# -*- coding: utf-8 -*-
"""
Created on Sun Feb  3 21:17:30 2019

@author: Suat
Decision Trees
"""

"""
Imagine that you are a medical researcher compiling data for a study. 
You have collected data about a set of patients, all of whom suffered from 
the same illness. During their course of treatment, each patient responded 
to one of 5 medications, Drug A, Drug B, Drug c, Drug x and y. 

Part of your job is to build a model to find out which drug might be 
appropriate for a future patient with the same illness. The feature sets of 
this dataset are Age, Sex, Blood Pressure, and Cholesterol of patients, and 
the target is the drug that each patient responded to. 

It is a sample of binary classifier, and you can use the training part of 
the dataset to build a decision tree, and then use it to predict the class 
of a unknown patient, or to prescribe it to a new patient.
"""


import numpy as np 
import pandas as pd
from sklearn.tree import DecisionTreeClassifier


my_data = pd.read_csv("https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/drug200.csv", delimiter=",")
my_data[0:5]

"""
PRE-PROCESSING
Using my_data as the Drug.csv data read by pandas, declare the 
following variables: 

X as the Feature Matrix (data of my_data)
y as the response vector (target)

Remove the column containing the target name since it doesn't contain 
numeric values.
"""

X = my_data[['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K']].values
X[0:5]

"""
As you may figure out, some features in this dataset are categorical such as 
Sex or BP. Unfortunately, Sklearn Decision Trees do not handle categorical 
variables. But still we can convert these features to numerical values. 
pandas.get_dummies() Convert categorical variable into dummy/indicator 
variables.
"""

from sklearn import preprocessing
le_sex = preprocessing.LabelEncoder()
le_sex.fit(['F','M'])
X[:,1] = le_sex.transform(X[:,1]) 


le_BP = preprocessing.LabelEncoder()
le_BP.fit([ 'LOW', 'NORMAL', 'HIGH'])
X[:,2] = le_BP.transform(X[:,2])


le_Chol = preprocessing.LabelEncoder()
le_Chol.fit([ 'NORMAL', 'HIGH'])
X[:,3] = le_Chol.transform(X[:,3]) 

X[0:5]


##Now we can fill the target variable.
y = my_data["Drug"]
y[0:5]

"""
Setting up the Decision Tree
We will be using train/test split on our decision tree. 
Let's import train_test_split from sklearn.cross_validation.
"""

from sklearn.model_selection import train_test_split

"""
Now train_test_split will return 4 different parameters. We will name them:
X_trainset, X_testset, y_trainset, y_testset 

The train_test_split will need the parameters: 
X, y, test_size=0.3, and random_state=3. 

The X and y are the arrays required before the split, the test_size 
represents the ratio of the testing dataset, and the random_state ensures 
that we obtain the same splits.
"""

X_trainset, X_testset, y_trainset, y_testset = train_test_split(X, y, test_size=0.3, random_state=3)

"""
Practice
Print the shape of X_trainset and y_trainset. Ensure that the dimensions match
"""
print(X_trainset.shape)
print(y_trainset.shape)

"""
MODELING
We will first create an instance of the DecisionTreeClassifier called drugTree.
Inside of the classifier, specify criterion="entropy" so we can see the 
information gain of each node.
"""

drugTree = DecisionTreeClassifier(criterion="entropy", max_depth = 4)
drugTree # it shows the default parameters


"""
Next, we will fit the data with the training feature matrix X_trainset and 
training response vector y_trainset
"""
drugTree.fit(X_trainset,y_trainset)

"""
Prediction
Let's make some predictions on the testing dataset and store it into a 
variable called predTree.
"""

predTree = drugTree.predict(X_testset)

##You can print out predTree and y_testset if you want to visually compare the 
##prediction to the actual values.

print (predTree [0:5])
print (y_testset [0:5])


"""
EVALUATION
Next, let's import metrics from sklearn and check the accuracy of our model.
"""

from sklearn import metrics
import matplotlib.pyplot as plt
print("DecisionTrees's Accuracy: ", metrics.accuracy_score(y_testset, predTree))


"""
Accuracy classification score computes subset accuracy: the set of labels 
predicted for a sample must exactly match the corresponding set of labels 
in y_true.

In multilabel classification, the function returns the subset accuracy. 
If the entire set of predicted labels for a sample strictly match with the 
true set of labels, then the subset accuracy is 1.0; otherwise it is 0.0.
"""

"""
VISUALIZATION
Lets visualize the tree
"""

# Notice: You might need to uncomment and install the pydotplus and graphviz libraries if you have not installed these before

from sklearn.externals.six import StringIO
import pydotplus
import matplotlib.image as mpimg
from sklearn import tree


dot_data = StringIO()
filename = "drugtree.png"
featureNames = my_data.columns[0:5]
targetNames = my_data["Drug"].unique().tolist()
out=tree.export_graphviz(drugTree,feature_names=featureNames, out_file=dot_data, class_names= np.unique(y_trainset), filled=True,  special_characters=True,rotate=False)  
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  
graph.write_png(filename)
img = mpimg.imread(filename)
plt.figure(figsize=(100, 200))
plt.imshow(img,interpolation='nearest')








