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Merge branch 'main' of https://github.com/giocoal/Data-Science-Lab
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giocoal committed Jan 5, 2023
2 parents 3f0fb5b + 4340915 commit 1e76ae7
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120 changes: 85 additions & 35 deletions Analisi predittive/Previsioni_RandomForest_PreCovid.Rmd
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Expand Up @@ -155,56 +155,106 @@ f_mod2 <- function(Y, h, newxreg = NULL) {
## Previsioni

```{r}
# Parametri globali
h = 73
initial = 538
window = NULL
# # Parametri globali
# h = 73
# initial = 538
# window = NULL
#
# # Calcolo tempo di computazione
# start.time <- Sys.time()
# print(start.time)
#
# # Pre processing
#
# rf_r1 <- xts(r1[, c("lordototale", "Festivo", "Pioggia")], as.Date(as.character(r1$data), format = "%Y-%m-%d"))
#
# rf_r1$Festivo[rf_r1$Festivo=="False"] <- 0
# rf_r1$Festivo[rf_r1$Festivo=="True"] <- 1
# rf_r1$Pioggia[rf_r1$Pioggia==""] <- 0
# rf_r1$Pioggia[rf_r1$Pioggia=="True"] <- 1
# yday <- yday(time(rf_r1))
# wday <- wday(time(rf_r1), week_start = getOption("lubridate.week.start", 1))
# data <- cbind(rf_r1, yday, wday)
#
# # CV su MODELLO 2
#
# e_2 <- tsCV_RandomForest(my_xts = data[,1], xreg = data[,-1], forecastfunction = f_mod2, h=h, initial = initial, window = window)
# e2 <- e_2$e
# e2_percentage <- e_2$e_percentage
# e2_estimate <- e_2$y_estimate
# e2_groundtruth <- e_2$y_groundtruth
#
# end.time <- Sys.time()
# print(end.time)
# time.taken <- end.time - start.time
# print(time.taken)
```

# Calcolo tempo di computazione
start.time <- Sys.time()
print(start.time)
```{r}
# covid_predictions <- e2_estimate[539] # Prima riga non nulla di e2_estimate
# covid_predictions <- as.data.frame(t(covid_predictions))
#
# prediction_dates <- seq(as.Date("2020-02-24"), as.Date("2020-05-06"), by = 1)
#
# covid_predictions <- cbind(covid_predictions, prediction_dates)
#
# rownames(covid_predictions) <- NULL
# colnames(covid_predictions) <- c("Previsioni", "Date")
```

# Pre processing
```{r}
# previsioni <- xts(covid_predictions[, c("Previsioni")], as.Date(as.character(covid_predictions$Date), format = "%Y-%m-%d"))
```

rf_r1 <- xts(r1[, c("lordototale", "Festivo", "Pioggia")], as.Date(as.character(r1$data), format = "%Y-%m-%d"))
```{r}
# previsioni <- cbind(previsioni, rf_r1$lordototale[prediction_dates])
# previsioni$perdite <- previsioni$previsioni - previsioni$lordototale
```

rf_r1$Festivo[rf_r1$Festivo=="False"] <- 0
rf_r1$Festivo[rf_r1$Festivo=="True"] <- 1
rf_r1$Pioggia[rf_r1$Pioggia==""] <- 0
rf_r1$Pioggia[rf_r1$Pioggia=="True"] <- 1
yday <- yday(time(rf_r1))
wday <- wday(time(rf_r1), week_start = getOption("lubridate.week.start", 1))
data <- cbind(rf_r1, yday, wday)
```{r}
# write.csv(previsioni, "../Dati ristoranti/previsioni_RF.csv")
```

# CV su MODELLO 2
```{r}
previsioni <- read.csv("..\\Dati ristoranti\\previsioni_RF.csv")
previsioni <- previsioni[-c(1)]
```

e_2 <- tsCV_RandomForest(my_xts = data[,1], xreg = data[,-1], forecastfunction = f_mod2, h=h, initial = initial, window = window)
e2 <- e_2$e
e2_percentage <- e_2$e_percentage
e2_estimate <- e_2$y_estimate
e2_groundtruth <- e_2$y_groundtruth
```{r}
# Analisi quantitativa

# Perdita medie e totale
perdita_media <- mean(previsioni$perdite)
perdita_totale <- sum(previsioni$perdite)

# Calcolo dell'incasso totale dell'anno precedente
r1_2019 <- r1[r1$Year == '2019', ]
incasso_2019 <- sum(r1_2019$lordototale)

end.time <- Sys.time()
print(end.time)
time.taken <- end.time - start.time
print(time.taken)
percentage <- perdita_totale/incasso_2019*100
```
```{r}
covid_predictions <- e2_estimate[539] # Prima riga non nulla di e2_estimate
covid_predictions <- as.data.frame(t(covid_predictions))

prediction_dates <- seq(as.Date("2020-02-24"), as.Date("2020-05-06"), by = 1)
Togliere commento seguente se vogliamo tenere anche la variabile "perdite"

covid_predictions <- cbind(covid_predictions, prediction_dates)
```{r}
# previsioni <- previsioni[-c(3)]
```

rownames(covid_predictions) <- NULL
colnames(covid_predictions) <- c("Previsioni", "Date")
```{r}
prediction_dates <- seq(as.Date("2020-02-24"), as.Date("2020-05-06"), by = 1)
previsioni <- cbind(prediction_dates, previsioni)
```

```{r}
previsioni <- xts(covid_predictions[, c("Previsioni")], as.Date(as.character(covid_predictions$Date), format = "%Y-%m-%d"))
previsioni <- melt(previsioni, id.vars = "prediction_dates")
```

```{r}
plot(previsioni)
p <- ggplot(previsioni,
aes(x = prediction_dates, y = value, col = variable)) + geom_line()
p + labs(x = "Data", y='Vendite') + ggtitle("Previsioni RF") +
theme(legend.title = element_blank(),
legend.position = c(0.9, 0.18),
legend.background = element_rect(fill = "white", color = "black"))
```

74 changes: 74 additions & 0 deletions Dati ristoranti/previsioni_RF.csv
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@@ -0,0 +1,74 @@
"","previsioni","lordototale","perdite"
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