# Author: Sanchita Kanade

# Class: Data Mining And Knowledge (Spring 2019)

# file: DecisionTreeInduction.py

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

from sklearn.tree import DecisionTreeClassifier, export_graphviz

from IPython.display import SVG

from graphviz import Source

from sklearn import tree

from sklearn.metrics import accuracy_score,confusion_matrix

# loading data

df = pd.read_csv (r'BlackFriday.csv')

# randomly selecting 200000 samples from dataframe.

df = df.sample(100000)

# selecting important features from the dataframe into featureVector

featureVector = pd.DataFrame(df, columns= ['Occupation','Marital_Status','Age','Gender'])

# creating labels

outputVector = pd.DataFrame(df,columns = ['Purchase'])

##### converting a label vector into binary values based on whether it is larger or smaller than the threshold ####

output = np.zeros(shape=(len(outputVector),1))

#threshold is the average of purchases

threshold = np.mean(outputVector['Purchase'])

# converting outputVector into binary values

for i in range(0, len(outputVector)):

if(outputVector.iloc[i,0] >= threshold):

output[i] = 1

else:

output[i] = 0

# converting Gender values from string to integer data type

featureVector['Gender'] = featureVector['Gender'].map({'M': 0, 'F': 1})

# converting Age values from string to integer.

featureVector['Age'] = featureVector['Age'].map({'0-17':0,'18-25':1,'26-35':2,'36-45':3,'46-50':4,'51-55':5,'55+':6})

# dividing data into training and testing datasets.

trainX = featureVector.iloc[0:60000]

trainY = output[0:60000]

testX = featureVector.iloc[60000:100000]

testY = output[60000:100000]

# training Decision Tree Classifier

clf = DecisionTreeClassifier(criterion="gini").fit(trainX,trainY)

# testing Decision Tree Classifier

predY = clf.predict(testX)

# calculating evaluation metrics

acc = accuracy_score(testY,predY)

#confusion matrix calculation

cfm = confusion_matrix(testY, predY,labels =[0,1])

print('Confusion Matrix:\n',cfm)

print('\nAccuracy: ',acc)

# calculating per class precision and per class recall

precision = []

recall = []

precision.append(cfm[0,0]/(cfm[0,0] + cfm[1,0]))

precision.append(cfm[1,1]/(cfm[0,1] + cfm[1,1]))

recall.append(cfm[0,0]/(cfm[0,0]+cfm[0,1]))

recall.append(cfm[1,1]/(cfm[1,0]+cfm[1,1]))

print('\nPrecision: ',precision)

print('\nRecall: ',recall)

# Decision tree visualization

# Note: Decision tree is huge,

# so some times it feels like there is no output, to see the whole tree please scroll down

print("\nTree Visualization")

graph = Source( tree.export_graphviz(clf, out_file=None, feature_names=['Occupation','Marital_Status','Age','Gender']))

SVG(graph.pipe(format='svg'))