspace(-10, 10, 100)
# Crea un array matriz usando seno.
y = np.sin(x)
# Gráfico de un array contra otro.
plt.plot(x, y, marker="x")
plt.show()
import pandas as pd
# create a simple dataset of people
data = {'Name': ["John", "Anna", "Peter", "Linda"],'Location' : ["New York", "Paris", "Berlin", "London"],
'Age' : [24, 13, 53, 33],}
data_pandas = pd.DataFrame(data)
display(data_pandas)
| Name | Location | Age | |
|---|---|---|---|
| 0 | John | New York | 24 |
| 1 | Anna | Paris | 13 |
| 2 | Peter | Berlin | 53 |
| 3 | Linda | London | 33 |
# Seleccionamos todos los registros con "Age" mayor a 30.
display(data_pandas[data_pandas["Age"] > 30])
| Name | Location | Age | |
|---|---|---|---|
| 2 | Peter | Berlin | 53 |
| 3 | Linda | London | 33 |
import sys
print("Python version: {}".format(sys.version))
import pandas as pd
print("pandas version: {}".format(pd.__version__))
import matplotlib
print("matplotlib version: {}".format(matplotlib.__version__))
import numpy as np
print("NumPy version: {}".format(np.__version__))
import scipy as sp
print("SciPy version: {}".format(sp.__version__))
import IPython
print("IPython version: {}".format(IPython.__version__))
import sklearn
print("scikit-learn version: {}".format(sklearn.__version__))
Python version: 3.10.12 (main, Nov 6 2024, 20:22:13) [GCC 11.4.0]
pandas version: 2.2.2
matplotlib version: 3.9.2
NumPy version: 1.26.4
SciPy version: 1.14.0
IPython version: 8.26.0
scikit-learn version: 1.5.1
Primera aplicación: clasificación de las especies de iris#
Vamos a abordar una primera aplicación de machine learning creando nuestro primer modelo.
Supongamos que un jardinero está interesado en distinguir las especies de algunas flores de iris que ha encontrado. El jardinero ha recopilado algunas medidas asociadas con cada iris:
El largo de los pétalos (petal length.)
El ancho de los pétalos (petal width).
El largo de los sépalos (sepal length).
El ancho de los sépalos (sepal width).
Todo lo anterior medido en centímetros (vea la Figura 1-2).
También tiene las medidas de los iris que han sido previamente identificados por un experto botánico como perteneciente a las especies:
setosa.
versicolor.
virginica.
Supongamos que estas son las únicas especies que nuestro jardinero aficionado encontrará en la naturaleza.
Objetivo: Construir un modelo de machine learning que pueda aprender de las mediciones de estos lirios cuya especie se conoce, de modo que podamos predecir la especie para un nuevo iris.
Solución:
Los datos que se usarán son los consignados en el DataFrame Iris un clásico de clásicos en los ejemplos de machine learning y estadística. En el módulo datasets de scikit-learn podemos obtener el conjunto de datos.
from sklearn.datasets import load_iris
iris_dataset = load_iris()
El objeto iris que devuelve load_iris es un objeto Bunch, que es muy similar a un diccionario. Es decir, estructura key-value.
print("Keys del iris_dataset: \n{}".format(iris_dataset.keys()))
Keys del iris_dataset:
dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])
El value con key DESCR es una breve descripción del conjunto de datos.
print(iris_dataset['DESCR'][:500])
.. _iris_dataset:
Iris plants dataset
--------------------
**Data Set Characteristics:**
:Number of Instances: 150 (50 in each of three classes)
:Number of Attributes: 4 numeric, predictive attributes and the class
:Attribute Information:
- sepal length in cm
- sepal width in cm
- petal length in cm
- petal width in cm
- class:
- Iris-Setosa
- Iris-Versicolour
- Iris-Virginica
:Summary Statistics:
============== ==== ==== ======= =====
Tenemos las especies de flor que queremos predecir:
print("Target names: {}".format(iris_dataset['target_names']))
Target names: ['setosa' 'versicolor' 'virginica']
Tenemos las características de los registros.
print("Features names: {}".format(iris_dataset['feature_names']))
Features names: ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']
Los datos están en la key “data”, ellos contienen las medidas de longitud y ancho de los pétalos y sépalos. Note que esta información está en un NumPy array.
print("Tipo de la data: {}".format(type(iris_dataset['data'])))
Tipo de la data: <class 'numpy.ndarray'>
print("Tamaño de la data: {}".format(iris_dataset['data'].shape))
Tamaño de la data: (150, 4)
## 5 primeros registros
iris_dataset['data'][:5]
array([[5.1, 3.5, 1.4, 0.2],
[4.9, 3. , 1.4, 0.2],
[4.7, 3.2, 1.3, 0.2],
[4.6, 3.1, 1.5, 0.2],
[5. , 3.6, 1.4, 0.2]])
A partir de estos datos, podemos ver que todas las primeras cinco flores tienen un ancho de pétalo de 0,2 cm, y que la primera flor tiene el sépalo más largo, con 5,1 cm.
