# -*- coding: utf-8 -*-
"""
Created on Thu Mar 23 14:43:36 2017

@author: ADB3HAMADL
"""

'''
===================================
Case Study 3 - Wine Classification
===================================
'''

#In this case study, we will analyze a dataset consisting of an assortment of 
#wines classified as "high quality" and "low quality" and will use the k-Nearest 
#Neighbors classifier to determine whether or not other information about the wine 
#helps us correctly predict whether a new wine will be of high quality.



#Exercise 1
#----------

#TODO: Read in the data as a pandas dataframe using pd.read_csv. 
#The data can be found at https://s3.amazonaws.com/demo-datasets/wine.csv.

import pandas as pd
import numpy as np

data = pd.read_csv('https://s3.amazonaws.com/demo-datasets/wine.csv') #read directly from the link

print data.head(5)
print np.shape(data) #(6497, 15)

#------------------------------------------------------------------------------

#Exercise 2
#----------

#TODO: The dataset remains stored as data. Two columns in data are is_red and color, 
#which are redundant. Drop color from the dataset, and save the new dataset as 
#numeric_data.

numeric_data = data.drop('color', axis=1)
print (numeric_data.head(5))
#------------------------------------------------------------------------------

#Exercise 3
#----------

#TODO: To ensure that each variable contributes equally to the kNN classifier, 
#we need to standardize the data. First, from each variable in numeric_data, 
#subtract its mean. Second, for each variable in numeric_data, divide by its 
#standard deviation. Store your standardized result as numeric_data.


numeric_data = (numeric_data - np.mean(numeric_data)) / np.std(numeric_data)

print(numeric_data.head(5))

#TODO: Principal component analysis is a way to take a linear snapshot of the data 
#from several different angles, with each snapshot ordered by how well it 
#aligns with variation in the data. The sklearn.decomposition module contains 
#the PCA class, which determines the most informative principal components of 
#the data (a matrix with columns corresponding to the principal components). 
#Use pca.fit(numeric_data).transform(numeric_data) to extract the first two 
#principal components from the data. Store this as principal_components.

import sklearn.decomposition
pca = sklearn.decomposition.PCA(n_components=2)
principal_components = pca.fit(numeric_data).transform(numeric_data)

#------------------------------------------------------------------------------

#Exercise 4
#----------

#TODO: The first two principal components can be accessed using principal_components[:,0] 
#and principal_components[:,1]. Store these as x and y respectively, and plot the 
#first two principal components. The high and low quality wines will be colored 
#using red and blue. How well are the two groups of wines separated by the first 
#two principal components?

import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from matplotlib.backends.backend_pdf import PdfPages
observation_colormap = ListedColormap(['red', 'blue'])
x = principal_components[:,0]
y = principal_components[:,1]
plt.plot(principal_components[:,0], principal_components[:,1])
plt.title("Principal Components of Wine")
plt.scatter(x, y, alpha = 0.2,
    c = data['high_quality'], cmap = observation_colormap, edgecolors = 'none')
plt.xlim(-8, 8); plt.ylim(-8, 8)
plt.xlabel("Principal Component 1"); plt.ylabel("Principal Component 2")
plt.show()

#------------------------------------------------------------------------------

#Exercise 5
#----------

#TODO: We are now ready to fit the wine data to our kNN classifier. 
#Create a function accuracy(predictions, outcomes) that takes two lists of the 
#same size as arguments and returns a single number, which is the percentage of 
#elements that are equal for the two lists.

def accuracy(predictions, outcomes):
    for i in predictions:
        for j in outcomes:
            if i==j:
                Percentage = 100*np.mean(predictions == outcomes)
    return Percentage
                
#TODO: Use accuracy to compare the percentage of similar elements in 
#x = np.array([1,2,3]) and y = np.array([1,2,4]).

x = np.array([1,2,3])
y = np.array([1,2,4])

percentage = accuracy(x, y)

#TODO: Print your answer.

print percentage 

#------------------------------------------------------------------------------

#Exercise 6
#----------

#TODO: The dataset remains stored as data. Because most wines in the dataset are 
#classified as low quality, one very simple classification rule is to predict 
#that all wines are of low quality. Use the accuracy function (preloaded into 
#memory as defined in Exercise 5) to calculate how many wines in the dataset 
#are of low quality. Accomplish this by calling accuracy with 0 as the first 
#argument, and data["high_quality"] as the second argument.

number_of_low_quality = accuracy(0, data["high_quality"])

#TODO: Print your result.

print number_of_low_quality

#------------------------------------------------------------------------------

#Exercise 7
#----------

#TODO: Use knn.predict(numeric_data) to predict which wines are high and low quality 
#and store the result as library_predictions.

from sklearn.neighbors import KNeighborsClassifier

knn = KNeighborsClassifier(n_neighbors = 5)
knn.fit(numeric_data, data['high_quality'])
library_predictions = knn.predict(numeric_data)


#TODO: Use accuracy to find the accuracy of your predictions, using library_predictions 
#as the first argument and data["high_quality"] as the second argument.

predictions_accuracy = accuracy(library_predictions, data["high_quality"])

#TODO: Print your answer. Is this prediction better than the simple classifier 
#in Exercise 6?

print (predictions_accuracy) # Yes, this is better!

#------------------------------------------------------------------------------

#Exercise 8
#----------

#TODO: Unlike the scikit-learn function, our homemade kNN classifier does not take 
#any shortcuts in calculating which neighbors are closest to each observation, 
#so it is likely too slow to carry out on the whole dataset. To circumvent this, 
#fix the random generator using random.seed(123), and select 10 rows from the 
#dataset using random.sample(range(n_nrows), 10). Store this selection as selection.

import random 

n_rows = data.shape[0]

random.seed(123)
selection = random.sample(range(n_rows), 10)

#------------------------------------------------------------------------------

#Exercise 9
#----------

#TODO: The sample of 10 row indices are stored as selection from the previous exercise. 
#For each predictor p in predictors[selection], 
#use knn_predict(p, predictors[training_indices,:], outcomes, k=5) to predict 
#the quality of each wine in the prediction set, and store these predictions 
#as a np.array called my_predictions. Note that knn_predict is already defined 
#as in the Case 3 videos.

predictors = np.array(numeric_data)
training_indices = [i for i in range(len(predictors)) if i not in selection]
outcomes = np.array(data["high_quality"])

my_predictions = [knn_predict(p, predictors[training_indices,:], outcomes, k=5) for p in predictors[selection]]


#TODO: Using the accuracy function, compare these results to the selected rows from 
#the high_quality variable in data using my_predictions as the first argument 
#and data.high_quality[selection] as the second argument. Store these results 
#as percentage.

percentage = accuracy(my_predictions, data.high_quality[selection] )

#TODO: Print your answer.

print(percentage) #Our accuracy is comparable to the library's function!

#------------------------------------------------------------------------------


