Actividad 4.2

Redes Neuronales

Una Red Neural Artificial (ANN) modela la relación entre un conjunto de entradas y una salida resolviendo un problema de aprendizaje

Ejemplos de aplicación de redes neuronales son:
1. La recomendación de contenido de netflix.
2. El feed de Instagram o TikTok. 3. Determinar el número o letra escrito a mano.

1. Instalar paquetes y llamar librería

library(neuralnet)

2. Alimentar con ejemplos.

examen <- c(20,10,30,20,80,30)
proyecto <- c(90,20,40,50,50,60)
estatus <- c(1,0,0,0,0,1)
df <- data.frame(examen, proyecto, estatus)

3. Generar la red neuronal.

red_neuronal <- neuralnet(estatus~ ., data=df)
plot((red_neuronal), rep = "best")

4. Predecir con la red neuronal.

prueba_examen <- c(30,40,85)
prueba_proyeto <- c(85, 50, 40)
prueba <- data.frame(prueba_examen, prueba_proyeto)
prediccion <- compute(red_neuronal, prueba)
probabilidad <- prediccion$net.result
resulado <- ifelse(probabilidad >0.5,1,0)
resulado

##      [,1]
## [1,]    0
## [2,]    0
## [3,]    0

Cancer de mama.

bd <- read.csv("/Users/santiago/Downloads/cancer_de_mama.csv")
summary(bd)

##   diagnosis          radius_mean      texture_mean   perimeter_mean  
##  Length:569         Min.   : 6.981   Min.   : 9.71   Min.   : 43.79  
##  Class :character   1st Qu.:11.700   1st Qu.:16.17   1st Qu.: 75.17  
##  Mode  :character   Median :13.370   Median :18.84   Median : 86.24  
##                     Mean   :14.127   Mean   :19.29   Mean   : 91.97  
##                     3rd Qu.:15.780   3rd Qu.:21.80   3rd Qu.:104.10  
##                     Max.   :28.110   Max.   :39.28   Max.   :188.50  
##    area_mean      smoothness_mean   compactness_mean  concavity_mean   
##  Min.   : 143.5   Min.   :0.05263   Min.   :0.01938   Min.   :0.00000  
##  1st Qu.: 420.3   1st Qu.:0.08637   1st Qu.:0.06492   1st Qu.:0.02956  
##  Median : 551.1   Median :0.09587   Median :0.09263   Median :0.06154  
##  Mean   : 654.9   Mean   :0.09636   Mean   :0.10434   Mean   :0.08880  
##  3rd Qu.: 782.7   3rd Qu.:0.10530   3rd Qu.:0.13040   3rd Qu.:0.13070  
##  Max.   :2501.0   Max.   :0.16340   Max.   :0.34540   Max.   :0.42680  
##  concave.points_mean symmetry_mean    fractal_dimension_mean   radius_se     
##  Min.   :0.00000     Min.   :0.1060   Min.   :0.04996        Min.   :0.1115  
##  1st Qu.:0.02031     1st Qu.:0.1619   1st Qu.:0.05770        1st Qu.:0.2324  
##  Median :0.03350     Median :0.1792   Median :0.06154        Median :0.3242  
##  Mean   :0.04892     Mean   :0.1812   Mean   :0.06280        Mean   :0.4052  
##  3rd Qu.:0.07400     3rd Qu.:0.1957   3rd Qu.:0.06612        3rd Qu.:0.4789  
##  Max.   :0.20120     Max.   :0.3040   Max.   :0.09744        Max.   :2.8730  
##    texture_se      perimeter_se       area_se        smoothness_se     
##  Min.   :0.3602   Min.   : 0.757   Min.   :  6.802   Min.   :0.001713  
##  1st Qu.:0.8339   1st Qu.: 1.606   1st Qu.: 17.850   1st Qu.:0.005169  
##  Median :1.1080   Median : 2.287   Median : 24.530   Median :0.006380  
##  Mean   :1.2169   Mean   : 2.866   Mean   : 40.337   Mean   :0.007041  
##  3rd Qu.:1.4740   3rd Qu.: 3.357   3rd Qu.: 45.190   3rd Qu.:0.008146  
##  Max.   :4.8850   Max.   :21.980   Max.   :542.200   Max.   :0.031130  
##  compactness_se      concavity_se     concave.points_se   symmetry_se      
##  Min.   :0.002252   Min.   :0.00000   Min.   :0.000000   Min.   :0.007882  
##  1st Qu.:0.013080   1st Qu.:0.01509   1st Qu.:0.007638   1st Qu.:0.015160  
##  Median :0.020450   Median :0.02589   Median :0.010930   Median :0.018730  
##  Mean   :0.025478   Mean   :0.03189   Mean   :0.011796   Mean   :0.020542  
##  3rd Qu.:0.032450   3rd Qu.:0.04205   3rd Qu.:0.014710   3rd Qu.:0.023480  
##  Max.   :0.135400   Max.   :0.39600   Max.   :0.052790   Max.   :0.078950  
##  fractal_dimension_se  radius_worst   texture_worst   perimeter_worst 
##  Min.   :0.0008948    Min.   : 7.93   Min.   :12.02   Min.   : 50.41  
##  1st Qu.:0.0022480    1st Qu.:13.01   1st Qu.:21.08   1st Qu.: 84.11  
##  Median :0.0031870    Median :14.97   Median :25.41   Median : 97.66  
##  Mean   :0.0037949    Mean   :16.27   Mean   :25.68   Mean   :107.26  
##  3rd Qu.:0.0045580    3rd Qu.:18.79   3rd Qu.:29.72   3rd Qu.:125.40  
##  Max.   :0.0298400    Max.   :36.04   Max.   :49.54   Max.   :251.20  
##    area_worst     smoothness_worst  compactness_worst concavity_worst 
##  Min.   : 185.2   Min.   :0.07117   Min.   :0.02729   Min.   :0.0000  
##  1st Qu.: 515.3   1st Qu.:0.11660   1st Qu.:0.14720   1st Qu.:0.1145  
##  Median : 686.5   Median :0.13130   Median :0.21190   Median :0.2267  
##  Mean   : 880.6   Mean   :0.13237   Mean   :0.25427   Mean   :0.2722  
##  3rd Qu.:1084.0   3rd Qu.:0.14600   3rd Qu.:0.33910   3rd Qu.:0.3829  
##  Max.   :4254.0   Max.   :0.22260   Max.   :1.05800   Max.   :1.2520  
##  concave.points_worst symmetry_worst   fractal_dimension_worst
##  Min.   :0.00000      Min.   :0.1565   Min.   :0.05504        
##  1st Qu.:0.06493      1st Qu.:0.2504   1st Qu.:0.07146        
##  Median :0.09993      Median :0.2822   Median :0.08004        
##  Mean   :0.11461      Mean   :0.2901   Mean   :0.08395        
##  3rd Qu.:0.16140      3rd Qu.:0.3179   3rd Qu.:0.09208        
##  Max.   :0.29100      Max.   :0.6638   Max.   :0.20750