La variable objetivo (target) conntiene las especies de cada una de las flores que se midieron, también como un NumPy array:
print("Tipo del target: {}".format(type(iris_dataset['target'])))
Tipo del target: <class 'numpy.ndarray'>
print("Tamaño de la variable target: {}".format(iris_dataset['target'].shape))
Tamaño de la variable target: (150,)
iris_dataset['target']
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
Los significados de los números vienen dados en:
iris['target_names']:
Es decir:
0 significa setosa.
1 significa versicolor.
2 significa virginica.
Notemos que conocemos la etiqueta correcta de cada registro de iris, en cuyo caso estamos en presencia de un problema de aprendizaje supervisado. En este problema, queremos predecir una de entre varias
opciones (las especies de iris). Este es un ejemplo de un problema de clasificación. Las posibles salidas (diferentes especies de iris) se llaman clases. Cada elemento en el conjunto de datos pertenece a una de tres clases, por lo que este problema es un problema de clasificación de tres clases (three-class). El output deseado para un registro (un iris) es la especie de esta flor. La especie a la que pertenece se llama etiqueta (label).
import pandas as pd
iris_dataframe = pd.DataFrame(iris_dataset['data'], columns = iris_dataset['feature_names'])
iris_dataframe.head()
| sepal length (cm) | sepal width (cm) | petal length (cm) | petal width (cm) | |
|---|---|---|---|---|
| 0 | 5.1 | 3.5 | 1.4 | 0.2 |
| 1 | 4.9 | 3.0 | 1.4 | 0.2 |
| 2 | 4.7 | 3.2 | 1.3 | 0.2 |
| 3 | 4.6 | 3.1 | 1.5 | 0.2 |
| 4 | 5.0 | 3.6 | 1.4 | 0.2 |
df_plot = iris_dataframe.copy()
df_plot['target_name'] = iris_dataset['target']
df_plot["target_name"] = df_plot['target_name'].replace({0:"setosa",1:"versicolor",2:"virginica"})
df_plot
| sepal length (cm) | sepal width (cm) | petal length (cm) | petal width (cm) | target_name | |
|---|---|---|---|---|---|
| 0 | 5.1 | 3.5 | 1.4 | 0.2 | setosa |
| 1 | 4.9 | 3.0 | 1.4 | 0.2 | setosa |
| 2 | 4.7 | 3.2 | 1.3 | 0.2 | setosa |
| 3 | 4.6 | 3.1 | 1.5 | 0.2 | setosa |
| 4 | 5.0 | 3.6 | 1.4 | 0.2 | setosa |
| ... | ... | ... | ... | ... | ... |
| 145 | 6.7 | 3.0 | 5.2 | 2.3 | virginica |
| 146 | 6.3 | 2.5 | 5.0 | 1.9 | virginica |
| 147 | 6.5 | 3.0 | 5.2 | 2.0 | virginica |
| 148 | 6.2 | 3.4 | 5.4 | 2.3 | virginica |
| 149 | 5.9 | 3.0 | 5.1 | 1.8 | virginica |
150 rows × 5 columns
Lo primero es lo primero: vamos a analizar un poco los datos, esto antes de construir nuestro modelo, es una buena idea inspeccionar los datos, esto con el objetivo de “ver” si la información deseada podría estar contenida en los datos. Además, inspeccionar los datos es una buena manera de encontrar anomalías y peculiaridades. Tal vez algunos iris se midieron usando pulgadas y no centímetros, por ejemplo. En el mundo real, inconsistencias en los datos y mediciones inesperadas son muy comunes.
Una de las mejores formas de inspeccionar los datos es visualizarlos. Una forma de hacerlo es mediante el uso de un scatter plots. Si tiene una pequeña cantidad de varibles, como las que tenemos aquí, esto es bastante razonable. Recuerde que este plot de pares no muestra la interacción de todas las características a la vez, por lo que algunos aspectos interesantes de los datos pueden que no se revelen al visualizarlo de esta manera.
Ejercicio 1.#
Construya un pairplot o un PairGrid en donde la diagonal principal tenga un histograma y un scatter-plot fuera de la diagonal principal. Haga uso del parámetro hue con las especies.
