#!/usr/bin/env python
# coding: utf-8

# In[1]:


# Tratamiento de datos
# ==============================================================================
import pandas as pd
import numpy as np

# Preprocesado y modelado
# ==============================================================================
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Configuración warnings
# ==============================================================================
import warnings
warnings.filterwarnings('ignore')


# In[2]:


#Cargamos los datos!
url = 'https://raw.githubusercontent.com/JoaquinAmatRodrigo/' \
       + 'Estadistica-machine-learning-python/master/data/ESL.mixture.csv'
datos = pd.read_csv(url)
datos.head()


# In[3]:


#Visualizacion!
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(6,4))
ax.scatter(datos.X1, datos.X2, c=datos.y);
ax.set_title("Datos");


# In[4]:


# División de los datos en train y test
X = datos.drop(columns = 'y')
y = datos['y']


# In[5]:


X


# In[6]:


y


# In[7]:


X_train, X_test, y_train, y_test = train_test_split(X,y.values.reshape(-1,1),train_size= 0.7,random_state = 42,shuffle=True)


# In[8]:


# Creación del modelo SVM 
modelo = SVC(C = 100, kernel = 'linear', random_state=42)
modelo.fit(X_train, y_train)


# In[9]:


#Predicciones!
y_test_pred = modelo.predict(X_test)


# A lo largo de este notebook, se solicita calcular las métricas requeridas como así también su correspondiente interpretación: 
# 
# 1. Calcular la métrica Accuracy.

# In[10]:


###Completar
from sklearn.metrics import accuracy_score
accuracy_score(y_test,y_test_pred)


# 2. Crear la Matriz de Confusión

# In[11]:


###Completar
from sklearn.metrics import confusion_matrix
confusion_matrix(y_test, y_test_pred) 


# 3. Calcular la métrica F1 score

# In[12]:


###Completar
from sklearn.metrics import f1_score
f1_score(y_test, y_test_pred) 


# Calcular todas las metricas al tiempo

# In[13]:


from sklearn.metrics import classification_report
reporte=classification_report(y_test,y_test_pred)
print(reporte)


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