# k means

# import the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# load the data set
import os

# clean everything
# %reset -f

dataFile = os.path.normpath("~/Documents/Programming/TrainingDataResources/K_Means/Mall_Customers.csv")
dataset = pd.read_csv(dataFile)

model_type = 'K-Means Clustering'

X = dataset.iloc[:,[3,4]].values

# use the elbow method to find clusters
from sklearn.cluster import KMeans

wcss = []
for i in range(1,11):
    kmeans = KMeans(n_clusters = i, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)
    kmeans.fit(X)
    wcss.append(kmeans.inertia_)
    

plt.plot(range(1,11), wcss)
plt.title('The Elbow Method')
plt.xlabel('Number of Clusters')
plt.ylabel('WCSS')
plt.show()

# apply k-means to the dataset with right number of clusters
kmeans = KMeans(n_clusters = 5, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)
y_kmeans = kmeans.fit_predict(X)

# visualize the clusters
plt.scatter(X[y_kmeans == 0,0], X[y_kmeans == 0,1], s = 100, c = 'red', label = 'Cluster 1')
plt.scatter(X[y_kmeans == 1,0], X[y_kmeans == 1,1], s = 100, c = 'blue', label = 'Cluster 2')
plt.scatter(X[y_kmeans == 2,0], X[y_kmeans == 2,1], s = 100, c = 'green', label = 'Cluster 3')
plt.scatter(X[y_kmeans == 3,0], X[y_kmeans == 3,1], s = 100, c = 'cyan', label = 'Cluster 4')
plt.scatter(X[y_kmeans == 4,0], X[y_kmeans == 4,1], s = 100, c = 'magenta', label = 'Cluster 5')
plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], s = 300, c = 'yellow', label = 'Centroids')
plt.title(model_type + ' of Clients')
plt.xlabel('Annual Income (k$)')
plt.ylabel('Spending Score (1-100)')
plt.legend()
plt.show()

# visualize the clusters with labels of behavior
plt.scatter(X[y_kmeans == 0,0], X[y_kmeans == 0,1], s = 100, c = 'red', label = 'Careful')
plt.scatter(X[y_kmeans == 1,0], X[y_kmeans == 1,1], s = 100, c = 'blue', label = 'Standard')
plt.scatter(X[y_kmeans == 2,0], X[y_kmeans == 2,1], s = 100, c = 'green', label = 'Target')
plt.scatter(X[y_kmeans == 3,0], X[y_kmeans == 3,1], s = 100, c = 'cyan', label = 'Careless')
plt.scatter(X[y_kmeans == 4,0], X[y_kmeans == 4,1], s = 100, c = 'magenta', label = 'Sensible')
plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], s = 300, c = 'yellow', label = 'Centroids')
plt.title(model_type + ' of Clients')
plt.xlabel('Annual Income (k$)')
plt.ylabel('Spending Score (1-100)')
plt.legend()
plt.show()