Respuesta:
## Celda de código para probar.
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_theme()
g = sns.PairGrid(df_plot,hue='target_name', palette = "dark")
g.map_diag(sns.histplot, hue = None, color = "orange", bins=20)
g.map_offdiag(sns.scatterplot)
g.add_legend()
g.fig.set_size_inches(15,15)
/home/andres-campos/github_public/Metodos-Estadisticos/venv/lib/python3.10/site-packages/seaborn/axisgrid.py:1513: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=vector, **plot_kwargs)
/home/andres-campos/github_public/Metodos-Estadisticos/venv/lib/python3.10/site-packages/seaborn/axisgrid.py:1513: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=vector, **plot_kwargs)
/home/andres-campos/github_public/Metodos-Estadisticos/venv/lib/python3.10/site-packages/seaborn/axisgrid.py:1513: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=vector, **plot_kwargs)
/home/andres-campos/github_public/Metodos-Estadisticos/venv/lib/python3.10/site-packages/seaborn/axisgrid.py:1513: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=vector, **plot_kwargs)
De las gráficas, podemos ver que las tres clases parecen estar relativamente bien separadas usando las medidas de sépalos y pétalos. Esto significa que un modelo de aprendizaje automático probablemente podrá aprender a separarlos.
Conjuntos de datos Training y Test#
Deseamos construir un modelo de machine learning a partir de este conjunto de datos, buscando que se pueda predecir la especie de iris para un nuevo conjunto de datos. Para esto, necesitamos construir un modelo y por supuesto saber si realmente funciona, es decir, si se debe confiar en sus predicciones.
Desafortunadamente, no podemos usar los datos que usamos para construir el modelo para evaluarlo. Esto se debe a que nuestro modelo siempre puede simplemente recordar todo el conjunto de datos con que se alimento el modelo, y por lo tanto, prediga siempre la etiqueta correcta para cualquier punto de dicho conjunto.
Este “recordar” no nos indica si nuestro modelo se generalizará bien (en otras palabras, si también funcionará bien con nuevos datos). Para evaluar el rendimiento del modelo, le mostramos nuevos datos (datos que no ha visto antes) para los que tenemos etiquetas.
Para solucionar dicho problema debemos dividir nuestro conjunto de datos, en 2 conjuntos, el primero tendrá los datos de entrenamiento (training-data) y el segundo tendrá los datos de prueba (test-data). Con la primera parte de la data vamos a construir nuestro modelo de machine learning. Con los datos de prueba se evaluará qué tan bien funciona el modelo.
scikit-learn contiene una función que mezcla el conjunto de datos y lo divide, la función train_test_split extrae el 75% de la data en los datos de entrenamiento, junto con las etiquetas correspondientes para estos datos. El 25% restante de la data, junto con las etiquetas restantes, se declara como los datos de prueba. Usar un test que contenga el 25% de la data es una buena regla general.
## DataFrame de trabajo.
iris_dataframe["target"] = iris_dataset["target"]
iris_dataframe.head()
| sepal length (cm) | sepal width (cm) | petal length (cm) | petal width (cm) | target | |
|---|---|---|---|---|---|
| 0 | 5.1 | 3.5 | 1.4 | 0.2 | 0 |
| 1 | 4.9 | 3.0 | 1.4 | 0.2 | 0 |
| 2 | 4.7 | 3.2 | 1.3 | 0.2 | 0 |
| 3 | 4.6 | 3.1 | 1.5 | 0.2 | 0 |
| 4 | 5.0 | 3.6 | 1.4 | 0.2 | 0 |
Por convención de las matemáticas tomamos \(X\) mayúscula para los datos consigandos en las features (matriz) y \(y\) para los datos en la variable target (vector).
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(iris_dataset['data'],
iris_dataset['target'],
random_state=0)
Antes de realizar la división, la función train_test_split mezcla el conjunto de datos mediante un generador de números pseudoaleatorios. Si solo tomamos el último 25 % de los datos como conjunto de prueba, todos los puntos de datos tendrían la etiqueta 2, ya que los puntos de datos están ordenados por la etiqueta. Usando un conjunto de prueba que contiene solo
una de las tres clases no nos diría mucho sobre qué tan bien generaliza nuestro modelo, entonces mezclamos nuestros datos para asegurarnos de que los datos en test contengan datos de todas las clases. Finalmente,para asegurarnos de que obtendremos el mismo resultado si ejecutamos la misma función varias veces
veces, le proporcionamos al generador de números pseudoaleatorios una semilla fija usando el
parámetro random_state. Esto hará que el resultado sea determinista, con lo que se garantiza tener el mismo resultado.
