#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Aug  2 11:25:59 2020

@author: ashesh
"""

# Load libraries
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.tree import DecisionTreeClassifier 
from sklearn.tree import export_text
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
import pandas as pd
import numpy as np



# Create URL
url = './data.csv'

# Load dataset
dataframe = pd.read_csv(url)
dataframe.dropna(inplace = True) 
featureframe = dataframe.iloc[:, 1 : -1 ]
targetframe = dataframe.iloc[:,  -1 ]

# For more on descriptive statistics in pandas
# see https://www.tutorialspoint.com/python_pandas/python_pandas_descriptive_statistics.htm
print("================ =============== ")
print(featureframe.describe())
print("================ =============== ")


dummy_cols = [ 'outlook', 'temp', 'humidity', 'wind'] 

ff = pd.get_dummies(featureframe, columns=dummy_cols, drop_first=True) 
feature_names = ff.columns
features = pd.DataFrame(ff).to_numpy( )

tf = pd.get_dummies(targetframe, columns=['play'] , drop_first=True) 
target_name = tf.columns
target = pd.DataFrame(tf).to_numpy( )
target = np.ravel(target)

# split into training and test sets
features_train, features_test, target_train, target_test = train_test_split(features, target, random_state=12)

#%%

tree_depth = 10
min_in_leaf = 1
#classifier =  DecisionTreeClassifier(random_state=0, max_depth=tree_depth)
classifier =  DecisionTreeClassifier(max_features="auto", random_state=12, min_samples_leaf=min_in_leaf, max_depth=tree_depth)


#%%
# train model 
decision_tree = classifier.fit(features_train, target_train)

#plot_tree(decision_tree) 
r = export_text(decision_tree, show_weights=True, feature_names= list(ff.columns)) 
print(r)
 
#%% now make predictions
target_predicted = decision_tree.predict(features_test)

#%%
# create confusion matrix
matrix = confusion_matrix(target_test, target_predicted)


#%%
# create pandas dataframe
class_names = ['Play_No', 'Play_Yes']
dataframe_Confusion = pd.DataFrame(matrix, index=class_names, columns=class_names)

# create heatmap
sns.heatmap(dataframe_Confusion, annot=True,  cmap="Blues", fmt=".0f")
plt.title("Confusion Matrix")
plt.tight_layout()
plt.ylabel("True Class")
plt.xlabel("Predicted Class")
plt.savefig('./confusion_matrix.png')
plt.show()
plt.close()