# -*- coding: utf-8 -*-
"""
Created on Sun Dec 13 21:12:54 2015
@author: ldierker
"""
# -*- coding: utf-8 -*-
from pandas import Series, DataFrame
import pandas as pd
import numpy as np
import os
import matplotlib.pylab as plt
from sklearn.cross_validation import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import classification_report
import sklearn.metrics
os.chdir("C:\TREES")
"""
Data Engineering and Analysis
"""
#Load the dataset
AH_data = pd.read_csv("tree_addhealth.csv")
data_clean = AH_data.dropna()
data_clean.dtypes
data_clean.describe()
"""
Modeling and Prediction
"""
#Split into training and testing sets
predictors = data_clean[['BIO_SEX','HISPANIC','WHITE','BLACK','NAMERICAN','ASIAN',
'age','ALCEVR1','ALCPROBS1','marever1','cocever1','inhever1','cigavail','DEP1',
'ESTEEM1','VIOL1','PASSIST','DEVIANT1','SCHCONN1','GPA1','EXPEL1','FAMCONCT','PARACTV',
'PARPRES']]
targets = data_clean.TREG1
#DIVISIÓN DE DATOS ENTRE ENTRENAMIENTO Y TEST
pred_train, pred_test, tar_train, tar_test = train_test_split(predictors, targets, test_size=.4)
pred_train.shape
pred_test.shape
tar_train.shape
tar_test.shape
#Build model on training data
classifier=DecisionTreeClassifier()
classifier=classifier.fit(pred_train,tar_train) #Pasamos el target y los trainers predictors
predictions=classifier.predict(pred_test)
sklearn.metrics.confusion_matrix(tar_test,predictions)
sklearn.metrics.accuracy_score(tar_test, predictions)
#Displaying the decision tree
from sklearn import tree
#from StringIO import StringIO
from io import StringIO
#from StringIO import StringIO
from IPython.display import Image
out = StringIO()
tree.export_graphviz(classifier, out_file=out)
import pydotplus
graph=pydotplus.graph_from_dot_data(out.getvalue())
Image(graph.create_png())