El output de la función train_test_split es X_train, X_test, y_train y y_test, que son todas arrays NumPy. X_train contiene el 75% de los registros de la data y X_test contiene el 25% restante:
print("El tamaño de X_train es {}".format(X_train.shape))
print("El tamaño de y_train es: {}".format(y_train.shape))
El tamaño de X_train es (112, 4)
El tamaño de y_train es: (112,)
print("El tamaño de X_test es {}".format(X_test.shape))
print("El tamaño de y_test es: {}".format(y_test.shape))
El tamaño de X_test es (38, 4)
El tamaño de y_test es: (38,)
## Muestra de la data en Train.
print("Muestra de la data de las features:\n\n",X_train[:5])
print("\nMuestra de la data en la variable target:\n\n",y_train[:5])
Muestra de la data de las features:
[[5.9 3. 4.2 1.5]
[5.8 2.6 4. 1.2]
[6.8 3. 5.5 2.1]
[4.7 3.2 1.3 0.2]
[6.9 3.1 5.1 2.3]]
Muestra de la data en la variable target:
[1 1 2 0 2]
## Muestra de la data en Test.
print("Muestra de la data de las features:\n\n",X_test[:5])
print("\nMuestra de la data en la variable target:\n\n",y_test[:5])
Muestra de la data de las features:
[[5.8 2.8 5.1 2.4]
[6. 2.2 4. 1. ]
[5.5 4.2 1.4 0.2]
[7.3 2.9 6.3 1.8]
[5. 3.4 1.5 0.2]]
Muestra de la data en la variable target:
[2 1 0 2 0]
Construcción de nuestro primer modelo: k-Nearest Neighbors#
El modelo KNN es un modelo de clasificación bastante fácil de enterder. Para construcción de este modelo, basta considerar sólo la data en train. Para hacer una predicción para un nuevo registro de datos, el algoritmo encuentra el punto en el conjunto train que está más cerca del nuevo punto. Luego asigna la etiqueta de registro que tiene este registro en train al nuevo registro. La \(k\) en k-Nearest Neighbors significa que en lugar de usar solo el vecino más cercano
al nuevo punto de datos, podemos considerar cualquier número fijo \(k\) de vecinos (por ejemplo, los tres o cinco vecinos más cercanos). Entonces, podemos hacer una predicción la cual será la clase mayoritaria entre estos vecinos. Más adelante profundizaremos este método de clasificación, de arranque usaremos solo un vecino.
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=1)
El objeto knn encapsula el algoritmo que se usará para construir el modelo a partir de los datos en train, así como el algoritmo para hacer predicciones sobre nuevos puntos de datos. También contienen la información que el algoritmo ha extraído de los datos de entrenamiento. En el caso de KNeighborsClassifier, solo almacenará el conjunto de train.
knn.get_params()
{'algorithm': 'auto',
'leaf_size': 30,
'metric': 'minkowski',
'metric_params': None,
'n_jobs': None,
'n_neighbors': 1,
'p': 2,
'weights': 'uniform'}
Lo anterior nos muestra qué parámetros
se usarán para crear el modelo. Casi todos ellos son los valores predeterminados, note que tenemos n_neighbors=1, que es el parámetro que pasamos. La mayoría de los modelos en scikit-learn tiene muchos parámetros, pero la mayoría de ellos son optimizaciones de velocidad o para casos de uso muy especiales. Por ahora, no nos preocupamos por el otros parámetros mostrados. Recuerde que más adelante cubriremos toda la temática.
Para construir el modelo en el conjunto train, llamamos al método fit() que es el ajuste del objeto knn, que toma como argumentos el NumPy array X_train que contiene la data train y el NumPy array en y_train de las etiquetas de train correspondientes:
knn.fit(X_train, y_train)
KNeighborsClassifier(n_neighbors=1)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
KNeighborsClassifier(n_neighbors=1)
Haciendo predicciones#
Podemos hacer predicciones usando este modelo sobre nuevos datos para los que quizás no se conoce la etiqueta correctas. Supongamos que tenemos un lirio con la siguiente información:
Un sépalo de 5 cm.
Un ancho de sépalo de 2,9 cm.
Un largo de pétalo de 1 cm.
Y un ancho de pétalo de 0,2 cm.
¿Qué especie de iris sería este lirio?