#count(bd, diagnosis, sort = TRUE)
bd$diagnosis = ifelse(bd$diagnosis == "M",1,0) 

red_neuronal <- neuralnet(diagnosis~ ., data=bd)
plot((red_neuronal), rep = "best")

prueba <- bd[c(19,20,21,22,34), ]
prediccion <- compute(red_neuronal, prueba)
prediccion$net.result

##          [,1]
## 19 0.98127178
## 20 0.01108065
## 21 0.01108065
## 22 0.01108065
## 34 0.98127178

probabilidad <- prediccion$net.result
resultado <- ifelse(probabilidad >0.5,1,0)
resultado

##    [,1]
## 19    1
## 20    0
## 21    0
## 22    0
## 34    1

Series de tiempo

Conceptos

Una serie de tiempo es una colección de observaciones sobre un determinado fenomeno efectuadas en momentos de tiempo sucesivos, ususalmente equiespaciados

Ejemplos de serie de tiempos son: 1. Precio de acciones.
2. Niveles de inventario. 3. Rotación de personal. 4. Ventas.
5. PIB.

1. Instalar paquetes y llamar librerías

#install.packages("forecast")
library(forecast)

## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo

2. Crear la serie de tiempo

# Ejemplo: Los siguientes datos de producción trimestral inician en el primer trimestre de 2020
# Se busca pronosticar la producción de los siguientes 5 trimestres

produccion <- c(50,53,55,57,55,60)

serie_de_tiempo <- ts(data=produccion, start = c(2020,1),
                      frequency = 4)
serie_de_tiempo

##      Qtr1 Qtr2 Qtr3 Qtr4
## 2020   50   53   55   57
## 2021   55   60

3. Crear el modelo ARIMA

ARIMA significa Autoagressive Integrated Moving Average o Modelo Autorregresivo Integrado de Promedio Movil.

modelo <- auto.arima(serie_de_tiempo, D=1)
modelo

## Series: serie_de_tiempo 
## ARIMA(0,0,0)(0,1,0)[4] with drift 
## 
## Coefficients:
##        drift
##       1.5000
## s.e.  0.1768
## 
## sigma^2 = 2.01:  log likelihood = -2.84
## AIC=9.68   AICc=-2.32   BIC=7.06

summary(modelo)

## Series: serie_de_tiempo 
## ARIMA(0,0,0)(0,1,0)[4] with drift 
## 
## Coefficients:
##        drift
##       1.5000
## s.e.  0.1768
## 
## sigma^2 = 2.01:  log likelihood = -2.84
## AIC=9.68   AICc=-2.32   BIC=7.06
## 
## Training set error measures:
##                      ME      RMSE       MAE        MPE      MAPE       MASE
## Training set 0.03333332 0.5787923 0.3666667 0.03685269 0.6429133 0.06111111
##                    ACF1
## Training set -0.5073047

4. Realizar el pronóstico

pronostico <- forecast(modelo, level = c(95), h=5)
pronostico

##         Point Forecast    Lo 95    Hi 95
## 2021 Q3             61 58.22127 63.77873
## 2021 Q4             63 60.22127 65.77873
## 2022 Q1             61 58.22127 63.77873
## 2022 Q2             66 63.22127 68.77873
## 2022 Q3             67 63.07028 70.92972

plot(pronostico)

Análisis de sentimientos

Shiny App (Precio Casas,Tiempo de procesamiento, análisis de sentimiento)

Shiny