---
title: Métricas modelo Churn
author: Squad B2C
format: 
  dashboard:
    orientation: columns
    theme: yeti
params:
  fichero_json: 
    value: x
---


```{r setup, include=FALSE}
library(flexdashboard)
library(jsonlite)
library(tidyverse)
```


```{r load_data}
## '/home/jose/canadasreche@gmail.com/h2o_production/epa_glm/experimental/modelDetails.json'
## '/home/jose/canadasreche@gmail.com/h2o_production/xgboost_model_target_jazztel_20191120_stable/experimental/modelDetails.json'
df <- fromJSON(params$fichero_json)
# df <- fromJSON("churn_iter13mojo/experimental/modelDetails.json")

metricas_training <- df$output$training_metrics$thresholds_and_metric_scores$data %>% t() %>% as.data.frame()


if(!is.null(df$output$validation_metrics)){
 metricas_validation <- df$output$validation_metrics$thresholds_and_metric_scores$data %>% t() %>% as.data.frame()
 colnames(metricas_validation) <- df$output$validation_metrics$thresholds_and_metric_scores$columns$name

}


colnames(metricas_training) <- df$output$training_metrics$thresholds_and_metric_scores$columns$name


colnames(metricas_training) <- df$output$training_metrics$thresholds_and_metric_scores$columns$name

```

# Training Metrics

## Column {width=10%}

### Row 1


```{r}
#| title: AUC
gauge(df$output$training_metrics$AUC, min = 0.5, max=1, abbreviateDecimals = 2)
```

### Row 2

```{r}
#| component: valuebox
#| title: F1
list(
  icon = "stopwatch",
  color = "primary",
  value = round(metricas_training[which.max(metricas_training$f1), c("f1")], 2)
)
```


## Column {width=40%}


### ROC

```{r}
plotly::ggplotly(ggplot(metricas_training, aes(x = fpr, y = tpr)) +
  geom_line(color="darkorange") + geom_abline(intercept = 0, slope = 1, size = rel(0.1)))
```


## Column {width=50%}

::: {.panel-tabset}

### Gain Lift

```{r}
t_gain_lift_data <- as.data.frame(t(df$output$training_metrics$gains_lift_table$data))
colnames(t_gain_lift_data) <- df$output$training_metrics$gains_lift_table$columns$name

t_gain_lift_data[,] <- sapply(t_gain_lift_data[,], function(x) as.numeric(as.character(x)))

DT::datatable(round(t_gain_lift_data,2), rownames = FALSE)
```


### Lift Plot
```{r}
ggplot(t_gain_lift_data, aes(x=cumulative_data_fraction, y = lift)) +
  geom_line(color="darkorange")
```

:::

# Validation Metrics
## Column {width=10%}

### Row 1


```{r}
#| title: AUC
gauge(df$output$validation_metrics$AUC, min = 0.5, max=1, abbreviateDecimals = 2)
```

### Row 2

```{r}
#| component: valuebox
#| title: F1
list(
  icon = "stopwatch",
  color = "primary",
  value = round(metricas_validation[which.max(metricas_validation$f1), c("f1")], 2)
)
```


## Column {width=40%}


### ROC

```{r}
plotly::ggplotly(ggplot(metricas_validation, aes(x = fpr, y = tpr)) +
  geom_line(color="darkorange") + geom_abline(intercept = 0, slope = 1, size = rel(0.1)))
```


## Column {width=50%}

::: {.panel-tabset}

### Gain Lift

```{r}
t_gain_lift_data <- as.data.frame(t(df$output$validation_metrics$gains_lift_table$data))
colnames(t_gain_lift_data) <- df$output$validation_metrics$gains_lift_table$columns$name

t_gain_lift_data[,] <- sapply(t_gain_lift_data[,], function(x) as.numeric(as.character(x)))

DT::datatable(round(t_gain_lift_data,2), rownames = FALSE)
```


### Lift Plot
```{r}
ggplot(t_gain_lift_data, aes(x=cumulative_data_fraction, y = lift)) +
  geom_line(color="darkorange")
```

:::


# Variable importance

## Column


### **Variable Importance**

```{r}
var_importance <- df$output$variable_importances$data %>% t() %>% as.data.frame()
colnames(var_importance) <- c("variable", "importance","rel_importance", "otra")
var_importance <- var_importance %>% 
  transmute(
     variable = variable,
     importance = importance %>% as.character() %>% as.numeric,
     rel_importance = rel_importance%>% as.character() %>% as.numeric)
# var_importance$variable <-  as.character(var_importance$variable)

p <- var_importance %>% 
  mutate(variable = fct_reorder(variable, rel_importance)) %>%
  top_n(25, rel_importance)  %>% 
  ggplot(
    aes( 
    x = variable,
    y = rel_importance 
    )) +
  geom_col(fill = "darkorange") +
  coord_flip()

plotly::ggplotly(p)

write_csv(var_importance, file = "variables_importantes.csv")


```