Debemos organizar esta información en un NumPy array, la forma de este array debe ser, el número de muestras (1) y multiplicado por el número de caracteristicas (4):
X_new = np.array([[5, 2.9, 1, 0.2]])
print("X_new.shape: {}".format(X_new.shape))
X_new.shape: (1, 4)
Notemos que tenemos las medidas de una sola flor en una fila del NumPy array bidimensional, esto pues scikit-learn siempre espera arrays bidimensionales
de los datos para hacer una predicción. Debemos invocar al método predict() del objeto knn:
prediction = knn.predict(X_new)
print("Predicción: {}".format(prediction))
print("Nombre de la etiqueta predicha: {}".format(iris_dataset['target_names'][prediction]))
Predicción: [0]
Nombre de la etiqueta predicha: ['setosa']
Nuestro modelo predice que este nuevo iris pertenece a la clase 0, lo que significa que su especie es setosa. ¿Cómo sabemos si podemos confiar en nuestro modelo? ¡No conocemos la especie correcta de esta muestra, que es el objetivo de construir el modelo!
Evaluación del modelo.#
¿Recuerda que tenemos un conjunto de test? Es en este lugar donde entra en juego. Sabemos que estos datos no se usaron para construir el modelo, pero sabemos cuál es la especie correcta para cada iris en el conjunto test. Por lo tanto, podemos hacer una predicción para cada iris en los datos de test y compararlos contra su etiqueta (la especie conocida). Podemos medir qué tan bien funciona el modelo calculando la precisión (accuracy), que es la fracción de flores para las se hizo una predicción correcta:
y_pred = knn.predict(X_test)
print("Predicciones en el conjunto test:\n\n {}".format(y_pred))
Predicciones en el conjunto test:
[2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0
2]
print("Score en el conjunto test: {:.2f}".format(np.mean(y_pred == y_test)))
print("="*51)
print("¿Porqué esta operacion nos proporciona el accuracy?")
Score en el conjunto test: 0.97
===================================================
¿Porqué esta operacion nos proporciona el accuracy?
También podemos usar el método score() del objeto knn, que calculará la puntuación con exactitud:
print("Score en el conjunto test: {:.2f}".format(knn.score(X_test, y_test)))
Score en el conjunto test: 0.97
Para este modelo, el accuracy en el conjunto test es de aproximadamente 0,97, lo que significa que hicimos un buen trabajo en la predicción para el 97% de los iris en el conjunto test. Bajo algunos supuestos matemáticos, esto significa que podemos esperar que nuestro modelo sea correcto el 97% del tiempo para nuevos
iris. Este alto nivel de precisión significa que nuestro
el modelo puede ser lo suficientemente confiable como para usarlo.
Conclusiones y comentarios#
Se realizó una breve introducción al machine learning y sus aplicaciones, seguido se discutió la distinción entre
aprendizaje supervisado y no supervisado y se brindó una descripción general de las herramientas. Finalmente resolvimos la tarea de construir un modelo para predecir qué especie de iris pertenece una flor en particular usando medidas físicas de la flor. Para ello, usamos una data con las medidas y clasificación conocida de la especie. En el contexto del problema se tenían tres posibles especies, setosa, versicolor o virginica, lo que hizo que estuvieramos en presencia de una tarea de clasificación de tres clases. Recordemos que las especies se denominan clases en el problema de clasificación, y la especie de un solo iris se llama su label. El conjunto de datos Iris consta de dos matrices NumPy: una que contiene los datos, que es
denominado \(X\) en scikit-learn, y uno que contiene los resultados correctos o deseados, llamada \(y\). El array \(X\) es un arreglo bidimensional, con una fila por cada registro y una columna por entidad. El array \(y\) es un arreglo unidimensional, el cualvaquí contiene una label de la clase, es decir, un número entero que va de 0 a 2, para cada una de las muestras. Seguido, dividimos nuestra dada en el conjunto train y en test, train para construir nuestro modelo y test para evaluar su desempeño, es decir, qué tan bien se generalizará nuestro modelo a datos nunca antes vistos. El algoritmo seleccionado fue \(knn\), que hace predicciones
para un nuevo punto de datos considerando su(s) vecino(s) más cercano(s) en el conjunto train. Para ello,instanciamos la clase, configurando parámetros. Luego construimos el modelo llamando el método fit() pasandole los datos en X_train y las labels correctas en y_train. Finalmente, evaluamos el modelo utilizando el método score(), que calcula la precisión del modelo. Esto se realiza sobre el conjunto test y se descubrió que nuestro modelo tiene una precisión de aproximadamente el 97 %, lo que significa que es correcto en un 97 % en el conjunto test.